The hematologic consequences of obesity
Open accessAbstract
The prevalence of obesity is increasing and progressively influencing physician-patient interactions. While there is a sizable amount of data demonstrating that obesity is a state of low-grade inflammation, to our knowledge, there is no single review summarizing its effects on hematologic parameters and thrombotic risk. We performed a literature search which largely surfaced observational studies, with a few systematic reviews and meta-analyses of these studies. We took care to review the mechanisms driving an inflammatory state and obesity's effect on white blood cells, red blood cells, platelets, and thrombotic risk. There is an observed relative, and sometimes absolute leukocytosis driven by this inflammatory state. Obesity is also associated with increased platelet counts and an increased risk for venous thromboembolism (VTE). Lastly, the association between obesity, iron deficiency (ID), and red blood cell counts may be present but remains uncertain. Recognizing the above associations may provide clinicians with reassurance regarding otherwise unexplained hematologic abnormalities in obese individuals. We hope this review will prompt future studies to further understand the underlying mechanisms driving these abnormalities and identify modifiable risk factors and potential therapeutic targets to prevent the development of probable obesity-associated conditions with significant morbidity and mortality, such as ID and VTE.
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Increased body mass index and obesity are associated with leukocytosis across multiple studies.
“Increased body mass index and obesity are associated with leukocytosis across multiple studies, 22”
From Comprehensive Long-Term Changes in Cardiovascular Risk Biomarkers With Tirzepatide (2026) · cites this paper as [22]
Increased platelet activity and thrombocytosis have been associated with obesity, particularly in women.
“Although increased platelet activity and thrombocytosis have been associated with obesity, particularly in women, 2,22”
From Comprehensive Long-Term Changes in Cardiovascular Risk Biomarkers With Tirzepatide (2026) · cites this paper as [22]
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Comprehensive Long-Term Changes in Cardiovascular Risk Biomarkers With Tirzepatide
Sattar, N., Linetzky, B., Ruotolo, G., Verma, S., Sourij, H., Wang, H., Vanderman, K., Wilson, J. M., Griffin, R. M., Stefanski, A., et al. (2026) · JACC · doi:10.1016/j.jacc.2026.04.044 · Cited by 5 · see citing works
- Authors
- Purdy, J. C., & Shatzel, J. J.
- Affiliations
- Oregon Health & Science University (US)
- Corresponding author
- Purdy, Johanna C.
- Venue
- European Journal Of Haematology
- Publisher
- Wiley
- Published
- 2020-12-13
- Volume
- 106
- Issue
- 3
- Pages
- 306-319
- ISSN
- 0902-4441 (ISSN-L)
- 1600-0609
- PMC
- PMC8086364
- OpenAlex
- W3106908788
- Cited by
- 227
- References
- 180
- Citations by year
- 2026: 43 · 2025: 51 · 2024: 54 · 2023: 52 · 2022: 19 · 2021: 8
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- European Journal Of Haematology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL151367)
- Topics
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Health Sciences › Medicine › Oncology › Inflammatory Biomarkers in Disease Prognosis
- Health Sciences › Medicine › Oncology › Cancer Risks and Factors
- Keywords
- obesity, hematological parameters, low-grade inflammation, leukocytosis, platelet count, venous thromboembolism, thrombosis risk, iron deficiency, red blood cell count, leukocytes
- MeSH terms
- Adipose Tissue, Animals, Disease Susceptibility, Humans, Inflammation, Leukocyte Count, Leukocytosis, Obesity, Sex Factors, Biomarkers, Cytokines, Inflammation Mediators
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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