AI Insight
Researchers developed plasma protein-based scores to predict body mass index (BMI) using data from over 50,000 participants across European, East Asian, South Asian, and African ancestries. These proteomic scores explained up to 48.7% of BMI variance, worked consistently across different ancestral groups and measurement platforms, and successfully predicted future obesity development. The analysis identified individuals with normal measured BMI but elevated proteomic BMI scores who exhibited metabolically unhealthy characteristics including higher visceral fat, elevated triglycerides, and reduced insulin sensitivity.
Why it matters
This approach could improve identification of cardiometabolic disease risk in individuals who appear healthy based on conventional BMI measurements alone, potentially enabling earlier intervention. The cross-ancestry validation suggests these proteomic tools could be applied equitably across diverse global populations, addressing a common limitation in precision medicine research.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Plasma proteomic scores for body mass index have been derived primarily in European populations, and it is unclear whether these scores are generalizable and useful in other ancestries. Here, we show that plasma proteomic BMI scores developed in participants of European, East Asian, South Asian and African ancestries from the UK Biobank (UKBB; N=50,621) and the South African Middle-Aged Soweto Cohort (MASC; N=948) are portable across ancestries, explain up to 48.7% of trait variance and significantly predict incident obesity. We identified eight protein biomarkers common across all scores, six of which showed causal associations with BMI in Mendelian randomization analyses and were associated with BMI across the Olink (UKBB, MASC) and SomaScan platforms (Qatar Biobank; N= 2410). Discrepancy analysis between the high predicted proteomic BMI and low measured BMI revealed metabolically unhealthy normal weight (MUNW) individuals, with higher visceral fat, elevated triglycerides, and low insulin sensitivity. Thus, plasma proteomic BMI scores may enhance the precision of cardiometabolic risk stratification across diverse populations