Objective:

To construct a syndrome load index using confirmatory factor analysis and item response theory, assess measurement invariance, and estimate adjusted intervention–analog associations with lipid and C-reactive protein biomarkers.

Approach:

• Statistical Methods: Used confirmatory factor analysis (CFA) and item response theory (IRT) to model syndrome load, and employed Gaussian generalized propensity score with stabilized inverse probability of treatment weighting and double machine learning cross-fitting for estimating associations.

Key Findings:

• CFA/IRT models showed good fit (CFI = 0.96, RMSEA = 0.042) and supported metric/scalar invariance across sex and age groups.
• A 1-SD higher syndrome load was associated with increased TG z-scores (+0.18; 95% CI: 0.12–0.24), LDL-C z-scores (+0.11; 95% CI: 0.05–0.18), and CRP z-scores (+0.16; 95% CI: 0.09–0.23).
• Associations were stronger among participants aged ≥65 years, those with diabetes, or those with BMI ≥ 28.

Interpretation:

Higher syndrome load was consistently associated with dyslipidemia and CRP elevation.

Limitations:

• The study is retrospective and conducted at a single center, which may limit generalizability.
• Further prospective intervention studies are needed to determine if the latent syndrome construct is directly modifiable.

Conclusion:

Findings support the potential for syndrome load to inform cardiometabolic risk stratification.