Rendimiento diagnóstico de once indicadores para resistencia a la insulina en una muestra de pobladores peruanos

Introducción: La resistencia a la insulina (RI) es una de las principales causas del desarrollo de patologías crónicas. Es indispensable su detección temprana, por ello es importante estudiar métodos más asequibles y menos costosos como los biomarcadores. Objetivo: Determinar la precisión diagnóstica de once biomarcadores para RI en una muestra de pobladores peruanos. Metodología: Estudio de pruebas diagnósticas. Análisis de base de datos secundario del estudio PERU MIGRANT. Para medir RI se utilizó como referencia la evaluación del modelo homeostático (HOMA-IR) ≥ 2,8. Los biomarcadores se basaron en la ratio de lípidos, los indicadores de lípido visceral, los indicadores con triglicéridos y glucosa (TyG), y los indicadores con cintura abdominal. Para la precisión se utilizó el análisis de la curva de características operativas del receptor y el área bajo la curva (AUC) con sus respectivos intervalos de confianza al 95% (IC95%). Resultados: Se estudió a 938 participantes. La prevalencia de RI fue del 9,91%. En relación con el análisis ROC, el índice TyG - índice de masa corporal (TyG - IMC) tuvo el mayor AUC, tanto en hombres: AUC=0,85 (0,81 - 0,90), corte=241,55; sens=92,5 (79,6 - 98,4) y esp=78,3 (73,9 - 82,2); como en mujeres: AUC=0,81 (0,76 - 0,85), corte=258,77; sens=79,2 (70,3 - 86,5) y esp= 82,1 (78,0 - 85,8). Discusión: Según los datos analizados, el índice TyG-IMC es el mejor indicador para medir RI. Es un índice simple que se puede tomar de manera rutinaria en la práctica clínica diaria. Es conveniente añadir futuros estudios prospectivos que confirmen su capacidad predictiva.

Introduction: Insulin resistance (IR) is one of the main causes of chronic disease. Early detection is essential, which is why it is important to study more affordable and less expensive methods, such as biomarkers. Objective: To determine the diagnostic accuracy of 11 biomarkers of IR in a sample of Peruvian residents. Method: diagnostic tests. Secondary Database Analysis of the PERU-MIGRANT Study. To measure RI, a homeostatic model evaluation (HOMA-IR) ≥ 2.8 was used as a reference. Biomarkers were based on the lipid ratio, visceral lipid indicators, indicators of triglycerides and glucose (TyG), and indicators of abdominal waist. For precision, the receiver operating characteristic curve and area under the curve (AUC) with their respective 95% confidence intervals (95%CI) were used. Results: A total of 938 participants were studied. The prevalence of IR was 9.91%. In relation to the ROC analysis, the TyG index - body mass index (TyG - BMI) had the highest AUC, both in men: AUC=0.85 (0.81 - 0.90), cut-off=241.55; sens=92.5 (79.6 - 98.4) and sp=78.3 (73.9 - 82.2); as in women: AUC=0.81 (0.76 - 0.85), cut-off=258.77; sens=79.2 (70.3 - 86.5) and esp= 82.1 (78.0 - 85.8). Discussion: According to the data analyzed, the TyG-IMC index is the best indicator for measuring IR. It is a simple index that can be routinely used in clinical practice. Future prospective studies are needed to confirm its predictive capacity.

Factors Associated with Symptoms of Depression among People with Obesity: Analysis of a 3-Year-Peruvian National Survey.

INTRODUCTION: Obesity and depression contribute to the global burden of economic cost, morbidity, and mortality. Nevertheless, not all people with obesity develop depression. OBJECTIVE: To determine the factors associated with depressive symptoms among people aged 15 or older with obesity from the National Demographic and Family Health Survey (ENDES in Spanish 2019-2021). METHODS: Cross-sectional analytical study. The outcome of interest was the presence of depressive symptoms, assessed using the Patient Health Questionnaire-9 (PHQ-9). Crude (cPR) and adjusted (aPR) prevalence ratios were estimated using GLM Poisson distribution with robust variance estimates. RESULTS: The prevalence of depression symptoms was 6.97%. In the multivariate analysis, a statistically significant association was found between depressive symptoms and female sex (PRa: 2.59; 95% CI 1.95-3.43); mountain region (PRa: 1.51; 95% CI 1.18-1.92); wealth index poor (PRa: 1.37; 95% CI 1.05-1.79, medium (PRa: 1.49; 95% CI 1.11-2.02), and rich (PRa: 1.65; 95% CI 1.21-2.26); daily tobacco use (PRa: 2.05, 95% CI 1.09-3.87); physical disability (PRa: 1.96, 95% CI 1.07-3.57); and a history of arterial hypertension (PRa: 2.05; 95% CI 1.63-2.55). CONCLUSION: There are several sociodemographic factors (such as being female and living in the Andean region) and individual factors (daily use of tobacco and history of hypertension) associated with depressive symptoms in Peruvian inhabitants aged 15 or older with obesity. In this study, the COVID-19 pandemic was associated with an increase in depressive symptoms.