
Sidhant Misra
VerifiedUniversity of Arizona · Software Engineering
Active 1986–2024
Research topics
- Political Science
- Medicine
- Internal medicine
- Endocrinology
- Intensive care medicine
- Genetics
- Family medicine
- Pathology
- Physical therapy
- Biology
Selected publications
Nature Medicine · 2023 · 194 citations
- Political Science
- Medicine
- Family medicine
Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
Precision gestational diabetes treatment: a systematic review and meta-analyses
Communications Medicine · 2023 · 29 citations
- Medicine
- Intensive care medicine
- Internal medicine
BACKGROUND: Gestational Diabetes Mellitus (GDM) affects approximately 1 in 7 pregnancies globally. It is associated with short- and long-term risks for both mother and baby. Therefore, optimizing treatment to effectively treat the condition has wide-ranging beneficial effects. However, despite the known heterogeneity in GDM, treatment guidelines and approaches are generally standardized. We hypothesized that a precision medicine approach could be a tool for risk-stratification of women to streamline successful GDM management. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, with more rapid normalization of hyperglycaemia, could have benefits for both mother and fetus. METHODS: We conducted two systematic reviews, to identify precision markers that may predict effective lifestyle and pharmacological interventions. RESULTS: There was a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We found a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). This included previous history of GDM, Body Mass Index and blood glucose concentrations at diagnosis. CONCLUSIONS: Clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown. These will be important to consider in future studies.
Precision subclassification of type 2 diabetes: a systematic review
Communications Medicine · 2023 · 92 citations
1st authorCorresponding- Political Science
- Medicine
- Political Science
BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.
Frequent coauthors
- 121 shared
Jonathan Valabhji
Chelsea and Westminster Hospital NHS Foundation Trust
- 66 shared
Gramos Begolli
- 65 shared
Julian H. Barth
Leeds Teaching Hospitals NHS Trust
- 64 shared
Norbert Stefan
Deutsches Diabetes-Zentrum e.V.
- 63 shared
Nick Oliver
Imperial College London
- 61 shared
Paul W. Franks
- 60 shared
Cécile Saint‐Martin
Assistance Publique – Hôpitaux de Paris
- 60 shared
Róbert Wágner
Heinrich Heine University Düsseldorf
Education
- 2005
MBBS with distinction in clinical and medical sciences
St Bartholomew's and the Royal London Medical School
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