Evidence map›Paper›PMID 42062622›Full record

ArticleNature medicine2026

Data-driven prioritization of high-risk individuals for weight loss interventions.

Kamil Demircan, Julia Carrasco-Zanini, Alice Williamson, Carl Beuchel, Linsey Jackson, Werner Römisch-Margl, Aleksander L Hansen, Sarah Finer, David A van Heel, Genes & Health Research Team and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Kamil DemircanPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.
Julia Carrasco-ZaniniPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0002-3988-7505
Alice WilliamsonPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.
Carl BeuchelComputational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0003-3224-3894
Linsey JacksonEli Lilly and Company, Indianapolis, IN, USA.
Werner Römisch-MarglInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Aleksander L HansenSteno Diabetes Center Copenhagen, Herlev, Denmark.ORCID http://orcid.org/0000-0002-9514-8942
Sarah FinerWolfson Institute of Population Health, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0002-2684-4653
David A van HeelPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0002-0637-2265
Genes & Health Research Team
Gabi KastenmüllerInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.ORCID http://orcid.org/0000-0002-2368-7322
Matthew CoghlanEli Lilly and Company, Indianapolis, IN, USA.ORCID http://orcid.org/0000-0002-0894-1505
Ida MoellerEli Lilly and Company, Indianapolis, IN, USA.
Nicholas J WarehamIMS Epidemiology, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.ORCID http://orcid.org/0000-0003-1422-2993
Maik PietznerPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0003-3437-9963
Claudia LangenbergPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK. claudia.langenberg@bih-charite.de.ORCID http://orcid.org/0000-0002-5017-7344

Funding

Cancer Research UK (CRUK) C864/A14136Deutsche Forschungsgemeinschaft (German Research Foundation) 547107463RCUK | Medical Research Council (MRC) MC_UU_00006/1RCUK | Medical Research Council (MRC) MR/N003284/1
6 · The paper itself

Abstract

New obesity medications have demonstrated efficacy in trials, but their real-world deployment is partly limited by the absence of approaches that identify individuals for treatment based on risks for obesity-related complications. Here we present a risk prediction model to guide prioritization of high-risk individuals. In a population-based sample of ~200,000 individuals with a body mass index (BMI) exceeding 27 kg m

Indexed as

ObesityWeight LossBody Mass IndexCardiovascular DiseasesFemaleHumansMachine LearningMaleMiddle AgedOverweightRisk FactorsTirzepatideTirzepatide

Identifiers

PMID42062622
PMCPMC13279260

What OpenQuestion holds

Texttitle and abstract
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.