BACKGROUND: Obesity and aging share biological processes, but their relationship remains unclear, especially in late life. Understanding how body mass index (BMI) and biological aging influence each other can guide strategies to reduce age- and obesity-related health risks. We examined the bidirectional, longitudinal association between changes in BMI and biological aging, measured by frailty index (FI) and functional aging index (FAI), across late life.

METHODS: This longitudinal cohort study used data from the Swedish Twin Registry substudies, GENDER, OCTO-Twin and SATSA, collected via in-person assessments from 1986 to 2014 at 2- to 4-year intervals. We analysed 6216-6512 evaluations from 1902 to 1976 Swedish twins. Dual change score models were applied to assess the bidirectional, longitudinal association between BMI and FI or FAI from ages 60.0-91.9. FI measured physiological aging, while FAI assessed functional aging through a composite score of functional abilities.

RESULTS: At first measurement, mean age was 74 ± 8, and 41% were males. BMI-FI relationship was bidirectional (p value ≤ 0.001): Higher BMI predicted a greater increase in FI over time (coupling effect [γ] = 0.86, 95% confidence interval [CI] = 0.65-1.06, p value ≤ 0.001), and higher FI predicted steeper decline in BMI (γ = -0.04, 95% CI = -0.05 to -0.03, p value ≤ 0.001). When including coupling from FI, BMI showed a nonlinear trajectory with a mean intercept of 26.32 kg/m² (95% CI = 25.76-26.88), declining more rapidly after age 75. When including BMI coupling, FI increased from a mean intercept of 7.91% (95% CI = 6.41-9.42), with steeper growth from ages 60-75. BMI-FAI relationship was unidirectional (p value ≤ 0.001): Higher FAI predicted a steeper BMI decline (γ = -0.02, 95% CI = -0.02 to -0.01, p value ≤ 0.001). By including FAI coupling, BMI had a mean intercept of 26.10 kg/m² (95% CI = 25.47-26.74), declining rapidly after age 75. FAI increased exponentially from a mean intercept of 36.49 (95% CI = 34.54-38.43).

CONCLUSIONS: Higher BMI predicted a steeper increase in FI, substantiating the hypothesis that obesity accelerates biological aging. Higher biological aging, measured as FI and FAI, drove a steeper BMI decline in late life, signalling that late-life weight loss may result from accelerated aging. Higher BMI may accelerate aspects of the aging process, and the aging process, in turn, accelerates late-life BMI decline, necessitating an integrated approach to manage both obesity and unintentional weight loss among older adults.

Background

Although women account for a greater portion of Alzheimer's Disease (AD) cases than men, the mechanisms driving this sex difference remain unclear. Given its documented neuroprotective effects, estrogen exposure (or the lack thereof) has been proposed as a moderator of AD risk in women. Specifically, greater estrogen exposure during the premenopausal period is hypothesized to provide prolonged neuroprotection through older adulthood, thereby reducing AD risk, while an earlier transition to the low-estrogen postmenopausal state may increase AD risk.

Method

To examine these hypotheses, we fit a series of Cox proportional hazards models to data from 9,793 spontaneously postmenopausal women in the Swedish Twin Registry. Chronological age was used as the model time scale and age 65 was designated as the start of model time. Women whose data collection occurred after age 65 were included as delayed entrants to prevent survivor bias in the results. Over an average of 14.43 years of follow-up 1,241 women developed dementia.

Result

Covarying for smoking history, education, and number of births, both reproductive span and age of spontaneous menopause showed significant non-linear relationships with dementia risk, where greater deviations from the mean were associated with increased risk. The models exhibited identical concordance values (0.57, SE = 0.009), indicating that both measures explain only a small proportion of the variance in dementia onset and that the models do not differ in their ability to correctly predict the order of dementia onset.

Conclusion

These results suggest that estrogen exposure plays a role in individual differences in dementia risk. However, the comparable concordance values across models limit the ability to further probe mechanistic explanations. Notably, although reproductive span is a commonly used index of lifetime estrogen exposure, it is inherently highly correlated with age of menopause given the larger variance in menopause age (here 15.66) compared with age of menarche (here 2.38). Future work employing more comprehensive measure of lifetime estrogen exposure are warranted to explore the mechanisms underlying the estrogen-AD connection.

Introduction: Both educational attainment and genetic propensity to education (PGSEdu) have been associated with geographic mobility. Socioeconomic conditions are, in turn, associated with individuals’ health. Geographic mobility could therefore lead to better health for some since it could provide better opportunities, like education. Our aim was to study how attained education and genetic predisposition for higher education are related to geographic mobility, and how they affect the association between geographic mobility and mortality. Methods: We used data from the Swedish Twin Registry (twins born 1926–1955; n = 14,211) in logistic regression models to test if attained education and PGSEdu predicted geographic mobility. Cox regression models were then performed to test if geographic mobility, attained education, and PGSEdu were associated with mortality. Results: The results show that both attained education and PGSEdu predicted geographic mobility, in both independent and joint effect models, with higher education associated with higher mobility. Geographic mobility was associated with lower mortality in the independent effect model, but joint effect models showed that this association was completely explained by attained education. Conclusions: To conclude, both attained education and PGSEdu were associated with geographic mobility. Moreover, attained education explained the relationship between geographic mobility and mortality.

While midlife adiposity is a risk factor for dementia, adiposity in late-life appears to be associated with lower risk. What drives the associations is poorly understood, especially the inverse association in late-life. Using results from genome-wide association studies, we identified inflammation and lipid metabolism as biological pathways involved in both adiposity and dementia. To test if these factors mediate the effect of midlife and/or late-life adiposity on dementia, we then used cohort data from the Swedish Twin Registry, with measures of adiposity and potential mediators taken in midlife (age 40-64, n = 5999) or late-life (age 65-90, n = 7257). Associations between body-mass index (BMI), waist-hip ratio (WHR), C-reactive protein (CRP), lipid levels, and dementia were tested in survival and mediation analyses. Age was used as the underlying time scale, and sex and education included as covariates in all models. Fasting status was included as a covariate in models of lipids. One standard deviation (SD) higher WHR in midlife was associated with 25% (95% CI 2-52%) higher dementia risk, with slight attenuation when adjusting for BMI. No evidence of mediation through CRP or lipid levels was present. After age 65, one SD higher BMI, but not WHR, was associated with 8% (95% CI 1-14%) lower dementia risk. The association was partly mediated by higher CRP, and suppressed when high-density lipoprotein levels were low. In conclusion, the negative effects of midlife adiposity on dementia risk were driven directly by factors associated with body fat distribution, with no evidence of mediation through inflammation or lipid levels. There was an inverse association between late-life adiposity and dementia risk, especially where the body's inflammatory response and lipid homeostasis is intact.

BACKGROUND: There is robust evidence that in midlife, higher body mass index (BMI) and metabolic syndrome (MetS), which often co-exist, are associated with increased mortality risk. However, late-life findings are inconclusive, and few studies have examined how metabolic health status (MHS) affects the BMI-mortality association in different age categories. We, therefore, aimed to investigate how mid- and late-life BMI and MHS interact to affect the risk of mortality. METHODS: This cohort study included 12,467 participants from the Swedish Twin Registry, with height, weight, and MHS measures from 1958-2008 and mortality data linked through 2020. We applied Cox proportional hazard regression with age as a timescale to examine how BMI categories (normal weight, overweight, obesity) and MHS (identification of MetS determined by presence/absence of hypertension, hyperglycemia, low HDL, hypertriglyceridemia), independently and in interaction, are associated with the risk of all-cause mortality. Models were adjusted for sex, education, smoking, and cardiovascular disease. RESULTS: The midlife group included 6,252 participants with a mean age of 59.6 years (range = 44.9-65.0) and 44.1% women. The late-life group included 6,215 participants with mean age 73.1 years (65.1-95.3) and 46.6% women. In independent effect models, metabolically unhealthy status in midlife increased mortality risks by 31% [hazard ratio 1.31; 95% confidence interval 1.12-1.53] and in late-life, by 18% (1.18;1.10-1.26) relative to metabolically healthy individuals. Midlife obesity increased the mortality risks by 30% (1.30;1.06-1.60) and late-life obesity by 15% (1.15; 1.04-1.27) relative to normal weight. In joint models, the BMI estimates were attenuated while those of MHS were less affected. Models including BMI-MHS categories revealed that, compared to metabolically healthy normal weight, the metabolically unhealthy obesity group had increased mortality risks by 53% (1.53;1.19-1.96) in midlife, and across all BMI categories in late-life (normal weight 1.12; 1.01-1.25, overweight 1.10;1.01-1.21, obesity 1.31;1.15-1.49). Mortality risk was decreased by 9% (0.91; 0.83-0.99) among those with metabolically healthy overweight in late-life. CONCLUSIONS: MHS strongly influenced the BMI-mortality association, such that individuals who were metabolically healthy with overweight or obesity in mid- or late-life did not carry excess risks of mortality. Being metabolically unhealthy had a higher risk of mortality independent of their BMI. 

The heritability of Alzheimer's disease estimated from twin studies is greater than the heritability derived from genome-based studies, for reasons that remain unclear. We apply both approaches to the same twin sample, considering both Alzheimer's disease polygenic risk scores and heritability from twin models, to provide insight into the role of measured genetic variants and to quantify uncaptured genetic risk. A population-based heritability and polygenic association study of Alzheimer's disease was conducted between 1986 and 2016 and is the first study to incorporate polygenic risk scores into biometrical twin models of Alzheimer's disease. The sample included 1586 twins drawn from the Swedish Twin Registry which were nested within 1137 twin pairs (449 complete pairs and 688 incomplete pairs) with clinically based diagnoses and registry follow-up (M-age = 85.28, SD = 7.02; 44% male; 431 cases and 1155 controls). We report contributions of polygenic risk scores at P < 1 x 10(-5), considering a full polygenic risk score (PRS), PRS without the APOE region (PRS.no.APOE) and PRS.no.APOE plus directly measured APOE alleles. Biometric twin models estimated the contribution of environmental influences and measured (PRS) and unmeasured genes to Alzheimer's disease risk. The full PRS and PRS.no.APOE contributed 10.1 and 2.4% to Alzheimer's disease risk, respectively. When APOE e4 alleles were added to the model with the PRS.no.APOE, the total contribution was 11.4% to Alzheimer's disease risk, where APOE e4 explained 9.3% and PRS.no.APOE dropped from 2.4 to 2.1%. The total genetic contribution to Alzheimer's disease risk, measured and unmeasured, was 71% while environmental influences unique to each twin accounted for 29% of the risk. The APOE region accounts for much of the measurable genetic contribution to Alzheimer's disease, with a smaller contribution from other measured polygenic influences. Importantly, substantial background genetic influences remain to be understood. Karlsson et al. report that measured polygenic scores from genome-based studies, including an outsized role for APOE, explain only a fraction of the heritability indicated by twin models of Alzheimer's disease, leaving most genetic risk for Alzheimer's disease unexplained. Sensitive designs are needed to capture all the genetic influences.

Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer’s disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast “resilient” unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

Late-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50–70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer’s disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer’s disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants that contribute to Alzheimer’s pathology.

Introduction To study if declining cognition drives weight loss in preclinical dementia, we examined the longitudinal association between body mass index (BMI) and cognitive abilities in individuals who did or did not later develop dementia. Methods Using data from individuals spanning age 50 to 89, we applied dual change score models separately in individuals who remained cognitively intact (n = 1498) and those who were diagnosed with dementia within 5 years of last assessment (n = 459). Results Among the cognitively intact, there was a bidirectional association: Stable BMI predicted stable cognition and vice versa. Among individuals who were subsequently diagnosed with dementia, the association was unidirectional: Higher BMI predicted declining cognition but cognition did not predict change in BMI. Discussion Although BMI and cognition stabilized each other when cognitive functioning was intact, this buffering effect was missing in the preclinical dementia phase. This finding indicates that weight loss in preclinical dementia is not driven by declining cognition.

Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease.