Diabetes & Metabolism Journal

Review

Drug/Regimen
Benefit and Safety of Sodium-Glucose Co-Transporter 2 Inhibitors in Older Patients with Type 2 Diabetes Mellitus: Ja Young Jeon, Dae Jung Kim; Diabetes Metab J. 2024;48(5):837-846. Published online September 12, 2024; DOI: https://doi.org/10.4093/dmj.2024.0317

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People with type 2 diabetes mellitus (T2DM) are at higher risk of developing cardiovascular disease, heart failure, chronic kidney disease, and premature death than people without diabetes. Therefore, treatment of diabetes aims to reduce these complications. Sodium-glucose co-transporter 2 (SGLT2) inhibitors have shown beneficial effects on cardiorenal and metabolic health beyond glucose control, making them a promising class of drugs for achieving the ultimate goals of diabetes treatment. However, despite their proven benefits, the use of SGLT2 inhibitors in eligible patients with T2DM remains suboptimal due to reports of adverse events. The use of SGLT2 inhibitors is particularly limited in older patients with T2DM because of the lack of treatment experience and insufficient long-term safety data. This article comprehensively reviews the risk-benefit profile of SGLT2 inhibitors in older patients with T2DM, drawing on data from prospective randomized controlled trials of cardiorenal outcomes, original studies, subgroup analyses across different age groups, and observational cohort studies.

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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.2024;[Epub] CrossRef

Original Article

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.

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

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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Abstract PDF Supplementary Material PubReader ePub

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.

Citations

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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
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Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm
Maryam Talebi Moghaddam, Yones Jahani, Zahra Arefzadeh, Azizallah Dehghan, Mohsen Khaleghi, Mehdi Sharafi, Ghasem Nikfar
BMC Medical Research Methodology.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
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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

Cardiovascular Risk/Epidemiology
Validation of Risk Prediction Models for Atherosclerotic Cardiovascular Disease in a Prospective Korean Community-Based Cohort: Jae Hyun Bae, Min Kyong Moon, Sohee Oh, Bo Kyung Koo, Nam Han Cho, Moon-Kyu Lee; Diabetes Metab J. 2020;44(3):458-469. Published online January 13, 2020; DOI: https://doi.org/10.4093/dmj.2019.0061

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Abstract PDF Supplementary Material PubReader

Background

To investigate the performance of the 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) in a large, prospective, community-based cohort in Korea and to compare it with that of the Framingham Global Cardiovascular Disease Risk Score (FRS-CVD) and the Korean Risk Prediction Model (KRPM).

Methods

In the Korean Genome and Epidemiology Study (KOGES)-Ansan and Ansung study, we evaluated calibration and discrimination of the PCE for non-Hispanic whites (PCE-WH) and for African Americans (PCE-AA) and compared their predictive abilities with the FRS-CVD and the KRPM.

Results

The present study included 7,932 individuals (3,778 men and 4,154 women). The PCE-WH and PCE-AA moderately overestimated the risk of atherosclerotic cardiovascular disease (ASCVD) for men (6% and 13%, respectively) but underestimated the risk for women (−49% and −25%, respectively). The FRS-CVD overestimated ASCVD risk for men (91%) but provided a good risk prediction for women (3%). The KRPM underestimated ASCVD risk for men (−31%) and women (−31%). All the risk prediction models showed good discrimination in both men (C-statistic 0.730 to 0.735) and women (C-statistic 0.726 to 0.732). Recalibration of the PCE using data from the KOGES-Ansan and Ansung study substantially improved the predictive accuracy in men.

Conclusion

In the KOGES-Ansan and Ansung study, the PCE overestimated ASCVD risk for men and underestimated the risk for women. The PCE-WH and the FRS-CVD provided an accurate prediction of ASCVD in men and women, respectively.

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Risk Factors for Infertility in Korean Women
Juyeon Lee, Chang-Woo Choo, Kyoung Yong Moon, Sang Woo Lyu, Hoon Kim, Joong Yeup Lee, Jung Ryeol Lee, Byung Chul Jee, Kyungjoo Hwang, Seok Hyun Kim, Sue K. Park
Journal of Korean Medical Science.2024;[Epub] CrossRef
Evaluating cardiovascular disease risk stratification using multiple-polygenic risk scores and pooled cohort equations: insights from a 17-year longitudinal Korean cohort study
Yi Seul Park, Hye-Mi Jang, Ji Hye Park, Bong-Jo Kim, Hyun-Young Park, Young Jin Kim
Frontiers in Genetics.2024;[Epub] CrossRef
Predictability of Cardiovascular Risk Scores for Carotid Atherosclerosis in Community-Dwelling Middle-Aged and Elderly Adults
Chao-Liang Chou, Chun-Chieh Liu, Tzu-Wei Wu, Chun-Fang Cheng, Shu-Xin Lu, Yih-Jer Wu, Li-Yu Wang
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F. O. Ushanova, T. Yu. Demidova, T. N. Korotkova
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Sangwoo Park, Yong-Giun Kim, Soe Hee Ann, Young-Rak Cho, Shin-Jae Kim, Seungbong Han, Gyung-Min Park
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Triglyceride-Glucose Index Predicts Future Atherosclerotic Cardiovascular Diseases: A 16-Year Follow-up in a Prospective, Community-Dwelling Cohort Study
Joon Ho Moon, Yongkang Kim, Tae Jung Oh, Jae Hoon Moon, Soo Heon Kwak, Kyong Soo Park, Hak Chul Jang, Sung Hee Choi, Nam H. Cho
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Mahin Nomali, Davood Khalili, Mehdi Yaseri, Mohammad Ali Mansournia, Aryan Ayati, Hossein Navid, Saharnaz Nedjat, Hean Teik Ong
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Kyung-Soo Kim, Sangmo Hong, Kyungdo Han, Cheol-Young Park
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Kayoung Lee
Metabolic Syndrome and Related Disorders.2022; 20(6): 344. CrossRef
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Xiao-Ying Li, Li Li, Sang-Hoon Na, Francesca Santilli, Zhongwei Shi, Michael Blaha
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Hyejin Chun, Jongchul Oh, Miae Doo
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Management of Cardiovascular Risk in Perimenopausal Women with Diabetes
Catherine Kim
Diabetes & Metabolism Journal.2021; 45(4): 492. CrossRef
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Alessandra M. Campos-Staffico, David Cordwin, Venkatesh L. Murthy, Michael P. Dorsch, Jasmine A. Luzum
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Epidemiology
Development and Validation of the Korean Diabetes Risk Score: A 10-Year National Cohort Study: Kyoung Hwa Ha, Yong-ho Lee, Sun Ok Song, Jae-woo Lee, Dong Wook Kim, Kyung-hee Cho, Dae Jung Kim; Diabetes Metab J. 2018;42(5):402-414. Published online July 6, 2018; DOI: https://doi.org/10.4093/dmj.2018.0014

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Abstract PDF Supplementary Material PubReader

Background

A diabetes risk score in Korean adults was developed and validated.

Methods

This study used the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) of 359,349 people without diabetes at baseline to derive an equation for predicting the risk of developing diabetes, using Cox proportional hazards regression models. External validation was conducted using data from the Korean Genome and Epidemiology Study. Calibration and discrimination analyses were performed separately for men and women in the development and validation datasets.

Results

During a median follow-up of 10.8 years, 37,678 cases (event rate=10.4 per 1,000 person-years) of diabetes were identified in the development cohort. The risk score included age, family history of diabetes, alcohol intake (only in men), smoking status, physical activity, use of antihypertensive therapy, use of statin therapy, body mass index, systolic blood pressure, total cholesterol, fasting glucose, and γ glutamyl transferase (only in women). The C-statistics for the models for risk at 10 years were 0.71 (95% confidence interval [CI], 0.70 to 0.73) for the men and 0.76 (95% CI, 0.75 to 0.78) for the women in the development dataset. In the validation dataset, the C-statistics were 0.63 (95% CI, 0.53 to 0.73) for men and 0.66 (95% CI, 0.55 to 0.76) for women.

Conclusion

The Korean Diabetes Risk Score may identify people at high risk of developing diabetes and may be an effective tool for delaying or preventing the onset of condition as risk management strategies involving modifiable risk factors can be recommended to those identified as at high risk.

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Review

Clinical Care/Education
A Clinical Practice Guideline to Guide a System Approach to Diabetes Care in Hong Kong: Ip Tim Lau; Diabetes Metab J. 2017;41(2):81-88. Published online April 14, 2017; DOI: https://doi.org/10.4093/dmj.2017.41.2.81

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Abstract PDF PubReader

The Hospital Authority of Hong Kong is a statutory body that manages all the public medical care institutions in Hong Kong. There are currently around 400,000 diabetic patients under its care at 17 hospitals (providing secondary care for 40%) and 73 General Outpatient Clinics (providing primary care for 60%). The patient population has been growing at 6% to 8% per year over the past 5 years, estimated to include over 95% of all diagnosed patients in Hong Kong. In order to provide equitable and a minimal level of care within resources and local system factors constraints, a Clinical Practice Guideline on the management of type 2 diabetes mellitus was drawn in 2013 to guide a system approach to providing diabetes care. There is an algorithm for the use of various hypoglycemic agents. An organizational drug formulary governs that less expansive options have to be used first. A number of clinical care and patient empowerment programs have been set up to support structured and systematic diabetes care. With such a system approach, there have been overall improvements in diabetes care with the percentage of patients with glycosylated hemoglobin <7% rising from 40% in 2010 to 52% in 2015.

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Original Articles

Agreement between Framingham Risk Score and United Kingdom Prospective Diabetes Study Risk Engine in Identifying High Coronary Heart Disease Risk in North Indian Population: Dipika Bansal, Ramya S. R. Nayakallu, Kapil Gudala, Rajavikram Vyamasuni, Anil Bhansali; Diabetes Metab J. 2015;39(4):321-327. Published online July 8, 2015; DOI: https://doi.org/10.4093/dmj.2015.39.4.321

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33 Download
11 Web of Science
12 Crossref

Abstract PDF PubReader

Background

The aim of the study is to evaluate the concurrence between Framingham Risk score (FRS) and United Kingdom Prospective Diabetes Study (UKPDS) risk engine in identifying coronary heart disease (CHD) risk in newly detected diabetes mellitus patients and to explore the characteristics associated with the discrepancy between them.

Methods

A cross-sectional study involving 489 subjects newly diagnosed with type 2 diabetes mellitus was conducted. Agreement between FRS and UKPDS in classifying patients as high risk was calculated using kappa statistic. Subjects with discrepant scores between two algorithms were identified and associated variables were determined.

Results

The FRS identified 20.9% subjects (range, 17.5 to 24.7) as high-risk while UKPDS identified 21.75% (range, 18.3 to 25.5) as high-risk. Discrepancy was observed in 17.9% (range, 14.7 to 21.7) subjects. About 9.4% had high risk by UKPDS but not FRS, and 8.6% had high risk by FRS but not UKPDS. The best agreement was observed at high-risk threshold of 20% for both (κ=0.463). Analysis showed that subjects having high risk on FRS but not UKPDS were elderly females having raised systolic and diastolic blood pressure. Patients with high risk on UKPDS but not FRS were males and have high glycosylated hemoglobin.

Conclusion

The FRS and UKPDS (threshold 20%) identified different populations as being at high risk, though the agreement between them was fairly good. The concurrence of a number of factors (e.g., male sex, low high density lipoprotein cholesterol, and smoking) in both algorithms should be regarded as increasing the CHD risk. However, longitudinal follow-up is required to form firm conclusions.

Citations

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Metabolic Syndrome versus Framingham Risk Score for Association of Self-Reported Coronary Heart Disease: The 2005 Korean Health and Nutrition Examination Survey: Hye Mi Kang, Dong-Jun Kim; Diabetes Metab J. 2012;36(3):237-244. Published online June 14, 2012; DOI: https://doi.org/10.4093/dmj.2012.36.3.237

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31 Download
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Abstract PDF PubReader

Background

Several studies in Western populations have indicated that metabolic syndrome (MetS) is inferior to the Framingham risk score (FRS) in predicting coronary heart disease (CHD). However there has been no study about the predictability of MetS vs. FRS for CHD in Korea.

Methods

Among the 43,145 persons from the third Korea National Health and Nutrition Examination Survey in 2005, laboratory test and nutritional survey data from 5,271 persons were examined. Participants were also asked to recall a physician's diagnosis of CHD.

Results

The median age was 46 (range, 20 to 78) in men (n=2,257) and 44 (range, 20 to 78) years in women (n=3,014). Prevalence of self-reported CHD was 1.7% in men and 2.1% in women. Receiver operating characteristic curves and their respective area under the curve (AUC) were used to compare the ability of the FRS and the number of components of MetS to predict self-reported CHD in each sex. In men, AUC of FRS was significantly larger than that of MetS (0.767 [0.708 to 0.819] vs. 0.677 [0.541 to 0.713], P<0.01). In women, AUC of FRS was comparable to that of MetS (0.777 [0.728 to 0.826] vs. 0.733 [0.673 to 0.795]), and was not significant.

Conclusion

The data suggested that FRS was more closely associated with CHD compared to MetS in Korean men.

Citations

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