|Year : 2020 | Volume
| Issue : 1 | Page : 11-21
Obesity subtypes, related biomarkers & heterogeneity
Laura Perez-Campos Mayoral1, Gabriel Mayoral Andrade1, Eduardo Perez-Campos Mayoral1, Teresa Hernandez Huerta2, Socorro Pina Canseco1, Francisco J Rodal Canales1, Héctor Alejandro Cabrera-Fuentes3, Margarito Martinez Cruz4, Alma Dolores Pérez Santiago4, Juan José Alpuche1, Edgar Zenteno1, Hector Martínez Ruíz1, Ruth Martínez Cruz1, Julia Hernandez Jeronimo1, Eduardo Perez-Campos5
1 Research Centre-Faculty of Medicine, National Autonomous University of Mexico-Benito Juárez Autonomous University of Oaxaca, Oaxaca, Mexico
2 CONACyT-Faculty of Medicine, Benito Juárez Autonomous University of Oaxaca, Oaxaca, Mexico
3 Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore, Singapore; Institute of Biochemistry, Medical School, Justus-Liebig University, Giessen, Germany
4 National Technological Institute of Mexico, ITOaxaca, Oaxaca, Mexico
5 National Technological Institute of Mexico, ITOaxaca; Clinical Pathology Laboratory ‘Dr. Eduardo Pérez Ortega' Oaxaca, Mexico
|Date of Submission||07-Nov-2017|
|Date of Web Publication||24-Feb-2020|
Dr Eduardo Perez-Campos
National Technological Institute of Mexico, ITOaxaca, Oaxaca
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Obesity is a serious medical condition worldwide, which needs new approaches and recognized international consensus in treating diseases leading to morbidity. The aim of this review was to examine heterogeneous links among the various phenotypes of obesity in adults. Proteins and associated genes in each group were analysed to differentiate between biomarkers. A variety of terms for classification and characterization within this pathology are currently in use; however, there is no clear consensus in terminology. The most significant groups reviewed include metabolically healthy obese, metabolically abnormal obese, metabolically abnormal, normal weight and sarcopenic obese. These phenotypes do not define particular genotypes or epigenetic gene regulation, or proteins related to inflammation. There are many other genes linked to obesity, though the value of screening all of those for diagnosis has low predictive results, as there are no significant biomarkers. It is important to establish a consensus in the terminology used and the characteristics attributed to obesity subtypes. The identification of specific molecular biomarkers is also required for better diagnosis in subtypes of obesity.
Keywords: Adipose tissue - biomarkers - body fat - genome-wide association studies - heterogeneity - HOMA - obesity - subtypes
|How to cite this article:|
Mayoral LP, Andrade GM, Mayoral EP, Huerta TH, Canseco SP, Rodal Canales FJ, Cabrera-Fuentes HA, Cruz MM, Pérez Santiago AD, Alpuche JJ, Zenteno E, Ruíz HM, Cruz RM, Jeronimo JH, Perez-Campos E. Obesity subtypes, related biomarkers & heterogeneity. Indian J Med Res 2020;151:11-21
|How to cite this URL:|
Mayoral LP, Andrade GM, Mayoral EP, Huerta TH, Canseco SP, Rodal Canales FJ, Cabrera-Fuentes HA, Cruz MM, Pérez Santiago AD, Alpuche JJ, Zenteno E, Ruíz HM, Cruz RM, Jeronimo JH, Perez-Campos E. Obesity subtypes, related biomarkers & heterogeneity. Indian J Med Res [serial online] 2020 [cited 2020 Jun 5];151:11-21. Available from: http://www.ijmr.org.in/text.asp?2020/151/1/11/279134
| Introduction|| |
Over the last few decades, obesity has become an increasing public health problem worldwide, and its related conditions differ by region. For example, in China, Russia and South Africa, obesity is associated with hypertension, angina, diabetes and arthritis, whereas in India, it is associated with hypertension. Obesity can also lead to a wide variety of other illnesses,. Overall, obesity is defined as the excessive accumulation or abnormal distribution of body fat (BF), affecting health. It is classified, primarily, by body mass index (BMI, kg/m), which is a very limited criterion. Obesity is complicated by other diseases such as type 2 diabetes mellitus (T2DM), hepatic steatosis, cardiovascular diseases, stroke, dyslipidaemia, hypertension, gallbladder problems, osteoarthritis, sleep apnoea and other breathing problems and certain types of cancer (endometrial, breast, ovary, prostate, liver, gallbladder, kidney and colon), all of which can lead to an increased risk of mortality. Cases related to pituitary, thyroid and adrenal gland diseases are considered an independent pathology but may indicate obesity,.
Multifactorial polygenic obesity involves several polymorphic genes. This subtype is caused by environmental factors such as diet, lack of physical exercise, ultra-processed foods, fast food, microbiome and the chemical contaminants, which can alter gene expression. This review was aimed to make a thorough investigation of the heterogeneous links and differences among various phenotypes for diagnosis and treatment of polygenic obesity in adults and in the relationship between genes and proteins as possible biomarkers. [Table 1] shows the definitions for the different obesity subtypes,,,.
|Table 1: Definitions used for heterogeneity subtypes in obese individuals|
Click here to view
A selective search of two databases (PubMed and the Cochrane Library) between 1998 and 2017 resulted in the selection of the most commonly reported subtypes of obesity and heterogeneity in adults. The terminology used for searches was as follows: (i) metabolically obese (MO), metabolically unhealthy obese (MUO), metabolically abnormal obese (MAO); (ii) metabolically healthy obese (MHO); (iii) metabolically unhealthy normal weight, metabolically abnormal normal-weight, normal weight obese; (iv) sarcopenic obese (SO); and (v) metabolically healthy normal-weight. All these terms were cross-checked with the words, genes, epigenetic, genome-wide association studies (GWAS), biomarkers and receiver operating characteristic (ROC) analysis. The four most common obesity phenotypes are shown in [Table 2].
| Heterogeneity in obese individuals|| |
Among overweight and obese individuals, significant heterogeneity of phenotypes occurs, which is directly related to the participation of molecules, genes and cells, in addition to environmental, social and economic factors. For example, central obesity (also known as visceral obesity) is evident from an apple or android-shaped body, and confers a greater risk of developing metabolic complications. On the other hand, peripheral obesity, or peripheral fat accumulation in the gluteofemoral region, gives a pear-shaped body and has a gynecoid phenotype associated with reduced metabolic risk.
One of the most commonly accepted diagnoses for obesity in a caucasian population is evidence of a BMI equal to or >30 kg/m. However, BMIs differ with ethnicity. A study on Dual-energy X-ray absorptiometry (DEXA) indicates that a BMI of 28 kg/m in men, and of 24 kg/m in women correlates better with adiposity. It is generally acknowledged that BMI indicates general adiposity, and the waist:height ratio (WHtR) indicates abdominal adiposity. People with ≥0·5 WHtR are classified as having high abdominal adiposity, although it may vary in different populations. A discrepancy also exists, particularly in individuals who have higher muscle mass.
| Metabolically healthy obese (MHO)|| |
MHO group or metabolically normal obese, or metabolically benign obese has been studied extensively, and, depending on the method of classification, represents 6-40 per cent of the obese population. However, these terms are inconsistent with the pathology, leaving no clear consensus on phenotype. The metabolic spectrum is defined in numerous studies. The homeostatic model assessment (HOMA) index is also used in MHO classification to identify an increased risk of mortality. In all MHO individuals, insulin levels and insulin resistance indices for HOMA, quantitative insulin-sensitivity check index (QUICKI), and Mffm/l, high-sensivity C-reactive protein (hsCRP) and interleukin 6 (IL-6) are similar to a healthy population. In addition, higher or lower HOMA, Quicki or Mffm/l results are not specific to any particular obesity phenotype. However, MHO individuals show increase in other biomarkers, such as, leptin.
MHO individuals have a higher risk of developing metabolic syndrome when compared to healthy individuals of normal weight. Over time, there has been a transition from a metabolically healthy overweight/obese phenotype to a metabolically abnormal overweight/obese phenotype. Wang et al found that MHO, in particular, was associated with subclinical cardiovascular dysfunction, lower global longitudinal systolic strain, dyssynchrony and early diastolic dysfunction. Chang et al reported that MHO individuals had a higher prevalence of subclinical coronary atherosclerosis than metabolically healthy normal-weight individuals; however, later studies suggested that these problems of MHO individuals might be even higher than in the metabolically unhealthy group.
The inflammatory state is reduced in MHO and may be explained by the fatty acid profile of myristic, palmitic, stearic, oleic and linoleic acids. MHO is also associated with lower levels of proinflammatory proteins and higher levels of anti-inflammatory molecules, such as overexpression of fetuin-A (AHSG), histidine-rich glycoprotein (HRG) and retinol-binding histidin-rich protein 4 (RBP4), and downregulation of histamine releasing peptide (HRP), hsCRP, complement factor 4A (C4A), and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4). Together, these opposing effects counteract each other creating a pro-/anti-inflammatory profile.
One particular feature of MHO is an abnormality in Bromodomain and extra terminal (BET) proteins. Wang et al discovered a connection between Brd2 obesity and T2DM. The Brd2 isoform promotes pancreatic β-cell function and proliferation and is one of the protein factors regulating gene transcription. It binds with acetylated lysines in nucleosomal chromatin and plays a role in energy metabolism. In MHO, a disruption of the BRD2 gene in the promoter region results in a reduced level of activity. BRD2 knockdown in mice protects them from insulin resistance and pancreatic β-cell dysfunction. Inhibition of BET proteins may increase insulin production and improve pancreatic β-cell function.
| Metabolically abnormal obese (MAO)|| |
A significant number of individuals in this group are overweight and have central obesity with metabolic syndrome, T2DM, cardiovascular or cerebrovascular disease and are likely to present diastolic or systolic high blood pressure and increased waist-hip circumference. This group differs significantly from the metabolic healthy obese subtype in levels of postprandial blood glucose, high-density lipoprotein cholesterol, triglycerides, insulin and adiponectin. Some of these are measured on the HOMA-IR despite variations. Certain biomarkers associated with metabolic syndrome, such as alanine aminotransferase, can increase greatly, but are still within the normal range of reference. In addition, the International Diabetes Federation (IDF), American Heart Association and the National Heart, Lung and Blood Institute (AHA/NHLBI) have published a document on harmonizing the metabolic syndrome. The consensus criteria for a clinical diagnosis of metabolic syndrome is based on this document.
In the overweight and obese individuals, cardiometabolic risk is one of the main problems for which waist circumference (WC), and WHtR are used for identification. The other examples of heterogeneity expression are observed in the pro-inflammatory cytokines IL-6, IL-8, monocyte chemoattractant protein 1 (MCP-1), regulated on activation, normal T cell expressed, and secreted (RANTES), macrophage inflammatory protein 1 alpha (MIP1α), and plasminogen activator inhibitor-1 (PAI 1) in visceral adipose tissue (VAT), whereas leptin and interferon inducible protein 10 are expressed mainly in subcutaneous AT (SAT),. VAT is related to metabolic disorder and to upregulated activation and expression. Leucine rich repeat containing receptor family pyrin domain containing 3 (NLRP3) gene and IL1b are upregulated in VAT, which is infiltrated by proinflammatory macrophages in the MUO/MAO subgroup. Marques-Vidal et al showed increased levels of hsCRP and also tumor necrosis factor-alpha (TNF-α) in a Swiss population based study which was associated with an increase in WC in men, and BMI in women.
It has been shown that high carbohydrate comsumption and environmental factors among others modulate genotype interactions increases risk of obesity. Therefore, epigenetic mechanisms increase the number of changes in the genome, which may be related to the different phenotypes of obesity.
All gene variants are related to an increased risk of obesity; for example, the fat mass and obesity associated gene (FTO rs9939609) significantly predisposes an individual to diabetes and increased BMI and hip circumference,. However, Veerman explained that the predictive power of this gene was attenuated significantly by its incomplete penetrance, suggesting that exploring gene expression in medical practice has limited relevance. Subgroups or subtypes of heterogeneity have also been reported in other studies. A clinical subgroup of MAO is the hypertriglyceridaemic-waist phenotype (HTGW), which is classified by increased WC and increased fasting triglyceride levels, and a cluster of factors related to metabolic syndrome. An epigenetic mechanism, known as DNA methylation, which is found in the HTGW phenotype in carnitine palmitoyltransferase 1A (CPT1A) and ATP binding cassette subfamily G member 1 (ABCG1) genes, may modify gene function through the addition of methyl to DNA. This process is strongly associated with HTGW in epigenome-wide analysis.
A number of methylated CpG loci are also associated with obesity. Crujeiras et al showed that DNA methylation levels in obese insulin resistant or insulin sensitive patients could be classified by the clamp technique. Through genome-wide epigenetic analysis, 982 differentially methylated CpG sites (DMCpGs) were found in VAT. As proposed by Huang et al, most of these DMCpGs could be related to the insulin pathway, and some could be used as markers. Pietiläinen et al studied SAT in monozygotic twins with different body masses and found 17 obesity-associated genes with differentially methylated 22 CpGs regions.
| Metabolically obese normal weight (MONW)|| |
The MONW is also known as metabolically abnormal with no obesity, metabolically abnormal individuals with no obesity (MANO), normal weight dyslipidaemia, or pre-obesity. As in other subtypes, MONW has multiple definitions, most of which are inconsistent. Metabolically abnormal individuals with a normal BMI and no visual signs of obesity are also known as pre-obese individuals. More than 23 per cent BF is evident in men and 30 per cent in women and both may have a visceral fat area (VFA) of ≥100 cm2 with a variable BMI cut off of <23, <25, or <26 kg/m2. The abnormal accumulation of BF in MONW, accounts for only a small number of cases but takes into consideration VFA and BF percentage. These individuals may also develop prediabetes or borderline dyslipidaemia with upper-normal WC.
In studies conducted in the USA, 24 per cent of adults of normal weight (BMI <25 kg/m) are considered metabolically abnormal and are at a high-risk of chronic diseases such as T2DM and cardiovascular disease. These individuals are physically inactive, have a BMI in the range of 20-27 kg/m and a fat mass of 2-10 kg, which is more than healthy controls of the same age.
In MONW, some members of the same family may be hypertensive and have metabolic syndrome or cardiovascular disease, and a small number may be diabetic, although it is notable that the risk of developing diabetes mellitus is not dependent on central obesity, it depends on a number of factors in positive metabolic syndrome. The adipose mass represents an important source of proinflammatory cytokines in obese individuals, and circulating concentrations of hsCRP, TNF-α, IL-1 α, IL-1β, IL-6 and IL-8 are elevated,. HsCRP in adults is strongly associated with a number of factors also seen in metabolic syndrome, central obesity and increased cardiovascular risk; however, it may not be specific to any obesity phenotype,. Yaghootkar et al reported on monogenic forms of insulin resistance in a subtype of MONW with a 'lipodystrophy-like' phenotype linked to 11 genetic variants. It can lead to hypertension, coronary artery disease and diabetes mellitus.
| Sarcopenic obesity|| |
Sarcopenic obesity, or sarcopenically obese, is defined as a reduction in lean mass and is associated with predicting factors such as increased age, low socio-economic status, smoking, decreased physical activity, atherosclerosis and pulmonary disease. These factors are related to an accumulation of BF and a decrease in skeletal muscle mass and muscle strength. The prevalence of sarcopenic obesity in adults over 65 yr is higher in countries such as Mexico (10.2%), South Africa (10.3%) and Spain (11%).
For diagnosis, the under quintile of the skeletal muscle index (muscle skeletal/BMI) is commonly used, along with the measurement for grip strength (<30 kg for men and <20 kg for women). BF is measured by skinfold thickness, bioelectrical impedance analysis (BIA), DEXA, or calculation of predictive formulae, among other criteria. DEXA not only detects adiposity but also shows osteopenia and osteoporosis. BIA, is quick, inexpensive and non-invasive and is useful in clinical practice. It measures body composition and is based on resistance and reactance. Although there is no direct relation between resistance, reactance and adiposity, a different BIA prediction equation has been found which gives a positive predictive value for fat-free mass (FFM) in adults, for males and females.
In particular, in sarcopenia studies with BIA, there are three main issues that need to be considered: (i) lack of standardization in the definition of sarcopenia, (ii) selection of adequate/appropriate equations to calculate FFM or appendicular lean soft tissue, and (iii) selection of population-specific cut-off points. Sarcopenic obesity can exist in individuals of different ages, not only in the older adult. Kim et al showed the prevalence of non-sarcopenic non-obese (53%), sarcopenic non-obese (10%), non-sarcopenic obese (20%) and sarcopenic obese (15%) individuals. They found an increase in the systolic blood pressure in the sarcopenic groups.
Inflammatory markers, such as hsCRP, increase in males with sarcopenic obesity. Further, an increase in MCP-1 in serum marks the proinflammatory state. Several loci are associated with sarcopenic obesity, such as those located in PTPRD, CDK14 and IMMP2L genes. Similarly, single nucleotide polymorphism (SNPs), such as the TP53 polymorphism, predict the risk of sarcopenia, contrasting with other kinds of obesity. An association between −308 G/A TNF-α polymorphism and sarcopenic obesity was also established.
| Adipose tissue, biomarkers and heterogeneity|| |
There are three varieties of adipocytes: brown, white and beige. In humans, brown adipocytes are found in the neck, interscapular and supraclavicular areas. White adipocytes are found in subcutaneous and visceral regions, while beige are found in the supraclavicular region, inguinal canal and near the carotid sheath and the long muscle of the neck (musculus longus colli). White adipose tissue (WAT) has an intrinsic heterogeneity with depot-specific differences. Subcutaneous depot expresses higher levels of TBx15 gene (T-Box transcription factor 15) and adiponectin in visceral WAT than other markers. Percentages of arachidonic acid and docosahexaenoic acid are higher in subcutaneous WAT and have an upregulation of 5-lipoxygenase in T2DM in women, in contrast to VAT (vWAT).
Other methods providing quantitative non-invasive biomarkers include magnetic resonance imaging, near-infrared-based optical spectroscopy and nuclear magnetic resonance (NMR), the last two of which have been validated by determining hepatic fat content through a minimally invasive needle-like probe. In addition, high-resolution pulsed field gradient diffusion NMR spectroscopy might delineate WAT and brown AT.
The adipocytes produce a number of cytokines including adiponectin, leptin, interleukin (IL-6), PAI-1, adipsin, TNF-α, resistin, angiotensinogen, aromatase and CRP. These are related to obesity, hypertension, atherosclerosis, diabetes and thrombosis, and some have a strong association with eating behaviours, chronic inflammation and metabolic disease.
Abdominal obesity is associated with an increase in IL-6, while BMI and WC relate to TNF-α levels. Lim et al found that BMI was a poor indicator of excess adiposity in the elderly and showed that WC was a better marker. They also associated MCP-1 with the proinflammatory state, in accordance with studies by Yang et al in which they found an increase in hsCRP in elderly males with sarcopenic obesity.
| Accuracy and limitations in terminology and biomarkers|| |
When considering the main group classifications for, monogenic, polygenic, multifactorial obesity and mixed cases, monogenic is proved to be the most useful in confirming the specific type by molecular methods, and subsequently, implementing strategies for personalized medicine. In cases linked to multiple genes or polygenic phenotypes, the study of genetic markers is not beneficial in clinical diagnosis. This takes into consideration that genetic predisposition is not equal to inevitability of disease in wider concept. A wide spectrum of disease susceptibility may be evident from the genes found in polygenic obesity (for example, in genes LEPR, MC4R, PCK1, POMC and PPARG), and is also significant in monogenic obesity. This indicates that highly penetrant rare variants may be related to severe obesity, and genes with common variants could be related to more common obesity. In addition, FTO, the gene most strongly associated with obesity, only explains 0.34 per cent of phenotypic variance, which increases to 1.45 per cent with 32 GWAS. Several of studies claimed that parental BMI, birth weight, maternal occupation, maternal gestational weight and gestational smoking gave a better predictive risk of obesity than GWAS. Therefore, genetic studies should be endorsed only in individuals with early-onset obesity if they have intellectual disabilities or exhibit developmental delays, or in syndromic types.
Without agreed terminology, at present, no research or clinical diagnoses define the different phenotypes sufficiently. Paradoxically, if the individual has normal biochemical blood parameters, they are considered healthy. The question, originally raised by Scully, still remains, as to how to properly distinguish between a real disease and merely disturbing risk factors, defects or deficits. One other concern of MHO diagnosis is the doctor´s bias towards, or perception of a patient. Other obesity subgroups related to diet, physical activity chemical compounds and endocrine disruptors (dichloro-diphenyl-dichloro-ethylene, bisphenol A, polychlorinated biphenols, phthalates, phytoestrogens, glycyrrhetinic acid and tricyclic antidepressants among others), have not been taken into consideration, that will very likely be participating.
| Perspectives|| |
Despite a lack of clear definitions to classify obesity subgroups, there are markers or indeces that are useful to make basic differentiations such as VAT, and fat mass, that together with BMI, WC and WHtR, all related with intra-abdominal adiposity could help in the subgroups classification [Figure 1].
|Figure 1: Differences between phenotypes of obesity.aNormal weight (NW) metabolically healthy and normal visceral adipose tissue (VAT) and normal BMI.bMetabolically healthy obese (MHO) individuals have high body mass index (BMI) and healthy metabolic profile, characterized by having excessive body fat, high insulin sensitivity, low VAT/total body fat mass index and low VAT.cMetabolically abnormal obese (MAO) individuals present high BMI, are associated with abnormal metabolic profile, high VAT and increased uric acid.dSarcopenically obese (SO) are characterized by loss of skeletal muscle mass and function, increases risk of metabolic alterations mainly in older individuals and have high VAT with BMI between 25 and 30 kg/m.eMetabolically obese normal weight (MONW) individuals are characterized by high VAT and a normal BMI. Source: Ref. 17.|
Click here to view
To differentiate the presence or absence of the metabolic component, VAT is useful because it is mechanistically related and strongest predictor to insulin resistance, T2DM, hypertension dyslipidaemia and cardiovascular disease. The drawback of measuring VAT is the high cost and difficulty in carrying out these procedures. It may not be not accurate, but is useful if factors such as age, race, ethnicity and gender are taken into consideration. For examples, WC has a good correlation with DEXA measures of trunk fat mass percentage and metabolic syndrome. To predict estimated per cent BF in older Caucasian American females and males, use of Siri-Brozeck equations is recommended. A simple index, which was evaluated in a cross-sectional study with 17,029 non-diabetic individuals from the Korea National Health and Nutrition Examination Survey, discriminated individuals with MONW from MHO is the triglyceride glucose (TyG) index. Others markers of visceral obesity: the visceral adiposity index and the lipid accumulation product (LAP) are good to identify MONW phenotype; these were evaluated in 3552 normal-weight individuals from the China Health and Nutrition Survey 2009 and identified people predisposed to develop metabolic diseases.
| Conclusion|| |
Although all obese individuals have excess BF, there are important heterogenic differences between the subtypes. After reviewing various clinical, biochemical and genetic reports it is found that important progress has been made by the different groups in identifying specific differences in types of obesity, and the present criteria can help in diagnosis and treatment of obesity. Ideally, we need progress in two ways, first, to find better markers to distinguish each subtype of obesity more accurately for improvements in treatment, and second, to have an international consensus on terminology.
Acknowledgment: Authors thank Ms Charlotte Grundy for her editorial assistance.
Financial support & sponsorship: This work was supported by the Clinical Pathology Laboratory 'Dr. Eduardo Perez Ortega', Oaxaca, Mexico, and the Department of Biochemistry and Immunology, National Institute of Technology of Mexico.
Conflicts of Interest: None.
| References|| |
Shukla A, Kumar K, Singh A. Association between obesity and selected morbidities: A study of BRICS countries. PLoS One
Cabrera-Fuentes HA, Aragones J, Bernhagen J, Boening A, Boisvert WA, Bøtker HE, et al
. From basic mechanisms to clinical applications in heart protection, new players in cardiovascular diseases and cardiac theranostics: Meeting report from the third international symposium on 'New frontiers in cardiovascular research'. Basic Res Cardiol
Cabrera-Fuentes HA, Alba-Alba C, Aragones J, Bernhagen J, Boisvert WA, Bøtker HE, et al
. Meeting report from the 2nd
International Symposium on New Frontiers in Cardiovascular Research. Protecting the cardiovascular system from ischemia: Between bench and bedside. Basic Res Cardiol
Bray GA. Evaluation of obesity. Who are the obese? Postgrad Med
: 19-27, 38.
Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al
. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond)
Purnamasari D, Badarsono S, Moersadik N, Sukardji K, Tahapary DL. Identification, evaluation and treatment of overweight and obesity in adults: Clinical practice guidelines of the obesity clinic, Wellness Cluster Cipto Mangunkusumo Hospital, Jakarta, Indonesia. JAFES
Sidhu S, Parikh T, Burman KD. Endocrine Changes in Obesity. In: Feingold KR, Anawalt B, Boyce A, Chrousos G, Dungan K, Grossman A, et al
, editors. Endotext
. South Dartmouth (MA): MDText.com, Inc.; 2000.
Álvarez-Castro P, Sangiao-Alvarellos S, Brandón-Sandá I, Cordido F. [Endocrine function in obesity]. Endocrinol Nutr
Muñoz Yáñez C, García Vargas GG, Pérez-Morales R. Monogenic, polygenic and multifactorial obesity in children: Genetic and environmental factors. Austin J Nutr Metab
Zhang YP, Zhang YY, Duan DD. From genome-wide association study to phenome-wide association study: New paradigms in obesity research. Prog Mol Biol Transl Sci
Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, et al
. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: Prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med
Du T, Yu X, Zhang J, Sun X. Lipid accumulation product and visceral adiposity index are effective markers for identifying the metabolically obese normal-weight phenotype. Acta Diabetol
Conus F, Rabasa-Lhoret R, Péronnet F. Characteristics of metabolically obese normal-weight (MONW) subjects. Appl Physiol Nutr Metab
Lee DC, Shook RP, Drenowatz C, Blair SN. Physical activity and sarcopenic obesity: Definition, assessment, prevalence and mechanism. Future Sci OA
Phillips CM, Perry IJ. Does inflammation determine metabolic health status in obese and nonobese adults? J Clin Endocrinol Metab
Du T, Zhang J, Yuan G, Zhang M, Zhou X, Liu Z, et al
. Nontraditional risk factors for cardiovascular disease and visceral adiposity index among different body size phenotypes. Nutr Metab Cardiovasc Dis
Berezina A, Belyaeva O, Berkovich O, Baranova E, Karonova T, Bazhenova E, et al
. Prevalence, risk factors, and genetic traits in metabolically healthy and unhealthy obese individuals. Biomed Res Int
Kjaer IG, Kolle E, Hansen BH, Anderssen SA, Torstveit MK. Obesity prevalence in Norwegian adults assessed by body mass index, waist circumference and fat mass percentage. Clin Obes
Xia L, Dong F, Gong H, Xu G, Wang K, Liu F, et al
. Association between indices of body composition and abnormal metabolic phenotype in normal-weight chinese adults. Int J Environ Res Public Health
. pii: E391.
Hermans MP, Amoussou-Guenou KD, Bouenizabila E, Sadikot SS, Ahn SA, Rousseau MF. The normal-weight type 2 diabetes phenotype revisited. Diabetes Metab Syndr
Lee SH, Han K, Yang HK, Kim MK, Yoon KH, Kwon HS, et al
. Identifying subgroups of obesity using the product of triglycerides and glucose: The Korea National Health and Nutrition Examination Survey, 2008-2010. Clin Endocrinol (Oxf)
Yang CW, Li CI, Li TC, Liu CS, Lin CH, Lin WY, et al
. Association of sarcopenic obesity with higher serum high-sensitivity c-reactive protein levels in chinese older males - A community-based study (taichung community health study-elderly, TCHS-E). PLoS One
Hsu Y, Newton E, McLean R, Kiel D. Genome-wide association study of sarcopenic-obesity, identifies novel candidate genes. The Framingham study adiposity & its sequelae
). Houston: The Endocrine Society's 94th
Annual Meeting and Expo; 2012.
Sakuma K, Yamaguchi A. Sarcopenic obesity and endocrinal adaptation with age. Int J Endocrinol
Lee MJ, Wu Y, Fried SK. Adipose tissue heterogeneity: Implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med
Chambers AJ, Parise E, McCrory JL, Cham R. A comparison of prediction equations for the estimation of body fat percentage in non-obese and obese older Caucasian adults in the United States. J Nutr Health Aging
Shah NR, Braverman ER. Measuring adiposity in patients: The utility of body mass index (BMI), percent body fat, and leptin. PLoS One
Kowalkowska J, Poínhos R, Franchini B, Afonso C, Correia F, Pinhão S, et al
. General and abdominal adiposity in a representative sample of Portuguese adults: Dependency of measures and socio-demographic factors' influence. Br J Nutr
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. Obes Rev
Yoo EG. Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Korean J Pediatr
Traissac P, Pradeilles R, El Ati J, Aounallah-Skhiri H, Gartner A, Delpeuch F. Within-subject non-concordance of abdominal v. general high adiposity: Definition and analysis issues. Br J Nutr
Muñoz-Garach A, Cornejo-Pareja I, Tinahones FJ. Does Metabolically Healthy Obesity Exist? Nutrients
. pii: E320.
Perreault M, Zulyniak MA, Badoud F, Stephenson S, Badawi A, Buchholz A, et al
. A distinct fatty acid profile underlies the reduced inflammatory state of metabolically healthy obese individuals. PLoS One
Ferrer R, Pardina E, Rossell J, Oller L, Viñas A, Baena-Fustegueras JA, et al
. Morbidly 'healthy' obese are not metabolically healthy but less metabolically imbalanced than those with type 2 diabetes or dyslipidemia. Obes Surg
Bradshaw PT, Monda KL, Stevens J. Metabolic syndrome in healthy obese, overweight, and normal weight individuals: The Atherosclerosis Risk in Communities Study. Obesity (Silver Spring)
Wang YC, Liang CS, Gopal DM, Ayalon N, Donohue C, Santhanakrishnan R, et al
. Preclinical systolic and diastolic dysfunctions in metabolically healthy and unhealthy obese individuals. Circ Heart Fail
Chang Y, Kim BK, Yun KE, Cho J, Zhang Y, Rampal S, et al
. Metabolically-healthy obesity and coronary artery calcification. J Am Coll Cardiol
Sahakyan KR, Somers VK, Rodriguez-Escudero JP, Hodge DO, Carter RE, Sochor O, et al
. Normal-weight central obesity: Implications for total and cardiovascular mortality. Ann Intern Med
Doumatey AP, Zhou J, Zhou M, Prieto D, Rotimi CN, Adeyemo A. Proinflammatory and lipid biomarkers mediate metabolically healthy obesity: A proteomics study. Obesity (Silver Spring)
Wang F, Liu H, Blanton WP, Belkina A, Lebrasseur NK, Denis GV. Brd2 disruption in mice causes severe obesity without Type 2 diabetes. Biochem J
Wang F, Deeney JT, Denis GV. Brd2 gene disruption causes 'metabolically healthy' obesity: Epigenetic and chromatin-based mechanisms that uncouple obesity from type 2 diabetes. Vitam Horm
Deeney JT, Belkina AC, Shirihai OS, Corkey BE, Denis GV. BET bromodomain Proteins Brd2, Brd3 and Brd4 selectively regulate metabolic pathways in the pancreatic β-Cell. PLoS One
Salgado AL, Carvalho Ld, Oliveira AC, Santos VN, Vieira JG, Parise ER. Insulin resistance index (HOMA-IR) in the differentiation of patients with non-alcoholic fatty liver disease and healthy individuals. Arq Gastroenterol
Mojiminiyi OA, Abdella NA, Al Mohammedi H. Higher levels of alanine aminotransferase within the reference range predict unhealthy metabolic phenotypes of obesity in normoglycemic first-degree relatives of patients with type 2 diabetes mellitus. J Clin Hypertens (Greenwich)
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al
. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation
Vikram NK, Latifi AN, Misra A, Luthra K, Bhatt SP, Guleria R, et al
. Waist-to-height ratio compared to standard obesity measures as predictor of cardiometabolic risk factors in Asian Indians in North India. Metab Syndr Relat Disord
Lee MJ, Fried SK. Depot-specific biology of adipose tissues: Links to fat distribution and metabolic risk. In: Leff T, James G. Granneman JG, editors. Adipose tissue in health and disease
, Ch. 15. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA; 2010. p. 283-306.
Farmer SR. Molecular determinants of brown adipocyte formation and function. Genes Dev
Esser N, L'homme L, De Roover A, Kohnen L, Scheen AJ, Moutschen M, et al
. Obesity phenotype is related to NLRP3
inflammasome activity and immunological profile of visceral adipose tissue. Diabetologia
Marques-Vidal P, Bochud M, Bastardot F, Lüscher T, Ferrero F, Gaspoz JM, et al
. Association between inflammatory and obesity markers in a Swiss population-based sample (CoLaus Study). Obes Facts
Huang T, Hu FB. Gene-environment interactions and obesity: Recent developments and future directions. BMC Med Genomics
(Suppl 1) : S2.
Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al
. A common variant in the FTO
gene is associated with body mass index and predisposes to childhood and adult obesity. Science
Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, et al
. Genome-wide association scan shows genetic variants in the FTO
gene are associated with obesity-related traits. PLoS Genet
Veerman JL. On the futility of screening for genes that make you fat. PLoS Med
Lemieux I, Poirier P, Bergeron J, Alméras N, Lamarche B, Cantin B, et al
. Hypertriglyceridemic waist: A useful screening phenotype in preventive cardiology? Can J Cardiol
(Suppl B) : 23B-31B.
Mamtani M, Kulkarni H, Dyer TD, Göring HH, Neary JL, Cole SA, et al
. Genome- and epigenome-wide association study of hypertriglyceridemic waist in Mexican American families. Clin Epigenetics
Crujeiras AB, Diaz-Lagares A, Moreno-Navarrete JM, Sandoval J, Hervas D, Gomez A, et al
. Genome-wide DNA methylation pattern in visceral adipose tissue differentiates insulin-resistant from insulin-sensitive obese subjects. Transl Res
Huang RC, Garratt ES, Pan H, Wu Y, Davis EA, Barton SJ, et al
. Genome-wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood. Epigenetics
Pietiläinen KH, Ismail K, Järvinen E, Heinonen S, Tummers M, Bollepalli S, et al
. DNA methylation and gene expression patterns in adipose tissue differ significantly within young adult monozygotic BMI-discordant twin pairs. Int J Obes (Lond)
Højland Ipsen D, Tveden-Nyborg P, Lykkesfeldt J. Normal weight dyslipidemia: Is it all about the liver? Obesity (Silver Spring)
Nagatomo A, Nishida N, Fukuhara I, Noro A, Kozai Y, Sato H, et al
. Daily intake of rosehip extract decreases abdominal visceral fat in preobese subjects: A randomized, double-blind, placebo-controlled clinical trial. Diabetes Metab Syndr Obes
Madeira FB, Silva AA, Veloso HF, Goldani MZ, Kac G, Cardoso VC, et al
. Normal weight obesity is associated with metabolic syndrome and insulin resistance in young adults from a middle-income country. PLoS One
Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S, Korinek J, et al
. Normal weight obesity: A risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J
Okada R, Yasuda Y, Tsushita K, Wakai K, Hamajima N, Matsuo S. Upper-normal waist circumference is a risk marker for metabolic syndrome in normal-weight subjects. Nutr Metab Cardiovasc Dis
Ruderman N, Chisholm D, Pi-Sunyer X, Schneider S. The metabolically obese, normal-weight individual revisited. Diabetes
Lee IT, Chiu YF, Hwu CM, He CT, Chiang FT, Lin YC, et al
. Central obesity is important but not essential component of the metabolic syndrome for predicting diabetes mellitus in a hypertensive family-based cohort. Results from the Stanford Asia-pacific program for hypertension and insulin resistance (SAPPHIRe) Taiwan follow-up study. Cardiovasc Diabetol
Berg AH, Scherer PE. Adipose tissue, inflammation, and cardiovascular disease. Circ Res
De Lorenzo A, Del Gobbo V, Premrov MG, Bigioni M, Galvano F, Di Renzo L. Normal-weight obese syndrome: Early inflammation? Am J Clin Nutr
Bennett NR, Ferguson TS, Bennett FI, Tulloch-Reid MK, Younger-Coleman NO, Jackson MD, et al
. High-sensitivity C-reactive protein is related to central Obesity and the number of metabolic syndrome components in Jamaican young adults. Front Cardiovasc Med
Yoshikane H, Yamamoto T, Ozaki M, Matsuzaki M. Clinical significance of high-sensitivity C-reactive protein in lifestyle-related disease and metabolic syndrome. J Cardiol
Yaghootkar H, Scott RA, White CC, Zhang W, Speliotes E, Munroe PB, et al
. Genetic evidence for a normal-weight 'metabolically obese' phenotype linking insulin resistance, hypertension, coronary artery disease, and type 2 diabetes. Diabetes
Tyrovolas S, Koyanagi A, Olaya B, Ayuso-Mateos JL, Miret M, Chatterji S, et al
. Factors associated with skeletal muscle mass, sarcopenia, and sarcopenic obesity in older adults: A multi-continent study. J Cachexia Sarcopenia Muscle
Abellan van Kan G, Houles M, Vellas B. Identifying sarcopenia. Curr Opin Clin Nutr Metab Care
Peppa M, Stefanaki C, Papaefstathiou A, Boschiero D, Dimitriadis G, Chrousos GP. Bioimpedance analysis vs. DEXA as a screening tool for osteosarcopenia in lean, overweight and obese Caucasian postmenopausal females. Hormones (Athens)
Ricciardi R, Talbot LA. Use of bioelectrical impedance analysis in the evaluation, treatment, and prevention of overweight and obesity. J Am Acad Nurse Pract
Barbosa-Silva MC, Barros AJ, Wang J, Heymsfield SB, Pierson RN Jr., Bioelectrical impedance analysis: Population reference values for phase angle by age and sex. Am J Clin Nutr
Kumar S, Dutt A, Hemraj S, Bhat S, Manipadybhima B. Phase angle measurement in healthy human subjects through bio-impedance analysis. Iran J Basic Med Sci
Kyle UG, Genton L, Karsegard L, Slosman DO, Pichard C. Single prediction equation for bioelectrical impedance analysis in adults aged 20–94 years. Nutrition
Gonzalez MC, Barbosa-Silva TG, Heymsfield SB. Bioelectrical impedance analysis in the assessment of sarcopenia. Curr Opin Clin Nutr Metab Care
Kim JH, Cho JJ, Park YS. Relationship between sarcopenic obesity and cardiovascular disease risk as estimated by the Framingham risk score. J KoreanMed Sci
Di Renzo L, Gratteri S, Sarlo F, Cabibbo A, Colica C, De Lorenzo A. Individually tailored screening of susceptibility to sarcopenia using p53 codon 72 polymorphism, phenotypes, and conventional risk factors. Dis Markers
Di Renzo L, Sarlo F, Petramala L, Iacopino L, Monteleone G, Colica C, et al
. Association between -308 G/A TNF-α polymorphism and appendicular skeletal muscle mass index as a marker of sarcopenia in normal weight obese syndrome. Dis Markers
van Marken Lichtenbelt WD, Vanhommerig JW, Smulders NM, Drossaerts JM, Kemerink GJ, Bouvy ND, et al
. Cold-activated brown adipose tissue in healthy men. N Engl J Med
Cedikova M, Kripnerová M, Dvorakova J, Pitule P, Grundmanova M, Babuska V, et al
. Mitochondria in white, brown, and beige adipocytes. Stem Cells Int
Kwok KH, Lam KS, Xu A. Heterogeneity of white adipose tissue: Molecular basis and clinical implications. Exp Mol Med
Gesta S, Blüher M, Yamamoto Y, Norris AW, Berndt J, Kralisch S, et al
. Evidence for a role of developmental genes in the origin of obesity and body fat distribution. Proc Natl Acad Sci U S A
Heemskerk MM, Giera M, Bouazzaoui FE, Lips MA, Pijl H, van Dijk KW, et al
. Increased PUFA content and 5-lipoxygenase pathway expression are associated with subcutaneous adipose tissue inflammation in obese women with type 2 diabetes. Nutrients
Reeder SB, Cruite I, Hamilton G, Sirlin CB. Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging
Nachabé R, van der Hoorn JW, van de Molengraaf R, Lamerichs R, Pikkemaat J, Sio CF, et al
. Validation of interventional fiber optic spectroscopy with MR spectroscopy, MAS-NMR spectroscopy, high-performance thin-layer chromatography, and histopathology for accurate hepatic fat quantification. Invest Radiol
Verma SK, Nagashima K, Yaligar J, Michael N, Lee SS, Xianfeng T, et al
. Differentiating brown and white adipose tissues by high-resolution diffusion NMR spectroscopy. J Lipid Res
Sethi JK, Vidal-Puig AJ. Thematic review series: Adipocyte biology. Adipose tissue function and plasticity orchestrate nutritional adaptation. J Lipid Res
Lim JP, Leung BP, Ding YY, Tay L, Ismail NH, Yeo A, et al
. Monocyte chemoattractant protein-1: A proinflammatory cytokine elevated in sarcopenic obesity. Clin Interv Aging
Pigeyre M, Yazdi FT, Kaur Y, Meyre D. Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin Sci (Lond)
Turksen K. End of inevitability: Programming and reprogramming. Stem CellRev Rep
Ng MC, Bowden DW. Is genetic testing of value in predicting and treating obesity? N C Med J
Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al
. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet
Morandi A, Meyre D, Lobbens S, Kleinman K, Kaakinen M, Rifas-Shiman SL, et al
. Estimation of newborn risk for child or adolescent obesity: Lessons from longitudinal birth cohorts. PLoS One
Scully JL. What is a disease? EMBO Rep
Young ME, Norman GR, Humphreys KR. The role of medical language in changing public perceptions of illness. PLoS One
Munoz Yanez C, Garcia Vargas GG, Perez-Morales R. Monogenic, polygenic and multifactorial obesity in children: Genetic and Environmental Factors. Austin J Nutr Metab
Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: A meta-analysis. J Clin Epidemiol
Sun Q, van Dam RM, Spiegelman D, Heymsfield SB, Willett WC, Hu FB. Comparison of dual-energy x-ray absorptiometric and anthropometric measures of adiposity in relation to adiposity-related biologic factors. Am J Epidemiol
[Table 1], [Table 2]