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766 "Diabetes mellitus"
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Complications
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Association of Succinate and Adenosine Nucleotide Metabolic Pathways with Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus
Inha Jung, Seungyoon Nam, Da Young Lee, So Young Park, Ji Hee Yu, Ji A Seo, Dae Ho Lee, Nan Hee Kim
Received October 23, 2023  Accepted May 6, 2024  Published online July 1, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0377    [Epub ahead of print]
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AbstractAbstract PDF
Background
Although the prevalence of diabetic kidney disease (DKD) is increasing, reliable biomarkers for its early detection are scarce. This study aimed to evaluate the association of adenosine and succinate levels and their related pathways, including hyaluronic acid (HA) synthesis, with DKD.
Methods
We examined 235 participants and categorized them into three groups: healthy controls; those with diabetes but without DKD; and those with DKD, which was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. We compared the concentrations of urinary adenosine, succinate, and HA and the serum levels of cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation, among the groups with DKD or albuminuria. In addition, we performed multiple logistic regression analysis to evaluate the independent association of DKD or albuminuria with the metabolites after adjusting for risk factors. We also showed the association of these metabolites with eGFR measured several years before enrollment. This study was registered with the Clinical Research Information Service (https://cris.nih.go.kr; Registration number: KCT0003573).
Results
Urinary succinate and serum CD39 levels were higher in the DKD group than in the control and non-DKD groups. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR, albuminuria, and ΔeGFR, which was calculated retrospectively. However, among the various metabolites studied, only urinary succinate was identified as an independent indicator of DKD and albuminuria.
Conclusion
Among several potential metabolites, only urinary succinate was independently associated with DKD. These findings hold promise for clinical application in the management of DKD.
Pathophysiology
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Recent Glycemia Is a Major Determinant of β-Cell Function in Type 2 Diabetes Mellitus
Ji Yoon Kim, Jiyoon Lee, Sin Gon Kim, Nam Hoon Kim
Received October 11, 2023  Accepted March 26, 2024  Published online June 17, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0359    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Progressive deterioration of β-cell function is a characteristic of type 2 diabetes mellitus (T2DM). We aimed to investigate the relative contributions of clinical factors to β-cell function in T2DM.
Methods
In a T2DM cohort of 470 adults (disease duration 0 to 41 years), β-cell function was estimated using insulinogenic index (IGI), disposition index (DI), oral disposition index (DIO), and homeostasis model assessment of β-cell function (HOMA-B) derived from a 75 g oral glucose tolerance test (OGTT). The relative contributions of age, sex, disease duration, body mass index, glycosylated hemoglobin (HbA1c) levels (at the time of the OGTT), area under the curve of HbA1c over time (HbA1c AUC), coefficient of variation in HbA1c (HbA1c CV), and antidiabetic agents use were compared by standardized regression coefficients. Longitudinal analyses of these indices were also performed.
Results
IGI, DI, DIO, and HOMA-B declined over time (P<0.001 for all). Notably, HbA1c was the most significant factor affecting IGI, DI, DIO, and HOMA-B in the multivariable regression analysis. Compared with HbA1c ≥9%, DI was 1.9-, 2.5-, 3.7-, and 5.5-fold higher in HbA1c of 8%–<9%, 7%–<8%, 6%–<7%, and <6%, respectively, after adjusting for confounding factors (P<0.001). Conversely, β-cell function was not affected by the type or duration of antidiabetic agents, HbA1c AUC, or HbA1c CV. The trajectories of the IGI, DI, DIO, and HOMA-B mirrored those of HbA1c.
Conclusion
β-Cell function declines over time; however, it is flexible, being largely affected by recent glycemia in T2DM.
Brief Report
Complications
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Diabetic Ketoacidosis as an Effect of Sodium-Glucose Cotransporter 2 Inhibitor: Real World Insights
Han-Sang Baek, Chaiho Jeong, Yeoree Yang, Joonyub Lee, Jeongmin Lee, Seung-Hwan Lee, Jae Hyoung Cho, Tae-Seo Sohn, Hyun-Shik Son, Kun-Ho Yoon, Eun Young Lee
Received January 22, 2024  Accepted May 13, 2024  Published online June 10, 2024  
DOI: https://doi.org/10.4093/dmj.2024.0036    [Epub ahead of print]
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AbstractAbstract PDFPubReader   ePub   
One of the notable adverse effects of sodium-glucose cotransporter 2 (SGLT2) inhibitor is diabetic ketoacidosis (DKA) often characterized by euglycemia. In this retrospective review of patients with DKA from 2015 to 2023, 21 cases of SGLT2 inhibitorassociated DKA were identified. Twelve (57.1%) exhibited euglycemic DKA (euDKA) while nine (42.9%) had hyperglycemic DKA (hyDKA). More than 90% of these cases were patients with type 2 diabetes mellitus. Despite similar age, sex, body mass index, and diabetes duration, individuals with hyDKA showed poorer glycemic control and lower C-peptide levels compared with euDKA. Renal impairment and acidosis were worse in the hyDKA group, requiring hemodialysis in two patients. Approximately one-half of hyDKA patients had concurrent hyperosmolar hyperglycemic state. Common symptoms included nausea, vomiting, general weakness, and dyspnea. Seizure was the initial manifestation of DKA in two cases. Infection and volume depletion were major contributors, while carbohydrate restriction and inadequate insulin treatment also contributed to SGLT2 inhibitor-associated DKA. Despite their beneficial effects, clinicians should be vigilant for SGLT2 inhibitor risk associated with DKA.
Original Articles
Others
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Serum Magnesium Levels Are Negatively Associated with Obesity and Abdominal Obesity in Type 2 Diabetes Mellitus: A Real-World Study
Man-Rong Xu, Ai-Ping Wang, Yu-Jie Wang, Jun-Xi Lu, Li Shen, Lian-Xi Li
Received November 8, 2023  Accepted March 6, 2024  Published online May 29, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0401    [Epub ahead of print]
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Background
There remains controversy over the relationship between serum magnesium levels and obesity in type 2 diabetes mellitus (T2DM). Therefore, the aim of this study was to assess whether there is any association of serum magnesium levels with obesity and abdominal obesity in T2DM.
Methods
This cross-sectional, real-world study was conducted in 8,010 patients with T2DM, which were stratified into quintiles according to serum magnesium levels. The clinical characteristics and the prevalence of obesity and abdominal obesity were compared across serum magnesium quintiles in T2DM. Regression analyses were used to evaluate the relationship of serum magnesium with obesity and abdominal obesity in T2DM (clinical trial registration number: ChiCTR1800015893).
Results
After adjustment for age, sex, and duration of diabetes, the prevalence of obesity and abdominal obesity was significantly declined across magnesium quintiles (obesity: 51.3%, 50.8%, 48.9%, 45.3%, and 43.8%, respectively, P<0.001 for trend; abdominal obesity: 71.5%, 70.5%, 68.2%, 66.4%, and 64.5%, respectively, P=0.001 for trend). After controlling for confounders, there were clearly negative associations of serum magnesium levels and quintiles with obesity and abdominal obesity in T2DM. Moreover, C-reactive protein partly mediates the effect of serum magnesium on obesity and abdominal obesity (P=0.016 and P=0.004, respectively).
Conclusion
The significantly negative relationship between serum magnesium and the risk of obesity and abdominal obesity was observed in T2DM. Furthermore, the independently negative association of serum magnesium with obesity may be explained by its anti-inflammatory functions. Serum magnesium levels may be applied to assess the risk of obesity and abdominal obesity in T2DM.
Basic Research
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Single-Cell Landscape and a Macrophage Subset Enhancing Brown Adipocyte Function in Diabetes
Junfei Gu, Jiajia Jin, Xiaoyu Ren, Xinjie Zhang, Jiaxuan Li, Xiaowei Wang, Shucui Zhang, Xianlun Yin, Qunye Zhang, Zhe Wang
Received August 16, 2023  Accepted February 7, 2024  Published online May 29, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0278    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Metabolic dysregulation is a hallmark of type 2 diabetes mellitus (T2DM), in which the abnormalities in brown adipose tissue (BAT) play important roles. However, the cellular composition and function of BAT as well as its pathological significance in diabetes remain incompletely understood. Our objective is to delineate the single-cell landscape of BAT-derived stromal vascular fraction (SVF) and their characteristic alterations in T2DM rats.
Methods
T2DM was induced in rats by intraperitoneal injection of low-dose streptozotocin and high-fat diet feeding. Single-cell mRNA sequencing was then performed on BAT samples and compared to normal rats to characterize changes in T2DM rats. Subsequently, the importance of key cell subsets in T2DM was elucidated using various functional studies.
Results
Almost all cell types in the BAT-derived SVF of T2DM rats exhibited enhanced inflammatory responses, increased angiogenesis, and disordered glucose and lipid metabolism. The multidirectional differentiation potential of adipose tissue-derived stem cells was also reduced. Moreover, macrophages played a pivotal role in intercellular crosstalk of BAT-derived SVF. A novel Rarres2+macrophage subset promoted the differentiation and metabolic function of brown adipocytes via adipose-immune crosstalk.
Conclusion
BAT SVF exhibited strong heterogeneity in cellular composition and function and contributed to T2DM as a significant inflammation source, in which a novel macrophage subset was identified that can promote brown adipocyte function.
Others
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Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea
Byung-Joon Kim, Jun-Seop Shin, Byoung-Hoon Min, Jong-Min Kim, Chung-Gyu Park, Hee-Jung Kang, Eung Soo Hwang, Won-Woo Lee, Jung-Sik Kim, Hyun Je Kim, Iov Kwon, Jae Sung Kim, Geun Soo Kim, Joonho Moon, Du Yeon Shin, Bumrae Cho, Heung-Mo Yang, Sung Joo Kim, Kwang-Won Kim
Received August 7, 2023  Accepted January 17, 2024  Published online May 21, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0260    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans.
Methods
A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness.
Conclusion
A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.
Metabolic Risk/Epidemiology
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Association of Body Composition Changes with the Development of Diabetes Mellitus: A Nation-Wide Population Study
Hyung Jun Kim, Hyung-Woo Lee, Min-Kyoung Kang, Gwang Hyun Leem, Min-Ho Kim, Tae-Jin Song
Received August 31, 2023  Accepted January 26, 2024  Published online May 21, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0243    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We investigated the association between body composition changes and new-onset diabetes mellitus (DM) development according to the body mass index (BMI) in a longitudinal setting in the general Korean population.
Methods
From 2010 to 2011 (1st) and 2012 to 2013 (2nd), we included 1,607,508 stratified random sample participants without DM from the National Health Insurance Service-Health Screening dataset of Korean. The predicted appendicular skeletal muscle mass index (pASMMI), body fat mass index (pBFMI), and lean body mass index (pLBMI) were calculated using pre-validated anthropometric prediction equations. A prediction equation was constructed by combining age, weight, height, waist circumference, serum creatinine levels, alcohol consumption status, physical activity, and smoking history as variables affecting body composition.
Results
Decreased pASMMI (men: hazard ratio [HR], 0.866; 95% confidence interval [CI], 0.830 to 0.903; P<0.001; women: HR, 0.748; 95% CI, 0.635 to 0.881; P<0.001), decreased pLBMI (men: HR, 0.931; 95% CI, 0.912 to 0.952; P<0.001; women: HR, 0.906; 95% CI, 0.856 to 0.959; P=0.007), and increased pBFMI (men: HR, 1.073; 95% CI, 1.050 to 1.096; P<0.001; women: HR, 1.114; 95% CI, 1.047 to 1.186; P=0.007) correlated with the development of new-onset DM. Notably, only in the overweight and obese BMI categories, decreases in pASMMI and pLBMI and increases in pBFMI associated with new-onset DM, regardless of gender.
Conclusion
Decreased pASMMI and pLBMI, and increased pBFMI with excess fat accumulation may enhance the risk of newonset DM. Therefore, appropriate changes in body composition can help prevent new-onset DM.
Drug/Regimen
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Efficacy and Safety of Metformin and Atorvastatin Combination Therapy vs. Monotherapy with Either Drug in Type 2 Diabetes Mellitus and Dyslipidemia Patients (ATOMIC): Double-Blinded Randomized Controlled Trial
Jie-Eun Lee, Seung Hee Yu, Sung Rae Kim, Kyu Jeung Ahn, Kee-Ho Song, In-Kyu Lee, Ho-Sang Shon, In Joo Kim, Soo Lim, Doo-Man Kim, Choon Hee Chung, Won-Young Lee, Soon Hee Lee, Dong Joon Kim, Sung-Rae Cho, Chang Hee Jung, Hyun Jeong Jeon, Seung-Hwan Lee, Keun-Young Park, Sang Youl Rhee, Sin Gon Kim, Seok O Park, Dae Jung Kim, Byung Joon Kim, Sang Ah Lee, Yong-Hyun Kim, Kyung-Soo Kim, Ji A Seo, Il Seong Nam-Goong, Chang Won Lee, Duk Kyu Kim, Sang Wook Kim, Chung Gu Cho, Jung Han Kim, Yeo-Joo Kim, Jae-Myung Yoo, Kyung Wan Min, Moon-Kyu Lee
Received March 8, 2023  Accepted June 28, 2023  Published online May 20, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0077    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
It is well known that a large number of patients with diabetes also have dyslipidemia, which significantly increases the risk of cardiovascular disease (CVD). This study aimed to evaluate the efficacy and safety of combination drugs consisting of metformin and atorvastatin, widely used as therapeutic agents for diabetes and dyslipidemia.
Methods
This randomized, double-blind, placebo-controlled, parallel-group and phase III multicenter study included adults with glycosylated hemoglobin (HbA1c) levels >7.0% and <10.0%, low-density lipoprotein cholesterol (LDL-C) >100 and <250 mg/dL. One hundred eighty-five eligible subjects were randomized to the combination group (metformin+atorvastatin), metformin group (metformin+atorvastatin placebo), and atorvastatin group (atorvastatin+metformin placebo). The primary efficacy endpoints were the percent changes in HbA1c and LDL-C levels from baseline at the end of the treatment.
Results
After 16 weeks of treatment compared to baseline, HbA1c showed a significant difference of 0.94% compared to the atorvastatin group in the combination group (0.35% vs. −0.58%, respectively; P<0.0001), whereas the proportion of patients with increased HbA1c was also 62% and 15%, respectively, showing a significant difference (P<0.001). The combination group also showed a significant decrease in LDL-C levels compared to the metformin group (−55.20% vs. −7.69%, P<0.001) without previously unknown adverse drug events.
Conclusion
The addition of atorvastatin to metformin improved HbA1c and LDL-C levels to a significant extent compared to metformin or atorvastatin alone in diabetes and dyslipidemia patients. This study also suggested metformin’s preventive effect on the glucose-elevating potential of atorvastatin in patients with type 2 diabetes mellitus and dyslipidemia, insufficiently controlled with exercise and diet. Metformin and atorvastatin combination might be an effective treatment in reducing the CVD risk in patients with both diabetes and dyslipidemia because of its lowering effect on LDL-C and glucose.
Metabolic Risk/Epidemiology
Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning
Chuan Yun, Fangli Tang, Zhenxiu Gao, Wenjun Wang, Fang Bai, Joshua D. Miller, Huanhuan Liu, Yaujiunn Lee, Qingqing Lou
Received February 3, 2023  Accepted May 27, 2023  Published online April 30, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0033    [Epub ahead of print]
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AbstractAbstract PDFPubReader   ePub   
Background
This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
Methods
The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance.
Results
The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05).
Conclusion
The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.
Drug/Regimen
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Efficacy and Safety of Alogliptin-Pioglitazone Combination for Type 2 Diabetes Mellitus Poorly Controlled with Metformin: A Multicenter, Double-Blind Randomized Trial
Ji-Yeon Park, Joonyub Lee, Yoon-Hee Choi, Kyung Wan Min, Kyung Ah Han, Kyu Jeung Ahn, Soo Lim, Young-Hyun Kim, Chul Woo Ahn, Kyung Mook Choi, Kun-Ho Yoon, the Practical Evidence of Antidiabetic Combination Therapy in Korea (PEAK) study investigators
Received August 7, 2023  Accepted November 30, 2023  Published online April 23, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0259    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Guidelines for switching to triple combination therapy directly after monotherapy failure are limited. This study investigated the efficacy, long-term sustainability, and safety of either mono or dual add-on therapy using alogliptin and pioglitazone for patients with type 2 diabetes mellitus (T2DM) who did not achieve their target glycemic range with metformin monotherapy.
Methods
The Practical Evidence of Antidiabetic Combination Therapy in Korea (PEAK) was a multicenter, placebo-controlled, double-blind, randomized trial. A total of 214 participants were randomized to receive alogliptin+pioglitazone (Alo+Pio group, n=70), alogliptin (Alo group, n=75), or pioglitazone (Pio group, n=69). The primary outcome was the difference in glycosylated hemoglobin (HbA1c) levels between the three groups at baseline to 24 weeks. For durability, the achievement of HbA1c levels <7% and <6.5% was compared in each group. The number of adverse events was investigated for safety.
Results
After 24 weeks of treatment, the change of HbA1c in the Alo+Pio, Alo, and Pio groups were –1.38%±0.08%, –1.03%±0.08%, and –0.84%±0.08%, respectively. The Alo+Pio group had significantly lower HbA1c levels than the other groups (P=0.0063, P<0.0001) and had a higher proportion of patients with target HbA1c achievement. In addition, insulin sensitivity and β-cell function, lipid profiles, and other metabolic indicators were also improved. There were no significant safety issues in patients treated with triple combination therapy.
Conclusion
Early combination triple therapy showed better efficacy and durability than the single add-on (dual) therapy. Therefore, combination therapy with metformin, alogliptin, and pioglitazone is a valuable early treatment option for T2DM poorly controlled with metformin monotherapy.
Metabolic Risk/Epidemiology
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Biologically Informed Polygenic Scores for Brain Insulin Receptor Network Are Associated with Cardiometabolic Risk Markers and Diabetes in Women
Jannica S. Selenius, Patricia P. Silveira, Mikaela von Bonsdorff, Jari Lahti, Hannu Koistinen, Riitta Koistinen, Markku Seppälä, Johan G. Eriksson, Niko S. Wasenius
Received February 10, 2023  Accepted November 25, 2023  Published online March 25, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0039    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
To investigate associations between variations in the co-expression-based brain insulin receptor polygenic score and cardiometabolic risk factors and diabetes mellitus.
Methods
This cross-sectional study included 1,573 participants from the Helsinki Birth Cohort Study. Biologically informed expression-based polygenic risk scores for the insulin receptor gene network were calculated for the hippocampal (hePRS-IR) and the mesocorticolimbic (mePRS-IR) regions. Cardiometabolic markers included body composition, waist circumference, circulating lipids, insulin-like growth factor 1 (IGF-1), and insulin-like growth factor-binding protein 1 and 3 (IGFBP-1 and -3). Glucose and insulin levels were measured during a standardized 2-hour 75 g oral glucose tolerance test and impaired glucose regulation status was defined by the World Health Organization 2019 criteria. Analyzes were adjusted for population stratification, age, smoking, alcohol consumption, socioeconomic status, chronic diseases, birth weight, and leisure-time physical activity.
Results
Multinomial logistic regression indicated that one standard deviation increase in hePRS-IR was associated with increased risk of diabetes mellitus in all participants (adjusted relative risk ratio, 1.17; 95% confidence interval, 1.01 to 1.35). In women, higher hePRS-IR was associated with greater waist circumference and higher body fat percentage, levels of glucose, insulin, total cholesterol, low-density lipoprotein cholesterol, triglycerides, apolipoprotein B, insulin, and IGFBP-1 (all P≤0.02). The mePRS-IR was associated with decreased IGF-1 level in women (P=0.02). No associations were detected in men and studied outcomes.
Conclusion
hePRS-IR is associated with sex-specific differences in cardiometabolic risk factor profiles including impaired glucose regulation, abnormal metabolic markers, and unfavorable body composition in women.
Type 1 Diabetes
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A New Tool to Identify Pediatric Patients with Atypical Diabetes Associated with Gene Polymorphisms
Sophie Welsch, Antoine Harvengt, Paola Gallo, Manon Martin, Dominique Beckers, Thierry Mouraux, Nicole Seret, Marie-Christine Lebrethon, Raphaël Helaers, Pascal Brouillard, Miikka Vikkula, Philippe A. Lysy
Received May 26, 2023  Accepted November 25, 2023  Published online March 22, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0166    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Recent diabetes subclassifications have improved the differentiation between patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus despite several overlapping features, yet without considering genetic forms of diabetes. We sought to facilitate the identification of monogenic diabetes by creating a new tool that we validated in a pediatric maturity-onset diabetes of the young (MODY) cohort.
Methods
We first created the DIAgnose MOnogenic DIAbetes (DIAMODIA) criteria based on the pre-existing, but incomplete, MODY calculator. This new score is composed of four strong and five weak criteria, with patients having to display at least one weak and one strong criterion.
Results
The effectiveness of the DIAMODIA criteria was evaluated in two patient cohorts, the first consisting of patients with confirmed MODY diabetes (n=34) and the second of patients with T1DM (n=390). These DIAMODIA criteria successfully detected 100% of MODY patients. Multiple correspondence analysis performed on the MODY and T1DM cohorts enabled us to differentiate MODY patients from T1DM. The three most relevant variables to distinguish a MODY from T1DM profile were: lower insulin-dose adjusted A1c score ≤9, glycemic target-adjusted A1c score ≤4.5, and absence of three anti-islet cell autoantibodies.
Conclusion
We validated the DIAMODIA criteria, as it effectively identified all monogenic diabetes patients (MODY cohort) and succeeded to differentiate T1DM from MODY patients. The creation of this new and effective tool is likely to facilitate the characterization and therapeutic management of patients with atypical diabetes, and promptly referring them for genetic testing which would markedly improve clinical care and counseling, as well.
Review
Metabolic Risk/Epidemiology
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One-Carbon Metabolism Nutrients, Genetic Variation, and Diabetes Mellitus
Jie Zhu, Gunjana Saikia, Xiaotao Zhang, Xiaoxi Shen, Ka Kahe
Diabetes Metab J. 2024;48(2):170-183.   Published online March 12, 2024
DOI: https://doi.org/10.4093/dmj.2023.0272
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Diabetes mellitus (DM) affects about 9.3% of the population globally. Hyperhomocysteinemia (HHcy) has been implicated in the pathogenesis of DM, owing to its promotion of oxidative stress, β-cell dysfunction, and insulin resistance. HHcy can result from low status of one-carbon metabolism (OCM) nutrients (e.g., folate, choline, betaine, vitamin B6, B12), which work together to degrade homocysteine by methylation. The etiology of HHcy may also involve genetic variation encoding key enzymes in OCM. This review aimed to provide an overview of the existing literature assessing the link between OCM nutrients status, related genetic factors, and incident DM. We also discussed possible mechanisms underlying the role of OCM in DM development and provided recommendations for future research and practice. Even though the available evidence remains inconsistent, some studies support the potential beneficial effects of intakes or blood levels of OCM nutrients on DM development. Moreover, certain variants in OCM-related genes may influence metabolic handling of methyl-donors and presumably incidental DM. Future studies are warranted to establish the causal inference between OCM and DM and examine the interaction of OCM nutrients and genetic factors with DM development, which will inform the personalized recommendations for OCM nutrients intakes on DM prevention.
Original Articles
Metabolic Risk/Epidemiology
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A Prospective 1-Year Follow-Up of Glycemic Status and C-Peptide Levels of COVID-19 Survivors with Dysglycemia in Acute COVID-19 Infection
David Tak Wai Lui, Chi Ho Lee, Ying Wong, Carol Ho Yi Fong, Kimberly Hang Tsoi, Yu Cho Woo, Kathryn Choon Beng Tan
Received June 5, 2023  Accepted October 13, 2023  Published online March 11, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0175    [Epub ahead of print]
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AbstractAbstract PDFPubReader   ePub   
Background
We evaluated changes in glycemic status, over 1 year, of coronavirus disease 2019 (COVID-19) survivors with dysglycemia in acute COVID-19.
Methods
COVID-19 survivors who had dysglycemia (defined by glycosylated hemoglobin [HbA1c] 5.7% to 6.4% or random glucose ≥10.0 mmol/L) in acute COVID-19 were recruited from a major COVID-19 treatment center from September to October 2020. Matched non-COVID controls were recruited from community. The 75-g oral glucose tolerance test (OGTT) were performed at baseline (6 weeks after acute COVID-19) and 1 year after acute COVID-19, with HbA1c, insulin and C-peptide measurements. Progression in glycemic status was defined by progression from normoglycemia to prediabetes/diabetes, or prediabetes to diabetes.
Results
Fifty-two COVID-19 survivors were recruited. Compared with non-COVID controls, they had higher C-peptide (P< 0.001) and trend towards higher homeostasis model assessment of insulin resistance (P=0.065). Forty-three COVID-19 survivors attended 1-year reassessment. HbA1c increased from 5.5%±0.3% to 5.7%±0.2% (P<0.001), with increases in glucose on OGTT at fasting (P=0.089), 30-minute (P=0.126), 1-hour (P=0.014), and 2-hour (P=0.165). At baseline, 19 subjects had normoglycemia, 23 had prediabetes, and one had diabetes. Over 1 year, 10 subjects (23.8%; of 42 non-diabetes subjects at baseline) had progression in glycemic status. C-peptide levels remained unchanged (P=0.835). Matsuda index decreased (P=0.007) and there was a trend of body mass index increase from 24.4±2.7 kg/m2 to 25.6±5.2 (P=0.083). Subjects with progression in glycemic status had more severe COVID-19 illness than non-progressors (P=0.030). Reassessment was not performed in the control group.
Conclusion
Subjects who had dysglycemia in acute COVID-19 were characterized by insulin resistance. Over 1 year, a quarter had progression in glycemic status, especially those with more severe COVID-19. Importantly, there was no significant deterioration in insulin secretory capacity.
Type 1 Diabetes
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Optimal Coefficient of Variance Threshold to Minimize Hypoglycemia Risk in Individuals with Well-Controlled Type 1 Diabetes Mellitus
Jee Hee Yoo, Seung Hee Yang, Sang-Man Jin, Jae Hyeon Kim
Diabetes Metab J. 2024;48(3):429-439.   Published online March 4, 2024
DOI: https://doi.org/10.4093/dmj.2023.0083
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Background
This study investigated the optimal coefficient of variance (%CV) for preventing hypoglycemia based on real-time continuous glucose monitoring (rt-CGM) data in people with type 1 diabetes mellitus (T1DM) already achieving their mean glucose (MG) target.
Methods
Data from 172 subjects who underwent rt-CGM for at least 90 days and for whom 439 90-day glycemic profiles were available were analyzed. Receiver operator characteristic analysis was conducted to determine the cut-off value of %CV to achieve time below range (%TBR)<54 mg/dL <1 and =0.
Results
Overall mean glycosylated hemoglobin was 6.8% and median %TBR<54 mg/dL was 0.2%. MG was significantly higher and %CV significantly lower in profiles achieving %TBR<54 mg/dL <1 compared to %TBR<54 mg/dL ≥1 (all P<0.001). The cut-off value of %CV for achieving %TBR<54 mg/dL <1 was 37.5%, 37.3%, and 31.0%, in the whole population, MG >135 mg/dL, and ≤135 mg/dL, respectively. The cut-off value for %TBR<54 mg/dL=0% was 29.2% in MG ≤135 mg/dL. In profiles with MG ≤135 mg/dL, 94.2% of profiles with a %CV <31 achieved the target of %TBR<54 mg/dL <1, and 97.3% with a %CV <29.2 achieved the target of %TBR<54 mg/ dL=0%. When MG was >135 mg/dL, 99.4% of profiles with a %CV <37.3 achieved %TBR<54 mg/dL <1.
Conclusion
In well-controlled T1DM with MG ≤135 mg/dL, we suggest a %CV <31% to achieve the %TBR<54 mg/dL <1 target. Furthermore, we suggest a %CV <29.2% to achieve the target of %TBR<54 mg/dL =0 for people at high risk of hypoglycemia.

Diabetes Metab J : Diabetes & Metabolism Journal
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