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.
Since type 2 diabetes (T2D) is a heterogenous disease, treatment of T2D appears to be better personalized depending on their underlining conditions. A recent attempt to classify adult-onset DM based on only 6 DM related variables (GAD antibodies, age at diagnosis, BMI, HbA1c, and HOMA2 indices) , showed that DM can be classified into 5 clusters, among them 4 represent T2D (Lancet Diabetes Endocrinol. 2018;6:361) . We here classified non-diabetic population to find risk cluster for incident DM, or to extend strategies for prevention of T2D. The participants were recruited from Iwaki study, a health promotion study of Japanese people. Among 1167 participants in 2014 (baseline) , 868 non-diabetic individuals who attended at least one time in 2015 to 20were included in this prospective study. A hierarchical cluster analysis was performed based on 5 variables (age, BMI, HbA1c, HOMA2-β, and HOMA2-IR) . The hazard ratios (HRs) for incident T2D of clusters observed were examined using Cox proportional hazards model. Traits (mean) of 5 clusters observed were as follows: age (clusters; 1: 60.1, 2: 58.7, 3: 63.6, 4: 36.1, 5: 48.3) , BMI (1: 20.8, 2: 24.2, 3: 22.9, 4: 20.7, 5: 27.7) , HbA1c (1: 5.54, 2: 5.67, 3: 6.01, 4: 5.41, 5: 5.73) , HOMA2-β (1: 82.8, 2: 101.8, 3: 78.5, 4: 101.4, 5: 143.1) , and HOMA2-IR (1: 0.53, 2: 0.79, 3: 0.73, 4: 0.57, 5: 1.16) . The HR for incident T2D was significantly higher in cluster 3 than in clusters 1, 2, and 4, even after adjustment for age and sex (HR (p) : 5.00 (0.005) , 4.90 (0.005) , and 10.4 (0.048) , respectively) . Accordingly, cluster 3 had significantly higher age and HbA1c than other clusters, lower HOMA2-β than clusters 2, 4, and 5, and lower HOMA2-IR and BMI than clusters 2 and 5. In summary, cluster analysis of a general non-diabetic population revealed a risk cluster for incident T2D, and, thus, individuals with characteristics representing the cluster (less insulin resistant but decreased insulin secretion, older age, and poor metabolic control) should be a target for prevention of incident T2D. Disclosure R.Ito: None. M.Daimon: None. S.Mizushiri: None. S.Ono: None. Y.Nishiya: None. A.Tamura: None. M.Murabayashi: None. A.Kamba: None.
Non-alcoholic fatty liver disease (NAFLD) is associated with a high risk of type 2 diabetes (DM), therefore, early diagnosis of NAFLD is important to prevent incident DM. FIB-4 index, a biomarker, often used to evaluate severity of NAFLD, may be useful to evaluate risk for incident DM in ordinary clinical setting. Here, we determined the association of FIB-4 index with changes in indices representing glucose metabolism with aging in a non-diabetic population. From among the participants of the population-based Iwaki study of Japanese people conducted during 2014-2017, 1,268 non-diabetic individuals with complete data sets (age: 51.4 ± 15.9 years; men/women: 485/773) were enrolled in a cross-sectional study. In addition, of the participants, 439 who attended consecutive appointments between 2014 and 2017 were enrolled in a longitudinal study that aimed to evaluate the changes in insulin secretion and resistance with aging (age: 53.1 ± 13.7 years; men/women: 178/261). The cross-sectional study showed significant correlations of FIB-4 index with homeostasis model of assessment (HOMA) indices, even after adjustment for multiple factors (HOMA-β: β = - 0.254, p < 0.001; HOMA-R: β = - 0.247, p < 0.001). The longitudinal study showed a significant association between FIB-4 index and the change in HOMA-β (p < 0.001) but not HOMA-R (p = 0.639) during the 3-year study period. Use of the optimal cut-off value of the FIB-4 index for the prediction of decreased insulin secretion (HOMA-β < 30), determined using receiver operating characteristic analysis (1.592), showed that individuals at risk had a hazard ratio of 2.22 (confidence interval 1.17-4.06) for decreased insulin secretion, after adjustment for confounders. FIB-4 index may represent a useful predictor of a subsequent decrease in insulin secretion, at least in a non-diabetic Japanese population.
Objective: Chronic hyperglycemia increases glycation as well as oxidization, and, thus, facilitates production of advanced glycation end products (AGEs), whose accumulation is involved in the pathophysiology of diabetic complications. Although association between AGE and insulin resistance has been reported thoroughly, association between AGE and insulin secretion has not been well examined, especially in relation with age. Research Design: Among participants of the population-based Iwaki study of Japanese held in 2014, those who attended the Iwaki study consecutively in 2014 and 2015 were enrolled in this study (n=518; model A), and we excluded fasting blood glucose levels <63 and >140 to calculate HOMA-β and IR (n=489; model B). We used skin autofluorescence as AGE, and also measured blood levels of Pentosidine and urine albumin-to-creatinine ratio. Results: 1. AGE levels significantly increased from 1.93±0.52 to 1.97±0.44 (p=0.014) in the 1 year period. 2. Regression analysis showed correlations between ∆AGE (change in AGE in the 1 year period) and age, BUN, FPG and log10uACR in model A, and, age, log10uACR, and log10HOMA-ß in model B. However, these correlations became none-significant after adjustment for multiple factors mentioned above as well as factors representing glucose control levels such as HbA1c, except for age in models A and B, respectively. 3. Subjects were stratified into tertiles (mildly, moderately, rapidly progress) based on their ∆AGE, Logistic analysis revealed the aging is a significant risk for the accumulation of AGE (rapidly progress) (OR: 1.02. 95%CI: 1.01-1.03). ROC analysis showed the age of 60 as an optimal cut-off value for determining the risk subjects for the increase in AGE (OR: 1.74, 95%CI: 1.20-2.51). Conclusions: The accumulation of AGEs may increase with increasing speed, which becomes rapid over the age of 60 independent of glycemic levels, uACR and HOMA-ß, indicating that not glycemic control levels but aging per se is a major contributor for AGE accumulation. Disclosure S. Mizushiri: None. M. Daimon: None. H. Murakami: None. A. Kamba: None. M. Murabayashi: None. Y. Nishiya: None.
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.
Abstract Since type 2 diabetes (DM) is a life-style related disease, life-style should be considered when association between genetic factors and DM are examined. However, most studies did not examine genetic associations in consideration with lifestyle. Glucagon-like peptide-1 (GLP-1) receptor (GLP1R) mediates the insulinotropic action of GLP-1 in β-cells. We here examined the association while taking into consideration of interactions between the gene polymorphism and various nutrient factors. Participants from the population-based Iwaki study of Japanese subjects held in 2014–2017 with information on nutritional intake evaluated by self-administered dietary history questionnaire, and GLP1R genotype (rs3765467: A/G), were included (n = 1,560). Although not significant, insulin secretion indices assessed by homeostasis model assessment of β-cell function (HOMA-β) in subjects with the GG genotype tended to be lower than in those with the AA+AG genotypes in most groups stratified into tertiles based on daily nutrient consumptions (high, middle, and low). Stratification also showed that the GG genotype was a significant risk for decreased insulin secretion (HOMA-β ≤ 30) even after adjustment for multiple factors (age, body mass index, alcohol consumption), but only in the highest tertiles of energy, protein and carbohydrate consumption in men [odds ratios (95% confidence interval) 3.95 (1.03–15.1), 15.83 (1.58–158.9), and 4.23 (1.10–11.2), respectively]. A polymorphism of the GLP1R gene was associated with decreased insulin secretion in a nutrient consumption-dependent manner in Japanese men, indicating an interaction between GLP1R and nutritional factors in the pathophysiology of DM.
Objectives: Although obese individuals without obesity related metabolic abnormalities are often defined as metabolically healthy, risks for developing such abnormalities have not been evaluated well. Recently, a prospective study showed that metabolically healthy obese subjects were at higher risk of diabetes, stroke and cardiovascular disease than metabolically healthy subjects without obesity in UK. We here analyzed the association between metabolically healthy obesity and a risk for developing diabetes in a Japanese population. Research Design and Methods: The participants were recruited from Iwaki study, a health promotion study of Japanese people aimed to prevent lifestyle-related disease. Among 1167 participants of Iwaki study held in 2014, 931 individuals attended at least one time in 2015 to 2019. We excluded 7 and 75 individuals without complete data sets and who were diagnosed with diabetes in 2014 respectively, thus 849 subjects were enrolled in this study. Obesity was defined as BMI >25 kg/m2. We used the National Cholesterol Education Program ATP III Guideline to categorize metabolically unhealthy as participants meeting at least two criteria of metabolic abnormalities. Results: In 2014, the number of metabolically healthy subjects without and with obesity were 6 and 1 respectively. Cox proportional hazard model showed metabolically healthy subjects with obesity were at higher risk of diabetes compared with those without obesity (Hazard ratio 3.70, p=0.002) , even if after adjusted by age and sex (Hazard ratio 3.32, p=0.005) . Conclusions: Our analysis suggest an importance of encouraging people with obesity to reduce body weight even though they are metabolically healthy. We are extending such analyses in association with other lifestyle-related diseases. Disclosure S.Mizushiri: None. M.Daimon: None. A.Kamba: None. M.Murabayashi: None. Y.Nishiya: None. R.Ito: None. A.Tamura: None. S.Ono: None. K.Matsuki: None.
Heparin resistance has been observed in patients with active severe COVID‐19 infection. The red blood cell distribution (RDW), a component of the complete blood count that reflects cellular volume variation, has been shown to be associated with elevated risk for morbidity and mortality in a wide range of diseases. Cutaneous manifestations, RDW, and levels of LD and D‐dimer might be useful biomarkers in triage of patients with COVID‐19.
The optimal timing for initiating extracorporeal membrane oxygenation (ECMO) after starting mechanical ventilation has yet to be clarified. We report herein the cases of two patients with coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) who were successfully managed with an early ECMO induction strategy. Case 1 involved a 64-year-old man admitted in respiratory distress with polymerase chain reaction–confirmed COVID-19. On day 5 at hospital, he was intubated, but oxygenation remained unimproved despite mechanical ventilation treatment with high positive end-expiratory pressure (PEEP) (PaO 2 /FiO 2 [P/F] ratio, 127; Respiratory ECMO Survival Prediction [RESP] score, 4). ECMO was initiated 4 hours after intubation, and stopped on day 16 at hospital. The patient was discharged from hospital on day 36. Case 2 involved a 49-year-old man who had been admitted 8 days prior. He was intubated on hospital on day 2. High PEEP mechanical ventilation did not improve oxygenation (P/F ratio, 93; RESP score, 7). ECMO was stopped on hospital on day 7 and he was discharged from hospital on day 21. The strategy of early initiation of ECMO in these two cases may have minimized the risk of ventilation-related lung injury and contributed to the achievement of favorable outcomes.
