Biomarker signatures of sickle cell disease severity

Du, Mengtian; Ness, Sarah Van; Nouraie, Mehdi; Nekhai, Sergeï; Gladwin, Mark T.; Steinberg, Martin H.; Sebastiani, Paola

doi:10.1016/j.bcmd.2018.05.001

Cited by 24 publications

(23 citation statements)

References 47 publications

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“…Our findings are in agreement with the pathophysiological mechanism of VO due to i) heightened ability of sickle RBC to adhere to the vascular endothelium and promote activation of endothelial cells and leukocytes and ii) sickle RBC have the lifespan shortened which also contributes to anemia [42]. Cluster grouping is a very useful approach to identify biomarkers of SCD severity [43]. We designed a cluster analysis in order to group the laboratory parameters of each genotype.…”

Section: Discussionsupporting

confidence: 83%

Sickle cell disease: A distinction of two most frequent genotypes (HbSS and HbSC)

et al. 2020

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Sickle cell disease (SCD) consists of a group of hemoglobinopathies in which individuals present highly variable clinical manifestations. Sickle cell anemia (SCA) is the most severe form, while SC hemoglobinopathy (HbSC) is thought to be milder. Thus, we investigated the clinical manifestations and laboratory parameters by comparing each SCD genotype. We designed a cross-sectional study including 126 SCA individuals and 55 HbSC individuals in steady-state. Hematological, biochemical and inflammatory characterization was performed as well as investigation of previous history of clinical events. SCA patients exhibited most prominent anemia, hemolysis, leukocytosis and inflammation, whereas HbSC patients had increased lipid determinations. The main cause of hospitalization was pain crises on both genotypes. Vaso-occlusive events and pain crises were associated with hematological, inflammatory and anemia biomarkers on both groups. Cluster analysis reveals hematological, inflammatory, hemolytic, endothelial dysfunction and anemia biomarkers in HbSC disease as well as SCA. The results found herein corroborate with previous studies suggesting that SCA and HbSC, although may be similar from the genetic point of view, exhibit different clinical manifestations and laboratory alterations which are useful to monitor the clinical course of each genotype.

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Section: Discussionsupporting

confidence: 83%

Sickle cell disease: A distinction of two most frequent genotypes (HbSS and HbSC)

et al. 2020

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“…Another study of the same cohort reported a higher baseline hematocrit was associated with 1 or more painful crises [ 22 ]. However, a detailed study of biomarker clusters in this cohort found patients with less intravascular hemolysis had a reduction in pain and acute chest syndrome in addition to a trend to reduced mortality and stroke [ 23 ]. High hemoglobin F levels have also been associated with reduced incidence of acute painful episodes [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

Oral carbon monoxide therapy in murine sickle cell disease: Beneficial effects on vaso-occlusion, inflammation and anemia

et al. 2018

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Carbon monoxide (CO) at low, non-toxic concentrations has been previously demonstrated to exert anti-inflammatory protection in murine models of sickle cell disease (SCD). However CO delivery by inhalation, CO-hemoglobin infusion or CO-releasing molecules presents problems for daily CO administration. Oral administration of a CO-saturated liquid avoids many of these issues and potentially provides a platform for self-administration to SCD patients. To test if orally-delivered CO could modulate SCD vaso-occlusion and inflammation, a liquid CO formulation (HBI-002) was administered by gavage (10 ml/kg) once-daily to NY1DD and Townes-SS transgenic mouse models of SCD. Baseline CO-hemoglobin (CO-Hb) levels were 1.6% and 1.8% in NY1DD and Townes-SS sickle mice and 0.6% in Townes-AS control mice. CO-Hb levels reached 5.4%, 4.7% and 3.0% within 5 minutes in NY1DD, SS and AS mice respectively after gavage with HBI-002. After ten treatments, each once-daily, hemoglobin levels rose from 5.3g/dL in vehicle-treated Townes-SS mice to 6.3g/dL in HBI-002-treated. Similarly, red blood cell (RBC) counts rose from 2.36 x 106/μL in vehicle-treated SS mice to 2.89 x 106/μL in HBI-002-treated mice. In concordance with these findings, hematocrits rose from 26.3% in vehicle-treated mice to 30.0% in HBI-002-treated mice. Reticulocyte counts were not significantly different between vehicle and HBI-002-treated SS mice implying less hemolysis and not an increase in RBC production. White blood cell counts decreased from 29.1 x 103/μL in vehicle-treated versus 20.3 x 103/μL in HBI-002-treated SS mice. Townes-SS mice treated with HBI-002 had markedly increased Nrf2 and HO-1 expression and decreased NF-κB activation compared to vehicle-treated mice. These anti-inflammatory effects were examined for the ability of HBI-002 (administered orally once-daily for up to 5 days) to inhibit vaso-occlusion induced by hypoxia-reoxygenation. In NY1DD and Townes-SS sickle mice, HBI-002 decreased microvascular stasis in a duration-dependent manner. Collectively, these findings support HBI-002 as a useful anti-inflammatory agent to treat SCD and warrants further development as a therapeutic.

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“…This group has also published analysis of laboratory measurements of carbon monoxide and heme oxygenase for acute pain crisis prediction [41]. Other groups have studied red blood cell mechanical sensitivity and biomarker signatures of SCD severity [42,43]. The use of machine learning in a variety of areas of medicine including outcome prediction for chemoradiotherapy, breast cancer survival prediction, and early prediction of asthma exacerbations have recently been published [44-46].…”

Section: Discussionmentioning

confidence: 99%

Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study

Johnson¹,

Yang²,

Gollarahalli³

et al. 2019

JMIR Mhealth Uhealth

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BackgroundSickle cell disease (SCD) is an inherited red blood cell disorder affecting millions worldwide, and it results in many potential medical complications throughout the life course. The hallmark of SCD is pain. Many patients experience daily chronic pain as well as intermittent, unpredictable acute vaso-occlusive painful episodes called pain crises. These pain crises often require acute medical care through the day hospital or emergency department. Following presentation, a number of these patients are subsequently admitted with continued efforts of treatment focused on palliative pain control and hydration for management. Mitigating pain crises is challenging for both the patients and their providers, given the perceived unpredictability and subjective nature of pain.ObjectiveThe objective of this study was to show the feasibility of using objective, physiologic measurements obtained from a wearable device during an acute pain crisis to predict patient-reported pain scores (in an app and to nursing staff) using machine learning techniques.MethodsFor this feasibility study, we enrolled 27 adult patients presenting to the day hospital with acute pain. At the beginning of pain treatment, each participant was given a wearable device (Microsoft Band 2) that collected physiologic measurements. Pain scores from our mobile app, Technology Resources to Understand Pain Assessment in Patients with Pain, and those obtained by nursing staff were both used with wearable signals to complete time stamp matching and feature extraction and selection. Following this, we constructed regression and classification machine learning algorithms to build between-subject pain prediction models.ResultsPatients were monitored for an average of 3.79 (SD 2.23) hours, with an average of 5826 (SD 2667) objective data values per patient. As expected, we found that pain scores and heart rate decreased for most patients during the course of their stay. Using the wearable sensor data and pain scores, we were able to create a regression model to predict subjective pain scores with a root mean square error of 1.430 and correlation between observations and predictions of 0.706. Furthermore, we verified the hypothesis that the regression model outperformed the classification model by comparing the performances of the support vector machines (SVM) and the SVM for regression.ConclusionsThe Microsoft Band 2 allowed easy collection of objective, physiologic markers during an acute pain crisis in adults with SCD. Features can be extracted from these data signals and matched with pain scores. Machine learning models can then use these features to feasibly predict patient pain scores.

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Biomarker signatures of sickle cell disease severity

Cited by 24 publications

References 47 publications

Sickle cell disease: A distinction of two most frequent genotypes (HbSS and HbSC)

Sickle cell disease: A distinction of two most frequent genotypes (HbSS and HbSC)

Oral carbon monoxide therapy in murine sickle cell disease: Beneficial effects on vaso-occlusion, inflammation and anemia

Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study

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