Unveiling sex-specific cardiometabolic and adiposity risk profiles for precision prevention.

Men displayed a less favorable overall cardiometabolic profile compared to women across multiple risk markers.

• Cross-sectional study conducted on 1715 individuals (929 females / 786 males; mean age 58.1 ± 13.5 years)
• Clinical and biochemical variables were compared by sex, including markers of metabolic, atherogenic cardiovascular, and renal risk
• Sex differences were evident with men showing worse cardiometabolic profiles
• All parameters were measured using certified and standardized methods to ensure reproducibility

In females, the principal variance in cardiometabolic risk was mainly explained by triglyceride-related indices, adiposity measures, and blood pressure.

• PCA was applied to explore underlying patterns of association across the full cohort and stratified by sex
• Female-specific principal components were driven by triglycerides (TG), TG/HDL ratio, and atherogenic index of plasma (AIP)
• Adiposity measures and blood pressure also contributed substantially to variance in females
• These patterns suggest triglyceride metabolism is a dominant axis of cardiometabolic risk in women

In males, lipid and atherogenic markers predominated as the primary drivers of cardiometabolic variance, with adiposity and blood pressure forming separate clusters.

• Male-specific principal components were driven by total cholesterol (TC), LDL, Castelli Risk Index (CRI) indices, non-HDL cholesterol, and lipid composite index (LCI)
• Adiposity and blood pressure formed separate, distinct clusters in males
• This contrasts with females, where triglyceride-related indices were the leading contributors
• Adiposity and blood pressure formed separate clusters in both sexes

PCA identified coherent clusters of cardiometabolic variables: lipid/atherogenic markers, glycemic and insulin-resistance indices, adiposity measures, and renal function as a distinct domain.

• PCA revealed groupings that univariate analyses did not fully capture
• Lipid/atherogenic markers formed one cluster; glycemic and insulin-resistance indices formed a second cluster; adiposity measures formed a third
• Renal function emerged as a distinct, separate domain from the other cardiometabolic clusters
• These coherent clusters suggest largely independent effects among different cardiometabolic risk domains

Correlation analyses revealed significant associations only between insulin resistance markers and atherogenic cardiovascular risk indices, suggesting largely independent effects of other parameters.

• Univariate correlation analyses were performed across the full set of clinical and biochemical variables
• Significant correlations were confined to the relationship between insulin resistance markers and atherogenic cardiovascular risk indices
• Other parameter pairs showed weak or non-significant associations, indicating independence across risk domains
• This finding was superseded in interpretive richness by PCA, which identified broader clustering patterns

Lifestyle variables including smoking, alcohol consumption, and fatigue modulated cardiometabolic risk, whereas family history of diabetes and hypertension showed weak associations.

• Smoking, alcohol consumption, and fatigue were among the lifestyle variables assessed
• These lifestyle factors demonstrated meaningful modulation of cardiometabolic risk profiles
• Family history of diabetes and family history of hypertension showed only weak associations with cardiometabolic risk
• Lifestyle variables were incorporated into the PCA and univariate analytical frameworks

Cardiometabolic risk is a multidimensional construct shaped by distinct yet overlapping biological and behavioral domains, supporting the need for sex-specific prevention strategies.

• The study combined univariate analyses and multivariate PCA to characterize risk structure
• Biological domains (lipid, glycemic/insulin resistance, adiposity, renal) and behavioral domains (lifestyle) were identified as contributors
• Sex-specific differences in the structure of risk suggest that prevention strategies should be individually tailored by sex
• The study protocol was registered in ClinicalTrials.gov (ID: NCT0642756)