Physiological and pathological population dynamics of circulating human red blood cells

Higgins, John M.; Mahadevan, Lakshmi

doi:10.1073/pnas.1012747107

Cited by 83 publications

(103 citation statements)

References 18 publications

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“…(We use the symbol

\overset{{normalM}_{RBC}}{true}

to represent an estimate of the patient’s true M RBC· ) Recent work directly measuring (4) and modeling (23–25) M RBC suggests it is tightly regulated within individuals, and we therefore hypothesized that we can derive a patient-specific

\overset{{normalM}_{RBC}}{true}

at one point in time and use it prospectively in Equation (4) to improve the accuracy of future AG estimates made from future

HbA 1 normalc : A G = \frac{italicHbA 1 c - italicHbA 1 c false(0 false)}{false[1 - italicHbA 1 c false(0 false) false] \cdot k_{g} \cdot \overset{{normalM}_{RBC}}{true}}

. See Figure 4 for two examples.…”

Section: Personalizing the Model For Increased Accuracy Of Prospectivmentioning

confidence: 99%

Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring

2016

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The glycated hemoglobin assay (HbA1c) is essential for the diagnosis and management of diabetes because it provides the best estimate of a patient’s average blood glucose (AG) over the preceding 2–3 months and is the best predictor of disease complications. However, there is substantial unexplained glucose-independent variation in HbA1c that makes AG estimation inaccurate and limits the precision of medical care for diabetics. The true AG of a non-diabetic and a poorly-controlled diabetic may differ by less than 15 mg/dL, but patients with identical HbA1c and thus identical HbA1c-based estimates of AG may have true AG that differs by more than 60 mg/dl. We combine a mechanistic mathematical model of hemoglobin glycation and red blood cell flux with large sets of intra-patient glucose measurements to derive patient-specific estimates of non-glycemic determinants of HbA1c including mean red blood cell age (MRBC). We find that interpatient variation in derived MRBC explains all glucose-independent variation in HbA1c. We then use our model to personalize prospective estimates of AG and reduce errors by more than 50% in four independent sets of more than 200 patients. The current standard of care provided AG estimates with errors > 15 mg/dL for 1 in 3 patients. Our patient-specific method reduced this error rate to 1 in 10. This personalized approach to estimating AG from HbA1c should improve medical care for diabetes using existing clinical measurements.

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“…(We use the symbol

\overset{{normalM}_{RBC}}{true}

\overset{{normalM}_{RBC}}{true}

at one point in time and use it prospectively in Equation (4) to improve the accuracy of future AG estimates made from future

HbA 1 normalc : A G = \frac{italicHbA 1 c - italicHbA 1 c false(0 false)}{false[1 - italicHbA 1 c false(0 false) false] \cdot k_{g} \cdot \overset{{normalM}_{RBC}}{true}}

. See Figure 4 for two examples.…”

Section: Personalizing the Model For Increased Accuracy Of Prospectivmentioning

confidence: 99%

Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring

2016

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“…In the patient population used in their study, Higgins and Mahadevan were able to identify IDA 75% of the time and at least 1 month before traditional tests (1 ). Clinicians know that extra time is critical for IDA, because it is often symptomatic of other illness, such as colon cancer in postmenopausal women and in adult men.…”

Section: Why Is This Invention Important?mentioning

confidence: 99%

Mathematical Models to Enhance the Value of Information from Current Laboratory Platforms

Webster¹,

Kumar²

2012

Clinical Chemistry

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“…The above equation tracks the evolution of the cellular characteristics as they deviate from or fluctuate around a healthy state (13). In Eq.…”

Section: Significancementioning

confidence: 99%

White blood cell population dynamics for risk stratification of acute coronary syndrome

Chaudhury

Noiret

Higgins

2017

Proc. Natl. Acad. Sci. U.S.A.

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The complete blood count (CBC) provides a high-level assessment of a patient's immunologic state and guides the diagnosis and treatment of almost all diseases. Hematology analyzers evaluate CBCs by making high-dimensional single-cell measurements of size and cytoplasmic and nuclear morphology in high throughput, but only the final cell counts are commonly used for clinical decisions. Here, we utilize the underlying single-cell measurements from conventional clinical instruments to develop a mathematical model guided by cellular mechanisms that quantifies the population dynamics of neutrophil, lymphocyte, and monocyte characteristics. The dynamic model tracks the evolution of the morphology of WBC subpopulations as a patient transitions from a healthy to a diseased state. We show how healthy individuals and hospitalized patients with similar WBC counts can be robustly classified based on their WBC population dynamics. We combine the model with supervised learning techniques to riskstratify patients under evaluation for acute coronary syndrome.In particular, the model can identify more than 70% of patients in our study population with initially negative screening tests who will be diagnosed with acute coronary syndrome in the subsequent 48 hours. More generally, our study shows how mechanistic modeling of existing clinical data can help realize the vision of precision medicine.acute coronary syndrome | mathematical modeling | white blood cells | disease prognosis | population dynamics C irculating blood cells continuously interrogate almost all tissues in high throughput, and their collective states of maturation, activation, proliferation, and senescence reflect current pathophysiologic conditions: healthy quiescence, acute response to pathology, chronic compensation for disease, and ultimately, decompensation. Routine complete blood counts (CBCs) involve measurements of single-cell characteristics for tens of thousands of blood cells and provide a high-level view of these pathophysiologic states. Each routine CBC measures high-dimensional single-cell information, but clinical decisions are currently based on only a few derived statistics. The vast potential of the complete set of CBC measurements has been well-appreciated, with previous efforts attempting early detection of infection by identifying immature granulocytes or prognosis for some malignancies by counting the number of WBCs with atypical characteristics (1-3). These efforts have had limited impact but hint at the potential for enhanced clinical decision support (4, 5).Here, we develop mathematical models of the population dynamics of neutrophils, lymphocytes, and monocytes using these CBC measurements. To test the hypothesis that qualitative aspects of WBC population dynamics are altered in disease, we first compare WBC population dynamics in healthy individuals with those in patients with an acute illness requiring treatment in a hospital. We ensure that overall WBC counts are normal to investigate the effect of acute illness on WBC dynamics independ...

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Physiological and pathological population dynamics of circulating human red blood cells

Cited by 83 publications

References 18 publications

Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring

Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring

Mathematical Models to Enhance the Value of Information from Current Laboratory Platforms

White blood cell population dynamics for risk stratification of acute coronary syndrome

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