The good news for these types of clients is that visceral fat, which is below the surface of the skin in between the organs, is a bit easier than subcutaneous fat to get rid of with moderate aerobic exercise and a healthy diet.

Waist circumference measurements are an easy way to assess a client for abdominal adiposity and help your understand if your client is at risk for many of the negative health outcomes that stem from a larger waist.

If a client has a waist measurement taken from the smallest portion of the trunk between the hips and armpits of greater than 35 inches for women and greater than 40 inches for men, they are at an increased risk for weight-related health issues, including insulin resistance, high blood pressure, high cholesterol, lower HDL levels and an increased risk for metabolic syndrome.

For those clients who are interested in reducing their overall girth measurement, moderate-intensity aerobic exercise has been shown to effectively decrease visceral fat.

Along with a healthy diet, performing a minimum of minutes of aerobic exercise weekly has been shown to decrease many health risks. However, according to the Physical Activity Guidelines , an additional minutes per week total, or 60 minutes per day, five days per week has been proven to further increase health benefits and help in the weight-loss process.

Performing 30 to 60 minutes of moderate-intensity aerobic exercise jogging, cycling, hiking, swimming, etc. is an effective way to reduce visceral fat stores, as well as increase HDL levels.

Other resistance-training exercises that are helpful to this type of clientele include core stability exercises and full-body circuits.

Trunk exercises that help to support the low back and correct poor posture are particularly effective because many clients with abdominal adiposity suffer from low-back pain due to the stress that is placed on the anterior portion of their body from the excess weight.

By performing full-body resistance training circuits that include core stability training, strength and weight-loss goals can be reached. Here are two workouts to use with clients who want to reduce abdominal fat and improve overall health and well-being. Full-body Resistance-training Circuit:.

Perform each exercise for 30 seconds, with a second rest in between to remain in the aerobic-training zone. Repeat the cycle two to four times to keep the heart rate up and calories burning.

Rest one to two minutes in between each cycle. Using a treadmill or short hill, do to second intervals RPE of on a scale of 10 five to 10 times. Walk for 60 to 90 seconds in between each interval. If you do two sets of five sprints, rest five to 10 minutes between sets by doing active recovery walking, or light jogging.

Jacque Crockford, DHSc, is an ACE Certified Personal Trainer and Senior Product Manager at ACE. She has been a personal trainer and performance coach for 20 years. Jacque grew up in the fitness industry, participating in YMCA sports and teaching gymnastics and swimming from a young age. Sign up to receive relevant, science-based health and fitness information and other resources.

Get answers to all your questions! Things like: How long is the program? Program Design. Body Type Workouts: How to Train Clients With an Android Body Type. by Jacqueline Crockford, DHSc on June 05, Filter By Category.

View All Categories. Participants Data from 66 obese children and adolescents coming to the hospital for medical consultation were used in this study.

Main Outcome Measures Subjects were stratified into tertiles of android to gynoid fat ratio determined by dual-energy x-ray absorptiometry. Insulin resistance was assessed by the homeostasis model of insulin resistance HOMA-IR index.

Results There were no differences in weight, body mass index, and body fat percentage between tertiles. Values of HOMA-IR were significantly increased in the 2 higher tertiles mean [SD], tertile 2, 2. Conclusions Android fat distribution is associated with an increased insulin resistance in obese children and adolescents.

An android to gynoid fat ratio based on dual-energy x-ray absorptiometry measurements is a useful and simple technique to assess distribution of body fat associated with an increased risk of insulin resistance.

The rising prevalence of childhood obesity represents an early risk factor for the development of metabolic and cardiovascular diseases in adults. Among obese children and adolescents, there is also an increased number of cases of type 2 diabetes mellitus, which was once considered as an adult-onset disease.

Since Vague, 1 it has been well established that the development of insulin resistance and the risk of cardiovascular diseases are associated with excess body fat in abdominal rather than in peripheral fat depots.

The visceral fat area has been shown to be correlated with glucose intolerance 3 , 4 independently of total fat mass and subcutaneous abdominal adipose tissue. A high intramyocellular lipid deposition has been shown to occur early during childhood and adolescence in association with peripheral insulin resistance.

Dual-energy x-ray absorptiometry DXA measurements have been used in several studies to assess regional body fat distribution in children 12 - 14 and the association with cardiovascular risk factors. Little attention has been paid to the association between gynoid fat storage and insulin resistance in obese children.

We hypothesized that children with a high android to gynoid fat ratio would exhibit an increased insulin resistance. Participants in this study were 66 obese children and adolescents 31 girls and 35 boys and their parents coming to the Department of Pediatrics, University Hospital, Clermont-Ferrand, France, for medical consultation.

Parents and children who agreed to take part to the study signed an informed consent. The experimental protocol of this study was approved by the local ethics committee Comité de Protection des Personnes, Sud Est IV. Children included in this study were higher than the 95th percentile of body mass index BMI for age and sex defined by the International Obesity Task Force.

Medical examination and anthropometric measurements were performed for each subject by a pediatrician. Body mass was measured to the nearest 0. Height was measured with a standing stadiometer and recorded with a precision of 1 mm. Body mass index was calculated as weight in kilograms divided by height in meters squared.

Body mass index and waist circumference z scores were calculated for age and sex reference values. All subjects were free of medication known to affect energy metabolism and none of the subjects had evidence of significant disease, non—insulin-dependent diabetes mellitus, or other endocrine disease.

Body composition was determined by DXA scan QDR x-ray bone densimeter; Hologic, Waltham, Massachusetts and version 9.

Children were asked to lie down in a supine position on the DXA table and to stay still until the end of the scanning procedure.

They were also instructed to keep their arms separated from their trunk and their legs separated from one another. Percentage of abdominal fat was determined manually by an experienced experimenter by drawing a rectangular box around the region of interest between vertebral bodies L1 and L4.

Gynoid fat deposition was assessed by lower limb fat percentage. Android to gynoid fat ratio was determined by using fat percentage in lower limbs and in the abdominal region.

To test the hypothesis that an android to gynoid fat ratio is associated with an impairment of insulin sensitivity, study subjects were grouped into tertiles. We used tertiles to ensure a number of subjects in each subgroup sufficient to give meaningful results.

Blood samples were drawn between 8 AM and 10 AM in a fasted state from an antecubital vein. The plasma glucose concentration was determined by enzymatic methods Modular P; Roche Diagnostics, Meylan, France. Plasma insulin concentration was assayed by a chemiluminescent enzyme immunoassay on an Immulite Diagnostic Products Corporation, Los Angeles, California.

Two indexes of insulin resistance were calculated from glucose and insulin concentrations. Results are expressed as mean SD. Normality of the distribution was checked with the Kolmogorov-Smirnov test for each variable. Dependent variables were compared between the 3 groups by using a 1-way analysis of variance.

Android to gynoid fat ratio and abdominal fat percentage were similar between boys and girls in the 3 groups. Hence, boys and girls were grouped together in each tertile. Spearman correlation coefficients were used to describe associations between continuous variables.

We also used a multiple stepwise regression to explain the variance of HOMA-IR values. Age, waist circumference z score, BMI, body fat percentage, and the android to gynoid fat ratio were included as independent variables. All statistical analyses were carried out with Statview software, version 5.

Descriptive results of the population are presented for boys and girls in Table 1. Body mass, percentage of body fat, and lean body mass were similar in the 3 tertiles.

Tertiles were also similar for the number of boys and girls. There was no significant difference for percentage of fat mass in lower limbs between tertiles.

Mean SD HOMA-IR values were significantly higher in tertiles 2 2. Mean SD quantitative insulin-sensitivity check index values were also significantly higher in tertile 1 0. Differences were not significant between tertiles 2 and 3.

Results are shown in Figure 1 and Figure 2. Mean SD homeostasis model of insulin resistance HOMA-IR index values in tertiles of android to gynoid fat ratio.

Mean SD quantitative insulin-sensitivity check index QUICKI values in tertiles of android to gynoid fat ratio. Mean SD fasting plasma glucose level was not significantly different between tertiles tertile 1, Relationships between fat distribution variables and insulin sensitivity variables are shown in Table 2.

Neither body fat percentage nor lower limbs fat percentage were significantly correlated with insulin sensitivity variables or glucose and insulin concentrations. None of the fat distribution variables had significant correlation with fasting glucose concentration.

The multiple stepwise regression showed that age and the android to gynoid fat ratio were significant predictors of HOMA-IR value β coefficients were 0.

Adjusted R 2 was 0. Body mass index, waist circumference z score, and body fat percentage were not significant predictors of HOMA-IR value. Our hypothesis was that a preferential fat storage at the abdominal level rather than in the lower limbs would be associated with increased insulin resistance.

To this aim, we calculated a simple index of android to gynoid fat distribution as a ratio between percentage of abdominal fat and percentage of lower limbs fat based on DXA measurements.

Insulin resistance was estimated by using simple indexes based on fasting plasma glucose and insulin concentrations. Indexes such as HOMA-IR and the quantitative insulin-sensitivity check index calculated from fasting samples have been shown to be valid to assess insulin resistance during puberty when compared with direct measurement with a glucose clamp.

Furthermore, insulin resistance was associated with abdominal adiposity without distinction between subcutaneous and visceral fat depots. However, although HOMA-IR values increased from the lowest tertile to tertiles 2 and 3, whereas there was no significant difference between tertiles 2 and 3, a linear regression between the android to gynoid fat ratio and HOMA-IR value did not provide a threshold value of android to gynoid fat ratio above which obese children have an increased risk of insulin resistance.

Indeed, in the present study, there was no significant association between percentage of body fat and insulin resistance. Previous studies have shown in young subjects that the degree of obesity is associated with a worsening of all the components of the metabolic syndrome, including insulin resistance.

Despite a similar degree of obesity, a lower prevalence of impaired glucose tolerance and type 2 diabetes have been reported in European than in American children. Hence, together with a reduced number of subjects with severe obesity in comparison with other studies, only mild alterations of insulin sensitivity may explain the lack of association between percentage of body fat and insulin resistance.

The development of abdominal obesity during puberty may be favored by pubertal insulin resistance and its consequent hyperinsulinemia.

Logically, age was a significant predictor of insulin resistance. Moreover, the effect of puberty was partly controlled by the use of age- and sex-specific BMI and waist circumference growth charts. Several studies have already used DXA to provide measurements of abdominal fat mass.

Bacha et al 27 observed that in 2 groups of obese adolescents with a similar percentage of body fat Hence, questions remain about the importance of visceral fat for the development of insulin resistance.

Finally, significant correlations between waist circumference or waist circumference z score and HOMA-IR confirm that simple anthropometric measurements are also reliable to assess an association between upper body adiposity and insulin resistance.

We did not observe any association between lower body fat percentage and insulin resistance. This result is similar to previous findings in adults. Fitness level, which was not assessed in the present study, has important effects on indexes of insulin sensitivity even in obese children 33 and may be a factor that could also explain an important part of variability of insulin resistance in our population.

To conclude, the present study showed that an android rather than gynoid fat distribution was associated with an increased insulin resistance in obese children and adolescents. Hence, an android to gynoid fat ratio based on DXA measurement may be a useful and simple technique to assess a pattern of body fat distribution associated with an increased insulin resistance.

This study also confirmed that the severity of insulin resistance is associated with abdominal obesity, which can be assessed by waist circumference measurement, whether fat is located essentially in visceral or subcutaneous adipose tissue in children and adolescents. Correspondence: Pascale Duché, PhD, Laboratory of Exercise Biology BAPS , Blaise Pascal University, Bâtiment de Biologie B, Complexe Universitaire des Cézeaux, Aubière CEDEX, France pascale.

duche univ-bpclermont. Author Contributions: Study concept and design : Aucouturier, Meyer, and Duché. Acquisition of data : Aucouturier, Thivel, and Taillardat.

Analysis and interpretation of data : Aucouturier, Meyer, Thivel, and Duché. Drafting of the manuscript : Aucouturier. Critical revision of the manuscript for important intellectual content : Aucouturier, Meyer, Thivel, Taillardat, and Duché.

Statistical analysis : Aucouturier, Thivel, Taillardat, and Duché. Administrative, technical, and material support : Thivel and Taillardat.

Study supervision : Aucouturier, Meyer, and Duché. Aucouturier J , Meyer M , Thivel D , Taillardat M , Duché P. Effect of Android to Gynoid Fat Ratio on Insulin Resistance in Obese Youth.

Arch Pediatr Adolesc Med. Artificial Intelligence Resource Center. Select Your Interests Customize your JAMA Network experience by selecting one or more topics from the list below. Save Preferences. Privacy Policy Terms of Use. X Facebook LinkedIn. This Issue.

Figure 1. Correlation matrix of fat distribution and NAFLD-related risk factors by sex. A All people, B male subgroup, and C female subgroup. A complex sample logistic regression was used to investigate the relationship between fat depots and the prevalence of NAFLD Table 3.

In the crude model, android percent fat was positively related to NAFLD OR: 1. We further conducted multivariable logistic regression analyses, additionally adjusting for BMI, hypertension, diabetes, ALT, AST, gamma-glutamyl-transpeptidase, total cholesterol, triglycerides, HDL, LDL, and uric acid, in which there were similar OR values resembling the two previous models.

Fat distribution and NAFLD categorized by gender are displayed in Table 5. More body fat in both the android area and gynoid areas was found in women than in men. Overall, the NAFLD group showed a similar pattern, except for the first and second quartiles, in which the proportion of women did not decline correspondingly as in the other two groups Figure 2.

Figure 2. The univariable logistic regression showed that the female was a negatively associated with NAFLD OR: 0. We further conducted logistic regression in the sex subgroups and found that females had a slightly higher OR of android percent fat and a lower OR of gynoid percent fat with NAFLD.

Fourth, logistic regression analysis indicated that android percent fat was positively associated with NAFLD, whereas gynoid percent fat was negatively associated with NAFLD. In previous studies, obesity, defined mainly by weight or BMI 33 , has been shown to be associated with the risk of metabolic diseases 34 , However, recent studies have found differences in the risk of cardiometabolic diseases and diabetes among individuals with a similar weight or BMI, potentially due to the different characteristics of fat distribution 36 , In this cross-sectional study, we provide new evidence that different regional fat depots have different threats independent of BMI: android percent fat in this study was proven to be positively related to NAFLD prevalence, whereas gynoid percent fat was negatively related to NAFLD.

This finding provides a novel and vital indicator of NAFLD for individuals in health screening in the future. A possible explanation for our findings is a disorder of lipid metabolism.

Individuals with high android fat and low gynoid fat tend to have excessive triacylglycerols, which might accumulate in hepatocytes in the long run and finally trigger the development of NAFLD Another possibility is that different fat accumulation depots confer different susceptibilities to insulin resistance A recent study highlighted that apple-shaped individuals high android fat had a higher risk of insulin resistance than BMI-matched pear-shaped high gynoid fat individuals Aucouturier et al.

Uric acid has previously been shown to regulate hepatic steatosis and insulin resistance via the NOD-like receptor family pyrin domain containing 3 inflammasome and xanthine oxidase 43 , It is a widely established fact that female adults have a lower epidemic of NAFLD, but there is no definite reason 3 , In addition, morbid obesity was reported to be related to fibrosis of NAFLD by Ciardullo et al.

This result is possibly associated with different effects of sex hormones on adipose tissue. Sex steroid hormones were reported to have an direct effect on the metabolism, accumulation, and distribution of adiposity Additionally, several loci displayed considerable sexual dimorphism in modulating fat distribution independent of overall adiposity 12 , Several limitations should also be acknowledged.

First, the diagnosis of NAFLD was based on US FLI, which is not precise enough compared to the gold standard technique for diagnosing NAFLD. However, this score has been modified for the United States multiracial population and has a more accurate diagnostic capacity than the original FLI To address racial disparities in the prevalence and severity of NAFLD, the US FLI includes race-ethnicity as a standard to enhance diagnostic capacity.

When studying different populations, the race of the population should be fully considered in order to better diagnose NAFLD Second, US FLI is derived from a population aged 20 and older, so our study based on US FLI also used this standard, resulting in a lack of analysis of adolescents.

Third, Given the lack of data, selection bias might exist. Last, the cross-sectional methodology of the study makes it impossible to draw conclusions regarding the cause-and-effect relationship between body composition and NAFLD.

Additional studies investigating the reasons are needed. Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

LY and CX conceived the study idea and designed the study. LY, HH, ZL, and JR performed the statistical analyses. LY wrote the manuscript. HH and CX revised the manuscript. All authors contributed to the article and approved the submitted version. This work was supported by the National Key Research and Development Program YFA , the National Natural Science Foundation of China , and the Key Research and Development Program of Zhejiang Province C The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Chalasani, N, Younossi, Z, Lavine, JE, Charlton, M, Cusi, K, Rinella, M, et al.

The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. doi: CrossRef Full Text Google Scholar. Stefan, N, and Cusi, K. A global view of the interplay between non-alcoholic fatty liver disease and diabetes.

Lancet Diabetes Endocrinol. PubMed Abstract CrossRef Full Text Google Scholar. Riazi, K, Azhari, H, Charette, JH, Underwood, FE, King, JA, Afshar, EE, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol.

Younossi, Z, Tacke, F, Arrese, M, Chander Sharma, B, Mostafa, I, Bugianesi, E, et al. Global perspectives on nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Kim, D, Konyn, P, Sandhu, KK, Dennis, BB, Cheung, AC, and Ahmed, A.

Metabolic dysfunction-associated fatty liver disease is associated with increased all-cause mortality in the United States. J Hepatol. Peiris, AN, Sothmann, MS, Hoffmann, RG, Hennes, MI, Wilson, CR, Gustafson, AB, et al.

Adiposity, fat distribution, and cardiovascular risk. Ann Intern Med. Nabi, O, Lacombe, K, Boursier, J, Mathurin, P, Zins, M, and Serfaty, L. Prevalence and risk factors of nonalcoholic fatty liver disease and advanced fibrosis in general population: the French Nationwide NASH-CO study.

Jarvis, H, Craig, D, Barker, R, Spiers, G, Stow, D, Anstee, QM, et al. Metabolic risk factors and incident advanced liver disease in non-alcoholic fatty liver disease NAFLD : a systematic review and meta-analysis of population-based observational studies.

PLoS Med. Huang, H, and Xu, C. Retinol-binding protein-4 and nonalcoholic fatty liver disease. Chin Med J. Guenther, M, James, R, Marks, J, Zhao, S, Szabo, A, and Kidambi, S. Adiposity distribution influences circulating adiponectin levels. Transl Res. Okosun, IS, Seale, JP, and Lyn, R. Commingling effect of gynoid and android fat patterns on cardiometabolic dysregulation in normal weight American adults.

Nutr Diabetes. Fu, J, Hofker, M, and Wijmenga, C. Apple or pear: size and shape matter. Cell Metab. Kang, SM, Yoon, JW, Ahn, HY, Kim, SY, Lee, KH, Shin, H, et al. Android fat depot is more closely associated with metabolic syndrome than abdominal visceral fat in elderly people.

PLoS One. Fuchs, A, Samovski, D, Smith, GI, Cifarelli, V, Farabi, SS, Yoshino, J, et al. Associations among adipose tissue immunology, inflammation, exosomes and insulin sensitivity in people with obesity and nonalcoholic fatty liver disease.

Polyzos, SA, Kountouras, J, and Mantzoros, CS. Obesity and nonalcoholic fatty liver disease: from pathophysiology to therapeutics. Metab Clin Exp. Adab, P, Pallan, M, and Whincup, PH.

Is BMI the best measure of obesity? Manolopoulos, KN, Karpe, F, and Frayn, KN. Gluteofemoral body fat as a determinant of metabolic health.

Int J Obes. Karastergiou, K, Smith, SR, Greenberg, AS, and Fried, SK. Sex differences in human adipose tissues—the biology of pear shape. Biol Sex Differ. Bedogni, G, Bellentani, S, Miglioli, L, Masutti, F, Passalacqua, M, Castiglione, A, et al. The fatty liver index: a simple and accurate predictor of hepatic steatosis in the general population.

BMC Gastroenterol. Kahl, S, Straßburger, K, Nowotny, B, Livingstone, R, Klüppelholz, B, Keßel, K, et al. Comparison of liver fat indices for the diagnosis of hepatic steatosis and insulin resistance. Cuthbertson, DJ, Weickert, MO, Lythgoe, D, Sprung, VS, Dobson, R, Shoajee-Moradie, F, et al.

External validation of the fatty liver index and lipid accumulation product indices, using 1H-magnetic resonance spectroscopy, to identify hepatic steatosis in healthy controls and obese, insulin-resistant individuals. Eur J Endocrinol. Ruhl, CE, and Everhart, JE. Fatty liver indices in the multiethnic United States National Health and nutrition examination survey.

Aliment Pharmacol Ther. Tavaglione, F, Jamialahmadi, O, De Vincentis, A, Qadri, S, Mowlaei, ME, Mancina, RM, et al.

Development and validation of a score for fibrotic nonalcoholic steatohepatitis. Clin Gastroenterol Hepatol. Centers for Disease Control and Prevention.

National Health and Nutrition Examination Survey Data Documentation, Codebook, and Frequencies. htm Accessed February Google Scholar.

htm Accessed October National Health and Nutrition Examination Survey Data Documentation, Codebook, and Frequencies. Matthews, DR, Hosker, JP, Rudenski, AS, Naylor, BA, Treacher, DF, and Turner, RC.

Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Thompson, ML, Myers, JE, and Kriebel, D.

Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done? Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies.

Lancet ; Korea Centers for Disease Control and Prevention. Statistics for Korea National Health in [Internet]. Cheongju: Korea Centers for Disease Control and Prevention; [cited Mar 6]. Thornhill R, Gangestad SW.

The evolutionary biology of human female sexuality. New York NY : Oxford University Press; Ley CJ, Lees B, Stevenson JC. Sex- and menopause-associated changes in body-fat distribution. Am J Clin Nutr ; Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endocrinol ; Min KB, Min JY.

Android and gynoid fat percentages and serum lipid levels in United States adults. Clin Endocrinol Oxf ; Guglielmi V, Sbraccia P. Obesity phenotypes: depot-differences in adipose tissue and their clinical implications.

Eat Weight Disord ; Pasquali R, Cantobelli S, Casimirri F, Capelli M, Bortoluzzi L, Flamia R, et al. The hypothalamic-pituitary-adrenal axis in obese women with different patterns of body fat distribution.

J Clin Endocrinol Metab ; Epel EE, Moyer AE, Martin CD, Macary S, Cummings N, Rodin J, et al. Stress-induced cortisol, mood, and fat distribution in men. Obes Res ; Pasquali R, Anconetani B, Chattat R, Biscotti M, Spinucci G, Casimirri F, et al.

Metabolism ; Vicennati V, Pasquali R. Abnormalities of the hypothalamicpituitary- adrenal axis in nondepressed women with abdominal obesity and relations with insulin resistance: evidence for a central and a peripheral alteration.

Zumoff B, Strain GW, Miller LK, Rosner W, Senie R, Seres DS, et al. Plasma free and non-sex-hormone-binding-globulinbound testosterone are decreased in obese men in proportion to their degree of obesity.

Vermeulen A, Kaufman JM, Deslypere JP, Thomas G. Attenuated luteinizing hormone LH pulse amplitude but normal LH pulse frequency, and its relation to plasma androgens in hypogonadism of obese men.

Isidori AM, Caprio M, Strollo F, Moretti C, Frajese G, Isidori A, et al. Leptin and androgens in male obesity: evidence for leptin contribution to reduced androgen levels. Pasquali R, Vicennati V. Obesity and hormonal abnormalities.

In: Björntorp P, editors. International textbook of obesity. Taylor RW, Grant AM, Williams SM, Goulding A. Sex differences in regional body fat distribution from pre- to postpuberty. Obesity Silver Spring ; Mongraw-Chaffin ML, Anderson CA, Allison MA, Ouyang P, Szklo M, Vaidya D, et al.

Association between sex hormones and adiposity: qualitative differences in women and men in the multi-ethnic study of atherosclerosis.

J Clin Endocrinol Metab ;E Dhindsa S, Miller MG, McWhirter CL, Mager DE, Ghanim H, Chaudhuri A, et al. Testosterone concentrations in diabetic and nondiabetic obese men. Diabetes Care ; Allan CA, McLachlan RI. Androgens and obesity.

Curr Opin Endocrinol Diabetes Obes ; Arner P, Lithell H, Wahrenberg H, Brönnegard M. Expression of lipoprotein lipase in different human subcutaneous adipose tissue regions.

J Lipid Res ; Ramirez ME, McMurry MP, Wiebke GA, Felten KJ, Ren K, Meikle AW, et al. Evidence for sex steroid inhibition of lipoprotein lipase in men: comparison of abdominal and femoral adipose tissue. Cnop M, Havel PJ, Utzschneider KM, Carr DB, Sinha MK, Boyko EJ, et al.

Relationship of adiponectin to body fat distribution, insulin sensitivity and plasma lipoproteins: evidence for independent roles of age and sex.

Diabetologia ; Khaw KT, Barrett-Connor E. Lower endogenous androgens predict central adiposity in men. Ann Epidemiol ; Grossmann M. Low testosterone in men with type 2 diabetes: significance and treatment.

Michalakis K, Mintziori G, Kaprara A, Tarlatzis BC, Goulis DG. The complex interaction between obesity, metabolic syndrome and reproductive axis: a narrative review. King N, Byrne NM, Hunt A, Hills A. Comparing exercise prescribed with exercise completed: effects of gender and mode of exercise.

J Sports Sci ; Ismail I, Keating SE, Baker MK, Johnson NA. A systematic review and meta-analysis of the effect of aerobic vs. resistance exercise training on visceral fat.

Obes Rev ; Sanal E, Ardic F, Kirac S. Effects of aerobic or combined aerobic resistance exercise on body composition in overweight and obese adults: gender differences: a randomized intervention study.

Eur J Phys Rehabil Med ; Aadland E, Jepsen R, Andersen JR, Anderssen SA. Differences in fat loss in response to physical activity among severely obese men and women. J Rehabil Med ; Aadland E, Robertson L.

Physical activity is associated with weight loss and increased cardiorespiratory fitness in severely obese men and women undergoing lifestyle treatment.

J Obes ; Boutcher SH, Dunn SL. Factors that may impede the weight loss response to exercise-based interventions. McMurray RG, Hackney AC. Interactions of metabolic hormones, adipose tissue and exercise. Sports Med ; Zouhal H, Jacob C, Delamarche P, Gratas-Delamarche A.

Catecholamines and the effects of exercise, training and gender. Kuk JL, Ross R. Influence of sex on total and regional fat loss in overweight and obese men and women. Int J Obes Lond ; Niskanen L, Laaksonen DE, Punnonen K, Mustajoki P, Kaukua J, Rissanen A.

Changes in sex hormone-binding globulin and testosterone during weight loss and weight maintenance in abdominally obese men with the metabolic syndrome.

Diabetes Obes Metab ; Hammoud A, Gibson M, Hunt SC, Adams TD, Carrell DT, Kolotkin RL, et al. Effect of Roux-en-Y gastric bypass surgery on the sex steroids and quality of life in obese men.

Ross R, Dagnone D, Jones PJ, Smith H, Paddags A, Hudson R, et al. Reduction in obesity and related comorbid conditions after diet-induced weight loss or exercise-induced weight loss in men: a randomized, controlled trial.

Ann Intern Med ; Rolls BJ, Fedoroff IC, Guthrie JF. Gender differences in eating behavior and body weight regulation. Health Psychol ; Wirth A, Steinmetz B. Gender differences in changes in subcutaneous and intra-abdominal fat during weight reduction: an ultrasound study.

Lemon SC, Rosal MC, Zapka J, Borg A, Andersen V. Contributions of weight perceptions to weight loss attempts: differences by body mass index and gender. Body Image ; French SA, Jeffery RW, Wing RR. Sex differences among participants in a weight-control program.

Addict Behav ; Robertson C, Archibald D, Avenell A, Douglas F, Hoddinott P, van Teijlingen E, et al. Systematic reviews of and integrated report on the quantitative, qualitative and economic evidence base for the management of obesity in men. Health Technol Assess ; Young MD, Morgan PJ, Plotnikoff RC, Callister R, Collins CE.

Effectiveness of male-only weight loss and weight loss maintenance interventions: a systematic review with meta-analysis.