Impact of structured cardiometabolic intervention in a high-risk cardiovascular population
results of a pilot study
DOI:
https://doi.org/10.37951/2675-5009.2026v7i19.193Keywords:
Cholesterol, Cardiometabolic risk factors, Treatment adherence and compliance, Obesity, Weight lossAbstract
Introduction: Cardiometabolic risk is associated with the likelihood of cardiovascular system damage when multiple factors occur concurrently. These include visceral obesity, dyslipidemia, hypertension, insulin resistance, and inflammation—aspects that comprise the pathophysiology of cardiometabolic diseases. Pharmacological and non-pharmacological interventions seek to control these factors and improve clinical outcomes. Objective: This study aims to demonstrate the effect of a multidisciplinary care intervention focused on adjusting cardiometabolic risk factors in a population with a high cardiovascular risk profile. Methods: This is a pilot cohort study conducted in a specialized outpatient clinic (cardiologist, endocrinologist, and nutritionist). Individuals underwent a systematic approach involving laboratory tests, cardiovascular risk imaging, and bioimpedance for body composition analysis. Dietary patterns, metabolic activity, and preexisting diseases were established. The protocol included a diet (caloric restriction), protein supplementation, resistance training, and gradual pharmacological treatment. Following the cardiometabolic diagnosis, an intervention plan was devised with structured feedback and follow-ups every 90 days via an app and direct contact with the team. Monitoring lasted six months, with constant reevaluations and adjustments. Statistical analysis was performed using Student’s t-test p≤0.05), with data expressed as mean ± standard deviation. Results: Twenty-seven individuals were followed (age: 54.2±8.2 years, 59.3% male, weight: 100.0±15.4 kg). The initial prevalence of risk factors was: coronary artery disease: 51.9%, diabetes: 33.3%, hypertension: 14.8%, dyslipidemia: 88.9%. 92.6% of the sample was specific to body weight, with a pre-test BMI of 34.9±4.3 kg/cm² vs. post-test 31.1±7.6 kg/cm² (p: 0.03). Bioimpedance analysis showed a decrease in fat mass (pre: 39.6±6.7% vs post 35.8±6.4%, p: 0.05) and maintenance of lean mass (pre: 34.0±4.5% vs post 36.1±4.0%, p: 0.10). LDL cholesterol decreased (pre: 96.3±49.4 mg/dL vs post 65.6±39.9 mg/dL, p: 0.03). The same occurred with triglycerides (pre: 199.3±139.0 mg/dL vs post 111.9±69.7 mg/dL, p: 0.01). Fasting glucose, glycated hemoglobin (HbA1c), high-density lipoprotein (HDL), and abdominal diseases did not show significant changes. Statins were used in 77.8% of cases and GLP-1 agonists in 40.7%. Conclusion: Preliminary data suggest that a care program focused on cardiometabolism has high potential to alter risk-associated parameters. Longer follow-up is necessary to determine longterm adherence and the reduction of clinical events.
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