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Young KG, McInnes EH, Massey RJ, Kahkohska AR, Pilla SJ, Raghaven S, Stanislawski MA, Tobias DK, McGovern AP, Dawed AY, Jones AG, Pearson ER, Dennis JM. Precision medicine in type 2 diabetes: A systematic review of treatment effect heterogeneity for GLP1-receptor agonists and SGLT2-inhibitors. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.21.23288868. [PMID: 37131814 PMCID: PMC10153311 DOI: 10.1101/2023.04.21.23288868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Background A precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. Results After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity. Conclusions Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care. Plain language summary This review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.
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Affiliation(s)
- Katherine G Young
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
| | - Eram Haider McInnes
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Robert J Massey
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Anna R Kahkohska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott J Pilla
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sridharan Raghaven
- Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, USA, 80045
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew P McGovern
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
| | - Adem Y Dawed
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Angus G Jones
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - John M Dennis
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
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Abegaz TM, Ahmed M, Sherbeny F, Diaby V, Chi H, Ali AA. Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data. Healthcare (Basel) 2023; 11:1138. [PMID: 37107973 PMCID: PMC10137945 DOI: 10.3390/healthcare11081138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.
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Affiliation(s)
- Tadesse M. Abegaz
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL 32307, USA
| | - Muktar Ahmed
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Fatimah Sherbeny
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL 32307, USA
| | - Vakaramoko Diaby
- College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
| | - Hongmei Chi
- The Department of Computer and Information Sciences, Florid A&M University, Tallahassee, FL 32307, USA
| | - Askal Ayalew Ali
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL 32307, USA
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Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope? Digit Health 2023; 9:20552076231203879. [PMID: 37786401 PMCID: PMC10541760 DOI: 10.1177/20552076231203879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023] Open
Abstract
Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.
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Affiliation(s)
- Xiantong Zou
- Xiantong Zou, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
| | | | - Linong Ji
- Linong Ji, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
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Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14:196. [PMID: 36572938 PMCID: PMC9793536 DOI: 10.1186/s13098-022-00969-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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Barreto J, Wolf V, Bonilha I, Luchiari B, Lima M, Oliveira A, Vitte S, Machado G, Cunha J, Borges C, Munhoz D, Fernandes V, Kimura-Medorima ST, Breder I, Fernandez MD, Quinaglia T, Oliveira RB, Chaves F, Arieta C, Guerra-Júnior G, Avila S, Nadruz W, Carvalho LSF, Sposito AC. Rationale and design of the Brazilian diabetes study: a prospective cohort of type 2 diabetes. Curr Med Res Opin 2022; 38:523-529. [PMID: 35174749 DOI: 10.1080/03007995.2022.2043658] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Optimal control of traditional risk factors only partially attenuates the exceeding cardiovascular mortality of individuals with diabetes. Employment of machine learning (ML) techniques aimed at the identification of novel features of risk prediction is a compelling target to tackle residual cardiovascular risk. The objective of this study is to identify clinical phenotypes of T2D which are more prone to developing cardiovascular disease. METHODS The Brazilian Diabetes Study is a single-center, ongoing, prospective registry of T2D individuals. Eligible patients are 30 years old or older, with a confirmed T2D diagnosis. After an initial visit for the signature of the informed consent form and medical history registration, all volunteers undergo biochemical analysis, echocardiography, carotid ultrasound, ophthalmologist visit, dual x-ray absorptiometry, coronary artery calcium score, polyneuropathy assessment, advanced glycation end-products reader, and ambulatory blood pressure monitoring. A 5-year follow-up will be conducted by yearly phone interviews for endpoints disclosure. The primary endpoint is the difference between ML-based clinical phenotypes in the incidence of a composite of death, myocardial infarction, revascularization, and stroke. Since June/2016, 1030 patients (mean age: 57 years, diabetes duration of 9.7 years, 58% male) were enrolled in our study. The mean follow-up time was 3.7 years in October/2021. CONCLUSION The BDS will be the first large population-based cohort dedicated to the identification of clinical phenotypes of T2D at higher risk of cardiovascular events. Data derived from this study will provide valuable information on risk estimation and prevention of cardiovascular and other diabetes-related events. CLINICALTRIALS.GOV IDENTIFIER NCT04949152.
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Affiliation(s)
- Joaquim Barreto
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Vaneza Wolf
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Growth and Body Composition Lab, Center for Investigation in Pediatrics, Faculty of Medical Sciences, University of Campinas, São Paulo, Brazil
| | - Isabella Bonilha
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Beatriz Luchiari
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Marcus Lima
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Alessandra Oliveira
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Sofia Vitte
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Gabriela Machado
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Jessica Cunha
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Cynthia Borges
- Nephrology Division, Clinics Hospital, University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Daniel Munhoz
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Vicente Fernandes
- Department of Ophthalmology, Clinics Hospital, University of Campinas, Sao Paulo, Brazil
| | - Sheila Tatsumi Kimura-Medorima
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Ikaro Breder
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Marta Duran Fernandez
- Clarity Healthcare Intelligence, Sao Paulo, Brazil
- School of Electrical and Computer Engineering, Unicamp, Sao Paulo, Brazil
| | - Thiago Quinaglia
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Rodrigo B Oliveira
- Nephrology Division, Clinics Hospital, University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Fernando Chaves
- Department of Ophthalmology, Clinics Hospital, University of Campinas, Sao Paulo, Brazil
| | - Carlos Arieta
- Department of Ophthalmology, Clinics Hospital, University of Campinas, Sao Paulo, Brazil
| | - Gil Guerra-Júnior
- Growth and Body Composition Lab, Center for Investigation in Pediatrics, Faculty of Medical Sciences, University of Campinas, São Paulo, Brazil
| | - Sandra Avila
- School of Electrical and Computer Engineering, Unicamp, Sao Paulo, Brazil
- Institute of Computing, Unicamp, Sao Paulo, Brazil
| | - Wilson Nadruz
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Luiz Sergio F Carvalho
- Clarity Healthcare Intelligence, Sao Paulo, Brazil
- Laboratory of Data for Quality of Care and Outcomes Research, Institute of Strategic Management in Healthcare, Brasılia, Federal District, Brazil
| | - Andrei C Sposito
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
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Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021; 159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 12/08/2021] [Accepted: 12/27/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice. METHODS Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered. RESULTS Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice. CONCLUSIONS ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.
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Affiliation(s)
- Ezekwesiri Michael Nwanosike
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Barbara R Conway
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Hamid A Merchant
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Syed Shahzad Hasan
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom; School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, Australia.
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