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.

The gut microbiota has emerged as an important environmental factor associated with obesity, type 2 diabetes, and cardiovascular disease, through its interactions with dietary factors. Therefore, we analyzed the composition of the fecal microbiota and levels of biochemical markers related to metabolic disease according to dietary pattern in Korean adolescents. We collected fecal samples from 112 student subjects aged 13–16 years with sufficient information available regarding clinical biomarkers and diet, and performed 16S rRNA targeted gene sequencing. Regarding bacterial composition according to taxonomic rank, we found that traditional dietary patterns enriched in plant-based and fermented foods were associated with higher proportions of Bacteroides (Bacteroidaceae) and Bifidobacterium (Bifidobacteriaceae-Actinobacteria) and a lower proportion of Prevotella (Prevotellaceae) relative to modified Western dietary patterns (a greater proportion of animal-based foods). Specifically, the proportion of Bacteroides (Bacteroidaceae) was associated with intake of plant-based nutrients such as fiber; however, that of Prevotella (Prevotellaceae) was negatively associated with these factors. Additionally, we observed that the increase of prevotella (Prevotellaceae) and decrease of Bacteroides (Bacteroidaceae) and Ruminococcaceae had a higher risk of obesity. We also found that the traditional dietary pattern was negatively associated with general and central adiposity and levels of clinical biomarkers, including AST, ALT, total cholesterol, triglyceride, hs-CRP, insulin, and HOMA-IR, whereas the positive associations were found for a modified Western dietary pattern. These findings suggest that the gut microbiota composition differs markedly according to dietary intake and suggest a role for diet in promoting a gut microbiome associated with the pathogenesis of metabolic disease.

Childhood obesity has increased worldwide, and many clinical and public interventions have attempted to reduce morbidity. We aimed to determine the metabolomic signatures associated with weight control interventions in children with obesity. Forty children from the “Intervention for Children and Adolescent Obesity via Activity and Nutrition (ICAAN)” cohort were selected according to intervention responses. Based on changes in body mass index z-scores, 20 were responders and the remaining non-responders. Their serum metabolites were quantitatively analyzed using capillary electrophoresis time-of-flight mass spectrometry at baseline and after 6 and 18 months of intervention. After 18 months of intervention, the metabolite cluster changes in the responders and non-responders showed a difference on the heatmap, but significant metabolites were not clear. However, regardless of the responses, 13 and 49 metabolites were significant in the group of children with obesity intervention at 6 months and 18 months post-intervention compared to baseline. In addition, the top five metabolic pathways (D-glutamine and D-glutamate metabolism; arginine biosynthesis; alanine, aspartate, and glutamate metabolism; TCA cycle (tricarboxylic acid cycle); valine, leucine, and isoleucine biosynthesis) including several amino acids in the metabolites of obese children after 18 months were significantly changed. Our study showed significantly different metabolomic profiles based on time post obesity-related intervention. Through this study, we can better understand and predict childhood obesity through metabolite analysis and monitoring.

This cross-sectional study was performed to examine the association between alcohol consumption and insulin secretion and sensitivity using the Korean Genome and Epidemiology Study. Alcohol consumption levels were categorized into four groups: (i) abstainers, (ii) low (<5 g/day), (iii) intermediate (<30 g/day), and (iv) high (≥30 g/day) alcohol consumption. β-cell function and insulin sensitivity were estimated using the insulinogenic index (IGI₆₀), and Matsuda insulin sensitivity index (ISI), respectively. IGI₆₀ and ISI were dichotomized into high and low groups using median cut-off values and four groups were defined (G-I: high IGI₆₀/high ISI; G-II: high IGI₆₀/low ISI; G-III: low IGI₆₀/high ISI; and G-IV: low IGI₆₀/low ISI). Men consumed 26.5 g alcohol per day on average, whereas women only consumed 5.7 g/day, so women were excluded from subsequent analyses due to their low drinking levels. Alcohol consumption was positively associated with high-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (AST), and triglycerides (TG) in men, but was negatively associated with IGI₆₀ (p < 0.05). TG levels were only increased in individuals with decreased insulin sensitivity (G-II) or decreased β-cell function (G-III) with high alcohol consumption. In addition, alcohol consumption increased HDL cholesterol in the four groups (p < 0.001). In subjects with decreased insulin sensitivity (G-II), intermediate and high alcohol consumption increased the risk of high cholesterol and TG. In individuals with decreased β-cell function (G-III), alcohol consumption increased the risk of high TG and high AST levels. High alcohol consumption was significantly associated with reduced insulin secretion. In addition, alcohol consumption was related to some metabolic risk factors depending on insulin secretion or sensitivity.