The Future of the Obesity Management Landscape: Challenges, Opportunities and Solutions

Alexander Dimitri Miras; Alexander Kokkinos; Abd Tahrani; Ricardo Reynoso; Jörg Tomaszewski; Ildiko Lingvay; Domenica Rubino; Jonathan Q. Purnell; Muzamil Hussain; Amy Hall; Dimitrios Pournaras; Philip Schauer; Carel W. le Roux; Luiza Borowska; Chiedza Kaitano; Ishita Doshi; Dominique Gregoire; Helen Haggart; María Morán Gortaire; Stephanie Mutchler; Barbara Sleypen; Elena Startseva; Priya Sumithran; Francis M. Finucane

doi:10.1002/osp4.70127

The Future of the Obesity Management Landscape: Challenges, Opportunities and Solutions

Alexander Dimitri Miras
, Alexander Kokkinos
, Abd Tahrani
, Ricardo Reynoso
, Jörg Tomaszewski
, Ildiko Lingvay
, Domenica Rubino
, Jonathan Q. Purnell
, Muzamil Hussain
, Amy Hall
, Dimitrios Pournaras
, Philip Schauer
, Carel W. le Roux
, Luiza Borowska
, Chiedza Kaitano
, Ishita Doshi
, Dominique Gregoire
, Helen Haggart
, María Morán Gortaire
, Stephanie Mutchler

Barbara Sleypen, Elena Startseva, Priya Sumithran, Francis M. Finucane

Research output: Contribution to journal › Article › peer-review

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Abstract

Objective: In July 2023 a diverse group of international experts from academia, healthcare and medical technology/pharmacological companies gathered to discuss the current global landscape in the management of obesity, identify the clinical challenges healthcare systems are facing, and the gaps in scientific knowledge.

Approach: We proposed ways that the academia‐industry‐healthcare‐interface can be strengthened to offer solutions to these challenges and fill the gaps in knowledge.

Conclusion: We identified these five priorities for action: (1) Enhancing the academia‐healthcare‐industry collaboration in a way that prioritizes the patient with obesity and healthcare economic value. (2) Identifying reliable biomarkers and predictors of obesity treatment response to determine as early as possible whether a specific therapy is likely to work. (3) Defining specific and individualized treatment targets that take account of heterogeneity of obesity‐related complications risk, the presence of multimorbidity, and patient preference. (4) Addressing bias and discrimination against people with obesity amongst clinicians, health policy makers and the wider public. (5) Combining randomized controlled trial and cohort study data to apply next generation “machine learning” and “artificial intelligence” methods to large datasets that accelerates identification of factors associated with response heterogeneity and successful treatment response prediction.

Original language	English
Article number	e70127
Pages (from-to)	1-13
Number of pages	13
Journal	Obesity Science and Practice
Volume	12
Issue number	2
Early online date	18 Mar 2026
DOIs	https://doi.org/10.1002/osp4.70127
Publication status	Published (in print/issue) - 1 Apr 2026

Bibliographical note

Publisher Copyright:
© 2026 The Author(s). Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.

Funding

The authors have nothing to report.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

obesity related misperceptions
pharmacotherapy
biology
bariatric surgery

Access to Document

10.1002/osp4.70127

osp4.70127.pdfFinal published version, 694 KBLicence: CC BY

Cite this

Miras, A. D., Kokkinos, A., Tahrani, A., Reynoso, R., Tomaszewski, J., Lingvay, I., Rubino, D., Purnell, J. Q., Hussain, M., Hall, A., Pournaras, D., Schauer, P., le Roux, C. W., Borowska, L., Kaitano, C., Doshi, I., Gregoire, D., Haggart, H., Morán Gortaire, M., ... Finucane, F. M. (2026). The Future of the Obesity Management Landscape: Challenges, Opportunities and Solutions. Obesity Science and Practice, 12(2), 1-13. Article e70127. https://doi.org/10.1002/osp4.70127

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title = "The Future of the Obesity Management Landscape: Challenges, Opportunities and Solutions",

abstract = "Objective: In July 2023 a diverse group of international experts from academia, healthcare and medical technology/pharmacological companies gathered to discuss the current global landscape in the management of obesity, identify the clinical challenges healthcare systems are facing, and the gaps in scientific knowledge. Approach: We proposed ways that the academia‐industry‐healthcare‐interface can be strengthened to offer solutions to these challenges and fill the gaps in knowledge. Conclusion: We identified these five priorities for action: (1) Enhancing the academia‐healthcare‐industry collaboration in a way that prioritizes the patient with obesity and healthcare economic value. (2) Identifying reliable biomarkers and predictors of obesity treatment response to determine as early as possible whether a specific therapy is likely to work. (3) Defining specific and individualized treatment targets that take account of heterogeneity of obesity‐related complications risk, the presence of multimorbidity, and patient preference. (4) Addressing bias and discrimination against people with obesity amongst clinicians, health policy makers and the wider public. (5) Combining randomized controlled trial and cohort study data to apply next generation “machine learning” and “artificial intelligence” methods to large datasets that accelerates identification of factors associated with response heterogeneity and successful treatment response prediction.",

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author = "Miras, \{Alexander Dimitri\} and Alexander Kokkinos and Abd Tahrani and Ricardo Reynoso and J{\"o}rg Tomaszewski and Ildiko Lingvay and Domenica Rubino and Purnell, \{Jonathan Q.\} and Muzamil Hussain and Amy Hall and Dimitrios Pournaras and Philip Schauer and \{le Roux\}, \{Carel W.\} and Luiza Borowska and Chiedza Kaitano and Ishita Doshi and Dominique Gregoire and Helen Haggart and \{Mor{\'a}n Gortaire\}, Mar{\'i}a and Stephanie Mutchler and Barbara Sleypen and Elena Startseva and Priya Sumithran and Finucane, \{Francis M.\}",

note = "Publisher Copyright: {\textcopyright} 2026 The Author(s). Obesity Science \& Practice published by World Obesity and The Obesity Society and John Wiley \& Sons Ltd.",

year = "2026",

month = apr,

day = "1",

doi = "10.1002/osp4.70127",

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Miras, AD, Kokkinos, A, Tahrani, A, Reynoso, R, Tomaszewski, J, Lingvay, I, Rubino, D, Purnell, JQ, Hussain, M, Hall, A, Pournaras, D, Schauer, P, le Roux, CW, Borowska, L, Kaitano, C, Doshi, I, Gregoire, D, Haggart, H, Morán Gortaire, M, Mutchler, S, Sleypen, B, Startseva, E, Sumithran, P & Finucane, FM 2026, 'The Future of the Obesity Management Landscape: Challenges, Opportunities and Solutions', Obesity Science and Practice, vol. 12, no. 2, e70127, pp. 1-13. https://doi.org/10.1002/osp4.70127

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T1 - The Future of the Obesity Management Landscape: Challenges, Opportunities and Solutions

AU - Miras, Alexander Dimitri

AU - Kokkinos, Alexander

AU - Tahrani, Abd

AU - Reynoso, Ricardo

AU - Tomaszewski, Jörg

AU - Lingvay, Ildiko

AU - Rubino, Domenica

AU - Purnell, Jonathan Q.

AU - Hussain, Muzamil

AU - Hall, Amy

AU - Pournaras, Dimitrios

AU - Schauer, Philip

AU - le Roux, Carel W.

AU - Borowska, Luiza

AU - Kaitano, Chiedza

AU - Doshi, Ishita

AU - Gregoire, Dominique

AU - Haggart, Helen

AU - Morán Gortaire, María

AU - Mutchler, Stephanie

AU - Sleypen, Barbara

AU - Startseva, Elena

AU - Sumithran, Priya

AU - Finucane, Francis M.

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N2 - Objective: In July 2023 a diverse group of international experts from academia, healthcare and medical technology/pharmacological companies gathered to discuss the current global landscape in the management of obesity, identify the clinical challenges healthcare systems are facing, and the gaps in scientific knowledge. Approach: We proposed ways that the academia‐industry‐healthcare‐interface can be strengthened to offer solutions to these challenges and fill the gaps in knowledge. Conclusion: We identified these five priorities for action: (1) Enhancing the academia‐healthcare‐industry collaboration in a way that prioritizes the patient with obesity and healthcare economic value. (2) Identifying reliable biomarkers and predictors of obesity treatment response to determine as early as possible whether a specific therapy is likely to work. (3) Defining specific and individualized treatment targets that take account of heterogeneity of obesity‐related complications risk, the presence of multimorbidity, and patient preference. (4) Addressing bias and discrimination against people with obesity amongst clinicians, health policy makers and the wider public. (5) Combining randomized controlled trial and cohort study data to apply next generation “machine learning” and “artificial intelligence” methods to large datasets that accelerates identification of factors associated with response heterogeneity and successful treatment response prediction.

AB - Objective: In July 2023 a diverse group of international experts from academia, healthcare and medical technology/pharmacological companies gathered to discuss the current global landscape in the management of obesity, identify the clinical challenges healthcare systems are facing, and the gaps in scientific knowledge. Approach: We proposed ways that the academia‐industry‐healthcare‐interface can be strengthened to offer solutions to these challenges and fill the gaps in knowledge. Conclusion: We identified these five priorities for action: (1) Enhancing the academia‐healthcare‐industry collaboration in a way that prioritizes the patient with obesity and healthcare economic value. (2) Identifying reliable biomarkers and predictors of obesity treatment response to determine as early as possible whether a specific therapy is likely to work. (3) Defining specific and individualized treatment targets that take account of heterogeneity of obesity‐related complications risk, the presence of multimorbidity, and patient preference. (4) Addressing bias and discrimination against people with obesity amongst clinicians, health policy makers and the wider public. (5) Combining randomized controlled trial and cohort study data to apply next generation “machine learning” and “artificial intelligence” methods to large datasets that accelerates identification of factors associated with response heterogeneity and successful treatment response prediction.

KW - obesity related misperceptions

KW - pharmacotherapy

KW - biology

KW - bariatric surgery

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DO - 10.1002/osp4.70127

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SN - 2055-2238

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ER -

The Future of the Obesity Management Landscape: Challenges, Opportunities and Solutions

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

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