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.

Rendimiento diagnóstico de tres índices antropométricos de peso y talla para síndrome metabólico en trabajadores

Introducción: La presencia del síndrome metabólico está asociada con enfermedades crónicas a largo plazo, por lo que se buscan diferentes formas de obtener un diagnóstico temprano. Objetivo: Determinar el rendimiento diagnóstico de 3 índices antropométricos de peso y talla para síndrome metabólico en una muestra de trabajadores peruanos. Métodos: La población son trabajadores de 18 a 65 años, de ambos sexos, ocupación operarios y administrativos. Las variables estudiadas son: edad, sexo, ocupación, peso, talla, perímetro de cintura, antecedentes de diabetes mellitus tipo 2, presión arterial sistólica, diastólica, glucosa en ayunas, triglicéridos y lipoproteína de alta densidad. Se incluyeron 370 trabajadores, se crearon curvas características operativa del receptor con su respectiva área bajo la curva (AUC), se obtuvo la sensibilidad y especificidad de cada índice. Resultados: Del total, el 20 % presentó síndrome metabólico; el 46,76 % fueron mujeres, el 60 % tomaron alcohol alguna vez, el 5,14 % señaló haber fumado. El índice de masa corporal tuvo la mayor AUC= 0,73; corte= 26,04; sensibilidad= 78,4 y especifidad= 67,9) seguido del nuevo índice de masa corporal (AUC= 0,70; corte= 27,85; sensibilidad= 68,9 y especificidad= 70,6); el último lugar lo ocupa el índice triponderal (AUC= 0,66; corte= 16,67; sensibilidad= 67,6 y especificidad= 64,5); los parámetros para síndrome metabólico mostraron asociación estadísticamente significativa. Conclusión: El índice de masa corporal es el de mejor rendimiento diagnóstico para síndrome metabólico; podría ser un predictor útil para detectar este síndrome.

Introduction: Metabolic syndrome is associated with long-term chronic diseases, which is why different ways of obtaining an early diagnosis are sought. Objective: To determine the diagnostic yield of 3 anthropometric indices of weight and height for metabolic syndrome in a sample of Peruvian workers. Methods: The population are workers from 18 to 65 years old, both sexes, occupation operators and administrators; the studied variables were: age, sex, occupation, weight, height, waist circumference, history of type 2 diabetes mellitus, pressure systolic and diastolic blood pressure, fasting glucose, triglycerides, and high-density lipoprotein; 370 workers were included, receiver operating characteristic curves (ROC) were created with their respective area under the curve, obtaining the sensitivity and specificity of each of the indices. Results: Of the total number of workers, 20% presented Metabolic Syndrome; 46.76% were women, 60% drank alcohol at some time, and 5.14% reported having smoked. The Body Mass Index the greatest ROC= 0.73; cutoff= 26.04; sensitivity= 78.4 and specificity= 67.9) followed by the New Body Mass Index (ROC= 0.70; cutoff= 27.85; sensitivity= 68.9 and specificity= 70.6), the last place was occupied by the Triponderal Index (ROC= 0.66; cutoff= 16.67; sensitivity= 67.6 and specificity= 64.5); the parameters for metabolic syndrome showed a statistically significant association. Conclusion: Body Mass Index is the best diagnostic yield for Metabolic Syndrome and could be a useful predictor to detect this syndrome.

The Fruit Intake-Adiposity Paradox: Findings from a Peruvian Cross-Sectional Study.

Due to the increase in obesity worldwide, international organizations have promoted the adoption of a healthy lifestyle, as part of which fruit consumption stands out. However, there are controversies regarding the role of fruit consumption in mitigating this disease. The objective of the present study was to analyze the association between fruit intake and body mass index (BMI) and waist circumference (WC) in a representative sample of Peruvians. This is an analytical cross-sectional study. Secondary data analysis was conducted using information from the Demographic and Health Survey of Peru (2019-2021). The outcome variables were BMI and WC. The exploratory variable was fruit intake, which was expressed in three different presentations: portion, salad, and juice. A generalized linear model of the Gaussian family and identity link function were performed to obtain the crude and adjusted beta coefficients. A total of 98,741 subjects were included in the study. Females comprised 54.4% of the sample. In the multivariate analysis, for each serving of fruit intake, the BMI decreased by 0.15 kg/m2 (ß = -0.15; 95% CI -0.24 to -0.07), while the WC was reduced by 0.40 cm (ß = -0.40; 95% CI -0.52 to -0.27). A negative association between fruit salad intake and WC was found (ß = -0.28; 95% CI -0.56 to -0.01). No statistically significant association between fruit salad intake and BMI was found. In the case of fruit juice, for each glass of juice consumed, the BMI increased by 0.27 kg/m2 (ß = 0.27; 95% CI 0.14 to 0.40), while the WC increased by 0.40 cm (ß = 0.40; 95% CI 0.20 to 0.60). Fruit intake per serving is negatively related to general body adiposity and central fat distribution, while fruit salad intake is negatively related to central distribution adiposity. However, the consumption of fruit in the form of juices is positively associated with a significant increase in BMI and WC.

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.