Publications

IMPORTANCE: Lipoprotein(a) (Lp[a]) is a genetically determined risk-enhancing factor for atherosclerotic cardiovascular disease (ASCVD). The Lp(a) distribution among the diverse Hispanic or Latino community residing in the US has not been previously described, to the authors' knowledge.

OBJECTIVE: To determine the distribution of Lp(a) levels across a large cohort of diverse Hispanic or Latino adults living in the US and by key demographic groups.

DESIGN, SETTING, AND PARTICIPANTS: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a prospective, population-based, cohort study of diverse Hispanic or Latino adults living in the US. At screening, participants aged 18 to 74 years were recruited between 2008 and 2011 from 4 US metropolitan areas (Bronx, New York; Chicago, Illinois; Miami, Florida; San Diego, California). HCHS/SOL included 16 415 noninstitutionalized adults recruited through probability sampling of randomly selected households. The study population represents Hispanic or Latino participants from diverse self-identified geographic and cultural backgrounds: Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American. This study evaluated a subset of HCHS/SOL participants who underwent Lp(a) measurement. Sampling weights and surveys methods were used to account for HCHS/SOL sampling design. Data for this study were analyzed from April 2021 to April 2023.

EXPOSURE: Lp(a) molar concentration was measured by a particle-enhanced turbidimetric assay with minimized sensitivity to apolipoprotein(a) size variation.

MAIN OUTCOME AND MEASURE: Lp(a) quintiles were compared using analysis of variance among key demographic groups, including self-identified Hispanic or Latino background. Median percentage genetic ancestry (Amerindian, European, West African) were compared across Lp(a) quintiles.

RESULTS: Lp(a) molar concentration was measured in 16 117 participants (mean [SD] age, 41 [14.8] years; 9680 female [52%]; 1704 Central American [7.7%], 2313 Cuban [21.1%], 1436 Dominican [10.3%], 6395 Mexican [39.1%], 2652 Puerto Rican [16.6%], 1051 South American [5.1%]). Median (IQR) Lp(a) level was 19.7 (7.4-59.7) nmol/L. Across Hispanic or Latino background groups, there was significant heterogeneity in median Lp(a) levels ranging from 12 to 41 nmol/L in those reporting a Mexican vs Dominican background. Median (IQR) West African genetic ancestry was lowest in the first quintile of Lp(a) level and highest in the fifth quintile (5.5% [3.4%-12.9%] and 12.1% [5.0%-32.5%]; respectively; P < .001), whereas the converse was seen for Amerindian ancestry (32.8% [9.9%-53.2%] and 10.7% [4.9%-30.7%], respectively; P < .001).

CONCLUSIONS AND RELEVANCE: Results of this cohort study suggest that differences in Lp(a) level distribution across the diverse US Hispanic or Latino population may carry important implications for the use of Lp(a) level in ASCVD risk assessment for this group. Cardiovascular outcomes data are needed to better understand the clinical impact of differences in Lp(a) levels by Hispanic or Latino background.

AIMS/HYPOTHESIS: Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes.

METHODS: One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals.

RESULTS: Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAFAfr]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9×10-9); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAFAfr=0.0536), was associated with a reduced response to metformin (p=2.4×10-8), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increase in HbA1c of 0.08% and non-carriers had an HbA1c increase of 0.01% after 1 year of treatment (p=3.3×10-3). We also identified associations between type 2 diabetes-associated variants and glycaemic response, including the type 2 diabetes-protective C allele of rs703972 near ZMIZ1 and increased levels of active glucagon-like peptide 1 (GLP-1) (p=1.6×10-5), supporting the role of alterations in incretin levels in type 2 diabetes pathophysiology.

CONCLUSIONS/INTERPRETATION: We present a well-phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common glucose-lowering medications and provide insight into mechanisms of action of type 2 diabetes-related variation.

DATA AVAILABILITY: The complete summary statistics from this study are available at the Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org ) and the GWAS Catalog ( www.ebi.ac.uk/gwas/ , accession IDs: GCST90269867 to GCST90269899).

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

Applying causal inference methods, such as weighting and matching methods, to a survey sampled population requires properly incorporating the survey weights and design to obtain effect estimates that are representative of the target population and correct standard errors (SEs). With a simulation study, we compared various approaches for incorporating the survey weights and design into weighting and matching-based causal inference methods. When the models were correctly specified, most approaches performed well. However, when a variable was treated as an unmeasured confounder and the survey weights were constructed to depend on this variable, only the matching methods that used the survey weights in causal estimation and as a covariate in matching continued to perform well. If unmeasured confounders are potentially associated with the survey sample design, we recommend that investigators include the survey weights as a covariate in matching, in addition to incorporating them in causal effect estimation. Finally, we applied the various approaches to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and found that insomnia has a causal association with both mild cognitive impairment (MCI) and incident hypertension 6-7 years later in the US Hispanic/Latino population.

Both short (≤6 h per night) and long sleep duration (≥9 h per night) are associated with increased risk of chronic diseases. Despite evidence linking habitual sleep duration and risk of disease, the genetic determinants of sleep duration in the general population are poorly understood, especially outside of European (EUR) populations. Here, we report that a polygenic score of 78 European ancestry sleep duration single-nucleotide polymorphisms (SNPs) is associated with sleep duration in an African (n = 7288; P = 0.003), an East Asian (n = 13 618; P = 6 × 10-4) and a South Asian (n = 7485; P = 0.025) genetic ancestry cohort, but not in a Hispanic/Latino cohort (n = 8726; P = 0.71). Furthermore, in a pan-ancestry (N = 483 235) meta-analysis of genome-wide association studies (GWAS) for habitual sleep duration, 73 loci are associated with genome-wide statistical significance. Follow-up of five loci (near HACD2, COG5, PRR12, SH3RF1 and KCNQ5) identified expression-quantitative trait loci for PRR12 and COG5 in brain tissues and pleiotropic associations with cardiovascular and neuropsychiatric traits. Overall, our results suggest that the genetic basis of sleep duration is at least partially shared across diverse ancestry groups.

Rationale: Obstructive sleep apnea is characterized by frequent reductions in ventilation, leading to oxygen desaturations and/or arousals. Objectives: In this study, association of hypoxic burden with incident cardiovascular disease (CVD) was examined and compared with that of "ventilatory burden" and "arousal burden." Finally, we assessed the extent to which the ventilatory burden, visceral obesity, and lung function explain variations in hypoxic burden. Methods: Hypoxic, ventilatory, and arousal burdens were measured from baseline polysomnograms in the Multi-Ethnic Study of Atherosclerosis (MESA) and the Osteoporotic Fractures in Men (MrOS) studies. Ventilatory burden was defined as event-specific area under ventilation signal (mean normalized, area under the mean), and arousal burden was defined as the normalized cumulative duration of all arousals. The adjusted hazard ratios for incident CVD and mortality were calculated. Exploratory analyses quantified contributions to hypoxic burden of ventilatory burden, baseline oxygen saturation as measured by pulse oximetry, visceral obesity, and spirometry parameters. Measurements and Main Results: Hypoxic and ventilatory burdens were significantly associated with incident CVD (adjusted hazard ratio [95% confidence interval] per 1 SD increase in hypoxic burden: MESA, 1.45 [1.14, 1.84]; MrOS, 1.13 [1.02, 1.26]; ventilatory burden: MESA, 1.38 [1.11, 1.72]; MrOS, 1.12 [1.01, 1.25]), whereas arousal burden was not. Similar associations with mortality were also observed. Finally, 78% of variation in hypoxic burden was explained by ventilatory burden, whereas other factors explained only <2% of variation. Conclusions: Hypoxic and ventilatory burden predicted CVD morbidity and mortality in two population-based studies. Hypoxic burden is minimally affected by measures of adiposity and captures the risk attributable to ventilatory burden of obstructive sleep apnea rather than a tendency to desaturate.

Sleep-disordered breathing (SDB) is a prevalent disorder characterized by recurrent episodic upper airway obstruction. In a dataset from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we applied principal component analysis (PCA) on seven measures characterizing SDB-associated respiratory events. We estimated the association of the top two SDB PCs with serum levels of 617 metabolites, in both single-metabolite analysis, and a joint, penalized regression analysis using the least absolute shrinkage and selection operator (LASSO). Discovery analysis included n = 3,299 HCHS/SOL individuals; associations were validated in a separate dataset of n = 1,522 HCHS/SOL individuals. Seven metabolite associations with SDB PCs were discovered and replicated. Metabolite risk scores (MRSs) developed based on LASSO association results and representing metabolite signatures associated with the two SDB PCs were associated with 6-year incident hypertension and incident diabetes. MRSs have the potential to serve as biomarkers for SDB, guiding risk stratification and treatment decisions.

BACKGROUND: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases.

METHODS: We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables.

FINDINGS: We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P < 7.94E-04) that can be explored as potential therapeutic targets.

INTERPRETATION: By integrating eQTM and MR analyses in general and clinical asthma populations, our findings provide a deeper understanding of the multidimensional inter-relations of DNA methylation, gene expression, and IgE levels.

FUNDING: US NIH/NHLBI grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.

STUDY OBJECTIVES: Rest-activity rhythms (RAR) may mark development, aging, and physical and mental health. Understanding how they differ between people may inform intervention and health promotion efforts. However, RAR characteristics across the lifespan have not been well-studied. Therefore, we investigated the association between RAR measures with demographic and lifestyle factors in a US nationally representative study.

METHODS: RAR metrics of interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), and mean amplitude and timing of high (M10) and low (L5) activity were derived from 2011 to 2012 and 2013 to 2014 National Health and Nutrition Examination Survey (NHANES) actigraphy data. Population-weighted linear and logistic regression models were fit to examine the associations of age, gender, smoking, alcohol, season, body mass index (BMI), income-to-poverty ratio, and race/ethnicity with RAR. Significance was based on a false-discovery rate-corrected P-value of <0.05.

RESULTS: Among n = 12 526 NHANES participants (3-≥80 years), IS (higher = greater day-to-day regularity) and RA (higher = greater rhythm strength) generally decreased with age and were lower among males, whereas IV (higher = greater rhythm fragmentation) increased with age (p < 0.05). Dynamic changes in RAR trajectories were observed during childhood and adolescence. Income, BMI, smoking, and alcohol use were associated with RAR metrics, as well as season among children and teenagers (p < 0.05). RAR also differed by race/ethnicity (p < 0.05), with trajectories initially diverging in childhood and continuing into adulthood.

CONCLUSIONS: RAR differed by demographic and health-related factors, representing possible windows for public health intervention and sleep health promotion. RAR differences by race/ethnicity begin in childhood, are evident in early adolescence, and persist throughout adulthood.