Obesity is an increasing public health concern worldwide. According to the latest Organization for Economic Co-operation and Development (OECD) report (2014), the incidence of child obesity in Korea has exceeded the OECD average. To better understand and control this condition, the present study examined the composition of the gut microbial community in normal and obese adolescents. Fecal samples were collected from 67 obese (body mass index [BMI] ≥ 30 kg/m², or ≥ 99^th BMI percentile) and 67 normal (BMI < 25 kg/m² or < 85^th BMI percentile) Korean adolescents aged 13–16 years and subjected to 16S rRNA gene sequencing. Analysis of bacterial composition according to taxonomic rank (genus, family, and phylum) revealed marked differences in the Bacteroides and Prevotella populations in normal and obese samples (p < 0.005) at the genus and family levels; however, there was no difference in the Firmicutes-to-Bacteroidetes (F/B) ratio between normal and obese adolescents samples at the phylum level (F/B normal = 0.50 ± 0.53; F/B obese = 0.56 ± 0.86; p = 0.384). Statistical analysis revealed a significant association between the compositions of several bacterial taxa and child obesity. Among these, Bacteroides and Prevotella showed the most significant association with BMI (p < 0.0001 and 0.0001, respectively). We also found that the composition of Bacteroides was negatively associated with triglycerides (TG), total cholesterol, and high-sensitive C-reactive protein (hs-crp) (p = 0.0049, 0.0023, and 0.0038, respectively) levels, whereas that of Prevotella was positively associated with TG and hs-crp levels (p = 0.0394 and 0.0150, respectively). We then applied the association rule mining algorithm to generate “rules” to identify the association between the populations of multiple bacterial taxa and obesity; these rules were able to discriminate obese from normal states. Therefore, the present study describes a systemic approach to identify the association between bacterial populations in the gut and childhood obesity.

We investigated the relationship among socioeconomic status factors, the risk of anemia, and iron deficiency among school-aged children in Korea. The sample consisted of fourth-grade students aged 10 y recruited from nine elementary schools in Korean urban areas in 2008 (n = 717). Anthropometric and blood biochemistry data were obtained for this cross-sectional observational study. Anemia was defined as hemoglobin levels lower than 11.5 g/dl. Iron deficiency was defined as serum iron levels lower than 40 ug/dl. We also obtained data on parental education from questionnaires and on children's diets from 3-day food diaries. Parental education was categorized as low or high, with the latter representing an educational level beyond high school. Children with more educated mothers were less likely to develop anemia (P = 0.0324) and iron deficiency (P = 0.0577) than were those with less educated mothers. This group consumed more protein (P = 0.0004) and iron (P = 0.0012) from animal sources than did the children of less educated mothers, as reflected by their greater consumption of meat, poultry, and derivatives (P < 0.0001). Logistic regression analysis revealed a significant inverse relationship between maternal education and the prevalence of anemia (odds ratio: 0.52; 95% confidence interval: 0.32, 0.85). As a contributor to socioeconomic status, maternal education is important in reducing the risk of anemia and iron deficiency and in increasing children's consumption of animal food sources.

This study aimed to develop a multidisciplinary lifestyle intervention program targeted at children and adolescents with moderate to severe obesity, and assess the additional effects of exercise intervention when compared to usual care. Overall, the 103 enrolled participants were ≥85th percentile of age and sex-specific body mass index (BMI). Participants were divided into groups that received 16 weeks of either usual care or exercise intervention. The BMI z-score of the overall completers decreased by about 0.05 after the 16-week intervention (p = 0.02). After the intervention, only the exercise group had a significantly lower BMI z-score than the baseline score by about 0.1 (p = 0.03), but no significant group by time interaction effects were observed. At the 16-week follow-up, significant group by time interaction effects were observed in percentage body fat (%BF) (β = −1.52, 95%CI = −2.58–−0.45), lean body mass (LM) (β = 1.20, 95%CI = 0.12–2.29), diastolic blood pressure (β = −5.24, 95%CI = −9.66–−0.83), high-sensitivity C-reactive protein (β = −1.67, 95%CI = −2.77–−1.01), and wall sit test score (β = 50.74, 95%CI = 32.30–69.18). We developed a moderate-intensity intervention program that can be sustained in the real-world setting and is practically applicable to both moderate and severe obesity. After interventions, the exercise group had lower %BF and cardiometabolic risk markers, and higher LM and leg muscle strength compared to the usual care group.

The discovery of metabolomics-based biomarkers has been a focus of recent kidney dysfunction research. In the present study, we aimed to identify metabolites associated with chronic kidney disease (CKD) in the general population using a cross-sectional study design. At baseline, 6.5% of subjects had CKD. Pearson correlation analysis showed that 28 metabolites were significantly associated with estimated glomerular filtration rate (eGFR) after Bonferroni correction. Among these metabolites, 4 acylcarnitines, 12 amino acids, 4 biogenic amines, 1 phosphatidylcholine, and 1 sphingolipid were associated with CKD (p < 0.05). After eight years, 13.5% of subjects had CKD. Three amino acid metabolites were positively associated with new-onset CKD: citrulline [odds ratio (OR): 2.41, 95% confidence interval (CI): 1.26–4.59], kynurenine (OR: 1.98, 95% CI: 1.05–3.73), and phenylalanine (OR: 2.68, 95% CI: 1.00–7.16). The kynurenine:tryptophan ratio was also associated with CKD (OR: 3.20; 95% CI: 1.57–6.51). The addition of multiple metabolites significantly improved the CKD prediction by C statistics (0.756–0.85, p < 0.0001), and the net reclassification improvement was 0.84 (95% CI: 0.72–0.96). Elevated hs-C reactive protein (CRP) was associated with new-onset CKD (OR: 1.045, 95% CI: 1.005–1.086); however, this association disappeared following adjustment with the kynurenine:tryptophan ratio. The levels of citrulline and kynurenine and their ratio to tryptophan in CKD patients with proteinuria were worse than those with one or neither characteristic. Together, the results of this study demonstrate that amino acid metabolites are associated with CKD eight years after initial metabolite assessment. These results could improve the identification of subjects at high risk of CKD who have modified amino acid metabolism.

Insulin resistance is an important clinical feature of metabolic syndrome, which includes obesity and type 2 diabetes. Increased adipose energy storage in obesity promote insulin resistance and other metabolic adverse effects. To identify a new link between adipocyte and insulin resistance, we performed targeted metabolite profiling of differentiated adipocytes and studied the association between adipogenic metabolites and insulin resistance. We found a correlation between 2-aminoadipic acid (2-AAA) and adipogenic differentiation. Also, circulatory 2-AAA was positively associated with obesity-related factors (fat mass, fat percent, waist circumference, BMI, BMI z-score, triglycerides, insulin, and HOMA-IR) at baseline and after 2 years in the children cohort study. Of these factors, increased BMI z-score and HOMA-IR were the primary independent factors associated with higher 2-AAA levels, and the baseline 2-AAA level was an indicator of the BMI z-score after 2 years. To validate the relationship between 2-AAA and obesity-related factors, we analyzed changes in 2-AAA levels following obesity intervention programs in two independent studies. In both studies, changes in 2-AAA levels during the intervention period were positively correlated with changes in the BMI z-score and HOMA-IR after adjusting for confounders. Moreover, the 2-AAA levels were increased in cell and mouse models of obesity-related insulin resistance. Excess 2-AAA levels led to impaired insulin signaling in insulin-sensitive cells (liver, skeletal muscle and adipose cells) and caused abnormal gluconeogenesis. Our results demonstrate that 2-AAA is associated with adipogenesis and insulin resistance. In this regard, 2-AAA could be a potential biomarker of obesity and obesity-related metabolic disorders.