Clinical Relevance of Red Cell Distribution Width and Reticulocytes Hemoglobin Content in Children with Fever without Source

Introduction

The main reason to consult in a pediatric emergency service is fever [1]. Up to 20% of these fever episodes have no apparent source in children under 3 years old and represent a specific nosological entity called fever without source (FWS) [2]. FWS represents a frequent and challenging situation, because both the timely distinction between a viral and bacterial etiology, as well as the early identification of individual with benign and selflimiting disease prone to be treated in an ambulatory settings, still remain partially met clinical needs [2,3]. FWS stratification tools include several clinical prediction rules, biological parameters, such as leukocytosis, C-reactive protein (CRP), and procalcitonin (PCT). So far these risk stratifications have been mostly dedicated to distinguish between viral and severe bacterial infections (SBI) with suboptimal discriminant accuracy [4]. If PCT values below 0.3 ng/ml have shown some promise to effectively rule-out SBI [5,6], knowing whether this single biomarker would outperform clinical prediction rules, enhance their discriminate accuracy for SBI or display sufficient negative predictive value (NPV) is still uncertain [3,6]. Considering these limitations, the integration of multiple biomarkers into a biomarker-based score showed some promise. To this respect the Lab-Score combining CRP, PCT, and urine analysis results probably represent the most discriminatory algorithm available so far [2,7-9], especially when used in a step by step approach in combination with clinical presentation, age and absolute neutrophils count, with optimal NPV for SBI [10]. Nevertheless, because of the lack of specific markers for viral infection, approximately 50% of children with FWS of viral etiology are currently exposed to unnecessary antibiotic treatment and are hospitalized [11].

Therefore, the identification of a biomarker highly specific for viral infections or allowing the early distinction between FWS patients with self-limited disease from those requiring hospitalization could be of considerable interest to optimize patient triage at the emergency room. Among emerging biomarkers of possible interest in FWS, several new hematological parameters automatically provided by SysmexTM analyzers could represent appealing candidates [12-16]. Among them, the red blood cell distribution width (RDW-CV) measuring the degree of heterogeneity of erythrocyte volume, and the reticulocytes hemoglobin content (Ret-He) indicating the iron availability for erythropoiesis, may be promising. In adults suffering from Influenza infections or septic shock, RDW-CV elevation was found to be associated with a worse prognosis [12,13]. On the other hand, in community acquired pneumonia, Ret-He has been shown to be decreased transiently in response to the Interleukin6–dependent hepcidin production leading to iron sequestration in other compartments than those involved in red blood cells maturation [14,15]. Whether Ret-He changes could reflect the infection severity or be of clinical relevance especially in infectious settings is still elusive. Therefore, in this pilot study we investigated whether RDW-CV and Ret-He, already available at no additional costs, could provide meaningful diagnostic and prognostic information in FWS when compared to the Lab-score, and whether these parameters would improve the discriminant accuracy of the Lab-score, both in term of hospitalization duration prediction and ability to confirm the presence of a viral infection

Materials and Methods

The research ethics committee of Geneva University Hospitals approved the study protocol (CER 15-082), and Informed consent given by parent or legal representative before enrolment. The study was performed in accordance with the declaration of Helsinki.

Patient Population and Study Design

This ancillary study was derived from a soon published cohort [17]. Participants for this prospective, single-center, and epidemiological diagnostic study were enrolled in the emergency room (ER) division of the Geneva University Hospitals between November 2015 and December 2017. Briefly, 241 patients aged <3 years-old were admitted to the pediatric ER of Geneva University Hospitals (a tertiary care hospital) with a diagnosis of FWS. FWS was defined as a febrile episode of less than 7 days with no cause determined by the history or the physical exam. The exclusion criterias for this study were unavailable blood samples or unavailable SysmexTM datas, comorbidities predisposing to infections such as cancer, primary or secondary immunodeficiency, and iatrogenic immunosuppression. From the initial 241 patients, 170 had to be excluded because of missing RDW-CV and Ret-He data, leaving 71 patients available for this exploratory study (Figure 1). Besides usual blood investigations for the normal care of children presenting with FWS, blood and urine culture were obtained for all patients. Real-time PCR was used for Adenovirus (AdV, quantitative assay, Argene commercial kit) and Herpes Human Virus- 6 (HHV- 6, qualitative assay, in-house assay followed by quantitative assay, Genesig commercial kit) [18], whereas quantitative and semiquantitative, real-time, reverse- transcription (RT)-PCR were used for Hepatitis E virus (HEV) [19] and Human Parechovirus (HPeV) respectively [20]. Semi-quantitative results were reported as cycle threshold (CT) values; samples with CT values <40 were considered positive. Quantitative results were reported in copies/ml (17). Medical history and the Lab-score, were obtained at admission and relevant information was recorded on an individual case report form [17].

Study Endpoints

Two predetermined endpoints were considered for this explorative study. The primary endpoint consisted in hospitalization duration >24h (HD>24h). The secondary endpoint consisted in a final diagnosis of viral infection defined by the identification of aforementioned viral pathogens or in presence of a highly suggestive clinical presentation in absence of documented bacterial infection. Purely bacterial, mixed infections or undefined etiologies were considered as other etiologies. The endpoints adjudication was performed by one senior physician blinded to the participant’s biological data.

Biological Analyses

Venous blood samples were collected in heparinate lithium and Ethylene Di-amino Tetra Acetate (EDTA) vials on patient admission to the pediatric ER, prior to treatment initiation. Samples were immediately processed for routine requirement. PCT and CRP measurements were performed on Cobas 8000 instruments (Roche, module c801 and module c702, respectively).

Generic and Specialized Hematological Parameters

Blood samples were collected in pediatric tubes containing EDTA and then analyzed for CBC-DIFF and reticulocytes count on a Sysmex XN-10 instrument. Besides classical hematological parameters, such as the leucocytes count, hemoglobin concentration and the neutrophils count, the XN-10 provides new parameters delivering complementary information for the granulocytic lineage and the red lineage [16]. We studied 6 of these new parameters. Three for the granulocytic lineage: the Neutrophil Reactive Intensity (NEUT–RI), the Neutrophil Granularity Intensity (NEUTGI) and Neutrophils Width on y axis (NEUT-WY); and 3 for the red lineage: the hemoglobin content of reticulocytes (Ret-He), the difference in cellular hemoglobin content between reticulocytes and erythrocytes (Delta-He) and the Red Cell Distribution Width CV (RDW-CV). Further details regarding these specialized hematological parameters can be found in Figures 1-3.

Figure 1: White blood cells scattergram in the WBC channel. The scattergram in the WBC channel represents on the x axis the structure of the cells (SSC) and on the y axis the fluorescence (SFL). The NEUT-RI represents the mean fluorescence of neutrophils and is related to the activation and the immaturity of the cells. The NEUT-GI represents the mean value of high angle diffraction and represents the complexity of the neutrophils (nucleus, granulations, …) [16]. The red cloud named EO represents eosinophils, the blue cloud named NEUT + BASO represents neutrophils and basophils, the green cloud named MONO represents monocytes, the purple cloud named LYMPH represents lymphocyts.

Figure 2: Red blood cells scattergram in the RET channel. The scattergram in the RET channel represents on the x axis the fluorescence and on the y axis the size of the red blood cells. The Ret– He is calculated from the Ret–Y (mean value of the red blood cell size on the y axis) and represents the mean hemoglobin content of red blood cell precursors (Reticulocytes). The Delta-He is the difference in cellular hemoglobin content between reticulocytes and erythrocytes.

Figure 3: Red Cell Distribution Width Standard Deviation (RDW-SD) and Red Cell Distribution Width Coefficient of Variation (RDW-CV) derived from the red blood cells curve in impedance. The RDW-CV is calculated from the RDW-SD which is the width of the impedance curve of the red blood cells 20% above the base line.

Statistical Analyses

Table 1: Determinant of the Lab-score.

Note: *Positive urine dipstick: positive leukocytes esterase, or nitrite test result. LE: leucocytes; NI: Nitrites.

Analyses were performed using STATISTICA™ software (StatSoft, Tulsa, OK, USA). Fisher’s bilateral exact test and Mann– Whitney U-test were used where appropriate. Associations between biomarkers and study endpoints are presented as the odds ratio (OR) and corresponding 95% confidence interval (95% CI). Multivariable analyses with logistic regression were used to assess associations between continuous variables. In this model, endpoints were set as dependent variables, and the Lab-Score (Table 1) was set as the unique confounder because of the limited sample size. Adjusted analyses were performed only in case of significant univariate analyses. ROC analyses were performed using ANALYSE-IT™ software for Excel (Microsoft, Redmond, WA, USA) to identify the biomarker with the best area under the curve (AUC). AUC comparisons were performed according to the nonparametric approach proposed by DeLong, et al. [21]. The optimal cut-off was determined in a post-hoc based upon ROC curve results. Corresponding sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) with the respective 95% CIs are given. A value of p<0.05 was considered statistically significant.

Results

Baseline Clinical Demographic and Biological Characteristics

The clinical features of our population are summarized in Table 2. A total of 71 patients were included in the cohort. Among them 19 were discharged <24 hours, and 38 had a final diagnosis of viral infection. Among the latter, 11 had an enteroviral meningitis (15.5%), 12 a viral upper respiratory tract infections (16.9 %), 12 a viral gastroenteritis (16.9%), 1 a hand-foot-and-mouth disease (1.41%), 1 a viral rash (1.41%), and 1 a viral meningitis (1.41%). The remaining patients included had either bacterial infections only, mixed infections (bacterial and viral) (n=7), or undefined etiologies (n=3). A total of 52 patients had a hospital stay superior or equal to 24 hours (73.24%).

RDW-CV as an Independent Predictor of the Hospitalization Duration >24h (HD >24)

Table 2 shows that patients with HD>24h had higher median levels of RDW-CV upon inclusion when compared to those with HD<24h (14.1% versus 13.1% p<0.0001). The results were further confirmed by the ROC curves analyses (Table 3) showing that the AUC of the RDW-CV for an HD>24h was 0.79 (95%CI:0.67-0.92, p<0.0001), which was the highest for all parameters tested. In comparison, the ROC curve of the Lab-score displayed an AUC of 0.66 (95%CI: 0.53-0.79, p=0.0068). The AUC difference between RDW-CV and the Lab-score was nevertheless not found to be significant according to the Delong method (delta: 0.13, p= 0.11; Table 3). Adding RDW-CV to the Lab-score significantly increased the latter AUC from 0.66 to 0.84 (95CI%: 0.72-0.95; delta: 0.18; p=0.001, Table 3). Furthermore, logistic regression analyses indicated that for each percent of RDW-CV

increase, there was a concomitant 3.28-fold increase in the risk of HD>24h (OR: 3.28, 95%CI:1.57-6.87, p=0.0015) which remained unchanged after the adjustment for the Lab- score (OR: 3.76, 95%CI: 1.11-12.67, p=0.03) (Table 4). Conversely, the risk association for the Lab-score was independent of RDW-CV (adjusted OR: 1.68,95% CI: 1.07 - 2.65; p=0.03). Taken together, these results indicate that both RDW-CV and the Lab-score are independently associated with HD>24h, with an apparent superior strength of association Privileging the PPV, the optimal cut-off of RDW-CV was found to be of 15.2 % with a PPV of 100 % (95%CI: 63-100), a NPV of 31% (95%CI: 20-44), a SN of 17% (95%CI:9-31), and a SP of 100% (95%CI:79-100; Table 5).

Table 2: Patient Baseline Characteristics.

Note: *Correspond to significant AUC differences.

GB: WBC: White Blood Cells; PNN: Neutrophils; IG#: Immature Granulocytes; NRBC: Nucleated Red Blood Cells; GR: RBC : Red blood cells

Table 3: Discriminant accuracies of hematological parameters for hospitalization duration ≥ 24h and infections of viral etiology.

Note: *Correspond to significant AUC differences.

Table 4: Risk associations for hospitalization duration ≥24h.

Note: *Adjusted for the Lab-score, except when **. Adjusted analyses were performed only in case of significant univariate analyses. ** adjusted for RDW-CV. *** adjusted for Labscore and age

Table 5: Optimal cut-off values.

Note: *Based upon ROC curves analyses. **Set in order to maximize PPV.

Ret-He as a Non-Meaningful Marker of Infections of Viral Etiologies

Table 2 shows that patients with a final diagnosis of viral infection had higher median levels of Ret-He upon study inclusion when compared to those with infections of other etiologies (29.9 pg vs 26.25 pg, p=0.004). ROC curves analyses (Table 3) indicated that Ret-He had an AUC of 0.70 (95%CI: 0.57-0.84, p=0.002), whereas the Lab- score (cut-off: 3 points (22)) (displayed the highest diagnostic accuracy with an AUC of 0.88 (95%CI: 0.79- 0.96, p<0.0001) to detect an infection of viral etiology. The AUC comparison between Lab-score and Ret-He, indicated that the AUC difference was significant with a delta of 0.18 and a p-value of 0.047. Logistic regression analyses indicated that if Ret-He was significantly associated with a final diagnosis of viral infection in unadjusted analysis (OR:1.31;95%CI: 1.09-1.57, p=0.004), the association was lost after adjusting for the Lab-score. Furthermore, none of the parameters tested remained significant when adjusted for the Lab-score (Table 6). Privileging the PPV value, the optimal cut-off value found was 29.8 pg with a PPV of 76% (95% CI: 0.53- 0.89), a SP of 81% (95% CI:0.63-0.92), a SN of 53% (95% CI:0.35- 0.70), and NPV of 60% (95% CI: 0.46-0.76; Table 5).

Table 6: Risk associations for infections of viral etiology.

Note: *Adjusted for the Lab-score. Adjusted analyses were performed only in case of significant univariate analyses.

Discussion

The key finding of this pilot study is that both RDW-CV and the Lab-score were independent predictors of a HD>24h in children with FWS, whereas the remaining hematological parameters tested were not, after the adjustment for the Lab-score. To the best of our knowledge, this is the first report of the Lab-score as predictor of hospital duration. Indeed, so far most of the studies performed analyzed the capacity to distinguish patients with SBI [2,7,10,22]. Nevertheless, despite being significant, the AUC was rather modest (0.66) and whether it would be enough to influence patient management remains to be tested in other larger studies. On the other hand, RDW-CV tended to have a better AUC (0.79) and displayed an optimal positive predictive value of 100% at the 15.2% cut-off. Although derived in a post-hoc manner in order to optimize positive predictive value, this cut-off is very close to previously reported RDW-CV cut- offs (between 14.5 % and 15.5%) predicting mortality in patients with SBI or septic shock [12,23- 25]. Furthermore, if the AUC difference between RDW-CV and the Lab- score (0.79 vs 0.66; p=0.13) was not found to be significant according to the Delong method [21], adding RDW-CV to the Labscore nevertheless substantially increased the latter AUC from 0.66 to 0.84 (p=0.001) (Table 6). These results indicate that RDWCV values above 15.2% in FWS would allow the clinicians to early identify patients requiring prolonged hospitalization regardless of infection etiology, and accordingly to improve patients triage in the emergency room.

Furthermore, being automated and available 24h/24h with a turn-around time around 1 minute, RDW-CV results would meet most of the requirements needed for an emergency test. Nevertheless, these appealing preliminary results need to be replicated and validated at a larger scale before any clinical recommendation can be made. Also, knowing whether RDW-CV should be introduced into the Lab-score or considered separately to optimally identify patients requiring hospitalization awaits clarifications. The other principal findings concerning the Ret-He is that this parameter was not significant when adjusted with the Labscore to predict the viral etiology in FWS, as the other parameters tested. However, it is interesting to mention that the median value of Ret-He for viral infections (29.9 pg vs 26.25 pg) was higher than the one retrieved in other etiologies (including bacterial, mixed infections and undefined etiologies), which can be explained by the fact that iron sequestration is more severe in case of septic conditions (26,27). The Ret-He AUC to predict a uniquely viral infection was not found to be optimal (AUC: 0.70), especially as it was not found to be independently associated with this diagnosis, when adjusted for the Lab-score. Furthermore, privileging the PPV, the optimal post-hoc retrieved cut-off (29.8 pg) only displayed a PPV of 76%, which is too low to be considered for rule-in purposes, especially given the lower end of the 95%CI (53%). There are several limitations in this study. Firstly, due to the limited sample size of this pilot study, we could not provide a proper interpretation of non-significant findings reported. Nevertheless, given the strength and independent nature of the association between RDWCV and HD>24h, those preliminary results clearly indicate that RDW-CV could represent an appealing biomarker to early identify FWS patients requiring hospitalization.

A second important limitation resided in the fact that the optimal cut-off for RDW-CV (and Ret-He) was determined in a post-hoc manner. Therefore, the current proposed cut-off would require further independent validation in other larger prospective studies. Finally, the fact that our population exclusively included children where reference intervals for RDW-CV and Ret-He are still undetermined, we could not further extrapolate on the relevance of the proposed cut-offs from adult populations. However, this pilot study opens some new perspectives in the research of new but readily automatically available biomarkers to optimize patient management flux presenting to the ER with FWS.

Conclusion

In conclusion, it appears that the RDW-CV is a good independent predictor of the hospitalization duration superior or equal to 24 hours with an optimal PPV of 100%. Moreover, when added to the Lab-score, the RDW-CV was found to increase the prognostic capacity of the Lab-score, one of the best available risk stratification tools in FWS. When above 15.2% RDW-CV has the potential to help the clinician to early identify FWS patients requiring hospitalization, and as such could facilitate patient management flux in the emergency room. On the other hand, none of the biomarker tested was found to outcompete the Lab-score in distinguishing between fevers of purely viral origin from fevers of other etiologies. Those preliminary findings need to be replicated and validated at a larger and multicenter scale before any clinical recommendation can be done.

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Appraisal of Target Definition for Management of Paraspinal Ewing Tumors with Modern Radiation Therapy (RT): An Original Article

Introduction

Ewing sarcoma, initially described by James Ewing in 1921, may be broadly categorized as a high-grade osteolytic bone tumor which may occur at several localizations throughout the skeleton albeit with a tendency to involve the diaphysis of long bones [1- 11]. Children and adolescents are more frequently affected, and multidisciplinary management is required for improved therapeutic outcomes [3-11]. Radiation therapy (RT) plays a major role in treatment of Ewing sarcoma, and there have been improvements in radiotherapeutic management recently [3-11]. Since younger patients are more commonly diagnosed with Ewing sarcoma, adverse effects of irradiation should be thoroughly considered before radiotherapeutic management. While RT is a viable therapeutic option for a variety of cancers, pediatric patients should be more vigilantly considered for irradiation in view of the toxicity and consequences regarding quality of life. Younger patients still in the process of growing may be negatively affected by adverse irradiation effects. Nevertheless, RT may be utilized as part of multidisciplinary Ewing sarcoma management. Every effort is made to avoid radiation induced toxicity in radiotherapeutic management of Ewing sarcoma. Exploitation of image guided RT (IGRT) techniques, adaptive RT (ART), and improved target definition are among the several considerations for contemporary radiotherapeutic management with an improved toxicity profile. Currently, majority of cancer centers utilize Computed Tomography (CT) simulation for RT planning for Ewing sarcoma. While CT is an effective imaging modality, incorporation of other imaging modalities such as Magnetic Resonance Imaging (MRI) may result in improved target definition for radiotherapeutic management. In this study, we assessed RT target definition for Ewing sarcoma by use of multimodality imaging.

Materials and Methods

Patients receiving RT for Ewing sarcoma were assessed with comparative analysis to explore whether multimodality imaging improves target volume definition, interobserver and intraobserver variations for radiotherapeutic management of Ewing sarcoma. To address this critical issue, we comparatively assessed RT target volume determination by integration of MRI or by CT-simulation images only. Ground truth target volume has been determined for every patient on a collaborative basis by board certified radiation oncologists after detailed assessment, colleague peer review, and consensus for actual treatment and comparison purposes. Included patients had paraspinal Ewing sarcoma, and management with RT was decided after close collaboration and detailed multidisciplinary evaluation on an individual basis. We considered optimal therapeutic approaches and protocols by meticulous evaluation of patient, tumor, and treatment characteristics. Decision making procedure included thorough consideration of lesion sizes, localization and association with critical structures, contemplated outcomes of treatment, patient symptomatology and preferences along with logistical issues. RT delivery has been accomplished by use of Synergy (Elekta, UK) linear accelerator (LINAC) available at our tertiary referral institution. CT-simulation has been individually performed for each patient at the CT-simulator (GE Lightspeed RT, GE Healthcare, Chalfont St. Giles, UK) to acquire high quality RT planning images. Following the CT-simulation procedure, acquired RT planning images were sent to the delineation workstation (SimMD, GE, UK) by use of the network. Structure sets including treatment volumes and critical structures have been meticulously determined. Target volume definition was performed by either the CT-simulation images only or by registered CT and MR images. We conducted a comparative analysis for assessment of target definition by CT only and with incorporation of CT-MR registrationbased imaging to investigate the impact of multimodality imaging.

Results

Patients referred for radiotherapeutic management of paraspinal Ewing sarcoma at the Department of Radiation Oncology, Gulhane Medical Faculty, University of Health Sciences have been studied for target volume determination by either CT-only imaging or by CT-MR registration-based imaging in this original study. Evaluated tumor related parameters included lesion size, localization and association with the spinal cord, extent of bony invasion, and other characteristics. Additionally, patient age, symptomatology, performance status, lesion location and association with other critical structures have also been assessed. We considered the reports by American Association of Physicists in Medicine (AAPM) and International Commission on Radiation Units and Measurements (ICRU) in precise RT planning. In view of contemporary guidelines and clinical experience, radiation physicists have generated plans by taking into account relevant critical organ dose constraints. Tissue heterogeneity, electron density, CT number and HU values in CT images have been considered by the radiation physicist in RT planning. A critical objective of RT planning included achieving optimal target volume coverage without violation of critical organ dose constraints. The definition of ground truth target volume has beeen accomplished by board certified radiation oncologists after thorough evaluation, colleague peer review, and consensus. Ground truth target volume has been used for actual treatment and for comparison purposes. Treatment delivery with Synergy (Elekta, UK) LINAC has been performed by incorporation of IGRT techniques including the kilovoltage cone beam CT and electronic digital portal imaging. Ground truth target volume has been found to be identical with CTMR registration-based imaging in this study for radiotherapeutic management of paraspinal Ewing sarcoma.

Discussion

Ewing sarcoma has been initially described by James Ewing in 1921 and may be defined as a high-grade osteolytic bone tumor which may occur at several localizations throughout the skeleton albeit with a tendency to involve the diaphysis of long bones [1-11]. Ewing sarcoma more frequently affects children and adolescents, and improved therapeutic outcomes may be achieved through collaborative multidisciplinary management [3-11]. RT composes a critical weapon in the therapeutic armamentarium for treatment of Ewing sarcoma, and there have been several improvements in radiotherapeutic management lately [3-11]. Given that younger patients are more frequently diagnosed with Ewing sarcoma, thorough consideration of adverse effects is mandatory. RT offers a viable therapeutic option for a variety of cancers, however, pediatric patients should be more vigilantly considered for irradiation in given the risk of toxicity and consequences affecting quality of life. Younger patients still in the process of growing may be more prone to be negatively affected by adverse irradiation effects. Even so, RT may be utilized as part of multidisciplinary Ewing sarcoma management. Every effort should be made to avoid radiation induced toxicity in radiotherapeutic management of Ewing sarcoma. Exploitation of IGRT techniques, ART, and improved target definition are among the several considerations for contemporary radiotherapeutic management with an improved toxicity profile. Multimodality imaging techniques and image fusion methods have clearly contributed to improving target definition for several cancers, and there is now growing body of evidence suggesting the use of multimodality imaging for target definition of several tumors throughout the human body [12-45].

In the meantime, majority of cancer centers utilize CT simulation for RT planning for Ewing sarcoma. CT has been an effective imaging modality, however, incorporation of other imaging modalities such as MRI may result in improved target definition for radiotherapeutic management. In this study, we assessed RT target definition for Ewing sarcoma by use of multimodality imaging and found that target definition is improved by multimodality imaging. Within this context, this study may add to accumulating body of data suggesting improved target volume definition by use of multimodality imaging. Clearly, recent years have witnessed several advances in the spectrum of radiation oncology through the introduction of molecular imaging methods, automatic segmentation techniques, stereotactic RT, intensity modulated RT (IMRT), IGRT, and ART [46-84]. In line with these innovatory advances, accuracy and precision in target volume definition has been a more critical aspect of contemporary radiotherapeutic approaches. From this perspective, we consider that our study may have relevant clinical implications for routinization of multimodality imaging for target volume definition in radiotherapeutic management of paraspinal Ewing sarcoma. In conclusion, this study suggests improved target volume definition for radiotherapeutic management of paraspinal Ewing sarcoma by incorporation of MRI in RT planning procedure. Admittedly, there is need for further supporting evidence.

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Hematopoietic Elements in Osteoarthritic Femurs Compared to Normal Bone Marrow as Evaluated by Immunohistochemistry

Introduction

Affecting over 60% of the elderly population, osteoarthritis (OA) has come to be one of the most common joint diseases to affect aging society and leads to the incapacity of joint movement [1]. Caused primarily by mechanical factors including age, obesity, inactive lifestyle, joint injury, and occupation, alterations in gene expression in cartilage and subchondral bone tissue may accelerate the rate of OA disease progression [2]. During alteration of joints in OA, the cartilage covering the ends of the bone become eroded, so the bone surfaces no longer glide across each other, but grind into each other releasing pieces of cartilage and bone into the joint space thereby causing pain and disability. The degenerative changes which include thinning of cartilage layers, increase in subchondral bone content, increased underlying bone remodeling, and fissures which penetrate both cartilage and bone have been well documented [2-9]. Qualitative and quantitative studies have evaluated adipocyte, T-cell, B-cell, macrophage, megakaryocyte, granulocyte, osteoblast, osteoclast, and osteocyte activity that affects the cartilage and subchondral bone in OA [10-18]. The goal of the study was to evaluate the hematopoietic elements in the proximal end of the osteoarthritic femur head as they compare to hematopoietic elements in normal bone marrow aspirates collected from the traditional posterior iliac crest to determine whether the joint pressure and bone degeneration influences the underlying blood cell fractions.

Materials and Methods

This study was approved by the University of Tennessee Health Science Center Internal Review Board IRB number 22-08576-NHSR. Twenty-one formalin-fixed paraffin-embedded osteoarthritic femur head tissues and 13 normal bone marrow biopsy tissues were obtained from the UT shared tissue resource. Only samples evidencing areas of 40% - 60% hematopoietic cells compared with adipocytes were selected for evaluation, and only those selected areas were used for image collection and analysis. Tissue sections were cut at 4 micrometers, were mounted on plus-charged microscope slides, and were placed in a 60°C oven overnight to dry. A hematoxylin and eosin stain was used to visualize the tissue elements and determine appropriate regions of interest.

An iron stain was performed by deparaffinizing tissue sections through several changes of xylene, 1 minute each, and graded alcohols and deionized (DI) water, 10 seconds each. Equal portions of 10% potassium ferrocyanide (424135000, ACROS Organics, NJ, USA) and 20% hydrochloric acid (A144S-212, Fisher Scientific, NJ, USA) were mixed and sections were submersed in this solution for 20 minutes. After a 1-minute tap water rinse, the sections were stained in nuclear fast red solution (STNFRLT, American MasterTech Scientific, Inc., CA, USA) for 5 minutes, rinsed briefly in running tap water, and then were dehydrated through several changes of absolute ethanol and xylene before being cover slipped with a resinous mounting medium.

For immunohistochemistry (IHC) applications, the Bond™ Polymer Refine Detection Kit (DS9800, Leica Biosystems, IL, USA) was used for visualization of the following antibodies: CD3 (103R- 96, Cell Marque/Sigma Millipore, MO, USA), low pH, 1:400, CD20 (ACR3004B, BioCare Medical, CA, USA) high pH, 1:100, CD68 (M0876, Dako/Agilent, CA, USA), high pH, 1:100, Myeloperoxidase light chain (MPO) (sc-365463, Santa Cruz Biotechnology, CA, USA), high pH, 1:500, CD42b (SZ2) (sc-59052, Santa Cruz), high pH, 1:100, and CD71 (NCL-L CD71-309, Leica Biosystems), low pH, 1:80. The tissue sections underwent heat-induced antigen retrieval using a decloaking instrument and the appropriate solution pH as indicated for each antibody above. The IHC steps consisted of the following: A peroxidase block was applied for 5 minutes and then rinsed briefly using tris buffered saline (TBS) 1X Envision™ Flex Wash buffer (DM831, Dako/Agilent) before application of the primary antibody at room temperature for 20 minutes. Thereafter, sections were rinsed with TBS, the kit post primary antibody was applied for 8 minutes, and another TBS rinse followed. The kit polymer reagent was used for 8 minutes followed by TBS and DI water washes before the kit diaminobenzidine reagent was applied for 2 minutes for chromogenic labeling. A kit hematoxylin counterstain was applied for 5 minutes followed by TBS and DI water rinses. The sections were allowed to air dry and were briefly immersed in xylene prior to cover slipping with a resinous mounting medium.

For CD3, T-cell marker, and CD20, B-cell marker, 3 images were obtained using a 40x objective on an Olympus BX45 light microscope (Olympus Corp, Tokyo, Japan) and CellSens software (Olympus). Positively-labeled cells were counted in each field and results reported as an average. For MPO, myeloid cell marker, CD71, red blood cell precursor marker, and CD68, macrophage marker, 3 images were also captured using a 40x objected. However, because positive cell numbers were very large and because the cells adjacent to each other were difficult to reliably count, positivity was evaluated using NIH ImageJ software (https://imagej.nih.gov). The counts represent the amount of brown chromogen present as a percentage of hematopoietic area analyzed. Adipocytes were subtracted from the areas analyzed. Because the kit utilized an amplification step to increase the chromogenic labeling and therefor overall area occupied by each positively-labeled cell, analyzed fraction percentages may exceed 100% when added together. However, with the exception of the myeloid:erythroid, the fraction populations were not compared against each other, but instead were compared only between the OA patient samples and control group samples. Megakaryocyte counts using CD42b were performed on 3 high-powered fields (40x) and iron positivity was graded with a low power (10x) objective using the grading scale 0 - 3 where 0 = no iron present, 1 = minimal iron stores, 2 = moderate iron stores, and 3 = marked iron stores present. For all cell fractions, only areas which were 40% - 60% hematopoietic cellularity were analyzed. Statistical analysis was performed using Microsoft Excel data analysis tool t-Test: Two-Sample Assuming Equal Variances.

Results

B-lymphocytes, T-lymphocytes, and macrophages were found to be slightly increased in OA specimens as compared to control specimens, but the increases were not statistically significant (Table 1, Figures 1 & 2). The myeloid cell component was found to be decreased in OA as compared to that of the control group, but, again, there was no statistical significance to that value. However, the megakaryocyte component was found to be significantly decreased and the CD71+ erythroid fraction was found to be significantly increased in OA cases over the control group (Table 1, Figures 1-3). Because the red blood cell component was increased with no corresponding increase in the myeloid component, the myeloid to erythroid ratio (M:E) in OA cases was decreased (0.98 ± 0.26) compared to the control group (1.66 ± 0.71) and the difference was statistically significant (p-value 0.0005). The presence of iron stores was noted to be distinctly decreased in OA samples (0.16 ± 0.37) as compared to control bone marrow samples (1.08 ± 1.04) with a p-value of 0.001.

Table 1: Hematopoietic cell fractions in osteoarthritis bone marrow compared to normal bone marrow biopsies.

Note: *Measured as percentage of hematopoietic area occupied.

Figure 1: Average number of T-cells, B-cells, and megakaryocytes per high power field (40x) in osteoarthritis bone marrow as compared with control bone marrow. While numbers of T-cells and B-cells present were not significantly different, the number of megakaryocytes in osteoarthritis cases was significantly decreased.

Figure 2: The relative fraction of macrophages, myeloid cells, and erythroid precursor cells given as a percentage of the available hematopoietic cells in osteoarthritis and normal bone marrow. Although the macrophage and myeloid components were not significantly altered, there were more erythroid precursors found in osteoarthritis bone marrow.

Figure 3: Immunohistochemistry assays on bone marrow. Adipocytes (*) occupied 40% - 60% of bone marrow space in the tested samples. CD71 labeling (green arrows) with brown chromogen highlights red blood cell precursors while unlabeled regions (black arrows) identify non-erythroid hematopoietic elements.

a) Control tissue demonstrated approximately 30% positivity while,

b) Osteoarthritis marrow demonstrated approximately 40% positivity. Scale bar = 50 micrometers.

Figure 4: Iron pigment (black arrows) as assessed by Prussian Blue stain for iron, grades 0-3. Scale bar = 50 micrometers.

Discussion

Cartilage growth and maintenance are carefully regulated by appropriate load bearing on the joint. Increase in water content in cartilage softens the matrix allowing for microtears which enlarge with load bearing and eventually lead to flaking of bone and cartilage into the synovial space [1]. Breakdown of cartilage and subchondral bone may allow for exchange of larger-than-normal molecules to influence signaling pathways and increase cross-talk between the normally unconnected compartments of synovium and bone marrow [19]. Studies have shown cellular fractions such as macrophages, osteoblasts, osteocytes, adipocytes, T-cells, B-cells, megakaryocytes, granulocytes, and osteoblasts found in the bone/bone marrow may affect, or be affected by, the cartilage and subchondral bone changes seen in OA joints [10-18].

This study reviewed the quantity of hematopoietic cells found in subchondral bone marrow in OA proximal femurs. Most hematopoietic elements including myeloid cells, macrophages, T-lymphocytes, and B-lymphocytes were similar in quantity to those found in normal bone marrow biopsy specimens taken from the posterior iliac crest. However, the erythroid precursor fraction was increased in OA and there was a corresponding decrease in iron stores. Although excessive iron can give rise to osteoarthritis symptoms [20,21], the relationship between iron deficiency and OA is scarce in the literature. The most common cause of hyperproliferative red blood cell progenitors in bone marrow is increased erythropoietin released in response to hypoxia sensed by the juxtaglomerular apparatus in the kidneys, typically in response to anemia. Hyperproliferation of red blood cells in the bone marrow in conjunction with decreased iron stores is characteristic of iron deficiency anemia. However, lack of access to patient clinical information including hemoglobin levels, serum iron, transferrin, and ferritin precludes a definitive diagnosis. Indeed, a previous studies suggested that OA patients over 70 years are less likely to be anemic than those who have fractured a femur [22]. While bone marrow aspirates collected from the iliac crest are thought to represent the general bone marrow population in the entire organism, it is uncertain whether discrete bone marrow compartments may evidence increases or decreases in hematopoietic lines based on local environment signals. Joints affected by OA are known to produce a myriad of chemokines, proteases, and interleukins among others which could stimulate or suppress one or more hematopoietic lineages [23]. Thus, the increase in erythroid precursors, decrease in megakaryocytes, and decrease in iron stores may reflect the state of the organismal bone marrow. Alternately, the shift in hematopoietic element production may be specifically localized to the femur head due to the mechanical stress related to OA changes. Estimated to make up <1% of the hematopoietic population under normal circumstances, the decrease in megakaryocytes could be directly related to the increased erythroid fraction resulting in reduced space in the bone marrow for megakaryocytes. Additionally, because megakaryocytes and erythroid precursors putatively stem from the same multipotent hematopoietic cell, signals from erythropoietin may drive differentiation from the megakaryocytic pathway and toward the erythrocytic pathway [24].

One limitation of this project pertains to the fact that the methods used in bone decalcification vary and can have adverse effects on subsequent tissue stains [25]. Decalcifying agents containing acid can reduce iron visualization in tissue sections. While both control and OA bone samples underwent acid decalcification with the same reagent, the length of time each was subjected to the acid reagent varied. Larger tissues such as femur heads undergo decalcification for several hours whereas bone marrow biopsies are decalcified for a brief 45 minutes. Because OA bones were subjected to longer decalcification procedure, the possibility that iron may have been leached out of those tissues cannot be excluded. However, the decalcification solution was composed of a weak acid and two of the OA bone samples did evidence clear iron deposits in tissues outside of the hematopoietic compartment suggesting that the decalcification procedures may not have adversely affected the iron stores present.

In conclusion, erythrocyte precursors in the bone marrow were found to be increased in proximal femurs in OA patients as compared to normal bone marrow biopsy specimens taking from the posterior iliac crest of control subjects. The myeloid component was not significantly decreased but nonetheless resulted in a decreased myeloid to erythroid ratio of cell fractions in the bone marrow. The iron stores were lower in OA patients which, when taken with the increase in red blood cell precursors, suggests possible iron deficiency or altered iron metabolism within the proximal femur compartment in persons with OA. Future studies are needed to assess the serum iron, transferrin, ferritin, and complete blood cell count of OA patients to determine anemia status and possible elucidation for the increased red blood cell components

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