Exposome-wide patterns predict brain health in aging

Domain: neuroscience
Published: 2026-04-10
Editorial outcome: minor_revision

Abstract
Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age - relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.

Introduction
Healthy brain aging is a critical societal challenge, as it underpins motor and cognitive abilities while also contributing to the reduction of neurodegenerative disease incidence. Promoting brain health is therefore essential not only for enhancing individual well-being and quality of life but also for alleviating the growing burden of aging on healthcare systems worldwide. Addressing this issue requires an urgent focus on global strategies to promote individual brain health, as acknowledged by several recent national initiatives around the world (e.g., the European CSA BrainHealth https://www.brainhealth-partnership.eu/, Healthy Brain Initiative Collaborative https://www.alz.org/hbi-collaborative#about or Brain Health Diplomacy1).

At the scientific level, several initiatives have also been settled to identify key factors that influence neurocognitive health. One example among the most rigorous and influential initiatives is the Lancet Commission on Dementia, which regularly publishes reports on risk factors, prevention strategies, and interventions for dementia. In its most recent report, the commission highlighted 14 modifiable risk factors—including depression, diabetes, smoking, hypertension, excessive alcohol consumption, air pollution, and visual impairment—that collectively account for an estimated 45% of potentially modifiable dementia risk factors2. While this report provides invaluable insights into dementia prevention, these factors are drawn from disparate studies with varying methodologies, making it unclear how they interact with one another, with additional minor risk factors, and with potential protective factors.

To provide a normative, whole brain, indicator of brain health for each individual, many studies have recently capitalized on a Brain Age framework3. With this approach, a gap (“Brain Age Gap” BAG) between the apparent age of the brain (or “biological” age) and the true age (or “chronological” age) is used as an estimator of the brain health of any individual4. This indicator is seductive for population studies because it offers a normative estimator, integrating whole-brain structural patterns while minimizing the influence of traditional covariates/confounders. Furthermore, this framework is particularly insightful in aging populations in which a wide diversity of aging paces can be observed, with greater paces being generally associated with brain pathology, such as dementia5.

Further understanding and promoting neurocognitive health at the individual level in a precision brain health perspective requires a holistic approach, taking into account a myriad of factors and considering how all these factors taken altogether explain neurocognitive health of individuals from the population. A relevant conceptual framework in that context is the exposome, which encompasses the cumulative lifelong exposure to environmental, social, and biological factors affecting brain health6. While many of the individual factors mentioned above have long been studied in isolation, the exposome concept insists on their co-occurrence, co-dependence, and potential interactions7. The contemporary definition of the exposome encompasses the external exposome with factors such as education, socio-economical deprivation, social support, stress, and environmental features (air pollution, area density or greenness, for instance)6, but also the “internal exposome” with biomedical aspects such as inflammation. We further propose to use the term expotype to refer a person’s unique exposome profile. This perspective acknowledges that an individual’s expotype can combine risk factors for diseases (e.g., smoking since adolescence) with more protective factors (e.g., regular physical activity since childhood) that may interact with, or mitigate the effect of the former factors on brain health.

Using a BAG-based grey matter health estimator, some studies have emphasized that grey matter health is tightly linked to several aspects of body health in aging populations8–10, others have highlighted relationships with early life factors11 and socio-affective/mental health-related factors (e.g., long-term depressive symptoms12). Given the wide variety of factors that can influence brain health, some studies have applied a phenome-wide association analysis of BAG13,14. With such approach, for each and every available non-genetic variable, its association with BAG is tested separately, typically for a linear association. Such approaches hence do not account for redundancy and interaction among different factors altogether forming the expotype of an individual. These previous studies may thus have missed important factors appearing only in interaction with others in shaping brain health in aging. Indeed, while some factors are protective (e.g., nutrition diet15), others may accelerate brain aging (such as smoking and alcohol consumption16,17), and they often interact with other factors. For example, the detrimental effect of diabetes on brain health can be mitigated by a healthier lifestyle18. Accordingly, the grey matter health of an individual should be seen as the outcome of his/her internal phenotype formed by the interaction between different organ systems, but which also relates or interacts in turn with early life factors, socio-affective factors, as well as lifestyle factors. Yet, currently, a holistic view is missing to better understand how individual grey matter health is shaped by the expotype. Capturing the complex interplay between a range of internal and external factors together forming the exposome and relating it to grey matter health requires multivariate approaches and predictive modeling.

In this study, we employed machine learning approaches to examine the extent to which an individual’s expotype can predict grey matter health, as reflected by grey matter BAG, in an aging population. We then investigated how the set of 261 distinct exposome variables spanning across biomedical domains (such as blood pressure, diabetes, illness and cancers, hearing loss, hips circumference, arterial stiffness), mental health/socio-affective domain (such as work/job satisfaction, family relationship satisfaction), socioeconomical factors (such as ethnicity, qualification), early life factors (such as birth weights, maternal smoking around birth), adversity in childhood factors (such as felt loved as a child, sexually molested as a child), but also lifestyle factors (such as nutrition, diet, alcohol intake, smoking, among others) and specific external environmental exposure (such as sun exposure, noisy workplace, population density in home area) contributes to the prediction. This was possible by leveraging the extensive assessment provided for a large population cohort in the UK Biobank project. Such a machine learning framework with additional replication analyses enables us to draw solid conclusions on the factors that contribute to predicting grey matter health in an aging population.

Results
Brain age gap indicator of grey matter health
We focused on grey matter health as a key aspect of brain health (see Methods for the rationale). In this aim, we first designed a high-performing Brain Age prediction model from grey matter volume after assessing different algorithms and parcellation schemes (see Supplementary Table 1). To build an estimator that would later be sensitive to deviance from a healthy reference norm, the models were trained and explicitly tested in a subset of 5025 healthy participants from the UK Biobank (age range 46–82 years, mean 62.12 ± 7.16 years, 2579 females, see Methods). The best model was the Ridge regression algorithm with 1054 grey matter features. When this model was applied to the remaining population data (n = 34,365, age range: 44–82 years, mean 63.86 ± 7.57 years, 18,128 females), it demonstrated an overall very good performance in capturing the brain aging process across the UK Biobank whole population with a high correlation between the chronological and predicted age (r = 0.76) and a Mean Absolute Error (MAE) of 3.93 years. The gap (BAG) between the predicted and chronological (real) age was then computed to provide an estimator of grey matter health. As could be expected this estimator was normally distributed with a group-averaged mean of zero. In the whole population, 90% of participants had BAG values between −9.20 and +10.18 years. Furthermore, 5% of participants had a BAG value inferior to −9.20 and 5% of participants had a BAG value superior to 10.18. This indicates that while the group-averaged mean BAG was close to zero, some participants showed a positive gap, indicating that their brain was estimated to be older than their chronological age, and some showed a negative gap, reflecting relatively preserved grey matter compared to their chronological age. In other words, a wide range of variations could be observed in grey matter health within the UK Biobank populations. Next, we sought to predict these variations based on the individual expotype.

Prediction of grey matter health from the expotype
The prediction of grey matter health from the exposome variables was performed in subsets of participants with data available for a wide range of exposome variables. As illustrated in Fig. 1, for our main analysis, we identified a main subset of 3706 participants with data for 261 distinct exposome variables (see Supplementary Table 2, and descriptive tables: Supplementary Table 3 for continuous variables, and Supplementary Table 4 for categorical variables) within the UK Biobank population. We additionally identified a bigger subset of participants for replication who only missed the left and right bone density measurements (“replication subset”, 4292 participants, 259 distinct exposome variables; Supplementary Table 2). Third, we also identified a very large subset of participants but with data for only 201 distinct exposome variables (“variables-restricted subset” mainly missing mental health and socio-affective related variables; Supplementary Table 2). These three subsets enable us to examine the stability of our main findings across different subsamples, varying in sample size. The prediction of grey matter health in these subsets was performed using a random forest algorithm, which has the advantage of accounting for non-linear relationships between the exposome variables and grey matter health. However, our results were also replicated using two other popular algorithms: support vector regression (SVR) and ridge regression.

We could significantly predict grey matter health based on the expotype in the main subset (r = 0.23; p = 0.002; r2 = 0.05; p = 0.002, FDR-corrected; Supplementary Table 5). We could replicate this achievement in the replication and variables-restricted subsets (which had less exposome variables but a higher number of participants) with similar accuracies (replication subset: r = 0.23; p = 0.004; r2 = 0.05; p = 0.004; and variables-restricted subset: r = 0.24; p = 0.002; r2 = 0.06; p = 0.002, FDR-corrected; Supplementary Table 5) confirming the robustness of our results across subsamples. Furthermore, grey matter health could also be predicted using alternative algorithms, although achieving numerically slightly lower prediction accuracy (with r = 0.17; p = 0.002; r2 = 0.03; p = 0.002 for Ridge Regression and r = 0.16; p = 0.002; r2 = 0.02; p = 0.002 for SVR; FDR-corrected; Supplementary Table 5). It is noteworthy that p-values were estimated using permutation testing with 500 permutations. Consequently, the smallest p-value that can be obtained under this procedure is approximately 0.002, and the true p-values may be smaller if a larger number of permutations were used.

Exposome factors’ contribution to the prediction
In order to get more insight into how different exposome factors contribute to the prediction of grey matter health, we used the SHAP explainer. To examine the stability across subsets and algorithms, we ran the explainer on replication and variables-restricted subsets (in addition to the main subset), as well as for additional algorithms in the main subset (see Supplementary Figs. 1–5 and Supplementary Tables 6, 7). The predictive model makes use of the broad spectrum of variables with no specific variable or set of variables showing a disproportionate contribution to the model (see feature importance ranking in Supplementary Tables 6 and 7). In other words, the prediction does not appear to be disproportionately driven by a few specific variables, but instead, it relies on the combination of information across a wide spectrum of variables. However, when looking at the top 30 variables (Fig. 2), some exposome factors appear particularly important for predicting grey matter health, and this is consistent across prediction algorithms. This group of factors mainly combines cardiovascular factors (such as variables pertaining to blood pressure, but also to smoking) with nutrition, diet, alcohol, and diabetes. Additionally, bone density and hip circumference also appear in the top contributing variables consistently for the main algorithm and one of the alternative algorithms.

Additional insight into the nature of the association between these most contributing variables and grey matter health can be obtained by examining the distribution of SHAP value in Fig. 3. This figure illustrates how the value (from low to high) of the exposome variable is associated with the BAG prediction (from negative which corresponds to better grey matter health to positive which corresponds to poorer brain health). Additionally, to provide some information about effect size and the relationships between the 30 top exposome features and BAG, we performed a traditional linear regression approach. The effect size of each top exposome variable on BAG (when accounting for confounds) is illustrated by plotting standardized beta for each regression analysis in the supplementary fig. 7. It should be noted here that such model does not account for interaction between exposome features and non-linear relationships with BAG. Accordingly, the effect size of any single exposome variable on BAG can be considered as relatively small, in line with the literature on UK Biobank. Whatever the approach, however, overall, most of the associations follow an expected pattern.

Concretely, for factors that are generally considered as risk factors for brain diseases, higher exposure is associated with poorer brain health (i.e., higher BAG). These include high blood pressure, smoking, and alcohol, as well as diabetes. It should be noted here that for these domains, the variables that show the highest impact on the predicted grey matter health pertain to the duration of the exposure and the life periods in which the exposure happens. Hence, for smoking, duration, age at start and age at stop appear as complementary information instead of redundant variables for the prediction. Longer smoking duration, younger age when started smoking and older age when stopped smoking are all associated with poorer predicted brain health. Similarly, for high blood pressure, a longer duration and an older age at diagnosis (likely reflecting an older age for treatment) both lead to poorer predicted brain health. Along the same line, our results show that when diabetes diagnosis happens at an older age, poorer brain health is predicted. This is also the case for other non-cancer illness: diagnosis (and likely treatment) at an older age is associated with poorer predicted brain health. In contrast, the relationship between operations (be it first or second operation) and the predicted grey matter health goes in the opposite direction: operations performed at younger age appear to relate to poorer brain health.

For the remaining top contributing variables different patterns can be observed. In particular, for variables related to nutrition diet, while high coffee intake is associated with poorer predicted brain health, low dried fruit and cereal intake leads also to poorer predicted brain health. Finally, low hip circumference and low bone density are both associated with poorer brain health.

The variables that are lower in the contribution ranking can be found in Supplementary Table 6. It can be noted that some variables do pertain to the general factors listed above, but do not importantly contribute to the model. Examples of such variables are current smoking status (ranked 207 with random forest), vascular/heart problems diagnosed by doctor (ranked 154 with random forest) with the main algorithm, diabetes diagnosed by doctor (ranked 231 with random forest), history of injury caused by alcohol consumption (“Ever been injured or injured someone else through drinking alcohol”’, ranked 240 with random forest). In contrast to variables from the same domain appearing in the top contributing group, these variables do not contain information about the life period or duration of the exposure. This pattern suggests that the most important aspect of cardiovascular, metabolic, and lifestyle factors on grey matter health is the life period and chronicity of the exposure.

We can also note that a range of other variables show a relatively low contribution to the prediction of grey matter health. These include mental health related variables (such as history of unusual and psychotic experience, ever had period of mania/excitability and depression-related problems) and socio-affective factors (such as friendships satisfaction), early life factors (such as maternal smoking around birth and “someone to take to physician when needed as a child”), ethnicity, but also specific exposure such as cannabis use and sun exposure. Although these factors can potentially contribute to predicting grey matter health, their influence on the model is relatively negligible.

Finally, these patterns are not importantly influenced by sex. Although in the main analysis, we treated sex as a covariate, in a supplementary analysis, we instead included it as a predictor variable in the model. We observed overall similar results. Furthermore, the contribution (SHAP value) of sex was very low, indicating minimal influence on the predictions. This suggests that the model’s performance remains stable and generalizable, regardless of whether sex is accounted for as a covariate or included as a predictor variable.

Taken together, our results suggest that factors pertaining to the internal expotype, including cardiovascular, metabolic, and musculoskeletal aspects, together with smoking, alcohol, and diet, are the strongest predictors of grey matter health. Importantly, variables informing about the chronicity of the life period and the duration of the exposure are the most important for individual prediction.

Methods
General workflow
Machine learning models were developed in two main steps in this study. First, a brain age prediction model based on grey matter data was developed. This model was needed to derive a grey matter health indicator (BAG) based on the discrepancy between the predicted and the chronological age. Second, a grey matter health (BAG) prediction model was developed based on exposome data. Both steps were carried on in the UK Biobank, but the two models were developed in strictly separated subsets of participants (to avoid data leakage) as illustrated in Fig. 4 below.

To develop a brain age prediction model, we identified a large subset of 5025 (cognitively) healthy participants (“healthy sample”) within UK Biobank. Within the remaining population of the UK Biobank, we identified one main subset, a replication subset and a “variables-restricted” subset of participants (see Supplementary Table 2) to which the previously developed brain age model was applied to compute a BAG for each and every participant. These subsets were defined based on the availability of exposome variables in the participants (see Fig. 1). In the second step, machine learning models were developed to predict grey matter health (i.e., BAG) in the participants of these three subsets.

Participants
Detailed explanation of data collection in UK Biobank cohort can be found in https://www.ukbiobank.ac.uk/media/gnkeyh2q/study-rationale.pdf. This study focuses on participants with available imaging data (39,390 participants, aged 44–82 years, mean 63.64 ± 7.54 years, n = 20,707 females). Written informed consent was obtained from all participants. The present analyses were conducted under data application number 41655 and were approved by the ethical committee of the Heinrich Heine University Düsseldorf.

Cognitively healthy participants (“’healthy sample”) had no self-reported long-standing illness disability or infirmity (UK Biobank data field #2188), no self-reported diabetes (field #2443), no stroke history (field #4056), no ICD-10 diagnosis and good or excellent self-reported health (field #2178). These criteria were defined in line with a previous study20 and led to a sample of 5025 healthy participants (age range 46–82 years, mean 62.12 years ± std 7.16 years, 2579 females), while leaving 34365 participants (age range: 44–82 years, mean 63.86 ± 7.57 years, 18,128 females) to define subsets for exposome based prediction of grey matter health (see Supplementary Table 2).

Grey matter features
To obtain an estimator of grey matter health, we used grey matter volume features. It should be noted that when using a Brain Age Gap Indicator, depending on the neuroimaging features which are used, different neurobiological aspects of brain health are probed. Many recent studies in large neuroimaging databases have capitalized on multimodal neuroimaging data (typically including functional, white matter, and grey matter features) to derive a Brain Age Gap estimator. This combination of different features was generally for the sake of optimizing the model’s accuracy4, but it results in a global brain health indicator (combining different aspects of brain structure and function) that may lack specificity from a neurobiological standpoint. In contrast, unimodal estimators, such as those explicitly based on grey matter, can be derived with close accuracies to indicator derived a multimodal model4,20 while providing a more specific insight into the healthiness of grey matter and its relationship to different factors.

Comprehensive information regarding the neuroimaging data from the UK Biobank is available here: https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf. T1-weighted MRI images were acquired by 3 Tesla Siemens Skyra scanners with 32 channel head coils and used an MPRAGE sequence with 1-mm isotropic resolution, with Field-of-view: 208 ´ 256 ´ 256. For Imaging data preprocessing, we used an in-house developed framework for computationally reproducible processing of large-scale data (FAIRly big51). For this purpose, a singularity container with a pipeline to perform voxel-based morphometry (VBM)52 on individual T1-weighted MRI images based on the Computational Anatomy Toolbox (CAT)53 was created. All T1-weighted anatomical images were processed with the CAT version 12.7. After normalisation and segmentation, the grey matter volume segments were modulated for non-linear transformations and smoothed. Grey matter was parcellated using a combination of the Schaefer atlas for 200, 400, 600, 800, and 1000 cortical regions54 and the Melbourne subcortex atlas for 32 and 54 subcortical regions55 leading to five levels of representations of grey matter (grey matter volume for either 232, 454, 654, 854, and 1054 regions). After evaluation of brain age prediction performance with grey matter features at these different levels of granularity (see Supplementary Table 1), the atlas of 1054 regions was selected as the optimal grey matter representation for BAG computation in the UK Biobank population.

Brain age prediction model
Four predictive algorithms were evaluated to design a Brain Age Prediction Model. These included Linear Regression, Ridge Regression56, Support Vector Regression (SVR)57,58, and Random Forest (RF)59 as implemented in scikit-learn60 and Julearn packages61. They were all trained to predict an individual’s chronological age using the five different sets of grey matter features (with different levels of granularity) in healthy sample (n = 5025). Predictive brain age models were trained on 80% of the healthy sample (n = 4020) using 10-fold nested cross-validation with 5 repeats for estimating the chronological age based on the five different sets of individual’s grey matter features. Furthermore, a 10-fold inner cross-validation loop was implemented for hyperparameter tuning using a grid search approach. The optimized brain age models were then validated on held-out set made by the remaining 20% of the healthy sample (n = 1005) (Supplementary Fig. 8A). Model performance was quantified using the Pearson correlation coefficient (r) and mean absolute error (MAE) between predicted and chronological age in the held-out set.

Finally, the model with the minimum prediction error in held-out set was selected and fitted on the entire healthy sample (training + held-out) and used to estimate the chronological age in population set (n = 34,365, age range: 44–82 years, mean 63.86 ± 7.57 years, 18,128 females) in the UK Biobank dataset. According to the results presented in the Supplementary Table 1, the best model was Ridge regression with 1054 grey matter features.

Importantly, several studies have brought attention to an age bias/age dependency in brain age prediction requiring a so-called age-bias correction4. This was done here by regressing out the effects of age on the predicted age62. Specifically, we calculated a regression line with chronological age as the predictor and predicted brain age as the outcome, using only the training dataset. The resulting slope and intercept were then used to adjust the predicted brain age values in the testing dataset. This adjustment involved subtracting the intercept from each predicted brain age and then dividing by the slope. In other words, the slope and intercept of the regression line between chronological age and the predicted age were estimated in the training set and used to adjust the predicted age in the test and the population samples. As expected, and illustrated in Supplementary Table 11, this age-bias correction procedure substantially reduced the artefactual correlation between the subsequently computed BAG and age.

Brain age gap (BAG) as an indicator of grey matter health
The predicted age was then used to compute an indicator of grey matter health for each individual participant based on the discrepancy between his/her (true) chronological age and the “apparent” (i.e., predicted) age of his/her brain grey matter. This was done by calculating the so-called “brain age gap” (BAG) by subtracting the actual chronological age from the adjusted predicted brain age. Accordingly, it provided a normalized measure of the extent to which an individual’s brain appeared older (BAG > 0) or younger (BAG < 0) than same-aged peers. The BAG offers a distinctive advantage as it is a personalized measure by nature. Moreover, it is cross-validated and does not convey age-related information (or age-related statistical signal) following age-bias correction, allowing it to directly assess deviations from population norms. In agreement with this view, this estimator was normally distributed in the UK Biobank whole population, as well as within our main subset, the replication subset and the variable-restricted subset (see Fig. 5). In other words, across all samples, most participants showed an almost null gap (i.e., an apparent brain that corresponds to their chronological age) while some participants show a positive gap indicating that their brain is estimated older than their chronological age and some other participants show a negative gap hence reflecting relatively preserved grey matter compared to their chronological age.

Exposome variables
To represent the exposome, we used a wide range of non-imaging variables encompassing biomedical, lifestyle, socio-economical and early life factors as illustrated in Fig. 1 and the full list of exposome variables are available in Supplementary Table 2. The selection of variables was guided by previous publications reporting either associations (with correlation or regression usually) with brain health as measured by the brain age gap10,11,18–21,25,63,64 or associations with grey matter structure26,65–69 in the UK Biobank. This choice was also constrained by data availability. To avoid potential biases associated with substantial missing values across participants, we focused on variables that were available in at least 2000 participants including both males and females. Of note, we carefully considered multiple imputation. However, the missingness structure in the dataset posed substantial challenges. Specifically, participants with neuroimaging data exhibited block-like patterns of missingness, resulting in limited overlap across many variables (see Supplementary Fig. 9). Under such conditions, chained-equations multiple imputation would be prone to instability and strong model dependence, as many conditional models would need to be estimated from sparse shared information. Moreover, implementing multiple imputation across several hundred heterogeneous variables (continuous and categorical) within a cross-validation framework—while avoiding data leakage—would substantially increase both methodological complexity and computational burden, with uncertain gains in bias reduction. Considering these limitations, we adopted a minimum-availability filtering strategy, prioritizing variables with sufficient observations while maintaining analytical tractability.

We also performed some additional data curation processes, which included: uniqueness constraint (we ensured that each field ID and participant was represented uniquely in the dataset and we removed potential duplicate records), aberrant value exclusion (we verified that all variable values fell within the valid range or allowed set of categories e.g., no out-of-range numerical values or invalid category codes), and uninformative value exclusion (we excluded implausible or non-informative responses such as “don’t know” or “prefer not to answer”). Furthermore, we also calculated duration variables for medical conditions by subtracting onset age from the age when attended assessment center (chronological age). Finally, it is noteworthy to mention that most exposome variables were measured at the initial UK Biobank assessment visit (2006–2010), and when data were not available at that time point, we used measurements from the first repeat assessment visit (2012–2013).

As illustrated in Fig. 1, these procedures led us to define a main subset in which 261 distinct exposome variables were available for 3706 participants. By dropping out two variables (left and right heel bone density), we could create a bigger subset of 4292 participants that served as a replication subset. Finally, we also identified a bigger subset of 7736 participants with data for 201 distinct exposome variables. In this “’variables-restricted subset”, mainly variables related to socio-affective and mental health domains were missing compared to the main and replication subsets. Standard preprocessing was performed on exposome variables, including variables standardization (within the cross-validation scheme to avoid any data leakage). The redundancy between the 261 exposome variables was relatively low as illustrated by the correlation heatmap (Supplementary Fig. 10). Although some clusters of correlated variables can be observed, these pertain to instrumentally related body morphology and composition variables and overall, a substantial degree of heterogeneity can be observed. The distribution of the variables across different exposome domains is illustrated for the reader’s information in Supplementary Fig. 11. Of note, from this set of variables, we did not perform any redundancy reduction since we subsequently used machine learning algorithms which are designed to be able to weight or select relevant features (see next section).

Grey matter health prediction model
To predict grey matter health at the individual level, we first implemented a random forest algorithm, which used a decision tree-based approach and has the advantage of accounting for non-linear relationships between the exposome variables and grey matter health. Random Forest was trained and tested for each subset (i.e. the main subset, the replication subset and the “’variables-restricted”’ subset separately), However, for the sake of replication, we also implemented two additional popular algorithms: ridge regression (which was also used for the brain age model) and support vector regression (SVR). We selected these three algorithms because they offer complementary strengths: random forest as an ensemble method, has the ability to capture non-linearity and complex interaction among features. Furthermore, by combining multiple decision trees, it usually improves prediction accuracy and robustness. While ridge regression provides regularization to prevent overfitting, SVR is very popular and relatively more robust to outliers than ridge regression.

Each additional algorithm was trained and tested on the main subset. Each model was trained using BAG as the target variable (representing grey matter health) and exposome variables as input features within a nested cross-validated scheme. This ensured unbiased performance estimation, with 5 inner folds for hyperparameter tuning and 5 outer folds with 5 repeats to estimate generalization performance. Grid search and Optuna search70 was employed in the inner folds for hyperparameter tuning.

To mitigate the disproportionate influence of larger-scale features, standardization was applied using the StandardScaler function from scikit-learn package60. Controlling for covariates was performed on the exposome variables within the cross-validation framework (using estimated from the train set). The set of covariates/confounds include age, square of age, sex, height, and volumetric scaling from T1 head image to standard space. Thus, all care was taken to neutralize the influence of usual confounds by using a normative indicator of grey matter health and by additionally controlling for covariates within the prediction analysis. It is worth noting that sex was defined based on UK Biobank data-field 31, which reflects sex recorded at recruitment and may include updates provided by participants. Sex was included as a covariate to control for potential confounding effects, but sex-stratified analyses were not performed, as the aim of the study was to model population-level patterns of brain health rather than sex-specific effects.

Model performances were evaluated using multiple metrics, including the mean absolute error, mean squared error, root mean squarederror, r-square, and Pearson correlation between predicted BAG and target BAG from the test sets for each outer fold. For each subset and each algorithm, permutation test was performed to assess significance against a null distribution by shuffling the scores of the Grey Matter Health variable in 500 repeats of 5-fold cross-validation. Multiple comparisons across different metrics were corrected using false discovery rate (FDR; Benjamini and Hochberg 1995) of p < 0.05. Additionally, after selecting the best model, SHapley Additive exPlanations (SHAP)71,72 were employed to provide an insight into how individual exposome variables contribute to the prediction (Supplementary Fig. 8B). Indeed, SHAP offers a cohesive framework for interpreting predictions in explainable Artificial Intelligence, assigning importance to each variable contributing to a prediction. The stability of SHAP patterns across folds is demonstrated in Supplementary Fig. 12. All analysis steps were conducted using Python, SHAP71,72, scikit-learn60, and the Julearn package61. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In their study, Mahdipour et al. seek to shed light on how a broad spectrum of biomedical, lifestyle and psychosocial exposures jointly relate to individual differences in “brain age gap” — the discrepancy between an individual's age predicted from structural MRI data and their chronological age. Here, brain-age gap is used as a proxy for overall brain health. The authors implement a two-step pipeline using data from the UK Biobank: (i) three machine learning algorithms (ridge regression, support vector regression, and random forest) are trained on grey-matter parcels from 5,025 healthy adults to predict brain age in 34,365 independent participants; (ii) a subset of these participants, scored on up to 261 exposome variables spanning cardiovascular-metabolic, behavioural, socio-economic, and early-life domains, are used to predict the brain-age gap via a random forest model, with SVR and ridge regression used as sensitivity analyses. Model performance is assessed using nested cross-validation, and feature importances are quantified using SHAP values. The exposome explains approximately 5% of the variance in brain-age gap (r ≈ 0.23, p = 0.002). Key contributors include alcohol intake, cardiometabolic burden (e.g., blood pressure history, age at diabetes onset), smoking behaviour, musculoskeletal factors, and dietary patterns (e.g., coffee consumption), while mental health, socio-affective, and environmental variables contribute comparatively little. The authors conclude that modifiable lifestyle and metabolic exposures modestly—but reliably—affect overall brain health, emphasizing the importance of early prevention in cardiovascular and metabolic domains to promote healthy brain ageing.\nThe manuscript is well-written, methodologically sound, and the analyses are logically organised. I particularly commend the authors for releasing their full codebase in a public GitHub repository, which greatly enhances transparency and reproducibility. The topic is timely and relevant to researchers in neuroscience, ageing, and epidemiology. A notable strength is the study’s explicit attempt to disentangle the individual contributions of a broad range of exposures to brain health, thereby illuminating modifiable risk factors. Nevertheless, the work’s capacity for causal inference is limited, and several of the associations—though now examined in a multivariate framework—have been reported previously in similar UK Biobank analyses. That said, the work still adds valuable evidence and provides timely insights into modifiable influences on brain health. In the following, I outline a number of concerns that, if addressed, would further strengthen the manuscript.\n[1] Novelty and prior work: A broad spectrum of phenotypic associations between lifestyle, health factors, and brain ageing have already been explored in UK Biobank data (e.g., Smith et al.: https://doi.org/10.1016/j.neuroimage.2019.06.017; Jawinski et al.: https://doi.org/10.1101/2023.12.26.23300533). How do the current findings extend or differ from these prior studies? Please clarify how the present multivariate approach advances beyond earlier univariate correlation analyses.\n[2] Variable selection and interpretation: It remains somewhat unclear how the 261 exposome variables were selected. Were these variables chosen purely based on availability and relevance, or were they also selected to represent independent clusters of health-related domains? How distinct are the included variables from one another in terms of conceptual overlap and statistical collinearity? Providing a hierarchical clustering analysis or correlation heatmap could help demonstrate the structure and independence of the variable set.\n[3] Training sample characteristics: In lines 120-125, the age range is reported for the test sample, but not for the training sample. For clarity and consistency, it would be helpful to also provide the age range of the training sample, ideally in the same section.\n[4] Prediction accuracy and implications for BAG: Higher age prediction accuracies have previously been reported in several UK Biobank studies, with a particularly strong example by Peng et al. achieving a mean absolute error (MAE) of 2.14 years (https://doi.org/10.1016/j.media.2020.101871). How might such improvements in prediction accuracy affect the ability to detect associations between brain-age gap (BAG) and exposome variables? Is it conceivable that, as models continue to improve, their precision could reduce true inter-individual variability in BAG to the extent that biologically meaningful differences become diminished?\n[5] Residual confounding by age and sex: The brain-age gap is known to be confounded by age due to regression dilution. The authors regressed out age effects from predicted age using the slope and intercept from the training sample. However, depending on how well the model generalizes to the test sample—even though all data are from the UK Biobank with harmonized scanner protocols—some residual confounding may remain. It would be helpful if the authors explicitly demonstrated that age (and sex) do not confound the BAG–exposome associations, for example by including a plot similar to Supplementary Figure 6.\n[6] Sample independence and relatedness: The UK Biobank includes many participants with first-, second-, and third-degree familial relationships. Were related individuals excluded across the training, validation, and test sets to prevent potential data leakage? If not, how did the authors ensure that familial relatedness did not introduce bias into the results? If related individuals were not accounted for, this could represent a methodological limitation worth acknowledging.\n[7] Algorithm choice rationale: While ridge regression, SVR, and random forest are valid choices, many other machine learning approaches are available for tabular data. What was the rationale for selecting these three algorithms? Have the authors considered ensemble methods?\n[8] P-values across datasets: In the main subset, the exposome model yields r = 0.23 with p = 0.002. In the larger replication and variable-restricted samples, the correlation remains similar (r = 0.23 and 0.24, respectively), yet the reported p-values are higher. Given the increased sample size, this is unexpected—typically, larger samples lead to smaller p-values for equivalent effect sizes. The authors mention that p-values were FDR-corrected, but it remains unclear which specific tests were included in the correction procedure. I suggest reporting both the raw and FDR-corrected p-values side by side in Supplementary Table 3, possibly along with a brief table note explaining which tests were included in the FDR correction.\n[9] Language clarity: The phrase “worst grey matter health” (and similar expressions such as “worst predicted grey matter health”) appears multiple times throughout the manuscript, and I found it a bit unusual. The authors might consider rephrasing these instances using more standard alternatives such as “poorer brain health”).\n[10] Sex/gender: Did the authors use self-reported sex or genetically inferred sex in their analyses? Unless a specific gender variable was used, referring to \"sex\" would be more appropriate in my view. Were discrepancies between self-reported and genetic sex taken into account in the analysis (e.g., by excluding individuals with reported vs. genetic sex-mismatch)?\n[11] Interpretation of \"almost null gap\": The manuscript states that “most participants showed an almost null gap...,” which may suggest that individual brain-age gaps are negligible. However, the reported mean absolute error is approximately 3.9 years. Do the authors instead refer to the group-averaged mean of the BAG distribution, which may be close to zero by design? I suggest clarifying whether this statement refers to the distribution mean or to individual-level deviations, and consider revising the wording to avoid potential confusion.\n[12] GitHub repository: I very much appreciate the inclusion of the GitHub repository, which appears well-organized, clear, and greatly enhances the transparency of the analysis workflow. The authors recommend using virtual environments such as conda, which is good practice. To further improve reproducibility, could the authors provide the corresponding environment.yml file(s) (or similar) with package versions? Additionally, it would be helpful to specify the system used to run the analyses.\n[13] The exposome concept: The concept of the exposome was relatively new to me, as I am more familiar with genome- and phenome-focused approaches. As I understand it, the exposome is generally defined as the totality of non-genetic internal and external exposures across the lifespan. However, many of the variables assessed in this study—such as dietary habits, smoking behaviour, or socioeconomic status—are known to have some degree of genetic influence. This suggests that the set of predictors used here may not reflect purely non-genetic exposures in the strictest sense. In my view, this overlap between genetic predisposition and environmental exposure highlights a broader conceptual limitation of the exposome framework. It might be helpful to briefly acknowledge this nuance in the discussion—perhaps in the context of causality, where the exposome framework alone does not resolve the challenge of causal inference, particularly in observational cohorts like the UK Biobank.", "reviewer_2": "I thank the Editor and Authors for the chance to review this interesting paper! Please find my structured comments below.\nKey results\nThis study presents an interesting approach to answering the question of “What predicts individual brain health?” by using rich data from the UK Biobank. Grey matter-based brain age models are trained and then used to predict brain age in a sub-sample of the UK Biobank. The difference between the individuals’ brain age and chronological age (brain age gap) is used as a proxy for brain health. In a further step, the brain age gap is predicted from a set of variables labelled here as the exposome. The authors present that exposome variable predict between 3%-5% of the variance in the brain age gaps. Using SHAP values, the authors present a ranking of the exposome features in explaining the brain age gap.\nValidity\nWhile the manuscript draws on rich and high-quality data, there are multiple uncertainties around BAG still limiting its interpretability. The extend to which BAG reflects brain health remains unclear.\nAdditionally, the results indicate relatively weak associations between the exposome and BAG. These effect sizes were not discussed, which would however be important for context when presenting SHAP values and making conclusions about exposome data being able to predict brain health.\nSignificance\nThe integration of exposome data with neuroimaging-based brain age prediction is timely and potentially impactful, particularly in the context of identifying modifiable lifestyle or environmental factors associated with brain health. The use of large-scale population data (UK Biobank) and open methods further enhance the manuscript’s relevance. However, the modest variance explained and the unresolved issues around the interpretation of BAG somewhat limit the strength of the conclusions. While the work is incremental in its current form, stronger positioning within the existing literature and a more critical discussion of the results would increase its contribution to the field. Nevertheless, the findings support a forming body of literature highlighting that the state of the brain (or potentially brain health) is multivariate, being an useful contribution to the field.\nData and methodology\nThe dataset (UK Biobank) is appropriate and well-regarded. However, important methodological details are missing or underspecified. These include:\n- Rationale for the choice of machine learning algorithms.\n- Whether training and test subsets were strictly independent.\n- The method and covariates used for age-bias correction.\n- How exposome variables were selected and grouped (e.g., based on UKB codes or theoretical assumptions).\n- Quality control steps for both exposome variables and MRI data.\n- Treatment of variable redundancy and imbalanced group sizes across exposome domains.\nAdditionally, it is unclear why brain age models were evaluated only with MAE and Pearson’s r, whereas exposome-to-BAG models used a broader set of metrics. A consistent evaluation framework would aid interpretability.\nAgain, while brain age provides a simplified metric of the state of the brain, which can be described as a proxy of brain health, its limitations need to be discussed in order to not mislead readers who are not familiar with the concept.\nFinally, I appreciate the authors’ open science approach and well-organized GitHub repository!\nAnalytical approach\nThe use of ensemble models such as Random Forests and SHAP value interpretation is appropriate. Nonetheless, the manuscript lacks information on the stability and robustness of the feature importances across methods. Are the SHAP results stable when compared to, for example, permutation importance or bootstrapped variable selection?\nThe choice of performance metrics should also be reconsidered. Reporting R² in the main text -- not only in the Supplement -- is essential for gauging practical significance. Moreover, the authors should ensure that statistical tests are labeled clearly; for example, the description of a t-test on a Pearson correlation is technically accurate but might be confusing in context.\nSuggested improvements\nMost importantly, the conceptual framing of brain age gap (BAG) could benefit from a more cautious and precise treatment. Currently, several statements risk implying that BAG reflects a direct measure of biological ageing, rather than deviation from a normative reference model. It would be helpful to more clearly separate BAG from ageing processes per se, especially in light of recent evidence questioning this link. In line with this, we suggest revising Figure 1 to avoid labeling the x-axis as “true BAG,” which may unintentionally suggest the existence of an objective ground truth. The methodological transparency would also benefit from further detail. The rationale for selecting specific machine learning algorithms should be articulated, along with an assessment of model stability and generalizability. Similarly, please specify how the age-bias correction was implemented—was it linear or non-linear, which covariates were included, and was the slope from the training set used? These details are important for reproducibility and interpretability.\nI also recommend expanding the description of the exposome variables, particularly regarding how they were selected, grouped, and whether any steps were taken to mitigate potential imbalance across groups (e.g., lifestyle vs. environmental exposures). Relatedly, it would be helpful to clarify whether weighting or redundancy reduction was applied to address overrepresentation or multicollinearity.\nWhile performance metrics like Pearson’s r are useful, including R² values in the main text, rather than relegating them to the supplement, would give readers a more complete sense of the model’s explanatory power. This is particularly relevant given that only a modest proportion of the variance in BAG is explained. These small effects should be clearly stated and considered when discussing the results.\nMoreover, the limitations of BAG as a derived and relatively noisy variable should be more directly acknowledged and discussed in a dedicated limitations section.\nFinally, some sections of the Discussion could be condensed to reduce repetition and instead used to place the findings in context of comparable studies. Phrasing such as “optimal lifestyle” and broad claims like “all factors…constitute the exposome” could also be softened or made more precise to avoid overinterpretation. Addressing these points would, in my view, substantially enhance the clarity and impact of the manuscript.\nClarity and context\nThe manuscript is generally well-structured, but several areas require clarification. Some sentences need rephrasing for precision and scientific tone (e.g., “leads to a worst predicted grey matter health”). The Discussion section currently repeats earlier results rather than synthesizing them with broader literature. A more critical and integrative discussion would improve clarity and impact.\nReferences\nMultiple references could be added to foster discussion in the Discussion section. This includes both brain age-specific and brain age-unspecific research.", "reviewer_3": "In this paper, the authors investigated the association between expotypes and brain age gap. The use of the large UK biobank dataset is a key strength of the study. Further clarification and justification regarding the methodology, reporting and discussion of the results, particularly in the context of existing literature, is required.\n• The authors suggest that to avoid potential biases associated with substantial missing values across participants, they focused on variables that were available in at least 2000 participants including both males and females - however, this would introduce bias, as has been well documented via complete cases analysis. Why was a robust method for addressing missing data not conducted, such as multiple imputation?\n• It is not clear to me how the statement “BAG does not rely on chronological age” is true, given the brain age gap subtracts the chronological age from the predicted age.\n• I do not see how an individualised approach has been adopted here. Fundamentally, these models show relationships between a set of variables and brain age gap, but do not provide an individualised approach to predicting brain age gap.\n• At what timepoint were the exposures measured, in addition to the imaging data? Was the time between measures considered in the model?\n• The entire discussion is missing a meaningful discussion of effect sizes i.e., how many additional years in brain ageing were observed as a result of each additional year for the exposure? This is needed to provide a meaningful interpretation of the results.\n• It is important that all statements are supported by citations/evidence\n• Please include a descriptive table for variables used in study"}, "author_response": "Dear Editors and Reviewers,\nThank you for your letter and for the reviewers’ comments concerning our manuscript entitled “What\npredicts individual brain health? a machine learning study spanning the exposome”. Your comments\nwere highly insightful and enabled us to greatly improve the quality of our manuscript. We have studied\ncomments and made corrections carefully which we hope to meet with approval. In the following pages\nare our point-by-point responses to each of the comments of the reviewers. Revisions in the text are\nshown using blue colour.\nResponses to the comments of Reviewers:\nReviewer #1 (Remarks to the Author):\nIn their study, Mahdipour et al. seek to shed light on how a broad spectrum of biomedical, lifestyle and\npsychosocial exposures jointly relate to individual differences in “brain age gap” — the discrepancy\nbetween an individual's age predicted from structural MRI data and their chronological age. Here, brain-\nage gap is used as a proxy for overall brain health. The authors implement a two-step pipeline using\ndata from the UK Biobank: (i) three machine learning algorithms (ridge regression, support vector\nregression, and random forest) are trained on grey-matter parcels from 5,025 healthy adults to\npredict brain age in 34,365 independent participants; (ii) a subset of these participants, scored on up to 261\nexposome variables spanning cardiovascular-metabolic, behavioural, socio-economic, and early-life\ndomains, are used to predict the brain-age gap via a random forest model, with SVR and ridge regression\nused as sensitivity analyses. Model performance is assessed using nested cross-validation, and feature\nimportances are quantified using SHAP values. The exposome explains approximately 5% of the\nvariance in brain-age gap (r ≈ 0.23, p = 0.002). Key contributors include alcohol intake, cardiometabolic\nburden (e.g., blood pressure history, age at diabetes onset), smoking behaviour, musculoskeletal factors,\nand dietary patterns (e.g., coffee consumption), while mental health, socio-affective, and environmental\nvariables contribute comparatively little. The authors conclude that modifiable lifestyle and metabolic\nexposures modestly—but reliably—affect overall brain health, emphasizing the importance of early\nprevention in cardiovascular and metabolic domains to promote healthy brain ageing.\nThe manuscript is well-written, methodologically sound, and the analyses are logically organised. I\nparticularly commend the authors for releasing their full codebase in a public GitHub repository, which\ngreatly enhances transparency and reproducibility. The topic is timely and relevant to researchers in\nneuroscience, ageing, and epidemiology. A notable strength is the study’s explicit attempt to disentangle\nthe individual contributions of a broad range of exposures to brain health, thereby illuminating\nmodifiable risk factors. Nevertheless, the work’s capacity for causal inference is limited, and several of\nthe associations—though now examined in a multivariate framework—have been reported previously\nin similar UK Biobank analyses. That said, the work still adds valuable evidence and provides timely\ninsights into modifiable influences on brain health. In the following, I outline a number of concerns\nthat, if addressed, would further strengthen the manuscript.\nWe thank the reviewer for the positive evaluation of our manuscript, as well as his/her comments which\nhave contributed to improve its quality.\n[1] Novelty and prior work: A broad spectrum of phenotypic associations between lifestyle, health\nfactors, and brain ageing have already been explored in UK Biobank data (e.g., Smith et\nal.: https://doi.org/10.1016/j.neuroimage.2019.06.017; Jawinski et\nal.: https://doi.org/10.1101/2023.12.26.23300533). How do the current findings extend or differ from\n1\nthese prior studies? Please clarify how the present multivariate approach advances beyond earlier\nunivariate correlation analyses.\nWe thank the reviewers to give us the opportunity to discuss the novelty of our work, in particular when\ncompared to previous phenome-wide association studies of BAG, such as Smith et al. (2019)1 and\nJawinski et al. (2023)2. The main difference lies in the approach: while phenome-wide studies typically\ntest association with BAG for each phenotypical variable separately, our exposome holistic approach\nconsidered all variables conjointly with a predictive modelling accounting for redundancy and\ninteraction.\nA consequence of the redundancy or collinearity among exposome variable in a mass bivariate testing\nas done in Smith et al. (2019)1 and Jawinski et al. (2023)2 is that all variables that are colinear will\nappear as significantly associated with BAG. Consequently, there is no indication whether one might\nbe more predictive for BAG than the other related variable and this may lead the authors to make\nsubjective decision on how to filter the set of related variables for reporting and interpretation. For\nexample, in Smith et al., the authors reported “we have manually removed largely-redundant variables\nfor purposes of readability”’ and accordingly they listed only the strongest linear association\n(correlation) for a set of similar variables. Despite this statement, the authors still report many related\nvariables in their main results, such as “Alcohol intake frequency, Average weekly beer plus cider\nintake and average weekly red wine intake’’. In contrast, our approach shows that “Alcohol intake”\non its own contribute to predict individual brain health and that the type of alcohol does not really play\na role in the prediction.\nAnother consequence of bivariate mass testing is that interaction effects are completely ignored\npotentially missing relevant modulating factor. We argue that this is the reason why some factors\nappear specifically relevant when predicting individual brain health with a multivariate predictive\nmodelling approach in our study. In particular, coffee intake and dried fruit intake did not appear as\nkey factors in previous phenome-wide association studies. Furthermore, hip circumference was also\nrarely reported in association to brain health and was not linearly associated to BAG in our study,\nalthough it appears to play an important role in the prediction of individual brain health. Therefore,\nwe now further discuss the role of these variables in interaction with other factors. In particular, we\ndiscuss the protective role of gluteofemoral fat in cardiovascular and metabolic conditions, as well as\nthe protective role of dried fruits intake on cardiovascular conditions based on evidence from previous\nstudies. In addition, we also discuss potential detrimental interaction between coffee intake and\ncardiovascular factors (such as blood pressure).\nFinally, but very importantly, we would also like to point out that our study for the first time points to\nthe relevance of information pertaining to the window and duration of detrimental exposures, such as\nsmoking, high blood pressure and diabetes. To the best of our knowledge, this aspect has rarely been\ndiscussed before, while according to our model the time period of exposure play an important role in\ndetermining individual brain health.\nFollowing the reviewers’ comments, the novelty of our work has been further discussed in the revised\nmanuscript:\nIn the introduction (page 3, line 7-15):\n“Using a BAG-based grey matter health estimator, some studies have emphasized that grey\nmatter health is tightly linked to several aspects of body health in aging populations3-5, others\nhave highlighted relationships with early life factors6 and socio-affective/mental health-related\nfactors (e.g., long-term depressive symptoms7). Given the wide variety of factors that can\ninfluence brain health, some studies have applied a phenome-wide association analysis of BAG1,8.\nis tested separately, typically for a linear association. Such approaches hence do not account for\nredundancy and interaction among different factors altogether forming the expotype of an\nindividual. These previous studies may thus have missed important factors appearing only in\ninteraction with others in shaping brain heath in aging.”\nIn the discussion (page 8, line 6-18):\n“Altogether these results generally point to the fact that the major threats for brain health in our\naging populations are metabolic and cardiovascular factors.\nHowever, in contrast to previous phenome-wide association studies of BAG1,8, our multivariate\npredictive modeling approach allows us to identify that the top contributing variables for the key\nother risk factors that are smoking, high blood pressure and diabetes pertain to the life period at\nwhich the exposure has taken place and the duration of the exposure. For smoking and higher\nblood pressure, we can even note that both, variables pertaining to life period, and variables\npertaining to duration conjointly contribute to the prediction. Hence our multivariate predictive\nmodeling approach allows us to demonstrate that life period and chronicity of the exposure are\ncomplementary (rather than redundant) information for determining individual brain health.\nOverall, the sooner these detrimental exposures are discarded (by stopping smoking and by\ndiagnosing and treating high blood pressure), the lower the negative impact on brain health. Thus\nour study crucially highlights the need of early prevention campaigns9 targeting diabetes,\nsmoking and blood pressure monitoring for public health strategies.”\n(page 9, line 3-9):\n“However, previous phenome-wide association studies did not highlight coffee consumption as\nstrongly associated with BAG when taken in isolation1,8. This suggests that the detrimental effect\nof high coffee consumption on brain health can potentially be explained by interaction with other\nrisk factors discussed above, in particular with cardiovascular factors. It has for example been\nshowed that in UK Biobank participants with genetic predisposition to elevated intraocular\npressure, greater caffeine consumption was associated with higher intraocular pressure and\nhigher glaucoma prevalence10.”\n(page 9, line 19-24):\n“Furthermore, dried fruits intake have been demonstrated to play a protective role against\ncardiovascular disorders in UK Biobank data14. Although further studies are needed in the future\nto better understand how diet and other factors interact with metabolic and cardiovascular\nfactors to influence individual brain health, our current study suggests that high (whole-grain)\n3\ncereal and dried fruits intake are important factors contributing to promote grey matter health\nin aging.”\n(page 9, line 25- page 10, line 8):\n“In our machine learning models, hip circumference also appears to contribute to the prediction\nof individual grey matter health. More concretely, lower (than the mean of 102 cm, see\nSupplementary table 10) hip circumference is associated with poorer individual brain health in\nthe prediction model. However, additional analyses show that, when taken in isolation, hip\ncircumference is not associated with grey matter health in the main subset (null correlation with\nBAG, see Supplementary Figure 6). This suggests that hip circumference plays a role as a\nmoderator variable in the individual prediction of grey matter health. In other words, lower hip\ncircumference on its own does not result in poorer brain health, but in interaction with other\nfactors, it can lead to poorer predicted brain heath. It can for example be assumed that a\ncombination of low hip circumference and low bone density reflects physical frailty, a body\ncondition associated with neurocognitive impairment11. In this vein, when hip circumference is\ntaken in conjunction with other variables, it can indicate specifically local adiposity, which can\nhave a protective effect against some metabolic and cardiovascular conditions. In particular, hip\ncircumference is an indicator of gluteofemoral fat which, in contrast to visceral fat, is associated\nwith reduced risks of insulin-resistance, type-II diabetes and cardiovascular problems, as well as,\nto metabolic health12,13. Since we observe here that hips circumference, a proxy for gluteofemoral\nfat, does contribute to predict grey matter health while it is not directly linearly associated to grey\nmatter health in the sample, we can assume that gluteofemoral fat may modulate the influence of\ncardiovascular and metabolic risks factors on brain degeneration through complex pathways.\nThis is an interesting question for future studies which should determine exactly how hips\ncircumference/gluteofemoral fat modulates the relationship between cardiovascular-metabolic\nhealth and brain structural health.”\n(page 11, line 20-25):\n“Despite these limitations, the current study revealed a set of factors primarily contributing to\npredict structural brain health in an aging population, highlighting cardiovascular health, bone\nhealth and diabetes as key factors, confirming previous studies based on univariate, linear and\nnot cross-validated modeling1,8,15. More importantly, our study pointed to additional factors,\nwhich may not show strong linear association to brain structural health when taking in isolation\nand which therefore are assumed to interact with other factors, such as nutrition diet and coffee\nintake, as well as hip circumference.”\n[2-A] Variable selection and interpretation: It remains somewhat unclear how the 261 exposome\nvariables were selected. Were these variables chosen purely based on availability and relevance, or were\nthey also selected to represent independent clusters of health-related domains? How distinct are the\nincluded variables from one another in terms of conceptual overlap and statistical collinearity?\nProviding a hierarchical clustering analysis or correlation heatmap could help demonstrate the structure\nand independence of the variable set.\nIn short, we took a two-step process: we first adopted a top-down approach to select relevant exposome\ndomains and then constrain the variable set based on availability. Accordingly, the variables were\nmainly selected to represent independent clusters of health-related domains, based on the literature in\nthis field.\nTo clarify this point, below we provide a detailed explanation of how the exposome variables were\nselected:\n Literature:\nWe reviewed more than 50 scientific articles, mainly in two categories:\no Studies that investigated associations between Brain Age Gap (BAG) and exposome\nvariables.\no Studies that investigated associations between brain structure/brain health and\nexposome variables.\nSome studies were theory-driven and focused on a single variable or category. For example,\nCherbuin et al., 202116 investigated the relationship between group of blood pressure (BP)\nvariables and brain health, as measured by BrainAGE, over a 12-year period in middle-aged\nand older adults; or Gray et al., 202017 assessed the relationship of cigarette smoking with gray\nmatter (GM) and white matter (WM) in the UK Biobank, controlling for numerous confounding\ndemographic and health variables.\nOther studies used an agnostic approach (phenome-wide association) or considered a wide\nrange of variables. For instance Miller et al., 201618 investigated the relationship between 1100\nnon-imaging derived phenotypes (coming from 378 data fields) and 2501 imaging derived\nphenotypes (IDPs). In the brain age context, Smith et al., 202019 and Dinsdale et al., 202120\nused 8787 non-imaging derived phenotypes (coming from more almost 500 data fields) to\ninvestigate the relationship of BAG and other phenotypical variables. Similarly, Tian et al.,\n20235, by using UK Biobank imaging data, designed 3 brain and 7 body (organs) age models.\nThey have done extensive work on investigating the relationship of the gap between brain and\nbody model. They have also investigated the associations between organ-specific age gaps,\nincluding brain age gap, and several environmental/lifestyle measures. They have used 148\n(non-imaging) Field IDs as exposome variables. These exposome variables include smoking,\nalcohol consumption, socioeconomic inequality, air pollution, greenspace, natural\nenvironment coverage, exercise, education, sleep hygiene, and maternal nutrition.\n Exposome variables:\nNext, we listed all variables reported in previous literature and accessible in UK Biobank for\nour study. For previous studies conducted in UK Biobank, we could straightforwardly identify\nthe variables based on field IDs. For studies based on other cohorts, we mapped the exposome\nvariables used there to equivalent variables in UK Biobank (where possible). After initial\nlisting, we have done a preliminary exclusion to exclude variables not pertaining to the\nexposome or that were less relevant duplicated of another field. Concretely, we have excluded\nthe following categories and Instance IDs:\no Mother category of 100026 (Cognitive function) and all her children’s categories\no Mother Category of 116 (Cognitive function online) and all her children’s categories\no Instance ID number 1 (Name: Diet Questionnaire Cycles): We excluded Instance ID 1,\nwhich corresponds to 'diet questionnaire cycles', due to its focus on daily food\nconsumption rather than overall dietary patterns. Instead, we utilized variables from\n5\nthe broader Category 100052 (Diet), which provides information on the frequency of\nfood consumption, aligning better with our study's focus on long-term dietary habits.\nThis approach allowed us to focus on variables that reflect long-term dietary patterns,\nwhich are more relevant to our study of Brain Age Gap.\no Instance ID 12 (Name: Coronavirus serology study wave)\no Instance ID 27 (Name: Coronavirus serology study symptoms questionnaire)\no Instance ID 93 (Name: Accelerometer wear)\no Instance ID 154 (Name: Covid-19 antibody study phase)\no Instance ID 178 (Name: Online cognitive assessment)\no Instance ID 604 (Name: Vaccination event)\no Instance ID 9000001 (Name: Death registry reports)\no Instance ID 9000002 (Name: Cancer registry reports)\nIn total, we compiled 659 Field IDs across 67 categories. From these, we selected 397 Field\nIDs, prioritizing those most frequently used in the literature. These included 291 common fields\n(39 categories) with the literature covering major domains such as body morphology and\ncomposition, diet, lifestyle, smoking, alcohol consumption, and cardiovascular health. The\nremaining 106 fields were mainly related to body injuries, illnesses, and socio-affective aspects.\n Parsing data from UK Biobank:\nAs described in the Methods section of the manuscript, we used the ukbb_parser Python\npackage Brandes et al., 202021 to extract data for the 397 Field IDs. This resulted in a table\nwith 2,599 columns, because each Field ID may include multiple time instances (e.g., baseline\nand follow-up), and categorical fields with multiple responses are expanded into even more\ncolumns. Thus, the number of columns substantially exceeded the initial number of Field IDs.\nUsing the Missingno package, we examined missing data patterns (Figure 1), which highlighted\nsubstantial missingness as a major challenge in selecting Field IDs as exposome features.\nFigure 1. Missingness pattern in the literature-based a-priori selected data fields (Missingno package). The grey\ncolours are the filled values in women participants. Blue colours are the filled values in men participants. White\ncolour reflects missing values, and red colour indicates non-informative answers (i.e., I don’t know, I prefer not to\nanswer etc,.).\n Data curation:\nIn the next step, we curate the data to ensure validity and meaningfulness:\n6\n1. Handling missing values: We retained columns with at least 2,000 filled values.\n2. Variable type (categorical vs. continuous) assignment: We identified which features\nwere categorical (discrete groups) and which were continuous (numeric\nmeasurements), to apply the appropriate preprocessing and modelling strategies.\n3. Uniqueness constraint: We ensured that each field ID (and participant) was\nrepresented uniquely in the dataset and removed potential duplicate records.\n4. Aberrant value exclusion: We verified that all variable values fell within the valid\nrange or allowed set of categories (e.g., no out-of-range numerical values or invalid\ncategory codes).\n5. Uninformative value exclusion: We excluded implausible or non-informative\nresponses. For example, some participants selected “do not know” or “prefer not to\nanswer,” which were removed from further analyses.\n Calculation of Durations:\nIn the final step, there were some variables from medical conditions and medical information\n(body injuries and illnesses) category, that they were related to the age when a disease happed.\nSubtracting these ages from the age when attended assessment centre (chronological age)\nprovided the duration of those illnesses.\nAt the end, and considering all mentioned steps, this led us to define three subsets. As mentioned in the\nmanuscript, in the main subset 261 distinct exposome variables were available for 3706 participants.\nBy dropping out two variables (left and right heel bone density), we could create a bigger subset of\n4292 participants that served as a replication subset. Finally, we also identified a bigger subset of 7736\nparticipants with data for 201 distinct exposome variables.\nAdditional information regarding these steps has now been provided in the revised manuscript:\n(page 15, line 6-29):\n“Exposome variables\nTo represent the exposome, we used a wide range of non-imaging variables encompassing\nbiomedical, lifestyle, socio-economical and early life factors as illustrated in Figure 1 and the full\nlist of exposome variables are available in Supplementary Table 2. The selection of variables was\nguided by previous publications reporting either associations (with correlation or regression\nusually) with brain health as measured by the brain age gap5,6,15,22-27 or associations with grey\nmatter structure18,28-32 in the UK Biobank. This choice was also constrained by data availability.\nTo avoid potential biases associated with substantial missing values across participants, we\nfocused on variables that were available in at least 2000 participants including both males and\nfemales. We also performed some additional data curation processes, which included: uniqueness\nconstraint (we ensured that each field ID and participant was represented uniquely in the dataset\nand we removed potential duplicate records), aberrant value exclusion (we verified that all\nvariable values fell within the valid range or allowed set of categories e.g., no out-of-range\nnumerical values or invalid category codes), and uninformative value exclusion (we excluded\nimplausible or non-informative responses such as “do not know” or “prefer not to answer”).\nFurthermore, we also calculated duration variables for medical conditions by subtracting onset\nage from the age when attended assessment center (chronological age).\nAs illustrated in Figure 1, these procedures led us to define a main subset in which 261 distinct\nexposome variables were available for 3706 participants. By dropping out two variables (left and\nright heel bone density), we could create a bigger subset of 4292 participants that served as a\nreplication subset. Finally, we also identified a bigger subset of 7736 participants with data for\n201 distinct exposome variables. In this “’variables-restricted subset”, mainly variables related\nto socio-affective and mental health domains were missing compared to the main and replication\nsubsets. Standard preprocessing was performed on exposome variables including variables\nstandardization (within the cross-validation scheme to avoid any data leakage).”\n[2-B] Regarding variables collinearity and potential redundancy (‘’ How distinct are the included\nvariables from one another in terms of conceptual overlap and statistical collinearity? Providing a\nhierarchical clustering analysis or correlation heatmap could help demonstrate the structure and\nindependence of the variable set’’:\nTo address the reviewer's query regarding the distinctiveness and potential collinearity of the 261\nexposome variables, we have generated a correlation heatmap and performed a hierarchical clustering\nanalysis.\nThe hierarchical clustering analysis (see Figure 2 below, a high-resolution PDF file has also been\nuploaded with the revisions file) provides a visual representation of the relationships between the\nvariables. As can be seen in the dendrogram, the variables cluster into distinct groups generally\naligning with broad categories such as diet, body composition, cardiovascular health and socio-\naffective domain. This suggests that while some variables within these categories may be correlated,\nthe overall variable set captures a diverse range of exposome domains. The branches show how features\nare related based on distance.\nFigure 2 Hierarchical clustering: Using pre-processed features (exposome variables) from the main subset, a hierarchical\nclustering was performed. The variables’ names have been colour-coded by domain.\nhas also been uploaded with the revisions file) provides a more detailed view of the pairwise\ncorrelations between variables. Only a few clusters of more correlated variables are apparent (red\nblocks), and these pertain to body composition and body morphology variables, which could be\nexpected to be importantly correlated. Apart from these clusters, overall, the heatmap shows a\nsubstantial degree of heterogeneity, indicating that many variables exhibit relatively low correlations\nwith one another. This suggests that the variable set, while capturing some expected relationships, also\nincludes a good degree non-redundant, distinct information piece. We did not perform explicit\nredundancy reduction, as the Random Forest algorithm is inherently robust to multicollinearity and\ncapable of selecting the most informative variables. We believe the heatmap sufficiently illustrates any\ninherent redundancy within the variable set, without the need for further pre-processing steps.\nFigure 3 (Supplemental Figure 8). correlation heatmap of the preprocessed features. Clusters of more correlated variables\npertain to body composition and body morphology variables\n(page 15, line 29- page 16, line 2):\n“The redundancy between the 261 exposome variables were relatively low as illustrated by the\ncorrelation heatmap (Supplementary Figure 8). Although some clusters of correlated variables\ncan be observed, these pertain to instrumentally related body morphology and composition\nvariables and overall, a substantial degree of heterogeneity can be observed. The distribution of\nthe variables across different exposome domains is illustrated for reader’s information in\nSupplementary Figure 9. Of note, from this set of variables, we did not perform any redundancy\nreduction since we subsequently used machine learning algorithms which are designed to be able\nto weight or select relevant features (see next section).”\n[3] Training sample characteristics: In lines 120-125, the age range is reported for the test sample, but\nnot for the training sample. For clarity and consistency, it would be helpful to also provide the age range\nof the training sample, ideally in the same section.\nWe appreciate the reviewer's attention. To improve clarity and consistency, we added the age range of\nthe training sample to the Results section, in addition to its current location in the Participants section\n(Method section). The revised sentence reads (page 4, line 8-11):\n“To build an estimator that would later be sensitive to deviance from a healthy reference norm,\nthe models were trained and explicitly tested in a subset of 5025 healthy participants from the UK\nBiobank (age range 46-82 years, mean 62.12±7.16 years, 2579 females, see Methods).”\n[4] Prediction accuracy and implications for BAG: Higher age prediction accuracies have previously\nbeen reported in several UK Biobank studies, with a particularly strong example by Peng et al. achieving\na mean absolute error (MAE) of 2.14 years (https://doi.org/10.1016/j.media.2020.101871). How might\nsuch improvements in prediction accuracy affect the ability to detect associations between brain-age\ngap (BAG) and exposome variables? Is it conceivable that, as models continue to improve, their\nprecision could reduce true inter-individual variability in BAG to the extent that biologically\nmeaningful differences become diminished?\nWe appreciate the reviewer's point regarding the potential impact of increased brain age prediction\naccuracy on the brain-age gap (BAG) and its relationship with exposome variables. We acknowledge\nthat Peng et al33 achieved a higher brain age prediction accuracy (MAE = 2.14 years) in the UK\nBiobank compared to our model (MAE = 3.93 years). This difference likely arises from methodological\nchoices, including our selection criteria for a healthy training set (resulting in a smaller training\nsample), our focus on grey matter volume features to have a strict indicator of grey matter health, and\nthe specific regression algorithms employed.\nIn this study, we aimed for relevant indicator of grey matter health in the population instead of a highly\naccurate brain age prediction model. The reason for this choice is, as stated by the reviewer, that model\naccuracy does not imply, and can even result in lower, sensitivity for interindividual variability in brain\nhealth. This counterintuitive fact about brain age prediction accuracy has been recently demonstrated\nin UK Biobank34.\nBAG values will likely become narrower, reflecting a reduction in unexplained variance. This\nrefinement of BAG has implications for detecting associations with exposome variables. Specifically,\nwith a narrower BAG distribution, the ability to detect relationships may be limited to exposome\nfeatures that exhibit stronger correlations or larger effect sizes on brain aging. In other words, the\n'signal' from weaker associations may be overshadowed by the increased precision of the BAG\nmeasurement. Furthermore, while Peng et al.'s33 model demonstrates impressive overall accuracy,\nour gray matter-focused model may be more sensitive to the specific effects of certain exposome variables\non grey matter alterations.\nIn conclusion, there is a trade-off between model accuracy and BAG variance. The optimal balance\nbetween these factors depends on the specific research question. Furthermore, the extent to which the\ninformation conveys by BAG depends on how the brain age prediction model has been developed\nremains a field of study. Accordingly, and in line with other reviewers’ comment, we have now\nacknowledged current limitations in the validity and interpretation of BAG as an index of brain aging\nand brain health in the revised manuscript:\nIn the discussion (page 11, line 4-12):\n“Second, it should also be acknowledged that although BAG is a very convenient indicator of the\ndegree of relative preservation or degeneration of a whole grey matter pattern, it remains a\nderived indicator instead of a direct measurement, which implies some noise. Furthermore, its\nconceptual validity and interpretability remain importantly discussed35. First, a BAG framework\nassumes a unique aging trajectory and deviance (older brain age) from this trajectory is\nconsidered as advanced or pathological aging35. Yet, this assumption can be questioned. Second\nand relatedly, some studies suggest that deviance (older brain age) may reflect brain phenotype\npresent since early life instead of an aging phenotype36. Overall these considerations question the\nextent to which BAG can be used as an indicator of brain health in aging.”\n[5] Residual confounding by age and sex: The brain-age gap is known to be confounded by age due to\nregression dilution. The authors regressed out age effects from predicted age using the slope and\nintercept from the training sample. However, depending on how well the model generalizes to the test\nsample—even though all data are from the UK Biobank with harmonized scanner protocols—some\nresidual confounding may remain. It would be helpful if the authors explicitly demonstrated that age\n(and sex) do not confound the BAG–exposome associations, for example by including a plot similar\nto Supplementary Figure 6.\nWe thank the reviewer for raising this important point. In this project, we took great care to ensure that\nresults are not confounded by age. We addressed this issue first by applying a bias correction to the\npredicted age using the slope and intercept derived from the training set. As suggested by the reviewer,\nwe examined the correlation between BAG and age before and after this correction (see Table\n1/Supplementary Table 11). While a very small correlation remains (r = -0.123, -0.135, and -0.130 for\nthe Variables-restricted subset, Replication subset, and Main subset, respectively), it represents a\nsubstantial reduction compared to the correlation before correction (r = -0.668, -0.674, and -0.672,\nrespectively). To further mitigate any potential residual confounding effects in the BAG-exposome\nassociations, as well as some potential confounding effect of sex, we included age, age2 and sex as\nconfounds within the cross-validation scheme in the exposome-based prediction of BAG.\nCorrection\nMAIN REPLICATION VARIABLES-RESTRICTED\nCORRELA\nTION (R)\nBEFORE -0. 672 -0.674 -0.668\nCORRECT\nION\nCORRELA\nTION (R)\nAFTER -0.130 -0.135 -0.123\nCORRECT\nION\nSCATTER\nPLOT\n(CHRONO\nLOGICAL\nAGE VS.\nBAG)\nNote: Pearson's correlation coefficients (r) between chronological age and brain age gap (BAG) in\ndifferent subsets of the UK Biobank cohort, before and after applying bias correction to the predicted\nage. Scatter plots illustrating the relationship between chronological age and BAG are also shown. The\nbias correction was performed using the slope and intercept derived from the training set.\nAge Bias correction in the methods (page 14, line 12-22):\n“Importantly, several studies have brought attention to an age bias/age dependency in brain age\nprediction requiring a so-called age-bias correction37. This was done here by regressing out the\neffects of age on the predicted age38. Specifically, we calculated a regression line with\nchronological age as the predictor and predicted brain age as the outcome, using only the training\ndataset. The resulting slope and intercept were then used to adjust the predicted brain age values\nin the testing dataset. This adjustment involved subtracting the intercept from each predicted\nbrain age and then dividing by the slope. In other words, the slope and intercept of the regression\nline between chronological age and the predicted age were estimated in the training set and used\nto adjust the predicted age in the test and the population samples. As expected, and illustrated in\nsupplementary Table 11, this age-bias correction procedure substantially reduced the artefactual\ncorrelation between the subsequently computed BAG and age.”\nAge and sex as confounders in exposome-based predictive model of BAG (page 16, line21-26):\n“Controlling for covariates was performed on the exposome variables within the cross-validation\nframework (using estimated from the train set). The set of covariates/ confounds include age,\nsquare of age, sex, height, and volumetric scaling from T1 head image to standard space. Thus,\nall care was taken to neutralize the influence of usual confounds by using a normative indicator\n12\nof grey matter health and by additionally controlling for covariates within the prediction\nanalysis.”\n[6] Sample independence and relatedness: The UK Biobank includes many participants with first-,\nsecond-, and third-degree familial relationships. Were related individuals excluded across the training,\nvalidation, and test sets to prevent potential data leakage? If not, how did the authors ensure that familial\nrelatedness did not introduce bias into the results? If related individuals were not accounted for, this\ncould represent a methodological limitation worth acknowledging.\nWe thank the reviewer for drawing our attention to this point. We acknowledge that our analysis did\nnot explicitly exclude or account for related individuals across the training, validation, and test sets.\nThe presence of related individuals could potentially slightly influence the prediction performance as\nfamily members may share similar genetic and environmental characteristics, leading to non-\nindependent observations.\nTo the best of our knowledge and potentially due to data protection protocols, there is no directly\naccessible field ID in the UK Biobank that definitively identifies familial relationships for all\nparticipants. We have identified genomic-related field IDs that provide some information on genetic\nrelatedness: 22011 (Genetic relatedness pairing), 22012 (Genetic relatedness factor), 22013 (Genetic\nrelatedness IBS0), and 22018 (Genetic relatedness exclusions). However, these fields are only\ncalculated for a subset of 17,296 participants in our dataset, which includes over 35,000 individuals.\nTherefore, we acknowledge that not accounting for familial relatedness represents a limitation of our\nstudy. This limitation has now been stated in the revised manuscript (page 10, line 22-25):\n“However, several limitations should be noted. First, at the methodological level, in this study\nwe did not account for family relatedness. The UK Biobank includes several participants with first-,\nsecond-, and third-degree familial relationships which can potentially bias the results.”\n[7] Algorithm choice rationale: While ridge regression, SVR, and random forest are valid choices, many\nother machine learning approaches are available for tabular data. What was the rationale for selecting\nthese three algorithms? Have the authors considered ensemble methods?\nWe thank the reviewer for raising this point. When selecting the algorithms, we took into account brain\nage prediction literature and complementary among algorithms. Thus we selected ridge regression,\nsupport vector regression (SVR), and random forest (RF) due to their established use and effectiveness\nwith tabular data in the field of brain age prediction39-41. Using these well-established algorithms, as\ntheir performances are comparable41, allow us to compare our brain age prediction results with other\nstudies in the field. We also focused on those three algorithms for exposome-based prediction of brain\nhealth because they offer complementary strengths: ridge regression provides regularization to prevent\noverfitting, SVR is very popular and relatively more robust to outliers than ridge regression, and\nrandom forest as an ensemble method, has the ability to capture non-linearity and complex interaction\namong features, furthermore by combining multiple decision trees it usually improves prediction\naccuracy and robustness. This information has now been added in the revised manuscript:\n(page 16, line 10-14):\n“We selected these three algorithms because they offer complementary strengths: random forest\nas an ensemble method, has the ability to capture non-linearity and complex interaction among\nfeatures. Furthermore, by combining multiple decision trees it usually improves prediction\naccuracy and robustness. While ridge regression provides regularization to prevent overfitting,\nSVR is very popular and relatively more robust to outliers than ridge regression.”\n[8] P-values across datasets: In the main subset, the exposome model yields r = 0.23 with p = 0.002. In\nthe larger replication and variable-restricted samples, the correlation remains similar (r = 0.23 and 0.24,\nrespectively), yet the reported p-values are higher. Given the increased sample size, this is unexpected—\ntypically, larger samples lead to smaller p-values for equivalent effect sizes. The authors mention that\np-values were FDR-corrected, but it remains unclear which specific tests were included in the correction\nprocedure. I suggest reporting both the raw and FDR-corrected p-values side by side in Supplementary\nTable 3, possibly along with a brief table note explaining which tests were included in the FDR\ncorrection.\nWe thank the reviewer for raising this important point regarding the p-values in Supplementary Table\n3. We fully agree with the reviewer’s point that p-values tend to decrease with increasing sample size,\neven for similar effect sizes. However, we would like to clarify that the p-values reported in\nSupplementary Table 3 were obtained using permutation testing, and we used 500 permutations for all\ndatasets. This means that the number of permutations—and therefore the basis for estimating the p-\nvalues—was identical across the three datasets, regardless of their sample sizes.\nAs requested, we have updated the Supplementary Table 3 (Table 2 here, Supplementary Table 5 in\nrevised version ) to report both the raw p-values and the FDR-corrected significance. One clarification\nwe would like to emphasize is that in the previous version of the table, we had reported raw p_values\nwhile their significance was determined based on FDR-corrected p_value threshold. In the revised\ntable, we now provide the actual raw p-values corresponding to those tests, along with the FDR-\ncorrected results, for transparency.\nmain subset (n = 3706), replication subset (n = 4202), and variable-restricted subset (n = 7736).\nRidge\nAlgorithm Random Forest SVR(Linear)\nregression\nVariables-\nSubset Main Replication Main Main\nrestricted\nMean Absolute Error\n4.48 4.52 4.53 4.54 4.53\n(MAE)\nUncorrected p-value 0.03 0.02 0.002 0.002 0.02\nCorrected p-value 0.03 0.02 0.003 0.003 0.003\nMean Squared Error\n32.22 32.63 32.66 33.14 33.07\n(MSE)\nUncorrected p-value 0.006 0.02 0.002 0.002 0.002\nCorrected p-value 0.007 0.02 0.003 0.003 0.003\nroot Mean Squared Error\n5.67 5.71 5.71 5.75 5.75\n(rMSE)\nUncorrected p-value 0.004 0.04 0.002 0.002 0.002\nCorrected p-value 0.005 0.04 0.003 0.003 0.003\nr-square\n0.05 0.05 0.06 0.02 0.03\nUncorrected p-value 0.002 0.004 0.002 0.02 0.002\nCorrected p-value 0.004 0.01 0.002 0.016 0.002\nPearson Correlation\n0.23 0.23 0.24 0.16 0.17\n(r)\nUncorrected p-value 0.002 0.004 0.002 0.002 0.002\nCorrected p-value 0.003 0.005 0.003 0.003 0.003\n[9] Language clarity: The phrase “worst grey matter health” (and similar expressions such as “worst\npredicted grey matter health”) appears multiple times throughout the manuscript, and I found it a bit\nunusual. The authors might consider rephrasing these instances using more standard alternatives such\nas “poorer brain health”).\nWe agree with the better phrasing suggested by the reviewer. We have now replaced all instances of\nphrases like 'worst grey matter health' with more standard alternatives such as 'poorer brain health’.\n(page 6, line 1-23):\n“Additional insight into the nature of the association between these most contributing variables\nand grey matter health can be obtained by examining the distribution of SHAP value in Figure 3.\nThis figure illustrates how the value (from low to high) of the exposome variable is associated\nwith the BAG prediction (from negative which corresponds to better grey matter heath to positive\nwhich corresponds to poorer brain health). Overall, most of the associations follow an expected\npattern.\nexposure is associated with poorer brain health (i.e. higher BAG). These include high blood\npressure, smoking and alcohol, as well as diabetes. It should be noted here that for these domains,\nthe variables that show the highest impact on the predicted grey matter health pertain to the\nduration of the exposure and the life periods in which the exposure happens. Hence, for smoking,\nduration, age at start and age at stop appear as complementary information instead of redundant\nvariables for the prediction. Longer smoking duration, younger age when started smoking and\nolder age when stopped smoking are all associated with poorer predicted brain health. Similarly\nfor high blood pressure, a longer duration and an older age at diagnosis (likely reflecting an older\nage for treatment) both lead to poorer predicted brain health. Along the same line, our results\nshow that when diabetes diagnosis happens at an older age, a poorer brain health is predicted.\nThis is also the case for other non-cancer illness: diagnosis (and likely treatment) at an older age\nis associated with younger. In contrast, the relationship between operations (be it first or second\noperation) and the predicted grey matter health goes in the opposite direction: operations\nperformed at younger age appear to relate to a poorer predicted brain health.\nFor the remaining top contributing variables different patterns can be observed. In particular,\nfor variables related to nutrition diet, while high coffee intake is associated with poorer predicted\nbrain health, low dried fruit and cereal intake leads also to poorer predicted brain health. Finally,\nlow hip circumference and low bone density are both associated with poorer brain health.”\n(page 8, line 31- page 9, line 2):\n“Beyond the critical effect of body health, our multivariate model also revealed the contribution\nof nutrition and diet on grey matter health. In particular, coffee, dried fruit, and cereals intake\nappear in the top most contributing factors consistently across algorithms. A higher coffee intake\nleads to poorer predicted brain health. Although a neuroprotective role for coffee is often\ndiscussed in the literature, our results demonstrate that a daily consumption above the moderate\nrange (around 2 cups/day, the mean of the sample, see summary statistics in Supplementary Table\n8) leads to a poorer predicted brain health.”\n(page 9, line 25-32):\n“In our machine learning models, hip circumference also appears to contribute to the prediction\nof individual grey matter health. More concretely, lower (than the mean of 102 cm, see\nSupplementary table 10) hip circumference is associated with poorer individual brain health in\nthe prediction model. However, additional analyses show that, when taken in isolation, hip\ncircumference is not associated with grey matter health in the main subset (null correlation with\nBAG, see Supplementary Figure 6). This suggests that hip circumference plays a role as a\nmoderator variable in the individual prediction of grey matter health. In other words, lower hip\ncircumference on its own does not result in poorer brain health, but in interaction with other\nfactors, it can lead to poorer predicted brain heath.”\n(page 21, line 7-13):\n“Figure 3. SHAP value for the common most contributing variables when using random forest in\nthe main subset (n = 3706, 261 exposome variables). The x axis represents the impact on the\nprediction: zero could be considered as the default prediction, while a shift to the right reflects a\nhigher predicted BAG (i.e., poorer predicted brain health) and a shift to the left represents a\nlower predicted BAG (i.e., a better predicted grey matter health). Blue indicates a lower value for\nthe variable, while pink/red indicates a higher value. The right panel summarizes which direction\nof the variable leads to poorer brain health (i.e. to higher BAG).”\n[10] Sex/gender: Did the authors use self-reported sex or genetically inferred sex in their analyses?\nUnless a specific gender variable was used, referring to \"sex\" would be more appropriate in my view.\nWere discrepancies between self-reported and genetic sex taken into account in the analysis (e.g., by\nexcluding individuals with reported vs. genetic sex-mismatch)?\nIn this study, we used self-reported sex (Data-Field 31) in line with previous related literature using\nUK Biobank15,18-20. Because this self-related information was not cross-validated with genetic sex\ninformation (Data-Field 2200), we assumed that some participants may have reported their gender\nidentity and therefore we used “’sex/gender”’. However, following the comment of the reviewer, we\nrealized that the use of “sex/gender’’ can be misleading and we therefore now use “’sex” terminology\nas suggested by the reviewer. Of note, we do not have access to Data-Field 2200, so we could not\ninvestigate potential discrepancies between self-reported and genetic sex.\n(page 7, line 6-11):\n“Finally, these patterns do not importantly influence by sex. Although in the main analysis, we\ntreated sex as a covariate, in a supplementary analysis, we instead included it as a predictor\nvariable in the model. We observed overall similar results. Furthermore, the contribution (SHAP\nvalue) of sex was very low, indicating minimal influence on the predictions. This suggests that the\nmodel’s performance remains stable and generalizable, regardless of whether sex is accounted\nfor as a covariate or included as a predictor variable.”\n[11] Interpretation of \"almost null gap\": The manuscript states that “most participants showed an almost\nnull gap...,” which may suggest that individual brain-age gaps are negligible. However, the reported\nmean absolute error is approximately 3.9 years. Do the authors instead refer to the group-averaged mean\nof the BAG distribution, which may be close to zero by design? I suggest clarifying whether this\nstatement refers to the distribution mean or to individual-level deviations and consider revising the\nwording to avoid potential confusion.\nWe thank the reviewer for pointing out the potential for misinterpretation regarding the 'almost null\ngap' statement. We have now revised the manuscript to clarify that this statement refers to the group-\naveraged mean of the Brain Age Gap (BAG) distribution, which is close to zero by design (page 4, line\n18-24):\n“As could be expected this estimator was normally distributed (see Methods, Figure 5) with a\ngroup-averaged mean of zero. In the whole population, 90% of participants had BAG values\nbetween -9.20 and +10.18 years. Furthermore, 5% of participants had a BAG value inferior to -\n9.20 and 5% of participants had a BAG value superior to 10.18. This indicates that while the\ngroup-averaged mean BAG was close to zero, some participants showed a positive gap, indicating\nthat their brain was estimated to be older than their chronological age, and some showed a\nnegative gap, reflecting relatively preserved grey matter compared to their chronological age.”\n[12] GitHub repository: I very much appreciate the inclusion of the GitHub repository, which appears\nwell-organized, clear, and greatly enhances the transparency of the analysis workflow. The authors\nrecommend using virtual environments such as conda, which is good practice. To further improve\nreproducibility, could the authors provide the corresponding environment.yml file(s) (or similar) with\npackage versions? Additionally, it would be helpful to specify the system used to run the analyses.\nWe thank the reviewer for the positive feedback on our efforts to design and document the GitHub\nrepository, as well as for the suggestion for completeness. The environment file has now been added to\nthe GitHub repository.\nLink:\nhttps://github.com/MostafaMahdipour/Predicting_Brain_Age_Gap_BAG_using_UKB_exposome/blo\nb/main/README.md\n[13] The exposome concept: The concept of the exposome was relatively new to me, as I am more\nfamiliar with genome- and phenome-focused approaches. As I understand it, the exposome is generally\ndefined as the totality of non-genetic internal and external exposures across the lifespan. However,\nmany of the variables assessed in this study—such as dietary habits, smoking behaviour, or\nsocioeconomic status—are known to have some degree of genetic influence. This suggests that the set\nof predictors used here may not reflect purely non-genetic exposures in the strictest sense. In my view,\nthis overlap between genetic predisposition and environmental exposure highlights a broader\nconceptual limitation of the exposome framework. It might be helpful to briefly acknowledge this\nnuance in the discussion—perhaps in the context of causality, where the exposome framework alone\ndoes not resolve the challenge of causal inference, particularly in observational cohorts like the UK\nBiobank.\nWe agree with the reviewer that the exposome concept has some limitations. This is mainly an heuristic\nframework that promotes a holistic view on the range of factors that may influence a given aspect of\nhuman health. Accordingly, while in a phenome-wide approach (such as done in some previous UK\nBiobank studies)1,42 each variable is taken in isolation, the exposome concept insists on their co-\noccurrence, co-dependence, and potential interactions. This shift from reductionist to systemic\nperspectives aim to more realistically model the multifactorial nature of health and disease. However,\nwe also agree with the reviewer that there is sometimes an intricate link between some exposome\nvariables (in particular those pertaining to body health and to life style) and genetic factors, that\nprevents causal inferences. In line with reviewer’s comment, the limitation has now been noted in the\nrevised manuscript:\n18\n(page 10, line 31- page 11, line 3):\n“In the same vein, our study points to relatively new factors (coffee intake, dried fruits intake and\nhip circumference) that seem to interact with metabolic and cardiovascular risk factors in\npredicting brain structural health. However, although we initially considered body and life style\nfactors from the lens of the exposome, many factors (such as metabolic and cardiovascular\nconditions) may obviously be importantly influenced by genetic factors. Future studies should\nthus crucially also include genetic factors, which have been found to also partly explain\ninterindividual variability in brain health measured with BAG8,43,44. Leveraging individual\ngenetic profiles to examine to which extent the combination of the individual genotype and the\nindividual expotype can predict individual brain health in aging with predictive modeling would\nhence be a crucial step towards precision brain health.”\nReviewer #1 (Remarks on code availability):\n[14] I briefly reviewed the GitHub repository, and it appears well-organized, well-documented, and\ncomprehensive. To further improve reproducibility, the addition of a conda environment file and basic\nsystem information would be beneficial.\nThank you again for the positive feedback and the useful suggestions.\nI thank the Editor and Authors for the chance to review this interesting paper! Please find my\nstructured comments below.\nKey results\nThis study presents an interesting approach to answering the question of “What predicts individual brain\nhealth?” by using rich data from the UK Biobank. Grey matter-based brain age models are trained and\nthen used to predict brain age in a sub-sample of the UK Biobank. The difference between the\nindividuals’ brain age and chronological age (brain age gap) is used as a proxy for brain health. In a\nfurther step, the brain age gap is predicted from a set of variables labelled here as the exposome. The\nauthors present that exposome variable predict between 3%-5% of the variance in the brain age gaps.\nUsing SHAP values, the authors present a ranking of the exposome features in explaining the brain age\ngap.\nWe thank the reviewer for the positive evaluation of our manuscript, as well as his/her comments which\nhave contributed to improve its quality.\nValidity\n[1] While the manuscript draws on rich and high-quality data, there are multiple uncertainties around\nBAG still limiting its interpretability. The extend to which BAG reflects brain health remains unclear.\nWe thank the reviewer for raising this important point. We acknowledge the ongoing debate\nsurrounding the interpretation of brain age gap and its validity as an indicator of individual brain\nhealth. In order to raise the reader’s awareness on the potential limitations of the interpretation of\nBAG, we have now discussed this point in the revised manuscript:\n(page 11, line 4-19):\n“Second, it should also be acknowledged that although BAG is a very convenient indicator of the\ndegree of relative preservation or degeneration of a whole grey matter pattern, it remains a\nderived indicator instead of a direct measurement, which implies some noise. Furthermore, its\nconceptual validity and interpretability remain importantly discussed35. First, a BAG framework\nassumes a unique aging trajectory and deviance (older brain age) from this trajectory is\nconsidered as advanced or pathological aging35. Yet, this assumption can be questioned. Second\nand relatedly, some studies suggest that deviance (older brain age) may reflect brain phenotype\npresent since early life instead of an aging phenotype36. Overall, these considerations question the\nextent to which BAG can be used as an indicator of brain health in aging. With these conceptual\nlimitations in mind, in this study, we did not use the popular “brain (biological) aging” concept\nwhen referring to BAG on purpose because it is not clear whether BAG represents any single\nphenomenon or set of phenomena that can be referred to as aging35. Actually, more research on\nthe underlying neurobiological properties detected by brain age prediction will be required to\ndifferentiate which phenomena BAG is in fact sensitive to. In that perspective, systematic\ncomparison with other markers of neurocognitive health in future studies could contribute to\n20\nbetter understand the neurobiological phenomenon captured by each marker and how each and\nevery marker is shaped by the exposome.”\n[2] Additionally, the results indicate relatively weak associations between the exposome and BAG.\nThese effect sizes were not discussed, which would however be important for context when presenting\nSHAP values and making conclusions about exposome data being able to predict brain health.\nWe agree with the reviewer that in our study the exposome explains a relatively small part of the\nvariance in grey matter health (3 to 5%). However, it should be noted that the model performance that\nwe report here are in out-of-sample prediction, this is of crucial relevance for interpreting our results\nin the context of the literature. To the best of our knowledge, out-of-sample model fit or prediction has\nnever been shown in previous studies examining associations between some exposome factors and a\nmarker of brain structure. Yet, predictive modelling with out-of-sample validation targeting other\naspects of human phenotype, such as behavioral phenotype, typically show very limited prediction\naccuracy and variance explained45. Thus, the out-of-sample effect size in this study is well in the range\nthat could be expected for a predictive modelling approach with proper out-of-sample evaluation.\nFurthermore, if we look at studies that have examined associations between exposome factors and\nmarkers of brain structure, when effect sizes are provided, these are typically very small. For example,\nin Smith et al. (2019)1, the highest within sample correlation (observed for bone mineral density) is -\n0.10. As another example, alcohol consumption generally explained less than one 1% of variance in\nbrain structural estimate 46,47 in other studies. Along the same line, standardized betas of biomedical\nand life style variables in regression model of brain structural estimates are generally below 0.1 (eg:\n28,48,49) while the model is evaluated in the sample in which it has been fitted. Thus, the current study,\nactually show that when a wide range of exposome variable are combined with multivariate modeling,\na greater part of variance in brain structural health can be explained.\nIn that context, despite the limited variance explained, we believe that the SHAP values still provide\nvaluable insights into the relative importance of different exposures and can help generate hypotheses\nfor future research as suggested in our discussion. Identifying even small contributions of modifiable\nfactors could be valuable for informing targeted interventions.\nNevertheless, we acknowledge that, in the absolute, our ability to predict individual brain\nhealth/explain interindividual variability in brain health from the individual expotype remain limited.\nAccordingly, this is now emphasize in our revised manuscript:\n(page 10, line 25-29):\n“Second, while the prediction accuracy in the out-of-sample test set is significant and\ndemonstrates the model generalizability, the effect size (r value) remains relatively moderate.\nRelatedly, the explained variance in brain structural health (i.e. in BAG) in the test set is relatively\nlimited (~5%). This suggests that additional factors should be taken into account.”\n(page 10, line 35- page 11, line 3):\n“Future studies should thus crucially also include genetic factors, which have been found to also\npartly explain interindividual variability in brain health measured with BAG8,43,44. Leveraging\nindividual genetic profiles to examine to which extent the combination of the individual genotype\nand the individual expotype can predict individual brain health in aging with predictive modeling\nwould hence be a crucial step towards precision brain health.”\n[3] The integration of exposome data with neuroimaging-based brain age prediction is timely and\npotentially impactful, particularly in the context of identifying modifiable lifestyle or environmental\nfactors associated with brain health. The use of large-scale population data (UK Biobank) and open\nmethods further enhance the manuscript’s relevance. However, the modest variance explained and the\nunresolved issues around the interpretation of BAG somewhat limit the strength of the conclusions.\nWhile the work is incremental in its current form, stronger positioning within the existing literature and\na more critical discussion of the results would increase its contribution to the field. Nevertheless, the\nfindings support a forming body of literature highlighting that the state of the brain (or potentially brain\nhealth) is multivariate, being a useful contribution to the field.\nWe thank the reviewer for their balanced assessment of our work. We appreciate the recognition of the\ntimeliness and potential impact of our study, as well as the strengths of our approach. We also\nacknowledge the reviewer's concerns regarding the modest variance explained and the limitations of\nBAG. As detailed in our answer to comment [1] of first reviewer and below, we have now acknowledged\nthese limitations in the revised manuscript.\nFollowing reviewer #1’s and reviewer #2’s comments, in the revised manuscript, we have now stronger\npositioned our work within the existing literature. This implies a clearer justification of the novelty of\nour work in the introduction by using a machine learning’s approach that account for interaction\nbetween exposome factors and their non-linear relationships with brain health in the introduction. To\nfurther demonstrate the new insight provided by such an approach, we have also emphasized new\nfindings relative to previous studies that have used a phenome-wide approach (i.e. studies that have\ntested the association of each variable with BAG separately). These new findings mainly suggest diet\n(coffee intake, cereals intake, dried fruits intake) and body composition factors (hip circumference as\na proxy for gluteofemoral fat) that interact with the already well-established metabolic (diabetes) and\ncardiovascular risk factors factors, in either a detrimental (eg: coffee intake) or protective way (ie.\ndried fruits intake). This additional discussion has been reported in our response to reviewer #1 first\ncomment above, please see the answer to comment #1 of first reviewer.\nData and methodology: The dataset (UK Biobank) is appropriate and well-regarded. However,\nimportant methodological details are missing or underspecified. These include:\n[4] Rationale for the choice of machine learning algorithms.\nWe thank the reviewer for raising this point. When selecting the algorithms, we took into account brain\nage prediction literature and complementary among algorithms. Thus we selected ridge regression,\nsupport vector regression (SVR), and random forest (RF) due to their established use and effectiveness\nwith tabular data in the field of brain age prediction39-41. Using these well-established algorithms, as\ntheir performances are comparable41, allow us to compare our brain age prediction results with other\nstudies in the field. We also focused on those three algorithms for exposome-based prediction of brain\nhealth because they offer complementary strengths: ridge regression provides regularization to prevent\noverfitting, SVR is very popular and relatively more robust to outliers than ridge regression, and\nrandom forest as an ensemble method, has the ability to capture non-linearity and complex interaction\namong features, furthermore by combining multiple decision trees it usually improves prediction\naccuracy and robustness. This information has now been added in the revised manuscript:\n22\n(page 16, line 10-14):\n“We selected these three algorithms because they offer complementary strengths: random forest\nas an ensemble method, has the ability to capture non-linearity and complex interaction among\nfeatures. Furthermore, by combining multiple decision trees it usually improves prediction\naccuracy and robustness. While ridge regression provides regularization to prevent overfitting,\nSVR is very popular and relatively more robust to outliers than ridge regression.”\n[5] Whether training and test subsets were strictly independent.\nWe thank the reviewer for raising this important point. When designing the study, we took great care\nthat the samples for brain age prediction model and the sample for exposome-based prediction of BAG\nwere strictly independent, within each sample we further ensure that training and test subsets were\nstrictly independent. Concretely, following the methodology described in Cole et al. (2020), we first\ncreated a 'healthy' subset of participants for training our brain age prediction models. The remaining\nparticipants formed the 'rest of population' set, which was used for subsequent BAG calculation using\nthe trained models. Furthermore, within the 'healthy' training set, we employed an out-of-sample\nvalidation set that was never used during the training process, ensuring independence and preventing\ndata leakage. A similar procedure was followed for BAG prediction in the population subsets. This\ncomplex design to prevent data leakage is now illustrated in Figure 4/Supplemental Figure 7 and below:\nFigure 4 (Supplementary Figure 7). Study design for models’ development and test preventing data leakage between models,\nas well as between training and test sets. A) Brain Age Model development, B) Exposome-BAG-Model.\n[6] The method and covariates used for age-bias correction.\nWe appreciate the reviewer's suggestion to elaborate on our age-bias correction method. Recognizing\nthe proportional bias described in previous studies1,15—whereby the difference between chronological\nage and predicted brain age tends to be negatively correlated with chronological age—we implemented\na linear correction procedure. Specifically, we calculated a regression line with chronological age as\nthe predictor and predicted brain age as the outcome, using only the training dataset. The resulting\nslope and intercept were then used to adjust the predicted brain age values in the testing dataset. This\nadjustment involved subtracting the intercept from each predicted brain age and then dividing by the\n23\nslope. Following this age-bias correction, we calculated the brain-age gap (BAG) by subtracting\nchronological age from the adjusted predicted brain age. We have added a detailed description of this\nprocedure to the manuscript to improve clarity and reproducibility.\nAdditionally, to further ensure that the calculated BAG did not contain statistical signal pertaining to\nage, we also regress out age and square of age in the exposome-based prediction of BAG.\nAge Bias correction in the methods (page 14, line 12-22):\n“Importantly, several studies have brought attention to an age bias/age dependency in brain age\nprediction requiring a so-called age-bias correction37. This was done here by regressing out the\neffects of age on the predicted age38. Specifically, we calculated a regression line with\nchronological age as the predictor and predicted brain age as the outcome, using only the training\ndataset. The resulting slope and intercept were then used to adjust the predicted brain age values\nin the testing dataset. This adjustment involved subtracting the intercept from each predicted\nbrain age and then dividing by the slope. In other words, the slope and intercept of the regression\nline between chronological age and the predicted age were estimated in the training set and used\nto adjust the predicted age in the test and the population samples. As expected, and illustrated in\nsupplementary Table 11, this age-bias correction procedure substantially reduced the artefactual\ncorrelation between the subsequently computed BAG and age.”\nAge and sex as confounders in exposome-based predictive model of BAG (page 16, line 21-26):\n“Controlling for covariates was performed on the exposome variables within the cross-validation\nframework (using estimated from the train set). The set of covariates/ confounds include age,\nsquare of age, sex, height, and volumetric scaling from T1 head image to standard space. Thus,\nall care was taken to neutralize the influence of usual confounds by using a normative indicator\nof grey matter health and by additionally controlling for covariates within the prediction\nanalysis.”\n[7] How exposome variables were selected and grouped (e.g., based on UKB codes or theoretical\nassumptions).\nIn response to the reviewer's comment, our selection of exposome variables involved a structured, multi-\nstage approach, building upon prior research and focusing on data quality. Initially, we conducted an\nextensive literature review, examining over 50 scientific articles to identify exposome variables linked\nto brain age gap (BAG) and brain health. This review guided our efforts to represent independent\nclusters of health-related domains. We then compiled a comprehensive list of available variables within\nthe UK Biobank, mapping variables from other cohorts when possible. We applied preliminary\nexclusion criteria based on the UK Biobank Showcase to remove irrelevant categories and instances.\nFrom an initial pool of 659 Field IDs, we prioritized 397 covering key domains like anthropometry,\ndiet, and lifestyle. Data extraction was performed using the ukbb_parser package, resulting in 2,599\ncolumns due to multiple time points and categorical variable expansions. Data curation was then\nconducted, which included addressing missing data by retaining columns with at least 2,000 filled\nvalues (as informed by Missingno package analysis), verifying variable types, applying uniqueness and\nmembership constraints, and excluding implausible responses. Finally, we defined three subsets for\n24\nanalysis. This approach combined insights from the literature, data accessibility, and rigorous data\ncuration to ensure a relevant and high-quality set of exposome variables.\nFor a more detailed, step-by-step explanation, please refer to our response to the comment #2 from\nReviewer #1.\n[8] Quality control steps for both exposome variables and MRI data.\n1- MRI data: An internal data quality controlled is performed by the UK Biobank team before releasing\nImaging-Derived-Phenotype. Importantly, UK Biobank only releases IDPs after internal QC. Although\nwe did not used these IDPs in our study, all T1 images that were used have been selected from the\nparticipants for which IDPs have been released. Furthermore, from the calculation of grey matter\nvolume of different parcels of the Brain using CAT 1250 within an in-house developed framework for\ncomputationally reproducible processing of large-scale data (FAIRly big)51, some quality variables are\navailable. These include IQR (Image Quality Rating), NCR (Noise-Contrast-Ratio), ICR\n(Inhomogeneity-Contrast-Ratio) and total GVM (Gray Matter Volume). Using these metrics we\nconducted a quality control ( Figure 5). As illustrated below, the relationships between these metrics\nconfirmed overall good data quality, with only a small number of participants showing values outside\nthe main distribution (e.g., low NCR with high IQR, extreme ICR values, or unusually low GMV). In\nparticular, the histogram of total GMV revealed a small tail on the lower end, which could suggest\npotential outliers. To identify whether these cases reflected segmentation errors or true biological\nvariation, we visually inspected the corresponding T1 images. This confirmed that the scans were of\nsufficient technical quality, and the low GMV values reflected genuine brain atrophy patterns rather\nthan artefacts. Thus, while our QC identified a small number of participants with atypical values, no\nparticipants were excluded solely on this basis, as the images themselves were of adequate quality for\nanalysis.\nand Noise-Contrast-Ratio (NCR), (B) Scatterplot of IQR versus Inhomogeneity-Contrast-Ratio (ICR), and (C) Histogram of\ntotal grey matter (GM) volume.\n2- Exposome variables:\nWe provided a detailed explanation of our quality control steps in our response to reviewer #1’s\ncomment. Briefly, data curation included: handling missing values, variable type (categorical vs.\ncontinuous) assignment, uniqueness constraint, aberrant value exclusion and uninformative value\nexclusion.\n[9] Treatment of variable redundancy and imbalanced group sizes across exposome domains.\nFollowing reviewer #1’s suggestions, we performed hierarchical clustering and generated a\ncorrelation heatmap (see Figure 3/Supplementary Figure 8 and Figure 2) to assess the redundancy and\nrelationships between the 261 exposome variables. The hierarchical clustering analysis (Figure 2)\nshows that the variables cluster into distinct groups, generally aligning with broad categories such as\ndiet, body composition, socio-affective/mental health, and cardiovascular factors. The correlation\nheatmap Figure 3/Supplementary Figure 8) reveals some clusters of correlated variables, but this is\nmainly for body morphology and composition variables. Overall, a substantial degree of heterogeneity\ncan be observed, indicating that many variables exhibit relatively low correlations with one another.\nWe acknowledged that imbalance exists across exposome domains. For full transparency, this is now\nillustrated in a pie chart in Figure 6/Supplementary Figure 9 (see below). However, we did not perform\n26\nexplicit redundancy reduction or weighting, because the Random Forest algorithm is inherently robust\nto multicollinearity and capable of selecting the most informative variables. Accordingly, our results\nshow that there is no relationship between the extent to which a domain is represented (i.e. the number\nof variables from this domain) and the likelihood for these variables to appear as key predictors of\nbrain health. For example, although socio-affective domain is overrepresented compared to cardio-\nvascular health, variable form this latter was consistently picked up by the predictive model while\nvariables from the former did not appear in the key findings.\nFigure 6 (Supplementary Figure 9). Distribution of exposome variables across different exposome domains.\nFollowing the reviewer’s comment, this information has been included in the revised manuscript (page\n15, line 29- page 16, line 2):\n“The redundancy between the 261 exposome variables were relatively low as illustrated by the\ncorrelation heatmap (Supplementary Figure 8). Although some clusters of correlated variables\ncan be observed, these pertain to instrumentally related body morphology and composition\nvariables and overall, a substantial degree of heterogeneity can be observed. The distribution of\nthe variables across different exposome domains is illustrated for reader’s information in\nSupplementary Figure 9. Of note, from this set of variables, we did not perform any redundancy\nreduction since we subsequently used machine learning algorithms which are designed to be able\nto weight or select relevant features (see next section). “\n[10] Additionally, it is unclear why brain age models were evaluated only with MAE and Pearson’s r,\nwhereas exposome-to-BAG models used a broader set of metrics. A consistent evaluation framework\nwould aid interpretability.\nRegarding the use of different evaluation metrics for brain age models versus exposome-to-BAG\nmodels, we acknowledge the reviewer's point about consistency. However, for brain age prediction,\na large body of literature exists, with the vast majority reporting MAE and Pearson's r. To facilitate\ncomparison with these existing studies and avoid potential misunderstandings, we primarily reported\nthese two scores. For the exposome-to-BAG models, as this is a new area with no established\nbenchmarks, we reported a broader set of metrics to provide a more comprehensive evaluation and to\npotentially serve as a reference for future research.\n[11] Again, while brain age provides a simplified metric of the state of the brain, which can be described\nas a proxy of brain health, its limitations need to be discussed in order to not mislead readers who are\nnot familiar with the concept.\nWe thank the reviewer for raising this important point. We acknowledge the ongoing debate\nsurrounding the interpretation of brain age gap and its validity as an indicator of individual brain\nhealth. In order to raise the reader’s awareness on the potential limitations of the interpretation of\nBAG, we have now discussed this point in the revised manuscript:\n(page 11, line 4-19):\n“Second, it should also be acknowledged that although BAG is a very convenient indicator of the\ndegree of relative preservation or degeneration of a whole grey matter pattern, it remains a\nderived indicator instead of a direct measurement, which implies some noise. Furthermore, its\nconceptual validity and interpretability remain importantly discussed35. First, a BAG framework\nassumes a unique aging trajectory and deviance (older brain age) from this trajectory is\nconsidered as advanced or pathological aging35. Yet, this assumption can be questioned. Second\nand relatedly, some studies suggest that deviance (older brain age) may reflect brain phenotype\npresent since early life instead of an aging phenotype36. Overall, these considerations question the\nextent to which BAG can be used as an indicator of brain health in aging. With these conceptual\nlimitations in mind, in this study, we did not use the popular “brain (biological) aging” concept\nwhen referring to BAG on purpose because it is not clear whether BAG represents any single\nphenomenon or set of phenomena that can be referred to as aging35. Actually, more research on\nthe underlying neurobiological properties detected by brain age prediction will be required to\ndifferentiate which phenomena BAG is in fact sensitive to. In that perspective, systematic\ncomparison with other markers of neurocognitive health in future studies could contribute to\nbetter understand the neurobiological phenomenon captured by each marker and how each and\nevery marker is shaped by the exposome.”\nFinally, I appreciate the authors’ open science approach and well-organized GitHub repository!\nThanks a lot for the positive feedback on our GitHub repository.\nAnalytical approach\n28\n[12] The use of ensemble models such as Random Forests and SHAP value interpretation is appropriate.\nNonetheless, the manuscript lacks information on the stability and robustness of the feature importances\nacross methods. Are the SHAP results stable when compared to, for example, permutation importance\nor bootstrapped variable selection?\nTo address the reviewer’s comment and assess the stability of SHAP-derived feature importances,\nwe performed a cross-validation–based stability analysis. Specifically, SHAP values were computed\nindependently for the test set of each outer folds. For each fold, we calculated the mean absolute SHAP\nvalue per feature, resulting in fold-specific importance profiles.\nWe then quantified the similarity of these profiles across folds by computing pairwise correlations.\nThe resulting fold–fold correlation matrix showed consistently high agreement, with an average correlation\nof 0.73 ± 0.10 and many fold–fold pairs exceeding 0.7 (Figure 7/Supplementary Figure 10). This\ndemonstrates that the relative importance of features is stable across different resampling splits.\nWhile we acknowledge that alternative methods (such as permutation importance or bootstrapped\nfeature selection) could also be applied, our analysis confirms that SHAP importances are stable across\nsets. This stability analysis has now been included in the Supplemental material.\nFigure 7 (Supplementary Figure 10). fold-to-fold mean absolute SHAP values’ correlations.\n16, line 35- page 17, line 2):\n“Indeed, SHAP offers a cohesive framework for interpreting predictions in explainable Artificial\nIntelligence, assigning importance to each variable contributing to a prediction. The stability of\nSHAP patterns across folds is demonstrated in Supplementary Figure 10.”\n[13] The choice of performance metrics should also be reconsidered. Reporting R² in the main text --\nnot only in the Supplement -- is essential for gauging practical significance. Moreover, the authors\nshould ensure that statistical tests are labelled clearly; for example, the description of a t-test on a\nPearson correlation is technically accurate but might be confusing in context.\nFollowing the reviewer’s comment, we have now included the R² values in the main manuscript.\n(page 5, line 9-18):\n“We could significantly predict individual grey matter health based on the expotype in the main\nsubset (r = 0.23; p = 0.002; r2 = 0.05; p = 0.002, FDR-corrected; Supplementary Table 5). We\ncould replicate this achievement in the replication and variables-restricted subsets (which had\nless exposome variables but a higher number of participants) with similar accuracies (replication\nsubset: r = 0.23; p = 0.004 ; r2 = 0.05; p = 0.004; and variables-restricted subset: r = 0.24; p =\n0.002; r2 = 0.06; p = 0.002, FDR-corrected; Supplementary Table 5) confirming the robustness of\nour results across subsamples. Furthermore, individual grey matter health could also be\npredicted using alternative algorithms, although achieving numerically slightly lower prediction\naccuracy (with r = 0.17; p = 0.002; r2 = 0.03; p = 0.002 for Ridge Regression and r = 0.16; p =\n0.002; r2 = 0.02; p = 0.002 for SVR; FDR-corrected; Supplementary Table 5).”\nAdditionally, as we agree with the reviewer that mentioning a t-test for a correlation value might be\nconfusing without reference to the methods, we have now removed the notion of t-test from the results.\nSuggested improvements\n[14] Most importantly, the conceptual framing of brain age gap (BAG) could benefit from a more\ncautious and precise treatment. Currently, several statements risk implying that BAG reflects a direct\nmeasure of biological ageing, rather than deviation from a normative reference model. It would be\nhelpful to more clearly separate BAG from ageing processes per se, especially in light of recent\nevidence questioning this link.\nWe appreciate the reviewer's suggestion for a more cautious framing of the brain age gap (BAG) 1. As\nwe discussed in previous comments, we have now explicitly acknowledged the issues surrounding the\ninterpretation of BAG as an indicator of biological aging in the revised manuscript (page 11, line 9-\n12):\n“Second and relatedly, some studies suggest that deviance (older brain age) may reflect brain\nphenotype\npresent since early life instead of an aging phenotype36. Overall, these considerations question the\nextent to which BAG can be used as an indicator of brain health in aging.”\nthe revised manuscript.\n(page 7, line 32-34):\nIn that context, our study more specifically suggests that alcohol consumption, smoking, high\nblood pressure and diabetes are the strongest risk factors for altered grey matter.\n[15] In line with this, we suggest revising Figure 1 to avoid labeling the x-axis as “true BAG,” which\nmay unintentionally suggest the existence of an objective ground truth.\nWe appreciate reviewer’s comment and to avoid misunderstanding we have now changed the label from\n“true BAG” to the “Target BAG”.\n[16] The methodological transparency would also benefit from further detail. The rationale for selecting\nspecific machine learning algorithms should be articulated.\nAs we discussed in our responses to Reviewer #1's comment #7 and Reviewer #2's comment #4, when\nselecting the algorithms, we took into account brain age prediction literature and complementary\namong algorithms. Thus we selected ridge regression, support vector regression (SVR), and random\nforest (RF) due to their established use and effectiveness with tabular data in the field of brain age\nprediction39-41. Using these well-established algorithms, as their performances are comparable41, allow\nus to compare our brain age prediction results with other studies in the field. We also focused on those\nthree algorithms for exposome-based prediction of brain health because they offer complementary\nstrengths: ridge regression provides regularization to prevent overfitting, SVR is very popular and\nrelatively more robust to outliers than ridge regression, and random forest as an ensemble method, has\nthe ability to capture non-linearity and complex interaction among features, furthermore by combining\nmultiple decision trees it usually improves prediction accuracy and robustness. This information has\nnow been added in the revised manuscript as suggested by the reviewer:\n(page 16, line 10-14):\n“We selected these three algorithms because they offer complementary strengths: random forest\nas an ensemble method, has the ability to capture non-linearity and complex interaction among\nfeatures. Furthermore, by combining multiple decision trees it usually improves prediction\naccuracy and robustness. While ridge regression provides regularization to prevent overfitting,\nSVR is very popular and relatively more robust to outliers than ridge regression.”\n[17] along with an assessment of model stability and generalizability.\nFollowing the reviewer’s comment, the stability of the SHAP values have now been demonstrated in\nFigure 7/Supplementary Figure 10.; Following the reviewer’s comment, this information has been\nincluded in the revised manuscript (page 16, line 35- page 17, line 2):\n“Indeed, SHAP offers a cohesive framework for interpreting predictions in explainable Artificial\nIntelligence, assigning importance to each variable contributing to a prediction. The stability of\nSHAP patterns across folds is demonstrated in Supplementary Figure 10.”\ngeneralizability. This has now been clarified in the revised manuscript (page 10, line 25-27):\n“Second, while the prediction accuracy in the out-of-sample test set is significant and\ndemonstrates the model generalizability, the effect size (r value) remains relatively moderate.”\n[19] Similarly, please specify how the age-bias correction was implemented—was it linear or non-\nlinear, which covariates were included, and was the slope from the training set used? These details are\nimportant for reproducibility and interpretability.\nFollowing the reviewer’s comment, we have now provided detailed explanation of how the age-bias\ncorrection was implemented (page 14, line 12-22):\n“Importantly, several studies have brought attention to an age bias/age dependency in brain age\nprediction requiring a so-called age-bias correction37. This was done here by regressing out the\neffects of age on the predicted age38. Specifically, we calculated a regression line with\nchronological age as the predictor and predicted brain age as the outcome, using only the training\ndataset. The resulting slope and intercept were then used to adjust the predicted brain age values\nin the testing dataset. This adjustment involved subtracting the intercept from each predicted\nbrain age and then dividing by the slope. In other words, the slope and intercept of the regression\nline between chronological age and the predicted age were estimated in the training set and used\nto adjust the predicted age in the test and the population samples. As expected, and illustrated in\nsupplementary Table 11, this age-bias correction procedure substantially reduced the artefactual\ncorrelation between the subsequently computed BAG and age. “\n[20] I also recommend expanding the description of the exposome variables, particularly regarding how\nthey were selected, grouped, and whether any steps were taken to mitigate potential imbalance across\ngroups (e.g., lifestyle vs. environmental exposures).\nWe appreciate the reviewer's suggestion to expand the description of the exposome variables and the\nselection process. As detailed in our response to Reviewer #1's comment #2 and further elaborated in\nour responses to Reviewer #2's comments #7 and #9, the selection of exposome variables was a multi-\nstage process, primarily driven by a desire to represent independent clusters of health-related domains\nbased on existing literature.\nThe main steps (detailed above) are:\n1. Literature Review\n2. Initial Variable Listing\n3. Preliminary Exclusion Criteria\n4. Variable Prioritization\n5. Data Extraction (parsing UK Biobank data)\n6. Data Curation\n7. Subset Definition\nTo further clarify this process, we have now provided more detailed descriptions in the revised\nmanuscript (page15, line 6-29):\n32\n“Exposome variables\nTo represent the exposome, we used a wide range of non-imaging variables encompassing\nbiomedical, lifestyle, socio-economical and early life factors as illustrated in Figure 1 and the full\nlist of exposome variables are available in Supplementary Table 2. The selection of variables was\nguided by previous publications reporting either associations (with correlation or regression\nusually) with brain health as measured by the brain age gap5,6,15,22-27 or associations with grey\nmatter structure18,28-32 in the UK Biobank. This choice was also constrained by data availability.\nTo avoid potential biases associated with substantial missing values across participants, we\nfocused on variables that were available in at least 2000 participants including both males and\nfemales. We also performed some additional data curation processes, which included: uniqueness\nconstraint (we ensured that each field ID and participant was represented uniquely in the dataset\nand we removed potential duplicate records), aberrant value exclusion (we verified that all\nvariable values fell within the valid range or allowed set of categories e.g., no out-of-range\nnumerical values or invalid category codes), and uninformative value exclusion (we excluded\nimplausible or non-informative responses such as “do not know” or “prefer not to answer”).\nFurthermore, we also calculated duration variables for medical conditions by subtracting onset\nage from the age when attended assessment center (chronological age).\nAs illustrated in Figure 1, these procedures led us to define a main subset in which 261 distinct\nexposome variables were available for 3706 participants. By dropping out two variables (left and\nright heel bone density), we could create a bigger subset of 4292 participants that served as a\nreplication subset. Finally, we also identified a bigger subset of 7736 participants with data for\n201 distinct exposome variables. In this “’variables-restricted subset”, mainly variables related\nto socio-affective and mental health domains were missing compared to the main and replication\nsubsets. Standard preprocessing was performed on exposome variables including variables\nstandardization (within the cross-validation scheme to avoid any data leakage). “\n[21] Relatedly, it would be helpful to clarify whether weighting or redundancy reduction was applied\nto address overrepresentation or multicollinearity.\nWe have now clarified in the revised manuscript that no a-priori weighting or redundancy reduction\nhas been applied:\n(page 15, line 34- page 16, line 2):\n“Of note, from this set of variables, we did not perform any redundancy reduction since we\nsubsequently used machine learning algorithms which are designed to be able to weight or select\nrelevant features (see next section).”\n33\n[22] While performance metrics like Pearson’s r are useful, including R² values in the main text, rather\nthan relegating them to the supplement, would give readers a more complete sense of the model’s\nexplanatory power.\nFollowing the reviewer’s comment, we have now incorporate the R² values alongside the Pearson's r\nvalues in the main manuscript (page 5, line 9-18):\n“We could significantly predict individual grey matter health based on the expotype in the main\nsubset (r = 0.23; p = 0.002; r2 = 0.05; p = 0.002, FDR-corrected; Supplementary Table 5). We\ncould replicate this achievement in the replication and variables-restricted subsets (which had\nless exposome variables but a higher number of participants) with similar accuracies (replication\nsubset: r = 0.23; p = 0.004 ; r2 = 0.05; p = 0.004; and variables-restricted subset: r = 0.24; p =\n0.002; r2 = 0.06; p = 0.002, FDR-corrected; Supplementary Table 5) confirming the robustness of\nour results across subsamples. Furthermore, individual grey matter health could also be\npredicted using alternative algorithms, although achieving numerically slightly lower prediction\naccuracy (with r = 0.17; p = 0.002; r2 = 0.03; p = 0.002 for Ridge Regression and r = 0.16; p =\n0.002; r2 = 0.02; p = 0.002 for SVR; FDR-corrected; Supplementary Table 5).”\n[23] This is particularly relevant given that only a modest proportion of the variance in BAG is\nexplained. These small effects should be clearly stated and considered when discussing the results.\nFollowing the reviewer’s suggestion, effect size have now been critically discussed in the revised\nmanuscript (page 10, line 25-28):\n“while the prediction accuracy in the out-of-sample test set is significant and demonstrates the\nmodel generalizability, the effect size (r value) remains relatively moderate. Relatedly, the\nexplained variance in brain structural health (i.e. in BAG) in the test set is relatively limited\n(~5%).”\n[24] Moreover, the limitations of BAG as a derived and relatively noisy variable should be more\ndirectly acknowledged and discussed in a dedicated limitations section.\nFollowing all reviewers’ comments, a specific section has now been devoted to point out the\nlimitations of BAG (page 11, line 4-12):\n“Second, it should also be acknowledged that although BAG is a very convenient indicator of the\ndegree of relative preservation or degeneration of a whole grey matter pattern, it remains a\nderived indicator instead of a direct measurement, which implies some noise. Furthermore, its\nconceptual validity and interpretability remain importantly discussed48. First, a BAG framework\nassumes a unique aging trajectory and deviance (older brain age) from this trajectory is\nconsidered as advanced or pathological aging48. Yet, this assumption can be questioned. Second\nand relatedly, some studies suggest that deviance (older brain age) may reflect brain phenotype\n34\npresent since early life instead of an aging phenotype49. Overall, these considerations question the\nextent to which BAG can be used as an indicator of brain health in aging.”\n[25] Finally, some sections of the Discussion could be condensed to reduce repetition and instead used\nto place the findings in context of comparable studies. Phrasing such as “optimal lifestyle” and broad\nclaims like “all factors…constitute the exposome” could also be softened or made more precise to avoid\noverinterpretation. Addressing these points would, in my view, substantially enhance the clarity and\nimpact of the manuscript.\nAs discussed in previous comments, we have now better situated our results in the context of previous\nliterature. Following the reviewer’s comment, we have also carefully rephrased our statements in the\ndiscussion. For example, at page 3 line 18, we have replaced “optimal lifestyle” by “healthier\nlifestyle”’. Furthermore, we have rephrased “all factors, collectively constitute the exposome” (page\n2 line 32-34) as “’while many of the individual factors mentioned above have long been studied in\nisolation, the exposome concept insists on their co-occurrence, co-dependence, and potential\ninteractions”’.\nClarity and context\n[26] The manuscript is generally well-structured, but several areas require clarification. Some\nsentences need rephrasing for precision and scientific tone (e.g., “leads to a worst predicted grey\nmatter health”).\nWe have now replaced replace all instances of phrases like 'worst grey matter health' with more\nstandard alternatives such as 'poorer brain health’. Please see the answer to comment #9 of\nReviewer #1.\n[27] The Discussion section currently repeats earlier results rather than synthesizing them with\nbroader literature. A more critical and integrative discussion would improve clarity and impact.\nFollowing the reviewers’ comments, we have now importantly revised the discussion. First, we have\nbetter integrated our findings with previous literature. Second, we have also further highlighted the\nnovelty of our findings and their potential interpretation at the light of previous evidence of\nrelationships between different biomedical factors. Third, we have also now discussed the potential\nlimitations of our study, in particular when considering critical literature on the validity an\ninterpretation of the brain age gap.\nReferences\n[28] Multiple references could be added to foster discussion in the Discussion section. This includes\nboth brain age-specific and brain age-unspecific research.\nIn line with previous comments, we have now better acknowledged previous literature. In particular,\nwe have reported previous phenome-wide associations studies in UK Biobank, we have also integrated\nother UK Biobank studies that have highlighted relationships between different biomedical factors and\nwe have also included additional, more critical, pieces on the limitations of the brain age gap.\n[29] The GitHub repository is well organized and the most important bits of the code are available. I\ndid not run the code, but after reading it, I do not see a reason why it would not run (at least after some\nrefinements). Only critique: The code could be commented better, but this is more or less always the\ncase for all code one is not writing oneself.\nWe thank the reviewer for the positive feedback on our GitHub repository. Following the reviewer’s\ncomment, we have further documented the code.\nIn this paper, the authors investigated the association between expotypes and brain age gap. The use\nof the large UK biobank dataset is a key strength of the study. Further clarification and justification\nregarding the methodology, reporting and discussion of the results, particularly in the context of\nexisting literature, is required.\nWe thank the reviewer for the positive evaluation of our manuscript, as well as his/her comments which\nhave contributed to improve its quality.\n[1] The authors suggest that to avoid potential biases associated with substantial missing values across\nparticipants, they focused on variables that were available in at least 2000 participants including both\nmales and females - however, this would introduce bias, as has been well documented via complete\ncases analysis. Why was a robust method for addressing missing data not conducted, such as multiple\nimputation?\nWe appreciate the reviewer’s concern about potential bias from restricting analyses to variables\nobserved in ≥2,000 participants. We agree that complete-case strategies can be biased. In this study,\nwe considered multiple imputation (MI) carefully. However, we note several issues or challenges here\nfor adopting this approach. First, as showed in Figure 1 (reported below), we encounter massive\nmissing values for a wide range of variables.\nFigure 1. Missingness pattern in the literature-based a-priori selected data fields (Missingno package). The gray\ncolours are the filled values in women participants. Blue colours are the filled values in men participants. White\ncolour reflects missing values, and red colour indicates non-informative answers (i.e., I don’t know, I prefer not to\nanswer etc,.).\nIndeed, as can be seen in Figure 1, the set of participants with neuroimaging data exhibits block-like\npatterns of missingness (large participant groups with many variables missing), which substantially\nlimits the overlap of observed information across variables. In such settings, chained-equations MI is\nlikely to become unstable, invalid or heavily model-dependent because many conditional models must\nbe estimated with little shared data. Second, the imputation model should include all variables related\nto missingness and different variable types (continuous vs. categorical) typically require different\nconditional models (e.g., predictive mean matching, logistic/multinomial models) which would make\nthe process extremely tedious and complex for a set of several hundreds of variables. This would also\nrepresent a major computational burden52. Finally, this massive preprocessing would have to be\nimplemented without the cross-validation loop (i.e. by inferring parameters from the training to\ncomplete missing information in the training and test set) to avoid data leakage. Thus, such an approach\n37\nwould substantially increase the complexity and computational demands of the analysis, while the\nbenefit and remaining potential bias would remain uncertain. Considering these issues, we chose a\nminimum-availability filter (≥2,000 participants) to retain variables with sufficient observed support,\nreduce reliance on heavy modeling assumptions, and keep computation tractable.\n[2] It is not clear to me how the statement “BAG does not rely on chronological age” is true, given the\nbrain age gap subtracts the chronological age from the predicted age.\nWe thank the reviewer for pointing out that our statement can be misleading. We initially aimed to state\nthat there was no information related to age in the Brain Age Gap, especially after age-bias correction.\nFollowing the reviewer’s comment, our statement has been rephrased to avoid misunderstanding in the\nrevised manuscript (page 14, line 31-33):\n“Moreover, it is cross-validated and does not convey age-related information (or age-related\nstatistical signal) following age-bias correction, allowing it to directly assess deviations from\npopulation norms.”\n[3] I do not see how an individualised approach has been adopted here. Fundamentally, these models\nshow relationships between a set of variables and brain age gap, but do not provide an individualised\napproach to predicting brain age gap.\nWe appreciate the reviewer's concern regarding the degree to which our model provides individualized\ninsights. We acknowledge that our primary analysis focuses on identifying generalizable relationships\nbetween exposome variables and brain age gap (BAG) at a population level. However, by their very\nnature, machine learning models are trained to infer an outcome in any new individual that it has not\nseen before. Thus, we can expect that in the future, if prediction accuracy increases, such models can\nbe useful for the prediction of vulnerability and resilience at the individual level. The ability of a\nmultivariate machine learning model to consider hundreds of interacting exposome variables in order\nto make predictions at the individual level can be seen from the examination of the SHAP values. To\nillustrate this, we have reported below (Figure 8) the SHAP values for the top 30 variables for two\ndifferent participants who were both predicted to have higher BAG (i.e. poorer grey matter heath).\nWe can see from these plots that the contributing pattern for the first participant includes a long smoking\nduration, a long history of high blood pressure, a high coffee intake, cannabis at older age, asthma\ndiagnosed at older age, pulmonary embolism diagnosis age, etc… while for the second participant, its\ncontributing pattern includes a more recently diagnosed diabetes, a high pulse rate, a low water intake,\na low cereals intake, etc… Such level of complexity to infer about individual brain outcome can not be\ncaptured by phenome-wide correlation approaches for example. However, we acknowledge that the\ncurrent level of accuracy achieved does not provide fine prediction at the individual level for\npersonalized recommendation/prevention/intervention at this stage and accordingly, we ensure that our\nstatements about individual-level prediction in the revised manuscript pertain to the potential of\nexposome-based prediction in a future perspective and not to the results of the current study.\nbrain health) using waterfall plot.\n[4] At what timepoint were the exposures measured, in addition to the imaging data? Was the time\nbetween measures considered in the model?\nDifferent exposome variables may have been measured at different time points. We have included a\ncolumn called 'Instance' in Supplementary Table 2. This column indicates the UK Biobank assessment\nvisit from which the data for each variable was obtained.\nThe 'Instance' coding is based on the UK Biobank Showcase (instancing coding 2) and primarily reflects\ndata collected during Assessment Centre Visits. The approximate time ranges for these instances are\nshown in Table 3.\nTable 3 instancing coding 2 from UK Biobank showcase.\nIndex Description\nInitial assessment visit (2006-2010) at which participants were recruited and consent\n0\ngiven\nIn general, most of the exposome variables were measured at the initial assessment visit (2006-2010)\nor the first repeat assessment visit (2012-2013), while the imaging data was primarily collected during\nthe imaging visit (2014+). For more information about instancing, please check this link: “What is an\ninstance index?”. In this study, we took care to select measurements that were taken before or at the\nsame time of the imaging visit, but not later.\nRegarding whether the time between measures was considered in the model: we did not explicitly model\nthe time between exposure measurements and imaging data. However, we prioritized measurements\nthat pertain to relatively chronic exposures when several types of measurement were available (for\nexample we selected weekly intake information for diet over food intake over the 24h before the visit).\nFurthermore, to account for different time of exposure, we calculated duration (such as smoking\n39\nduration, diagnosis duration,…). By doing so we believe that the time between a given exposome\nmeasurement and the brain scan should not significantly influence the prediction. In other words, the\nsmall variations in the time between the exposome measurement across subjects is unlikely to influence\nthe pattern of the results.\n[5] The entire discussion is missing a meaningful discussion of effect sizes i.e., how many additional\nyears in brain ageing were observed as a result of each additional year for the exposure? This is needed\nto provide a meaningful interpretation of the results.\nWe agree with the reviewer that effect size is a relevant issue to be discussed in this study. Following\nthe reviewer’s comment, we have now discussed the limited effect size of our model in out-of-sample\ntest sets (page 10, line 25-29):\n“while the prediction accuracy in the out-of-sample test set is significant and demonstrates the\nmodel generalizability, the effect size (r value) remains relatively moderate. Relatedly, the\nexplained variance in brain structural health (i.e. in BAG) in the test set is relatively limited\n(~5%). This suggests that additional factors should be taken into account.”\nAlthough our machine learning model allows us to derive an overall effect size for the exposome\nvariables set in explaining brain structural health in test samples, it does not allow any inference at the\nexposome variable (i.e. feature) level. This is why we used a SHAP explainer. However, while the SHAP\napproach can inform us on which features matter most, and whether they generally increase or decrease\npredictions, it cannot indicate how much the outcome changes per unit increase in a feature in the\nclassical regression sense. \n[6] It is important that all statements are supported by citations/evidence\nWe thank the reviewer for raising out that point. We have now careful checked that all statements are\nsupported by citations.\n• [7] Please include a descriptive table for variables used in study\nFollowing the reviewer’s request, two separate descriptive tables (one for continuous and one for\ncategorical variables) have been included in the Supplemental material (for the main subset) in docx\nformat (`Supplementary Table 3.docx ` for continuous variables and ` Supplementary Table 4.docx `for\ncategorical variables).\nReviewer #3 (Remarks on code availability):\nCode is well documented\nThank you for your positive feedback on our codes.\n1. Smith, S.M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T.E. & Miller, K.L. Estimation of\nbrain age delta from brain imaging. NeuroImage 200, 528-539 (2019).\n2. Jawinski, P., et al. Genome-wide analysis of brain age identifies 25 associated loci and\nunveils relationships with mental and physical health. medRxiv, 2023.2012. 2026.23300533\n(2023).\n3. De Lange, A.-M.G., et al. 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Translational psychiatry 12, 1-9 (2022).\n50. Gaser, C., et al. CAT–A computational anatomy toolbox for the analysis of structural MRI\ndata. biorxiv, 2022.2006. 2011.495736 (2022).\n51. Wagner, A.S., et al. FAIRly big: A framework for computationally reproducible processing of\nlarge-scale data. Scientific data 9, 80 (2022).\n52. Von Hippel, P.T. How many imputations do you need? A two-stage calculation using a\nquadratic rule. Sociological Methods & Research 49, 699-718 (2020).\nThank you for your letter and for the reviewers’ comments concerning our manuscript entitled “What\npredicts individual brain health? a machine learning study spanning the exposome”. Your comments\nwere helpful in raising important points that allowed us to finalize and further improve the manuscript.\nIn the following pages, we provide our point-by-point responses to each of the reviewers’ comments.\nRevisions in the text are shown using Blue colour.\nResponses to the comments of Reviewers:\nReviewer #1:\nRemarks to the Author\nThe authors responded to my revision requests with notable rigor and thoroughness, which substantially\nimproved the manuscript. All my previous concerns have been fully addressed. I have only two\nremaining comments / tips for finalization.\nDear Dr. Jawinski, we thank you for your positive evaluation of our manuscript, as well as for your\ncomments, which have contributed to improving its quality.\n[1] Relatedness field: Unfortunately, the tabular data fields referring to relatedness, as mentioned by the\nauthors, are not very informative. There is a file containing pre-calculated kinship coefficients that could\npreviously be downloaded using the UK Biobank tool gfetch with the -rel flag. Within the Research\nAnalysis Platform, this relatedness file including kinship coefficients can now be found under\nBulk/Genotype Results/Genotype calls/ukb_rel.dat. Possibly something that could be considered in a\nfuture study. However, access to this file may require a Tier 3 plan.\nWe thank you for pointing out the availability of pre-calculated kinship coefficients in the UK Biobank.\nIt is indeed a helpful suggestion. At the time of the present study, we did not have access to ‘Tier 3 plan’\nwith in our UK Biobank application (application ID: 41655). Therefore, we were unable to explicitly\naccount for kinship coefficients in the current analyses. Following the reviewer’s comment, we have\nnow expanded the limitations section of the manuscript to clarify this point and to explicitly\nacknowledge that future studies could integrate these pre-calculated kinship coefficients to address\nfamilial relatedness more rigorously:\n(page 10, lines 31-36):\n“However, several limitations should be noted. First, at the methodological level, in this study\nwe did not account for family relatedness. The UK Biobank includes several participants with\nfirst-, second-, and third-degree familial relationships which can potentially bias the results. The\ngenetic relatedness between pairs of participants has been summarized in the pre-calculated\nkinship coefficients, which could be incorporated in future work to explicitly model or control\nfor familial relatedness.”\n[2] P-values and permutations: Thank you for the clarification. With 500 permutations, the lowest\nachievable p-value is < 0.002 (i.e., when none of the permutations yields a similarly extreme test\nstatistic). It may be worth mentioning this explicitly, as it explains why many p-values appear as\n0.002. I also noticed that for SVR (linear) and r-squared, the corrected p-value is lower than the\nuncorrected p-value; please re-check this. Additionally, consider reporting all p-values in this table\nwith three decimal places for consistency.\nWe thank you for highlighting the limitation imposed by the number of permutations on the achievable\np-values. As correctly noted, when permutation testing is performed with 500 permutations, the\nsmallest possible p-value that can be obtained is approximately 0.002. This explains why several\nresults in Supplementary Table 5 show p-values at or close to this threshold.\nImportantly, these p-values indicate that the observed prediction accuracies are significantly above\nchance level and therefore non-random. However, with the current number of permutations, it is not\npossible to further quantify how extremely non-random these effects are. Estimating more precise (i.e.,\nsmaller) p-values would require running a larger number of permutations.\nWe have now clarified this point explicitly in the manuscript, where we state that p-values were\nestimated using permutation testing with 500 permutations and that the minimum attainable p-value\nunder this procedure is approximately 0.002.\n(Page 5 lines 9-20):\n“We could significantly predict grey matter health based on the expotype in the main subset (r =\n0.23; p = 0.002; r2 = 0.05; p = 0.002, FDR-corrected; Supplementary Table 5). We could replicate\nthis achievement in the replication and variables-restricted subsets (which had less exposome\nvariables but a higher number of participants) with similar accuracies (replication subset: r =\n0.23; p = 0.004 ; r2 = 0.05; p = 0.004; and variables-restricted subset: r = 0.24; p = 0.002; r2 =\n0.06; p = 0.002, FDR-corrected; Supplementary Table 5) confirming the robustness of our results\nacross subsamples. Furthermore, grey matter health could also be predicted using alternative\nalgorithms, although achieving numerically slightly lower prediction accuracy (with r = 0.17; p =\n0.002; r2 = 0.03; p = 0.002 for Ridge Regression and r = 0.16; p = 0.002; r2 = 0.02; p = 0.002 for\nSVR; FDR-corrected; Supplementary Table 5). It is noteworthy that p-values were estimated\nusing permutation testing with 500 permutations. Consequently, the smallest p-value that can be\nobtained under this procedure is approximately 0.002, and the true p-values may be smaller if a\nlarger number of permutations were used.”\nRegarding the lower corrected p-value than the uncorrected p-value for SVR (linear) and r-squared, we\nconfirmed that this was due to a typographical error (happening during files format conversion for the\nsubmission). We have now carefully re-checked all p-values and corrected the table accordingly.\nFinally, we have reported them with three decimal places for consistency (Table 1, supplementary table\n5) as you suggested.\nTable 1(supplementary table 5), Grey matter health prediction performance across different subsets/algorithms including\nmain subset (n = 3706), replication subset (n = 4202), and variable-restricted subset (n = 7736).\nRidge re-\nAlgorithm Random Forest SVR(Linear)\ngression\nVariables-re-\nSubset Main Replication Main Main\nstricted\nMean Absolute Error\n4.48 4.52 4.53 4.54 4.53\n(MAE)\nUncorrected p-value 0.034 0.024 0.002 0.002 0.002\nCorrected p-value 0.035 0.026 0.003 0.003 0.003\nMean Squared Error\n32.22 32.63 32.66 33.14 33.07\n(MSE)\nUncorrected p-value 0.006 0.022 0.002 0.002 0.002\nCorrected p-value 0.007 0.024 0.003 0.003 0.003\nroot Mean Squared Error\n5.67 5.71 5.71 5.75 5.75\n(rMSE)\nUncorrected p-value 0.004 0.044 0.002 0.002 0.002\nCorrected p-value 0.005 0.044 0.003 0.003 0.003\nr2\n0.05 0.05 0.06 0.02 0.03\nUncorrected p-value 0.002 0.004 0.002 0.002 0.002\nCorrected p-value 0.003 0.005 0.003 0.003 0.003\nPearson Correlation\n0.23 0.23 0.24 0.16 0.17\n(r)\nUncorrected p-value 0.002 0.004 0.002 0.002 0.002\nCorrected p-value 0.003 0.005 0.003 0.003 0.003\nI congratulate the authors on this strong revision.\nBest wishes,\nPhilippe Jawinski\nWe thank you again for your careful assessment of the revised manuscript and for your supportive and\nconstructive final comments.\nRemarks on code availability:\nThe GitHub repository is well-organized, well-documented, and comprehensive. As requested, the\nauthors have added a conda environment file during the revision, which further enhances\nreproducibility.\nThank you for the positive assessment of the GitHub repository and code availability. We are glad that\nthe added conda environment file improves the clarity and reproducibility of the analyses.\nReviewer #2:\nRemarks to the Author:\nThe authors have addressed my comments satisfactorily. Congratulations on a well-executed study!\nThank you for the positive evaluation of our manuscript and thank you for your valuable comments.\nRemarks on code availability:\nThe authors have addressed my comments satisfactorily. Congratulations on a well-executed study!\nThank you for your positive assessment of the code availability and reproducibility.\nReviewer #3:\nRemarks to the Author:\nThe authors have made considerable progress in addressing the initial comments and should be\ncommended for this. However, further clarification and justification is required for transparency.\n[1] The authors’ rationale for not conducting multiple imputation is understandable, but this justification\nmust be included directly in the manuscript. This is important because the decision not to impute has\nimplications for potential bias. A concise explanation of the missingness structure, the limitations of\nMI under these conditions, and the trade-offs involved should be made clear to readers.\nWe appreciate your concern about including a direct justification. Following your suggestion, we have\nnow referred to the block-like pattern of the missingness in the data (included Supplementary Figure 9\nin the supplement) and state the limitations of MI under these conditions, as well as the trade-off\ninvolved in our strategy in the revised manuscript. (page 16, lines 18-30):\n“To avoid potential biases associated with substantial missing values across participants, we\nfocused on variables that were available in at least 2000 participants including both males and\nfemales. Of note, we carefully considered multiple imputation. However, the missingness structure\nin the dataset posed substantial challenges. Specifically, participants with neuroimaging data\nexhibited block-like patterns of missingness, resulting in limited overlap across many variables\n(see Supplementary Figure 9). Under such conditions, chained-equations multiple imputation\nwould be prone to instability and strong model dependence, as many conditional models would\nneed to be estimated from sparse shared information. Moreover, implementing multiple\nimputation across several hundred heterogeneous variables (continuous and categorical) within\na cross-validation framework—while avoiding data leakage—would substantially increase both\nmethodological complexity and computational burden, with uncertain gains in bias reduction.\nConsidering these limitations, we adopted a minimum-availability filtering strategy, prioritizing\nvariables with sufficient observations while maintaining analytical tractability.”\nFigure 1(Supplementary Figure 9). Missingness pattern in the literature-based a-priori selected data fields (Missingno\npackage). The grey colours are the filled values in women participants. Blue colours are the filled values in men\nparticipants. White colour reflects missing values, and red colour indicates non-informative answers (i.e., I don’t know, I\nprefer not to answer etc,.).\n[2] The response regarding the “individualised approach” is appreciated, but the manuscript should\nmore clearly distinguish between what the current model can achieve and what may be possible in future\napplications throughout. At present, the predictive accuracy is not sufficient to support individual-level\ninference, and any statements implying personalised prediction should be reframed to refer explicitly\nto future potential rather than current capability.\nWe thank you for this important clarification. We agree that, given the current level of predictive\naccuracy, the model does not support individual-level inference or personalised prediction at present.\nWe have therefore carefully revised the manuscript to clearly distinguish between the current\ncapabilities of the model and the potential future applications of exposome-based predictive modeling\nfor individual prediction.\nSpecifically, we now explicitly state that while the model generates individual-level predictions and\nexplanations (e.g. SHAP values), these are intended to illustrate model behaviour rather than to support\npersonalised clinical inference. All statements referring to individual-level prediction have been\nreframed to refer explicitly to future potential, contingent on substantially improved predictive\naccuracy.\nFinally, we have also explicitly discussed the fact that our model currently does not support individual\nprediction for application (page 11, lines 13-21):\n“Third, while the prediction accuracy in the out-of-sample test set is significant and demonstrates\nthe model generalizability, the effect size (r value) remains relatively moderate. Relatedly, the\nexplained variance in brain structural health (i.e. in BAG) in the test set is relatively limited\n(~5%). Although the model generates predictions and feature attributions at the individual level,\nthe current level of predictive accuracy does not support personalized inference or individual-\nlevel decision-making. Accordingly, individual SHAP explanations are presented to illustrate\nmodel behaviour rather than to enable personalized interpretation. The potential utility of\nexposome-based machine learning models for individual-level vulnerability or resilience\nprediction should therefore be considered as a future perspective, contingent on substantial\nimprovements in predictive performance.”\n[3] The authors state that “small variations in the time between exposome measurement across subjects\nis unlikely to influence the pattern of results,” but this assumption needs to be transparently stated in\nthe manuscript with the justification provided, as there is currently no evidence to support this.\nWe agree that the assumption regarding the timing of exposome measurements should be explicitly\nstated and justified in the manuscript.\nIn the present study, we did not explicitly model the time interval between exposome assessments and\nthe imaging visit. Instead, when multiple measurements were available for a given variable, we\nprioritized indicators of relatively chronic exposure (e.g., weekly dietary intake rather than 24-hour\nrecall), and where relevant we derived duration-based measures (e.g., smoking duration, disease\nduration). These choices were intended to reduce sensitivity to short-term fluctuations and partially\naccount for between-subject variability in the timing of exposure measurement relative to brain\nimaging.\nNevertheless, this approach relies on the assumption that modest between-subject variation in the time\ninterval between exposome assessment and imaging does not substantially alter the observed patterns.\nWe acknowledge that this assumption cannot be directly tested within the current study and therefore\nrepresents a methodological limitation. Future longitudinal studies explicitly modeling time-varying\nexposures and exposure–outcome lag effects will be important to further clarify these temporal\nrelationships.\n(page 16 lines 31–35 and page 17 lines 1–5):\n“We also performed some additional data curation processes, which included: uniqueness\nconstraint (we ensured that each field ID and participant was represented uniquely in the dataset\nand we removed potential duplicate records), aberrant value exclusion (we verified that all\nvariable values fell within the valid range or allowed set of categories e.g., no out-of-range\nnumerical values or invalid category codes), and uninformative value exclusion (we excluded\nimplausible or non-informative responses such as “do not know” or “prefer not to answer”).\nFurthermore, we also calculated duration variables for medical conditions by subtracting onset\nage from the age when attended assessment center (chronological age). Finally, it is noteworthy\nto mention that most exposome variables were measured at the initial UK Biobank assessment\nvisit (2006–2010), and when data were not available at that time point, we used measurements\nfrom the first repeat assessment visit (2012–2013).”\n(page 10 line 36 and page 11 lines 1-11):\n“Second, as mentioned in method section, most exposome variables were measured at the initial\nUK Biobank assessment visit (2006–2010), and when data were not available at that time point,\nwe used measurements from the first repeat assessment visit (2012–2013). We did not explicitly\nmodel the time interval between exposome assessment and the imaging visit. To mitigate potential\neffects of temporal variability, we prioritized measures reflecting relatively chronic exposures\n(e.g., weekly dietary intake rather than 24-hour recall) and derived duration-based variables\nwhere possible (e.g., smoking duration, disease duration). Nevertheless, this approach assumes\nthat modest between-subject variation in the timing of exposome assessment relative to imaging\ndoes not substantially influence the observed associations. This assumption cannot be formally\ntested within the present study and therefore represents a methodological limitation. Future\nlongitudinal analyses explicitly modeling time-varying exposures and exposure–outcome lag\neffects will be important to further clarify these temporal relationships.”\n[4] The discussion of effect sizes remains limited. While classical regression-style effect estimates\ncannot be derived from the modelling approach used, readers still require a meaningful way to interpret\nthe magnitude and direction of key associations. The authors are encouraged to add brief clarifying text\nin the discussion noting these limitations, and include SHAP dependence plots or similar visualisations\nto illustrate how the most influential exposures relate to BAG across their value ranges.\nWe agree that the reviewer that the effect size may be of interest for some readers. It is noteworthy to\nmention that, although SHAP-based explanations help us in the interpretation of how changes in\nexposure values are associated with increases or decreases in predicted BAG across their observed\nranges, but do not provide effect sizes in the traditional regression sense. We should here also mention\nthat using SHAP dependence plots to infer about effect size can be misleading and leads to reader’s\nmisunderstanding in our study for several reasons. First, typical SHAP dependence plots illustrate\nSHAP values for a given feature against the feature values (which does not provide information about\nassociations between how exposure relates to BAG). Second, it is very important to note that confounds\nremoval has been performed on the variables and therefore exposome variables values are actually\nadjusted standardized values. This leads for example to negative values for age at onset and duration\nwhich can be really difficult to interpret for the reader if SHAP dependence plots would be provided.\nNevertheless to provide some information about effect size and the relationships between key exposome\nfeatures and BAG, we used a traditional linear regression approach for our top 30 exposome features.\nThe effect size of each top exposome variable on BAG (when accounting for confounds) is now\nillustrated by plotting standardized beta for each regression analysis together in Figure 1 (Supplementary\nFigure 7). It should be noted here that such model does not account for interaction between exposome\nfeatures and non-linear relationships with BAG. Accordingly, the effect size of any single exposome\nvariable on BAG can be considered as relatively small, in line with the literature on UK Biobank. This\nhas also been discussed in the revised manuscript:\n(Page 6, line 7–14):\n“Additionally, to provide some information about effect size and the relationships between the 30\ntop exposome features and BAG, we perform a traditional linear regression approach. The effect\nsize of each top exposome variable on BAG (when accounting for confounds) is illustrated by\nplotting standardized beta for each regression analysis in the supplementary figure 7. It should\nbe noted here that such model does not account for interaction between exposome features and\nnon-linear relationships with BAG. Accordingly, the effect size of any single exposome variable\non BAG can be considered as relatively small, in line with the literature on UK Biobank. Whatever\nthe approach, however, overall, most of the associations follow an expected pattern.”\n(Page 11, lines 22–32):\n“Along the same line, at the level of individual exposome variables, classical regression-style effect\nsizes (e.g. years of brain ageing per unit increase in exposure) cannot be directly inferred from\nthe multivariate machine learning models used here, due to their non-linear structure and the\ncomplex interactions between predictors. To facilitate interpretation of the direction and shape of\nassociations, we therefore relied on SHAP-based explanations, which illustrate how changes in\nexposure values are associated with increases or decreases in predicted BAG across their observed\nranges, but do not provide effect sizes in the traditional regression sense. When considering each\nof the top exposome variable individually in relation to BAG (supplementary figure 7) using a\ntraditional linear regression model, the effect size of any single exposome variable on BAG can be\nconsidered as relatively small, in line with the literature on the UK Biobank. Overall, this\nreinforces the view that brain health emerges from the cumulative and interacting effects of many\nfactors rather than large effects of single exposures.”\nFigure 2 (supplementary figure 7). Standardized regression coefficients for the top 30 exposome features associated with brain\nage gap (BAG) in the main subset. The figure shows standardized beta coefficients derived from generalized linear models\nrelating each exposome feature separately to BAG, when accounting for age, age², sex, height, and volumetric scaling from\nT1 head image to standard space. Both predictors and outcome were standardized to allow comparison of effect magnitudes\nacross variables. Red dots illustrate significant associations after correcting for multiple comparisons (while blue dots are not\nsignificant). These analyses were conducted for reader’s information about the magnitude of each association when each\nexposome variable is considered in isolation in relation to BAG.\nRemarks on code availability:\nHave not re-reviewed code.\nThank you again for the positive feedback and the useful suggestions.\n1. Smith, S.M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T.E. & Miller, K.L. Estimation of\nbrain age delta from brain imaging. NeuroImage 200, 528-539 (2019).\n2. Jawinski, P., et al. Genome-wide analysis of brain age identifies 25 associated loci and\nunveils relationships with mental and physical health. medRxiv, 2023.2012. 2026.23300533\n(2023).\n3. De Lange, A.-M.G., et al. Multimodal brain-age prediction and cardiovascular risk: The\nWhitehall II MRI sub-study. NeuroImage 222, 117292 (2020).\n4. Wagen, A.Z., et al. 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CAT–A computational anatomy toolbox for the analysis of structural MRI\ndata. biorxiv, 2022.2006. 2011.495736 (2022).\n51. Wagner, A.S., et al. FAIRly big: A framework for computationally reproducible processing of\nlarge-scale data. Scientific data 9, 80 (2022).\n52. Von Hippel, P.T. How many imputations do you need? A two-stage calculation using a\nquadratic rule. Sociological Methods & Research 49, 699-718 (2020).\nThank you for your letter and for the reviewers’ comments concerning our manuscript entitled “What\npredicts individual brain health? a machine learning study spanning the exposome”. Your comments\nwere helpful in raising important points that allowed us to finalize and further improve the manuscript.\nIn the following pages, we provide our point-by-point responses to each of the reviewers’ comments.\nRevisions in the text are shown using Blue colour.\nResponses to the comments of Reviewers:\nReviewer #1:\nRemarks to the Author\nThe authors responded to my revision requests with notable rigor and thoroughness, which substantially\nimproved the manuscript. All my previous concerns have been fully addressed. I have only two\nremaining comments / tips for finalization.\nDear Dr. Jawinski, we thank you for your positive evaluation of our manuscript, as well as for your\ncomments, which have contributed to improving its quality.\n[1] Relatedness field: Unfortunately, the tabular data fields referring to relatedness, as mentioned by the\nauthors, are not very informative. There is a file containing pre-calculated kinship coefficients that could\npreviously be downloaded using the UK Biobank tool gfetch with the -rel flag. Within the Research\nAnalysis Platform, this relatedness file including kinship coefficients can now be found under\nBulk/Genotype Results/Genotype calls/ukb_rel.dat. Possibly something that could be considered in a\nfuture study. However, access to this file may require a Tier 3 plan.\nWe thank you for pointing out the availability of pre-calculated kinship coefficients in the UK Biobank.\nIt is indeed a helpful suggestion. At the time of the present study, we did not have access to ‘Tier 3 plan’\nwith in our UK Biobank application (application ID: 41655). Therefore, we were unable to explicitly\naccount for kinship coefficients in the current analyses. Following the reviewer’s comment, we have\nnow expanded the limitations section of the manuscript to clarify this point and to explicitly\nacknowledge that future studies could integrate these pre-calculated kinship coefficients to address\nfamilial relatedness more rigorously:\n(page 10, lines 31-36):\n“However, several limitations should be noted. First, at the methodological level, in this study\nwe did not account for family relatedness. The UK Biobank includes several participants with\nfirst-, second-, and third-degree familial relationships which can potentially bias the results. The\ngenetic relatedness between pairs of participants has been summarized in the pre-calculated\nkinship coefficients, which could be incorporated in future work to explicitly model or control\nfor familial relatedness.”\n[2] P-values and permutations: Thank you for the clarification. With 500 permutations, the lowest\nachievable p-value is < 0.002 (i.e., when none of the permutations yields a similarly extreme test\nstatistic). It may be worth mentioning this explicitly, as it explains why many p-values appear as\n0.002. I also noticed that for SVR (linear) and r-squared, the corrected p-value is lower than the\nuncorrected p-value; please re-check this. Additionally, consider reporting all p-values in this table\nwith three decimal places for consistency.\nWe thank you for highlighting the limitation imposed by the number of permutations on the achievable\np-values. As correctly noted, when permutation testing is performed with 500 permutations, the\nsmallest possible p-value that can be obtained is approximately 0.002. This explains why several\nresults in Supplementary Table 5 show p-values at or close to this threshold.\nImportantly, these p-values indicate that the observed prediction accuracies are significantly above\nchance level and therefore non-random. However, with the current number of permutations, it is not\npossible to further quantify how extremely non-random these effects are. Estimating more precise (i.e.,\nsmaller) p-values would require running a larger number of permutations.\nWe have now clarified this point explicitly in the manuscript, where we state that p-values were\nestimated using permutation testing with 500 permutations and that the minimum attainable p-value\nunder this procedure is approximately 0.002.\n(Page 5 lines 9-20):\n“We could significantly predict grey matter health based on the expotype in the main subset (r =\n0.23; p = 0.002; r2 = 0.05; p = 0.002, FDR-corrected; Supplementary Table 5). We could replicate\nthis achievement in the replication and variables-restricted subsets (which had less exposome\nvariables but a higher number of participants) with similar accuracies (replication subset: r =\n0.23; p = 0.004 ; r2 = 0.05; p = 0.004; and variables-restricted subset: r = 0.24; p = 0.002; r2 =\n0.06; p = 0.002, FDR-corrected; Supplementary Table 5) confirming the robustness of our results\nacross subsamples. Furthermore, grey matter health could also be predicted using alternative\nalgorithms, although achieving numerically slightly lower prediction accuracy (with r = 0.17; p =\n0.002; r2 = 0.03; p = 0.002 for Ridge Regression and r = 0.16; p = 0.002; r2 = 0.02; p = 0.002 for\nSVR; FDR-corrected; Supplementary Table 5). It is noteworthy that p-values were estimated\nusing permutation testing with 500 permutations. Consequently, the smallest p-value that can be\nobtained under this procedure is approximately 0.002, and the true p-values may be smaller if a\nlarger number of permutations were used.”\nRegarding the lower corrected p-value than the uncorrected p-value for SVR (linear) and r-squared, we\nconfirmed that this was due to a typographical error (happening during files format conversion for the\nsubmission). We have now carefully re-checked all p-values and corrected the table accordingly.\nFinally, we have reported them with three decimal places for consistency (Table 1, supplementary table\n5) as you suggested.\nTable 1(supplementary table 5), Grey matter health prediction performance across different subsets/algorithms including\nmain subset (n = 3706), replication subset (n = 4202), and variable-restricted subset (n = 7736).\nRidge re-\nAlgorithm Random Forest SVR(Linear)\ngression\nVariables-re-\nSubset Main Replication Main Main\nstricted\nMean Absolute Error\n4.48 4.52 4.53 4.54 4.53\n(MAE)\nUncorrected p-value 0.034 0.024 0.002 0.002 0.002\nCorrected p-value 0.035 0.026 0.003 0.003 0.003\nMean Squared Error\n32.22 32.63 32.66 33.14 33.07\n(MSE)\nUncorrected p-value 0.006 0.022 0.002 0.002 0.002\nCorrected p-value 0.007 0.024 0.003 0.003 0.003\nroot Mean Squared Error\n5.67 5.71 5.71 5.75 5.75\n(rMSE)\nUncorrected p-value 0.004 0.044 0.002 0.002 0.002\nCorrected p-value 0.005 0.044 0.003 0.003 0.003\nr2\n0.05 0.05 0.06 0.02 0.03\nUncorrected p-value 0.002 0.004 0.002 0.002 0.002\nCorrected p-value 0.003 0.005 0.003 0.003 0.003\nPearson Correlation\n0.23 0.23 0.24 0.16 0.17\n(r)\nUncorrected p-value 0.002 0.004 0.002 0.002 0.002\nCorrected p-value 0.003 0.005 0.003 0.003 0.003\nI congratulate the authors on this strong revision.\nBest wishes,\nPhilippe Jawinski\nWe thank you again for your careful assessment of the revised manuscript and for your supportive and\nconstructive final comments.\nRemarks on code availability:\nThe GitHub repository is well-organized, well-documented, and comprehensive. As requested, the\nauthors have added a conda environment file during the revision, which further enhances\nreproducibility.\nThank you for the positive assessment of the GitHub repository and code availability. We are glad that\nthe added conda environment file improves the clarity and reproducibility of the analyses.\nReviewer #2:\nRemarks to the Author:\nThe authors have addressed my comments satisfactorily. Congratulations on a well-executed study!\nThank you for the positive evaluation of our manuscript and thank you for your valuable comments.\nRemarks on code availability:\nThe authors have addressed my comments satisfactorily. Congratulations on a well-executed study!\nThank you for your positive assessment of the code availability and reproducibility.\nReviewer #3:\nRemarks to the Author:\nThe authors have made considerable progress in addressing the initial comments and should be\ncommended for this. However, further clarification and justification is required for transparency.\n[1] The authors’ rationale for not conducting multiple imputation is understandable, but this justification\nmust be included directly in the manuscript. This is important because the decision not to impute has\nimplications for potential bias. A concise explanation of the missingness structure, the limitations of\nMI under these conditions, and the trade-offs involved should be made clear to readers.\nWe appreciate your concern about including a direct justification. Following your suggestion, we have\nnow referred to the block-like pattern of the missingness in the data (included Supplementary Figure 9\nin the supplement) and state the limitations of MI under these conditions, as well as the trade-off\ninvolved in our strategy in the revised manuscript. (page 16, lines 18-30):\n“To avoid potential biases associated with substantial missing values across participants, we\nfocused on variables that were available in at least 2000 participants including both males and\nfemales. Of note, we carefully considered multiple imputation. However, the missingness structure\nin the dataset posed substantial challenges. Specifically, participants with neuroimaging data\nexhibited block-like patterns of missingness, resulting in limited overlap across many variables\n(see Supplementary Figure 9). Under such conditions, chained-equations multiple imputation\nwould be prone to instability and strong model dependence, as many conditional models would\nneed to be estimated from sparse shared information. Moreover, implementing multiple\nimputation across several hundred heterogeneous variables (continuous and categorical) within\na cross-validation framework—while avoiding data leakage—would substantially increase both\nmethodological complexity and computational burden, with uncertain gains in bias reduction.\nConsidering these limitations, we adopted a minimum-availability filtering strategy, prioritizing\nvariables with sufficient observations while maintaining analytical tractability.”\nFigure 1(Supplementary Figure 9). Missingness pattern in the literature-based a-priori selected data fields (Missingno\npackage). The grey colours are the filled values in women participants. Blue colours are the filled values in men\nparticipants. White colour reflects missing values, and red colour indicates non-informative answers (i.e., I don’t know, I\nprefer not to answer etc,.).\n[2] The response regarding the “individualised approach” is appreciated, but the manuscript should\nmore clearly distinguish between what the current model can achieve and what may be possible in future\napplications throughout. At present, the predictive accuracy is not sufficient to support individual-level\ninference, and any statements implying personalised prediction should be reframed to refer explicitly\nto future potential rather than current capability.\nWe thank you for this important clarification. We agree that, given the current level of predictive\naccuracy, the model does not support individual-level inference or personalised prediction at present.\nWe have therefore carefully revised the manuscript to clearly distinguish between the current\ncapabilities of the model and the potential future applications of exposome-based predictive modeling\nfor individual prediction.\nSpecifically, we now explicitly state that while the model generates individual-level predictions and\nexplanations (e.g. SHAP values), these are intended to illustrate model behaviour rather than to support\npersonalised clinical inference. All statements referring to individual-level prediction have been\nreframed to refer explicitly to future potential, contingent on substantially improved predictive\naccuracy.\nFinally, we have also explicitly discussed the fact that our model currently does not support individual\nprediction for application (page 11, lines 13-21):\n“Third, while the prediction accuracy in the out-of-sample test set is significant and demonstrates\nthe model generalizability, the effect size (r value) remains relatively moderate. Relatedly, the\nexplained variance in brain structural health (i.e. in BAG) in the test set is relatively limited\n(~5%). Although the model generates predictions and feature attributions at the individual level,\nthe current level of predictive accuracy does not support personalized inference or individual-\nlevel decision-making. Accordingly, individual SHAP explanations are presented to illustrate\nmodel behaviour rather than to enable personalized interpretation. The potential utility of\nexposome-based machine learning models for individual-level vulnerability or resilience\nprediction should therefore be considered as a future perspective, contingent on substantial\nimprovements in predictive performance.”\n[3] The authors state that “small variations in the time between exposome measurement across subjects\nis unlikely to influence the pattern of results,” but this assumption needs to be transparently stated in\nthe manuscript with the justification provided, as there is currently no evidence to support this.\nWe agree that the assumption regarding the timing of exposome measurements should be explicitly\nstated and justified in the manuscript.\nIn the present study, we did not explicitly model the time interval between exposome assessments and\nthe imaging visit. Instead, when multiple measurements were available for a given variable, we\nprioritized indicators of relatively chronic exposure (e.g., weekly dietary intake rather than 24-hour\nrecall), and where relevant we derived duration-based measures (e.g., smoking duration, disease\nduration). These choices were intended to reduce sensitivity to short-term fluctuations and partially\naccount for between-subject variability in the timing of exposure measurement relative to brain\nimaging.\nNevertheless, this approach relies on the assumption that modest between-subject variation in the time\ninterval between exposome assessment and imaging does not substantially alter the observed patterns.\nWe acknowledge that this assumption cannot be directly tested within the current study and therefore\nrepresents a methodological limitation. Future longitudinal studies explicitly modeling time-varying\nexposures and exposure–outcome lag effects will be important to further clarify these temporal\nrelationships.\n(page 16 lines 31–35 and page 17 lines 1–5):\n“We also performed some additional data curation processes, which included: uniqueness\nconstraint (we ensured that each field ID and participant was represented uniquely in the dataset\nand we removed potential duplicate records), aberrant value exclusion (we verified that all\nvariable values fell within the valid range or allowed set of categories e.g., no out-of-range\nnumerical values or invalid category codes), and uninformative value exclusion (we excluded\nimplausible or non-informative responses such as “do not know” or “prefer not to answer”).\nFurthermore, we also calculated duration variables for medical conditions by subtracting onset\nage from the age when attended assessment center (chronological age). Finally, it is noteworthy\nto mention that most exposome variables were measured at the initial UK Biobank assessment\nvisit (2006–2010), and when data were not available at that time point, we used measurements\nfrom the first repeat assessment visit (2012–2013).”\n(page 10 line 36 and page 11 lines 1-11):\n“Second, as mentioned in method section, most exposome variables were measured at the initial\nUK Biobank assessment visit (2006–2010), and when data were not available at that time point,\nwe used measurements from the first repeat assessment visit (2012–2013). We did not explicitly\nmodel the time interval between exposome assessment and the imaging visit. To mitigate potential\neffects of temporal variability, we prioritized measures reflecting relatively chronic exposures\n(e.g., weekly dietary intake rather than 24-hour recall) and derived duration-based variables\nwhere possible (e.g., smoking duration, disease duration). Nevertheless, this approach assumes\nthat modest between-subject variation in the timing of exposome assessment relative to imaging\ndoes not substantially influence the observed associations. This assumption cannot be formally\ntested within the present study and therefore represents a methodological limitation. Future\nlongitudinal analyses explicitly modeling time-varying exposures and exposure–outcome lag\neffects will be important to further clarify these temporal relationships.”\n[4] The discussion of effect sizes remains limited. While classical regression-style effect estimates\ncannot be derived from the modelling approach used, readers still require a meaningful way to interpret\nthe magnitude and direction of key associations. The authors are encouraged to add brief clarifying text\nin the discussion noting these limitations, and include SHAP dependence plots or similar visualisations\nto illustrate how the most influential exposures relate to BAG across their value ranges.\nWe agree that the reviewer that the effect size may be of interest for some readers. It is noteworthy to\nmention that, although SHAP-based explanations help us in the interpretation of how changes in\nexposure values are associated with increases or decreases in predicted BAG across their observed\nranges, but do not provide effect sizes in the traditional regression sense. We should here also mention\nthat using SHAP dependence plots to infer about effect size can be misleading and leads to reader’s\nmisunderstanding in our study for several reasons. First, typical SHAP dependence plots illustrate\nSHAP values for a given feature against the feature values (which does not provide information about\nassociations between how exposure relates to BAG). Second, it is very important to note that confounds\nremoval has been performed on the variables and therefore exposome variables values are actually\nadjusted standardized values. This leads for example to negative values for age at onset and duration\nwhich can be really difficult to interpret for the reader if SHAP dependence plots would be provided.\nNevertheless to provide some information about effect size and the relationships between key exposome\nfeatures and BAG, we used a traditional linear regression approach for our top 30 exposome features.\nThe effect size of each top exposome variable on BAG (when accounting for confounds) is now\nillustrated by plotting standardized beta for each regression analysis together in Figure 1 (Supplementary\nFigure 7). It should be noted here that such model does not account for interaction between exposome\nfeatures and non-linear relationships with BAG. Accordingly, the effect size of any single exposome\nvariable on BAG can be considered as relatively small, in line with the literature on UK Biobank. This\nhas also been discussed in the revised manuscript:\n(Page 6, line 7–14):\n“Additionally, to provide some information about effect size and the relationships between the 30\ntop exposome features and BAG, we perform a traditional linear regression approach. The effect\nsize of each top exposome variable on BAG (when accounting for confounds) is illustrated by\nplotting standardized beta for each regression analysis in the supplementary figure 7. It should\nbe noted here that such model does not account for interaction between exposome features and\nnon-linear relationships with BAG. Accordingly, the effect size of any single exposome variable\non BAG can be considered as relatively small, in line with the literature on UK Biobank. Whatever\nthe approach, however, overall, most of the associations follow an expected pattern.”\n(Page 11, lines 22–32):\n“Along the same line, at the level of individual exposome variables, classical regression-style effect\nsizes (e.g. years of brain ageing per unit increase in exposure) cannot be directly inferred from\nthe multivariate machine learning models used here, due to their non-linear structure and the\ncomplex interactions between predictors. To facilitate interpretation of the direction and shape of\nassociations, we therefore relied on SHAP-based explanations, which illustrate how changes in\nexposure values are associated with increases or decreases in predicted BAG across their observed\nranges, but do not provide effect sizes in the traditional regression sense. When considering each\nof the top exposome variable individually in relation to BAG (supplementary figure 7) using a\ntraditional linear regression model, the effect size of any single exposome variable on BAG can be\nconsidered as relatively small, in line with the literature on the UK Biobank. Overall, this\nreinforces the view that brain health emerges from the cumulative and interacting effects of many\nfactors rather than large effects of single exposures.”\nFigure 2 (supplementary figure 7). Standardized regression coefficients for the top 30 exposome features associated with brain\nage gap (BAG) in the main subset. The figure shows standardized beta coefficients derived from generalized linear models\nrelating each exposome feature separately to BAG, when accounting for age, age², sex, height, and volumetric scaling from\nT1 head image to standard space. Both predictors and outcome were standardized to allow comparison of effect magnitudes\nacross variables. Red dots illustrate significant associations after correcting for multiple comparisons (while blue dots are not\nsignificant). These analyses were conducted for reader’s information about the magnitude of each association when each\nexposome variable is considered in isolation in relation to BAG.\nRemarks on code availability:\nHave not re-reviewed code.\nThank you again for the positive feedback and the useful suggestions.\n1. Smith, S.M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T.E. & Miller, K.L. Estimation of\nbrain age delta from brain imaging. NeuroImage 200, 528-539 (2019).\n2. Jawinski, P., et al. Genome-wide analysis of brain age identifies 25 associated loci and\nunveils relationships with mental and physical health. medRxiv, 2023.2012. 2026.23300533\n(2023).\n3. De Lange, A.-M.G., et al. Multimodal brain-age prediction and cardiovascular risk: The\nWhitehall II MRI sub-study. NeuroImage 222, 117292 (2020).\n4. Wagen, A.Z., et al. 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There is a file containing pre-calculated kinship coefficients that could previously be downloaded using the UK Biobank tool gfetch with the -rel flag. Within the Research Analysis Platform, this relatedness file including kinship coefficients can now be found under Bulk/Genotype Results/Genotype calls/ukb_rel.dat. Possibly something that could be considered in a future study. However, access to this file may require a Tier 3 plan.\nP-values and permutations: Thank you for the clarification. With 500 permutations, the lowest achievable p-value is < 0.002 (i.e., when none of the permutations yields a similarly extreme test statistic). It may be worth mentioning this explicitly, as it explains why many p-values appear as 0.002. I also noticed that for SVR (linear) and r-squared, the corrected p-value is lower than the uncorrected p-value; please re-check this. Additionally, consider reporting all p-values in this table with three decimal places for consistency.\nI congratulate the authors on this strong revision.\nBest wishes,\nPhilippe Jawinski", "reviewer_2": "The authors have addressed my comments satisfactorily. Congratulations on a well-executed study!", "reviewer_3": "The authors have made considerable progress in addressing the initial comments and should be commended for this. However, further clarification and justification is required for transparency.\n- The authors’ rationale for not conducting multiple imputation is understandable, but this justification must be included directly in the manuscript. This is important because the decision not to impute has implications for potential bias. A concise explanation of the missingness structure, the limitations of MI under these conditions, and the trade-offs involved should be made clear to readers.\n- The response regarding the “individualised approach” is appreciated, but the manuscript should more clearly distinguish between what the current model can achieve and what may be possible in future applications throughout. At present, the predictive accuracy is not sufficient to support individual-level inference, and any statements implying personalised prediction should be reframed to refer explicitly to future potential rather than current capability.\n- The authors state that “small variations in the time between exposome measurement across subjects is unlikely to influence the pattern of results,” but this assumption needs to be transparently stated in the manuscript with the justification provided, as there is currently no evidence to support this.\n- The discussion of effect sizes remains limited. While classical regression-style effect estimates cannot be derived from the modelling approach used, readers still require a meaningful way to interpret the magnitude and direction of key associations. The authors are encouraged to add brief clarifying text in the discussion noting these limitations, and include SHAP dependence plots or similar visualisations to illustrate how the most influential exposures relate to BAG across their value ranges."}, "author_response": "Dear Editors and Reviewers,\nThank you for your letter and for the reviewers’ comments concerning our manuscript entitled “What\npredicts individual brain health? a machine learning study spanning the exposome”. Your comments\nwere helpful in raising important points that allowed us to finalize and further improve the manuscript.\nIn the following pages, we provide our point-by-point responses to each of the reviewers’ comments.\nRevisions in the text are shown using Blue colour.\nResponses to the comments of Reviewers:\nReviewer #1:\nRemarks to the Author\nThe authors responded to my revision requests with notable rigor and thoroughness, which substantially\nimproved the manuscript. All my previous concerns have been fully addressed. I have only two\nremaining comments / tips for finalization.\nDear Dr. Jawinski, we thank you for your positive evaluation of our manuscript, as well as for your\ncomments, which have contributed to improving its quality.\n[1] Relatedness field: Unfortunately, the tabular data fields referring to relatedness, as mentioned by the\nauthors, are not very informative. There is a file containing pre-calculated kinship coefficients that could\npreviously be downloaded using the UK Biobank tool gfetch with the -rel flag. Within the Research\nAnalysis Platform, this relatedness file including kinship coefficients can now be found under\nBulk/Genotype Results/Genotype calls/ukb_rel.dat. Possibly something that could be considered in a\nfuture study. However, access to this file may require a Tier 3 plan.\nWe thank you for pointing out the availability of pre-calculated kinship coefficients in the UK Biobank.\nIt is indeed a helpful suggestion. At the time of the present study, we did not have access to ‘Tier 3 plan’\nwith in our UK Biobank application (application ID: 41655). Therefore, we were unable to explicitly\naccount for kinship coefficients in the current analyses. Following the reviewer’s comment, we have\nnow expanded the limitations section of the manuscript to clarify this point and to explicitly\nacknowledge that future studies could integrate these pre-calculated kinship coefficients to address\nfamilial relatedness more rigorously:\n(page 10, lines 31-36):\n“However, several limitations should be noted. First, at the methodological level, in this study\nwe did not account for family relatedness. The UK Biobank includes several participants with\nfirst-, second-, and third-degree familial relationships which can potentially bias the results. The\ngenetic relatedness between pairs of participants has been summarized in the pre-calculated\nkinship coefficients, which could be incorporated in future work to explicitly model or control\nfor familial relatedness.”\n[2] P-values and permutations: Thank you for the clarification. With 500 permutations, the lowest\nachievable p-value is < 0.002 (i.e., when none of the permutations yields a similarly extreme test\nstatistic). It may be worth mentioning this explicitly, as it explains why many p-values appear as\n0.002. I also noticed that for SVR (linear) and r-squared, the corrected p-value is lower than the\nuncorrected p-value; please re-check this. Additionally, consider reporting all p-values in this table\nwith three decimal places for consistency.\nWe thank you for highlighting the limitation imposed by the number of permutations on the achievable\np-values. As correctly noted, when permutation testing is performed with 500 permutations, the\nsmallest possible p-value that can be obtained is approximately 0.002. This explains why several\nresults in Supplementary Table 5 show p-values at or close to this threshold.\nImportantly, these p-values indicate that the observed prediction accuracies are significantly above\nchance level and therefore non-random. However, with the current number of permutations, it is not\npossible to further quantify how extremely non-random these effects are. Estimating more precise (i.e.,\nsmaller) p-values would require running a larger number of permutations.\nWe have now clarified this point explicitly in the manuscript, where we state that p-values were\nestimated using permutation testing with 500 permutations and that the minimum attainable p-value\nunder this procedure is approximately 0.002.\n(Page 5 lines 9-20):\n“We could significantly predict grey matter health based on the expotype in the main subset (r =\n0.23; p = 0.002; r2 = 0.05; p = 0.002, FDR-corrected; Supplementary Table 5). We could replicate\nthis achievement in the replication and variables-restricted subsets (which had less exposome\nvariables but a higher number of participants) with similar accuracies (replication subset: r =\n0.23; p = 0.004 ; r2 = 0.05; p = 0.004; and variables-restricted subset: r = 0.24; p = 0.002; r2 =\n0.06; p = 0.002, FDR-corrected; Supplementary Table 5) confirming the robustness of our results\nacross subsamples. Furthermore, grey matter health could also be predicted using alternative\nalgorithms, although achieving numerically slightly lower prediction accuracy (with r = 0.17; p =\n0.002; r2 = 0.03; p = 0.002 for Ridge Regression and r = 0.16; p = 0.002; r2 = 0.02; p = 0.002 for\nSVR; FDR-corrected; Supplementary Table 5). It is noteworthy that p-values were estimated\nusing permutation testing with 500 permutations. Consequently, the smallest p-value that can be\nobtained under this procedure is approximately 0.002, and the true p-values may be smaller if a\nlarger number of permutations were used.”\nRegarding the lower corrected p-value than the uncorrected p-value for SVR (linear) and r-squared, we\nconfirmed that this was due to a typographical error (happening during files format conversion for the\nsubmission). We have now carefully re-checked all p-values and corrected the table accordingly.\nFinally, we have reported them with three decimal places for consistency (Table 1, supplementary table\n5) as you suggested.\nTable 1(supplementary table 5), Grey matter health prediction performance across different subsets/algorithms including\nmain subset (n = 3706), replication subset (n = 4202), and variable-restricted subset (n = 7736).\nRidge re-\nAlgorithm Random Forest SVR(Linear)\ngression\nVariables-re-\nSubset Main Replication Main Main\nstricted\nMean Absolute Error\n4.48 4.52 4.53 4.54 4.53\n(MAE)\nUncorrected p-value 0.034 0.024 0.002 0.002 0.002\nCorrected p-value 0.035 0.026 0.003 0.003 0.003\nMean Squared Error\n32.22 32.63 32.66 33.14 33.07\n(MSE)\nUncorrected p-value 0.006 0.022 0.002 0.002 0.002\nCorrected p-value 0.007 0.024 0.003 0.003 0.003\nroot Mean Squared Error\n5.67 5.71 5.71 5.75 5.75\n(rMSE)\nUncorrected p-value 0.004 0.044 0.002 0.002 0.002\nCorrected p-value 0.005 0.044 0.003 0.003 0.003\nr2\n0.05 0.05 0.06 0.02 0.03\nUncorrected p-value 0.002 0.004 0.002 0.002 0.002\nCorrected p-value 0.003 0.005 0.003 0.003 0.003\nPearson Correlation\n0.23 0.23 0.24 0.16 0.17\n(r)\nUncorrected p-value 0.002 0.004 0.002 0.002 0.002\nCorrected p-value 0.003 0.005 0.003 0.003 0.003\nI congratulate the authors on this strong revision.\nBest wishes,\nPhilippe Jawinski\nWe thank you again for your careful assessment of the revised manuscript and for your supportive and\nconstructive final comments.\nRemarks on code availability:\nThe GitHub repository is well-organized, well-documented, and comprehensive. As requested, the\nauthors have added a conda environment file during the revision, which further enhances\nreproducibility.\nThank you for the positive assessment of the GitHub repository and code availability. We are glad that\nthe added conda environment file improves the clarity and reproducibility of the analyses.\nReviewer #2:\nRemarks to the Author:\nThe authors have addressed my comments satisfactorily. Congratulations on a well-executed study!\nThank you for the positive evaluation of our manuscript and thank you for your valuable comments.\nRemarks on code availability:\nThe authors have addressed my comments satisfactorily. Congratulations on a well-executed study!\nThank you for your positive assessment of the code availability and reproducibility.\nReviewer #3:\nRemarks to the Author:\nThe authors have made considerable progress in addressing the initial comments and should be\ncommended for this. However, further clarification and justification is required for transparency.\n[1] The authors’ rationale for not conducting multiple imputation is understandable, but this justification\nmust be included directly in the manuscript. This is important because the decision not to impute has\nimplications for potential bias. A concise explanation of the missingness structure, the limitations of\nMI under these conditions, and the trade-offs involved should be made clear to readers.\nWe appreciate your concern about including a direct justification. Following your suggestion, we have\nnow referred to the block-like pattern of the missingness in the data (included Supplementary Figure 9\nin the supplement) and state the limitations of MI under these conditions, as well as the trade-off\ninvolved in our strategy in the revised manuscript. (page 16, lines 18-30):\n“To avoid potential biases associated with substantial missing values across participants, we\nfocused on variables that were available in at least 2000 participants including both males and\nfemales. Of note, we carefully considered multiple imputation. However, the missingness structure\nin the dataset posed substantial challenges. Specifically, participants with neuroimaging data\nexhibited block-like patterns of missingness, resulting in limited overlap across many variables\n(see Supplementary Figure 9). Under such conditions, chained-equations multiple imputation\nwould be prone to instability and strong model dependence, as many conditional models would\nneed to be estimated from sparse shared information. Moreover, implementing multiple\nimputation across several hundred heterogeneous variables (continuous and categorical) within\na cross-validation framework—while avoiding data leakage—would substantially increase both\nmethodological complexity and computational burden, with uncertain gains in bias reduction.\nConsidering these limitations, we adopted a minimum-availability filtering strategy, prioritizing\nvariables with sufficient observations while maintaining analytical tractability.”\nFigure 1(Supplementary Figure 9). Missingness pattern in the literature-based a-priori selected data fields (Missingno\npackage). The grey colours are the filled values in women participants. Blue colours are the filled values in men\nparticipants. White colour reflects missing values, and red colour indicates non-informative answers (i.e., I don’t know, I\nprefer not to answer etc,.).\n[2] The response regarding the “individualised approach” is appreciated, but the manuscript should\nmore clearly distinguish between what the current model can achieve and what may be possible in future\napplications throughout. At present, the predictive accuracy is not sufficient to support individual-level\ninference, and any statements implying personalised prediction should be reframed to refer explicitly\nto future potential rather than current capability.\nWe thank you for this important clarification. We agree that, given the current level of predictive\naccuracy, the model does not support individual-level inference or personalised prediction at present.\nWe have therefore carefully revised the manuscript to clearly distinguish between the current\ncapabilities of the model and the potential future applications of exposome-based predictive modeling\nfor individual prediction.\nSpecifically, we now explicitly state that while the model generates individual-level predictions and\nexplanations (e.g. SHAP values), these are intended to illustrate model behaviour rather than to support\npersonalised clinical inference. All statements referring to individual-level prediction have been\nreframed to refer explicitly to future potential, contingent on substantially improved predictive\naccuracy.\nFinally, we have also explicitly discussed the fact that our model currently does not support individual\nprediction for application (page 11, lines 13-21):\n“Third, while the prediction accuracy in the out-of-sample test set is significant and demonstrates\nthe model generalizability, the effect size (r value) remains relatively moderate. Relatedly, the\nexplained variance in brain structural health (i.e. in BAG) in the test set is relatively limited\n(~5%). Although the model generates predictions and feature attributions at the individual level,\nthe current level of predictive accuracy does not support personalized inference or individual-\nlevel decision-making. Accordingly, individual SHAP explanations are presented to illustrate\nmodel behaviour rather than to enable personalized interpretation. The potential utility of\nexposome-based machine learning models for individual-level vulnerability or resilience\nprediction should therefore be considered as a future perspective, contingent on substantial\nimprovements in predictive performance.”\n[3] The authors state that “small variations in the time between exposome measurement across subjects\nis unlikely to influence the pattern of results,” but this assumption needs to be transparently stated in\nthe manuscript with the justification provided, as there is currently no evidence to support this.\nWe agree that the assumption regarding the timing of exposome measurements should be explicitly\nstated and justified in the manuscript.\nIn the present study, we did not explicitly model the time interval between exposome assessments and\nthe imaging visit. Instead, when multiple measurements were available for a given variable, we\nprioritized indicators of relatively chronic exposure (e.g., weekly dietary intake rather than 24-hour\nrecall), and where relevant we derived duration-based measures (e.g., smoking duration, disease\nduration). These choices were intended to reduce sensitivity to short-term fluctuations and partially\naccount for between-subject variability in the timing of exposure measurement relative to brain\nimaging.\nNevertheless, this approach relies on the assumption that modest between-subject variation in the time\ninterval between exposome assessment and imaging does not substantially alter the observed patterns.\nWe acknowledge that this assumption cannot be directly tested within the current study and therefore\nrepresents a methodological limitation. Future longitudinal studies explicitly modeling time-varying\nexposures and exposure–outcome lag effects will be important to further clarify these temporal\nrelationships.\n(page 16 lines 31–35 and page 17 lines 1–5):\n“We also performed some additional data curation processes, which included: uniqueness\nconstraint (we ensured that each field ID and participant was represented uniquely in the dataset\nand we removed potential duplicate records), aberrant value exclusion (we verified that all\nvariable values fell within the valid range or allowed set of categories e.g., no out-of-range\nnumerical values or invalid category codes), and uninformative value exclusion (we excluded\nimplausible or non-informative responses such as “do not know” or “prefer not to answer”).\nFurthermore, we also calculated duration variables for medical conditions by subtracting onset\nage from the age when attended assessment center (chronological age). Finally, it is noteworthy\nto mention that most exposome variables were measured at the initial UK Biobank assessment\nvisit (2006–2010), and when data were not available at that time point, we used measurements\nfrom the first repeat assessment visit (2012–2013).”\n(page 10 line 36 and page 11 lines 1-11):\n“Second, as mentioned in method section, most exposome variables were measured at the initial\nUK Biobank assessment visit (2006–2010), and when data were not available at that time point,\nwe used measurements from the first repeat assessment visit (2012–2013). We did not explicitly\nmodel the time interval between exposome assessment and the imaging visit. To mitigate potential\neffects of temporal variability, we prioritized measures reflecting relatively chronic exposures\n(e.g., weekly dietary intake rather than 24-hour recall) and derived duration-based variables\nwhere possible (e.g., smoking duration, disease duration). Nevertheless, this approach assumes\nthat modest between-subject variation in the timing of exposome assessment relative to imaging\ndoes not substantially influence the observed associations. This assumption cannot be formally\ntested within the present study and therefore represents a methodological limitation. Future\nlongitudinal analyses explicitly modeling time-varying exposures and exposure–outcome lag\neffects will be important to further clarify these temporal relationships.”\n[4] The discussion of effect sizes remains limited. While classical regression-style effect estimates\ncannot be derived from the modelling approach used, readers still require a meaningful way to interpret\nthe magnitude and direction of key associations. The authors are encouraged to add brief clarifying text\nin the discussion noting these limitations, and include SHAP dependence plots or similar visualisations\nto illustrate how the most influential exposures relate to BAG across their value ranges.\nWe agree that the reviewer that the effect size may be of interest for some readers. It is noteworthy to\nmention that, although SHAP-based explanations help us in the interpretation of how changes in\nexposure values are associated with increases or decreases in predicted BAG across their observed\nranges, but do not provide effect sizes in the traditional regression sense. We should here also mention\nthat using SHAP dependence plots to infer about effect size can be misleading and leads to reader’s\nmisunderstanding in our study for several reasons. First, typical SHAP dependence plots illustrate\nSHAP values for a given feature against the feature values (which does not provide information about\nassociations between how exposure relates to BAG). Second, it is very important to note that confounds\nremoval has been performed on the variables and therefore exposome variables values are actually\nadjusted standardized values. This leads for example to negative values for age at onset and duration\nwhich can be really difficult to interpret for the reader if SHAP dependence plots would be provided.\nNevertheless to provide some information about effect size and the relationships between key exposome\nfeatures and BAG, we used a traditional linear regression approach for our top 30 exposome features.\nThe effect size of each top exposome variable on BAG (when accounting for confounds) is now\nillustrated by plotting standardized beta for each regression analysis together in Figure 1 (Supplementary\nFigure 7). It should be noted here that such model does not account for interaction between exposome\nfeatures and non-linear relationships with BAG. Accordingly, the effect size of any single exposome\nvariable on BAG can be considered as relatively small, in line with the literature on UK Biobank. This\nhas also been discussed in the revised manuscript:\n(Page 6, line 7–14):\n“Additionally, to provide some information about effect size and the relationships between the 30\ntop exposome features and BAG, we perform a traditional linear regression approach. The effect\nsize of each top exposome variable on BAG (when accounting for confounds) is illustrated by\nplotting standardized beta for each regression analysis in the supplementary figure 7. It should\nbe noted here that such model does not account for interaction between exposome features and\nnon-linear relationships with BAG. Accordingly, the effect size of any single exposome variable\non BAG can be considered as relatively small, in line with the literature on UK Biobank. Whatever\nthe approach, however, overall, most of the associations follow an expected pattern.”\n(Page 11, lines 22–32):\n“Along the same line, at the level of individual exposome variables, classical regression-style effect\nsizes (e.g. years of brain ageing per unit increase in exposure) cannot be directly inferred from\nthe multivariate machine learning models used here, due to their non-linear structure and the\ncomplex interactions between predictors. To facilitate interpretation of the direction and shape of\nassociations, we therefore relied on SHAP-based explanations, which illustrate how changes in\nexposure values are associated with increases or decreases in predicted BAG across their observed\nranges, but do not provide effect sizes in the traditional regression sense. When considering each\nof the top exposome variable individually in relation to BAG (supplementary figure 7) using a\ntraditional linear regression model, the effect size of any single exposome variable on BAG can be\nconsidered as relatively small, in line with the literature on the UK Biobank. Overall, this\nreinforces the view that brain health emerges from the cumulative and interacting effects of many\nfactors rather than large effects of single exposures.”\nFigure 2 (supplementary figure 7). Standardized regression coefficients for the top 30 exposome features associated with brain\nage gap (BAG) in the main subset. The figure shows standardized beta coefficients derived from generalized linear models\nrelating each exposome feature separately to BAG, when accounting for age, age², sex, height, and volumetric scaling from\nT1 head image to standard space. Both predictors and outcome were standardized to allow comparison of effect magnitudes\nacross variables. 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Decision letter