Dietary amino acids and anthropometric indices: Tehran Lipid and Glucose Study

ABSTRACT Objective: Recent studies investigated the role of amino acids (AAs) in weight management. We aimed to determine the association between AAs and three-year change of anthropometric indices and incident obesity. Materials and methods: Height, weight, hip, and waist circumference (WC) were collected at baseline and follow up. Three-year changes in anthropometric indices and obesity incident according to body mass index (BMI) (overweight & obesity) and WC cutoffs (obesity-WC) were ascertained. Dietary intakes of AAs were collected at baseline, using a food frequency questionnaire. Data analyses were conducted on 4976 adult participants and two subsamples, including 1,570 and 2,918 subjects, for assessing the AAs relationship with 3-year changes on anthropometric indices and obesity incident. Results: Lysine and aspartic acid were positively associated with higher weight change, whereas acidic AAs, cysteine, and glutamic acid showed a negative correlation with weight change. Furthermore, a weak positive correlation was shown for alkaline AAs, lysine, and valine with WC; however, acidic AAs, tryptophan, cysteine, and glutamic acid were negatively associated with WC. Aromatic and acidic AAs also demonstrated a weak negative relation with changes in BAI. Phenylalanine and Aromatic AAs showed a negative association with overweight &obesity incidence adjusting for potential confounders. Each quartile increases the dietary lysine, arginine, alanine, methionine, aspartic acid, and alkaline AAs related to a greater risk of obesity-WC, while tryptophan, glutamic acid, proline, and acidic AAs associated with lower obesity-WC risk. Conclusion: Our results suggested that certain dietary AAs may potentially change anthropometric indices and risk of obesity incident.

Value of body adiposity index in the prediction of metabolic disease / 中国综合临床

Objective To discuss the value of body adiposity index( BAI) in the prediction of the risk of dysglycemia,dyslipidemia and hyperuricemia.Methods Five thousand and thirty residents as the participants from Jinshan New Area and nearby of Jinshan,Shanghai were enrolled,including 2004 males and 3026 females. The receiver operating characteristic curve( ROC) was drew to predict dysglycemia,dyslipidemia and hyperurice?mia by body mass index( BMI) ,waist circumference( WC) ,waist?hip ratio( WHR) and BAI in different gender groups.Medcale soft was used to compare the area under the curve.Results According to the ROC analysis,in males the area under curve in the prediction of dysglycemia of BMI,WC,WHR and BAI was 0.553,0.556,0. 538,0.540(P<0.05),and 0.513,0.523,0.523,0.535 in females(P<0.05).The area under curve in the predic?tion of dyslipidemia of BMI,WC,WHR and BAI was 0.641,0.626,0.563,0.588(P<0.05) in males and 0.617, 0.613,0.597,0.587(P<0.05) in females.The area under curve in the prediction of hyperuricemia of BMI,WC, WHR and BAI was 0.685,0.665,0.609,0.577(P<0.05) in males and 0.730,0.708,0.656,0.649(P<0.05) in females,and BMI, WC of AUC were higher than BAI both in males and females(P<0.05).Conclusion BAI could be a predictor for the risk of metabolic disease,but less than BMI and WC.

Índice de adiposidade corporal modificado para determinação de gordura corporal de adultos com obesidade mórbida / Modified Body Adiposity Index to determine body fat in morbid obese adults

INTRODUÇÃO: A obesidade mórbida tornou-se um importante problema de saúde pública. A medida de massa corporal não é capaz de identificar deficiências ou excessos dos diferentes componentes corporais, surgindo a necessidade de se avaliar a composição corporal. Não há consenso sobre o melhor método para esse fim em obesos mórbidos. O Índice de Adiposidade Corporal (IAC) foi proposto para ser um método simples e preciso para uma população de diversificada quantidade de gordura corporal (GC). OBJETIVO: Avaliar a eficácia do IAC em determinar GC de adultos com obesidade mórbida. MÉTODOS: O IAC foi comparado à Bioimpedância (BIA) em 240 adultos obesos mórbidos (Grupo 1= G1), uma equação específica para determinar GC em obesidade mórbida foi desenvolvida e, posteriormente, validada em outra amostra de 158 indivíduos (Grupo 2 = G2). RESULTADOS: Observou-se diferença significativa entre os dois métodos (p=0,039). A quantidade média de GC no G1 foi 52,3±6,1% segundo a BIA e 51,6±8,1% segundo o IAC, com uma diferença de 0,6±5,1% entre os métodos. Algumas variáveis, como gênero, RCQ e gravidade da obesidade confundiram o IAC. Para minimizar esses erros uma equação (Índice de Adiposidade Corporal Modificado = IACM) foi desenvolvida por meio de regressão linear (IACM% = 23,6 + 0,5 x (IAC); somar 2,2 se IMC >= 50kg/m2 e 2,4 se RCQ >= 1,05). A equação foi aplicada no G2 e possibilitou a redução da diferença entre os métodos (1,2±5,9% para 0,4±4,0%) e o fortalecimento da correlação entre eles (0,6 para 0,7). CONCLUSÕES: O IAC apresenta limitações para determinar porcentagem de gordura corporal de obesos mórbidos, já a equação sugerida (IACM) foi eficaz, não se apresentando significativamente diferente da Bioimpedância e corrigindo as limitações anteriormente apresentadas pelo IAC.

BACKGROUND: Morbid obesity has become a public health problem. As body mass is not able to identify deficiencies or excesses of body components, the need to assess body composition emerged. There is no consensus of the best method to measure body composition in morbidly obese adults and a simple, accurate, reproducible and inexpensive method is desirable. The Body Adiposity Index (BAI) has been proposed to be a simple and accurate method for a population with a diverse amount of body fat (BF). OBJECTIVE: Evaluate the efficacy of BAI in determining BF of morbid obese adults. METHODS: BAI was compared to bioimpedance (BIA) in 240 morbidly obese adults (Group One= G1) and a specific equation for morbid obesity has been developed to determine BF and then validated on another sample of 158 subjects (Group Two= G2). RESULTS: There was a significant difference between the two methods (p=0,039). The average amount of BF in G1 was 52.3±6.1%, according to BIA and 51.6±8.1% according to BAI, with a difference of 0.6±5.1% between methods. Some variables, such as gender, WHR and severity of obesity mistook BAI. To minimize these errors an equation (Modified Body Adiposity Index = MBAI) was developed by linear regression (MBAI% = 23.6 + 0.5 x (BAI); add 2.2 if BMI >= 50kg/m2 and 2.4 if WHR >= 1.05). The equation was applied to G2 and resulted in a reduction in the difference between methods (1.2±5.9% to 0.4±4.12%) and strengthening of the correlation between them (0.6 to 0.7). CONCLUSIONS: BAI has limitations in determine BF in morbid obesity. The suggested equation (MBAI) was effective for predicting body fat in morbid obese adults; MBAI wasn't significant different from BIA and was able to correct BAI limitations.

Comparison of the Body Adiposity Index to Body Mass Index in Korean Women

PURPOSE: Obesity is a major public health issue and is associated with many metabolic abnormalities. Consequently, the assessment of obesity is very important. A new measurement, the body adiposity index (BAI), has recently been proposed to provide valid estimates of body fat percentages. The objective of this study was to compare the BAI and body mass index (BMI) as measurements of body adiposity and metabolic risk. MATERIALS AND METHODS: This was a cross-sectional analysis performed on Korean women. The weight, height, and hip circumferences of 2950 women (mean age 25+/-5 years old, 18-39 years) were measured, and their BMI and BAI [hip circumference (cm)/height (m)(1.5)-18] values were calculated. Bioelectric impedance analysis was used to evaluate body fat content. Glucose tolerance status was assessed with a 75-g oral glucose tolerance test, and insulin sensitivity was estimated with the insulin sensitivity index. RESULTS: BMI was more significantly correlated with fat mass and fat percentage. Additionally, BMI was also more significantly associated with metabolic parameters, including fasting glucose, post-load 2-h glucose, fasting insulin, post-load 2-h insulin, triglycerides, and high density lipoprotein cholesterol than BAI. Receiver operating characteristic curve analysis revealed that BMI was a better tool for predicting body fat percentage than BAI. Insulin sensitivity and metabolic syndrome were more significantly associated with BMI than with BAI. CONCLUSION: In Korean women, the current BMI-based classifications for obesity might be superior to BAI-based measurements for determining obesity and predicting metabolic risk.