In the Era of Precision Medicine and Big Data, Who Is Normal?
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Guidelines state that as few as 120 individuals define reference intervals for clinical factors (though often smaller numbers from only one subpopulation are used) and there is no clear definition of who is "normal".
“Currently, guidelines state that as few as 120 individuals define reference intervals for clinical factors (though often smaller numbers from only one subpopulation are used) and there is no clear definition of who is “normal”64.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [64]
In “Defining Normality,” the paper says CLSI guidelines call for 120 reference individuals, that laboratories may use fewer, and that health lacks a universal definition. The “Multiplicity” section mentions sampling from the same subpopulation only as a simulation example; it does not establish that smaller samples are often drawn from just one subpopulation. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIndividual clinical tests, biomarkers, and prescription drug efficacy may vary across populations in their utility, but are fundamentally informed by the same underlying biology.
“Individual clinical tests, biomarkers, and prescription drug efficacy may vary across populations in their utility, but are fundamentally informed by the same underlying biology64,65.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [64]
The paper’s discussion of population variation in laboratory tests and its Potential Solutions section (page not provided) support the point that a clinical test’s utility can differ across populations. The copy does not establish population differences in prescription-drug efficacy or the claim that all three are informed by the same underlying biology. (LLM, conf=0.86)
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Cites
Association of Sickle Cell Trait With Hemoglobin A<sub>1c</sub> in African Americans
Lacy, M. E., Wellenius, G. A., Sumner, A. E., Correa, A., Carnethon, M. R., Liem, R. I., Wilson, J. G., Sacks, D. B., Jacobs, D. R., Carson, A. P., et al. (2017) · JAMA · doi:10.1001/jama.2016.21035 · Cited by 141 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [1]International Expert Committee. International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care. 2009; 32 (7): 1327– 1334. 19502545 PMC2699715
- [2]Lacy ME, Wellenius GA, Sumner AE, Association of sickle cell trait with hemoglobin A1c in African Americans. JAMA. 2017; 317 (5): 507– 515. 28170479 PMC5713881
- [3]Toward NRC. Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington, DC: National Academies Press; 2011. 22536618
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- [7]Rotimi CN, Jorde LB. Ancestry and disease in the age of genomic medicine. N Engl J Med. 2010; 363 (16): 1551– 1558. 20942671
Cited by
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Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,831 · see citing works
Ensuring fairness in machine learning to advance health equity.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018) · Annals of Internal Medicine · doi:10.7326/m18-1990 · Cited by 1,341 · see citing works
Performance of plasma phosphorylated tau 181 and 217 in the community
Mielke, M. M., Dage, J. L., Frank, R. D., Algeciras-Schimnich, A., Knopman, D. S., Lowe, V. J., Bu, G., Vemuri, P., Graff-Radford, J., Jack, C. R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01822-2 · Cited by 465 · see citing works
Reinforcement learning for clinical decision support in critical care: comprehensive review
Liu, S., See, K. C., Ngiam, K. Y., Celi, L. A., Sun, X., & Feng, M. (2020) · Journal of Medical Internet Research · doi:10.2196/18477 · Cited by 225 · see citing works
White, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176 · see citing works
Medical Artificial Intelligence and Human Values
Yu, K.-H., Healey, E., Leong, T.-Y., Kohane, I. S., & Manrai, A. K. (2024) · New England Journal of Medicine · doi:10.1056/nejmra2214183 · Cited by 149 · see citing works
Self-citationCurrent clinical use of polygenic scores will risk exacerbating health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2018) · doi:10.1101/441261 · Cited by 2,813 · see citing works
- Authors
- Manrai, A. K., Patel, C. J., & Ioannidis, J. P. A.
- Affiliations
- Harvard University (US)
- Stanford University (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2018-05-15
- Volume
- 319
- Issue
- 19
- Pages
- 1981
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC7572221
- OpenAlex
- W2801088249
- Cited by
- 98
- References
- 1
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 7 · 2023: 11 · 2022: 11 · 2021: 12 · 2020: 19 · 2019: 21 · 2018: 10
- FWCI
- 12.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K01HL138259)
- National Institute of Environmental Health Sciences (R00ES023504)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Medicine, History, and Philosophy
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- precision medicine, big data, reference values, clinical decision-making, data-driven medicine
- MeSH terms
- Adult, Female, Humans, Male, Nutrition Surveys, Reference Values, Precision Medicine, Datasets as Topic
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