Trajectory of Cognitive Decline Before and After Stroke in 14 Population Cohorts.

Importance: Poststroke cognitive impairment is common, but the cognitive trajectory following a first stroke, relative to prestroke cognitive function, remains unclear. Objective: To map the trajectory of cognitive function before any stroke and after stroke in global cognition and in 4 cognitive domains, as well as to compare the cognitive trajectory prestroke in stroke survivors with the trajectory of individuals without incident stroke over follow-up. Design, Setting, and Participants: The study used harmonized and pooled data from 14 population-based cohort studies included in the Cohort Studies of Memory in an International Consortium collaboration. These studies were conducted from 1993 to 2019 across 11 countries among community-dwelling older adults without a history of stroke or dementia. For this study, linear mixed-effects models were used to estimate trajectories of cognitive function poststroke relative to a stroke-free cognitive trajectory. The full model adjusted for demographic and vascular risk factors. Data were analyzed from July 2022 to March 2024. Exposure: Incident stroke. Main outcomes and measures: The primary outcome was global cognition, defined as the standardized average of 4 cognitive domains (language, memory, processing speed, and executive function). Cognitive domain scores were formed by selecting the most commonly administered test within each domain and standardizing the scores. Results: The study included 20 860 participants (12 261 [58.8%] female) with a mean (SD) age of 72.9 (8.0) years and follow-up of 7.51 (4.2) years. Incident stroke was associated with a substantial acute decline in global cognition (-0.25 SD; 95% CI, -0.33 to -0.17 SD), the Mini-Mental State Examination, and all cognitive domains (ranging from -0.17 SD to -0.22 SD), as well as accelerated decline in global cognition (-0.038 SD per year; 95% CI, -0.057 to -0.019 SD per year) and all domains except memory (ranging from -0.020 to -0.055 SD per year), relative to a stroke-free cognitive trajectory. There was no significant difference in prestroke slope in stroke survivors compared with the rate of decline in individuals without stroke in all cognitive measures. The mean rate of decline without a previous stroke was -0.049 SD per year (95% CI, -0.051 to -0.047 SD) in global cognition. Conclusions and relevance: In this cohort study using pooled data from 14 cohorts, incident stroke was associated with acute and accelerated long-term cognitive decline in older stroke survivors.

Independent and joint associations of cardiometabolic multimorbidity and depression on cognitive function: findings from multi-regional cohorts and generalisation from community to clinic.

Background: Cardiometabolic multimorbidity (CMM) and depression are often co-occurring in older adults and associated with neurodegenerative outcomes. The present study aimed to estimate the independent and joint associations of CMM and depression on cognitive function in multi-regional cohorts, and to validate the generalizability of the findings in additional settings, including clinical. Methods: Data harmonization was performed across 14 longitudinal cohort studies within the Cohort Studies of Memory in an International Consortium (COSMIC) group, spanning North America, South America, Europe, Africa, Asia, and Australia. Three external validation studies with distinct settings were employed for generalization. Participants were eligible for inclusion if they had data for CMM and were free of dementia at baseline. Baseline CMM was defined as: 1) CMM 5, ≥2 among hypertension, hyperlipidemia, diabetes, stroke, and heart disease and 2) CMM 3 (aligned with previous studies), ≥2 among diabetes, stroke, and heart disease. Baseline depression was primarily characterized by binary classification of depressive symptom measurements, employing the Geriatric Depression Scale and the Center for Epidemiological Studies-Depression scale. Global cognition was standardized as z-scores through harmonizing multiple cognitive measures. Longitudinal cognition was calculated as changes in global cognitive z-scores. A pooled individual participant data (IPD) analysis was utilized to estimate the independent and joint associations of CMM and depression on cognitive outcomes in COSMIC studies, both cross-sectionally and longitudinally. Repeated analyses were performed in three external validation studies. Findings: Of the 32,931 older adults in the 14 COSMIC cohorts, we included 30,382 participants with complete data on baseline CMM, depression, and cognitive assessments for cross-sectional analyses. Among them, 22,599 who had at least 1 follow-up cognitive assessment were included in the longitudinal analyses. The three external studies for validation had 1964 participants from 3 multi-ethnic Asian older adult cohorts in different settings (community-based, memory clinic, and post-stroke study). In COSMIC studies, each of CMM and depression was independently associated with cross-sectional and longitudinal cognitive function, without significant interactions between them (Ps > 0.05). Participants with both CMM and depression had lower cross-sectional cognitive performance (e.g. ß = -0.207, 95% CI = (-0.255, -0.159) for CMM5 (+)/depression (+)) and a faster rate of cognitive decline (e.g. ß = -0.040, 95% CI = (-0.047, -0.034) for CMM5 (+)/depression (+)), compared with those without either condition. These associations remained consistent after additional adjustment for APOE genotype and were robust in two-step random-effects IPD analyses. The findings regarding the joint association of CMM and depression on cognitive function were reproduced in the three external validation studies. Interpretation: Our findings highlighted the importance of investigating age-related co-morbidities in a multi-dimensional perspective. Targeting both cardiometabolic and psychological conditions to prevent cognitive decline could enhance effectiveness. Funding: Natural Science Foundation of China and National Institute on Aging/National Institutes of Health.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

The Add Health Parent Study: A Biosocial Resource for the Study of Multigenerational Racial/Ethnic Disparities in Alzheimer's Disease and Alzheimer's Disease-Related Dementias.

Background: Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD) have increased in prevalence. Objective: This article describes the Add Health Parent Study (AHPS) Phase 2, a study of social, behavioral, and biological factors influencing healthy aging and risk for AD/ADRD, in a national sample of adults aged 58-90. Methods: Sample members are parents of the National Longitudinal Study of Adolescent to Adult Health (Add Health) cohort, initially interviewed in Add Health in midlife (1994-95). AHPS Phase 1 (2015-17) collected longitudinal data on a random subsample of parents and their spouse/partners, who were mostly Non-Hispanic (NH) White. AHPS Phase 2 will collect the same longitudinal socio-behavioral, and health survey data on all remaining NH Black and Hispanic parents (Black and Hispanic Supplement, BHS). Additionally, Phase 2 will collect cognitive and DNA data from AHPS Phase 1 and BHS sample parents and their current spouse/partners. Results: Funded by the National Institute on Aging, recruitment will occur between June 2025 and May 2026, producing an expected total AHPS sample of 5506 parents and their spouse/partners. Conclusions: The AHPS will be the first longitudinal cohort study powered to address multigenerational racial/ethnic disparities in AD/ADRD risk and protective factors across race/ethnic groups and socioeconomic strata.

Blood Pressure, Antihypertensive Use, and Late-Life Alzheimer and Non-Alzheimer Dementia Risk: An Individual Participant Data Meta-Analysis.

BACKGROUND AND OBJECTIVES: Previous randomized controlled trials and longitudinal studies have indicated that ongoing antihypertensive use in late life reduces all-cause dementia risk, but the specific impact on Alzheimer dementia (AD) and non-AD risk remains unclear. This study investigates whether previous hypertension or antihypertensive use modifies AD or non-AD risk in late life and the ideal blood pressure (BP) for risk reduction in a diverse consortium of cohort studies. METHODS: This individual participant data meta-analysis included community-based longitudinal studies of aging from a preexisting consortium. The main outcomes were risk of developing AD and non-AD. The main exposures were hypertension history/antihypertensive use and baseline systolic BP/diastolic BP. Mixed-effects Cox proportional hazards models were used to assess risk and natural splines were applied to model the relationship between BP and the dementia outcomes. The main model controlled for age, age2, sex, education, ethnoracial group, and study cohort. Supplementary analyses included a fully adjusted model, an analysis restricting to those with >5 years of follow-up and models that examined the moderating effect of age, sex, and ethnoracial group. RESULTS: There were 31,250 participants from 14 nations in the analysis (41% male) with a mean baseline age of 72 (SD 7.5, range 60-110) years. Participants with untreated hypertension had a 36% (hazard ratio [HR] 1.36, 95% CI 1.01-1.83, p = 0.0406) and 42% (HR 1.42, 95% CI 1.08-1.87, p = 0.0135) increased risk of AD compared with "healthy controls" and those with treated hypertension, respectively. Compared with "healthy controls" both those with treated (HR 1.29, 95% CI 1.03-1.60, p = 0.0267) and untreated hypertension (HR 1.69, 95% CI 1.19-2.40, p = 0.0032) had greater non-AD risk, but there was no difference between the treated and untreated groups. Baseline diastolic BP had a significant U-shaped relationship (p = 0.0227) with non-AD risk in an analysis restricted to those with 5-year follow-up, but otherwise there was no significant relationship between baseline BP and either AD or non-AD risk. DISCUSSION: Antihypertensive use was associated with decreased AD but not non-AD risk throughout late life. This suggests that treating hypertension throughout late life continues to be crucial in AD risk mitigation. A single measure of BP was not associated with AD risk, but DBP may have a U-shaped relationship with non-AD risk over longer periods in late life.

Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.

Indicators of inequity: Exploring the complexities of operationalizing area-level structural racism.

•Examines two types of area-level indicators commonly used in structural racism and health research.•Uses choropleth maps to assess the spatial patterning of the area-level indicators.•Offers methodological considerations for using area-level indicators.•Highlights the prospects and limits of using area-level indicators as proxies of structural racism.

Elevated stress-responsive biomarkers are associated with HIV acquisition in young women in rural South Africa.

OBJECTIVE: Biological markers of stress have been associated with HIV progression and pathogenesis but not with HIV incidence. We sought to determine if elevated stress-responsive biomarkers would be associated with incident HIV among adolescent girls and young women (AGYW). DESIGN: We conducted a case-cohort study within the HIV Prevention Trials Network (HPTN) 068 study among 949 AGYW in South Africa. Cases were AGYW who tested HIV-positive during the eight-year follow-up. Unmatched controls were randomly selected from the HIV-negative population at enrollment. METHODS: Dried blood spots from cases and controls were tested from enrollment (2011-2012) for C-reactive protein (CRP), herpes simplex virus type-1 (HSV-1) antibody titers, and cytomegalovirus (CMV) antibody titers. Cox proportional hazards models estimated the association between each biomarker and time to incident HIV. RESULTS: Compared to AGYW with the lowest CRP levels, those with medium and high CRP levels had a higher hazard ratio (HR) of incident HIV [HR: 1.45, 95% confidence interval (CI): 0.95, 2.21; HR: 1.50, 95% CI: 0.98, 2.30, respectively], although not statistically significant. The relative hazard of incident HIV was also higher among AGYW who were CMV seropositive vs. seronegative (low antibodies HR: 2.18, 95% CI: 1.2, 3.87; medium HR: 2.25, 95% CI: 1.28, 3.95; high HR: 1.78, 95% CI: 0.99, 3.21). Those with the highest HSV-1 antibody levels experienced an increased hazard of HIV compared to those who were HSV-1 seronegative (HR: 1.58, 95% CI: 1.03, 2.44). CONCLUSIONS: Biological stress may increase AGYW's susceptibility to HIV acquisition through changes in immune function, viral infection, and increased biological vulnerability to disease.

The embodiment of the neighborhood socioeconomic environment in the architecture of the immune system.

There is growing recognition of the importance of immune health for understanding the origins of ageing-related disease and decline. Numerous studies have demonstrated consistent associations between the social determinants of health and immunosenescence (i.e. ageing of the immune system). Yet few studies have interrogated the relationship between neighborhood socioeconomic status (nSES) and biologically specific measures of immunosenescence. We used data from the US Health and Retirement Study to measure immunosenescence linked with neighborhood socioeconomic data from the National Neighborhood Data Archive to examine associations between indicators of nSES and immunosenescence. We found associations between both the ratio of terminally differentiated effector memory to naïve (EMRA:Naïve) CD4+ T cells and cytomegalovirus (CMV) immunoglobulin G (IgG) levels and nSES. For the CD4+ EMRA:Naïve ratio, each 1% increase in the neighborhood disadvantage index was associated with a 0.005 standard deviation higher value of the EMRA:Naïve ratio (95% CI: 0.0003, 0.01) indicating that living in a neighborhood that is 10% higher in disadvantage is associated with a 0.05 higher standardized value of the CD4+ EMRA:Naïve ratio. The results were fully attenuated when adjusting for both individual-level SES and race/ethnicity. For CMV IgG antibodies, a 1% increase in neighborhood disadvantage was associated a 0.03 standard deviation higher value of CMV IgG antibodies (ß = 0.03; 95% CI: 0.002, 0.03) indicating that living in a neighborhood that is 10% higher in disadvantage is associated with a 0.3 higher standardized value of CMV. This association was attenuated though still statistically significant when controlling for individual-level SES and race/ethnicity. The findings from this study provide compelling initial evidence that large, nonspecific social exposures, such as neighborhood socioeconomic conditions, can become embodied in cellular processes of immune ageing.

Sociodemographic and Lifestyle Factors and Epigenetic Aging in US Young Adults: NIMHD Social Epigenomics Program.

Importance: Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most research is based on samples of older adults who already have measurable chronic disease. Objective: To investigate whether and how sociodemographic and lifestyle characteristics are associated with biological aging in a younger adult sample across a wide array of epigenetic clock measures. Design, Setting, and Participants: This cohort study was conducted using data from the National Longitudinal Study of Adolescent to Adult Health, a US representative cohort of adolescents in grades 7 to 12 in 1994 followed up for 25 years to 2018 over 5 interview waves. Participants who provided blood samples at wave V (2016-2018) were analyzed, with samples tested for DNA methylation (DNAm) in 2021 to 2024. Data were analyzed from February 2023 to May 2024. Exposure: Sociodemographic (sex, race and ethnicity, immigrant status, socioeconomic status, and geographic location) and lifestyle (obesity status by body mass index [BMI] in categories of reference range or underweight [<25], overweight [25 to <30], obesity [30 to <40], and severe obesity [≥40]; exercise level; tobacco use; and alcohol use) characteristics were assessed. Main Outcome and Measure: Biological aging assessed from banked blood DNAm using 16 epigenetic clocks. Results: Data were analyzed from 4237 participants (mean [SD] age, 38.4 [2.0] years; percentage [SE], 51.3% [0.01] female and 48.7% [0.01] male; percentage [SE], 2.7% [<0.01] Asian or Pacific Islander, 16.7% [0.02] Black, 8.7% [0.01] Hispanic, and 71.0% [0.03] White). Sociodemographic and lifestyle factors were more often associated with biological aging in clocks trained to estimate morbidity and mortality (eg, PhenoAge, GrimAge, and DunedinPACE) than clocks trained to estimate chronological age (eg, Horvath). For example, the ß for an annual income less than $25 000 vs $100 000 or more was 1.99 years (95% CI, 0.45 to 3.52 years) for PhenoAgeAA, 1.70 years (95% CI, 0.68 to 2.72 years) for GrimAgeAA, 0.33 SD (95% CI, 0.17 to 0.48 SD) for DunedinPACE, and -0.17 years (95% CI, -1.08 to 0.74 years) for Horvath1AA. Lower education, lower income, higher obesity levels, no exercise, and tobacco use were associated with faster biological aging across several clocks; associations with GrimAge were particularly robust (no college vs college or higher: ß = 2.63 years; 95% CI, 1.67-3.58 years; lower vs higher annual income: <$25 000 vs ≥$100 000: ß = 1.70 years; 95% CI, 0.68-2.72 years; severe obesity vs no obesity: ß = 1.57 years; 95% CI, 0.51-2.63 years; no weekly exercise vs ≥5 bouts/week: ß = 1.33 years; 95% CI, 0.67-1.99 years; current vs no smoking: ß = 7.16 years; 95% CI, 6.25-8.07 years). Conclusions and Relevance: This study found that important social and lifestyle factors were associated with biological aging in a nationally representative cohort of younger adults. These findings suggest that molecular processes underlying disease risk may be identified in adults entering midlife before disease is manifest and inform interventions aimed at reducing social inequalities in heathy aging and longevity.

Familial Loss of a Loved One and Biological Aging: NIMHD Social Epigenomics Program.

Importance: The link between familial loss of a loved one and long-term health decline is complex and not fully understood. Objective: To test associations of losing a parent, sibling, child, or partner or spouse with accelerated biological aging. Design, Setting, and Participants: Data from the National Longitudinal Study of Adolescent to Adult Health, a US population-based longitudinal cohort study, were analyzed. Participants were enrolled from 1994 to 1995 for wave 1, while in grades 7 to 12, and followed up through wave 5 in 2018. The study analyzed participant reports of loss collected at each wave from 1 to 5 over 24 years and used a banked wave 5 blood sample for subsequent DNA methylation testing and epigenetic clock calculation from 2018 to 2024. Data were analyzed from January 2022 to July 2024. Exposure: Loss of biological parents or parental figures, partners or spouses, siblings, or children at waves 1 to 3 or during childhood, adolescence (aged <18 years), or adulthood at wave 4 to wave 5 (aged 18-43 years). Main Outcomes and Measures: Biological aging assessed from blood DNA methylation using the Horvath, PhenoAge, GrimAge, and DunedinPACE epigenetic clocks at wave 5. Results: Data from 3963 participants were analyzed, with a weighted mean (range) age of 38.36 (36.78-39.78) years at wave 5; 2370 (50.3%) were male, 720 (15.97%) were Black, 400 (8.18%) were Hispanic, and 2642 (72.53%) were White. Nearly 40% of participants experienced loss by wave 5 when they were aged 33 to 43 years, and participants who were Black (379 participants [56.67%]), Hispanic (152 participants [41.38%]), and American Indian (18 participants [56.08%]) experienced a greater proportion of losses compared with White participants (884 participants [34.09%]). Those who experienced 2 or more losses tended to have older biological ages for several of the clocks (PhenoAge ß = 0.15; 95% CI, 0.02 to 0.28; GrimAge ß = 0.27; 95% CI, 0.09 to 0.45; DunedinPACE ß = 0.22; 95% CI, 0.10 to 0.34) compared with those with no losses. In contrast, there were no associations with 2 or more losses for the Horvath clock (ß = -0.08; 95% CI, -0.23 to 0.06). Conclusions and Relevance: This study reveals associations between various measures of loss experienced from childhood to adulthood and biological aging in a diverse sample of the US population. These findings underscore the potentially enduring impact of loss on biological aging even before middle age and may contribute to understanding racial and ethnic disparities in health and mortality.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

Background: The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. Methods: As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. Results: We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. Conclusions: This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

Trajectories of intimate partner violence and their relationship to stress among young women in South Africa: An HPTN 068 study.

BACKGROUND: One in four South African women will experience intimate partner violence (IPV) in their lifetime, potentially increasing their biological stress. In South Africa, limited IPV and stress research has utilized multiple timepoints or examined modifying factors. Cash transfers (CTs) are associated with reduced IPV and stress and may be an intervention target. AIMS: We used data-driven methods to identify longitudinal IPV trajectory groups among South African adolescent girls and young women (AGYW), estimate each group's association with stress, and assess modification by a CT. METHODS: A total of 2,183 South African AGYW ages 13 to 24 years from the HIV Prevention Trials Network 068 study were randomized to a CT or control group. Physical IPV was measured five times (2011-2017), and stress was captured once (2018-2019). Stress measures included the Cohen Stress Scale and stress biomarkers (C-reactive protein (CRP), cytomegalovirus (CMV), herpes simplex virus type-1 (HSV-1)). Group-based trajectory modeling identified IPV trajectories; ordinal logistic regression estimated the association between trajectory group and stress. RESULTS: A two-group quadratic trajectory model was identified (higher trajectory group = 26.7% of AGYW; lower trajectory group = 73.3%). In both groups, the probability of IPV increased from ages 13 to 17 years before declining in early adulthood. However, the higher group's probability peaked later and declined gradually. The higher trajectory group was associated with an increased odds of elevated CRP (OR: 1.41, 95% CI [1.11, 1.80]), but not with other stress measures. The CT modified the relationship with CMV: a positive association was observed among the usual care arm (OR: 1.59, 95% CI [1.11, 2.28]) but not the CT arm (OR: 0.85, 95% CI [0.61, 1.19]). CONCLUSIONS: Sustained IPV risk during adolescence was associated with elevated CRP in young adulthood. The relationship between IPV and elevated CMV was attenuated among those receiving a CT, suggesting that CTs could possibly reduce biological stress due to IPV.

Lifestyle and incident dementia: A COSMIC individual participant data meta­analysis.

INTRODUCTION: The LIfestyle for BRAin Health (LIBRA) index yields a dementia risk score based on modifiable lifestyle factors and is validated in Western samples. We investigated whether the association between LIBRA scores and incident dementia is moderated by geographical location or sociodemographic characteristics. METHODS: We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort-specific Cox proportional hazard regression analyses in a two-step individual participant data meta-analysis. RESULTS: A one-standard-deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow-up. No interactions with sex, education, or socioeconomic position were observed. DISCUSSION: Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups. HIGHLIGHTS: A two-step individual participant data meta-analysis was conducted. This was done at a global scale using data from 21 ethno-regionally diverse cohorts. The association between a modifiable dementia risk score and dementia was examined. The association was modified by geographical region and age at baseline. Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions.

The Sociodemographic and Lifestyle Correlates of Epigenetic Aging in a Nationally Representative U.S. Study of Younger Adults.

Importance: Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most of this research is based on older adult samples who already have measurable chronic disease. Objective: To investigate whether and how sociodemographic and lifestyle characteristics are related to biological aging in a younger adult sample across a wide array of epigenetic clock measures. Design: Nationally representative prospective cohort study. Setting: United States (U.S.). Participants: Data come from the National Longitudinal Study of Adolescent to Adult Health, a national cohort of adolescents in grades 7-12 in U.S. in 1994 followed for 25 years over five interview waves. Our analytic sample includes participants followed-up through Wave V in 2016-18 who provided blood samples for DNA methylation (DNAm) testing (n=4237) at Wave V. Exposure: Sociodemographic (sex, race/ethnicity, immigrant status, socioeconomic status, geographic location) and lifestyle (obesity status, exercise, tobacco, and alcohol use) characteristics. Main Outcome: Biological aging assessed from blood DNAm using 16 epigenetic clocks when the cohort was aged 33-44 in Wave V. Results: While there is considerable variation in the mean and distribution of epigenetic clock estimates and in the correlations among the clocks, we found sociodemographic and lifestyle factors are more often associated with biological aging in clocks trained to predict current or dynamic phenotypes (e.g., PhenoAge, GrimAge and DunedinPACE) as opposed to clocks trained to predict chronological age alone (e.g., Horvath). Consistent and strong associations of faster biological aging were found for those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use. Conclusions and Relevance: Our study found important social and lifestyle factors associated with biological aging in a nationally representative cohort of younger-aged adults. These findings indicate that molecular processes underlying disease risk can be identified in adults entering midlife before disease is manifest and represent useful targets for interventions to reduce social inequalities in heathy aging and longevity. Key Points: Question: Are epigenetic clocks, measures of biological aging developed mainly on older-adult samples, meaningful for younger adults and associated with sociodemographic and lifestyle characteristics in expected patterns found in prior aging research?Findings: Sociodemographic and lifestyle factors were associated with biological aging in clocks trained to predict morbidity and mortality showing accelerated aging among those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use.Meaning: Age-related molecular processes can be identified in younger-aged adults before disease manifests and represent potential interventions to reduce social inequalities in heathy aging and longevity.

Educational Mobility, Pace of Aging, and Lifespan Among Participants in the Framingham Heart Study.

Importance: People who complete more education live longer lives with better health. New evidence suggests that these benefits operate through a slowed pace of biological aging. If so, measurements of the pace of biological aging could offer intermediate end points for studies of how interventions to promote education will affect healthy longevity. Objective: To test the hypothesis that upward educational mobility is associated with a slower pace of biological aging and increased longevity. Design, Setting, and Participants: This prospective cohort study analyzed data from 3 generations of participants in the Framingham Heart Study: (1) the original cohort, enrolled beginning in 1948; (2) the Offspring cohort, enrolled beginning in 1971; and (3) the Gen3 cohort, enrolled beginning in 2002. A 3-generation database was constructed to quantify intergenerational educational mobility. Mobility data were linked with blood DNA-methylation data collected from the Offspring cohort in 2005 to 2008 (n = 1652) and the Gen3 cohort in 2009 to 2011 (n = 1449). Follow-up is ongoing. Data analysis was conducted from June 2022 to November 2023 using data obtained from the National Institutes of Health database of Genotypes and Phenotypes (dbGaP). Exposure: Educational mobility was measured by comparing participants' educational outcomes with those of their parents. Main Outcomes and Measures: The pace of biological aging was measured from whole-blood DNA-methylation data using the DunedinPACE epigenetic clock. For comparison purposes, the analysis was repeated using 4 other epigenetic clocks. Survival follow-up was conducted through 2019. Results: This study analyzed data from 3101 participants from the Framingham Heart Study; 1652 were in the Offspring cohort (mean [SD] age, 65.57 [9.22] years; 764 [46.2%] male) and 1449 were in the Gen3 cohort (mean [SD] age, 45.38 [7.83] years; 691 [47.7%] male). Participants who were upwardly mobile in educational terms tended to have slower pace of aging in later life (r = -0.18 [95% CI, -0.23 to -0.13]; P < .001). This pattern of association was similar across generations and held in within-family sibling comparisons. There were 402 Offspring cohort participants who died over the follow-up period. Upward educational mobility was associated with lower mortality risk (hazard ratio, 0.89 [95% CI, 0.81 to 0.98]; P = .01). Slower pace of aging accounted for approximately half of this association. Conclusions and Relevance: This cohort study's findings support the hypothesis that interventions to promote educational attainment may slow the pace of biological aging and promote longevity. Epigenetic clocks have potential as near-term outcome measures of intervention effects on healthy aging. Experimental evidence is needed to confirm findings.

The association between incarceration and housing insecurity and advanced immune age during late life.

Emerging evidence suggests that psychosocial stress ages the immune system. Accordingly, immune aging may be an important potential mechanism linking psychosocial stress to aging-related decline and disease. Incarceration and housing insecurity represent severe and complex experiences of a multitude of psychosocial stressors, including discrimination, violence, and poverty. In this study, we investigated the association between incarceration and/or housing insecurity and advanced immune age in adults aged 55 and older. Our sample was derived from the Health and Retirement Survey (HRS), with n = 7003 individuals with valid housing insecurity data and n = 7523 with valid incarceration data. From 2016 Venous Blood Study data, we assessed immune aging using a comprehensive set of immune markers including inflammatory markers (IL-6, CRP, s-TNFR1), markers of viral control (CMV IgG antibodies), and ratios of T cell phenotypes (CD8+:CD4+, CD+ Memory: Naïve, CD4+ Memory: Naïve, CD8+ Memory: Naïve ratios). We found that both incarceration and housing insecurity were strongly associated with more advanced immune aging as indicated by increased inflammation, reduced viral control, and reduction in naïve T cells relative to memory T cells. Given that those who experienced incarceration, housing insecurity, and/or are racialized minorities were less likely to be included in this study, our results likely underestimated these associations. Despite these limitations, our study provided strong evidence that experiencing incarceration and/or housing insecurity may accelerate the aging of the immune system.

Sociodemographic patterns in biomarkers of aging in the Add Health cohort.

Biomarkers in population health research serve as indicators of incremental physiological deterioration and contribute to our understanding of mechanisms through which social disparities in health unfold over time. Yet, few population-based studies incorporate biomarkers of aging in early midlife, when disease risks may emerge and progress across the life course. We describe the distributions of several biomarkers of inflammation and neurodegeneration and their variation by sociodemographic characteristics using blood samples collected during Wave V of the National Longitudinal Study of Adolescent to Adult Health (ages 33-44 years). Higher mean levels of inflammatory and neurodegenerative biomarkers were associated with greater socioeconomic disadvantage. For example, the neurodegenerative markers, Neurofilament Light Chain and total Tau proteins were higher among lower income groups, though the relationship was not statistically significant. Similarly, proinflammatory marker Tumor Necrosis Factor-α (TNF-α) levels were higher among those with lower education. Significant differences in the mean levels of other proinflammatory markers were observed by race/ethnicity, sex, census region, BMI, and smoking status. These descriptive findings indicate that disparities in biomarkers associated with aging are already evident among young adults in their 30s and attention should focus on age-related disease risk earlier in the life course.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

Background: Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. Methods: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. Results: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. Conclusion: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.