Studies have shown how air pollution and temperature affect blood pressure. We investigated the combined effect of air pollution and temperature on blood pressure in a cohort of older German women. We conducted a cross-sectional analysis of systolic (SBP) and diastolic blood pressure (DBP) data from follow-up 3 (2012-2013) of the Study on the influence of Air pollution on lung function inflammation and ageing (SALIA) cohort. Short-term data on daily air pollutants and temperature were obtained from the German Weather Bureau and the German Environment Agency. A generalized additive model was used to capture their combined effect. Stratified analyses were performed to quantify the variation in the estimated effects. The core model was adjusted for potential covariates. We observed a combined association of temperature and air pollution with blood pressure. We found that lower temperatures and higher levels of air pollutants such as PM2.5 and NO2 were associated with higher SBP and DBP. However, higher O3 exposure was generally associated with lower SBP and DBP. Stratified analyses showed that the associations between temperature, air pollution, and blood pressure were stronger among women living in urban areas and those with lower socio-economic status. The combined effect of low ambient temperature and high air pollution substantially increased BP among older German women.

BACKGROUND:Synthetic data hold substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while preserving privacy. METHODS:We propose adversarial random forests (ARF) as an efficient and convenient method for synthesizing tabular epidemiological data. To evaluate its performance, we replicated statistical analyses from six epidemiological publications covering blood pressure, anthropometry, myocardial infarction, accelerometry, loneliness, and diabetes, from the German National Cohort (NAKO Gesundheitsstudie), the Bremen STEMI Registry U45 Study, and the Guelph Family Health Study. We further assessed how dataset dimensionality and variable complexity affect the quality of synthetic data, and contextualized ARF's performance by comparison with commonly used tabular data synthesizers in terms of utility, privacy, generalization, and runtime. RESULTS:Across all replicated studies, results on ARF-generated synthetic data consistently aligned with original findings. Even for datasets with relatively low sample size-to-dimensionality ratios, replication outcomes closely matched the original results across descriptive and inferential analyses. Reduced dimensionality and variable complexity further enhanced synthesis quality. ARF demonstrated favourable performance regarding utility, privacy preservation, and generalization relative to other synthesizers and superior computational efficiency. CONCLUSIONS:In summary, ARF reliably generates high-quality synthetic data that replicate diverse epidemiological analyses while offering a competitive privacy-utility trade-off.

Introduction and Objective: Air pollutants such as particulate matter (PM2.5, PM10) and nitrogen dioxide (NO2) have been linked to increased risk of diabetes-related comorbidities. However, whether such exposures contribute to the early development of comorbidities remains unknown. This study aims to determine whether long-term exposure to PM2.5, PM10 and NO2 is associated with diabetes-related comorbidities in individuals with recent-onset T1D and T2D and diabetes subtypes. Methods: Data are based on 985 adults from the German Diabetes Study (GDS; 358 with T1D, 627 with T2D, time since diagnosis <1 year). Diabetes-related risk factors and comorbidities included 10-year cardiovascular disease (CVD) risk (SCORE2-Diabetes), urinary albumin as an indicator of impaired kidney function and clinical diagnosis of distal sensorimotor polyneuropathy (DSPN). Air pollution exposures were derived from the data provided by the German Federal Environment Agency. Exposures to PM2.5, PM10 and NO2 were calculated as average of exposures during the 5 years preceding study enrolment. Each exposure-outcome pair were estimated using regression adjusted for age, sex, smoking, socioeconomic status, season and physical activity. Results: In participants with T2D, exposure to higher PM2.5 per interquartile range increase was associated with higher CVD risk (exp(β) = 1.12 (95% CI 1.03-1.22)), higher urinary albumin (exp(β) = 1.78 (1.49-2.13)) and higher odds of DSPN (OR = 1.46 (1.01-2.10)). Similar associations were observed for PM10, and NO2. Among individuals with T1D, higher exposures to PM2.5 and PM10 were also associated with higher urinary albumin (exp(β) = 1.52 (1.20-1.93) and 1.32 (1.05-1.65), respectively). Exploratory analyses suggested differences in associations across diabetes subtypes. Conclusion: Long-term exposure to air pollution was consistently associated with an increased burden of diabetes-related risk factors and comorbidities already in recent-onset T2D, while associations were less consistent in T1D. Disclosure N. Singh: None. C. Wigmann: None. S. Kress: None. O. Zaharia: None. K.B. Bódis: Other - travel support; Ended; Sanofi. Other - lecture honoraria; Ended; Pfizer Inc. S. Trenkamp: None. B. Sun: None. G.J. Bönhof: None. K.A. Jandeleit-Dahm: None. S. Schlesinger: Other - Speaker honorarium; Ended; Novo Nordisk. R. Wagner: Advisory Panel; Current; Sanofi. Speaker's Bureau; Ended; Daiichi Sankyo, Novo Nordisk. T. Schikowski: None. M. Roden: Advisory Panel; Current; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly, Madrigal Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Echosens. C. Herder: None. Funding The German Diabetes Center initiated and financed the German Diabetes Study (GDS), which is funded by the German Federal Ministry of Health (Berlin, Germany), the Ministry of Innovation, Science, Research and Technology of the state North Rhine-Westphalia (Düsseldorf, Germany) and grants from the Federal Ministry of Research, Technology and Space (BMFTR) to the German Centre for Diabetes Research e. V. (DZD). M.R. is further supported by grants from the European Funds for Regional Development (EFRE-0400191), EUREKA Eurostars-2 (E! 113230 DIAPEP), European Community (HORIZON-HLTH-2022-STAYHLTH-02-01: P.A), the German Science Foundation (DFG; GRK 2576 vivid) and the Schmutzler Stiftung. The IUF is funded by the Ministry of Culture and Science of North Rhine-Westphalia (MKW) and the Federal Ministry of Research, Technology and Space (BMFTR).

Allergies have been linked to immune dysfunction, genetics, and environmental factors. However, environmental exposures are often highly correlated similar to genetic predictors making the cumulative assessment of exposures difficult. Here, we aim to investigate the relative contribution of genetic variants as well as different individual and environmental factors on the presence or absence of allergies in 450 elderly German women enrolled in the SALIA cohort study living in the Ruhr area by using genetic risk score (GRS) and exposomal risk scores (ERS). We used the novel cross leverage scores (CLS) to select genetic variants to be included in the GRS. The weights of the risk scores were obtained through bootstrapped and cross-validated ridge regression. We characterized the relative contributions of the risk scores to presence of allergies such as atopic dermatitis, asthma, or allergic rhinitis using McFadden’s Pseudo R-squared. Overall, our model was able to explain 11.13% of the variance of allergy diagnosis. The modest variance explained is consistent with prior work on complex polygenic and environmentally influenced traits, reflecting that no single exposure or domain is likely to fully capture individual risk. The GRS had the highest relative contribution at 3.80%, followed by the meteorological risk score with 1.13%. This method can easily be adapted to other diseases and can facilitate health risk assessments of exposomal factors. In addition, the results may aid policy-making, for example, by regulating specific sources of exposure.

This exploratory in vivo study investigated the impact of solar-simulated ultraviolet (UV) radiation (UVB plus UVA) on the composition of the human skin microbiome in healthy male volunteers. Thirty Caucasian men were exposed to suberythemal and erythemal doses of UV radiation (0.5, 0.7, and 1.0 minimal erythema dose, MED) on defined areas of the lower back. Skin swabs were collected from both irradiated (n = 243) and nonirradiated control sites (n = 81) 30 min, 24 h, and 96 h postexposure. The microbial profiles were generated using flow cytometry, and the data were analyzed via the open-access bioinformatic platform FlowSoFine™. The results revealed pronounced alterations in the microbial composition, with changes already detectable 30 min after exposure. Although partial recovery was observed over time, certain microbial shifts persisted. Further analysis indicated dose-dependent trends in microbiome changes, suggesting a potential relationship between the extent of microbial alteration and the applied UV dose. These results suggest that even low, nonerythematous exposure to solar-simulated UV radiation can rapidly alter the microbial balance of the skin and emphasize the role of UV radiation as a potent modulator of the skin microbial homeostasis.

Background Climate change and extreme temperatures significantly impact public health by influencing morbidity and mortality. However, the cause-and-effect relationship remains unclear due to limited data on individual characteristics. Objective This study investigates the association between extreme temperature and all-cause and cause-specific mortality in elderly German women, using data from the SALIA cohort (4,756 women, with 718 deaths recorded from a mortality follow-up in 2008). Methods We used a time-stratified case-crossover study design with a distributed lag nonlinear model (DLNM) with up to 5-day lags. Effects of temperature on mortality were expressed as odds ratios (OR), which were the result of a conditional logistic regression. Results A nonlinear association between mean ambient temperature (Tmean) and all-cause and cause-specific mortality was found. Higher temperatures (15°C; 75th percentile & 21°C; 95th percentile) had a significant delayed association with all-cause mortality starting at lag 4. Lower temperatures (-1°C; 5th percentile & 5°C; 25th percentile) showed no significant effects on mortality. Moreover, no significant associations were observed for cause-specific mortality, such as mortality due to cardiorespiratory disease (CRD), cardiovascular disease (CVD), or ischemic heart disease (IHD). Sensitivity analyses further confirmed the robustness of the findings. Conclusion Higher temperatures had significant delayed effects on all-cause mortality, while lower temperatures and cause-specific mortality showed no significant associations. Cohort studies are essential for elucidating temperature-mortality relationships and guiding preventive strategies, though further research is needed to understand the complex interplay between temperature and health outcomes, considering various individual factors.

OBJECTIVE:To investigate the interplay between the genetic predisposition to successful ageing and air pollution on lung disease in healthy aged German women under the hypothesis that ageing and lung diseases share mechanisms of oxidative stress and inflammation that can be regulated by genetic predisposition and environmental factors. DESIGN:German Study on the influence of Air pollution on Lung function, Inflammation and Aging prospective cohort between baseline (1985-1994) and follow-up (2007-2010). SETTING:Urban Ruhr area and the adjacent rural Münsterland in Germany. PARTICIPANTS:At baseline, 4874 women aged 55 years living between 1985 and 1994 in the setting and at follow-up examination, 834 of them participated. MAIN OUTCOME MEASURES:Chronic lung disease was defined as any of asthma, chronic bronchitis, cough (with sputum) or chronic obstructive pulmonary disease. Chronic individual exposures to nitrogen dioxide (NO2), nitrogen oxides, particulate matter with median aerodynamic diameters <2.5 (PM2.5), PM10, PMcoarse and PM2.5 absorbance based on European Study of Cohorts for Air Pollution Effects land-use regression models were used. Main and interaction effects between the genetic risk score (77 single-nucleotide polymorphisms (SNPs) related to successful ageing) and air pollutant exposures were investigated using adjusted logistic regression models. RESULTS:In 560 women (67-80 years), chronic lung disease was present in 156. Higher exposure to air pollution was associated with increased odds by up to 43% per IQR-increase in NO2 (IQR=11.6 µg/m³, 95% CI 1.15 to 1.77). The genetic make-up reduced the negative impact of air pollution (gene-environment interaction with NO2: OR=0.66, 95% CI 0.45 to 0.96), while a healthy lifestyle further strengthens this association. CONCLUSIONS:In elderly women, genetic predisposition based on successful ageing SNPs likely reduces the negative impact of air pollution on chronic lung disease, while a healthy lifestyle further strengthens this association.

Chronic subclinical inflammation, which gradually increases with age, is a major risk factor in the development of several chronic diseases, including cardiovascular disease, type 2 diabetes, cancer, and mental disorders. Environmental factors, including air pollution and lifestyle, influence subclinical inflammation. However, evidence of the influence of ambient temperature (AT) on subclinical inflammation is limited. This study addresses this gap by examining the association between AT and biomarkers of subclinical inflammation in a cohort of older German women. We investigated 359 women (age range: 68.6-79.1 years) from the German SALIA cohort during the 2007-2010 follow-up. Data on 12 plasma biomarkers of subclinical inflammation, climatic factors (AT and relative humidity), and air pollution were collected and analyzed. Linear regression models with moving average lag periods (lag 0-1, lag 0-2, …, lag 0-14) were used to estimate associations, adjusting for age, BMI, smoking, alcohol consumption, socioeconomic status, cardiovascular disease, and respiratory disease. We observed consistent and significant associations between higher AT and increased levels of TGF-β1 and MCP-1/CCL2, as well as between higher AT and decreased levels of C3c and sICAM-1. For example, the lag 0-7 moving average results showed that a 5 °C increase in AT was associated with a 9.97 % increase in TGF-β1 (95 % CI: 1.54 %, 19.10 %), an 8.96 % increase in MCP-1/CCL2 (95 % CI: 4.38 %, 13.75 %), a 5.84 % decrease in C3c (95 % CI: -8.87 %, -2.71 %), and a 3.97 % decrease in sICAM-1 (95 % CI: -7.22 %, -0.59 %). These findings underscore the role of environmental factors in subclinical inflammation and the link between AT and immune mediators associated with chronic disease risk.

ABSTRACT Sensitive skin is characterised by unpleasant skin sensations in response to normally non‐provocative stimuli. While its pathophysiology remains incompletely understood, environmental factors and impaired barrier function are key contributors. Because many of these environmental factors also promote extrinsic skin aging, a link between sensitive skin and skin aging phenotypes has been proposed. To examine this hypothesis, we analysed data from 810 participants of the Chinese Taizhou Longitudinal Study (2012–2014). Sensitive skin was classified into subtypes based on questionnaire responses, and skin aging phenotypes were assessed using a subset of items from the SCINEXA (Score of Intrinsic and Extrinsic Skin Aging). Associations between sensitive skin subtypes and specific skin aging phenotypes were examined using multivariate regression models. Environmentally triggered sensitive skin was associated with the presence of pigment spots on the cheeks in participants aged ≥ 50 years, particularly among women. Intrinsically triggered sensitive skin was associated with perioral wrinkles, again most prominently in older women. This is the first large‐scale study demonstrating objective associations between sensitive skin subtypes and specific skin aging phenotypes. The findings identify subpopulations potentially more vulnerable to environmental stressors, underscoring the need for targeted prevention strategies.

Background: There is no doubt that global warming, with its extreme heat events, is having an increasing impact on human health. Heat is not independent of ambient temperature but acts synergistically with relative humidity (RH) to increase the risk of several diseases, such as cardiovascular and pulmonary diseases. Although the skin is the organ in direct contact with the environment, it is currently unknown whether skin health is similarly affected. Objective: While mechanistic studies have demonstrated the mechanism of thermal aging, this is the first epidemiological study to investigate the effect of long-term exposure to heat index (HI) as a combined function of elevated ambient temperature and RH on skin aging phenotypes in Indian women. Methods: The skin aging phenotypes of 1510 Indian women were assessed using the Score of Intrinsic and Extrinsic Skin Aging (SCINEXA (TM)) scoring tool. We used data on ambient temperature and RH, combined into an HI with solar ultraviolet radiation (UVR), and air pollution (particulate matter <2.5 m [PM2.5]; nitrogen dioxide [NO2]) from secondary data sources with a 5-year mean residential exposure window. An adjusted ordinal multivariate logistic regression model was used to assess the effects of HI on skin aging phenotypes. Results: HI increased pigmentation such as hyperpigmented macula on the forehead (odds ratios [OR]: 1.31, 95% confidence interval [CI]: 1.12, 1.54) and coarse wrinkles such as crow's feet (OR: 1.17, 95% CI: 1.05, 1.30) and under-eye wrinkles (OR: 1.3, 95% CI: 1.15, 1.47). These associations were robust to the confounding effects of solar UVR and age. Prolonged exposure to extreme heat, as indicated by high HI, contributes to skin aging phenotypes. Conclusion: Thus, ambient temperature and RH are important factors in assessing the skin aging exposome.

Although desert dust promotes morbidity and mortality, it is exempt from regulations. Its health effects have been related to its inflammatory properties, which can vary between source regions. It remains unclear which constituents cause this variability. Moreover, whether long-range transported desert dust potentiates the hazardousness of local particulate matter (PM) is still unresolved. We aimed to assess the influence of long-range transported desert dust on the inflammatory potency of PM2.5 and PM10 collected in Cape Verde and to examine associated constituents.During a reference period and two Saharan dust events, 63 PM2.5 and PM10 samples were collected at four sampling stations. The content of water-soluble ions, elements, and organic and elemental carbon was measured in all samples and endotoxins in PM10 samples. The PM-induced release of inflammatory cytokines from differentiated THP-1 macrophages was evaluated. The association of interleukin (IL)-1β release with PM composition was assessed using principal component (PC) regressions.PM2.5 from both dust events and PM10 from one event caused higher IL-1β release than PM from the reference period. PC regressions indicated an inverse relation of IL-1β release with sea spray ions in both size fractions and organic and elemental carbon in PM2.5. The PC with the higher regression coefficient suggested that iron and manganese may contribute to PM2.5-induced IL-1β release. Only during the reference period, endotoxin content strongly differed between sampling stations and correlated with inflammatory potency.Our results demonstrate that long-range transported desert dust amplifies the hazardousness of local air pollution and suggest that, in PM2.5, iron and manganese may be important. Our data indicate that endotoxins are contained in local and long-range transported PM10 but only explain the variability in inflammatory potency of local PM10. The increasing inflammatory potency of respirable and inhalable PM from desert dust events warrants regulatory measures and risk mitigation strategies.

Objective: Temperature changes will put vulnerable groups of the population, such as the elderly, at a higher risk of ill mental health. In this study, we investigated the short-term association between ambient temperature and depressive symptoms in older German women. Methods: The study used clinical data from the first follow-up of the well-characterised SALIA cohort, collected between 2007 and 2010. Depressive symptoms were defined using the Centre for Epidemiological Studies Depression Scale (CESD-R). Daily data on meteorological parameters were obtained from the COSMO reanalysis model and air pollution data from the German Federal Environment Agency. A cross-sectional study design with a generalised linear model with quasi-Poisson regression adjusted for covariates and confounders was used for the analysis. Results: A total of 763 older German women with a mean age (±SD) of 73·8 (± 2·9) years were included. We observed a negative linear exposure-response curve between mean ambient temperature and depressive symptoms in older German women and found that low temperatures had a significant and persistent effect on the risk of depression. Each 5-degree Celsius decrease in ambient temperature was associated with a 1·200-fold increase in the number of depressive symptoms ([95% CI: 1·125, 1·279], p<0-0001). The association was stronger on the day of the study (Lag0), but remained significant over several lag days. Sensitivity analyses showed that the results were robust to further adjustments in the model. Interpretation: The present study suggests that a decrease in temperature may be an important factor in increased mental disorders such as depression. Funding: The SALIA follow-up study was funded by the German Statutory Accident Insurance (DGUV) Grant No: 617.0-FP266. Declaration of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical Approval: The University of Bochum Ethics Committee approved the study. All the participants gave written informed consent.

BACKGROUND AND AIM: Depression has been recognized as a common, but serious, mental illness that is influenced by a range of environmental factors. Ambient temperature is an important environmental factor however, there is limited evidence for an association with a depressive state. This study explored the short-term association between temperature and depression in elderly women. METHOD: We investigated 834 elderly women from the 2007–2010 follow-up of the German SALIA cohort. Self-reported frequency of depressive symptoms and cognitive performance was evaluated using the CESD-R Scale and the CERAD-Plus test. Modelled climate and air pollution data were assigned to the residential addresses. Semi parametric quasi-Poisson generalized additive regression model adjusted for potential confounding factors were used. Effect modification by season, individual characteristics, and cognition level was investigated. Single and cumulative lag effect up to 10 days was estimated. Because of inverse association relative risk (RR)1 is considered an adverse risk. RESULTS: We observed that every 5°C reduction in the current day's average temperature was associated with a 16.7 % increase in depression (RR = 0.83, 95% confidence interval (CI): 0.77–0.90, P0.001). Subgroup analyses showed that women with low socioeconomic conditions (RR = 0.75, 95% CI: 0.64–0. 88, P 0.001), living in rural areas (RR = 0.80, 95% CI: 0.71– 0.91, P 0.001), living isolated (RR = 0.76, 95% CI: 0.67–0.86, P 0.001) and having a low cognitive function (RR = 0.81, 95% CI: 0.73–0.90, P 0.001) were more vulnerable. Seasonal analysis showed that decrease in temperature during the winter months were more strongly and adversely associated with higher depression than summer months. Sensitivity analysis brings forth that the effect estimates varied by exposure days but remained significant up to lag 0–10 days. CONCLUSIONS: Low temperature was inversely associated with depression in elderly women. The association is modified by season, individual specific characteristics, and cognitive performance.

The pathogenesis of atopic dermatitis (AD) involves an impairment of the skin barrier by an interplay of genetic and environmental factors, incl. particulate matter (PM). A gene that is upregulated in PM-exposed lung cells and correlates with inflammatory responses encodes carbonyl reductase 3 (CBR3). CBR3 is also inducible by air pollutants in keratinocytes, so we constructed aCBR3 genetic risk score (GRS) for AD under constant chronic PM exposure. We used data between birth and age 15 (years 2011–13) from 428 adolescents enrolled in the GINIplus/LISA study. AD at age 15 was defined as ever-diagnosed by a physician. Individual exposures to PMs with a median aerodynamic diameter of ≤2.5, ≤10, 2.5–10 μm (PM2.5, PM10, PMcoarse), and the reflectance of PM2.5 filters (PMabsorbance) were modeled within the ESCAPE project using land-use regression. Chronic exposures were estimated using the centered means of cumulative exposures to each PM over 15 years. Out of 52 CBR3 single nucleotide polymorphisms (SNPs), we constructed and evaluated a GRS utilizing bagging and random forests as the statistical learning procedure. The effect of the GRS on AD adjusted for the exposure to each PM separately and potential confounders were investigated by logistic regression. Our results showed significant effects of CBR3 GRS on AD under constant chronic exposure to each PM (AD diagnoses: 34.8%; PM2.5, PM10, PMabsorbance: p-value=0.009; PMcoarse: p-value=0.008). Furthermore, we identified 8 SNPs presenting the highest protective (rs881712, rs879892, rs12626189, rs9305578, rs1971504, rs2242803, and rs10470174) or risk (rs45622637) trends of CBR3 SNPs. In conclusion the individual susceptibility to AD in adolescents under constant chronic PM exposure is likely affected by CBR3 variations. A combination of epidemiological and mechanistic studies is required to gain further insights into the underlying pathophysiology.

Background and aim: The fractional exhaled nitric oxide (FeNO) concentration in the exhaled breath is a biomarker for eosinophilic airway inflammation. We explored the interplay between chronic air pollution exposure and polygenic susceptibility to airway inflammation at different critical age stages.Methods: Adolescents (15 yr) enrolled in the GINIplus/LISA birth cohorts (n = 2434) and 220 elderly women (75 yr on average) enrolled in the SALIA cohort with FeNO measurements available were investigated. Environmental main effects of the mean of ESCAPE land-use regression air pollutant concentrations within a time window of 15 years and main effects of the polygenic risk scores (PRS) using internal weights from elastic net regression of genome-wide derived single nucleotide polymorphisms were investigated. Furthermore, we examined gene-environment interac-tion (GxE) effects on natural log-transformed FeNO levels by adjusted linear regression models.Results: While we observed no significant environmental and polygenic main effects on airway inflammation in either age group, we found robust harmful effects of chronic nitrogen dioxide (NO2) exposure in the GxE models for elderly women (16.2 % increase in FeNO, p-value = 0.027). Stratified analyses found GxE effects between the PRS and chronic NO2 exposure in never-smoker elderly women and in adolescents without any inflammatory respiratory conditions.Conclusions: FeNO measurement is a useful biomarker to detect higher risk of NO2-induced eosinophilic airway inflam-mation in the elderly. There was limited evidence for GxE effects on airway inflammation in adolescents or the elderly. Further GxE studies in subpopulations should be conducted to investigate the assumption that susceptibility to airway inflammation differs between age stages.

BACKGROUND:Genetic risk scores (GRS) summarize genetic features such as single nucleotide polymorphisms (SNPs) in a single statistic with respect to a given trait. So far, GRS are typically built using generalized linear models or regularized extensions. However, these linear methods are usually not able to incorporate gene-gene interactions or non-linear SNP-response relationships. Tree-based statistical learning methods such as random forests and logic regression may be an alternative to such regularized-regression-based methods and are investigated in this article. Moreover, we consider modifications of random forests and logic regression for the construction of GRS.RESULTS:In an extensive simulation study and an application to a real data set from a German cohort study, we show that both tree-based approaches can outperform elastic net when constructing GRS for binary traits. Especially a modification of logic regression called logic bagging could induce comparatively high predictive power as measured by the area under the curve and the statistical power. Even when considering no epistatic interaction effects but only marginal genetic effects, the regularized regression method lead in most cases to inferior results.CONCLUSIONS:When constructing GRS, we recommend taking random forests and logic bagging into account, in particular, if it can be assumed that possibly unknown epistasis between SNPs is present. To develop the best possible prediction models, extensive joint hyperparameter optimizations should be conducted.

Genetic and exposomal factors contribute to the development of human aging. For example, genetic polymorphisms and exposure to environmental factors (air pollution, tobacco smoke, etc.) influence lung and skin aging traits. For prevention purposes it is highly desirable to know the extent to which each category of the exposome and genetic factors contribute to their development. Estimating such extents, however, is methodologically challenging, mainly because the predictors are often highly correlated. Tackling this challenge, this article proposes to use weighted risk scores to assess combined effects of categories of such predictors, and a measure of relative importance to quantify their relative contribution. The risk score weights are determined via regularized regression and the relative contributions are estimated by the proportion of explained variance in linear regression. This approach is applied to data from a cohort of elderly Caucasian women investigated in 2007–2010 by estimating the relative contribution of genetic and exposomal factors to skin and lung aging. Overall, the models explain 17% (95% CI: [9%, 28%]) of the outcome’s variance for skin aging and 23% ([11%, 34%]) for lung function parameters. For both aging traits, genetic factors make up the largest contribution. The proposed approach enables us to quantify and rank contributions of categories of exposomal and genetic factors to human aging traits and facilitates risk assessment related to common human diseases in general. Obtained rankings can aid political decision making, for example, by prioritizing protective measures such as limit values for certain exposures.