Lesson 10: Child and Adolescent – Obesity Definition and Anthropometric Considerations

Table of Contents


In the last two lessons I introduced the topic of obesity in the childhood & adolescent age groups, discussing the physiologic impacts of obesity on health, appetite dysregulation associated with obesity, and psychological considerations of obesity and its treatment. With that background showing why obesity is an important topic, in this lesson I will define obesity and discuss various considerations related to how it is defined and whether this definition encapsulates health status in an optimal fashion.

Some of these topics parallel those discussed in Lesson 3 of the Nutrition and Weight Management course that is designed more for adults; I will focus on the pediatric literature here.

Body mass index (“BMI”)

In children and adolescents the BMI (defined as weight in kilograms divided by height in meters squared, or kg/m2) is used to define overweight and obesity. In the United states overweight is defined as being at or above the 85th percentile (“%ile”) line on the BMI curve for age and gender, over the time span of 2-18 years. Obesity is defined in the same manner but ≥95th %ile. Regarding subtypes:

  • Class I obesity: If the BMI meets the above definition (≥95th %ile) for age and gender.
  • Class II obesity: If the BMI is ≥120% of the 95th %ile for age and gender or if the BMI is ≥35.
  • Class III obesity: If the BMI is ≥140% of the 95th %ile for age and gender or if the BMI is ≥40.
  • Some literature extends the definition of obesity under age 2 years as >97.7 %ile on the World Health Organization weight-for-length curves.(Styne, 2017)

Of note, these growth curves were generated from data prior to the current obesity epidemic. Thus, while in theory only 5% of children should be >95th %ile, in today’s society many more than 5% are defined as having obesity. The following table indicates the prevalence in various geographical locations.

Reproduced from: Negrea MO, Neamtu B, Dobrotă I, Sofariu CR, Crisan RM, Ciprian BI, Domnariu CD, Teodoru M. Causative Mechanisms of Childhood and Adolescent Obesity Leading to Adult Cardiometabolic Disease: A Literature Review. Applied Sciences. 2021; 11(23):11565. doi: 10.3390/app112311565

In the United States the rate of childhood obesity has continued to rise, reaching 22.4% in August, 2020, with a rapid increase of 3.1% from August, 2019, resulting from the COVID-19 pandemic.

Note: A 2020 study used data from European children aged 6-18 years to attempt to determine age & sex-specific reference percentiles for body composition in comparison to other studies.(Ofenheimer, 2020) The authors examined several allometric relationships. The idea here is that across all of adulthood a healthy BMI range is static (ie, 25 is always the threshold between the normal and overweight range (though in East/Southeast Asian adults a threshold of 23 may be more appropriate)), but in children this changes with age. The authors were attempting to determine if using different relationships than kg/m2 would generate numbers that were static across childhood and adolescence; this would eliminate the need to consider age in the definition.

Thus, while BMI is traditionally defined as kg/m2, they determined in childhood it would be better to use kg/m3. For the fat mass index (“FMI”) using an exponent of 2.5 would be better (thus, kg/m2.5), for the lean body mass index (“LMI”) 3 would be better (thus, kg/m3), and for the appendicular lean mass index (the “ALM”, considered a better marker of skeletal muscle mass) 3.5 would be best (thus, kg/m3.5). The authors note more studies in more populations need to be conducted to see if these findings can be generalized.

However, since much of the literature discussing childhood obesity has referred to BMI defined as kg/m2, it is likely that we will continue to use this as the standard definition in children for the foreseeable future.

As BMI only represents a relationship between height and weight, it does not give any specific information about underlying fat mass, fat-free mass (“FFM”), body fat distribution, or general health. There are also differences in BMI seen by race and ethnicity.(Shypailo, 2020) Many studies have been done that do find correlations between BMI and other anthropometric measurements as well as general health, but these correlations are not perfect, and thus BMI is not a perfect measure to define one’s health.

Body composition

As BMI does not differentiate body fat from lean body mass (“LBM”), it is worth considering what implications body fat percentage (“BF%”) has on overall health that may be distinct from considerations of BMI.

A few informative publications regarding BF% include:

  • A 2015 systematic review and meta-analysis (“SR/MA”) evaluating the ability of BMI to identify obesity based on body fat percentage (“BF%”) in children and adolescents included 37 studies and found an overall sensitivity of 0.73 with an overall specificity of 0.92.(Javed, 2015) There was considerable variability between studies regarding what BMI thresholds and what BF% thresholds were used for the definitions, but this implies potentially 1/4 youth with a normal BMI may actually have elevated body fat, while almost 1/10 youth with an elevated BMI may have a healthy body composition.
  • In a 2020 cross-sectional study of 557 youth (mostly white) with a mean age of 13.8 years, the authors determined breakpoints above which for every 1% increase in BF% there was a substantial increase in visceral adipose tissue (which is known to have poor health implications).(Kelly, 2020) Above these thresholds the % of visceral adipose tissue was associated with most of the examined cardiometabolic risk factors (blood pressure, inflammation, insulin levels, cholesterol, and triglycerides). When considering the children <12 years old the threshold was ~0.83 of the BMI 95th %ile, which came to ~34% body fat. In children aged ≥12 years the breakpoint was at ~0.85 of the BMI 95th %ile (~30% body fat). Thus, even slightly below the 85th %ile for BMI (traditionally considered the threshold for “overweight”), there seems to be increased cardiometabolic risk with further weight gain.
  • One study found children with a normal BMI but elevated BF% had insulin resistance relative to children with a normal BMI and healthy BF%; increased cardiorespiratory fitness only partially ameliorated this association.(Fairchild, 2018)
  • A 2021 study of >500 youth aged 10-19 years in Brazil found 13.8% had normal weight obesity (with a BMI in the healthy range but an elevated waist circumference or BF%).(Cota, 2021) Of those with normal weight obesity, 25.0% had elevated triglycerides, 17.0% had low HDL-cholesterol (the “good” type), 25.0% had elevated blood pressure, and 33.3% had abnormal blood glucose control, with these risks generally considerably higher than in the children with normal weight without obesity.

The prior publications collectively show that many youth will potentially develop excess body fat and associated negative health consequences even before the traditional BMI cutoffs for overweight and obesity. Further evidence of this was also seen in a separate analysis that found adolescents with BMIs near the top of the normal range had higher risks of mortality as young and middle-aged adults compared to adolescents with BMIs in the lower portion of the normal range, though it’s possible this may have been mediated by the BMI in adulthood itself.(Twig, 2016)

There are also considerations of total LBM or FFM, as shown in the following series of publications:

  • A 2021 study of Chinese children examined the association of LBM and fat mass with hypertension, hyperlipidemia, blood glucose, and insulin resistance.(Xiao, 2021) BMI and fat mass were positively associated with all of the metrics. However, the LMI was inversely correlated with cholesterol, blood glucose, and insulin resistance metrics. Children with obesity and a high LMI did not have an increased risk of high LDL-cholesterol, high blood sugar (in male children only), and insulin resistance (in female children only).
  • A 2021 study in children with varying levels of obesity found LBM positively associated with bone mineral density while increasing amounts of body fat negatively associated with bone mineral density.(Seo, 2021)
  • A 2021 study examined body composition with body impedance analysis of 210 children aged 5-18 years who were referred to an endocrinology clinic and who did not have medical conditions associated with body composition or cardiometabolic health.(Salton, 2021) 89 of these children had overweight and 121 had obesity by BMI. 148 of the children had at least 1 component of metabolic syndrome (the components included high fasting blood glucose, elevated blood pressure, elevated triglycerides, or low HDL-cholesterol). The authors determined the muscle-to-fat ratio based on the total appendicular skeletal muscle mass divided by total body fat. The odds of having metabolic syndrome components were 1.4 for every 3% increase in BF% or truncal fat % and 3.3 for every 1 standard deviation decrease in the muscle-to-fat ratio.

The prior publications collectively show that an increased level of LBM has a protective effect against the harmful impacts of elevated body fat levels.


  • BMI may underestimate the negative health effects experienced by individuals who are close to the “overweight” threshold if they have elevated body fat levels.
  • Increased LBM, which increases the BMI to a degree, can actually have significant health benefits and protective properties against elevated body fat levels.

Waist circumference

As indicated in the prior sections, BMI does not make a distinction between body fat and LBM, and these two separate compartments have different health implications. Another consideration is waist circumference; individuals with a BMI in the healthy range can still have excess abdominal fat which may be associated with health risks. A 2020 study determined age and sex-specific waist circumference percentile cutoffs using data from 8 countries (Bulgaria, China, Iran, Korea, Malaysia, Poland, Seychelles, and Switzerland) from the ages of 6-18 years.(Xi, 2020) Waist circumference was measured halfway between the lowest rib & the superior border of the iliac crest at the end of normal expiration. Data was weighted according to the population size of each survey from the 8 countries, and they calculated thresholds for 4 samples:

  1. All of the children
  2. All of the children except those classified as having obesity by BMI
  3. All of the children except those classified as having overweight or obesity by BMI
  4. All of the children except those classified as having underweight, overweight, or obesity by BMI

3 of the populations (China, Iran, and Korea) had data on cardiovascular risk and the authors defined elevated cardiovascular risk as having at least 3 of 6 potential risk factors (high blood pressure, high triglycerides, low HDL-cholesterol, high total cholesterol, high LDL-cholesterol, or high glucose). Of the 4 samples above, samples 3 and 4 did a better job of predicting cardiovascular risk when using the 90th %ile cutoffs, so they moved forward with sample 4. They also compared this to US adolescent data. They chose the 90th %ile to use as a cutoff as this matched the criteria used by the International Diabetes Federation and the National Cholesterol Education Program ATP III report, but in addition this threshold linked best at 18 years of age to adult criteria and did a better job in predicting cardiovascular risk. Ultimately, using their proposed thresholds the area under the curve for estimating cardiovascular risk was 0.69 for boys and 0.63 for girls.

Note: For those unfamiliar with area under the curve, you can click here for an overview of what this means with receiver operating characteristics curves. Basically, a score of 1.00 is perfect, and a score of 0.50 means the test is useless. Scores in the 0.6-0.7 range indicate these waist circumference thresholds have some utility, but they are not great. This is likely because many children do not yet have health complications from obesity, and some children will have genetic influences leading to metabolic health consequences even without having obesity. Therefore, waist circumference is informative to a degree when used in isolation, but this is not a strong enough correlation to indicate it should replace BMI.

For those curious, I have plotted the data (taken from Table 7 in their publication(Xi, 2020)) for sample 4 (only individuals with a BMI in the healthy range) in the graphs below; as shown in the plots the 90th percentile corresponds to the red lines.

Of interest, event though these plots only include children with a BMI in the healthy range, there is a wide disparity in the waist circumference sizes. Part of this is going to be due to children having different heights at any given age (a taller child is expected to have a larger waist circumference than a shorter child), but some of this is likely due to some children having increased abdominal adiposity despite a normal range BMI. In the next section I discuss literature where the waist circumference is normalized by height, as this may provide additional useful information for determining health risks.

Waist-to-height ratio (“WHtR”) thresholds

As indicated above, waist circumference measurements can vary based on height, and thus normalizing the waist circumference by the height with the WHtR may prove more optimal in delineating cardiometabolic health risks. When this is elevated this would imply increased abdominal adiposity, which may correlate with increased visceral adipose tissue and worse health. I will discuss several publications that have evaluated this here:

  • A 2016 SR/MA included 5 articles assessing the correlation of BMI with body fat and the correlation of WHtR with body fat in children with overweight or obesity and found varying correlations between different studies but generally the r2 = 0.65-0.74 for both metrics.(Martin-Calvo, 2016) Thus, both of these can correlate fairly well with body fat levels, though BMI will not give much indication of the body fat distribution (ie, abdominal adiposity vs. more body fat on the hips and thighs).
    • Importantly, the authors noted that BMI likely correlates better with body fat in individuals with overweight and obesity than normal weight, and since this study was limited to individuals with overweight and obesity this likely influenced the results.
    • Thus, using WHtR may be more effective to find individuals with increased abdominal adiposity in particular when they are in the healthy BMI range.
  • A 2021 study evaluated the prevalence of central obesity among normal-weight youth in Shandong, China, including >29,000 students aged 7-18 years.(Zhang, 2021) They defined central obesity using recently published thresholds (from (Xi, 2020) shown above) as well as by using the more common WHtR ≥ 0.5. Using the published thresholds shown above the total prevalence of central obesity was 9.90% in males and 8.11% in females, while using the 0.5 cutoff the total prevalence was 2.97% in males and 2.44% in females. The authors indicate it is unclear if the 0.5 cutoff is too high or if the newly published thresholds are too low.
    • However, as seen in a publication discussed below in this section, it has been shown that East/Southeast Asian countries typically have people develop cardiometabolic risks at lower BMI and waist circumference thresholds, so the 0.5 cutoff was likely too high in this population.
  • A 2021 MA evaluated the use of WHtR to identify cardiometabolic risk in children aged 6-20 years, including 53 studies from 24 countries with almost all being cross-sectional.(Jiang, 2021) Different studies used different WHtR thresholds. The authors considered cardiometabolic risk factors including elevated fasting blood glucose, elevated blood pressure, dyslipidemia, and central obesity. The studies found in general:
    • When optimizing the WHtR threshold chosen from considering receiver operator characteristic curves (see the prior note for an explanation of this if curious), the area under the curve was 0.92 for 3 risk factors, 0.85 for 2 risk factors, and 0.75 for 1 risk factor.
    • In particular for identifying children with 3 risk factors the sensitivity and specificity were both 84% with a diagnostic odds ratio = 28; this implies that above the threshold a child would have a 28x greater odds of having 3 risk factors compared to a child below the threshold.
    • The area under the curve for the specific components were 0.59 for elevated fasting blood glucose, 0.69 for elevated blood pressure, 0.66 for dyslipidemia, and 0.96 for central obesity.
    • There was a lot of heterogeneity in the MA and the authors believe most of this was due to different WHtR cutoffs being used in different studies; I have pulled the data from their supplementary material to indicate the distribution of WHtR cutoffs used in different studies in the image below. As shown in the image, most studies had an optimized WHtR in the 0.45-0.50 range, with some outliers at both ends of the distribution.
  • A 2021 SR/MA evaluating WHtR thresholds to screen for cardiometabolic risk in children and adolescents aged 3-19 years included 41 cross-sectional studies from various regions around the world, with 10 including a cutoff of 0.41-0.45, 23 including a cutoff of 0.46-0.50, and 12 including a cutoff of ≥0.51.(Ezzatvar, 2021) Cutoffs of 0.41-0.45 had worse accuracy than the higher thresholds. For the threshold of 0.46-0.50, the sensitivity was 75%, the specificity was 78%, and the area under summary receiver operating characteristic value was 0.83 (as mentioned earlier, this was discussed in an above note). The same values for the cutoff of ≥0.51 were 76%, 84%, and 0.87.
    • Thus, using a threshold of >0.50 seems optimal to denote cardiometabolic risks.
    • However, there was some variability between regions, as higher thresholds were superior in Latin American countries (ie, 0.54) while East/Southeast Asian countries were more accurate with lower thresholds (ie, 0.46).
  • A 2022 study evaluated the utility of the WHtR to assess for cardiometabolic risk in children aged 5, 8, and 11 years old, finding that while a ratio of 0.55 was the most specific and potentially the best option at age 5 and 8 years, it was not as sensitive particularly at age 11 years, where a ratio of 0.50 may be more appropriate. (Muñoz-Hernando, 2022) The BMI threshold for obesity had similar results at age 11 years as the WHtR. The authors noted that several other studies have found variable results when evaluating the WHtR in children, with a range of values from 0.47-0.59 being found to be optimal depending on the age range and outcome metric used.

Thus, the WHtR seems to be a promising metric, with a threshold of >0.50 in most youth (but potentially >0.46 in East and Southeast Asian countries and >0.54 in Latin American countries) being good indicators of elevated cardiometabolic risk markers. More research is needed to help determine optimal thresholds when considering differences in both ethnicity and age.

Tip: So if defining obesity by BMI, waist circumference, and WHtR all have potential flaws, at least in part due to none of these directly assessing body fat function (ie, visceral vs. subcutaneous adipose tissue) and underlying health markers, what should you use to determine if a child has excess body weight?

The vast majority of the research associating obesity with health outcomes uses definitions based on BMI. As indicated in a note above, some individuals with an elevated BMI may seem metabolically healthy at a given point in time, but there is a substantial risk of them converting to a metabolically unhealthy state as time goes on if they do not make any lifestyle changes. For this reason I generally favor using BMI.

Using waist circumference and in particular WHtR seems to have more utility when assessing individuals with a borderline elevated BMI; these individuals may have excess abdominal adiposity and relatively low skeletal muscle mass, and the WC and WHtR may be useful to make this distinction. Waist circumference and WHtR also likely have utility for youth with more “athletic” builds; if there is an elevated BMI but the waist circumference and/or WHtR is in a healthy range then there may not be increased cardiometabolic risk. This latter point is more speculative as I’m not aware of any substantial body of literature assessing this.


In this lesson I discussed various aspects of the definition of childhood, indicating that:

  • Overweight, obesity, and the severity of obesity are defined by a child’s BMI.
  • Negative health consequences of increasing body fat may start slightly below the overweight category by BMI.
  • Increasing levels of lean body mass will help mitigate the health risks of an elevated BMI.
  • There is potentially additional useful information that can be obtained by examining a child’s waist circumference and waist-to-height ratio. More research is needed to better determine how to use these values in addition to the BMI, but if the waist-to-height ratio is >0.5 this indicates potential health concerns even if the BMI is not elevated.
    • A threshold of 0.46 may be more appropriate in East/Southeast Asian countries and a threshold of 0.54 may be more appropriate in Latin American countries. We will need more research to more conclusively determine various thresholds based on ethnicity and age.

In the next lesson I will discuss additional aspects of growth that can be gleaned from examining a child’s growth curve, as well as desirable growth trajectories for youth with obesity who are attempting to improve their health.

Click here to proceed to Lesson 11


  1. Cota BC, Priore SE, Ribeiro SAV, Juvanhol LL, de Faria ER, de Faria FR, Pereira PF. Cardiometabolic risk in adolescents with normal weight obesity. Eur J Clin Nutr. 2021 Oct 28. doi: 10.1038/s41430-021-01037-7. Epub ahead of print. PMID: 34711932.
  2. Ezzatvar Y, Izquierdo M, Ramírez-Vélez R, Del Pozo Cruz B, García-Hermoso A. Accuracy of different cutoffs of the waist-to-height ratio as a screening tool for cardiometabolic risk in children and adolescents: A systematic review and meta-analysis of diagnostic test accuracy studies. Obes Rev. 2021 Nov 8:e13375. doi: 10.1111/obr.13375. Epub ahead of print. PMID: 34751482.
  3. Fairchild TJ, Klakk H, Heidemann M, Grøntved A, Wedderkopp N. Insulin sensitivity is reduced in children with high body-fat regardless of BMI. Int J Obes (Lond). 2018 Jun;42(5):985-994. doi: 10.1038/s41366-018-0043-z. Epub 2018 Feb 23. PMID: 29523875.
  4. Javed A, Jumean M, Murad MH, Okorodudu D, Kumar S, Somers VK, Sochor O, Lopez-Jimenez F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr Obes. 2015 Jun;10(3):234-44. doi: 10.1111/ijpo.242. Epub 2014 Jun 25. PMID: 24961794.
  5. Jiang Y, Dou Y, Chen H, Zhang Y, Chen X, Wang Y, Rodrigues M, Yan W. Performance of waist-to-height ratio as a screening tool for identifying cardiometabolic risk in children: a meta-analysis. Diabetol Metab Syndr. 2021 Jun 14;13(1):66. doi: 10.1186/s13098-021-00688-7. PMID: 34127061; PMCID: PMC8201900.
  6. Kelly AS, Kaizer AM, Bosch TA, Rudser KD, Ryder JR, Gross AC, Chow LS, Fox CK, Dengel DR. Reaching the Tipping Point: Identification of Thresholds at which Visceral Adipose Tissue May Steeply Increase in Youth. Obesity (Silver Spring). 2020 Jan;28(1):139-145. doi: 10.1002/oby.22679. Epub 2019 Nov 26. PMID: 31773904; PMCID: PMC6925316.
  7. Martin-Calvo N, Moreno-Galarraga L, Martinez-Gonzalez MA. Association between Body Mass Index, Waist-to-Height Ratio and Adiposity in Children: A Systematic Review and Meta-Analysis. Nutrients. 2016 Aug 20;8(8):512. doi: 10.3390/nu8080512. PMID: 27556485; PMCID: PMC4997425.
  8. Muñoz-Hernando J, Escribano J, Ferré N, Closa-Monasterolo R, Grote V, Koletzko B, Gruszfeld D, ReDionigi A, Verduci E, Xhonneux A, Luque V. Usefulness of the waist-to-height ratio for predicting cardiometabolic risk in children and its suggested boundary values. Clin Nutr. 2022 Feb;41(2):508-516. doi: 10.1016/j.clnu.2021.12.008. Epub 2021 Dec 8. PMID: 35016145.
  9. Negrea MO, Neamtu B, Dobrotă I, Sofariu CR, Crisan RM, Ciprian BI, Domnariu CD, Teodoru M. Causative Mechanisms of Childhood and Adolescent Obesity Leading to Adult Cardiometabolic Disease: A Literature Review. Applied Sciences. 2021; 11(23):11565. doi: 10.3390/app112311565
  10. Ofenheimer A, Breyer-Kohansal R, Hartl S, Burghuber OC, Krach F, Schrott A, Franssen FME, Wouters EFM, Breyer MK. Reference charts for body composition parameters by dual-energy X-ray absorptiometry in European children and adolescents aged 6 to 18 years-Results from the Austrian LEAD (Lung, hEart, sociAl, boDy) cohort. Pediatr Obes. 2021 Jan;16(1):e12695. doi: 10.1111/ijpo.12695. Epub 2020 Jul 2. PMID: 32618143; PMCID: PMC7757249.
  11. Salton N, Kern S, Interator H, Lopez A, Moran-Lev H, Lebenthal Y, Brener A. Muscle-to-Fat Ratio for Predicting Metabolic Syndrome Components in Children with Overweight and Obesity. Child Obes. 2021 Sep 21. doi: 10.1089/chi.2021.0157. Epub ahead of print. PMID: 34550798.
  12. Seo YG, Kim Y, Lim H, Kang MJ, Park KH. Relationship Between Bone Mineral Density and Body Composition According to Obesity Status in Children. Endocr Pract. 2021 Oct;27(10):983-991. doi: 10.1016/j.eprac.2021.06.006. Epub 2021 Jun 16. PMID: 34144210.
  13. Shypailo RJ, Wong WW. Fat and fat-free mass index references in children and young adults: assessments along racial and ethnic lines. Am J Clin Nutr. 2020 Sep 1;112(3):566-575. doi: 10.1093/ajcn/nqaa128. PMID: 32469402.
  14. Styne DM, Arslanian SA, Connor EL, Farooqi IS, Murad MH, Silverstein JH, Yanovski JA. Pediatric Obesity-Assessment, Treatment, and Prevention: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2017 Mar 1;102(3):709-757. doi: 10.1210/jc.2016-2573. PMID: 28359099; PMCID: PMC6283429.
  15. Twig G, Yaniv G, Levine H, Leiba A, Goldberger N, Derazne E, Ben-Ami Shor D, Tzur D, Afek A, Shamiss A, Haklai Z, Kark JD. Body-Mass Index in 2.3 Million Adolescents and Cardiovascular Death in Adulthood. N Engl J Med. 2016 Jun 23;374(25):2430-40. doi: 10.1056/NEJMoa1503840. Epub 2016 Apr 13. PMID: 27074389.
  16. Xi B, Zong X, Kelishadi R, Litwin M, Hong YM, Poh BK, Steffen LM, Galcheva SV, Herter-Aeberli I, Nawarycz T, Krzywińska-Wiewiorowska M, Khadilkar A, Schmidt MD, Neuhauser H, Schienkiewitz A, Kułaga Z, Kim HS, Stawińska-Witoszyńska B, Motlagh ME, Ruzita AT, Iotova VM, Grajda A, Ismail MN, Krzyżaniak A, Heshmat R, Stratev V, Różdżyńska-Świątkowska A, Ardalan G, Qorbani M, Świąder-Leśniak A, Ostrowska-Nawarycz L, Yotov Y, Ekbote V, Khadilkar V, Venn AJ, Dwyer T, Zhao M, Magnussen CG, Bovet P. International Waist Circumference Percentile Cutoffs for Central Obesity in Children and Adolescents Aged 6 to 18 Years. J Clin Endocrinol Metab. 2020 Apr 1;105(4):e1569–83. doi: 10.1210/clinem/dgz195. PMID: 31723976; PMCID: PMC7059990.
  17. Xiao P, Cheng H, Yan Y, Liu J, Zhao X, Li H, Mi J. High BMI with Adequate Lean Mass Is Not Associated with Cardiometabolic Risk Factors in Children and Adolescents. J Nutr. 2021 May 11;151(5):1213-1221. doi: 10.1093/jn/nxaa328. PMID: 33245131.
  18. Zhang YX, Chen J, Liu XH. Screening of central obesity among normal-weight children and adolescents in Shandong, China. Br J Nutr. 2021 Sep 28;126(6):950-955. doi: 10.1017/S0007114520004882. Epub 2020 Dec 9. PMID: 33292920.
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