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Polasek TM, Peck RW. Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses. Clin Pharmacol Ther 2024; 116:602-612. [PMID: 38328977 DOI: 10.1002/cpt.3197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024]
Abstract
The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.
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Affiliation(s)
- Thomas M Polasek
- Centre for Medicine Use and Safety, Monash University, Melbourne, Victoria, Australia
- CMAX Clinical Research, Adelaide, South Australia, Australia
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research & Development (pRED), Roche Innovation Center Basel, Basel, Switzerland
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2
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Tlili NE, Robert L, Gerard E, Lemaitre M, Vambergue A, Beuscart JB, Quindroit P. A systematic review of the value of clinical decision support systems in the prescription of antidiabetic drugs. Int J Med Inform 2024; 191:105581. [PMID: 39106772 DOI: 10.1016/j.ijmedinf.2024.105581] [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: 02/22/2024] [Revised: 07/23/2024] [Accepted: 07/28/2024] [Indexed: 08/09/2024]
Abstract
INTRODUCTION The management of chronic diabetes mellitus and its complications demands customized glycaemia control strategies. Polypharmacy is prevalent among people with diabetes and comorbidities, which increases the risk of adverse drug reactions. Clinical decision support systems (CDSSs) may constitute an innovative solution to these problems. The aim of our study was to conduct a systematic review assessing the value of CDSSs for the management of antidiabetic drugs (AD). MATERIALS AND METHODS We systematically searched the scientific literature published between January 2010 and October 2023. The retrieved studies were categorized as non-specific or AD-specific. The studies' quality was assessed using the Mixed Methods Appraisal Tool. The review's results were reported in accordance with the PRISMA guidelines. RESULTS Twenty studies met our inclusion criteria. The majority of AD-specific studies were conducted more recently (2020-2023) compared to non-specific studies (2010-2015). This trend hints at growing interest in more specialized CDSSs tailored for prescriptions of ADs. The nine AD-specific studies focused on metformin and insulin and demonstrated positive impacts of the CDSSs on different outcomes, including the reduction in the proportion of inappropriate prescriptions of ADs and in hypoglycaemia events. The 11 nonspecific studies showed similar trends for metformin and insulin prescriptions, although the CDSSs' impacts were not significant. There was a predominance of metformin and insulin in the studied CDSSs and a lack of studies on ADs such as sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists. CONCLUSION The limited number of studies, especially randomized clinical trials, interested in evaluating the application of CDSS in the management of ADs underscores the need for further investigations. Our findings suggest the potential benefit of applying CDSSs to the prescription of ADs particularly in primary care settings and when targeting clinical pharmacists. Finally, establishing core outcome sets is crucial for ensuring consistent and standardized evaluation of these CDSSs.
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Affiliation(s)
- Nour Elhouda Tlili
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France.
| | - Laurine Robert
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France; Institut de Pharmacie, CHU Lille, F-59000 Lille, France
| | - Erwin Gerard
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France; Institut de Pharmacie, CHU Lille, F-59000 Lille, France
| | - Madleen Lemaitre
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France; CHU Lille, Department of Diabetology, Endocrinology, Metabolism and Nutrition, Lille University Hospital, F-59000, Lille, France
| | - Anne Vambergue
- CHU Lille, Department of Diabetology, Endocrinology, Metabolism and Nutrition, Lille University Hospital, F-59000, Lille, France; European Genomic Institute for Diabetes, Lille University School of Medicine, F-59000 Lille, France
| | - Jean-Baptiste Beuscart
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France
| | - Paul Quindroit
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France
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Güdemann LM, Young KG, Thomas NJM, Hopkins R, Challen R, Jones AG, Hattersley AT, Pearson ER, Shields BM, Bowden J, Dennis JM, McGovern AP. Safety and effectiveness of SGLT2 inhibitors in a UK population with type 2 diabetes and aged over 70 years: an instrumental variable approach. Diabetologia 2024:10.1007/s00125-024-06190-9. [PMID: 38836934 DOI: 10.1007/s00125-024-06190-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/11/2024] [Indexed: 06/06/2024]
Abstract
AIMS/HYPOTHESIS Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis. METHODS Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013-2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: <70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument. RESULTS Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged <70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those <70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA1c reduction was similar between SGLT2i and DPP4i (-0.3 mmol/mol [-1.6, 1.1], -0.02% [0.1, 0.1]), but in those <70, SGLT2i were more effective (-4 mmol/mol [4.8, -3.1], -0.4% [-0.4, -0.3]). CONCLUSIONS/INTERPRETATION Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes.
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Affiliation(s)
- Laura M Güdemann
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK.
| | - Katie G Young
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Nicholas J M Thomas
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Rhian Hopkins
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Robert Challen
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Angus G Jones
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Andrew T Hattersley
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Ewan R Pearson
- Division of Molecular & Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Beverley M Shields
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Jack Bowden
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - John M Dennis
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Andrew P McGovern
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
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Carrillo-Larco RM, Bravo-Rocca G, Castillo-Cara M, Xu X, Bernabe-Ortiz A. A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes - An exploratory analysis. Prim Care Diabetes 2024; 18:327-332. [PMID: 38616442 DOI: 10.1016/j.pcd.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/17/2024] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
AIMS Machine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived with diabetes unknowingly. We aimed to develop a model to predict self-reported diabetes duration. METHODS We used the Brazilian Multilabel Ophthalmological Dataset. Unit of analysis was the fundus image and its meta-data, regardless of the patient. We included people 40 + years and fundus images without diabetic retinopathy. Fundus images and meta-data (sex, age, comorbidities and taking insulin) were passed to the MedCLIP model to extract the embedding representation. The embedding representation was passed to an Extra Tree Classifier to predict: 0-4, 5-9, 10-14 and 15 + years with self-reported diabetes. RESULTS There were 988 images from 563 people (mean age = 67 years; 64 % were women). Overall, the F1 score was 57 %. The group 15 + years of self-reported diabetes had the highest precision (64 %) and F1 score (63 %), while the highest recall (69 %) was observed in the group 0-4 years. The proportion of correctly classified observations was 55 % for the group 0-4 years, 51 % for 5-9 years, 58 % for 10-14 years, and 64 % for 15 + years with self-reported diabetes. CONCLUSIONS The machine learning model had acceptable accuracy and F1 score, and correctly classified more than half of the patients according to diabetes duration. Using large foundational models to extract image and text embeddings seems a feasible and efficient approach to predict years living with self-reported diabetes.
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Affiliation(s)
- Rodrigo M Carrillo-Larco
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Emory Global Diabetes Research Center, Emory University, Atlanta, GA, USA.
| | | | | | - Xiaolin Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China; School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
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Lopes J, Faria M, Santos MF. Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review. PLoS One 2024; 19:e0302697. [PMID: 38728308 PMCID: PMC11086907 DOI: 10.1371/journal.pone.0302697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/09/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVE In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context. METHODS A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study. RESULTS The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions. CONCLUSION The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.
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Affiliation(s)
- João Lopes
- ALGORITMI Research Center, University of Minho, Braga, Portugal
| | - Mariana Faria
- ALGORITMI Research Center, University of Minho, Braga, Portugal
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Selby JV, Maas CCHM, Fireman BH, Kent DM. Impact of the PATH Statement on Analysis and Reporting of Heterogeneity of Treatment Effect in Clinical Trials: A Scoping Review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.06.24306774. [PMID: 38766150 PMCID: PMC11100853 DOI: 10.1101/2024.05.06.24306774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement provides guidance for using predictive modeling to identify differences (i.e., heterogeneity) in treatment effects (benefits and harms) among participants in randomized clinical trials (RCTs). It distinguished risk modeling, which uses a multivariable model to predict risk of trial outcome(s) and then examines treatment effects within strata of predicted risk, from effect modeling, which predicts trial outcomes using models that include treatment, individual participant characteristics and interactions of treatment with selected characteristics. Purpose To describe studies of heterogeneous treatment effects (HTE) that use predictive modeling in RCT data and cite the PATH Statement. Data Sources The Cited By functions in PubMed, Google Scholar, Web of Science and SCOPUS databases (Jan 7, 2020 - June 5, 2023). Study Selection 42 reports presenting 45 predictive models. Data Extraction Double review with adjudication to identify risk and effect modeling and examine consistency with Statement consensus statements. Credibility of HTE findings was assessed using criteria adapted from the Instrument to assess Credibility of Effect Modification Analyses (ICEMAN). Clinical importance of credible HTE findings was also assessed. Data Synthesis The numbers of reports, especially risk modeling reports, increased year-on-year. Consistency with consensus statements was high, except for two: only 15 of 32 studies with positive overall findings included a risk model; and most effect models explored many candidate covariates with little prior evidence for effect modification. Risk modeling was more likely than effect modeling to identify both credible HTE (14/19 vs 5/26) and clinically important HTE (10/19 vs 4/26). Limitations Risk of reviewer bias: reviewers assessing credibility and clinical importance were not blinded to adherence to PATH recommendations. Conclusions The PATH Statement appears to be influencing research practice. Risk modeling often uncovered clinically important HTE; effect modeling was more often exploratory.
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Affiliation(s)
- Joe V Selby
- Division of Research, Kaiser Permanente Northern California, Oakland, CA (emeritus)
| | - Carolien C H M Maas
- Tufts Predictive Analytics and Comparative Effectiveness Center, Tufts University School of Medicine, Boston MA
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bruce H Fireman
- Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | - David M Kent
- Tufts Predictive Analytics and Comparative Effectiveness Center, Tufts University School of Medicine, Boston MA
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Cardoso P, Young KG, Nair ATN, Hopkins R, McGovern AP, Haider E, Karunaratne P, Donnelly L, Mateen BA, Sattar N, Holman RR, Bowden J, Hattersley AT, Pearson ER, Jones AG, Shields BM, McKinley TJ, Dennis JM. Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes. Diabetologia 2024; 67:822-836. [PMID: 38388753 PMCID: PMC10955037 DOI: 10.1007/s00125-024-06099-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/04/2024] [Indexed: 02/24/2024]
Abstract
AIMS/HYPOTHESIS A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA). METHODS We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events. RESULTS Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. CONCLUSIONS/INTERPRETATION Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries.
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Affiliation(s)
- Pedro Cardoso
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Katie G Young
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Anand T N Nair
- Division of Molecular & Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Rhian Hopkins
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Andrew P McGovern
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Eram Haider
- Division of Molecular & Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Piyumanga Karunaratne
- Division of Molecular & Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Louise Donnelly
- Division of Molecular & Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Rury R Holman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Jack Bowden
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Andrew T Hattersley
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Ewan R Pearson
- Division of Molecular & Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Angus G Jones
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Beverley M Shields
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Trevelyan J McKinley
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK
| | - John M Dennis
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK.
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Harada M, Han S, Shi M, Ge J, Yu S, Adam J, Adamski J, Scheerer MF, Neschen S, de Angelis MH, Wang-Sattler R. Metabolic effects of SGLT2i and metformin on 3-hydroxybutyric acid and lactate in db/db mice. Int J Biol Macromol 2024; 265:130962. [PMID: 38503370 DOI: 10.1016/j.ijbiomac.2024.130962] [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: 09/06/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 03/21/2024]
Abstract
Combining a Sodium-Glucose-Cotransporter-2-inhibitor (SGLT2i) with metformin is recommended for managing hyperglycemia in patients with type 2 diabetes (T2D) who have cardio-renal complications. Our study aimed to investigate the metabolic effects of SGLT2i and metformin, both individually and synergistically. We treated leptin receptor-deficient (db/db) mice with these drugs for two weeks and conducted metabolite profiling, identifying 861 metabolites across kidney, liver, muscle, fat, and plasma. Using linear regression and mixed-effects models, we identified two SGLT2i-specific metabolites, X-12465 and 3-hydroxybutyric acid (3HBA), a ketone body, across all examined tissues. The levels of 3HBA were significantly higher under SGLT2i monotherapy compared to controls and were attenuated when combined with metformin. We observed similar modulatory effects on metabolites involved in protein catabolism (e.g., branched-chain amino acids) and gluconeogenesis. Moreover, combination therapy significantly raised pipecolate levels, which may enhance mTOR1 activity, while modulating GSK3, a common target of SGLT2i and 3HBA inhibition. The combination therapy also led to significant reductions in body weight and lactate levels, contrasted with monotherapies. Our findings advocate for the combined approach to better manage muscle loss, and the risks of DKA and lactic acidosis, presenting a more effective strategy for T2D treatment.
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Affiliation(s)
- Makoto Harada
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Siyu Han
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany; School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Mengya Shi
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany; School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Jianhong Ge
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany; School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Shixiang Yu
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Jonathan Adam
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Markus F Scheerer
- Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Susanne Neschen
- Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Martin Hrabe de Angelis
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany; Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Chair of Experimental Genetics, School of Life Sciences, Technical University of Munich (TUM), Freising, Germany
| | - Rui Wang-Sattler
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
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9
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Costello E, Goodrich JA, Patterson WB, Walker DI, Chen J(C, Baumert BO, Rock S, Gilliland FD, Goran MI, Chen Z, Alderete TL, Conti DV, Chatzi L. Proteomic and Metabolomic Signatures of Diet Quality in Young Adults. Nutrients 2024; 16:429. [PMID: 38337712 PMCID: PMC10857402 DOI: 10.3390/nu16030429] [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: 11/27/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
The assessment of "omics" signatures may contribute to personalized medicine and precision nutrition. However, the existing literature is still limited in the homogeneity of participants' characteristics and in limited assessments of integrated omics layers. Our objective was to use post-prandial metabolomics and fasting proteomics to identify biological pathways and functions associated with diet quality in a population of primarily Hispanic young adults. We conducted protein and metabolite-wide association studies and functional pathway analyses to assess the relationships between a priori diet indices, Healthy Eating Index-2015 (HEI) and Dietary Approaches to Stop Hypertension (DASH) diets, and proteins (n = 346) and untargeted metabolites (n = 23,173), using data from the MetaAIR study (n = 154, 61% Hispanic). Analyses were performed for each diet quality index separately, adjusting for demographics and BMI. Five proteins (ACY1, ADH4, AGXT, GSTA1, F7) and six metabolites (undecylenic acid, betaine, hyodeoxycholic acid, stearidonic acid, iprovalicarb, pyracarbolid) were associated with both diets (p < 0.05), though none were significant after adjustment for multiple comparisons. Overlapping proteins are involved in lipid and amino acid metabolism and in hemostasis, while overlapping metabolites include amino acid derivatives, bile acids, fatty acids, and pesticides. Enriched biological pathways were involved in macronutrient metabolism, immune function, and oxidative stress. These findings in young Hispanic adults contribute to efforts to develop precision nutrition and medicine for diverse populations.
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Affiliation(s)
- Elizabeth Costello
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Jesse A. Goodrich
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - William B. Patterson
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (W.B.P.); (T.L.A.)
| | - Douglas I. Walker
- Gangarosa Department of Environmental Health, Emory University, Atlanta, GA 30329, USA;
| | - Jiawen (Carmen) Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Brittney O. Baumert
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Sarah Rock
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Frank D. Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Michael I. Goran
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
- Department of Pediatrics, Children’s Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA 90027, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Tanya L. Alderete
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (W.B.P.); (T.L.A.)
| | - David V. Conti
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Lida Chatzi
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
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10
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Cardoso P, Dennis JM, Bowden J, Shields BM, McKinley TJ. Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy. BMC Med Inform Decis Mak 2024; 24:12. [PMID: 38191403 PMCID: PMC10773072 DOI: 10.1186/s12911-023-02400-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 12/11/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. METHODS We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors. RESULTS We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome. CONCLUSIONS When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data.
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Affiliation(s)
- Pedro Cardoso
- University of Exeter, Medical School, Exeter, England
| | - John M Dennis
- University of Exeter, Medical School, Exeter, England
| | - Jack Bowden
- University of Exeter, Medical School, Exeter, England
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11
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Galiero R, Caturano A, Vetrano E, Monda M, Marfella R, Sardu C, Salvatore T, Rinaldi L, Sasso FC. Precision Medicine in Type 2 Diabetes Mellitus: Utility and Limitations. Diabetes Metab Syndr Obes 2023; 16:3669-3689. [PMID: 38028995 PMCID: PMC10658811 DOI: 10.2147/dmso.s390752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM) is one of the most widespread diseases in Western countries, and its incidence is constantly increasing. Epidemiological studies have shown that in the next 20 years. The number of subjects affected by T2DM will double. In recent years, owing to the development and improvement in methods for studying the genome, several authors have evaluated the association between monogenic or polygenic genetic alterations and the development of metabolic diseases and complications. In addition, sedentary lifestyle and socio-economic and pandemic factors have a great impact on the habits of the population and have significantly contributed to the increase in the incidence of metabolic disorders, obesity, T2DM, metabolic syndrome, and liver steatosis. Moreover, patients with type 2 diabetes appear to respond to antihyperglycemic drugs. Only a minority of patients could be considered true non-responders. Thus, it appears clear that the main aim of precision medicine in T2DM is to identify patients who can benefit most from a specific drug class more than from the others. Precision medicine is a discipline that evaluates the applicability of genetic, lifestyle, and environmental factors to disease development. In particular, it evaluated whether these factors could affect the development of diseases and their complications, response to diet, lifestyle, and use of drugs. Thus, the objective is to find prevention models aimed at reducing the incidence of pathology and mortality and therapeutic personalized approaches, to obtain a greater probability of response and efficacy. This review aims to evaluate the applicability of precision medicine for T2DM, a healthcare burden in many countries.
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Affiliation(s)
- Raffaele Galiero
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Alfredo Caturano
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Erica Vetrano
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Marcellino Monda
- Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Raffaele Marfella
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Celestino Sardu
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Teresa Salvatore
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Luca Rinaldi
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Ferdinando Carlo Sasso
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
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12
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Franks PW, Cefalu WT, Dennis J, Florez JC, Mathieu C, Morton RW, Ridderstråle M, Sillesen HH, Stehouwer CDA. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diabetes Endocrinol 2023; 11:822-835. [PMID: 37804856 DOI: 10.1016/s2213-8587(23)00165-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.
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Affiliation(s)
- Paul W Franks
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
| | - William T Cefalu
- Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - John Dennis
- Institute of Biomedical and Clinical Science, Royal Devon and Exeter Hospital, University of Exeter, Exeter, UK
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, UZ Gasthuisberg, KU Leuven, Leuven, Belgium
| | - Robert W Morton
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | | | - Henrik H Sillesen
- Department of Clinical Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Coen D A Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands; Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, Netherlands
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13
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Leslie RD, Ma RCW, Franks PW, Nadeau KJ, Pearson ER, Redondo MJ. Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes. Lancet Diabetes Endocrinol 2023; 11:848-860. [PMID: 37804855 DOI: 10.1016/s2213-8587(23)00159-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 10/09/2023]
Abstract
Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.
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Affiliation(s)
| | - Ronald Ching Wan Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China; Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China; Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Paul W Franks
- Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmo, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Kristen J Nadeau
- Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
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14
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Misra S, Aguilar-Salinas CA, Chikowore T, Konradsen F, Ma RCW, Mbau L, Mohan V, Morton RW, Nyirenda MJ, Tapela N, Franks PW. The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries. Lancet Diabetes Endocrinol 2023; 11:836-847. [PMID: 37804857 DOI: 10.1016/s2213-8587(23)00164-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/08/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic diseases are the leading preventable causes of death in most geographies. The causes, clinical presentations, and pathogenesis of cardiometabolic diseases vary greatly worldwide, as do the resources and strategies needed to prevent and treat them. Therefore, there is no single solution and health care should be optimised, if not to the individual (ie, personalised health care), then at least to population subgroups (ie, precision medicine). This optimisation should involve tailoring health care to individual disease characteristics according to ethnicity, biology, behaviour, environment, and subjective person-level characteristics. The capacity and availability of local resources and infrastructures should also be considered. Evidence needed for equitable precision medicine cannot be generated without adequate data from all target populations, and the idea that research done in high-income countries will transfer adequately to low-income and middle-income countries (LMICs) is problematic, as many migration studies and transethnic comparisons have shown. However, most data for precision medicine research are derived from people of European ancestry living in high-income countries. In this Series paper, we discuss the case for precision medicine for cardiometabolic diseases in LMICs, the barriers and enablers, and key considerations for implementation. We focus on three propositions: first, failure to explore and implement precision medicine for cardiometabolic disease in LMICs will enhance global health disparities. Second, some LMICs might already be placed to implement cardiometabolic precision medicine under appropriate circumstances, owing to progress made in treating infectious diseases. Third, improvements in population health from precision medicine are most probably asymptotic; the greatest gains are more likely to be obtained in countries where health-care systems are less developed. We outline key recommendations for implementation of precision medicine approaches in LMICs.
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Affiliation(s)
- Shivani Misra
- Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK; Department of Diabetes and Endocrinology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Carlos A Aguilar-Salinas
- Dirección de Nutricion, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, México
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Flemming Konradsen
- Novo Nordisk Foundation, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | | | - Viswanathan Mohan
- Madras Diabetes Research Foundation, ICMR Centre for Advanced Research in Diabetes, Chennai, India; Dr Mohan's Diabetes Specialties Centre, IDF Centre of Excellence in Diabetes Care, Chennai, India
| | | | - Moffat J Nyirenda
- MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; London School of Hygiene and Tropical Medicine, London, UK
| | - Neo Tapela
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana; International Consortium for Health Outcomes Measurement, Oxford, UK
| | - Paul W Franks
- Novo Nordisk Foundation, Copenhagen, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
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15
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Trischitta V, Menzaghi C, Copetti M. Unveiling Novel Markers and Modeling Clinical Prediction of Treatment Effects Are Equally Important for Implementing Precision Therapeutics. Diabetes 2023; 72:1057-1059. [PMID: 37471601 DOI: 10.2337/dbi22-0039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/28/2023] [Indexed: 07/22/2023]
Affiliation(s)
- Vincenzo Trischitta
- Research Unit of Diabetes and Endocrine Diseases, Fondazione IRCCS "Casa Sollievo della Sofferenza," San Giovanni Rotondo, Italy
- Department of Experimental Medicine, "Sapienza" University, Rome, Italy
| | - Claudia Menzaghi
- Research Unit of Diabetes and Endocrine Diseases, Fondazione IRCCS "Casa Sollievo della Sofferenza," San Giovanni Rotondo, Italy
| | - Massimiliano Copetti
- Biostatistics Unit, Fondazione IRCCS "Casa Sollievo della Sofferenza," San Giovanni Rotondo, Italy
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16
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Venkatasubramaniam A, Mateen BA, Shields BM, Hattersley AT, Jones AG, Vollmer SJ, Dennis JM. Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine. BMC Med Inform Decis Mak 2023; 23:110. [PMID: 37328784 PMCID: PMC10276367 DOI: 10.1186/s12911-023-02207-2] [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: 11/07/2022] [Accepted: 06/01/2023] [Indexed: 06/18/2023] Open
Abstract
OBJECTIVE Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. METHODS Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). RESULTS Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1). CONCLUSIONS Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.
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Affiliation(s)
| | - Bilal A Mateen
- The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, UK
- University College London, Institute of Health Informatics, 222 Euston Rd, London, NW1 2DA, UK
| | - Beverley M Shields
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Andrew T Hattersley
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Angus G Jones
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | | | - John M Dennis
- University of Exeter Medical School, Institute of Biomedical & Clinical Science, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK.
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17
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Yu OHY, Shin JY. Treating type 2 diabetes: moving towards precision medicine. Lancet Digit Health 2022; 4:e851-e852. [PMID: 36427948 DOI: 10.1016/s2589-7500(22)00197-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/06/2022] [Indexed: 11/24/2022]
Affiliation(s)
- Oriana Hoi Yun Yu
- Center for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada; Division of Endocrinology and Metabolism, Jewish General Hospital, McGill University, Montreal, QC, Canada.
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, South Korea; Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, South Korea; Department of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, South Korea
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