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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Melissa Lamar

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Open access Jul 2026

Social vulnerability index and inflammation: a proteomic analysis linking social context to biological risk in older black adults.

BACKGROUND Inflammation is a key driver of age-related disease and has been associated with social conditions. We examined how cumulative community-level social and structural disadvantage is associated with inflammatory proteomic profiles in older Black adults. METHODS We employed data from the Minority Aging Research Study and the Rush Clinical Core, including the Social Vulnerability Index (SVI; global score and four domains) and 92 plasma inflammatory proteins (Olink® Target-96 Inflammation). Cross-sectional associations between each SVI metric and inflammatory proteins (principal component (PC)-derived global proteomic profile and protein-specific) were assessed using multivariable linear models (demographic, behavioral, and individual-level socioeconomic factors adjusted), with additional sex-by-SVI interaction terms. In a secondary analysis, we replaced SVI with the Index of Concentration at the Extremes (ICE) for household income (ICEincome). RESULTS A total of 580 participants (mean (SD) age of 74.9 (6.50) years; 79.7% women) had global SVI and proteomics assessed. Lower household composition (SVIHHC) was associated with the primary global proteomic profile, represented by the 1st proteomic PC (beta = -0.429, p-value = 0.019). A secondary exploratory analysis using the first five proteomic PCs showed that higher minority status/language (SVIMSL) and socioeconomic status (SVISES) were associated with an inflammatory proteomic profile (SVIMSL-PC3: beta=0.160, p-value = 0.048; SVISES-PC2: beta = 0.286, p-value = 0.012). In protein-specific analyses, no SVI-protein associations were found. We found an SVIHHC-by-sex interaction for interleukin-10 receptor alpha (IL-10RA; beta = -0.258, p-value = 5.01×10-4), and among men, SVIMSL was associated with Sirtuin 2 (beta = -0.397, p-value = 8.51×10-04) and STAM binding protein (beta = -0.304, p-value = 9.87×10-04). ICEincome was inversely associated with the global proteomic profile (beta = -0.233, p-value = 0.051), and an ICEincome-by-sex interaction was found for IL-10RA (beta=0.279, p-value = 4.66×10-04). CONCLUSIONS By analyzing associations between community-level factors and inflammation-related proteins, our study provides new molecular insights into how social context may relate to biological risk, identifies proteomic patterns that could inform the development of community-level interventions, and underscores the utility of integrating multi-omics approaches to investigate biological pathways relevant to health disparities research.

Anat Yaskolka Meir, H. Adeola, S. Tasaki et al. · 0 citations
Open access Jul 2026

Using Polygenic Risk Scores to Evaluate Definitions of Self-Reported Sleep Phenotypes Across Cohorts.

STUDY OBJECTIVES Since genome-wide association studies (GWAS) of sleep phenotypes have been conducted in differing populations and definitions of sleep phenotypes vary across studies, we investigated associations between several polygenic risk scores (PRSs) and potential sleep definitions among multiethnic cohorts. METHODS Using data from four cohorts (HCHS/SOL, ARIC, MESA, BHS, N = 16 895), we considered multiple definitions of short and long sleep, insomnia, and excessive daytime sleepiness (EDS). PRSs were developed based on summary statistics from GWAS in European ancestry individuals from the UK Biobank (UKB) and from GWAS conducted in a multiethnic population from the Million Veteran Program (MVP). Study-specific analyses estimated associations between sleep PRSs and corresponding sleep measures per 1 standard deviation increase in the PRS. Models were adjusted for age, sex, ancestral principal components, and center and race as appropriate. Results were meta-analyzed across studies. RESULTS PRSs based on European ancestry UKB GWAS had statistically significant associations with multiple definitions of the corresponding sleep phenotypes. Associations that were most consistent across studies included: short sleep PRS with ≤6 hours (OR = 1.23,p = 1.80x10-7,phet = 0.97); long sleep PRS with ≥9 hours (OR = 1.09,p = 6.76x10-4,phet = 0.77); insomnia PRS with the Women's Health Initiative Insomnia Rating Scale (WHIIRS) ≥10 or a subset of three questions ≥6 in ARIC (OR = 1.17,p = 5.51x10-10,phet = 0.69); and EDS PRS with Epworth Sleepiness Scale (ESS) ≥11 (OR = 1.23,p = 1.83x10-13,phet = 0.83). PRSs based on multi-ancestry MVP GWAS had weaker associations compared to those based on European ancestry only. CONCLUSIONS By evaluating several types of sleep PRSs and sleep phenotypes, we were able to highlight which sleep PRS performed well across diverse populations and which sleep definitions better captured genetic underpinnings.

A. Wyss, Michael Brown, Xiang Li et al. · 0 citations