Cardiometabolic Phenotypes and Dietary Patterns in Albanian University-Enrolled Young Adults: Cross-Sectional...

Background: Albania is undergoing rapid nutrition transition, yet cardiometabolic (CM) risk in young adults is poorly characterized. We report baseline, cross-sectional findings from a WHO-aligned sentinel study examining diet, physical activity and early CM phenotypes, with fat quality examined as a modifiable exposure. Methods: Young adults recruited on campus (n = 262; median age, 21 years; 172 women, 90 men) underwent standardized anthropometry, seated blood pressure (BP) and fasting glucose (FG). Diet was assessed by two interviewer-administered 24 h recalls and activity outlined by the IPAQ-short form. We derived potential renal acid load (PRAL) and a MASLD-oriented nutrient score, computed a composite CM risk score (cCMRS: sex-standardized mean of WHtR, mean arterial pressure, FG) and fitted prespecified energy-partition models for isocaloric +5% of energy substitutions (SFA → PUFA; SFA → MUFA) with Benjamini-Hochberg false discovery rate (FDR) control. Results: Despite normal average BMI (23.4), risk clustering was common: elevated BP in 63% of men and 30% of women, impaired FG (100-125 mg/dL) in almost one third and central adiposity (WHtR ≥ 0.5) in 51% of men and 24% of women. Diets were SFA-rich (~17-19%E), sodium-dense and low in fiber and several micronutrients (e.g., vitamin D, folate, potassium). In isocaloric models, SFA → PUFA was associated with more favorable nutrient signatures: MASLD-oriented score -28% (p < 0.001; FDR-significant) and PRAL -33% (p = 0.007; FDR-borderline/suggestive). Conclusions: A waist-centric CM subphenotype-central adiposity co-occurring with upward BP shifts and intermittent dysglycemia-was detectable in young adults despite normal average BMI, against a background of poor diet quality and low activity. These baseline surveillance signals are not causal effects. Integration into routine with WHO-aligned NCD surveillance is feasible. Prospective follow-up (biomarker calibration, device-based activity, repeated waves) will refine inferences and inform scalable proactive prevention.