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Haematological setpoints are a stable and patient-specific deep phenotype

Abstract

The complete blood count (CBC) is an important screening tool for healthy adults and a common test at periodic exams. However, results are usually interpreted relative to one-size-fits-all reference intervals1,2, undermining the precision medicine goal to tailor care for patients on the basis of their unique characteristics3,4. Here we study thousands of diverse patients at an academic medical centre and show that routine CBC indices fluctuate around stable values or setpoints5, and setpoints are patient-specific, with the typical healthy adult’s nine CBC setpoints distinguishable as a group from those of 98% of other healthy adults, and setpoint differences persist for at least 20 years. Haematological setpoints reflect a deep physiologic phenotype enabling investigation of acquired and genetic determinants of haematological regulation and its variation among healthy adults. Setpoints in apparently healthy adults were associated with significant variation in clinical risk: absolute risk of some common diseases and morbidities varied by more than 2% (heart attack and stroke, diabetes, kidney disease, osteoporosis), and absolute risk of all-cause 10 year mortality varied by more than 5%. Setpoints also define patient-specific reference intervals and personalize the interpretation of subsequent test results. In retrospective analysis, setpoints improved sensitivity and specificity for evaluation of some common conditions including diabetes, kidney disease, thyroid dysfunction, iron deficiency and myeloproliferative neoplasms. This study shows CBC setpoints are sufficiently stable and patient-specific to help realize the promise of precision medicine for healthy adults.

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Fig. 1: Haematological setpoints are stable over decades in states of health.
Fig. 2: Setpoints are a deep phenotype and generate a strong signal for heritability analysis.
Fig. 3: Haematological setpoints are associated with all-cause mortality.
Fig. 4: Setpoints are associated with disease diagnosis and may enhance diagnostic accuracy.

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Data availability

Clinical data were collected using the MGB Research Patient Data Registry and Electronic Data Warehouse (for MGB patients) and the UW DLMP Data Warehouse (for UWMC data). Owing to restrictions on sharing protected health information, individual patient data have not been shared. Previous estimates of inter- and intra-patient marker variation were obtained from the online EFLM database: https://biologicalvariation.eu/, via manual queries for each individual marker on 13 March 2023. GWAS summary data have been uploaded to the GWAS Catalog and are available under accession numbers: GCST90292591 (HCT), GCST90292592 (HGB), GCST90292593 (MCH), GCST90292594 (MCHC), GCST90292595 (MCV), GCST90292596 (PLT), GCST90292597 (RBC), GCST90292598 (RDW) and GCST90292599 (WBC). Supplementary methods and tables are given in Supplementary Information. Data for significant hits and loci are given in Supplementary Tables. Summary data for primary figures are given in Supplementary Data.

Code availability

Code for calculation of setpoints is included in Supplementary Code. The same code is also available at GitHub (https://github.com/BrodyFoy/setpoint_calculation/). Owing to restrictions on sharing of protected health information, an artificial dataset of simulated patient data is provided for illustrative purposes.

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Acknowledgements

The authors thank the Mass General Brigham Biobank for providing access to genomic and health information data, and the MGB Electronic Data Warehouse and Research Patient Data Repository groups and University of Washington Department of Laboratory Medicine and Pathology Informatics group for facilitating access to electronic health records. The authors thank R. Gupta, J. Stefely, C. MacRae, D. Louis, S. Spitalnik, C. Brugnara and S. Dzik for helpful discussions. We thank K. Lewandrowski and the MGH core clinical laboratory for facilitating prospective laboratory testing. J.M.H. reports funding from the National Institutes of Health (grants R01HD104756 and R01DK123330).

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Authors and Affiliations

Authors

Contributions

J.M.H. and B.H.F. conceived the project and its design. Data collection was performed by B.H.F., R.P., M.T.R., C.Z., C.M., H.R.P., C.H.P., S.N.H., E.L., C.E.P., R.P.H., V.T. and J.M.H. Data analysis was performed by B.H.F., R.P., M.T.R., C.Z., D.C.D., K.J.K., V.T. and J.M.H. with input from all authors. All authors contributed to interpretation of results, writing and editing of the manuscript.

Corresponding authors

Correspondence to Brody H. Foy or John M. Higgins.

Ethics declarations

Competing interests

Mass General Brigham submitted a provisional patent application (63/695,679) on 17 September 2024, related to diagnostic and prognostic applications of haematological setpoints that includes B.H.F., M.T.R., V.T. and J.M.H. as inventors.

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Extended data figures and tables

Extended Data Fig. 1 Characteristics of setpoint coefficients of variation.

a, Mean inter- and intra-patient coefficients of variation (CVs) for each setpoint are shown stratified by subject age. b, Mean intra-patient CV over various time periods. c, Association between setpoint value and setpoint CV. All results were calculated using cohort A. d, Median (+IQR) absolute difference between setpoints in cohort A estimated using variable numbers of datapoints against setpoints estimated with 20 CBCs (n = 1203). Error bars in a-b represent 95% confidence intervals, generated via bootstrapping with 10,000 samples. For results in b, each patient’s CV was calculated using the single time period with the highest number of isolated outpatient CBCs during the study period (2002–2021). Note that results in d do not include MPV, due to lower frequency of collection than the other 9 markers.

Extended Data Fig. 2 Correlations between setpoints and other laboratory tests.

a, Correlation of CBC setpoints with other CBC setpoints and setpoints of other haematologic markers derived from extended research parameters of the Sysmex XN-9000. b, Correlation of setpoints with mean marker values for a wide range of common laboratory tests. c, Correlation of setpoints with common laboratory test marker differences in 10 pairs of demographically matched subjects with differences in a selected setpoint. Results in (c) reflect differences in the marker between matched patients, normalized by the mean and s.d. of the marker difference across the 10 pairs. X-axis labels in c reflect the male pairs (M1, M2, …, M5) and female pairs (F1, F2, …, F5) of the cohort. Axis ordering for a-c was performed via hierarchical clustering. Black boxes in a-b refer to regions where absolute correlation coefficients exceed 0.3. Black boxes in c refer to markers for which P > 0.05 in a 2-sided t-test. Full details of correlation calculations and cohort definition are provided in Supplementary Methods. Details of tests in a-c (full names, units, patient data range, etc.) are listed in Supplementary Tables 1011, and Supplementary Methods.

Extended Data Fig. 3 Setpoints shift in some pathophysiologic settings.

a, A long-term HCT trajectory in a patient pre- and post-menopause illustrates a shift in the HCT setpoint. b, This shift is often seen following menopause. c–f, Other shifts tend to occur in patient setpoints after pathophysiologic events (c, hypothyroidism, d, splenectomy, e, liver disease) and (f) after pregnancy. g, Effect sizes (mean marker changes illustrated in panels b-f) are similar when using setpoints or a randomly chosen single isolated CBC. h, Precision is higher with setpoints, based on a ratio of P values from a t-test of effect sizes using setpoints or isolated CBCs, on a log10 scale. Summary characteristics of cohorts in b-f are given in Supplementary Table 9. Lines in b-f reflect unity. Box plots in b-f represent the median (center line), interquartile range (box), with tails extending 1.5 times the interquartile range beyond the box, and all other outliers are plotted as separate points. P values in b-f were derived from a 1-sample, 2-sided t-test. Exact P values are 1 × 10−46, 1.5 × 10−18, 0.002, 1.5 × 10−10, and 1 × 10−14 for b-f respectively.

Extended Data Fig. 4 Heritability of haematologic setpoints.

a-b, Associations of each haematologic setpoint between first-degree relatives (a) and partners (b) in cohorts A-C. c, Heritability estimates of each CBC index derived from studies in the literature7,8,9,10,11. Plots in a-b have been age- and gender-corrected via linear regression.

Extended Data Fig. 5 Manhattan and QQ-plots for each setpoint GWAS analysis.

a, Manhattan plots for the setpoints not shown in Fig. 2. b, Quintile-quintile plots for each setpoint GWAS. Annotations correspond to nearby genes for the primary SNP in each associated locus. Red annotations correspond to novel loci. A full list of significant hits, loci, and nearby genes is given in Supplementary Tables 36. P values in a-b were derived from a linear regression test, and to adjust for multiple comparisons, a significance threshold of 5 × 10−8 was used.

Extended Data Fig. 6 Comparison of significance and effect sizes between single marker and setpoint GWAS.

a-b, Comparison of effect estimates (a. beta coefficients for SNPs in a GWAS) and corresponding P values (b) for significant SNPs from GWAS using a single, randomly-chosen isolated CBC or the setpoint. The dashed line in a-b represents unity, and highlighted colors reflect whether either or both markers achieved significance at P < 5 × 10−8. For brevity, hits where both P values were above 5 × 10−8 have been excluded. P values in a-b were derived from a linear regression test, and to adjust for multiple comparisons, a significance threshold of 5 × 10−8 was used.

Extended Data Fig. 7 Number of significant loci identified when using averaged CBC values compared to setpoints.

Results are from GWAS analyses following the same quality control measures as the primary analysis but limited to the patients with at least 8 isolated CBCs (n = 19,773). 1, 2, 4, and 8-point averages were taken from a randomly chosen subset of each patient’s isolated CBCs, but limited to the same set of 8 measurements, such that each higher point average contains all the data from the lower point average.

Extended Data Fig. 8 Setpoint-mortality associations over various time periods and in different cohorts.

a-b, Calculations of setpoint-mortality associations in Cohort B over 2 years (a) and 5 years (b). c, Calculation of setpoint-mortality associations in a less-restricted MGB cohort with setpoint estimates from 2006–2011 (excluding any members of cohorts A-C; n = 50,423), without requirement of no major inpatient stays during the study period. d, Calculation of setpoint-mortality associations in a distinct cohort from the University of Washington Medical Center (UWMC), with setpoints estimated from 2014–2018 (= 13,864). Error bars in a-d reflect the 95% confidence interval on the mortality rate. All results shown are after exclusion of setpoints outside the reference range (using MGB reference range for a-c, and UWMC reference range for d). Characteristics of the UWMC cohort are given in Supplementary Table 12.

Extended Data Fig. 9 Validation of associations between setpoints and risk of disease diagnosis at UWMC.

a, Associations between setpoints (estimated between 01-2014 - 01-2019) and future diagnoses are given for patients from UWMC (n = 13,864 patients), based on ICD code analysis. b, Age- and sex-corrected hazard ratios for future disease diagnosis based on a 1-s.d. increase in each setpoint (n = 13,864 patients). c, Time from presentation with a pre-diabetic HbA1c (5.7–6.4%) until a diabetic HbA1c (>6.4%) stratified by change in MCHC from setpoint (n = 2,173 patients). d, Likelihood of an elevated TSH result (>5 mIU/L) stratified by presenting MCV and MCV setpoint (n = 7,510 patients). e, Likelihood of low ferritin (<10 ng/dL) stratified by presenting HGB and HGB setpoint (n = 6,285 patients). Each pair of survival curves in a is significantly different (P < 1 × 10−5). Error bars in b reflect the 95% confidence interval for the hazard ratio.

Extended Data Fig. 10 Associations of setpoints and presenting lab values with mortality.

1-year mortality rates stratified by setpoint value (estimated from 2002–2006) and worst lab value in 2007. Results show similar stratifications to Fig. 4c. Note that numbers for WBC results may differ slightly from Fig. 4c, due to use of percentile cut-offs instead of specific clinical cut-offs.

Supplementary information

Supplementary Information

Supplementary methods, Figs. 1 and 2 and Tables 1, 2 and 7–12 (Supplementary Tables 3–6 are supplied separately).

Reporting Summary

Supplementary Tables

Supplementary Tables 3–6. Four supplementary tables related to the genome-wide association study.

Supplementary Data

Raw data underlying plots in the four primary manuscript figures. Note that not all raw data could be shared owing to restrictions on sharing of protected health information.

Supplementary Code

MATLAB code for calculation of setpoints from a marker time series. A readme and toy dataset is included. This file is also available from GitHub at: https://github.com/BrodyFoy/setpoint_calculation/.

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Foy, B.H., Petherbridge, R., Roth, M.T. et al. Haematological setpoints are a stable and patient-specific deep phenotype. Nature 637, 430–438 (2025). https://doi.org/10.1038/s41586-024-08264-5

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