2000 character limit reached
Routine haematological markers can predict and discriminate health status and biological age even from noisy sources (2303.01444v9)
Published 2 Mar 2023 in q-bio.QM
Abstract: For more than two decades, advances in personalised medicine and precision healthcare have largely been based on genomics and other omics data. These strategies aim to tailor interventions to individual patient profiles, promising greater treatment efficacy and more efficient allocation of healthcare resources. Here, we show that widely collected common haematologic markers can reliably predict and discriminate individual chronological age and health status from even noisy sources. Our analysis includes synthetic and real retrospective patient data, including medically relevant and extreme cases, and draws on more than 100\,000 complete blood count records over 13 years from the United States Centers for Disease Control and Prevention's National Health and Nutrition Examination Survey (CDC NHANES). We combine fully explainable risk assessment scores with machine and deep learning techniques to focus on clinically significant patterns and characteristics without functioning purely as a ''black-box model allowing interpretation and control. We validated the results with the UK Biobank, a larger cohort independent of the CDC NHANES and with very different collection techniques, the former a survey and the second a longitudinal study. Unlike current biological ageing indicators, this approach may offer rapid, and scalable implementations of personalised, precision and predictive approaches to healthcare and medicine without or before requiring other specialised, uncommon or costly tests.
- “Platelet activation status decreases after menopause” In Gynecol Endocrinol 20.5, 2005, pp. 249–57 DOI: 10.1080/09513590500097549.
- “A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring” In Nat Med 25.3, 2019, pp. 487–495 DOI: 10.1038/s41591-019-0381-y.
- “Usefulness of a complete blood count-derived risk score to predict incident mortality in patients with suspected cardiovascular disease” In Am J Cardiol. 99.2, 2007, pp. 169–74 DOI: 10.1016/j.amjcard.
- “Changes in Haematological Indices of Women at Different Fertility Periods in Nnewi, South-East, Nigeria” In J Med Res 2, 2016, pp. 166–169 DOI: 10.31254/jmr.2016.2610
- Petter Brodin and Mark M Davis “Human immune system variation” In Nat Rev Immunol. 17.1, 2017, pp. 21–29 DOI: 10.1038/nri.2016.125.
- “Variation in the human immune system is largely driven by non-heritable influences” In Cell 160.1-2, 2015, pp. 37–47 DOI: 10.1016/j.cell.2014.12.020.
- “Case Index by Patient History” In University of Pittsburgh, Department of Pathology, 2022, pp. Accessed on URL: https://path.upmc.edu/cases/
- “Case Index by Patient History” In NHS Foundation Trust, York Teaching Hospital URL: https://www.yorkhospitals.nhs.uk/seecmsfile/?id=2396
- J.M. Cruickshank “Some Variations in the Normal Haemoglobin Concentration” In British Journal of Haematology 18.5, 1970, pp. 523–530 DOI: https://doi.org/10.1111/j.1365-2141.1970.tb00773.x
- “The Society of Thoracic Surgeons National Cardiac Surgery Database: current risk assessment” In Ann Thorac Surg. 63.3, 1997, pp. 903–8 DOI: 10.1016/s0003-4975(97)00017-9.
- I. Erdemir “The comparison of blood parameters between morning and evening exercise” In European Journal of Experimental Biology 3.1, 2013, pp. 559–563
- “Inflamm-aging. An evolutionary perspective on immunosenescence” In Ann N Y Acad Sci. 908, 2000, pp. 244–54 DOI: 10.1111/j.1749-6632.2000.tb06651.x.
- “Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases” In J Gerontol A Biol Sci Med Sci. 69.1, 2014, pp. 4–9 DOI: 10.1093/gerona/glu057.
- “Inflammaging and anti-inflammaging: a systemic perspective on aging and longevity emerged from studies in humans” In Mech Ageing Dev. 128.1, 2006, pp. 92–105 DOI: 10.1016/j.mad.2006.11.016.
- “Inflammaging and ’Garb-aging”’ In Trends Endocrinol Metab 28.3, 2017, pp. 199–212 DOI: 10.1016/j.tem.2016.09.005.
- “Chronic inflammation in the etiology of disease across the life span” In Nat Med 25.12, 2019, pp. 1822–1832 DOI: 10.1038/s41591-019-0675-0.
- “Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states” In Nat Med 23.2, 2017, pp. 174–184 DOI: 10.1038/nm.4267.
- “Complete blood count risk score and its components, including RDW, are associated with mortality in the JUPITER trial” In Eur J Prev Cardiol. 22.4, 2014, pp. 519–26 DOI: 10.1177/2047487313519347.
- “The Intermountain Risk Score (including the red cell distribution width) predicts heart failure and other morbidity endpoints” In Eur J Heart Fail 12.11, 2010, pp. 1203–13
- “Diagnostic and Prognostic Role of Neutrophil-to-Lymphocyte Ratio in Early and Late Phase of Sepsis” In Indian J Crit Care Med. 22.9, 2018, pp. 660–663 URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6161585/
- “Design and protocol of Estrogenic Regulation of Muscle Apoptosis (ERMA) study with 47 to 55-year-old women’s cohort: novel results show menopause-related differences in blood count” In Menopause 25.9, 2018, pp. 1020–1032 DOI: 10.1097/GME.0000000000001117.
- “Routine blood tests are associated with short term mortality and can improve emergency department triage: a cohort study of >12000absent12000>12000> 12000 patients” In Scand J Trauma Resusc Emerg Med. 25.1, 2017, pp. 115 DOI: 10.1186/s13049-017-0458-x.
- “A new anatomic score for prognosis after cardiac catheterization in patients with previous bypass surgery” In J Am Coll Cardiol. 46.9, 2005, pp. 1684–92 DOI: 10.1016/j.jacc.2005.06.074.
- “The Hallmarks of Aging” In Cell 153.6, 2013, pp. 1194–217 DOI: 10.1016/j.cell.2013.05.039
- “Neutrophil-lymphocyte ratio in the early diagnosis of sepsis in an intensive care unit: a case-control study” In Rev Bras Ter Intensiva. 31.1, 2019, pp. 63–70 URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443306/#:~:targetText=The%5C%20presence%5C%20of%5C%20a%5C%20neutrophil,were%5C%20related%5C%20to%5C%20patient%5C%20mortality.
- Omid Fatemi Mohammad Madjid “Components of the complete blood count as risk predictors for coronary heart disease: in-depth review and update” In Tex Heart Inst J. 40.1, 2013, pp. 17–29
- “Estrogen increases haematopoietic stem-cell self-renewal in females and during pregnancy” In Nature 505.7484, 2014, pp. 555–558 DOI: 10.1038/nature12932
- “National Health and Nutrition Examination Survey” In Centers for Disease Control and Prevention, National Center for Health Statistics, 2016 URL: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx
- “Risk stratification based on components of the complete blood count in patients with acute coronary syndrome: A classification and regression tree analysis” In Sci Rep. 8.1, 2018, pp. 2838 DOI: 10.1038/s41598-018-21139-w.
- “Development and Validation of a Predictive Model for Short- and Medium-Term Hospital Readmission Following Heart Valve Surgery” In J Am Heart Assoc. 5.9, 2016 DOI: 10.1161/JAHA.116.003544.
- “Prognostic value of angiographic indices of coronary artery disease from the Coronary Artery Surgery Study (CASS)” In J Clin Invest. 71.6, 1983, pp. 1854–66 DOI: 10.1172/jci110941.
- “Prediction of coronary heart disease using risk factor categories” In Circulation 97.18, 1998, pp. 1837–47 DOI: 10.1161/01.cir.97.18.1837.
- “Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients” In Crit Care Med. 34.5, 2006, pp. 1297–310 DOI: 10.1097/01.CCM.0000215112.84523.F0.
- “A four-factor immune risk score signature predicts the clinical outcome of patients with spinal chordoma” In Clin Transl Med. 10.1, 2020, pp. 224–237 DOI: 10.1002/ctm2.4.