Wenhui Jiang1, Jingyu Wang1, Xiaofang Shen1, Wenli Lu2, Yuan Wang2, Wen Li2, Zhongai Gao1, Jie Xu1, Xiaochen Li1, Ran Liu1, Miaoyan Zheng1, Bai Chang3, Jing Li1, Juhong Yang3, Baocheng Chang3. 1. NHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China. 2. Department of Epidemiology and Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China. 3. NHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China changbc1970edu@yeah.net yangjuhongpro@126.com.
Abstract
BACKGROUND: Identifying patients at high risk of diabetic kidney disease (DKD) helps improve clinical outcome. PURPOSE: To establish a model for predicting DKD. DATA SOURCES: The derivation cohort was from a meta-analysis. The validation cohort was from a Chinese cohort. STUDY SELECTION: Cohort studies that reported risk factors of DKD with their corresponding risk ratios (RRs) in patients with type 2 diabetes were selected. All patients had estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 and urinary albumin-to-creatinine ratio (UACR) <30 mg/g at baseline. DATA EXTRACTION: Risk factors and their corresponding RRs were extracted. Only risk factors with statistical significance were included in our DKD risk prediction model. DATA SYNTHESIS: Twenty cohorts including 41,271 patients with type 2 diabetes were included in our meta-analysis. Age, BMI, smoking, diabetic retinopathy, hemoglobin A1c, systolic blood pressure, HDL cholesterol, triglycerides, UACR, and eGFR were statistically significant. All these risk factors were included in the model except eGFR because of the significant heterogeneity among studies. All risk factors were scored according to their weightings, and the highest score was 37.0. The model was validated in an external cohort with a median follow-up of 2.9 years. A cutoff value of 16 was selected with a sensitivity of 0.847 and a specificity of 0.677. LIMITATIONS: There was huge heterogeneity among studies involving eGFR. More evidence is needed to power it as a risk factor of DKD. CONCLUSIONS: The DKD risk prediction model consisting of nine risk factors established in this study is a simple tool for detecting patients at high risk of DKD.
BACKGROUND: Identifying patients at high risk of diabetic kidney disease (DKD) helps improve clinical outcome. PURPOSE: To establish a model for predicting DKD. DATA SOURCES: The derivation cohort was from a meta-analysis. The validation cohort was from a Chinese cohort. STUDY SELECTION: Cohort studies that reported risk factors of DKD with their corresponding risk ratios (RRs) in patients with type 2 diabetes were selected. All patients had estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 and urinary albumin-to-creatinine ratio (UACR) <30 mg/g at baseline. DATA EXTRACTION: Risk factors and their corresponding RRs were extracted. Only risk factors with statistical significance were included in our DKD risk prediction model. DATA SYNTHESIS: Twenty cohorts including 41,271 patients with type 2 diabetes were included in our meta-analysis. Age, BMI, smoking, diabetic retinopathy, hemoglobin A1c, systolic blood pressure, HDL cholesterol, triglycerides, UACR, and eGFR were statistically significant. All these risk factors were included in the model except eGFR because of the significant heterogeneity among studies. All risk factors were scored according to their weightings, and the highest score was 37.0. The model was validated in an external cohort with a median follow-up of 2.9 years. A cutoff value of 16 was selected with a sensitivity of 0.847 and a specificity of 0.677. LIMITATIONS: There was huge heterogeneity among studies involving eGFR. More evidence is needed to power it as a risk factor of DKD. CONCLUSIONS: The DKD risk prediction model consisting of nine risk factors established in this study is a simple tool for detecting patients at high risk of DKD.
Authors: Christine P Limonte; Erkka Valo; Daniel Montemayor; Farsad Afshinnia; Tarunveer S Ahluwalia; Tina Costacou; Manjula Darshi; Carol Forsblom; Andrew N Hoofnagle; Per-Henrik Groop; Rachel G Miller; Trevor J Orchard; Subramaniam Pennathur; Peter Rossing; Niina Sandholm; Janet K Snell-Bergeon; Hongping Ye; Jing Zhang; Loki Natarajan; Ian H de Boer; Kumar Sharma Journal: Am J Nephrol Date: 2020-10-14 Impact factor: 3.754