Publications

OBJECTIVES: Exposure to allergens may trigger inflammatory pathways contributing to poor sleep. We investigated the associations between indoor allergen concentrations with multiple sleep dimensions in children.

METHODS: Bedroom dust aeroallergens were collected in participants' homes. The outcomes were caregiver-reported sleep-related daytime impairment and sleep disturbance (pediatric PROMIS instruments), sleep-disordered breathing (SDB; apnea-hypopnea index or oxygen desaturation index >5), and actigraphy-based short sleep (<8 hours) duration and poor sleep continuity (sleep fragmentation index > 75th percentile). Logistic regression was used to examine associations between aeroallergens (>50% detectable levels) and sleep outcomes, adjusting for potential confounders (i.e., sociodemographic, environmental, and health-related).

RESULTS: The sample included an urban cohort of 256 children (41% Hispanic, 29% Black; 43% female) aged 6-12 years. Mouse (Mus m 1), cat (Fel d 1), and dog (Can f 1) allergens were detected in 81%, 72%, and 53% of households, respectively. Elevated mouse allergen exposure (>0.55 μg/g-75th percentile) was associated with a 2.6-fold (95% CI: 1.34, 5.03) increased odds for sleep-related daytime impairment (PROMIS T-score > 55) after adjusting for demographic factors. This association persisted after further adjusting for inflammatory-related health factors (asthma, allergic rhinitis, obesity, and environmental tobacco smoke), neighborhood disadvantage, and SDB. There was attenuation of this association with poor sleep consolidation. Associations were not observed for other allergens or other sleep outcomes.

CONCLUSIONS: Exposure to elevated mouse dust was associated with increased sleep-related daytime impairment symptoms. The role of household pest exposure as a potentially modifiable target for improving sleep health should be further studied.

Directional replicability addresses the question of whether an effect studied across n independent studies is present with the same direction in at least r of them, for r ≥ 2 . When the expected direction of the effect is not specified in advance, the state of the art recommends assessing replicability separately by combining one-sided p -values for both directions (left and right), and then doubling the smaller of the two resulting combined p -values to account for multiple testing. In this work, we show that this multiplicative correction is not always necessary, and give a sufficient and necessary condition under which it can be safely omitted.

BACKGROUND: Sepsis remains a leading cause of mortality, and optimizing treatment is challenging due to patient heterogeneity. Identification of cardiac phenotypes may inform precision medicine approaches and guide resuscitation. We performed a clustering analysis of patients with sepsis using echocardiographic data without using any a priori definitions of cardiac dysfunction or outcomes to establish the subgroups.

METHODS: This was a retrospective cohort study of patients admitted to the hospital with sepsis at a single academic center. Patients were identified using sepsis-related ICD codes, and those who had echocardiogram performed within 14 days of admission underwent chart review to ensure sepsis-3 criteria were met. Those with preexisting heart disease were excluded. Clustering by echocardiographic variables was performed using latent profile analysis. Clinical features such as patient characteristics, laboratory studies, sepsis source, and outcomes were compared across the clusters.

RESULTS: There were 2,071 patients included in the analysis. Our cluster analysis yielded five phenotypes: cluster 1, elevated mean E/e' 24.5 (SD 9.6); cluster 2, reduced ejection fraction, mean 33.1% (SD 10.6), and cardiac index 2.6 L/min/m2 (SD 0.9); cluster 3, right ventricular dilation with right ventricular basal diameter 4.5 cm (SD 0.9) and elevated tricuspid regurgitation gradient 60.0 mmHg (SD 13.5); cluster 4, hyperdynamic with mean left ventricular ejection fraction 75% (SD10.9) and mean cardiac index 6.6 L/min/m2 (SD 2.6); and lastly cluster 5, normal echocardiographic parameters. Group 3 had the highest mortality compared to the normal group (36.9% vs. 19.6%, p = 0.002), with an odds ratio of 2.3 (95%CI 1.4-3.9).

CONCLUSIONS: Using an unsupervised clustering analysis, we identified five phenotypes of cardiac function in sepsis based on commonly recorded echocardiographic data: diastolic dysfunction, left ventricular systolic dysfunction with low cardiac index, right ventricular dilation with elevated tricuspid regurgitation gradient, hyperdynamic cardiac function, and normal. The right ventricular dilation group had the highest mortality. Future research should explore mechanisms and potential treatment implications for these groups.

BACKGROUND: Apolipoprotein E (APOE) alleles are well-established genetic risk factors for Alzheimer's disease (AD), but their effects on AD biomarkers (amyloid beta [Aβ]42/40, phosphorylated tau [p-tau]181, neurofilament light chain [NfL], and glial fibrillary acidic protein [GFAP]) may vary across populations due to ancestry-, age-, and sex-related differences. We hypothesized that these effects vary across Hispanic/Latino background groups with distinct ancestral admixture.

METHODS: We analyzed ε2 and ε4 allele associations with AD biomarkers using survey-weighted linear regression models, adjusting for demographic covariates. Secondary analyses examined genetic analysis group- and ancestry-specific effects.

RESULTS: ε4 was associated with lower Aβ42/40 and higher p-tau181and GFAP levels, but not with NfL, suggesting its role in Aβ and tau deposition and neuroinflammation. ε4 associations were stronger in those with higher European and lower African ancestry.

DISCUSSION: These findings expand on prior studies suggesting that genetic ancestry modifies APOE-associated AD risk in Hispanic/Latino populations and highlight the importance of capturing ancestry-based heterogeneity in AD biomarker research.

BACKGROUND: Sleep traits, including sleep apnoea (SA), insomnia, daytime sleepiness, and snoring, frequently co-occur with cardiometabolic diseases (CMDs), with shared genetic factors suspected to underlie these associations. However, the contribution of shared genetic determinants to these associations is not fully understood.

METHODS: We conducted a genome-wide pleiotropic association study applying sequential genetic methods to identify shared genetic variants, genes, pathways and causal associations between the four sleep traits and seven CMDs, including LDSC, high-definition likelihood analysis, colocalisation, gene-based tests, enrichment analysis and Mendelian randomisation. Next, validation of those pleiotropic variants was performed in individuals from the All of Us and MVP studies.

FINDINGS: Among 28 pairs of sleep traits and CMDs, 25 showed significant genetic correlations. Pleiotropic analysis identified 754 independent SNPs (691 unique) and 102 colocalized loci (85 unique). Among these, 47 SNPs (44 unique) were validated as significantly associated with both traits in the pairs, and notably, rs429358 (19q13.32, APOE) demonstrated pleiotropic effects across SA, insomnia and type 2 diabetes (T2D). Forty-eight annotated genes were validated by gene-based tests. Shared genes were enriched in phenotypes related to mortality and growth. Pathway analysis highlighted Cushing syndrome, hormone secretion, and cGMP-PKG, Ras and calcium signalling pathways. After adjusting for glycaemic traits and blood pressure, genetically predicted T2D increased risk of SA, sleepiness, and snoring. Conversely, SA was positively associated with heart failure and T2D independently.

INTERPRETATION: This study of sleep traits and CMDs reveals shared genetic determinants that may partially explain their epidemiologic association and suggests potential treatment targets.

FUNDING: Described in Acknowledgements.

Population stratification is one of the source of inflation in epigenome-wide association studies (EWAS) when not properly accounted for. To address this, we developed methylation population scores (MPSs) to predict genetic principal components (GPCs) using a feature selection approach. We used multi-ethnic DNA methylation data from Illumina EPIC arrays across five cohorts, including MESA (n = 929), CARDIA (n = 1123), JHS (n = 1365), ARIC (n = 2338), and HCHS/SOL (n = 1475), randomly splitting participants into training (85%) and test (15%) sets. Within each cohort, associations between GPCs and CpG sites were estimated using linear regression adjusting for age, sex, smoking and alcohol use, race/ethnicity, body mass index, and cell type proportions, followed by meta-analysis and selection of CpGs with FDR <0.05. We then applied a two-stage weighted least squares Lasso regression to construct MPSs, adjusting for the aforementioned covariates. In the test dataset, MPSs showed strong correlation with GPCs, with R² ranging from 0.27 (MPS7 vs. GPC7) to 0.98 (MPS1 vs. GPC1). Visualization demonstrated that MPSs recapitulated the pattern shown by GPCs in differentiating self-reported White, Black, and Hispanic/Latino groups and outperformed methylation-based principal components constructed using alternative published methods. Additionally, MPSs showed comparable performance to GPCs in reducing inflation in EWAS. Overall, MPSs uses supervised learning with covariate adjustment to capture genetic structure across diverse populations, and provide a reliable estimate of population structure in the data and can complement GPCs when genetic data are absent.

Omics signatures for psychiatric conditions have been under-investigated. In this study, we used data from a representative observational cohort study of Hispanic/Latino adults to identify serum metabolites associated with symptoms of depression or anxiety and to develop and assess metabolite risk scores (MRSs) of these phenotypes. We conducted a metabolome-wide association study of 768 metabolites with depression and anxiety symptoms, using two separate assay batches measured from baseline samples collected between June 2008 and July 2011 for discovery (batch 1: n = 4002 adults) and replication (batch 2: n = 2178 adults). We estimated the associations via survey-based generalized linear regression, and applied Least Absolute Shrinkage and Selection Operator (LASSO) regression for metabolite selection and for obtaining MRS weights. In analyses adjusted for age, sex, field center and Hispanic/Latino background, we identified five metabolites of replicated associations with depression symptoms and six metabolites with anxiety symptoms. Key pathways related to the identified metabolites included arginine and proline, vitamin A, phospholipid, fatty acid and tyrosine metabolism. MRSs were associated with higher depression symptom score (1.07 points, 95% CI: 0.67 - 1.46, per 1 SD increase in depression MRS) and anxiety symptom score (1.05 points, 95% CI: 0.67 - 1.43, per 1 SD increase in anxiety MRS) in the replication dataset. Our study supports the associations between serum metabolites and symptoms of depression and anxiety in Hispanic/Latino adults. The developed MRSs may facilitate earlier and more objective screening of individuals for depression and anxiety apart from traditional risk factors.

Polygenic risk score values vary with genetic ancestry due to differences in population-specific allele frequencies and linkage disequilibrium patterns. We present a framework to calibrate polygenic risk scores based on ancestral makeup. We propose the "expected polygenic risk score" or ePRS, defined as the expected value of a polygenic risk score based on one's global or local admixture patterns. We further define the "residual polygenic risk score" or rPRS as measuring the deviation of the polygenic risk score from the ePRS. The ePRS reflects the baseline ancestry-driven component of genetic risk, whereas the rPRS isolates an ancestry-agnostic measure of genetic liability. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the polygenic risk score-outcome association without further adjusting for principal components. Using the TOPMed and the All of Us datasets, effect size estimates for the rPRS (adjusted for ePRS) are similar to those obtained from polygenic risk scores adjusting for genetic principal components. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to interpret genetic risk across diverse populations.

Prediction models for cognitive aging measures have largely evaluated demographic variables and APOE carrier status in populations of European ancestry. To comprehensively assess prediction models among Hispanic/Latinos, we considered 12 models (6 predictor sets and 2 methods) for global cognitive score change (GCSC) and mild cognitive impairment (MCI) in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) (N = 5856). Based on the average mean squared error (MSE) for GCSC or average area under the curve (AUC) for MCI across 100 randomly split testing and training sets, performance was similar across models, but slightly better for the following models: the chronic conditions model and genetic model (mean MSEs = 0.2464) using gradient-boosted trees for GCSC prediction and the chronic conditions model (mean AUC = 62%) and metabolite model (mean AUC = 60%) using logistic regression for MCI prediction. Using the Shapley Additive Explanations (SHAP) method, age at baseline, time between exams, and sex were the most important predictors for GCSC, followed by diabetes and global ancestral proportions. Diabetes and the metabolite ribitol had the highest influence on prediction of MCI. Although prediction performance was not especially high and did not vary greatly across models, incorporating information on diabetes, ancestry and metabolites may help improve prediction of GCSC and MCI.