Social determinants of health, accelerated biological aging, and long-term health outcomes

Domain: chemistry
Published: 2025-12-24
Editorial outcome: major_revision

Abstract
Social determinants of health (SDHs) are the primary drivers of health inequalities, but whether biological aging plays a role in linking SDHs to health outcomes remains unclear. Here we utilize detailed information on social determinants across five domains, clinical parameters and electronic health records from the UK Biobank and US NHANES to examine the associations between combined SDHs, accelerated biological aging, and health outcomes. Compared with participants in the favourable SDH group, participants in the unfavourable SDH group had increased KDM-BA and phenotypic age acceleration. Moreover, unfavourable SDHs were associated with elevated risks of mortality and incident diseases. Accelerated biological aging significantly mediated the association between SDHs and all-cause and cause-specific mortality (UK Biobank: mediation proportion 13.46%-25.21%; US NHANES: 7.62%-22.16%). Also, accelerated biological aging served as a mediator between SDHs and incident diseases in the UK Biobank, with the mediation proportions ranging from 6.20% to 30.48%. The estimates were likely specific to the UK Biobank cohort considering its healthy volunteer bias and limited socioeconomic diversity. Overall, our study reveals that the biological aging discrepancy partially explains the associations of combined SDHs with mortality and chronic diseases. Assessing and delaying aging acceleration may be an effective way to narrow the health disparities caused by SDHs.

Introduction
Aging is a complex process involving a progressive loss in the resilience capacity of cells, tissues, and organs over time, ultimately resulting in impaired function and increased morbidity and death1. It is estimated that aging-related diseases accounted for 51.3% of all global disease burden in 20172. Several biological age measures have been proposed to capture the aging process3,4. Among these measures, the algorithms derived from composite clinical parameters like Klemera–Doubal biological age (KDM-BA) and phenotypic age have shown excellent capability of predicting morbidity and mortality risks across diverse populations and prove to be cost-effective5–7. Given the expanding aging population worldwide, broadening knowledge of the aging process and its contributing factors is increasingly important for informing public health strategies.

Social determinants of health (SDHs) are the conditions where people are born, grow, live, work, and age, which have been associated with differences in morbidity and mortality8. Although the globe has witnessed socioeconomic progress over recent decades, the high rate of wealth inequalities exists and even increases; particularly, socioeconomic inequity in survival is rising in the UK and the US9,10. Apart from their profound influence on health outcomes, including death, cardiometabolic disease, chronic respiratory disease, and chronic kidney disease11–14, certain individual disadvantaged SDHs (i.e., low income and chronic psychosocial burden) were associated with accelerated biological aging15–17. Nevertheless, different disadvantaged SDHs tend to cluster in individuals, and the pathways linking them to health are interconnected18. In addition, disregarding the synergistic effects of combined SDHs will reduce the power to efficiently identify the groups who are socially vulnerable for targeted interventions. Therefore, it is of great importance to perform a comprehensive assessment of SDHs and establish the association between overall SDHs and accelerated biological aging. Previous studies have suggested that biological aging is an indicator of morbidity and mortality in later life19,20, but whether SDHs could increase the susceptibility to adverse health outcomes through accelerated biological aging remains unclear.

In this work, we use data from the UK Biobank and US National Health and Nutrition Examination Survey (NHANES) to investigate the associations between combined SDHs, accelerated biological aging, and a range of health outcomes (Fig. 1). The results reveal that accelerated biological aging significantly mediate the associations of unfavorable SDHs with increased risks of mortality and chronic diseases, which are consistent across two cohorts from varied social contexts. These findings support biological aging as a promising target when developing interventions aimed at narrowing the SDHs-related health disparities.

Results
Population characteristics
Among 266,029 participants from UK Biobank (mean [standard deviation, SD] age 56.1 [8.1] years, 52.4% females), 55,610 (20.9%) were categorized into the favorable SDH group, 135,445 (50.9%) into the medium SDH group and 74,974 (28.2%) into the unfavorable SDH group (Table 1). Among 32,018 participants from the US NHANES (mean [SD] age 46.7 [16.8] years, 51.8% females), 8,297 (25.9%) were categorized into the favorable SDH group, 16,040 (50.1%) into the medium SDH group, and 7,681 (24.0%) into the unfavorable SDH group. Overall, participants in the unfavorable SDH group were more likely to be female, to have unhealthy lifestyles, higher body mass index (BMI), higher prevalence of cardiovascular disease and chronic respiratory disease, and increased biological aging acceleration (Tables 1 and S1). Baseline characteristics by weighted SDH scores are presented in Tables S2 and S3. Excluded participants due to missing data were generally comparable to included participants except for slightly higher proportions of comorbidities and medication use (Table S4).

Association of combined SDHs with accelerated biological aging
As shown in Fig. 2, KDM-BA and phenotypic age acceleration were positively correlated with combined SDH scores in the UK Biobank (Spearman’s R=0.141 for KDM-BA and R=0.158 for phenotypic age acceleration, P<2.2e-16) and the US NHANES (Spearman’s R=0.105 for KDM-BA and R=0.123 for phenotypic age acceleration, P<2.2e-16). Table 2 shows the associations between combined SDHs and accelerated biological aging. In UK Biobank, compared with participants in the favorable SDH group, participants in the medium SDH group had increased KDM-BA and phenotypic age acceleration of 0.56 (95% confidence interval [CI]: 0.49, 0.62) and 0.33 (0.28, 0.38) years, and those in the unfavorable SDH group had increased KDM-BA and phenotypic age acceleration of 1.50 (1.42, 1.57) and 1.07 (1.02, 1.13) years, respectively. In US NHANES, participants in the medium and unfavorable SDH group also had an increase in KDM-BA acceleration (medium SDH, 1.24 [0.77, 1.71] years; unfavorable SDH, 1.78 [1.14, 2.41] years) and phenotypic age acceleration (medium SDH, 0.45 [0.29, 0.60] years; unfavorable SDH, 0.82 [0.60, 1.05] years). The results remained consistent in sensitivity analyses using weighted combined SDH scores, additionally adjusting for waist circumference and sleep score, without adjustment for lifestyle factors, using imputation for all missing variables, and adopting different classifications of SDHs (Tables S5–S9). Across the five SDH domains, financial circumstances showed the strongest association with KDM-BA (0.69 [0.67-0.72] years) and phenotypic age acceleration (0.44 [0.42–0.46] years) in UK Biobank, while education access and quality and social and community context showed the strongest association with KDM-BA (0.77 [0.59-0.94] years) and phenotypic age acceleration (0.33 [0.26−0.40] years) in US NHANES, respectively (Table S10). Furthermore, we examined the associations between SDHs and change in biological aging and found that SDHs were associated with increased KDM-BA and phenotypic age acceleration over time (Table S11). In stratified analyses, consistent associations of combined SDHs with increased KDM-BA and phenotypic age acceleration were found among all subgroups, but seemed to be more pronounced among males, current or former smokers, and individuals with an unhealthy diet or comorbidities in the UK Biobank and among males in the US NHANES (P <0.05) (Figs. S1 and S2).

Associations of combined SDHs with mortality and incident diseases
During a median follow-up of 15.1 (IQR, 14.4–15.8) years, the UK Biobank documented 20,773 deaths, including 4327 deaths from cardiovascular disease, 1269 from respiratory disease, and 10,359 from cancer. During a median follow-up of 11.1 (IQR, 4.0–15.2) years, the US NHANES documented 5116 deaths, including 155 deaths from cardiovascular disease, 283 from respiratory disease, and 90 from cancer. More unfavorable SDHs were associated with higher risks of all-cause and cause-specific mortality (Figs. 3 and S3). For example, in the UK Biobank, each 1-point increase in the SDH scores was associated with a hazard ratio (HR) of 1.07 (95% CI: 1.07–1.08) for all-cause mortality. In the US NHANES, each 1-point increase in the SDH scores was associated with an HR of 1.22 (95% CI: 1.19–1.25) for all-cause mortality.

Over a median follow-up of up to 15.1 (IQR, 14.3–15.8) years in the UK Biobank, 34,350 participants developed cardiovascular disease, 10,468 developed type 2 diabetes, 8507 developed chronic liver disease, 11,159 developed chronic respiratory disease, and 12,545 developed chronic kidney disease. Similar graded associations between unfavorable SDHs and risks of incident diseases were detected (Figs. 3 and S3). For instance, in UK Biobank, each 1-point increase in the SDH scores was associated with an HR of 1.04 (95% CI: 1.03–1.04) for cardiovascular disease, 1.10 (1.09-1.11) for type 2 diabetes, 1.08 (1.07–1.09) for liver disease, 1.13 (1.13–1.14) for chronic respiratory disease, and 1.06 (1.05–1.07) for chronic kidney disease. These associations remained robust when using weighted SDH scores, adjusting for waist circumference and sleep score, without adjustment for lifestyle factors, or taking into account the competing risk (Tables S12–S16). Furthermore, the combined effects of genetic risk and overall SDHs on incident diseases expressed in a dose-response manner, and significant interactions were observed between combined SDHs and genetic risk for type 2 diabetes and chronic respiratory disease (Table S17).

Mediation analysis
Both KDM-BA and phenotypic age acceleration showed positive associations with risks of mortality and incident diseases (Table S18). We further performed mediation analyses and found that accelerated biological aging significantly mediated the associations between combined SDHs and all-cause and cause-specific mortality (Fig. 4). For all-cause mortality, the estimates for the indirect effect of KDM-BA and phenotypic age acceleration were 0.50 (95% CI: 0.46, 0.54) and 0.69 (0.64, 0.74) in UK Biobank, respectively; the proportion mediated by KDM-BA acceleration was 13.46% (12.13%, 14.97%) and the proportion mediated by phenotypic age acceleration was 18.55% (16.89%, 20.45%). The mediation proportions of KDM-BA and phenotypic age acceleration in US NHANES were 7.62% (5.98%, 9.42%) and 9.41% (7.72%, 11.26%), respectively. In addition, accelerated biological aging significantly mediated the relationships between combined SDHs and incident diseases in the UK Biobank. The mediation proportions attributed to KDM-BA (22.19% [19.46%, 25.61%]) and phenotypic age acceleration (30.48% [26.93%, 34.91%]) were largest for chronic kidney disease, followed by cardiovascular disease (KDM-BA: 15.38% [13.36%, 17.83%]; phenotypic age: 15.51% [13.49%, 17.96%]). Results were largely unchanged in all sensitivity analyses (Tables S19–S22). Additionally, structural equation modeling revealed that KDM-BA and phenotypic age acceleration exhibited distinct mediation patterns across different SDH domains. For instance, in the UK Biobank cohort, 10.5% (95% CI: 9.4–11.7%) of the association between financial circumstance and all-cause mortality was mediated by KDM-BA acceleration, and 13.1% (11.7–14.5%) was mediated by phenotypic age acceleration. In the US NHANES cohort, for the association between social and community context and all-cause mortality, 9.5% (4.6–14.4%) was mediated by KDM-BA acceleration, and 20.2% (12.2–28.2%) was mediated by phenotypic age acceleration (Tables S23–S26).

Methods
Study design and population
The UK Biobank is a large-scale cohort that recruited over 500,000 participants aged 37–73 years from 22 assessment centers in England, Wales, and Scotland between 2006 and 2010. The study design and data collection procedures have been reported in detail previously32. Among the 502,415 participants, we excluded those without information on SDHs and biological aging. A total of 266,029 participants were included to examine the associations of SDHs with accelerated biological aging and mortality (Fig. S4). For the analysis of the joint effects of SDHs and genetic susceptibility on disease outcomes, we restricted the sample to individuals of European descent who had genetic information (n=253,783). Additionally, participants without outcomes of interest at baseline were excluded for the analysis of incident diseases.

The US NHANES is an ongoing cross-sectional survey with a nationally representative sample of the civilian resident population of the USA using a complex, multistage probability design in 2-year cycles since 1999–2000. Details have been described elsewhere33. We included 55,081 participants aged 20 years and older from cycles 1999 to 2018. After excluding those with missing data on SDHs and biological aging, 32,018 participants were retained in the analysis (Fig. S5).

Assessment of SDHs
We systematically reviewed standardized questionnaires from the UK Biobank and US NHANES, mapping variables to the five domains guided by the Healthy People 203034 and World Health Organization35: economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social and community context. The selection of specific variables within these domains was informed by prior studies, which demonstrated strong associations between such variables and health outcomes, including mortality and major chronic diseases11,12,36,37. Specifically, we chose 18 SDH variables (household income, employment status, income quality, education attainment, education quality, healthcare, accommodation stability, housing quality, local crime rate, natural environment, psychosocial problems, race/ethnicity, living alone/with partners, social support, social activity, social isolation, loneliness, and emotional distress) in UK Biobank. Loneliness, the subjective experience of social disconnection, has also been recognized as a significant health risk factor and was therefore included in this study38. For the US NHANES, 10 SDH variables (household income, employment status, food security, education attainment, healthcare, health insurance, accommodation stability, psychosocial problems, race/ethnicity, and marital status) were considered. All variables were collected from standardized questionnaires at baseline or linkage to the index of multiple deprivation for the corresponding Lower-layer Super Output Area in the UK. Details of SDHs assessment are provided in Table S27.

Each SDH was dichotomized into advantaged and disadvantaged levels using conventional cut points (Table S28) established in prior literature to enhance interpretability and facilitate the application of SDH score11,12,36,37. We constructed an unweighted score by assigning 0 points for the advantaged level of each SDH and 1 point for the disadvantaged level. As a sensitivity analysis, we also constructed weighted SDH scores to account for varying magnitude of the associations (absolute \beta coefficients) between individual SDHs and biological aging acceleration (Table S29). In UK Biobank, weighted SDHs score = (\beta 1 \times SDH 1 + ... + \beta 18 \times SDH 18) \times (18 / sum of \beta coefficients). In the US NHANES, weighted SDHs score = (\beta 1 \times SDH 1 + ... + \beta 10 \times SDH 10) \times (10 / sum of \beta coefficients). The unweighted and weighted SDH score ranged from 0 to 18 in the UK Biobank and from 0 to 10 in US NHANES (Fig. S6), with higher scores indicating more unfavorable SDHs. Participants were classified into three groups by tertiles: favorable SDH group, medium SDH group, and unfavorable SDH group. Furthermore, we examined the associations of different SDH domains with biological aging. SDH domain scores were calculated by summing up individual scores for each SDH within specific domains and were standardized to facilitate comparisons across different SDH domains.

Assessment of biological age and age acceleration
Two best-trained biological age algorithms, KDM-BA and phenotypic age, were used. These algorithms have been originally developed and validated in the US NHANES23,39,40 and subsequently adapted for application in the UK Biobank using available biomarker data19,41,42. The clinical parameters used for biological age calculation, including anthropometric measurements and biochemical markers, were collected at baseline. Due to the differences in study designs and populations, different parameters were collected in the UK Biobank (See Table S30 for the corresponding data field IDs) and US NHANES, which referred to previous studies conducted within these two cohorts19,42,43.

An individual’s KDM-BA prediction reflects the chronological age at which their physiological state would be considered approximately normal. KDM-BA is derived from a series of regressions of individual biomarkers on chronological age within a reference population44. In the UK Biobank, we utilized nine biomarkers to calculate KDM-BA: systolic blood pressure, forced expiratory volume in one second (FEV1), total cholesterol, glycated hemoglobin, blood urea nitrogen, albumin, creatinine, C-reactive protein, and alkaline phosphatase19,42. The US NHANES included four additional biomarkers: uric acid, lymphocyte percentage, mean cell volume, and white blood cell count, while omitting FEV1, resulting in a total of twelve biomarkers43. Phenotypic age, initially developed using elastic-net Gompertz regression of mortality on 42 biomarkers among NHANES participants, can be interpreted as the age at which an individual’s mortality risk aligns with the predicted average mortality risk27. In this study, we calculated an individual’s phenotypic age using chronological age and nine biomarkers: lymphocyte percentage, mean cell volume, glucose, red cell distribution width, white blood cell count, albumin, creatinine, C-reactive protein, and alkaline phosphatase in the UK Biobank19,42. In the US NHANES, we utilized chronological age and the same twelve biomarkers for KDM-BA calculation43.

Computation of KDM-BA and phenotypic age values was conducted using the R package BioAge (https://github.com/dayoonkwon/BioAge). Age acceleration was calculated as the residual by regressing KDM-BA and phenotypic age on chronological age42. An age acceleration value greater than 0 indicated an advanced state of biological aging, while an age acceleration value less than 0 indicated a delayed biological aging state45.

Polygenic risk scores
Genetic data were obtained from the UK Biobank, and details of the single-nucleotide polymorphisms (SNPs) regarding genotyping, imputation, and quality control have been reported previously46. For cardiovascular disease and type 2 diabetes, we used polygenic risk scores (PRS) that were calculated by using external genome-wide association studies (GWAS) data and were made available in the UK Biobank (Category 301, https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=301, Table S31)47. For other diseases with no PRS provided in the UK Biobank, we retrieved Polygenic Score (PGS) catalog48 or GWAS summary statistics derived in predominantly European populations (Table S31). The number of associated effect alleles was weighted by the strengths of associations with diseases and summed to derive a PRS in UK Biobank individuals of European ancestry (Supplementary Methods). Participants were then categorized into low genetic risk (tertile 1), medium genetic risk (tertile 2), and high genetic risk (tertile 3) according to PRS tertiles.

Assessment of health outcomes
Health outcomes were ascertained using International Classification of Diseases 10th Revision (ICD-10) codes. The primary outcomes included all-cause mortality and cause-specific mortality (from cardiovascular disease, respiratory disease, and cancer). In the UK Biobank, information on the date and cause of death was obtained from death certificates held within the National Health Service (NHS) Information Centre (England and Wales) and the NHS Central Register (Scotland) to April 30, 202449 (Table S32). In the US NHANES, deaths were ascertained through linkage to death certificates from the National Death Index records until December 31, 201950. For cardiovascular disease mortality, only deaths from heart disease were available in the US NHANES.

The secondary outcomes were incident diseases, including cardiovascular disease, type 2 diabetes, liver disease, chronic respiratory disease, and chronic kidney disease, which were only available from the UK Biobank. Disease diagnoses were obtained from hospital inpatient data, primary care records, and death register linkage according to the ICD-10 codes up to April 30, 202451 (Table S32).

Covariates
Information on demographic characteristics, lifestyle factors, and medication use was collected through self-report questionnaires. The demographic characteristics included age and sex. Lifestyle factors included BMI, waist circumference, smoking status (never, previous, or current), alcohol drinking (moderate or no drinking, or heavy drinking), diet (healthy or unhealthy), and physical activity (active or inactive). Prevalent diseases, including cardiovascular disease, cancer, diabetes, high cholesterol, hypertension, and respiratory disease, were obtained from self-reports (both cohorts) and disease registers (UK Biobank only). Medication use for high cholesterol, hypertension, and diabetes was extracted. Weight, height, and waist circumference were measured by trained technicians, and BMI was calculated as weight in kg divided by height in m2. Details of covariates are shown in Supplementary Methods.

Statistical analysis
All statistical analyses in the US NHANES accounted for the complex survey design and yielded population-weighted estimates representative of the US adults. Baseline characteristics of participants were described as mean (SD) for continuous variables and absolute numbers with percentages for categorical variables across SDH groups. In the US NHANES, means, SDs, and percentages were population-weighted. Differences across SDH groups were tested using analysis of variance for continuous variables and the Rao-Scott \chi² test for categorical variables, whereby sampling weights were adjusted. In the UK Biobank, analysis of variance and the \chi² test were employed for continuous and categorical variables, respectively. Correlations between SDH score and biological aging acceleration were assessed using Spearman’s correlation coefficient. Multiple imputation by chained equations was utilized to impute the missing values of covariates.

We used multivariable linear regression models to assess the associations of combined SDH score with KDM-BA acceleration and phenotypic age acceleration. Model 1 was adjusted for age and sex. Model 2 was further adjusted for BMI, smoking status, alcohol drinking, diet, physical activity, cardiovascular disease, cancer, diabetes, high cholesterol, hypertension, respiratory disease, and medication use for diabetes, high cholesterol, and hypertension. The P value for trend across SDH groups was calculated using integer values. Moreover, we explored the associations of individual SDHs with biological aging.

We performed stratified analyses to test the potential modification effects on the association between combined SDH score and accelerated biological aging by age (<50, 50–59, vs \ge 60 years), sex (female vs male), cardiovascular disease (yes vs no), diabetes (yes vs no), high cholesterol (yes vs no), hypertension (yes vs no), BMI (<30 vs \ge 30 kg/m2), smoking (never, previous, or current), moderate or no drinking (yes vs no), physically active (yes vs no), and healthy diet (yes vs no).

Cox proportional hazards regression models were used to estimate the associations of combined SDH score and biological aging acceleration with health outcomes. Person years were calculated from baseline until the diagnosis of the incident diseases, death, or the end of follow-up, whichever occurred first. Proportional hazard assumptions were satisfied, as indicated by Schoenfeld residual test. All the covariates in Model 2 were included for adjustment. Additionally, we explored the joint effects of combined SDHs and genetic risk on incident diseases, with additional adjustment for genetic kinship and the 10 genetic principal components. The interaction between combined SDHs and genetic risk was assessed by adding a cross-product term in the models.

We further investigated whether accelerated biological aging mediated the associations of combined SDH score with mortality and incident diseases. Causal mediation analyses were conducted using the Aalen additive hazards model to estimate the total effect of SDH on the outcome, adjusting for covariates included in Model 2, which was decomposed into the natural direct effect and the natural indirect effect52,53. The effect estimates are presented as the number of additional events per person-time, offering a more interpretable metric from a public health perspective. The 95% CIs for direct and indirect effects and proportions mediated were estimated using simulation with 100,000 repeats, as previously described52. In addition, generalized structural equation modeling was performed to assess the mediating role of biological aging in the relationships between specific SDH domains and health outcomes. The 95% CIs for direct and indirect effects and proportions mediated were estimated by 10,000-iteration nonparametric bootstrap approach54.

A series of sensitivity analyses was conducted. First, a weighted SDH score was constructed to test the robustness of the results. Second, we additionally adjusted for waist circumference and healthy sleep score to reduce potential residual confounding. Third, as lifestyle behaviors may act as potential mediators in the associations between specific SDHs, biological aging, and health outcomes, we repeated the analyses without adjusting for lifestyle behaviors to detect the differences in effect estimates. Fourth, Fine and Gray’s proportional sub-hazards models were applied to account for competing risk of death for cause-specific mortality (with deaths from other causes as competing events) and incident diseases (with death as a competing event). Fifth, to reduce temporal uncertainty, we assessed the association between SDHs and changes in biological aging among 4273 UK Biobank participants with biomarker data at both baseline and the first repeat visit (2012–2013). The changes in biological aging were calculated as the difference between KDM-BA and phenotypic age acceleration at two time points. Sixth, we performed multiple imputation for all variables with missing data, including SDHs, biological aging markers, and covariates. Last, to mitigate potential information loss from dichotomizing SDHs, we classified SDHs according to the following principles: SDHs obtained from questionnaires were categorized into three or more groups based on the original questions, while those derived from area-level scores were classified into three categories according to the tertiles of their distributions (Tables S33 and S34).

All analyses were performed using R (version 4.4.1). A two-tailed P value of less than 0.05 was considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is another paper using UKB and NHANES data to explore the correlation network surrounding the popular blood chemistry algorithms introduced by Morgan Levine some time ago and now very widely reported on, especially in these two datasets. There are two bits that are somewhat new here. One is looking at combination of genetic and social-determinants risks. But this novelty is undercut by the reliance on GWAS conducted in UKB (the dataset being analyzed to test the hypothesis) to develop the polygenic scores used to operationalize genetic influences on the biological age measurements. The other is the mediation analysis evaluating whether differences in the computed BA measures explain social gradients in health. That last analysis is worthy of attention and should be the primary focus of the paper.\nGiven that the primary contribution here is re social determinants of health (hereafter “SDH”), the authors need to spend more time on describing how they measure the construct and establishing its validity. Frankly, they also need to do a better job of measuring the construct to begin with. This paper is worth the attention of Nat Comms readers because of the SDH analysis. So it needs to be a serious SDH paper. Simply dichotomizing a bunch of stuff and adding them up as if each variable contained equal amounts of information both throws away a lot of information and also diminishes the precision of the measurement.\nRegardless of how the authors choose to refine their SDH index, they should include with their paper\n1) A table or structured section of the manuscript listing the individual measurements combined into the SDH index and their Mean + SD or, if they stick with dichotomizing, the % “disadvantaged”.\n2) They need to visualize the distribution of the index\n3) They should visualize the association of the index with their 2 bio age measures as a scatterplot (if they develop a continuously distributed index) or, e.g. by generating box-plots of BA measure distributions by SDH score (if they stick with summing dichotomized indicators).\n4) They should report associations of the SDH measure with their health outcomes. Suggest denominating the indices in deciles to compute these effect-sizes.\n5) They should be concrete about not just the % of the SDH risk mediated by the BA variables, but precisely what that differential morbidity/mortality amounts to – in terms of risk increments or years of life/health.\n6) They should engage seriously in their discussion with the finding that BA measures appear not to mediate substantial fractions of the SDH risk measured in these datasets. They should compare and contrast these findings with the results from Graf et al. 2021 Am J Epid and Graf et al. 2024 JAMA Network Open reporting more substantial mediation of SDH associations with healthspan/lifespan by bio aging measurements.\nThere is by now a great deal of work reporting associations of SDH with bio age measurements and of bio age measurements with health outcomes. But few papers take the time to do the statistical analysis of mediation. If it’s true that measurements of biological aging do not appear to account for social gradients in health, that’s a significant observation for the field. But it depends on the authors doing just as good job of measuring SDH as they do of measuring bio aging.\nFinally, for what it’s worth, the genetic analysis would be more interesting (and also make more sense) if instead of polygenic scores for the biological age algorithms, the authors were using polygenic scores for the outcomes they are analyzing.", "reviewer_3": "This study examines the associations between social determinants of health (SDHs), biological aging acceleration, and health outcomes using data from the UK Biobank and US NHANES cohorts. Below are my comments for the authors.\nMajor Comments\nThe paper addresses an important topic in public health and aging research. The analysis of how social determinants affect biological aging and subsequent health outcomes is timely and valuable. However, I have several concerns:\nThe mediation analysis appears to assume a causal pathway from SDHs to biological aging to health outcomes. Since this is an observational study with cross-sectional assessment of both SDHs and biological aging at baseline, establishing temporality is problematic. The authors acknowledge this limitation but should more explicitly address the potential for reverse causation or bidirectional relationships.\nThe construction of the combined SDH score treats all SDH variables equally by assigning 0 or 1 values. While the authors performed a sensitivity analysis using weighted SDH scores, the main analysis may mask the differential impacts of specific SDHs. Consider presenting results showing which specific SDH domains are most strongly associated with biological aging.\nThe explanation of the different biomarkers used for biological age estimation in UK Biobank vs. US NHANES is unclear. The authors should provide more details on how these differences might affect comparability between cohorts and the generalizability of findings.\nWhile genetic analyses are presented, the authors should clarify how genetic risk scores were developed. The stated reliance on \"SNPs associated with KDM-BA and PhenoAge\" needs more detailed description of how these associations were established and whether they represent causal relationships.\nThe missing data handling approach requires more detail. The exclusion of participants with missing data on SDHs and biological aging could introduce selection bias. A comparison of included vs. excluded participants showed systematic differences, which should be addressed more thoroughly.\nAdditional Analyses Suggested\nThe answers seem to be confused by the naming of phenoage and phenotypic age. Please refer to Morgan's paper. Specifically, the phenoage usually only used to represent DNA methylation-based phenoage while these phenotypic marker-based clock usually is referred to phenotypic age.\nPerform path analysis or structural equation modeling to better understand the complex relationships between specific SDH domains, biological aging, and health outcomes.\nAnalyze change in biological aging measures over time for the subset of participants with repeated biomarker measurements to strengthen causal inference.\nConduct age-stratified analyses to determine if the association between SDHs and biological aging varies across different life stages.\nExplore interactions between specific SDHs (not just the combined score) and genetic risk.\nConsider using alternative methods for handling missing data, such as multiple imputation for all variables, not just covariates.\nMinor Comments\nTable 1 is extensive but would benefit from a clearer presentation of the distribution of individual SDH variables across groups.\nFigure 2 boxplots are informative, but dot density makes patterns difficult to discern. Consider using violin plots or reducing opacity of individual points.\nThe discussion of differences in effect sizes between the two cohorts needs elaboration. The stronger associations in UK Biobank compared to US NHANES deserve more attention.\nThe authors should clarify whether the PhenoAge and KDM-BA estimates were developed independently or whether there is overlap in the biomarkers used.\nWhen reporting mediation results, please include absolute effect estimates along with proportions mediated to give readers a sense of the magnitude of effects.\nThis paper has the potential to make a valuable contribution to our understanding of social determinants and biological aging. Addressing these concerns would strengthen the manuscript considerably."}, "author_response": "Point-by-point response to comments\nReviewer #1 (Remarks to the Author):\n1. This is another paper using UKB and NHANES data to explore the correlation\nnetwork surrounding the popular blood chemistry algorithms introduced by\nMorgan Levine some time ago and now very widely reported on, especially in\nthese two datasets. There are two bits that are somewhat new here. One is\nlooking at combination of genetic and social-determinants risks. But this novelty\nis undercut by the reliance on GWAS conducted in UKB (the dataset being\nanalyzed to test the hypothesis) to develop the polygenic scores used to\noperationalize genetic influences on the biological age measurements. The other\nis the mediation analysis evaluating whether differences in the computed BA\nmeasures explain social gradients in health. That last analysis is worthy of\nattention and should be the primary focus of the paper.\nResponse: We sincerely appreciate your thorough evaluation of our paper and\ninsightful and constructive comments, which enable us to prepare a greatly improved\nmanuscript. We attach great importance to these feedbacks and have made\ncomprehensive and critical revisions according to your valuable suggestions. The\nfollowing is our detailed response to each comment.\nWe totally agree with the reviewer’s opinion on the novelty of our research,\nparticularly the mediation analysis evaluating whether differences in the computed\nBA measures explain social gradients in various health outcomes. In the revised\npaper, we have added extensive analyses to illustrate this novel finding, including the\nuse of the Aalen additive hazards model to facilitate interpretations and structural\nequation modeling to explore the mediation effect for specific SDH domains.\nRegarding the concern about using UKB-based GWAS to construct polygenic risk\nscores (PRS) for joint analysis of genetic risk and social determinants of health\n(SDHs) in UKB, we acknowledge this may limit the independence of the genetic\nmeasures. To our knowledge, no large-scale GWAS of KDM-BA or phenotypic age\nhave been conducted in other European-ancestry populations except UKB.\nFurthermore, in the original GWAS study1 we referred to, the lead SNPs associated\nwith KDM-BA and phenotypic age identified in UKB were replicated in an\nindependent cohort from the US Health and Retirement Study (HRS). Therefore, we\nbelieve that the use of UKB-derived SNPs for constructing PRS is unlikely to\nintroduce substantial bias. In the revised paper, we have added this in the “Methods”\nsection.\n“Line 228-230: The lead SNPs were replicated in an independent European ancestry\ncohort from the U.S. Health and Retirement Study (HRS).”\nReference\nbiological age measures point to distinct aging phenotypes. Aging Cell 20, e13376 (2021).\n2. Given that the primary contribution here is re social determinants of health\n(hereafter “SDH”), the authors need to spend more time on describing how they\nmeasure the construct and establishing its validity. Frankly, they also need to do\na better job of measuring the construct to begin with. This paper is worth the\nattention of Nat Comms readers because of the SDH analysis. So it needs to be a\nserious SDH paper. Simply dichotomizing a bunch of stuff and adding them up\nas if each variable contained equal amounts of information both throws away a\nlot of information and also diminishes the precision of the measurement.\nResponse: Thank you for the insightful and constructive comment. We agree that\naccurately conceptualizing and measuring SDH is of great importance to this paper. In\nthe revised paper, we added more descriptions of how we determined the SDH\nvariables in the “Methods” section:\n“Line 130-137: We systematically reviewed standardized questionnaires from the UK\nBiobank and US NHANES, mapping variables to the five domains guided by the\nHealthy People 20301 and World Health Organization2: economic stability, education\naccess and quality, health care access and quality, neighborhood and built\nenvironment, and social and community context. The selection of specific variables\nwithin these domains was informed by prior studies which demonstrated the\nassociations between such variables and health outcomes, including mortality and\nmajor chronic diseases3-6.”\nOn the other hand, we acknowledge that simply dichotomizing individual SDH\nvariables and summing them with equal weights may lead to information loss and\nobscure the differential contributions. Given that the SDH variables comprised a mix\nof binary (e.g., living alone), categorical (e.g., employment status), and continuous\n(e.g., income quality) measures, we dichotomized them using well-established cut\npoints in prior literature to enhance interpretability and comparability, which may\nfacilitate application of the SDH score in public health practice3-6. To address the\noversimplified hypothesis of equal contribution of each variable (unweighted\nmethod), we revised our analytic approach to construct weighted SDH scores in the\nmain analysis, which assigned a weight to each SDH variable based on its association\nwith KDM-BA acceleration and phenotypic age acceleration, respectively.\nAdditionally, to mitigate potential information loss from dichotomizing SDHs, we\nconducted a sensitivity analysis in which SDHs were reclassified according to the\nfollowing principles: SDH variables obtained from questionnaires were categorized\ninto three or more groups based on the questions, while variables derived from area-\nlevel scores were classified into three categories according to tertiles of their\ndistributions. Results from these analyses were broadly similar. We have made the\nrevision in the “Methods” section and updated all results.\nMethods section:\n“Line 155-170: Each SDH was dichotomized into advantaged and disadvantaged\nlevels using conventional cut points (Table S2) established in prior literature to\nenhance interpretability and facilitate the application of SDH score3-6. We constructed\na weighted SDH score to account for varying magnitude of the associations (absolute\nβ coefficients) between individual SDHs and biological aging acceleration (Table S3).\nIn UK Biobank, weighted SDHs score = (β × SDH + … + β × SDH ) × (18/sum\n1 1 18 18\nof β coefficients). In US NHANES, weighted SDHs score = (β × SDH + … + β ×\n1 1 10\nSDH ) × (10/sum of β coefficients). As a sensitivity analysis, we also constructed an\n10\nunweighted score by assigning 0 points for the advantaged level of each SDH and 1\npoint for the disadvantaged level. The weighted and unweighted SDHs score ranged\nfrom 0 to 18 in UK Biobank and from 0 to 10 in US NHANES (Figure S3), with higher\nscores indicating more unfavourable SDHs.”\n“Line 350-355: Sixth, to mitigate potential information loss from dichotomizing\nSDHs, we reclassified SDHs according to the principles: SDHs obtained from\nquestionnaires were categorized into three or more groups based on the original\nquestions, while those derived from area-level scores were classified into three\ncategories according to the tertiles of their distributions (Table S8-S9).”\nResults section:\n“Line 395-400: The results remained consistent in sensitivity analyses using\nunweighted combined SDH scores, additionally adjusting for waist circumference and\nsleep score, using imputation for all missing variables, and adopting different\nclassifications of SDHs (Table S13-S16).\nSupplementary data:\n-Table S8. Different classifications of SDHs in UK Biobank\n-Table S9. Different classifications of SDHs in US NHANES\n-Table S16. Associations of combined SDHs with accelerated biological aging in the\nUK Biobank and US NHANES cohorts: using different classifications of SDHs and\nweighted SDH scores\n-Table S30. Mediating effect of accelerated biological aging on the relationships\nbetween combined SDHs and health outcomes in UK Biobank and US NHANES:\nusing different classifications of SDHs and weighted SDH scores\nReferences\nHealth. 2023. https://odphp.health.gov/healthypeople/priority-areas/social-determinants-health\n(accessed Oct 25 2024).\nhealth equity through action on the social determinants of health. The lancet 372, 1661-1669\n(2008).\n1999 to 2018: a national cohort study. Lancet Public Health 8, e422-e431 (2023).\nrisks among individuals with type 2 diabetes: two nationwide cohort studies in the UK and USA.\nLancet Healthy Longev 5, e542-e551 (2024).\nDeterminants of Health. JAMA Netw Open 7, e2445309 (2024).\ncancer survivors population. BMC Med 22, 343 (2024).\n3. Regardless of how the authors choose to refine their SDH index, they should\ninclude with their paper\n1) A table or structured section of the manuscript listing the individual\nmeasurements combined into the SDH index and their Mean + SD or, if they\nstick with dichotomizing, the % “disadvantaged”.\nResponse: Thank you for this thoughtful comment. We have added the distribution of\nindividual SDH variables across groups using % “disadvantaged” in Table 1.\nPlease see the table below and Table 1 in the revised manuscript.\n2) They need to visualize the distribution of the index.\nResponse: Thank you for the helpful suggestion. We have added a histogram to\nillustrate the distribution of the SDH index, including the weighted SDH score\nderived from KDM-BA acceleration, weighted SDH score derived from phenotypic\nage acceleration, and unweighted SDH score. Please see the figure below and\nSupplementary data (Figure S3) in the revised paper.\n3) They should visualize the association of the index with their 2 bio age\nmeasures as a scatterplot (if they develop a continuously distributed index) or,\ne.g. by generating box-plots of BA measure distributions by SDH score (if they\nstick with summing dichotomized indicators).\nResponse: Thanks for the insightful comment. The weighted combined SDH scores\nwere constructed as a continuous variable ranging from 0 to 18 in UK Biobank and\nfrom 0 to 10 in US NHANES. Accordingly, we visualized the association of the SDH\nindex with the two biological aging measures using a scatterplot as suggested. Please\nsee the figure below and Figure 2 in the revised paper.\n4) They should report associations of the SDH measure with their health\noutcomes. Suggest denominating the indices in deciles to compute these effect-\nsizes.\nResponse: Thank you for the constructive suggestion. Actually, we reported the\nassociation between the SDH measure with health outcomes in our original\nsubmission. In response to the reviewer’s comment, we reevaluated the associations\nbetween the weighted SDH scores and health outcomes by categorizing the SDH\nscores into deciles in UK Biobank and quintiles in US NHANES. Due to the limited\nnumber of deaths in US NHANES, we used quintiles instead of deciles to ensure\nsufficient statistical power. Please see the figure below and supplementary data\n(Figure S8) in the revised paper. We have reported the associations of the SDH\nmeasure with health outcomes in the “Results” section.\n“Line 436-444: More unfavourable SDHs were associated with higher risks of all-\ncause and cause-specific mortality (Figure S8, Table S20-S21). For example, in UK\nBiobank, each 1-point increase in weighted SDH scores derived from KDM-BA\nacceleration was associated with a hazard ratio (HR) of 1.08 (95% CI: 1.07-1.08) for\nall-cause mortality, and the corresponding HR for SDH scores derived from\nphenotypic age acceleration was 1.09 (1.09-1.10). In US NHANES, each 1-point\nincrease in the weighted SDH scores derived from KDM-BA acceleration was\nassociated with a HR of 1.20 (95% CI: 1.17-1.23) for all-cause mortality, and the\ncorresponding HR for SDH scores derived from phenotypic age acceleration was 1.17\n(1.15-1.20).”\n“Line 458-462: For instance, in UK Biobank, each 1-point increase in the weighted\nSDH scores derived from KDM-BA acceleration was associated with a HR of 1.04\n(95% CI: 1.03-1.04) for cardiovascular disease, 1.11 (1.10-1.12) for type 2 diabetes,\n1.08 (1.07-1.09) for liver disease, 1.14 (1.13-1.15) for chronic respiratory disease,\nand 1.07 (1.07-1.08) for chronic kidney disease. ”\n5) They should be concrete about not just the % of the SDH risk mediated by the\nBA variables, but precisely what that differential morbidity/mortality amounts to\n– in terms of risk increments or years of life/health.\nResponse: We are grateful for the constructive suggestion. To address this issue as the\nreviewer pointed out, we used the Aalen additive hazard model to conduct the\nmediation analysis, where the direct effect and indirect effect can be interpreted as the\nnumber of additional events per person-time and are more interpretable from the\npublic health perspective1,2. For instance, in UK biobank, each 1-point increase in the\nSDH score was associated with 3.14 (2.87, 3.42) additional deaths per 10,000 person-\nyears through the direct pathway of SDHs, and 0.68 (0.63, 0.73) additional deaths\nattributable to the pathway mediated by KDM-BA acceleration. Please see Figure 4 in\nthe revised paper. We made the revision in the “Methods” section and updated\ncorresponding results.\n“Line 316-324: Causal mediation analyses were conducted using the Aalen additive\nhazards model to estimate the total effect of SDH on the outcome adjusting for\ncovariates included in Model 2, which was decomposed into the natural direct effect\nand the natural indirect effect1,2. The effect estimates are presented as the number of\nadditional events per person-time, offering a more interpretable metric from a public\nhealth perspective. The 95% confidence intervals (CIs) for the direct and indirect\neffects and proportions mediated were estimated using simulation with 100,000\nrepeats, as previously described1.”\nReferences\n581 (2011).\nin tobacco: Malmo Diet and Cancer Cohort Study. Environ Health 18, 56 (2019).\n6) They should engage seriously in their discussion with the finding that BA\nmeasures appear not to mediate substantial fractions of the SDH risk measured\nin these datasets. They should compare and contrast these findings with the\nresults from Graf et al. 2021 Am J Epid and Graf et al. 2024 JAMA Network\nOpen reporting more substantial mediation of SDH associations with\nhealthspan/lifespan by bio aging measurements.\nResponse: We sincerely appreciate this insightful comment. Graf et al. (2022)\nreported that biological aging measures including the blood-chemistry-based\nphenotypic age, the DNA-methylation (DNAm) GrimAge clock, and DNAm pace-of-\naging measure (DunedinPoAm) accounted for 52%-80% of the Black-White\ndifferences in mortality in the Health and Retirement Study1. Additionally, Graf et al.\n(2024) reported that the DunedinPoAm mediated 50% of the association between\neducational mobility and mortality risk in the Framingham Heart Study2. By contrast,\nthe proportion mediated by accelerated biological aging in our study was lower\n(17.85% to 28.69% in UK Biobank and 8.52% to 25.19% in US NHANES). The\nobserved discrepancies may be partly explained by differences in SDH measurement.\nIn our study, the composite SDH score that integrated five distinct domains could\nreflect the complex interconnection of different SDHs with biological aging. Thus, the\nmediation effects might be attenuated through diffuse biological pathways, while\nprevious studies focusing on isolated SDHs (e.g., race or educational mobility) may\nhave captured more specific aging-mediated mechanisms. Another explanation might\nbe the covariate adjustment. Graf’s studies primarily adjusted for age and sex,\nwhereas our analyses incorporated a broader set of covariates, including lifestyle\nfactors and baseline health status, which might reduce residual confounding.\nFollowing the reviewer’s advice, we have added the discussion with previous studies\nin the “Discussion” section.\n“Line 558-576: Of note, the mediation proportion of biological aging in the\nassociation between SDHs and mortality risk ranged from 17.85% to 28.69% in UK\nBiobank and 8.52% to 25.19% in US NHANES, which were lower than those shown in\nprior studies. Graf et al.(2022) reported that biological aging measures, including the\nblood-chemistry-based phenotypic age, the DNA-methylation (DNAm) GrimAge\nclock, and DNAm pace-of-aging measure (DunedinPoAm), accounted for 52%-80%\nof the Black-White differences in mortality in the Health and Retirement Study1.\nAdditionally, Graf et al. (2024) reported that the DunedinPoAm mediated 50% of the\nassociation between educational mobility and mortality risk in the Framingham Heart\nStudy2. The observed discrepancies may be partly explained by the differences in SDH\nmeasurement. In our study, the composite SDH score that integrated five distinct\ndomains reflects the complex interconnection of different SDHs with biological aging.\nThis potentially leads to attenuated mediation effects through diffuse biological\npathways. In contrast, previous studies focusing on isolated SDHs (e.g., race or\neducational mobility) may have captured more specific aging-mediated mechanisms.\nAdditionally, prior studies primarily adjusted for age and sex in the model, whereas\nour analyses incorporated a broader set of covariates, including lifestyle factors and\nbaseline health status, which could reduce potential residual confounding.”\nReferences\nUnited States: Analysis of DNA-Methylation and Blood-Chemistry Methods. Am J Epidemiol 191,\n613-625 (2022).\nFramingham Heart Study. JAMA Netw Open 7, e240655 (2024).\n4. There is by now a great deal of work reporting associations of SDH with bio\nage measurements and of bio age measurements with health outcomes. But few\npapers take the time to do the statistical analysis of mediation. If it’s true that\nmeasurements of biological aging do appear to account for social gradients in\nhealth, that’s a significant observation for the field. But it depends on the authors\ndoing just as good job of measuring SDH as they do of measuring bio aging.\nResponse: Thank you for the insightful comment. The mediation analysis and\nsignificant mediation effect of two biological aging metrics in the association of SDH\nand various health outcomes represents an important finding of our paper. We agree\nwith the reviewer’s idea that a reliable measurement of SDH is responsible for our\nsignificant observation. As demonstrated in our detailed reply to Comment 2, we\nadopted multiple approaches to measure SDH in our study that not only make our\nresults comparable to previous studies, but also confirm the robustness of our\nconclusion.\nFor the selection of SDH variables, we systematically reviewed standardized\nquestionnaires from the UK Biobank and US NHANES, mapping variables to the five\ndomains guided by the Healthy People 2030 and World Health Organization:\neconomic stability, education access and quality, health care access and quality,\nneighborhood and built environment, and social and community context. Then, the\nspecific variables within these domains were determined according to prior studies\nwhich demonstrated strong associations between these variables and health outcomes,\nincluding mortality and chronic diseases.\nFor the construction of SDH variables, we developed weighted SDH scores to account\nfor varying magnitude of the associations (absolute β coefficients) between individual\nSDHs and biological aging acceleration (Table S3). To accurately reflect the effect\nsize associated with different aging metrics, we constructed SDH scores based on the\nassociations of individual SDH variables with KDM-BA acceleration and phenotypic\nage acceleration, respectively. Meanwhile, we built unweighted SDH scores in the\nsensitivity analysis for comparison. Finally, to mitigate potential information loss\nfrom dichotomizing SDHs (we used dichotomous SDH variables in the main analysis\ndue to the well-established cut points informed in previous studies and increased\ncomparability), we also conducted a sensitivity analysis in which SDHs were\nreclassified according to the following principles: SDHs obtained from questionnaires\nwere categorized into three or more groups based on the original questions, while\nthose derived from area-level scores were classified into three categories according to\ntertiles of their distributions (Table S8-S9). Results for the associations between\ncombined SDHs using alternative classification approaches and biological aging were\nconsistent with the main results (Table S16, Table S30).\nIn addition, since mediation is a major innovative aspect of our study, we performed\nmore in-depth analyses to decipher how biological aging accounts for social gradients\nin health. Besides the use of the Aalen additive hazard model to reveal the number of\nadditional events per person-time through mediation pathway, we also applied\nstructural equation modeling to demonstrate the mediating role of biological aging in\nlinking specific SDH domains to health outcomes, as suggested by another reviewer.\nThe results indicated that biological aging acceleration exhibited distinct mediation\npatterns across different SDH domains. Please see Table S31-S34 in the\nsupplementary data.\nWe sincerely hope that our modifications and supplementary analyses of measuring\nSDH could adequately address your concerns. Should you have any additional\nfeedback or suggestions, we would be pleased to make further modifications to\nenhance the manuscript.\n5. Finally, for what it’s worth, the genetic analysis would be more interesting\n(and also make more sense) if instead of polygenic scores for the biological age\nalgorithms, the authors were using polygenic scores for the outcomes they are\nanalyzing.\nResponse: According to the reviewer’s comment, we conducted genetic analysis to\nexamine the joint association between combined SDHs and genetic susceptibility\n(using polygenic scores for individual diseases) on incident diseases. Please see the\ntable below and Supplementary Table S26 in the revised paper. We have added this in\nthe “Method” and “Results” sections.\nMethod section:\n“Line 233-239: For cardiovascular disease and type 2 diabetes, we used PRS that\nwere calculated using external GWAS data and were made available in UK Biobank\n(Table S6). Methods for deriving these PRS have been described previously1. For\nother diseases with no PRS in UK Biobank, we retrieved Polygenic Score catalog2 or\nGWAS summary statistics derived in predominantly European populations (Table S6).\nWe calculated these PRS using the same approach as for biological aging.”\n“Line 313-314: Additionally, we explored the joint effects of combined SDHs and\ngenetic risk on incident diseases.”\nResults section:\n“Line 468-472: Furthermore, the combined effects of genetic risk and overall SDHs\non incident diseases expressed in a dose-response manner, and significant interactions\nwere observed between combined SDHs and genetic risk for cardiovascular disease\nand type 2 diabetes (Table S26)”.\nReferences\nBiobank Polygenic Risk Score (PRS) Release. PLoS One 19, e0307270 (2024).\nsystematic evaluation. Nat Genet 53, 420-425 (2021).\nThank you again for the meticulous review and valuable suggestions on our paper. We\nbelieve that these revisions, based on your comments, have significantly strengthened\nthis manuscript and provide a more robust foundation for our claims. We look forward\nto your additional feedback or guidance.\nReviewer #3 (Remarks to the Author):\nThis study examines the associations between social determinants of health\n(SDHs), biological aging acceleration, and health outcomes using data from the\nUK Biobank and US NHANES cohorts. Below are my comments for the authors.\nResponse: We sincerely appreciate your thorough evaluation of our paper and\ninsightful and constructive comments, which enable us to prepare a greatly improved\nmanuscript. We attach great importance to these feedbacks and have made\ncomprehensive and critical revisions according to your valuable suggestions. The\nfollowing is our detailed response to each comment.\nMajor Comments\nThe paper addresses an important topic in public health and aging research. The\nanalysis of how social determinants affect biological aging and subsequent health\noutcomes is timely and valuable. However, I have several concerns:\n1. The mediation analysis appears to assume a causal pathway from SDHs to\nbiological aging to health outcomes. Since this is an observational study with\ncross-sectional assessment of both SDHs and biological aging at baseline,\nestablishing temporality is problematic. The authors acknowledge this limitation\nbut should more explicitly address the potential for reverse causation or\nbidirectional relationships.\nResponse: Thank you for your insightful comments. We acknowledge that the cross-\nsectional association between SDHs and biological aging leads to temporal\nuncertainty. To address this issue, we additionally performed a longitudinal analysis\namong 4,273 UK Biobank participants with biomarker data available at both baseline\nand the first repeat assessment (2012-2013). We calculated changes in KDM-BA\nacceleration and phenotypic age acceleration between the two time points. Our results\nshowed that SDHs were significantly associated with increased KDM-BA and\nphenotypic age acceleration over time, supporting a longitudinal relationship between\nSDHs and accelerated biological aging. These results complement our main analysis\nand reduce the possibility of reverse causation or bidirectional relationships. Please\nsee the table below and supplementary data (Table S18) in the revised paper. Details\nof this analysis have been incorporated into the “Methods” and “Results” sections,\nand the limitation paragraph in the “Discussion” section.\nMethods section:\n“Line 341-345: Fourth, to reduce temporal uncertainty, we assessed the association\nbetween SDHs and changes in biological aging among 4,273 UK Biobank\nparticipants with biomarker data at both baseline and the first repeat visit (2012-\n2013). The changes in biological aging were calculated as the difference between\nKDM-BA and phenotypic age acceleration at two time points.”\nResults section:\n“Line 406-408: Furthermore, we examined the associations between SDHs and\nchange in biological aging and found that SDHs were associated with increased\nKDM-BA and phenotypic age acceleration over time (Table S18).”\nDiscussion section:\n“Line 622-627: Fourth, since combined SDHs and biomarker data were collected at\nbaseline, the temporal relationship between SDHs and biological aging was\nuncertain. To address this concern, we used the first repeat measurement of\nbiomarkers (2012-2013) in UK Biobank to assess the change in biological aging and\nfound that SDHs were associated with accelerated biological aging over time.”\n2. The construction of the combined SDH score treats all SDH variables equally\nby assigning 0 or 1 values. While the authors performed a sensitivity analysis\nusing weighted SDH scores, the main analysis may mask the differential impacts\nof specific SDHs. Consider presenting results showing which specific SDH\ndomains are most strongly associated with biological aging.\nResponse: Thank you for your insightful comments. In the revised paper, we\nconstructed weighted SDH scores in the main analysis, which assign the weight to\neach SDH variable based on its association with KDM-BA and phenotypic age\nacceleration, respectively. We have revised accordingly in the “Methods” section and\nupdated all results.\n“Line 155-170: Each SDH was dichotomized into advantaged and disadvantaged\nlevels using conventional cut points (Table S2) established in prior literature to\nenhance interpretability and facilitate the application of SDH scores1-4. We\nconstructed a weighted SDH score to account for varying magnitude of the\nassociations (absolute β coefficients) between individual SDHs and biological aging\nacceleration (Table S3). In UK Biobank, weighted SDHs score = (β × SDH + … +\n1 1\nβ × SDH ) × (18/sum of β coefficients). In US NHANES, weighted SDHs score =\n18 18\n(β × SDH + … + β × SDH ) × (10/sum of β coefficients). As a sensitivity\n1 1 10 10\nanalysis, we also constructed an unweighted score by assigning 0 points for the\nadvantaged level of each SDH and 1 point for the disadvantaged level. The weighted\nand unweighted SDHs score ranged from 0 to 18 in UK Biobank and from 0 to 10 in\nUS NHANES (Figure S3), with higher scores indicating more unfavourable SDHs.”\nAccording to the reviewer’s suggestion, we further examined which specific SDH\ndomains were most strongly associated with biological aging. Specifically, we\ncalculated weighted SDH domain score = (β1 × SDH1 + β2 × SDH2 + … +βn ×\nSDHn) × (number of SDHs within specific SDH domains/sum of β coefficients for\nthe SDHs within specific SDH domains). The weighted SDH domain scores were\nstandardized to facilitate comparisons across different SDH domains. Across the five\nSDH domains, financial circumstances showed the strongest association with KDM-\nBA (0.71 [0.68-0.73] years) and phenotypic age acceleration (0.46 [0.44-0.48] years)\nin UK Biobank (Table S17), while social and community context showed the\nstrongest association with KDM-BA (0.87 [0.68-1.06] years) and phenotypic age\nacceleration (0.39 [0.32-0.45] years) in US NHANES (Table S17, also shown below).\nWe have revised accordingly in the “Methods” section and “Results” section.\nMethods section:\n“Line 172-177: Furthermore, we examined the associations of different SDH domains\nwith biological aging. Specifically, we calculated weighted SDH domain score = (β1\n× SDH1 + β2 × SDH2 + … +βn × SDHn) × (number of SDHs within specific SDH\ndomains/sum of β coefficients for the SDHs within specific SDH domains). The SDH\ndomain scores were standardized to facilitate comparisons across different SDH\ndomains.”\nResults section:\n“Line 401-406: Across the five SDH domains, financial circumstances showed the\nstrongest association with KDM-BA (0.71 [0.68-0.73] years) and phenotypic age\nacceleration (0.46 [0.44-0.48] years) in UK Biobank, while social and community\ncontext showed the strongest association with KDM-BA (0.87 [0.68-1.06] years) and\nphenotypic age acceleration (0.39 [0.32-0.45] years) in US NHANES (Table S17).”\nReferences\n1999 to 2018: a national cohort study. Lancet Public Health 8, e422-e431 (2023).\nrisks among individuals with type 2 diabetes: two nationwide cohort studies in the UK and USA.\nLancet Healthy Longev 5, e542-e551 (2024).\nDeterminants of Health. JAMA Netw Open 7, e2445309 (2024).\ncancer survivors population. BMC Med 22, 343 (2024).\n3. The explanation of the different biomarkers used for biological age estimation\nin UK Biobank vs. US NHANES is unclear. The authors should provide more\ndetails on how these differences might affect comparability between cohorts and\nthe generalizability of findings.\nResponse: Thank you for the constructive comment. Due to the differences in\nparameters measured in UK Biobank and US NHANES, the biomarkers used for\nbiological age estimation between these two cohorts were slightly different1-3.\nConsistent with previous studies, the biological aging algorithms used in our study\nwere originally developed and validated in US NHANES and subsequently adapted\nfor application in UK Biobank using the available biomarker data. Although\ndifferences in biomarker selection may limit direct comparability across cohorts, the\nuse of cohort-specific validated measures ensures internal validity within each cohort.\nImportantly, we observed consistent associations between SDHs and accelerated\nbiological aging across both cohorts, and this consistency remained in serial\nsensitivity analyses. These results support the broader relevance and generalizability\nof our findings. We have added more details in the “Methods” and “Discussion”\nsections.\nMethods section:\n“Line 184-188: Two best-trained biological age algorithms, KDM-BA and phenotypic\nage, were used. These algorithms have been originally developed and validated in the\nUS NHANES4-6 and subsequently adapted for application in the UK Biobank using\navailable biomarker data.1,2,7.”\nDiscussion section:\n“Line 609-613: Likewise, biological aging metrics were established using available\nclinical biomarkers in each cohort, which may limit direct comparability across the\ntwo cohorts. However, the use of cohort-specific validated measures ensures internal\nvalidity within each cohort and the consistent association between SDHs and\naccelerated biological aging supports the broader relevance and generalizability of\nour findings.”\nReferences\nand mortality. Nat Cardiovasc Res 3, 332-342 (2024).\n424,299 UK Biobank participants. Nat Commun 14, 2277 (2023).\norgan function test data: BioAge. Geroscience 43, 2795-2808 (2021).\nsubpopulations from NHANES IV: A cohort study. PLoS Med 15, e1002718 (2018).\nAgeing Dev 127, 240-248 (2006).\naccurately than chronological age? J Gerontol A Biol Sci Med Sci 68, 667-674 (2013).\nbiological age measures point to distinct aging phenotypes. Aging Cell 20, e13376 (2021).\n4. While genetic analyses are presented, the authors should clarify how genetic\nrisk scores were developed. The stated reliance on \"SNPs associated with KDM-\nBA and PhenoAge\" needs more detailed description of how these associations\nwere established and whether they represent causal relationships.\nResponse: Thank you for the valuable comment. In the original GWAS study1, the\nreported association between KDM-BA or phenotypic age with each SNP was\nexamined using an efficient Bayesian linear mixed effects model, adjusting for age,\nsex, genotyping array, and assessment center. Genome-wide significant SNPs (P\n<5×10-8) were identified and independent signals were selected through Conditional\n& Joint association analysis, excluding those with multiple regression R2 >0.9 with\nalready pre-selected SNPs. Filtered SNPs were mapped to genes based on\nGRCh37/hg19 coordinates. This rigorous approach ensured the robust identification\nof genetic variants associated with each biological aging measure. Although these\nassociations may not represent causal relationships, their biological plausibility is\nsupported by pathway enrichment analyses, which revealed enrichment of lipid-\nrelated pathways for SNPs associated with KDM-BA, and enrichment of immune,\ncellular, and carbohydrate metabolism pathways for SNPs associated with phenotypic\nage. Furthermore, the replication of lead SNPs in an independent European ancestry\ncohort from the US Health and Retirement Study (HRS) reinforces the robustness of\nthe associations.\nWe have added more details regarding this in the “Methods” sections.\n“Line 223-230: The associations between KDM-BA or phenotypic age with individual\nSNPs were examined using an efficient Bayesian linear mixed effects model1.\nGenome-wide significant SNPs (P <5×10-8) were identified and independent signals\nwere selected through Conditional & Joint association analysis. The biological\nplausibility of these associations was supported by pathway enrichment analyses and\nthe lead SNPs were replicated in an independent European ancestry cohort from the\nU.S. Health and Retirement Study (HRS).”\nReference\nbiological age measures point to distinct aging phenotypes. Aging Cell 20, e13376 (2021).\n5. The missing data handling approach requires more detail. The exclusion of\nparticipants with missing data on SDHs and biological aging could introduce\nselection bias. A comparison of included vs. excluded participants showed\nsystematic differences, which should be addressed more thoroughly.\nResponse: Thank you for the constructive comment. We acknowledge that the\nexclusion of participants with missing data on SDHs and biological aging may\nintroduce selection bias, even though the characteristics between included and\nexcluded participants were broadly comparable except for slightly higher proportions\nof comorbidities among excluded participants than included participants (Table S12).\nTo address this potential concern, we conducted a sensitivity analysis using multiple\nimputation for all missing variables, including SDHs, biological aging markers, and\ncovariates. The results from the imputed dataset were largely consistent with our\nprimary findings, suggesting that the impact of exclusion of participants with missing\ndata on SDHs and biological aging would be minor. Please see the tables below and\nsupplementary data (Table S15 and Table S29) in the revised paper. We have added\nthis in the “Methods”, “Results” and “Discussion” sections.\nMethods section:\n“Line 334-350: Several sensitivity analyses were conducted…Fifth, we performed\nmultiple imputation for all variables with missing data, including SDHs, biological\naging biomarkers, and covariates.”\nResults section:\n“Line 395-400: The results remained consistent in sensitivity analyses using\nunweighted combined SDH scores, additionally adjusting for waist circumference and\nsleep score, using imputation for all missing variables, and adopting different\nclassifications of SDHs (Table S13-S16).”\n“Line 494-495: Results were largely unchanged in all sensitivity analyses (Table S28-\nS30).”\nDiscussion section:\n“Line 614-622: Third, the differences in certain characteristics between included and\nexcluded participants may raise the concern about selection bias. Nevertheless, we\nconducted a sensitivity analysis using multiple imputation for all missing variables,\nincluding SDHs, biological aging markers, and covariates. The results from the\nimputed dataset were largely consistent with our primary findings, suggesting that the\nimpact of exclusion of participants with missing data on SDHs and biological aging\nwould be minor.”\nAdditional Analyses Suggested\n6. The answers seem to be confused by the naming of phenoage and phenotypic\nage. Please refer to Morgan's paper. Specifically, the phenoage usually only used\nto represent DNA methylation-based phenoage while these phenotypic marker-\nbased clock usually is referred to phenotypic age.\nResponse: Thank you for underlining this inaccurate expression. As suggested, we\nhave revised the manuscript to use “phenotypic age” throughout the manuscript.\n7. Perform path analysis or structural equation modeling to better understand\nthe complex relationships between specific SDH domains, biological aging, and\nhealth outcomes.\nResponse: Thank you for the insightful comment. In response, we conducted\nstructural equation modeling to examine the mediating role of biological aging in the\nassociations between specific SDH domains and health outcomes. The results\nindicated that KDM-BA and phenotypic age acceleration exhibited distinct mediation\npatterns across different SDH domains (Table S31-S34). For instance, in UK Biobank,\n9.4% (95% CI: 8.4%-10.4%) of the association between financial circumstance and\nall-cause mortality was mediated by KDM-BA acceleration, and 13.4% (12.1%-\n14.8%) was mediated by phenotypic age acceleration. For the association between\nsocial and community context and all-cause mortality, 16.7% (12.4%-21.0%) was\nmediated by KDM-BA acceleration, and 20.4% (17.0%-23.8%) was mediated by\nphenotypic age acceleration. These findings highlight the complex relationships\namong social determinants, biological aging, and health outcomes. Please see below\nresults using weighted SDH domain scores derived from KDM-BA acceleration in\nUK Biobank. The complete results are shown in Supplementary Table S31-S34 in the\nrevised paper. We have added the descriptions in the “Methods” and “Results”\nsections.\nMethods section:\n“Line 324-328: In addition, generalized structural equation modelling was performed\nto assess the mediating role of biological aging in the relationships between specific\nSDH domains and health outcomes. The 95% CIs for direct and indirect effects and\nproportions mediated were estimated by 10,000-iteration nonparametric bootstrap\napproach1.”\nResults section:\n“Line 495-503: Additionally, structural equation modeling revealed that KDM-BA\nand phenotypic age acceleration exhibited distinct mediation patterns across different\nSDH domains. For instance, in the UK Biobank cohort, 9.4% (95% CI: 8.4%-10.4%)\nof the association between financial circumstance and all-cause mortality was\nmediated by KDM-BA acceleration, and 13.4% (12.1%-14.8%) was mediated by\nphenotypic age acceleration. For the association between social and community\ncontext and all-cause mortality, 16.7% (12.4%-21.0%) was mediated by KDM-BA\nacceleration, and 20.4% (17.0%-23.8%) was mediated by phenotypic age\nacceleration (Table S31-S34).”\nReference\nbetween sleep duration, cognition and mental health. Nat Aging 2, 425-437 (2022).\n8. Analyze change in biological aging measures over time for the subset of\nparticipants with repeated biomarker measurements to strengthen causal\ninference.\nResponse: Thank you for your helpful comments. Following your advice, we\nconducted a longitudinal analysis among 4,273 UK Biobank participants with\nbiomarker data available at both baseline and the first repeat assessment (2012-2013).\nWe calculated changes in biological aging using the difference in KDM-BA and\nphenotypic age acceleration between the two time points. Our findings showed that\nSDHs were significantly associated with increased KDM-BA and phenotypic age\nacceleration over time, supporting a longitudinal relationship between SDHs and\nbiological aging acceleration. Please see the table below and Table S18 in the revised\npaper. Details of this analysis have been incorporated into the “Methods” and\n“Results” sections and “Discussion” section.\nMethods section:\n“Line 341-345: Fourth, to reduce temporal uncertainty, we assessed the association\nbetween SDHs and changes in biological aging among 4,273 UK Biobank\nparticipants with biomarker data at both baseline and the first repeat visit (2012-\n2013). The changes in biological aging were calculated as the difference between\nKDM-BA and phenotypic age acceleration at two time points.”\nResults section:\n“Line 406-408: Furthermore, we examined the associations between SDHs and\nchange in biological aging and found that SDHs were associated with increased\nKDM-BA and phenotypic age acceleration over time (Table S18).”\nDiscussion section:\n“Line 622-627: Fourth, since combined SDHs and biomarker data were collected at\nbaseline, the temporal relationship between SDHs and biological aging was\nuncertain. To address this concern, we used the first repeat measurement of\nbiomarkers (2012-2013) in UK Biobank to assess the change in biological aging and\nfound that SDHs were associated with accelerated biological aging over time.”\n9. Conduct age-stratified analyses to determine if the association between SDHs\nand biological aging varies across different life stages.\nResponse: Thank you for this kind suggestion. We conducted stratified analysis by\nage groups and other characteristics. In stratified analyses by age (<50, 50-59, vs ≥60\nyears), we found that the association between SDHs and biological aging remained\nsimilar across different age groups (P >0.05). Please see the table below and\ninteraction\nFigure S4-Figure S5 in the revised paper.\n10. Explore interactions between specific SDHs (not just the combined score) and\ngenetic risk.\nResponse: According to the reviewer’s comment, we explored the joint effects and\ninteractions between individual SDHs and genetic risk on biological aging. Our\nanalyses revealed that individuals with both unfavorable SDH profiles and high\ngenetic risk exhibited the greatest acceleration in biological aging. Notably,\nsignificant interactions were observed between genetic risk and several specific SDHs\nincluding low household income, unemployed, low education attainment, low\neducation quality, accommodation instability and emotional distress on KDM-BA or\nphenotypic age acceleration. Please see figures below and Figure S6-S7 in the revised\npaper. We have added this in the “Methods” and “Results” sections.\nMethod section:\n“Line 298-299: Moreover, we explored the associations of individual SDHs with\nbiological aging and their interactions with genetic risk.”\nResults section:\n“Line 424-429: Likewise, individuals with unfavorable individual SDH profiles and\nhigh genetic risk exhibited the greatest acceleration in biological aging (Figure S6-\nS7). Notably, significant interactions were observed between genetic risk and several\nSDHs including low household income, unemployed, low education attainment, low\neducation quality, accommodation instability and emotional distress.”\n11. Consider using alternative methods for handling missing data, such as\nmultiple imputation for all variables, not just covariates.\nResponse: Thank you for the insightful comment. As shown in our previous reply to\nyour comment (5th comment), we conducted a sensitivity analysis using multiple\nimputation for all missing variables, including SDHs, biological aging biomarkers,\nand covariates. The results from the imputed dataset were largely consistent with our\nprimary findings. Please see Table S15 and Table S29 in the revised paper. We have\nadded the description in the “Methods” section and “Results” section.\nMethods section:\n“Line 348-350: Fifth, we performed multiple imputation for all variables with missing\ndata, including SDHs, biological aging biomarkers, and covariates.”\nResults section:\n“Line 395-400: The results remained consistent in sensitivity analyses using\nunweighted combined SDH scores, additionally adjusting for waist circumference and\nsleep score, using imputation for all missing variables, and adopting different\nclassifications of SDHs (Table S13-S16).”\n“Line 494-495: Results were largely unchanged in all sensitivity analyses (Table S28-\nS30).”\nMinor Comments\n12. Table 1 is extensive but would benefit from a clearer presentation of the\ndistribution of individual SDH variables across groups.\nResponse: Thank you for the insightful comment and we have added the distribution\nof individual SDH variables across groups in Table 1. Please see the table below and\nTable 1 in the revised manuscript.\n13. Figure 2 boxplots are informative, but dot density makes patterns difficult to\ndiscern. Consider using violin plots or reducing opacity of individual points.\nResponse: Thank you for your constructive comment. The weighted combined SDH\nscores were constructed as a continuous variable ranging from 0 to 18 in UK Biobank\nand from 0 to 10 in US NHANES. Accordingly, we visualized the association of the\nSDH index with the two biological aging measures using scatterplots, as suggested by\nthe reviewer 1. Please see the figure below and Figure 2 in the revised paper.\n14. The discussion of differences in effect sizes between the two cohorts needs\nelaboration. The stronger associations in UK Biobank compared to US NHANES\ndeserve more attention.\nResponse: Thank you for the kind reminder. Actually, we observed stronger\nassociations between SDHs and KDM-BA acceleration in US NHANES, whereas the\nassociations between SDHs and phenotypic age acceleration were more pronounced\nin UK Biobank. These differences may partly reflect variations in the construction of\nthe combined SDH scores, which were based on the availability of SDH variables in\neach cohort. Moreover, KDM-BA and phenotypic age capture distinct dimensions of\nbiological aging, which may differentially reflect the impact of SDHs across\npopulations. We have added this in the “Discussion” section.\n“Line 579-585: We observed stronger associations of SDHs with KDM-BA\nacceleration in US NHANES, whereas the associations with phenotypic age\nacceleration were more pronounced in UK Biobank. These differences may partly\nreflect variations in the construction of the combined SDH scores, which were based\non the availability of SDH variables in each cohort. Moreover, KDM-BA and\nphenotypic age capture distinct dimensions of biological aging, which may\ndifferentially reflect the impact of SDHs across populations.”\n15. The authors should clarify whether the PhenoAge and KDM-BA estimates\nwere developed independently or whether there is overlap in the biomarkers\nused.\nResponse: Thank you for the helpful comment. KDM-BA1 and phenotypic age2 are\ntwo independently developed biological age algorithms, each derived using distinct\nmodeling approaches. KDM-BA is derived from a series of regressions of individual\nbiomarkers on chronological age within a reference population1, whereas phenotypic\nage is developed via mortality risk prediction using Gompertz regression2. Although\ndeveloped independently, the two algorithms share some overlapping biomarkers,\nsuch as albumin, creatinine, C-reactive protein, and alkaline phosphatase.\nNevertheless, they also include distinct biomarkers indicative of their respective\ndesign frameworks. For instance, KDM-BA in the UK Biobank incorporates FEV1\nand glycated hemoglobin, whereas phenotypic age includes red cell distribution width\nand glucose. These differences underscore the unique biological domains each\nalgorithm emphasizes in the assessment of aging. We clearly demonstrated the\ndevelopment of PhenoAge and KDM-BA and listed biomarkers used for construction\nof these two aging metrics under the heading “Assessment of biological age and age\nacceleration” in the Methods section. Please see line 180-207.\nReferences\naccurately than chronological age? J Gerontol A Biol Sci Med Sci 68, 667-674 (2013).\nsubpopulations from NHANES IV: A cohort study. PLoS Med 15, e1002718 (2018).\n16. When reporting mediation results, please include absolute effect estimates\nalong with proportions mediated to give readers a sense of the magnitude of\neffects.\nResponse: We are grateful for the valuable suggestion. In response, we have reported\nboth the absolute effect estimates and the proportions mediated to better present the\nmagnitude of the mediation effects in Figure 4. Following the advice from Reviewer\n1, we used the Aalen additive hazard model to conduct mediation analysis, where the\nresults can be interpreted as the number of additional events per person-time and are\nmore interpretable from the public health perspective1,2. Please see the figure below\nand Figure 4 in the revised manuscript. We have revised accordingly in the “Methods”\nsection and “Results” section.\nMethods section\n“Line 316-324: Causal mediation analyses were conducted using the Aalen additive\nhazards model to estimate the total effect of SDH on the outcome adjusting for\ncovariates included in Model 2, which was decomposed into the natural direct effect\nand the natural indirect effect1,2. The effect estimates are presented as the number of\nadditional events per person-time, offering a more interpretable metric from a public\nhealth perspective. The 95% confidence intervals (CIs) for the direct and indirect\neffects and proportions mediated were estimated using simulation with 100,000\nrepeats, as previously described1.”\nResults section\n“Line 475-485: We further performed mediation analyses and found that accelerated\nbiological aging significantly mediated the associations between combined SDHs and\nall-cause and cause-specific mortality (Figure 4). For all-cause mortality, the\nestimates for the indirect effect of KDM-BA and phenotypic age acceleration were\n0.68 (0.63, 0.73) and 0.83 (0.78, 0.89) in UK Biobank, respectively; the proportion\nmediated by KDM-BA acceleration was 17.85% (16.20%, 19.69%) and the proportion\nmediated by phenotypic age acceleration was 18.23% (16.73%, 19.90%)…”\nReferences\n581 (2011).\nin tobacco: Malmo Diet and Cancer Cohort Study. Environ Health 18, 56 (2019).\n17. This paper has the potential to make a valuable contribution to our\nunderstanding of social determinants and biological aging. Addressing these\nconcerns would strengthen the manuscript considerably.\nResponse: We sincerely appreciate your positive feedback and recognition of our\nresearch work. We have carefully read the comments and revised the manuscript\naccordingly to strengthen the clarity, rigor, and overall quality of our work. We hope\nthat our critical revision can adequately address your concerns. Should you have any\nadditional feedback or suggestions, we would be pleased to make further\nmodifications to enhance the manuscript.\nPoint-by-point response to comments\nReviewer #1 (Remarks to the Author):\nComments:\nThe authors have done a lot of work to respond to my comments. For the most\npart they have addressed my concerns. In a few cases, there is more that needs to\nbe done. And, in the case of comment 2, the changes made need to be reversed\nbecause they are biasing the analysis.\nResponse: We sincerely thank the reviewer for the thorough re-evaluation and\nconstructive feedback. In response to comment 2, we have reversed the prior change\nand restored the original approach to avoid bias. Below, we provide detailed responses\nto each of the comments.\n1. In response to comment 1, the authors justify their reliance on UKB GWAS of\nthe biological age variables with the argument that no alternative GWAS are\navailable. This is not an argument. It's an excuse. And it's not a good one. The\nanswer is simply to omit the genetic component of the analysis. (if they are\npersuaded of the value of the replication results in the other studies, then they\ncan build their PGI from the replicated top hits and their weights from the\nreplication studies).\nResponse: We appreciate the reviewer’s concern regarding reliance on UKB GWAS\nresults. In the initial study of UKB GWAS, the replication in the US Health\nRetirement Study (HRS) actually did not detect genome-wide significance for the\nSNPs identified from UK Biobank possibly due to the limited sample size, but the\neffect size for lead SNPs were replicated (Table S5)1. Given the lack of genome-wide\nsignificance for the SNPs in the replication analysis and unavailability of other large-\nscale GWAS on biological age, we prefer to rely on UKB GWAS in the main analysis,\nand meanwhile, we constructed polygenic risk score (PRS) using effect estimates\nfrom US HRS replication results in sensitivity analysis. This strategy reduces the risk\nof overfitting and enhances the robustness and generalizability of our findings. The\nassociations of PRS with accelerated biological aging, as well as the joint effects of\ncombined SDHs and genetic susceptibility, were consistent with those in the main\nanalysis. Please see the table and figure below. In the revised paper, we have added\nthis in the “Methods” and “Results” sections.\nMethods section:\n“Line 223-225: To mitigate potential overfitting, we also constructed PRS using\nweights from HRS replication results. Detailed information on the SNPs used for\nconstructing PRS of KDM-BA and phenotypic age is provided in Table S5.”\nResults section:\n“Line 406-408: Sensitivity analyses using PRS constructed with weights from HRS\nproduced consistent findings (Table S21 and Figure S6), with no significant\ninteractions observed for either KDM-BA (P = 0.363) or phenotypic age acceleration\n(P = 0.124).”\nReference\nbiological age measures point to distinct aging phenotypes. Aging Cell 20, e13376 (2021).\nhttps://doi.org/10.1111/acel.13376\n2. In response to comment 2, the authors include the response \"To address the\noversimplified hypothesis of equal contribution of each variable (unweighted\nmethod), we revised our analytic approach to construct weighted SDH scores in\nthe main analysis, which assigned a weight to each SDH variable based on its\nassociation with KDM-BA acceleration and phenotypic age acceleration,\nrespectively.\" I appreciate the authors efforts to address my comment. But this is\na step in the wrong direction. Weighting these variables according to their\nassociation with the putative mediator (Biological Age) will induce bias. They\nshould revert to their original approach. The added justification for the cut-\npoints used in dichotomization is sufficient.\nResponse: We sincerely thank the reviewer for this valuable comment. In line with\nyour suggestion, we have reverted to using the unweighted SDH score as the primary\nanalysis. The results based on the weighted SDH score, which were presented in\nRound 1 revision, have been moved to the Supplementary Information for reference.\nWe believe this revision strengthens the rigor of our analytic approach while ensuring\ntransparency. The description of how unweighted and weighted SDH scores were\nbuilt has been clarified in Methods section, and the cut-off points used for\ndichotomizing each SDH variable have also been clearly justified in the manuscript.\nMethods section:\n“Line 151-163: We constructed an unweighted score by assigning 0 points for the\nadvantaged level of each SDH and 1 point for the disadvantaged level. As a sensitivity\nanalysis, we also constructed weighted SDH scores to account for varying magnitude\nof the associations (absolute β coefficients) between individual SDHs and biological\naging acceleration (Table S3). In UK Biobank, weighted SDHs score = (β × SDH\n1 1\n+ … + β × SDH ) × (18/sum of β coefficients). In US NHANES, weighted SDHs\n18 18\nscore = (β × SDH + … + β × SDH ) × (10/sum of β coefficients).”\n1 1 10 10\n3. In response to comment 6, the authors offer that one reason mediation\nfractions might be smaller relative to previous reports is that their analysis\nincludes more covariates. Fair enough. But this is an easy hypothesis to test. They\nshould repeat their analysis omitting the health behavior covariates and re-\nevaluate the consistency of their results with the ones in the Graf papers. In my\nview, the health behavior covariates should not be included anyway because they\nare on the causal path from SHD to BA (SDH->health behavior->BA->disease)\nResponse: Thanks a lot for the reviewer’s constructive suggestion. Following your\nadvice, we repeated the mediation analysis without adjusting for lifestyle behavior\ncovariates. The results were largely unchanged (Table S31), with mediation fractions\nremaining lower than those reported by Graf et al1,2. We therefore removed this\nexplanation from the Discussion section, as also implicated by the reviewer in the\ncomment 4 that the weaker associations could be mainly attributed to healthy\nvolunteer bias in the UK Biobank3. In addition, the observed discrepancies may be\nrelated to the differences in SDH measurements. In our study, the composite SDH\nscore that integrated five distinct domains reflects the complex interconnection of\ndifferent SDHs with biological aging. This potentially leads to attenuated mediation\neffects through diffuse biological pathways. In contrast, previous studies focusing on\nisolated SDHs (e.g., race or educational mobility) may have captured more specific\naging-mediated mechanisms.\nSince non-SDH-related factors could also potentially impact lifestyle behaviors4,\nwe retained lifestyle behaviors in the primary analysis to reduce residual confounding.\nMoreover, several SDHs (e.g., psychosocial stress, social isolation and loneliness)\nmay affect biological aging directly through stress-related, metabolic, inflammatory,\nand neuroendocrine pathways, independent of lifestyle behaviors5-7.Therefore, we\nadjusted for lifestyle behaviors to keep consistency with previous studies8-11. The\nresults without adjustment for lifestyle factors were presented in the supplementary\nmaterial (please also see the table below). We have clarified this in the Methods and\nDiscussion section.\nMethods section:\n“Line 325-328: Fourth, as lifestyle behaviors may act as potential mediators in the\nassociations between specific SDHs, biological aging, and health outcomes, we\nrepeated the analyses without adjusting for lifestyle behaviors to detect the differences\nin effect estimates.”\nDiscussion section:\n“Line 544-549: Additionally, the weaker associations observed between SDHs,\nbiological aging, and health outcomes in UK Biobank may partly be explained by the\nhealthy volunteer bias where participants tend to be healthier than the general\npopulation3. In this case, participants with high levels of adverse SDHs who\nparticipated in UK Biobank are not representative of the general population with\nsimilar levels of adverse SDHs.”\nReferences\nthe United States: Analysis of DNA-Methylation and Blood-Chemistry Methods. Am J\nEpidemiol 191, 613-625 (2022). https://doi.org/10.1093/aje/kwab281\nthe Framingham Heart Study. JAMA Netw Open 7, e240655 (2024).\nhttps://doi.org/10.1001/jamanetworkopen.2024.0655\nrepresentativeness. Lancet 393, 1297 (2019). https://doi.org/10.1016/s0140-6736(18)33067-8\nmortality. Nat Med (2025). https://doi.org/10.1038/s41591-024-03483-9\nParkinson Disease. JAMA Neurol 80, 1138-1144 (2023).\nhttps://doi.org/10.1001/jamaneurol.2023.3382\nfacial aging acceleration: evidence from the UK Biobank. BMC Med 22, 359 (2024).\nhttps://doi.org/10.1186/s12916-024-03578-7\nAmong Individuals With Type 2 Diabetes Mellitus. Am J Kidney Dis 84, 557-566.e551 (2024).\nhttps://doi.org/10.1053/j.ajkd.2024.05.004\nhealth risks among individuals with type 2 diabetes: two nationwide cohort studies in the UK\nand USA. Lancet Healthy Longev 5, e542-e551 (2024). https://doi.org/10.1016/S2666-\n7568(24)00116-8\nYoung Adults: NIMHD Social Epigenomics Program. JAMA Netw Open 7, e2427889 (2024).\nhttps://doi.org/10.1001/jamaneuworkopen.2024.27889\nParkinsons Dis 10, 79 (2024). https://doi.org/10.1038/s41531-024-00700-7\nwomen: A 12-year cohort study in Australia. Alzheimers Dement 21, e70065 (2025).\nhttps://doi.org/10.1002/alz.70065\n4. Finally, the additional reporting included in the revised manuscript makes\nclear that both sets of associations in the mediation analysis (i.e. SDH-BA and\nSDH-Disease) are pretty weak. The SDH-BA associations are smaller in\nmagnitude relative to some prior reports (here between r=0.1-0.2 vs. 0.2-0.3 in\nother studies). And the SDH-disease associations also seem attenuated. I suspect\nthis is more about UKB than it is about the models the authors are using. But\nthis is worth checking. The selectivity of the UKB sample is well documented\n(e.g. Keyes & Westreich 2019 Lancet). And there is reason to believe that the\nparticipants with high levels of adverse SDH who participate in UKB are not\nrepresentative of the general population with similar levels of adverse SDH. This\npotential source of bias should be acknowledged as a limitation. There are\noptions to attempt to correct for this bias (e.g. van Alten et al. 2024 IJE). But I\nthink simply acknowledging the limitation as a potential explanation for the\nrelatively weaker associations (if they are in fact weaker after the health behavior\ncovariates are removed) is sufficient.\nResponse: We appreciate the reviewer for this insightful comment. In our study, the\ncorrelation between SDH score and biological aging shown in Figure 2 were\ncalculated without adjusting for any covariates. The correlations were lower than\nthose reported in prior studies1,2. Moreover, when we re-examined associations in the\nmultivariable models without adjusting for health behavior covariates, the\nassociations of SDH with diseases remained largely unchanged (Table S26, please\nalso see the table below). We double checked the results and confirmed that our data\nand statistical models were correct. As the reviewer mentioned, one possible\nexplanation for the relatively weaker associations observed in our study is that the UK\nBiobank is subject to healthy volunteer bias, where participants tend to be healthier\nthan the general population3. We have added this in the Discussion section by\nacknowledging the potential source of bias as a limitation.\nDiscussion section:\n“Line 544-549: Additionally, the weaker associations observed between SDHs,\nbiological aging, and health outcomes in UK Biobank may partly be explained by the\nhealthy volunteer bias where participants tend to be healthier than the general\npopulation3. In this case, participants with high levels of adverse SDHs who\nparticipated in UK Biobank are not representative of the general population with\nsimilar levels of adverse SDHs.”\nReferences\nthe United States: Analysis of DNA-Methylation and Blood-Chemistry Methods. Am J\nEpidemiol 191, 613-625 (2022). https://doi.org/10.1093/aje/kwab281\nthe Framingham Heart Study. JAMA Netw Open 7, e240655 (2024).\nhttps://doi.org/10.1001/jamanetworkopen.2024.0655\nrepresentativeness. Lancet 393, 1297 (2019). https://doi.org/10.1016/s0140-6736(18)33067-8\nPoint-by-point response to comments\nReviewer #1 (Remarks to the Author):\nThe authors have responded extensively to my comments.\nResponse: We sincerely thank the reviewer for the thoughtful third-round evaluation.\nIn response to the concern regarding the use of UK Biobank GWAS to construct the\npolygenic score for biological aging, and in line with the editor’s guidance, we have\nremoved the genetic analysis related to biological aging. We appreciate the reviewer’s\ninsight and believe that this revision has strengthened the rigor of the manuscript.\nBelow, we provide detailed responses to each of the comments.\n1. I remain unpersuaded regarding reliance on UKB GWAS to form a polygenic\nscore to include as a covariate. My view is that the genetic analysis should simply\nbe removed. But I defer to the editor's judgement as to whether to force this\nchange.\nResponse: We sincerely thank you for the thoughtful third-round review and we have\nremoved the genetic analysis related to biological aging.\n2. I appreciate the changes made to the mediation analysis and discussion\nthereof. However, the notion that mediation fractions are smaller relative to, e.g.\nGraft et al., because there are more diverse SDH pathways represented is, again,\nempirically testable within their data. My guess is that, as with the covariates,\nthis is not why the fractions are smaller. Rather, it seems like biological aging (or\nthe measurements of it they are using) are not key mediators of SDH associations\nwith health outcomes in UKB. I don't want to review this paper again. But my\nview is that the discussion should engage with the substantive conclusion implied\nby the data -- that these BioAge measures are not capturing the processes\nmediating the social determination of health in this dataset. I think that the\ncurious makeup of UKB is the obvious explanation here. And the take home\nought to be that we shouldn't try and do serious social epi in a dataset like this\none that was built for a very different purpose.\nResponse: We sincerely thank you for the insightful feedback. We agree that the\nlimitations of the UK Biobank, which was not designed for social epidemiology, are\nimportant for interpreting our findings. In the discussion, we have expanded on these\nlimitations, including healthy volunteer bias, limited socioeconomic diversity, and\npredominantly European ancestry, which likely contribute to the weaker mediation\neffects observed for accelerated biological aging. We also acknowledge, as suggested,\nthat the biological age measures used including KDM-BA and phenotypic age may\nnot fully capture the aging processes mediating SDHs and health outcomes. While\nwidely used and cost-effective, these measures do not cover all clinical biomarkers of\naging and therefore capture only specific aspects of the aging process. We have added\nthese in the “Discussion” section.\n“Line 267-273: Additionally, the weaker associations observed between SDHs,\nbiological aging, and health outcomes in UK Biobank may be attributed to healthy\nvolunteer bias , limited socioeconomic diversity, and predominantly European\nancestry within the cohort1. In this case, the mediation estimates generated are\nspecific to this population and may not be applicable to more diverse groups. The\nrelatively low mediation proportions also suggest that KDM-BA and phenotypic age\nmay not fully capture the aging processes mediating SDHs and health outcomes in\nour cohorts2,3.”\n“Line 324-325: Moreover, the cohort’s limited socioeconomic diversity and\npredominantly European ancestry further restrict the representativeness of the\nsample.”\nReferences\nrepresentativeness. Lancet 393, 1297 (2019).\nfrom two cohort studies. Lancet Reg Health West Pac 42, 100955 (2024).\nbiological ages across 20-years follow-up. Elife 9 (2020).\n3. Otherwise, this is a great paper and i look forward to seeing it in print and\nciting it.\nResponse: We sincerely appreciate the reviewer’s positive feedback on our work.\nWe are grateful for the time and effort you invested in reviewing this manuscript. We\nhave carefully considered your comments above and revised the manuscript to enhance\nits clarity, rigor, and overall quality."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Comments :\nThe authors have done a lot of work to respond to my comments. For the most part they have addressed my concerns. In a few cases, there is more that needs to be done. And, in the case of comment 2, the changes made need to be reversed because they are biasing the analysis.\nIn response to comment 1, the authors justify their reliance on UKB GWAS of the biological age variables with the argument that no alternative GWAS are available. This is not an argument. It's an excuse. And it's not a good one. The answer is simply to omit the genetic component of the analysis. (if they are persuaded of the value of the replication results in the other studies, then they can build their PGI from the replicated top hits and their weights from the replication studies)\nIn response to comment 2, the authors include the response \"To address the oversimplified hypothesis of equal contribution of each variable (unweighted method), we revised our analytic approach to construct weighted SDH scores in the main analysis, which assigned a weight to each SDH variable based on its association with KDM-BA acceleration and phenotypic age acceleration, respectively.\" I appreciate the authors efforts to address my comment. But this is a step in the wrong direction. Weighting these variables according to their association with the putative mediator (Biological Age) will induce bias. They should revert to their original approach. The added justification for the cut-points used in dichotomization is sufficient.\nIn response to comment 6, the authors offer that one reason mediation fractions might be smaller relative to previous reports is that their analysis includes more covariates. Fair enough. But this is an easy hypothesis to test. They should repeat their analysis omitting the health behavior covariates and re-evaluate the consistency of their results with the ones in the Graf papers. In my view, the health behavior covariates should not be included anyway because they are on the causal path from SHD to BA (SDH->health behavior->BA->disease)\nFinally, the additional reporting included in the revised manuscript makes clear that both sets of associations in the mediation analysis (i.e. SDH-BA and SDH-Disease) are pretty weak. The SDH-BA associations are smaller in magnitude relative to some prior reports (here between r=0.1-0.2 vs. 0.2-0.3 in other studies). And the SDH-disease associations also seem attenuated. I suspect this is more about UKB than it is about the models the authors are using. But this is worth checking. The selectivity of the UKB sample is well documented (e.g. Keyes & Westreich 2019 Lancet). And there is reason to believe that the participants with high levels of adverse SDH who participate in UKB are not representative of the general population with similar levels of adverse SDH. This potential source of bias should be acknowledged as a limitation. There are options to attempt to correct for this bias (e.g. van Alten et al. 2024 IJE). But I think simply acknowledging the limitation as a potential explanation for the relatively weaker associations (if they are in fact weaker after the health behavior covariates are removed) is sufficient.", "reviewer_3": "Thank you for the revision. I think the author already addressed most of my comments. The manuscript is much better than the previous version. I do not have further comments."}, "author_response": "Point-by-point response to comments\nReviewer #1 (Remarks to the Author):\nComments:\nThe authors have done a lot of work to respond to my comments. For the most\npart they have addressed my concerns. In a few cases, there is more that needs to\nbe done. And, in the case of comment 2, the changes made need to be reversed\nbecause they are biasing the analysis.\nResponse: We sincerely thank the reviewer for the thorough re-evaluation and\nconstructive feedback. In response to comment 2, we have reversed the prior change\nand restored the original approach to avoid bias. Below, we provide detailed responses\nto each of the comments.\n1. In response to comment 1, the authors justify their reliance on UKB GWAS of\nthe biological age variables with the argument that no alternative GWAS are\navailable. This is not an argument. It's an excuse. And it's not a good one. The\nanswer is simply to omit the genetic component of the analysis. (if they are\npersuaded of the value of the replication results in the other studies, then they\ncan build their PGI from the replicated top hits and their weights from the\nreplication studies).\nResponse: We appreciate the reviewer’s concern regarding reliance on UKB GWAS\nresults. In the initial study of UKB GWAS, the replication in the US Health\nRetirement Study (HRS) actually did not detect genome-wide significance for the\nSNPs identified from UK Biobank possibly due to the limited sample size, but the\neffect size for lead SNPs were replicated (Table S5)1. Given the lack of genome-wide\nsignificance for the SNPs in the replication analysis and unavailability of other large-\nscale GWAS on biological age, we prefer to rely on UKB GWAS in the main analysis,\nand meanwhile, we constructed polygenic risk score (PRS) using effect estimates\nfrom US HRS replication results in sensitivity analysis. This strategy reduces the risk\nof overfitting and enhances the robustness and generalizability of our findings. The\nassociations of PRS with accelerated biological aging, as well as the joint effects of\ncombined SDHs and genetic susceptibility, were consistent with those in the main\nanalysis. Please see the table and figure below. In the revised paper, we have added\nthis in the “Methods” and “Results” sections.\nMethods section:\n“Line 223-225: To mitigate potential overfitting, we also constructed PRS using\nweights from HRS replication results. Detailed information on the SNPs used for\nconstructing PRS of KDM-BA and phenotypic age is provided in Table S5.”\nResults section:\n“Line 406-408: Sensitivity analyses using PRS constructed with weights from HRS\nproduced consistent findings (Table S21 and Figure S6), with no significant\ninteractions observed for either KDM-BA (P = 0.363) or phenotypic age acceleration\n(P = 0.124).”\nReference\nbiological age measures point to distinct aging phenotypes. Aging Cell 20, e13376 (2021).\nhttps://doi.org/10.1111/acel.13376\n2. In response to comment 2, the authors include the response \"To address the\noversimplified hypothesis of equal contribution of each variable (unweighted\nmethod), we revised our analytic approach to construct weighted SDH scores in\nthe main analysis, which assigned a weight to each SDH variable based on its\nassociation with KDM-BA acceleration and phenotypic age acceleration,\nrespectively.\" I appreciate the authors efforts to address my comment. But this is\na step in the wrong direction. Weighting these variables according to their\nassociation with the putative mediator (Biological Age) will induce bias. They\nshould revert to their original approach. The added justification for the cut-\npoints used in dichotomization is sufficient.\nResponse: We sincerely thank the reviewer for this valuable comment. In line with\nyour suggestion, we have reverted to using the unweighted SDH score as the primary\nanalysis. The results based on the weighted SDH score, which were presented in\nRound 1 revision, have been moved to the Supplementary Information for reference.\nWe believe this revision strengthens the rigor of our analytic approach while ensuring\ntransparency. The description of how unweighted and weighted SDH scores were\nbuilt has been clarified in Methods section, and the cut-off points used for\ndichotomizing each SDH variable have also been clearly justified in the manuscript.\nMethods section:\n“Line 151-163: We constructed an unweighted score by assigning 0 points for the\nadvantaged level of each SDH and 1 point for the disadvantaged level. As a sensitivity\nanalysis, we also constructed weighted SDH scores to account for varying magnitude\nof the associations (absolute β coefficients) between individual SDHs and biological\naging acceleration (Table S3). In UK Biobank, weighted SDHs score = (β × SDH\n1 1\n+ … + β × SDH ) × (18/sum of β coefficients). In US NHANES, weighted SDHs\n18 18\nscore = (β × SDH + … + β × SDH ) × (10/sum of β coefficients).”\n1 1 10 10\n3. In response to comment 6, the authors offer that one reason mediation\nfractions might be smaller relative to previous reports is that their analysis\nincludes more covariates. Fair enough. But this is an easy hypothesis to test. They\nshould repeat their analysis omitting the health behavior covariates and re-\nevaluate the consistency of their results with the ones in the Graf papers. In my\nview, the health behavior covariates should not be included anyway because they\nare on the causal path from SHD to BA (SDH->health behavior->BA->disease)\nResponse: Thanks a lot for the reviewer’s constructive suggestion. Following your\nadvice, we repeated the mediation analysis without adjusting for lifestyle behavior\ncovariates. The results were largely unchanged (Table S31), with mediation fractions\nremaining lower than those reported by Graf et al1,2. We therefore removed this\nexplanation from the Discussion section, as also implicated by the reviewer in the\ncomment 4 that the weaker associations could be mainly attributed to healthy\nvolunteer bias in the UK Biobank3. In addition, the observed discrepancies may be\nrelated to the differences in SDH measurements. In our study, the composite SDH\nscore that integrated five distinct domains reflects the complex interconnection of\ndifferent SDHs with biological aging. This potentially leads to attenuated mediation\neffects through diffuse biological pathways. In contrast, previous studies focusing on\nisolated SDHs (e.g., race or educational mobility) may have captured more specific\naging-mediated mechanisms.\nSince non-SDH-related factors could also potentially impact lifestyle behaviors4,\nwe retained lifestyle behaviors in the primary analysis to reduce residual confounding.\nMoreover, several SDHs (e.g., psychosocial stress, social isolation and loneliness)\nmay affect biological aging directly through stress-related, metabolic, inflammatory,\nand neuroendocrine pathways, independent of lifestyle behaviors5-7.Therefore, we\nadjusted for lifestyle behaviors to keep consistency with previous studies8-11. The\nresults without adjustment for lifestyle factors were presented in the supplementary\nmaterial (please also see the table below). We have clarified this in the Methods and\nDiscussion section.\nMethods section:\n“Line 325-328: Fourth, as lifestyle behaviors may act as potential mediators in the\nassociations between specific SDHs, biological aging, and health outcomes, we\nrepeated the analyses without adjusting for lifestyle behaviors to detect the differences\nin effect estimates.”\nDiscussion section:\n“Line 544-549: Additionally, the weaker associations observed between SDHs,\nbiological aging, and health outcomes in UK Biobank may partly be explained by the\nhealthy volunteer bias where participants tend to be healthier than the general\npopulation3. In this case, participants with high levels of adverse SDHs who\nparticipated in UK Biobank are not representative of the general population with\nsimilar levels of adverse SDHs.”\nReferences\nthe United States: Analysis of DNA-Methylation and Blood-Chemistry Methods. Am J\nEpidemiol 191, 613-625 (2022). https://doi.org/10.1093/aje/kwab281\nthe Framingham Heart Study. JAMA Netw Open 7, e240655 (2024).\nhttps://doi.org/10.1001/jamaneworkopen.2024.0655\nrepresentativeness. Lancet 393, 1297 (2019). https://doi.org/10.1016/s0140-6736(18)33067-8\nmortality. Nat Med (2025). https://doi.org/10.1038/s41591-024-03483-9\nParkinson Disease. JAMA Neurol 80, 1138-1144 (2023).\nhttps://doi.org/10.1001/jamaneurol.2023.3382\nfacial aging acceleration: evidence from the UK Biobank. BMC Med 22, 359 (2024).\nhttps://doi.org/10.1186/s12916-024-03578-7\nAmong Individuals With Type 2 Diabetes Mellitus. Am J Kidney Dis 84, 557-566.e551 (2024).\nhttps://doi.org/10.1053/j.ajkd.2024.05.004\nhealth risks among individuals with type 2 diabetes: two nationwide cohort studies in the UK\nand USA. Lancet Healthy Longev 5, e542-e551 (2024). https://doi.org/10.1016/S2666-\n7568(24)00116-8\nYoung Adults: NIMHD Social Epigenomics Program. JAMA Netw Open 7, e2427889 (2024).\nhttps://doi.org/10.1001/jamaneuworkopen.2024.27889\nParkinsons Dis 10, 79 (2024). https://doi.org/10.1038/s41531-024-00700-7\nwomen: A 12-year cohort study in Australia. Alzheimers Dement 21, e70065 (2025).\nhttps://doi.org/10.1002/alz.70065\n4. Finally, the additional reporting included in the revised manuscript makes\nclear that both sets of associations in the mediation analysis (i.e. SDH-BA and\nSDH-Disease) are pretty weak. The SDH-BA associations are smaller in\nmagnitude relative to some prior reports (here between r=0.1-0.2 vs. 0.2-0.3 in\nother studies). And the SDH-disease associations also seem attenuated. I suspect\nthis is more about UKB than it is about the models the authors are using. But\nthis is worth checking. The selectivity of the UKB sample is well documented\n(e.g. Keyes & Westreich 2019 Lancet). And there is reason to believe that the\nparticipants with high levels of adverse SDH who participate in UKB are not\nrepresentative of the general population with similar levels of adverse SDH. This\npotential source of bias should be acknowledged as a limitation. There are\noptions to attempt to correct for this bias (e.g. van Alten et al. 2024 IJE). But I\nthink simply acknowledging the limitation as a potential explanation for the\nrelatively weaker associations (if they are in fact weaker after the health behavior\ncovariates are removed) is sufficient.\nResponse: We appreciate the reviewer for this insightful comment. In our study, the\ncorrelation between SDH score and biological aging shown in Figure 2 were\ncalculated without adjusting for any covariates. The correlations were lower than\nthose reported in prior studies1,2. Moreover, when we re-examined associations in the\nmultivariable models without adjusting for health behavior covariates, the\nassociations of SDH with diseases remained largely unchanged (Table S26, please\nalso see the table below). We double checked the results and confirmed that our data\nand statistical models were correct. As the reviewer mentioned, one possible\nexplanation for the relatively weaker associations observed in our study is that the UK\nBiobank is subject to healthy volunteer bias, where participants tend to be healthier\nthan the general population3. We have added this in the Discussion section by\nacknowledging the potential source of bias as a limitation.\nDiscussion section:\n“Line 544-549: Additionally, the weaker associations observed between SDHs,\nbiological aging, and health outcomes in UK Biobank may partly be explained by the\nhealthy volunteer bias where participants tend to be healthier than the general\npopulation3. In this case, participants with high levels of adverse SDHs who\nparticipated in UK Biobank are not representative of the general population with\nsimilar levels of adverse SDHs.”\nReferences\nthe United States: Analysis of DNA-Methylation and Blood-Chemistry Methods. Am J\nEpidemiol 191, 613-625 (2022). https://doi.org/10.1093/aje/kwab281\nthe Framingham Heart Study. JAMA Netw Open 7, e240655 (2024).\nhttps://doi.org/10.1001/jamaneuworkopen.2024.0655\nrepresentativeness. Lancet 393, 1297 (2019). https://doi.org/10.1016/s0140-6736(18)33067-8"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have responded extensively to my comments.\nI remain unpersuaded regarding reliance on UKB GWAS to form a polygenic score to include as a covariate. My view is that the genetic analysis should simply be removed. But I defer to the editor's judgement as to whether to force this change.\nI appreciate the changes made to the mediation analysis and discussion thereof. However, the notion that mediation fractions are smaller relative to, e.g. Graft et al., because there are more diverse SDH pathways represented is, again, empirically testable within their data. My guess is that, as with the covariates, this is not why the fractions are smaller. Rather, it seems like biological aging (or the measurements of it they are using) are not key mediators of SDH associations with health outcomes in UKB. I don't want to review this paper again. But my view is that the discussion should engage with the substantive conclusion implied by the data -- that these BioAge measures are not capturing the processes mediating the social determination of health in this dataset. I think that the curious makeup of UKB is the obvious explanation here. And the take home ought to be that we shouldn't try and do serious social epi in a dataset like this one that was built for a very different purpose.\nOtherwise, this is a great paper and i look forward to seeing it in print and citing it."}, "author_response": "Point-by-point response to comments\nReviewer #1 (Remarks to the Author):\nThe authors have responded extensively to my comments.\nResponse: We sincerely thank the reviewer for the thoughtful third-round evaluation.\nIn response to the concern regarding the use of UK Biobank GWAS to construct the\npolygenic score for biological aging, and in line with the editor’s guidance, we have\nremoved the genetic analysis related to biological aging. We appreciate the reviewer’s\ninsight and believe that this revision has strengthened the rigor of the manuscript.\nBelow, we provide detailed responses to each of the comments.\n1. I remain unpersuaded regarding reliance on UKB GWAS to form a polygenic\nscore to include as a covariate. My view is that the genetic analysis should simply\nbe removed. But I defer to the editor's judgement as to whether to force this\nchange.\nResponse: We sincerely thank you for the thoughtful third-round review and we have\nremoved the genetic analysis related to biological aging.\n2. I appreciate the changes made to the mediation analysis and discussion\nthereof. However, the notion that mediation fractions are smaller relative to, e.g.\nGraft et al., because there are more diverse SDH pathways represented is, again,\nempirically testable within their data. My guess is that, as with the covariates,\nthis is not why the fractions are smaller. Rather, it seems like biological aging (or\nthe measurements of it they are using) are not key mediators of SDH associations\nwith health outcomes in UKB. I don't want to review this paper again. But my\nview is that the discussion should engage with the substantive conclusion implied\nby the data -- that these BioAge measures are not capturing the processes\nmediating the social determination of health in this dataset. I think that the\ncurious makeup of UKB is the obvious explanation here. And the take home\nought to be that we shouldn't try and do serious social epi in a dataset like this\none that was built for a very different purpose.\nResponse: We sincerely thank you for the insightful feedback. We agree that the\nlimitations of the UK Biobank, which was not designed for social epidemiology, are\nimportant for interpreting our findings. In the discussion, we have expanded on these\nlimitations, including healthy volunteer bias, limited socioeconomic diversity, and\npredominantly European ancestry, which likely contribute to the weaker mediation\neffects observed for accelerated biological aging. We also acknowledge, as suggested,\nthat the biological age measures used including KDM-BA and phenotypic age may\nnot fully capture the aging processes mediating SDHs and health outcomes. While\nwidely used and cost-effective, these measures do not cover all clinical biomarkers of\naging and therefore capture only specific aspects of the aging process. We have added\nthese in the “Discussion” section.\n“Line 267-273: Additionally, the weaker associations observed between SDHs,\nbiological aging, and health outcomes in UK Biobank may be attributed to healthy\nvolunteer bias , limited socioeconomic diversity, and predominantly European\nancestry within the cohort1. In this case, the mediation estimates generated are\nspecific to this population and may not be applicable to more diverse groups. The\nrelatively low mediation proportions also suggest that KDM-BA and phenotypic age\nmay not fully capture the aging processes mediating SDHs and health outcomes in\nour cohorts2,3.”\n“Line 324-325: Moreover, the cohort’s limited socioeconomic diversity and\npredominantly European ancestry further restrict the representativeness of the\nsample.”\nReferences\nrepresentativeness. Lancet 393, 1297 (2019).\nfrom two cohort studies. Lancet Reg Health West Pac 42, 100955 (2024).\nbiological ages across 20-years follow-up. Elife 9 (2020).\n3. Otherwise, this is a great paper and i look forward to seeing it in print and\nciting it.\nResponse: We sincerely appreciate the reviewer’s positive feedback on our work.\nWe are grateful for the time and effort you invested in reviewing this manuscript. We\nhave carefully considered your comments above and revised the manuscript to enhance\nits clarity, rigor, and overall quality."}]

Decision letter