Adverse Pregnancy Outcomes and Pharyngeal Flow Limitation during Sleep: Nulliparous Pregnancy Outcomes Study Monitoring Mothers-to-be (nuMoM2b).

RATIONALE: Pharyngeal flow limitation during pregnancy may be a risk factor for adverse pregnancy outcomes but was previously challenging to quantify. OBJECTIVE: To determine whether a novel objective measure of flow limitation identifies an increased risk of preeclampsia (primary outcome) and other adverse outcomes in a prospective cohort: Nulliparous Pregnancy Outcomes Study Monitoring Mothers-to-be. 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 preeclampsia, 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 (age: 27.0±5.4 yr [mean±sd], BMI: 27.7±6.1 kg·m-2), 5.8% developed preeclampsia, 12.7% developed HDP, and 4.5% developed GDM. Greater flow limitation was associated with increased preeclampsia risk: adjusted Odds Ratio (OR) 2.49, 95% Confidence Interval [1.69, 3.69], per 2SD increase in severity. Findings persisted in women without sleep apnea (apnea-hypopnea index <5 events·hr-1). Flow limitation was associated with HDP (OR: 1.77 [1.33, 2.38]) and reduced infant birthweight (83.7 [31.8, 135.6] g), but not GDM. CONCLUSIONS: Greater flow limitation is associated with increased risk of preeclampsia, HDP, and lower infant birthweight. Flow limitation may provide an early target for mitigating the consequences of sleep disordered breathing during pregnancy.

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.

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.

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 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.

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

RATIONALE: While patients with obstructive sleep apnea (OSA) have a higher risk for COVID-19 hospitalization, the causal relationship has remained unexplored. OBJECTIVES: To understand the causal relationship between OSA and COVID-19 leveraging data from vaccination and electronic health records, genetic risk factors from genome-wide association studies (GWAS) and Mendelian randomization. METHODS: We elucidated genetic risk factors for OSA using FinnGen (N total = 377,277 individuals) performing genome-wide association. We used the associated variants as instruments for univariate and multivariate Mendelian randomization (MR) analyses and computed absolute risk reduction (ARR) against COVID-19 hospitalization with or without vaccination. MEASUREMENTS AND MAIN RESULTS: We identified 9 novel loci for OSA and replicated our findings in the Million Veterans Program. Furthermore, MR analysis showed that OSA was a causal risk factor for severe COVID-19 (P=9.41x10-4). Probabilistic modelling showed that the strongest genetic risk factor for OSA at the FTO locus reflected a signal of higher 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 (MVMR) analysis showed that the causality for severe COVID-19 was driven by body mass index (BMI), (P MVMR = 5.97x10-6, beta=0.47). Finally, vaccination reduced the risk for COVID-19 hospitalization more in the OSA patients than in the non-OSA controls: ARR = 13.3% vs. ARR = 6.3% in the OSA vs. non-OSA population. 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.

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.

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.

Objectively regular sleep patterns and mortality in a prospective cohort: The Multi-Ethnic Study of Atherosclerosis.

Irregular sleep and non-optimal sleep duration separately have been shown to be associated with increased disease and mortality risk. We used data from the prospective cohort Multi-Ethnic Study of Atherosclerosis sleep study (2010-2013) to investigate: do aging adults whose sleep is objectively high in regularity in timing and duration, and of sufficient duration tend to have increased survival compared with those whose sleep is lower in regularity and duration, in a diverse US sample? At baseline, sleep was measured by 7-day wrist actigraphy, concurrent with at-home polysomnography and questionnaires. Objective metrics of sleep regularity and duration from actigraphy were used for statistical clustering using sparse k-means clustering. Two sleep patterns were identified: "regular-optimal" (average duration: 7.0 ± 1.0 hr obtained regularly) and "irregular-insufficient" (duration: 5.8 ± 1.4 hr obtained with twice the irregularity). Using proportional hazard models with multivariate adjustment, we estimated all-cause mortality hazard ratios. Among 1759 participants followed for a median of 7.0 years (Q1-Q3, 6.4-7.4 years), 176 deaths were recorded. The "regular-optimal" group had a 39% lower mortality hazard than did the "irregular-insufficient" sleep group (hazard ratio [95% confidence interval]: 0.61 [0.45, 0.83]) after adjusting for socio-demographics, lifestyle, medical comorbidities and sleep disorders. In conclusion, a "regular-optimal" sleep pattern was significantly associated with a lower hazard of all-cause mortality. The regular-optimal phenotype maps behaviourally to regular bed and wake times, suggesting sleep benefits of adherence to recommended healthy sleep practices, with further potential benefits for longevity.

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.

Light exposure during sleep is bidirectionally associated with irregular sleep timing: The multi-ethnic study of atherosclerosis (MESA).

Exposure to light at night (LAN) may influence sleep timing and regularity. Here, we test whether greater light exposure during sleep (LEDS) is bidirectionally associated with greater irregularity in sleep onset timing in a large cohort of older adults in cross-sectional and short-term longitudinal (days) analyses. Light exposure and activity patterns, measured via wrist-worn actigraphy (ActiWatch Spectrum), were analyzed in 1933 participants with 6+ valid days of data in the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 5 Sleep Study. Summary measures of LEDS averaged across nights were evaluated in linear and logistic regression analyses to test the association with standard deviation (SD) in sleep onset timing (continuous variable) and irregular sleep onset timing (SD > 90 min, binary). Night-to-night associations between LEDS and absolute differences in nightly sleep onset timing were also evaluated with distributed lag non-linear models and mixed models. In between-individual linear and logistic models adjusted for demographic, health, and seasonal factors, every 5-lux unit increase in LEDS was associated with a 7.8-min increase in sleep onset SD (ß = 0.13 h, 95%CI:0.09-0.17) and 32% greater odds (OR = 1.32, 95%CI:1.17-1.50) of irregular sleep onset. In within-individual night-to-night mixed model analyses, every 5-lux unit increase in LEDS the night prior was associated with a 2.2-min greater deviation of sleep onset the next night (ß = 0.036 h, p < 0.05). Conversely, every 1-h increase in sleep deviation was associated with a 0.35-lux increase in future LEDS (ß = 0.348 lux, p < 0.05). LEDS was associated with greater irregularity in sleep onset in between-individual analyses and subsequent deviation in sleep timing in within-individual analyses, supporting a role for LEDS in irregular sleep onset timing. Greater deviation in sleep onset was also associated with greater future LEDS, suggesting a bidirectional relationship. Maintaining a dark sleeping environment and preventing LEDS may promote sleep regularity and following a regular sleep schedule may limit LEDS.

Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes. METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted ß=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05). CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

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.

Anaerobic pathogens associated with OSA may contribute to pathophysiology via amino-acid depletion.

BACKGROUND: The human microbiome is linked to multiple metabolic disorders such as obesity and diabetes. Obstructive sleep apnoea (OSA) is a common sleep disorder with several metabolic risk factors. We investigated the associations between the gut microbiome composition and function, and measures of OSA severity in participants from a prospective community-based cohort study: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). METHODS: Bacterial-Wide Association Analysis (BWAS) of gut microbiome measured via metagenomics with OSA measures was performed adjusting for clinical, lifestyle and co-morbidities. This was followed by functional analysis of the OSA-enriched bacteria. We utilized additional metabolomic and transcriptomic associations to suggest possible mechanisms explaining the microbiome effects on OSA. FINDINGS: Several uncommon anaerobic human pathogens were associated with OSA severity. These belong to the Lachnospira, Actinomyces, Kingella and Eubacterium genera. Functional analysis revealed enrichment in 49 processes including many anaerobic-related ones. Severe OSA was associated with the depletion of the amino acids glycine and glutamine in the blood, yet neither diet nor gene expression revealed any changes in the production or consumption of these amino acids. INTERPRETATION: We show anaerobic bacterial communities to be a novel component of OSA pathophysiology. These are established in the oxygen-poor environments characteristic of OSA. We hypothesize that these bacteria deplete certain amino acids required for normal human homeostasis and muscle tone, contributing to OSA phenotypes. Future work should test this hypothesis as well as consider diagnostics via anaerobic bacteria detection and possible interventions via antibiotics and amino-acid supplementation. FUNDING: Described in methods.

Light exposure during sleep is associated with irregular sleep timing: the Multi-Ethnic Study of Atherosclerosis (MESA).

Objective: Exposure to light at night (LAN) may influence sleep timing and regularity. Here, we test whether greater light exposure during sleep (LEDS) associates with greater irregularity in sleep onset timing in a large cohort of older adults. Methods: Light exposure and activity patterns, measured via wrist-worn actigraphy (ActiWatch Spectrum), were analyzed in 1,933 participants with 6+ valid days of data in the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 5 Sleep Study. Summary measures of LEDS averaged across nights were evaluated in linear and logistic regression analyses to test the association with standard deviation (SD) in sleep onset timing (continuous variable) and irregular sleep onset timing (SD≥1.36 hours, binary). Night-to-night associations between LEDS and absolute differences in nightly sleep onset timing were also evaluated with distributed lag non-linear models and mixed models. Results: In between-individual linear and logistic models adjusted for demographic, health, and seasonal factors, every 5-lux unit increase in LEDS was associated with an increase of 7.8 minutes in sleep onset SD (ß=0.13 hours, 95%CI:0.09-0.17) and 40% greater odds (OR=1.40, 95%CI:1.24-1.60) of irregular sleep onset. In within-individual night-to-night mixed model analyses, every 5-lux unit increase in LEDS the night prior (lag0) was associated with a 2.2-minute greater deviation of sleep onset the next night (ß=0.036 hours, p<0.05). Conversely, every 1-hour increase in sleep deviation (lag0) was associated with a 0.35-lux increase in future LEDS (ß=0.347 lux, p<0.05). Conclusion: LEDS was associated with greater irregularity in sleep onset in between-individual analyses and subsequent deviation in sleep timing in within-individual analyses, supporting a role for LEDS in exacerbating irregular sleep onset timing. Greater deviation in sleep onset was also associated with greater future LEDS, suggesting a bidirectional relationship. Maintaining a dark sleeping environment and preventing LEDS may promote sleep regularity and following a regular sleep schedule may limit LEDS.

Rest-activity rhythms across the lifespan: cross-sectional findings from the US representative National Health and Nutrition Examination Survey.

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.

A polygenic risk score for Alzheimer's disease constructed using APOE-region variants has stronger association than APOE alleles with mild cognitive impairment in Hispanic/Latino adults in the U.S.

INTRODUCTION: Polygenic Risk Scores (PRSs) are summaries of genetic risk alleles for an outcome. METHODS: We used summary statistics from five GWASs of AD to construct PRSs in 4,189 diverse Hispanics/Latinos (mean age 63 years) from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). We assessed the PRS associations with MCI in the combined set of people and in diverse subgroups, and when including and excluding the APOE gene region. We also assessed PRS associations with MCI in an independent dataset from the Mass General Brigham Biobank. RESULTS: A simple sum of 5 PRSs ("PRSsum"), each constructed based on a different AD GWAS, was associated with MCI (OR = 1.28, 95% CI [1.14, 1.41]) in a model adjusted for counts of the APOE-[Formula: see text] and APOE-[Formula: see text] alleles. Associations of single-GWAS PRSs were weaker. When removing SNPs from the APOE region from the PRSs, the association of PRSsum with MCI was weaker (OR = 1.17, 95% CI [1.04,1.31] with adjustment for APOE alleles). In all association analyses, APOE-[Formula: see text] and APOE-[Formula: see text] alleles were not associated with MCI. DISCUSSION: A sum of AD PRSs is associated with MCI in Hispanic/Latino older adults. Despite no association of APOE-[Formula: see text] and APOE-[Formula: see text] alleles with MCI, the association of the AD PRS with MCI is stronger when including the APOE region. Thus, APOE variants different than the classic APOE alleles may be important predictors of MCI in Hispanic/Latino adults.