Prediction of diabetic kidney disease risk using machine learning models: a population-based cohort study of Asian adults
Abstract
Background: Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multi-dimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).
Methods: We utilized longitudinal data from 1365 Chinese, Malay and Indian participants aged 40-80 years with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 with at least 25% decrease in eGFR at follow-up from baseline. 339 features including participant characteristics, retinal imaging, genetic and blood metabolites were used as predictors. Performances of several ML models were compared to each other and to logic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).
Results: ML model, Elastic Net (EN) had the best AUC (95% confidence interval) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2% and 65.9% vs. 73.0% and 72.8% by LR. The top-15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR and metabolites related to lipids, lipoproteins, fatty acids and ketone bodies.
Conclusions: Our results showed ML together with feature selection improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors including metabolites.
Funding: This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability
The authors declare that the data supporting the findings of this study are available within the article and its supplementary materials. The individual participant-level data cannot be shared publicly due to privacy and confidentiality concerns. However, de-identified data may be available upon request with a project proposal to qualified individuals, subject to approval by the Singapore Eye Research Institute and access will require a data sharing agreement. Interested researchers can send data access requests to Prof. Ching-Yu Cheng at the Singapore Eye Research Institute using the following email address: cheng.ching.yu@seri.com.sgProcessed version of the datasets are provided in Supplementary Tables S1-S5.
Article and author information
Author details
Funding
National Medical Research Council (NMRC/OFLCG/001/2017)
- Gavin Tan
National Medical Research Council (NMRC/HCSAINV/MOH-001019-00)
- Charumathi Sabanayagam
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: SEED was conducted in accordance with the Declaration of Helsinki and was approved by the SingHealth Centralised Institutional Review Board [2018/2717, 2018/ 2921, 2012/487/A, 2015/2279, 2018/2006, 2018/2594, 2018/2570]. Informed consent was obtained from all participants.
Copyright
© 2023, Sabanayagam et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,095
- views
-
- 251
- downloads
-
- 4
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Epidemiology and Global Health
- Microbiology and Infectious Disease
Background:
Few national-level studies have evaluated the impact of ‘hybrid’ immunity (vaccination coupled with recovery from infection) from the Omicron variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Methods:
From May 2020 to December 2022, we conducted serial assessments (each of ~4000–9000 adults) examining SARS-CoV-2 antibodies within a mostly representative Canadian cohort drawn from a national online polling platform. Adults, most of whom were vaccinated, reported viral test-confirmed infections and mailed self-collected dried blood spots (DBSs) to a central lab. Samples underwent highly sensitive and specific antibody assays to spike and nucleocapsid protein antigens, the latter triggered only by infection. We estimated cumulative SARS-CoV-2 incidence prior to the Omicron period and during the BA.1/1.1 and BA.2/5 waves. We assessed changes in antibody levels and in age-specific active immunity levels.
Results:
Spike levels were higher in infected than in uninfected adults, regardless of vaccination doses. Among adults vaccinated at least thrice and infected more than 6 months earlier, spike levels fell notably and continuously for the 9-month post-vaccination. In contrast, among adults infected within 6 months, spike levels declined gradually. Declines were similar by sex, age group, and ethnicity. Recent vaccination attenuated declines in spike levels from older infections. In a convenience sample, spike antibody and cellular responses were correlated. Near the end of 2022, about 35% of adults above age 60 had their last vaccine dose more than 6 months ago, and about 25% remained uninfected. The cumulative incidence of SARS-CoV-2 infection rose from 13% (95% confidence interval 11–14%) before omicron to 78% (76–80%) by December 2022, equating to 25 million infected adults cumulatively. However, the coronavirus disease 2019 (COVID-19) weekly death rate during the BA.2/5 waves was less than half of that during the BA.1/1.1 wave, implying a protective role for hybrid immunity.
Conclusions:
Strategies to maintain population-level hybrid immunity require up-to-date vaccination coverage, including among those recovering from infection. Population-based, self-collected DBSs are a practicable biological surveillance platform.
Funding:
Funding was provided by the COVID-19 Immunity Task Force, Canadian Institutes of Health Research, Pfizer Global Medical Grants, and St. Michael’s Hospital Foundation. PJ and ACG are funded by the Canada Research Chairs Program.
-
- Epidemiology and Global Health
Background:
The Zanzibar archipelago of Tanzania has become a low-transmission area for Plasmodium falciparum. Despite being considered an area of pre-elimination for years, achieving elimination has been difficult, likely due to a combination of imported infections from mainland Tanzania and continued local transmission.
Methods:
To shed light on these sources of transmission, we applied highly multiplexed genotyping utilizing molecular inversion probes to characterize the genetic relatedness of 282 P. falciparum isolates collected across Zanzibar and in Bagamoyo district on the coastal mainland from 2016 to 2018.
Results:
Overall, parasite populations on the coastal mainland and Zanzibar archipelago remain highly related. However, parasite isolates from Zanzibar exhibit population microstructure due to the rapid decay of parasite relatedness over very short distances. This, along with highly related pairs within shehias, suggests ongoing low-level local transmission. We also identified highly related parasites across shehias that reflect human mobility on the main island of Unguja and identified a cluster of highly related parasites, suggestive of an outbreak, in the Micheweni district on Pemba island. Parasites in asymptomatic infections demonstrated higher complexity of infection than those in symptomatic infections, but have similar core genomes.
Conclusions:
Our data support importation as a main source of genetic diversity and contribution to the parasite population in Zanzibar, but they also show local outbreak clusters where targeted interventions are essential to block local transmission. These results highlight the need for preventive measures against imported malaria and enhanced control measures in areas that remain receptive to malaria reemergence due to susceptible hosts and competent vectors.
Funding:
This research was funded by the National Institutes of Health, grants R01AI121558, R01AI137395, R01AI155730, F30AI143172, and K24AI134990. Funding was also contributed from the Swedish Research Council, Erling-Persson Family Foundation, and the Yang Fund. RV acknowledges funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 program supported by the European Union. RV also acknowledges funding by Community Jameel.