Association between cytokine levels and anthropometric measurements: a population-based study.

Obesity is currently considered a public health problem with pandemic proportions and is associated with chronic low-grade inflammation, which can predispose to the development of several chronic diseases and metabolic complications. This cross-sectional population-based study, conducted with 743 Brazilian adults, aimed to evaluate the association between inflammatory cytokines with anthropometric measurements. Socio-demographic, anthropometric, behavioural and biochemical variables were collected. Multiple linear regression stratified by sex and adjusted for confounding factors was performed. In men, waist circumference (WC) was associated with IL-1ß (3·52 pg/ml; 95 % CI 0·60, 6·45), IL-6 (6·35 pg/ml; 95 % CI 0·35, 12·34), IL-8 (8·77 pg/ml; 95 % CI 2·37, 15·17), IL-10 (3·09 pg/ml; 95 % CI 0·56, 5·61), IL12p70 (8·31 pg/ml; 95 % CI 3·11, 13·52) and TNF-α (4·22 pg/ml; 95 % CI 0·20, 10·48). Waist:height ratio was associated with IL-6 (3·21 pg/ml; 95 % CI 0·02, 6·39). BMI was associated with IL-1ß (1·50 pg/ml; 95 % CI 0·46, 2·34), IL-6 (2·97 pg/ml; 95 % CI 0·78, 5·16), IL-8 (4·48 pg/ml; 95 % CI 2·21, 6·75), IL-10 (1·31 pg/ml; 95 % CI 0·30, 2·31), IL-12p70 (3·59 pg/ml; 95 % CI 1·24, 5·95) and TNF-α (2·00 pg/ml; 95 % CI 0·81, 3·19). In women, WC was associated with IL-6 (5·10 pg/ml; 95 % CI 0·68, 9·51) and IL-10 (4·16 pg/ml; 95 % CI 1·26, 7·06). BMI was associated with IL-6 (2·67 pg/ml; 95 % CI 0·34, 4·99), and WHR was associated with TNF-α (2·84 pg/ml; 95 % IC 0·86-6·54). The results highlight the importance of anthropometric assessment in clinical practice and the need to develop public policies and interventions to reduce the prevalence of obesity and, consequently, of inflammation and possible metabolic complications.

Prevalence of body composition phenotypes and their associations with glycemic, lipidic, and inflammatory biomarkers: a population-based study.

We aimed to verify the prevalence of body composition phenotypes and the association of glycemic, lipidic, and inflammatory biomarkers with such phenotypes. This is a cross-sectional, population-based study, with 720 participants aged 20 to 59 years. Body composition was assessed by dual-energy X-ray absorptiometry. Obesity was defined as body fat percentage ≥ 25% in males and ≥ 32% in females and sarcopenia by appendicular muscle mass index < 7.0kg/m2 in males and < 5.5kg/m2 in females. Sarcopenic obesity (SO) was defined as the presence of both sarcopenia and obesity. The prevalence of obesity, sarcopenia, and SO were 62.5%, 4.5%, and 6.2%, respectively. The association between biomarkers and phenotypes was verified using multinomial logistic regression models adjusted for confounding factors. The models showed that increased glycemia (OR = 3.39; 95%CI: 1.83-6.27), total cholesterol (TC) (OR = 2.24; 95%CI: 1.35-3.70), LDL-c (OR = 1.01; 95%CI: 1.00-1.02), VLDL-c (OR = 1.04; 95%CI: 1.02-1.06), non-HDL-c (OR = 1.02; 95%CI: 1.01-1.03), triglycerides (Tg) (OR = 3.66; 95%CI: 2.20-6.06), and decreased HDL-c (OR = 0.97; 95%CI: 0.95-0.98) were significantly associated with the obesity phenotype. Increased HOMA-IR (OR = 3.94; 95%CI: 1.69-9.21), LDL-c (OR = 1.01; 95%CI: 1.00-1.02), non-HDL-c (OR = 1.01; 95%CI: 1.00-1.02), and hs-CRP (OR = 2.42; 95%CI: 1.04-5.66) were independently associated with SO phenotype. Our findings indicate that increased glycemia, TC, Tg, LDL-c, VLDL-c, non-HDL-c, and decreased HDL-c may be indicators of the obesity phenotype and that increased hs-CRP, HOMA-IR, LDL-c, and non-HDL-c appear to be indicators of the SO phenotype. Those parameters may be used as additional markers for screening.

Credibility at stake: only two-thirds of randomized trials of nutrition interventions are registered and lack transparency in outcome and treatment effect definitions.

OBJECTIVES: This study aimed to investigate the adherence of randomized controlled trials of nutrition interventions to transparency practices informing assessments of selective reporting biases, including the availability of a trial registration entry, protocol and statistical analysis plan (SAP). STUDY DESIGN AND SETTING: Retrospective observational study with cross-sectional design. We systematically searched for trials published from 1 July 2019, to 30 June 2020, and included a randomly selected sample of 400 studies. We searched for registry entries, protocols, and SAPs for all included studies. We extracted data to characterize the disclosure of sufficient information in the available materials to inform assessments of selective reporting biases, considering the definition of outcome domain, measure, metric, method of aggregation, time point, analysis population, methods to handle missing data and method of adjustment. RESULTS: Most trials (69%) were registered, but these often lacked sufficient specification of outcomes and intended treatment effects. Protocols and SAPs provided more details but were less often available (14% and 3%, respectively), and even then, almost all studies presented limited information to inform the assessments of risk of bias due to the selection of the reported result. CONCLUSION: Lack of full specification of outcomes and intended treatment effects hinder a full adherence of randomized controlled trials of nutrition interventions to transparency practices and may affect their credibility.

Metabolically unhealthy and overweight phenotypes are associated with increased levels of inflammatory cytokines: a population-based study.

OBJECTIVE: The aim of this study was to determine the association between cytokine levels in metabolic phenotypes. Our hypothesis was that an unhealthy metabolic profile is associated to higher levels of proinflammatory cytokines. METHODS: The study sample was composed of 743 Brazilian adults classified in four phenotypes: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight (MHOW), and metabolically unhealthy overweight (MUOW). Sociodemographic, anthropometric, clinical, and biochemical parameters were collected. Six different cytokines were analyzed from blood samples using the CBA Human Inflammatory cytokines kit and the values divided in quartiles for analysis. Logistic regression models were constructed to assess the association between metabolic phenotypes and cytokines concentrations, adjusted for potential confounders and P < 0.05 was used. RESULTS: The MUOW phenotype showed a higher risk for increased levels of all cytokines analyzed compared with the reference group (MHNW). CONCLUSIONS: These results indicated that excess weight and altered metabolic profile are related to inflammation, especially when both conditions are associated, possibly linked to visceral adiposity. Therefore, the categorization of metabolic phenotypes in populations is an important factor for prevention of chronic diseases, as inflammation is associated with cardiovascular risk and obesity is not the only influencing factor.

Prevalence of body composition phenotypes and their associations with glycemic, lipidic, and inflammatory biomarkers: a population-based study / Prevalência de fenótipos corporais e suas associações com biomarcadores dos perfis glicídico, lipídico e inflamatório: um estudo de base populacional / Prevalencia de fenotipos corporales y sus asociaciones con biomarcadores de perfiles glucídicos, lipídicos e inflamatorios: un estudio de base poblacional

Abstract: We aimed to verify the prevalence of body composition phenotypes and the association of glycemic, lipidic, and inflammatory biomarkers with such phenotypes. This is a cross-sectional, population-based study, with 720 participants aged 20 to 59 years. Body composition was assessed by dual-energy X-ray absorptiometry. Obesity was defined as body fat percentage ≥ 25% in males and ≥ 32% in females and sarcopenia by appendicular muscle mass index < 7.0kg/m2 in males and < 5.5kg/m2 in females. Sarcopenic obesity (SO) was defined as the presence of both sarcopenia and obesity. The prevalence of obesity, sarcopenia, and SO were 62.5%, 4.5%, and 6.2%, respectively. The association between biomarkers and phenotypes was verified using multinomial logistic regression models adjusted for confounding factors. The models showed that increased glycemia (OR = 3.39; 95%CI: 1.83-6.27), total cholesterol (TC) (OR = 2.24; 95%CI: 1.35-3.70), LDL-c (OR = 1.01; 95%CI: 1.00-1.02), VLDL-c (OR = 1.04; 95%CI: 1.02-1.06), non-HDL-c (OR = 1.02; 95%CI: 1.01-1.03), triglycerides (Tg) (OR = 3.66; 95%CI: 2.20-6.06), and decreased HDL-c (OR = 0.97; 95%CI: 0.95-0.98) were significantly associated with the obesity phenotype. Increased HOMA-IR (OR = 3.94; 95%CI: 1.69-9.21), LDL-c (OR = 1.01; 95%CI: 1.00-1.02), non-HDL-c (OR = 1.01; 95%CI: 1.00-1.02), and hs-CRP (OR = 2.42; 95%CI: 1.04-5.66) were independently associated with SO phenotype. Our findings indicate that increased glycemia, TC, Tg, LDL-c, VLDL-c, non-HDL-c, and decreased HDL-c may be indicators of the obesity phenotype and that increased hs-CRP, HOMA-IR, LDL-c, and non-HDL-c appear to be indicators of the SO phenotype. Those parameters may be used as additional markers for screening.

Resumo: Objetivou-se verificar a prevalência de fenótipos corporais e suas associações com biomarcadores dos perfis glicídico, lipídico e inflamatório. Trata-se de um estudo transversal, de base populacional, com 720 indivíduos de 20 a 59 anos. A composição corporal foi avaliada por absorciometria com raios X de dupla energia. Obesidade foi definida como percentual de gordura corporal ≥ 25% em homens e ≥ 32% em mulheres e sarcopenia pelo índice de massa muscular apendicular < 7,0kg/m2 em homens e < 5,5kg/m2 em mulheres. A obesidade sarcopênica foi definida como a coexistência de sarcopenia e obesidade. As prevalências de obesidade, sarcopenia e obesidade sarcopênica foram de 62,5%, 4,5% e 6,2%, respectivamente. A associação entre biomarcadores e fenótipos foi verificada por meio de modelos de regressão logística multinomial ajustados por variáveis de confusão. Os modelos mostraram que níveis aumentados de glicemia (OR = 3,39; IC95%: 1,83-6,27), colesterol total (OR = 2,24; IC95%: 1,35-3,70), LDL-c (OR = 1,01; IC95%: 1,00-1,02), VLDL-c (OR = 1,04; IC95%: 1,02-1,06), não HDL-c (OR = 1,02; IC95%: 1,01-1,03), triglicerídeos (OR = 3,66; IC95%: 2,20-6,06) e diminuição do HDL-c (OR = 0,97; IC95%: 0,95-0,98) foram significativamente associados ao fenótipo de obesidade. Índices aumentados de HOMA-IR (OR = 3,94; IC95%: 1,69-9,21), LDL-c (OR = 1,01; IC95%: 1,00-1,02), não HDL-c (OR = 1,01; IC95%: 1,00-1,02) e PCR-us (OR = 2,42; IC95%: 1,04-5,66) foram independentemente associados ao fenótipo de obesidade sarcopênica. Nossos resultados sugerem que níveis aumentados de glicemia, colesterol total, triglicerídeos, LDL-c, VLDL-c, não HDL-c e graus reduzidos de HDL-c são indicadores do fenótipo de obesidade e que o aumento em níveis de PCR-us, HOMA-IR, LDL-c e não HDL-c são indicadores do fenótipo de obesidade sarcopênica. Esses parâmetros podem ser usados como marcadores adicionais para triagem.

Resumen: El objetivo de este estudio fue evaluar la prevalencia de fenotipos corporales y sus asociaciones con biomarcadores de perfiles lipídicos, glucídicos e inflamatorios. Se trata de un estudio transversal, de base poblacional, realizado con 720 individuos de entre 20 y 59 años. La composición corporal se evaluó mediante absorciometría de rayos X de energía dual. La obesidad se estimó como porcentaje de grasa corporal ≥ 25% en hombres y ≥ 32% en mujeres, y la sarcopenia como índice de masa muscular apendicular < 7,0kg/m2 en hombres y < 5,5kg/m2 en mujeres. La obesidad sarcopénica se evaluó como la coexistencia de sarcopenia y obesidad. Las prevalencias de obesidad, sarcopenia y obesidad sarcopénica fueron del 62,5%, 4,5% y 6,2%, respectivamente. La asociación entre biomarcadores y fenotipos se comprobó mediante modelos de regresión logística multinomial ajustados por variables de confusión. Los modelos mostraron que el incremento de los niveles de glucosa en la sangre (OR = 3,39; IC95%: 1,83-6,27), colesterol total (OR = 2,24; IC95%: 1,35-3,70), LDL-c (OR = 1,01; IC95%: 1,00-1,02), VLDL-c (OR = 1,04; IC95%: 1,02-1,06), no HDL-c (OR = 1,02; IC95%: 1,01-1,03), triglicéridos (OR = 3,66; IC95%: 2,20-6,06) y disminución de HDL-c (OR = 0,97; IC95%: 0,95-0,98) se asociaron significativamente con el fenotipo de obesidad. Las tasas aumentadas de HOMA-IR (OR = 3,94; IC95%: 1,69-9,21), LDL-c (OR = 1,01; IC95%: 1,00-1,02), no-HDL-c (OR = 1,01; IC95%: 1,00-1,02) y PCR-us (OR = 2,42; IC95%: 1,04-5,66) se asociaron de manera independiente con el fenotipo de obesidad sarcopénica. Los resultados demuestran que el aumento de los niveles de glucosa en la sangre, colesterol total, triglicéridos, LDL-c, VLDL-c, no-HDL-c y grados reducidos de HDL-c son indicadores del fenotipo de obesidad y que el incremento de los niveles de PCR-us, HOMA-IR, LDL-c y no-HDL-c son indicadores del fenotipo de obesidad sarcopénica. Estos parámetros se pueden utilizar como marcadores adicionales para el cribado.