Prevalence and clinical characteristics of metabolically healthy obese versus metabolically unhealthy obese school children

Cardiometabolic Health in Adolescents with Obesity: The Role of Protein Intake, Diet Quality, and Physical Activity

Background: Although adolescents with obesity have an increased risk of cardiometabolic disease, a subset maintains a healthy cardiometabolic profile. Unhealthy lifestyle behaviors may determine cardiometabolic risk. We aimed to characterize the lifestyle behaviors of adolescents with obesity, compare differences between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO), and assess associations between lifestyle behaviors and cardiometabolic profiles. Methods: Participants aged 10-18 years with body mass index (BMI) ≥ 95th percentile were included. Dietary intake (DI) was estimated from 3-day food records, and diet quality (DQ) was assessed using the Healthy Eating Index-Canadian Adaptation. Physical activity (PA), body composition, anthropometrics, blood markers, and blood pressure (BP) were objectively measured. MUO was defined as having high triglycerides, BP, glucose, or low high-density lipoprotein. Regression analyses were performed between lifestyle behaviors and cardiometabolic markers. Results: Thirty-nine participants (BMI z-score 2.8 [2.5-3.5], age 12.5 [10.9-13.5] years, 56.4% female) were included. A high proportion of participants failed to meet lifestyle recommendations, particularly for DQ (94.7%, n = 36), fiber (94.7%, n = 36), and PA (90.9%, n = 30). No differences in lifestyle behaviors were found between MUO (59.0%, n = 22) and MHO (41.0%, n = 16). Protein intake was negatively associated with BMI and waist circumference z-scores, fat mass index, insulin resistance, low-density lipoprotein, and C-reactive protein, whereas higher DQ was associated with lower C-reactive protein. Higher light PA levels were associated with lower total cholesterol and triglycerides. Conclusion: Adolescents with either MUO or MHO displayed low adherence to DQ, DI, and PA recommendations; no differences in lifestyle behaviors were found. Protein intake, DQ, and PA were associated with a healthier cardiometabolic profile.

Lower adiponectin is associated with higher anthropometry and insulin resistance but not with low cardiorespiratory fitness in adolescents

PURPOSE

The aim of this study was to analyze the relationship between adiposity, cardiometabolic risk and cardiorespiratory fitness (CRF) according to different groups of adiponectin concentration.

METHODS

255 adolescents of both sexes, aged 11-17 years old, participated. Anthropometric and biochemical parameters such as body mass, height, abdominal circumference (AC), waist circumference (WC), fat mass, fat-free mass, total cholesterol (TC), high-density lipoprotein (HDL-c), low-density lipoprotein (LDL-c), triglycerides (TG), glucose, insulin, adiponectin, blood pressure, peak oxygen consumption (VO2peak) were measured. Body mass index (BMI), z-score BMI (BMI-z), triponderal mass index (TMI), waist-to-height ratio (WHtR), homeostasis model to assessment insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) were calculated. Adiponectin was categorized: low adiponectin concentration (LAC  ≤ 5.18 µg/mL-1), intermediate (IAC = 5.18 and 7.63 µg/mL-1) and high (HAC ≥ 7.63 µg/ml-1).

RESULTS

LAC showed higher BMI, BMI-z and TMI than the other groups (p < 0.05) and higher AC, WC and WHtR that the HAC (p < 0.05). IAC showed lower values of TC, LDL-c and TG, and the LAC presented the highest values of insulin, HOMA-IR and QUICKI (p < 0.05) to the IAC and HAC. HAC presented the lower VO2peak than the other groups (p < 0.01). BMI, TMI, glucose, insulin, HOMA-IR showed inverse, and QUICKI a direct and weak correlation with adiponectin (p < 0.05). No significant association was found between adiponectin and VO2peak (p > 0.05).

CONCLUSION

The LAC group had higher means in the anthropometric variables and the worst results related to insulin resistance and sensitivity. Thus, adiponectin may play an important role in obesity and reduced concentration may be a factor in the development of obesity-associated morbidities.

Identifying subgroups of childhood obesity by using multiplatform metabotyping

Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.

Prebiotics, Probiotics, and Synbiotics-A Research Hotspot for Pediatric Obesity

Childhood obesity is a major public health problem worldwide with an increasing prevalence, associated not only with metabolic syndrome, insulin resistance, hypertension, dyslipidemia, and non-alcoholic fatty liver disease (NAFLD), but also with psychosocial problems. Gut microbiota is a new factor in childhood obesity, which can modulate the blood lipopolysaccharide levels, the satiety, and fat distribution, and can ensure additional calories to the host. The aim of this review was to assess the differences and the impact of the gut microbial composition on several obesity-related complications such as metabolic syndrome, NAFLD, or insulin resistance. Early dysbiosis was proven to be associated with an increased predisposition to obesity. Depending on the predominant species, the gut microbiota might have either a positive or negative impact on the development of obesity. Prebiotics, probiotics, and synbiotics were suggested to have a positive effect on improving the gut microbiota and reducing cardio-metabolic risk factors. The results of clinical trials regarding probiotic, prebiotic, and synbiotic administration in children with metabolic syndrome, NAFLD, and insulin resistance are controversial. Some of them (Lactobacillus rhamnosus bv-77, Lactobacillus salivarius, and Bifidobacterium animalis) were proven to reduce the body mass index in obese children, and also improve the blood lipid content; others (Bifidobacterium bifidum, Bifidobacterium longum, Lactobacillus acidophilus, Lacticaseibacillus rhamnosus, Enterococcus faecium, and fructo-oligosaccharides) failed in proving any effect on lipid parameters and glucose metabolism. Further studies are necessary for understanding the mechanism of the gut microbiota in childhood obesity and for developing low-cost effective strategies for its management.

Circulating MicroRNA-30a, Beclin1 and Their Association with Different Variables in Females with Metabolically Healthy /Unhealthy Obesity

Background

Obesity is associated with metabolic and cardiovascular co-morbidities. It is important to determine the factors associated with metabolic derangement in obesity. Autophagy plays a major role in the pathogenesis of metabolic syndrome. MicroRNA-30a targets beclin1, the main regulator of autophagy.

Purpose

We assess circulating microRNA-30a and serum beclin1 in women with metabolically unhealthy obesity (MUO), women with metabolically healthy obesity (MHO) and non-obese healthy control and determine their relationship with different clinical and metabolic variables in women with obesity.

Patients and Methods

This cross-sectional study included 34 women with MHO, 34 with MUO, and 20 healthy non-obese women. Blood pressure, body mass index (BMI), and waist circumference were recorded. Glycemic and lipid indices, urinary albumin-to-creatinine ratio, ALT, AST, microRNA-30a expression in serum were measured using real-time polymerase chain reaction and beclin1 by enzyme-linked immunosorbent assay were measured.

Results

The expression of microRNA-30a was significantly higher, and beclin1 level was significantly lower in women with MUO compared to those in women with MHO (P<0.001; for both). People with MUO were significantly older (P<0.001) and had higher TSH (P=0.006), HbA1c (P<0.001), triglyceride (P<0.001), and ALT (P<0.001) compared to women with MHO. However, there was no significant difference between the two groups in any anthropometric measurements, HDL-C or LDL-C. In univariate analyses, age, ALT, TSH, microRNA-30a, and beclin1 were significantly correlated with the MUO phenotype (P<0.001; for all). Significance was confirmed in the multivariate analysis for microRNA-30a (95% CI 1.317-28.252; P=0.021).

Conclusion

MicroRNA-30a, beclin1, age, and ALT and TSH levels were significantly associated with the MUO phenotype, among which microRNA-30a was the best indicator of metabolic syndrome in women with obesity.

Differences in specific abdominal fat depots between metabolically healthy and unhealthy children with overweight/obesity: The role of cardiorespiratory fitness

OBJECTIVES

Fat depots localization has a critical role in the metabolic health status of adults. Nevertheless, whether that is also the case in children remains under-studied. Therefore, the aims of this study were: (i) to examine the differences between metabolically healthy (MHO) and unhealthy (MUO) overweight/obesity phenotypes on specific abdominal fat depots, and (ii) to further explore whether cardiorespiratory fitness plays a major role in the differences between metabolic phenotypes among children with overweight/obesity.

METHODS

A total of 114 children with overweight/obesity (10.6 ± 1.1 years, 62 girls) were included. Children were classified as MHO (n = 68) or MUO. visceral (VAT), abdominal subcutaneous (ASAT), intermuscular abdominal (IMAAT), psoas, hepatic, pancreatic, and lumbar bone marrow adipose tissues were measured by magnetic resonance imaging. Cardiorespiratory fitness was assessed using the 20 m shuttle run test.

RESULTS

MHO children had lower VAT and ASAT contents and psoas fat fraction compared to MUO children (difference = 12.4%-25.8%, all p < 0.035). MUO-unfit had more VAT and ASAT content than those MUO-fit and MHO-fit (difference = 34.8%-45.3%, all p < 0.044). MUO-unfit shows also greater IMAAT fat fraction than those MUO-fit and MHO-fit peers (difference = 16.4%-13.9% respectively, all p ≤ 0.001). In addition, MHO-unfit presented higher IMAAT fat fraction than MHO-fit (difference = 13.4%, p < 0.001). MUO-unfit presented higher psoas fat fraction than MHO-fit (difference = 29.1%, p = 0.008).

CONCLUSIONS

VAT together with ASAT and psoas fat fraction, were lower in MHO than in MUO children. Further, we also observed that being fit, regardless of metabolic phenotype, has a protective role over the specific abdominal fat depots among children with overweight/obesity.

Skeptical Look at the Clinical Implication of Metabolic Syndrome in Childhood Obesity

Metabolic syndrome (MetS) is defined by a cluster of several cardio-metabolic risk factors, specifically visceral obesity, hypertension, dyslipidemia, and impaired glucose metabolism, which together increase risks of developing future cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D). This article is a narrative review of the literature and a summary of the main observations, conclusions, and perspectives raised in the literature and the study projects of the Working Group of Childhood Obesity (WGChO) of the Italian Society of Paediatric Endocrinology and Diabetology (ISPED) on MetS in childhood obesity. Although there is an agreement on the distinctive features of MetS, no international diagnostic criteria in a pediatric population exist. Moreover, to date, the prevalence of MetS in childhood is not certain and thus the true value of diagnosis of MetS in youth as well as its clinical implications, is unclear. The aim of this narrative review is to summarize the pathogenesis and current role of MetS in children and adolescents with particular reference to applicability in clinical practice in childhood obesity.

Childhood Obesity and the Cryptic Language of the Microbiota: Metabolomics’ Upgrading

The growing obesity epidemic in childhood is increasingly concerning for the related physical and psychological consequences, with a significant impact on health care costs in both the short and the long term. Nonetheless, the scientific community has not yet completely clarified the complex metabolic mechanisms underlying body weight alterations. In only a small percentage of cases, obesity is the result of endocrine, monogenic, or syndromic causes, while in much more cases, lifestyle plays a crucial role in obesity development. In this context, the pediatric age appears to be of considerable importance as prevention strategies together with early intervention can represent important therapeutic tools not only to counteract the comorbidities that increasingly affect children but also to hinder the persistence of obesity in adulthood. Although evidence in the literature supporting the alteration of the microbiota as a critical factor in the etiology of obesity is abundant, it is not yet fully defined and understood. However, increasingly clear evidence is emerging regarding the existence of differentiated metabolic profiles in obese children, with characteristic metabolites. The identification of specific pathology-related biomarkers and the elucidation of the altered metabolic pathways would therefore be desirable in order to clarify aspects that are still poorly understood, such as the consequences of the interaction between the host, the diet, and the microbiota. In fact, metabolomics can characterize the biological behavior of a specific individual in response to external stimuli, offering not only an eventual effective screening and prevention strategy but also the possibility of evaluating adherence and response to dietary intervention.