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Qi H, Zhang T, Hou L, Li Q, Huang R, Ma L. Research progress on risk prediction models for the diabetic foot. Acta Diabetol 2025:10.1007/s00592-025-02505-3. [PMID: 40252103 DOI: 10.1007/s00592-025-02505-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2024] [Accepted: 03/29/2025] [Indexed: 04/21/2025]
Abstract
OBJECTIVE This study aimed to comprehensively review the latest advancements in diabetic foot risk prediction models over the past four years to address the severe challenges posed by diabetic foot ulcers, which are among the leading causes of disability and mortality among diabetic patients. Diabetic foot ulcers are characterized by their complex aetiology, pose a grave threat to life and impose enormous social and economic burdens, thus becoming a critical issue in public health that urgently requires attention. By accurately predicting the risk of diabetic foot and implementing early intervention strategies, this study aimed to reduce its incidence and mortality rates. METHODS This study employed a systematic review and comprehensive analysis framework, conducted extensive searches of electronic databases (including PubMed, EMBASE, the Cochrane Library, CNKI, etc.) and supplemented these searches with manual literature collection to ensure comprehensive information coverage. During the literature screening and evaluation phase, strict adherence to the predetermined inclusion and exclusion criteria was maintained to guarantee the high quality of the included studies. Further detailed quality assessments, data extraction, and analysis of the selected literature were conducted, with a focus on exploring the construction strategies of risk prediction models, the selection of key variables, the evaluation indicators of model performance, and the validation methods. RESULTS By comparing and analysing the differences among studies in terms of methodology, model effectiveness, and practical application potential, this study summarized the development trends of diabetic foot risk prediction models and anticipated future research directions. These findings indicate that with the assistance of advanced diabetic foot risk prediction models, potential risk factors can be identified and addressed early on, thereby effectively reducing the incidence of diabetic foot and significantly improving patients' quality of life. CONCLUSION This study revealed that diabetic foot risk prediction models have significant effects on accurately identifying risk factors and guiding early interventions, serving as effective tools to reduce the incidence of diabetic foot. Through early identification and intervention, the prognosis and quality of life of patients can be significantly improved, providing important references and guidance for the field of public health.
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Affiliation(s)
- Haixia Qi
- School of Nursing, Lanzhou University, Lanzhou, 730011, China
| | - Tao Zhang
- The 940th Hospital of Chinese People's Liberation Army, Lanzhou, 730050, China
| | - Lijie Hou
- Department of Endocrinology, The First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Qi Li
- Department of Neurology, The First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Ruiping Huang
- Department of Endocrinology, The First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Lihua Ma
- School of Nursing, Lanzhou University, Lanzhou, 730011, China.
- Department of Endocrinology, The First Hospital of Lanzhou University, Lanzhou, 730000, China.
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Sun CF, Lin YH, Ling GX, Gao HJ, Feng XZ, Sun CQ. Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements. World J Diabetes 2025; 16:101310. [PMID: 40236862 PMCID: PMC11947933 DOI: 10.4239/wjd.v16.i4.101310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Revised: 12/17/2024] [Accepted: 02/12/2025] [Indexed: 02/28/2025] Open
Abstract
BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy (DPN) is increasing, but few studies focus on the quality of the model and its practical application. AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN. METHODS A meticulous search was conducted in PubMed, EMBASE, Cochrane, CNKI, Wang Fang DATA, and VIP Database to identify studies published until October 2023. The included and excluded criteria were applied by the researchers to screen the literature. Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool. Disagreements were resolved through consultation with a third investigator. Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. Additionally, the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool. RESULTS The systematic review included 14 studies with a total of 26 models. The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938. All studies had high risks of bias, mainly due to participants, outcomes, and analysis. The most common predictors included glycated hemoglobin, age, duration of diabetes, lipid abnormalities, and fasting blood glucose. CONCLUSION The predictor model presented good differentiation, calibration, but there were significant methodological flaws and high risk of bias. Future studies should focus on improving the study design and study report, updating the model and verifying its adaptability and feasibility in clinical practice.
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Affiliation(s)
- Chao-Fan Sun
- Department of Endocrinology, Tsinghua University Yuquan Hospital, Beijing 100040, China
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yu-Han Lin
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Guo-Xing Ling
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Hui-Juan Gao
- Department of Endocrinology, Tsinghua University Yuquan Hospital, Beijing 100040, China
| | - Xing-Zhong Feng
- Department of Endocrinology, Tsinghua University Yuquan Hospital, Beijing 100040, China
| | - Chun-Quan Sun
- Office of Drug Clinical Trial Institution, Health Management Center (Preventive Treatment of Disease) Tsinghua University Yuquan Hospital, Beijing 100040, China
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Fu J, Deng T, Zheng T, Shi P, Zhu W, Tao M, Wen Z, Wu X. Development and Validation of a Predictive Nomogram for Myelosuppression Risk in Chronic Hepatitis B Patients Treated with Peginterferon. Infect Drug Resist 2025; 18:1793-1805. [PMID: 40225103 PMCID: PMC11994083 DOI: 10.2147/idr.s508538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Accepted: 03/28/2025] [Indexed: 04/15/2025] Open
Abstract
Purpose Peginterferon (Peg-IFN) is a common treatment for chronic hepatitis B (CHB); however, some patients developing myelosuppression as a side-effect. In this study, we identified risk factors associated with increased myelosuppression, and incorporated them into a predictive nomogram. Patients and Methods This study is designed as a case-control study. A total of 312 CHB patients treated with Peg-IFN from two medical centers were retrospectively enrolled between December 2019 and December 2022. Patients from the First Affiliated Hospital of Nanchang University were randomly divided into a training cohort (n=153) and a test cohort (n=55) at a 3:1 ratio. Patients from the Jiangxi Provincial People's Hospital composed the validation cohort (n= 104). In the training cohort, based on the blood routine results of patients 1 week after Peg-IFN treatment, patients were further divided into Normal (myelosuppression grades 0-I) and Myelosuppression (grades II-IV) groups. Then uni- and multivariate logistic regression analyses were carried out to identify myelosuppression risk factors, which were subsequently incorporated into a predictive nomogram. The capability of the predictive nomogram was validated using an area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA) were used to evaluate the nomogram. Finally, the developed predictive nomogram was validated both internally and externally using separate test and validation cohorts. Results Body mass index (BMI; odds ratio [OR]=0.841, 95% confidence interval [CI] 0.738-0.959, P=0.010), white blood cell counts (WBC; OR=0.657, 95% CI 0.497-0.868, P=0.003), globulin (GLB; OR=0.796, 95% CI 0.713-0.889, P<0.001) and serum creatinine levels (SCR; OR=1.029, 95% CI 1.002-1.058, P=0.038) are independent risk factors for myelosuppression in Peg-IFN-treated CHB patients. A predictive nomogram was constructed by incorporating the above independent risk factors, and its performance was assessed across the training, test, and validation cohorts. The model demonstrated AUC values of 0.824 (95% CI 0.757-0.891), 0.812 (95% CI 0.701-0.923), and 0.870 (95% CI 0.802-0.940), respectively, highlighting its good predictive accuracy. As for Hosmer-Lemeshow, it was P=0.351, (χ2= 8.898) for training, P=0.514 (χ2=6.226) for the test, and P=0.442 (χ2=7.918) for the validation cohort. The results of the calibration curves and DCA demonstrated good concordance between predicted probabilities and observed outcomes, with the model showing higher clinical net benefit. Conclusion Lower BMI, WBC counts, GLB, and higher SCR levels are independent risk factors for myelosuppression among Peg-IFN-treated CHB patients. The predictive nomogram, based on those factors, is able to identify high-risk individuals for myelosuppression, thereby aiding in early alleviation of this side-effect.
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Affiliation(s)
- Jiwei Fu
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
| | - Ting Deng
- Second Department of Cardiovascular Medicine, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, 330006, People’s Republic of China
| | - Ting Zheng
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
| | - Pei Shi
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
| | - Wentao Zhu
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
| | - Mengyu Tao
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
| | - Zhilong Wen
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
| | - Xiaoping Wu
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
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Liu L, Bi B, Gui M, Zhang L, Ju F, Wang X, Cao L. Development and internal validation of an interpretable risk prediction model for diabetic peripheral neuropathy in type 2 diabetes: a single-centre retrospective cohort study in China. BMJ Open 2025; 15:e092463. [PMID: 40180384 PMCID: PMC11969608 DOI: 10.1136/bmjopen-2024-092463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2024] [Accepted: 03/07/2025] [Indexed: 04/05/2025] Open
Abstract
OBJECTIVE Diabetic peripheral neuropathy (DPN) is a common and serious complication of diabetes, which can lead to foot deformity, ulceration, and even amputation. Early identification is crucial, as more than half of DPN patients are asymptomatic in the early stage. This study aimed to develop and validate multiple risk prediction models for DPN in patients with type 2 diabetes mellitus (T2DM) and to apply the Shapley Additive Explanation (SHAP) method to interpret the best-performing model and identify key risk factors for DPN. DESIGN A single-centre retrospective cohort study. SETTING The study was conducted at a tertiary teaching hospital in Hainan. PARTICIPANTS AND METHODS Data were retrospectively collected from the electronic medical records of patients with diabetes admitted between 1 January 2021 and 28 March 2023. After data preprocessing, 73 variables were retained for baseline analysis. Feature selection was performed using univariate analysis combined with recursive feature elimination (RFE). The dataset was split into training and test sets in an 8:2 ratio, with the training set balanced via the Synthetic Minority Over-sampling Technique. Six machine learning algorithms were applied to develop prediction models for DPN. Hyperparameters were optimised using grid search with 10-fold cross-validation. Model performance was assessed using various metrics on the test set, and the SHAP method was used to interpret the best-performing model. RESULTS The study included 3343 T2DM inpatients, with a median age of 60 years (IQR 53-69), and 88.6% (2962/3343) had DPN. The RFE method identified 12 key factors for model construction. Among the six models, XGBoost showed the best predictive performance, achieving an area under the curve of 0.960, accuracy of 0.927, precision of 0.969, recall of 0.948, F1-score of 0.958 and a G-mean of 0.850 on the test set. The SHAP analysis highlighted C reactive protein, total bile acids, gamma-glutamyl transpeptidase, age and lipoprotein(a) as the top five predictors of DPN. CONCLUSIONS The machine learning approach successfully established a DPN risk prediction model with excellent performance. The use of the interpretable SHAP method could enhance the model's clinical applicability.
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Affiliation(s)
- Lianhua Liu
- Department of Biostatistics, School of Public Health, Hainan Medical University, Haikou, Hainan, China
| | - Bo Bi
- Department of Biostatistics, School of Public Health, Hainan Medical University, Haikou, Hainan, China
| | - Mei Gui
- Department of Biostatistics, School of Public Health, Hainan Medical University, Haikou, Hainan, China
| | - Linli Zhang
- Department of Mathematics, Physics, and Chemistry teaching, Hainan University, Haikou, Hainan, China
| | - Feng Ju
- Department of Endocrinology, The Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Xiaodan Wang
- Department of Biostatistics, School of Public Health, Hainan Medical University, Haikou, Hainan, China
| | - Li Cao
- Department of Biostatistics, School of Public Health, Hainan Medical University, Haikou, Hainan, China
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Zhao Q, Wang J, Liu F, Jiang H, Ma Y. Early diagnosis and risk factors of diabetic peripheral neuropathy in type 1 diabetes: insights from current perception threshold testing. Front Endocrinol (Lausanne) 2025; 16:1496635. [PMID: 40230481 PMCID: PMC11994408 DOI: 10.3389/fendo.2025.1496635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2024] [Accepted: 03/05/2025] [Indexed: 04/16/2025] Open
Abstract
Objective This study investigates nerve fiber dysfunction in type 1 diabetes (T1D) patients and identifies risk factors for diabetic peripheral neuropathy (DPN). It evaluates the relationship between current perception threshold (CPT) tests and nerve conduction velocity (NCV), and assesses CPT's diagnostic accuracy for early DPN detection. Research design and methods This study enrolled 110 patients with T1D and 26 healthy controls between January 2020 and December 2021, in accordance with predefined inclusion/exclusion criteria. CPT testing at 2000 Hz, 250 Hz, and 5 Hz assessed Aβ, Aδ, and C fiber function, while NCV was measured in 47 patients. Subgroups were stratified by disease duration (>5 years vs ≤5 years). Multivariate logistic regression identified DPN risk factors, and CPT-NCV correlation was analyzed using Chi-square and Kappa tests. Receiver operating characteristic (ROC) curves evaluated CPT diagnostic efficacy. Results The incidence of DPN in 110 T1D patients was 78%, with no significant difference between disease duration subgroups (78.3% vs. 78.0%). Neurological abnormalities were significantly more common in the lower extremities compared to the upper extremities (67.27% vs. 49.09%, P < 0.05). Multivariate logistic regression analysis revealed that a waist-to-hip ratio (WHR) greater than 0.85 was an independent risk factor for DPN (OR = 3.01, 95% CI: 1.03-8.80, P < 0.05). Patients with a disease duration >5 years demonstrated significantly higher 2000Hz abnormality rates (68.09% vs. 46.15%, P < 0.05) and more severe neurological lesions (57.45% vs. 35.90%, P < 0.05). In contrast, those with disease duration ≤5 years exhibited elevated 5Hz abnormality rates (30.77% vs. 10.64%, P < 0.05) with predominantly milder lesions (56.41% vs. 31.91%, P < 0.05). Statistical analyses demonstrated a significant association between CPT and NCV (P<0.001), with moderate diagnostic consistency further supported by Cohen's Kappa Test (κ=0.515, P<0.001). ROC curve analysis demonstrated that CPT exhibited moderate diagnostic accuracy in detecting DPN at the 5Hz, with an area under the curve (AUC) of 0.723. Conclusions CPT showed moderate diagnostic accuracy for early unmyelinated (C) fibers detection, routine CPT screening in high-risk groups (central obesity/short disease duration) enables timely intervention to prevent irreversible damage.
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Affiliation(s)
- Qian Zhao
- Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Luoyang Key Laboratory of Clinical Multiomics and Translational Medicine, Henan Key Laboratory of Rare Diseases, Luoyang, China
| | - Jialin Wang
- Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Luoyang Key Laboratory of Clinical Multiomics and Translational Medicine, Henan Key Laboratory of Rare Diseases, Luoyang, China
| | - Fangfang Liu
- Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Luoyang Key Laboratory of Clinical Multiomics and Translational Medicine, Henan Key Laboratory of Rare Diseases, Luoyang, China
| | - Hongwei Jiang
- Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Luoyang Key Laboratory of Clinical Multiomics and Translational Medicine, Henan Key Laboratory of Rare Diseases, Luoyang, China
| | - Yujin Ma
- Endocrinology and Metabolism Center, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Luoyang Key Laboratory of Clinical Multiomics and Translational Medicine, Henan Key Laboratory of Rare Diseases, Luoyang, China
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Tian Z, Zhang J, Fan Y, Sun X, Wang D, Liu X, Lu G, Wang H. Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study. BMC Med Inform Decis Mak 2025; 25:90. [PMID: 39966886 PMCID: PMC11837659 DOI: 10.1186/s12911-025-02932-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2024] [Accepted: 02/11/2025] [Indexed: 02/20/2025] Open
Abstract
AIMS Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of DPN is essential for successful early intervention. Traditional Chinese medicine (TCM) tongue diagnosis, one of the four diagnostic methods, lacks specific algorithms for TCM symptoms and tongue features. This study aims to develop machine learning (ML) models based on TCM to predict the risk of diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM). METHODS A total of 4723 patients were included in the analysis (4430 with T2DM and 293 with DPN). TFDA-1 was used to obtain tongue images during a questionnaire survey. LASSO (least absolute shrinkage and selection operator) logistic regression model with fivefold cross-validation was used to select imaging features, which were then screened using best subset selection. The synthetic minority oversampling technique (SMOTE) algorithm was applied to address the class imbalance and eliminate possible bias. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model's performance. Four ML algorithms, namely logistic regression (LR), random forest (RF), support vector classifier (SVC), and light gradient boosting machine (LGBM), were used to build predictive models for DPN. The importance of covariates in DPN was ranked using classifiers with better performance. RESULTS The RF model performed the best, with an accuracy of 0.767, precision of 0.718, recall of 0.874, F-1 score of 0.789, and AUC of 0.77. With a value of 0.879, the LGBM model appeared to be the best regarding recall Age, sweating, dark red tongue, insomnia, and smoking were the five most significant RF features. Age, yellow coating, loose teeth, smoking, and insomnia were the five most significant features of the LGBM model. CONCLUSIONS This cross-sectional study demonstrates that the RF and LGBM models can screen for high-risk DPN in T2DM patients using TCM symptoms and tongue features. The identified key TCM-related features, such as age, tongue coating, and other symptoms, may be advantageous in developing preventative measures for T2DM patients.
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Affiliation(s)
- Zhikui Tian
- School of Rehabilitation Medicine, Qilu Medical University, Shandong, 255300, China
| | - JiZhong Zhang
- School of Rehabilitation Medicine, Qilu Medical University, Shandong, 255300, China
| | - Yadong Fan
- Medical College of Yangzhou University, YangZhou, 225000, China
| | - Xuan Sun
- College of Traditional Chinese Medicine, Binzhou Medical University, Shandong, China
| | - Dongjun Wang
- College of Traditional Chinese Medicine, North China University of Science and Technology, Tangshan, 063000, China
| | - XiaoFei Liu
- School of Rehabilitation Medicine, Qilu Medical University, Shandong, 255300, China
| | - GuoHui Lu
- School of Rehabilitation Medicine, Qilu Medical University, Shandong, 255300, China.
| | - Hongwu Wang
- School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
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Zhang YY, Yang XY, Wan Q. Association between atherogenic index of plasma and type 2 diabetic complications: a cross-sectional study. Front Endocrinol (Lausanne) 2025; 16:1537303. [PMID: 39968299 PMCID: PMC11832369 DOI: 10.3389/fendo.2025.1537303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2024] [Accepted: 01/14/2025] [Indexed: 02/20/2025] Open
Abstract
Background The Atherogenic Index of Plasma (AIP) was originally developed primarily as a marker for assessing atherosclerosis. Consequently, this study investigates the potential association between AIP and type 2 diabetic complications through a cross-sectional design. Methods The National Metabolic Management Center(MMC) serves as a comprehensive platform dedicated to the establishment of standardized protocols for the diagnosis, treatment, and long-term follow-up of metabolic diseases. Following the relevant inclusion and exclusion criteria, a total of 3,094 patients were enrolled for subsequent analysis. In this study, logistic regression, restricted cubic splines, and subgroup analyses were employed to evaluate the association between the AIP and four major complications of type 2 diabetes, namely, type 2 diabetes with carotid atherosclerosis (DA), diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic peripheral neuropathy (DPN). Results The logistic regression results demonstrate that in the fully adjusted model, each SD increase in AIP correlates with an elevated risk of type 2 diabetic kidney disease (DKD), with the risk of kidney damage intensifying alongside higher AIP groupings. The RCS analysis and subgroup analyses similarly revealed a dose-response relationship between AIP levels and the risk of DKD. Furthermore, the AIP was not found to be statistically significantly associated with DA, DR,and DPN. Conclusions The AIP may serve as a valuable predictive indicator for evaluating kidney damage in patients with type 2 diabetes, and regular screening of AIP in this population could provide significant benefits in the prevention of DKD.
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Affiliation(s)
- Yue-Yang Zhang
- Department of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Xiao-Yu Yang
- Department of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
| | - Qin Wan
- Department of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China
- Sichuan Clinical Research Center for Diabetes and Metabolism, Luzhou, China
- Sichuan Clinical Research Center for Nephropathy, Luzhou, China
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China
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Amiri Dashatan P, Soylu H, Elbistan M, Atmaca A, Keskin A, Celik ZB, Yigit S. Evaluation of ACE I/D and ATIR A1166C variants in patients with diabetes mellitus with and without peripheral neuropathy in Turkish patients. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2025:1-10. [PMID: 39819424 DOI: 10.1080/15257770.2025.2451382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 01/03/2025] [Indexed: 01/19/2025]
Abstract
OBJECTIVE Type 2 Diabetes Mellitus (T2DM) can lead to long-term vascular complications such as diabetic peripheral neuropathy (DPN). This study aimed to investigate the role of angiotensin-converting enzyme (ACE) insertion (I)/deletion (D) and angiotensin II type 1 receptor (AT1R) A1166C variants in the predisposition to T2DM in the Turkish population and their association with DPN. METHODS The study included 90 T2DM patients (42 with DPN) and 50 healthy individuals. ACE I/D and ATIR A1166C gene regions were analyzed for the variant. Both the general genotype distribution of these variants and the observed genotype ratios were examined separately. RESULTS In the T2DM group, the proportion of individuals with the AA genotype of the AT1R A1166C variant was lower than in the control group, and the proportion of individuals with the AC genotype was higher. There was no significant difference in the genotype distribution between the groups for the ACE I/D variant. There was no significant difference in the genotype distribution of the ACE I/D and ATIR A1166C variants in patients with and without DPN. CONCLUSION In the Turkish population, no significant difference was observed in the overall genotype distribution of ACE I/D and AT1R A1166C variants between T2DM patients and healthy individuals, whereas the AC genotype of the AT1R A1166C variant was more frequent in T2DM patients, and the AA genotype was less frequent. For both variants, no significant difference was observed in the genotype distribution between T2DM patients with and without DPN.
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Affiliation(s)
- Payam Amiri Dashatan
- Department of Medical Biology, Faculty of Medicine, Ondokuz Mayis University, Samsun, Turkey
| | - Huseyin Soylu
- Department of Internal Medicine, Faculty of Medicine, Ondokuz Mayis University, Samsun, Turkey
| | - Mehmet Elbistan
- Department of Medical Biology, Faculty of Medicine, Ondokuz Mayis University, Samsun, Turkey
| | - Aysegul Atmaca
- Department of Internal Medicine, Faculty of Medicine, Ondokuz Mayis University, Samsun, Turkey
| | - Adem Keskin
- Department of Biochemistry (Medicine), Institute of Health Sciences, Aydın Adnan Menderes University, Aydin, Turkey
| | - Zulfinaz Betul Celik
- Department of Medical Biology, Faculty of Medicine, Samsun University, Samsun, Turkey
| | - Serbulent Yigit
- Department of Veterinary Genetics, Faculty of Veterinary, Ondokuz Mayıs University, Samsun, Turkey
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Zhan JY, Chen J, Yu JZ, Xu FP, Xing FF, Wang DX, Yang MY, Xing F, Wang J, Mu YP. Prognostic model for esophagogastric variceal rebleeding after endoscopic treatment in liver cirrhosis: A Chinese multicenter study. World J Gastroenterol 2025; 31:100234. [PMID: 39811510 PMCID: PMC11684194 DOI: 10.3748/wjg.v31.i2.100234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Revised: 09/28/2024] [Accepted: 10/25/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding (EGVB) is a severe complication that is associated with high rates of both incidence and mortality. Despite its clinical importance, recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking. AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding. METHODS This study included 477 EGVB patients across 2 cohorts: The derivation cohort (n = 322) and the validation cohort (n = 155). The primary outcome was rebleeding events within 1 year. The least absolute shrinkage and selection operator was applied for predictor selection, and multivariate Cox regression analysis was used to construct the prognostic model. Internal validation was performed with bootstrap resampling. We assessed the discrimination, calibration and accuracy of the model, and performed patient risk stratification. RESULTS Six predictors, including albumin and aspartate aminotransferase concentrations, white blood cell count, and the presence of ascites, portal vein thrombosis, and bleeding signs, were selected for the rebleeding event prediction following endoscopic treatment (REPET) model. In predicting rebleeding within 1 year, the REPET model exhibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort, alongside 0.862 and 0.127 in the validation cohort. Furthermore, the REPET model revealed a significant difference in rebleeding rates (P < 0.01) between low-risk patients and intermediate- to high-risk patients in both cohorts. CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive performance, which will improve the clinical management of rebleeding in EGVB patients.
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Affiliation(s)
- Jun-Yi Zhan
- Cell Biology Laboratory, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Institute of Liver Diseases, Shanghai Academy of Chinese Medicine, Shanghai 201203, China
- Clinical Key Laboratory of Traditional Chinese Medicine of Shanghai, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Key Laboratory of Liver and Kidney Disease of the Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jie Chen
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
- Shanghai Institute of Liver Disease, Fudan University, Shanghai 200032, China
- Evidence-Based Medicine Center, Fudan University, Shanghai 200032, China
| | - Jin-Zhong Yu
- Department of Gastrointestinal Endoscopy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Fei-Peng Xu
- Cell Biology Laboratory, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Institute of Liver Diseases, Shanghai Academy of Chinese Medicine, Shanghai 201203, China
- Clinical Key Laboratory of Traditional Chinese Medicine of Shanghai, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Key Laboratory of Liver and Kidney Disease of the Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Fei-Fei Xing
- Cell Biology Laboratory, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Institute of Liver Diseases, Shanghai Academy of Chinese Medicine, Shanghai 201203, China
- Clinical Key Laboratory of Traditional Chinese Medicine of Shanghai, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Key Laboratory of Liver and Kidney Disease of the Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - De-Xin Wang
- Cell Biology Laboratory, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Institute of Liver Diseases, Shanghai Academy of Chinese Medicine, Shanghai 201203, China
- Clinical Key Laboratory of Traditional Chinese Medicine of Shanghai, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Key Laboratory of Liver and Kidney Disease of the Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Ming-Yan Yang
- Cell Biology Laboratory, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Institute of Liver Diseases, Shanghai Academy of Chinese Medicine, Shanghai 201203, China
- Clinical Key Laboratory of Traditional Chinese Medicine of Shanghai, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Key Laboratory of Liver and Kidney Disease of the Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Feng Xing
- Department of Hepatology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jian Wang
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
- Shanghai Institute of Liver Disease, Fudan University, Shanghai 200032, China
| | - Yong-Ping Mu
- Cell Biology Laboratory, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Institute of Liver Diseases, Shanghai Academy of Chinese Medicine, Shanghai 201203, China
- Clinical Key Laboratory of Traditional Chinese Medicine of Shanghai, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
- Key Laboratory of Liver and Kidney Disease of the Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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Kapoor Y, Hasija Y. Continuous glucose monitoring using machine learning models and IoT device data: A meta-analysis. Technol Health Care 2025; 33:577-591. [PMID: 39269871 DOI: 10.3233/thc-241403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
BACKGROUND Machine learning offers diverse options for effectively managing blood glucose levels in diabetes patients. Selecting the right ML algorithm is critical given the array of available choices. Integrating data from IoT devices presents promising opportunities to enhance real-time blood glucose management models. OBJECTIVE This meta-analysis aims to evaluate the effectiveness of machine learning models utilizing IoT device data for predicting blood glucose levels. METHODS We systematically searched electronic databases for studies published between 2019 and 2023. We excluded studies lacking ML model derivation or performance metrics. The Quality Assessment of Diagnostic Accuracy Studies tool assessed study quality. Our primary outcomes compared ML models for BG level prediction across different prediction horizons (PHs). RESULTS We analyzed ten eligible studies across prediction horizons of 15, 30, 45, and 60 minutes. ML models exhibited mean absolute RMSE values of 15.02 (SD 1.45), 21.488 (SD 2.92), 30.094 (SD 3.245), and 35.89 (SD 6.4) mg/dL, respectively. Random Forest demonstrated superior performance across these PHs. CONCLUSION We observed significant heterogeneity across all subgroups, indicating diverse sources of variability. As the PH lengthened, the RMSE for blood glucose prediction by the ML model increased, with Random Forest showing the highest relative performance among the ML models.
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11
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Liu H, Liu Q, Chen M, Lu C, Feng P. Construction and validation of a nomogram model for predicting diabetic peripheral neuropathy. Front Endocrinol (Lausanne) 2024; 15:1419115. [PMID: 39736870 PMCID: PMC11682957 DOI: 10.3389/fendo.2024.1419115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 12/02/2024] [Indexed: 01/01/2025] Open
Abstract
Objective Diabetic peripheral neuropathy (DPN) is a chronic complication of diabetes that can potentially escalate into ulceration, amputation and other severe consequences. The aim of this study was to construct and validate a predictive nomogram model for assessing the risk of DPN development among diabetic patients, thereby facilitating the early identification of high-risk DPN individuals and mitigating the incidence of severe outcomes. Methods 1185 patients were included in this study from June 2020 to June 2023. All patients underwent peripheral nerve function assessments, of which 801 were diagnosed with DPN. Patients were randomly divided into a training set (n =711) and a validation set (n = 474) with a ratio of 6:4. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis was performed to identify independent risk factors and develop a simple nomogram. Subsequently, the discrimination and clinical value of the nomogram was extensively validated using receiver operating characteristic (ROC) curves, calibration curves and clinical decision curve analyses (DCA). Results Following LASSO regression analysis, a nomogram model for predicting the risk of DPN was eventually established based on 7 factors: age (OR = 1.02, 95%CI: 1.01 - 1.03), hip circumference (HC, OR = 0.94, 95%CI: 0.92 - 0.97), fasting plasma glucose (FPG, OR = 1.06, 95%CI: 1.01 - 1.11), fasting C-peptide (FCP, OR = 0.66, 95%CI: 0.56 - 0.77), 2 hour postprandial C-peptide (PCP, OR = 0.78, 95%CI: 0.72 - 0.84), albumin (ALB, OR = 0.90, 95%CI: 0.87 - 0.94) and blood urea nitrogen (BUN, OR = 1.08, 95%CI: 1.01 - 1.17). The areas under the curves (AUC) of the nomogram were 0.703 (95% CI 0.664-0.743) and 0.704 (95% CI 0.652-0.756) in the training and validation sets, respectively. The Hosmer-Lemeshow test and calibration curves revealed high consistency between the predicted and actual results of the nomogram. DCA demonstrated that the nomogram was valuable in clinical practice. Conclusions The DPN nomogram prediction model, containing 7 significant variables, has exhibited excellent performance. Its generalization to clinical practice could potentially help in the early detection and prompt intervention for high-risk DPN patients.
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Affiliation(s)
| | | | | | | | - Ping Feng
- Department of Endocrinology, Taizhou Central Hospital (Taizhou University
Hospital), Taizhou, China
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12
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Fan X, Cao J, Yan G, Zhao Y, Wang Y, Wang X, Mi J. A protocol for research on the use of acupuncture in the management of diabetic peripheral neuropathy in individuals with type 2 diabetes: A systematic review and meta-analysis. PLoS One 2024; 19:e0310732. [PMID: 39541380 PMCID: PMC11563441 DOI: 10.1371/journal.pone.0310732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 09/04/2024] [Indexed: 11/16/2024] Open
Abstract
INTRODUCTION Diabetic peripheral neuropathy (DPN), a prevalent complication among individuals diagnosed with type 2 diabetes, has a significant impact on both the well-being of patients and their financial situation. Acupuncture has been employed for thousands of years within China and is regarded as one of the primary characteristic therapies of traditional Chinese medicine. Research has indicated that acupuncture has the potential to enhance microcirculation, decrease the generation of free radicals, and augment nerve conduction velocity. There had been several meta-analyses of acupuncture on DPN. Nevertheless, there has been inadequate attention given to the assessment of blood glucose control, and scores related to quality of life. Hence, we get additional evidence by enhancing the quantity and quality of studies to draw more distinct findings. METHODS We will conduct a comprehensive search for reports published from the beginning until June 2023 using various databases including Web of Science, Embase, Cochrane Library, PubMed, AMED, Wanfang database, VIP database, China National Knowledge Infrastructure, and Chinese Biomedical Literature database. Only randomized controlled trials will be considered, with no exclusion of quasi-randomized control trials. Articles in both English and Chinese will be taken into account without any limitations on publication dates. The data will be extracted, managed, and analyzed by two researchers working independently. The primary outcomes will include improvement of symptom scores, change of nerve conduction velocity, and quality of life scores. Additional outcomes will encompass blood glucose levels after fasting and 2 hours after eating, levels of glycosylated hemoglobin, and any adverse events associated with acupuncture. We plan to use the RevMan V.5.4 application and the random-effects model for conducting the meta-analysis. The assessment of potential prejudice can be conducted by Cochrane's 'risk of bias' 2 (RoB 2) tool. Registration: PROSPERO (registration number: CRD42023425203). DISCUSSION Our goal is to perform a meta-analysis that offers an unbiased approach to treating individuals with type 2 DPN. At the same time, it also provides doctors with more choices in the treatment of diabetes peripheral neuropathy.
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Affiliation(s)
- Xuechun Fan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Jingsi Cao
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Guanchi Yan
- Department of Endocrinology, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - Yunyun Zhao
- Department of Endocrinology, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - Ying Wang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Xiuge Wang
- Department of Endocrinology, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - Jia Mi
- Department of Endocrinology, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
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Zhang JM, Liu XB, Li YX, Li HJ, Fan J, Xue C, Yin YF, Zhang Y, Nong YX, Wang YN, Zheng Z, Zhong DL, Li J, Jin RJ. Characteristic Activation Pattern and Network Connectivity of Prefrontal Cortex in Patients with Type 2 Diabetes Mellitus and Major Depressive Disorder during a Verbal Fluency Task: A Functional Near-Infrared Spectroscopy Study Based on Network-Based Statistic Prediction. Neuroendocrinology 2024; 114:1112-1123. [PMID: 39471791 DOI: 10.1159/000542235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 10/08/2024] [Indexed: 11/01/2024]
Abstract
INTRODUCTION Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) together occur frequently among the elderly population. However, the inconsistency in assessments and limited medical resources in the community make it challenging to identify depression in patients with T2DM. This cross-sectional study aimed to investigate the activation pattern and network connectivity of prefrontal cortex (PFC) during a verbal fluency task (VFT) in patients with T2DM and MDD using functional near-infrared spectroscopy (fNIRS). METHODS Three parallel groups (T2DM with MDD group, T2DM group, and healthy group) with 100 participants in each group were included in the study. Recruitment took place from August 1, 2020, to December 31, 2023. Due to the close association between the PFC and depressive emotions, fNIRS was used to monitor brain activation and network connectivity of PFC in all participants during a task of Chinese-language phonological VFT. Network-based statistic prediction was adopted as data analysis method. RESULTS Patients in the T2DM with MDD group showed characteristic activation pattern and network connectivity in contrast with patients with T2DM and healthy controls, including decreased activation in PFC, and decreased network connectivity of right dorsolateral prefrontal cortex (DLPFC). Furthermore, the network connectivity of the right DLPFC in patients with T2DM and MDD was negatively correlated with scores of Hamilton Depression Scale-24 (HAMD-24). CONCLUSIONS There was a distinctive activation pattern and network connectivity of the PFC in patients with T2DM and MDD. The right DLPFC could serve as a potential target for the diagnosis and intervention of MDD in patients with T2DM.
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Affiliation(s)
- Jia-Ming Zhang
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China,
- Department for Neural Function Detection and Regulation, West China Xiamen Hospital, Sichuan University, Xiamen, China,
| | - Xiao-Bo Liu
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yu-Xi Li
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hui-Jing Li
- College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jin Fan
- School of Acupuncture Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Chen Xue
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yun-Fang Yin
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yuan Zhang
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yu-Xuan Nong
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yi-Nan Wang
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhong Zheng
- Department for Neural Function Detection and Regulation, West China Xiamen Hospital, Sichuan University, Xiamen, China
| | - Dong-Ling Zhong
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Juan Li
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Rong-Jiang Jin
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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Fan Z, Huang J, Liu Y, Xie H, Yang Q, Liang Y, Ding H. Multifactorial analysis of risk factors for foot ulcers in patients with neurovascular complications of diabetes. Front Endocrinol (Lausanne) 2024; 15:1399924. [PMID: 39464185 PMCID: PMC11502377 DOI: 10.3389/fendo.2024.1399924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 09/23/2024] [Indexed: 10/29/2024] Open
Abstract
Background Diabetic foot ulcers (DFU) are a major complication associated with significant morbidity and mortality. While numerous studies have investigated risk factors for these ulcers in general, few have focused specifically on patients with Neurovascular Complications of Diabetes. This study aimed to evaluate the prevalence and risk factors for DFU in this specific population. Methods We analyzed data from the National Institutes of Health (NIS) database for the years 2017-2019, involving a cohort of 161,834 patients aged over 18 who were diagnosed with neurovascular complications of diabetes. Demographic characteristics (age, gender, ethnicity), hospital characteristics, comorbidities, and other relevant data were included for analysis. A binary logistic regression model was generated to identify independent risk factors for DFU. Results The prevalence of DFU among patients with neurovascular complications of diabetes was 29.4% during the period from 2017 to 2019. Compared to patients without DFU, those with DFU had longer hospitalization times and higher costs. The multiple regression analysis revealed that Iron-deficiency anemia (OR, 1.10; 95% CI, 1.01-1.11; P=0.019), Hypertension (OR, 1.07; 95% CI, 1.03-1.11; P=0.001), Obesity (OR, 1.08; 95% CI, 1.06-1.11; P<0.001), Peripheral vascular disorders (PVD) (OR, 1.69; 95% CI, 1.65-1.74; P<0.001), Osteomyelitis (OR, 7.10; 95% CI, 6.89-7.31; P<0.001), Tinea pedis (OR, 1.89; 95% CI, 1.59-2.26; P<0.001), Sepsis (OR, 1.24; 95% CI, 1.20-1.28; P<0.001), and onychomycosis (OR, 1.26; 95% CI, 1.13-1.42; P<0.001) were independent predictors for DFU in this population. Conclusion The study found a high prevalence of DFU in patients with neurovascular complications of diabetes. Identifying and addressing risk factors such as deficiency anemia, hypertension, obesity, PVD, infections, and foot conditions may contribute to reducing the prevalence of DFU in this vulnerable population.
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Affiliation(s)
- Zibo Fan
- Department of Nursing, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Jinyan Huang
- School of Nursing, Southern Medical University, Guangzhou, Guangdong, China
| | - Yue Liu
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Precision Anaesthesia and Perioperative Organ Protection, Guangzhou, Guangdong, China
| | - Hao Xie
- Division of Orthopaedic Surgery, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Qinfeng Yang
- Division of Orthopaedic Surgery, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yue Liang
- Department of Nursing, Nanping First Hospital affiliated with Fujian Medical University, Nanping, Fujian, China
| | - Hong Ding
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Precision Anaesthesia and Perioperative Organ Protection, Guangzhou, Guangdong, China
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15
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Sui C, Li M, Zhang Q, Li J, Gao Y, Zhang X, Wang N, Liang C, Guo L. Increased brain iron deposition in the basial ganglia is associated with cognitive and motor dysfunction in type 2 diabetes mellitus. Brain Res 2024; 1846:149263. [PMID: 39369777 DOI: 10.1016/j.brainres.2024.149263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 09/28/2024] [Accepted: 10/03/2024] [Indexed: 10/08/2024]
Abstract
OBJECTIVE Compared with those in type 2 diabetes mellitus (T2DM) patients without diabetic peripheral neuropathy (DPN), alterations in brain iron levels in the basal ganglia (an iron-rich region) and motor and cognitive dysfunction in T2DM patients with DPN have not been fully elucidated. We aimed to explore changes in brain iron levels in the basal ganglia in T2DM patients with DPN using quantitative susceptibility mapping (QSM). METHODS Thirty-four patients with DPN, fifty-five patients with diabetes without DPN (non-DPN, NDPN), and fifty-one healthy controls (HCs) were recruited and underwent cognitive and motor assessments, blood biochemical tests, and brain QSM imaging. One-way ANOVA was applied to evaluate the variations in cognitive, motor and blood biochemical indicators across the three groups. Then, we performed multiple linear regression analysis to identify the possible factors associated with the significant differences in susceptibility values of the basal ganglia subregions between the two T2DM groups. RESULTS Susceptibility values in the putamen and the caudate nucleus were greater in the T2DM patients than in the HCs (DPN patients vs. HCs, p < 0.05; NDPN patients vs. HCs, p < 0.05, FDR correction), and there were no significant differences between the DPN patients and NDPN patients. Multiple linear regression analysis revealed that age and history of diabetes played crucialroles in brain iron deposition in the putamen and the caudate nucleus. Notably, DPN in T2DM patients had no effect on brain iron deposition in the putamen or the caudate nucleus. The susceptibility values of the putamen was positively correlated with the Timed Up and Go test score and negatively correlated with gait speed, the Montreal Cognitive Assessment score, and the Symbol Digit Modalities Test score in T2DM patients. CONCLUSIONS Iron-based susceptibility in the putamen, measured by QSM, can reflect motor function in T2DM patients and might indicate micropathological changes in brain tissue in T2DM patients.
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Affiliation(s)
- Chaofan Sui
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jing-wu Road, Jinan, Shandong 250021, China.
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg-Halle, Germany.
| | - Qihao Zhang
- Department of Radiology, Weill Cornell Medical College, New York. 407 East 61st Street, New York, NY 10065, USA.
| | - Jing Li
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Yian Gao
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jing-wu Road, Jinan, Shandong 250021, China.
| | - Xinyue Zhang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jing-wu Road, Jinan, Shandong 250021, China.
| | - Na Wang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jing-wu Road, Jinan, Shandong 250021, China.
| | - Changhu Liang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jing-wu Road, Jinan, Shandong 250021, China.
| | - Lingfei Guo
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jing-wu Road, Jinan, Shandong 250021, China.
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Dong S, Ding L, Zheng X, Wang O, Cai S. Phenolic Compositions of Different Fractions from Coffee Silver Skin and Their Antioxidant Activities and Inhibition towards Carbohydrate-Digesting Enzymes. Foods 2024; 13:3083. [PMID: 39410118 PMCID: PMC11475555 DOI: 10.3390/foods13193083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Revised: 09/18/2024] [Accepted: 09/24/2024] [Indexed: 10/20/2024] Open
Abstract
Seeking food-derived antioxidants and inhibitors of α-glucosidase and α-amylase has been recognized as an effective way for managing diabetes. Coffee silver skin (CSS) is rich in phenolic compounds, which may be potential agents as antioxidants and for α-glucosidase and α-amylase inhibition. But whether phenolics in different forms show similar bioactivity remains unknown. In this study, phenolic compounds in CSS were extracted as free phenolics (FPs), esterified phenolics (EPs), and bound phenolics (BPs). The phenolic profiles and antioxidant activities of them were investigated. Their inhibitory effects on α-glucosidase and α-amylase were analyzed, and the inhibitory mechanisms were elucidated by molecular docking and molecular dynamic simulation. Results showed that FPs exhibited the best antioxidant ability and inhibitory effects on α-glucosidase and α-amylase. A total of 17 compounds were identified in FPs with 3-caffeoylquinic acid, 4-feruloylquinic acid, and dicaffeoylquinic acids as the dominant ones. Typical phenolics in FPs could bind to α-glucosidase and α-amylase through hydrogen bonds and form hydrophobic interaction with several key amino acid residues. In addition, 3,4-dicaffeoylquinic acid and 3-caffeoylquinic acid might be the principal components that account for the inhibitory effect of FPs on α-glucosidase. The results of this study may provide some scientific support for CSS utilization as a health-beneficial component in functional food development for type 2 diabetes mellitus management.
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Affiliation(s)
- Shiyu Dong
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Yunnan Engineering Research Center for Fruit & Vegetable Products, Yunnan Key Laboratory of Plateau Food Advanced Manufacturing, Kunming 650500, China; (S.D.); (L.D.); (X.Z.)
| | - Lixin Ding
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Yunnan Engineering Research Center for Fruit & Vegetable Products, Yunnan Key Laboratory of Plateau Food Advanced Manufacturing, Kunming 650500, China; (S.D.); (L.D.); (X.Z.)
| | - Xiuqing Zheng
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Yunnan Engineering Research Center for Fruit & Vegetable Products, Yunnan Key Laboratory of Plateau Food Advanced Manufacturing, Kunming 650500, China; (S.D.); (L.D.); (X.Z.)
| | - Ou Wang
- NHC Key Laboratory of Public Nutrition and Health, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Shengbao Cai
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Yunnan Engineering Research Center for Fruit & Vegetable Products, Yunnan Key Laboratory of Plateau Food Advanced Manufacturing, Kunming 650500, China; (S.D.); (L.D.); (X.Z.)
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Wei Z, Wang X, Lu L, Li S, Long W, Zhang L, Shen S. Construction of an Early Risk Prediction Model for Type 2 Diabetic Peripheral Neuropathy Based on Random Forest. Comput Inform Nurs 2024; 42:665-674. [PMID: 38913980 DOI: 10.1097/cin.0000000000001157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Diabetic peripheral neuropathy is a major cause of disability and death in the later stages of diabetes. A retrospective chart review was performed using a hospital-based electronic medical record database to identify 1020 patients who met the criteria. The objective of this study was to explore and analyze the early risk factors for peripheral neuropathy in patients with type 2 diabetes, even in the absence of specific clinical symptoms or signs. Finally, the random forest algorithm was used to rank the influencing factors and construct a predictive model, and then the model performance was evaluated. Logistic regression analysis revealed that vitamin D plays a crucial protective role in preventing diabetic peripheral neuropathy. The top three risk factors with significant contributions to the model in the random forest algorithm eigenvalue ranking were glycosylated hemoglobin, disease duration, and vitamin D. The areas under the receiver operating characteristic curve of the model ware 0.90. The accuracy, precision, specificity, and sensitivity were 0.85, 0.83, 0.92, and 0.71, respectively. The predictive model, which is based on the random forest algorithm, is intended to support clinical decision-making by healthcare professionals and help them target timely interventions to key factors in early diabetic peripheral neuropathy.
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Affiliation(s)
- Zhengang Wei
- Author Affiliations: Department of Nursing, Affiliated Hospital of Zunyi Medical University (Mr Wei; Mss Lu, Long, and Zhang; and Dr Shen); Department of Endocrinology and Metabolic Diseases, Affiliated Hospital of Zunyi Medical (Ms Li); and Department of Information Technology, Affiliated Hospital of Zunyi Medical University (Dr Wang), China
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Bao P, Sun Y, Qiu P, Li X. Development and validation of a nomogram to predict the risk of vancomycin-related acute kidney injury in critical care patients. Front Pharmacol 2024; 15:1389140. [PMID: 39263571 PMCID: PMC11387168 DOI: 10.3389/fphar.2024.1389140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 08/14/2024] [Indexed: 09/13/2024] Open
Abstract
Background Vancomycin-associated acute kidney injury (AKI) leads to underestimated morbidity in the intensive care unit (ICU). It is significantly important to predict its occurrence in advance. However, risk factors and nomograms to predict this AKI are limited. Methods This was a retrospective analysis of two databases. A total of 1,959 patients diagnosed with AKI and treated with vancomycin were enrolled from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. According to the 7:3 ratio, the training set (n = 1,372) and the internal validation set (n = 587) were randomly allocated. The external validation set included 211 patients from the eICU Collaborative Research Database (eICU). Next, to screen potential variables, the least absolute shrinkage and selection operator (LASSO) regression was utilized. Subsequently, the nomogram was developed by the variables of the selected results in the multivariable logistic regression. Finally, discrimination, calibration, and clinical utility were evaluated to validate the nomogram. Results The constructed nomogram showed fine discrimination in the training set (area under the receiver operator characteristic curve [AUC] = 0.791; 95% confidence interval [CI]: 0.758-0.823), internal validation set (AUC = 0.793; 95% CI: 0.742-0.844), and external validation set (AUC = 0.755; 95% CI: 0.663-0.847). Moreover, it also well demonstrated calibration and clinical utility. The significant improvement (P < 0.001) in net reclassification improvement (NRI) and integrated differentiation improvement (IDI) confirmed that the predictive model outperformed others. Conclusion This established nomogram indicated promising performance in determining individual AKI risk of vancomycin-treated critical care patients, which will be beneficial in making clinical decisions.
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Affiliation(s)
- Peng Bao
- Fuwai Central China Cardiovascular Hospital, Zhengzhou University, Zhengzhou, China
| | - Yuzhen Sun
- Fuwai Central China Cardiovascular Hospital, Zhengzhou University, Zhengzhou, China
| | - Peng Qiu
- Department of Rehabilitation, First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, China
| | - Xiaohui Li
- Fuwai Central China Cardiovascular Hospital, Zhengzhou University, Zhengzhou, China
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Fan X, Yan G, Cao J, Zhao Y, Wang Y, Wang X, Mi J. Effectiveness and safety of photobiomodulation therapy in diabetic peripheral neuropathy: Protocol for a systematic review and meta-analysis. PLoS One 2024; 19:e0308537. [PMID: 39186566 PMCID: PMC11346721 DOI: 10.1371/journal.pone.0308537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 07/25/2024] [Indexed: 08/28/2024] Open
Abstract
INTRODUCTION Diabetic peripheral neuropathy (DPN), a widely prevalent complication in patients with type 2 diabetes, exerts a significant influence on patients' overall health and financial circumstances. Photobiomodulation therapy is one of the means of physical therapy for DPN. Although preliminary findings suggest the efficacy of photobiomodulation therapy in alleviating peripheral neuropathy, the existing literature lacks substantial evidence regarding its safety and effectiveness specifically in the context of diabetes-related peripheral neuropathy. Therefore, we plan to arrive at more distinct findings through systematic evaluation and meta-analysis. METHODS We will conduct a comprehensive search for studies published from the beginning until October 1, 2023, using various databases including Web of Science, Embase, Cochrane Library, PubMed, AMED, Wanfang database, VIP database, China National Knowledge Infrastructure, and the Chinese Biomedical Literature database. Simultaneously, we will also search for the WHO International Clinical Trial Registration Platform, China Clinical Trial Registration Platform, and Clinical Trials.gov. Gray literature will be retrieved using Google Scholar and opengrey.edu. Only randomized controlled trials in Chinese and English were included, with no restrictions on publication status. The primary outcomes will include change of symptom scores, change of nerve conduction velocity. Additional outcomes will encompass quality of life, change in pain, blood glucose levels after fasting and 2 hours after eating, levels of glycosylated hemoglobin, and any adverse events associated with photobiomodulation therapy. Reman V.5.4 and R language will be used for the meta-analysis. Assessment of potential bias will be conducted through Cochrane risk of bias 2 tool (RoB 2.0) and Physiotherapy Evidence Database (PEDro) scale. Registration: PROSPERO (registration number: CRD42023466586). DISCUSSION This meta-analysis aims to assess the efficacy and safety of photobiomodulation therapy as a potential treatment for diabetic peripheral neuropathy (DPN), and providing a straightforward and convenient therapeutic for patients. Additionally, it expands the range of treatment alternatives available to healthcare professionals managing DPN.
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Affiliation(s)
- Xuechun Fan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Guanchi Yan
- Department of Endocrinology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Jingsi Cao
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Yunyun Zhao
- Department of Endocrinology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Ying Wang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Xiuge Wang
- Department of Endocrinology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Jia Mi
- Department of Endocrinology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
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Shen X, Zhang XH, Yang L, Wang PF, Zhang JF, Song SZ, Jiang L. Development and validation of a nomogram of all-cause mortality in adult Americans with diabetes. Sci Rep 2024; 14:19148. [PMID: 39160223 PMCID: PMC11333764 DOI: 10.1038/s41598-024-69581-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 08/06/2024] [Indexed: 08/21/2024] Open
Abstract
This study aimed to develop and validate a predictive model of all-cause mortality risk in American adults aged ≥ 18 years with diabetes. 7918 participants with diabetes were enrolled from the National Health and Nutrition Examination Survey (NHANES) 1999-2016 and followed for a median of 96 months. The primary study endpoint was the all-cause mortality. Predictors of all-cause mortality included age, Monocytes, Erythrocyte, creatinine, Nutrition Risk Index (NRI), neutrophils/lymphocytes (NLR), smoking habits, alcohol consumption, cardiovascular disease (CVD), urinary albumin excretion rate (UAE), and insulin use. The c-index was 0.790 (95% CI 0.779-0.801, P < 0.001) and 0.792 (95% CI: 0.776-0.808, P < 0.001) for the training and validation sets, respectively. The area under the ROC curve was 0.815, 0.814, 0.827 and 0.812, 0.818 and 0.829 for the training and validation sets at 3, 5, and 10 years of follow-up, respectively. Both calibration plots and DCA curves performed well. The model provides accurate predictions of the risk of death for American persons with diabetes and its scores can effectively determine the risk of death in outpatients, providing guidance for clinical decision-making and predicting prognosis for patients.
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Affiliation(s)
- Xia Shen
- Department of Nursing, School of Health and Nursing, Wuxi Taihu University, 68 Qian Rong Rode, Bin Hu District, Wuxi, China
| | - Xiao Hua Zhang
- Cardiac Catheter Room, Wuxi People's Hospital, Jiangsu, No.299 Qing Yang Road, Wuxi, 214000, China
| | - Long Yang
- Department of Pediatric Cardiothoracic Surgery, The First Affiliated Hospital of Xinjiang Medical University, 137 Li Yu Shan Road, Urumqi, 830054, China
| | - Peng Fei Wang
- Department of Traditional Chinese Medicine, Fuzhou University Affiliated Provincial Hospital, 134 East Street, Gu Lou District, Fuzhou, 350001, China
| | - Jian Feng Zhang
- Research and Teaching Department, Taizhou Hospital of Integrative Medicine, Jiangsu Province, No. 111, Jiang Zhou South Road, Taizhou City, Jiangsu, China
| | - Shao Zheng Song
- Department of Basci, School of Health and Nursing, Wuxi Taihu University, 68 Qian Rong Rode, Bin Hu District, Wuxi, China.
| | - Lei Jiang
- Department of Radiology, The Convalescent Hospital of East China, No.67 Da Ji Shan, Wuxi, 214065, China.
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21
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Mittal R, McKenna K, Keith G, McKenna E, Sinha R, Lemos JRN, Hirani K. Systematic review of translational insights: Neuromodulation in animal models for Diabetic Peripheral Neuropathy. PLoS One 2024; 19:e0308556. [PMID: 39116099 PMCID: PMC11309513 DOI: 10.1371/journal.pone.0308556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
Diabetic Peripheral Neuropathy (DPN) is a prevalent and debilitating complication of diabetes, affecting a significant proportion of the diabetic population. Neuromodulation, an emerging therapeutic approach, has shown promise in the management of DPN symptoms. This systematic review aims to synthesize and analyze the current advancements in neuromodulation techniques for the treatment of DPN utilizing studies with preclinical animal models. A comprehensive search was conducted across multiple databases, including PubMed, Scopus, and Web of Science. Inclusion criteria were focused on studies utilizing preclinical animal models for DPN that investigated the efficacy of various neuromodulation techniques, such as spinal cord stimulation, transcranial magnetic stimulation, and peripheral nerve stimulation. The findings suggest that neuromodulation significantly alleviated pain symptoms associated with DPN. Moreover, some studies reported improvements in nerve conduction velocity and reduction in nerve damage. The mechanisms underlying these effects appeared to involve modulation of pain pathways and enhancement of neurotrophic factors. However, the review also highlights the variability in methodology and stimulation parameters across studies, highlighting the need for standardization in future research. Additionally, while the results are promising, the translation of these findings from animal models to human clinical practice requires careful consideration. This review concludes that neuromodulation presents a potentially effective therapeutic strategy for DPN, but further research is necessary to optimize protocols and understand the underlying molecular mechanisms. It also emphasizes the importance of bridging the gap between preclinical findings and clinical applications to improve the management of DPN in diabetic patients.
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Affiliation(s)
- Rahul Mittal
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Keelin McKenna
- Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, United States of America
| | - Grant Keith
- School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Evan McKenna
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Rahul Sinha
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Joana R. N. Lemos
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Khemraj Hirani
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida, United States of America
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Tu Z, Du J, Ge X, Peng W, Shen L, Xia L, Jiang X, Hu F, Huang S. Triglyceride Glucose Index for the Detection of Diabetic Kidney Disease and Diabetic Peripheral Neuropathy in Hospitalized Patients with Type 2 Diabetes. Diabetes Ther 2024; 15:1799-1810. [PMID: 38907937 PMCID: PMC11263315 DOI: 10.1007/s13300-024-01609-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
INTRODUCTION The triglyceride-glucose index (TyG) has been identified as a dependable and simple indicator marker of insulin resistance (IR). Research has demonstrated a correlation between macrovascular complications and TyG. However, limited research exists regarding the relationship between TyG and diabetic microvascular complications. Consequently, the objective of this study is to investigate the association between TyG and diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN). METHODS This is a cross-sectional, observational study. A total of 2048 patients from Tongren Hospital, Shanghai Jiao Tong University School of Medicine were enrolled. The primary outcomes are DKD and DPN. Quantile regression analysis was employed to investigate the implicit factors of TyG quartiles. Subsequently, based on implicit factors, logistic regression models were constructed to further examine the relationship between TyG and DKD and DPN. RESULTS In the baseline, TyG exhibited higher values across patients with DKD, DPN, and co-existence of DKD and DPN (DKD + DPN) in type 2 diabetes (T2D). Univariate logistic regressions demonstrated a significant association between an elevated TyG and an increased risk of DKD (OR = 1.842, [95% CI] 1.317-2.578, P for trend < 0.01), DPN (OR = 1.516, [95% CI] 1.114-2.288, P for trend < 0.05), DKD + DPN (OR = 2.088, [95% CI] 1.429-3.052, P for trend < 0.05). Multivariable logistic regression models suggested a statistically significant increase in the risk of DKD (OR = 1.581, [95% CI] 1.031-2.424, p < 0.05), DKD + DPN (OR = 1.779, [95% CI] 1.091-2.903, p < 0.05) after adjusting the implicit factors of TyG quartiles. However, no significant relationship was observed between TyG and DPN in the multivariable regression analysis. CONCLUSIONS Elevated TyG was significantly associated with an increased risk of DKD in T2D, but no significant relationship was shown with DPN. This finding provided further evidence for the clinical significance of integrating TyG into the initial assessment of diabetic microvascular complications.
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Affiliation(s)
- Zhihui Tu
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China
| | - Juan Du
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China
| | - Xiaoxu Ge
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China
| | - Wenfang Peng
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China
| | - Lisha Shen
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China
| | - Lili Xia
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China
| | - Xiaohong Jiang
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China.
| | - Fan Hu
- Shanghai Jiao Tong University School of Medicine, No. 227, Chongqing South Road, Huangpu District, Shanghai, China.
| | - Shan Huang
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Changning District, Shanghai, China.
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de Paula AVL, Dykstra GM, da Rocha RB, Magalhães AT, da Silva BAK, Cardoso VS. The association of diabetic peripheral neuropathy with cardiac autonomic neuropathy in individuals with diabetes mellitus: A systematic review. J Diabetes Complications 2024; 38:108802. [PMID: 38971002 DOI: 10.1016/j.jdiacomp.2024.108802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/08/2024]
Abstract
This systematic review aimed to explore the relationship between diabetic peripheral neuropathy (DPN) and cardiac autonomic neuropathy (CAN) in individuals with type 1 and 2 diabetes mellitus (DM). METHODS The systematic review follow the protocol registered in Prospero (CRD42020182899). Two authors independently searched the PubMed, Scopus, Embase, Cochrane, and Web of Science databases. Discrepancies were resolved by a third author. The review included observational studies investigating the relationship between CAN and DPN in individuals with DM. RESULTS Initially, out of 1165 studies, only 16 were selected, with 42.8 % involving volunteers with one type of diabetes, 14.3 % with both types of diabetes and 14.3 % not specify the type. The total number of volunteers was 2582, mostly with type 2 DM. It was analyzed that there is a relationship between CAN and DPN. It was observed that more severe levels of DPN are associated with worse outcomes in autonomic tests. Some studies suggested that the techniques for evaluating DPN might serve as risk factors for CAN. CONCLUSION The review presents a possible relationship between DPN and CAN, such as in their severity.
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Affiliation(s)
- Ana Vitoria Lima de Paula
- Programa de Pós-Graduação em Ciências Biomédicas, Universidade Federal do Delta do Parnaíba, Parnaíba, PI, Brazil
| | - Gabrielly Menin Dykstra
- Curso de Bacharelado em Fisioterapia, Universidade Federal do Delta do Parnaíba, Parnaíba, PI, Brazil
| | - Rebeca Barbosa da Rocha
- Programa de Pós Graduação em Biotecnologia, Universidade Federal do Delta do Parnaíba, Parnaíba, PI, Brazil
| | - Alessandra Tanuri Magalhães
- Curso de Bacharelado em Fisioterapia, Universidade Federal do Delta do Parnaíba, Parnaíba, PI, Brazil; Centro Integrado de Especialidades Médicas, Universidade Federal do Piauí (UFPI), Parnaíba, PI, Brazil
| | | | - Vinicius Saura Cardoso
- Programa de Pós-Graduação em Ciências Biomédicas, Universidade Federal do Delta do Parnaíba, Parnaíba, PI, Brazil; Centro Integrado de Especialidades Médicas, Universidade Federal do Piauí (UFPI), Parnaíba, PI, Brazil.
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Zhang X, Zhang X, Li X, Zhao X, Wei G, Shi J, Yang Y, Fan S, Zhao J, Zhu K, Du J, Guo J, Cao W. Association between serum uric acid levels and diabetic peripheral neuropathy in type 2 diabetes: a systematic review and meta-analysis. Front Endocrinol (Lausanne) 2024; 15:1416311. [PMID: 39072278 PMCID: PMC11272597 DOI: 10.3389/fendo.2024.1416311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/25/2024] [Indexed: 07/30/2024] Open
Abstract
Background The evidence supporting a connection between elevated serum uric acid (SUA) levels and diabetic peripheral neuropathy (DPN) is controversial. The present study performed a comprehensive evaluation of this correlation by conducting a systematic review and meta-analysis of relevant research. Method PubMed, Web of Science (WOS), Embase, and the Cochrane Library were searched for published literature from the establishment of each database to January 8, 2024. In total, 5 cohort studies and 15 cross-sectional studies were included, and 2 researchers independently screened and extracted relevant data. R 4.3.0 was used to evaluate the included literature. The present meta-analysis evaluated the relationship between SUA levels and the risk of DPN in type 2 diabetes (T2DM) by calculating the ratio of means (RoM) and 95% confidence intervals (CIs) using the method reported by JO Friedrich, and it also analyzed continuous outcome measures using standardized mean differences (SMDs) and 95% CIs to compare SUA levels between DPN and non-DPN groups. Funnel plot and Egger's test were used to assess publication bias. Sensitivity analysis was conducted by sequentially removing each study one-by-one. Results The meta-analysis included 20 studies, with 12,952 T2DM patients with DPN and 16,246 T2DM patients without DPN. There was a significant correlation between SUA levels and the risk of developing DPN [odds ratio (OR) = 1.23; 95% CI: 1.07-1.41; p = 0.001]. Additionally, individuals with DPN had higher levels of SUA compared to those without DPN (SMD = 0.4; 95% CI: -0.11-0.91; p < 0.01). Conclusion T2DM patients with DPN have significantly elevated SUA levels, which correlate with a heightened risk of peripheral neuropathy. Hyperuricemia (HUA) may be a risk indicator for assessing the risk of developing DPN in T2DM patients. Systematic review registration https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024500373.
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Affiliation(s)
- Xieyu Zhang
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Xinwen Zhang
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Xiaoxu Li
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Xin Zhao
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Guangcheng Wei
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Jinjie Shi
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Yue Yang
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Su Fan
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Jiahe Zhao
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Ke Zhu
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Jieyang Du
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
| | - Junyi Guo
- Robotics Movement Department, Amazon, Boston, MA, United States
| | - Wei Cao
- Department of Rheumatology, Wangjing Hospital, China Academy of Chinese Medicine Science, Beijing, China
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Yu J, Wang C, Liu Y, Tao T, Yang L, Liu R, Liang D, Zhang Y, He Z, Sun Y. A comparative study of urinary levels of multiple metals and neurotransmitter correlations between GDM and T2DM populations. J Trace Elem Med Biol 2024; 84:127447. [PMID: 38733832 DOI: 10.1016/j.jtemb.2024.127447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/26/2024] [Accepted: 04/04/2024] [Indexed: 05/13/2024]
Abstract
OBJECTIVE The pathogenesis of GDM and T2DM are closely related to various metals in vivo, and changes in the concentration of these metal exposures can lead to neuropathy through the DNA damage pathway caused by the accumulation of ROS. METHOD Urine samples were analyzed for heavy metals and trace elements by ICP-MS, neurotransmitter metabolites by HPLC, 8-OH-dG by HPLC-MS and metabolomics by UPLC-MS. RESULT Cd and Hg were risk factors for T2DM. There was a positive correlation between 8-OH-dG and neurotransmitter metabolites in both two populations. For GDM, the metabolite with the largest down-regulation effect was desloratadine and the largest up-regulation effect was D-glycine. That tyrosine and carbon metabolites were upregulated in the GDM population and downregulated in the T2DM population. CONCLUSION The BMI, urinary Cd and Hg endo-exposure levels correlated with elevated blood glucose, and the latter may cause changes in the DNA damage marker 8-OH-dG in both study populations and trigger common responses to neurological alterations changes in the neurotransmitter. Tyrosine, carbonin metabolites, alanine, aspartate, and glutamate were signature metabolites that were altered in both study populations. These indicators and markers have clinical implications for monitoring and prevention of neurological injury in patients with GDM and T2DM.
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Affiliation(s)
- Jia Yu
- Department of Toxicology, Guilin Medical University, Guilin 541004, China
| | - Caimei Wang
- Department of Laboratory Medicine, Affiliated Hospital of Guilin medical University, Guilin, China
| | - Yun Liu
- Department of Gynaecolog, Tangshan Central Hospital, Tangshan, Hebei 063000, China
| | - Tao Tao
- Department of Toxicology, Guilin Medical University, Guilin 541004, China
| | - Liuxue Yang
- Department of Endocrinology, The Second Affiliated Hospital of Guilin Medical College, China
| | - Ruxi Liu
- Department of Toxicology, Guilin Medical University, Guilin 541004, China
| | - Dan Liang
- Department of Toxicology, Guilin Medical University, Guilin 541004, China
| | - Ying Zhang
- Department of Toxicology, Guilin Medical University, Guilin 541004, China
| | - Zhuohong He
- Department of Toxicology, Guilin Medical University, Guilin 541004, China
| | - Yi Sun
- Department of Toxicology, Guilin Medical University, Guilin 541004, China.
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Liu M, Pan N. Quantitative ultrasound imaging parameters in patients with cancerous thyroid nodules: development of a diagnostic model. Am J Transl Res 2024; 16:2645-2653. [PMID: 39006293 PMCID: PMC11236663 DOI: 10.62347/wedg9279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/24/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE This study aimed to develop a diagnostic model utilizing quantitative ultrasound parameters to accurately differentiate benign from malignant thyroid nodules. METHODS A retrospective analysis of 194 patients with thyroid nodules, encompassing 65 malignant and 129 benign cases, was performed. Clinical data, ultrasound characteristics, and hemodynamic indicators were compared. Receiver operating characteristic (ROC) curves and logistic regression analysis identified independent diagnostic markers. RESULTS No significant differences in clinical data were observed between the groups (P>0.05). Malignant nodules, however, were more likely to exhibit solid composition, hypoechoicity, irregular shapes, calcifications, central blood flow, and unclear margins (P<0.05). Hemodynamic parameters showed that malignant nodules had lower end-diastolic volume (EDV) but higher peak systolic velocity (PSV), resistive index (RI), and vascularization flow index (VFI) (P<0.001). Independent diagnostic factors identified included calcification, margin definition, RI, and VFI. A risk prediction model was formulated, demonstrating significantly lower scores for benign nodules (P<0.0001), achieving an ROC area of 0.964. CONCLUSION Color Doppler ultrasound effectively distinguishes malignant from benign thyroid nodules. The diagnostic model emphasizes the importance of calcification, margin clarity, RI, and VFI as critical elements, enhancing the accuracy of thyroid nodule characterization and facilitating informed clinical decisions.
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Affiliation(s)
- Mingyang Liu
- Department of Ultrasound, Xingtai People's Hospital No. 16 Hongxing Street, Xingtai 054500, Hebei, China
| | - Na Pan
- Department of Hematology, Xingtai People's Hospital No. 16 Hongxing Street, Xingtai 054500, Hebei, China
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Jiang A, Li J, Wang L, Zha W, Lin Y, Zhao J, Fang Z, Shen G. Multi-feature, Chinese-Western medicine-integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP. Diabetes Metab Res Rev 2024; 40:e3801. [PMID: 38616511 DOI: 10.1002/dmrr.3801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 09/18/2023] [Accepted: 03/14/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Clinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning-based multi-featured Chinese-Western medicine-integrated prediction model for DPN using clinical features of TCM. MATERIALS AND METHODS The clinical data of 1581 patients with Type 2 diabetes mellitus (T2DM) treated at the Department of Endocrinology of the First Affiliated Hospital of Anhui University of Chinese Medicine were collected. The data (including general information, laboratory parameters and TCM features) of 1142 patients with T2DM were selected after data cleaning. After baseline description analysis of the variables, the data were divided into training and validation sets. Four prediction models were established and their performance was evaluated using validation sets. Meanwhile, the accuracy, precision, recall, F1 score and area under the curve (AUC) of ROC were calculated using ten-fold cross-validation to further assess the performance of the models. An explanatory analysis of the results of the DPN prediction model was carried out using the SHAP framework based on machine learning-based prediction models. RESULTS Of the 1142 patients with T2DM, 681 had a comorbidity of DPN, while 461 did not. There was a significant difference between the two groups in terms of age, cause of disease, systolic pressure, HbA1c, ALT, RBC, Cr, BUN, red blood cells in the urine, glucose in the urine, and protein in the urine (p < 0.05). T2DM patients with a comorbidity of DPN exhibited diverse TCM symptoms, including limb numbness, limb pain, hypodynamia, thirst with desire for drinks, dry mouth and throat, blurred vision, gloomy complexion, and unsmooth pulse, with statistically significant differences (p < 0.05). Our results showed that the proposed multi-featured Chinese-Western medicine-integrated prediction model was superior to conventional models without characteristic TCM indicators. The model showed the best performance (accuracy = 0.8109, precision = 0.8029, recall = 0.9060, F1 score = 0.8511, and AUC = 0.9002). SHAP analysis revealed that the dominant risk factors that caused DPN were TCM symptoms (limb numbness, thirst with desire for drinks, blurred vision), age, cause of disease, and glycosylated haemoglobin. These risk factors were exerted positive effects on the DPN prediction models. CONCLUSIONS A multi-feature, Chinese-Western medicine-integrated prediction model for DPN was established and validated. The model improves early-stage identification of high-risk groups for DPN in the diagnosis and treatment of T2DM, while also providing informative support for the intelligent management of chronic conditions such as diabetes.
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Affiliation(s)
- Aijuan Jiang
- Anhui University of Chinese Medicine, Hefei, China
| | - Jiajie Li
- Anhui University of Chinese Medicine, Hefei, China
| | - Lujie Wang
- Anhui University of Chinese Medicine, Hefei, China
| | - Wenshu Zha
- Hefei University of Technology, Hefei, China
| | - Yixuan Lin
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Jindong Zhao
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Zhaohui Fang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Guoming Shen
- Anhui University of Chinese Medicine, Hefei, China
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Lou B, Hu YL, Jiang ZH. Predictive Value of Combined HbA1c and Neutrophil-to-Lymphocyte Ratio for Diabetic Peripheral Neuropathy in Type 2 Diabetes. Med Sci Monit 2024; 30:e942509. [PMID: 38561932 PMCID: PMC10998473 DOI: 10.12659/msm.942509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/24/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Diabetic peripheral neuropathy (DPN) is a prevalent complication affecting over 60% of type 2 diabetes patients. Early diagnosis is challenging, leading to irreversible impacts on quality of life. This study explores the predictive value of combining HbA1c and Neutrophil-to-Lymphocyte Ratio (NLR) for early DPN detection. MATERIAL AND METHODS An observational study was conducted at the First People's Hospital of Linping District, Hangzhou spanning from May 2019 to July 2020. Data on sex, age, biochemical measurements were collected from electronic medical records and analyzed. Employing multivariate logistic regression analysis, we sought to comprehend the factors influencing the development of DPN. To assess the predictive value of individual and combined testing for DPN, a receiver operating characteristic (ROC) curve was plotted. The data analysis was executed using R software (Version: 4.1.0). RESULTS The univariate and multivariate logistic regression analysis identified the level of glycated hemoglobin (HbA1C) (OR=1.94, 95% CI: 1.27-3.14) and neutrophil-to-lymphocyte ratio (NLR) (OR=4.60, 95% CI: 1.15-22.62, P=0.04) as significant risk factors for the development of DPN. Receiver operating characteristic (ROC) curve analysis demonstrated that HbA1c, NLR, and their combined detection exhibited high sensitivity in predicting the development of DPN (71.60%, 90.00%, and 97.2%, respectively), with moderate specificity (63.8%, 45.00%, and 50.00%, respectively). The area under the curve (AUC) for these predictors was 0.703, 0.661, and 0.733, respectively. CONCLUSIONS HbA1c and NLR emerge as noteworthy risk indicators associated with the manifestation of DPN in patients with type 2 diabetes. The combined detection of HbA1c and NLR exhibits a heightened predictive value for the development of DPN.
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Gao L, Qin J, Chen Y, Jiang W, Zhu D, Zhou X, Ding J, Qiu H, Zhou Y, Dong Q, Guan Y. Risk Factors for Subclinical Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus. Diabetes Metab Syndr Obes 2024; 17:417-426. [PMID: 38288341 PMCID: PMC10823870 DOI: 10.2147/dmso.s433024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/21/2023] [Indexed: 01/31/2024] Open
Abstract
Purpose To investigate the risk factors associated with subclinical diabetic peripheral neuropathy (sDPN) in patients with type 2 diabetes mellitus (T2DM). Patients and Methods This cross-sectional, retrospective study involved 311 patients with T2DM who were successively admitted from January 2018 to December 2021 without any neurological symptoms. All participants underwent a nerve conduction study (NCS), and those asymptomatic patients with abnormal nerve conduction were diagnosed with sDPN. Differences between groups were evaluated by the chi-squared, Wilcoxon, or Fisher's exact test. Binary logistic regression analysis was performed to determine the independent risk factors for sDPN. Receiver operating characteristic (ROC) curves were constructed, and the areas under curves (AUCs) were detected. Results Among 311 asymptomatic patients with T2DM, 142 (45.7%) with abnormal nerve conduction were diagnosed with sDPN. Patients with sDPN significantly differed from those without diabetic peripheral neuropathy (DPN) in age, history of hypertension, duration of diabetes, anemia, neutrophil-to-lymphocyte ratio, fasting C-peptide level, serum creatinine level, and albuminuria (all p<0.05). Furthermore, the duration of diabetes (odds ratio [OR]: 1.062, 95% confidence interval [CI]: 1.016-1.110), fasting C-peptide level (OR: 2.427, 95% CI: 1.126-5.231), and presence of albuminuria (OR: 2.481, 95% CI: 1.406-4.380) were independently associated with the development of sDPN (all p<0.05). The AUCs for fasting C-peptide level, duration of diabetes, and the two factors combined were 0.6229 (95% CI: 0.5603-0.6855, p=0.0002), 0.6738 (95% CI: 0.6142-0.7333, p<0.0001), and 0.6808 (95% CI: 0.6212-0.7404, p<0.0001), respectively. Conclusion For patients with T2DM and longer duration of diabetes, lower fasting C-peptide levels, and presence with albuminuria, the risk for developing DPN is higher even if they have no clinical signs or symptoms. Identifying potential risk factors for the development of sDPN and effectively controlling them early are critical for the successful management of DPN.
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Affiliation(s)
- Li Gao
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Jiexing Qin
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Ying Chen
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Wenqun Jiang
- Department of Laboratory Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Desheng Zhu
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Xiajun Zhou
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Jie Ding
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Huiying Qiu
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Yan Zhou
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Qing Dong
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Yangtai Guan
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
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Zhao M, Zhou H, Wang J, Liu Y, Zhang X. A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learning. Technol Health Care 2024; 32:3393-3408. [PMID: 38875060 DOI: 10.3233/thc-240128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
BACKGROUND The theory of Chinese medicine (TCM) constitution contributes to the optimisation of individualised healthcare programmes. However, at present, TCM constitution identification mainly relies on inefficient questionnaires with subjective bias. Efficient and accurate TCM constitution identification can play an important role in individualised medicine and healthcare. OBJECTIVE Building an efficient model for identifying traditional Chinese medicine constitutions using objective tongue features and machine learning techniques. METHODS The DS01-A device was applied to collect tongue images and extract features. We trained and evaluated five machine learning models: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), and CatBoost (CB). Among these, we selected the model with the best performance as the base classifier for constructing our heterogeneous ensemble learning model. Using various performance metrics, including classification accuracy, precision, recall, F1 score, and area under curve (AUC), to comprehensively evaluate model performance. RESULTS A total of 1149 tongue images were obtained and 45 features were extracted, forming dataset 1. RF, LGBM, and CB were selected as the base learners for the RLC-Stacking. On dataset 1, RLC-Stacking1 achieved an accuracy of 0.8122, outperforming individual classifiers. After feature selection, the classification accuracy of RLC-Stacking2 improved to 0.8287, an improvement of 0.00165 compared to RLC-Stacking1. RLC-Stacking2 achieved an accuracy exceeding 0.85 for identifying each TCM constitution type, indicating excellent identification performance. CONCLUSION The study provides a reliable method for the accurate and rapid identification of TCM constitutions and can assist clinicians in tailoring individualized medical treatments based on personal constitution types and guide daily health care. The information extracted from tongue images serves as an effective marker for objective TCM constitution identification.
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Spallone V. Diabetic neuropathy: Current issues in diagnosis and prevention. CHRONIC COMPLICATIONS OF DIABETES MELLITUS 2024:117-163. [DOI: 10.1016/b978-0-323-88426-6.00016-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Xu HM, Shen XJ, Liu J. Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus. World J Diabetes 2023; 14:1793-1802. [PMID: 38222787 PMCID: PMC10784791 DOI: 10.4239/wjd.v14.i12.1793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/20/2023] [Accepted: 11/27/2023] [Indexed: 12/14/2023] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is associated with periodontitis. Currently, there are few studies proposing predictive models for periodontitis in patients with T2DM. AIM To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models. METHODS In this a retrospective study, 300 patients with T2DM who were hospitalized at the First People's Hospital of Wenling from January 2022 to June 2022 were selected for inclusion, and their data were collected from hospital records. We used logistic regression to analyze factors associated with periodontitis in patients with T2DM, and random forest and logistic regression prediction models were established. The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve (AUC). RESULTS Of 300 patients with T2DM, 224 had periodontitis, with an incidence of 74.67%. Logistic regression analysis showed that age [odds ratio (OR) = 1.047, 95% confidence interval (CI): 1.017-1.078], teeth brushing frequency (OR = 4.303, 95%CI: 2.154-8.599), education level (OR = 0.528, 95%CI: 0.348-0.800), glycosylated hemoglobin (HbA1c) (OR = 2.545, 95%CI: 1.770-3.661), total cholesterol (TC) (OR = 2.872, 95%CI: 1.725-4.781), and triglyceride (TG) (OR = 3.306, 95%CI: 1.019-10.723) influenced the occurrence of periodontitis (P < 0.05). The random forest model showed that the most influential variable was HbA1c followed by age, TC, TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showed that in the training dataset, the AUC of the random forest model was higher than that of the logistic regression model (AUC = 1.000 vs AUC = 0.851; P < 0.05). In the validation dataset, there was no significant difference in AUC between the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915; P > 0.05). CONCLUSION Both random forest and logistic regression models have good predictive value and can accurately predict the risk of periodontitis in patients with T2DM.
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Affiliation(s)
- Hong-Miao Xu
- Department of Stomatology, The First People’s Hospital of Wenling, Taizhou 317500, Zhejiang Province, China
| | - Xuan-Jiang Shen
- Department of Stomatology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou 318000, Zhejiang Province, China
| | - Jia Liu
- Department of Stomatology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou 318000, Zhejiang Province, China
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Chu W, Ma LL, Li BX, Li MC. Clinical significance of vascular endothelial growth factor and endothelin-1 in serum levels as novel indicators for predicting the progression of diabetic nephropathy. EUR J INFLAMM 2023. [DOI: 10.1177/1721727x231151526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Objective: Early diagnosis and intervention of diabetic nephropathy (DN) is necessary to optimize therapy in order to delay the progression of diabetes. This research aimed to reveal the change of vascular endothelial growth factor (VEGF) and endothelin-1 (ET-1) in patients with DN, and to assess possible correlations with glycated hemoglobin (HbAlc) values. Methods: The present study was a retrospective, single-center study conducted at a teaching hospital in the northeast China. A total of 120 patients were divided into proteinuria-positive group ( n = 40), the microalbuminuria group ( n = 40), and the high proteinuria group ( n = 40) according to the urinary albumin excretion rate (UAER), and 40 healthy volunteers were selected as the control group. The levels of VEGF, ET-1 and HbA1c were measured in all subjects and principal component analysis (PCA) was performed to classify and reveal correlations between VEGF, ET-1 and HbA1c. Results: Compared to the control group, a significant difference in the increase of HbA1c was detected in group I, II and III. A significant increase in the concentrations of serum VEGF and ET-1 was also observed. HbA1c in DN patients had proven to be positively correlated with VEGF (r = 0.7941; p < 0. 0001) and ET-1 (r = 0.8504; p < 0.0001) respectively. Conclusion: The elevated levels of VEGF and ET-1 in serum have been proposed as being able to supplement the additional information about the progression of DN. These data suggest that the decrease in endothelial function may be related to poor glycemic control.
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Affiliation(s)
- Wei Chu
- Department of Renal Medicine, People’s Hospital of Jilin, Jilin, China
| | - Lin-Lin Ma
- Department of Clinical Laboratory, Beihua University, Jilin, China
| | - Bin-Xian Li
- Department of Clinical Laboratory, Beihua University, Jilin, China
| | - Ming-Cheng Li
- Department of Molecular diagnosis, Beihua University, Jilin, China
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Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, Liu S, Pu C, Chen C, Wan Y. Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis. JMIR Med Inform 2023; 11:e47833. [PMID: 37983072 PMCID: PMC10696506 DOI: 10.2196/47833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/21/2023] [Accepted: 10/12/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. OBJECTIVE In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. METHODS PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. RESULTS In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. CONCLUSIONS Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. TRIAL REGISTRATION PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.
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Affiliation(s)
- Kui Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Linyi Li
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yifei Ma
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Jun Jiang
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zhenhua Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zichen Ye
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Shuang Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Chen Pu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Changsheng Chen
- Department of Health Statistics, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yi Wan
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
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Gelaw NB, Muche AA, Alem AZ, Gebi NB, Chekol YM, Tesfie TK, Tebeje TM. Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021. PLoS One 2023; 18:e0276472. [PMID: 37643198 PMCID: PMC10465000 DOI: 10.1371/journal.pone.0276472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 07/23/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians. OBJECTIVE Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021. METHODS A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model. RESULTS The incidence proportion of diabetic neuropathy among DM patients was 21.29% (95% CI; 18.59, 24.25). In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment, white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2% (95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7 (95% CI; 67.2%, 75.9%). It had less optimism coefficient (0.015). To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2% (95% CI; 65.8%, 74.6%). The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree. CONCLUSION The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.
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Affiliation(s)
- Negalgn Byadgie Gelaw
- Department of Public Health, Mizan Aman College of Health Sciences, Mizan-Aman, Ethiopia
| | - Achenef Asmamaw Muche
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Adugnaw Zeleke Alem
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Nebiyu Bekele Gebi
- Department of Internal Medicine, School of Medicine, University of Gondar Comprehensive Specialized Hospital, Gondar, Ethiopia
| | - Yazachew Moges Chekol
- Department of Health Information Technology, Mizan Aman College of Health Sciences, Mizan-Aman, Ethiopia
| | - Tigabu Kidie Tesfie
- Department of Public Health, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Tsion Mulat Tebeje
- Unit of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Sciences, Dilla University, Dilla, Ethiopia
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Luo X, Sun J, Pan H, Zhou D, Huang P, Tang J, Shi R, Ye H, Zhao Y, Zhang A. Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining. PLoS One 2023; 18:e0289749. [PMID: 37552706 PMCID: PMC10409378 DOI: 10.1371/journal.pone.0289749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 07/26/2023] [Indexed: 08/10/2023] Open
Abstract
In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell's concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744-0.792) and 0.745 (95% CI, 0.669-0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30-54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.
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Affiliation(s)
- Xin Luo
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jijia Sun
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hong Pan
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dian Zhou
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ping Huang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jingjing Tang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rong Shi
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hong Ye
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ying Zhao
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - An Zhang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Lian X, Qi J, Yuan M, Li X, Wang M, Li G, Yang T, Zhong J. Study on risk factors of diabetic peripheral neuropathy and establishment of a prediction model by machine learning. BMC Med Inform Decis Mak 2023; 23:146. [PMID: 37533059 PMCID: PMC10394817 DOI: 10.1186/s12911-023-02232-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/12/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND Diabetic peripheral neuropathy (DPN) is a common complication of diabetes. Predicting the risk of developing DPN is important for clinical decision-making and designing clinical trials. METHODS We retrospectively reviewed the data of 1278 patients with diabetes treated in two central hospitals from 2020 to 2022. The data included medical history, physical examination, and biochemical index test results. After feature selection and data balancing, the cohort was divided into training and internal validation datasets at a 7:3 ratio. Training was made in logistic regression, k-nearest neighbor, decision tree, naive bayes, random forest, and extreme gradient boosting (XGBoost) based on machine learning. The k-fold cross-validation was used for model assessment, and the accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were adopted to validate the models' discrimination and clinical practicality. The SHapley Additive exPlanation (SHAP) was used to interpret the best-performing model. RESULTS The XGBoost model outperformed other models, which had an accuracy of 0·746, precision of 0·765, recall of 0·711, F1-score of 0·736, and AUC of 0·813. The SHAP results indicated that age, disease duration, glycated hemoglobin, insulin resistance index, 24-h urine protein quantification, and urine protein concentration were risk factors for DPN, while the ratio between 2-h postprandial C-peptide and fasting C-peptide(C2/C0), total cholesterol, activated partial thromboplastin time, and creatinine were protective factors. CONCLUSIONS The machine learning approach helped established a DPN risk prediction model with good performance. The model identified the factors most closely related to DPN.
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Affiliation(s)
- Xiaoyang Lian
- Affiliated Hospital of Nanjing University of Chinese Medicine，Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China
| | - Juanzhi Qi
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Mengqian Yuan
- Affiliated Hospital of Nanjing University of Chinese Medicine，Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China
| | - Xiaojie Li
- Jiangsu Health Vocational College, Nanjing, 210036, Jiangsu, China
| | - Ming Wang
- Geriatric Hospital of Nanjing Medical University, Jiangsu Province Official Hospital, Nanjing, Jiangsu, 210036, China
| | - Gang Li
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Tao Yang
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
| | - Jingchen Zhong
- Affiliated Hospital of Nanjing University of Chinese Medicine，Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China.
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Anumah FE, Lawal Y, Mshelia-Reng R, Omonua SO, Odumodu K, Shuaibu R, Itanyi UD, Abubakar AI, Kolade-Yunusa HO, David ZS, Ogunlana B, Clarke A, Adediran O, Ehusani CO, Abbas Z. Common and contrast determinants of peripheral artery disease and diabetic peripheral neuropathy in North Central Nigeria. Foot (Edinb) 2023; 55:101987. [PMID: 36867948 DOI: 10.1016/j.foot.2023.101987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 12/27/2022] [Accepted: 02/23/2023] [Indexed: 03/05/2023]
Abstract
BACKGROUND Peripheral artery disease (PAD) and diabetic peripheral neuropathy (DPN) are two of the leading causes of non-traumatic amputation worldwide with tremendous negative effects on the quality of life, psychosocial well-being of persons with diabetes mellitus; and a great burden on health care expenditure. It is therefore imperative, to identify the common and contrast determinants of PAD and DPN in order to ease adoption of common and specific strategies for their early prevention. METHODS This was a multi-center cross-sectional study which involved the consecutive enrolment of one thousand and forty (1040) participants following consent and waiver of ethical approval. Relevant medical history, anthropometric measurements, other clinical examinations including measurement of ankle-brachial index (ABI) and neurological examinations were undertaken. IBM SPSS version 23 was used for statistical analysis and logistic regression was used to assess for the common and contrast determinants of PAD and DPN. Significance level used was p < 0.05. RESULTS Multiple stepwise logistic regression showed that common predictors of PAD vs DPN respectively include age, odds ratio (OR) 1.51 vs 1.99, 95 % confidence interval (CI) 1.18-2.34 vs 1.35-2.54, p = 0.033 vs 0.003; duration of DM (OR 1.51 vs 2.01, CI 1.23-1.85 vs 1.00-3.02, p = <.001 vs 0.032); central obesity (OR 9.77 vs 1.12, CI 5.07-18.82 vs 1.08-3.25, p = <.001 vs 0.047); poor SBP control (OR 2.47 vs 1.78, CI 1.26-4.87 vs 1.18-3.31, p = .016 vs 0.001); poor DBP control (OR 2.45 vs 1.45, CI 1.24-4.84 vs 1.13-2.59, p = .010 vs 0.006); poor 2HrPP control (OR 3.43 vs 2.83, CI 1.79-6.56 vs 1.31-4.17, p = <.001 vs 0.001); poor HbA1c control (OR 2.59 vs 2.31, CI 1.50-5.71 vs 1.47-3.69, p = <.001 vs 0.004). Common negative predictors or probable protective factors of PAD and DPN respectively include statins (OR 3.01 vs 2.21, CI 1.99-9.19 vs 1.45-3.26, p = .023 vs 0.004); and antiplatelets (OR 7.14 vs 2.46, CI 3.03-15.61 vs 1.09-5.53, p = .008 vs 0.030). However, only DPN was significantly predicted by female gender (OR 1.94, CI 1.39-2.25, p = 0.023), height (OR 2.02, CI 1.85-2.20, p = 0.001), generalized obesity (OR 2.02, CI 1.58-2.79, p = 0.002), and poor FPG control (OR 2.43, CI 1.50-4.10, p = 0.004) CONCLUSION: Common determinants of PAD and DPN included age, duration of DM, central obesity, and poor control of SBP, DBP, and 2HrPP control. Additionally, the use of antiplatelets and statins use were common inverse determinants of PAD and DPN which means they may help protect against PAD and DPN. However, only DPN was significantly predicted by female gender, height, generalized obesity, and poor control of FPG.
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Affiliation(s)
| | - Yakubu Lawal
- College of Health Sciences, University of Abuja, Abuja, Nigeria.
| | | | | | | | - Ramatu Shuaibu
- College of Health Sciences, University of Abuja, Abuja, Nigeria
| | | | | | | | | | | | - Andrew Clarke
- Andrew Clarke Podiatry Clinic, Suite 315, Library Square, Wilderness Road, Claremont, Cape Town 7708, South Africa
| | | | | | - Zulfiqarali Abbas
- Muhimbili University College of Health Science and Abbas Medical Centre, P O Box 21361, Dar es Salaam, Tanzania
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Zhang W, Chen L. A Nomogram for Predicting the Possibility of Peripheral Neuropathy in Patients with Type 2 Diabetes Mellitus. Brain Sci 2022; 12:brainsci12101328. [PMID: 36291262 PMCID: PMC9599450 DOI: 10.3390/brainsci12101328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/25/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
Abstract
Background and Purpose: Diabetic peripheral neuropathy (DPN) leads to ulceration, noninvasive amputation, and long-term disability. This study aimed to develop and validate a nomogram for forecasting the probability of DPN in type 2 diabetes mellitus patients. Methods: From February 2017 to May 2021, 778 patients with type 2 diabetes mellitus were included in this study. We confirmed the diagnosis of DPN according to the Toronto Expert Consensus. Patients were randomly divided into a training cohort (n = 519) and a validation cohort (n = 259). In the training cohort, univariate and multivariate logistic regression analyses were performed, and a simple nomogram was built using the stepwise method. The receiver operating characteristic (ROC), calibration curve, and decision curve analysis were computed in order to validate the discrimination and clinical value of the nomogram model. Results: About 65.7% and 72.2% of patients were diagnosed with DPN in the training and validation cohorts. We developed a novel nomogram to predict the probability of DPN based on the parameters of age, gender, duration of diabetes, body mass index, uric acid, hemoglobin A1c, and free triiodothyronine. The areas under the curves (AUCs) of the nomogram model were 0.763 in the training cohort and 0.755 in the validation cohort. The calibration plots revealed well-fitted accuracy between the predicted and actual probability in the training and validation cohorts. Decision curve analysis confirmed the clinical value of the nomogram. In subgroup analysis, the predictive ability of the nomogram model was strong. Conclusions: The nomogram of age, gender, duration of diabetes, body mass index, uric acid, hemoglobin A1c, and free triiodothyronine may assist clinicians with the early identification of DPN in patients with type 2 diabetes mellitus.
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Affiliation(s)
| | - Lingli Chen
- Correspondence: ; Tel./Fax: +86-577-555-54543
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Prediction Model for the Risk of HIV Infection among MSM in China: Validation and Stability. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19021010. [PMID: 35055826 PMCID: PMC8776241 DOI: 10.3390/ijerph19021010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 01/08/2022] [Accepted: 01/11/2022] [Indexed: 12/04/2022]
Abstract
The impact of psychosocial factors on increasing the risk of HIV infection among men who have sex with men (MSM) has attracted increasing attention. We aimed to develop and validate an integrated prediction model, especially incorporating emerging psychosocial variables, for predicting the risk of HIV infection among MSM. We surveyed and collected sociodemographic, psychosocial, and behavioral information from 547 MSM in China. The participants were split into a training set and a testing set in a 3:1 theoretical ratio. The prediction model was constructed by introducing the important variables selected with the least absolute shrinkage and selection operator (LASSO) regression, applying multivariate logistic regression, and visually assessing the risk of HIV infection through the nomogram. Receiver operating characteristic curves (ROC), Kolmogorov–Smirnov test, calibration plots, Hosmer–Lemeshow test and population stability index (PSI) were performed to test validity and stability of the model. Four of the 15 selected variables—unprotected anal intercourse, multiple sexual partners, involuntary subordination and drug use before sex—were included in the prediction model. The results indicated that the comprehensive prediction model we developed had relatively good predictive performance and stability in identifying MSM at high-risk for HIV infection, thus providing targeted interventions for high-risk MSM.
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