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Nakamura K, Kojima R, Uchino E, Ono K, Yanagita M, Murashita K, Itoh K, Nakaji S, Okuno Y. Health improvement framework for actionable treatment planning using a surrogate Bayesian model. Nat Commun 2021; 12:3088. [PMID: 34035243 PMCID: PMC8149666 DOI: 10.1038/s41467-021-23319-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 04/23/2021] [Indexed: 02/04/2023] Open
Abstract
Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. A prominent issue is the development of objective treatment processes in clinical situations. This study proposes a framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the actionability for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model. We first evaluate the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework is applied to an actual health checkup dataset comprising data from 3132 participants, to lower systolic blood pressure and risk of chronic kidney disease at the individual level. We confirm that the computed treatment processes are actionable and consistent with clinical knowledge for improving these values. We also show that the improvement processes presented by the framework can be clinically informative. These results demonstrate that our framework can contribute toward decision-making in the medical field, providing clinicians with deeper insights.
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Affiliation(s)
- Kazuki Nakamura
- Research and Business Development Department, Kyowa Hakko Bio Co., Ltd., Tokyo, Japan
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Eiichiro Uchino
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koh Ono
- Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Motoko Yanagita
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
| | - Koichi Murashita
- Center of Innovation Research Initiatives Organization, Hirosaki University, Hirosaki, Japan
| | - Ken Itoh
- Department of Stress Response Science, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shigeyuki Nakaji
- Department of Social Health, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
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Noordam R, Vermond D, Drenth H, Wijman CA, Akintola AA, van der Kroef S, Jansen SWM, Huurman NC, Schutte BAM, Beekman M, Slagboom PE, Mooijaart SP, van Heemst D. High Liver Enzyme Concentrations are Associated with Higher Glycemia, but not with Glycemic Variability, in Individuals without Diabetes Mellitus. Front Endocrinol (Lausanne) 2017; 8:236. [PMID: 28955304 PMCID: PMC5601417 DOI: 10.3389/fendo.2017.00236] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 08/28/2017] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Elevated concentrations of liver enzymes have been associated with an increased risk of developing type 2 diabetes mellitus. However, it remains unclear to which specific aspects of diurnal glucose metabolism these associate most. We aimed to investigate the associations between liver enzyme concentrations and 24 h-glucose trajectories in individuals without diabetes mellitus from three independent cohorts. METHODS This cross-sectional study included 436 participants without diabetes mellitus from the Active and Healthy Aging Study, the Switchbox Study, and the Growing Old Together Study. Fasting blood samples were drawn to measure gamma-glutamyltransferase (GGT), alanine transaminase, and aspartate transaminase. Measures of glycemia (e.g., nocturnal and diurnal mean glucose levels) and glycemic variability (e.g., mean amplitude of glucose excursions) were derived from continuous glucose monitoring. Analyses were performed separately for the three cohorts; derived estimates were additionally meta-analyzed. RESULTS After meta-analyses of the three cohorts, elevated liver enzyme concentrations, and specifically elevated GGT concentrations, were associated with higher glycemia. More specific, participants in the highest GGT tertile (GGT ≥37.9 U/L) had a 0.39 mmol/L (95% confidence interval: 0.23, 0.56) higher mean nocturnal glucose (3:00 to 6:00 a.m.) and a 0.23 mmol/L (0.10, 0.36) higher diurnal glucose (6:00 to 0:00 a.m.) than participants in the lowest GGT tertile (GGT <21.23 U/L). However, elevated liver enzyme concentrations were not associated with a higher glycemic variability. CONCLUSION Though elevated liver enzyme concentrations did not associate with higher glycemic variability in participants without diabetes mellitus, specifically, elevated GGT concentrations associated with higher glycemia.
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Affiliation(s)
- Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
- *Correspondence: Raymond Noordam,
| | - Debbie Vermond
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
- Leyden Academy on Vitality and Ageing, Leiden, Netherlands
| | - Hermijntje Drenth
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
- Leyden Academy on Vitality and Ageing, Leiden, Netherlands
| | - Carolien A. Wijman
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Abimbola A. Akintola
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Sabrina van der Kroef
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Steffy W. M. Jansen
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Neline C. Huurman
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Bianca A. M. Schutte
- Department of Medical Statistics and Bioinformatics, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Marian Beekman
- Department of Medical Statistics and Bioinformatics, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - P. Eline Slagboom
- Department of Medical Statistics and Bioinformatics, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Simon P. Mooijaart
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
- Institute for Evidence-Based Medicine in Old Age, IEMO, Leiden, Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
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