Admixture mapping of cognitive function in diverse Hispanic and Latino adults: Results from the Hispanic Community Health Study/Study of Latinos.

INTRODUCTION: We conducted admixture mapping and fine-mapping analyses to identify ancestry-of-origin loci influencing cognitive abilities. METHODS: We estimated the association of local ancestry intervals across the genome with five neurocognitive measures in 7140 diverse Hispanic and Latino adults (mean age 55 years). We prioritized genetic variants in associated loci and tested them for replication in four independent cohorts. RESULTS: We identified nine local ancestry-associated regions for the five neurocognitive measures. There was strong biological support for the observed associations to cognitive function at all loci and there was statistical evidence of independent replication at 4q12, 9p22.1, and 13q12.13. DISCUSSION: Our study identified multiple novel loci harboring genes implicated in cognitive functioning and dementia, and uncovered ancestry-relevant genetic variants. It adds to our understanding of the genetic architecture of cognitive function in Hispanic and Latino adults and demonstrates the power of admixture mapping to discover unique haplotypes influencing cognitive function, complementing genome-wide association studies. HIGHLIGHTS: We identified nine ancestry-of-origin chromosomal regions associated with five neurocognitive traits. In each associated region, we identified single nucleotide polymorphisms (SNPs) that explained, at least in part, the admixture signal and were tested for replication in independent samples of Black, non-Hispanic White, and Hispanic/Latino adults with the same or similar neurocognitive tests. Statistical evidence of independent replication of the prioritized SNPs was observed for three of the nine associations, at chr4q12, chr9p22.1, and chr13q12.13. At all loci, there was strong biological support for the observed associations to cognitive function and dementia, prioritizing genes such as KIT, implicated in autophagic clearance of neurotoxic proteins and on mast cell and microglial-mediated inflammation; SLC24A2, implicated in synaptic plasticity associated with learning and memory; and MTMR6, implicated in phosphoinositide lipids metabolism.

Environmental Bright Light Exposure, Depression Symptoms, and Sleep Regularity.

This cross-sectional study examines the associations between bright light therapy, sleep regularity, and depression symptoms among adults in the US.

Association Between Accelerometer-Measured Irregular Sleep Duration and Type 2 Diabetes Risk: A Prospective Cohort Study in the UK Biobank.

OBJECTIVE: To evaluate the association between irregular sleep duration and incident diabetes in a U.K. population over 7 years of follow-up. RESEARCH DESIGN AND METHODS: Among 84,421 UK Biobank participants (mean age: 62 years) who were free of diabetes at the time of providing accelerometer data in 2013-2015 and prospectively followed until May 2022, sleep duration variability was quantified by the within-person SD of 7-night accelerometer-measured sleep duration. We used Cox proportional hazard models to estimate hazard ratios (HRs) for incident diabetes (identified from medical records, death register, and/or self-reported diagnosis) according to categories of sleep duration SD. RESULTS: There were 2,058 incident diabetes cases over 622,080 person-years of follow-up. Compared with sleep duration SD ≤ 30 min, the HR (95% CI) was 1.15 (0.99, 1.33) for 31-45 min, 1.28 (1.10, 1.48) for 46-60 min, 1.54 (1.32, 1.80) for 61-90 min, and 1.59 (1.33, 1.90) for ≥91 min, after adjusting for age, sex, and race. We found a nonlinear relationship (p nonlinearity 0.0002), with individuals with a sleep duration SD of >60 vs. ≤60 min having 34% higher diabetes risk (95% CI 1.22, 1.47). Further adjustment for lifestyle, comorbidities, environmental factors, and adiposity attenuated the association (HR comparing sleep duration SD of >60 vs. ≤60 min: 1.11; 95% CI 1.01, 1.22). The association was stronger among individuals with lower diabetes polygenic risk score (PRS; P interaction ≤ 0.0264) and longer sleep duration (P interaction ≤ 0.0009). CONCLUSIONS: Irregular sleep duration was associated with higher diabetes risk, particularly in individuals with a lower diabetes PRS and longer sleep duration.

Intracranial Volume Is Driven by Both Genetics and Early Life Exposures: The SOL-INCA-MRI Study.

Intracranial volume (ICV) reflects maximal brain development and is associated with later-life cognitive abilities. We quantified ICV among first- and second-generation Hispanic and Latino adults from the Study of Latinos-Investigation of Cognitive Aging - MRI (SOL-INCA-MRI), estimated ICV heritability, and tested its associations with previously reported genetic variants, both individually and as a genetic risk score (GRS). We also estimated the association of ICV with early life environmental measures: nativity or age of immigration and parental education. The estimated heritability of ICV was 19% (95% CI, 0.1%-56%) in n=1781 unrelated SOL-INCA-MRI individuals. Four of 10 tested genetic variants were associated with ICV and an increase of 1 SD of the ICV-GRS was associated with an increase of 10.37 cm3 in the ICV (95% CI, 5.29-15.45). Compared to being born in the continental United States, immigrating to the United States at age 11 years or older was associated with 24 cm3 smaller ICV (95% CI, -39.97 to -8.06). Compared to both parents having less than high-school education, at least 1 parent completing high-school education was associated with 15.4 cm3 greater ICV (95% CI, 4.46-26.39). These data confirm the importance of early life health on brain development.

Genome-wide association study reveals shared and distinct genetic architecture underlying fatty acid and bioactive oxylipin metabolites in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Bioactive fatty acid-derived oxylipin molecules play key roles in mediating inflammation and oxidative stress, which underlie many chronic diseases. Circulating levels of fatty acids and oxylipins are influenced by both environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biological pathways. Thus, we performed a genome wide association study (GWAS) of n=81 fatty acids and oxylipins in n=11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years, standard deviation = 13.8 years). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Heritability estimates ranged from 0% to 47.9%, and 48 of the 81oxylipins and fatty acids were significantly heritable. Moreover, 40 (49.4%) of the 81 oxylipins and fatty acids had at least one genome-wide significant (p< 6.94E-11) variant resulting in 19 independent genetic loci involved in fatty acid and oxylipin synthesis, as well as downstream pathways. Four loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including the desaturase-encoding FADS and the OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with four or more fatty acids and oxylipins. The majority of the 15 remaining loci (87.5%) (lead variant MAF range = 0.03-0.45, mean = 0.23) were only associated with one oxylipin or fatty acid, demonstrating evidence of distinct genetic effects. Finally, while most loci identified in two-degree-of-freedom tests were previously identified in our main effects analyses, we also identified an additional rare variant (MAF = 0.002) near CARS2, a locus previously implicated in inflammation. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating future multi-omics work to characterize these compounds and elucidate their roles in disease pathways.

MethParquet: an R package for rapid and efficient DNA methylation association analysis adopting Apache Parquet.

SUMMARY: Genome-wide DNA methylation (DNAm) profiling is indispensable for unveiling how DNAm regulates biological pathways and individual phenotypes. However, managing and analyzing extensive DNAm data generated from large cohort studies present computational obstacles. Apache Parquet is a data file format that allows for efficient data storage, retrieval, and manipulation, alleviating computational hurdles associated with conventional row-based formats. We here introduce MethParquet, the first R package leveraging the columnar Parquet format for efficient DNAm data analysis. It can be used for data extraction, methylation risk score calculation, epigenome-wide association analyses, and other standard post-quality control tasks. The package flexibly implements diverse regression models. Via a public methylation dataset, we show the efficiency of this package in reducing running time and RAM usage in large-scale EWAS. AVAILABILITY AND IMPLEMENTATION: The MethParquet R package is publicly available on the GitHub repository https://github.com/ZWangTen/MethParquet. It includes a vignette and a toy dataset derived from a public resource.

Untargeted Metabolome Atlas for Sleep Phenotypes in the Hispanic Community Health Study/Study of Latinos.

Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep phenotypes and blood metabolites. Utilizing data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep phenotypes, grouped in several domains (i.e., sleep disordered breathing (SDB), sleep duration, timing, insomnia symptoms, and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualize and interpret the associations between sleep phenotypes and metabolites. The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, some xenobiotic metabolites were associated with sleep duration and heart rate phenotypes (e.g. 1H-indole-7-acetic acid, 4-allylphenol sulfate), while ketone bodies and fatty acid metabolism metabolites were associated with sleep timing measures (e.g. 3-hydroxybutyrate (BHBA), 3-hydroxyhexanoylcarnitine (1)). Heart rate phenotypes had the overall largest number of detected metabolite associations. Many of these associations were shared with both SDB and with sleep timing phenotypes, while SDB phenotypes shared relatively few metabolite associations with sleep duration measures. A number of metabolites were associated with multiple sleep phenotypes, from a few domains. The amino acids vanillylmandelate (VMA) and 1-carboxyethylisoleucine were associated with the greatest number of sleep phenotypes, from all domains other than insomnia. This atlas of sleep-metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks.

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.

A comparison of caregiver-reported snoring and objective snoring in children from urban neighborhoods.

OBJECTIVE: Sleep Disordered Breathing (SDB) is both prevalent and under-recognized in pediatric minority populations. Recognition of SDB is often triggered by symptoms of caregiver-reported snoring. However, the validity and utility of caregiver reports likely vary across populations. Our objective is to assess the association between caregiver-reported snoring and objectively recorded snoring in a low-income urban community and explore factors associated with agreement between objective and subjective snoring. METHODS: 169 6 to 12 year old participants underwent at-home sleep studies with a WatchPAT device as part of the Environmental Assessment of Sleep in Youth (EASY) cohort study. Differences in subjective snoring, objective snoring, and concordance between subjective and objective snoring based on socioeconomic and clinical characteristics were assessed. RESULTS: The sample had a high proportion of non-white (78.9 %) and low income (39.6 %) children. Caregivers reported snoring for 20.7 % of the children and snoring was measured objectively for 21.9 %. Of those with objective snoring, only 29.7 % were identified as snorers by caregiver report (sensitivity: 0.30; specificity: 0.82). Primary Spanish language and co-sleeping were associated with increased caregiver reported snoring, and allergy was associated with increased objective snoring. Older child age and normal range BMI percentile were associated with higher concordance between caregiver and objective snoring. CONCLUSIONS: Among a community-based, predominantly minority sample, caregiver-reported snoring resulted in under-estimation of prevalence of objectively assessed snoring. Reliance on caregiver report may poorly identify children with snoring or SDB in clinical practice.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

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. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

A sociodemographic index identifies non-biological sex-related effects on insomnia in the Hispanic Community Health Study/Study of Latinos.

Background: Sex differences are related to both biological factors and the gendered environment. To untangle sex-related effects on health and disease it is important to model sex-related differences better. Methods: Data came from the baseline visit of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a longitudinal cohort study following 16,415 individuals recruited at baseline from four study sites: Bronx NY, Miami FL, San Diego CA, and Chicago IL. We applied LASSO penalized logistic regression of male versus female sex over sociodemographic, acculturation, and psychological factors jointly. Two "gendered indices", GISE and GIPSE, summarizing the sociodemographic environment (GISE, primary) and psychosocial and sociodemographic environment (GIPSE, secondary) associated with sex, were calculated by summing these variables, weighted by their regression coefficients. We examined the association of these indices with insomnia derived from self-reported symptoms assessed via the Women Health Initiative Insomnia Rating Scale (WHIIRS), a phenotype with strong sex differences, in sex-adjusted and sex-stratified analyses. All analyses were adjusted for age, Hispanic/Latino background, and study center. Results: The distribution of GISE and GIPSE differed by sex with higher values in male individuals, even when constructing and validating them on separate, independent, subsets of HCHS/SOL individuals. In an association model with insomnia, male sex was associated with lower likelihood of insomnia (odds ratio (OR)=0.60, 95% CI (0.53, 0.67)). Including GISE in the model, the association was slightly weaker (OR=0.63, 95% CI (0.56, 0.70)), and weaker when including instead GIPSE in the association model (OR=0.78, 95% CI (0.69, 0.88)). Higher values of GISE and of GIPSE, more common in male sex, were associated with lower likelihood of insomnia, in analyses adjusted for sex (per 1 standard deviation of the index, GISE OR= 0.92, 95% CI (0.87, 0.99), GIPSE OR=0.65, 95% CI (0.61, 0.70)). Conclusions: New measures such as GISE and GIPSE capture sex-related differences beyond binary sex and have the potential to better model and inform research studies of health. However, such indices do not account for gender identity and may not well capture the environment experienced by intersex and non-binary persons.

Adverse pregnancy outcomes and pharyngeal flow limitation during sleep: Nulliparous Pregnancy Outcomes Study Monitoring Mothers-to-be (nuMoM2b).

BACKGROUND: Pharyngeal flow limitation during pregnancy may be a risk factor for adverse pregnancy outcomes but was previously challenging to quantify. Our objective was to determine whether a novel objective measure of flow limitation identifies an increased risk of pre-eclampsia (primary outcome) and other adverse outcomes in a prospective cohort: Nulliparous Pregnancy Outcomes Study Monitoring Mothers-to-be (nuMoM2b). METHODS: Flow limitation severity scores (0%=fully obstructed, 100%=open airway), quantified from breath-by-breath airflow shape, were obtained from home sleep tests during early (6-15 weeks) and mid (22-31 weeks) pregnancy. Multivariable logistic regression quantified associations between flow limitation (median overnight severity, both time-points averaged) and pre-eclampsia, adjusting for maternal age, body mass index (BMI), race, ethnicity, chronic hypertension and flow limitation during wakefulness. Secondary outcomes were hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM) and infant birthweight. RESULTS: Of 1939 participants with flow limitation data at both time-points (mean±sd age 27.0±5.4 years and BMI 27.7±6.1 kg·m-2), 5.8% developed pre-eclampsia, 12.7% developed HDP and 4.5% developed GDM. Greater flow limitation was associated with increased pre-eclampsia risk: adjusted OR 2.49 (95% CI 1.69-3.69) per 2sd increase in severity. Findings persisted in women without sleep apnoea (apnoea-hypopnoea index <5 events·h-1). Flow limitation was associated with HDP (OR 1.77 (95% CI 1.33-2.38)) and reduced infant birthweight (83.7 (95% CI 31.8-135.6) g), but not GDM. CONCLUSIONS: Greater flow limitation is associated with increased risk of pre-eclampsia, HDP and lower infant birthweight. Flow limitation may provide an early target for mitigating the consequences of sleep disordered breathing during pregnancy.

Many roads to a gene-environment interaction.

Despite the importance of gene-environment interactions (GxEs) in improving and operationalizing genetic discovery, interpretation of any GxEs that are discovered can be surprisingly difficult. There are many potential biological and statistical explanations for a statistically significant finding and, likewise, it is not always clear what can be claimed based on a null result. A better understanding of the possible underlying mechanisms leading to a detected GxE can help investigators decide which are and which are not relevant to their hypothesis. Here, we provide a detailed explanation of five "phenomena," or data-generating mechanisms, that can lead to nonzero interaction estimates, as well as a discussion of specific instances in which they might be relevant. We hope that, given this framework, investigators can design more targeted experiments and provide cleaner interpretations of the associated results.

Metabolomic profiles of sleep-disordered breathing are associated with hypertension and diabetes mellitus development.

Sleep-disordered breathing (SDB) is a prevalent disorder characterized by recurrent episodic upper airway obstruction. Using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we apply principal component analysis (PCA) to seven SDB-related measures. We estimate the associations of the top two SDB PCs with serum levels of 617 metabolites, in both single-metabolite analysis, and a joint penalized regression analysis. The discovery analysis includes 3299 individuals, with validation in a separate dataset of 1522 individuals. Five metabolite associations with SDB PCs are discovered and replicated. SDB PC1, characterized by frequent respiratory events common in older and male adults, is associated with pregnanolone and progesterone-related sulfated metabolites. SDB PC2, characterized by short respiratory event length and self-reported restless sleep, enriched in young adults, is associated with sphingomyelins. Metabolite risk scores (MRSs), representing metabolite signatures associated with the two SDB PCs, are associated with 6-year incident hypertension and diabetes. These MRSs have the potential to serve as biomarkers for SDB, guiding risk stratification and treatment decisions.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

Genetic Analysis of Obstructive Sleep Apnea and Its Relationship with Severe COVID-19.

Rationale: Although patients with obstructive sleep apnea (OSA) have a higher risk for coronavirus disease (COVID-19) hospitalization, the causal relationship has remained unexplored. Objectives: To understand the causal relationship between OSA and COVID-19 by leveraging data from vaccination and electronic health records, genetic risk factors from genome-wide association studies, and Mendelian randomization. Methods: We elucidated genetic risk factors for OSA using FinnGen (total N = 377,277), performing genome-wide association. We used the associated variants as instruments for univariate and multivariate Mendelian randomization (MR) analyses and computed absolute risk reduction against COVID-19 hospitalization with or without vaccination. Results: We identified nine novel loci for OSA and replicated our findings in the Million Veteran Program. Furthermore, MR analysis showed that OSA was a causal risk factor for severe COVID-19 (P = 9.41 × 10-4). Probabilistic modeling showed that the strongest genetic risk factor for OSA at the FTO locus reflected a signal of higher body mass index (BMI), whereas BMI-independent association was seen with the earlier reported SLC9A4 locus and a MECOM locus, which is a transcriptional regulator with 210-fold enrichment in the Finnish population. Similarly, multivariate MR analysis showed that the causality for severe COVID-19 was driven by BMI (multivariate MR P = 5.97 × 10-6, ß = 0.47). Finally, vaccination reduced the risk for COVID-19 hospitalization more in the patients with OSA than in the non-OSA controls, with respective absolute risk reductions of 13.3% versus 6.3%. Conclusions: Our analysis identified novel genetic risk factors for OSA and showed that OSA is a causal risk factor for severe COVID-19. The effect is predominantly explained by higher BMI and suggests BMI-dependent effects at the level of individual variants and at the level of comorbid causality.

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Cardiac Abnormalities in Hispanic/Latina Women With Prior De Novo Hypertensive Disorders of Pregnancy.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are associated with long-term maternal risks for cardiovascular disease for reasons that remain incompletely understood. METHODS: The HCHS/SOL (Hispanic Community Health Study/Study of Latinos), a multi-center community-based cohort of Hispanic/Latino adults recruited 2008 to 2011, was used to evaluate the associations of history of de novo HDP (gestational hypertension, preeclampsia, eclampsia) with echocardiographic measures of cardiac structure and function in Hispanic/Latina women with ≥1 prior pregnancy and the proportion of association mediated by current hypertension (>140/90 mm Hg or antihypertensive therapy). RESULTS.: The study cohort included 5168 Hispanic/Latina women with an average age (SD) of 58.7 (9.7) years at time of echocardiogram. Prior de novo HDP was reported by 724 (14%) of the women studied and was associated with lower left ventricle (LV) ejection fraction -0.66 (95% confidence interval [CI], -1.21 to -0.11), higher LV relative wall thickness 0.09 (95% CI, 0-0.18), and 1.39 (95% CI, 1.02-1.89) higher risk of abnormal LV geometry after adjusting for blood pressure and other confounders. The proportion of the association mediated by current hypertension between HDP and LV ejection fraction was 0.09 (95% CI, 0.03-0.45), LV relative wall thickness was 0.28 (95% CI, 0.16-0.51), abnormal LV geometry was 0.14 (95% CI, 0.12-0.48), concentric left ventricular hypertrophy was 0.31 (95% CI, 0.19-0.86), and abnormal LV diastolic dysfunction was 0.58 (95% CI, 0.26-0.79). CONCLUSIONS.: In a large cohort of Hispanic/Latina women those with history of de novo HDP had detectable and measurable subclinical alterations in cardiac structure and both systolic and diastolic dysfunction that were only partially mediated by current hypertension.

Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos.

Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.