Yu, D ORCID: https://orcid.org/0000-0002-8449-7725, Shang, J, Cai, Y, Wang, Z, Zhao, B, Zhao, Z and Simmons, D (2019) A low‐cost laboratory‐based method for predicting newly diagnosed biopsy‐proven diabetic nephropathy in people with type 2 diabetes. Diabetic Medicine.

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To identify significant prognostic factors for newly diagnosed biopsy‐proven diabetic nephropathy using routine laboratory measures, from which to derive a low‐cost explanatory model, and to use this model to examine associations between the potential low‐cost test panels and the risk of diabetic nephropathy in people with type 2 diabetes with normal kidney function.

A population‐based case–control study was undertaken to test the association between diabetic nephropathy and 47 laboratory variables using a ‘hypothesis‐free’ strategy and five routinely recorded factors in diabetes care (BMI, systolic and diastolic blood pressure, HbA1c, fasting glucose). Factors that were significant after Bonferroni correction were included in different test panels and used to develop diabetic nephropathy (outcome) explanatory models. Models were derived using risk‐set sampling among 950 biopsy‐proven diabetic nephropathy cases newly diagnosed in the period between 2012 and 2018 and among 4750 age‐ and gender‐matched controls.

A total of 15 Bonferroni‐corrected significant laboratory predictors in the three test panels (blood cell, serum electrolytes and blood coagulation) were identified through multivariable analysis and used to develop the three explanatory models. The optimism‐adjusted C‐statistics and calibration slope were 0.725 (95% CI 0.723–0.728) and 0.978 (95% CI 0.912–0.999) for the blood cell model, 0.688 (95% CI 0.686–0.690) and 0.923 (95% CI 0.706–0.977) for the serum electrolytes model, 0.648 (95% CI 0.639–0.658) and 0.914 (95% CI 0.641–1.115) for the blood coagulation model, respectively.

A total of 15 predictors were significantly associated with newly diagnosed biopsy‐proven diabetic nephropathy in type 2 diabetes. The blood cell model appeared to be the low‐cost model with the best predictive ability.

Item Type: Article
Additional Information: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Wiley at https://doi.org/10.1111/dme.14195 - please refer to any applicable terms of use of the publisher.
Subjects: R Medicine > RC Internal medicine > RC660 Diabetes
Divisions: Faculty of Medicine and Health Sciences > School of Primary, Community and Social Care
Depositing User: Symplectic
Date Deposited: 13 Dec 2019 13:59
Last Modified: 03 Dec 2020 01:30
URI: https://eprints.keele.ac.uk/id/eprint/7366

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