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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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Koraishy FM, Mann FD, Waszczuk MA, Kuan PF, Jonas K, Yang X, Docherty A, Shabalin A, Clouston S, Kotov R, Luft B. Polygenic association of glomerular filtration rate decline in world trade center responders. BMC Nephrol 2022; 23:347. [PMID: 36307804 PMCID: PMC9615399 DOI: 10.1186/s12882-022-02967-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/26/2022] [Accepted: 10/06/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The factors associated with estimated glomerular filtrate rate (eGFR) decline in low risk adults remain relatively unknown. We hypothesized that a polygenic risk score (PRS) will be associated with eGFR decline. METHODS We analyzed genetic data from 1,601 adult participants with European ancestry in the World Trade Center Health Program (baseline age 49.68 ± 8.79 years, 93% male, 23% hypertensive, 7% diabetic and 1% with cardiovascular disease) with ≥ three serial measures of serum creatinine. PRSs were calculated from an aggregation of single nucleotide polymorphisms (SNPs) from a recent, large-scale genome-wide association study (GWAS) of rapid eGFR decline. Generalized linear models were used to evaluate the association of PRS with renal outcomes: baseline eGFR and CKD stage, rate of change in eGFR, stable versus declining eGFR over a 3-5-year observation period. eGFR decline was defined in separate analyses as "clinical" (> -1.0 ml/min/1.73 m2/year) or "empirical" (lower most quartile of eGFR slopes). RESULTS The mean baseline eGFR was ~ 86 ml/min/1.73 m2. Subjects with decline in eGFR were more likely to be diabetic. PRS was significantly associated with lower baseline eGFR (B = -0.96, p = 0.002), higher CKD stage (OR = 1.17, p = 0.010), decline in eGFR (OR = 1.14, p = 0.036) relative to stable eGFR, and the lower quartile of eGFR slopes (OR = 1.21, p = 0.008), after adjusting for established risk factors for CKD. CONCLUSION Common genetic variants are associated with eGFR decline in middle-aged adults with relatively low comorbidity burdens.
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Affiliation(s)
- Farrukh M Koraishy
- Division of Nephrology, Department of Medicine, Stony Brook University, 100 Nicolls Road, HSCT16-080E, Stony Brook, NY, USA.
| | - Frank D Mann
- Department of Family, Population, and Preventative Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University, North Chicago, IL, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Katherine Jonas
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Xiaohua Yang
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Anna Docherty
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Andrey Shabalin
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Sean Clouston
- Department of Family, Population, and Preventative Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Benjamin Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
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Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int 2022; 101:1126-1141. [PMID: 35460632 PMCID: PMC9922534 DOI: 10.1016/j.kint.2022.03.019] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/16/2022] [Accepted: 03/29/2022] [Indexed: 01/19/2023]
Abstract
Numerous genes for monogenic kidney diseases with classical patterns of inheritance, as well as genes for complex kidney diseases that manifest in combination with environmental factors, have been discovered. Genetic findings are increasingly used to inform clinical management of nephropathies, and have led to improved diagnostics, disease surveillance, choice of therapy, and family counseling. All of these steps rely on accurate interpretation of genetic data, which can be outpaced by current rates of data collection. In March of 2021, Kidney Diseases: Improving Global Outcomes (KDIGO) held a Controversies Conference on "Genetics in Chronic Kidney Disease (CKD)" to review the current state of understanding of monogenic and complex (polygenic) kidney diseases, processes for applying genetic findings in clinical medicine, and use of genomics for defining and stratifying CKD. Given the important contribution of genetic variants to CKD, practitioners with CKD patients are advised to "think genetic," which specifically involves obtaining a family history, collecting detailed information on age of CKD onset, performing clinical examination for extrarenal symptoms, and considering genetic testing. To improve the use of genetics in nephrology, meeting participants advised developing an advanced training or subspecialty track for nephrologists, crafting guidelines for testing and treatment, and educating patients, students, and practitioners. Key areas of future research, including clinical interpretation of genome variation, electronic phenotyping, global representation, kidney-specific molecular data, polygenic scores, translational epidemiology, and open data resources, were also identified.
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Yu Z, Jin J, Tin A, Köttgen A, Yu B, Chen J, Surapaneni A, Zhou L, Ballantyne CM, Hoogeveen RC, Arking DE, Chatterjee N, Grams ME, Coresh J. Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. J Am Soc Nephrol 2021; 32:3161-3173. [PMID: 34548389 PMCID: PMC8638405 DOI: 10.1681/asn.2020111599] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 08/29/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. METHODS We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS ( n =765,348) and UK Biobank GWAS (90% of the cohort; n =451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data ( n =45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study ( n =8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR. RESULTS The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α -1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR. CONCLUSIONS A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.
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Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts,Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Centre–University of Freiburg, Freiburg, Germany
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Jingsha Chen
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Aditya Surapaneni
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Linda Zhou
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | | | - Ron C. Hoogeveen
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Dan E. Arking
- McKusick-Nathans Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
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Cao X, Yang B, Zhou J. Scoring model to predict risk of chronic kidney disease in Chinese health screening examinees with type 2 diabetes. Int Urol Nephrol 2021; 54:1629-1639. [PMID: 34724145 PMCID: PMC9184348 DOI: 10.1007/s11255-021-03045-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 10/24/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE As health screening continues to increase in China, there is an opportunity to integrate a large number of demographic as well as subjective and objective clinical data into risk prediction modeling. The aim of this study was to develop and validate a prediction model for chronic kidney disease (CKD) in Chinese health screening examinees with type 2 diabetes mellitus (T2DM). METHODS We conducted a retrospective cohort study consisting of 2051 Chinese T2DM patients between 35 and 78 years old who were enrolled in the XY3CKD Follow-up Program between 2009 and 2010. All participants were randomly assigned into a derivation set or a validation set at a 2:1 ratio. Cox proportional hazards regression model was selected for the analysis of risk factors for the development of the proposed risk model of CKD. We established a prediction model with a scoring system following the steps proposed by the Framingham Heart Study. RESULTS The mean follow-up was 8.52 years, with a total of 315 (23.20%) and 189 (27.27%) incident CKD cases in the derivation set and validation set, respectively. We identified the following risk factors: age, gender, body mass index, duration of type 2 diabetes, variation of fasting blood glucose, stroke, and hypertension. The points were summed to obtain individual scores (from 0 to 15). The areas under the curve of 3-, 5- and 10-year CKD risks were 0.843, 0.799 and 0.780 in the derivation set and 0.871, 0.803 and 0.785 in the validation set, respectively. CONCLUSIONS The proposed scoring system is a promising tool for further application of assisting Chinese medical staff for early prevention of T2DM complications among health screening examinees.
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Affiliation(s)
- Xia Cao
- Department of Health Management, Health Management Research Center of Central South University, The Third Xiangya Hospital, Central South University, Changsha, 410013 Hunan Province China
| | - Binfang Yang
- Department of Health Management, Health Management Research Center of Central South University, The Third Xiangya Hospital, Central South University, Changsha, 410013 Hunan Province China
| | - Jiansong Zhou
- Department of Psychiatry & Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province China
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