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Abstract 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- $$\epsilon 2$$ ϵ 2 and APOE- $$\epsilon 4$$ ϵ 4 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- $$\epsilon 4$$ ϵ 4 and APOE- $$\epsilon 2$$ ϵ 2 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- $$\epsilon 4$$ ϵ 4 and APOE- $$\epsilon 2$$ ϵ 2 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.

Abstract 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.

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 (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 β=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.

Polygenic risk scores have potential to predict an individual’s risk of disease based on genetic markers. Here, the authors develop a polygenic risk score for hypertension and test it in a multi-ethnic cohort, finding that the score is associated with higher likelihood of hypertension development 4-6 years later.

Abstract Obstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Iron and heme metabolism, implicated in ventilatory control and OSA comorbidities, was associated with OSA phenotypes in recent admixture mapping and gene enrichment analyses. However, its causal contribution was unclear. In this study, we performed pathway-level transcriptional Mendelian randomization (MR) analysis to investigate the causal relationships between iron and heme related pathways and OSA. In primary analysis, we examined the expression level of four iron/heme Reactome pathways as exposures and four OSA traits as outcomes using cross-tissue cis-eQTLs from the Genotype-Tissue Expression portal and published genome-wide summary statistics of OSA. We identify a significant putative causal association between up-regulated heme biosynthesis pathway with higher sleep time percentage of hypoxemia (p = 6.14 × 10–3). This association is supported by consistency of point estimates in one-sample MR in the Multi-Ethnic Study of Atherosclerosis using high coverage DNA and RNA sequencing data generated by the Trans-Omics for Precision Medicine project. Secondary analysis for 37 additional iron/heme Gene Ontology pathways did not reveal any significant causal associations. This study suggests a causal association between increased heme biosynthesis and OSA severity.

Thirty-eight individuals (mean age: 34.8 years old) participating in a 3-month yoga and meditation retreat were assessed before and after the intervention for psychometric measures, brain derived neurotrophic factor (BDNF), circadian salivary cortisol levels, and pro- and anti-inflammatory cytokines. Participation in the retreat was found to be associated with decreases in self-reported anxiety and depression as well as increases in mindfulness. As hypothesized, increases in the plasma levels of BDNF and increases in the magnitude of the cortisol awakening response (CAR) were also observed. The normalized change in BDNF levels was inversely correlated with BSI-18 anxiety scores at both the pre-retreat (r = 0.40, p < 0.05) and post-retreat (r = 0.52, p < 0.005) such that those with greater anxiety scores tended to exhibit smaller pre- to post-retreat increases in plasma BDNF levels. In line with a hypothesized decrease in inflammatory processes resulting from the yoga and meditation practices, we found that the plasma level of the anti-inflammatory cytokine Interleukin-10 was increased and the pro-inflammatory cytokine Interleukin-12 was reduced after the retreat. Contrary to our initial hypotheses, plasma levels of other pro-inflammatory cytokines, including Interferon Gamma (IFN-γ), Tumor Necrosis Factor (TNF-α), Interleukin-1β (IL-1β), Interleukin-6 (IL-6), and Interleukin-8 (IL-8) were increased after the retreat. Given evidence from previous studies of the positive effects of meditative practices on mental fitness, autonomic homeostasis and inflammatory status, we hypothesize that these findings are related to the meditative practices throughout the retreat; however, some of the observed changes may also be related to other aspects of the retreat such as physical exercise-related components of the yoga practice and diet. We hypothesize that the patterns of change observed here reflect mind-body integration and well-being. The increased BDNF levels observed is a potential mediator between meditative practices and brain health, the increased CAR is likely a reflection of increased dynamic physiological arousal, and the relationship of the dual enhancement of pro- and anti-inflammatory cytokine changes to healthy immunologic functioning is discussed.

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.