Adipose Tissue Insulin Resistance Is Not Associated With Changes in the Degree of Obesity in Children and Adolescents

Rana Halloun; Alfonso Galderisi; Sonia Caprio; Ram Weiss

doi:10.1210/clinem/dgac700

. 2022 Dec 5;108(5):1053–1060. doi: 10.1210/clinem/dgac700

Adipose Tissue Insulin Resistance Is Not Associated With Changes in the Degree of Obesity in Children and Adolescents

Rana Halloun ¹, Alfonso Galderisi ², Sonia Caprio ³, Ram Weiss ^4,^✉

PMCID: PMC10306082 PMID: 36469736

Abstract

Context

The “carbohydrate-insulin model” claims that adipose tissue insulin sensitivity explains development of obesity via adipocyte energy storage and/or low postprandial metabolic fuel levels.

Objective

We tested whether adipose tissue insulin sensitivity predicts changes in the degree of obesity over time.

Methods

This secondary analysis of an observational study of youth with obesity included 213 youths at a pediatric weight management clinic. Adipose tissue insulin sensitivity/resistance and whole-body insulin sensitivity were evaluated using oral glucose tolerance test (OGTT)-derived surrogates in the face of changes in the degree of obesity over time. The main outcome measure was change in body mass index (BMI) z score.

Results

Mean BMI z change was 0.05 ± 0.28 (range, −1.15 to 1.19), representing a broad distribution of changes in the degree of obesity over a follow-up period of 1.88 ± 1.27 years. Adipose tissue insulin resistance was not associated with changes in the degree of obesity in univariate or multivariate analyses (adjusted for baseline age, BMI z score, sex, ethnicity, and time of follow-up). Low postprandial free fatty acid concentrations or their suppression during the OGTT were not associated with changes in the degree of obesity in univariate or multivariate analyses. Whole-body insulin sensitivity was not associated with changes in the degree of obesity in univariate or multivariate analyses.

Conclusion

In this secondary analysis, in youth with obesity, adipose tissue insulin resistance is not protective from increases of the degree of obesity and skeletal muscle insulin resistance is not associated with increases of the degree of obesity.

The analysis was performed using data derived from NCT00000112 and NCT00536250.

Keywords: adipose insulin sensitivity, insulin resistance, carbohydrate-insulin model

The mainstream model of obesity physiology (the energy balance model) posits that weight is tightly regulated by central (hypothalamic, basal ganglia, brain stem, and other) brain regions that integrate signals of internal organs as well as dietary components and the environment to modulate food intake and energy expenditure (1, 2). This model explains the metabolic responses to varying energy input imbalances by the hormonal responses they induce and their resulting autonomic output (3). Perturbations in body weight and energy balance lead to a multifactorial systemic (autonomic/neuroendocrine) response that acts in coordination to maintain body weight. An alternative model to explain obesity development is the carbohydrate-insulin model (CIM) which puts more emphasis on adipose tissue (4). According to this model, consumption of greater glycemic loads induces a hormonal response dominated by insulin, which leads to aggregation of nutrients in adipose tissue causing “semi-starvation” of other metabolically active tissues such as the brain, liver, or muscle, which leads to an increased drive for further energy consumption (5). This model claims that specific nutrients, such as carbohydrates in general and fructose in particular (and not overall energy consumption), induce a systemic metabolic response that affects energy balance and thus weight dynamics beyond their simple energy value (6).

Testing these models in humans is difficult due to the limitations of precisely controlling caloric input for the prolonged periods that are needed to evaluate dietary effects on weight and body composition. Despite these limitations, the CIM model of obesity gives rise to several hypotheses that can be tested empirically in humans. One of them is that adipose tissue insulin resistance (lower suppression of lipolysis in response to insulin) protects against obesity development and that skeletal muscle insulin resistance drives obesity development (7). In order to test these hypotheses, we used our well-characterized cohort of youth with obesity who have been followed longitudinally (8-10). Per the CIM model, we hypothesized that subjects with higher adipose insulin sensitivity will increase their degree of obesity more than those with lower adipose insulin sensitivity. In addition, those with lower whole-body insulin sensitivity (reflecting mainly skeletal muscle insulin resistance) will increase their degree of obesity more than those with greater whole-body insulin sensitivity.

Methods

Participants of this analysis are part of the Yale Pathophysiology of Type 2 Diabetes in Youth Study. This is a long-term, multiethnic cohort aimed at studying early alternations in glucose metabolism in children and adolescents with obesity (NCT00000112 and NCT00536250). The present investigation is a secondary analysis of this unique cohort aimed at testing the relation of baseline adipose insulin sensitivity/resistance and changes in the degree of obesity over time. Eligibility criteria were age of 7 to 20 years and a body mass index (BMI) that exceeded the 95th centile for age and sex (11). Participants received standard care of clinic-based behavioral modification therapy aimed at the child and the parents. Participants attended the clinic biannually and received diet and exercise guidance along with psychosocial counseling. The dietary recommendations were mainly to reduce consumption of soft drinks and energy-dense highly processed snacks and did not aim at limiting consumption of any food group specifically (low fat or low carbohydrate diets). Subjects who performed more than one oral glucose tolerance test (OGTT), were nondiabetic and off any medication that can affect glucose or lipid metabolism and had all available relevant data were included in the current analysis. The first OGTT was used to calculate indices of adipose tissue insulin sensitivity/resistance, and these were used as predictors of change in the degree of obesity during the follow-up period until the second OGTT. Some of the participants of this analysis have been described in previous publications (12-14). Between October 2002 and October 2006, free fatty acid levels (FFAs) were analyzed as part of the OGTT and all participants of this analysis had baseline OGTTs during this time frame.

Weight and body composition parameters (percent body fat and lean mass) were measured using a scale (Tanita Corp) and height was measured in triplicate with a wall-mounted stadiometer. An oral glucose tolerance test (OGTT, 1.75 g/kg body weight up to 75 g) was performed to establish glucose tolerance status. Subjects were studied at the Yale Pediatric Clinical Center at 08:00 Am after a 10-h overnight fast as previously reported (15). Blood samples were drawn for the measurement of free fatty acids (FFAs) at 0, 30, 60, and 120 minutes of the OGTT. Blood samples were immediately put on ice, centrifuged for 30 minutes, and stored at −80 °C. FFA samples were allowed to clot first, and then, serum was separated per the instructions of the assay manufacturer. Subjects signed an informed assent and parents signed an informed consent to participate in the study. The Human Investigations Committee of the Yale School of Medicine ethically approved the study.

Analytical Methods

Plasma glucose was determined using YSI 2700 Analyzer (Yellow Springs Instruments). Plasma insulin levels were measured using double antibody RIAs (Millipore Inc. RRID: AB_93727; insulin intra- and inter-assay coefficients of variation are 6.8 and 11.6%, respectively). Plasma FFAs were determined using an enzymatic colorimetric method assay for the quantitative determination of non-esterified fatty acids in serum (Wakochem, Ind.).

Calculations

Insulin sensitivity was calculated using the OGTT-derived whole-body insulin sensitivity index (WBISI, known as the Matsuda index) (16). WBISI is based on the values of insulin (µ units/mL), glucose (mg/dL) obtained throughout the OGTT, and the corresponding fasting values and correlates well with euglycemic-hyperinsulinemic clamp–derived insulin sensitivity in this population (17). WBISI mainly represents the effect of insulin to stimulate skeletal muscle glucose uptake. Two surrogates of fasting adipose insulin resistance (Adipo-IR) were calculated: first, the product of fasting insulin and FFA concentrations (18) (Adipo-IR1); second, an index of fasting adipose insulin sensitivity to circulating FFA was calculated with the formula: 2/(insulin [mU/L] × FFA [mmol/L]) + 1)(19) (Adipo-IS1). These indices were derived and validated in vivo using a stepwise insulin clamp in combination with indirect calorimetry and infusion of labeled palmitate (20) as well as in vitro in adipocyte cell cultures (21). An additional index of adipose insulin resistance in the prandial state was calculated using the product of mean fasting insulin and mean FFA concentrations at time 0, 30, 60, and 120 minutes at the OGTT (22). Area under the curve (AUC) of FFAs during 120 minutes of the OGTT, as means to express FFA suppression during the study (greater AUC representing lower suppression) was calculated using the trapezoidal rule. Percent suppression of FFAs during the OGTT was calculated as 1 minus the minimal FFA value divided by fasting FFA.

Statistical Analyses

Data are presented as means ± SD. Parameters that did not have a normal distribution were natural log transformed for the sake of the analysis. Linear regression models were used to identify factors associated with changes in the BMI z score (the primary outcome of the analysis) and changes in percent body fat and percent BMI above 95th centile for age (secondary outcomes) over time. Independent variables in these models included age and BMI z score at baseline, sex, race, and time of follow-up between the OGTTs. All post hoc multiple comparisons between parameters (tertiles of the predictors tested) were adjusted using the Bonferonni correction. The analysis was performed using SPSS 24.

Results

Characteristics of the Study Participants

The analysis included 213 nondiabetic children and adolescents with obesity (81 male and 132 female), with a mean age of 12.76 ± 2.84 years at baseline. All participants were with obesity (mean BMI z score 2.30 ± 0.51). Mean BMI z change over time was −0.05 ± 0.28 (range, −1.15 to 1.19) representing a broad distribution of changes in the degree of obesity over a mean follow-up period of 1.88 ± 1.27 years. The weight change of participants was 11.70 ± 15.36 kg. As shown in Table 1, the levels of adipose insulin sensitivity/resistance indices of participants represent a broad range of values despite the fact that all participants were by definition with obesity at baseline (at least an order of magnitude for fasting adipose insulin resistance/sensitivity indices and even 2 orders of magnitude for the prandial index of adipose insulin resistance) (Table 1).

Table 1.

Characteristics of study participants

	Mean ± SD	Range
Sex (m/f)	81/132
Race (Caucasian/African American/Hispanic)	94/66/53
Age (yrs)	12.76 ± 2.84	7–20
Height (cm)	157 ± 13	113–184
Weight (kgs)	85.86 ± 24.73	30.0–145.7
BMI (kg/m²)	34.03 ± 6.92	20.86–53.27
BMI Z score	2.30 ± 0.51	1.98–3.05
Fasting Glucose (mg/dL)	93 ± 10	73–124
Fasting Insulin (μU/mL)	38 ± 34	4–158
Fasting FFA (mmol)	0.53 ± 0.16	0.16–1.02
Adipo-IR1 (nmol/L × μU/mL)	19.93 ± 13.55	5–118
Adipo-IS1 (1/(nmol/L × μU/mL))	0.13 ± 0.10	0.02–0.78
Prandial Adipo-IR	53.54 ± 51.02	3.78–336.30
AUC_FFA (mmol *min/L)	23.87 ± 8.47	8.10–53.60
WBISI	1.81 ± 1.23	0.30–8.19

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Abbreviations: Adipo-IR, fasting adipose insulin resistance; Adipo-IR1, fasting adipose insulin resistance index 1 (fasting insulin × FFA); Adipo-IS1, fasting adipose insulin sensitivity index 1; AUC, area under the curve; BMI, body mass index; FFA, free fatty acid; WBISI, whole-body insulin sensitivity (Matsuda) index.

Fasting Adipose Insulin Resistance Index 1 and Change in the Degree of Obesity

We divided the cohort into tertiles of fasting Adipo-IR1 levels as a measure of fasting adipose insulin resistance. The BMI z score change over time was comparable across the Adipo-IR1 index tertiles (P ANOVA = 0.35, Fig 1A). We used the BMI z change as a dependent variable in a linear regression model and introduced sex, ethnicity, time between the OGTTs, baseline age, baseline BMI z score, and fasting Adipo-IR1 tertile as independent variables. Adipo-IR1 tertiles were not significantly associated with the outcome (P = 0.63, Fig. 1D), including post hoc comparisons between tertiles (tertile 1 vs tertile 3, P = 1.0 in the adjusted model). Introducing Adipo-IR1 as a continuous variable and not as tertiles into this model led to similar results (P = 0.98).

Figure 1. — BMI z score change by Adipo IR1, Adipo IS1, and prandial Adipo IR1 tertiles. Panels a-c are results of univariate comparisons. Adjusted values (panels d-f) are derived from the linear regression models. Values presented as means ± standard errors. The values for Adipo IR1 tertiles are: tertile 1 < 12.88; 12.88 ≤ tertile 2 ≤ 22.59; tertile 3 > 22.59 (Adipo IR1 expressed in nmol/L × μU/mL). The values for Adipo IS1 tertiles are: tertile 1 < 0.08; 0.08 ≤ tertile 2 ≤ 0.14; tertile 3 > 0.14 (Adipo IS1 expressed in 1/(nmol/L × μU/Ml)).

Fasting Adipose Insulin Sensitivity Index and Change in the Degree of Obesity

We divided the cohort into tertiles of fasting Adipo-IS1 index levels as a measure of fasting adipose insulin sensitivity. BMI z score change over time was similar across Adipo-IS1 tertiles (P ANOVA = 0.37, Fig 1B). In the adjusted analysis, we used BMI z change as a dependent variable in a linear regression model and introduced sex, ethnicity, time between the OGTTs, baseline age, baseline BMI z score, and fasting Adipo-IS1 tertile as independent variables. Adipo-IS1 tertiles were not significantly associated with BMI z score change over time (P = 0.68, Fig. 1E), including post hoc comparisons between tertiles (tertile 1 vs tertile 3, P = 1.0). Introducing Adipo-IS1 as a continuous variable and not as tertiles into this model led to similar results (P = 0.79).

Prandial Adipose Insulin Resistance and Change in the Degree of Obesity

We divided the cohort into tertiles of prandial Adipo-IR index levels. BMI z score change over time was significantly different across tertiles (P ANOVA = 0.03, Fig 1C). In post hoc comparisons between tertiles, BMI z score change was significantly higher in the lower percentile of prandial Adipo-IR (tertile 1 vs tertile 3, P = 0.034), reflecting a greater increase in the degree of obesity in those with greater adipose prandial insulin sensitivity (as predicted by the CIM). We used the BMI z change as a dependent variable in a linear regression model and introduced sex, ethnicity, time between the OGTTs, baseline age, baseline BMI z score, and prandial Adipo-IR tertile as independent variables. Prandial Adipo-IR tertiles were not significantly associated with the outcome (P = 0.10, Fig. 1F) and the change in BMI z score was comparable between tertile 1 and 3 (P = 1.0). Introducing prandial insulin resistance as a continuous variable into the model and not as tertiles into this model led to similar results (P = 0.98).

Prandial AUC FFAs During OGTT and Change in the Degree of Obesity

We divided the cohort into tertiles of AUC_FFA as a measure of prandial adipose insulin resistance (greater prandial AUC_FFA reflecting greater adipose insulin resistance). BMI z score change over time was similar across AUC_FFA tertiles (P ANOVA = 0.96, Fig. 2A). Upon adjustment in the regression model, using BMI z change as a dependent variable and introducing sex, ethnicity, time between the OGTTs, baseline age, baseline BMI z score, and prandial AUC_FFA tertile as independent variables, baseline AUC_FFA was not associated with BMI z score change over time (P = 0.97, Fig. 2C). Introducing AUC_FFA as a continuous variable into this model led to similar results (P = 0.77). In a post hoc comparisons between tertiles, tertile 1 vs tertile 3 had similar changes in BMI z score (P = 1.0).

Figure 2. — BMI Z score changes by AUC_FFA and FFA suppression tertiles. Panels a, b are results of univariate comparisons. Adjusted values (panels c, d) are derived from the linear regression models. Values presented as means ± standard errors. The values for AUC FFA tertiles are: tertile 1 < 18.97; 18.97 ≤ tertile 2 ≤ 27.5; tertile 3 > 27.5 (AUC FFA expressed in mmol *min/L). The values for FFA suppression tertiles are: tertile 1 < 9.3; 9.3 ≤ tertile 2 ≤ 15; tertile 3 > 15 (FFA suppression expressed in 1/(nmol/L × μU/Ml)).

Suppression of FFAs During the OGTT

We divided the cohort into tertiles of FFAs suppression during OGTT testing as a measure of adipose tissue insulin sensitivity. BMI z score over time was similar across the tertiles (P ANOVA = 0.25, Fig. 2B). Using BMI z score a dependent variable in a linear regression model and introducing sex, ethnicity, time between the OGTTs, baseline age, baseline BMI z score, and percent FFA suppression tertile as independent variables, suppression of FFAs during OGTT was not associated with BMI z score change over time (P = 0.39, Fig. 2D). Introducing suppression of FFAs during OGTT as a continuous variable into this model led to similar results (P = 0.12).In a post hoc comparison between tertiles, BMI z score was comparable between tertile 1 and 3, P = 0.58 in the adjusted model. We further tested whether lower FFA concentrations during the OGTT (reflecting the greatest FFA suppression and thus the highest adipose insulin sensitivity and lowest postprandial FFA availability as a metabolic fuel) are associated with greater increases in the degree of obesity. Upon comparing tertiles of minimal FFA concentrations during the OGTT, BMI z score change over time was comparable between tertiles (−0.03 ± 0.31 vs −0.02 ± 0.24 vs −0.09 ± 0.28 for tertiles 1, 2, and 3 respectively, for the lowest quintile vs the rest of the cohort respectively, P = 0.35). We used the BMI z change as a dependent variable in a linear regression model and introduced sex, ethnicity, time between the OGTTs, baseline age, baseline BMI z score, and tertiles of minimal levels of FFAs as independent variables. Having lower FFA concentrations (tertile 1) during the OGTT was not a significant predictor of BMI z change over time (P = 0.28).

Whole-Body Insulin Sensitivity and Change in the Degree of Obesity

We divided the cohort into tertiles of WBISI as a measure of skeletal muscle insulin sensitivity (Fig. 3). BMI z score over time was similar across tertiles (P ANOVA = 0.25 Fig. 3A). Using BMI z score as a dependent variable in a linear regression model, adjusted for sex, ethnicity, time between the OGTTs, baseline age, baseline BMI z score, and WBISI tertile as independent variables, WBISI was not significantly associated with BMI z score change over time (P = 0.29 Fig. 3B). Introducing WBISI as a continuous variable into this model led to similar results (P = 0.57). In post hoc comparisons between tertiles, BMI z score was comparable between tertile 1 and 3, P = 1.0).

Figure 3. — BMI Z score changes by WBISI tertiles. Panel a is result of univariate comparisons. Adjusted values (panel b) are derived from the linear regression model. Values presented as means ± standard errors. The values for WBISI tertiles are: tertile 1 < 1.21; 1.21 ≤ tertile 2 ≤ 1.97; tertile 3 > 1.97.

Associations of Indices of Adipose Tissue Insulin Sensitivity/Resistance and Whole-Body Insulin Sensitivity With Changes in Percent Body Fat and Changes in Percent BMI Above the 95th Centile

We repeated the analysis as described above using the change in percent body fat as a dependent variable in similar regression models. Complete data was available for 176 participants (108 female/68 male, 80 Caucasian, 50 African American, and 46 Hispanic). The regression models included baseline age and percent body fat, sex, age, and the time of follow-up and the adipose tissue indices of interest as independent variables. Tertiles of Adipo-IR1, Adipo-IS1, WBISI, FFA suppression during the OGTT and minimal free fatty acids (FFS) during the OGTT were not associated with changes in percent body fat in our study participants (data not shown). Upon introducing tertiles of the prandial adipose insulin resistance index into the model, it did emerge as a significant predictor of changes in percent body fat (P = 0.01). Interestingly, the second tertile of prandial adipose tissue insulin resistance was significantly lower than tertile 1 (0.46 vs −2.98% for the first and second tertiles respectively, P = 0.01) and marginally lower than tertile 3 (−2.98 vs −0.27% for the second vs the third tertiles respectively, P = 0.06). Of note, this finding does not imply that there is a significant linear trend for an association of prandial adipose tissue insulin resistance and changes in percent body fat over time in our study population.

We similarly repeated the analysis using the change in the BMI percent above the 95th BMI centile for age and sex as an outcome variable. The regression models included baseline age, percent BMI above the 95th centile for age and sex, sex, age, the time of follow-up, and the adipose tissue indices of interest as independent variables. None of the relevant predictors of interest (tertiles of Adipo-IR1, Adipo-IS1, WBISI, FFA suppression during the OGTT and minimal FFS during the OGTT) were associated with change in the BMI percent above the 95th BMI centile for age and sex in our study participants (data not shown).

Discussion

The carbohydrate-insulin model (CIM) of obesity highlights the role of adipose tissue insulin sensitivity as a major driver of increased caloric intake and weight gain. One of the testable hypotheses derived from the CIM is that adipose tissue insulin resistance protects against weight gain and obesity development while skeletal muscle insulin resistance facilitates obesity (7). This secondary analysis of a long-term follow-up of a cohort of youth with obesity is the first to test adipose tissue insulin sensitivity/resistance as a predictor of changes in the degree of obesity. While testing participants with a large variability in baseline indices of adipose insulin sensitivity, our data indicate that baseline adipose tissue insulin sensitivity/resistance, calculated using 2 fasting indices and 1 prandial index, is not associated with changes in the degree of obesity in adolescents over time. Furthermore, whole-body insulin sensitivity, representing mainly skeletal muscle, is similarly not associated with changes in the degree of obesity over time in this population. Importantly, the changes in the degree of obesity were tested using anthropometric (BMI z score and percent above the 95th centile of BMI) as well as body composition (percent body fat) outcome measures complementing each other and yielding a similar result.

The CIM posits that obesity development is not the net result of a single “push” mechanism of increased calorie consumption and reduced physical activity induced by our obesogenic environment and leading to a positive energy balance. The model adds a “pull” mechanism resulting from alterations of fuel partitioning favoring insulin-induced substrate deposition in adipose tissue and resulting in “semi-starvation” of critical metabolically active tissues that leads to increased caloric intake and potentially also to reduced energy expenditure (23, 24). The CIM thus highlights an “adipocentric” view of obesity development due to consumption of high glycemic loads that via changes in the insulin to glucagon ratio (and probably other hormones and molecules) leads to adipose tissue expansion and a compensatory increased calorie consumption leading to further weight gain (5). This line of thought implies that greater adipose tissue insulin sensitivity should be associated with a greater tendency to increase the degree of obesity. We tested 3 different surrogates of adipose tissue insulin sensitivity/resistance derived from OGTTs performed in children and adolescents followed at a pediatric obesity clinic. None were associated with changes in the degree of obesity over time, even after adjustments for relevant baseline anthropometric parameters. We additionally tested FFA suppression during the OGTT as a marker of adipose tissue insulin sensitivity and found no relation to changes in the degree of obesity. Furthermore, in contrast with the prediction of the CIM, whole-body insulin sensitivity (reflecting mainly skeletal muscle) was not associated with changes in the degree of obesity in this cohort. Repeating the same analysis using changes in percent body fat and the change in the percent above the 95th centile for BMI as complementary outcomes yielded similar results.

Adipose insulin resistance has been previously shown to worsen with greater degrees of obesity (and intra-abdominal fat) and altered glucose metabolism in children and adolescents with obesity (12, 13, 25, 26). Similarly, in adults discordant for whole-body and adipose insulin resistance, it was shown that those with higher adipose insulin sensitivity were leaner and had less visceral fat (27). While previous studies were cross sectional, the current analysis is the first to specifically test the ability of adipose tissue insulin sensitivity/resistance to predict changes in the degree of obesity over time (without an active pharmacological or intensive behavior modification intervention) and failed to detect such effect. Lustig et al (28) have previously shown that in children with obesity, those with lower whole-body insulin sensitivity responded better to metformin and those with insulin hyper-secretion (in the context of a prior brain insult) responded better to Octreotide, with regard to weight loss. These authors did not evaluate adipose insulin sensitivity yet did show a differential response to a pharmacological intervention dependent on whole-body insulin sensitivity and insulin hyper-secretion.

The semi-starvation of other metabolically active and sensing tissues in the postprandial state induced by the accumulation of metabolic fuels in the insulin sensitive adipose tissue was tested herein using an analysis of the lower FFA concentrations during the OGTT. Similar to our previous findings regarding postprandial lower glucose levels (29), we found no evidence suggesting that lower postprandial fuel availability (in this case lower postprandial FFA concentrations) is associated with greater increases in the degree of obesity. Of note, while our findings do not support the hypothesis of adipose tissue insulin sensitivity as a driver of obesity development, they are derived from a clinic-based cohort and not from an intervention aimed specifically at adipose tissue metabolism. It has recently been demonstrated that blockade of the glucocorticoid receptor with mifepristone selectively improves adipose tissue insulin sensitivity in overweight/obese adults without affecting whole-body insulin sensitivity (30). A prospective randomized controlled study evaluating the effect of such intervention on weight changes (while controlling for energy intake and physical activity) may shed further light on the role of adipose tissue insulin sensitivity in the development of obesity.

The complementary hypothesis derived from the CIM is that skeletal muscle insulin resistance predisposes to increasing degrees of obesity. In adolescents with a broad range of BMI values, fasting surrogates of insulin resistance did not predict gains in BMI, fat mass (31) or were negatively associated with BMI SD changes (32). Our analysis used the whole-body insulin sensitivity index, which correlates better than fasting surrogates with euglycemic-hyperinsulinemic clamp–derived insulin sensitivity in obese children (33) and failed to show an impact of muscle insulin resistance on changes in the degree of obesity. We have previously shown that the degree of insulin response during the OGTT, a marker of hyperinsulinemia induced by peripheral insulin resistance in subjects without diabetes, does not predict changes in the degree of obesity over approximately 2 years of follow-up (29). Similarly, hyperinsulinemia during an OGTT in nonobese adults was not associated with weight gain during a follow-up period of 14 years (34). In intervention studies, whole-body insulin resistance (reflecting mainly skeletal muscle) did not predict the degree of weight loss in response to hypocaloric diets (35). Moreover, in a pharmacological (Sibutramine) weight loss intervention, there was no difference in weight loss between insulin sensitive and insulin resistant participants (36). Taken together, these findings do not support a mechanistic role for whole-body insulin resistance (mainly skeletal) as a driver of increasing the degree of obesity.

Our study suffers from limitations—the participants were all children and adolescents with obesity to begin with. As adipose insulin sensitivity tends to decrease in those who are more obese, there is still a need to test the ability of adipose insulin sensitivity to predict changes in the degree of obesity in nonobese or overweight adolescents and adults (prior to the development of significant degrees of obesity). FFAs were sampled only for 2 hours of the OGTT and perhaps longer periods of postprandial measurements are needed to evaluate and detect lower troughs of metabolic fuel concentrations potentially driving increased caloric consumption. Moreover, the follow-up period may be too short for evaluating longer term changes in the degree of obesity. We did not evaluate pubertal changes in this analysis, yet in youth with obesity, the impact of pubertal changes on whole-body insulin sensitivity is much smaller in comparison with lean youth. The advantages of the analysis include the thorough metabolic phenotyping of our participants and the large variability of the main outcome parameter (BMI z change) and all the predictor surrogates tested. Moreover, we tested 3 indices of change in adiposity (BMI z score, change in percent BMI above the 95th centile and change in percent body fat) that complement the outcome parameter of interest adding relevant changes in body composition that may be missed when evaluating BMI z scores or centiles (37). FFAs and insulin were measured in participants who performed the OGTT in a very narrow time frame of 4 years which excludes the remote and unlikely possibility of potential bias related to the insulin assay (38). Despite the fact that we do not have accurate dietary assessments for the follow-up period, the participants received dietary recommendations focusing on reduced consumption of soft drinks and highly processed energy-dense items without any emphasis on total carbohydrate or fat reductions, thus one can safely assume that the vast majority consumed a typical Western diet and that no dietary effect modification is relevant for the interpretation of our results.

Our results show no protective effect of adipose tissue insulin resistance against increasing degrees of obesity and no facilitating effect of whole-body (skeletal and hepatic) insulin resistance on increasing degrees of obesity (evaluated using changes in BMI z score, changes in percent BMI above the 95th centile and in percent body fat) in children and adolescents with obesity followed over time. Longer studies of participants of different age groups and lower degrees of obesity at baseline are needed to validate our findings, preferably prospective studies aimed specifically at testing the CIM longitudinally with and without interventions aimed at affecting adipose tissue insulin sensitivity. As the present study is a secondary analysis of an existing longitudinal cohort designed at testing the dynamics of glucose metabolism in youth with obesity, these findings do not refute the CIM, rather they highlight the complexity of defining a unifying model of obesity dynamics for populations of different ages, races, metabolic status, and dietary patterns. The “adipocentric” view of the CIM may be distilled to specific adipose tissue depots, such as discrete layers of subcutaneous fat (deep vs superficial), intrahepatic, intrapancreatic, intramuscular, or intra-abdominal fat, as they have been shown to correlate well with specific adverse metabolic phenotypes (39, 40). The endocrine/paracrine responses of such depots in response to consumption of specific nutrients may potentially play a role in energy balance and weight dynamics. Further studies are warranted for evaluation of the impact of specific adipose depots and lipid partitioning in general on obesity dynamics and energy metabolism.

Acknowledgments

The authors thank the participants and families for their willingness and cooperation.

Abbreviations

adipo-IR: fasting adipose insulin resistance
adipo-IR1: fasting adipose insulin resistance index 1 (fasting insulin × FFA)
adipo-IS1: fasting adipose insulin sensitivity index 1
AUC: area under the curve
BMI: body mass index
CIM: carbohydrate-insulin model
FFA: free fatty acid
OGTT: oral glucose tolerance test
WBISI: whole-body insulin sensitivity (Matsuda) index

Contributor Information

Rana Halloun, Department of Pediatrics, Ruth Children's Hospital, Rambam Medical Center, Haifa 3109601, Israel.

Alfonso Galderisi, Department of Women and Child Health, University of Padova, Via Giustiniani, 3, 35128 Padova, Italy.

Sonia Caprio, Department of Pediatrics, Yale University, Yale school of Medicine, 333 Cedar St, New Haven, CT 06510, USA.

Ram Weiss, Department of Pediatrics, Ruth Children's Hospital, Rambam Medical Center, Haifa 3109601, Israel.

Funding

This study was supported by the National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development (grants R01-HD-40787, R01DK111038, R01-HD-28016, and K24-HD-01464 to S.C.), the National Center for Research Resources (Clinical and Translational Science Award UL1-RR-0249139 to S.C.), the American Diabetes Association (Distinguished Clinical Scientist Award to S.C.), and the National Institute of Diabetes and Digestive and Kidney Diseases (grants R01-DK-111038 to S.C. and R01-DK-114504-01A1).

Author Contributions

R.H. and R.W. performed the analysis and wrote the manuscript. A.G. took part in the clinical care and added to the discussion. S.C. and R.W. designed the study, took part in the clinical care, the analysis, and manuscript composition.

Disclosures

The authors have nothing to disclose.

Data Availability

Restrictions apply to the availability of some or all data generated or analyzed during this study to preserve patient confidentiality or because they were used under license. The corresponding author will on request detail the restrictions and any conditions under which access to some data may be provided.

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6. Weiss R, Bremer AA, Lustig RH. What is metabolic syndrome, and why are children getting it? Ann N Y Acad Sci. 2013;1281(1):123‐140. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Ludwig DS, Aronne LJ, Astrup A, et al. The carbohydrate-insulin model: a physiological perspective on the obesity pandemic. Am J Clin Nutr. 2021;114(6):1873‐1885. [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Weiss R, Dziura J, Burgert TS, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004;350(23):2362‐2374. [DOI] [PubMed] [Google Scholar]
9. Weiss R, Taksali SE, Tamborlane WV, Burgert TS, Savoye M, Caprio S. Predictors of changes in glucose tolerance status in obese youth. Diabetes Care. 2005;28(4):902‐909. [DOI] [PubMed] [Google Scholar]
10. Weiss R, Cali AM, Dziura J, Burgert TS, Tamborlane WV, Caprio S. Degree of obesity and glucose allostasis are major effectors of glucose tolerance dynamics in obese youth. Diabetes Care. 2007;30(7):1845‐1850. [DOI] [PubMed] [Google Scholar]
11. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth charts for the United States: methods and development. Vital Health Stat. 2002;11(246):1‐190. [PubMed] [Google Scholar]
12. Hershkop K, Besor O, Santoro N, Pierpont B, Caprio S, Weiss R. Adipose insulin resistance in obese adolescents across the Spectrum of glucose tolerance. J Clin Endocrinol Metab. 2016;101(6):2423‐2431. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Hagman E, Besor O, Hershkop K, et al. Relation of the degree of obesity in childhood to adipose tissue insulin resistance. Acta Diabetol. 2019;56(2):219‐226. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Weiss R, Taksali SE, Dufour S, et al. The “obese insulin-sensitive” adolescent: importance of adiponectin and lipid partitioning. J Clin Endocrinol Metab. 2005;90(6):3731‐3737. [DOI] [PubMed] [Google Scholar]
15. Sinha R, Fisch G, Teague B, et al. Prevalence of impaired glucose tolerance among children and adolescents with marked obesity. N Engl J Med. 2002;346(11):802‐810. [DOI] [PubMed] [Google Scholar]
16. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22(9):1462‐1470. [DOI] [PubMed] [Google Scholar]
17. Yeckel CW, Weiss R, Dziura J, et al. Validation of insulin sensitivity indices from oral glucose tolerance test parameters in obese children and adolescents. J Clin Endocrinol Metab. 2004;89(3):1096‐1101. [DOI] [PubMed] [Google Scholar]
18. Groop LC, Bonadonna RC, DelPrato S, et al. Glucose and free fatty acid metabolism in non-insulin-dependent diabetes mellitus. Evidence for multiple sites of insulin resistance. J Clin Invest. 1989;84(1):205‐213. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Belfiore F, Iannello S, Volpicelli G. Insulin sensitivity indices calculated from basal and OGTT-induced insulin, glucose, and FFA levels. Mol Genet Metab. 1998;63(2):134‐141. [DOI] [PubMed] [Google Scholar]
20. Søndergaard E, Espinosa De Ycaza AE, Morgan-Bathke M, Jensen MD. How to measure adipose tissue insulin sensitivity. J Clin Endocrinol Metab. 2017;102(4):1193‐1199. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Rydén M, Andersson DP, Arner P. Usefulness of surrogate markers to determine insulin action in fat cells. Int J Obes (Lond). 2020;44(12):2436‐2443. [DOI] [PubMed] [Google Scholar]
22. Gastaldelli A, Gaggini M, DeFronzo RA. Role of adipose tissue insulin resistance in the natural history of type 2 diabetes: results from the San Antonio metabolism study. Diabetes. 2017;66(4):815‐822. [DOI] [PubMed] [Google Scholar]
23. Ludwig DS, Sørensen TIA. An integrated model of obesity pathogenesis that revisits causal direction. Nat Rev Endocrinol. 2022;18(5):261‐262. [DOI] [PubMed] [Google Scholar]
24. Shimy KJ, Feldman HA, Klein GL, Bielak L, Ebbeling CB, Ludwig DS. Effects of dietary carbohydrate content on circulating metabolic fuel availability in the postprandial state. J Endocr Soc. 2020;4(7):bvaa062. [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Kim JY, Bacha F, Tfayli H, Michaliszyn SF, Yousuf S, Arslanian S. Adipose tissue insulin resistance in youth on the Spectrum from Normal weight to obese and from Normal glucose tolerance to impaired glucose tolerance to type 2 diabetes. Diabetes Care. 2019;42(2):265‐272. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Reinehr T, Kiess W, Andler W. Insulin sensitivity indices of glucose and free fatty acid metabolism in obese children and adolescents in relation to serum lipids. Metab Clin Exp. 2005;54(3):397‐402. [DOI] [PubMed] [Google Scholar]
27. Song Y, Søndergaard E, Jensen MD. Unique metabolic features of adults discordant for indices of insulin resistance. J Clin Endocrinol Metab. 2020;105(8):e2753‐e2763. [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Lustig RH, Mietus-Snyder ML, Bacchetti P, Lazar AA, Velasquez-Mieyer PA, Christensen ML. Insulin dynamics predict body mass index and z-score response to insulin suppression or sensitization pharmacotherapy in obese children. J Pediatr. 2006;148(1):23‐29. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Halloun R, Galderisi A, Caprio S, Weiss R. Lack of evidence for a causal role of hyperinsulinemia in the progression of obesity in children and adolescents: A longitudinal study. Diabetes Care. 2022;45(6):1400‐1407. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Gubbi S, Muniyappa R, Sharma ST, Grewal S, McGlotten R, Nieman LK. Mifepristone improves adipose tissue insulin sensitivity in insulin resistant individuals. J Clin Endocrinol Metab. 2021;106(5):1501‐1515. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Sedaka NM, Olsen CH, Yannai LE, et al. A longitudinal study of serum insulin and insulin resistance as predictors of weight and body fat gain in African American and Caucasian children. Int J Obes (Lond). 2017;41(1):61‐70. [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Uysal Y, Wolters B, Knop C, Reinehr T. Components of the metabolic syndrome are negative predictors of weight loss in obese children with lifestyle intervention. Clin Nutr. 2014;33(4):620‐625. [DOI] [PubMed] [Google Scholar]
33. Levy-Marchal C, Arslanian S, Cutfield W, et al. Insulin resistance in children: consensus, perspective, and future directions. J Clin Endocrinol Metab. 2010;95(12):5189‐5198. [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Zavaroni I, Zuccarelli A, Gasparini P, Massironi P, Barilli A, Reaven GM. Can weight gain in healthy, nonobese volunteers be predicted by differences in baseline plasma insulin concentration? J Clin Endocrinol Metab. 1998;83(10):3498‐3500. [DOI] [PubMed] [Google Scholar]
35. McLaughlin T, Abbasi F, Carantoni M, Schaaf P, Reaven G. Differences in insulin resistance do not predict weight loss in response to hypocaloric diets in healthy obese women. J Clin Endocrinol Metab. 1999;84(2):578‐581. [DOI] [PubMed] [Google Scholar]
36. McLaughlin T, Abbasi F, Lamendola C, Kim HS, Reaven GM. Metabolic changes following sibutramine-assisted weight loss in obese individuals: role of plasma free fatty acids in the insulin resistance of obesity. Metab Clin Exp. 2001;50(7):819‐824. [DOI] [PubMed] [Google Scholar]
37. Vanderwall C, Eickhoff J, Randall Clark R, Carrel AL. BMI z-score in obese children is a poor predictor of adiposity changes over time. BMC Pediatr. 2018;18(1):187. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Ludwig DS, Ebbeling CB, Rimm EB. Carbohydrates, insulin secretion, and “precision nutrition”. Diabetes Care. 2022;45(6):1303‐1305. [DOI] [PubMed] [Google Scholar]
39. Weiss R. Fat distribution and storage: how much, where, and how? Eur J Endocrinol. 2007;157(Suppl 1):S39‐S45. [DOI] [PubMed] [Google Scholar]
40. Kahn DE, Bergman BC. Keeping it local in metabolic disease: adipose tissue paracrine signaling and insulin resistance. Diabetes. 2022;71(4):599‐609. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

[dgac700-B1] 1. Speakman JR, Hall KD. Carbohydrates, insulin, and obesity. Science. 2021;372(6542):577‐578. [DOI] [PubMed] [Google Scholar]

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[dgac700-B5] 5. Ludwig DS, Friedman MI. Increasing adiposity: consequence or cause of overeating? JAMA. 2014;311(21):2167‐2168. [DOI] [PubMed] [Google Scholar]

[dgac700-B6] 6. Weiss R, Bremer AA, Lustig RH. What is metabolic syndrome, and why are children getting it? Ann N Y Acad Sci. 2013;1281(1):123‐140. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B7] 7. Ludwig DS, Aronne LJ, Astrup A, et al. The carbohydrate-insulin model: a physiological perspective on the obesity pandemic. Am J Clin Nutr. 2021;114(6):1873‐1885. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B8] 8. Weiss R, Dziura J, Burgert TS, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004;350(23):2362‐2374. [DOI] [PubMed] [Google Scholar]

[dgac700-B9] 9. Weiss R, Taksali SE, Tamborlane WV, Burgert TS, Savoye M, Caprio S. Predictors of changes in glucose tolerance status in obese youth. Diabetes Care. 2005;28(4):902‐909. [DOI] [PubMed] [Google Scholar]

[dgac700-B10] 10. Weiss R, Cali AM, Dziura J, Burgert TS, Tamborlane WV, Caprio S. Degree of obesity and glucose allostasis are major effectors of glucose tolerance dynamics in obese youth. Diabetes Care. 2007;30(7):1845‐1850. [DOI] [PubMed] [Google Scholar]

[dgac700-B11] 11. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth charts for the United States: methods and development. Vital Health Stat. 2002;11(246):1‐190. [PubMed] [Google Scholar]

[dgac700-B12] 12. Hershkop K, Besor O, Santoro N, Pierpont B, Caprio S, Weiss R. Adipose insulin resistance in obese adolescents across the Spectrum of glucose tolerance. J Clin Endocrinol Metab. 2016;101(6):2423‐2431. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B13] 13. Hagman E, Besor O, Hershkop K, et al. Relation of the degree of obesity in childhood to adipose tissue insulin resistance. Acta Diabetol. 2019;56(2):219‐226. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B14] 14. Weiss R, Taksali SE, Dufour S, et al. The “obese insulin-sensitive” adolescent: importance of adiponectin and lipid partitioning. J Clin Endocrinol Metab. 2005;90(6):3731‐3737. [DOI] [PubMed] [Google Scholar]

[dgac700-B15] 15. Sinha R, Fisch G, Teague B, et al. Prevalence of impaired glucose tolerance among children and adolescents with marked obesity. N Engl J Med. 2002;346(11):802‐810. [DOI] [PubMed] [Google Scholar]

[dgac700-B16] 16. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22(9):1462‐1470. [DOI] [PubMed] [Google Scholar]

[dgac700-B17] 17. Yeckel CW, Weiss R, Dziura J, et al. Validation of insulin sensitivity indices from oral glucose tolerance test parameters in obese children and adolescents. J Clin Endocrinol Metab. 2004;89(3):1096‐1101. [DOI] [PubMed] [Google Scholar]

[dgac700-B18] 18. Groop LC, Bonadonna RC, DelPrato S, et al. Glucose and free fatty acid metabolism in non-insulin-dependent diabetes mellitus. Evidence for multiple sites of insulin resistance. J Clin Invest. 1989;84(1):205‐213. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B19] 19. Belfiore F, Iannello S, Volpicelli G. Insulin sensitivity indices calculated from basal and OGTT-induced insulin, glucose, and FFA levels. Mol Genet Metab. 1998;63(2):134‐141. [DOI] [PubMed] [Google Scholar]

[dgac700-B20] 20. Søndergaard E, Espinosa De Ycaza AE, Morgan-Bathke M, Jensen MD. How to measure adipose tissue insulin sensitivity. J Clin Endocrinol Metab. 2017;102(4):1193‐1199. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B21] 21. Rydén M, Andersson DP, Arner P. Usefulness of surrogate markers to determine insulin action in fat cells. Int J Obes (Lond). 2020;44(12):2436‐2443. [DOI] [PubMed] [Google Scholar]

[dgac700-B22] 22. Gastaldelli A, Gaggini M, DeFronzo RA. Role of adipose tissue insulin resistance in the natural history of type 2 diabetes: results from the San Antonio metabolism study. Diabetes. 2017;66(4):815‐822. [DOI] [PubMed] [Google Scholar]

[dgac700-B23] 23. Ludwig DS, Sørensen TIA. An integrated model of obesity pathogenesis that revisits causal direction. Nat Rev Endocrinol. 2022;18(5):261‐262. [DOI] [PubMed] [Google Scholar]

[dgac700-B24] 24. Shimy KJ, Feldman HA, Klein GL, Bielak L, Ebbeling CB, Ludwig DS. Effects of dietary carbohydrate content on circulating metabolic fuel availability in the postprandial state. J Endocr Soc. 2020;4(7):bvaa062. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B25] 25. Kim JY, Bacha F, Tfayli H, Michaliszyn SF, Yousuf S, Arslanian S. Adipose tissue insulin resistance in youth on the Spectrum from Normal weight to obese and from Normal glucose tolerance to impaired glucose tolerance to type 2 diabetes. Diabetes Care. 2019;42(2):265‐272. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B26] 26. Reinehr T, Kiess W, Andler W. Insulin sensitivity indices of glucose and free fatty acid metabolism in obese children and adolescents in relation to serum lipids. Metab Clin Exp. 2005;54(3):397‐402. [DOI] [PubMed] [Google Scholar]

[dgac700-B27] 27. Song Y, Søndergaard E, Jensen MD. Unique metabolic features of adults discordant for indices of insulin resistance. J Clin Endocrinol Metab. 2020;105(8):e2753‐e2763. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B28] 28. Lustig RH, Mietus-Snyder ML, Bacchetti P, Lazar AA, Velasquez-Mieyer PA, Christensen ML. Insulin dynamics predict body mass index and z-score response to insulin suppression or sensitization pharmacotherapy in obese children. J Pediatr. 2006;148(1):23‐29. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B29] 29. Halloun R, Galderisi A, Caprio S, Weiss R. Lack of evidence for a causal role of hyperinsulinemia in the progression of obesity in children and adolescents: A longitudinal study. Diabetes Care. 2022;45(6):1400‐1407. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B30] 30. Gubbi S, Muniyappa R, Sharma ST, Grewal S, McGlotten R, Nieman LK. Mifepristone improves adipose tissue insulin sensitivity in insulin resistant individuals. J Clin Endocrinol Metab. 2021;106(5):1501‐1515. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B31] 31. Sedaka NM, Olsen CH, Yannai LE, et al. A longitudinal study of serum insulin and insulin resistance as predictors of weight and body fat gain in African American and Caucasian children. Int J Obes (Lond). 2017;41(1):61‐70. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B32] 32. Uysal Y, Wolters B, Knop C, Reinehr T. Components of the metabolic syndrome are negative predictors of weight loss in obese children with lifestyle intervention. Clin Nutr. 2014;33(4):620‐625. [DOI] [PubMed] [Google Scholar]

[dgac700-B33] 33. Levy-Marchal C, Arslanian S, Cutfield W, et al. Insulin resistance in children: consensus, perspective, and future directions. J Clin Endocrinol Metab. 2010;95(12):5189‐5198. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B34] 34. Zavaroni I, Zuccarelli A, Gasparini P, Massironi P, Barilli A, Reaven GM. Can weight gain in healthy, nonobese volunteers be predicted by differences in baseline plasma insulin concentration? J Clin Endocrinol Metab. 1998;83(10):3498‐3500. [DOI] [PubMed] [Google Scholar]

[dgac700-B35] 35. McLaughlin T, Abbasi F, Carantoni M, Schaaf P, Reaven G. Differences in insulin resistance do not predict weight loss in response to hypocaloric diets in healthy obese women. J Clin Endocrinol Metab. 1999;84(2):578‐581. [DOI] [PubMed] [Google Scholar]

[dgac700-B36] 36. McLaughlin T, Abbasi F, Lamendola C, Kim HS, Reaven GM. Metabolic changes following sibutramine-assisted weight loss in obese individuals: role of plasma free fatty acids in the insulin resistance of obesity. Metab Clin Exp. 2001;50(7):819‐824. [DOI] [PubMed] [Google Scholar]

[dgac700-B37] 37. Vanderwall C, Eickhoff J, Randall Clark R, Carrel AL. BMI z-score in obese children is a poor predictor of adiposity changes over time. BMC Pediatr. 2018;18(1):187. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dgac700-B38] 38. Ludwig DS, Ebbeling CB, Rimm EB. Carbohydrates, insulin secretion, and “precision nutrition”. Diabetes Care. 2022;45(6):1303‐1305. [DOI] [PubMed] [Google Scholar]

[dgac700-B39] 39. Weiss R. Fat distribution and storage: how much, where, and how? Eur J Endocrinol. 2007;157(Suppl 1):S39‐S45. [DOI] [PubMed] [Google Scholar]

[dgac700-B40] 40. Kahn DE, Bergman BC. Keeping it local in metabolic disease: adipose tissue paracrine signaling and insulin resistance. Diabetes. 2022;71(4):599‐609. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Adipose Tissue Insulin Resistance Is Not Associated With Changes in the Degree of Obesity in Children and Adolescents

Rana Halloun

Alfonso Galderisi

Sonia Caprio

Ram Weiss

Abstract

Context

Objective

Methods

Results

Conclusion

Methods

Analytical Methods

Calculations

Statistical Analyses

Results

Characteristics of the Study Participants

Table 1.

Fasting Adipose Insulin Resistance Index 1 and Change in the Degree of Obesity

Figure 1.

Fasting Adipose Insulin Sensitivity Index and Change in the Degree of Obesity

Prandial Adipose Insulin Resistance and Change in the Degree of Obesity

Prandial AUC FFAs During OGTT and Change in the Degree of Obesity

Figure 2.

Suppression of FFAs During the OGTT

Whole-Body Insulin Sensitivity and Change in the Degree of Obesity

Figure 3.

Associations of Indices of Adipose Tissue Insulin Sensitivity/Resistance and Whole-Body Insulin Sensitivity With Changes in Percent Body Fat and Changes in Percent BMI Above the 95th Centile

Discussion

Acknowledgments

Abbreviations

Contributor Information

Funding

Author Contributions

Disclosures

Data Availability

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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