Christine P Limonte1,2, Erkka Valo3,4,5, Daniel Montemayor6,7, Farsad Afshinnia8, Tarunveer S Ahluwalia9,10, Tina Costacou11, Manjula Darshi6,7, Carol Forsblom3,4,5, Andrew N Hoofnagle12,13, Per-Henrik Groop3,4,5, Rachel G Miller11, Trevor J Orchard11, Subramaniam Pennathur14, Peter Rossing9,15, Niina Sandholm3,4,5, Janet K Snell-Bergeon16, Hongping Ye6,7, Jing Zhang17, Loki Natarajan17, Ian H de Boer12,18,19, Kumar Sharma6,7. 1. Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington, USA, climonte@uw.edu. 2. Kidney Research Institute, University of Washington, Seattle, Washington, USA, climonte@uw.edu. 3. Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland. 4. Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. 5. Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 6. Division of Nephrology, UT Health Science Center San Antonio, San Antonio, Texas, USA. 7. Center for Renal Precision Medicine, Division of Nephrology, Department of Medicine, University of Texas Health San Antonio, San Antonio, Texas, USA. 8. Department of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, Michigan, USA. 9. Steno Diabetes Center Copenhagen, Copenhagen, Denmark. 10. The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark. 11. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 12. Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington, USA. 13. Department of Laboratory Medicine, University of Washington, Seattle, Washington, USA. 14. Departments of Medicine-Nephrology and Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA. 15. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 16. Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. 17. Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health and UC San Diego Moores Comprehensive Cancer Center, La Jolla, California, USA. 18. Kidney Research Institute, University of Washington, Seattle, Washington, USA. 19. Puget Sound VA Healthcare System, Seattle, Washington, USA.
Abstract
BACKGROUND: Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict. METHODS: We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics" studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques. RESULTS: At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001). CONCLUSION: Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.
BACKGROUND: Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict. METHODS: We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics" studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques. RESULTS: At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001). CONCLUSION: Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.
Authors: Tomohito Gohda; Monika A Niewczas; Linda H Ficociello; William H Walker; Jan Skupien; Florencia Rosetti; Xavier Cullere; Amanda C Johnson; Gordon Crabtree; Adam M Smiles; Tanya N Mayadas; James H Warram; Andrzej S Krolewski Journal: J Am Soc Nephrol Date: 2012-01-19 Impact factor: 10.121
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Authors: Daniela Schlatzer; David M Maahs; Mark R Chance; Jean-Eudes Dazard; Xiaolin Li; Fred Hazlett; Marian Rewers; Janet K Snell-Bergeon Journal: Diabetes Care Date: 2012-01-11 Impact factor: 19.112
Authors: Christine P Limonte; Erkka Valo; Viktor Drel; Loki Natarajan; Manjula Darshi; Carol Forsblom; Clark M Henderson; Andrew N Hoofnagle; Wenjun Ju; Matthias Kretzler; Daniel Montemayor; Viji Nair; Robert G Nelson; John F O'Toole; Robert D Toto; Sylvia E Rosas; John Ruzinski; Niina Sandholm; Insa M Schmidt; Tomas Vaisar; Sushrut S Waikar; Jing Zhang; Peter Rossing; Tarunveer S Ahluwalia; Per-Henrik Groop; Subramaniam Pennathur; Janet K Snell-Bergeon; Tina Costacou; Trevor J Orchard; Kumar Sharma; Ian H de Boer Journal: Diabetes Care Date: 2022-06-02 Impact factor: 17.152
Authors: Farsad Afshinnia; Thekkelnaycke M Rajendiran; Chenchen He; Jaeman Byun; Daniel Montemayor; Manjula Darshi; Jana Tumova; Jiwan Kim; Christine P Limonte; Rachel G Miller; Tina Costacou; Trevor J Orchard; Tarunveer S Ahluwalia; Peter Rossing; Janet K Snell-Bergeon; Ian H de Boer; Loki Natarajan; George Michailidis; Kumar Sharma; Subramaniam Pennathur Journal: Diabetes Care Date: 2021-07-08 Impact factor: 17.152