Valentin Max Vetter

Objectives: Awareness of age-related change (AARC) reflects how individuals perceive age-related gains and losses, with positive perceptions consistently predicting better health and well-being. The Awareness of Age-Related Change theoretical framework has long highlighted physical and psychosocial health as distal antecedents of AARC, but their actual importance remains poorly understood. In this preregistered study, we empirically test this key model component. Methods: We operationalize distal antecedents as 9-year trajectories in physical health (morbidity, grip strength, hearing, vision), cognitive functioning (recall, processing speed, working memory), and self-reported loneliness. We apply structural equation modeling to 3-wave longitudinal data from the Berlin Aging Study II (N = 753, aged 61–81 at baseline, 49% female) and test if these trajectories predict late-life AARC. Results: Older adults with better baseline recall and grip strength reported fewer AARC losses, whereas those with higher baseline loneliness perceived more losses and fewer gains. Steeper declines in recall and stronger increases in morbidity were associated with heightened awareness of losses. Processing speed, working memory, hearing, and vision were not associated with AARC. Discussion: Our results suggest several refinements of the Awareness of Age-Related Change framework: (a) within a given construct space (e.g., cognition), some dimensions (e.g., recall) are more strongly associated with perceiving age-related losses than others (e.g., processing speed), (b) long-term trajectories of recall memory and morbidity (rather than status alone) are particularly relevant for loss-focused awareness of aging, and (c) higher levels of loneliness are most consistently associated with awareness of age-related gains.

Background: The disproportionate increase in lifespan compared to healthspan over the past decades results in a growing proportion of life marked by diseases, even if incidence rates are falling in some cases. However, not everyone ages at the same pace and some people remain in good health and preserve physical and cognitive function into old age. To quantify inter-individual differences in the biological aging process, numerous indicators of biological age have been developed. Methods: In this study, we analyzed 16 measures of biological aging including epigenetic clocks, proteomics clock, telomere length, and SkinAge, laboratory composite markers (BioAge, Allostatic Load), psychological aging, and Brain Age. These age markers were evaluated cross-sectionally as well as longitudinally in the context of age-associated outcomes covering frailty, mobility, cognitive function, depressive symptoms, autonomy in daily life, nutrition, morbidity, and chronic disease in participants of the Berlin Aging Study II (BASE-II). Results: Here, we analyze longitudinal data from 1083 participants (mean age of 68.3 years at baseline, 52% women) with an average follow-up period of 7.4 years. Allostatic Load Index and DunedinPACE show the strongest and most consistent cross-sectional and longitudinal associations with age-associated phenotypes. Furthermore, both biomarkers individually increase the accuracy of a logistic regression model trained to predict incident cases of Metabolic Syndrome, high cardiovascular risk (Lifes’s Simple 7) as well as incident frailty (Fried’s frailty index) 7.4 years after baseline examination by up to 24 percentage points. Conclusions: Our findings support the previously shown distinction between indicators of aging and provide a comprehensive overview of their individual strengths and weaknesses in the context of wide variety of age-associated phenotypes.

1 Background: Epigenetic clocks based on DNA methylation (DNAm) have emerged as promising biomarkers of biological aging, yet their associations with cognitive performance remain inconsistent. This study investigates the relationship between epigenetic age acceleration and cognitive performance in older adults using 14 DNAm clocks from five generations of development. Methods: We analyzed data from the Berlin Aging Study II (BASE-II) using genome-wide DNAm profiles and cognitive assessments ascertained at baseline (T0) and two follow-up time points (T1, T2) in up to 1,014 individuals. DNAm-based age and age acceleration estimates were calculated using Biolearn and MethylCIPHER. Analyses focused on cross-sectional and longitudinal associations between DNAm clock estimates and cognitive performance, including sex-specific effects and comparisons with frailty as non-cognitive positive control. Results: Among all tested DNAm clocks, DunedinPACE (a third-generation clock) showed the strongest and most consistent associations with cognitive performance. In addition, the fifth-generation SystemsAge framework also demonstrated robust associations with cross-sectional and longitudinal cognitive outcomes. In contrast, second-generation clocks (GrimAge [v2], PhenoAge) showed occasional nominal associations, while first-generation clocks (Horvath [v1], Hannum) and the causally-informed, fourth-generation clocks (e.g. YingCausAge, YingDamAge) showed no noteworthy signals. Likewise, telomere length estimated from DNAm was not strongly associated with cognitive performance in this dataset. Conclusions: Our findings highlight DunedinPACE as a particularly informative biomarker for various aspects of cognitive aging, while other DNAm aging measures showed no consistent associations. Future work should further refine domain-specific epigenetic biomarkers to improve biological aging assessments and achieve a more reliable early detection of cognitive decline.

Background: In many countries, lifespan has been increasing faster than healthspan, leading to more years spent with late-life disease and highlighting the need for reliable biomarkers to measure biological aging. Methods: We used data from the Berlin Aging Study II (BASE-II, 60–80 years of age at baseline, average follow-up 7.4 ± 1.5 years, range 3.9–10.4, n = 1,083) to compare 14 biomarkers of aging recently consented by an expert panel for the use as outcome measures in intervention studies: physiological (insulin-like growth factor 1 (IGF-1), growth-differentiating factor-15 (DNA methylation derived, DNAmGDF15)), inflammatory (high sensitivity C-reactive protein (CRP), interleukin-6 (IL-6)), functional (muscle mass, muscle strength, hand grip strength (HGS), Timed-Up-and-Go (TUG), gait speed, standing balance test, frailty phenotype (FP), cognitive health, blood pressure), and epigenetic (epigenetic clock, DunedinPACE). Cox proportional hazard regression analyses were performed to investigate their role in prediction of all-cause as well as cause-specific mortality. Results were adjusted for age, sex, lifestyle factors, and genetic ancestry. Results: In adjusted models of all-cause mortality, HGS, IL-6, standing balance, cognitive health, and the epigenetic clock (DunedinPACE) statistically significantly predicted mortality, with the epigenetic clock (DunedinPACE) emerging as the strongest predictor. CRP, gait speed, IGF-1, blood pressure, muscle mass, DNAmGDF15, FP and TUG were not associated with mortality in this study. These results were corroborated in subgroup analyses stratified by cause of death. Feature selection identified a minimal biomarker set consisting of muscle mass, standing balance, and epigenetic clock (DunedinPACE) that predicted mortality with nearly the same discriminative accuracy (C-index = 0.63) as the full model including all biomarkers (C-index = 0.65). Conclusion: Among the fourteen investigated biomarkers of aging, DunedinPACE emerged with the strongest and most consistent association with mortality.

Physicians face intense work-related stress, which can harm their health, increase the risk of medical errors, lower healthcare quality, and increase costs within the healthcare system. In this 4-week intervention study, individual-level and population-level effects of two short and easy-to-perform breathing exercises designed to reduce stress are evaluated among 76 physicians in residency in Germany in a series of N-of-1 trials. Levels of stress and levels of stress expected for the following day were assessed electronically every day via the StudyU App (protocol adherence: 91.9%). Average intervention effects were estimated using Bayesian linear regression models. They were overall small on the population level, but they showed large heterogeneity between individuals, with strong effects for selected individuals, with stress reduction of up to 3 points on a 1 to 10 stress scale. Thirty-one participants benefited from the anti-stress exercises. Three (mindfulness breathing) and seven participants (box breathing) had a ≥70% probability for a daily stress reduction of ≥0.5 points and thereby fulfilled our responder criteria. Of the 17 participants who completed the follow-up survey about 4.5 months after completion of the individual N-of-1 trials, 58% reported that they felt they had benefited from the intervention and 42% planned to use it in the future. The results highlight the value of personalized perspectives: while the studied interventions showed only small positive benefits for the “average person”, they may well help actual individual persons, here 10 of 76 or even 31 of 76 participants.

Background: Age-related declines in gonadal and cognitive function are commonly observed, but their relationship is still not completely understood. Methods: 615 men and 607 women from the Berlin Aging Study II aged ≥60 years were analyzed at baseline, of which 308 men and 297 women were re-assessed on average 7.2 years later. Total testosterone (TT) was measured, and free testosterone was estimated using the Vermeulen (FTV), Sartorius (FTS), and Free Androgen Index (FAI) equations. Cognitive performance was assessed using the Digit Symbol Substitution Test (DSST) and latent factor scores established from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD)-Plus test battery representing 4 cognitive domains (verbal memory, visuo-construction, executive functions and processing speed, and verbal fluency). Associations between testosterone measures and cognitive function were analyzed using linear regression. Results: Cross-sectional analyses showed negative associations in women between visuo-construction and TT (β = −.215, p = .010) and FTS (β = −.013, p = .03) as well as between verbal fluency and TT (β = −.189, p = .006), FAI (β = −.062, p = .03), FTS (β = −.013, p = .008), and FTV (β = −.012, p = .01). In men, FAI was positively associated with DSST performance (β = .103, p = .003). Longitudinally, higher FAI and FTV at baseline were associated with a less steep decline in DSST performance in men (β = .066 and .007, both p = .03). In women, declines in FAI and DSST scores were positively associated (β = 1.794, p = .03). Conclusions: Our findings suggest sex-specific associations between testosterone levels and cognitive function in older adults. Higher testosterone levels were predominantly associated with better DSST performance in men, but with poorer visuo-construction and verbal fluency in women.

In many countries, lifespan has been increasing faster than healthspan, leading to more years spent with late-life disease and highlighting the need for reliable biomarkers to measure biological aging and to plan personalized interventions to extend healthspan. We used data from the Berlin Aging Study II (BASE-II, 60-80 years of age at baseline, average follow-up 7.4±1.5 years, range 3.9-10.4, n=1,083) to compare 14 biomarkers of aging recently consented by an expert panel for the use as outcome measures in intervention studies: Insulin-like growth factor 1 (IGF-1), growth-differentiating factor-15 (DNA methylation derived, DNAmGDF15), high sensitivity C-reactive protein (CRP), interleukin-6 (IL 6), muscle mass, muscle strength, hand grip strength (HGS), Timed-Up-and-Go (TUG), gait speed, standing balance test, frailty index (FI), cognitive health, blood pressure, and epigenetic age (DunedinPACE). Cox proportional hazard regression analyses were performed to investigate the predictive role for all-cause mortality and to identify subgroups of the three most frequent causes of death observed in BASE-II. Results were adjusted for age, sex, lifestyle factors, and genetic ancestry. In adjusted models of all-cause mortality, HGS, IL 6, standing balance, cognitive health, and epigenetic age (DunedinPACE) significantly predicted mortality, with the epigenetic age (DunedinPACE) emerging as the strongest predictor. In contrast, CRP, Gait Speed, IGF-1, blood pressure, muscle mass, DNAmGDF15, FI and TUG were not associated with mortality. These results were corroborated in subgroup analyses stratified by cause of death. Feature selection identified a minimal biomarker set comprising muscle mass, standing balance, and epigenetic age (DunedinPACE) that predicted mortality with nearly the same discriminative accuracy (C-index = 0.63) as the full model including all biomarkers (C-index = 0.65).

Background: Aim of the study was a comparative analysis of different epigenetic clocks with regard to their ability to predict a future onset of the Metabolic Syndrome (MetS). In addition, cross-sectional relationships between epigenetic age measures and MetS were investigated. Methods: MetS was diagnosed in participants of the Berlin Aging Study II at baseline (n = 1671, mean age 68.8 ± 3.7 years, 51.6% women) and at follow-up (n = 1083; 7.4 ± 1.5 years later). DNA methylation age acceleration (DNAmAA) was calculated for a total of ten epigenetic clocks at baseline. In addition, DunedinPACE, a DNAm-based measure of the pace of aging, was calculated. The relationship between MetS, DNAmAA, and DunedinPACE was investigated by fitting regression models adjusted for potential confounders and calculating receiver operating characteristic statistics. Results: Among all biomarkers investigated, DunedinPACE was the only DNAm-based predictor that was significantly associated with incident MetS at follow-up on average 7.4 years later (OR: 9.84, P = .028). Logistic regression models predicting MetS that either included solely clinical parameters or solely epigenetic clock estimates (DNAmAA) or DunedinPACE revealed that GrimAge DNAmAA had an area under the curve most comparable to the model considering clinical variables only. Cross-sectional differences between participants with and without MetS remained statistically significant for DunedinPACE only after covariate adjustment (baseline: β = 0.042, follow-up: β = 0.031, P < .0001 in both cases). Conclusion: Comparison of epigenetic clocks in relation to MetS showed strong and consistent associations with DunedinPACE. Our results highlight the potential of using certain DNAm-based measures of biological ageing in predicting the onset of clinical outcomes, such as MetS.

Background: Frailty is a common geriatric syndrome characterized by increased vulnerability to stressors, reduced physiological reserves and heightened vulnerability and. It is also associated with adverse health outcomes. Given its complex phenotypes and underlying pathophysiology, there is a pressing need for robust, multidimensional biomarkers of frailty to advance personalized care. The objective of this study was to identify serum metabolomics signatures associated with different frailty phenotypes and related features. Methods: We analyzed Nuclear Magnetic Resonance (NMR) metabolomics signatures in 901 subjects, 47.5% of them males (average age 68.34 ± 3.51 years) from the Berlin Aging Study II, categorized as non-frail, pre-frail, or frail based on Fried’s criteria at baseline (T0) and follow-up (T1, on average 7 years later). Linear models were used to assess associations between metabolite levels, frailty phenotypes, and frailty-related parameters. Results: At baseline (T0), only 1% of the population was classified as frail, increasing to 4.8% at T1 (p-valuep -valuep -value=0.0297) positively associated with fat mass in men. In women, dimethylsulfone was positively associated with the percentage of evolution in the hand grip strength between T0 and T1 ( p -value=0.0442), and glycerol was positively associated with appendicular lean mass at T0 ( p -value=0.0049). Additionally, 22 metabolites were positively correlated with nutritional status at T0 in men ( p -value<0.05): many of these were linked to carbohydrate (e.g., maltose, fructose, glucose, galactitol, mannose, lactate, acetylcarnitine) and amino acid metabolism (e.g., valine, tyrosine, isoleucine, alpha-hydroxybutyrate). Conclusions: We may conclude that while serum metabolome revealed a weak association with frailty, significant associations were observed (particularly in men) between metabolomics signatures and frailty-related features such as muscle strength and nutritional status. These findings point to insulin sensitivity as a central feature, with early markers of impaired insulin sensitivity potentially impacting muscle health.