Browsing by Author "Faúndez, Cristian"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Lipidomic Signature of Healthy Diet Adherence and Its Association with Cardiometabolic Risk in American Adults
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Berkowitz, Loni; Echeverría, Guadalupe; Salazar, Cristian; Faúndez, Cristian; Coe, Christopher L.; Ryff, Carol; Rigotti, Attilio
    The aim of this study was to identify the blood lipidomic profile associated with a healthy eating pattern in a middle-aged US population sample and to determine its relationship with metabolic disorders and cardiovascular risk (CVR). Self-reported information about diet and blood samples were obtained from 2114 adult participants in the Midlife in the United States study (MIDUS). Food intake data were used to design a Healthy Diet Index (MIDUS-HEI) and to evaluate the predictive value by examining its association with health variables. The associated lipid signature (HEI-LS) was constructed using Lasso regression, from lipidomic data (LC/MS). Associations between HEI-LS, cardiometabolic biomarkers, and estimated CVR were assessed using multiple linear regression. MIDUS-HEI score was a robust indicator of dietary quality and inversely associated with body mass index (p < 0.001) and metabolic syndrome (p = 0.012). A lipidomic signature comprising 57 distinct lipid species was highly correlated with the MIDUS-HEI score (r = 0.39, p < 10⁻16). It was characterized by lower levels of saturated fatty acid and adrenic acid (n-6) and higher levels of docosahexaenoic acid (n-3). Healthier HEI-LS scores were strongly associated with better cardiometabolic indicators and lower estimated CVR (OR 0.89 CI 95% 0.87–0.91). The MIDUS-HEI effectively assessed dietary quality, confirming the link between poor diet quality and metabolic disorders in American population. Lipidomic profiling offered an objective assessment of dietary patterns and provided insights into the relationship between diet quality, metabolic responses, and CVR. This approach supports precision nutrition strategies for at-risk populations.