RESUMO
Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clusters, of which four correspond to type 2 diabetes (T2DM). Here, we classified nondiabetic individuals to identify risk clusters for incident T2DM to facilitate the refinement of prevention strategies. Of the 1167 participants in the population-based Iwaki Health Promotion Project in 2014 (baseline), 868 nondiabetic individuals who attended at least once during 2015-2019 were included in a prospective study. A hierarchical cluster analysis was performed using four variables (BMI, HbA1c, and HOMA2 indices). Of the four clusters identified, cluster 1 (n = 103), labeled as "obese insulin resistant with sufficient compensatory insulin secretion", and cluster 2 (n = 136), labeled as "low insulin secretion", were found to be at risk of diabetes during the 5-year follow-up period: the multiple factor-adjusted HRs for clusters 1 and 2 were 14.7 and 53.1, respectively. Further, individuals in clusters 1and 2 could be accurately identified: the area under the ROC curves for clusters 1and 2 were 0.997 and 0.983, respectively. The risk of diabetes could be better assessed on the basis of the cluster that an individual belongs to.
RESUMO
The relationship between serum adiponectin concentration (S-Adipo) and various diseases, such as type 2 diabetes (T2D) is conflicting. We hypothesized that the extent of kidney damage in patients with T2D may be responsible for this inconsistency and, thus, examined association between S-Adipo and T2D after consideration for the extent of kidney damage present. Of the 1816 participants in the population-based Iwaki study of Japanese people, 1751 participants with a complete dataset were included. Multivariate logistic regression analyses revealed that low S-Adipo was independently associated with T2D (<0.001), as was high urinary albumin to creatinine ratio (uACR) (<0.001). Principal components analysis showed that the relative value of S-Adipo to uACR (adiponectin relative excess) was significantly associated with T2D (odds ratio: 0.49, p < 0.001). Receiver operating curve analyses revealed that an index of adiponectin relative excess the ratio of S-Adipo to uACR was superior to S-Adipo per se as a marker of T2D (area under the curve: 0.746 vs. 0.579, p < 0.001). This finding indicates that the relationship between S-Adipo and T2D should be evaluated according to the extent of kidney damage present and may warrant similar analyses of the relationships between S-Adipo and other medicalconditions, such as cardiovascular disease.
RESUMO
Upon food digestion, the gut microbiota plays a pivotal role in energy metabolism, thus affecting the development of type 2 diabetes (DM). We aimed to examine the influence of the composition of selected nutrients consumed on the association between the gut microbiota and DM. This cross-sectional study of a general population was conducted on 1019 Japanese volunteers. Compared with non-diabetic subjects, diabetic subjects had larger proportions of the genera Bifidobacterium and Streptococcus but smaller proportions of the genera Roseburia and Blautia in their gut microbiotas. The genera Streptococcus and Roseburia were positively correlated with the amounts of energy (p = 0.027) and carbohydrate and fiber (p = 0.007 and p = 0.010, respectively) consumed, respectively. In contrast, the genera Bifidobacterium and Blautia were not correlated with any of the selected nutrients consumed. Cluster analyses of these four genera revealed that the Blautia-dominant cluster was most negatively associated with DM, whereas the Bifidobacterium-dominant cluster was positively associated with DM (vs. the Blautia-dominant cluster; odds ratio 3.97, 95% confidence interval 1.68-9.35). These results indicate the possible involvement of nutrient factors in the association between the gut microbiota and DM. Furthermore, independent of nutrient factors, having a Bifidobacterium-dominant gut microbiota may be a risk factor for DM compared to having a Blautia-dominant gut microbiota in a general Japanese population.
RESUMO
There was an error in the original publication [...].
