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What Happens Inside Your Body When You Burn FatBody composition and metabolism -
Oflaz et al. In this study, intima media thickness of the common carotid was significantly higher, and flow-mediated dilation was significantly lower in MHO individuals despite a normal metabolic profile.
Also in that study, lipid profile, blood pressure, insulin sensitivity homeostasis model assessment , and anthropometric measurements could not explain the flow-mediated dilation or intima media thickness in the MHO and lean individuals.
In addition, early atherosclerotic changes in MHO individuals are evident compared with healthy lean individuals, suggesting that factors other than obesity-related risks could be responsible for this observation.
Thus, it is not our intention to convey the idea that MHO individuals are at an optimal state of health, as evidenced by the work of Oflaz et al. A more prudent statement would be that MHO individuals are at a lower risk than at risk obese individuals, but at a higher risk than the general population.
Visceral fat has been associated with a decrease in insulin sensitivity, which could lead to an increase risk of cardiovascular disease The higher levels of insulin sensitivity in MHO individuals may be due in part to lower amounts of visceral fat despite the presence of large amounts of total body fatness.
Further investigations may want to consider the examination of adipocytes as a source of potential differences in insulin sensitivity.
Larger adipocytes have been associated with an increase in insulin resistance 19 , and normal size adipocytes have been associated with early onset of obesity 5.
Therefore, the measurement of cell size and number in adipose tissue could indicate whether there is an increased number of normal sized adipocytes in MHO individuals and, in turn, could explain at least in part the higher insulin sensitivity observed in MHO individuals.
Future research is needed to clarify this hypothesis. MONW persons are a subgroup of individuals who have normal weight and body mass index BMI , but display a cluster of obesity-related abnormalities. Although there has long been clinical recognition of this group of individuals, to our knowledge they were first described in detail in the s 3 , 4 and recently reviewed As described, these individuals can be young and display premature signs of insulin resistance, hyperinsulinemia, and dyslipidemia that may eventually increase their risk for the development of diabetes and cardiovascular disease.
It has been suggested, however, that body composition and body fat distribution abnormalities may play an important role in the development of metabolic complications in these individuals Although it has been suggested that there is a high prevalence of MONW individuals in the general population 20 , the exact percentage is unclear.
However, in the study by Dvorak et al. Differences in metabolic characteristics in MONW individuals and normal healthy individuals. Several recent studies have examined the clustering of phenotypes in the MONW individual. For example, Zavaroni et al. In a recent study, Katsuki et al.
The researchers concluded that increased levels of visceral fat and plasma triglycerides were associated with insulin resistance. Dvorak et al. In this study BMI, body mass, and fat-free mass were not significantly different between groups. Despite no differences in BMI, differences in body composition and body fat distribution were noted.
Several selected metabolic characteristics in MONW and metabolically healthy individuals. Data were adapted from Dvorak et al. Physical inactivity and low cardiorespiratory fitness 24 could be considered important risk factors in the development of type 2 diabetes.
In the study by Dvorak et al. The logic is that differences in energy expenditure may help explain differences in body composition and body fat distribution.
The researchers reported no significant differences in maximal cardiorespiratory fitness between the groups. The researchers suggested that physical daily energy expenditure appears to influence insulin sensitivity and other cardiovascular disease risk factors primarily through its effects on energy balance and body composition In addition, low levels of physical activity energy expenditure in the MONW individual could favor a positive energy balance and, in turn, may increase, in part, total fat mass.
Similar results were observed in a recent study from our laboratory unpublished indicating that young MONW women, despite having normal BMI, showed distinct differences in body composition compared with healthy normal young women.
In this study MONW women showed a higher relative fat mass, a lower fat-free mass, and a tendency for greater central fat mass. In addition, MONW women showed significantly higher total cholesterol and low density lipoprotein cholesterol levels, but plasma triglycerides were similar in both groups.
This could suggest that the percent body fat mass even within a normal BMI range may be predictive of reduced insulin sensitivity in MONW, young, normal weight women. Collectively, the relative level of body fatness may be an important first step to screen and identify MONW subjects.
In addition, the researchers concluded that the higher fat mass in MONW women could be mediated indirectly by low cardiorespiratory fitness, as demonstrated by lower levels of the maximum rate of oxygen uptake VO 2max and reduced physical activity energy expenditure as shown by low leisure time physical activity and greater time spent watching TV Conus, F.
Allison, R. Rabasa-Lhoret, M. St-Onge, D. St-Pierre, A. Tremblay-Lebeau, and E. Poehlman, unpublished observations. These findings suggest that physical inactivity may be an important marker of the MONW young woman in relation to body composition.
Several recent studies have investigated in more detail other metabolic disturbances in the MONW individual. For instance, excessive fat on the upper part of the body or abdomen, as measured by the waist to hip ratio, is associated with an increase risk of diabetes and cardiovascular disease Indeed, there is evidence to suggest that young healthy men 27 and in young adults 28 who fit the description of the MONW individual show an increase in intraabdominal fat, and this is associated with a decrease in insulin sensitivity as well as an increased risk for cardiovascular disease.
Finally, it may be hypothesized that MONW individuals have a decrease in fat storage in adipose tissue. This, in turn, could explain the increase in plasma triglycerides levels observed in the previous studies 22 , In addition, this could increase fat storage in nonphysiological depots such as liver and muscle.
In support of this conclusion, Seppala-Lindroos et al. The researchers suggested that the increase in liver fat could be associated with a decrease in insulin sensitivity. Although not systematically examined, future areas of investigation may want to target measures of adipose and GI hormones to help us understand health profiles in MHO and MONW individuals.
Proteins such as acylation-stimulating protein, leptin, adiponectin, resistin, and other novel GI hormones ghrelin could influence adipose and overall metabolism as well as insulin sensitivity Thus, a key issue is whether MHO and MONW individuals display a hormonal profile that distinguishes them from either at risk obese individuals or normal healthy individuals, respectively.
Recent studies show that levels of stomach-derived ghrelin 31 , 32 and adipose tissue-derived adiponectin 33 , 34 are inversely related to insulin resistance. Interestingly, increased visceral fat accumulation is believed to down-regulate both ghrelin 31 and adiponectins 33 , 35 level.
The levels of other adipose tissue-derived hormones, such as resistin 35 , leptin 31 , and acylation-stimulating protein 36 , 37 , are reported to be positively correlated with insulin resistance and visceral fat accumulation. To our knowledge only one study has examined plasma levels of adiponectin in Japanese MONW individuals In that study, plasma levels of adiponectin showed no significant differences between MONW and normal subjects; however, a significant correlation between plasma levels of adiponectin and the rate of glucose infusion was observed in MONW subjects.
This suggests that adiponectin could be involved in the development of insulin resistance in MONW individuals. An understanding of the MHO individual has important implications for both clinical and academic work.
Sims 39 underscored the need to appreciate the effects of subtypes of obesity in clinical and research aspects. The overall success rate for losing weight and maintaining a reduced body weight is quite poor. MHO individuals may contribute to this poor record by their strong tendency to regain lost body weight.
One may even question the need to aggressively treat MHO individuals, given their favorable metabolic profile. In contrast, early identification and treatment of the metabolic abnormalities of MONW individuals could be effective in the primary prevention i.
Moreover, in clinical research, volunteers are frequently excluded from participation if they have one or more of the phenotypes of the metabolic syndrome. This recruitment or exclusion strategy could increase the percentage of MHO individuals within a given protocol.
In addition, MONW individuals are frequently undetected and undiagnosed. Therefore, recruiting MONW individuals with lean individuals could also render data difficult to interpret. Finally, in medical education, it is important to educate health care professionals and physicians about the different needs of subsets of obese individuals.
Identifying the physiological and behavioral factors that could classify an individual as MHO or MONW would be valuable. This area of investigation could have important implications for therapeutic medical decision-making i.
whether to treat individuals , subject characterization in research protocols, and medical education. Interesting future areas of investigation may want to target free fatty acid trapping A greater understanding of the regulation of free fatty acid transport, storage, and utilization in MHO and MONW individuals would be valuable in identifying mechanisms that could regulate these subsets.
In addition, genetic studies using the microarray technique will offer the possibility to engage in discovery-driven research. Simply put, is there a distinct profile of genes found in MHO and MONW individuals?
Ultimately, after the gene expression is measured, one may attempt to identify those genes for which there is differential expression in MHO and at risk obese individuals as well as MONW and metabolically healthy individuals.
This area of research could potentially broaden our knowledge of factors that predispose subtypes of obese individuals to develop metabolic complications. This work was supported by grants from the Canadian Institutes of Health Research and the Canadian Foundation of Innovation.
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Sims EA Are there persons who are obese, but metabolically healthy? Metabolism 50 : — Frayn KN Adipose tissue as a buffer for daily lipid flux. However, these imaging techniques are expensive, require a well-trained observer, and are not always available in the clinical practice [ 77 ].
Obesity is defined as an excess of adiposity, with the amount of this excess correlating with comorbidity development [ 8 ]. Although the BMI shows a good correlation with adiposity in large population studies, it presents a very high error rate when we study patients at the individual level; this fact is very remarkable in the era of personalized medicine.
Moreover, these incorrectly classified patients exhibited numerous risk factors above the thresholds established for predicting cardiometabolic risk. Our data, together with other studies [ 16 , 17 ] evidence that there is a substantial degree of misclassification in the diagnosis of obesity in clinical practice using the BMI, in particular in those considered as having overweight, and that we are missing opportunities to treat patients with this life-threatening condition.
Vertical dashed lines indicate cut-offs for defining overweight OW and obesity OB according to BMI The number of subjects in each quadrant is indicated. The availability of devices to determine body composition has been increasing in recent years and nowadays it is very common to have them for example BIA devices, whose accuracy has increased over the years available in consultations with nutritionists, endocrinologists or even in primary care offices.
Most of the studies aimed to determine the influence of adiposity on cardiometabolic alterations have focused more on estimators of body fat distribution than on the amount of body fat per se.
However, a growing number of studies indicate that the amount of body fat is also exerting a fundamental role in the increased cardiometabolic risk [ 12 , 36 , , , , , , ].
Body composition provides a scientific explanation that may help to understand the observed increased cardiovascular risk in metabolically unhealthy normal weight MUNW subjects with high adiposity [ 43 , , ]. This is of particular relevance due to the pathophysiological implications that increased adiposity may have in the context of NW or overweight.
Although BMI is widely used as a proxy indicator of body adiposity, it does not provide an actual measure of body composition as previously evidenced [ 12 ]. A high number of patients with obesity are being underdiagnosed, and, therefore, opportunities for cardiometabolic risk assessment and instauration of appropriate treatment measures are being lost.
In this sense, the inclusion of body composition determination together with the evaluation of metabolic alterations in the routine clinical practice both for the diagnosis and the instauration of the most adequate management of obesity should be pursued [ 8 ].
An elevated VAT is a hallmark sign of increased cardiometabolic risk, even among NW subjects [ , , ].
Imaging techniques using first CT and MRI thereafter revealed that the amount of VAT is a major determinant of the cardiometabolic risk [ , ]. There is no consensus for VAT area cut-off points to define increased metabolic risk, although it has been proposed that in both females and males a value of cm 2 was linked to significant changes in the risk profile for CVD, and when values of more than cm 2 of VAT were attained, a further elevation of the cardiometabolic risk was seen [ ].
In addition, these techniques have been improved and volumetric data obtained from multislice imaging has confirmed that even after taking into consideration the usual anthropometric measures VAT is still more strongly linked to a harmful cardiometabolic risk profile [ ].
Therefore, VAT determination may provide a more detailed picture of the obesity-associated cardiometabolic risk. The information obtained thanks to body composition techniques has allowed to detect the presence of a condition with important functional implications that consists of the simultaneous presence of excess adiposity and a deficit in skeletal muscle mass and function sarcopenia , which has been defined as sarcopenic obesity [ , , ].
The matrix resulting from the combination of low and high body adiposity and low and high skeletal muscle mass results in the establishment of another classification system for obesity-related phenotypes Fig. The diagnosis is based on skeletal muscle functional parameters for example hand-grip strength adjusted by body mass and, if a dysfunction is detected, the process will continue with body composition to identify potential increased fat mass and reduced skeletal muscle mass.
When both situations concur the presence of sarcopenic obesity can be diagnosed [ ]. The lack of clear diagnostic criteria during the last years made it difficult to adequately study the cardiometabolic risk associated with these phenotypes.
With this approach it was shown that sarcopenic obesity is associated with an increase in cardiometabolic risk [ , ]. In the same line, other studies using this phenotyping system have suggested that the maintenance of skeletal muscle mass with ageing reduced the development of T2D [ ].
Phenotyping system according to fat mass and skeletal muscle mass. The evaluation regarding skeletal muscle mass includes amount and functionality. The diagnostic criteria are reported in a consensus statement [ ]. Both BMI and WC may have useful applications in routine clinical practice.
As commented above, patient stratification according to BMI and WC simultaneously allows a better prediction of cardiovascular or death risk [ 80 , 81 , 85 , 86 , 87 ].
However those studies were not aimed to establish different phenotypes according to both amount and distribution of adiposity. However, although the cardiometabolic risk estimated with this approach is more precise than the use of BMI or WC alone it still maintains the mentioned BMI limitations.
This phenotyping system Fig. By using this approach, a very accurate stratification of cardiometabolic risk factors is achieved unpublished results. This phenotyping system establishes nine different types 1a to 3c clustered in five different phenotypes according to the cardiometabolic risk.
Green: no risk; yellow: slightly increased risk; orange: increased risk; dark orange: high risk and red: very high risk. It seems clear that the current obesity classification systems do not allow a good diagnosis and prediction of the comorbidity risk of the patients and, therefore, their clinical management.
More than a decade ago it was proposed that obesity phenotyping should be more sophisticated including dynamic rather than static phenotypes taking into account the variability in body composition and its influence on metabolism and the physiological responses to diet and exercise [ ].
Some advances have been achieved in the phenotyping of obesities with the incorporation, for example, of functionality or metabolic health.
Moreover, the evolution of software tools is allowing the development of new approaches incorporating machine learning for establishing new classification systems according to the combination of genetics, adipocyte morphology and metabolic traits [ ], the calculation of adiposity by using smartphones [ ] or the development of whole-body three-dimensional optical scanning to predict the metabolic risk [ ].
Improvements in the resolution and availability of body composition equipments have also produced a greater implementation of these techniques. Body composition has allowed the identification of sarcopenic obesity, to establish more refined obesity phenotypes and to better define obesity-associated cardiometabolic risk.
It is time to implement these advances in routine clinical practice in a more constant way to prevent the development of overweight and obesity, as well as to achieve a better management of people living with obesity. Blüher M. Obesity: global epidemiology and pathogenesis.
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Metabolically healthy but obese, a matter of time? Findings from the prospective pizarra study. Lin L, Zhang J, Jiang L, Du R, Hu C, et al. Transition of metabolic phenotypes and risk of subclinical atherosclerosis according to BMI: a prospective study.
Appleton SL, Seaborn CJ, Visvanathan R, Hill CL, Gill TK, et al. Diabetes and cardiovascular disease outcomes in the metabolically healthy obese phenotype: a cohort study. Morkedal B, Vatten LJ, Romundstad PR, Laugsand LE, Janszky I. Risk of myocardial infarction and heart failure among metabolically healthy but obese individuals.
The HUNT study, Norway. Hinnouho GM, Czernichow S, Dugravot A, Batty GD, Kivimaki M, et al. Metabolically healthy obesity and risk of mortality: does the definition of metabolic health matter? Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?
Ann Intern Med. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, et al. Yumuk V, Frühbeck G, Oppert JM, Woodward E, Toplak H. An EASO position statement on multidisciplinary obesity management in adults.
Neeland IJ, Poirier P, Despres JP. Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Pérez-Pevida B, Núñez-Cordoba JM, Romero S, Miras AD, Ibañez P, et al.
Discriminatory ability of anthropometric measurements of central fat distribution for prediction of post-prandial hyperglycaemia in patients with normal fasting glucose: the DICAMANO Study. J Transl Med. Vague J. The degree of masculine differentiation of obesities: a factor determining predisposition to diabetes, atherosclerosis, gout, and uric calculous disease.
Am J Clin Nutr. Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, et al. General and abdominal adiposity and risk of death in Europe. Cerhan JR, Moore SC, Jacobs EJ, Kitahara CM, Rosenberg PS et al.
A pooled analysis of waist circumference and mortality in , adults. Lassale C, Tzoulaki I, Moons KGM, Sweeting M, Boer J, et al. Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-european case-cohort analysis.
Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on visceral obesity.
Ardern CI, Janssen I, Ross R, Katzmarzyk PT. Development of health-related waist circumference thresholds within BMI categories. Obes Res. Janssen I, Katzmarzyk PT, Ross R. Body mass index, waist circumference, and health risk: evidence in support of current National Institutes of Health guidelines.
Ardern CI, Katzmarzyk PT, Janssen I, Ross R. Discrimination of health risk by combined body mass index and waist circumference. Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, et al. Obesity and the risk of myocardial infarction in 27, participants from 52 countries: a case-control study.
Nazare JA, Smith J, Borel AL, Aschner P, Barter P, et al. Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile from the INSPIRE ME IAA study.
Am J Cardiol. Vazquez G, Duval S, Jacobs DR Jr, Silventoinen K. Epidemiol Rev. Qiao Q, Nyamdorj R. Is the association of type II diabetes with waist circumference or waist-to-hip ratio stronger than that with body mass index?
de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies.
Czernichow S, Kengne AP, Stamatakis E, Hamer M, Batty GD. Body mass index, waist circumference and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk?
CAS PubMed PubMed Central Google Scholar. Aune D, Sen A, Norat T, Janszky I, Romundstad P, et al. Body mass index, abdominal fatness and heart failure incidence and mortality: a systematic review and dose-response meta-analysis of prospective studies.
Treatment of obesity: need to focus on high risk abdominally obese patients. Ashwell M, Cole TJ, Dixon AK. Ratio of waist circumference to height is strong predictor of intra-abdominal fat. Ashwell M, Gibson S. BMC Med. Savva SC, Lamnisos D, Kafatos AG.
Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis. Diabetes Metab Syndr Obes. Gruson E, Montaye M, Kee F, Wagner A, Bingham A, et al. Anthropometric assessment of abdominal obesity and coronary heart disease risk in men: the PRIME study.
Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0. Nutr Res Rev. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis.
BMJ Open. Hwaung P, Heo M, Kennedy S, Hong S, Thomas DM, et al. Optimum waist circumference-height indices for evaluating adult adiposity: an analytic review.
Müller MJ, Braun W, Enderle J, Bosy-Westphal A, Beyond BMI. Conceptual issues related to overweight and obese patients. Gonzalez MC, Correia M, Heymsfield SB. A requiem for BMI in the clinical setting. Curr Opin Clin Nutr Metab Care. Holmes CJ, Racette SB. The utility of body composition assessment in nutrition and clinical practice: an overview of current methodology.
Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Favaretto F, Bettini S, Busetto L, Milan G, Vettor R. Adipogenic progenitors in different organs: pathophysiological implications.
Rev Endocr Metab Disord. Klein S, Gastaldelli A, Yki-Järvinen H, Scherer PE. Why does obesity cause diabetes? Sakers A, De Siqueira MK, Seale P, Villanueva CJ. Adipose-tissue plasticity in health and disease. Rattarasarn C, Leelawattana R, Soonthornpun S, Setasuban W, Thamprasit A, et al.
Relationships of body fat distribution, insulin sensitivity and cardiovascular risk factors in lean, healthy non-diabetic thai men and women.
Diabetes Res Clin Pract. Dervaux N, Wubuli M, Megnien JL, Chironi G, Simon A. Comparative associations of adiposity measures with cardiometabolic risk burden in asymptomatic subjects. Body adiposity and type 2 diabetes: increased risk with a high body fat percentage even having a normal BMI.
Gómez-Ambrosi J, Catalán V, Rodríguez A, Salvador J, Frühbeck G. Does body adiposity better predict obesity-associated cardiometabolic risk than body mass index? Gómez-Ambrosi J, Moncada R, Valentí V, Silva C, Ramírez B, et al.
Cardiometabolic profile related to body adiposity identifies patients eligible for bariatric surgery more accurately than BMI.
Obes Surg. Gómez-Ambrosi J, Andrada P, Valentí V, Rotellar F, Silva C, et al. Dissociation of body mass index, excess weight loss and body fat percentage trajectories after 3 years of gastric bypass: relationship with metabolic outcomes. Segal KR, Dunaif A, Gutin B, Albu J, Nyman A, et al.
Body composition, not body weight, is related to cardiovascular disease risk factors and sex hormone levels in men. De Lorenzo A, Del Gobbo V, Premrov MG, Bigioni M, Galvano F, et al. Normal-weight obese syndrome: early inflammation?
Deurenberg P, Andreoli A, Borg P, Kukkonen-Harjula K, de Lorenzo A, et al. The validity of predicted body fat percentage from body mass index and from impedance in samples of five european populations. De Lorenzo A, Deurenberg P, Pietrantuono M, Di Daniele N, Cervelli V, et al.
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Home » Blogs » Caloric restriction and kidney function Metabolism and Your Body Composition. But you should. People metabolismm naturally compoxition of Snake envenomation therapy metabolism composution and the weight gain they know comes with it. To some extent, those worries are well-founded. Metabolism is linked with weight gain and loss because of its a biological process involved with energy and calories. The Mayo Clinic defines metabolism as:.Body composition and metabolism -
Visceral fat has been associated with a decrease in insulin sensitivity, which could lead to an increase risk of cardiovascular disease The higher levels of insulin sensitivity in MHO individuals may be due in part to lower amounts of visceral fat despite the presence of large amounts of total body fatness.
Further investigations may want to consider the examination of adipocytes as a source of potential differences in insulin sensitivity. Larger adipocytes have been associated with an increase in insulin resistance 19 , and normal size adipocytes have been associated with early onset of obesity 5.
Therefore, the measurement of cell size and number in adipose tissue could indicate whether there is an increased number of normal sized adipocytes in MHO individuals and, in turn, could explain at least in part the higher insulin sensitivity observed in MHO individuals.
Future research is needed to clarify this hypothesis. MONW persons are a subgroup of individuals who have normal weight and body mass index BMI , but display a cluster of obesity-related abnormalities.
Although there has long been clinical recognition of this group of individuals, to our knowledge they were first described in detail in the s 3 , 4 and recently reviewed As described, these individuals can be young and display premature signs of insulin resistance, hyperinsulinemia, and dyslipidemia that may eventually increase their risk for the development of diabetes and cardiovascular disease.
It has been suggested, however, that body composition and body fat distribution abnormalities may play an important role in the development of metabolic complications in these individuals Although it has been suggested that there is a high prevalence of MONW individuals in the general population 20 , the exact percentage is unclear.
However, in the study by Dvorak et al. Differences in metabolic characteristics in MONW individuals and normal healthy individuals. Several recent studies have examined the clustering of phenotypes in the MONW individual.
For example, Zavaroni et al. In a recent study, Katsuki et al. The researchers concluded that increased levels of visceral fat and plasma triglycerides were associated with insulin resistance.
Dvorak et al. In this study BMI, body mass, and fat-free mass were not significantly different between groups. Despite no differences in BMI, differences in body composition and body fat distribution were noted.
Several selected metabolic characteristics in MONW and metabolically healthy individuals. Data were adapted from Dvorak et al. Physical inactivity and low cardiorespiratory fitness 24 could be considered important risk factors in the development of type 2 diabetes. In the study by Dvorak et al. The logic is that differences in energy expenditure may help explain differences in body composition and body fat distribution.
The researchers reported no significant differences in maximal cardiorespiratory fitness between the groups. The researchers suggested that physical daily energy expenditure appears to influence insulin sensitivity and other cardiovascular disease risk factors primarily through its effects on energy balance and body composition In addition, low levels of physical activity energy expenditure in the MONW individual could favor a positive energy balance and, in turn, may increase, in part, total fat mass.
Similar results were observed in a recent study from our laboratory unpublished indicating that young MONW women, despite having normal BMI, showed distinct differences in body composition compared with healthy normal young women.
In this study MONW women showed a higher relative fat mass, a lower fat-free mass, and a tendency for greater central fat mass. In addition, MONW women showed significantly higher total cholesterol and low density lipoprotein cholesterol levels, but plasma triglycerides were similar in both groups.
This could suggest that the percent body fat mass even within a normal BMI range may be predictive of reduced insulin sensitivity in MONW, young, normal weight women. Collectively, the relative level of body fatness may be an important first step to screen and identify MONW subjects.
In addition, the researchers concluded that the higher fat mass in MONW women could be mediated indirectly by low cardiorespiratory fitness, as demonstrated by lower levels of the maximum rate of oxygen uptake VO 2max and reduced physical activity energy expenditure as shown by low leisure time physical activity and greater time spent watching TV Conus, F.
Allison, R. Rabasa-Lhoret, M. St-Onge, D. St-Pierre, A. Tremblay-Lebeau, and E. Poehlman, unpublished observations.
These findings suggest that physical inactivity may be an important marker of the MONW young woman in relation to body composition. Several recent studies have investigated in more detail other metabolic disturbances in the MONW individual.
For instance, excessive fat on the upper part of the body or abdomen, as measured by the waist to hip ratio, is associated with an increase risk of diabetes and cardiovascular disease Indeed, there is evidence to suggest that young healthy men 27 and in young adults 28 who fit the description of the MONW individual show an increase in intraabdominal fat, and this is associated with a decrease in insulin sensitivity as well as an increased risk for cardiovascular disease.
Finally, it may be hypothesized that MONW individuals have a decrease in fat storage in adipose tissue. This, in turn, could explain the increase in plasma triglycerides levels observed in the previous studies 22 , In addition, this could increase fat storage in nonphysiological depots such as liver and muscle.
In support of this conclusion, Seppala-Lindroos et al. The researchers suggested that the increase in liver fat could be associated with a decrease in insulin sensitivity. Although not systematically examined, future areas of investigation may want to target measures of adipose and GI hormones to help us understand health profiles in MHO and MONW individuals.
Proteins such as acylation-stimulating protein, leptin, adiponectin, resistin, and other novel GI hormones ghrelin could influence adipose and overall metabolism as well as insulin sensitivity Thus, a key issue is whether MHO and MONW individuals display a hormonal profile that distinguishes them from either at risk obese individuals or normal healthy individuals, respectively.
Recent studies show that levels of stomach-derived ghrelin 31 , 32 and adipose tissue-derived adiponectin 33 , 34 are inversely related to insulin resistance. Interestingly, increased visceral fat accumulation is believed to down-regulate both ghrelin 31 and adiponectins 33 , 35 level.
The levels of other adipose tissue-derived hormones, such as resistin 35 , leptin 31 , and acylation-stimulating protein 36 , 37 , are reported to be positively correlated with insulin resistance and visceral fat accumulation. To our knowledge only one study has examined plasma levels of adiponectin in Japanese MONW individuals In that study, plasma levels of adiponectin showed no significant differences between MONW and normal subjects; however, a significant correlation between plasma levels of adiponectin and the rate of glucose infusion was observed in MONW subjects.
This suggests that adiponectin could be involved in the development of insulin resistance in MONW individuals. An understanding of the MHO individual has important implications for both clinical and academic work.
Sims 39 underscored the need to appreciate the effects of subtypes of obesity in clinical and research aspects. The overall success rate for losing weight and maintaining a reduced body weight is quite poor. MHO individuals may contribute to this poor record by their strong tendency to regain lost body weight.
One may even question the need to aggressively treat MHO individuals, given their favorable metabolic profile. In contrast, early identification and treatment of the metabolic abnormalities of MONW individuals could be effective in the primary prevention i.
Moreover, in clinical research, volunteers are frequently excluded from participation if they have one or more of the phenotypes of the metabolic syndrome.
This recruitment or exclusion strategy could increase the percentage of MHO individuals within a given protocol. In addition, MONW individuals are frequently undetected and undiagnosed. Therefore, recruiting MONW individuals with lean individuals could also render data difficult to interpret.
Finally, in medical education, it is important to educate health care professionals and physicians about the different needs of subsets of obese individuals. Identifying the physiological and behavioral factors that could classify an individual as MHO or MONW would be valuable.
This area of investigation could have important implications for therapeutic medical decision-making i. whether to treat individuals , subject characterization in research protocols, and medical education. Interesting future areas of investigation may want to target free fatty acid trapping A greater understanding of the regulation of free fatty acid transport, storage, and utilization in MHO and MONW individuals would be valuable in identifying mechanisms that could regulate these subsets.
In addition, genetic studies using the microarray technique will offer the possibility to engage in discovery-driven research. Simply put, is there a distinct profile of genes found in MHO and MONW individuals? Ultimately, after the gene expression is measured, one may attempt to identify those genes for which there is differential expression in MHO and at risk obese individuals as well as MONW and metabolically healthy individuals.
This area of research could potentially broaden our knowledge of factors that predispose subtypes of obese individuals to develop metabolic complications. This work was supported by grants from the Canadian Institutes of Health Research and the Canadian Foundation of Innovation.
WHO Obesity. Preventing and managing the global epidemic. Geneva : WHO. Andres R Effect of obesity on total mortality. Int J Obes 4 : — Google Scholar. Am J Clin Nutr 34 : — Ruderman NB , Berchtold P , Schneider S Obesity-associated disorders in normal-weight individuals: some speculations.
Int J Obes 6 : — Sims EA Characterization of the syndromes of obesity, diabetes mellitus and obesity. Ferrannini E , Haffner SM , Mitchell BD , Stern MP Hyperinsulinaemia: the key feature of a cardiovascular and metabolic syndrome. Diabetologia 34 : — Ferrannini E , Vichi S , Beck-Nielsen H , Laakso M , Paolisso G , Smith U Insulin action and age.
European Group for the Study of Insulin Resistance EGIR. Diabetes 45 : — Ferrannini E , Natali A , Bell P , Cavallo-Perin P , Lalic N , Mingrone G Insulin resistance and hypersecretion in obesity. J Clin Invest : — Brochu M , Tchernof A , Dionne IJ , Sites CK , Eltabbakh GH , Sims EA , Poehlman ET What are the physical characteristics associated with a normal metabolic profile despite a high level of obesity in postmenopausal women?
J Clin Endocrinol Metab 86 : — Bonora E , Kiechl S , Willeit J , Oberhollenzer F , Egger G , Targher G , Alberiche M , Bonadonna RC , Muggeo M Prevalence of insulin resistance in metabolic disorders: the Bruneck Study.
Diabetes 47 : — Marin P , Andersson B , Ottosson M , Olbe L , Chowdhury B , Kvist H , Holm G , Sjostrom L , Bjorntorp P The morphology and metabolism of intraabdominal adipose tissue in men. Metabolism 41 : — Albu JB , Curi M , Shur M , Murphy L , Matthews DE , Pi-Sunyer FX Systemic resistance to the antilipolytic effect of insulin in black and white women with visceral obesity.
Am J Physiol : E — E Muscelli E , Camastra S , Gastaldelli A , Natali A , Masoni A , Pecori N , Ferrannini E Influence of duration of obesity on the insulin resistance of obese non-diabetic patients. Int J Obes Relat Metab Disord 22 : — Circ J 66 : — BMI values are interpreted differently for children, because body fatness changes with age and can be different between boys and girls.
When researchers have looked at BMI and health in large groups of people, they generally find that the lowest risk of disease and of dying younger is in the range of BMI of 20 to As BMI values increase into the overweight and obese ranges, the risk of developing type 2 diabetes, cardiovascular disease and stroke, and even cancer increase, as well as other complications of obesity, such as osteoarthritis.
On the flipside, BMI can underestimate body fatness in someone with very low muscle mass, such as a person who is elderly and frail. At the same BMI, women tend to carry more body fat than men. Also at the same BMI, a Black person tends to have less body fat, and an Asian person tends to have more body fat, compared to a white person.
This means that a high BMI may overestimate health risk in a Black person and underestimate health risk in an Asian person. BMI is also not useful for estimating body fatness in a pregnant person, because pregnancy weight gain includes placental and fetal tissues.
BMI is perhaps most useful for tracking changes in body composition over time, whether of a population or an individual. For example, the data on average BMI in the U. show a clear increase over the last several decades, and the most likely explanation for that is not that people in the U. The weight on the scale does not distinguish between these different components, but body composition measurements can.
Figure 7. The four main components of body weight are water, fat, lean body mass and other components like minerals. An individual might use body composition measurements to track their progress in building muscle with a new strength training program.
Since increased body fat is often a risk factor for diseases like cardiovascular diseases and diabetes, researchers are often interested in this type of data. There are several different methods used to measure body composition, each with advantages and limitations.
Calipers used to assess body fat during skinfold testing. Body composition measurement with whole-body air displacement plethysmography ADP technology or BodPod.
Dual-Energy X-ray Absorptiometry DXA. Keep in mind that body composition can be hard to measure accurately when using inexpensive and accessible techniques like skinfold testing and BIA. Your best bet is to pick one method and use that method over time to compare numbers and see how they change.
Total body fat is one predictor of health; another is how the fat is distributed in the body. The location of fat is important, because people who store fat more centrally apple-shaped have a higher risk for chronic disease—like cardiovascular disease and type 2 diabetes—compared to people who store fat in the hips, thighs, and buttocks pear-shaped.
This is because visceral fat that surrounds vital organs common in central obesity or apple-shaped fat patterning is more metabolically active, releasing more hormones and inflammatory factors thought to contribute to disease risk compared to subcutaneous fat.
Subcutaneous fat stored just below the skin common in pear-shaped fat patterning does not seem to significantly increase the risk for chronic disease. Fat can be located in the abdominal region apple shape or hips, thighs, and buttocks pear shape.
Dual energy X-ray absorptiometry DXA or DEXA uses low-energy X-rays to directly assess body composition and can measure the density of tissues in the body [15]. For those reasons, DEXA is typically only used in health research studies, to diagnose and assess the progression of diseases, and to monitor the body composition of professional athletes.
However, DEXA is becoming widely recognized as a useful tool for measuring and tracking body composition and is gaining popularity and use [16]. Contrary to popular belief, not all body fat is bad.
While excess fat tissue can be harmful, our bodies require fat for basic metabolic functions. Fat tissue is made up of cells called adipocytes, which are hormonally active cells. The greater the number of fat cells or the larger the fat cells are, then the higher the body fat percentage.
Where fat is located in the body matters [17]. Scientists have also found that a higher fat deposition in your midsection , or a large waistline, is strongly associated with worse health outcomes [18].
This is because having a lot of belly fat may be more inflammatory a large waistline is actually one of the markers of metabolic syndrome. A higher percentage of belly fat is also associated with an increased risk for metabolic syndrome, T2D, and death [19]. In general, the higher proportion of fat mass to lean mass, the greater amount of insulin resistance you will have [19].
This is especially true in people with excess belly fat and is caused by increased free fatty acids and triglycerides in the blood, leading to insulin resistance [20].
This is an issue for several reasons. When you have insulin resistance, your body stops responding properly to insulin, making it harder for your cells to absorb glucose from the bloodstream. This can lead to higher levels of circulating glucose and insulin in your blood — which promotes fat storage and triggers a vicious cycle that makes it hard to manage your weight.
High levels of glucose in your bloodstream over a long period can also have severe implications for your heart health , leading to decreased elasticity of your blood vessels making it harder for blood to pass through and increasing your risk of heart disease. That said, while having more fat mass than muscle mass increases your risk of insulin resistance, having increased muscle mass has a number of benefits, including improved insulin sensitivity, lipid profile, and blood pressure control as well as reduced mortality risk.
This is because muscles need glucose for energy, and the more muscle mass you have, the more glucose your muscles will absorb from your bloodstream. On top of that, muscle is more metabolically active than fat i.
Put simply, having more muscle mass and less fat mass can improve your metabolic health — and paying attention to body composition is more important than simply seeing the number on a scale go down. But a healthier body composition can lead to better metabolism, and the first area of focus apart from a healthy diet is focusing on building lean mass, or muscle mass, to help you manage your weight.
Resistance training is key to improving body composition. Resistance training itself, even without dietary restrictions, can also lead to improved glycemic control, decreases in fat mass mostly visceral fat tissue , and better cardiovascular health outcomes.
Even just one hour per week of resistance training spread out between 2 sessions can help improve insulin sensitivity and build muscle mass. Moderate-intensity exercise can improve glucose tolerance and overall metabolic health, even without drastic changes in weight [23].
Aim to get 2. See our full list of exercises to improve insulin sensitivity. Losing weight may change body composition, but this weight may not be all fat mass — you may also be losing muscle mass. You can improve your metabolic health and maintain muscle mass by having a diet that is overall high in protein, fiber , and fat, but lower in refined carbs [24].
These types of foods help give you stable blood sugar.
Most articles on gut microbiota argue the importance Thyroid Maintenance Products caloric restriction and kidney function composition assessment aand patients; however, body Muscle recovery foods assessments are metablism ie, metaolism methodological limitations in metanolism most caloric restriction and kidney function studies. To present two suggestions for further research using the human body composition andd. The methods used in this metabooism are based on a Pinto et al article published in Nutrition Reviews. obtained from the PubMed, SCOPUS, LILACS, and Web of Science databases, Pinto et al provided a current survey of intermittent fasting protocols and an understanding of the outcomes to date in terms of the profile of the intestinal microbiota in obese organisms. Of the 82 original articles identified from the databases, 35 were eliminated because of duplication and 32 were excluded for not meeting the inclusion criteria. Two additional articles found in a new search were added, yielding a total of 17 studies to be included in this review.
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