When Being Lean Is Not Enough: The Metabolically Unhealthy Normal Weight Phenotype and Cardiometabolic Disease (original) (raw)

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Copyright © 2024. Korean Society of CardioMetabolic Syndrome

Review

Dahyun Park, PhD,1,2 Min-Jeong Shin, PhD,3 and Faidon Magkos, PhD4

Received May 17, 2024; Revised June 24, 2024; Accepted July 11, 2024.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Conventional wisdom associates increased body weight with suboptimal health. However, it has become apparent in recent years that excess weight and total body fat do not necessarily impair metabolic function and increase the risk of cardiometabolic disease. Vice versa, the absence of excess body weight and fat does not necessarily reflect better health and a low disease risk. Individuals with obesity typically present with hypertension or metabolic abnormalities such as hyperglycemia, hyperinsulinemia, and dyslipidemia. These risk factors are also present in a subset of people with normal body weight (“metabolically unhealthy normal weight” or MUNW), particularly among Asian populations. Compared with control subjects with the same body mass index and total body fat, MUNW individuals have dysregulated glucose homeostasis (insulin resistance and hyperinsulinemia) and greater accumulation of fat in the upper body (intraabdominal region and liver) than in the lower body (legs). Mild diet-induced weight loss in MUNW individuals improves body composition and metabolic function in a manner that is qualitatively and quantitatively similar to that in people with obesity. However, weight loss cannot be a long-term treatment solution for this phenotype; therefore, alternative therapies need to be developed together with screening programs for early identification.

Keywords

Obesity; Prediabetes; Fatty liver; Intra-abdominal fat; Weight loss

INTRODUCTION

Obesity remains a major public health challenge and affects a significant proportion of the population, including both sexes and all age groups worldwide.1 The World Health Organization (WHO) defines obesity as a disorder involving excessive fat accumulation to an extent that health is adversely affected; this involves an increased risk of type 2 diabetes, heart disease, and certain cancers; compromised bone health and reproductive function; and an overall decrease in the quality of life.2 Obesity is characterized by excess deposition of fat everywhere in the body,3 and although there is large variability in body fat distribution between individuals,4 obesity is typically defined on the basis of the body mass index (BMI), which is a simple index of weight relative to height.5 Accordingly, the belief that BMI is a direct indicator of health is widespread among the public, to the point that most individuals self-perceive their health status in relation to their BMI.6 This often also leads to the perception that the absence of obesity, i.e., having a normal body weight, indicates good health and a low risk of disease.7

BMI, TOTAL BODY FAT, AND ADIPOSE TISSUE DEPOTS

In recent decades, it became clear that the amount of total body fat is not necessarily the best predictor of obesity-related metabolic abnormalities or the risk of cardiometabolic disease.8, 9, 10 Instead, the distribution of fat in the body and its deposition in some key anatomical locations may be more important (Figure 1). The vast majority of adipose tissue in the adult human body comprises white adipocytes with very few mitochondria and a single large lipid droplet occupying the cytosol; brown adipocytes, which have many mitochondria and many small cytosolic lipid droplets, are found around the neck (cervical and supraclavicular) and along the spine (paravertebral), although they comprise a very small fraction of the total adipose tissue (<300 g or 0.5% of the total body weight).11 Most body fat is stored as subcutaneous adipose tissue (>90% of the total or approximately 60 kg in obesity) in depots beneath the skin in the upper (e.g., abdominal region) and lower (e.g., gluteal and femoral regions) body.12, 13, 14 A relatively small amount of fat (2–5 kg) is stored in the intra-abdominal cavity, inside the peritoneum, and in the area surrounding the splanchnic organs (e.g., omentum, mesentery); this is collectively referred to as visceral fat.12 Very small amounts of fat (a few hundred grams) can also be stored in organs and tissues that are not traditionally associated with fat storage, such as the liver (intrahepatic fat), skeletal and heart muscles (intramuscular and intramyocardial fat, respectively), and pancreas (intrapancreatic fat); this is collectively referred to as ectopic fat13, 14 (“ectopic” means “out of place” in Greek). BMI correlates directly with the body fat percentage,15, 16, 17 waist circumference,18, 19 abdominal subcutaneous and visceral adipose tissues,20 and intrahepatic fat.21 However, for any given BMI value, there is very large inter-individual variability in percentage body fat, distribution of fat in the upper and lower body, size of the visceral adipose depot, and liver fat content,15, 16, 17, 18, 19, 20, 21 and this is thought to confer vastly different levels of risk of cardiometabolic disease. For instance, among adult subjects with a BMI of 25 kg/m2 (i.e., the upper limit of normal for Western populations) who participated in the US Third National Health and Nutrition Examination Survey (NHANES III), the body fat percentage ranged from 13.8% to 35.3% among men and from 26.4% to 42.8% among women.22

In addition to the well-known limitation of BMI in terms of accurate reflection of differences in percentage body fat between women and men (higher body fat in the former than in the latter for any given BMI value) and between athletic/trained and sedentary/untrained individuals (lower body fat in the former than in the latter for any given BMI value), ethnicity has emerged in recent decades as another important factor affecting the accuracy of BMI as an indicator of adiposity.23, 24, 25 Compared to Caucasians, and for any given BMI value, Asian populations of both Chinese and Indian descents have a greater percentage body fat (about 3–6 percentage units) (Figure 2),26, 27 unfavorable body fat distribution (e.g., more visceral relative to subcutaneous abdominal adipose tissue),28 and greater risk of cardiometabolic diseases.28, 29, 30, 31 This prompted an Expert WHO Panel to recommend lower cutoff values for defining overweight and obesity in Asian populations, such that the lower BMI thresholds for Asians reflect health risks equivalent to those reflected by the higher BMI thresholds in Caucasians.32 Asian countries have variably adopted these recommendations. For instance, in China, overweight (“pre-obesity”) is defined as a BMI of 24–28 kg/m2 and obesity is defined as a BMI of ≥28.0 kg/m2.33 In Malaysia, overweight is defined as a BMI of 23–27.5 kg/m2 and obesity is defined as a BMI of ≥27.5 kg/m2.34 In Korea, Japan, and India, overweight is defined as a BMI of 23–25 kg/m2 and obesity is defined as a BMI of ≥25 kg/m2.35, 36

Figure 2
Age-adjusted body fat percentage, measured by deuterium dilution, in relation to BMI in men and women from Europe (The Netherlands, n=106) and Asia (Indonesia, n=110).
The regression lines are determined from data points extracted from Figure 2 in the study by Gurrici et al.26

BMI = body mass index.

BMI AND RISK OF CARDIOMETABOLIC DISEASE

Obesity adversely affects all organ systems of the body and all physiological functions (Figure 3), and it is often, but not always, accompanied by metabolic dysfunction and multiple cardiometabolic risk factors such as hyperglycemia, impaired glucose tolerance, hypertension, dyslipidemia (increased triglyceride and decreased high-density lipoprotein cholesterol concentrations), and insulin resistance, which collectively heighten the incidence of and mortality from cardiometabolic diseases.37, 38, 39, 40 However, among most populations, higher BMI values are associated with increased values and greater prevalences of many cardiometabolic risk factors (e.g., increased blood pressure, triglyceride, and low-density lipoprotein cholesterol concentrations and decreased insulin sensitivity and high-density lipoprotein cholesterol concentrations), even within the normal range (18.5–25 kg/m2).29, 41, 42, 43, 44, 45 Thus, there is no apparent “safe” lower BMI limit (Figure 4). Furthermore, Mendelian randomization analysis in approximately 13,000 young adults indicated a causal association between increasing BMI or body fat within the non-obese range and adverse changes in several cardiometabolic risk markers.46 Remarkably, results from the multiethnic US Patient Outcomes Research To Advance Learning (PORTAL) study conducted in approximately 5 million people indicated that prediabetes and type 2 diabetes are highly prevalent even among individuals with normal BMI, with corresponding age-adjusted prevalence rates of 24–34% and 5–18%, respectively.47 This demonstrates that 30–55% of lean individuals, depending on ethnicity, suffer from prediabetes or type 2 diabetes, with higher estimates generally observed among Asian subgroups47 Likewise, in a prospective study of approximately 24,000 Japanese adults followed-up for approximately 8 years, it was observed that approximately 40% cases of newly-diagnosed type 2 diabetes occurred in lean individuals (average baseline BMI of 21.5 kg/m2) and in the absence of weight gain.48

Figure 4
Prevalence of at least one cardiometabolic risk factor (elevated total cholesterol, elevated total cholesterol to high-density lipoprotein cholesterol ratio, elevated triglyceride, hypertension, or diabetes mellitus) in relation to BMI. Data are from a multiethnic Asian population of 4,723 adults (64% Chinese, 21% Malay, and 15% Indian) who participated in the 1998 National Health Survey in Singapore. The top and bottom limits of the red bands represent the estimates for men and women, respectively, and the black line represents the average for both sexes in each BMI category. Created with data extracted from Figure 1a in the study by Deurenberg-Yap et al.41
BMI = body mass index.

THE METABOLICALLY UNHEALTHY NORMAL-WEIGHT PHENOTYPE

It has long been recognized that the same cardiometabolic diseases that are often present in individuals with obesity can also occur in a subset of lean individuals; this phenotype was coined “metabolically obese normal weight” or “metabolically unhealthy normal weight” (MUNW).49 More recently, the term “thin-on-the-outside fat-on-the-inside” was used to describe a phenotype of excess adiposity (total and/or visceral and/or ectopic fat) with normal body weight.50 Although the pathogenesis of MUNW remains elusive, genetic studies suggest that it is predominantly characterized by variability in genes regulating adipocyte differentiation, lipogenesis, and lipolysis as well as insulin-mediated glucose metabolism.51, 52 It has been hypothesized that individuals have a “personal fat threshold” that is independent of BMI; conversion from a metabolically healthy to a metabolically unhealthy phenotype occurs if this threshold is exceeded under the influence of adverse lifestyle and environmental factors.53

Several cross-sectional studies have been conducted to evaluate metabolic function and delineate the physiological traits of the MUNW phenotype (Figure 5). However, many of them ended up recruiting groups of people with different BMI or body fat values (which were typically higher in MUNW than in control subjects, even though they were within the normal range in both groups); in other words, these studies compared a group of lean people (MUNW) with a group of relatively leaner people (controls).54 This makes interpretation of the results difficult, given the direct relationship between BMI within the normal range and a variety of cardiometabolic risk factors (Figure 4).29, 41, 42, 43, 44, 45 Other studies used, in addition to BMI within the normal range, an additional body composition criterion to identify MUNW participants,55, 56, 57 such as increased body fat percentage, waist circumference, or visceral adiposity, thus making it counterintuitive to use their results to conclude that MUNW is associated with differences in total fat mass and body fat distribution.54

Figure 5
Individuals with MUNW have a BMI within the normal range and may or may not have increased relative adiposity (percent body fat). Nevertheless, they present with several cardiometabolic abnormalities that typically accompany obesity, including decreased insulin sensitivity and increased insulin secretion. Compared to metabolically healthy lean subjects with the same BMI, MUNW individuals have the same or greater excess accumulation of fat in the upper body (particularly in the intra-abdominal area) at the expense of the fat in the lower body (leg fat), which is not necessarily reflected in different waist or hip circumferences; excess fat deposition in the liver (but not in skeletal muscle); inferior aerobic fitness, lower skeletal muscle mass, and lower muscle strength; and unhealthy dietary habits. Modified with permission from the study by Klitgaard et al.54 (Copyright holder: Springer Nature).
BMI = body mass index; MUNW = metabolically unhealthy normal weight; SFA = saturated fatty acid.

In studies that carefully matched MUNW and control groups for BMI, sex, and age, no differences in body fat percentage were observed,58, 59, 60 suggesting that metabolic dysfunction in MUNW is not simply the result of increased whole-body adiposity. Corroborating this notion, a large study in about 3,250 normal weight Asians found that >60% of all MUNW individuals had a normal body fat percentage relative to their BMI.61 Interestingly, neither waist nor hip circumference was found to differ between MUNW subjects and controls matched for BMI.54 Waist circumference is a surrogate marker of both subcutaneous and visceral abdominal fat,20 and the ratio of waist-to-hip circumferences is an indicator of the distribution of total fat in the upper body relative to that in the lower body.62 These observations cast doubt on the usefulness of such crude indices to identify individuals with MUNW. To illustrate, the visceral adipose tissue volume can vary by nearly 9-fold for the same waist circumference among normal weight individuals.55 Truncal fat mass (measured by whole-body dual energy X-ray absorptiometry) has been reported to be 25–80% greater61, 63, 64 while intra-abdominal adipose tissue (measured by magnetic resonance imaging or computed tomography) has been reported to be 25–135% greater56, 58, 59, 63, 65 in subjects with MUNW than in controls, and this may happen at the expense of lower body (i.e. leg) fat.66 There is also evidence of excess fat deposition in the liver (maybe also the pericardium31), but not in the skeletal muscle, in MUNW subjects.58, 60 Consequently, there is a greater prevalence of “metabolic dysfunction-associated fatty liver disease” (previously known as “non-alcoholic fatty liver disease”).66, 67, 68 In fact, the fat content of the liver, but not the muscles, correlates directly with insulin sensitivity among non-obese individuals.60, 69 Not much is known about ectopic fat deposition in the pancreas in subjects with MUNW.54, 70 Collectively, these findings suggest that increased visceral adipose tissue and intrahepatic fat content are important components of the MUNW phenotype.

Although there is no consensus on the definition of MUNW, and multiple definitions have been used in different studies, an indication of lower insulin action in the face of normal BMI has been a consistent selection criterion; this implies that insulin-resistant glucose metabolism is the hallmark of MUNW.58, 71, 72 The adverse effects of decreased insulin action are mitigated by augmented pancreatic insulin secretion, which is likely responsible for the fasting and postprandial hyperinsulinemia that is commonly observed in MUNW.58 However, it is true that MUNW does not represent a homogeneous phenotype; instead, it can include individuals with different metabolic abnormalities.73 Depending on the definition and characteristics of the population, prevalence rate estimates of MUNW among all normal weight individuals range from as low as 5% to as high as 45%, with a worldwide average of approximately 30% (95% confidence interval: 26–36%).31, 71, 74 Data from the Korean National Health and Nutrition Examination Survey (KNHANES) and the Ansan-Ansung cohort of the Korean Genome and Epidemiology Study (ASAS-KoGES) indicate that the prevalence of MUNW (defined as the presence of diabetes, hypertension, or dyslipidemia) ranges from 31.6% to 43.4% among all Koreans with BMI <25 kg/m2 (equivalent to 18–28% of the whole population).75 Meta-analyses of prospective cohorts studies clearly demonstrated that being lean but metabolically unhealthy is associated with a 3–4-fold higher risk of type 2 diabetes and cardiovascular disease.37, 73, 76, 77 Likewise, over 8.2–17.4 years of follow-up in the KNHANES and ASAS-KoGES, hazard ratios of 1.64–1.77 for cardiovascular disease incidence and mortality were reported for Koreans with MUNW at baseline.75

It is important to point out that “metabolic health” is, in fact, a temporary trait. In other words, the percentage of subjects who are metabolically healthy decreases almost linearly with time, probably as a result of aging; this is true within all BMI strata (i.e., lean, overweight, and obese).78 Results from a meta-analysis of 12 cohort studies including 26,203 participants (aged 36–63 years) who were lean and healthy at baseline and followed-up for 3–10 years indicated that more than one-in-four individuals (27%; 95% confidence interval: 18–36%) will develop at least one cardiometabolic abnormality within this timeframe and, consequently, convert to an MUNW phenotype.79 Importantly, over 16 years of follow-up in the ASAS-KoGES, the prevalence of obesity did not change, but the proportion of people who were metabolically healthy at baseline decreased significantly, resulting in an increase in the prevalence of MUNW from 29% to 60% of all normal weight individuals (or from 16.4% to 34% of the whole population; Figure 6).75 Perhaps not surprisingly, the conversion from a metabolically healthy phenotype to MUNW is accompanied by a significant increase in the risks of type 2 diabetes and cardiovascular disease in both Caucasian and Asian populations.78, 80

Figure 6
Sixteen-year trends in the prevalence of people with a normal body mass index (<25 kg/m2) who were MHNW or MUNW among 3,995 Korean men and women who participated in the Korean Genome and Epidemiology Study and had data for baseline and all eight F/Up examinations.
Created with data extracted from Figure 2 in the study by Lee et al.75

F/Up = follow-up; MHNW = metabolically healthy normal weight; MUNW = metabolically unhealthy normal weight.

Therefore, it becomes apparent that there is an urgent need to intervene early to prevent conversion to MUNW, but more importantly, there is an urgent need to identify individuals with MUNW and facilitate their transition back to a metabolically healthy phenotype. The relevance of MUNW for public health is exemplified by the fact that lean individuals are less likely to be selected for screening (“they look healthy”); thus, they are more likely to go undiagnosed for a longer period and less likely to be treated before the development of clinically overt disease.71 Accordingly, the consequences of not discovering the “true positives” (i.e., MUNW) can be significant for public health. This is in sharp contrast to the manner in which healthcare systems treat individuals with obesity, who are more likely to be selected for screening (“they look unhealthy”) and receive timely diagnosis and treatment.

MANAGEMENT OF MUNW BY LIFESTYLE INTERVENTION

Lifestyle modification focusing on diet and exercise to facilitate a reduction in body weight and body fat is the cornerstone of obesity treatment.81 Weight loss in people with obesity results in multiple improvements in cardiometabolic risk factors, typically in a dose-dependent manner.82, 83 Accordingly, conversion from an obese to a non-obese phenotype, even without complete normalization of the metabolic risk factor profile, is associated with considerable reductions in the risk of cardiovascular disease by 37–45%.75 This weight loss-induced improvement is comparable to the risk reduction (by 40%) observed during the transition from MUNW back to a metabolically healthy phenotype.75 Suboptimal lifestyle behaviors are common among individuals with MUNW. Compared to their metabolically healthy peers, MUNW individuals often report lower consumption of fruits, vegetables, and dairy foods and higher consumption of saturated fat, carbohydrates, and refined foods.84 Moreover, despite inconclusive data on self-reported physical activity,54 studies that used the doubly-labeled water technique63 and studies that used accelerometers in large groups of MUNW (n>500) individuals85 indicated that MUNW individuals spend more time being sedentary and less time being active compared to their metabolically healthy peers; this result was not documented in studies including small groups of MUNW individuals (n<30).60, 64 Accordingly, MUNW individuals have lower muscle mass, muscle strength, and cardiorespiratory fitness (maximum oxygen uptake).54 These observations suggest that lifestyle modification holds great potential to manage the MUNW phenotype. This notion is corroborated by results from metabolomics studies that have been able to identify specific metabolome signatures of metabolic dysregulation and use them to calculate a “metabolic BMI” score, which is amenable to change by diet and exercise.86, 87

MUNW individuals have long been known to respond to calorie restriction and weight loss.49 In particular, for the same amount of diet-induced weight loss (5% or 10% from the initial body weight), MUNW individuals lose relatively more lean mass than do individuals with obesity (approximately 50% and 25% of lost weight, respectively);83, 88, 89 however, aside from this difference in body composition, all metabolic effects of weight loss are qualitatively and quantitatively similar. This includes a decrease in all components of total energy expenditure (both resting and non-resting energy expenditure),90 a decrease in abdominal fat (both subcutaneous and visceral adipose tissues) and intrahepatic lipids,83, 88, 89 and an increase in the sensitivity of skeletal muscle and adipose tissue to insulin without changes in pancreatic insulin secretion.83, 88, 89

Certainly, the excess loss of metabolically active lean tissue mass after diet-induced weight loss in MUNW individuals is concerning, particularly because these individuals often have reduced muscle mass to begin with.54 Despite that, there are several examples of normal weight individuals who successfully lost considerable amounts of body weight for prolonged periods of time, such as those in the Minnesota experiment on human starvation,91 the Biosphere 2 project,92 and the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy trial.92, 93 The mere fact that individuals with MUNW have normal body weight likely limits their ability to lose a large amount of weight and challenges the clinical utility of diet-induced weight loss as a long-term treatment option for this phenotype. Thus, increased physical activity should also be considered and, perhaps, be the primary focus of the treatment regimen.

CONCLUSION

Evidently, just like the presence of obesity (high BMI and total body fat) is not necessarily accompanied by the presence of an increased risk of cardiometabolic disease,37, 38, 73 the absence of obesity does not imply the absence of this risk. Accumulation of fat in the upper body rather than the lower body, particularly in the visceral depot and liver, is associated with a metabolically unhealthy phenotype independent of BMI. Weight reduction in MUNW individuals causes relatively greater loss of fat-free mass, although it has beneficial effects on body fat accumulation and distribution and the sensitivity of muscle and adipose tissue to insulin, thus resembling the effects of weight loss among subjects with obesity. Because excessive weight loss may not be a feasible treatment option for the MUNW phenotype in the long-term, the focus should be on adopting a more physically active lifestyle and healthier eating habits without necessarily cutting down on the calorie intake. Last, but not the least, research on the mechanisms and metabolic pathways that differ significantly between MUNW individuals and controls by using novel animal models of MUNW94 will likely reveal important cellular targets for the development of pharmaceuticals. Preventing and treating metabolic disease among people with normal body weight is an underappreciated challenge for many countries, particularly in Asia.

Funding:None.

Conflict of Interest:The authors have no conflicts of interest.

Author Contributions:

References

    1. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism 2019;92:6–10.
    1. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Geneva: World Health Organization; 2000.
    1. Saladin KS. In: Anatomy & Physiology: The Unity of Form and Function. 10 ed. New York (NY): McGraw Hill LLC; 2024.
    1. Thomas EL, Fitzpatrick JA, Malik SJ, Taylor-Robinson SD, Bell JD. Whole body fat: content and distribution. Prog Nucl Magn Reson Spectrosc 2013;73:56–80.
    1. Keys A, Fidanza F, Karvonen MJ, Kimura N, Taylor HL. Indices of relative weight and obesity. J Chronic Dis 1972;25:329–343.
    1. Narciso J, Croome N. How does body mass index impact self-perceived health? A pan-European analysis of the European Health Interview Survey Wave 2. BMJ Nutr Prev Health 2022;5:235–242.
    1. Mbogori T, Arthur TM. Perception of body weight status is associated with the health and food intake behaviors of adolescents in the United States. Am J Lifestyle Med 2019;15:347–355.
    1. Jensen MD. Role of body fat distribution and the metabolic complications of obesity. J Clin Endocrinol Metab 2008;93 Suppl 1:S57–S63.
    1. Britton KA, Fox CS. Ectopic fat depots and cardiovascular disease. Circulation 2011;124:e837–e841.
    1. Goossens GH. The metabolic phenotype in obesity: fat mass, body fat distribution, and adipose tissue function. Obes Facts 2017;10:207–215.
    1. Hachemi I, U-Din M. Brown adipose tissue: activation and metabolism in humans. Endocrinol Metab (Seoul) 2023;38:214–222.
    1. Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest 1995;96:88–98.
    1. Lee MJ, Wu Y, Fried SK. Adipose tissue heterogeneity: implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med 2013;34:1–11.
    1. Walker GE, Marzullo P, Ricotti R, Bona G, Prodam F. The pathophysiology of abdominal adipose tissue depots in health and disease. Horm Mol Biol Clin Investig 2014;19:57–74.
    1. Jackson AS, Stanforth PR, Gagnon J, et al. The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. Int J Obes 2002;26:789–796.
    1. Gallagher D, Visser M, Sepúlveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996;143:228–239.
    1. Meeuwsen S, Horgan GW, Elia M. The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr 2010;29:560–566.
    1. Després JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation 2012;126:1301–1313.
    1. Schulze MB. Metabolic health in normal-weight and obese individuals. Diabetologia 2019;62:558–566.
    1. Estrada S, Lu R, Conjeti S, et al. FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI. Magn Reson Med 2020;83:1471–1483.
    1. Wilman HR, Kelly M, Garratt S, et al. Characterisation of liver fat in the UK Biobank cohort. PLoS One 2017;12:e0172921
    1. Romero-Corral A, Somers VK, Sierra-Johnson J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes 2008;32:959–966.
    1. Yajnik CS, Yudkin JS. The Y-Y paradox. Lancet 2004;363:163.
    1. Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes 1998;22:1164–1171.
    1. Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes 2000;24:1011–1017.
    1. Gurrici S, Hartriyanti Y, Hautvast JG, Deurenberg P. Relationship between body fat and body mass index: differences between Indonesians and Dutch Caucasians. Eur J Clin Nutr 1998;52:779–783.
    1. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev 2002;3:141–146.
    1. Haldar S, Chia SC, Henry CJ. Body composition in Asians and Caucasians: Comparative analyses and influences on cardiometabolic outcomes. Adv Food Nutr Res 2015;75:97–154.
    1. Deurenberg-Yap M, Yian TB, Kai CS, Deurenberg P, VAN Staveren WA. Manifestation of cardiovascular risk factors at low levels of body mass index and waist-to-hip ratio in Singaporean Chinese. Asia Pac J Clin Nutr 1999;8:177–183.
    1. Caleyachetty R, Barber TM, Mohammed NI, et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. Lancet Diabetes Endocrinol 2021;9:419–426.
    1. Gujral UP, Vittinghoff E, Mongraw-Chaffin M, et al. Cardiometabolic abnormalities among normal-weight persons from five racial/ethnic groups in the united states: a cross-sectional analysis of two cohort studies. Ann Intern Med 2017;166:628–636.
    1. Expert Consultation WH. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–163.
    1. Chen K, Shen Z, Gu W, et al. Meinian Investigator Group. Prevalence of obesity and associated complications in China: a cross-sectional, real-world study in 15.8 million adults. Diabetes Obes Metab 2023;25:3390–3399.
    1. Zainudin S, Daud Z, Mohamad M, Boon AT, Mohamed WM. A summary of the Malaysian clinical practice guidelines on management of obesity 2004. J ASEAN Fed Endocr Soc 2014;26:101–104.
    1. Haam JH, Kim BT, Kim EM, et al. Diagnosis of obesity: 2022 update of clinical practice guidelines for obesity by the Korean Society for the Study of Obesity. J Obes Metab Syndr 2023;32:121–129.
    1. Misra A, Chowbey P, Makkar BM, et al. Concensus Group. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India 2009;57:163–170.
    1. Magkos F. Metabolically healthy obesity: what’s in a name? Am J Clin Nutr 2019;110:533–539.
    1. Smith GI, Mittendorfer B, Klein S. Metabolically healthy obesity: facts and fantasies. J Clin Invest 2019;129:3978–3989.
    1. Bhoyrul S, Lashock J. The physical and fiscal impact of the obesity epidemic: The impact of comorbid conditions on patients and payers. J Manag Care Med 2008;11:10–17.
    1. Whitlock G, Lewington S, Sherliker P, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009;373:1083–1096.
    1. Deurenberg-Yap M, Chew SK, Lin VF, Tan BY, van Staveren WA, Deurenberg P. Relationships between indices of obesity and its co-morbidities in multi-ethnic Singapore. Int J Obes 2001;25:1554–1562.
    1. Abbasi F, Brown BW Jr, Lamendola C, McLaughlin T, Reaven GM. Relationship between obesity, insulin resistance, and coronary heart disease risk. J Am Coll Cardiol 2002;40:937–943.
    1. Bernabe-Ortiz A, Carrillo-Larco RM, Miranda JJ. Association between body mass index and blood pressure levels across socio-demographic groups and geographical settings: analysis of pooled data in Peru. PeerJ 2021;9:e11307
    1. Bradley D, Magkos F, Klein S. Effects of bariatric surgery on glucose homeostasis and type 2 diabetes. Gastroenterology 2012;143:897–912.
    1. Khoo CM, Leow MK, Sadananthan SA, et al. Body fat partitioning does not explain the interethnic variation in insulin sensitivity among Asian ethnicity: the Singapore adults metabolism study. Diabetes 2014;63:1093–1102.
    1. Würtz P, Wang Q, Kangas AJ, et al. Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change. PLoS Med 2014;11:e1001765
    1. Zhu Y, Sidell MA, Arterburn D, et al. Racial/ethnic disparities in the prevalence of diabetes and prediabetes by BMI: Patient Outcomes Research To Advance Learning (PORTAL) multisite cohort of adults in the US. Diabetes Care 2019;42:2211–2219.
    1. Kuwahara K, Honda T, Nakagawa T, Yamamoto S, Hayashi T, Mizoue T. Body mass index trajectory patterns and changes in visceral fat and glucose metabolism before the onset of type 2 diabetes. Sci Rep 2017;7:43521.
    1. Ruderman NB, Schneider SH, Berchtold P. The “metabolically-obese,” normal-weight individual. Am J Clin Nutr 1981;34:1617–1621.
    1. Thomas EL, Frost G, Taylor-Robinson SD, Bell JD. Excess body fat in obese and normal-weight subjects. Nutr Res Rev 2012;25:150–161.
    1. Stefan N. Metabolically healthy and unhealthy normal weight and obesity. Endocrinol Metab (Seoul) 2020;35:487–493.
    1. Park JM, Park DH, Song Y, et al. Understanding the genetic architecture of the metabolically unhealthy normal weight and metabolically healthy obese phenotypes in a Korean population. Sci Rep 2021;11:2279.
    1. Taylor R, Holman RR. Normal weight individuals who develop type 2 diabetes: the personal fat threshold. Clin Sci (Lond) 2015;128:405–410.
    1. Klitgaard HB, Kilbak JH, Nozawa EA, Seidel AV, Magkos F. Physiological and lifestyle traits of metabolic dysfunction in the absence of obesity. Curr Diab Rep 2020;20:17.
    1. Thomas EL, Parkinson JR, Frost GS, et al. The missing risk: MRI and MRS phenotyping of abdominal adiposity and ectopic fat. Obesity (Silver Spring) 2012;20:76–87.
    1. Kim TN, Park MS, Yang SJ, et al. Body size phenotypes and low muscle mass: the Korean sarcopenic obesity study (KSOS). J Clin Endocrinol Metab 2013;98:811–817.
    1. 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 2007;85:40–45.
    1. Ding C, Chan Z, Chooi YC, et al. Regulation of glucose metabolism in nondiabetic, metabolically obese normal-weight Asians. Am J Physiol Endocrinol Metab 2018;314:E494–E502.
    1. Hyun YJ, Koh SJ, Chae JS, et al. Atherogenecity of LDL and unfavorable adipokine profile in metabolically obese, normal-weight woman. Obesity (Silver Spring) 2008;16:784–789.
    1. Takeno K, Tamura Y, Kawaguchi M, et al. Relation between insulin sensitivity and metabolic abnormalities in Japanese men with BMI of 23-25 kg/m2. J Clin Endocrinol Metab 2016;101:3676–3684.
    1. Lu YC, Lin YC, Yen AM, Chan WP. Dual-energy X-ray absorptiometry-assessed adipose tissues in metabolically unhealthy normal weight Asians. Sci Rep 2019;9:17698.
    1. World Health Organization. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation. Geneva: World Health Organization; 2011.
    1. Dvorak RV, DeNino WF, Ades PA, Poehlman ET. Phenotypic characteristics associated with insulin resistance in metabolically obese but normal-weight young women. Diabetes 1999;48:2210–2214.
    1. Conus F, Allison DB, Rabasa-Lhoret R, et al. Metabolic and behavioral characteristics of metabolically obese but normal-weight women. J Clin Endocrinol Metab 2004;89:5013–5020.
    1. Katsuki A, Sumida Y, Urakawa H, et al. Increased visceral fat and serum levels of triglyceride are associated with insulin resistance in Japanese metabolically obese, normal weight subjects with normal glucose tolerance. Diabetes Care 2003;26:2341–2344.
    1. Stefan N, Schick F, Häring HU. Causes, characteristics, and consequences of metabolically unhealthy normal weight in humans. Cell Metab 2017;26:292–300.
    1. Heianza Y, Arase Y, Tsuji H, et al. Metabolically healthy obesity, presence or absence of fatty liver, and risk of type 2 diabetes in Japanese individuals: Toranomon Hospital Health Management Center Study 20 (TOPICS 20). J Clin Endocrinol Metab 2014;99:2952–2960.
    1. Wei Y, Wang J, Han X, et al. Metabolically healthy obesity increased diabetes incidence in a middle-aged and elderly Chinese population. Diabetes Metab Res Rev 2020;36:e3202
    1. Chan Z, Ding C, Chooi YC, et al. Ectopic fat and aerobic fitness are key determinants of glucose homeostasis in nonobese Asians. Eur J Clin Invest 2019;49:e13079
    1. Sequeira IR, Yip WC, Lu LW, et al. Pancreas fat, an early marker of metabolic risk? A magnetic resonance study of Chinese and Caucasian women: TOFI_Asia study. Front Physiol 2022;13:819606
    1. Ding C, Chan Z, Magkos F. Lean, but not healthy: the ‘metabolically obese, normal-weight’ phenotype. Curr Opin Clin Nutr Metab Care 2016;19:408–417.
    1. Pluta W, Dudzińska W, Lubkowska A. Metabolic obesity in people with normal body weight (MONW)-review of diagnostic criteria. Int J Environ Res Public Health 2022;19:624.
    1. Stefan N, Schulze MB. Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment. Lancet Diabetes Endocrinol 2023;11:426–440.
    1. Wang B, Zhuang R, Luo X, et al. Prevalence of metabolically healthy obese and metabolically obese but normal weight in adults worldwide: a meta-analysis. Horm Metab Res 2015;47:839–845.
    1. Lee J, Kwak SY, Park D, Kim GE, Park CY, Shin MJ. Prolonged or transition to metabolically unhealthy status, regardless of obesity status, is associated with higher risk of cardiovascular disease incidence and mortality in Koreans. Nutrients 2022;14:1644.
    1. Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?: a systematic review and meta-analysis. Ann Intern Med 2013;159:758–769.
    1. Lotta LA, Abbasi A, Sharp SJ, et al. Definitions of metabolic health and risk of future type 2 diabetes in BMI categories: a systematic review and network meta-analysis. Diabetes Care 2015;38:2177–2187.
    1. Eckel N, Li Y, Kuxhaus O, Stefan N, Hu FB, Schulze MB. Transition from metabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90 257 women (the Nurses’ Health Study): 30 year follow-up from a prospective cohort study. Lancet Diabetes Endocrinol 2018;6:714–724.
    1. Lin H, Zhang L, Zheng R, Zheng Y. The prevalence, metabolic risk and effects of lifestyle intervention for metabolically healthy obesity: a systematic review and meta-analysis: a PRISMA-compliant article. Medicine (Baltimore) 2017;96:e8838
    1. Kim JA, Kim DH, Kim SM, et al. Impact of the dynamic change of metabolic health status on the incident type 2 diabetes: a nationwide population-based cohort study. Endocrinol Metab (Seoul) 2019;34:406–414.
    1. Klein S, Wadden T, Sugerman HJ. AGA technical review on obesity. Gastroenterology 2002;123:882–932.
    1. Magkos F. Metabolic effects of progressive weight loss. In: Bray GA, Katzmarzyk PT, Kirwan JP, Redman LM, Bouchard C, Schauer PR, editors. Handbook of Obesity, Volume 2: Clinical Applications. 5th ed. Boca Raton (FL): CRC Press/Taylor & Francis Group; 2024. pp. 291-300.
    1. Magkos F, Fraterrigo G, Yoshino J, et al. Effects of moderate and subsequent progressive weight loss on metabolic function and adipose tissue biology in humans with obesity. Cell Metab 2016;23:591–601.
    1. Abiri B, Valizadeh M, Nasreddine L, Hosseinpanah F. Dietary determinants of healthy/unhealthy metabolic phenotype in individuals with normal weight or overweight/obesity: a systematic review. Crit Rev Food Sci Nutr 2023;63:5856–5873.
    1. de Rooij BH, van der Berg JD, van der Kallen CJ, et al. Physical activity and sedentary behavior in metabolically healthy versus unhealthy obese and non-obese individuals - the Maastricht study. PLoS One 2016;11:e0154358
    1. Cirulli ET, Guo L, Leon Swisher C, et al. Profound perturbation of the metabolome in obesity is associated with health risk. Cell Metab 2019;29:488–500.e2.
    1. Beyene HB, Giles C, Huynh K, et al. Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts. Nat Commun 2023;14:6280.
    1. Chooi YC, Ding C, Chan Z, et al. Moderate weight loss improves body composition and metabolic function in metabolically unhealthy lean subjects. Obesity (Silver Spring) 2018;26:1000–1007.
    1. Magkos F. Is calorie restriction beneficial for normal-weight individuals? A narrative review of the effects of weight loss in the presence and absence of obesity. Nutr Rev 2022;80:1811–1825.
    1. Leibel RL, Rosenbaum M, Hirsch J. Changes in energy expenditure resulting from altered body weight. N Engl J Med 1995;332:621–628.
    1. Keys A, Brožek J, Henschel A, Mickelsen O, Taylor HL. In: The Biology of Human Starvation. Minneapolis (MN): University of Minnesota Press; 1950.
    1. Most J, Tosti V, Redman LM, Fontana L. Calorie restriction in humans: an update. Ageing Res Rev 2017;39:36–45.
    1. Kraus WE, Bhapkar M, Huffman KM, et al. 2 years of calorie restriction and cardiometabolic risk (CALERIE): exploratory outcomes of a multicentre, phase 2, randomised controlled trial. Lancet Diabetes Endocrinol 2019;7:673–683.
    1. Higazi AA, Maraga E, Baraghithy S, et al. Characterization of metabolic alterations in the lean metabolically unhealthy alpha defensin transgenic mice. iScience 2024;27:108802
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