Diabetes & Metabolism Journal

Review

Lifestyle
Ultra-Processed Foods and the Impact on Cardiometabolic Health: The Role of Diet Quality: Xiaowen Wang, Qi Sun; Diabetes Metab J. 2024;48(6):1047-1055. Published online November 1, 2024; DOI: https://doi.org/10.4093/dmj.2024.0659

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The consumption of ultra-processed foods (UPFs) has surged globally, raising significant public health concerns due to their associations with a range of adverse health outcomes. This review aims to elucidate potential health impacts of UPF intake and underscore the importance of considering diet quality when interpreting study findings. UPF group, as classified by the Nova system based on the extent of industrial processing, contains numerous individual food items with a wide spectrum of nutrient profiles, as well as differential quality as reflected by their potential health effects. The quality of a given food may well misalign with the processing levels so that a UPF food can be nutritious and healthful whereas a non-UPF food can be of low quality and excess intake of which may lead to adverse health consequences. The current review argues that it is critical to focus on the nutritional content and quality of foods and their role within the overall dietary pattern rather than only the level of processing. Further research should dissect health effects of diet quality and food processing, investigate the health impacts of ingredients that render the UPF categorization, understand roles of metabolomics and the gut microbiome in mediating and modulating the health effects of food processing, and consider environmental sustainability in UPF studies. Emphasizing nutrient-dense healthful foods and dietary patterns shall remain the pivotal strategy for promoting overall health and preventing chronic diseases.

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Social determinants of health and type 2 diabetes in Asia
Kyunghun Sung, Seung‐Hwan Lee
Journal of Diabetes Investigation.2025;[Epub] CrossRef

Original Articles

Complications
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; Diabetes Metab J. 2024;48(4):771-779. Published online April 30, 2024; DOI: https://doi.org/10.4093/dmj.2023.0033

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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.

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Trends and analysis of risk factor differences in the global burden of chronic kidney disease due to type 2 diabetes from 1990 to 2021: A population‐based study
Yifei Wang, Ting Lin, Jiale Lu, Wenfang He, Hongbo Chen, Tiancai Wen, Juan Jin, Qiang He
Diabetes, Obesity and Metabolism.2025; 27(4): 1902. CrossRef

Metabolic Risk/Epidemiology
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; Diabetes Metab J. 2024;48(5):960-970. Published online March 25, 2024; DOI: https://doi.org/10.4093/dmj.2023.0039

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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.

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A mesocorticolimbic insulin receptor gene co-expression network moderates the association between early life adversity and food approach eating behaviour style in childhood
Angela Marcela Jaramillo-Ospina, Roberta Dalle Molle, Sachin Patel, Shona Kelly, Irina Pokhvisneva, Carolina de Weerth, Patrícia Pelufo Silveira
Appetite.2025; 204: 107762. CrossRef

Complications
The Risk of Shoulder Adhesive Capsulitis in Individuals with Prediabetes and Type 2 Diabetes Mellitus: A Longitudinal Nationwide Population-Based Study: Jong-Ho Kim, Bong-Seoung Kim, Kyung-do Han, Hyuk-Sang Kwon; Diabetes Metab J. 2023;47(6):869-878. Published online August 23, 2023; DOI: https://doi.org/10.4093/dmj.2022.0275

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Background
This study aimed to investigate the association between type 2 diabetes mellitus (T2DM) and shoulder adhesive capsulitis (AC) using a large-scale, nationwide, population-based cohort in the Republic of Korea.
Methods
A total of 3,471,745 subjects aged over 20 years who underwent a National Health Insurance Service medical checkup between 2009 and 2010 were included in this study, and followed from the date of their medical checkup to the end of 2018. Subjects were classified into the following four groups based on the presence of dysglycemia and history of diabetes medication: normal, prediabetes, newly diagnosed T2DM (new-T2DM), and T2DM (claim history for antidiabetic medication). The endpoint was new-onset AC during follow-up. The incidence rates (IRs) in 1,000 person-years and hazard ratios (HRs) of AC for each group were analyzed using Cox proportional hazard regression models.
Results
The IRs of AC were 9.453 (normal), 11.912 (prediabetes), 14.933 (new-T2DM), and 24.3761 (T2DM). The adjusted HRs of AC in the prediabetes, new-T2DM, and T2DM groups were 1.084 (95% confidence interval [CI], 1.075 to 1.094), 1.312 (95% CI, 1.287 to 1.337), and 1.473 (95% CI, 1.452 to 1.494) compared to the normal group, respectively. This secular trend of the HRs of AC according to T2DM status was statistically significant (P<0.0001).
Conclusion
This large-scale, longitudinal, nationwide, population-based cohort study of 3,471,745 subjects confirmed that the risk of AC increases in prediabetic subjects and is associated with T2DM status.

Citations

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Higher body mass index increases the risk of shoulder adhesive capsulitis in young adults: a nationwide cohort study
Jong-Ho Kim, Jae-Yoon Baek, Kyung-Do Han, Bong-Seoung Kim, Hyuk-Sang Kwon
Journal of Shoulder and Elbow Surgery.2025; 34(1): 26. CrossRef
Classification of shoulder diseases in older adult patients: a narrative review
Hyo-Jin Lee, Jong-Ho Kim
The Ewha Medical Journal.2025;[Epub] CrossRef
Impacts of preoperative anxiety and depression on pain and range of motion after arthroscopic frozen shoulder release: a cohort study
Yahia Haroun, Ahmed Saeed Younis, Wessam Fakhery Ebied, Mohamed Amr Hemida, Ahmed H. Khater
International Orthopaedics.2024; 48(8): 2113. CrossRef
Subdiaphragmatic phrenic nerve supply: A systematic review
María Pérez-Montalbán, Encarna García-Domínguez, Ángel Oliva-Pascual-Vaca
Annals of Anatomy - Anatomischer Anzeiger.2024; 254: 152269. CrossRef
Effects of moderate physical activity on diabetic adhesive capsulitis: a randomized clinical trial
Raheela Kanwal Sheikh, Amna Toseef, Aadil Omer, Anam Aftab, Muhammad Manan Haider Khan, Saeed Bin Ayaz, Omar Althomli, Aisha Razzaq, Samra Khokhar, Nazia Jabbar, Waqar Ahmed Awan
PeerJ.2024; 12: e18030. CrossRef
A Narrative Review of Adhesive Capsulitis with Diabetes
Mu-Her Chen, Wen-Shiang Chen
Journal of Clinical Medicine.2024; 13(19): 5696. CrossRef
Sub‐Acromioclavicular Decompression Increases the Risk of Postoperative Shoulder Stiffness after Arthroscopic Rotator Cuff Repair
Cheng Li, Zhiling Wang, Maslah Idiris Ali, Yi Long, Ymuhanmode Alike, Min Zhou, Dedong Cui, Zhenze Zheng, Ke Meng, Jingyi Hou, Rui Yang
Orthopaedic Surgery.2024; 16(12): 2942. CrossRef
A comprehensive scoring system for the diagnosis and staging of adhesive capsulitis: development, application, and implications
Fabio Vita, Danilo Donati, Roberto Tedeschi, Marco Miceli, Paolo Spinnato, Flavio Origlio, Enrico Guerra, Marco Cavallo, Salvatore Massimo Stella, Luigi Tarallo, Giuseppe Porcellini, Stefano Galletti, Cesare Faldini
European Journal of Orthopaedic Surgery & Traumatology.2024; 34(8): 4113. CrossRef

Cardiovascular Risk/Epidemiology
Comparison of on-Statin Lipid and Lipoprotein Levels for the Prediction of First Cardiovascular Event in Type 2 Diabetes Mellitus: Ji Yoon Kim, Jimi Choi, Sin Gon Kim, Nam Hoon Kim; Diabetes Metab J. 2023;47(6):837-845. Published online August 23, 2023; DOI: https://doi.org/10.4093/dmj.2022.0217

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Abstract PDF Supplementary Material PubReader ePub: Background
A substantial cardiovascular disease risk remains even after optimal statin therapy. Comparative predictiveness of major lipid and lipoprotein parameters for cardiovascular events in patients with type 2 diabetes mellitus (T2DM) who are treated with statins is not well documented.
Methods
From the Korean Nationwide Cohort, 11,900 patients with T2DM (≥40 years of age) without a history of cardiovascular disease and receiving moderate- or high-intensity statins were included. The primary outcome was the first occurrence of major adverse cardiovascular events (MACE) including ischemic heart disease, ischemic stroke, and cardiovascular death. The risk of MACE was estimated according to on-statin levels of low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), highdensity lipoprotein cholesterol (HDL-C), and non-HDL-C.
Results
MACE occurred in 712 patients during a median follow-up period of 37.9 months (interquartile range, 21.7 to 54.9). Among patients achieving LDL-C levels less than 100 mg/dL, the hazard ratios for MACE per 1-standard deviation change in ontreatment values were 1.25 (95% confidence interval [CI], 1.07 to 1.47) for LDL-C, 1.31 (95% CI, 1.09 to 1.57) for non-HDL-C, 1.05 (95% CI, 0.91 to 1.21) for TG, and 1.16 (95% CI, 0.98 to 1.37) for HDL-C, after adjusting for potential confounders and lipid parameters mutually. The predictive ability of on-statin LDL-C and non-HDL-C for MACE was prominent in patients at high cardiovascular risk or those with LDL-C ≥70 mg/dL.
Conclusion
On-statin LDL-C and non-HDL-C levels are better predictors of the first cardiovascular event than TG or HDL-C in patients with T2DM.

Metabolic Risk/Epidemiology
The Risk of Type 2 Diabetes Mellitus according to Changes in Obesity Status in Late Middle-Aged Adults: A Nationwide Cohort Study of Korea: Joon Ho Moon, Yeonhoon Jang, Tae Jung Oh, Se Young Jung; Diabetes Metab J. 2023;47(4):514-522. Published online April 25, 2023; DOI: https://doi.org/10.4093/dmj.2022.0159

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Background
Although obesity is a well-known risk factor of type 2 diabetes mellitus (T2DM), there is scant data on discriminating the contribution of previous obesity and recent weight gain on developing T2DM.
Methods
We analyzed the Korean National Health Insurance Service-Health Screening Cohort data from 2002 to 2015 where Korean residents underwent biennial health checkups. Participants were classified into four groups according to their obesity status (body mass index [BMI] ≥25 kg/m²) before and after turning 50 years old: maintaining normal (MN), becoming obese (BO), becoming normal (BN), and maintaining obese (MO). Cox proportional hazards regression model was used to estimate the risk of T2DM factoring in the covariates age, sex, BMI, presence of impaired fasting glucose or hypertension, family history of diabetes, and smoking status.
Results
A total of 118,438 participants (mean age, 52.5±1.1 years; men, 45.2%) were prospectively evaluated for incident T2DM. A total of 7,339 (6.2%) participants were diagnosed with T2DM during a follow-up period of 4.8±2.6 years. Incidence rates of T2DM per 1,000 person-year were 9.20 in MN, 14.81 in BO, 14.42 in BN, 21.38 in MO. After factoring in covariates, participants in the groups BN (adjusted hazard ratio [aHR], 1.15; 95% confidence interval [CI], 1.04 to 1.27) and MO (aHR, 1.14; 95% CI, 1.06 to 1.24) were at increased risk of developing T2DM compared to MN, whereas BO (hazard ratio, 1.06; 95% CI, 0.96 to 1.17) was not.
Conclusion
Having been obese before 50 years old increased the risk of developing T2DM in the future, but becoming obese after 50 did not. Therefore, it is important to maintain normal weight from early adulthood to prevent future metabolic perturbations.

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Accounting for time-varying exposures and covariates in the relationship between obesity and diabetes: analysis using parametric g-formula
Boyoung Park, Junghyun Yoon, Thi Xuan Mai Tran
Journal of Epidemiology and Community Health.2024; 78(11): 729. CrossRef
Association of Uterine Leiomyoma with Type 2 Diabetes Mellitus in Young Women: A Population-Based Cohort Study
Ji-Hee Sung, Kyung-Soo Kim, Kyungdo Han, Cheol-Young Park
Diabetes & Metabolism Journal.2024; 48(6): 1105. CrossRef

Complications
Association of Body Mass Index and Fracture Risk Varied by Affected Bones in Patients with Diabetes: A Nationwide Cohort Study: Se-Won Lee, Kyungdo Han, Hyuk-Sang Kwon; Diabetes Metab J. 2023;47(2):242-254. Published online January 19, 2023; DOI: https://doi.org/10.4093/dmj.2022.0001

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Background
Body mass index (BMI) is a risk factor for the type 2 diabetes (T2DM), and T2DM accompanies various complications, such as fractures. We investigated the effects of BMI and T2DM on fracture risk and analyzed whether the association varied with fracture locations.
Methods
This study is a nationwide population-based cohort study that included all people with T2DM (n=2,746,078) who received the National Screening Program during 2009–2012. According to the anatomical location of the fracture, the incidence rate and hazard ratio (HR) were analyzed by dividing it into four categories: vertebra, hip, limbs, and total fracture.
Results
The total fracture had higher HR in the underweight group (HR, 1.268; 95% CI, 1.228 to 1.309) and lower HR in the obese group (HR, 0.891; 95% CI, 0.882 to 0.901) and the morbidly obese group (HR, 0.873; 95% CI, 0.857 to 0.89), compared to reference (normal BMI group). Similar trends were observed for HR of vertebra fracture. The risk of hip fracture was most prominent, the risk of hip fracture increased in the underweight group (HR, 1.896; 95% CI, 1.178 to 2.021) and decreased in the obesity (HR, 0.643; 95% CI, 0.624 to 0.663) and morbidly obesity group (HR, 0.627; 95% CI, 0.591 to 0.665). Lastly, fracture risk was least affected by BMI for limbs.
Conclusion
In T2DM patients, underweight tends to increase fracture risk, and overweight tends to lower fracture risk, but association between BMI and fracture risk varied depending on the affected bone lesions.

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Dysuricemia—A New Concept Encompassing Hyperuricemia and Hypouricemia
Naoyuki Otani, Motoshi Ouchi, Einosuke Mizuta, Asuka Morita, Tomoe Fujita, Naohiko Anzai, Ichiro Hisatome
Biomedicines.2023; 11(5): 1255. CrossRef
Association of Body Mass Index and Fracture Risk Varied by Affected Bones in Patients with Diabetes: A Nationwide Cohort Study (Diabetes Metab J 2023;47:242-54)
Se-Won Lee, Kyungdo Han, Hyuk-Sang Kwon
Diabetes & Metabolism Journal.2023; 47(3): 439. CrossRef
Association of Body Mass Index and Fracture Risk Varied by Affected Bones in Patients with Diabetes: A Nationwide Cohort Study (Diabetes Metab J 2023;47:242-54)
So Young Park
Diabetes & Metabolism Journal.2023; 47(3): 437. CrossRef
Effect of SGLT2 inhibitors on fractures, BMD, and bone metabolism markers in patients with type 2 diabetes mellitus: a systematic review and meta-analysis
Xin Wang, Fengyi Zhang, Yufeng Zhang, Jiayi Zhang, Yingli Sheng, Wenbo Wang, Yujie Li
Osteoporosis International.2023; 34(12): 2013. CrossRef

Review

Guideline/Fact Sheet
Screening for Prediabetes and Diabetes in Korean Nonpregnant Adults: A Position Statement of the Korean Diabetes Association, 2022: Kyung Ae Lee, Dae Jung Kim, Kyungdo Han, Suk Chon, Min Kyong Moon, on Behalf of the Committee of Clinical Practice Guideline of Korean Diabetes Association; Diabetes Metab J. 2022;46(6):819-826. Published online November 24, 2022; DOI: https://doi.org/10.4093/dmj.2022.0364

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Diabetes screening serves to identify individuals at high-risk for diabetes who have not yet developed symptoms and to diagnose diabetes at an early stage. Globally, the prevalence of diabetes is rapidly increasing. Furthermore, obesity and/or abdominal obesity, which are major risk factors for type 2 diabetes mellitus (T2DM), are progressively increasing, particularly among young adults. Many patients with T2DM are asymptomatic and can accompany various complications at the time of diagnosis, as well as chronic complications develop as the duration of diabetes increases. Thus, proper screening and early diagnosis are essential for diabetes care. Based on reports on the changing epidemiology of diabetes and obesity in Korea, as well as growing evidence from new national cohort studies on diabetes screening, the Korean Diabetes Association has updated its clinical practice recommendations regarding T2DM screening. Diabetes screening is now recommended in adults aged ≥35 years regardless of the presence of risk factors, and in all adults (aged ≥19) with any of the risk factors. Abdominal obesity based on waist circumference (men ≥90 cm, women ≥85 cm) was added to the list of risk factors.

Citations

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Graves’ Disease and the Risk of Type 2 Diabetes: A Korean Population-Based Study
Yoon Young Cho, Bongseong Kim, Sang-Man Jin, Chan-Hee Jung, Ji Oh Mok, Sun Wook Kim, Jae Hoon Chung, Kyung-Do Han, Tae Hyuk Kim
Thyroid®.2025; 35(1): 60. CrossRef
Workplace‐based continuous glucose monitoring with structured education for pre‐diabetes and type 2 diabetes: A prospective community cohort study
Ji‐Hee Ko, Sun‐Joon Moon, Ramzi A. Ajjan, Mi Yeon Lee, Hae‐Jeong Lee, Boram Choi, JiYeon Park, Seung‐Eun Lee, Jae‐Hyeon Kang, Cheol‐Young Park
Diabetes, Obesity and Metabolism.2025;[Epub] CrossRef
Oxidative Balance Score and New-Onset Type 2 Diabetes Mellitus in Korean Adults without Non-Alcoholic Fatty Liver Disease: Korean Genome and Epidemiology Study-Health Examinees (KoGES-HEXA) Cohort
Mid-Eum Moon, Dong Hyuk Jung, Seok-Jae Heo, Byoungjin Park, Yong Jae Lee
Antioxidants.2024; 13(1): 107. CrossRef
Efficacy and Safety of Once-Weekly Semaglutide Versus Once-Daily Sitagliptin as Metformin Add-on in a Korean Population with Type 2 Diabetes
Byung-Wan Lee, Young Min Cho, Sin Gon Kim, Seung-Hyun Ko, Soo Lim, Amine Dahaoui, Jin Sook Jeong, Hyo Jin Lim, Jae Myung Yu
Diabetes Therapy.2024; 15(2): 547. CrossRef
Triglyceride-glucose index predicts type 2 diabetes mellitus more effectively than oral glucose tolerance test-derived insulin sensitivity and secretion markers
Min Jin Lee, Ji Hyun Bae, Ah Reum Khang, Dongwon Yi, Mi Sook Yun, Yang Ho Kang
Diabetes Research and Clinical Practice.2024; 210: 111640. CrossRef
Association of sleep fragmentation with general and abdominal obesity: a population-based longitudinal study
Yu-xiang Xu, Shan-shan Wang, Yu-hui Wan, Pu-yu Su, Fang-biao Tao, Ying Sun
International Journal of Obesity.2024; 48(9): 1258. CrossRef
Oxidative balance score as a useful predictive marker for new-onset type 2 diabetes mellitus in Korean adults aged 60 years or older: The Korean Genome and Epidemiologic Study–Health Examination (KoGES-HEXA) cohort
Mid-Eum Moon, Dong Hyuk Jung, Seok-Jae Heo, Byoungjin Park, Yong Jae Lee
Experimental Gerontology.2024; 193: 112475. CrossRef
The optimal dose of metformin to control conversion to diabetes in patients with prediabetes: A meta-analysis
Xiaoyan Yi, Yongliang Pan, Huan Peng, Mengru Ren, Qin Jia, Bing Wang
Journal of Diabetes and its Complications.2024; 38(10): 108846. CrossRef
Changes in the Epidemiological Landscape of Diabetes in South Korea: Trends in Prevalence, Incidence, and Healthcare Expenditures
Kyoung Hwa Ha, Dae Jung Kim
Endocrinology and Metabolism.2024; 39(5): 669. CrossRef
Development and validation of machine learning models to predict prediabetes using dietary intake data in young adults in Korea: a cross-sectional study
Myoung-Lyun Heo
Journal of Korean Biological Nursing Science.2024; 26(4): 300. CrossRef
Cumulative muscle strength and risk of diabetes: A prospective cohort study with mediation analysis
Shanhu Qiu, Xue Cai, Yan Liang, Wenji Chen, Duolao Wang, Zilin Sun, Bo Xie, Tongzhi Wu
Diabetes Research and Clinical Practice.2023; 197: 110562. CrossRef
Revisiting the Diabetes Crisis in Korea: Call for Urgent Action
Jun Sung Moon
The Journal of Korean Diabetes.2023; 24(1): 1. CrossRef
2023 Clinical Practice Guidelines for Diabetes Mellitus of the Korean Diabetes Association
Jong Han Choi, Kyung Ae Lee, Joon Ho Moon, Suk Chon, Dae Jung Kim, Hyun Jin Kim, Nan Hee Kim, Ji A Seo, Mee Kyoung Kim, Jeong Hyun Lim, YoonJu Song, Ye Seul Yang, Jae Hyeon Kim, You-Bin Lee, Junghyun Noh, Kyu Yeon Hur, Jong Suk Park, Sang Youl Rhee, Hae J
Diabetes & Metabolism Journal.2023; 47(5): 575. CrossRef
2023 Clinical Practice Guidelines for Diabetes
Min Kyong Moon
The Journal of Korean Diabetes.2023; 24(3): 120. CrossRef

Original Articles

Guideline/Fact Sheet
Diabetes Fact Sheet in Korea 2021: Jae Hyun Bae, Kyung-Do Han, Seung-Hyun Ko, Ye Seul Yang, Jong Han Choi, Kyung Mook Choi, Hyuk-Sang Kwon, Kyu Chang Won, on Behalf of the Committee of Media-Public Relation of the Korean Diabetes Association; Diabetes Metab J. 2022;46(3):417-426. Published online May 25, 2022; DOI: https://doi.org/10.4093/dmj.2022.0106

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Background
This study aimed to investigate the prevalence and management of diabetes mellitus, risk-factor control, and comorbidities among Korean adults.
Methods
We conducted a cross-sectional analysis of data from the Korea National Health and Nutrition Examination Survey to assess the prevalence, treatment, risk factors, comorbidities, and self-management behaviors of diabetes mellitus from 2019 to 2020. We also analyzed data from the Korean National Health Insurance Service to evaluate the use of antidiabetic medications in people with diabetes mellitus from 2002 through 2018.
Results
Among Korean adults aged 30 years or older, the estimated prevalence of diabetes mellitus was 16.7% in 2020. From 2019 through 2020, 65.8% of adults with diabetes mellitus were aware of the disease and treated with antidiabetic medications. The percentage of adults with diabetes mellitus who achieved glycosylated hemoglobin (HbA1c) <6.5% was 24.5% despite the increased use of new antidiabetic medications. We found that adults with diabetes mellitus who achieved all three goals of HbA1c <6.5%, blood pressure (BP) <140/85 mm Hg, and low-density lipoprotein cholesterol <100 mg/dL were 9.7%. The percentage of self-management behaviors was lower in men than women. Excess energy intake was observed in 16.7% of adults with diabetes mellitus.
Conclusion
The prevalence of diabetes mellitus among Korean adults remained high. Only 9.7% of adults with diabetes mellitus achieved all glycemic, BP, and lipid controls from 2019 to 2020. Continuous evaluation of national diabetes statistics and a national effort to increase awareness of diabetes mellitus and improve comprehensive diabetes care are needed.

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Zuhier A. Awan, Haider Ali Khan, Alam Jamal, Sulaiman Shams, Guojun Zheng, Abdul Wadood, Muhammad Shahab, Mohammad Imran Khan, Abdulaziz A. Kalantan
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Seong-Uk Baek, Minseo Choi, Yu-Min Lee, Jin-Ha Yoon
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Diabetes, Obesity and Metabolism.2025; 27(1): 81. CrossRef
1-Hour Postload Glucose: Early Screening for High Risk of Type 2 Diabetes in Koreans With Normal Fasting Glucose
Min Jin Lee, Ji Hyun Bae, Ah Reum Khang, Dongwon Yi, Joo Yeon Kim, Su Hyun Kim, Dong Hee Kim, Dasol Kang, Sujin Park, Yun Kyung Jeon, Sang Soo Kim, Bo Hyun Kim, Mi Sook Yun, Yang Ho Kang
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Trends in prescribing sodium‐glucose cotransporter 2 inhibitors for individuals with type 2 diabetes with and without cardiovascular‐renal disease in South Korea, 2015–2021
Kyoung Hwa Ha, Soyoung Shin, EunJi Na, Dae Jung Kim
Journal of Diabetes Investigation.2025; 16(2): 215. CrossRef
Is avoidable diabetes-related hospitalization in older patients with type 2 diabetes mellitus associated with increased health expenditure?: A nationwide retrospective cohort study in South Korea
Woo-Ri Lee, Gyeong-Min Lee, Noorhee Son, Kyu-Tae Han, Sungyoun Chun, Yehrhee Son, Ki-Bong Yoo
Preventive Medicine Reports.2025; 49: 102946. CrossRef
New-Onset Type 1 and Type 2 Diabetes Among Korean Youths During the COVID-19 Pandemic
Da Hye Lee, Hwa Young Kim, Ji Young Park, Jaehyun Kim, Jae Hyeon Park
JAMA Pediatrics.2025; 179(2): 155. CrossRef
Graves’ Disease and the Risk of Type 2 Diabetes: A Korean Population-Based Study
Yoon Young Cho, Bongseong Kim, Sang-Man Jin, Chan-Hee Jung, Ji Oh Mok, Sun Wook Kim, Jae Hoon Chung, Kyung-Do Han, Tae Hyuk Kim
Thyroid®.2025; 35(1): 60. CrossRef
Current status of modifiable risk factors for cardiovascular disease in Korean women
In-Jeong Cho, Mi-Seung Shin
The Korean Journal of Internal Medicine.2025; 40(1): 15. CrossRef
Diabetes Fact Sheets in Korea 2024
Se Eun Park, Seung-Hyun Ko, Ji Yoon Kim, Kyuho Kim, Joon Ho Moon, Nam Hoon Kim, Kyung Do Han, Sung Hee Choi, Bong Soo Cha
Diabetes & Metabolism Journal.2025; 49(1): 24. CrossRef
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BMJ Open.2025; 15(3): e093598. CrossRef
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Journal of Clinical Medicine.2025; 14(6): 2065. CrossRef
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Diabetes, Obesity and Metabolism.2024; 26(2): 567. CrossRef
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American Journal of Preventive Medicine.2024; 66(4): 717. CrossRef
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Yonsei Medical Journal.2024; 65(1): 42. CrossRef
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Preventive Medicine.2024; 178: 107793. CrossRef
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Diabetes Therapy.2024; 15(2): 547. CrossRef
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Diabetes Research and Clinical Practice.2024; 208: 111109. CrossRef
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Cardiovascular Prevention and Pharmacotherapy.2024; 6(1): 33. CrossRef
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Scientific Reports.2024;[Epub] CrossRef
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BMJ Open Diabetes Research & Care.2024; 12(1): e003696. CrossRef
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Journal of Personalized Medicine.2024; 14(3): 280. CrossRef
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Scientific Reports.2024;[Epub] CrossRef
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Diabetes & Metabolism Journal.2024; 48(2): 279. CrossRef
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Diabetes Research and Clinical Practice.2024; 210: 111640. CrossRef
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Scandinavian Journal of Clinical and Laboratory Investigation.2024; 84(3): 168. CrossRef
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Journal of Clinical Medicine.2024; 13(8): 2311. CrossRef
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British Journal of Health Psychology.2024;[Epub] CrossRef
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Diabetes & Metabolism Journal.2024; 48(4): 790. CrossRef
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Diabetes & Metabolism Journal.2024; 48(4): 531. CrossRef
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JMIR Public Health and Surveillance.2024; 10: e59571. CrossRef
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Diabetes & Metabolism Journal.2024; 48(5): 929. CrossRef
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Journal of Korean Gerontological Nursing.2024; 26(3): 257. CrossRef
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Frontiers in Public Health.2024;[Epub] CrossRef
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Diabetes & Metabolism Journal.2024; 48(5): 937. CrossRef
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Diabetes & Metabolism Journal.2024; 48(5): 915. CrossRef
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European Journal of Cancer.2024; 213: 114312. CrossRef
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Scientific Reports.2024;[Epub] CrossRef
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Diabetes, Metabolic Syndrome and Obesity.2024; Volume 17: 3917. CrossRef
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Korean Journal of Health Education and Promotion.2024; 41(3): 73. CrossRef
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Clinical Hypertension.2024;[Epub] CrossRef
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PLOS ONE.2024; 19(10): e0309558. CrossRef
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Cardiovascular Prevention and Pharmacotherapy.2024; 6(4): 116. CrossRef
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종한 최, 민경 문
Public Health Weekly Report.2024; 17(44): 1905. CrossRef
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PLOS ONE.2024; 19(11): e0313988. CrossRef
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Respiratory Research.2024;[Epub] CrossRef
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Chronic Illness.2024;[Epub] CrossRef
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Journal of the Korean Medical Association.2024; 67(11): 686. CrossRef
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Diabetes & Metabolism Journal.2024; 48(6): 1084. CrossRef
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Drug/Regimen
Comparison of Serum Ketone Levels and Cardiometabolic Efficacy of Dapagliflozin versus Sitagliptin among Insulin-Treated Chinese Patients with Type 2 Diabetes Mellitus: Chi-Ho Lee, Mei-Zhen Wu, David Tak-Wai Lui, Darren Shing-Hei Chan, Carol Ho-Yi Fong, Sammy Wing-Ming Shiu, Ying Wong, Alan Chun-Hong Lee, Joanne King-Yan Lam, Yu-Cho Woo, Karen Siu-Ling Lam, Kelvin Kai-Hang Yiu, Kathryn Choon-Beng Tan; Diabetes Metab J. 2022;46(6):843-854. Published online April 28, 2022; DOI: https://doi.org/10.4093/dmj.2021.0319

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Abstract PDF Supplementary Material PubReader ePub

Background
Insulin-treated patients with long duration of type 2 diabetes mellitus (T2DM) are at increased risk of ketoacidosis related to sodium-glucose co-transporter 2 inhibitor (SGLT2i). The extent of circulating ketone elevation in these patients remains unknown. We conducted this study to compare the serum ketone response between dapagliflozin, an SGLT2i, and sitagliptin, a dipeptidyl peptidase-4 inhibitor, among insulin-treated T2DM patients.
Methods
This was a randomized, open-label, active comparator-controlled study involving 60 insulin-treated T2DM patients. Participants were randomized 1:1 for 24-week of dapagliflozin 10 mg daily or sitagliptin 100 mg daily. Serum β-hydroxybutyrate (BHB) levels were measured at baseline, 12 and 24 weeks after intervention. Comprehensive cardiometabolic assessments were performed with measurements of high-density lipoprotein cholesterol (HDL-C) cholesterol efflux capacity (CEC), vibration-controlled transient elastography and echocardiography.
Results
Among these 60 insulin-treated participants (mean age 58.8 years, diabetes duration 18.2 years, glycosylated hemoglobin 8.87%), as compared with sitagliptin, serum BHB levels increased significantly after 24 weeks of dapagliflozin (P=0.045), with a median of 27% increase from baseline. Change in serum BHB levels correlated significantly with change in free fatty acid levels. Despite similar glucose lowering, dapagliflozin led to significant improvements in body weight (P=0.006), waist circumference (P=0.028), HDL-C (P=0.041), CEC (P=0.045), controlled attenuation parameter (P=0.007), and liver stiffness (P=0.022). Average E/e’, an echocardiographic index of left ventricular diastolic dysfunction, was also significantly lower at 24 weeks in participants treated with dapagliflozin (P=0.037).
Conclusion
Among insulin-treated T2DM patients with long diabetes duration, compared to sitagliptin, dapagliflozin modestly increased ketone levels and was associated with cardiometabolic benefits.

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Dapagliflozin improves diabetic kidney disease by inhibiting ferroptosis through β-hydroxybutyrate production
Yan Tian, Chenxia Zhou, Qun Yan, Ziyi Li, Da Chen, Bo Feng, Jun Song
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Urna Kansakar, Crystal Nieves Garcia, Gaetano Santulli, Jessica Gambardella, Pasquale Mone, Stanislovas S. Jankauskas, Angela Lombardi
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Type 1 Diabetes
Abnormal Responses in Cognitive Impulsivity Circuits Are Associated with Glycosylated Hemoglobin Trajectories in Type 1 Diabetes Mellitus and Impaired Metabolic Control: Helena Jorge, Isabel C. Duarte, Sandra Paiva, Ana Paula Relvas, Miguel Castelo-Branco; Diabetes Metab J. 2022;46(6):866-878. Published online March 22, 2022; DOI: https://doi.org/10.4093/dmj.2021.0307

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Background
Risky health decisions and impulse control profiles may impact on metabolic control in type 1 diabetes mellitus (T1DM). We hypothesize that the neural correlates of cognitive impulsivity and decision-making in T1DM relate to metabolic control trajectories.
Methods
We combined functional magnetic resonance imaging (fMRI), measures of metabolic trajectories (glycosylated hemoglobin [HbA1c] over multiple time points) and behavioral assessment using a cognitive impulsivity paradigm, the Balloon Analogue Risk Task (BART), in 50 participants (25 T1DM and 25 controls).
Results
Behavioral results showed that T1DM participants followed a rigid conservative risk strategy along the iterative game. Imaging group comparisons showed that patients showed larger activation of reward related, limbic regions (nucleus accumbens, amygdala) and insula (interoceptive saliency network) in initial game stages. Upon game completion differences emerged in relation to error monitoring (anterior cingulate cortex [ACC]) and inhibitory control (inferior frontal gyrus). Importantly, activity in the saliency network (ACC and insula), which monitors interoceptive states, was related with metabolic trajectories, which was also found for limbic/reward networks. Parietal and posterior cingulate regions activated both in controls and patients with adaptive decision-making, and positively associated with metabolic trajectories.
Conclusion
We found triple converging evidence when comparing metabolic trajectories, patients versus controls or risk averse (non-learners) versus patients who learned by trial and error. Dopaminergic reward and saliency (interoceptive and error monitoring) circuits show a tight link with impaired metabolic trajectories and cognitive impulsivity in T1DM. Activity in parietal and posterior cingulate are associated with adaptive trajectories. This link between reward-saliency-inhibition circuits suggests novel strategies for patient management.

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Trust-based health decision-making recruits the neural interoceptive saliency network which relates to temporal trajectories of Hemoglobin A1C in Diabetes Type 1
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COVID-19
Association of Metabolic Syndrome with COVID-19 in the Republic of Korea: Woo-Hwi Jeon, Jeong-Yeon Seon, So-Youn Park, In-Hwan Oh; Diabetes Metab J. 2022;46(3):427-438. Published online November 26, 2021; DOI: https://doi.org/10.4093/dmj.2021.0105

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Abstract PDF PubReader ePub

Background
Metabolic syndrome (MetS) is reportedly a crucial risk factor for coronavirus disease 2019 (COVID-19). Since the epidemiological studies that examine this association are few and include small samples, we investigated the relationship between MetS and COVID-19 severity and death using a larger sample in the Republic of Korea.
Methods
We analyzed 66,321 patients, 4,066 of whom had COVID-19. We used chi-square tests to examine patients’ characteristics. We performed logistic regression analysis to analyze differences in COVID-19 infection and clinical outcomes according to the presence of MetS.
Results
Although MetS was not significantly associated with COVID-19 risk, acquiring MetS was significantly associated with the risk of severe COVID-19 outcomes (odds ratio [OR], 1.97; 95% confidence interval [CI], 1.34 to 2.91; P=0.001). The mortality risk was significantly higher in COVID-19 patients with MetS (OR, 1.74; 95% CI, 1.17 to 2.59; P=0.006). Patients with abnormal waist circumference were approximately 2.07 times more likely to develop severe COVID-19 (P<0.001), and high-density lipoprotein cholesterol (HDL-C) levels were significantly associated with COVID-19; the mortality risk due to COVID-19 was 1.74 times higher in men with an HDL-C level of <40 mg/dL and in women with an HDL-C level of <50 mg/dL (P=0.012).
Conclusion
COVID-19 is likely associated with severity and death in patients with MetS or in patients with MetS risk factors. Therefore, patients with MetS or those with abnormal waist circumference and HDL-C levels need to be treated with caution.

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Cardiovascular Risk/Epidemiology
Performance of Diabetes and Kidney Disease Screening Scores in Contemporary United States and Korean Populations: Liela Meng, Keun-Sang Kwon, Dae Jung Kim, Yong-ho Lee, Jeehyoung Kim, Abhijit V. Kshirsagar, Heejung Bang; Diabetes Metab J. 2022;46(2):273-285. Published online September 9, 2021; DOI: https://doi.org/10.4093/dmj.2021.0054

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Background
Risk assessment tools have been actively studied, and they summarize key predictors with relative weights/importance for a disease. Currently, standardized screening scores for type 2 diabetes mellitus (DM) and chronic kidney disease (CKD)—two key global health problems—are available in United States and Korea. We aimed to compare and evaluate screening scores for DM (or combined with prediabetes) and CKD, and assess the risk in contemporary United States and Korean populations.
Methods
Four (2×2) models were evaluated in the United States-National Health and Nutrition Examination Survey (NHANES 2015–2018) and Korea-NHANES (2016–2018)—8,928 and 16,209 adults. Weighted statistics were used to describe population characteristics. We used logistic regression for predictors in the models to assess associations with study outcomes (undiagnosed DM and CKD) and diagnostic measures for temporal and cross-validation.
Results
Korean adult population (mean age 47.5 years) appeared to be healthier than United States counterpart, in terms of DM and CKD risks and associated factors, with exceptions of undiagnosed DM, prediabetes and prehypertension. Models performed well in own country and external populations regarding predictor-outcome association and discrimination. Risk tests (high vs. low) showed area under the curve >0.75, sensitivity >84%, specificity >45%, positive predictive value >8%, and negative predictive value >99%. Discrimination was better for DM, compared to the combined outcome of DM and prediabetes, and excellent for CKD due to age.
Conclusion
Four easy-to-use screening scores for DM and CKD are well-validated in contemporary United States and Korean populations. Prevention of DM and CKD may serve as first-step in public health, with these self-assessment tools as basic tools to help health education and disparity.

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Review

Cardiovascular Risk/Epidemiology
Management of Cardiovascular Risk in Perimenopausal Women with Diabetes: Catherine Kim; Diabetes Metab J. 2021;45(4):492-501. Published online July 30, 2021; DOI: https://doi.org/10.4093/dmj.2020.0262

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Cardiovascular disease is the primary cause of mortality in women and men with diabetes. Due to age and worsening of risk factors over the menopausal transition, risk of coronary heart disease events increases in postmenopausal women with diabetes. Randomized studies have conflicted regarding the beneficial impact of estrogen therapy upon intermediate cardiovascular disease markers and events. Therefore, estrogen therapy is not currently recommended for indications other than symptom management. However, for women at low risk of adverse events, estrogen therapy can be used to minimize menopausal symptoms. The risk of adverse events can be estimated using risk engines for the calculation of cardiovascular risk and breast cancer risk in conjunction with screening tools such as mammography. Use of estrogen therapy, statins, and anti-platelet agents can be guided by such calculators particularly for younger women with diabetes. Risk management remains focused upon lifestyle behaviors and achieving optimal levels of cardiovascular risk factors, including lipids, glucose, and blood pressure. Use of pharmacologic therapies to address these risk factors, particularly specific hypoglycemic agents, may provide some additional benefit for risk prevention. The minimal benefit for women with limited life expectancy and risk of complications with intensive therapy should also be considered.

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Original Article

Complications
Screening Tools Based on Nomogram for Diabetic Kidney Diseases in Chinese Type 2 Diabetes Mellitus Patients: Ganyi Wang, Biyao Wang, Gaoxing Qiao, Hao Lou, Fei Xu, Zhan Chen, Shiwei Chen; Diabetes Metab J. 2021;45(5):708-718. Published online April 13, 2021; DOI: https://doi.org/10.4093/dmj.2020.0117

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Background
The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM.
Methods
A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools.
Results
Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ²=3.2756, P=0.9159), LBM1 (χ²=7.749, P=0.4584), LBM2 (χ²=10.023, P=0.2634), and simplified model (χ²=12.294, P=0.1387).
Conclusion
LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.

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