Association of cardiometabolic risk factors with insulin resistance in overweight and obese children

Daneshzad, Elnaz; Rostami, Sayeh; Aghamahdi, Fatemeh; Mahdavi-Gorabi, Armita; Qorbani, Mostafa

doi:10.1186/s12902-022-01245-7

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…The following variables were used as markers for increased cardiometabolic risk: WHtR parameters: There is no consensus on the appropriate cut points of WHtR in pediatric populations [ 15 – 18 , 20 , 28 ]. Previous reviews and analyses have indicated that dichotomized WHtR cut points at ≥ 0.5 [ 18 ] or 0.55 [ 29 ] are surrogates of increased risk in children; however, they may be insignificant when assessed for sensitivity and specificity to certain variables [ 30 – 32 ].…”

Section: Methodsmentioning

confidence: 99%

“…Waist-to-height ratio (WHtR), derived as a ratio of waist circumference in centimeters (cms) by height (cms), a simple screening tool [ 13 ], has recently been proposed as a diagnostic measure of early cardiometabolic risk in both children [ 3 , 14 – 16 ] and adults [ 16 – 19 ]. WHtR is more strongly associated with cardiovascular and cardiometabolic risk factors than individual anthropometric measures, such as waist circumference, Body Mass Index (BMI), and waist-hip ratio [ 15 , 17 , 20 – 24 ]. [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Waist-to-height ratio associated cardiometabolic risk phenotype in children with overweight/obesity

et al. 2023

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Background Childhood overweight/obesity has been associated with an elevated risk of insulin resistance and cardiometabolic disorders. Waist-to-height ratio (WHtR) may be a simple screening tool to quickly identify children at elevated risk for cardiometabolic disorders. The primary objective of the present study was to create sex-specific tertile cut points of WHtR and assess its association with Insulin resistance and elevated liver enzyme concentrations in children, factors using cross-sectional data from the randomized, controlled Family Weight Management Study. Methods Baseline data from 360 children (7–12 years, mean Body Mass Index (BMI) ≥ 85th percentile for age and sex) were used to calculate WHtR tertiles by sex, male: ≤ 0.55 (T1), > 0.55- ≤ 0.59 (T2), > 0.59 (T3); female: ≤ 0.56 (T1), > 0.56- ≤ 0.6 (T2), > 0.6 (T3). The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) was used to categorize participants as insulin-resistant (HOMA-IR ≥ 2.6) and insulin-sensitive (HOMA-IR < 2.6). Liver enzymes aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were categorized as normal vs. elevated (AST of < 36.0 µkat/L or ≥ 36.0 µkat/L; ALT of < 30.0 µkat/L or ≥ 30.0 µkat/L; ALT > 26 µkat/L males, > 22 µkat/L females). We examined differences in baseline cardiometabolic risk factors by WHtR tertiles and sex-specific multivariable logistic regression models to predict HOMA-IR and elevation of liver enzymes. Results Study participants had a mean WHtR of 0.59 ([SD: 0.06]). Irrespective of sex, children in WHtR T3 had higher BMIz scores, blood pressure, triglycerides, 2-h glucose, fasting 2-h insulin, and lower high-density lipoprotein cholesterol (HDL-C) concentrations than those in T2 and T1. After adjusting for covariates, the odds of elevated HOMA-IR (> 2.6) were over five-fold higher among males in T3 versus T1 [OR, 95%CI: 5.83, 2.34–14.52] and T2 [OR, 95%CI: 4.81, 1.94–11.92] and females in T3 [OR, 95%CI: 5.06, 2.10–12.20] versus T1. The odds of elevated ALT values (≥ 30) were 2.9 [95%CI: 1.01–8.41] fold higher among females in T3 compared to T1. Conclusion In public health settings, WHtR may be a practical screening tool in pediatric populations to identify children at risk of metabolic syndrome.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Waist-to-height ratio associated cardiometabolic risk phenotype in children with overweight/obesity

et al. 2023

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“…Diabetes and prediabetes, which are a continuum of impaired insulin resistance and/or secretion, develop even in adolescents [ 1 ]. Impaired glucose metabolism in adolescents is associated with increased cardiovascular risk [ 2 ]. The influence of dietary behaviors on type 2 diabetes (T2D) incidence has been documented extensively in the literature [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Dietary Amino Acid Composition and Glycemic Biomarkers in Japanese Adolescents

Okuda,

Sasaki

2024

Nutrients

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Protein intake reportedly increases the risk of diabetes; however, the results have been inconsistent. Diabetes in adulthood may be attributed to early life dietary amino acid composition. This study aimed to investigate the association between amino acid composition and glycemic biomarkers in adolescents. Dietary intake was assessed using a food frequency questionnaire, and fasting glucose and insulin levels were measured in 1238 eighth graders. The homeostatic model assessment (HOMA) indices (insulin resistance and β-cell function) were calculated. Anthropometrics were measured and other covariates were obtained from a questionnaire. Amino acid composition was isometric log transformed according to the compositional data analysis, which was used as explanatory variables in multivariate linear regression models for glucose, insulin, and HOMA indices. Only the association between glucose and leucine was significant. In replacement of other amino acids with leucine, an increase of 0.1% of total amino acids correlated with a lower glucose level (−1.02 mg/dL). One-to-one substitution of leucine for isoleucine or methionine decreased glucose (−2.98 and −2.28 mg/dL, respectively). Associations with other biomarkers were not observed. In conclusion, compositional data analysis of amino acids revealed an association only with glucose in adolescents; however, the results of this study should be verified in other populations.

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“…Obesity is a common, serious, and multifactorial disease associated with genetics [ 4 ], environment [ 5 ], physiology [ 6 ], increased consumption of hypercaloric foods [ 7 ], and sedentary lifestyles [ 8 ]. Increased body mass index (BMI) and obesity during adolescence carry significant long-term adverse health outcomes, including the development of chronic diseases and mortality, such as cardiovascular disease, stroke, insulin resistance diabetes, and cancer [ 9 , 10 , 11 , 12 , 13 ]. Therefore, efficient interventions and predictive biomarkers for the development of obesity are needed.…”

Section: Introductionmentioning

confidence: 99%

Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis

Singh,

Kinnebrew,

Hsu

et al. 2023

Metabolites

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Obesity in children and adolescents has increased globally. Increased body mass index (BMI) during adolescence carries significant long-term adverse health outcomes, including chronic diseases such as cardiovascular disease, stroke, diabetes, and cancer. Little is known about the metabolic consequences of changes in BMI in adolescents outside of typical clinical parameters. Here, we used untargeted metabolomics to assess changing BMI in male adolescents. Untargeted metabolomic profiling was performed on urine samples from 360 adolescents using UPLC–QTOF-MS. The study includes a baseline of 235 subjects in a discovery set and 125 subjects in a validation set. Of them, a follow-up of 81 subjects (1 year later) as a replication set was studied. Linear regression analysis models were used to estimate the associations of metabolic features with BMI z-score in the discovery and validation sets, after adjusting for age, race, and total energy intake (kcal) at false-discovery-rate correction (FDR) ≤ 0.1. We identified 221 and 16 significant metabolic features in the discovery and in the validation set, respectively. The metabolites associated with BMI z-score in validation sets are glycylproline, citrulline, 4-vinylsyringol, 3′-sialyllactose, estrone sulfate, carnosine, formiminoglutamic acid, 4-hydroxyproline, hydroxyprolyl-asparagine, 2-hexenoylcarnitine, L-glutamine, inosine, N-(2-Hydroxyphenyl) acetamide glucuronide, and galactosylhydroxylysine. Of those 16 features, 9 significant metabolic features were associated with a positive change in BMI in the replication set 1 year later. Histidine and arginine metabolism were the most affected metabolic pathways. Our findings suggest that obesity and its metabolic outcomes in the urine metabolome of children are linked to altered amino acids, lipid, and carbohydrate metabolism. These identified metabolites may serve as biomarkers and aid in the investigation of obesity’s underlying pathological mechanisms. Whether these features are associated with the development of obesity, or a consequence of changing BMI, requires further study.

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Association of cardiometabolic risk factors with insulin resistance in overweight and obese children

Cited by 4 publications

References 36 publications

Waist-to-height ratio associated cardiometabolic risk phenotype in children with overweight/obesity

Waist-to-height ratio associated cardiometabolic risk phenotype in children with overweight/obesity

Dietary Amino Acid Composition and Glycemic Biomarkers in Japanese Adolescents

Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis

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