Diagnostic value of anthropometric indicators and body composition as indicators of aging rates and comorbidity in patients with cardiometabolic diseases

Abstract

Objective — to assess anthropometric indicators and body composition in patients with certain cardiometabolic diseases depending on their aging rates and the number of existing comorbid pathologies.
 Materials and methods. Examinations involved 92 patients with cardiometabolic diseases, including arterial hypertension (AH), non‑alcoholic fatty liver disease, obesity, dyslipidemia, insulin resistance. Female prevailed among patients (62.0%), the mean age was 49.2 [41.4; 55.4] years. All patients were divided into groups depending on calendar age (CA): young <45 years (n=32), middle age 45—59 years (n=49), elderly >60 years (n=11), and also divided by the rates of aging: with accelerated age delta (Δ) >0 years (n=14), with significantly slowed rates of ageing — Δ ≤–5 years (n=51) and intermediate ones — Δ ∈ (–5; 0] years (n=27). Anthropometric parameters and calculated indices were determined in all patients and included body mass index (BMI), waist circumference (WC), hip circumference (HC), WC/HC ratio, percentage of total fat (TF), percentage of skeletal muscle mass (SM), visceral fat (VF), weight‑adjusted waist index (WWI), a body shape index (ABSI) and abdominal volume index (AVI). As additional indicators of aging, the level of uric acid, the total mortality risk during the next 10 years (MR) and the level of global methylation (GM) of DNA were determined.
 Results. Significant differences between all groups divided according to CA were in BA and MR (p=0.0001 in all cases). Patients <45 years of age compared to the elderly had significantly higher rates of aging (p=0.019) and significantly lower levels of BMI (p=0.039), WC (p=0.034), WWI (p=0.002) and AVI (p=0.032). WWI was the only marker that significantly increased in the middle‑aged group of patients (p=0.033) compared to young patients. Patients re‑divided into groups according to  did not differ in the levels of anthropometric indicators and body composition. However, patients with significantly slower rates of aging had significantly lower uric acid levels compared to the group with accelerated (p=0.0001) and intermediate rates of aging (p=0.024), and also had higher CA compared to patients in the intermediate group (p=0.026). Various indicators of aging were significantly associated with weight, BMI, VF, WC, WC/HC, WWI and AVI, but only WC and AVI were simultaneously associated with all markers of aging (uric acid, BA, MR, GM). Only WWI was associated with CA, in addition it had the strongest association with BA and MR compared to other indicators. Patients with 4—5 comorbid pathologies had worse weight (p=0.045), BMI (p=0.0001), WC (p=0.002), WC/HC (p=0.002), TF (p=0.004), SM (p=0.010), WWI (p=0.006), AVI (p=0.002), WWI (p=0.040) and aging rates (p=0.008).
 Conclusions. Among anthropometric and body composition parameters in patients with cardiometabolic pathology, WWI is probably the most effective one for monitoring premature aging, as it is associated with both CA, markers of aging and cardiometabolic parameters. The higher calendar age among patients with cardiometabolic pathologies was accompanied by higher values of BMI, WC, WWI and AVI, so control of these indicators may be useful for prevention of premature aging. Assessment of weight, BMI, WC, WC/HC, TF, SM, WWI and AVI is effective for predicting aging rates among patients with multimorbidity.