The journals of gerontology. Series A, Biological sciences and medical sciences最新文献

Background: This study examined the longitudinal relationship between cumulative socioeconomic status (SES) risk and serum neurofilament light chain (NfL) levels to better understand the association between social factors and a biomarker of neurodegeneration.

Methods: We used data from the Family and Community Health Study (FACHS), collecting psychosocial and blood data at two waves (2008) and (2019) from 254 Black Americans (43 males and 211 females). Blood samples were analyzed at each wave for serum NfL concentrations. Regression analysis and mixed-effect modeling examined relationships between cumulative SES risk and serum NfL, controlling for covariates and assessing time effects.

Results: Utilizing 11-year longitudinal data, serum NfL levels increased with age. Higher cumulative SES risk at baseline correlated with elevated serum NfL at the 11-year follow-up and predicted a greater increase in NfL levels. Clinically, NfL is a sensitive biomarker for axonal injury and neurodegeneration, commonly used to detect early and preclinical stages of conditions such as Alzheimer's disease (AD), multiple sclerosis, and other neurodegenerative disorders.

Conclusions: Our results suggest that exposure to cumulative SES risk among Black adults may contribute to elevated levels of NfL, indicating potential early neurodegeneration. Given the established role of NfL in detecting neurodegenerative processes, these findings underscore the importance of interventions that bolster social safety nets and social connectedness to enhance brain health and mitigate neurodegenerative risks.

Aging is a complex process influenced by mechanisms operating at numerous levels of functioning. Multiple biomarkers of age have been identified, yet we know little about how the different alternative age indicators are intertwined. In the Berlin Aging Study II (nmin= 328; nmax= 1,517, women = 51%; 14.27 years of education), we examined how levels and seven-year changes in indicators derived from blood assays, MRI brain scans, other-ratings, and self-reports converge among older adults. We included eight epigenetic biomarkers (incl. five epigenetic "clocks"), a BioAge composite from clinical laboratory parameters, brain age, skin age, subjective age, subjective life expectancy, and future health horizon. We found moderate associations within aging domains, both cross-sectionally and longitudinally over seven years. However, associations across different domains were infrequent and modest. Notably, participants with older BioAge had correspondingly older epigenetic ages. Our results suggest that different aging clocks are only loosely interconnected and that more specific measures are needed to differentiate healthy from unhealthy aging.

Background: The Apolipoprotein E (APOE) ε4 allele, type 2 diabetes mellitus (T2DM), and cardiovascular disease (CVD) are well-established risk factors for dementia. Relationships between APOE and incidence of T2DM and CVD are not fully understood but may shed light on the mechanisms underlying dementia pathogenesis.

Methods: Postmenopausal women (N=6,795) from the Women's Health Initiative hormone therapy clinical trial with APOE genotyping and no prior diagnosis of T2DM or CVD were included. We examined associations of APOE status (APOE2+ [ε2/ε2, ε2/ε3], APOE3 [ε3/ε3], and APOE4+ [ε4/ε4, ε3/ε4] carriers) with incidence of T2DM, coronary heart disease (CHD), stroke, and total CVD events using Cox regression. CVD outcomes were examined in baseline non-statin users and adjusted for statin initiation over follow-up to account for possible confounding by statins.

Results: Among all participants (mean age 66.7±6.5 years, 100% non-Hispanic white), 451 (6.6%) were using statins at baseline. Over the follow-up (mean 14.9 and 16.0 years for T2DM and CVD, respectively), 1,564 participants developed T2DM and 1,578 developed CVD. T2DM incidence did not differ significantly by APOE status (ps≥0.09). Among non-statin users, APOE4+ had higher incidence of total CVD (hazard ratio [95% confidence interval]=1.18 [1.02-1.38], p=0.03) compared to APOE3 carriers, but risks for CHD (1.09 [0.87-1.36], p=0.47) and stroke (1.14 [0.91-1.44], p=0.27) were not significantly elevated when examined individually. CVD outcomes did not differ between APOE2+ and APOE3 carriers (ps≥0.11).

Conclusions: T2DM risk did not differ by APOE status among postmenopausal women, but APOE4+ carriers not using statins had an increased risk of total CVD events.

Background: In older patients, frailty and anemia frequently coexist. However, only few studies have been conducted in older patients with multimorbidity and several overlapping causes of anemia, such as inflammation, inadequate nutrition or certain pathologies. This analysis aims to decipher potential factors associated with anemia in older hospital patients with frailty.

Methods: Patients (n=208, age: 62-98 years) were categorized as pre-frail (n=68) and frail (n=140) using the Fried frailty phenotype. We quantified serum concentrations of markers of iron-metabolism (iron, ferritin, transferrin, soluble transferrin receptor, hepcidin), inflammation (interleukin (IL) 6, IL-10 C-reactive protein) and haematology (hemoglobin). Principal component analysis was conducted to evaluate biomarker patterns and associations with frailty were assessed with logistic regression analysis.

Results: Anemia prevalence was higher in patients with frailty (84.3% versus 70.6%, p=0.021). Three principal components (PC1-3) were identified. PC1 was characterized by high factor loadings representing inflammation and factor scores differed between patients with pre-frailty and frailty [-0.04 (IQR:1.45) versus -0.51 (IQR:0.87), p<0.001]. PC2 represents macrocytic anemia and thus vitamin B12 or folate deficiency, whereas PC3 indicates hematological pathologies. Only PC1 was associated with frailty status when controlled for age, sex, number of drugs and comorbidities (OR: 2.018, 95%CI: 1.316; 3.094, p=0.001). PC2 and PC3 were not associated with frailty.

Conclusion: Our results suggest that anemia in patients with frailty is driven by inflammation rather than being disease-related or solely the result of micronutrient deficiencies.

Background: Aging is characterized by loss of resilience, the ability to resist or recover from stressors. Network analysis has shown promise in investigating dynamic relationships underlying resilience. We aimed to use network analysis to measure resilience in a longitudinal cohort of older adults and quantify whole-system vulnerabilities associated with frailty.

Methods: We used data from the Rugao Longitudinal Ageing Study, including 71 biomarkers from participants classified as robust, prefrail, or frail. We quantified biomarker correlations and topological parameters. Additionally, we proposed propagation models to simulate damage and recovery dynamics, investigating network resilience under various conditions.

Results: We classified 1 754 individuals into robust (n = 369), prefrail (n = 1 103), and frail (n = 282) groups with 71 biomarkers. Several biomarkers were linked to frailty, including those related to blood pressure, electrocardiogram (ECG), kidney function, platelets, and white blood cells. Each frailty stage was associated with increased network correlations. The frail network showed increased average degree and connectance, decreased average path length and diameter, and reduced modularity compared to robust and prefrail networks. Hub biomarkers, particularly β2-microglobulin and platelet count, played a significant role, potentially propagating dysfunction across physiological systems. Simulations revealed that damage to critical hubs led to longer recovery times in the frail network than robust and prefrail networks.

Conclusions: Network analysis could serve as a valuable tool for quantifying resilience and identifying vulnerabilities in older adults with frailty. Our findings contribute to understanding frailty-related physiological disturbances and offer potential for personalized healthcare interventions targeting resilience in older populations.

It is becoming highly accepted that aging, age-related diseases, and geriatric healthcare can move forward if reductionist research is complemented by integrative research uniting knowledge on specific aging mechanisms, multiple biomedical, social, psychological, lifestyle, and environmental factors and their interactions. In this special issue, we present exciting papers that illustrate how complexity science theory and practice can be applied to aging research and provide a better understanding and quantification of healthy aging and vulnerability to disease. Recent insights on biomarkers, clocks of aging, frailty, and resilience are covered and studied in interaction with a dynamic multiscale perspective. The editorial and closing viewpoint guide you through basic principles of gerontological complexity science and shed light on new research horizons, including innovative systems-based interventions.

Maintaining balance is a complex motor problem that requires coordinated contributions from multiple biological systems. Aging inevitably lessens the fidelity of biological systems, which can result in an increased risk of falling and associated injuries. It is advantageous to land safely, but falls manifest in diverse ways, so different motor solutions are required to land safely. However, without considerable practice, it is difficult to recall the appropriate motor solution for a fall and then apply it effectively in the brief duration before hitting the ground. A complex systems perspective provides a lens through which to view the problem of safe(r) landing. It may be possible to use motor analogies to promote degeneracy within the perceptual motor system so that, regardless of the direction in which an older person falls, their body self-organizes to land with less likelihood of injury.

Background: DNA methylation-derived epigenetic clocks and frailty are well-established biological age measures capturing different aging processes. However, whether they are dynamically linked to each other across chronological age remains poorly understood.

Methods: This analysis included 1 309 repeated measurements in 524 individuals aged 50-90 years from the Swedish Adoption/Twin Study of Aging. Frailty was measured using a validated 42-item frailty index (FI). Five epigenetic clocks were calculated, including 4 principal component (PC)-based clocks trained on chronological age (PCHorvathAge and PCHannumAge) and aging-related physiological conditions (PCPhenoAge and PCGrimAge), and a pace of aging clock (DunedinPACE). Using dual change score models, we examined the dynamic, bidirectional associations between each of the epigenetic clocks and the FI over age to test for potential causal associations.

Results: The FI exhibited a nonlinear, accelerated increase across the older adulthood, whereas the epigenetic clocks mostly increased linearly with age. For PCHorvathAge, PCHannumAge, PCPhenoAge, and PCGrimAge, their associations with the FI were primarily due to correlated levels at age 50 but with no evidence of a dynamic longitudinal association. In contrast, we observed a unidirectional association between DunedinPACE and the FI, where a higher DunedinPACE predicted a subsequent increase in the FI, but not vice versa.

Conclusions: Our results highlight a temporal order between epigenetic aging and frailty such that changes in DunedinPACE precede changes in the FI. This potentially suggests that the pace of aging clock can be used as an early marker of the overall physiological decline at system level.

Background: Long-term care (LTC) costs create burdens on aging societies. Maintaining oral health through dental visits may result in shorter LTC periods, thereby decreasing LTC costs; however, this remains unverified. We examined whether dental visits in the past 6 months were associated with cumulative LTC insurance (LTCI) costs.

Methods: This cohort study of the Japan Gerontological Evaluation Study targeted independent adults aged≥65 years in 2010 over an 8-year follow-up. We used data from a self-reported questionnaire and LTCI records from the municipalities. The outcome was cumulative LTCI costs, and exposure was dental visits within 6 months for prevention, treatment, and prevention or treatment. A 2-part model was used to estimate the differences in the predicted cumulative LTCI costs and 95% confidence intervals (CIs) for each dental visit.

Results: The mean age of the 8 429 participants was 73.7 years (standard deviation [SD] = 6.0), and 46.1% were men. During the follow-up period, 17.6% started using LTCI services. The mean cumulative LTCI cost was USD 4 877.0 (SD = 19 082.1). The predicted cumulative LTCI costs were lower among those had dental visits than among those who did not. The differences in predicted cumulative LTCI cost were -USD 1 089.9 (95% CI = -1 888.5 to -291.2) for dental preventive visits, -USD 806.7 (95% CI = -1 647.4 to 34.0) for treatment visits, and -USD 980.6 (95% CI = -1 835.7 to -125.5) for preventive or treatment visits.

Conclusions: Dental visits, particularly preventive visits, were associated with lower cumulative LTCI costs. Maintaining oral health through dental visits may effectively reduce LTCI costs.