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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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Ulloque-Badaracco JR, Mosquera-Rojas MD, Hernandez-Bustamante EA, Alarcón-Braga EA, Ulloque-Badaracco RR, Al-kassab-Córdova A, Herrera-Añazco P, Benites-Zapata VA, Hernandez AV. Association between Lipid Profile and Apolipoproteins with Risk of Diabetic Foot Ulcer: A Systematic Review and Meta-Analysis. Int J Clin Pract 2022; 2022:5450173. [PMID: 36016824 PMCID: PMC9385316 DOI: 10.1155/2022/5450173] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/21/2022] [Accepted: 07/19/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND AIMS Biomarkers are necessary to stratify the risk of diabetic foot ulcers (DFUs). This systematic review and meta-analysis aimed to evaluate the association between the lipid profile and apolipoproteins with the risk of DFU. METHODS A systematic search was conducted in PubMed, Scopus, Cochrane Library, and Web of Science among adult patients. Cohort and case-control studies were included. Random-effects models were used for meta-analyses, and the effects were expressed as odds ratio (OR) and their 95% confidence intervals (CIs). We evaluated publication bias through Egger's test and funnel plot. RESULTS A total of 12 cohort studies and 26 case-control studies were included, with 17076 patients. We found that the higher values of total cholesterol (TC), low-density lipoprotein (LDL), triglycerides, and lipoprotein(a) (Lp(a)) were associated with a higher risk of developing DFU (OR: 1.47, OR: 1.47, OR: 1.5, OR: 1.85, respectively). Otherwise, the lower values of HDL were associated with a higher risk of developing DFU (OR: 0.49). Publication bias was not found for associations between TC, HDL, LDL, or TG and the risk of DFU. CONCLUSIONS The high values of LDL, TC, TG, and Lp(a) and low values of HDL are associated with a higher risk of developing DFU. Furthermore, we did not find a significant association for VLDL, ApoA1, ApoB, and ApoB/ApoA1 ratio.
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Affiliation(s)
- Juan R. Ulloque-Badaracco
- Escuela de Medicina, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
- Sociedad Científica de Estudiantes de Medicina de la Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | - Melany D. Mosquera-Rojas
- Escuela de Medicina, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
- Sociedad Científica de Estudiantes de Medicina de la Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | - Enrique A Hernandez-Bustamante
- Sociedad Científica de Estudiantes de Medicina de la Universidad Nacional de Trujillo, Trujillo, Peru
- Grupo Peruano de Investigación Epidemiológica, Unidad para la Generación y Síntesis de Evidencias en Salud, Universidad San Ignacio de Loyola, Lima, Peru
| | - Esteban A Alarcón-Braga
- Escuela de Medicina, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
- Sociedad Científica de Estudiantes de Medicina de la Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | | | | | - Percy Herrera-Añazco
- Universidad Privada San Juan Bautista, Lima, Peru
- Instituto de Evaluación de Tecnologías en Salud e Investigación—IETSI, EsSalud, Lima, Peru
| | - Vicente A. Benites-Zapata
- Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Vicerrectorado de Investigación, Universidad San Ignacio de Loyola, Lima, Peru
| | - Adrian V. Hernandez
- Unidad de Revisiones Sistemáticas y Meta-Análisis, Guías de Práctica Clínica y Evaluaciones de Tecnología Sanitaria, Vicerrectorado de Investigación, Universidad San Ignacio de Loyola, Lima, Peru
- Health Outcomes, Policy, and Evidence Synthesis (HOPES) Group, University of Connecticut School of Pharmacy, Mansfield, CT, USA
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