Department of Biological and Medical Science, Faculty of Physical Education and Sport, Comenius University in Bratislava, Bratislava, Slovakia.
Biomedical Center, Institute of Clinical and Translational Research, Slovak Academy of Sciences, Bratislava, Slovakia.
Metab Syndr Relat Disord. 2023 Jun;21(5):243-253. doi: 10.1089/met.2022.0071. Epub 2023 Apr 20.
Gut microbial composition seems to change in association with prediabetes. The purpose of this prospective cross-sectional study was to compare the composition of gut microbiota and energy metabolites between individuals with class III obesity but without type 2 diabetes mellitus (OB) and healthy normal weight controls. The subjects of this prospective cross-sectional study were participants recruited from a previous clinical trial (No: NCT02325804), with intervention focused on weight loss. We recruited 19 OB [mean age ± standard deviation (SD) was 35.4 ± 7.0 years, mean body mass index (BMI) ± SD was 48.8 ± 6.7 kg/m] and 23 controls (mean age ± SD was 31.7 ± 14.8 years, mean BMI ± SD was 22.2 ± 1.7 kg/m). Their fecal microbiota was categorized using specific primers targeting the V1-V3 region of 16S rDNA, whereas serum metabolites were characterized by nuclear magnetic resonance spectroscopy. Multivariate statistical analysis and Random Forest models were applied to discriminate predictors with the highest variable importance. We observed a significantly lower microbial α-diversity ( = 0.001) and relative abundance of beneficial bacterium ( = 0.001) and the short-chain fatty acid-producing bacteria ( = 0.019), ( = 0.024), ( = 0.010), and ( = 0.050) and a higher abundance of the pathogenic bacteria ( = 0.018) and ( = 0.022) in OB compared with controls. Notably, the Random Forest machine learning analysis identified energy metabolites (citrate and acetate), HOMA-IR, and insulin as important predictors capable of discriminating between OB and controls. Our results suggest that changes in gut microbiota and in serum acetate and citrate are additional promising biomarkers before progression to Type 2 diabetes. The non-invasive manipulation of gut microbiota composition in OB through a healthy lifestyle, thus, offers a new approach for managing class III obesity and associated disorders. ClinicalTrials.gov identifier: NCT02325804.
肠道微生物组成似乎与前驱糖尿病有关。本前瞻性横断面研究的目的是比较 III 类肥胖但无 2 型糖尿病（OB）个体与健康正常体重对照者的肠道微生物群组成和能量代谢物。本前瞻性横断面研究的受试者为先前临床试验（编号：NCT02325804）的参与者，干预重点是减肥。我们招募了 19 名 OB [平均年龄 ± 标准差（SD）为 35.4 ± 7.0 岁，平均体重指数（BMI）± SD 为 48.8 ± 6.7 kg/m]和 23 名对照者（平均年龄 ± SD 为 31.7 ± 14.8 岁，平均 BMI ± SD 为 22.2 ± 1.7 kg/m）。他们的粪便微生物群使用针对 16S rDNA V1-V3 区的特异性引物进行分类，而血清代谢物则通过核磁共振波谱进行表征。应用多元统计分析和随机森林模型来区分具有最高变量重要性的预测因子。我们观察到微生物 α-多样性显著降低（ = 0.001），有益菌（ = 0.001）和短链脂肪酸产生菌（ = 0.019），（ = 0.024），（ = 0.010）和（ = 0.050）的相对丰度降低，而致病菌（ = 0.018）和（ = 0.022）的丰度升高OB 与对照组相比。值得注意的是，随机森林机器学习分析确定能量代谢物（柠檬酸和乙酸盐）、HOMA-IR 和胰岛素是能够区分 OB 和对照组的重要预测因子。我们的研究结果表明，肠道微生物群和血清乙酸盐和柠檬酸的变化是进展为 2 型糖尿病之前的另一个有前途的生物标志物。通过健康的生活方式对 OB 肠道微生物群组成进行非侵入性干预，从而为管理 III 类肥胖和相关疾病提供了一种新方法。临床试验标识符：NCT02325804。
