Identifying Critical Age Periods for the Prevention of Metabolic Complications in Obesity: An Integrative Analysis of Body Composition, Biochemical Profiles and Nutritional Recommendations in 29,544 Adults

Background/Objectives: Evidence-based nutritional recommendations for obesity management require understanding of sex-specific and age-specific body composition patterns and their associations with metabolic biomarkers, habitual dietary intake and chronic low-grade inflammation. This study aimed to characterize body composition phenotypes in a large clinical cohort of adults with obesity, to evaluate associated metabolic and inflammatory biomarker patterns, to contextualise these patterns against habitual nutrient intake assessed in a dietary subcohort, and to derive age- and sex-specific nutritional recommendations based on the identified patterns. Methods: We performed a cross-sectional analysis of 29,544 adults with obesity (BMI ≥ 30; 21,374 women, 8170 men; age 30–69) who underwent multi-frequency bioelectrical impedance analysis (BIA; InBody 770). Biochemical assessments (fasting glucose, lipid profile, uric acid, HbA1c, insulin) were available for 2019 hospitalized patients from the same population. Habitual dietary intake was quantitatively assessed in a dietary subcohort of 423 patients using the validated Russian software-based questionnaire “Scientific Nutrition Analysis Tool”. Inflammatory biomarkers (high-sensitivity CRP, IL-6) and adipokines (leptin, adiponectin) together with serum 25(OH)D were measured in an inflammation/adipokine subcohort of 116 patients. A body composition phenotype with low relative muscle mass and high visceral fat (VFA ≥ 100 cm2) was defined using FNIH criteria (ALM/BMI < 0.789 men, <0.512 women). Benjamini–Hochberg FDR correction (q < 0.05) was applied for multiple comparisons. Results: The body composition phenotype prevalence increased progressively with age: men 24.6% (30–39) to 42.0% (60–69); women 10.3% (30–39) to 31.8% (60–69). Skeletal muscle mass (SMM) was positively associated with uric acid (r = +0.347, p < 0.001, FDR q < 0.05) and inversely associated with HDL-cholesterol (r = −0.321, p < 0.001, FDR q < 0.05)—both associations with direct nutritional implications. BMI was associated with fasting insulin (r = +0.233, p < 0.001, FDR q < 0.05). Women showed significant age-related metabolic differences between the 30–39 and 60–69 age groups: fasting glucose +12.9%, triglycerides +34.8%, uric acid +15.0% (all p < 0.001); in men, significant differences were observed for fasting glucose (+7.0%) and HbA1c (+5.2%) (both p < 0.001), while lipid parameters did not reach significance. In the dietary subcohort, habitual saturated-fat intake exceeded recommended values in 70–72% of patients of both sexes, dietary fibre intake was below recommended levels in 73–85%, and habitual calcium intake decreased significantly with age in women (1022 → 746 mg/day, p = 0.028). Serum CRP was elevated (median 5.59 mg/L, n = 59). In a separate extended laboratory subcohort, serum oestradiol declined markedly with age in women (55.0 → 16.8 pmol/L between 30–39 and 50–59 years, p < 0.001), consistent with the menopausal transition; serum testosterone in men remained stable across age groups; and 25(OH)D insufficiency (<30 ng/mL) was prevalent in 49.7–55.8% of patients. Conclusions: The identified sex-specific and age-specific body composition patterns provide a rationale, supported by observed dietary and inflammatory patterns, for targeted nutritional intervention: increased dietary protein, omega-3 fatty acids supplementation, low-glycemic-index dietary patterns, and purine restriction with hyperuricemia. Routine BIA-based nutritional screening combined with quantitative dietary assessment should begin at age 30, with preventive monitoring at age 40 and intensification of control at age 50, to guide personalized dietary planning in obesity.