Diabetes & Metabolism Journal

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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Abstract PDF PubReader 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.

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; 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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Abstract PDF Supplementary Material PubReader 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.

Review

Others
Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus: Ying-Guat Ooi, Tharsini Sarvanandan, Nicholas Ken Yoong Hee, Quan-Hziung Lim, Sharmila S. Paramasivam, Jeyakantha Ratnasingam, Shireene R. Vethakkan, Soo-Kun Lim, Lee-Ling Lim; Diabetes Metab J. 2024;48(2):196-207. Published online January 26, 2024; DOI: https://doi.org/10.4093/dmj.2023.0244

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Abstract PDF Supplementary Material PubReader ePub: People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.

Original Articles

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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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
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.2024;[Epub] 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.

Review

Complications
Dyslipidemia in Patients with Chronic Kidney Disease: An Updated Overview: Sang Heon Suh, Soo Wan Kim; Diabetes Metab J. 2023;47(5):612-629. Published online July 24, 2023; DOI: https://doi.org/10.4093/dmj.2023.0067

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Dyslipidemia is a potentially modifiable cardiovascular risk factor. Whereas the recommendations for the treatment target of dyslipidemia in the general population are being more and more rigorous, the 2013 Kidney Disease: Improving Global Outcomes clinical practice guideline for lipid management in chronic kidney disease (CKD) presented a relatively conservative approach with respect to the indication of lipid lowering therapy and therapeutic monitoring among the patients with CKD. This may be largely attributed to the lack of high-quality evidence derived from CKD population, among whom the overall feature of dyslipidemia is considerably distinctive to that of general population. In this review article, we cover the characteristic features of dyslipidemia and impact of dyslipidemia on cardiovascular outcomes in patients with CKD. We also review the current evidence on lipid lowering therapy to modify the risk of cardiovascular events in this population. We finally discuss the association between dyslipidemia and CKD progression and the potential strategy to delay the progression of CKD in relation to lipid lowering therapy.

Citations

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Statin Therapy and Lipid Indices in Chronic Kidney Disease: A Systematic Review and Meta-analysis of Randomized Control Trials
Jafar Karami, Bahman Razi, Danyal Imani, Saeed Aslani, Mahdi Pakjoo, Mahdieh Fasihi, Keyhan Mohammadi, Amirhossein Sahebkar
Current Pharmaceutical Design.2024; 30(5): 362. CrossRef
Lipoprotein glomerulopathy with markedly increased arterial stiffness successfully treated with a combination of fenofibrate and losartan: a case report
Junichiro Kato, Hideo Okonogi, Go Kanzaki, Haruki Katsumata, Yasuyuki Nakada, Makoto Sagasaki, Kazumasa Komine, Kenji Ito, Takao Saito, Akira Matsunaga, Koh Tokutou, Kazuho Honda, Nobuo Tsuboi, Takashi Yokoo
BMC Nephrology.2024;[Epub] CrossRef
Atherogenic index of plasma: a new indicator for assessing the short-term mortality of patients with acute decompensated heart failure
Meng Yu, Hongyi Yang, Maobin Kuang, Jiajun Qiu, Changhui Yu, Guobo Xie, Guotai Sheng, Yang Zou
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Rabab Mahmoud Ahmed, Nehal Kamal Rakha, Ahmed Yousry, Amin Roshdy Soliman
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Original Articles

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.

Citations

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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; : jech-2023-221882. 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
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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.

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Triglyceride-glucose index predicts type 2 diabetes mellitus more effectively than oral glucose tolerance test-derived insulin sensitivity and secretion markers
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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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Cardiovascular Prevention and Pharmacotherapy.2024; 6(1): 33. CrossRef
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Journal of Korean Gerontological Nursing.2024; 26(1): 31. CrossRef
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Journal of Personalized Medicine.2024; 14(3): 280. CrossRef
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Cancer Medicine.2024;[Epub] CrossRef
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Triglyceride-glucose index predicts type 2 diabetes mellitus more effectively than oral glucose tolerance test-derived insulin sensitivity and secretion markers
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Diabetes Research and Clinical Practice.2024; 210: 111640. CrossRef
Real-World Outcomes of Individualized Targeted Therapy with Insulin Glargine 300 Units/mL in Insulin-Naïve Korean People with Type 2 Diabetes: TOBE Study
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Nutrition Research and Practice.2024; 18(2): 239. CrossRef
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The Journal of Korean Diabetes.2024; 25(1): 4. CrossRef
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Diabetes, Obesity and Metabolism.2024; 26(9): 3642. 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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Incheol Seo, Jin-Mo Park
Neurological Sciences.2024; 45(9): 4573. CrossRef
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Kyuho Kim, Jae-Seung Yun, Joonyub Lee, Yeoree Yang, Minhan Lee, Yu-Bae Ahn, Jae Hyoung Cho, Seung-Hyun Ko
Endocrinology and Metabolism.2024; 39(2): 344. CrossRef
Impact of electronic cigarette use on the increased risk of diabetes: the Korean Community Health Survey
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Epidemiology and Health.2024; : e2024029. CrossRef
Evolution and global research trends of immunity in diabetic nephropathy: a bibliometric and visual analysis from 2004 to 2023
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International Urology and Nephrology.2024;[Epub] CrossRef
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Endocrine.2024;[Epub] CrossRef
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Korean Journal of Family Practice.2024; 14(2): 105. CrossRef
Impact of Antidiabetic Drugs on Clinical Outcomes of COVID-19: A Nationwide Population-Based Study
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Endocrinology and Metabolism.2024; 39(3): 479. CrossRef
Association Between Benzene and Other Volatile Organic Compounds Exposure and Diabetes Mellitus Among Korean Adults: Findings from the Nationwide Biomonitoring Data
Seong-Uk Baek, Minseo Choi, Yu-Min Lee, Jin-Ha Yoon
Exposure and Health.2024;[Epub] CrossRef
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Diabetes and Heart Failure: A Literature Review, Reflection and Outlook
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Diabetes & Metabolism Journal.2024; 48(4): 518. CrossRef
Association of ABO genetic Polymorphisms and Type 2 Diabetes Mellitus Susceptibility in the Korean Population
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Biomedical Science Letters.2024; 30(2): 65. CrossRef
Impact of Education as a Social Determinant on the Risk of Type 2 Diabetes Mellitus in Korean Adults
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Healthcare.2024; 12(14): 1446. 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
Transcriptional regulation of multi-tissue mRNA expression by L-arabinose mitigates metabolic disorders in leptin receptor-deficient mice
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2023 Clinical Practice Guidelines for Diabetes Management in Korea: Full Version Recommendation of the Korean Diabetes Association
Jun Sung Moon, Shinae Kang, Jong Han Choi, Kyung Ae Lee, Joon Ho Moon, Suk Chon, Dae Jung Kim, Hyun Jin Kim, Ji A Seo, Mee Kyoung Kim, Jeong Hyun Lim, Yoon Ju Song, Ye Seul Yang, Jae Hyeon Kim, You-Bin Lee, Junghyun Noh, Kyu Yeon Hur, Jong Suk Park, Sang
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Diabetes & Metabolism Journal.2024; 48(4): 709. CrossRef
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Diabetes & Metabolism Journal.2024; 48(4): 531. CrossRef
Efficacy of Daily Walking as a Potential Predictor of Improved Health-Related Quality of Life in Patients with Type 2 Diabetes in Korea
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Prevalence, Awareness, Treatment, and Control of Type 2 Diabetes in South Korea (1998 to 2022): Nationwide Cross-Sectional Study
Wonwoo Jang, Seokjun Kim, Yejun Son, Soeun Kim, Hyeon Jin Kim, Hyesu Jo, Jaeyu Park, Kyeongmin Lee, Hayeon Lee, Mark A Tully, Masoud Rahmati, Lee Smith, Jiseung Kang, Selin Woo, Sunyoung Kim, Jiyoung Hwang, Sang Youl Rhee, Dong Keon Yon
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Heashoon Lee
Journal of Korean Gerontological Nursing.2024; 26(3): 257. CrossRef
Financial Benefits of Renal Dose-Adjusted Dipeptidyl Peptidase-4 Inhibitors for Patients with Type 2 Diabetes and Chronic Kidney Disease
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Endocrinology and Metabolism.2024; 39(4): 622. CrossRef
Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration
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Applied Sciences.2024; 14(17): 7480. CrossRef
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Endocrinology and Metabolism.2023; 38(4): 426. CrossRef
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The Korean Journal of Internal Medicine.2023; 38(1): 93. CrossRef
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International Journal of Infectious Diseases.2023; 127: 1. CrossRef
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The Journal of Clinical Endocrinology & Metabolism.2023; 108(4): e58. CrossRef
Lipid Management in Korean People With Type 2 Diabetes Mellitus: Korean Diabetes Association and Korean Society of Lipid and Atherosclerosis Consensus Statement
Ye Seul Yang, Hack-Lyoung Kim, Sang-Hyun Kim, Min Kyong Moon
Journal of Lipid and Atherosclerosis.2023; 12(1): 12. CrossRef
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Diabetes & Metabolism Journal.2023; 47(1): 1. CrossRef
Evaluation and Management of Patients with Diabetes and Heart Failure: A Korean Diabetes Association and Korean Society of Heart Failure Consensus Statement
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Acta Diabetologica.2023; 60(5): 655. CrossRef
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Electronic Journal of General Medicine.2023; 20(3): em477. CrossRef
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BMJ Open.2023; 13(3): e069642. CrossRef
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Korean Journal of Community Nutrition.2023; 28(1): 38. CrossRef
Insulin Fact Sheet in Type 1 and 2 Diabetes Mellitus and Trends of Antidiabetic Medication Use in Insulin Users with Type 2 Diabetes Mellitus: 2002 to 2019
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Diabetes & Metabolism Journal.2023; 47(2): 211. CrossRef
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Diabetes & Metabolism Journal.2023; 47(2): 201. CrossRef
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Translational and Clinical Pharmacology.2023; 31(1): 59. CrossRef
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Diabetes, Obesity and Metabolism.2023; 25(7): 1922. CrossRef
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The Journal of Korean Diabetes.2023; 24(1): 29. CrossRef
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The Journal of Korean Diabetes.2023; 24(1): 1. CrossRef
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Journal of Clinical Medicine.2023; 12(8): 2899. CrossRef
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Journal of Korean Medical Science.2023;[Epub] CrossRef
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Nutrients.2023; 15(10): 2248. CrossRef
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Diabetes & Metabolism Journal.2023; 47(3): 307. CrossRef
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Diabetes & Metabolism Journal.2023; 47(3): 347. CrossRef
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Journal of Preventive Medicine and Public Health.2023; 56(3): 248. CrossRef
Development and Validation of Multiple Equations for Low-Density Lipoprotein and Apolipoprotein B in Korean Patients Visiting Local Clinics and Hospitals
Rihwa Choi, Sang Gon Lee, Eun Hee Lee
Nutrients.2023; 15(12): 2786. CrossRef
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Frontiers in Cardiovascular Medicine.2023;[Epub] CrossRef
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BMC Nursing.2023;[Epub] CrossRef
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Diabetes Research and Clinical Practice.2023; 203: 110820. CrossRef
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The Journal of Korean Diabetes.2023; 24(2): 59. CrossRef
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Journal of the Korean Medical Association.2023; 66(7): 421. CrossRef
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Journal of the Korean Medical Association.2023; 66(7): 404. CrossRef
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Diabetes Research and Clinical Practice.2023; 203: 110866. CrossRef
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Diabetes Research and Clinical Practice.2023; 203: 110864. CrossRef
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Journal of the American Heart Association.2023;[Epub] CrossRef
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Scientific Reports.2023;[Epub] CrossRef
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Ji Hye Huh, Serim Kwon, Gui Ok Kim, Bo Yeon Kim, Kyoung Hwa Ha, Dae Jung Kim
Diabetes Care.2023; 46(9): 1700. CrossRef
Refined Diagnostic Protocol for Diabetic Polyneuropathy: Paving the Way for Timely Detection
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Annals of Rehabilitation Medicine.2023; 47(4): 234. CrossRef
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Journal of Nutrition and Health.2023; 56(4): 377. CrossRef
2023 Clinical Practice Guidelines for Diabetes Mellitus of the Korean Diabetes Association
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Diabetes & Metabolism Journal.2023; 47(5): 643. CrossRef
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The Journal of Korean Diabetes.2023; 24(3): 111. CrossRef
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The Journal of Korean Diabetes.2023; 24(3): 120. CrossRef
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Hepatology Communications.2023;[Epub] CrossRef
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Endocrinology and Metabolism.2023; 38(5): 525. CrossRef
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Journal of the American College of Surgeons.2023; 237(6): 902. CrossRef
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Diabetes & Metabolism Journal.2023; 47(6): 808. CrossRef
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Journal of Metabolic and Bariatric Surgery.2023; 12(2): 26. CrossRef
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The Journal of Korean Diabetes.2023; 24(4): 173. CrossRef
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Cardiovascular Diabetology.2022;[Epub] CrossRef
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Cardiovascular Prevention and Pharmacotherapy.2022; 4(3): 106. CrossRef
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Diabetes & Metabolism Journal.2022; 46(4): 552. CrossRef
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Public Health Weekly Report.2022; 15(35): 2474. CrossRef
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International Journal of Environmental Research and Public Health.2022; 19(18): 11832. CrossRef
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Journal of Korean Medical Science.2022;[Epub] CrossRef
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Diabetes & Metabolism Journal.2022; 46(5): 667. CrossRef
Association Between DPP4 Inhibitor Use and the Incidence of Cirrhosis, ESRD, and Some Cancers in Patients With Diabetes
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Gi Yeon Lee
The Journal of Korean Diabetes.2022; 23(3): 201. CrossRef
Recent Updates on Phytoconstituent Alpha-Glucosidase Inhibitors: An Approach towards the Treatment of Type Two Diabetes
Hamdy Kashtoh, Kwang-Hyun Baek
Plants.2022; 11(20): 2722. CrossRef
Epidemiological characteristics of diabetes mellitus in Korea
Soon Young Lee
Journal of the Korean Medical Association.2022; 65(10): 640. CrossRef
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
Diabetes & Metabolism Journal.2022; 46(6): 819. CrossRef
Oldies but Goodies: Thiazolidinedione as an Insulin Sensitizer with Cardioprotection
Eun-Hee Cho
Diabetes & Metabolism Journal.2022; 46(6): 827. CrossRef
The Mediating Effect of Acceptance Action in the Relationship between Diabetes Distress and Self-stigma among Old Adults with Diabetes in South Korea
Hyesun Kim, Kawoun Seo
Journal of Korean Academy of Community Health Nursing.2022; 33(4): 446. CrossRef

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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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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Serum thrombospondin‐2 level changes with liver stiffness improvement in patients with type 2 diabetes
Jimmy Ho Cheung Mak, David Tak‐Wai Lui, Carol Ho‐Yi Fong, Chloe Yu‐Yan Cheung, Ying Wong, Alan Chun‐Hong Lee, Ruby Lai‐Chong Hoo, Aimin Xu, Kathryn Choon‐Beng Tan, Karen Siu‐Ling Lam, Chi‐Ho Lee
Clinical Endocrinology.2024; 100(3): 230. CrossRef
SGLT-2 inhibitors as novel treatments of multiple organ fibrosis
Junpei Hu, Jianhui Teng, Shan Hui, Lihui Liang
Heliyon.2024; 10(8): e29486. CrossRef
Innovations and applications of ketone body monitoring in diabetes care
Naoki Sakane
Diabetology International.2024; 15(3): 370. CrossRef
Effect of dapagliflozin on readmission and loop diuretics use in patients with acute heart failure: a retrospective propensity score-matched cohort study
Dong Wu, Zhen Ma, Xiaoying Wang, Xiaowu Wang, Xiaojuan Wang
BMC Cardiovascular Disorders.2024;[Epub] CrossRef
Effect of sodium-glucose cotransporter protein-2 inhibitors on left ventricular hypertrophy in patients with type 2 diabetes: A systematic review and meta-analysis
Yao Wang, Yujie Zhong, Zhehao Zhang, Shuhao Yang, Qianying Zhang, Bingyang Chu, Xulin Hu
Frontiers in Endocrinology.2023;[Epub] CrossRef
Effects of SGLT2 inhibitors on hepatic fibrosis and steatosis: A systematic review and meta-analysis
Peipei Zhou, Ying Tan, Zhenning Hao, Weilong Xu, Xiqiao Zhou, Jiangyi Yu
Frontiers in Endocrinology.2023;[Epub] CrossRef
The impact of sodium-glucose Cotransporter-2 inhibitors on lipid profile: A meta-analysis of 28 randomized controlled trials
Gang Fan, Dian long Guo, Hong Zuo
European Journal of Pharmacology.2023; 959: 176087. CrossRef

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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Glycated hemoglobin, type 2 diabetes, and poor diabetes control are positively associated with impulsivity changes in aged individuals with overweight or obesity and metabolic syndrome
Carlos Gómez‐Martínez, Nancy Babio, Lucía Camacho‐Barcia, Jordi Júlvez, Stephanie K. Nishi, Zenaida Vázquez, Laura Forcano, Andrea Álvarez‐Sala, Aida Cuenca‐Royo, Rafael de la Torre, Marta Fanlo‐Maresma, Susanna Tello, Dolores Corella, Alejandro Arias Vás
Annals of the New York Academy of Sciences.2024;[Epub] CrossRef
The usefulness of an intervention with a serious video game as a complementary approach to cognitive behavioural therapy in eating disorders: A pilot randomized clinical trial for impulsivity management
Cristina Vintró‐Alcaraz, Núria Mallorquí‐Bagué, María Lozano‐Madrid, Giulia Testa, Roser Granero, Isabel Sánchez, Janet Treasure, Susana Jiménez‐Murcia, Fernando Fernández‐Aranda
European Eating Disorders Review.2023; 31(6): 781. CrossRef
Adaptations of the balloon analog risk task for neuroimaging settings: a systematic review
Charline Compagne, Juliana Teti Mayer, Damien Gabriel, Alexandre Comte, Eloi Magnin, Djamila Bennabi, Thomas Tannou
Frontiers in Neuroscience.2023;[Epub] CrossRef
Trust-based health decision-making recruits the neural interoceptive saliency network which relates to temporal trajectories of Hemoglobin A1C in Diabetes Type 1
Helena Jorge, Isabel C. Duarte, Miguel Melo, Ana Paula Relvas, Miguel Castelo-Branco
Brain Imaging and Behavior.2023; 18(1): 171. CrossRef

Others
Development of Various Diabetes Prediction Models Using Machine Learning Techniques: Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim; Diabetes Metab J. 2022;46(4):650-657. Published online March 11, 2022; DOI: https://doi.org/10.4093/dmj.2021.0115

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Background
There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.
Methods
Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.
Results
The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.
Conclusion
We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

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Predictive modeling for the development of diabetes mellitus using key factors in various machine learning approaches
Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Kenichi Kato, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi
Diabetes Epidemiology and Management.2024; 13: 100191. CrossRef
Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data
Hye Ah Lee, Hyesook Park, Young Sun Hong
Journal of Korean Medical Science.2024;[Epub] CrossRef
Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques
Rishita Konda, Anuraag Ramineni, Jayashree J, Niharika Singavajhala, Sai Akshaj Vanka
EAI Endorsed Transactions on Pervasive Health and Technology.2024;[Epub] CrossRef
The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use
Ji-Won Chun, Hun-Sung Kim
Journal of Korean Medical Science.2023;[Epub] CrossRef
Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features
Nikita Jain, Bhaumik Patel, Manjesh Hanawal, Anurag R. Lila, Saba Memon, Tushar Bandgar, Ashutosh Kumar
Metabolomics.2023;[Epub] CrossRef
Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid‐stimulating hormone level
Hyunah Kim, Da Young Jung, Seung‐Hwan Lee, Jae‐Hyoung Cho, Hyeon Woo Yim, Hun‐Sung Kim
Journal of Diabetes.2022; 14(9): 620. CrossRef
Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
Journal of Personalized Medicine.2022; 12(11): 1899. CrossRef

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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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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Heterogeneity in familial clustering of metabolic syndrome components in the multiethnic GENNID study
Jia Y. Wan, Deborah Goodman, Sukh Makhnoon, Trina M. Norden‐Krichmar, Baolin Wu, Karen L. Edwards
Obesity.2024; 32(1): 176. CrossRef
Identification of shared genetic risks underlying metabolic syndrome and its related traits in the Korean population
Jun Young Kim, Yoon Shin Cho
Frontiers in Genetics.2024;[Epub] CrossRef
Metabolic health is more strongly associated with the severity and mortality of coronavirus disease 2019 than obesity
Hye Yeon Koo, Jae-Ryun Lee, Jin Yong Lee, Hyejin Lee
Archives of Public Health.2024;[Epub] CrossRef
Associated Factors with Changes of Metabolic Abnormalities among General Population in COVID-19 Pandemic
Eunjoo Kwon, Eun-Hee Nah, Suyoung Kim, Seon Cho, Hyeran Park
Korean Journal of Health Promotion.2023; 23(2): 55. CrossRef
Association between metabolic syndrome and mortality in patients with COVID-19: A nationwide cohort study
Hyo Jin Park, Jin-Hyung Jung, Kyungdo Han, Jean Shin, Yoojeong Lee, Yujin Chang, Kyeyeung Park, Yoon Jeong Cho, Youn Seon Choi, Seon Mee Kim, Ga Eun Nam
Obesity Research & Clinical Practice.2022; 16(6): 484. CrossRef

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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A meta‐analysis of diabetes risk prediction models applied to prediabetes screening
Yujin Liu, Sunrui Yu, Wenming Feng, Hangfeng Mo, Yuting Hua, Mei Zhang, Zhichao Zhu, Xiaoping Zhang, Zhen Wu, Lanzhen Zheng, Xiaoqiu Wu, Jiantong Shen, Wei Qiu, Jianlin Lou
Diabetes, Obesity and Metabolism.2024; 26(5): 1593. CrossRef
Performance Analysis and Assessment of Type 2 Diabetes Screening Scores in Patients with Non-Alcoholic Fatty Liver Disease
Norma Latif Fitriyani, Muhammad Syafrudin, Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani, Chuan-Kai Yang, Jongtae Rhee, Muhammad Anshari
Mathematics.2023; 11(10): 2266. CrossRef
A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction
Norma Latif Fitriyani, Muhammad Syafrudin, Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani, Muhammad Anshari
Mathematics.2022; 10(21): 4027. CrossRef

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