Estado nutricional de trabalhadores : implicações nos parâmetros bioquímicos, inflamatórios e adipocitocinas.

Abstract

Overweight and obesity are recognized as a public health problem. Obesity is considered a chronic disease with a multifactorial etiology. Body fat is mainly distributed in two types of adipose tissue, with different biochemical and metabolic characteristics: subcutaneous adipose tissue and visceral adipose tissue. Increased subcutaneous fat is associated with the risk of disturbances in glucose metabolism and dyslipidemia. Increased visceral adipose tissue is positively correlated with elevated hepatic lipase activity of Triglycerides. With obesity occurs the unbalanced production of pro-inflammatory adipocytokines that contributes to the development of metabolic diseases such as insulin resistance, diabetes mellitus type 2 and cardiovascular diseases. The present study aimed to verify the association of nutritional status with inflammatory, biochemical and metabolic parameters in workers in rural and industrial sectors. Abstract of article I: Correlation of three indices of risk estimation to insulin resistance using triglycerides: a Study with Rural and Industry Workers. Introduction: Overweight is a risk factor for metabolic dysfunctions such as dyslipidemia and insulin resistance (IR). Indices for determining adipose and visceral tissue and lipid-based insulin action have been suggested to help identify IR. Objective: to correlate the fasting plasma glucose and triglyceride (TyG), Visceral Adiposity Index (VAI) and Lipid Accumulation Product (LAP) indices with the anthropometric profile and biochemical indicators in rural workers and industry. Methods: A cross-sectional, analytical study was performed with 97 workers (57 females), mean age 43 ± 11. Data were collected: sex, age, weight, height, Body Mass Index (BMI), waist circumference (CC), total cholesterol, HDL-c, LDL-c, triglycerides and fasting glycemia. For the Visceral Fat Area (AVG) and Body Fat Percentage (% G) the In-Body 720 was used. The TyG, VAI and LAP indices were calculated. Data were expressed as mean ± standard deviation. Pearson and Spearman tests were performed to correlate the TyG, VAI and LAP data with anthropometric and biochemical data. The level of significance was 5%. Results: Among the three indexes, LAP showed a strong correlation with all anthropometric indicators (BMI, CC,% G and AGV), in the male and female, and in individuals under 50 years of age. TyG and LAP were correlated with AGV in males and females. VAI presented an inverse correlation with HDL-c in both sexes, younger than 50 years, and in males older than 50 years. Conclusion: TyG, VAI and LAP are useful because they are based on standardized and widely available measures such as BMI, CC, TG, fasting glycemia and HDL-c. Abstract of the article II: Nutritional Status, Insulin Resistance and Cytokine Biomarkers: A Principal Component Analysis Approach. Introduction: The high prevalence of overweight and obesity represents a growing public health problem. Several anthropometric indices have been used to define overweight and obesity based on fat distribution and insulin resistance, proposed as tools to reproduce adipose tissue dysfunction. Objective: We aimed to explore the clustering of risk factors and associations between nutritional status, biochemical parameters, insulin resistance, and cytokine biomarkers in rural and industrial workers. Methods: A cross-sectional study involving 71 individuals, 41 females and 30 males. Data were collected: sex, age, body mass index, waist circumference, total cholesterol, HDL-c, LDL-c, triglycerides and fasting glycemia. For the Visceral fat area and the fat percentage the In-Body 720 was used. The fasting plasma glucose and triglycerides (TyG), Visceral Adiposity Index (VAI) and Lipid Accumulation Product (LAP) indexes were calculated ). Cytokines (IL-6, IL-1β, IL-8, TNF-α, and IL-10) were measured using the custom Human Cytokine Magnetic 10 plex kit. Factor analysis was performed to reduce 17 intercorrelated variables in groups of independent factors and ANOVA. Results: Five factors explained most of the intrinsic variation of the data. Factor 1 was related to overweight, obesity and visceral fat, factor 2 composed of insulin resistance variables, and factor 3 included cytokines (IL-8, IL-6, IL-1β) and fasting glycemia. Differences were found after grouping the variables in sex, BMI and work sector. Conclusion: This factorial analysis of the main components found that factor 1 demonstrated maximum variation in factor clustering, followed by factors of insulin resistance and inflammatory cytokines. After grouping, based on factor scores, differences were found in sex, BMI and the work sector.