Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study.

People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36-75 of Round 4 (2003-2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987-1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.

Bear in mind: the role of personal background in semantic animal fluency - The SMART-MR study.

Objectives: Semantic fluency is a prominent neuropsychological task, typically administered within the category 'animals'. With the increasing development of novel item-level metrics of semantic fluency, a concern around the validity of item-level analyses could be that personal background factors (e.g., hobbies like birdwatching or fishing) may disproportionally influence performance. We analyzed animal fluency performance at the item level and investigated the prevalence of individuals with abundant knowledge in specific classes of animals (e.g., birds, fish, insects) and the relationship of such knowledge with personal background factors and other cognitive tasks (episodic memory and executive functioning). Method: Participants included 736 Dutch middle-aged to older adults from the SMART-MR cohort (mean age 58 ± 9.4 years, 18% women). Individuals were asked to name as many animals as possible for 2 min. Number of people with abundant animal class knowledge was calculated for the ability to recall a series of minimum ≥5 and up to ≥15 animals within a specific class with at most one interruption by an animal from another class. Subsequent analyses to investigate relationships of abundant class knowledge with sociodemographic characteristics (t-tests and chi-square tests) and cognitive performance (linear regressions) were performed for a cut-off of ≥10 animals within a specific class (90th percentile), with a sensitivity analysis for ≥7 animals (67th percentile). Results: A total of 416 (56.2%) participants recalled a series of ≥5 animals from a specific class, 245 (33.3%) participants recalled ≥7, 78 (10.6%) participants recalled ≥10, and 8 (1.1%) participants recalled ≥15. Those who recalled a series of at least 10 animals within a class were older, more often men, and more often retired than those who did not. Moreover, they had a higher total score on animal fluency, letter fluency (i.e., executive functioning), and episodic memory tasks compared to those who did not. Discussion: Our results suggest that the benefit of abundant animal class knowledge gained by personal background does not disproportionally influence animal fluency performance as individuals with such knowledge also performed better on other cognitive tasks unrelated to abundant knowledge of animal classes.

Frailty Combined with Loneliness or Social Isolation: An Elevated Risk for Mortality in Later Life.

BACKGROUND/OBJECTIVES: Frailty, loneliness, and social isolation are all associated with adverse outcomes in older adults, but little is known about their combined impact on mortality. DESIGN: Prospective cohort study. SETTING: The Longitudinal Aging Study Amsterdam. PARTICIPANTS: Community-dwelling older adults aged 65 and older (n = 1,427). MEASUREMENTS: Frailty was measured with the frailty phenotype (Fried criteria). Loneliness was assessed with the De Jong Gierveld Loneliness Scale. Social isolation was operationalized using information on partner status, social support, and network size. Two categorical variables were created, for each possible combination regarding frailty and loneliness (FL) and frailty and social isolation (FS), respectively. Mortality was monitored over a period of 22 years (1995-2017). Survival curves and Cox proportional hazard models were used to study the effects of the FL and FS combinations on mortality. Analyses were adjusted for sociodemographic factors, depression, chronic diseases, and smoking. RESULTS: Frailty prevalence was 13%, and 5.9% of the sample were frail and lonely, and 6.2% frail and socially isolated. In fully adjusted models, older adults who were only frail had a higher risk of mortality compared with people without any of the conditions (hazard ratio [HR] range = 1.40-1.48; P < .01). However, the highest risk of mortality was observed in people with a combined presence of frailty and loneliness or social isolation (HRFL = 1.83; 95% confidence interval [CI] = 1.42-2.37; HRFS = 1.77; 95% CI = 1.36-2.30). Sensitivity analyses using a frailty index based on the deficit accumulation approach instead of the frailty phenotype showed similar results, confirming the robustness of our findings. CONCLUSION: Frail older adults are at increased risk of mortality, but this risk is even higher for those who are also lonely or socially isolated. To optimize well-being and health outcomes in physically frail older adults, targeted interventions focusing on both subjective and objective social vulnerability are needed.