Dataset Information

Differences in Cardiometabolic Risk between Insulin-Sensitive and Insulin-Resistant Overweight and Obese Children.

ABSTRACT: It is known that 15-30% overweight/obese adults do not suffer cardiometabolic consequences. There is limited literature examining factors that can be used to assess cardiometabolic health in overweight/obese children. If such factors can be identified, they would aid in differentiating those most in need for aggressive management.Baseline data from 7- to 12-year-old, overweight, and obese children enrolled in a weight management program at an urban hospital were analyzed. Homeostatic model assessment for insulin resistance (HOMA-IR) <2.6 was used to define insulin-sensitive and HOMA-IR ?2.6 was used to defined insulin-resistant participants. Demographics, physical activity measures, and cardiometabolic risk factors were compared between the two phenotypes. Odds ratios (ORs) examining the association between intermediate endpoints (metabolic syndrome [MetS], nonalcoholic fatty liver disease [NAFLD], systemic inflammation, and microalbuminuria) and the two metabolic phenotypes were evaluated.Of the 362 overweight/obese participants, 157 (43.5%) were insulin sensitive and 204 (56.5%) were insulin resistant. Compared to the insulin-sensitive group, the insulin-resistant group was older (8.6±1.6 vs. 9.9±1.7; p<0.001) and had a higher BMI z-score (1.89±0.42 vs. 2.04±0.42; p=0.001). After multivariable adjustment, compared to the insulin-sensitive group, the insulin-resistant group had higher odds of having MetS (OR, 5.47; 95% confidence interval [CI]: 1.72, 17.35; p=0.004) and NAFLD (OR, 8.66; 95% CI, 2.48, 30.31; p=0.001), but not systemic inflammation (OR, 1.06; 95% CI: 0.56, 2.03; p=0.86) or microalbuminuria (OR, 1.71; 95% CI, 0.49, 6.04; p=0.403).Using a HOMA-IR value of ?2.6, clinical providers can identify prepubertal and early pubertal children most at risk. Focusing limited resources on aggressive weight interventions may lead to improvement in cardiometabolic health.

SUBMITTER: Khan UI

PROVIDER: S-EPMC4485365 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Differences in Cardiometabolic Risk between Insulin-Sensitive and Insulin-Resistant Overweight and Obese Children.

Khan Unab I UI McGinn Aileen P AP Isasi Carmen R CR Groisman-Perelstein Adriana A Diamantis Pamela M PM Ginsberg Mindy M Wylie-Rosett Judith J

Childhood obesity (Print) 20150316 3

<h4>Background</h4>It is known that 15-30% overweight/obese adults do not suffer cardiometabolic consequences. There is limited literature examining factors that can be used to assess cardiometabolic health in overweight/obese children. If such factors can be identified, they would aid in differentiating those most in need for aggressive management.<h4>Methods</h4>Baseline data from 7- to 12-year-old, overweight, and obese children enrolled in a weight management program at an urban hospital wer ...[more]

PMID: 25774664

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Project description:BackgroundWe aimed to assess the associations between adiposity distribution and cardiometabolic risk factors among overweight and obese adults in China, and to demonstrate the sex differences in these associations.MethodsA total of 1221 participants (455 males and 766 females) were included in this study. Percentage of body fat (PBF) of the whole body and regional areas, including arm, thigh, trunk, android, and gynoid, were measured by the dual-energy X-ray absorptiometry method. Central adiposity was measured by waist circumference. Clustered cardiometabolic risk was defined as the presence of two or more of the six cardiometabolic risk factors, namely, high triglyceride, low high density lipoprotein, elevated glucose, elevated blood pressure, elevated high sensitivity C-reactive protein, and low adiponectin. Linear regression models and multivariate logistic regression models were used to assess the associations between whole body or regional PBF and cardiometabolic risk factors.ResultsIn females, except arm adiposity, other regional fat (thigh, trunk, android, gynoid) and whole-body PBF are significantly associated with clustered cardiometabolic risk, adjusting for age, smoking, alcohol drinking, physical activity, and whole-body PBF. One-SD increase in Z scores of the thigh and gynoid PBF were significantly associated with 80 and 78% lower odds of clustered cardiometabolic risk (OR: 0.20, 95%CI: 0.12-0.35 and OR: 0.22, 95%CI: 0.12-0.41). Trunk, android and whole-body PBF were significantly associated with higher odds of clustered risk with OR of 1.90 (95%CI:1.02-3.55), 2.91 (95%CI: 1.75-4.85), and 2.01 (95%CI: 1.47-2.76), respectively. While in males, one-SD increase in the thigh and gynoid PBF are associated with 94% (OR: 0.06, 95%CI: 0.02-0.23) and 83% lower odds (OR: 0.17, 95%CI: 0.05-0.57) of clustered cardiometabolic risk, respectively. Android and whole-body PBF were associated with higher odds of clustered cardiometabolic risk (OR: 3.39, 95%CI: 1.42-8.09 and OR: 2.45, 95%CI: 1.53-3.92), but the association for trunk PBF was not statistically significant (OR: 1.16, 95%CI: 0.42-3.19).ConclusionsAdiposity distribution plays an important role in the clustered cardiometabolic risk in participants with overweight and obese and sex differences were observed in these associations. In general, central obesity (measured by android PBF) could be the best anthropometric measurement for screening people at risk for CVD risk factors for both men and women. Upper body fat tends to be more detrimental to cardiometabolic health in women than in men, whereas lower body fat is relatively more protective in men than in women.

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

Dataset Information

Differences in Cardiometabolic Risk between Insulin-Sensitive and Insulin-Resistant Overweight and Obese Children.

Publications

Differences in Cardiometabolic Risk between Insulin-Sensitive and Insulin-Resistant Overweight and Obese Children.

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