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Fu M, He R, Zhang Z, Ma F, Shen L, Zhang Y, Duan M, Zhang Y, Wang Y, Zhu L, He J. Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome. Sci Rep 2023; 13:20535. [PMID: 37996510 PMCID: PMC10667512 DOI: 10.1038/s41598-023-47783-5] [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: 08/25/2023] [Accepted: 11/18/2023] [Indexed: 11/25/2023] Open
Abstract
A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.
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Affiliation(s)
- Meijiao Fu
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Ruhua He
- Department of Cardiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Zhihan Zhang
- Department of Cardiology, Hanzhong Central Hospital, Hanzhong, 723200, Shanxi, China
| | - Fuqing Ma
- Department of Cardiology, The Fifth People's Hospital of Ningxia, Shizuishan, 753000, Ningxia, China
| | - Libo Shen
- Center for Cardiovascular Diseases, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750002, Ningxia, China
| | - Yu Zhang
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Mingyu Duan
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Yameng Zhang
- Department of Cardiology, The Second Affiliated Hospital of Henan University of Science and Technology, Luoyang, 471000, Henan, China
| | - Yifan Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Li Zhu
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
| | - Jun He
- Department of Cardiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
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Boileau P, Qi NT, van der Laan MJ, Dudoit S, Leng N. A flexible approach for predictive biomarker discovery. Biostatistics 2023; 24:1085-1105. [PMID: 35861622 DOI: 10.1093/biostatistics/kxac029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/01/2022] [Accepted: 06/27/2022] [Indexed: 11/14/2022] Open
Abstract
An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates. The higher than expected number of false positives translates to wasted resources when conducting follow-up experiments for drug target identification and diagnostic assay development. Patient outcomes are in turn negatively affected. We propose a variable importance parameter for directly assessing the importance of potentially predictive biomarkers and develop a flexible nonparametric inference procedure for this estimand. We prove that our estimator is double robust and asymptotically linear under loose conditions in the data-generating process, permitting valid inference about the importance metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from nonpredictive biomarkers than procedures whose primary purpose is treatment rule estimation. An open-source software implementation of the methodology, the uniCATE R package, is briefly introduced.
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Affiliation(s)
- Philippe Boileau
- Graduate Group in Biostatistics and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Nina Ting Qi
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Mark J van der Laan
- Division of Biostatistics, Department of Statistics, Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Sandrine Dudoit
- Division of Biostatistics, Department of Statistics, Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Ning Leng
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
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Rudolph KE, Williams NT, Díaz I, Luo SX, Rotrosen J, Nunes EV. Optimally Choosing Medication Type for Patients With Opioid Use Disorder. Am J Epidemiol 2023; 192:748-756. [PMID: 36549900 PMCID: PMC10423632 DOI: 10.1093/aje/kwac217] [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: 03/07/2022] [Revised: 09/16/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Patients with opioid use disorder (OUD) tend to get assigned to one of 3 medications based on the treatment program to which the patient presents (e.g., opioid treatment programs tend to treat patients with methadone, while office-based practices tend to prescribe buprenorphine). It is possible that optimally matching patients with treatment type would reduce the risk of return to regular opioid use (RROU). We analyzed data from 3 comparative effectiveness trials from the US National Institute on Drug Abuse Clinical Trials Network (CTN0027, 2006-2010; CTN0030, 2006-2009; and CTN0051 2014-2017), in which patients with OUD (n = 1,459) were assigned to treatment with either injection extended-release naltrexone (XR-NTX), sublingual buprenorphine-naloxone (BUP-NX), or oral methadone. We learned an individualized rule by which to assign medication type such that risk of RROU during 12 weeks of treatment would be minimized, and then estimated the amount by which RROU risk could be reduced if the rule were applied. Applying our estimated treatment rule would reduce risk of RROU compared with treating everyone with methadone (relative risk (RR) = 0.79, 95% confidence interval (CI): 0.60, 0.97) or treating everyone with XR-NTX (RR = 0.71, 95% CI: 0.47, 0.96). Applying the estimated treatment rule would have resulted in a similar risk of RROU to that of with treating everyone with BUP-NX (RR = 0.92, 95% CI: 0.73, 1.11).
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Affiliation(s)
- Kara E Rudolph
- Correspondence to Dr. Kara Rudolph, 722 W. 168th Street, Room 522, New York, NY 10032 (e-mail: )
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