Journal of Diabetes Investigation最新文献

Aims/introduction: The impact of rare pathogenic variants on diabetic kidney disease (DKD) has not been investigated in detail. Previous studies have detected pathogenic variants in 22% of Caucasian patients with DKD; however, this proportion may vary depending on ethnicity and updates to the database. Therefore, we performed a whole-genome analysis of patients with DKD in type 2 diabetes mellitus in Japan, utilizing a recent database to investigate the prevalence of kidney-related pathogenic variants and describe the characteristics of these patients.

Materials and methods: Whole-genome sequencing was performed, and variants were analyzed following the GATK Best Practices. We extracted data on 790 genes associated with Mendelian kidney and genitourinary diseases. Pathogenic variants were defined based on the American College of Medical Genetics criteria, including both heterozygous and homozygous variants classified as pathogenic or likely pathogenic.

Results: Among 79 participants, heterozygous pathogenic variants were identified in 27 (34.1%), a higher prevalence than previously reported. No homozygous pathogenic variants were detected. The identified heterozygous pathogenic variants were roughly divided into 23.7% related to glomerulopathy, 36.8% related to tubulointerstitial disease, 10.5% related to cystic disease/ciliopathy, and 28.9% related to others. Diagnostic variants were found in 10 patients (12.7%) in seven genes (ABCC6, ALPL, ASXL1, BMPR2, GCM2, PAX2, and WT1), all associated with autosomal dominant congenital disease.

Conclusions: This study identified a considerable number of patients with DKD in Japan who carried kidney-related heterozygous pathogenic variants. These findings suggest potential ethnic differences and highlight the impact of database updates on variant detection.

This case report described a 78-year-old woman with type 1 diabetes who experienced a significant decrease in the hemoglobin glycation index (HGI) after switching her insulin pump from MiniMed™770G to 780G. Despite minimal changes in glycemic control, including a slight decrease in the average sensor glucose and % coefficient of variation (%CV) from continuous glucose monitoring metrics, her glycated hemoglobin level decreased from 8.2% to 7.3%, and HGI decreased from approximately 1.3-0.5. Previous reports have shown that HGI indicates differences in the glycation rate of hemoglobin between individuals. Moreover, a high HGI is a risk factor for diabetic complications. However, there are no reports on changes in HGI within an individual. Therefore, the significance of intraindividual HGI changes, as in this case, should be investigated.

LGR signaling plays a crucial role in metabolic regulation by coordinating muscle-neuron-adipose tissue crosstalk. In response to a high-sugar diet, muscle-derived BMP signaling activates neuronal Bursicon, which stimulates insulin secretion and enhances adipose insulin sensitivity. This evolutionarily conserved pathway underscores LGR4 as a promising therapeutic target for insulin resistance and diabetes.

There is no evidence of a causal relationship between GDM and PPD in European populations. Future research should explore this association in diverse ancestries and investigate the link between GDM and antenatal depression.

Introduction: Vitamin B12 deficiency is one of the adverse drug reactions of metformin and proton pump inhibitors (PPIs). As symptoms of gastroesophageal reflux disease are frequently reported in patients with diabetes, the concomitant use of metformin and PPI is expected. Therefore, this study aimed to investigate the effects of concurrent PPI use on the risk of vitamin B12 deficiency in patients with type 2 diabetes (T2DM) using metformin.

Materials and methods: This retrospective cohort study was conducted using a sample cohort database provided by the National Health Insurance Service. Among adult patients newly diagnosed with T2DM, new users of metformin who used metformin ≥4 months were included. The subjects were divided into two cohorts: metformin monotherapy and metformin + PPI. Vitamin B12 deficiency was defined by a diagnostic code or a prescription of medications for vitamin B12 supplementation. A Cox proportional hazards model was used to estimate the adjusted hazard ratio (aHR) with a 95% confidence interval (CI).

Results: In total, 11,200 subjects were included in the 1:1 propensity score-matched cohort. The risk of vitamin B12 deficiency was significantly higher in the metformin + PPI cohort compared with the metformin monotherapy cohort (aHR, 1.18; 95% CI, 1.02-1.35).

Conclusions: A significant association between the concurrent use of metformin and PPI and an increased risk of vitamin B12 deficiency was found, highlighting the need to carefully monitor the symptoms associated with vitamin B12 deficiency and regularly assess vitamin B12 levels when considering the concomitant use of PPI and metformin.

Aims/introduction: Previous observational studies have suggested an increased risk of type 2 diabetes associated with both short and long sleep duration. However, there remains uncertainty, particularly regarding the adverse effects of long sleep duration. We investigated the association between self-reported questionnaire-based and objectively measured accelerometer-derived sleep duration and the risk of type 2 diabetes using data from the UK Biobank.

Materials and methods: First, we performed conventional Cox regression analysis with restricted cubic splines to illustrate the potentially non-linear association between sleep duration and the risk of type 2 diabetes. Second, we performed non-linear Mendelian randomization (MR) analysis using the doubly-ranked method with 85 and 20 genetic variants associated with questionnaire-based and accelerometer-based sleep duration, respectively. Third, we performed two-sample MR analysis.

Results: The results of conventional analysis of accelerometer-derived sleep duration did not suggest a strong association between longer sleep duration and type 2 diabetes risk (hazard ratio [HR] of ≥10 h compared with 7-8 h, 1.08; 95% confidence interval [CI], 0.92-1.27). The results of non-linear MR showed no strong evidence for an increased risk of type 2 diabetes associated with questionnaire-based longer sleep duration (HR of 9 h compared with 7 h, 0.77; 95% CI, 0.52-1.15). This finding was consistent with non-linear MR of accelerometer-derived sleep duration (HR of 9 h compared with 7 h, 0.78; 95% CI, 0.29-2.06).

Conclusions: Our findings suggest that longer sleep duration does not play a major role in the development of type 2 diabetes.

Aims/introduction: This study aimed to improve efficiency of participant selection in Japan's Specific Health Guidance (SHG) program by developing a model to predict 3-year risk of diabetes complications. The targeted complications included macrovascular diseases (ischemic heart disease and cerebrovascular disease), microvascular diseases (diabetic nephropathy, diabetic neuropathy, diabetic retinopathy), and chronic kidney disease (CKD).

Materials and methods: We utilized the Kokuho Database to analyze individuals in Saga Prefecture who underwent Specific Health Checkups from 2016 to 2019 without diabetes complications at baseline. To evaluate risk of the complications across all examinees, we excluded medicated individuals ineligible for SHG, while including those without diabetes in the prediction cohort. The outcomes of diabetes complications were derived from claims data. Explanatory variables included health checkup results, questionnaire responses, medical diagnoses, and prescription records. The predictive model was constructed using logistic regression, and its performance was evaluated using the Area Under the Curve (AUC) from fivefold cross-validation.

Results: Through model optimization techniques, including stratification, the incorporation of quadratic terms, and variable selection, the AUC exceeded 0.7 for all conditions. Notably, the AUC for microvascular complications and CKD surpassed 0.8, indicating high predictive accuracy. The model identified a higher risk among individuals who met the health guidance criteria established by the Ministry of Health, Labour and Welfare, demonstrating alignment with existing standards.

Conclusions: This predictive model has potential to enhance the targeting process for health guidance in Japan, enabling more timely medical intervention. Its implementation could significantly contribute to the prevention of severe diabetes complications through earlier detection and treatment.

Aims/introduction: Obesity triggers various health disorders, but information on these disorders in real-world settings remains limited. To address this knowledge gap, we developed a database directly linked to electronic medical records (EMRs). We here present the baseline data for this database, designated Japan Obesity Research Based on electronIc healTh Records (J-ORBIT).

Materials and methods: Individuals with obesity disease diagnosed according to the criteria of the Japan Society for the Study of Obesity were registered in J-ORBIT from seven medical centers in Japan. We analyzed the relationship between body mass index (BMI), clinical characteristics, and the prevalence of obesity-related health disorders in this cohort.

Results: Data were obtained from 1,169 individuals, with a mean (±SD) age of 56.9 ± 15.3 years and a BMI of 31.4 ± 6.1 kg/m². The prevalence of health disorders varied substantially across BMI categories, with a higher BMI being associated with an increased prevalence of hyperuricemia or gout, obstructive sleep apnea syndrome or obesity hypoventilation syndrome, musculoskeletal disorders, and obesity-related kidney disease, as well as with a higher frequency of both a family history of obesity and of a history of childhood obesity. Among individuals with a BMI of ≥25 kg/m², the prevalence of hypertension and dyslipidemia did not increase with BMI, whereas that of glucose intolerance decreased with increasing BMI.

Conclusions: The J-ORBIT system, which collects clinical data in real time directly from EMRs, has the potential to provide insight into obesity and its associated health conditions, thereby contributing to improved care of affected individuals.