Evidence mapPaperPMID 41991772Full record

ReviewNature reviews. Endocrinology2026

Precision medicine in low-income settings and small island developing states.

Sushant Saluja, Fahmida Mannan, Guillaume Pare, Sonia S Anand, Neil A Hanchard, Claudia Langenberg, Simon G Anderson

Abstract readReview
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In one paragraph

Review in Nature reviews. Endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Sushant SalujaDivision of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Fahmida MannanDivision of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0002-3094-784X
Guillaume PareDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0000-0002-6795-4760
Sonia S AnandDepartment of Medicine, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0000-0003-3692-7441
Neil A HanchardChildhood Complex Disease Genomics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0003-1925-2665
Claudia LangenbergPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0002-5017-7344
Simon G AndersonDivision of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. simon.anderson@uwi.edu.ORCID http://orcid.org/0000-0002-8896-073X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precision medicine tailors prevention, diagnosis and treatment of cardiometabolic diseases to individual genetic, environmental and lifestyle determinants, with the potential to fundamentally change healthcare. However, low-income and middle-income countries (LMICs) and small island developing states (SIDS) experience severe implementation barriers: inadequate healthcare infrastructure, prohibitive costs, under-representation in genomic datasets and additional SIDS-specific constraints. This Perspective advances three specific contributions beyond generic equity calls. First, it delineates distinct precision medicine pathways for larger LMICs versus SIDS, highlighting SIDS opportunities for regional consortia, shared sequencing and/or biobanking hubs and technological leapfrogging via mobile health platforms and digital phenotyping. Second, it emphasizes practical and high-impact entry points that are financially sustainable. Additionally, it advocates for integrating polygenic risk-based stratification into existing non-communicable disease care pathways rather than establishing separate specialist services. Third, it delineates a staged implementation framework that prioritizes ethical oversight and robust data governance, underscoring the importance of privacy safeguards, data sovereignty, equitable benefit sharing, community consent mechanisms and alignment with the Sustainable Development Goals to minimize associated risks of exploitation. Equitable partnerships between LMICs and high-income countries, expansion of diverse genomic data and community-driven innovation will ensure that precision tools effectively target metabolic phenotypes in LMICs and SIDS while advancing global health equity.

Indexed as

Developing CountriesPovertyPrecision MedicineHumansResource-Limited Settings

Identifiers

What Socratic holds

Textmetadata
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 Socratic graph.