Introduction: A well-trained and experienced staff in epidemiological studies is essential for the quality of the study implementation and the data and analyses collected from it. A broad understanding of Good Epidemiological Practice (GEP) can increase the motivation of staff to collect evaluable data. There are no special training opportunities for non-academic staff. This report describes the development and evaluation of an “EPI Study Nurse” (ESN) pilot course to train staff in epidemiological studies. Project description: The development of a concept for a pilot course was initiated by a project group from the study centers of the German National Cohort (NAKO) in early 2020. It was carried out in close collaboration with the Robert Koch Institute (RKI) and the professional societies “Deutsche Gesellschaft für Sozialmedizin und Prävention” (DGSMP), “Deutsche Gesellschaft für Epidemiologie” (DGEpi), and “Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V.” (GMDS). A training plan with 12 modules covering the relevant contents of the GEP was defined and a timetable was drawn up. Those responsible for the modules and teachers as well as participants in the pilot course were recruited through inquiries from the institutions mentioned above. After each module, the expectations and satisfaction of participants and teachers were surveyed. The pilot course was financed by the participating institutions from their own resources. Discussion: The pilot phase of the continuing education course was successfully implemented within one year (2022–2023) (preparation of a curriculum, registration of participants and recruitment of lecturers, organisation and execution of the course and final examination) and evaluated. A particular challenge was the definition of the module-specific timeframes for the lectures and group work as well as the targeted consideration of the different levels of experience of the participants. For future courses, it must be taken into account that administrative tasks in particular require considerable human resources (e.g., creating the curriculum, organizing and moderating the course) and financial resources (e.g., fees, travel expenses). The project was approved as part of the application for the third funding phase of the NAKO (June 2024 – April 2028) and the courses will be offered once a year.

A growing number of epidemiological studies have suggested a causal link between air pollution and several common skin diseases. However, considerable variation in study design and heterogeneous results make it difficult for clinical dermatologists to judge the true relevance of air pollution as a risk factor for skin diseases. We therefore conducted a systematic review of epidemiological studies to investigate the associations of short- and long-term exposure to ambient air pollutants with atopic dermatitis, psoriasis, urticaria, acne, melanoma skin cancer, non-melanoma skin cancer, and skin aging. We systematically searched two comprehensive databases, SCOPUS and PubMed, from 1 January, 1990 to 30 April, 2025 for relevant observational studies. After screening 1393 eligible studies, 77 studies were selected. We defined the level of evidence for causality by assessing the risk of bias in such studies. Ambient air pollutants included particulate matter with an aerodynamic diameter of 10 µm or smaller, particulate matter with an aerodynamic diameter of 2.5 µm or smaller, and gaseous pollutants (nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide). We obtained five major results: (i) the majority of studies strongly advocated the harmful effects of air pollution on the above-mentioned skin diseases, but results across studies were heterogeneous in terms of direction and magnitude. (ii) For all skin diseases, the risk of bias assessment resulted in high risk, which was mainly observed in the domains of confounding, selection bias, and exposure assessment. Consequently, certainty in evidence or causal inference was usually low to very low. (iii) In most studies, high air pollution had a more immediate effect (same day) and lasted up to a week after exposure. (iv) The results on vulnerable subpopulations such as children, older people, or women were inconclusive. (v) Studies were mostly from the upper-middle and higher income countries. Despite numerous epidemiological studies on air pollution and skin diseases, the overall quality of evidence is low. We encourage more longitudinal studies, such as cohort studies or panel studies, to support causality and study change in disease severity over time and improved exposure assessments, and adjustment for critical confounding factors. Importantly, more studies are needed from low- to middle-income countries and on susceptible groups who are most vulnerable to climate change.

BACKGROUND:A large proportion of Europeans are exposed to high levels of transportation noise, which can cause physiological and psychological stress, leading to negative health impacts. Few studies have examined the association between transportation noise and self-rated health (SRH), a summary indicator of morbidity. OBJECTIVES:We aimed to assess the associations of SRH with both annual average road traffic noise exposure and nighttime transportation noise annoyance, examine geographic differences, evaluate potential effect modification and interaction by sex, and investigate whether annoyance mediates the relationship between road traffic noise and self-rated health. METHODS:Using NAKO baseline data (n = 174,956), we implemented a cross-sectional study using logistic regression to analyze associations of road traffic noise ≥55 dB(A) Lden and nighttime transportation noise annoyance with poor SRH, adjusting for relevant sociodemographic characteristics and environmental co-exposures, including air pollution and greenness. We examined geographic differences, tested for effect modification and interaction by sex, and used path analysis to assess mediation by annoyance. RESULTS:Road traffic noise ≥55 dB(A) (OR 1.06, 95 % CI 1.01; 1.10), and moderate (OR 1.28, 1.23; 1.32) and strong nighttime transportation noise annoyance (OR 1.73, 1.65; 1.81) were associated with higher odds of poor SRH. Associations were similar for males and females, but varied across study regions. The path analysis revealed that road traffic noise was associated with higher odds of poor SRH indirectly via nighttime transportation noise annoyance (indirect effect). CONCLUSIONS:In our study, nighttime transportation noise annoyance was more strongly and consistently associated with poor SRH than road traffic noise. Reducing both transportation noise and related annoyance could help protect population health.

Abstract Background The COVID-19 pandemic and accompanying social distancing measures might have caused adverse health consequences. We aimed to describe changes in participants’ self-rated health and mental health (depression, anxiety, and stress), and investigate factors associated with them. Methods We collected data from the German National Cohort (NAKO). We first described changes in participants’ self-rated health and mental health from the baseline examination (1 to 6 years earlier) to the early phase of the COVID-19 pandemic. We then applied the multinomial logistic regression model (self-rated health) and the quantile regression model (mental health) to investigate the potential factors associated with the health status and changes. Results After a median of 3.1 [2.1, 4.1] years from baseline to the early pandemic phase (N = 91,809), 39.3% of participants with good health and 69.7% with less good health status at baseline reported better health. However, the percentage of participants with high depression, anxiety, and stress scores (≥ 10) increased from 6.2%, 4.1%, and 4.3% to 8.6%, 5.6%, and 10.1%, respectively. In the multivariable models, we found that being younger, being male, highly educated, being employed, having higher life satisfaction at baseline, being more physically active, drinking heavily, and experiencing improved anxiety symptoms were associated with improved self-rated health. In contrast, smoking and having mental health disorders were all associated with worse self-rated health. Our results showed that being younger, being female, smoking, drinking heavily, and drinking more since baseline were associated with higher depression scores. Having had a coronavirus test was associated with worse self-rated health and more severe anxiety and stress. Conclusions During the early COVID-19 pandemic, many participants experienced improvements in self-rated health but suffered deterioration in mental health and physical activity engagement. Female participants, those who were physically inactive, and those with pre-existing mental disorders were more likely to report poorer health.

BACKGROUND:Chronic kidney disease (CKD) can be asymptomatic for many years and is often diagnosed late. Given the availability of new treatments, the early identification of relevant findings from screening of the kidney markers estimated glomerular filtration rate (eGFR) and albuminuria in the general population is becoming increasingly important. METHODS:In the NAKO study, self-reported medical diagnoses of kidney disease in 195 182 participants were compared with relevant findings from screening biomarkers (eGFR < 60 mL/min/1.73 m² and albuminuria). For the purpose of comparison, various equations for assessing kidney function were evaluated as well. RESULTS:2% of the participants reported having received a medical diagnosis of kidney disease, and 2% had an eGFR below 60 mL/min/1.73 m². There was, however, little overlap between these two groups: more than 80% of participants with an eGFR between 30 and 59 mL/min/1,73 m²) did not report any diagnosis of kidney disease. The additional inclusion of data on albuminuria did not materially affect this discrepancy: 6213 persons (17.5% of the cohort) with an abnormal eGFR or urinary albumin-to-creatinine ratio (UACR) did not report any diagnosis of kidney disease. Even among participants whose eGFR was in the range of 30-59 mL/min/1.73 m² and whose UACR was above 300 mg/g, less than half reported having a medically diagnosed kidney disease. CONCLUSION:These findings indicate a low level of awareness regarding the possible presence of CKD in the general population. Many people with abnormal screening findings needing further investigation due to their potential clinical relevance are unaware that they might be suffering from a kidney disease. As more effective treatments for kidney disease are now available, these findings indicate a need for structured screening and evaluation strategies to promote kidney health.

BACKGROUND:Food allergy (FA) arises from a complex interplay between an individual's genetic predisposition and environmental factors, and its prevalence is increasing. Genome-wide association studies to date have been hindered by small sample sizes and varying FA definitions. OBJECTIVE:We sought to identify novel FA risk loci by conducting a genome-wide association study meta-analysis in children and adults by using a multiphenotype approach to ensure a good trade-off between sufficient sample size and valid FA definitions. METHODS:Analyses were conducted separately in children and adults on the basis of the following FA phenotypes: self-report, doctor diagnosis, food-specific sensitization, and doctor diagnosis plus food-specific sensitization. A meta-analysis was performed of genome-wide association studies from up to 16 cohorts of people of European ancestry including 229,426 adults and 14,234 children. Models were adjusted for sex, age, principal components, and, if applicable, further study-specific confounders. Sensitivity models were additionally adjusted for hay fever. Replication was conducted in additional external cohorts and a validation in oral food challenge-defined FA cases. RESULTS:Thirty-seven single nucleotide polymorphisms met suggestive significance (P < 1 × 10-6), with two reaching genome-wide significance: rs116936231 (FGL1) in adult doctor-diagnosed FA plus food-specific sensitization phenotype (stable after additional hay fever adjustment) and rs8022829 (AKAP6-NPAS3), which was significant only in the hay fever-adjusted model in adults. However, neither variant was validated. Further, we identified 3 single nucleotide polymorphisms previously reported for FA and atopic disease. CONCLUSION:This study identified 37 single nucleotide polymorphisms suggestively associated with FA and demonstrated genetic differences across phenotypes. It highlights the need for a unified FA definition and sheds light on FA's shared genetic architecture with allergies.

Background The nasal microbiome plays an important role in respiratory and systemic health, but data from large adult population cohorts remain scarce. We analyzed nasal microbiota from 2,070 adults aged 21–73 years in the population-based German National Cohort (NAKO) to characterize community composition and identify host factors associated with variation in the anterior nares’ microbiome. Microbial profiles were obtained using 16S rRNA gene sequencing, and associations with host characteristics—including sex, age, body composition, tobacco smoking, pulmonary function, household context, and self-reported physician diagnoses—were evaluated using a two-stage regression framework, beta diversity analyses, and complementary Latent Dirichlet Allocation (LDA) community modeling. Results Despite detecting 358 genera across the cohort, more than 90% of all sequencing reads were assigned to only 15 genera. These core genera showed distinct associations with host physiology and lifestyle. Sex and body mass index were the strongest correlates of alpha and beta diversity, while pulmonary function and tobacco smoking were associated with differences in the relative abundance of several commensal taxa, including Lawsonella , Cutibacterium , and Dolosigranulum . Age was not related to overall diversity but exhibited characteristic shifts in community composition. Antibiotic use within the previous 12 months was associated with lower levels of multiple commensal genera and higher relative abundance of Staphylococcus . LDA-derived sub-communities largely mirrored the genus-level associations and indicated that these patterns reflect transitions between recurrent nasal community types rather than isolated changes in single taxa. Conclusions This nationwide study provides a detailed characterization of nasal microbiome patterns in the general adult population (up to 73 years) of Germany and highlights that multiple host factors are associated with both taxon-specific and community-level variation. The findings offer a reference for future investigations into the role of the nasal microbiome in respiratory and systemic health.

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.

Recent studies have suggested a potential association of particulate matter (PM) and noise with diabetes and obesity, but studies examining other environmental exposures and their sex-specific and joint associations remain limited. Therefore, we investigated sex-specific individual and joint associations of annual exposure to multiple environmental factors with diabetes and obesity-related measures using cross-sectional data from the population-based multi-center German National Cohort (NAKO). Outcomes included self-reported diabetes mellitus, body mass index (BMI), obesity (BMI ≥30 kg/m2), and waist circumference. Annual mean residential exposures included air pollutants, air temperature, day-evening-night road traffic noise (Lden) and surrounding greenness (normalized difference vegetation index (NDVI)). We used sex-stratified linear and logistic regression models to assess individual associations and quantile g-computation to assess joint associations. Among 174,955 adult participants (50.4% women), 5.6% reported a diabetes diagnosis and 20.9% were obese. An interquartile range increase in PM2.5 and Lden was consistently associated with diabetes and obesity-related measures (e.g., PM2.5-diabetes for men: odds ratio (OR) [95% confidence interval] = 1.12 [1.02; 1.22]; Lden-BMI for women: 0.22 kg/m2 [0.16; 0.27]). Greenness showed non-linear (inverted U-shaped) with all outcomes. An interquartile range increase in multiple exposures simultaneously was associated with higher odds of diabetes, obesity and higher obesity-related measures (e.g., mixture (PM2.5,Lden, lack of NDVI)-diabetes: OR = 1.20 [1.09; 1.33] for men; mixture (PM2.5,Lden, lack of NDVI)-BMI: 0.33 kg/m2 [0.21; 0.44] for women). While longitudinal studies need to confirm these findings, the study highlights that reducing multiple adverse environmental exposures could be potential targets for the prevention of diabetes and obesity.

BACKGROUND:Allergic diseases often develop jointly during early childhood. Potential disease trajectories and relevant early-life factors have been described, yet existing prediction approaches mostly focus on single allergic diseases cross-sectionally. Models addressing allergic multimorbidity and disease trajectories are lacking. We aim to predict allergic disease trajectories from birth up to adolescence using early-life factors. METHODS:Preceding research using data from 4646 adolescents of the German birth cohorts GINIplus and LISA identified seven allergic disease trajectories up to the age of 15 years. A set of predictors comprising parental and perinatal factors, early allergic or respiratory symptoms, lifestyle and environmental factors was used with an XGBoost machine learning approach to perform multiclass classification. In a subsample (N = 2109), polygenic risk scores (PRS) for asthma, allergic rhinitis, atopic dermatitis, and any allergy were added to the predictor set. RESULTS:Our approach revealed moderate classification success (multiclass area under the curve (AUC) = 0.69). A macro-averaged sensitivity of 0.26 and specificity of 0.89 were obtained. The most important predictors were early-life skin rash, respiratory symptoms, and air pollution. In the sub-analysis, the PRS were among the factors with high importance, but the prediction performance in external test data was not improved. CONCLUSIONS:Our prediction success was comparable to established prediction scores while accounting for multiple allergic disease trajectories and using solely early-life factors. This study cannot yet provide reliable individual-level prediction in a clinical setting but can inform development of future work on this.

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).

INTRODUCTION:The Lifestyle for Brain Health (LIBRA) index evaluates modifiable dementia risk, mainly in midlife and older adults. We examined the frequency of LIBRA factors and their individual and combined associations with cognitive functioning across adulthood (20-75 years), considering age, sex, and socioeconomic status (SES). METHODS:Data came from the population-based German National Cohort (NAKO baseline; n = 149,948). We calculated proportions for LIBRA factors, tested frequency trends, and analyzed cross-sectional associations with cognitive functioning using cluster-adjusted regression controlling for confounders. RESULTS:Behavioral and psychosocial risks (smoking, physical inactivity, depression) were more common in younger adults, while cardiovascular risks (hypertension, coronary heart disease, hypercholesterolemia) predominated in older age. Men had higher LIBRA scores. Higher scores were consistently linked to lower cognitive functioning and lower SES across age groups. DISCUSSION:Dementia risk factors were frequent and already associated with poorer cognition in younger adults, underscoring the need for early, targeted, and equity-oriented prevention.

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.

A strong increase in the prevalence and deaths of Parkinson’s disease (PD) has been reported over the last three decades. Ageing societies, genetic and environmental factors and access to health care contribute to these increases. Using data of the German National Cohort (NAKO) PD classifications based on self-report, claims data and a brief medication based algorithm to increase validity in the absence of neurologic examinations, were compared and the PD incidence, selected non-motor symptoms, functions and vital status were analysed. The algorithm based PD prevalence among 205.000 participants was 0.13% but it was 2.5-fold higher in claims data. Self-reported PD incidence was 0.13% over a median of 4.8 years follow-up, but cases had a 4-fold higher mortality, worse emotional, motoric and olfactory functions and higher prevalences of diabetes, obesity, depression and anxiety. The algorithm based PD prevalence and the mortality risk was similar to considerable older population studies from Europe.

IntroductionAccurate measurement of dietary intake remains challenging in large-scale nutritional studies. This study aimed to develop and evaluate both a practical dietary assessment strategy and a computationally efficient statistical method for estimating usual dietary intake in the German National Cohort (NAKO Gesundheitsstudie).MethodsWe developed a blended approach using data from NAKO. During baseline (2014–2019) and first follow-up examinations (2019–2024), up to four 24-h food lists (24 h-FLs) and one food frequency questionnaire (FFQ) were collected. We combined these dietary intake data sources using an adapted Multiple Source Method (MSM) and supplemented them with estimated consumption amounts based on data from the German National Nutrition Survey II (NVS II, 2005–2007) to generate measurement-error-corrected estimates of dietary intake. The adapted MSM was empirically evaluated against conventional logistic linear mixed-effects model (LLMM), which can be computationally complex for large datasets due to lengthy processing times. Additionally, a simulation study evaluated how varying the number of 24 h-FLs and FFQ assessments affected the consumption probability estimates.ResultsThe adapted MSM showed high statistical agreement with LLMM (correlation ≥0.97). The usual intake of 90 EPIC-SOFT food groups, 124 nutrients, and energy intake was estimated for 152,304 participants (75% of the cohort) who had at least one 24 h-FL and an FFQ available. Furthermore, the simulation showed that including repeated 24 h-FLs alongside an FFQ improved the accuracy of individual consumption probability estimates, particularly when only one or two 24 h-FLs were available.ConclusionThe adapted MSM offers a computationally efficient, practical alternative to LLMM, generated dietary intake estimates for over 150,000 NAKO participants to support future research. By integrating repeated 24 h-FLs, an FFQ, and external consumption data, this blended approach balances logistical feasibility with statistical precision, providing a scalable, cost-effective framework for large-scale nutritional studies.

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.