Proteomic signatures of smoking and their associations with risk of incident diseases and mortality in diverse populations

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

Abstract
Smoking is the most important behavioural determinant of morbidity and mortality. Using machine learning on plasma levels of 2,917 proteins in the UK Biobank (n=43,914), we develop a proteomic Smoking Index (pSIN) comprising 51 proteins that accurately distinguish current from never smokers (AUC=0.95; 95% CI 0.94–0.95). Validation in the China Kadoorie Biobank (n=3,977) shows similar accuracy (AUC=0.91; 95% CI 0.89–0.92). pSIN is significantly associated with the risk of all-cause mortality and 18 major chronic diseases, including cardiovascular, renal, pulmonary, neurodegenerative, and cancer outcomes. Among current and former smokers, pSIN predicts death and 11 diseases independently of self-reported smoking history and lifestyle factors. Genome-wide analysis identifies 125 genes (e.g., ALPP, CST5, IL12B) associated with pSIN, while exposome analysis highlights maternal smoking, diet, physical activity, and air pollution as key modifiers. Notably, pSIN tracks recovery among former smokers and identifies those whose disease risks remain comparable to current smokers. These findings demonstrate that plasma proteomics effectively capture the biological imprint of smoking and predict smoking-related morbidity and mortality, offering a more nuanced, molecularly grounded assessment of individual variation in biological response to smoking.

Introduction
Seventy years after the British Doctors Study first demonstrated an increased risk of death from lung cancer, myocardial infarction and chronic obstructive pulmonary disease (COPD) among smokers, smoking still accounts for about 14% of total deaths and 8% of disability-adjusted life years globally. Indeed, smoking is associated with higher risks of numerous diseases across different organ systems, including most cancers, respiratory diseases, cardiovascular diseases, and diseases of the liver, brain, kidney, and bladder, among others. While smoking is often initiated in youth, smoking-related diseases typically manifest in middle age or later. Throughout the life course, smoking behaviour, including the type and quantity of tobacco consumed, may fluctuate, and many individuals attempt to quit smoking multiple times without success due to its addictive nature. Additionally, as smoking is becoming socially unacceptable in an increasing number of societies, the validity of smoking history is further compromised. Consequently, there is an urgent need to develop objective measures that assess smoking history and possible differences in smoking behaviour dynamics, including the recovery status of previous smokers. Such biomarkers will provide more accurate measurements of smoking exposure and will help to identify molecular mechanisms linking smoking with disease risks.

Exhaled carbon monoxide (CO) and plasma cotinine levels are widely used to validate smoking status, but both capture smoking status within the last 24 h, with limited ability to characterise long-term smoking habits and health effects and to reveal mechanisms of action associated with smoking. There have been major advances in our understanding of the epigenomic signatures of cigarette smoking during the last decade, particularly using smoking-related DNA methylation. A meta-analysis of 16 epigenome-wide studies demonstrated smoking-induced DNA methylation of 2623 CpGs annotated to 1405 genes among current smokers. Of these, 185 CpGs showed persistent alterations in previous smokers, and 36 CpGs showed persistent alterations for up to 30 years after smoking cessation. While DNA methylation markers reflect smoking history in smokers and previous smokers, DNA methylation-based smoking profiles have only been associated with the risks of a limited number of conditions, including COPD, lung cancer, stroke and all-cause mortality, independent of smoking history.

Circulating proteins, however, may reflect not only smoking exposure as captured by DNA methylation but also the cumulative biological effects of responses to cigarette smoking (including oxidative stress or other mechanisms) or the biological responses to limit the hazards of smoking. Thus, plasma proteomic profiles may enhance the ability to quantify the direct effects and the physiological responses to smoking and ultimately to predict the risk of subsequent morbidity and mortality among current and previous smokers.

In this work, to address these limitations in the field, we develop a proteomic-based smoking profile in the UK Biobank (UKB) (n=43,914) using machine learning with high accuracy Area Under the Curve (AUC=0.95; CI: 0.94–0.95). We further externally validate the accuracy of our proteomic-based smoking score in participants from the China Kadoorie Biobank (CKB) (n=3977, AUC=0.91; CI: 0.89–0.92). We then explore the genetic and exposome factors influencing the smoking proteomic score. Finally, we study the associations between our proteomic-based smoking score and clinical risk factors, blood-based biomarkers, risks of 27 major diseases and mortality in the UKB. These findings demonstrate that plasma proteomics can be used to effectively capture smoking patterns and risk of smoking-related morbidity and mortality, and that proteomics-based estimation of smoking history provides more nuanced information about differences between individuals in the dynamics of biological response to smoking.

Results
To model the relationships between the plasma proteome and cigarette smoking, we used a subset of the UKB with available measurements of the plasma proteome. The study population consisted of 43,914 participants, with 4732 self-reported as current regular cigarette smokers, and 23,778 as never smokers. Baseline characteristics of study participants in the UKB are provided in Supplementary Data 1. We observed significant age (p=3.44×10−26) and sex (p=1.64×10−71) differences between current and never smokers. Therefore, we regressed out age and sex from the expression of each protein to eliminate their effects for downstream analyses. For external validation, we used a subset of the CKB, in which the plasma proteome was measured using an identical OLINK assay panel (Supplementary Data 1 for baseline characteristics).

In a training dataset comprising 70% of UKB participants, we developed a gradient boosting tree model using plasma protein levels as features to discriminate current smokers from never smokers (Fig. 1). The model achieved a high area under receiver operating characteristic (ROC) curve (AUC) after 5-fold cross-validation (CV) within the training dataset (mean AUC=0.96, SD=0.004; F1=0.98, SD=0.006; Averaged precision (AP)=0.93, SD=0.006; Balanced accuracy (BA)=0.90, SD=0.006) (Supplementary Fig. 1a) and showed similar performance in the 30% UKB holdout test dataset (AUC=0.95, SD=0.004, F1=0.98, SD=0.004; AP=0.91, SD=0.006; BA=0.88, SD=0.006). After using the Boruta algorithm to identify all proteins that contribute to predicting smoking status, we found that 51 of 2917 proteins discriminated current smokers and never smokers (98.3% reduction in protein size) with a mean AUC of 0.95 in 5-fold CV in the training dataset (SD=0.005) (F1=0.97, SD=0.005; AP=0.93, SD=0.004, BA=0.90, SD=0.004) (Supplementary Fig. 1b and Supplementary Data 3) and an AUC of 0.95 (SD=0.004) in the test dataset (F1=0.97, SD=1.11×10−16; AP=0.91, SD=3.33×10−16; BA=0.89, SD=1.11×10−16) (Fig. 2a). Due to the imbalance of the participant numbers between current smokers and never smokers (1:5), we also calculated the scoring matrix after undersampling the test dataset. When case to control ratio is 1:1, the AUC stays the same at 0.95 (SD=2.22×10−16) (F1=0.99, SD=1.11×10−16; AP=0.97, SD=2.22×10−16; BA=0.89, SD=3.33×10−16). This model yielded a sensitivity of 84.6% and a specificity of 95%. We also compared the performance of the model in males and females in the UKB test dataset. The result showed slightly higher performance in females compared to males, with an AUC of 0.96 (SD=1.11×10−16) and 0.94 (SD=2.06×10−296), respectively. To assess the added value of pSIN compared to using a single biomarker, we evaluated the predictive performance of the top three proteins ranked by SHAP value within the UKB test dataset (Supplementary Fig. 1c). ALPP and CXCL17 demonstrated relatively strong discrimination with AUCs of 0.88 and 0.87, respectively, while ACVRL1 showed lower performance with an AUC of 0.76. However, none of these individual proteins matched the predictive power of pSIN, which achieved an AUC of 0.95. These results highlight the advantage of using a multi-protein composite score over a single biomarker for more accurate stratification of current smokers and never smokers. The UKB model derived from the training set was subsequently externally validated in CKB, achieving an AUC of 0.91 (SD=2.22×10−16; Fig. 2a) (F1=0.93, SD=1.11×10−16; AP=0.87, SD=1.11×10−16; BA=0.79, SD=2.22×10−16), and a sensitivity of 70.8% and specificity of 95%. After undersampling the majority class, the balanced testing dataset achieved a similar AUC of 0.91 (SD=4.44×10−16) (F1=0.97, SD=4.44×10−16; AP=0.93, SD=1.11×10−16; BA=0.80, SD=1.11×10−16). We further compared the performance of the trained model between males and females in CKB. The model showed a slightly higher AUC for males (AUC=0.90, SD=2.06×10−296; F1=0.98, SD=4.44×10−16; AP=0.91, SD=2.06×10−296; AP=0.77, SD=2.22×10−16) comparing to females (AUC=0.89, SD=2.06×10−296; F1=0.98, SD=2.22×10−16; AP=0.90, SD=1.11×10−16; AP=0.74, SD=2.22×10−16). We next calculated a predicted score for the overall UKB population, including previous smokers defined as a Proteomic Smoking INdex (pSIN) (Fig. 2c). Although the curves largely overlapped, the mean pSIN was significantly higher in previous smokers (mean=2.8; SD=1.8) than in never smokers (mean=−3.7; SD=1.4, p<2.2×10−308).

Overall, the proteins selected were predominantly involved in biological processes including epithelial cell proliferation (IL12B, IGFBP4, TNFSF12, ACVRL1, MMP12, PRL, KIT), regulation of immune system (IL12B, MMP12, SCGB1A1, MERTK, PDCD1, IL7R), cell growth (IGFBP4, TNR, ACVRL1, SCGB3A1, ISLR2), and T cell activation (IL12B, CD1C, SCGB1A1, KIT, IL7R). Tissue enrichment analysis was performed among selected proteins using RNA expression data from the Genotype-Tissue Expression (GTEx) project. Results indicated that many of these proteins were differentially expressed in tissues either directly exposed to or affected by smoking, including lung, salivary glands, colon, oesophagus and adipose tissues (Supplementary Fig. 2a, b). The top proteins contributing to the model prediction (Fig. 2b) included ALPP, CXCL17 and ACVRL1, all of which showed higher levels in smokers. All three are predominantly expressed in the lungs, oesophagus and minor salivary glands. ALPP is synthesised in the liver by a metalloenzyme that catalyses the hydrolysis of phosphoric acid monoesters and was previously found to be associated with COPD and cancers. CXCL17 is a mucosal chemokine that attracts immature dendritic cells and blood monocytes to the lungs and was previously associated with lung cancer. ACVRL1 is an activin receptor-like kinase that has recently been shown to separately mediate transcytosis of low-density lipoprotein into arterial endothelium. Individuals with loss-of-function ACVRL1 DNA variants have lower rates of progression of atherosclerotic vascular diseases.

Determinants and correlates of pSIN
pSIN is predominantly determined by smoking history, as the number of years smoked, cigarettes smoked per day, and pack years that were smoked were each positively correlated with pSIN scores in both current and previous smokers (Supplementary Data 4). Among previous smokers, a longer duration since cessation was strongly associated with lower pSIN scores. Analysis of the data in 5-year intervals indicated a continuous decline in pSIN scores up to and exceeding 30 years of smoking cessation, suggesting ongoing recovery from regular smoking-related effects (Supplementary Data 4). In individuals who had never smoked, passive exposure to smoking was correlated with higher pSIN scores, demonstrating its high sensitivity. Similar trends were observed in CKB current smokers, where smoking duration, smoking exposure, and smoking intensity were all significantly and positively associated with pSIN. However, among former smokers, significant associations were observed only for years since smoking cessation and smoking duration, whereas associations with smoking intensity and smoking exposure were not statistically significant. This may be because there are more modifiers related to the difference in behavioural epidemiological characteristics between the Chinese and UK populations, such as relapse after quitting smoking in previous smokers than in current smokers. (Supplementary Data 5).

To understand the genetic architecture underlying the biological consequences of smoking pathology, as indexed by pSIN, we performed a genome-wide association study (GWAS) of pSIN in the UKB. We applied a linear mixed model in SAIGE, adjusting for age, sex, genotyping batch effects, and the first 40 principal components to account for population structure. We identified 95 lead-independent genome-wide significant variants mapped to 129 genes, of which 8 (ALPP, CST5, IL12B, ACVRL1, IL7R, SCGB1A1, NCAM1 and ICAM5) encoded one of the selected proteins in the pSIN score. Of the 95 significant lead-independent variants, 34 were cis-pQTLs mapping to 16 genes, 8 of which encoded proteins included in the pSIN model. Six cis-pQTLs were annotated for multiple closely located genes simultaneously (e.g. rs901886 and rs35929247 were cis-pQTLs for ICAM1, ICAM3, ICAM4 and ICAM5). Additionally, 7 variants were identified as trans-pQTLs mapping to 366 genes, 21 of which encoded proteins selected in the pSIN model. Among these, rs2519093 showed the broadest mapping, acting as a trans-pQTL for 358 genes and a cis-pQTL for two genes (ABO and DBH). Of the 129 genes associated with pSIN, 10 genes were previously identified as GWAS smoking loci, and 54 genes were previously found in epigenetic studies, 75 (58%) were novel and have previously been reported to be associated with body mass index (BMI), diabetes, cancer development, and immunological/haematological traits including lymphocyte counts, eosinophil counts, and white blood cell counts (Supplementary Fig. 3 and Supplementary Data 6–9).

Importantly, haematological measurements also showed strong associations with pSIN in the UKB before and after adjusting for smoking status (Supplementary Fig. 4A, B). Genetic correlations using LD score regression (LDSC) analysis highlighted strong genetic correlations of pSIN with current smoking (r=0.78, p=1.08×10−98), never smoking (r=−0.65, p=1.05×10−59), maternal smoking around birth (r=0.66, p=2.08×10−42), cannabis use (r=0.51, p=1.50×10−27), smoking-related lung disorders (lung cancer (r=0.71, p=1.14×10−06), COPD (r=0.54, p=1.04×10−31), depression (r=0.28, p=9.46×10−16), ADHD (r=0.47, p=8.26×10−16), multi-site chronic pain (r=0.31, p=7.31×10−16), obesity and fat distribution (BMI r=0.33, p=1.832×10−22), % of leg body fat (r=0.33, p=1.05×10−23) and diabetes (r=0.27, p=4.08×10−10) among others (Supplementary Data 10).

We then studied the relation of pSIN with all available environmental exposures in the UKB (i.e. the exposome). The exposome-wide analysis was conducted using generalised linear models, where each factor was tested separately for its association with pSIN. Models were adjusted for recruitment centre, ethnicity and smoking status. Favourable socio-economic indicators and healthier lifestyle choices, including better housing conditions, lower Townsend deprivation index, higher household income, higher levels of education, higher consumption of fruit and fibre intake, higher levels of physical activity and social interactions were associated with lower levels of pSIN (Supplementary Data 11). Conversely, unhealthy lifestyle choices, poor environment and mood disorders including high salt intake, high consumption of red/processed meat and coffee, alcohol consumption, exposure to air pollutants (PM10, PM2.5, NO2 and NO), maternal smoking during pregnancy, evening chronotype, sleeping greater than 9 h or less than 7 h or feeling of tiredness or low mood were associated with higher pSIN scores. These findings suggest that beyond smoking behaviour itself, a range of socio-economic, dietary, environmental and behavioural factors each contributed independently to smoking-related damage as reflected by effects on pSIN.

We next explored how different clinical biomarkers and risk factors that were read-outs of the exposome (e.g. obesity as a read-out of diet and physical activity) and those indicative of overall health status at baseline (e.g. lipid levels, creatinine and blood pressure) influenced the pSIN. Firstly, blood biochemistry profiles revealed significant correlations with pSIN. These included markers of inflammation, such as glycA (glycoprotein acetyl, antichymotrypsin) and C-reactive protein, indicators of glucose metabolism such as higher HbA1c levels and biomarkers related to kidney and liver function. In addition, significant associations were observed with sex hormones, lower vitamin D levels and ageing-related biomarkers like telomere length and insulin-like growth factor 1 (Supplementary Fig. 5a and Supplementary Data 12). Additional analyses of decline in clinical function indicated that self-rated health status, older facial ageing, but also clinically assessed ones, including systolic blood pressure, heel bone density, fluid intelligence and lung function, were positively correlated with pSIN (Supplementary Fig. 5b and Supplementary Data 12). Moreover, baseline disease, including type 2 diabetes and arterial stiffness, was strongly correlated with higher pSIN scores (Supplementary Data 12). The findings of this study highlight the substantial and diverse impact of smoking on physiological systems, as reflected by the associations of pSIN with a wide array of clinical biomarkers and health indicators at baseline. We conducted additional sensitivity analyses where the associations between pSIN and clinical biomarkers were further adjusted for smoking status (Supplementary Fig. 6a, b). We observed that while the effect sizes of biomarkers such as HbA1c, GlycA and Triglycerides were attenuated, they remained statistically significant. However, biomarkers such as APOA, HDL cholesterol and Creatinine became non-significant after this adjustment. Similarly, the association between pSIN and clinical risk factors, such as poor self-rated health, was weakened when adjusting for smoking status, while the associations with other risk factors were maintained at a similar level.

To explore the relative contributions of the genome, smoking history and exposome to pSIN, we used a gradient boosting model. We first regressed GWAS significant SNPs against pSIN and calculated the variance explained (Fig. 2d). In our analysis, genetic factors explained a larger proportion of variance in pSIN never smokers (2.7%) and previous smokers (1.6%) compared with current smokers (0.3%). However, overall, genetic factors had a minimal contribution to pSIN. We next estimated the contribution of the exposome, starting with the contribution of smoking history, then added sociodemographic and lifestyle factors and finally added clinical biomarkers and risk factors to the models, calculating the additional proportion of variance explained by each factor (Fig. 2d). Smoking history accounted for the largest proportion of pSIN variance in both the overall population (65.8%), current (52.1%) and previous smokers (16.4%); passive smoking explained 0.2% of the variance in never smokers. Sociodemographic and lifestyle factors contributed most of the variance in never smokers (4.8%), with factors such as air pollution, diet and alcohol consumption making a substantial contribution to levels of pSIN in this subgroup. Lastly, clinical biomarkers and general health indicators provided a comparable amount of additional information across current (2.0%), previous (1.9%) and never (2.0%) smokers.

We further conducted an analysis using multivariate linear models with ANOVA to assess the partial R2 explained by each category when all variables were included in a single model. In the overall UKB population, smoking history accounted for the largest proportion of variance (partial R2=42.5%), followed by clinical biomarkers (5.3%). In contrast, prevalent disease status and polygenic risk scores explained only 0.1 and 0.02% of the variance, respectively. Notably, technical factors—including fasting time, assessment centre and assay batch—contributed just 0.3% of the variance, underscoring the robustness of the model (Supplementary Fig. 7). When stratified by smoking status, clinical biomarkers explained the largest proportion of variance in current smokers (9.9%), followed by smoking history variables other than smoking status (7.8%) and lifestyle factors (3.7%). Clinical biomarkers also remained important contributors to pSIN in previous smokers (4.1%) and never smokers (4.2%). Among previous smokers, smoking history explained the greatest variance (6.9%), whereas in never smokers, it accounted for only 0.03% (passive smoking). Finally, technical factors contributed modestly to pSIN variance in current (1.4%), previous (0.4%) and never smokers (0.3%) (Supplementary Fig. 7).

These findings underscore the multifactorial nature of pSIN; it is predominantly determined by smoking history in the population overall and in current and previous smokers, but also the genome and exposome are related to pSIN, even in non-smokers.

Associations of pSIN with risk of morbidity and mortality
Over a mean follow-up time of 13.3 years (SD=2.2) in the UKB, 4615 deaths were observed. We tested the association of pSIN with 27 major disease outcomes and all-cause mortality. In the overall UKB population, pSIN was significantly associated (FDR<0.05) with 19/28 outcomes independent of the confounding factors. These included lung cancer (HR=1.97, CI: 1.83, 2.11), COPD (1.72, 1.67, 1.78) and head and neck cancer (1.64, 1.44, 1.86), ranking as the top 3 based on their hazard ratios per SD increase in pSIN. pSIN was able to differentiate subtypes of inflammatory bowel disease, with pSIN associating with higher risks in Crohn’s disease (p=2.34×10−2) but not ulcerative colitis (p=5.99×10−2), concordant with previous literature. Interestingly, pSIN displayed a protective effect for Parkinson’s disease (0.84, 0.75, 0.94) (Supplementary Fig. 8 and Supplementary Data 13), one of the few common disorders with a lower risk in smokers. pSIN was also significantly associated with mortality (1.32, 1.28, 1.36). Associations of individual proteins with incident health outcomes are shown in Supplementary Fig. 9. We further validated the association with lung cancer, COPD, any vascular disease, any respiratory disease, ischemic stroke, all stroke, ischemic heart disease and mortality in the CKB, all of which stayed significant after adjusting for confounding factors (Supplementary Data 14).

For mortality and the 18 diseases that were significantly associated with pSIN, we further tested if participants in the top, median and bottom quartiles of the pSIN exhibited divergent cumulative incidence in each outcome (Fig. 3). We found that 11 diseases showed more than 2-fold higher cumulative incidence when comparing the top and bottom quartiles of pSIN at age 75 years including lung cancer, peripheral artery disease, COPD, head and neck cancer, esophageal cancer, congestive cardiac failure, all-cause mortality, chronic liver disease, ischemic stroke, all stroke and bladder cancer. The cumulative incidence and number of risks in each age group are shown in Supplementary Data 15 and 16.

To evaluate the additional information provided by the pSIN compared to self-reported smoking habits, we analysed their associations with current smokers and previous smokers. In current smokers, pSIN was significantly associated with 6/15 major smoking-related health outcomes (with a more than 80 incident cases cutoff), including lung cancer, peripheral artery disease, COPD, all-cause mortality, osteoporosis and ischemic heart disease after adjusting for lifestyle factors and smoking pack-years (Fig. 4a, Supplementary Data 17). Further, participants in the highest quartile of pSIN had greater than 2-fold higher cumulative incidence at age 75 years compared to those in the lowest quartile for all evaluated outcomes, except for ischemic heart disease, which showed a 1.59-fold higher risk (Supplementary Fig. 10a). Cumulative incidence and number at risk at each age are provided in Supplementary Data 18 and 19.

Among previous smokers, pSIN was significantly associated with higher risks of 12 disease outcomes, including COPD, lung cancer, peripheral artery disease and mortality, among others, after adjusting for lifestyle factors, smoking pack-years and number of years since cessation (Fig. 4b and Supplementary Data 20). Participants in the highest quartile of pSIN had greater than 2-fold higher cumulative incidence at age 75 years compared to those in the lowest quartile in 2 of the morbidities, including lung cancer (3.43-fold higher risk) and COPD (2.06-fold higher risk). The cumulative incidence associated with pSIN for each disease is shown in Supplementary Fig. 10b and Supplementary Data 21, 22.

Use of pSIN to differentiate the recovery status of previous smokers
Analyses of changes in mean pSIN levels by years since smoking cessation among previous smokers indicated that it took only 2 years for the pSIN to decline to a threshold that differentiated current from never smokers at a false positive rate (FPR) of 0.05 (Supplementary Fig. 11). However, the variation of pSIN in each individual with the same cessation years is large and for a substantial number of people, the pSIN remains high despite quitting smoking. Indeed, at least 10 years were needed for more than 80% of the previous smokers to have a pSIN below the threshold, consistent with findings in the British Doctors study (Fig. 5a). Additionally, the mean levels of pSIN of previous smokers plateaued, approaching the levels seen in never smokers after approximately 25 years after cessation of smoking (Supplementary Fig. 11). Using this cut-off, we identified two clinically relevant subgroups among previous smokers: (1) those whose proteomic profile was similar to that of the current smokers and (2) those whose proteomic profiles were similar to the never smokers—henceforth referred to as the ‘recovered’ group (Fig. 5b; n=2576/15,404).

To determine whether this differentiation between these two groups of previous smokers was clinically relevant, we evaluated the disease and mortality risk differences between the two groups, adjusting for lifestyle factors, smoking pack-years and smoking cessation years. Within the group of previous smokers, the recovered group had significantly lower risks for 10 of the disease outcomes, including COPD (HR=0.33; CI: 0.29, 0.38), lung cancer (0.37, 0.27, 0.52) and all-cause mortality (0.52, 0.46, 0.59). (Supplementary Fig. 12, Supplementary Data 23). Importantly, for most diseases, cumulative lifetime risks for recovered groups were comparable to those for never smokers (Fig. 5c).

In contrast, previous smokers who had not recovered from their smoking damage exhibited lifetime cumulative incidences of asthma (p=0.72), chronic kidney disease (p=0.62), chronic liver disease (p=0.13) and congestive cardiac failure (p=0.12) that were comparable to those of current smokers. However, for most respiratory and cardiovascular diseases, their risk was significantly lower than that of current smokers. Notably, diseases such as lung cancer and peripheral artery disease showed more than a 50% reduction in cumulative incidence by age 75 (Supplementary Data 24 and 25).

In addition, we also identified current smokers whose pSIN scores were similar to those of never smokers (Supplementary Fig. 13a, n=737/4732). After adjusting for lifestyle factors and smoking pack-years, this population had significantly lower risks for peripheral artery disease (HR=0.13, CI: 0.03, 0.53), lung cancer (0.15, 0.05, 0.46) and COPD (0.25, 0.16, 0.39) in addition to all-cause mortality (0.53, 0.38, 0.73) (Supplementary Fig. 13b, Supplementary Data 26). Supplementary Fig. 13c shows that when current smokers had similar pSIN to never smokers, they exhibited cumulative disease risks comparable to those of never smokers for all tested health-related outcomes with the exception of COPD (risk at 70 years: 2.5%, p=9.5×10–5) and all-cause mortality (risk at 70: 6.4%, p=4.59×10–2) which were associated with a lower absolute risk compared to high-risk current smokers (Supplementary Data 27 and 28).

Methods
Study cohorts
Ethics approval. UKB data use (Project Application Number 61054) was approved by the UKB according to their established access procedures. UKB has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) and as such, researchers using UKB data do not require separate ethical clearance and can operate under the RTB approval. The CKB (Oxford Tropical Research Ethics Committee 025-04) complies with all the required ethical standards for medical research on human subjects. Ethical approvals were granted and have been maintained by the relevant institutional ethical research committees in the UK and China.

UK Biobank (UKB). The UKB is a prospective cohort study which includes 502,505 participants recruited between 2006 and 2010. Cohort population characteristics were summarised in Supplementary Data 1. Smoking behaviours, including smoking status, number of cigarettes per day, smoking start age, age stopped smoking, type of tobacco smoked, exposure to tobacco smoke at home and exposure to tobacco smoke outside, were collected at baseline using touchscreen-based questionnaires. Participants were classified as current smokers, previous smokers and never smokers. We classified ‘occasional smokers’ as never smokers to ensure consistency in our comparison groups and to align with prior epidemiological studies. Occasional smokers comprised a very small proportion of the population and did not report a sustained smoking history. Given that our study focuses on the long-term molecular impact of regular smoking, we categorised occasional smokers with never smokers to avoid potential misclassification bias and to maintain a clear distinction between individuals with significant smoking exposure and those without. Smoking pack-years were calculated as the Number of cigarettes per day/20 × (Age stopped smoking – Age started smoking). Passive exposure was defined as a binary variable, indicating whether an individual had been exposed to tobacco smoking either at home or outside. UKB blood biomarkers were measured using the non-fasting blood serum samples collected at baseline. Sample processing, quality control and technical variation adjustment steps were performed by UKB before data release and were previously described. Sociodemographic, lifestyle and environment, early life factors, family history, psychosocial factors and health and medical history collected through touchscreen-based questionnaires at baseline were used as exposures for exposome-wide association studies. 176 variables common to both males and females were used in the analysis. Continuous exposures were centred and standardised, except for age. Ordinal categorical exposures were recoded as ordered categorical variables. Nominal categorical exposures were analysed with the most common category set as the reference and dummy variables were generated accordingly. Details of the variables and processing methods were described elsewhere.

Missing data was imputed using a random-forest-based algorithm provided by the R package missRanger when used as a covariate in linear association models (Townsend deprivation index, IPAQ physical activity group, ethnicity, alcohol frequency, BMI and education years). Recruitment centre has a missingness of 0.00%, ethnicity has a missingness of 0.55%, alcohol frequency has a missingness of 0.30%, BMI has a missingness of 0.62%, IPQA activity group has a missingness of 19.93%, Townsend deprivation index has a missingness of 0.12% and education years has a missingness of 2.02%. Imputation was performed with default hyperparameters using a maximum of 10 iterations and 200 trees. Linked hospital inpatient data, primary care data and cancer register data were accessed from the UKB data portal on May 23, 2023, with a censoring date of Oct 31, 2022, Jul 31, 2021, Feb 28, 2018, for participants recruited in England, Scotland and Wales, respectively. The follow-up time is between 8 and 16 years. Mortality data and cause of death information were accessed from the UKB data portal on May 23, 2023, with a censoring date of November 30, 2022. The follow-up time is between 12 and 16 years. Methods and ICD diagnosis codes used to identify prevalent and incident chronic disease in UKB are shown in Supplementary Data 30.

Proteomics assessment. Proteomic assessments in the UKB were performed using the Olink Explore 3072 platform, where the biomarker library was determined by pre-existing knowledge of pathways involved in cardiometabolic, inflammation, neurology and oncology. After excluding participants with more than 20% missingness in protein measurements or those with smoking status information missing or answered ‘prefer not to say’ at the baseline assessment and removing the non-randomised samples (batches 0–7), 43,914 participants were retained for the analyses. The randomised participants selected for proteomic profiling were previously shown to be representative of the original UKB full cohort. Proteomics data were provided in Normalised Protein eXpression (NPX) values on log2 scale, where quality control and a 2-step approach of within-batches and cross-batches intensity normalisation was performed pre-releasing the data. Proteins missing in over 20% of the sample were excluded from the study (NPM1, PCOLCE, CTSS, GLIPR1), leaving a total of 2917 proteins for the analysis. After regressing out recruitment age and sex, the proteomic data were further processed by first rescaling to the value between 0 and 1 and then centring on the median. To address the concern regarding the potential residual signal of age and sex in the proteomic expression data after linear regression, we performed additional analyses to rigorously evaluate the effectiveness of the regression process. Specifically, we trained gradient boosting models to assess whether any predictive signal for age or sex remained in the regressed data. For age, we trained regression models to predict age using each protein in the regression dataset. The results showed a mean R² of −0.0042 (SD=0.0089), indicating that the models performed no better than random chance. This suggests that the linear regression successfully removed age-related signals from the proteomic data. For sex, we trained classification models to predict sex using each protein in the regressed dataset. The models achieved a mean AUC of 0.51 (SD=0.017), which is equivalent to random guessing. This confirm that sex-related signals were effectively removed by the regression process.

Genomics assessment. UKB genotyping was conducted by Affymetrix using a bespoke BiLEVE Axiom array for ~50k participants and the remaining ~450k on the Affymetrix UKB Axiom array. As the two arrays are broadly comparable, with over 95% overlap in assessed gene variants, they were combined. All genetic data were quality-controlled and imputed by the UKB. Detailed information on the genotyping process and technical methods is available online. Genetic data were phased prior to imputation with SHAPEIT3, followed by imputations using IMPUTE2. Details on genetic imputations are provided elsewhere.

China Kadoorie Biobank. The CKB is a prospective cohort study which includes 512,724 participants recruited from 10 geographically diverse regions between 2004 and 2008. Smoking variables were collected by laptop-based questionnaires at baseline. Smoking status was collected as never smokers, occasional smokers, ex-regular smokers and smokers. We then redefine never smokers and occasional smokers as never smokers, ex-regular smokers as previous smokers and smokers as current smokers. This study restricted the analysis to participants where the proteomic profile was measured with the Olink platform (n=3977). Details of the study design and methods were previously described.

Proteomics assessment. Proteomic assessments in CKB were performed using the Olink Explore 3072 platform. Plasma samples from selected participants were sub-aliquoted to 2 batches and were measured by the laboratory at Uppsala, Sweden (batch 1, 1463 proteins) and Boston, USA (batch 2, 1460 proteins) separately. The CKB Olink data were provided in NPX values on the log2 scale, where the CKB team has performed quality control, a 2-step approach of internal control and inter-plate control. Any protein was excluded if it was not available in either UKB or CKB (HLA_A, ERVV_1, CD97, FGFR10P, LRMP, CASC4, ADRS, HARS, WISP2, FOPNL, WISP1). After regressing out age and sex in the CKB cohort, proteomic data were further processed by first rescaling to the value between 0 and 1 and then centering on the median.

Statistical analysis
A descriptive analysis of population characteristics was performed using the R package CBCgrps. The study design and analysis pipeline are illustrated in Fig. 1.

Proteomic signatures of smoking. Proteomic profiles of smoking were constructed by comparing current smokers with never smokers, excluding passive smokers, using gradient boosting. Samples were randomly split into 70% training (n=13,343 for never smokers, n=3312 for current smokers) and 30% testing dataset (n=5719 for never smokers, n=1420 for current smokers). Within the training dataset, a gradient boosting machine learning model, including all 2911 proteins, was trained to differentiate never smokers and current smokers. The model was first hyperparameter-tuned using a Tree-structured Parzen Estimator-based method provided by the Optuna package in Python. Hyperparameters within a pre-set range were searched and optimised across 200 trials to maximise the 5-fold cross-validated ROC AUC score. After hyperparameter tuning, the performance of the best parameter in the training dataset with 5-fold cross-validation and in a 30% left-out testing dataset was assessed.

Feature interpretation and selection. To characterise the feature importance, SHapley Additive exPlanation (SHAP), a local tree explaining method based on game theory, was used. SHAP calculates the contribution of each feature to the outcome in each participant and extends these local explanations to also capture interactions between features directly. Compared to traditionally used permutation feature importance, SHAP plots can display the magnitude, prevalence and direction of a feature’s effect. We then used a SHAP-based Boruta selection method provided by the shap-hypetune package to select all relevant features contributing to smoking status prediction. The Boruta algorithm enhances feature selection by creating randomly permuted shadow features that serve as a baseline for comparison. Specifically, shadow features are duplicates of the original features with their values randomly shuffled to break any associations with the target variable. The algorithm compares the mean absolute SHAP values of the original features against those of the shadow features. Features are retained only if they demonstrate a higher mean SHAP value than the highest-scoring shadow feature, thereby ensuring their predictive importance is not due to random chance.

In our study, the algorithm was executed for 200 iterations to ensure robustness and stability in feature ranking. Features falling within the bottom 5% of importance scores (tail 5%) were systematically rejected as they were unlikely to contribute meaningfully to the model’s performance. Following the feature selection process, the refined model—consisting of Boruta-selected features—underwent another round of hyperparameter tuning to optimise its performance before further analysis. All model tuning and feature selection steps were performed within the 70% training set in UKB.

Proteomic Smoking Index (pSIN). The pSIN for the full UKB sample (n=43,914) was calculated using a robust methodology to mitigate the risk of overfitting. This process involved employing 5-fold cross-validation to ensure the reliability of the results. After identifying the best hyperparameters and selecting the proteins using the Boruta method, a gradient boosting model was trained within each fold. Subsequently, the predicted raw score for the corresponding test set was generated. For binary classification tasks, this raw score corresponds to the log odds of the positive class (in this case, being a current smoker). The LightGBM model typically outputs raw scores (logits) in the range of approximately −10 to 10, where a score of 0 indicates a neutral prediction, corresponding to a 50% probability of being in the positive class (sigmoid(0)=0.5). Scores closer to 10 indicate a high confidence prediction for the positive class (sigmoid(10)≈0.99995 or ~99.995% probability), while scores closer to −10 indicate a high confidence prediction for the negative class (sigmoid(−10)≈4.5×10⁻⁵ or ~0.0045% probability). In our analysis, we set the classification threshold at a raw score of −1.29, which corresponds to the point where the FPR is 0.05. This threshold was chosen to balance sensitivity and specificity in our predictions. pSIN higher than the threshold indicates a higher likelihood of being in the positive class (smoker), with larger values reflecting greater confidence, while pSIN smaller than the threshold indicates a higher likelihood of being in the negative class (non-smoker), with more negative values reflecting greater confidence. These predicted raw scores from the test sets of each fold were then aggregated to create a comprehensive measure of smoking protein profiles for the entire population. This approach allowed for a more robust estimation of the impact of smoking on protein profiles across the UKB cohort compared to using only one model trained on training data to calculate pSIN for the entire population. External validation in CKB was performed to further test the possibility of the overfitting problem. For external validation, a model with the optimised hyperparameter was trained in the UKB training dataset and was tested in the CKB. The performances of identifying current smokers from never smokers were compared.

Function annotation for proteins. Individual protein function annotation (GO: molecular functions) was extracted from the GO database using clusterProfiler v.4.2.2 in R. No enrichment analysis was performed. Tissue-specific protein expression data were extracted from the GTEx project database v.8 and the heatmap was plotted using the FUMA GWAS web tool. Values shown on the heatmap were the average of normalised expression per gene. Differential expression genes (DEG) were identified by a 2-sided t-test per tissue type versus all other tissue types. Genes with a Bonferroni corrected p<0.05 and absolute log fold change >0.58 were selected as DEG and were shown in red colour.

Association of smoking history, clinical biomarkers and risk factors, haematological measurements, exposome-wide association analysis with pSIN. To test the association between self-reported smoking habits, social-demographic factors, lifestyle factors, blood biochemistry biomarkers and clinical risk factors with pSIN, generalised linear models from the statsmodel v.0.14.0 package were used.

For associations between smoking history and pSIN, models were adjusted for basic socioeconomic factors, including recruitment centre, ethnicity, education years and the Townsend deprivation index. Each smoking-related exposure was considered separately. Similarly, smoking cessation years were analysed separately as either continuous or categorical variables in distinct models, with categorical analyses designed to explore dose-response relationships.

For associations between blood biomarkers/clinical risk factors/haematological measurements to pSIN, continuous exposure variables, standardisation was applied before inclusion in the models. Associations were adjusted additionally for lifestyle factors, including IPAQ activity group and alcohol intake frequency, as they are known to confound physiological status and liver damage. To explore the added value of pSIN compared to self-reported smoking status, a sensitivity analysis was performed, adjusting additionally for self-reported smoking status.

For exposome-wide association analysis (all available socio-economic and lifestyle variables available in UKB), models were only adjusted for the most basic recruitment centre, ethnicity and smoking status to allow exploration of a wider range of potential associations between the exposome and pSIN without masking potential signals by controlling for too many variables.

Social-economic and lifestyle confounding factors were chosen based on previous literature. P-values resulting from these analyses were corrected for FDR multiple testing.

Identification of genes influencing pSIN. GWAS was conducted using SAIGE software V1.09. For constructing a genetic relationship matrix (GRM) in step 1, we used the pruned genotype dataset. Genotype pruning was conducted in PLINK software using the ‘indep-pairwise’ option with an r2 of 0.5, a window size of 1000 markers and a step size of 10 markers. We further used the ‘LOCO=TRUE’ option to construct the GRM. The GWAS analyses were adjusted for age, sex, batch effects and 40 gene principal components identified within UKB genotyping data. Gene mapping was performed using the FUMA GWAS web tool, where the maximum p-value for lead SNP was set as 5×10-8 and the maximum distance to genes was set as 10 kb. LDSC analysis was performed using online tools Complex-Traits Genetics Virtual Labs (CTG-VL) where summary statistics of GWAS against pSIN were correlated to publicly available GWAS results of 1461 traits in the database. Significant correlations were identified if the FDR-adjusted p-value was smaller than 0.05.

Calculating the contribution of each category to pSIN. To investigate how much of the additional variance explained does each category of genetics, smoking history, social-demographic and lifestyle and clinical biomarkers and risk factors have on pSIN, we constructed four gradient boosting models. These models were sequentially augmented by adding each category, starting with genetics, followed by smoking history, then social-demographic and lifestyle factors and finally clinical biomarkers and risk factors. Each model was initially trained on the 70% training dataset and subsequently, the variance explained was assessed using the 30% testing dataset using the scikit-learn package. The additional variance explained by each category was determined by subtracting the variance explained by the model that contains the information on this category from that of the previous model that did not include this category. The same procedure was carried out for the whole population, but also in current smokers, previous smokers and never smokers to investigate how each category contributes to pSIN differently among the smoking status-stratified population. The additional variance explained was then plotted in a stacked bar chart for comparison.

Further, a multivariate linear regression model, followed by ANOVA using statsmodel was used to estimate the relative R2 for each variable. Sum of relative R2 of all variables from the same category was reported. In addition to the previous analysis, prevalent diseases of lung cancer and COPD, Polygenic Risk Scores (PRS) of smoking initiation, lung cancer and COPD, technical measurements including fasting time, OLINK batches and assessment centre, anthropological measures including standing height, BMI, weight, hip and waist circumference were also included in the model. Dietary factors were included in the lifestyle factors. For PRS calculation, we identified recent PRS from the Polygenic Score (PGS) catalogue, selecting scores derived in predominantly European populations that did not overlap with the UKB cohort. We calculated these PRS as weighted sums ∑(no. risk alleles × effect size) in the UKB v3 imputed genotyped data. Details of the method were described elsewhere. PGS catalogue entries used to calculate PRS were as follows: Smoking initiation: PGS00336028, Lung cancer: PGS00007860 and COPD: PGS00133261.

Associating pSIN with future health-related outcomes. To test the association between pSIN and incident health outcomes, all prevalent cases were removed beforehand. The multivariate Cox proportional hazard model provided by the lifeline v.0.27.8 package was used with a pre-set step size of 0.1. Survival outcomes were defined using follow-up time to event and the binary incident event indicator. For all incident outcomes in the whole UKB population, two successive models were tested with an increasing number of covariates: model 1 did not adjust for any additional covariate as age and sex had already been regressed out in protein level; model 2 was adjusted for recruitment centre, Townsend deprivation index, IPAQ physical activity group, ethnicity, alcohol frequency, BMI and education years. In current smokers, a third model was applied, which further adjusted for smoking pack years and the number of years smoked. For previous smokers, an additional third model was tested that included adjustments for smoking pack years and the number of years since cessation. P-values of the hazard ratio were corrected for FDR multiple testing. Forest plots were generated with a minimum sample size threshold of 80 to ensure adequate statistical power and reliable interpretation. Cumulative incidence plots were generated utilising the Kaplan-Meier Fitter function from the lifelines package. Due to limitations in case numbers or at-risk numbers at both ends, the x-axis of the plot was constrained to the age range of 45–75. This adjustment ensured a more focused visualisation of the cumulative incidence curve within a clinically relevant age range. P-values between cumulative incidence curves were calculated using a log-rank test with adjustment for FDR multiple testing.

A summary of all packages and software used is listed below. CBCgrps [https://cran.r-project.org/web/packages/CBCgrps/index.html], Optuna [https://optuna.org/], SHAP, shap-hypetune [https://github.com/cerlymarco/shap-hypetune], missRanger [https://cran.r-project.org/web/packages/missRanger/index.html], LightGBM [https://lightgbm.readthedocs.io/], scikit-learn [https://scikit-learn.org/], statsmodels [https://doi.org/10.25080/Majora-92bf1922-011], SAIGE, PLINK, SHAPEIT3, IMPUTE2 [https://mathgen.stats.ox.ac.uk/impute/impute_v2.html], FUMA, CTG-VL [https://vl.genoma.io/], clusterProfiler [https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html], lifelines [https://lifelines.readthedocs.io/].

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Xiao et al developed a protein signature to discriminate smoking status which was predictive of future disease risk in the general population (i.e. including in non-smokers) and could stratify risk among current (and previous) smokers. The analyses are well designed, and the paper is well written. The results are exciting as they pose the possibility to use and objective signature to identify groups at high risk among smokers that would be candidates for more intensive screening and early detection strategies. However, I have some comments particularly regarding some of the more aetiological analyses as it is unclear how these signatures can inform aetiology of smoking, as some of the observed associations may well be driven by independent effects of other factors on the component proteins of the score without any relationship whatsoever to smoking.\nMajor comments\n1. Some more details on the feature selection would be beneficial in the methods section. Specifically, was this done only in the 70% training set?\n2. Regarding the generation of the pSIN in the UKB sample. I appreciate the efforts to avoid overfitting through a CV framework. However, assuming feature selection was performed in the training set (70% of current and never smoker as asked in my previous comment?), these people would have already been seen and are therefore the group more prone to overfitting. While the validation in CKB provides confidence in the associations seen, it would be important to clarify this in the methods.\n3. More details are needed on the functional enrichment analysis. What did the authors use as a background, i.e. all genes or only those covered by the Olink platform? If using the former, authors should restrict the background to genes covered by Olink as are already over-represented for certain pathways.\n4. Lines 145-146: unclear how table s3 related to tissue annotations?? Furthermore, how tissue specific was the expression of these proteins, i.e. albeit they may be expressed in lung, is this expression specific or are these proteins rather expressed throughout many different tissues? Authors can use data from the human protein atlas to look-up specificity of tissue expression.\n5. Regarding the GWAS of the pSIN score. To what extent do the authors consider the hits they are getting are related to smoking or related to the proteins in the score but independently of any smoking related mechanisms. The authors report 32 are cis-pQTLs , how many are trans-pQTLs for any of the proteins in the pSIN.\n6. Lines 174 – 177: Not sure how figs3 and tables s6-9 relate to colocalization? These just show results from the GWAS but not from colocalization analyses. Table s9 shows some lookups from GWAS catalogue but this does not mean at all that the pSIN and the other traits share the same causal variant. For this formal colocalization analyses need to be performed.\n7. Lines 197 – 199: The causal relationships between environmental exposures and smoking are quite complex and some of these associations could be driven by independent effects of the environmental exposures on the proteins that are not due to “smoking related damage”. Authors should discuss this more clearly as a limitation of using such a score to try to understand aetiological mechanisms. This is one of the major limitations of this type of analyses that is very difficult to discern associations that have nothing to do with smoking but are coming up due to independent effects on the proteins composing the pSIN that have nothing to do with smoking.\n8. Lines 200 – 216: are this associations (clinical biomarkers and pSIN) similar to the associations between clinical biomarkers and smoking status. Is there any gain in using the pSIN.\n9. Regarding the value of pSIN to predict the risk of diseases in current and previous smokers. In my view this is extremely valuable and exciting as it provides an objective tool to identify a high risk population among smokers (or previous smokers) for early detection and screening. However, it would be important to understand the added values of the pSIN to do so, in comparison to a clinically more acceptable strategy such a using just one biomarker. How does the ability to stratify at risk populations among smokers (or previous smokers) differs when using the pSIN (51 proteins) to when using the single most discriminatory protein of smoking status?\nMinor comments\n10. Lines 85 – 89: This may be due to a much lower power and limited set of outcomes considered in these studies rather than a lack of association between the smoking methylation signature and other outcomes (as the sentence suggests)?\n11. Line 146 – 147: Quite a lot is known about the association of these proteins and incident diseases actually: https://insight.olink.com/data-stories/ukb-diseases\n12. Table s4: the labelling of the traits is unclear , further detail is needed, perhaps a data dictionary.\n13. Lines 348 – 350. Duplicated sentences?\n14. Unclear which variables were imputed from the description in the methods.\n15. Grammar should be revised throughout the methods section to improve readability.", "reviewer_2": "Thank you for the opportunity to review this interesting, comprehensive, and important article. The authors justified the need for their analysis well. They included a large sample for their initial analysis and tested their results using a reasonably large sample in a different demographic population. The figures are clear and help the reader understand the arguments. The authors went through all the expected steps for a high-quality investigation of this nature. My comments are minor in nature. Overall, I was impressed!\n1. L143-145: In a sentence or two, clarify how the current project leveraged the GTEx project. Methodologically, how were the findings of Lonsdale et al. used to create Figure s2?\n2. Table S4: Clarify whether all variables were analyzed together in a single regression model. It would not be appropriate to include all of smoking years, pack years, and number of cigarettes together in one model. Similarly, it would not be appropriate to include smoking cessation years as a continuous variable and as a categorical variable together in one model.\n3. Table S5/L166-168: Comment that the trends between CKB and UKB are more consistent for current smokers than for former smokers.\n4. L169-171: Very briefly, provide methodological details about how the GWAS was conducted such that the reader does not need to go to the end of the article to the Methods. What was the outcome being associated? Also clarify if this was done in UKB only.\n5. L172-L174: Add citations for the referenced studies.\n6. L187-188: Very briefly, provide methodological details about how the exposome analysis was conducted. Were all factors considered together in a single model? What statistical approach was taken? (I realize details are in the Methods, but help guide the readers through the many logical steps you took – it’s excellent, but a lot!).\n7. L103 and L240: Inconsistent number of major disease outcomes reported.\n8. L242: Add the second decimal place to the upper confidence interval limit.\n9. L308-311: Can be reworded for clarity.\n10. Figure S10 and s11: Clarify the reference group. Is this saying that higher pSIN scores are protective among these groups? Or is this in comparison to people with high-risk previous smokers (s10) and high-risk current smokers (s11)?\n11. In the discussion, comment on the fact that the external validation study was limited to only males, and that the health association analysis was not validated in this population.\n12. L461: Comment on the % missingness for key variables.\n13. L503: Justify why “occasional smokers” were considered never smokers.\n14. For the regression models throughout the paper, justify how covariates were chosen (e.g., section starting L583).", "reviewer_3": "This is an exemplary piece of analytical research into Proteomic Olink signatures and their association with multiple diseases.\nThe raw data was taken from the UKBB and the validation undertaken with the China Kadoorie Biobank, The AUC values are indeed very high, far higher than many similar biomarker analysis.\nThe sub-analysis was not just with smokers and never smokers but also with environmental exposures, health status (ie. obesity), sociodemographic and lifestyle factors and mortality data. The pSIN parameter was also used to differentiate recovery status of previous smokers, which is a completely new and impressive approach.\nThe indepth details provided for machine learning analysis and statistical tools are well described.\nThis is an academic exercise providing a very informative manuscript. The authors have also included a number of possible limitations in their discussion, even indicating that the study has only limited statistical power to detect associations between pSIN and common diseases.\nHowever, even considering all the positive elements provided in this analysis, there is no attempt to indicate how it maybe used or implemented in clinical practice. We are now in an era of not just discovery research but also translational research. The authors should have pencilled in a possible way their findings could be tested in a primary care clinical trial and the costs involved in utilising the 51 proteomic biomarkers (Olink or another platform) and the collection of the wide range of clinical, epidemiological, environmental data. And if this is a realistic way forward for identification of high-risk individuals?", "reviewer_4": "The work provides the first affinity proteomics signature to differentiate between “never smoking” and “currently smoking” subgroups. The signature is based on a machine learning model trained with gradient boosting and subsequent feature selection, resulting in a signature of 51 selected proteins. Based on this gradient boosting trained machine learning model, a smoking Index (pSIN) is defined. It is encouraging to see that within the 51 proteins around 5-10 are highly expressed in the lung, and other relevant tissues, while the signature is obtained from plasma samples. The proteins and pathways associated with the signature give a novel perspective on underlying biological changes, induced by smoking. Note that true causality is or course difficult to show given the study set up. Nevertheless, there is external validation for the male population based on an external cohort. The test also provides a novel and different perspective on self-reporting, and methylation profiles for smoking. Where the previously reported methylation profiles seem to be more closely associated with smoking history, while the plasma profiles seem to reflect the response to smoking – as the reported index becomes continuously lower after smoking cessation. Overall, the study provides a novel perspective on the bodies smoking response, and has the potential to monitor the impact of smoking on the body.\nMajor issues:\n1. Firstly, the added value of the proteomics signature, over the self-reported smoking and the existing methylation signature, should be made more clear. In addition, the added value should be highlighted more where contrastive results have already been provided (for example, the difference between methylation and proteomics profile in the “previous smoker group”). Moreover, in all associations reported with the pSIN value, the strength of associations with the self-reported smoking status, and methylation profile should be provided as a reference, to make the novelty and added value of the proteomics signature more clear; this includes the association analysis of major diseases and mortalities, genomics factors /GWAS results, haematological measures, social-demographic and lifestyle factors.\n2. The labels used to train the machine learning model (“current smokers” vs “never-smoker”) are extremely imbalanced in the dataset. This can give biases in several reporting measure of classification measures, including the AUC-ROC. A wider range of performance measures should be reported (both on validation and test set), including: F1, balanced accuracy, and the AUC-PR/RC. In addition, one can undersample the majority class in the held-out test set, to get a better performance estimate.\n3. The argument that the signature cannot be validated on the 2137 female smoking participants does not hold. It is possible to report accuracies and AUC ROC statistics on as little as 50–100 samples. Presumably, the signature does not translate to the female cohort. Instead of hiding this, the authors should report the separate accuracy statistics for the female only test external validation set, as well as a separate performance analysis on the female subset of the UK biobank cohort. If there are differences, and the signature does not translate well to female subjects, this is in itself interesting, and should be reported fairly.\n4. Missing data was imputed. Please describe more clearly what data was imputed, and in which analysis imputed data was used. It is essential that in the test data set, over which the performance of the classification model was measured, no imputed data was used for the labels (i.e. current smokers vs non-smokers). In addition, the variables tested for association with pSIN should be free from imputation or at least it should be tested if the association holds when imputation is not performed.\nMinor issues:\n1. In the abstract, the added value of pSIN with respect to self reported smoking history, and the methylation profile should be made more clear.\n2. In the abstract, the rationale behind the genetic variant associations is completely lost. (Why would one expect a link between a lifestyle choice of smoking and genetics?). Perhaps take this out if there is no sufficient space to explain this. It is very confusing to report this without appropriate context.\n3. Objectivity of signature:\na. Page 3, line 71/72 “… there is an urgent need to develop objective measures …”\nb. page 13 line 272 “This suggests pSIN can serve as an objective test …”\nMaking claims about objectivity, should be accompanied by a discussion on the accuracy of the model. What if this signature is used to check someone's smoking history for insurance purposes? What guarantee can be provided that someone with a high pSIN score has indeed smoked? Make the limitations of the model more clear, when objectivity of the model is claimed.\n4. “age” and “sex” are regressed out of the proteome expression data. As this is done via a linear model, and the machine learning model (gradient boosting) is non-linear, one cannot assume that there is no remaining signal left in the data to predict age or sex on the regression data. One way to test if the signal is taken out completely, is by training a classifier to predict sex or age on the regressed data set. Alternatively, one can discuss the impact on the results and signature if the regression does not remove all associations with age and sex in the proteome expression data.\n5. Please explain how the model probability score for a given class that comes out of the classification model is transformed to a pSIN score with a range of approximately -10.0-10.0. Make in the text of the results section more clear what a pSIN score of 0.0 indicates, or more generally how the reader can quantitatively interpret the pSIN score.\n6. Very strong associations between pSIN and haematological measurements and the blood biochemical composition are found (S4 and S5). Is there any clear link between the proteins found in the plasma based pSIN signature, and these findings? Would any of these measures by itself be sufficient to classify “current smokers” and “never-smokers”.\n7. P6 line 178-183. It is unclear what the rationale is for performing these genetic correlations. Are you looking for risk factors to start smoking? Or are you interested to see which genetic variants can worsen the effects of smoking, or perhaps cause a smoking-like signature in non-smokers? Make the aim of the analysis more clear, and justify the choice of datasets to ask this question.\n8. It may be helpful to the reader to explain better how the “smoking history” has been collected. Is this self-reported? P8 line 226: explain more clearly why it makes sense to look at variance explained from “smoking history” in a profile that was learned to classify current smokers vs never-smokers.\n9. P22 “Feature interpretation and selection” it should be clearly described over which data the feature selection and hyperparameter tuning was performed. Figure 1 suggests that this is done over the train/validation set and not over the hold-out test set; as it should be. However, this should be described explicitly in the text as well."}, "author_response": "Response to reviewers’ comments (Manuscript no.: 551097)\nWe would like to thank external reviewers for their detailed consideration and helpful\nfeedback which has helped improve the manuscript. We have incorporated the\nreviewers’ comments and expanded our methodology, results, and discussion to reflect\nthese requests. Responses to the reviewers’ comments (in bold) are detailed below,\nalong with the accompanying revisions to the paper.\nReviewer #1:\nXiao et al developed a protein signature to discriminate smoking status which\nwas predictive of future disease risk in the general population (i.e. including non-\nsmokers) and could stratify risk among current (and previous) smokers. The\nanalyses are well-designed, and the paper is well-written. The results are exciting\nas they pose the possibility to use and objective signature to identify groups at\nhigh risk among smokers that would be candidates for more intensive screening\nand early detection strategies. However, I have some comments particularly\nregarding some of the more aetiological analyses as it is unclear how these\nsignatures can inform aetiology of smoking, as some of the observed\nassociations may well be driven by independent effects of other factors on the\ncomponent proteins of the score without any relationship whatsoever to\nsmoking.\nWe thank the reviewer for their time and positive feedback. We have incorporated the\nsuggestions of the reviewer in the revised version of the manuscript.\nMajor comments\n1. Some more details on the feature selection would be beneficial in the methods\nsection. Specifically, was this done only in the 70% training set?\nResponse: We apologize for the omission of details. We have added the following\ndetails to the Methods section in the revised version of the manuscript (lines 634-647):\n“The Boruta algorithm enhances feature selection by creating randomly permuted\nshadow features that serve as a baseline for comparison. Specifically, shadow features\nare duplicates of the original features with their values randomly shuffled to break any\nassociations with the target variable. The algorithm compares the mean absolute SHAP\nvalues of the original features against those of the shadow features. Features are\nretained only if they demonstrate a higher mean SHAP value than the highest-scoring\nshadow feature, thereby ensuring their predictive importance is not due to random\nchance. In our study, the algorithm was executed for 200 iterations to ensure\nrobustness and stability in feature ranking. Features falling within the bottom 5% of\nimportance scores (tail 5%) were systematically rejected as they were unlikely to\ncontribute meaningfully to the model's performance. Following the feature selection\nprocess, the refined model—consisting of Boruta-selected features—underwent another\nround of hyperparameter tuning to optimize its performance before further analysis. All\nmodel tuning and feature selection steps were performed within the 70% training set in\nUKB.”.\n2. Regarding the generation of the pSIN in the UKB sample. I appreciate the\nefforts to avoid overfitting through a CV framework. However, assuming feature\nselection was performed in the training set (70% of current and never smokers as\nasked in my previous comment?), these people would have already been seen\nand are therefore the group more prone to overfitting. While the validation in CKB\nprovides confidence in the associations seen, it would be important to clarify this\nin the methods.\nResponse: We agree that external validation is a more robust way of testing overfitting.\nAs suggested, we have now provided further clarification in the Methods section lines\n671-677. “This approach allowed for a more robust estimation of the impact of smoking\non protein profiles across the UK Biobank cohort compared to using only one model\ntrained on training data to calculate pSIN for the entire population. External validation in\nCKB was performed to further test the possibility of the overfitting problem. Although the\nhyperparameter tuning step still gave the possibility of data leakage and potentially\noverfitting problems, we further performed external validation. For external validation, a\nmodel with the optimized hyperparameter was trained in the UKB training dataset and\nwas tested in the CKB. Performances of identifying current smokers from never\nsmokers were compared.”\n3. More details are needed on the functional enrichment analysis. What did the\nauthors use as a background, i.e. all genes or only those covered by the Olink\nplatform? If using the former, authors should restrict the background to genes\ncovered by Olink as are already over-represented for certain pathways.\nResponse: For clarification, the pathway annotations in Lines 152–155 were derived\nfrom GO molecular functions assigned to each protein using the clusterProfiler package.\nHowever, no formal enrichment analysis was performed. Instead, we focused on\nannotating molecular functions for individual proteins rather than assessing pathway\noverrepresentation relative to a background set. We have updated the Methods section\nlines 681-689 and changed the subtitle into function annotation for proteins to make this\nclearer.\n4. Lines 145-146: unclear how table s3 related to tissue annotations??\nFurthermore, how tissue specific was the expression of these proteins, i.e. albeit\nthey may be expressed in lung, is this expression specific or are these proteins\nrather expressed throughout many different tissues? Authors can use data from\nthe human protein atlas to look-up specificity of tissue expression.\nResponse: We apologize for the confusion. Table s3 was meant to show the annotations\nof the Boruta-selected proteins which should be referenced earlier in the text. Tissue\nenrichment analysis was performed based on the GTEX tissue mRNA expression levels\nusing the FUMA web tool (see method). Two plots were provided to show the mRNA\nexpression levels of each gene of the selected protein in available tissues and in which\ntissue does genes of selected proteins are differentially expressed. Protein expression\ndata available on human protein atlas does not provide quantitative measurements,\ninstead only four category values (i.e., low, medium, high and not detected) are\nprovided. The human protein atlas website has also been using tissue RNA expression\nfrom GTEX as a reference for tissue specificity analysis in the protein summary section.\n5. Regarding the GWAS of the pSIN score. To what extent do the authors consider\nthe hits they are getting are related to smoking or related to the proteins in the\nscore but independently of any smoking related mechanisms. The authors report\n32 are cis-pQTLs, how many are trans-pQTLs for any of the proteins in the pSIN.\nResponse: Of the 129 genes identified to be associated with pSIN, 75 were novel as in\nthey were not associated with smoking initiation before in both genetic and epigenetic\nassociation studies. LD score regression analysis shows the strongest correlation with\ncurrent smoking (r2 = 0.78) followed by lung cancer (r2=0.71) and COPD (r2=0.54),\nwhich suggests that pSIN GWAS is mainly capturing smoking and/or related\nmechanisms.\nFor the second question, we have now added the following details in the result section\nlines 190-196: Of the 95 significant lead independent variants, 34 were cis-pQTLs\nmapping to 16 genes, 8 of which encoded proteins included in the pSIN model. Six cis-\npQTLs were annotated for multiple closely located genes simultaneously (e.g.,\nrs901886 and rs35929247 were cis-pQTLs for ICAM1, ICAM3, ICAM4, and ICAM5).\nAdditionally, 7 variants were identified as trans-pQTLs mapping to 366 genes, 21 of\nwhich encoded proteins selected in the pSIN model. Among these, rs2519093 showed\nthe broadest mapping, acting as a trans-pQTL for 358 genes and a cis-pQTL for two\ngenes (ABO and DBH).\n6. Lines 174 – 177: Not sure how figs3 and tables s6-9 relate to colocalization?\nThese just show results from the GWAS but not from colocalization analyses.\nTable s9 shows some lookups from GWAS catalogue but this does not mean at all\nthat the pSIN and the other traits share the same causal variant. For this formal\ncolocalization analyses need to be performed.\nResponse: We apologize for incorrectly using the word “colocalization”, we did not\nperform formal colocalization analyses, as our focus was on identifying associations\nrather than establishing causal relationships between these variants and traits. To\nensure accuracy, we have revised the text to avoid the term \"colocalization\" which now\nstates (line196-201):\n\" Of the 129 genes associated with pSIN, 10 genes were previously identified as GWAS\nsmoking loci9,21, and 54 genes were previously found in epigenetic studies10,14,22, 75\n(58%) were novel and have previously been reported to be associated with body mass\nindex (BMI), diabetes, cancer development, and immunological/haematological traits\nincluding lymphocyte counts, eosinophil counts, and white blood cell counts (Fig S3,\nTable s6-s9).”\n7. Lines 197 – 199: The causal relationships between environmental exposures\nand smoking are quite complex and some of these associations could be driven\nby independent effects of the environmental exposures on the proteins that are\nnot due to “smoking related damage”. Authors should discuss this more clearly\nas a limitation of using such a score to try to understand aetiological\nmechanisms. This is one of the major limitations of this type of analysis that is\nvery difficult to discern associations that have nothing to do with smoking but are\ncoming up due to independent effects on the proteins composing the pSIN that\nhave nothing to do with smoking.\nResponse: Thanks for your comments. We did not declare a causal relationship in the\nmanuscript. However, to make this clearer, we have now expanded the discussion on\nstudy limitation and added the following (lines 457-467): “The causal relationships\nbetween environmental exposures and smoking are inherently complex. While our\nanalysis aimed to leverage protein signatures within pSIN to understand the\ndownstream pathology of smoking, it is essential to acknowledge that some\nassociations identified may not be directly attributable to smoking behaviour. Instead,\nthese associations could arise from independent effects of environmental exposures\nand smoking-related behaviours on proteins that are part of the pSIN. This limitation\nunderscores the challenges of disentangling causal pathways in observational data and\nhighlights the necessity for cautious interpretation when utilizing scores based on\nprotein networks to elucidate aetiological mechanisms. Future studies should consider\ncomplementary approaches, such as Mendelian randomization or experimental\nvalidation, to disentangle these independent effects and strengthen causal inferences.”\n8. Lines 200 – 216: are this associations (clinical biomarkers and pSIN) similar to\nthe associations between clinical biomarkers and smoking status. Is there any\ngain in using the pSIN.\nResponse: To answer this question, we conducted additional sensitivity analyses where\nthe associations between pSIN and clinical biomarkers were further adjusted for\nsmoking status (Figure below) and added to the result session (lines 243-250) (Fig\nS6a,b). We observed that while the effect sizes of biomarkers such as HbA1c, GlycA,\nand Triglycerides were attenuated, they remained statistically significant. However,\nbiomarkers such as APOA, HDL cholesterol, and Creatinine became non-significant\nafter this adjustment. Similarly, the association between pSIN and clinical risk factors,\nsuch as poor self-rated health, was weakened when adjusting for smoking status while\nthe associations with other risk factors remained unchanged. These findings suggest\nthat while pSIN captures smoking-related biological signatures, it also provides\nadditional information beyond conventional smoking status, particularly for certain\nbiomarkers and health outcomes. We have now further expanded the results and\ndiscussion sections on the findings of additional sensitivity analyses.\n9. Regarding the value of pSIN to predict the risk of diseases in current and\nprevious smokers. In my view this is extremely valuable and exciting as it\nprovides an objective tool to identify a high risk population among smokers (or\nprevious smokers) for early detection and screening. However, it would be\nimportant to understand the added values of the pSIN to do so, in comparison to\na clinically more acceptable strategy such a using just one biomarker. How does\nthe ability to stratify at risk populations among smokers (or previous smokers)\ndiffers when using the pSIN (51 proteins) to when using the single most\ndiscriminatory protein of smoking status?\nResponse: Thank you for your insightful comments. To assess the added value of pSIN\ncompared to using a single biomarker, we have evaluated the predictive performance of\nthe top three proteins ranked by SHAP value within the UKB test dataset with pSIN\n(Figure below). ALPP and CXCL17 demonstrated relatively strong discrimination with\nAUCs of 0.88 and 0.87, respectively, while ACVRL1 showed lower performance with an\nAUC of 0.76. However, none of these individual proteins matched the predictive power\nof pSIN, which achieved an AUC of 0.95. These results highlight the advantage of using\na multi-protein composite score over a single biomarker for more accurate stratification\nof current smokers and never smokers. We have added this to the result session (lines\n132-139) (Fig s1c).\nMinor comments:\n10. Lines 85 – 89: This may be due to a much lower power and limited set of\noutcomes considered in these studies rather than a lack of association between\nthe smoking methylation signature and other outcomes (as the sentence\nsuggests)?\nResponse: This could be a possible explanation as most studies of DNA methylation\nscores on smoking only consisted of several thousand participants which is magnitude\nsmaller than our study. It could also be that most of these cohorts also have short\nfollow-up times which result in short incident disease cases as well. This has been\nfurther discussed in the discussion section lines 397-400.\n11. Line 146 – 147: Quite a lot is known about the association of these proteins\nand incident diseases actually: https://insight.olink.com/data-stories/ukb-\ndiseases\nResponse: We would like to thank the reviewer for pointing out this resource. We have\nnow changed to: ALPP is synthesized in the liver by a metalloenzyme that catalyzes the\nhydrolysis of phosphoric acid monoesters and was previously found to be associated\nwith COPD and cancers. (lines 158-160)\n12. Table s4: the labelling of the traits is unclear, further detail is needed, perhaps\na data dictionary.\nResponse: As suggested, we have now revised the table with better labelling.\n13. Lines 348 – 350. Duplicated sentences?\nResponse: We have removed the duplicated sentence.\n14. Unclear which variables were imputed from the description in the methods.\nResponse: For clarification, we did not impute any exposure or outcome variables but\nonly co-variates of the linear association between biomarkers and pSIN, clinical risk\nfactors and pSIN, GWAS, exposome analysis and multivariable Cox model. We have\nchanged the relevant sentence to: “Missing data was imputed using a combined method\nwith random-forest-based algorithm and predictive mean matching provided by R\npackage missRanger1 when used as a covariate in the linear association between\nbiomarkers and pSIN, clinical risk factors and pSIN, GWAS, exposome analysis and\nmultivariable cox model (Townsend deprivation index, IPAQ physical activity group,\nethnicity, alcohol frequency, BMI, and education years)”. (line 534-540)\n15. Grammar should be revised throughout the methods section to improve\nreadability.\nResponse: The grammar and clarity of the methods have been revised.\nReviewer #2:\nThank you for the opportunity to review this interesting, comprehensive, and\nimportant article. The authors justified the need for their analysis well. They\nincluded a large sample for their initial analysis and tested their results using a\nreasonably large sample in a different demographic population. The figures are\nclear and help the reader understand the arguments. The authors went through\nall the expected steps for a high-quality investigation of this nature. My\ncomments are minor in nature. Overall, I was impressed!\nWe would like to thank the reviewer for their time, input and positive feedback. We have\nmade changes to the manuscript according to their suggestions.\n1. L143-145: In a sentence or two, clarify how the current project leveraged the\nGTEx project. Methodologically, how were the findings of Lonsdale et al. used to\ncreate Figure s2?\nResponse: For improved clarity, we have now revised the relevant sentence to: “Tissue\nenrichment analysis was performed among selected proteins using RNA expression\ndata from Genotype-Tissue Expression (GTEx) project2. Results indicated that many of\nthese proteins were differentially expressed in tissues either directly exposed to or\naffected by smoking including lung, salivary glands, colon, esophagus and adipose\ntissues.” In line152-155\nFigure s2 was created using the method stated in the Functional enrichment analysis of\nthe method section line(677-683): Tissue-specific protein expression data was extracted\nfrom the Genotype-Tissue Expression (GTEx) project2 database v.8 and the heatmap\nwas plotted using FUMAGWAS webtool. Values showed on the heatmap were the\naverage of normalized expression per gene. Differential expression genes (DEG) were\nidentified by a 2-sided t-test per tissue type versus all other tissue types. Genes with a\nBonferroni corrected p-value < 0.05 and absolute log fold change > 0.58 were selected\nas DEG and were shown as red colour. This is also added to Fig s2 description.\n2. Table S4: Clarify whether all variables were analyzed together in a single\nregression model. It would not be appropriate to include all of smoking years,\npack years, and number of cigarettes together in one model. Similarly, it would\nnot be appropriate to include smoking cessation years as a continuous variable\nand as a categorical variable together in one model.\nResponse: We apologize for the lack of clarity. Each smoking-related exposure was\nconsidered separately. Similarly, smoking cessation years were analyzed separately as\neither continuous or categorical variables in distinct models, with categorical analyses\ndesigned to explore dose-response relationships. The supplementary table was\nredesigned to make this clearer. In the revised version we have made it clearer (lines\n694-713)\n3. Table S5/L166-168: Comment that the trends between CKB and UKB are more\nconsistent for current smokers than for former smokers.\nResponse: We have added the following text to the results section lines 175-183:\n“Similar trends were observed in CKB current smokers, where smoking duration,\nsmoking exposure, and smoking intensity were all significantly and positively associated\nwith pSIN. However, among former smokers, significant associations were observed\nonly for years since smoking cessation and smoking duration, whereas associations\nwith smoking intensity and smoking exposure were not statistically significant. This may\nbe because there are more modifiers related to the difference in behavioural or\nepidemiological characteristics between Chinese and UK populations such as relapse\nafter quitting smoking in previous smokers than in current smokers.”\n4. L169-171: Very briefly, provide methodological details about how the GWAS\nwas conducted such that the reader does not need to go to the end of the article\nto the Methods. What was the outcome being associated? Also clarify if this was\ndone in UKB only.\nResponse: We have added the following text to the results lines 184-187: “To\nunderstand the genetic architecture underlying the biological consequences of smoking\npathology, as indexed by pSIN, we performed a genome-wide association study\n(GWAS) of pSIN in the UKB. We applied a linear mixed model in SAIGE, adjusting for\nage, sex, genotyping batch effects, and the first 40 principal components to account for\npopulation structure.”\n5. L172-L174: Add citations for the referenced studies.\nResponse: We apologize for the omission. We have added the references in the revised\nversion of the manuscript.\n6. L187-188: Very briefly, provide methodological details about how the exposome\nanalysis was conducted. Were all factors considered together in a single model?\nWhat statistical approach was taken? (I realize details are in the Methods, but\nhelp guide the readers through the many logical steps you took – it’s excellent,\nbut a lot!).\nResponse: We have added the following text to the revised version of the manuscript:\n“The exposome-wide analysis was conducted using generalized linear models, where\neach factor was tested separately for its association with pSIN. Models were adjusted\nfor recruitment centre, ethnicity, and smoking status.”. Lines 212-215.\n7. L103 and L240: Inconsistent number of major disease outcomes reported.\nResponse: For confirmation, the number in the L240 is correct. We have now changed\nL103 into: “Finally, we examined the associations between our proteomic-based\nsmoking score and clinical risk factors, blood-based biomarkers, and risks of 27 major\ndiseases and mortality in the UK Biobank”.\n8. L242: Add the second decimal place to the upper confidence interval limit.\nResponse: We have added the second decimal point for lung cancer (HR=1.97, CI:1.83,\n2.11) (line 276)\n9. L308-311: Can be reworded for clarity.\nResponse: We have rephrased the entire paragraph lines 340-346.\n10. Figure S10 and s11: Clarify the reference group. Is this saying that higher\npSIN scores are protective among these groups? Or is this in comparison to\npeople with high-risk previous smokers (s10) and high-risk current smokers\n(s11)?\nResponse: As requested, we have now clarified the reference groups in Figures S10\nand S11. In Figure S10, the comparisons are made between previous smokers with low\npSIN (i.e., those whose proteomic profiles resemble never-smokers) and high-risk\nprevious smokers with elevated pSIN scores. In Figure S11, the comparisons are\nbetween current smokers with low pSIN (whose proteomic profiles are similar to never\nsmokers) and high-risk current smokers with high pSIN.\nThe forest plot shows that those with proteomic profiles similar to never smokers indeed\nhad lower incident disease risks. The observed differences in health outcomes highlight\nthe ability of pSIN to stratify risk beyond traditional smoking classifications.\nWe have updated the figure legends to ensure this distinction is clear.\n11. In the discussion, comment on the fact that the external validation study was\nlimited to only males, and that the health association analysis was not validated\nin this population.\nResponse: Thank you for your comment. Initially, we limited the external validation in\nCKB to males due to the very low prevalence of smoking among females in this cohort.\nHowever, we recognize that restricting validation to only one sex may limit the\ngeneralizability of our findings. To address this, we have now expanded the validation to\ninclude both male and female participants. The model trained in the UKB training\ndataset was re-evaluated in the full CKB cohort, achieving an AUC of 0.91. Additionally,\nmajor diseases with a sufficient number of cases (including lung cancer, COPD, stroke,\nrespiratory disease (combined), and mortality) remained significantly associated with\npSIN in Cox proportional hazards models, further supporting the robustness of our\nfindings.\nWe have updated the result and discussion to match the new results Lines (100, 114-\n116,139-142,144-146,284-287)\n12. L461: Comment on the % missingness for key variables.\nResponse: We have added the % missingness of ley variables to the methods section\nlines 534-540.\n13. L503: Justify why “occasional smokers” were considered never smokers.\nResponse: We classified \"occasional smokers\" as never smokers to ensure consistency\nin our comparison groups and to align with conventions in CKB, UKB and prior\nepidemiological studies3–6, in which only those who had smoked at least one cigarette a\nday persistently for at least 6 months were considered as smokers. Occasional smokers\ncomprised a very small proportion of the population and did not report a sustained\nsmoking history, with similar levels of exhaled CO levels in CKB to that among never\nsmokers. Given that our study focuses on the long-term molecular impact of regular\nsmoking, we categorized occasional smokers with never smokers to avoid potential\nmisclassification bias and to maintain a clear distinction between individuals with\nsignificant smoking exposure and those without.\nWe have now added this justification in the text lines 514-520 to clarify our approach.\n14. For the regression models throughout the paper, justify how covariates were\nchosen (e.g., section starting L583).\nResponse: In our analyses, the selection of covariates for the regression models was\nbased on a combination of prior literature, biological plausibility, and statistical\nconsiderations to minimize confounding while avoiding overadjustment. To make the\nmethodology clearer, this has been changed to (lines 694-713):\nFor associations between smoking history and pSIN, models were adjusted for basic\nsocioeconomic factors including recruitment centre, ethnicity, education years, and\nTownsend deprivation index. Each smoking-related exposure was considered\nseparately. Similarly, smoking cessation years were analyzed separately as either\ncontinuous or categorical variables in distinct models, with categorical analyses\ndesigned to explore dose-response relationships.\nFor associations between blood biomarkers/clinical risk factors/haematological\nmeasurements to pSIN, continuous exposure variables, standardization was applied\nbefore inclusion in the models. Associations were adjusted additionally for lifestyle\nfactors including IPAQ activity group and alcohol intake frequency as they are known to\ncofound physiology status and liver damage. To explore the added value of pSIN\ncompared to self-reported smoking status, sensitivity analysis was performed adjusting\nadditionally for self-reported smoking status.\nFor exposome-wide association analysis (all available social-economic and lifestyle\nvariables available in UKB), models were only adjusted for the most basic recruitment\ncentre, ethnicity and smoking status to allow exploration of a wide range of potential\nassociations between the exposome and pSIN without masking potential signals by\ncontrolling for too many variables.\nSocial-economic and lifestyle confounding factors were chosen based on previous\nliteratures54–56.\nReviewer #2 (Remarks on code availability):\nNA\nReviewer #3 (Remarks to the Author):\nThis is an exemplary piece of analytical research into Proteomic Olink signatures\nand their association with multiple diseases.\nThe raw data was taken from the UKBB and the validation undertaken with the\nChina Kadoorie Biobank, The AUC values are indeed very high, far higher than\nmany similar biomarker analysis.\nThe sub-analysis was not just with smokers and never smokers but also with\nenvironmental exposures, health status (ie. obesity), sociodemographic and\nlifestyle factors and mortality data. The pSIN parameter was also used to\ndifferentiate recovery status of previous smokers, which is a completely new and\nimpressive approach.\nThe indepth details provided for machine learning analysis and statistical tools\nare well described.\nThis is an academic exercise providing a very informative manuscript. The\nauthors have also included a number of possible limitations in their discussion,\neven indicating that the study has only limited statistical power to detect\nassociations between pSIN and common diseases.\nHowever, even considering all the positive elements provided in this analysis,\nthere is no attempt to indicate how it maybe used or implemented in clinical\npractice. We are now in an era of not just discovery research but also\ntranslational research. The authors should have pencilled in a possible way their\nfindings could be tested in a primary care clinical trial and the costs involved in\nutilising the 51 proteomic biomarkers (Olink or another platform) and the\ncollection of the wide range of clinical, epidemiological, environmental data. And\nthis is a realistic way forward for identification of high-risk individuals?\nResponse: We thank the reviewer for their time, input and positive feedback. We\nacknowledge that while our study provides a strong foundation for understanding the\nmolecular signatures of smoking and its associations with disease risk, further steps are\nneeded to explore its potential clinical applications.\nTo bridge the gap between discovery and translational research, we propose that future\nstudies should evaluate the feasibility of integrating pSIN into primary care settings. This\ncould involve testing its utility in a clinical trial designed to assess whether proteomic-\nbased risk stratification improves early detection and targeted interventions for high-risk\nindividuals. Key considerations include the cost-effectiveness of measuring the 51\nproteomic biomarkers using Olink or alternative platforms, as well as the logistical\nchallenges of incorporating these assessments into routine clinical workflows.\nAdditionally, implementing pSIN in practice would require careful evaluation of whether\nit provides actionable information beyond traditional risk factors, and whether its use\ncould justify the costs associated with proteomic profiling and the collection of\ncomprehensive clinical, epidemiological, and environmental data. While high-throughput\nproteomics is currently expensive, costs are expected to decline with technological\nadvancements, potentially making it a viable tool for personalized risk assessment in\nthe future.\nWe have now included a discussion of these considerations in the manuscript to\nhighlight possible pathways for translation into clinical practice. Lines (468-486)\nReviewer #4 (Remarks to the Author):\nThe work provides the first affinity proteomics signature to differentiate between\n“never smoking” and “currently smoking” subgroups. The signature is based on\na machine learning model trained with gradient boosting and subsequent feature\nselection, resulting in a signature of 51 selected proteins. Based on this gradient\nboosting trained machine learning model, a smoking Index (pSIN) is defined. It is\nencouraging to see that within the 51 proteins around 5-10 are highly expressed\nin the lung, and other relevant tissues, while the signature is obtained from\nplasma samples. The proteins and pathways associated with the signature give a\nnovel perspective on underlying biological changes, induced by smoking. Note\nthat true causality is or course difficult to show given the study set up.\nNevertheless, there is external validation for the male population based on an\nexternal cohort. The test also provides a novel and different perspective on self-\nreporting, and methylation profiles for smoking. Where the previously reported\nmethylation profiles seem to be more closely associated with smoking history,\nwhile the plasma profiles seem to reflect the response to smoking – as the\nreported index becomes continuously lower after smoking cessation. Overall, the\nstudy provides a novel perspective on the bodies smoking response, and has the\npotential to monitor the impact of smoking on the body.\nMajor issues:\n1. Firstly, the added value of the proteomics signature, over the self-reported\nsmoking and the existing methylation signature, should be made more clear. In\naddition, the added value should be highlighted more where contrastive results\nhave already been provided (for example, the difference between methylation and\nproteomics profile in the “previous smoker group”). Moreover, in all associations\nreported with the pSIN value, the strength of associations with the self-reported\nsmoking status, and methylation profile should be provided as a reference, to\nmake the novelty and added value of the proteomics signature more clear; this\nincludes the association analysis of major diseases and mortalities, genomics\nfactors /GWAS results, haematological measures, social-demographic and\nlifestyle factors.\nResponse: We would like to thank the reviewer for their time and helpful comments. We\nagree that the added value of the pSIN over self-reported smoking status and the\nexisting methylation signature should be made clearer, particularly in association\nanalyses. To address this, we have performed additional sensitivity analyses, adjusting\nfor smoking status in associations with haematological measures, biological markers,\nclinical risk factors, and social-demographic and lifestyle factors. These analyses help to\nbetter isolate the specific contribution of pSIN, independent of self-reported smoking\nstatus. (line 243-250) (Fig s6a,b)\nRegarding associations with incident disease outcomes, we have already conducted\nanalyses stratified by smoking status, with further adjustments for variables such as\nsmoking duration, smoking exposure, and smoking cessation. This approach serves a\nsimilar purpose, ensuring that the associations we observe with pSIN do not simply\nreflect the effects of smoking history.\nIn the GWAS analysis, we focused on identifying all genomic factors associated with\npSIN, including those linked to smoking histories, smoking-related diseases, and other\nlifestyle factors. While we did not adjust for smoking status in this context, we did\nhighlight that 58% of the significantly associated variants were not found to be linked to\nsmoking initiation in previous genetic or epigenetic studies. This reinforces the novelty\nof the pSIN signature, showing that a substantial portion of the genetic variants\nassociated with pSIN are independent of smoking initiation, and may reflect broader\nbiological processes.\nFor comparison with methylation, unfortunately, there were currently no epigenetic data\navailable in either the UKB or the CKB. Additionally, the existing epigenetic cohorts did\nnot have proteomic data, and usually, these cohorts tend to have smaller sample sizes\nand shorter follow-up periods, making it challenging to establish the link between\nepigenetic signatures of smoking and future risks of smoking-related diseases.\nHowever, this limitation also presents one of the key advantages of our research, as the\ncombination of proteomics with a large sample size and long follow-up period in UKB\nand CKB offers a more comprehensive approach to understanding the biological\nconsequences of smoking.\nWe have updated the manuscript to include these clarifications, ensuring that the added\nvalue of the proteomics signature is highlighted in comparison to self-reported smoking\nstatus and methylation profiles and that the results from the additional analyses are\npresented.\n2. The labels used to train the machine learning model (“current smokers” vs\n“never-smoker”) are extremely imbalanced in the dataset. This can give biases in\nseveral reporting measure of classification measures, including the AUC-ROC. A\nwider range of performance measures should be reported (both on validation and\ntest set), including: F1, balanced accuracy, and the AUC-PR/RC. In addition, one\ncan undersample the majority class in the held-out test set, to get a better\nperformance estimate.\nResponse: To address the issues raised, we have now added F1, balanced accuracy\nand the averaged precision score in addition to AUC to give a more comprehensive\nassessment of the model performance. We have added all scoring matrices in the result\nsection. Lines 122-131, lines 140-141.\n3. The argument that the signature cannot be validated on the 2137 female\nsmoking participants does not hold. It is possible to report accuracies and AUC\nROC statistics on as little as 50–100 samples. Presumably, the signature does not\ntranslate to the female cohort. Instead of hiding this, the authors should report\nthe separate accuracy statistics for the female only test external validation set, as\nwell as a separate performance analysis on the female subset of the UK biobank\ncohort. If there are differences, and the signature does not translate well to\nfemale subjects, this is in itself interesting, and should be reported fairly.\nResponse: Please see our response to Reviewer 2’s 11th comments on the same issue.\nWe have now re-validated the model using the entire CKB cohort, which includes both\nmale and female participants. The UKB model derived in the training set was\nsubsequently externally validated in CKB, achieving an AUC of 0.91 (SD=2.22*10-16;\nFig 2a) (F1=0.93, SD=1.11*10-16; AP=0.87, SD=1.11*10-16; BA=0.79, SD=2.22*10-16),\nand a sensitivity of 70.8% and specificity of 95%. (line 139-142)\n4. Missing data was imputed. Please describe more clearly what data was\nimputed, and in which analysis imputed data was used. It is essential that in the\ntest data set, over which the performance of the classification model was\nmeasured, no imputed data was used for the labels (i.e. current smokers vs non-smokers).\nIn addition, the variables tested for association with pSIN should be free from\nimputation or at least it should be tested if the association holds when imputation is not\nperformed.\nResponse: Please see our response to Reviewer 1’s 14th comments on the same\nissue. Only co-variates of the linear association between biomarkers and pSIN, clinical\nrisk factors and pSIN, GWAS, exposome analysis and multivariable Cox model were\nimputed. No exposure or outcomes were imputed. No imputed data was used in the\nclassification model. (lines 534-537)\nMinor issues:\n1. In the abstract, the added value of pSIN with respect to self reported smoking\nhistory, and the methylation profile should be made more clear.\nResponse: We have stated that the association between pSIN and diseases was\nperformed stratified by smoking status and adjusted by other smoking histories (lines\n45-48). However, as mentioned earlier we do not have methylation information on the\nparticipants to include that.\n2. In the abstract, the rationale behind the genetic variant associations is\ncompletely lost. (Why would one expect a link between a lifestyle choice of\nsmoking and genetics?). Perhaps take this out if there is no sufficient space to\nexplain this. It is very confusing to report this without appropriate context.\nResponse: The purpose of GWAS is to understand the genetic architecture underlying\nthe biological consequences of smoking pathology, as indexed by pSIN. Specifically, we\nsought to investigate how genetic factors contribute to the proteomic signature of\nsmoking. We have added sentences to state more clearly the rationale of the GWAS\nanalysis (lines 47-49).\n3. Objectivity of signature:\na. Page 3, line 71/72 “… there is an urgent need to develop objective\nmeasures …”\nb. page 13 line 272 “This suggests pSIN can serve as an objective test …”\nMaking claims about objectivity, should be accompanied by a discussion on the\naccuracy of the model. What if this signature is used to check someone's\nsmoking history for insurance purposes? What guarantee can be provided that\nsomeone with a high pSIN score has indeed smoked? Make the limitations of the\nmodel more clear, when objectivity of the model is claimed.\nResponse: As with any predictive model, the accuracy of pSIN depends on the quality of\nthe data and the model’s generalizability. Although we have demonstrated high\npredictive accuracy (AUC = 0.95) in the UKB and CKB datasets, there are potential\nlimitations, particularly when applying the signature to new populations or for specific\nuses (e.g., insurance assessments). Although we demonstrated that a high pSIN score\nis strongly associated with smoking-related biological changes, it does not guarantee\nthat someone with a high score has indeed smoked. Other factors, including genetic\npredisposition, environmental exposures, and other health conditions, could influence\nproteomic profiles. Hence its use as a definitive marker of smoking history should be\napproached with caution.\nWe have revised the text to clarify these limitations and emphasize the need for further\nvalidation and careful interpretation when considering pSIN for specific applications,\nsuch as insurance or clinical risk assessment. These limitations are now addressed in\nboth the discussion and the conclusion to provide a more balanced view of the model’s\ncapabilities and potential uses. lines 484-491\n4. “age” and “sex” are regressed out of the proteome expression data. As this is\ndone via a linear model, and the machine learning model (gradient boosting) is\nnon-linear, one cannot assume that there is no remaining signal left in the data to\npredict age or sex on the regression data. One way to test if the signal is taken\nout completely, is by training a classifier to predict sex or age on the regressed\ndata set. Alternatively, one can discuss the impact on the results and signature if\nthe regression does not remove all associations with age and sex in the proteome\nexpression data.\nResponse: To address the concern regarding the potential residual signal of age and\nsex in the proteomic expression data after linear regression, we have performed\nadditional analyses to rigorously evaluate the effectiveness of the regression process.\nSpecifically, we trained gradient boosting models to assess whether any predictive\nsignal for age or sex remained in the regressed data.\nFor age, we trained regression models to predict age using each protein in the\nregressed dataset. The results showed a mean R² of -0.0042 (SD = 0.0089), indicating\nthat the models performed no better than random chance. This suggests that the linear\nregression successfully removed age-related signals from the proteomic data. For sex,\nwe trained classification models to predict sex using each protein in the regressed\ndataset. The models achieved a mean AUC of 0.51 (SD = 0.017), which is equivalent to\nrandom guessing. This confirms that sex-related signals were effectively removed by\nthe regression process.\nThese results demonstrate that the linear regression procedure successfully eliminated\nthe influence of age and sex from the proteomic expression data. Consequently, we can\nconfidently conclude that any associations identified in our downstream analyses are\nunlikely to be confounded by these variables. We have added these findings to the\nrevised manuscript to provide further transparency and support for our approach. lines\n565-575\n5. Please explain how the model probability score for a given class that comes\nout of the classification model is transformed to a pSIN score with a range of\napproximately -10.0-10.0. Make in the text of the results section clearer what a\npSIN score of 0.0 indicates, or more generally how the reader can quantitatively\ninterpret the pSIN score.\nResponse: The pSIN score is derived from the raw output of the LightGBM gradient\nboosting model, which represents the sum of the leaf values across all trees in the\nmodel. For binary classification tasks, this raw score corresponds to the log odds of the\npositive class (in this case, being a current smoker). The LightGBM model typically\noutputs raw scores (logits) in the range of approximately -10 to 10, where a score of 0\nindicates a neutral prediction, corresponding to a 50% probability of being in the positive\nclass (sigmoid(0) = 0.5). Scores closer to 10 indicate a high confidence prediction for\nthe positive class (sigmoid(10) ≈ 0.99995, or ~99.995% probability), while scores closer\nto -10 indicate a high confidence prediction for the negative class (sigmoid(-10) ≈ 4.5 ×\n10⁻⁵, or ~0.0045% probability).\nIn our analysis, we set the classification threshold at a raw score of -1.29, which\ncorresponds to the point where the false positive rate (FPR) is 0.05. This threshold was\nchosen to balance sensitivity and specificity in our predictions. pSIN higher than the\nthreshold indicates a higher likelihood of being in the positive class (smoker), with larger\nvalues reflecting greater confidence, while pSIN smaller than the threshold indicate a\nhigher likelihood of being in the negative class (non-smoker), with more negative values\nreflecting greater confidence. We have added this in the method section lines 656-669.\n6. Very strong associations between pSIN and haematological measurements and\nthe blood biochemical composition are found (S4 and S5). Is there any clear link\nbetween the proteins found in the plasma based pSIN signature, and these\nfindings? Would any of these measures by itself be sufficient to classify “current\nsmokers” and “never-smokers”.\nResponse: The purpose of the analysis is to investigate if pSIN is reflective of different\nfunctions represented by those biomarkers and if pSIN provides added value in addition\nto self-report smoking status.\nThe proteins included in the pSIN signature reflect a broad spectrum of biological\nprocesses, including immune regulation, cell proliferation, and inflammation, which are\nknown to be influenced by smoking. Many of the haematological and biochemical\nmeasures (e.g., inflammatory markers such as GlycA and CRP, lipid profiles, and kidney\nfunction biomarkers) are direct or indirect indicators of the systemic impact of smoking.\nFor example, pSIN proteins related to immune activation, such as IL12B and MMP12,\nare closely linked to inflammatory pathways, which are often altered in response to\nsmoking. These inflammatory changes are reflected in biomarkers such as GlycA and\nCRP, which we observed to be strongly associated with pSIN levels. Additionally,\nproteins involved in vascular remodeling and metabolic processes, such as ACVRL1\nand APOA, are associated with biochemical markers like triglycerides and HDL\ncholesterol, which are known to be affected by smoking.\nWhile the haematological and biochemical measures we identified show significant\nassociations with pSIN, none of these measures in isolation would be sufficient to\nreliably classify \"current smokers\" and \"never smokers.\" This is because smoking has\nbroad and complex effects on multiple biological systems, which are reflected in the\nproteomic signature. In contrast, the individual biomarkers may capture only specific\naspects of smoking-related changes (e.g., inflammation or lipid metabolism) and do not\nprovide a comprehensive view of the cumulative biological impact of smoking. pSIN,\nwith its multi-protein signature, captures this broader biological context, offering a more\naccurate and nuanced classification of smoking status. In addition, we tested the\nprediction power of haematological measurements against smoking status in UKB using\na gradient boosting model and in the testing dataset the AUC is 0.741 (Std=0.008).\n7. P6 line 178-183. It is unclear what the rationale is for performing these genetic\ncorrelations. Are you looking for risk factors to start smoking? Or are you\ninterested to see which genetic variants can worsen the effects of smoking, or\nperhaps cause a smoking-like signature in non-smokers? Make the aim of the\nanalysis more clear, and justify the choice of datasets to ask this question.\nResponse: We apologize for the lack of clarity. The fundamental reason for including\ngenetic associations in this analysis is to understand the genetic architecture underlying\nthe biological consequences of smoking pathology, as indexed by pSIN. Specifically, we\nsought to investigate how genetic factors contribute to the proteomic signature of\nsmoking. The goal is to identify potential genetic variants that influence the biological\neffects of smoking or exacerbate the health consequences of smoking, as well as\nwhether certain genetic variants might contribute to a \"smoking-like\" biological signature\nin non-smokers.\nWe utilized the GWAS Catalog, which includes publicly available datasets on smoking\nbehaviour and related health outcomes, to examine how genetic variants associated\nwith pSIN either directly overlap or correlate with those linked to other diseases or traits\ninfluenced by smoking. By using these datasets, we aimed to capture both the genetic\npredispositions for smoking behaviour and the genetic variants that influence the\nbiological consequences of smoking, thus building a comprehensive understanding of\nthe genetic architecture underlying pSIN and its implications for smoking-related\ndiseases. We have added the rationale for performing GWAS of pSIN to the revised\nversion of the manuscript, lines184-187\n8. It may be helpful to the reader to explain better how the “smoking history” has\nbeen collected. Is this self-reported? P8 line 226: explain more clearly why it\nmakes sense to look at variance explained from “smoking history” in a profile\nthat was learned to classify current smokers vs never-smokers.\nResponse: Smoking history was primarily collected through self-reported touch-screen\nquestionnaires in the UK Biobank and the CKB cohort. In these datasets, participants\nwere asked to report their smoking status, the number of cigarettes smoked per day, the\nage at which they started smoking, and the age at which they stopped (if applicable)\nthrough a questionnaire as indicated in the method section line 533, 594\nThe reason for assessing the variance explained by \"smoking history\" in the pSIN\nprofile is to understand the extent to which smoking-related biological effects are\ncaptured by the pSIN model, which was specifically trained to distinguish between\ncurrent smokers and never smokers. Although the model was trained to classify current\nsmokers vs. never smokers, examining variance explained by smoking history allows us\nto assess how much of the proteomic signature is attributable to smoking behaviour\nversus other variables that might influence the biological markers, such as genetics or\nother environmental factors.\nWe have clarified these points in manuscript lines 362-365 to provide a better\nexplanation for the rationale behind this analysis and the relevance of self-reported\nsmoking history in relation to pSIN.\n9. P22 “Feature interpretation and selection” it should be clearly described over\nwhich data the feature selection and hyperparameter tuning was performed.\nFigure 1 suggests that this is done over the train/validation set and not over the\nhold-out test set; as it should be. However, this should be described explicitly in\nthe text as well.\nResponse: We have now explicitly stated in the manuscript that feature selection and\nhyperparameter tuning were performed on the training/validation set, and not the hold-\nout test set, as Figure 1 suggests.\nTo clarify, the feature selection process using methods like the Boruta algorithm and\nhyperparameter tuning was carried out on the training set during the model\ndevelopment phase. This ensures that the model is optimized before testing on the\nhold-out set, which was reserved exclusively for model evaluation to avoid data leakage\nand ensure an unbiased performance assessment.\nWe have updated the text in method section lines 634-647 to explicitly describe this\napproach and provide more clarity regarding the data used for feature selection and\nhyperparameter tuning.\nReviewer #4 (Remarks on code availability):\nThe published code is not sufficient to redo the analysis. The readme file is not\nsufficient. It is not clear which input files are required to run the script. The code\nis poorly annotated / commented. Internal code review is required, before\nresubmission.\nResponse: With new analyses to address the issues raised by reviewers, we will\nprovide an updated and more readable version of the code upon acceptance of the\npaper.\nRESPONSES TO REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have addressed all comments nicely. I only have one outstanding comment relating\nto my previous comment #8. Authors have performed additional sensitivity analyses adjusting\nassociations between biomarkers or risk factor and pSIN by smoking status, and conclude :\n“while pSIN captures smoking-related biological signatures, it also provides additional\ninformation beyond conventional smoking status, particularly for certain biomarkers and health\noutcomes”. It would be important to understand to what extent pSIN is capturing other\nbiological factors if there were to be considered for any downstream translational application.\nTherefore, I strongly suggest authors perform a more in-depth analysis looking at the proportion\nof pSIN variance explained by a comprehensive set of biological (prevalent disease status,\nclinical biomarkers, anthropometric, lifestyle (including smoking status), dietary and\ndemographic factors), genetic (e.g. PRS for smoking, COPD, lung cancer, etc.) and technical\nvariables (potentially affecting proteome measurements, e.g. fasting time, study centre, etc.),\nwhen all variables compete in the same model.\nResponse: As suggested by the reviewer, we have now performed a linear model followed by\nANOVA to estimate the proportion of pSIN variance explained by each category of variables\n(partial R2), stratified by smoking status (Fig S7, lines280-294). In the overall UKB population,\nsmoking history accounted for the largest proportion of variance (partial R2 = 42.5%), followed\nby clinical biomarkers (5.3%). In contrast, prevalent disease status and polygenic risk scores\nexplained only 0.1% and 0.02% of the variance, respectively. Technical factors—including fasting\ntime, assessment centre, and assay batch—contributed just 0.3% of the variance, underscoring\nthe robustness of the model. Among the current smokers, clinical biomarkers explained the\nlargest proportion of variance in current smokers (9.9%), followed by smoking history variables\nother than smoking status (7.8%) and lifestyle factors (3.7%). Clinical biomarkers also remained\nimportant contributors to pSIN in previous smokers (4.1%) and never smokers (4.2%). Smoking\nhistory explained the greatest variance (6.9%) among previous smokers, whereas in never\nsmokers, it accounted for only 0.03% (passive smoking). Finally, technical factors contributed\nmodestly to pSIN variance in current (1.4%), previous (0.4%), and never smokers (0.3%),\nunderscoring the robustness of our model.\nDetailed methodology was updated in the method section (lines775-787).\nFigure s7 Relative contribution of each category to pSIN when competing in one model.\nLinear regression followed by ANOVA was performed to study the contribution of each category to pSIN stratified\nby smoking status. Partial R2 was shown, and the exact percentage was annotated if it is larger than 1%. For\nsmoking history, smoking status and pack years were included for the whole population, pack years, smoking years,\ntobacco type, number of cigarettes per day were included for current smokers, pack years, smoking years,\nsmoking cessation years, tobacco type, number of cigarettes per day were included for previous smokers, and passive\nsmoking exposure was included for never smokers.\nReviewer #4 (Remarks to the Author):\nThe clarity of the manuscript has much improved due to the revisions. In particular, the value\nand impact of the pSIN profile are much more explicitly described, and the added value is more\nclearly demonstrated by the additional analyses.\nOne minor issue remains:\nComment Reviewer 1, comment 3:\nThe background of the GO enrichment analysis may still be problematic. Olink panels are not a\nset of randomly chosen proteins (unlike RNAseq or untargeted MS proteomics), but are a\nselected set of proteins based on disease profiles. ClusterProfiler assumes an untargeted omics\nset. It would be good, to use a method, that can take the Olink target panel as background into\naccount, otherwise pathways may come up, that were simply selected within the Olink panels.\nResponse: We apologise for the lack of clarity. While we understand the concern of the\nreviewer, we would like to point out that we have not performed a pathway enrichment analysis\nof the identified proteins in the pSIN. We have only used the algorithm to annotate the function\nof the individual proteins. We hope this clarifies any confusion. We have also clarified in the\nMethods section (lines 708-709).\nReviewer #4 (Remarks on code availability):\nSome parts of the code are still quite poorly annotated, but the overall structure is now clear.\nResponse: More annotations added to the code to make it more readable."}]

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