Naoto Katakami

Patients with diabetes often develop abnormalities in lipid metabolism that can promote diabetic complications. Lipidomic studies have demonstrated that certain plasma lipid species are associated with the development of cardiovascular diseases. However, the plasma lipids associated with endothelial dysfunction remain unclear. To identify the lipids associated with flow-mediated vasodilation (FMD), an indicator of endothelial function, in patients with diabetes, we performed a cross-sectional analysis as a subanalysis of a previous lipidomic study. In total, 200 patients with type 2 diabetes were included in the analysis. A total of 55.0% were men, and the mean age (±standard deviation) was 61.1 ± 11.3 years. Their HbA1c and FMD values were 9.04 ± 1.79% (75.3 ± 19.5 mmol/mol) and 5.77 ± 2.55%, respectively. Supercritical fluid chromatography/mass spectrometry-based semi-targeted lipidomic analysis identified 349 lipids belonging to 16 lipid classes. We selected 13 distinctive lipids potentially associated with FMD, according to a sparse-group least absolute shrinkage and selection operator regularized regression model. Of these, seven phospholipids, including three phosphatidylethanolamine (PE) plasmalogens and three phosphatidylinositols (PIs), showed a positive association with FMD after adjusting for atherosclerotic risk factors. These findings may contribute to improving the assessment and management of endothelial dysfunction in patients with diabetes.

Aims/Introduction: Diabetes and periodontal disease, a bacterial infection involving the oral microbiome, have a bidirectional relationship. However, the relationship between poor glycemic control and the oral microbiome remains unclear. The aim of this study was to assess the direct effect of intensive glycemic control on the oral microbiome in patients with type 2 diabetes mellitus (T2DM). Materials and Methods: Twenty‐nine patients with T2DM and 29 controls were enrolled in this study. Patients underwent a 2‐week intensive glycemic control regimen. We collected subgingival dental plaque and conducted full‐length 16S ribosomal RNA gene sequencing to identify bacterial species. The focus was on the subgingival microbiome, particularly 29 species of oral bacteria related to periodontal disease, and the characteristics of the diabetic cohort. Periodontal inflammation was quantified using the periodontal inflamed surface area (PISA). Results: Prior to diabetes treatment, the relative abundance of Porphyromonas gingivalis ( P = 0.018), Tannerella forsythia ( P < 0.001), Fusobacterium nucleatum ( P = 0.041), and Prevotella intermedia ( P < 0.001), known for their high pathogenicity in periodontal disease, was higher in the diabetes group than in the control group. The bacterial species whose relative abundance changed significantly before and after treatment differed between the PISA improved and non‐improved groups in patients with diabetes. Conclusions: Oral bacteria changed pre‐ and post‐diabetes treatment, correlating with improvements in PISA.

Background: The glycation gap (GGap), defined as the discrepancy between glycated hemoglobin (HbA1c) and the value estimated from actual blood glucose level, is associated with diabetic complications, but its association with hypoglycemia remains unclear. Methods: We evaluated the association between GGap and continuous glucose monitoring (CGM)‐based hypoglycemic indices in patients with type 2 diabetes mellitus (T2DM). Baseline data from a multicenter cohort of 999 T2DM patients without cardiovascular disease were analyzed. The difference between HbA1c and estimated A1c (eA1c) was defined as the GGap, and various CGM indices were compared among low (≤0.16), medium (0.60) GGap tertile groups. Results: In the high GGap group, the average blood glucose was lower, while the Time Below Range ) and ), and low blood glucose index (LBGI) were higher than in the low and middle GGap groups. Patients with minimum blood glucose levels of ≥ 4%, and TBR ^{≥ 1% had significantly higher GGap values. Conclusion: This is the first study to show the strong association of high GGap with CGM‐based hypoglycemic indices in T2DM. To achieve diabetes treatment that effectively prevents the progression of diabetic complications, it is essential to assess the GGap of the individual patient before intensifying diabetes management.}

Background: Current guidelines recommend continuous glucose monitoring (CGM) to complement HbA1c in glycemic assessment. While cross-sectional studies have reported associations between CGM-derived metrics and arterial stiffness in type 2 diabetes (T2D), longitudinal evidence remains limited. Objective: This study aimed to investigate the longitudinal associations of CGM metrics, particularly time in range (TIR) and coefficient of variation (CV), with changes in brachial-ankle pulse wave velocity (baPWV). Methods: This exploratory study analyzed data collected over 260 weeks from an ongoing prospective, multicenter, observational cohort. A total of 348 participants with type 2 diabetes and no history of symptomatic cardiovascular disease underwent baPWV measurements at baseline, 104 weeks, and/or 260 weeks. Participants were divided into two groups based on the median values of CGM-derived metrics and HbA1c at baseline, and the associations between each baseline value and longitudinal changes in baPWV were evaluated using mixed-effects models for repeated measures, adjusting for conventional atherosclerotic risk factors. Results: The median change in baPWV from baseline was 60.1 cm/s (95% CI: 34.6 to 85.6) at 104 weeks and 130.3 cm/s (95% CI: 98.6 to 162.1) at 260 weeks (p < 0.001). The change over time in baPWV differed between the higher and lower TIR groups, with a significant interaction between group and time (p = 0.013) in the multivariable-adjusted model. This interaction remained significant even after further adjustment for HbA1c (p = 0.013). In contrast, baseline CV, other CGM-derived metrics, and HbA1c were not associated with longitudinal changes in baPWV. Conclusions: TIR was significantly associated with longitudinal changes in arterial stiffness in participants with type 2 diabetes, independent of HbA1c.

Context: The importance of utilizing continuous glucose monitoring (CGM) to optimize glycemic profiles and thereby prevent the onset of diabetic complications in patients with type 1 diabetes has been increasingly recognized. Nevertheless, studies evaluating the risk of diabetic complications in patients with type 1 diabetes through machine learning approaches based on CGM data remain limited. Objective: To classify the glycemic profiles of Japanese patients with type 1 diabetes using a data-driven cluster analysis based on CGM and clarify the association between these clusters and diabetic complications. Methods: In this cross-sectional study, a cluster analysis using glycemic metrics from CGM of 153 Japanese patients with type 1 diabetes was performed. Logistic regression analysis adjusted for age, sex, and duration of diabetes was performed to compare the risk of diabetic complications by cluster. Results: The cluster analysis identified four clusters. Cluster 1 (n = 53) exhibited an optimal glycemic profile. Cluster 2 (n = 46) demonstrated an extended duration of hyperglycemia and a higher risk of elevated brachial–ankle pulse wave velocity than Cluster 1. Cluster 3 (n = 39) demonstrated an extended duration of hypoglycemia and a higher risk of severe hypoglycemia than Cluster 1. Cluster 4 (n = 15) demonstrated large glycemic variability associated with hyperglycemia and hypoglycemia. Cluster 4 had higher risks of polyneuropathy, elevated brachial–ankle pulse wave velocity, and higher cardiovascular disease risk scores than Cluster 1. Conclusion: High-risk diabetic complications were identified for each cluster classified by glycemic profile.

Introduction: We aimed to examine the impact of gestational weight gain (GWG) on perinatal outcomes based on body mass index (BMI) category and gestational diabetes mellitus (GDM) status. Research design and methods: This retrospective study included 53 183 Japanese women with GDM and 734 028 with normal glucose tolerance (NGT). We assessed interaction effects of BMI categories (underweight <18.5, normal 18.5–24.9, overweight 25.0–29.9, obese ≥30.0 kg/m²) and GDM status on GWG and perinatal outcomes, including birth weight, hypertensive disorders of pregnancy, preterm delivery, mode of delivery, and a composite outcome, using generalized linear models with a logit link function. Results: BMI category and GDM status had significant interaction effects on the association between GWG and all outcomes (all p<0.05, using the likelihood ratio test). Notably, the impact of increased GWG on the risk of having large-for-gestational-age newborns was greater in women with GDM than in those with NGT, a trend that was most pronounced in the obese group. For GWG, from 5–6 kg to 15–16 kg, the predicted probability of large-for-gestational-age newborns increased by 10.0% (from 4.9% to 14.9%) for NGT compared with 15.6% (from 6.3% to 21.8%) for GDM in the normal BMI group, and by 14.4% (from 17.6% to 32.0%) and 23.1% (from 23.5% to 46.6%), respectively, in the obese group. Conclusions: The impact of GWG on perinatal outcomes differed according to the BMI category and GDM status. These findings support the need to take into account both factors when developing GWG recommendations to minimize the risk of adverse perinatal outcomes.

Objective: This study aimed to evaluate the effects of tofogliflozin, a sodium-glucose cotransporter 2 inhibitor, on health issues in patients with type 2 diabetes (T2DM) and overweight or obesity in real-world clinical practice. Methods: This post-hoc sub-analysis of the UTOPIA trial, a randomized prospective study, included 210 patients (102 in the tofogliflozin group and 108 in the conventional treatment group) with a body mass index of ≥ 25.0 kg/m². The primary outcome was the percentage of patients achieving a weight loss of ≥ 3% at 26 and 104 weeks. Secondary outcomes included improvements in obesity-related health problems. Results: At 26 weeks, 46.4% of the tofogliflozin group (95% confidence interval [CI] 36.2–56.8%) achieved a weight loss of 3% of more, significantly higher than 14.4% in the conventional treatment group (95% CI 8.3–22.7%, p < 0.001). At 104 weeks, 62.0% of the tofogliflozin group (95% CI 51.2–71.9%) achieved this outcome compared with 29.3% in the conventional treatment group (95% CI 20.6–39.3%, p < 0.001). The tofogliflozin group also showed greater improvements in glycated hemoglobin, fasting blood glucose, blood pressure, liver indices, high-density lipoprotein-cholesterol, serum uric acid, and quality of life (QOL). Additionally, arterial stiffness progression was significantly reduced (p < 0.05) and the increase in urinary albumin tended to be attenuated (p = 0.056) in the tofogliflozin group. Conclusions: In Japanese patients with T2DM and obesity, tofogliflozin effectively promotes weight loss and has a positive impact on various obesity-related health problems and QOL. These findings suggest its potential as a therapeutic option for improving both metabolic and cardiovascular health in this population. Trial registration: UMIN000017607 (https://www.umin.ac.jp/icdr/index.html).

This study aimed to investigate the relationship between the plasma lipidome and metabolic dysfunction–associated steatotic liver disease (MASLD) in type 2 diabetes. Initially, we conducted a plasma lipidome analysis using supercritical fluid chromatography–tandem mass spectrometry in 143 patients with type 2 diabetes with and without MASLD. Of the 349 lipid species identified, 13 had higher levels and a fold-change ≥2 in the MASLD group than in the non-MASLD group; 10 of these 13 lipids were triglycerides (TGs). The constituent fatty acid (FA) in TGs that exhibited the greatest difference between patients with and without MASLD was myristic acid (FA 14:0). The presence of MASLD was an independent explanatory factor for high FA 14:0 levels in TGs, even after adjusting for covariates. Next, we assessed whether the levels of lipids identified in the initial analysis were influenced by comprehensive diabetes treatment in 26 patients. After comprehensive diabetes treatment of 2 weeks, FA levels in many TGs significantly decreased; especially FA 14:0 levels, and this reduction was more pronounced in patients with MASLD. These results suggest that various plasma lipids, particularly TGs comprising FA 14:0, may be associated with the pathogenesis of MASLD in patients with type 2 diabetes. Article Highlights: Changes in blood lipid profiles in type 2 diabetes, particularly when complicated by metabolic dysfunction–associated steatotic liver disease (MASLD), have not been fully elucidated. Does MASLD influence the plasma lipidome in patients with type 2 diabetes? Patients with MASLD exhibited elevated levels of myristic acid (FA 14:0) in plasma triglycerides, which significantly decreased after comprehensive diabetes treatment. Elevated FA 14:0 levels in triglycerides may be associated with MASLD pathogenesis in type 2 diabetes.

Abnormalities in plasma lipoproteins observed in patients with diabetes promote atherosclerosis. However, the association between various lipid species and classes and atherosclerosis remains unclear. Here, we aimed to identify the plasma lipid characteristics associated with atherosclerosis progression in patients with diabetes. We performed semi-targeted lipidomic analysis of fasting plasma samples using supercritical fluid chromatography coupled with mass spectrometry in two independent patient groups with type 2 diabetes (n = 223 and 31) and evaluated cross-sectional associations between plasma lipids and carotid intima-media thickness (CIMT). Ten plasma lipid species, including eight diacylglycerols (DGs), and total DG levels were significantly associated with CIMT in both groups. Patients of the former group were partly observed for 5 years, and we investigated associations between DGs and CIMT progression in these patients (n = 101). As a result, 22 DGs among the 26 identified DGs and total DG (β = 0.398, p < 0.001) were significantly associated with the annual change in CIMT. Furthermore, plasma DG levels improved the predictive ability for CIMT progression, with an adjusted R-squared increase of 0.105 [95% confidence interval: 0.010, 0.232] in the models. Plasma DGs are associated with CIMT progression in patients with type 2 diabetes. Measurement of total plasma DG levels may be beneficial in assessing the risk of atherosclerosis progression.

Introduction: Despite the increasing number of studies using machine learning to develop individualized treatment strategies, only a few have been conducted in patients with type 1 diabetes. This study aimed to identify the characteristics of Japanese patients with type 1 diabetes, classified into subgroups using data‐driven cluster analysis based on pancreatic beta‐cell function, obesity, and glycemic control, and clarify the association between these subgroups and diabetic complications.Materials and Methods: In this cross‐sectional study, a cluster analysis using three variables (C‐peptide, body mass index, and glycated hemoglobin) in 206 Japanese patients with type 1 diabetes was performed. Multivariate logistic regression analysis was performed to compare the risk of diabetic complications by subgroup.Results: The cluster analysis identified four subgroups. Group 2 (n = 58), characterized by high body mass index levels, had a higher risk of hepatic steatosis than the control group (Group 1, n = 90). Meanwhile, Group 3 (n = 44), characterized by high glycated hemoglobin levels, had higher risks of retinopathy, polyneuropathy, elevated brachial–ankle pulse wave velocity, and hepatic steatosis than Group 1 and Group 4 (n = 14), characterized by residual endogenous insulin, had a higher risk of chronic kidney disease than Group 1.Conclusions: The risks of diabetic complications differed between subgroups of Japanese patients with type 1 diabetes. Tailored treatment approaches based on subgroup characteristics are a potential treatment option for reducing the risks of diabetic complications in this population.