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.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes.

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Baseline characteristics of SARS-CoV-2 vaccine non-responders in a large population-based sample.

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and "other" non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.

DNA Methylation-Derived Immune Cell Proportions and Cancer Risk, Including Lung Cancer, in Black Participants.

Prior cohort studies assessing cancer risk based on immune cell subtype profiles have predominantly focused on White populations. This limitation obscures vital insights into how cancer risk varies across race. Immune cell subtype proportions were estimated using deconvolution based on leukocyte DNA methylation markers from blood samples collected at baseline on participants without cancer in the Atherosclerosis Risk in Communities (ARIC) Study. Over a mean of 17.5 years of follow-up, 668 incident cancers were diagnosed in 2,467 Black participants. Cox proportional hazards regression was used to examine immune cell subtype proportions and overall cancer incidence and site-specific incidence (lung, breast, and prostate cancers). Higher T regulatory cell proportions were associated with statistically significantly higher lung cancer risk (hazard ratio = 1.22, 95% confidence interval = 1.06-1.41 per percent increase). Increased memory B cell proportions were associated with significantly higher risk of prostate cancer (1.17, 1.04-1.33) and all cancers (1.13, 1.05-1.22). Increased CD8+ naïve cell proportions were associated with significantly lower risk of all cancers in participants ≥55 years (0.91, 0.83-0.98). Other immune cell subtypes did not display statistically significant associations with cancer risk. These results in Black participants align closely with prior findings in largely White populations. Findings from this study could help identify those at high cancer risk and outline risk stratifying to target patients for cancer screening, prevention, and other interventions. Further studies should assess these relationships in other cancer types, better elucidate the interplay of B cells in cancer risk, and identify biomarkers for personalized risk stratification.

X-linked genetic associations in sporadic thoracic aortic dissection.

The male predominance in sporadic thoracic aortic aneurysm and dissection (TAD) suggests that the X chromosome contributes to TAD, but this has not been tested. We investigated whether X-linked variation-common (minor allele frequency [MAF] ≥0.01) and rare (MAF <0.01)-was associated with sporadic TAD in three cohorts of European descent (Discovery: 364 cases, 874 controls; Replication: 516 cases, 440,131 controls, and ARIC [Atherosclerosis Risk in Communities study]: 753 cases, 2247 controls). For analysis of common variants, we applied a sex-stratified logistic regression model followed by a meta-analysis of sex-specific odds ratios. Furthermore, we conducted a meta-analysis of overlapping common variants between the Discovery and Replication cohorts. For analysis of rare variants, we used a sex-stratified optimized sequence kernel association test model. Common variants results showed no statistically significant findings in the Discovery cohort. An intergenic common variant near SPANXN1 was statistically significant in the Replication cohort (p = 1.81 × 10-8). The highest signal from the meta-analysis of the Discovery and Replication cohorts was a ZNF182 intronic common variant (p = 3.5 × 10-6). In rare variants results, RTL9 reached statistical significance (p = 5.15 × 10-5). Although most of our results were statistically insignificant, our analysis is the most comprehensive X-chromosome association analysis of sporadic TAD to date.

NODAL variants are associated with a continuum of laterality defects from simple D-transposition of the great arteries to heterotaxy.

BACKGROUND: NODAL signaling plays a critical role in embryonic patterning and heart development in vertebrates. Genetic variants resulting in perturbations of the TGF-ß/NODAL signaling pathway have reproducibly been shown to cause laterality defects in humans. To further explore this association and improve genetic diagnosis, the study aims to identify and characterize a broader range of NODAL variants in a large number of individuals with laterality defects. METHODS: We re-analyzed a cohort of 321 proband-only exomes of individuals with clinically diagnosed laterality congenital heart disease (CHD) using family-based, rare variant genomic analyses. To this cohort we added 12 affected subjects with known NODAL variants and CHD from institutional research and clinical cohorts to investigate an allelic series. For those with candidate contributory variants, variant allele confirmation and segregation analysis were studied by Sanger sequencing in available family members. Array comparative genomic hybridization and droplet digital PCR were utilized for copy number variants (CNV) validation and characterization. We performed Human Phenotype Ontology (HPO)-based quantitative phenotypic analyses to dissect allele-specific phenotypic differences. RESULTS: Missense, nonsense, splice site, indels, and/or structural variants of NODAL were identified as potential causes of heterotaxy and other laterality defects in 33 CHD cases. We describe a recurrent complex indel variant for which the nucleic acid secondary structure predictions implicate secondary structure mutagenesis as a possible mechanism for formation. We identified two CNV deletion alleles spanning NODAL in two unrelated CHD cases. Furthermore, 17 CHD individuals were found (16/17 with known Hispanic ancestry) to have the c.778G > A:p.G260R NODAL missense variant which we propose reclassification from variant of uncertain significance (VUS) to likely pathogenic. Quantitative HPO-based analyses of the observed clinical phenotype for all cases with p.G260R variation, including heterozygous, homozygous, and compound heterozygous cases, reveal clustering of individuals with biallelic variation. This finding provides evidence for a genotypic-phenotypic correlation and an allele-specific gene dosage model. CONCLUSION: Our data further support a role for rare deleterious variants in NODAL as a cause for sporadic human laterality defects, expand the repertoire of observed anatomical complexity of potential cardiovascular anomalies, and implicate an allele specific gene dosage model.

Social determinants of health predict readmission following COVID-19 hospitalization: a health information exchange-based retrospective cohort study.

Introduction: Since February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States. Methods: A retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022. Results and discussion: In this cohort of 91,007 adult patients with a COVID-19-associated hospitalization, over 21% were readmitted to the hospital within 90 days (n = 19,679), and 13% were readmitted within 30 days (n = 11,912). In logistic regression analyses, Hispanic and non-Hispanic Asian patients were less likely to be readmitted within 90 days (adjusted odds ratio [aOR]: 0.8, 95% confidence interval [CI]: 0.7-0.9, and aOR: 0.8, 95% CI: 0.8-0.8), while non-Hispanic Black patients were more likely to be readmitted (aOR: 1.1, 95% CI: 1.0-1.1, p = 0.002), compared to non-Hispanic White patients. Area deprivation index displayed a clear dose-response relationship to readmission: patients living in the most disadvantaged neighborhoods were more likely to be readmitted within 30 (aOR: 1.1, 95% CI: 1.0-1.2), 60 (aOR: 1.1, 95% CI: 1.2-1.2), and 90 days (aOR: 1.2, 95% CI: 1.1-1.2), compared to patients from the least disadvantaged neighborhoods. Our findings demonstrate the lasting impact of COVID-19, especially among members of marginalized communities, and the increasing burden of COVID-19 morbidity on the healthcare system.

Metabolomic Association and Risk Prediction With Heart Failure in Older Adults.

BACKGROUND: Older adults have markedly increased risks of heart failure (HF), specifically HF with preserved ejection fraction (HFpEF). Identifying novel biomarkers can help in understanding HF pathogenesis and improve at-risk population identification. This study aimed to identify metabolites associated with incident HF, HFpEF, and HF with reduced ejection fraction and examine risk prediction in older adults. METHODS: Untargeted metabolomic profiling was performed in Black and White adults from the ARIC study (Atherosclerosis Risk in Communities) visit 5 (n=3719; mean age, 75 years). We applied Cox regressions to identify metabolites associated with incident HF and its subtypes. The metabolite risk score (MRS) was constructed and examined for associations with HF, echocardiographic measures, and HF risk prediction. Independent samples from visit 3 (n=1929; mean age, 58 years) were used for replication. RESULTS: Sixty metabolites (hazard ratios range, 0.79-1.49; false discovery rate, <0.05) were associated with incident HF after adjusting for clinical risk factors, eGFR, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Mannonate, a hydroxy acid, was replicated (hazard ratio, 1.36 [95% CI, 1.19-1.56]) with full adjustments. MRS was associated with an 80% increased risk of HF per SD increment, and the highest MRS quartile had 8.7× the risk of developing HFpEF than the lowest quartile. High MRS was also associated with unfavorable values of cardiac structure and function. Adding MRS over clinical risk factors and NT-proBNP improved 5-year HF risk prediction C statistics from 0.817 to 0.850 (∆C, 0.033 [95% CI, 0.017-0.047]). The association between MRS and incident HF was replicated after accounting for clinical risk factors (P<0.05). CONCLUSIONS: Novel metabolites associated with HF risk were identified, elucidating disease pathways, specifically HFpEF. An MRS was associated with HF risk and improved 5-year risk prediction in older adults, which may assist at at-risk population identification.

Prozone masks elevated SARS-CoV-2 antibody level measurements.

We report a prozone effect in measurement of SARS-CoV-2 spike protein antibody levels from an antibody surveillance program. Briefly, the prozone effect occurs in immunoassays when excessively high antibody concentration disrupts the immune complex formation, resulting in a spuriously low reported result. Following participant inquiries, we observed anomalously low measurement of SARS-CoV-2 spike protein antibody levels using the Roche Elecsys® Anti-SARS-CoV-2 S immunoassay from participants in the Texas Coronavirus Antibody Research survey (Texas CARES), an ongoing prospective, longitudinal antibody surveillance program. In July, 2022, samples were collected from ten participants with anomalously low results for serial dilution studies, and a prozone effect was confirmed. From October, 2022 to March, 2023, serial dilution of samples detected 74 additional cases of prozone out of 1,720 participants' samples. Prozone effect may affect clinical management of at-risk populations repeatedly exposed to SARS-CoV-2 spike protein through multiple immunizations or serial infections, making awareness and mitigation of this issue paramount.

Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data.

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Serum Metabolomic Markers of Protein-Rich Foods and Incident CKD: Results From the Atherosclerosis Risk in Communities Study.

Rationale & Objective: While urine excretion of nitrogen estimates the total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of 6 protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD). Study Design: Prospective cohort study. Setting & Participants: A total of 3,726 participants from the Atherosclerosis Risk in Communities study without CKD at baseline. Exposures: Dietary intake of 6 protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry), serum metabolites. Outcomes: Incident CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2 with ≥25% estimated glomerular filtration rate decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease). Analytical Approach: Multivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C statistics assessed the ability of the metabolites to improve the discrimination of highest versus lower 3 quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and the intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident CKD. Results: Thirty significant associations were identified between protein-rich foods and serum metabolites (fish, n = 8; nuts, n = 5; legumes, n = 0; red and processed meat, n = 5; eggs, n = 3; and poultry, n = 9). Metabolites collectively and significantly improved the discrimination of high intake of protein-rich foods compared with covariates alone (difference in C statistics = 0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; P < 1.00 × 10-16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR, 0.82; 95% CI, 0.75-0.89; P = 7.81 × 10-6). Limitations: Residual confounding and sample-storage duration. Conclusions: We identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with a lower risk of CKD.

In this study, we aimed to identify associations between protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry) and serum metabolites, which are small biological molecules involved in metabolism. Metabolites significantly associated with a protein-rich food individually and collectively improved the discrimination of the respective protein-rich food, suggesting that these metabolites should be prioritized in future diet biomarker research. We also studied associations between significant diet-related metabolites and incident kidney disease. One fish-related metabolite was associated with a lower kidney disease risk. This finding supports the recent nutritional guidelines recommending a Mediterranean diet, which includes fish as the main dietary protein source.

Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes.

BACKGROUND: Consistent evidence suggests diabetes-protective effects of dietary fiber intake. However, the underlying mechanisms, particularly the role of gut microbiota and host circulating metabolites, are not fully understood. We aimed to investigate gut microbiota and circulating metabolites associated with dietary fiber intake and their relationships with type 2 diabetes (T2D). METHODS: This study included up to 11 394 participants from the HCHS/SOL (Hispanic Community Health Study/Study of Latinos). Diet was assessed with two 24-hour dietary recalls at baseline. We examined associations of dietary fiber intake with gut microbiome measured by shotgun metagenomics (350 species/85 genera and 1958 enzymes; n=2992 at visit 2), serum metabolome measured by untargeted metabolomics (624 metabolites; n=6198 at baseline), and associations between fiber-related gut bacteria and metabolites (n=804 at visit 2). We examined prospective associations of serum microbial-associated metabolites (n=3579 at baseline) with incident T2D over 6 years. RESULTS: We identified multiple bacterial genera, species, and related enzymes associated with fiber intake. Several bacteria (eg, Butyrivibrio, Faecalibacterium) and enzymes involved in fiber degradation (eg, xylanase EC3.2.1.156) were positively associated with fiber intake, inversely associated with prevalent T2D, and favorably associated with T2D-related metabolic traits. We identified 159 metabolites associated with fiber intake, 47 of which were associated with incident T2D. We identified 18 of these 47 metabolites associated with the identified fiber-related bacteria, including several microbial metabolites (eg, indolepropionate and 3-phenylpropionate) inversely associated with the risk of T2D. Both Butyrivibrio and Faecalibacterium were associated with these favorable metabolites. The associations of fiber-related bacteria, especially Faecalibacterium and Butyrivibrio, with T2D were attenuated after further adjustment for these microbial metabolites. CONCLUSIONS: Among United States Hispanics/Latinos, dietary fiber intake was associated with favorable profiles of gut microbiota and circulating metabolites for T2D. These findings advance our understanding of the role of gut microbiota and microbial metabolites in the relationship between diet and T2D.

Clonal hematopoiesis of indeterminate potential is associated with acute kidney injury.

Age is a predominant risk factor for acute kidney injury (AKI), yet the biological mechanisms underlying this risk are largely unknown. Clonal hematopoiesis of indeterminate potential (CHIP) confers increased risk for several chronic diseases associated with aging. Here we sought to test whether CHIP increases the risk of AKI. In three population-based epidemiology cohorts, we found that CHIP was associated with a greater risk of incident AKI, which was more pronounced in patients with AKI requiring dialysis and in individuals with somatic mutations in genes other than DNMT3A, including mutations in TET2 and JAK2. Mendelian randomization analyses supported a causal role for CHIP in promoting AKI. Non-DNMT3A-CHIP was also associated with a nonresolving pattern of injury in patients with AKI. To gain mechanistic insight, we evaluated the role of Tet2-CHIP and Jak2V617F-CHIP in two mouse models of AKI. In both models, CHIP was associated with more severe AKI, greater renal proinflammatory macrophage infiltration and greater post-AKI kidney fibrosis. In summary, this work establishes CHIP as a genetic mechanism conferring impaired kidney function recovery after AKI via an aberrant inflammatory response mediated by renal macrophages.

Genetic sex validation for sample tracking in next-generation sequencing clinical testing.

OBJECTIVE: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. RESULTS: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors (49.09%), samples from transgender participants (3.64%) and stem cell or bone marrow transplant patients (7.27%) along with undetermined sample mix-ups (40%) for which sample swaps occurred prior to arrival at genome centers, however the exact cause of the events at the sampling sites resulting in the mix-ups were not able to be determined.

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.

Circulating microRNA Biomarkers of Thiazide Response in Hypertension.

BACKGROUND: Thiazide diuretics are the second most frequently prescribed class of antihypertensives, but up to 50% of patients with hypertension have minimal antihypertensive response to thiazides. We explored circulating microRNAs (miRNAs) in search of predictive biomarkers of thiazide response. METHODS AND RESULTS: We profiled 754 miRNAs in baseline plasma samples of 36 hypertensive European American adults treated with hydrochlorothiazide, categorized into responders (n=18) and nonresponders (n=18) on the basis of diastolic blood pressure response to hydrochlorothiazide. miRNAs with ≥2.5-fold differential expression between responders and nonresponders were considered for validation in 3 cohorts (n=50 each): hydrochlorothiazide-treated European Americans, chlorthalidone-treated European Americans, and hydrochlorothiazide-treated Black individuals. Different blood pressure phenotypes including categorical (responder versus nonresponder) and continuous diastolic blood pressure and systolic blood pressure were tested for association with the candidate miRNA expression using multivariate regression analyses adjusting for age, sex, and baseline blood pressure. After quality control, 74 miRNAs were available for screening, 19 of which were considered for validation. In the validation cohort, miR-193b-3p and 30d-5p showed significant associations with continuous SBP or diastolic blood pressure response or both, to hydrochlorothiazide in European Americans at Benjamini-Hochberg adjusted P<0.05. In the combined analysis of validation cohorts, let-7g (odds ratio, 0.6 [95% CI, 0.4-0.8]), miR-142-3p (odds ratio, 1.1 [95% CI, 1.0, 1.2]), and miR-423-5p (odds ratio, 0.7 [95% CI, 0.5-0.9]) associated with categorical diastolic blood pressure response at Benjamini-Hochberg adjusted P<0.05. Predicted target genes of the 5 miRNAs were mapped to key hypertension pathways: lysine degradation, fatty acid biosynthesis, and metabolism. CONCLUSIONS: The above identified circulating miRNAs may have a potential for clinical use as biomarkers for thiazide diuretic selection in hypertension. REGISTRATION: URL: ClinicalTrials.gov. Unique identifiers: NCT00246519, NCT01203852, NCT00005520.

A polygenic risk score of atrial fibrillation improves prediction of lifetime risk for heart failure.

AIMS: Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk-factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi-racial populations. METHODS AND RESULTS: Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow-up. PRSs of AF, BMI, and CHD were associated with incident HF (P < 0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41-1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10 year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009-0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27-1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33-1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P < 0.050). Protein analyses revealed that N-terminal pro-brain natriuretic peptide and other 98 proteins were associated with PRSAF. CONCLUSIONS: The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation.

Equilibrium points and their stability of COVID-19 in US.

This study aims to develop an advanced mathematic model and investigate when and how will the COVID-19 in the US be evolved to endemic. We employed a nonlinear ordinary differential equations-based model to simulate COVID-19 transmission dynamics, factoring in vaccination efforts. Multi-stability analysis was performed on daily new infection data from January 12, 2021 to December 12, 2022 across 50 states in the US. Key indices such as eigenvalues and the basic reproduction number were utilized to evaluate stability and investigate how the pandemic COVD-19 will evolve to endemic in the US. The transmissional, recovery, vaccination rates, vaccination effectiveness, eigenvalues and reproduction numbers ([Formula: see text] and [Formula: see text]) in the endemic equilibrium point were estimated. The stability attractor regions for these parameters were identified and ranked. Our multi-stability analysis revealed that while the endemic equilibrium points in the 50 states remain unstable, there is a significant trend towards stable endemicity in the US. The study's stability analysis, coupled with observed epidemiological waves in the US, suggested that the COVID-19 pandemic may not conclude with the virus's eradication. Nevertheless, the virus is gradually becoming endemic. Effectively strategizing vaccine distribution is pivotal for this transition.