Adiposity and the perilipin gene: The role of single locus and multilocus variation, obesity-related quantitative traits, and disease-related phenotypes in the Atherosclerosis Risk in Communities (ARIC) study

Obesity is a complex multifactorial disease and is a public health priority. Perilipin coats the surface of lipid droplets in adipocytes and is believed to stabilize these lipid bodies by protecting triglyceride from early lipolysis. This research project evaluated the association between genetic variation within the human perilipin (PLIN) gene and obesity-related quantitative traits and disease-related phenotypes in Non-Hispanic White (NHW) and African American (AA) participants from the Atherosclerosis Risk in Communities (ARIC) Study. ^ Multivariate linear regression, multivariate logistic regression, and Cox proportional hazards models evaluated the association between single gene variants (rs2304794, rs894160, rs8179071, and rs2304795) and multilocus variation (rs894160 and rs2304795) within the PLIN gene and both obesity-related quantitative traits (body weight, body mass index [BMI], waist girth, waist-to-hip ratio [WHR], estimated percent body fat, and plasma total triglycerides) and disease-related phenotypes (prevalent obesity, metabolic syndrome [MetS], prevalent coronary heart disease [CHD], and incident CHD). Single variant analyses were stratified by race and gender within race while multilocus analyses were stratified by race. ^ Single variant analyses revealed that rs2304794 and rs894160 were significantly related to plasma triglyceride levels in all NHWs and NHW women. Among AA women, variant rs8179071 was associated with triglyceride levels and rs2304794 was associated with risk-raising waist circumference (>0.8 in women). The multilocus effects of variants rs894160 and rs2304795 were significantly associated with body weight, waist girth, WHR, estimated percent body fat, class II obesity (BMI ≥ 35 kg/m2), class III obesity (BMI ≥ 35 kg/m2), and risk-raising WHR (>0.9 in men and >0.8 in women) in AAs. Variant rs2304795 was significantly related to prevalent MetS among AA males and prevalent CHD in NHW women; multilocus effects of the PLIN gene were associated with prevalent CHD among NHWs. Rs2304794 was associated with incident CHD in the absence of the MetS among AAs. These findings support the hypothesis that variation within the PLIN gene influences obesity-related traits and disease-related phenotypes. ^ Understanding these effects of the PLIN genotype on the development of obesity can potentially lead to tailored health promotion interventions that are more effective. ^

Genetic variability and the hypothalamic control of energy balance

Background. Obesity is a major health problem throughout the industrialized world. Despite numerous attempts to curtail the rapid growth of obesity, its incidence continues to rise. Therefore, it is crucial to better understand the etiology of obesity beyond the concept of energy balance.^ Aims. The first aim of this study was to first investigate the relationship between eating behaviors and body size. The second goal was to identify genetic variation associated with eating behaviors. Thirdly, this study aimed to examine the joint relationships between eating behavior, body size and genetic variation.^ Methods. This study utilized baseline data ascertained in young adults from the Training Interventions and Genetics of Exercise (TIGER) Study. Variables assessed included eating behavior (Emotional Eating Scale, Eating Attitudes Test-26, and the Block98 Food Frequency Questionnaire), body size (body mass index, waist and hip circumference, waist/hip ratio, and percent body fat), genetic variation in genes implicated related to the hypothalamic control of energy balance, and appropriate covariates (age, gender, race/ethnicity, smoking status, and physical activity. For the genetic association analyses, genotypes were collapsed by minor allele frequency, and haplotypes were estimated for each gene. Additionally, Bayesian networks were constructed in order to determine the relationships between genetic variation, eating behavior and body size.^ Results. We report that the EAT-26 score, Caloric intake, percent fat, fiber intake, HEAT index, and daily servings of vegetables, meats, grains, and fats were significantly associated with at least one body size measure. Multiple SNPs in 17 genes and haplotypes from 12 genes were tested for their association with body size. Variation within both DRD4 and HTR2A was found to be associated with EAT-26 score. In addition, variation in the ghrelin gene (GHRL) was significantly associated with daily Caloric intake. A significant interaction between daily servings of grains and the HEAT index and variation within the leptin receptor gene (LEPR) was shown to influence body size.^ Conclusion. This study has shown that there is a substantial genetic component to eating behavior and that genetic variation interacts with eating behavior to influence body size.^

Body trunk fat and insulin resistance in post-pubertal obese adolescents

CONTEXT AND OBJECTIVE: Insulin resistance is a metabolic disorder commonly associated with excess body fat accumulation that may increase chronic disease risk. The present study was undertaken to evaluate the relationship between body composition and insulin resistance among obese adolescents. DESIGN AND SETTING: Cross-sectional study, at the Adolescence Center, Pediatric Department, Universidade Federal de São Paulo. METHODS: Body composition was assessed using dual-energy X-ray absorptiometry. Dietary intake was evaluated using a three-day dietary record. The biochemical evaluation comprised glucose, insulin, serum lipid, leptin and ghrelin measurements. Insulin resistance was calculated by means of the homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS: Forty-nine post-pubertal obese adolescents participated in the study: 12 boys and 37 girls of mean age 16.6 (1.4) years and mean body mass index (BMI) of 35.0 (3.9) kg/m². The mean glucose, insulin and HOMA values were 90.3 (6.4) mg/dl, 16.6 (8.1) µIU/ml and 3.7 (1.9), respectively. Hyperinsulinemia and insulin resistance were observed in 40.2% and 57.1% of the subjects, respectively. Adolescents with insulin resistance had higher BMI and body trunk fat. There was a trend towards higher leptin concentration in obese individuals with insulin resistance. Insulin resistance was positively correlated with body trunk fat, BMI, body fat mass (kg), leptin and body fat percentage. Furthermore, there was a negative correlation between HOMA-IR and lean body mass. The body composition predicted 30% of the HOMA-IR levels, according to linear regression models. CONCLUSION: Body trunk fat was significantly associated with insulin resistance, demonstrating the clinical importance of abdominal obesity during adolescence.

Magnesium, insulin resistance and body composition in healthy postmenopausal women

Objective: This study was conducted to determine the association between magnesium (Mg), body composition and insulin resistance in 136 sedentary postmenopausal women, 50 to 77 years of age. Methods: Diabetics, hypertensives and women on hormonal replacement therapy were excluded and the remaining 74 were divided according to BMI≥25 (obese: OG) and BMI<25 kg/m2 (non-obese: NOG). Nutritional data disclosed that intakes were high for protein and saturated fat, low for carbohydrates, polyunsaturated fat and Mg and normal for the other nutrients, according to recommended dietary allowances (RDA). Mg values in red blood cells (RBC-Mg) and plasma (P-Mg), were determined, as were fasting glucose, and insulin levels, Homeostasis Model Assessment (HOMA), body mass index (BMI), body fat percent (BF %), abdominal fat (AF) and free fat mass (FFM). Results: RBC-Mg values were low in both groups when compared with normal values. There were significant differences in body composition parameters, HOMA and insulin levels, with higher basal insulin levels in OG. RBC-Mg was directly correlated with insulin, HOMA and FFM in both groups, according to Pearson correlations. HOMA in OG was also directly correlated with BMI, FFM and AF. In NOG, HOMA was only correlated with FFM. The low RBC-Mg levels observed were probably due to low Mg intake and to deregulation of factors that control Mg homeostasis during menopause. Conclusions: Both Mg deficit and obesity may independently lead to a higher risk for insulin resistance and cardiovascular disease.

Depression with atypical features and increase in obesity, body mass index, waist circumference, and fat mass: a prospective, population-based study.

IMPORTANCE: Depression and obesity are 2 prevalent disorders that have been repeatedly shown to be associated. However, the mechanisms and temporal sequence underlying this association are poorly understood. OBJECTIVE: To determine whether the subtypes of major depressive disorder (MDD; melancholic, atypical, combined, or unspecified) are predictive of adiposity in terms of the incidence of obesity and changes in body mass index (calculated as weight in kilograms divided by height in meters squared), waist circumference, and fat mass. DESIGN, SETTING, AND PARTICIPANTS: This prospective population-based cohort study, CoLaus (Cohorte Lausannoise)/PsyCoLaus (Psychiatric arm of the CoLaus Study), with 5.5 years of follow-up included 3054 randomly selected residents (mean age, 49.7 years; 53.1% were women) of the city of Lausanne, Switzerland (according to the civil register), aged 35 to 66 years in 2003, who accepted the physical and psychiatric baseline and physical follow-up evaluations. EXPOSURES: Depression subtypes according to the DSM-IV. Diagnostic criteria at baseline and follow-up, as well as sociodemographic characteristics, lifestyle (alcohol and tobacco use and physical activity), and medication, were elicited using the semistructured Diagnostic Interview for Genetic Studies. MAIN OUTCOMES AND MEASURES: Changes in body mass index, waist circumference, and fat mass during the follow-up period, in percentage of the baseline value, and the incidence of obesity during the follow-up period among nonobese participants at baseline. Weight, height, waist circumference, and body fat (bioimpedance) were measured at baseline and follow-up by trained field interviewers. RESULTS: Only participants with the atypical subtype of MDD at baseline revealed a higher increase in adiposity during follow-up than participants without MDD. The associations between this MDD subtype and body mass index (β = 3.19; 95% CI, 1.50-4.88), incidence of obesity (odds ratio, 3.75; 95% CI, 1.24-11.35), waist circumference in both sexes (β = 2.44; 95% CI, 0.21-4.66), and fat mass in men (β = 16.36; 95% CI, 4.81-27.92) remained significant after adjustments for a wide range of possible cofounding. CONCLUSIONS AND RELEVANCE: The atypical subtype of MDD is a strong predictor of obesity. This emphasizes the need to identify individuals with this subtype of MDD in both clinical and research settings. Therapeutic measures to diminish the consequences of increased appetite during depressive episodes with atypical features are advocated.

Fat oxidation in nonobese and obese adolescents: effect of body composition and pubertal development.

OBJECTIVES: To measure postabsorptive fat oxidation (F(ox)) and to assess its association with body composition (lean body mass [LBM] and body fat mass [BFM]) and pubertal development. DESIGN: We studied 235 control (male/female ratio = 116/119; age [mean +/- SD]: 13.1 +/- 1.7 years; weight: 45.3 +/- 10.5 kg; LBM: 34.3 +/- 7.1 kg; BFM: 11.0 +/- 4.5 kg) and 159 obese (male/female ratio = 93/66; age: 12.9 +/- 2.1 years; weight: 76.2 +/- 19.1 kg; LBM: 47.4 +/- 10.9 kg; BFM: 28.8 +/- 9.2 kg) adolescents. Postabsorptive F(ox) was calculated from oxygen consumption, carbon dioxide production, and urinary nitrogen as measured by indirect calorimetry and Kjeldahl's method, respectively. Body composition was determined by anthropometry. RESULTS: Postabsorptive F(ox) (absolute value and percentage of resting metabolic rate) was significantly (p < 0.001) higher in the obese adolescents (76.7 +/- 26.3 gm/24 hours, 42.3% +/- 18.7%) than in the control subjects (40.0 +/- 26.3 gm/24 hours, 28.7% +/- 17.0%), even if adjusted for LBM. F(ox) corrected for BFM was similar in control and in obese children, but was significantly lower in girls compared with boys (control male subjects: 62.1 +/- 29.1 gm/24 hours, control female subjects: 51.6 +/- 28.4 gm/24 hours, obese male subjects: 57.3 +/- 29 gm/24 hour, obese female subjects: 45.0 +/- 28.4 gm/24 hours). BFM and LBM showed a significant positive correlation with F(ox). By stepwise regression analysis the most important determinant of F(ox) was BFM in obese and LBM in control children. There was a significant rise in F(ox) during puberty; however, it was mainly explained by changes in body composition. CONCLUSIONS: Obese adolescents have higher F(ox) rates than their normal-weight counterparts. Both LBM and fat mass are important determinants of F(ox).

Role of fat oxidation in the long-term stabilization of body weight in obese women.

Two studies were performed to investigate the association between body fat mass and fat oxidation. The first, a cross-sectional study of 106 obese women maintaining stable body weight, showed that these two variables were significantly correlated (r = 0.56, P less than 0.001) and the regression coefficient indicated that a 10-kg change in fat mass corresponded to a change in fat oxidation of approximately 20 g/d. The second, a prospective study, validated this estimate and quantifies the long-term adaptations in fat oxidation resulting from body fat loss. Twenty-four moderately obese women were studied under controlled dietary conditions at stable weight before and after mean weight and fat losses of 12.7 and 9.8 kg, respectively. The reduction in fat oxidation was identical to that predicted by the above regression. We conclude that changes in fat mass significantly affect fat oxidation and that this process may contribute to the long-term regulation of fat and energy balance in obese individuals.

Validity of silhouette showcards as a measure of body size and obesity in a population in the African region : a practical research tool for general-purpose surveys.

BACKGROUND: The purpose of this study is to validate the Pulvers silhouette showcard as a measure of weight status in a population in the African region. This tool is particularly beneficial when scarce resources do not allow for direct anthropometric measurements due to limited survey time or lack of measurement technology in face-to-face general-purpose surveys or in mailed, online, or mobile device-based surveys. METHODS: A cross-sectional study was conducted in the Republic of Seychelles with a sample of 1240 adults. We compared self-reported body sizes measured by Pulvers' silhouette showcards to four measurements of body size and adiposity: body mass index (BMI), body fat percent measured, waist circumference, and waist to height ratio. The accuracy of silhouettes as an obesity indicator was examined using sex-specific receiver operator curve (ROC) analysis and the reliability of this tool to detect socioeconomic gradients in obesity was compared to BMI-based measurements. RESULTS: Our study supports silhouette body size showcards as a valid and reliable survey tool to measure self-reported body size and adiposity in an African population. The mean correlation coefficients of self-reported silhouettes with measured BMI were 0.80 in men and 0.81 in women (P < 0.001). The silhouette showcards also showed high accuracy for detecting obesity as per a BMI ≥ 30 (Area under curve, AUC: 0.91/0.89, SE: 0.01), which was comparable to other measured adiposity indicators: fat percent (AUC: 0.94/0.94, SE: 0.01), waist circumference (AUC: 0.95/0.94, SE: 0.01), and waist to height ratio (AUC: 0.95/0.94, SE: 0.01) amongst men and women, respectively. The use of silhouettes in detecting obesity differences among different socioeconomic groups resulted in similar magnitude, direction, and significance of association between obesity and socioeconomic status as when using measured BMI. CONCLUSIONS: This study highlights the validity and reliability of silhouettes as a survey tool for measuring obesity in a population in the African region. The ease of use and cost-effectiveness of this tool makes it an attractive alternative to measured BMI in the design of non-face-to-face online- or mobile device-based surveys as well as in-person general-purpose surveys of obesity in social sciences, where limited resources do not allow for direct anthropometric measurements.

Changes in fat, fat-free mass and body water in human normal pregnancy

Measurements of body weight, total body water and total body potassium (40K) were made serially on three occasions during pregnancy and once post partum in 27 normal pregnant women. Skinfold thickness and fat cell diameter were also measured. A model of body composition was formulated to permit the estimation of changes in fat, lean tissue and water content of the maternal body. Total maternal body fat increased during pregnancy, reaching a peak towards the end of the second trimester before diminishing. Serial measurements of fat cell diameter showed poor correlation, whilst total body fat calculated from skinfold thickness correlated well with our estimated values for total body fat in pregnancy.

Body Composition and Net Energy Requirements for Maintenance of Non-Castrated Water Buffaloes (Bubalus bubalis)

The research aimed to estimate body contents of protein and energy and net requirements of energy for maintenance of buffaloes, slaughtered at different stages of maturity. There were used 14 Mediterranean intact males with initial average body weight of 352.2 +/- 24.3 kg and average age of 24 months. The animais were randomly divided into four experimental groups. One group was designed to slaughter at the beginning of the experimental period (IS). The animals of another group were restricting fed, receiving, individually, levels of protein and energy 15% above maintenance (RF). The animals of the two remaining groups were individually fed ad libitum (SW450 and SW500) to reach weights corresponding to 100 and 110 percent of the mature weight of the buffalo cows (respectively 450 and 550 kg). The ration contained ground-corn cobs, soybean meal, urea, minerals, and signal-grass (Brachiaria decumbens) hay, with a concentrate: roughage ratio of 50: 50 and 13% of crude protein on a dry matter basis. To estimate changes in body composition inside the range of weights included in the trial, linear regression equations of log protein (kg), fat (kg) and energy (Mcal) as a function of log empty-body-weight (EBW), in kg, were fitted. Energy requirements for maintenance were obtained as estimated heat production at zero level of energy intake. Buffaloes submitted to fattening in feedlot presented early body fat deposition, and had with the same live weight lower protein content and higher fat content and energy per unit weight than european-zebu crossbred cattle.

Ultrasonographic measurements of kidney fat thickness and Longissimus muscle area in predicting body composition of pregnant goats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bioelectrical impedance analysis and anthropometry for the determination of body composition in rats: effects of high-fat and high-sucrose diets

Objective The aim of the present study was to determine the impedance of Wistar rats treated with high-fat and high-sucrose diets and correlate their biochemical and anthropometric parameters with chemical analysis of the carcass. Methods Twenty-four male Wistar rats were fed a standard (AIN-93), high-fat (50% fat) or high-sucrose (59% of sucrose) diet for 4 weeks. Abdominal and thoracic circumference and body length were measured. Bioelectrical impedance analysis was used to determine resistance and reactance. Final body composition was determined by chemical analysis. Results Higher fat intake led to a high percentage of liver fat and cholesterol and low total body water in the High-Fat group, but these changes in the biochemical profile were not reflected by the anthropometric measurements or bioelectrical impedance analysis variables. Anthropometric and bioelectrical impedance analysis changes were not observed in the High-Sucrose group. However, a positive association was found between body fat and three anthropometric variables: body mass index, Lee index and abdominal circumference. Conclusion Bioelectrical impedance analysis did not prove to be sensitive for detecting changes in body composition, but body mass index, Lee index and abdominal circumference can be used for estimating the body composition of rats.

Long-term leucine supplementation reduces fat mass gain without changing body protein status of aging rats

Objective: Aging is characterized by alterations in body composition such as an increase in body fat and decreases in muscle mass (sarcopenia) and bone density (osteopenia). Leucine supplementation has been shown to acutely stimulate protein synthesis and to decrease body fat. However, the long-term effect of consistent leucine supplementation is not well defined. This study investigated the effect of leucine supplementation during aging. Methods: Six-month-old rats were divided into three groups: an adult group (n = 10) euthanized at 6 mo of age, a leucine group (n = 16) that received a diet supplemented with 4% leucine for 40 wk, and a control group (n = 19) that received the control diet for 40 wk. The following parameters were evaluated: body weight, food intake, chemical carcass composition, indicators of acquired chronic diseases, and indicators of protein nutritional status. Results: Body weight and fat were lower in the leucine group after 40 wk of supplementation compared with the control group but still higher than in the adult group. The lipid and glycemic profiles were equally altered in the control and leucine groups because of aging. In addition, leucine supplementation did not affect the changes in protein status parameters associated with aging, such as decreases in body and muscle protein and total serum protein. Conclusion: The results indicate that leucine supplementation attenuates body fat gain during aging but does not affect risk indicators of acquired chronic diseases. Furthermore, supplemented animals did not show signs of a prevention of the decrease in lean mass associated with aging. (C) 2012 Elsevier Inc. All rights reserved.

Application of body mass index adjusted for fat mass (BMIfat) obtained by bioelectrical impedance in adults

Introduction: Body mass index (BMI) has been one of the methods most frequently used for diagnose obesity, but it isn't consider body composition. Objective: This study intends to apply one new adiposity index, the BMI adjusted for fat mass (BMIfat) developed by Mialich, et al. (2011), in a adult Brazilian sample. Methods: A cross-sectional study with 501 individuals of both genders (366 women, 135 men) aged 17 to 38 years and mean age was 20.4 ± 2.8 years, mean weight 63.0 ± 13.5 kg, mean height 166.9 ± 9.0 cm, and BMI 22.4 ± 3.4 kg/m². Results and discussion: High and satisfactory R2 values were obtained, i.e., 91.1%, 91.9% and 88.8% for the sample as a whole and for men and women, respectively. Considering this BMIfat were developed new ranges, as follows: 1.35 to 1.65 (nutritional risk for malnutrition), > 1.65 and ≤ 2.0 (normal weight) and > 2.0 (obesity). The BMIfat had a more accurate capacity of detecting obese individuals (0.980. 0.993, 0.974) considering the sample as a whole and women and men, respectively, compared to the traditional BMI (0.932, 0.956, 0.95). Were also defined new cut-off points for the traditional BMI for the classification of obesity, i.e.: 25.24 kg/m² and 28.38 kg/m² for men and women, respectively. Conclusion: The BMIfat was applied for the present population and can be adopted in clinical practice. Further studies are needed to determine its application to different ethnic groups and to compare this index to others previously described in the scientific literature.

Estimation of whole body muscle, adipose/fat mass, validation in health and during weight loss. Development of prediction equations using MRI as reference method

Background: Body composition is affected by diseases, and affects responses to medical treatments, dosage of medicines, etc., while an abnormal body composition contributes to the causation of many chronic diseases. While we have reliable biochemical tests for certain nutritional parameters of body composition, such as iron or iodine status, and we have harnessed nuclear physics to estimate the body’s content of trace elements, the very basic quantification of body fat content and muscle mass remains highly problematic. Both body fat and muscle mass are vitally important, as they have opposing influences on chronic disease, but they have seldom been estimated as part of population health surveillance. Instead, most national surveys have merely reported BMI and waist, or sometimes the waist/hip ratio; these indices are convenient but do not have any specific biological meaning. Anthropometry offers a practical and inexpensive method for muscle and fat estimation in clinical and epidemiological settings; however, its use is imperfect due to many limitations, such as a shortage of reference data, misuse of terminology, unclear assumptions, and the absence of properly validated anthropometric equations. To date, anthropometric methods are not sensitive enough to detect muscle and fat loss. Aims: The aim of this thesis is to estimate Adipose/fat and muscle mass in health disease and during weight loss through; 1. evaluating and critiquing the literature, to identify the best-published prediction equations for adipose/fat and muscle mass estimation; 2. to derive and validate adipose tissue and muscle mass prediction equations; and 3.to evaluate the prediction equations along with anthropometric indices and the best equations retrieved from the literature in health, metabolic illness and during weight loss. Methods: a Systematic review using Cochrane Review method was used for reviewing muscle mass estimation papers that used MRI as the reference method. Fat mass estimation papers were critically reviewed. Mixed ethnic, age and body mass data that underwent whole body magnetic resonance imaging to quantify adipose tissue and muscle mass (dependent variable) and anthropometry (independent variable) were used in the derivation/validation analysis. Multiple regression and Bland-Altman plot were applied to evaluate the prediction equations. To determine how well the equations identify metabolic illness, English and Scottish health surveys were studied. Statistical analysis using multiple regression and binary logistic regression were applied to assess model fit and associations. Also, populations were divided into quintiles and relative risk was analysed. Finally, the prediction equations were evaluated by applying them to a pilot study of 10 subjects who underwent whole-body MRI, anthropometric measurements and muscle strength before and after weight loss to determine how well the equations identify adipose/fat mass and muscle mass change. Results: The estimation of fat mass has serious problems. Despite advances in technology and science, prediction equations for the estimation of fat mass depend on limited historical reference data and remain dependent upon assumptions that have not yet been properly validated for different population groups. Muscle mass does not have the same conceptual problems; however, its measurement is still problematic and reference data are scarce. The derivation and validation analysis in this thesis was satisfactory, compared to prediction equations in the literature they were similar or even better. Applying the prediction equations in metabolic illness and during weight loss presented an understanding on how well the equations identify metabolic illness showing significant associations with diabetes, hypertension, HbA1c and blood pressure. And moderate to high correlations with MRI-measured adipose tissue and muscle mass before and after weight loss. Conclusion: Adipose tissue mass and to an extent muscle mass can now be estimated for many purposes as population or groups means. However, these equations must not be used for assessing fatness and categorising individuals. Further exploration in different populations and health surveys would be valuable.