Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury

被引:3
作者
Douville, Nicholas J. [1 ,2 ,3 ]
Larach, Daniel B. [4 ]
Lewis, Adam [5 ]
Bastarache, Lisa [5 ]
Pandit, Anita [6 ]
He, Jing [5 ]
Heung, Michael [7 ]
Mathis, Michael [1 ,2 ,3 ]
Wanderer, Jonathan P. [4 ,5 ]
Kheterpal, Sachin [1 ]
Surakka, Ida [8 ]
Kertai, Miklos D. [4 ]
机构
[1] Univ Michigan Hlth Syst, Dept Anesthesiol, Ann Arbor, MI USA
[2] Univ Michigan Hlth Syst, Ctr Computat Med & Bioinformat, Ann Arbor, MI USA
[3] Univ Michigan, Inst Healthcare Policy & Innovat, Michigan Integrated Ctr Hlth Analyt & Med Predict, Ann Arbor, MI USA
[4] Vanderbilt Univ, Med Ctr, Dept Anesthesiol, Nashville, TN 37232 USA
[5] Vanderbilt Univ, Med Ctr, Dept Biomed Informat, Nashville, TN USA
[6] Univ Michigan, Ctr Stat Genet & Precis Hlth Initiat, Ann Arbor, MI USA
[7] Univ Michigan, Dept Internal Med, Div Nephrol, Ann Arbor, MI USA
[8] Univ Michigan, Dept Internal Med, Div Cardiovasc Med, Ann Arbor, MI USA
关键词
perioperative genomics; acute kidney injury; cardiac surgery-associated acute kidney injury; precision medicine and genomics; anesthesiology [H02; 403; 066; polygenic risk score (PRS); GENOME-WIDE ASSOCIATION; RISK; OUTCOMES; SUSCEPTIBILITY; VARIANTS;
D O I
10.3389/fgene.2023.1094908
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury.Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models.Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 x 10(-8)) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 x 10(-6)). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model.Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.
引用
收藏
页数:12
相关论文
共 49 条
  • [1] Success of Intubation Rescue Techniques after Failed Direct Laryngoscopy in Adults A Retrospective Comparative Analysis from the Multicenter Perioperative Outcomes Group
    Aziz, Michael F.
    Brambrink, Ansgar M.
    Healy, David W.
    Willett, Amy Wen
    Shanks, Amy
    Tremper, Tyler
    Jameson, Leslie
    Ragheb, Jacqueline
    Biggs, Daniel A.
    Paganelli, William C.
    Rao, Janavi
    Epps, Jerry L.
    Colquhoun, Douglas A.
    Bakke, Patrick
    Kheterpal, Sachin
    [J]. ANESTHESIOLOGY, 2016, 125 (04) : 656 - 666
  • [2] Intraoperative Lung-Protective Ventilation Trends and Practice Patterns: A Report from the Multicenter Perioperative Outcomes Group
    Bender, S. Patrick
    Paganelli, William C.
    Gerety, Lyle P.
    Tharp, William G.
    Shanks, Amy M.
    Housey, Michelle
    Blank, Randal S.
    Colquhoun, Douglas A.
    Fernandez-Bustamante, Ana
    Jameson, Leslie C.
    Kheterpal, Sachin
    [J]. ANESTHESIA AND ANALGESIA, 2015, 121 (05) : 1231 - 1239
  • [3] Center for Statistical Genetics, 2017, MET MET AN HELP
  • [4] Developing and evaluating polygenic risk prediction models for stratified disease prevention
    Chatterjee, Nilanjan
    Shi, Jianxin
    Garcia-Closas, Montserrat
    [J]. NATURE REVIEWS GENETICS, 2016, 17 (07) : 392 - 406
  • [5] Tutorial: a guide to performing polygenic risk score analyses
    Choi, Shing Wan
    Mak, Timothy Shin-Heng
    O'Reilly, Paul F.
    [J]. NATURE PROTOCOLS, 2020, 15 (09) : 2759 - 2772
  • [6] Genetic mutations associated with susceptibility to perioperative complications in a longitudinal biorepository with integrated genomic and electronic health records
    Douville, Nicholas J.
    Kheterpal, Sachin
    Engoren, Milo
    Mathis, Michael
    Mashour, George A.
    Hornsby, Whitney E.
    Willer, Cristen J.
    Douville, Christopher B.
    [J]. BRITISH JOURNAL OF ANAESTHESIA, 2020, 125 (06) : 986 - 994
  • [7] Use of a Polygenic Risk Score Improves Prediction of Myocardial Injury After Non-Cardiac Surgery
    Douville, Nicholas J.
    Surakka, Ida
    Leis, Aleda
    Douville, Christopher B.
    Hornsby, Whitney E.
    Brummett, Chad M.
    Kheterpal, Sachin
    Willer, Cristen J.
    Engoren, Milo
    Mathis, Michael R.
    [J]. CIRCULATION-GENOMIC AND PRECISION MEDICINE, 2020, 13 (04): : e002817
  • [8] Polygenic Epidemiology
    Dudbridge, Frank
    [J]. GENETIC EPIDEMIOLOGY, 2016, 40 (04) : 268 - 272
  • [9] Foreword
    Eckardt, Kai-Uwe
    Kasiske, Bertram L.
    [J]. KIDNEY INTERNATIONAL SUPPLEMENTS, 2012, 2 (01) : 7 - 7
  • [10] Comorbidity measures for use with administrative data
    Elixhauser, A
    Steiner, C
    Harris, DR
    Coffey, RN
    [J]. MEDICAL CARE, 1998, 36 (01) : 8 - 27