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Xu W, Zhou Y, Jiang Q, Fang Y, Yang Q. Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis. Front Endocrinol (Lausanne) 2024; 15:1407348. [PMID: 39022345 PMCID: PMC11251916 DOI: 10.3389/fendo.2024.1407348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 06/07/2024] [Indexed: 07/20/2024] Open
Abstract
Objective This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models. Methods We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software. Results A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance. Conclusion Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings. Registration This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.
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Affiliation(s)
| | | | | | | | - Qian Yang
- School of Nursing, Chengdu Medical College, Chengdu, Sichuan, China
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Zoccali C, Mallamaci F, Lightstone L, Jha V, Pollock C, Tuttle K, Kotanko P, Wiecek A, Anders HJ, Remuzzi G, Kalantar-Zadeh K, Levin A, Vanholder R. A new era in the science and care of kidney diseases. Nat Rev Nephrol 2024; 20:460-472. [PMID: 38575770 DOI: 10.1038/s41581-024-00828-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2024] [Indexed: 04/06/2024]
Abstract
Notable progress in basic, translational and clinical nephrology research has been made over the past five decades. Nonetheless, many challenges remain, including obstacles to the early detection of kidney disease, disparities in access to care and variability in responses to existing and emerging therapies. Innovations in drug development, research technologies, tissue engineering and regenerative medicine have the potential to improve patient outcomes. Exciting prospects include the availability of new drugs to slow or halt the progression of chronic kidney disease, the development of bioartificial kidneys that mimic healthy kidney functions, and tissue engineering techniques that could enable transplantable kidneys to be created from the cells of the recipient, removing the risk of rejection. Cell and gene therapies have the potential to be applied for kidney tissue regeneration and repair. In addition, about 30% of kidney disease cases are monogenic and could potentially be treated using these genetic medicine approaches. Systemic diseases that involve the kidney, such as diabetes mellitus and hypertension, might also be amenable to these treatments. Continued investment, communication, collaboration and translation of innovations are crucial to realize their full potential. In addition, increasing sophistication in exploring large datasets, implementation science, and qualitative methodologies will improve the ability to deliver transformational kidney health strategies.
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Affiliation(s)
- Carmine Zoccali
- Kidney Research Institute, New York City, NY, USA.
- Institute of Molecular Biology and Genetics (Biogem), Ariano Irpino, Italy.
- Associazione Ipertensione Nefrologia Trapianto Kidney (IPNET), c/o Nefrologia, Grande Ospedale Metropolitano, Reggio Calabria, Italy.
| | - Francesca Mallamaci
- Nephrology, Dialysis and Transplantation Unit Azienda Ospedaliera "Bianchi-Melacrino-Morelli", Reggio Calabria, Italy
- CNR-IFC, Institute of Clinical Physiology, Research Unit of Clinical Epidemiology and Physiopathology of Kidney Diseases and Hypertension of Reggio Calabria, Reggio Calabria, Italy
| | - Liz Lightstone
- Department of Immunology and Inflammation, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, UK
| | - Vivek Jha
- George Institute for Global Health, UNSW, New Delhi, India
- School of Public Health, Imperial College, London, UK
- Prasanna School of Public Health, Manipal Academy of Medical Education, Manipal, India
| | - Carol Pollock
- Kolling Institute, Royal North Shore Hospital University of Sydney, Sydney, NSW, Australia
| | - Katherine Tuttle
- Providence Medical Research Center, Providence Inland Northwest, Spokane, Washington, USA
- Department of Medicine, University of Washington, Seattle, Spokane, Washington, USA
- Kidney Research Institute, Institute of Translational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Peter Kotanko
- Kidney Research Institute, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrzej Wiecek
- Department of Nephrology, Transplantation and Internal Medicine, Medical University of Silesia, 40-027, Katowice, Poland
| | - Hans Joachim Anders
- Division of Nephrology, Department of Medicine IV, Hospital of the Ludwig Maximilians University Munich, Munich, Germany
| | - Giuseppe Remuzzi
- Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Bergamo, Italy
| | - Kamyar Kalantar-Zadeh
- Harold Simmons Center for Kidney Disease Research and Epidemiology, California, USA
- Division of Nephrology and Hypertension, University of California Irvine, School of Medicine, Orange, Irvine, USA
- Veterans Affairs Healthcare System, Division of Nephrology, Long Beach, California, USA
| | - Adeera Levin
- University of British Columbia, Vancouver General Hospital, Division of Nephrology, Vancouver, British Columbia, Canada
- British Columbia, Provincial Kidney Agency, Vancouver, British Columbia, Canada
| | - Raymond Vanholder
- European Kidney Health Alliance, Brussels, Belgium
- Nephrology Section, Department of Internal Medicine and Paediatrics, University Hospital Ghent, Ghent, Belgium
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Ali AS, Pham C, Morahan G, Ekinci EI. Genetic Risk Scores Identify People at High Risk of Developing Diabetic Kidney Disease: A Systematic Review. J Clin Endocrinol Metab 2024; 109:1189-1197. [PMID: 38039081 PMCID: PMC11031242 DOI: 10.1210/clinem/dgad704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/20/2023] [Accepted: 11/29/2023] [Indexed: 12/03/2023]
Abstract
CONTEXT Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Measures to prevent and treat DKD require better identification of patients most at risk. In this systematic review, we summarize the existing evidence of genetic risk scores (GRSs) and their utility for predicting DKD in people with type 1 or type 2 diabetes. EVIDENCE ACQUISITION We searched MEDLINE, Embase, Web of Science, and Cochrane Reviews in June 2022 to identify all existing and relevant literature. Main data items sought were study design, sample size, population, single nucleotide polymorphisms of interest, DKD-related outcomes, and relevant summary measures of result. The Critical Appraisal Skills Programme checklist was used to evaluate the methodological quality of studies. EVIDENCE SYNTHESIS We identified 400 citations of which 15 are included in this review. Overall, 7 studies had positive results, 5 had mixed results, and 3 had negative results. Most studies with the strongest methodological quality (n = 9) reported statistically significant and favourable findings of a GRS's association with at least 1 measure of DKD. CONCLUSION This systematic review presents evidence of the utility of GRSs to identify people with diabetes that are at high risk of developing DKD. In practice, a robust GRS could be used at the first clinical encounter with a person living with diabetes in order to stratify their risk of complications. Further prospective research is needed.
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Affiliation(s)
- Aleena Shujaat Ali
- Department of Medicine, The University of Melbourne, Melbourne 3084, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
| | - Cecilia Pham
- Department of Medicine, The University of Melbourne, Melbourne 3084, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
| | - Grant Morahan
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
- Diabetes Research Foundation, The University of Western Australia, Perth 6009, Australia
| | - Elif Ilhan Ekinci
- Department of Medicine, The University of Melbourne, Melbourne 3084, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
- Department of Endocrinology, Austin Health, Melbourne 3084, Australia
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Sandholm N, Dahlström EH, Groop PH. Genetic and epigenetic background of diabetic kidney disease. Front Endocrinol (Lausanne) 2023; 14:1163001. [PMID: 37324271 PMCID: PMC10262849 DOI: 10.3389/fendo.2023.1163001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/10/2023] [Indexed: 06/17/2023] Open
Abstract
Diabetic kidney disease (DKD) is a severe diabetic complication that affects up to half of the individuals with diabetes. Elevated blood glucose levels are a key underlying cause of DKD, but DKD is a complex multifactorial disease, which takes years to develop. Family studies have shown that inherited factors also contribute to the risk of the disease. During the last decade, genome-wide association studies (GWASs) have emerged as a powerful tool to identify genetic risk factors for DKD. In recent years, the GWASs have acquired larger number of participants, leading to increased statistical power to detect more genetic risk factors. In addition, whole-exome and whole-genome sequencing studies are emerging, aiming to identify rare genetic risk factors for DKD, as well as epigenome-wide association studies, investigating DNA methylation in relation to DKD. This article aims to review the identified genetic and epigenetic risk factors for DKD.
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Affiliation(s)
- Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Emma H. Dahlström
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, Australia
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Liao LN, Li TC, Yeh CC, Li CI, Liu CS, Yang CW, Yang YF, Lin CH, Tsai FJ, Lin CC. Risk prediction of nephropathy by integrating clinical and genetic information among adult patients with type 2 diabetes. Acta Diabetol 2023; 60:413-424. [PMID: 36576562 DOI: 10.1007/s00592-022-02017-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 12/10/2022] [Indexed: 12/29/2022]
Abstract
AIMS Diabetic nephropathy (DN) is a major healthcare challenge. We developed and internally and externally validated a risk prediction model of DN by integrating clinical factors and SNPs from genes of multiple CKD-related pathways in the Han Chinese population. MATERIALS AND METHODS A total of 1526 patients with type 2 diabetes were randomly allocated into derivation (n = 1019) or validation (n = 507) sets. External validation was performed with 3899 participants from the Taiwan Biobank. We selected 66 SNPs identified from literature review for building our weighted genetic risk score (wGRS). The steps for prediction model development integrating clinical and genetic information were based on the Framingham Heart Study. RESULTS The AUROC (95% CI) for this DN prediction model with combined clinical factors and wGRS was 0.81 (0.78, 0.84) in the derivation set. Furthermore, by directly using the information of these 66 SNPs, our final prediction model had AUROC values of 0.85 (0.82, 0.87), 0.89 (0.86, 0.91), and 0.77 (0.74, 0.80) in the derivation, internal validation, and external validation sets, respectively. Under the combined model, the results with a cutoff point of 30% showed 70.91% sensitivity, 67.84% specificity, 51.54% positive predictive value, and 82.86% negative predictive value. CONCLUSIONS We developed and internally and externally validated a model with clinical factors and SNPs from genes of multiple CKD-related pathways to predict DN in Taiwan. This model can be used in clinical risk management practice as a screening tool to identify persons who are genetically predisposed to DN for early intervention and prevention.
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Affiliation(s)
- Li-Na Liao
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan, R.O.C
| | - Tsai-Chung Li
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan, R.O.C
- Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan, R.O.C
| | - Chih-Ching Yeh
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan, R.O.C
- School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan, R.O.C
- Master Program in Applied Epidemiology, College of Public Health, Taipei Medical University, Taipei, Taiwan, R.O.C
| | - Chia-Ing Li
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, R.O.C
- School of Medicine, College of Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C
| | - Chiu-Shong Liu
- School of Medicine, College of Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C
- Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan, R.O.C
| | - Chuan-Wei Yang
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, R.O.C
| | - Ya-Fei Yang
- Department of Nephrology, Everan Hospital, Taichung, Taiwan, R.O.C
| | - Chih-Hsueh Lin
- School of Medicine, College of Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C
- Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan, R.O.C
| | - Fuu-Jen Tsai
- School of Chinese Medicine, College of Chinese Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C..
- Human Genetic Laboratory, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, R.O.C..
| | - Cheng-Chieh Lin
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, R.O.C..
- School of Medicine, College of Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C..
- Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan, R.O.C..
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Hill C, Duffy S, Coulter T, Maxwell AP, McKnight AJ. Harnessing Genomic Analysis to Explore the Role of Telomeres in the Pathogenesis and Progression of Diabetic Kidney Disease. Genes (Basel) 2023; 14:609. [PMID: 36980881 PMCID: PMC10048490 DOI: 10.3390/genes14030609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
The prevalence of diabetes is increasing globally, and this trend is predicted to continue for future decades. Research is needed to uncover new ways to manage diabetes and its co-morbidities. A significant secondary complication of diabetes is kidney disease, which can ultimately result in the need for renal replacement therapy, via dialysis or transplantation. Diabetic kidney disease presents a substantial burden to patients, their families and global healthcare services. This review highlights studies that have harnessed genomic, epigenomic and functional prediction tools to uncover novel genes and pathways associated with DKD that are useful for the identification of therapeutic targets or novel biomarkers for risk stratification. Telomere length regulation is a specific pathway gaining attention recently because of its association with DKD. Researchers are employing both observational and genetics-based studies to identify telomere-related genes associated with kidney function decline in diabetes. Studies have also uncovered novel functions for telomere-related genes beyond the immediate regulation of telomere length, such as transcriptional regulation and inflammation. This review summarises studies that have revealed the potential to harness therapeutics that modulate telomere length, or the associated epigenetic modifications, for the treatment of DKD, to potentially slow renal function decline and reduce the global burden of this disease.
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Affiliation(s)
- Claire Hill
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
| | - Seamus Duffy
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
| | - Tiernan Coulter
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
| | - Alexander Peter Maxwell
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
- Regional Nephrology Unit, Belfast City Hospital, Belfast BT9 7AB, UK
| | - Amy Jayne McKnight
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
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Mohammedi K, Marre M, Hadjadj S, Potier L, Velho G. Redox Genetic Risk Score and the Incidence of End-Stage Kidney Disease in People with Type 1 Diabetes. Cells 2022; 11:cells11244131. [PMID: 36552894 PMCID: PMC9777489 DOI: 10.3390/cells11244131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/23/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
End-stage kidney disease (ESKD) is a multifactorial condition influenced by genetic background, but the extent to which a genetic risk score (GRS) improves ESKD prediction is unknown. We built a redox GRS on the base of previous association studies (six polymorphisms from six redox genes) and tested its relationship with ESKD in three cohorts of people with type 1 diabetes. Among 1012 participants, ESKD (hemodialysis requirement, kidney transplantation, eGFR < 15 mL/min/1.73 m2) occurred in 105 (10.4%) during a 14-year follow-up. High redox GRS was associated with increased ESKD risk (adjusted HR for the upper versus the lowest GRS tertile: 2.60 (95% CI, 1.51-4.48), p = 0.001). Each additional risk-allele was associated with a 20% increased risk of ESKD (95% CI, 8-33, p < 0.0001). High GRS yielded a relevant population attributable fraction (30%), but only a marginal enhancement in c-statistics index (0.928 [0.903-0.954]) over clinical factors 0.921 (0.892-0.950), p = 0.04). This is the first report of an independent association between redox GRS and increased risk of ESKD in type 1 diabetes. Our results do not support the use of this GRS in clinical practice but provide new insights into the involvement of oxidative stress genetic factors in ESKD risk in type 1 diabetes.
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Affiliation(s)
- Kamel Mohammedi
- Centre Hospitalier de Bordeaux, Department of Endocrinology, Diabetes and Nutrition, University Hospital of Bordeaux, 33604 Pessac, France
- Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, ON L8S 4L8, Canada
- Correspondence:
| | - Michel Marre
- Institut Necker-Enfants Malades, INSERM, Université de Paris, 75013 Paris, France
- Clinique Ambroise Paré, 92200 Neuilly-sur-Seine, France
| | - Samy Hadjadj
- Institut du Thorax, INSERM, CNRS, UNIV Nantes, CHU Nantes, 44109 Nantes, France
| | - Louis Potier
- Institut Necker-Enfants Malades, INSERM, Université de Paris, 75013 Paris, France
- Clinique Ambroise Paré, 92200 Neuilly-sur-Seine, France
- Service d’Endocrinologie Diabétologie Nutrition, Hôpital Bichat, AP-HP, 75013 Paris, France
| | - Gilberto Velho
- Institut Necker-Enfants Malades, INSERM, Université de Paris, 75013 Paris, France
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Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease. NPJ Digit Med 2022; 5:166. [PMID: 36323795 PMCID: PMC9630270 DOI: 10.1038/s41746-022-00713-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 10/18/2022] [Indexed: 11/27/2022] Open
Abstract
Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.
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Saputro SA, Pattanateepapon A, Pattanaprateep O, Aekplakorn W, McKay GJ, Attia J, Thakkinstian A. External validation of prognostic models for chronic kidney disease among type 2 diabetes. J Nephrol 2022; 35:1637-1653. [PMID: 34997924 PMCID: PMC9300508 DOI: 10.1007/s40620-021-01220-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/30/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Various prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). However, their generalisability and predictive performance in different populations remain largely unvalidated. This study aimed to externally validate several prognostic models of CKD in a T2D Thai cohort. METHODS A nationwide survey was linked with hospital databases to create a prospective cohort of patients with diabetes (n = 3416). We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. We updated prognostic models by including additional clinical parameters to optimise model performance in the Thai setting. RESULTS Six relevant previously published models were identified. At baseline, C-statistics ranged from 0.585 (0.565-0.605) to 0.786 (0.765-0.806) for CKD and 0.657 (0.610-0.703) to 0.760 (0.705-0.816) for end-stage renal disease (ESRD). All original CKD models showed fair calibration with Observed/Expected (O/E) ratios ranging from 0.999 (0.975-1.024) to 1.009 (0.929-1.090). Hosmer-Lemeshow tests indicated a good fit for all models. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low's of 0.114 for CKD and Elley's of 0.025 for ESRD. CONCLUSIONS All models showed moderate discrimination and fair calibration. Updating models to include routine clinical factors substantially enhanced their accuracy. Low's (developed in Singapore) and Elley's model (developed in New Zealand), outperformed the other models evaluated. These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand.
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Affiliation(s)
- Sigit Ari Saputro
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Phayathai, Bangkok, 10400, Thailand
- Department of Epidemiology Biostatistics Population and Health Promotion, Faculty of Public Health, Airlangga University, Surabaya, 60115, Indonesia
| | - Anuchate Pattanateepapon
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Phayathai, Bangkok, 10400, Thailand.
| | - Oraluck Pattanaprateep
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Phayathai, Bangkok, 10400, Thailand
| | - Wichai Aekplakorn
- Department of Community Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Phayathai, Bangkok, 10400, Thailand.
| | - Gareth J McKay
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - John Attia
- School of Medicine and Public Health, and Hunter Medical Research Institute, University of Newcastle, New Lambton, NSW, Australia
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Phayathai, Bangkok, 10400, Thailand
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Saputro SA, Pattanaprateep O, Pattanateepapon A, Karmacharya S, Thakkinstian A. Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis. Syst Rev 2021; 10:288. [PMID: 34724973 PMCID: PMC8561867 DOI: 10.1186/s13643-021-01841-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models. METHODS Prognostic models of diabetic microvascular complications were retrieved from PubMed and Scopus up to 31 December 2020. Studies were selected, if they developed or internally/externally validated models of any microvascular complication in type 2 diabetes (T2D). RESULTS In total, 71 studies were eligible, of which 32, 30 and 18 studies initially developed prognostic model for diabetic retinopathy (DR), chronic kidney disease (CKD) and end stage renal disease (ESRD) with the number of derived equations of 84, 96 and 51, respectively. Most models were derived-phases, some were internal and external validations. Common predictors were age, sex, HbA1c, diabetic duration, SBP and BMI. Traditional statistical models (i.e. Cox and logit regression) were mostly applied, otherwise machine learning. In cohorts, the discriminative performance in derived-logit was pooled with C statistics of 0.82 (0.73‑0.92) for DR and 0.78 (0.74‑0.83) for CKD. Pooled Cox regression yielded 0.75 (0.74‑0.77), 0.78 (0.74‑0.82) and 0.87 (0.84‑0.89) for DR, CKD and ESRD, respectively. External validation performances were sufficiently pooled with 0.81 (0.78‑0.83), 0.75 (0.67‑0.84) and 0.87 (0.85‑0.88) for DR, CKD and ESRD, respectively. CONCLUSIONS Several prognostic models were developed, but less were externally validated. A few studies derived the models by using appropriate methods and were satisfactory reported. More external validations and impact analyses are required before applying these models in clinical practice. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42018105287.
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Affiliation(s)
- Sigit Ari Saputro
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Pyathai, Bangkok, 10400, Thailand.,Department of Epidemiology Biostatistics Population and Health Promotion, Faculty of Public Health, Airlangga University, Surabaya, Indonesia
| | - Oraluck Pattanaprateep
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Pyathai, Bangkok, 10400, Thailand.
| | - Anuchate Pattanateepapon
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Pyathai, Bangkok, 10400, Thailand
| | - Swekshya Karmacharya
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Pyathai, Bangkok, 10400, Thailand
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Pyathai, Bangkok, 10400, Thailand
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