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Kidney Int. 2019 Jan;95(1):178-187. doi: 10.1016/j.kint.2018.08.026. Epub 2018 Nov 8.

Progression of diabetic kidney disease and trajectory of kidney function decline in Chinese patients with Type 2 diabetes.

Author information

1
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Shatin, New Territories, Hong Kong.
2
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
3
Steno Diabetes Centre, Copenhagen, Denmark.
4
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
5
Center for Genomics and Personalized Medicine Research and Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
6
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
7
Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
8
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
9
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
10
Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
11
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Shatin, New Territories, Hong Kong.
12
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong; CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Shatin, New Territories, Hong Kong. Electronic address: rcwma@cuhk.edu.hk.

Abstract

Diabetes is a major cause of end stage renal disease (ESRD), yet the natural history of diabetic kidney disease is not well understood. We aimed to identify patterns of estimated GFR (eGFR) trajectory and to determine the clinical and genetic factors and their associations of these different patterns with all-cause mortality in patients with type 2 diabetes. Among 6330 patients with baseline eGFR >60 ml/min per 1.73 m2 in the Hong Kong Diabetes Register, a total of 456 patients (7.2%) developed Stage 5 chronic kidney disease or ESRD over a median follow-up of 13 years (incidence rate 5.6 per 1000 person-years). Joint latent class modeling was used to identify different patterns of eGFR trajectory. Four distinct and non-linear trajectories of eGFR were identified: slow decline (84.3% of patients), curvilinear decline (6.5%), progressive decline (6.1%) and accelerated decline (3.1%). Microalbuminuria and retinopathy were associated with accelerated eGFR decline, which was itself associated with all-cause mortality (odds ratio [OR] 6.9; 95% confidence interval [CI]: 5.6-8.4 for comparison with slow eGFR decline). Of 68 candidate genetic loci evaluated, the inclusion of five loci (rs11803049, rs911119, rs1933182, rs11123170, and rs889472) improved the prediction of eGFR trajectories (net reclassification improvement 0.232; 95% CI: 0.057--0.406). Our study highlights substantial heterogeneity in the patterns of eGFR decline among patients with diabetic kidney disease, and identifies associated clinical and genetic factors that may help to identify those who are more likely to experience an accelerated decline in kidney function.

KEYWORDS:

albuminuria; diabetes; diabetic kidney disease; end-stage renal disease; latent trajectory; renal dysfunction

PMID:
30415941
DOI:
10.1016/j.kint.2018.08.026
[Indexed for MEDLINE]

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