Framingham Heart Study Participants Research Articles

Life's Essential 8 Cardiovascular Health Score and Cardiorespiratory Fitness in the Community.

The relation of cardiorespiratory fitness (CRF) to lifestyle behaviors and factors linked with cardiovascular health remains unclear. We aimed to understand how the American Heart Association's Life's Essential 8 (LE8) score (and its changes over time) relate to CRF and complementary exercise measures in community-dwelling adults. Framingham Heart Study (FHS) participants underwent maximum effort cardiopulmonary exercise testing for direct quantification of peak oxygen uptake (V̇O2). A 100-point LE8 score was constructed as the average across 8 factors: diet, physical activity, nicotine exposure, sleep, body mass index, lipids, blood glucose, and blood pressure. We related total LE8 score, score components, and change in LE8 score over 8 years with peak V̇O2 (log-transformed) and complementary CRF measures. In age- and sex-adjusted linear models (N=1838, age 54±9 years, 54% women, LE8 score 76±12), a higher LE8 score was associated favorably with peak V̇O2, ventilatory efficiency, resting heart rate, and blood pressure response to exercise (all P<0.0001). A clinically meaningful 5-point higher LE8 score was associated with a 6.0% greater peak V̇O2 (≈1.4 mL/kg per minute at sample mean). All LE8 components were significantly associated with peak V̇O2 in models adjusted for age and sex, but blood lipids, diet, and sleep health were no longer statistically significant after adjustment for all LE8 components. Over an ≈8-year interval, a 5-unit increase in LE8 score was associated with a 3.7% higher peak V̇O2 (P<0.0001). Higher LE8 score and improvement in LE8 over time was associated with greater CRF, highlighting the importance of the LE8 factors in maintaining CRF.

Enhancing selection of alcohol consumption-associated genes by random forest.

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria

Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants. We identified blood lipids and cardiovascular health measurements associated with microbiome and metabolome composition. Integrated analysis revealed microbial pathways implicated in CVD, including flavonoid, γ-butyrobetaine, and cholesterol metabolism. Species from the Oscillibacter genus were associated with decreased fecal and plasma cholesterol levels. Using functional prediction and invitro characterization of multiple representative human gut Oscillibacter isolates, we uncovered conserved cholesterol-metabolizing capabilities, including glycosylation and dehydrogenation. These findings suggest that cholesterol metabolism is a broad property of phylogenetically diverse Oscillibacter spp., with potential benefits for lipid homeostasis and cardiovascular health.

Abstract TP39: Multi-Marker Cerebral Small Vessel Disease Score and Risk of Incident Depression: The Framingham Heart Study

Introduction: The vascular depression hypothesis postulates that cerebral small vessel disease (CSVD) contributes to the development of depression. A multi-marker CSVD score based on brain magnetic resonance imaging (MRI) could serve as a valuable tool for predicting the risk of developing vascular depression. This is important as depression can mimic cognitive impairment, dementia and is treatable. This study aimed to determine the relation of a MRI multi-marker CSVD score with incident depression in community-dwelling individuals. Methods: Framingham Heart Study participants from the Original and Offspring cohorts free of stroke, dementia and depression with available data on depression and CSVD ratings on MRI were included. Depression was defined as Center for Epidemiological Studies Depression scale score ≥16 or use of antidepressant medication. The multi-marker CSVD score was calculated by summing 1 point for individual CSVD markers present (covert brain infarcts, cerebral microbleeds, extensive white matter hyperintensities, high burden perivascular spaces, cortical superficial siderosis), ranging 0-5. Multivariable logistic regression models were used to relate the MRI-multi-marker CSVD score and incident depression in a primary analysis and individual CSVD biomarkers and depression in an exploratory analysis. Models were adjusted for age, sex, FHS cohort, time interval between MRI and clinic exam, body mass index, smoking, prevalent hypertension and diabetes. Results: In 1768 participants included (mean age 54.4±12.4 years, 54% male), the incidence of depression was 11% (median follow-up 6.0 years, IQR: 5.3-6.7). No CSVD score group had a statistically significant association with incident depression, however a CSVD score 3+ showed a trend towards increased odds of developing incident depression (OR 2.65 [0.87, 8.06], p =0.09). Among the individual CSVD markers, visible perivascular spaces had the highest odds for incident depression (OR 1.55 [0.96, 2.51] p =0.07). Conclusion: A higher multi-marker CSVD score might be associated with increased odds of incident depression. Further studies with a larger sample may help elucidate a potential association between this CSVD burden and onset of depression.

Abstract WMP22: Higher Burden of Cerebral Small Vessel Disease is Associated With Risk of Incident Stroke in Community-Dwelling Individuals

Background: Mild manifestations of individual cerebral small vessel disease (CSVD) markers are common and may not denote increased risk, but high CSVD burden identifies individuals at increased risk of stroke and dementia. Scores incorporating multiple CSVD markers may better identify a person’s risk, but it remains unclear if they offer benefit beyond tools based on clinical factors alone such as the revised Framingham Stroke Risk Profile (FSRP). Therefore, we related a multi-marker CSVD score to risk of incident stroke and compared the findings with the FSRP in community-dwelling individuals. Methods: Framingham Heart Study (FHS) participants aged ≥55 years, free of stroke and dementia and with brain magnetic resonance imaging (MRI) ratings of CSVD markers were included. A multi-marker CSVD score reflecting increasing CSVD burden was used (one point each for presence of cerebral microbleeds, severe perivascular spaces, extensive white matter hyperintensities, covert brain infarcts, and cortical superficial siderosis, ranging 0-5). Multivariable Cox proportional hazards regression analyses were used to relate CSVD score to incident stroke. Results: Among 1,154 participants (46% male, mean age 70.9±8.7), 590 (51%) had score 0, 352 (31%) had score 1, 164 (14%) had score 2, and 48 (4%) had score ≥ 3. Over a median follow-up of 8.2 years (Q1-Q3: 5.1-12.5) from the time of MRI, 92 (8%) developed stroke. In models adjusting for time interval between clinic exam and MRI, FHS cohort, and FSRP score, number of CSVD manifestations was associated with increased risk of stroke in a dose-dependent manner: score 1 (HR: 1.73; 95% CI 1.05-2.84), score 2 (HR 1.83; 95% CI: 1.01-3.30), and score ≥ 3 (HR: 3.31; 95% CI: 1.50-7.28) in relation to participants with no CSVD markers. In comparison, a 5-percent increase in FSRP alone was associated with increased risk of stroke (HR: 1.25; 95% CI 1.15-1.36). Interpretation: Higher CSVD burden is associated with increased risk of stroke, beyond the effect explained by risk factors in the FSRP. These findings support consideration of CSVD burden to identify risk of stroke in community-dwelling individuals.

Abstract WMP16: Multi-Marker Cerebral Small Vessel Disease Score and Risk of Incident Dementia in the Framingham Heart Study

Background: Individual magnetic resonance imaging (MRI) markers of cerebral small vessel disease (CSVD) are associated with impaired cognition and dementia but may not reflect the overall burden of CSVD. It is unclear whether these markers provide additional value in evaluating the risk of dementia beyond assessments based solely on clinical risk factors. Thus, we aimed to determine whether the association of additive burden of multiple CSVD markers was independent of vascular risk factors and the Framingham Stroke Risk Profile (FSRP) and to also compare results with the FSRP. Methods: We included 1,152 Framingham Heart Study (FHS) participants over the age of 55, free of stroke and dementia, from the Original and Offspring cohorts with available MRI and FSRP data. CSVD score was calculated by summing the number of CSVD features detected in the MRI: cerebral microbleeds, cerebral brain infarcts, extensive white matter hyperintensities, severe perivascular spaces, and cortical superficial siderosis. Two Cox proportional hazards regression models were used to relate the multi-marker CSVD score to incident all-cause dementia and Alzheimer’s dementia. The first model adjusted for education, cohort, and clinical risk factors while the second adjusted for education, cohort, and FSRP. Results: Mean age was 70.9 years (SD: 8.7), 527 (46%) were male, and 211 (18%) had score ≥ 2. Over a median follow-up time of 6.4 years (Q1-Q3: 4.6-11.3), participants with score ≥ 2 had elevated risk of all-cause dementia compared to those with no CSVD markers after adjustment for FSRP (HR: 2.32; 95% CI: 1.46-3.69; p&lt;0.001) and vascular risk factors (HR: 1.76; 95% CI: 1.10-2.81; p=0.02). Score ≥ 2 was also significantly associated with AD after adjustment for FSRP (HR: 2.05; 95% CI: 1.19-3.62; p&lt;0.01), but not vascular risk factors (HR: 1.50; 95% CI: 0.87-2.60; p=0.15). In addition, a 5-percent increase in FSRP was independently associated with incident dementia after adjustment for education and FHS cohort (HR: 1.21; 95% CI: 1.13-1.30; p&lt;0.001). Conclusions: Our results support the use of CSVD score as an indicator for dementia risk and it may be as robust as the FSRP. Further studies are necessary to validate the use of CSVD score in quantifying dementia risk.

Depressive symptoms are not associated with clinically important levels of digital home blood pressure in the electronic Framingham Heart Study

BACKGROUNDDepressive symptoms are common and share many biopsychosocial mechanisms with hypertension. Association studies between depressive symptoms and blood pressure (BP) have been inconsistent. Home BP monitoring may provide insight. OBJECTIVETo investigate the association between depressive symptoms and digital home BP METHODSElectronic Framingham Heart Study (eFHS) participants were invited to obtain a smartphone app and digital BP cuff at research exam 3 (2016-2019). Participants with ≥3 weeks of home BP measurements within one year were included. Depressive symptoms were measured using the Center for Epidemiological Studies Depression Scale (CES-D). Multivariable linear mixed models were used to test the associations of continuous CES-D score and dichotomous depressive symptoms (CES-D ≥16) (independent) with home BP (dependent), adjusting for age, sex, cohort, number of weeks since baseline, lifestyle factors, diabetes, and CVD. RESULTSAmong 883 participants (mean age 54 years, 59% women, 91% White), the median CES-D score was 4. Depressive symptoms prevalence was 7.6%. Mean SBP and DBP at exam 3 were 119 and 76 mmHg; hypertension prevalence was 48%. A 1 SD higher CES-D score was associated with 0.9 (95% CI: 0.18 to 1.56, p=0.01) and 0.6 (95% CI: 0.06 to 1.07, p=0.03) mmHg higher home SBP and DBP respectively. Dichotomous depressive symptoms were not significantly associated with home BP(p>0.2). CONCLUSIONDepressive symptoms were not associated with clinically substantive levels of home BP. The association between depression and CVD risk factors warrants more data, which may be supported by mobile health measures.

Comparing Cognitive Tests and Smartphone-Based Assessment in 2 US Community-Based Cohorts.

Smartphone-based cognitive assessments have emerged as promising tools, bridging gaps in accessibility and reducing bias in Alzheimer disease and related dementia research. However, their congruence with traditional neuropsychological tests and usefulness in diverse cohorts remain underexplored. A total of 406 FHS (Framingham Heart Study) and 59 BHS (Bogalusa Heart Study) participants with traditional neuropsychological tests and digital assessments using the Defense Automated Neurocognitive Assessment (DANA) smartphone protocol were included. Regression models investigated associations between DANA task digital measures and a neuropsychological global cognitive Z score (Global Cognitive Score [GCS]), and neuropsychological domain-specific Z scores. FHS participants' mean age was 57 (SD, 9.75) years, and 44% (179) were men. BHS participants' mean age was 49 (4.4) years, and 28% (16) were men. Participants in both cohorts with the lowest neuropsychological performance (lowest quartile, GCS1) demonstrated lower DANA digital scores. In the FHS, GCS1 participants had slower average response times and decreased cognitive efficiency scores in all DANA tasks (P<0.05). In BHS, participants in GCS1 had slower average response times and decreased cognitive efficiency scores for DANA Code Substitution and Go/No-Go tasks, although this was not statistically significant. In both cohorts, GCS was significantly associated with DANA tasks, such that higher GCS correlated with faster average response times (P<0.05) and increased cognitive efficiency (all P<0.05) in the DANA Code Substitution task. Our findings demonstrate that smartphone-based cognitive assessments exhibit concurrent validity with a composite measure of traditional neuropsychological tests. This supports the potential of using smartphone-based assessments in cognitive screening across diverse populations and the scalability of digital assessments to community-dwelling individuals.

A novel neuroimaging signature for ADRD risk stratification in the community.

Early risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR). An MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD-ADRC) cohort. Participants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P<0.001). Greater thickness in the signature was related to better general cognition (P<0.01) and episodic memory (P=0.01). Results replicated in UCD-ADRC. We identified a robust neuroimaging biomarker for persons at increased risk of ADRD. Other cohorts will further test the validity of this biomarker.

USABILITY OF A SMARTPHONE APP DESIGNED TO COLLECT HEALTH INFORMATION IN OLDER ADULTS

Abstract Few studies evaluate the usability of mobile-phone assessments in older adults. The aim of this study was to identify design-based barriers and facilitators to mobile app survey completion among two samples of older adults; those in the Framingham Heart Study (n=15, mean age 72 years, 40% women, 100% non-Hispanic, White) and, to aid with generalization, a more diverse sample (n=15, mean age 71 years, 47% women, 53% non-White, 20% Hispanic). We used mixed methods to ascertain information about challenging or beneficial features and functions of the mobile app. A variety of measures with different response formats were tested including self-reported surveys, pictorial format assessments (image of a body with checkboxes to indicate pain sites), and cognitive testing tasks, including Trail Making Test (TMT) and Stroop. Participants completed each of the measures using a think-aloud task, while being audio- and video-recorded. Recordings were coded for usability errors by two pairs of coders. Participants completed the Mobile App Rating Scale (MARS) to assess functionality and aesthetics of the app (range 1-5). In general, participants were satisfied with the app. In Framingham Heart Study participants, the average MARS functionality score was 3.6 (SD=0.7) and aesthetics score was 3.9 (SD=0.7) with no significant differences in the diverse sample. The majority of older adults did not have difficulty completing the self-report measures, unless there were lengthy instructions or the pictorial measure, but participants had usability issues with the Stroop and TMT. Our methods and results can guide app development and survey construction for older adults.

MODELING HETEROGENEITY IN COGNITIVE TRAJECTORIES IN THE FRAMINGHAM HEART STUDY

Abstract The prevalence of cognitive impairment in the population is growing. Maintaining good cognitive health is central to successful aging, independence, and well-being. Although older age is generally associated with worse cognitive function, there are substantial heterogeneities in the rate of cognitive decline across different cognitive domains and in terms of the clinical onset age of dementia. Recently, harmonized factor scores measuring global cognitive function for the memory, executive function, and language domains have been created in the Framingham Heart Study (FHS). In this work, we identified FHS participants with two or more repeated factor scores after the age of 60 (n=2339; 57% female; 17% APOE-ϵ4 carriers; 64% attended colleges) and were dementia-free at the baseline visits. We fitted latent class mixed models (LCMM) to cluster cognitive trajectories from all three domains using the harmonized factor scores. Non-linear trends with age in the trajectories were modeled via piecewise linear LCMM models followed by stepwise selections to select cluster-specific change points. We identified different latent classes of participants, some characterized by an early cognitive decline before age 70 as compared to late decliners, across different domains. Using 10-fold cross-validation, we also showed that the subgroupings of participants are stable. Our findings indicate class-related differential patterns in cognitive aging in the FHS. Future associations between the identified subclasses, cognitive outcomes, and physical or biological markers may advance the knowledge of multiple pathways of cognitive aging.

Cell‐Based Genetic Subtypes Predict Neuroimaging Biomarkers and Clinical Conversion to Alzheimer’s Disease

AbstractBackgroundThe complex etiology and slow progression of AD have complicated efforts to establish robust patient subtypes resulting in high attrition rates among late‐stage clinical trials. We developed a novel procedure to stratify at risk individuals to develop AD using network‐based polygenic risk scores from brain cell‐type specific transcriptome signatures.MethodWe previously computed cell‐based polygenic risk scores (cbPRSs) in the Alzheimer’s Disease Neuroimaging Initiative (Sahelijo et al. 2022). Recently, we computed cbPRSs in 8,481 Framingham Heart Study (FHS) participants using the same genetic markers used in ADNI. We defined low‐ and high‐risk genetic subtypes using the first and the fourth quartiles of the cbPRS distributions, respectively. We conducted association tests with domain specific cognitive tests and neuroimaging biomarkers using subtype risk status as a binary outcome in cognitively normal (CN), mild cognitive impairment (MCI), AD, and all subjects from ADNI. We compared conversion rates from CN and MCI to AD between subtypes using a Cox proportional hazard regression in ADNI and FHS. Using brain‐wide gray matter volume maps with voxel‐based morphometry we compared brain atrophy patterns between subtypes in ADNI corrected for multiple comparisons using Standard FDR at q = 0.05.ResultGenetic subtypes for astrocytes (Ast‐M2) and oligodendrocytes (Oli‐M45) showed significant association with memory and executive function impairment, decreased hippocampal volume, increased amyloid deposition, and decreased glucose metabolism using all subjects from ADNI (P&lt;1.39E‐3). These associations for memory impairment, global amyloid deposition, and decreased glucose metabolism remained significant only in MCI subjects (best P&lt;7.23E‐09). Conversion rates for Ast‐M2 and Oli‐M45 genetic subtypes were significantly different in both ADNI (P&lt;8.67E‐5) and FHS (P&lt;3.90E‐9). These differential conversion rates for Ast‐M2 and Oli‐M45 in FHS were not fully explained by presence of APOE ɛ4 allele (P&lt;0.05 in ɛ4 non‐carriers). Voxel‐wise whole brain analysis identified significant atrophy in bilateral hippocampi, entorhinal cortex, and amygdala for Ast‐M2 (P = 0.0007) and Oli‐M45 (P = 0.001).ConclusionWe demonstrate that polygenic risk scores informed by cell type specific brain transcriptomic networks can stratify at risk subjects with AD related changes in imaging biomarkers and higher chance of progression to AD. These findings provide insight into early detection of cell‐type specific at‐risk individuals for AD.

Genome‐wide association study for Alzheimer disease in APOE ɛ4/ɛ4 carriers

AbstractBackgroundThe APOE ɛ4/ɛ4 genotype has the greatest risk for late‐onset Alzheimer disease (AD). However, modifying effects of other genes on AD risk in APOE ɛ4 homozygotes are not fully understood.MethodsWe conducted a genome‐wide association study (GWAS) for AD among 1,223 non‐Hispanic White APOE ɛ4/ɛ4 carriers assembled by the Alzheimer’s Disease Genetics Consortium (ADGC). Expression of genes within 50 kb of suggestively associated single nucleotide polymorphisms (SNPs, P&lt;10−6) were compared between AD and control brains from 576 Religious Orders Study and Memory and Aging Project (ROSMAP) subjects. We conducted survival analysis between carriers and non‐carriers of AD risk alleles for the suggestive SNPs in 4,287 Framingham Heart Study (FHS) participants. We compared expression of the top‐ranked genes between ɛ2/ɛ2 (APOE2) and ɛ4/ɛ4 (APOE4) human induced pluripotent stem cells (iPSC)‐derived neurons (iNeurons) and astrocytes (iAstrocytes) in a co‐culture system (Jun et al, 2022) and separately evaluated associations of their expression levels with neuropathological traits from 208 brains from FHS participants.ResultsWe identified association of AD with 11 SNPs at the suggestive level of which four near TRIB2, CTNNA2, ZNF443, and KLHL29 were genome‐wide significant (P&lt;5×10−8). Rs75983775 near TRIB2, the most significant finding (P = 9.4×10−11), was associated with decreased risk of AD in ADGC APOE ɛ4 homozygotes (odds ratio = 0.12) and increased TRIB2 expression in ROSMAP ɛ4‐carrier brains (P = 0.02). TRIB2 expression was increased in APOE2 compared to APOE4 iAstrocytes (log2 fold change (log2FC) = 0.41, adjusted P (adjP) = 0.03) and iNeurons (log2FC = 0.31, adjP = 0.002) and elevated the Iba1 level in FHS brains (P = 0.03). Association with TENM3 SNP rs56035906 was suggestively associated with reduced AD risk in ADGC APOE ɛ4 homozygotes (odds ratio = 0.30, P = 4.3×10−7) and with lower incidence of AD among FHS ɛ4 carriers (P = 0.03). TENM3 expression was decreased in APOE2 compared to APOE4 iNeurons (log2FC = 0.59, adjP = 2.4×10−4) and associated with increasing phosphorylated tau 181 (P = 0.007) and C4a protein expression (P = 9.6×10−4) in FHS brains.ConclusionWe identified variants that may protect against AD risk among APOE ɛ4/ɛ4 carriers, delay disease onset, modulate expression of nearby genes in iAstrocytes and iNeurons, and are associated with measures of tau and neuroinflammation in autopsied brains.

Sleep Patterns and Prospective Diffusion Weighted Imaging Biomarkers: the Sleep and Dementia Consortium (SDC)

AbstractBackgroundAdequate sleep is necessary to maintain brain health, with sleep disturbances associated with higher Alzheimer’s disease (AD) risk. Diffusion‐weighted imaging (DWI) metrics are increasingly recognized as useful neuroimaging biomarkers to detect AD‐related white matter degeneration. We assessed the relationship between sleep patterns and prospective DWI metrics in the Sleep and Dementia Consortium (SDC). The SDC studies associations between polysomnography (PSG)‐derived sleep with dementia risk and cognitive and MRI endophenotypes.MethodThe SDC includes five community‐based cohorts, two of which have DWI acquisitions: The Framingham Heart Study (FHS) and the Atherosclerosis Risk in Communities study (ARIC). Dementia‐free participants who underwent both PSG and DWI‐MRI were selected, including 354 FHS participants (56.6±7.1y, 57%W), and 184 ARIC participants (61.6±5.0y, 52%W). The MRI session was approximately 15 years after the PSG on average (FHS:17.0±1.3y; ARIC:15.8±0.8y). Fractional anisotropy (FA) and mean diffusivity (MD) were considered for both cohorts, in addition to free‐water (FW) in the FHS. Sleep metrics were harmonized centrally, distributed for cohort‐specific linear regressions, and study‐level estimates were pooled in random effects meta‐analyses. Analyses were adjusted for demographics, obesity, time between PSG and MRI, antidepressants and sedative medication usage. An interaction term by APOE4 allele carrier status in regression models was used to test its moderating effect.ResultIn the FHS, sleep fragmentation was associated with DWI measures in the expected direction: Higher Wake After Sleep Onset and lower Sleep Maintenance Efficiency were associated with lower FA (β±SE = ‐0.17±0.09,p = 0.04; β±SE = 0.23±0.10,p = 0.03), higher MD (β±SE = 0.15±0.07,p = 0.04; β±SE = ‐0.20±0.09,p = 0.03), and higher FW (β±SE = 0.17±0.07,p = 0.02; β±SE = ‐0.22±0.08,p = 0.008). Meta‐analysis of FHS and ARIC revealed significant pooled effects between lower Sleep Maintenance Efficiency and lower FA (β±SE = 0.17±0.08,p = 0.04). In the FHS, APOE4 allele significantly moderated the association between REM sleep proportion with FA and MD, where lower REM sleep percentage was associated with higher MD (β±SE = ‐4.89±1.95,p = 0.02) and lower FA (β±SE = 4.93±2.26,p = 0.03) in APOE4 carriers only.ConclusionIn the SDC, sleep fragmentation was associated with MRI markers of poorer white matter integrity 15 years later. Less REM sleep was associated with poorer white matter integrity in APOE4 allele carriers only, suggesting that disrupted sleep architecture may contribute and interact with neurodegenerative processes to affect brain integrity.

The Feasibility of Digital Testing using a Smartphone Application for Precision Brain Health Monitoring in the Framingham Heart Study

AbstractBackgroundEarly detection of cognitive decline is critical to identify effective treatments for Alzheimer’s disease and related dementias. The inherent heterogeneity of behaviors associated with brain health may obscure the signals derived from a typical neuropsychological evaluation, partly because of the limited frequency of assessment with which it can be conducted. This may lead to inaccurate or poor detection sensitivity. Digital health technology using smartphone applications enables frequent assessment and, thereby, more precise monitoring of brain health. The current study sought to examine the feasibility of using smartphone application‐based tasks in community‐based participants from the Framingham Heart Study (FHS).MethodThe smartphone application consists of three blocks of assessments, lasting approximately 15‐20 minutes (Figure 1). Block 1: Participants answer questions to ensure that they can see the stimuli and hear the instructions adequately. Block 2: Participants complete a series of cognitive tasks and respond to open‐ended questions which are voice recorded. Block 3: To ensure that performance is not influenced by external factors, participants respond to questions, for example, about their experiences with using the smartphone application.ResultTo date, 318 FHS participants have been enrolled into the study. Of these, as of 12/09/2022, 168 participants’ first assessment period has been completed within the two week period after being enrolled in the study. Of the 168 participants, a large majority (81%; n = 136) completed all the tasks, while the remaining 32 completed partial assessment. Table 1 reveals that the overall percentage of participants who have been enrolled in the study and the percentage of those who completed their first assessment are highly similar.ConclusionFindings reveal that older adults from a large, community‐based study are able to successfully download and complete the first set of tasks using their smartphone application. As data collection is ongoing, the number of older adults recruited into the study is expected to increase exponentially. These results indicate that it is feasible to monitor brain health on an on‐going basis using self‐administered smartphone applications, potentially creating an effective, lower cost, and scalable approach to detect significant changes in cognitive functions earlier and more accurately in the preclinical neurodegenerative process.

Abstract 12528: DNA Methylation Profiles of 3 Major CHIP Subtypes Provide Insight Into Underlying Epigenetic Mechanisms

Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related phenomenon characterized by the presence of clonal populations of hematopoietic-derived cells in whole blood that harbor functional leukemogenic somatic mutations. Despite the well-established associations between aging and CHIP and aging and genome-wide DNA methylation (DNAm), the association between CHIP and changes in DNAm remains to be fully elucidated. To fill this knowledge gap, an epigenome-wide association study of CHIP was performed in Framingham Heart Study participants from the Offspring cohort (n total = 1594; 54% women; mean age 67 years) using whole blood derived DNA to elucidate the DNAm profiles of any CHIP and the three most frequently mutated CHIP driver genes DNMT3A, TET2 , and ASXL1/2 . Linear mixed models were used to test associations between CHIP status as the predictor variable and genome-wide DNAm of CpG sites as the outcome, with adjustment for age, age 2 , sex, smoking, 33 surrogate variables, and familial relatedness. From the analysis, 189 CpGs were found to be associated with any CHIP, and 139, 107, and 4 were associated with DNMT3A , TET2 , and ASXL1/2 CHIP, respectively (Bonferroni corrected P&lt;1х10 -7 ). The CpGs associated with the DNMT3A , TET2 , and ASXL1/2 CHIP were distinct. Genome-wide DNAm directions of effect were similar for DNMT3A and ASXL1/2 CHIP but opposite to TET2 . Gene expression analysis identified CpG-gene transcript pairs for any CHIP and CHIP subtypes. The most significant gene sets were enriched in pathways related to immune response and hematopoietic cell lineage. Two-sample Mendelian randomization analysis was additionally conducted to investigate whether methylation at CHIP-associated CpGs causally influences risk of cardiometabolic traits and all-cause mortality. Evidence of a putative causal role of CHIP-associated CpGs in relation to several traits, including BMI and hypertension, and all-cause mortality was found. Taken together, we identified CpGs associated with CHIP and with the three major CHIP driver genes, elucidated their methylomic signatures, identified CpG-transcript pairs, and provided support for a potential causal role of CHIP-associated CpGs in relation to cardiometabolic traits and all-cause mortality.

Abstract 16138: Hepatic Steatosis, Cardiorespiratory Fitness, and Metabolic Mediators in the Community

Background: Cardiorespiratory fitness (CRF) is a powerful predictor of cardiovascular (CV) outcomes and multi-organ reserve capacity. Hypothesis: We hypothesize that hepatic steatosis, an emerging CV risk factor, is associated with lower CRF and that certain metabolites mediate this association. Objective: To determine if hepatic steatosis contributes to impaired CRF in community-dwelling individuals. Methods: Framingham Heart Study participants (n=2722) underwent vibration-controlled transient elastography for assessment of controlled attenuation parameter (CAP; a measure of hepatic steatosis) and maximal cardiopulmonary exercise testing (CPET). In individuals with available metabolomic data (n=1346), we evaluated whether blood metabolites mediate the association of CAP and peak CRF (peak oxygen uptake [VO 2 ]). Results: The study sample (mean age 54±9 years, 53% women) had a peak VO 2 of 95±20% of the predicted value, and 722 participants (26.5%) had hepatic steatosis (defined as CAP≥290 dB/m). In multivariable regression models adjusted for age, sex, alcohol use, smoking, systolic blood pressure, hypertensive medication use, diabetes, and prevalent CV disease, both CAP (continuous) and hepatic steatosis were associated with lower peak VO 2 (in ml/kg/min) and VO 2 at the anaerobic threshold, and unfavorable heart rate and blood pressure responses to exercise (at Bonferroni-adjusted P&lt;0.008 for all; Figure 1). Eighteen out of 201 measured circulating metabolites were associated with both peak VO 2 and CAP in separate multivariable models also adjusted for BMI (at FDR 5%). Of these metabolites, 16 significantly mediated the association between CAP and peak VO 2 , with dimethylguanidino valeric acid and lysophosphatidylcholine 20:1 demonstrating the highest proportion mediated. Conclusions: Hepatic steatosis is associated with impaired CRF in the community and this association is partially mediated by known and novel circulating metabolites.

Abstract 13280: Plasma Extracellular microRNAs Associated With Cardiovascular Disease Risk Factors in Middle-Aged and Older Populations

Background: Extracellular microRNAs (miRNAs) are a class of non-coding RNAs that resist degradation and remain stable in the extracellular milieu where they contribute to various physiological and pathological processes by facilitating intercellular signaling. Previous studies have reported associations between miRNAs and cardiovascular diseases (CVD); however, the plasma miRNA signatures of CVD and its risk factors have not been fully elucidated at the population level. Methods: Plasma miRNA levels were measured in 4,440 Framingham Heart Study (FHS) participants from the Third Generation and Omni II cohorts using qRT-PCR. Linear regression analyses were conducted to test the cross-sectional associations of each miRNA with CVD risk factors, including systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides, HDL and total cholesterol, blood glucose, BMI, and cigarette smoking. Prospective analyses of the associations of miRNAs with new-onset obesity, hypertension, type 2 diabetes, CVD, and all-cause mortality were conducted using proportional hazards regression. A false discovery rate threshold of 0.05 was applied for each risk factor and outcome individually. Replication was carried out in 2,000 Rotterdam Study (RS) participants with data on plasma miRNA levels measured using RNA-seq. Pathway enrichment analyses were conducted and gene targets were predicted for miRNAs associated with five or more risk factors in FHS. Results and Conclusions: In FHS, six miRNAs (miR-193b-3p, miR-122-5p, miR-365a-3p, miR-194-5p, miR-192-5p, miR-193a-5p) were associated with five or more CVD risk factors. This miRNA signature was enriched for biological processes and cellular pathways associated with CVD; many of the genes annotated to these pathways are predicted targets of the identified miRNAs. In RS, miR-193b-3p, miR-194-5p, and miR-193a-5p were associated with two or more risk factors. Prospective analysis revealed eight miRNAs associated with all-cause mortality only in FHS, including miR-193b-3p, miR-122-5p, miR-192-5p, and miR-193a-5p. These findings suggest plasma miRNA levels are associated with CVD risk factors and could provide opportunities to investigate their possible contributions to CVD pathogenesis.

Abstract 16232: Life’s Essential 8 Cardiovascular Health Score and Cardiorespiratory Fitness in the Community

Background: Cardiorespiratory fitness (CRF) is a key indicator of cardiovascular health (CVH) and is inversely associated with cardiovascular disease (CVD) and mortality, but its relations with lifestyle behaviors and factors linked with CVH are incompletely elucidated. Hypothesis: Greater CVH, as assessed by a score comprising lifestyle and standard risk factors, is associated with greater CRF. Aim: To evaluate the cross-sectional relations of AHA’s Life’s Essential 8 (LE8) metrics with CRF in the community. Methods: Maximal cardiopulmonary exercise tests (CPET) were performed in the Framingham Heart Study (FHS). An LE8 score was constructed for each participant by averaging scores across all LE8 components (ranging 0-100). We related CRF and other CPET measures with total LE8 score, individual LE8 components as three-level variables (optimal/suboptimal/poor), and changes in LE8 score over an ≈8 year interval. Results: In 1838 FHS participants (age 54±9 years, 54% women, BMI 28±5 kg/m 2 ), mean LE8 score was 76±12. In age- and sex-adjusted models, a higher LE8 score was associated with higher peak VO 2 , better ventilatory efficiency, lower resting heart rate, and favorable blood pressure response to exercise (p&lt;0.0001 for all). A clinically meaningful 5-point higher LE8 score was associated with a 6.0% greater peak VO 2 (≈1.4 ml/kg/min at the sample mean). There was no evidence of effect modification by age, sex, or CVD status. The eight LE8 score components were statistically significantly associated with peak VO 2 in models adjusted for age and sex, but blood lipids, diet, and sleep health were no longer statistically significant after adjustment for all other LE8 components ( Figure ). Over an ≈8-year interval, a 5-unit increase in LE8 score was associated with a 3.7% higher peak VO 2 (p&lt;0.0001). Conclusion: Higher LE8 score is associated with greater CRF in the community, highlighting the importance of CVH as assessed by LE8 metrics in maintaining and promoting CRF.

Association Between Slow-Wave Sleep Loss and Incident Dementia

Slow-wave sleep (SWS) supports the aging brain in many ways, including facilitating the glymphatic clearance of proteins that aggregate in Alzheimer disease. However, the role of SWS in the development of dementia remains equivocal. To determine whether SWS loss with aging is associated with the risk of incident dementia and examine whether Alzheimer disease genetic risk or hippocampal volumes suggestive of early neurodegeneration were associated with SWS loss. This prospective cohort study included participants in the Framingham Heart Study who completed 2 overnight polysomnography (PSG) studies in the time periods 1995 to 1998 and 1998 to 2001. Additional criteria for individuals in this study sample were an age of 60 years or older and no dementia at the time of the second overnight PSG. Data analysis was performed from January 2020 to August 2023. Changes in SWS percentage measured across repeated overnight sleep studies over a mean of 5.2 years apart (range, 4.8-7.1 years). Risk of incident all-cause dementia adjudicated over 17 years of follow-up from the second PSG. From the 868 Framingham Heart Study participants who returned for a second PSG, this cohort included 346 participants with a mean age of 69 years (range, 60-87 years); 179 (52%) were female. Aging was associated with SWS loss across repeated overnight sleep studies (mean [SD] change, -0.6 [1.5%] per year; P < .001). Over the next 17 years of follow-up, there were 52 cases of incident dementia. In Cox regression models adjusted for age, sex, cohort, positivity for at least 1 APOE ε4 allele, smoking status, sleeping medication use, antidepressant use, and anxiolytic use, each percentage decrease in SWS per year was associated with a 27% increase in the risk of dementia (hazard ratio, 1.27; 95% CI, 1.06-1.54; P = .01). SWS loss with aging was accelerated in the presence of Alzheimer disease genetic risk (ie, APOE ε4 allele) but not hippocampal volumes measured proximal to the first PSG. This cohort study found that slow-wave sleep percentage declined with aging and Alzheimer disease genetic risk, with greater reductions associated with the risk of incident dementia. These findings suggest that SWS loss may be a modifiable dementia risk factor.