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Social isolation might be considered as a marker of poor health and higher mortality. The aim of our analysis was to assess the association of social network index (SNI) with incident AF and death. We selected participants aged ≥ 55 years without prevalent AF from the Framingham Heart Study. We evaluated the association between social isolation measured by the Berkman-Syme Social Network Index (SNI), incident AF, and mortality without diagnosed AF. We assessed the risk factor-adjusted associations between SNI (the sum of 4 components: marriage status, close friends/relatives, religious service attendance, social group participation), incident AF, and mortality without AF by using Fine-Gray competing risk regression models. We secondarily examined the outcome of all-cause mortality. We included 3454 participants (mean age 67 ± 10 years, 58% female). During 11.8 ± 5.2 mean years of follow-up, there were 686 incident AF cases and 965 mortality without AF events. Individuals with fewer connections had lower rates of incident AF ( P  = 0.04) but higher rates of mortality without AF ( P  = 0.03). Among SNI components, only social group participation was associated with higher incident AF (subdistribution hazards ratio [sHR] 1.35, 95% CI 1.16–1.57, P  = 0.0001). For mortality without AF, social group participation (sHR = 0.81, 95% CI 0.71–0.93, P  = 0.002) and regular religious service attendance sHR = 0.76, 95% CI 0.67–0.87, P  < 0.0001) were associated with lower risk of death. Social isolation was associated with a higher rate of mortality without diagnosed AF. In contrast to our hypothesis, we observed that poor social connectedness was associated with a lower rate of incident AF. This finding should be interpreted cautiously since there were very few participants in the lowest social connectedness group. Additionally, the seemingly protective effect of social isolation on AF incidence may be simply an artifact of the strong association between social isolation and increased mortality rate in combination with the large number of deaths as compared to AF events in our study. Further study is warranted.

Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5 , TBX5/TBX3 , WNT9B and GATA4 . A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P  = 7.1 × 10 −13 ) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function. Genome-wide analyses of cardiac magnetic resonance imaging data identify loci associated with right heart structure and function. A polygenic predictor of right ventricular ejection fraction is associated with dilated cardiomyopathy risk.

Large language models (LLMs) have shown promising capabilities across diverse domains, yet their application to complex clinical prediction tasks remains limited. In this study, we present CARE-AD (Collaborative Analysis and Risk Evaluation for Alzheimer’s Disease), a multi-agent LLM-based framework for forecasting Alzheimer’s disease (AD) onset by analyzing longitudinal electronic health record (EHR) notes. CARE-AD assigns specialized LLM agents to extract signs and symptoms relevant to AD and conduct domain-specific evaluations—emulating a collaborative diagnostic process. In a retrospective evaluation, CARE-AD achieved higher accuracy (0.53 vs. 0.26–0.45) than baseline single-model approaches in predicting AD risk 10 years prior to the first recorded diagnosis code. These findings highlight the feasibility of using multi-agent LLM systems to support early risk assessment for AD and motivate further research on their integration into clinical decision support workflows.

Although there is some evidence of an association between Alzheimer’s disease polygenic risk score (AD PRS) and cognitive function, limited validations have been performed in large populations. We investigated the relationship between AD PRS and cognitive function in the UK Biobank in over 276,000 participants and further validated the association in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) sample. We developed the AD PRS (excluded the APOE variants) in the middle age UK Biobank participants (age ranged 39–72, mean age 57 years) of European ancestries by LDpred2. To validate the association of AD PRS and cognitive function internally in the UK Biobank, we linearly regressed standardized cognitive function on continuous standardized AD PRS with age at cognitive test, sex, genotyping array, first 10 principal components of genotyping, smoking, education in years, body mass index, and apolipoprotein E gene ε4 ( APOE4 ) risk allele dosages. To validate the associations externally, we ran the linear mixed effects model in the ADNI sample free of dementia (age ranged 55–91, mean age 73), including similar covariates as fixed effects and participants’ IDs as the random effect. Stratification by age, APOE4 carrier status, and cognitive status (cognitively normal or mild cognitive impairment) was also investigated. Our study validated the associations of AD PRS and cognitive function in both midlife and late-life observational cohorts. Although not all of the cognitive measures were significantly associated with AD PRS, non-verbal fluid reasoning (matrix pattern completion, β = − 0.022, P  = 0.003), processing speed (such as symbol digit substitution, β = − 0.017, P  = 1.08E−05), short-term memory and attention (such as pairs matching, β = − 0.014, P  = 1.66E−10), and reaction time (β = − 0.010, P  = 1.19E−06) were inversely associated with increasing AD PRS in the UK Biobank. Higher likelihood of cognitive impairment was also associated with higher AD PRS in the ADNI cognitive normal individuals (AD assessment scale β = 0.079, P  = 0.02). In summary, we confirmed that poorer cognitive function was associated with a higher polygenic AD risk, and suggested the potential utility of the AD PRS in identifying those who may be at risk for further cognitive decline.

Emerging research suggests that regular physical activity can reduce the risk of cognitive decline. However, most prior studies rely on self-reported measures, which are subject to recall bias, subjectivity, and limited temporal resolution. This study aims to investigate whether objectively measured physical activity, captured through passive accelerometry, is associated with incident cognitive impairment and neuropsychological test performance and to evaluate its potential as an early indicator of cognitive decline. We analyzed data from the Framingham Heart Study (FHS), a community-based cohort with longitudinal surveillance of cognitive impairment. Participants wore an Actical accelerometer for at least 3 days (excluding bathing). A total of 30 accelerometer-derived physical activity measures were categorized into intensity-specific durations, step and cadence summaries, and peak cadence. At FHS, diagnoses of cognitive impairment were established by a review committee based on established criteria. Cox proportional hazard models were used to examine the association with incident cognitive impairment, adjusting for age, gender, education, and accelerometer wear time. Time-dependent area under the receiver operating characteristic curves, derived from random survival forest models, were used to assess predictive performance. Linear regression models were used to evaluate the associations between physical activity measures and 18 neuropsychological test scores. Among 1212 participants from the FHS Offspring cohort (age: mean 70, SD 8 y; women: 651/1212, 53.71%; follow-up period: mean 9, SD 2 y), 10 physical activity measures, including all peak cadence metrics, were nominally associated with incident cognitive impairment. Higher peak 1-minute cadence (steps/min) was significantly associated with lower risk (hazard ratio 0.82; 95% CI 0.69-0.97; P=.02). Incorporating peak 1-minute cadence into the base model (age, gender, education, and accelerometer wear time) improved the 8-year prediction area under the curve by 10.2%. Peak 1-minute cadence and total steps per day were also associated with better performance on trail making tests A and B. This study highlights significant associations between accelerometer-based physical activity metrics and both incident cognitive impairment and cognitive test performance. In particular, moderate-intensity movement, as reflected by cadence measures, may serve as a valuable marker for cognitive health and a potential target for early intervention.

Gene function can be described using various measures. We integrated association studies of three types of omics data to provide insights into the pathophysiology of subclinical coronary disease and myocardial infarction (MI). Using multivariable regression models, we associated: (1) single nucleotide polymorphism, (2) DNA methylation, and (3) gene expression with coronary artery calcification (CAC) scores and MI. Among 3106 participants of the Framingham Heart Study, 65 (2.1%) had prevalent MI and 60 (1.9%) had incident MI, median CAC value was 67.8 [IQR 10.8, 274.9], and 1403 (45.2%) had CAC scores > 0 (prevalent CAC). Prevalent CAC was associated with AHRR (linked to smoking) and EXOC3 (affecting platelet function and promoting hemostasis). CAC score was associated with VWA1 (extracellular matrix protein associated with cartilage structure in endomysium). For prevalent MI we identified FYTTD1 (down-regulated in familial hypercholesterolemia) and PINK1 (linked to cardiac tissue homeostasis and ischemia–reperfusion injury). Incident MI was associated with IRX3 (enhancing browning of white adipose tissue) and STXBP3 (controlling trafficking of glucose transporter type 4 to plasma). Using an integrative trans-omics approach, we identified both putatively novel and known candidate genes associated with CAC and MI. Replication of findings is warranted.

Although brain amyloid and tau deposition measured by PET scans are established as biomarkers of Alzheimer’s disease (AD), they can emerge decades before symptoms are detectable on traditional neuropsychological (NP) tests. There is a pressing need for early AD detection tools that are more accessible, cost-effective, and non-invasive. The digital clock drawing test (dCDT), a digital version of the clock drawing test, has emerged as a promising cognitive assessment tool that takes minutes to administer and can reveal clinical symptoms earlier than paper–pencil NP tests. This study explored the association between 53 dCDT measures and amyloid and tau PET biomarkers using data from 87 low age risk participants in the Framingham Heart Study. Our findings revealed a significant association between a dCDT measure related to spatial reasoning function and global amyloid burden ( P  < 0.05), and 4 dCDT measures correlated with tau accumulation after adjusting for multiple comparisons. Notably, the combination of demographic variables and a composite dCDT score achieved a mean area under the receiver operating characteristics curve of 0.86 in detecting amyloid positivity. These results highlight the potential of dCDT measures as effective predictors of amyloid and tau pathology in preclinical AD.

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease. Here, the authors perform a whole-genome sequencing association analysis of genetic variants in ≤11,840 multi-ethnic participants with ≤1666 circulating metabolites, discovering 1985 novel variant-metabolite associations and insights into human disease.

Human voice has increasingly been recognized as an effective indicator for the detection of cognitive disorders. However, the association of acoustic features with specific cognitive functions and mild cognitive impairment (MCI) has yet to be evaluated in a large community-based population. This study aimed to investigate the association between acoustic features and neuropsychological (NP) tests across multiple cognitive domains and evaluate the added predictive power of acoustic composite scores for the classification of MCI. This study included participants without dementia from the Framingham Heart Study, a large community-based cohort with longitudinal surveillance for incident dementia. For each participant, 65 low-level acoustic descriptors were derived from voice recordings of NP test administration. The associations between individual acoustic descriptors and 18 NP tests were assessed with linear mixed-effect models adjusted for age, sex, and education. Acoustic composite scores were then built by combining acoustic features significantly associated with NP tests. The added prediction power of acoustic composite scores for prevalent and incident MCI was also evaluated. The study included 7874 voice recordings from 4950 participants (age: mean 62, SD 14 years; 4336/7874, 55.07% women), of whom 453 were diagnosed with MCI. In all, 8 NP tests were associated with more than 15 acoustic features after adjusting for multiple testing. Additionally, 4 of the acoustic composite scores were significantly associated with prevalent MCI and 7 were associated with incident MCI. The acoustic composite scores can increase the area under the curve of the baseline model for MCI prediction from 0.712 to 0.755. Multiple acoustic features are significantly associated with NP test performance and MCI, which can potentially be used as digital biomarkers for early cognitive impairment monitoring.

Alzheimer’s disease (AD) is a multifactorial disease that affects more than 5 million Americans. Multiple pathways might be involved in the AD pathogenesis. The implication of lipid genetic susceptibility on brain gene expression is yet to be investigated. The current study included 192 brain samples from AD patients who were enrolled in the ROSMAP study. The samples were genotyped and imputed to the HRC Reference Panel. Lipid polygenetic risk score was constructed from the weighted sum of genetic variants associated with low-density lipoprotein cholesterol (LDL-C). The gene expression was profiled by RNA sequencing, and the  association of gene expression with lipid polygenetic risk scores was tested by linear regression models adjusted for age, sex and APOE e4 alleles. Three genes were found to associate with lipid polygenetic risk scores, including HMCN2 ( P  = 3.6 × 10 –7 ), PDLIM5 ( P  = 1.2 × 10 –6 ), and FHL5 ( P  = 2.0 × 10 –6 ). Network analysis revealed multiple related pathways, including dopaminergic synapse ( P  = 4.5 × 10 –5 ), circadian entrainment ( P  = 1.1 × 10 –4 ), and cholinergic synapse ( P  = 2.3 × 10 –4 ). Our study underscores the importance of lipid regulation and metabolism to AD heterogeneity.