Biomedical subjects

Susan Redline

Publications and source records attributed to Susan Redline.

8 recordsLinked to original sources

PubMed2026

Obesity Polygenic Risk and Healthy Lifestyle Interactions on Weight Trajectories in Women and Men.

BACKGROUND: Genetics and environmental factors contribute to obesity risk, but the extent to which healthy behaviors can offset genetic susceptibility remains unclear. We examined the interaction between obesity polygenic risk and a composite healthy lifestyle score on body mass index (BMI) trajectories in women and men. METHODS: We analyzed 13 780 women from the Nurses' Health Study and 8242 men from the Health Professionals Follow-Up Study, all of European ancestry and free of major chronic disease at baseline. The lifestyle score comprised American Heart Association Essential 8 components (nonsmoking, physical activity, healthy eating, adequate sleep) plus moderate alcohol intake, modeled as a time-varying variable. A genome-wide polygenic score for BMI was derived from genome-wide association study. Adjusted linear mixed-effects models estimated associations and interactions on biennial BMI measures over up to 26 years. RESULTS: Each SD increase in the polygenic score was associated with 1.80 kg/m2 (95% CI, 1.72-1.87) and 1.12 kg/m2 (95% CI, 1.06-1.19) higher BMI in women and men, respectively. Significant interactions between the polygenic score and healthy lifestyle score (both P<0.05) showed a dose-response attenuation of the genetic effects with healthier lifestyles. Comparing the healthiest with the least healthy lifestyle groups, genetic effects on BMI were 35% lower in women and 28% lower in men. In sensitivity analyses, higher diet quality and physical activity consistently attenuated genetic associations in both cohorts, whereas current smoking showed similar effects in women only. CONCLUSIONS: Adherence to a healthier lifestyle attenuated the association between obesity polygenic risk and BMI in a dose-response manner.

Humans↗

PubMed2025

Sleep-disordered breathing subtypes and future diet quality in the Multi-Ethnic Study of Atherosclerosis.

OBJECTIVES: Sleep-disordered breathing (SDB) and diet quality impact cardiometabolic disease, but few studies have examined if SDB influences diet quality. This study estimated the association between SDB subtypes (with and without sleepiness) and future diet quality in the Multi-Ethnic Study of Atherosclerosis. METHODS: Probable SDB was characterized by self-reported physician-diagnosed sleep apnea (PDSA) or habitual snoring and subtyped by presence or absence of sleepiness. A food frequency questionnaire measured diet 1.6 years before, and 7.8 years after SDB assessment. Diet quality was measured with the Alternate Healthy Eating Index-2010 (AHEI). Mean differences in AHEI at follow-up by SDB subtypes were estimated with multivariable linear regression adjusting for baseline AHEI, demographic, and lifestyle factors. RESULTS: Among 3294 participants (mean age 62 years, 51% women), 29.5% had SDB. When grouped by sleepiness, 20.6% had SDB without, and 8.9% had SDB with, sleepiness. Adjusting for baseline diet and potential confounders, those with SDB had lower follow-up AHEI scores compared with unaffected individuals (mean AHEI difference [95% CI]: -1.02 [-1.69, -0.35]). Upon stratifying by sleepiness, both groups had lower AHEI scores at follow-up compared with unaffected individuals, and the difference was greater for those with sleepiness (mean score difference [95% CI]: -0.8 [-1.56, -0.04], without sleepiness; -1.52 [-2.59, -0.45], with sleepiness). The difference between those with and without sleepiness was not statistically significant. CONCLUSIONS: In a multi-ethnic cohort, SDB was associated with lower diet quality after 7.8 years and this association was larger among participants with SDB with sleepiness.

Humans↗

PubMed2025

Whole genome sequence analysis of low-density lipoprotein cholesterol across 246 K individuals.

BACKGROUND: Rare genetic variation provided by whole genome sequence datasets has been relatively less explored for its contributions to human traits. Meta-analysis of sequencing data offers advantages by integrating larger sample sizes from diverse cohorts, thereby increasing the likelihood of discovering novel insights into complex traits. Furthermore, emerging methods in genome-wide rare variant association testing further improve power and interpretability. RESULTS: Here, we conduct the largest meta-analysis of whole genome sequencing for low-density lipoprotein cholesterol (LDL-C), a therapeutic target for coronary artery disease, analyzing data from 246 K participants and integrating 1.23B variants from the UK Biobank and the Trans-Omics for Precision Medicine (TOPMed) program. We identify numerous rare coding and non-coding gene associations related to LDL-C, with replication across 86 K participants in All of Us. Our findings are based on single-variant analyses, rare coding and non-coding variant aggregation tests, and sliding window approaches. Through this comprehensive analysis, we identify 704 novel single-variant associations, 25 novel rare coding variant aggregates, 28 novel rare non-coding variant aggregates, and one novel sliding window aggregate. CONCLUSIONS: This study provides a meta-analysis framework for large-scale whole genome sequence association analyses from diverse population groups, yielding novel rare non-coding variant associations.

Humans↗

PubMed2025

Steroid hormone biosynthesis and dietary related metabolites associated with excessive daytime sleepiness.

BACKGROUND: Excessive daytime sleepiness (EDS) is a complex sleep problem that affects approximately 33% of the United States population. Although EDS usually occurs in conjunction with insufficient sleep and other sleep and circadian disorders, recent studies have shown unique genetic markers and metabolic pathways underlying EDS. Here, we aimed to further elucidate the biological profile of EDS using large-scale single- and pathway-level metabolomics analyses. METHODS: Metabolomics data were available for 877 metabolites in 6071 individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). EDS was assessed using the Epworth Sleepiness Scale (ESS) questionnaire. We performed linear regression for each metabolite on the continuous ESS score, adjusting for demographic, lifestyle, and physiological confounders, and in sex specific groups. Subsequently, gaussian graphical modelling was performed coupled with pathway and enrichment analyses to generate a holistic interactive network of the metabolomic profile of EDS associations. FINDINGS: We identified seven metabolites belonging to steroids, sphingomyelin, and long-chain fatty acids sub-pathways in the primary model associated with EDS, and an additional three metabolites in the male-specific analysis. INTERPRETATION: Our findings indicate that an EDS metabolomic profile is characterised by endogenous and dietary metabolites within the steroid hormone biosynthesis pathway, with some pathways that differ by sex. These pathways may be useful for understanding the causes or consequences of EDS and related sleep disorders. FUNDING: Details regarding funding supporting this work and all studies involved are provided in the acknowledgements section.

Humans↗

PubMed2025

Polygenic scores for obstructive sleep apnoea reveal pathways contributing to cardiovascular disease.

BACKGROUND: Obstructive sleep apnoea (OSA) is a common chronic condition, with obesity its strongest risk factor. Polygenic scores (PGSs) summarise the genetic liability to phenotype and can provide insights into relationships between phenotypes. Recently, large datasets that include genetic data and OSA status became available, providing an opportunity to utilise PGS approaches to study the genetic relationship between OSA and other phenotypes, while differentiating OSA-specific from obesity-specific genetic factors. METHODS: Using race/ethnic diverse samples from over 1.2 million individuals from the Million Veteran Program, FinnGen, TOPMed, All of Us (AoU), Geisinger's MyCode, MGB Biobank, and the Human Phenotype Project, we developed and assessed PGSs for OSA, both without (BMIunadjOSA-PGS) and with adjustment for the genetic contributions of BMI (BMIadjOSA-PGS). FINDINGS: Adjusted odds ratios (ORs) for OSA per 1 standard deviation of the PGSs ranged from 1.38 to 2.75. The associations of BMIadjOSA- and BMIunadjOSA-PGSs with CVD outcomes in AoU shared both common and distinct patterns. Only BMIunadjOSA-PGS was associated with type 2 diabetes, heart failure, and coronary artery disease, while both BMIadjOSA- and BMIunadjOSA-PGSs were associated with hypertension and stroke. Sex stratified analyses revealed that BMIadjOSA-PGS association with hypertension was driven by females (OR = 1.1, p-value = 0.002, OR = 1.01 p-value = 0.2 in males). OSA PGSs were also associated with body fat measures with some sex-specific associations. INTERPRETATION: Distinct components of OSA genetic risk are related and independent of obesity. Sex-specific associations with body fat distribution measures may explain differing OSA risks and associations with cardiometabolic morbidities between sexes. FUNDING: R01AG080598.

Humans↗

PubMed2025

Whole genome sequence-based association analysis of African American individuals with bipolar disorder and schizophrenia.

In studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole genome sequencing of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls. To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of BD association with single-variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD GWAS loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

Journal Article↗

PubMed2024

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup.

Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one's global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

Journal Article↗

PubMed2024

Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height.

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare ( < 0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal-regulatory, intergenic-regulatory and deep-intronic annotation. We observed 29 independent variants associated with height at P < after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7 cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5 cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed an approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

Humans↗