Publications

INTRODUCTION: Polygenic Risk Scores (PRSs) are summaries of genetic risk alleles for an outcome.

METHODS: We used summary statistics from five GWASs of AD to construct PRSs in 4,189 diverse Hispanics/Latinos (mean age 63 years) from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). We assessed the PRS associations with MCI in the combined set of people and in diverse subgroups, and when including and excluding the APOE gene region. We also assessed PRS associations with MCI in an independent dataset from the Mass General Brigham Biobank.

RESULTS: A simple sum of 5 PRSs ("PRSsum"), each constructed based on a different AD GWAS, was associated with MCI (OR = 1.28, 95% CI [1.14, 1.41]) in a model adjusted for counts of the APOE-[Formula: see text] and APOE-[Formula: see text] alleles. Associations of single-GWAS PRSs were weaker. When removing SNPs from the APOE region from the PRSs, the association of PRSsum with MCI was weaker (OR = 1.17, 95% CI [1.04,1.31] with adjustment for APOE alleles). In all association analyses, APOE-[Formula: see text] and APOE-[Formula: see text] alleles were not associated with MCI.

DISCUSSION: A sum of AD PRSs is associated with MCI in Hispanic/Latino older adults. Despite no association of APOE-[Formula: see text] and APOE-[Formula: see text] alleles with MCI, the association of the AD PRS with MCI is stronger when including the APOE region. Thus, APOE variants different than the classic APOE alleles may be important predictors of MCI in Hispanic/Latino adults.

OBJECTIVE: Exposure to light at night (LAN) may influence sleep timing and regularity. Here, we test whether greater light exposure during sleep (LEDS) associates with greater irregularity in sleep onset timing in a large cohort of older adults.

METHODS: Light exposure and activity patterns, measured via wrist-worn actigraphy (ActiWatch Spectrum), were analyzed in 1,933 participants with 6+ valid days of data in the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 5 Sleep Study. Summary measures of LEDS averaged across nights were evaluated in linear and logistic regression analyses to test the association with standard deviation (SD) in sleep onset timing (continuous variable) and irregular sleep onset timing (SD≥1.36 hours, binary). Night-to-night associations between LEDS and absolute differences in nightly sleep onset timing were also evaluated with distributed lag non-linear models and mixed models.

RESULTS: In between-individual linear and logistic models adjusted for demographic, health, and seasonal factors, every 5-lux unit increase in LEDS was associated with an increase of 7.8 minutes in sleep onset SD (β=0.13 hours, 95%CI:0.09-0.17) and 40% greater odds (OR=1.40, 95%CI:1.24-1.60) of irregular sleep onset. In within-individual night-to-night mixed model analyses, every 5-lux unit increase in LEDS the night prior (lag0) was associated with a 2.2-minute greater deviation of sleep onset the next night (β=0.036 hours, p<0.05). Conversely, every 1-hour increase in sleep deviation (lag0) was associated with a 0.35-lux increase in future LEDS (β=0.347 lux, p<0.05).

CONCLUSION: LEDS was associated with greater irregularity in sleep onset in between-individual analyses and subsequent deviation in sleep timing in within-individual analyses, supporting a role for LEDS in exacerbating irregular sleep onset timing. Greater deviation in sleep onset was also associated with greater future LEDS, suggesting a bidirectional relationship. Maintaining a dark sleeping environment and preventing LEDS may promote sleep regularity and following a regular sleep schedule may limit LEDS.

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

BACKGROUND: The human microbiome is linked to multiple metabolic disorders such as obesity and diabetes. Obstructive sleep apnoea (OSA) is a common sleep disorder with several metabolic risk factors. We investigated the associations between the gut microbiome composition and function, and measures of OSA severity in participants from a prospective community-based cohort study: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

METHODS: Bacterial-Wide Association Analysis (BWAS) of gut microbiome measured via metagenomics with OSA measures was performed adjusting for clinical, lifestyle and co-morbidities. This was followed by functional analysis of the OSA-enriched bacteria. We utilized additional metabolomic and transcriptomic associations to suggest possible mechanisms explaining the microbiome effects on OSA.

FINDINGS: Several uncommon anaerobic human pathogens were associated with OSA severity. These belong to the Lachnospira, Actinomyces, Kingella and Eubacterium genera. Functional analysis revealed enrichment in 49 processes including many anaerobic-related ones. Severe OSA was associated with the depletion of the amino acids glycine and glutamine in the blood, yet neither diet nor gene expression revealed any changes in the production or consumption of these amino acids.

INTERPRETATION: We show anaerobic bacterial communities to be a novel component of OSA pathophysiology. These are established in the oxygen-poor environments characteristic of OSA. We hypothesize that these bacteria deplete certain amino acids required for normal human homeostasis and muscle tone, contributing to OSA phenotypes. Future work should test this hypothesis as well as consider diagnostics via anaerobic bacteria detection and possible interventions via antibiotics and amino-acid supplementation.

FUNDING: Described in methods.

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10-8) and suggestive (p < 1 × 10-6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes.

METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted β=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05).

CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

BACKGROUND: Pediatric sleep-disordered breathing (SDB) disproportionately affects children with low socioeconomic status (SES). The multilevel risk factors that drive these associations are not well understood.

RESEARCH QUESTION: What are the associations between SDB risk factors, including individual health conditions (obesity, asthma, and allergies), household SES (maternal education), indoor exposures (environmental tobacco smoke [ETS] and pests), and neighborhood characteristics (neighborhood disadvantage), and pediatric SDB symptoms?

STUDY DESIGN AND METHODS: Cross-sectional analyses were performed on 303 children (aged 6-12 years) enrolled in the Environmental Assessment of Sleep Youth study from 2018 to 2022. Exposures were determined by caregiver reports, assays of measured settled dust from the child's bedroom, and neighborhood-level Census data (deriving the Childhood Opportunity Index to characterize neighborhood disadvantage). The primary outcome was the SDB-related symptom burden assessed by the OSA-18 questionnaire total score. Using linear regression models, we calculated associations between exposures and SDB-related symptom burden, adjusting for sociodemographic factors, then health conditions, indoor environment, and neighborhood factors.

RESULTS: The sample included 303 children (39% Hispanic, Latino, Latina, or Spanish origin; 30% Black or African American; 22% White; and 11% other). Increasing OSA-18 total scores were associated with low household SES after adjustment for demographic factors, and with asthma, allergies, ETS, pests (mouse, cockroach, and rodents), and an indoor environmental index (sum of the presence of pests and ETS; 0-2) after adjusting for sociodemographic factors. Even after further adjusting for asthma, allergies, and neighborhood disadvantage, ETS and pest exposure were associated with OSA-18 (ETS: β = 12.80; 95% CI, 7.07-18.53, also adjusted for pest; pest exposure: β = 3.69; 95% CI, 0.44-6.94, also adjusted for ETS).

INTERPRETATION: In addition to associations with ETS, a novel association was observed for indoor pest exposure and SDB symptom burden. Strategies to reduce household exposure to ETS and indoor allergens should be tested as approaches for reducing sleep health disparities.

INTRODUCTION: Blood metabolomics-based biomarkers may be useful to predict measures of neurocognitive aging.

METHODS: We tested the association between 707 blood metabolites measured in 1451 participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), with mild cognitive impairment (MCI) and global cognitive change assessed 7 years later. We further used Lasso penalized regression to construct a metabolomics risk score (MRS) that predicts MCI, potentially identifying a different set of metabolites than those discovered in individual-metabolite analysis.

RESULTS: We identified 20 metabolites predicting prevalent MCI and/or global cognitive change. Six of them were novel and 14 were previously reported as associated with neurocognitive aging outcomes. The MCI MRS comprised 61 metabolites and improved prediction accuracy from 84% (minimally adjusted model) to 89% in the entire dataset and from 75% to 87% among apolipoprotein E ε4 carriers.

DISCUSSION: Blood metabolites may serve as biomarkers identifying individuals at risk for MCI among US Hispanics/Latinos.