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Comparison of Novel Hematological Biomarkers in Alopecia Areata and Other Not-Cicatricial Alopecia: A Retrospective Study

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19 September 2024

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20 September 2024

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Abstract
Introduction & Objectives: Blood cells-(BC)count derived parameters have been inves-tigated in systemic inflammation, cancer, and various cutaneous diseases. Little is known about their usefulness in trichology, especially as prognostic and therapeutic biomarkers. We conducted a retrospective study on patients with not-cicatricial alopecia to assess differences in BC-count. Materials & Methods: Patients with alopecia areata (AA), telogen effluvium (TE), androgenic alopecia (AGA), psoriasis and hidradenitis suppurativa (HS) were included in the study. Baseline complete BC-count and derived parameters (i.e. NLR, PLR, MLR, SII; PIV) were recorded and analyzed for each patient and at different timepoints for AA patients on systemic agents. Results: 170 patients fulfilled inclusion criteria. BC-count and derived values were similar across the not-cicatricial alopecia groups, while general differences were observed compared to psoriasis and HS. TE displayed a closer inflammatory signature to psoriasis with respect to CRP, NLR, MLR, SII and PIV. We also found a significant reduction in RBCs in severe versus patchy-AA and an increase of SII in acute versus chronic AA. Modulation of platelet number and volume could be used as therapeutic marker in AA. Conclusions: In our experience, the BC-count and its derived parameters could represent potential tools in AA and TE.
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Subject: Medicine and Pharmacology  -   Dermatology

1. Introduction

Identification of validated biomarkers is an ideal goal for the majority of human diseases and, recently, a great attention has raised around whole blood cells (BC)-count and derived formulas, since they are low cost, easy to access and could be used for different purpose (e.g. staging, treatment bio-marker)[1,2].
Whole BC ratio such as Neutrophils (NLR), Monocytes (MLR) or Platelet (PLR) to Lymphocytes ratio, and more complex formulas, already validated as inflammatory biomarkers (i.e. SII and PIV), have been extensively investigated in systemic inflammation, cancer, and many cutaneous diseases [3,4,5,6,7,8,9,10,11,12,13,14]. Patients with psoriasis and hidradenitis suppurativa (HS) have increased NLR, PLR, SII, and MLR compared to healthy controls and therapeutic intervention with biologic agents in these patients is proven to restore the systemic inflammatory imbalance [8,15,16,17,18,19,20,21].
However, scant data exist so far for hair disorders and only few studies focused on AA patients [22,23,24,25]. The current knowledge in AA pathogenesis highlighted the relevant role of inflammatory Type 1 lymphocytes and the usefulness of their inhibition as therapeutic strategy [26,27,28,29]. Indeed, involvement of nails and body hair follicles, suggest a more complex systemic mechanism that could impair BC-count and inflammatory parameters [30,31,32,33]. Systemic diseases and general inflammatory status have been linked to telogen effluvium (TE) [34,35,36] as well. Despite androgenic alopecia (AGA) is usually considered a primary hair follicle disease, triggering factors such as smoking habits, obesity, psychological distress could cause systemic inflammation [37,38,39]. For these reasons, we conducted a retrospective study on patients with not cicatricial alopecia to assess differences in whole BC-count and derived formulas, also compared to psoriasis and HS.

2. Materials and Methods

Patients affected with AA, TE, AGA, moderate-to-severe psoriasis and moderate-to-severe HS who referred to Our Dermatologic Center over the last 24 months (January 2022 - January 2024) were included in the study. Patients were considered eligible if they had either long-standing or recently active disease and they were not on systemic therapy including food supplements over the last 3 months before our visit or performing routinary venous blood collection. Severity of AA was scored according to clinical phenotype and hair loss distribution in patchy (with Severity alopecia tool score SALT<50; pAA), moderate-to-severe (ms) alopecia (including SALT≥50, with or without beard, eyelashes and/or eyebrow loss; msAA) and universal alopecia (uAA). For TE, patients with either acute or chronic disease were considered eligible.
Patients with psoriasis or HS were included only if they had moderate-to-severe disease [Psoriasis Area and Severity Index (PASI)>10, and International Hidradenitis Suppurativa Severity Score System (IHS4)>4, for psoriasis and HS respectively].
Baseline characteristics, including age, gender, PASI, comorbid PsA, IHS4, SALT, body mass index (BMI), relevant medical and therapeutic history, and concomitant therapies were collected from patient’s records.
Baseline whole BC-count, ESR, CRP, thyroid profile, vitamin D and vitamin B12 were retrieved, recorded and analyzed for each patient. For patients with AA who started a systemic treatment, whole BC-count count was studied also at baseline (T0) and at the last available data between week 12-24 of treatment (last observation, LO) and analyzed prospectively. The ratio between blood cells, and derived formulas were calculated for each patient and when applicable, at treatment time points. The investigated BC-count derived formulas were: i) neutrophil to lymphocyte ratio (NLR), ii) platelet to lymphocyte ratio (PLR), iii) monocyte to leukocyte ratio (MLR), iv) systemic immune inflammation index (SII) and v) pan immune inflammation value (PIV). The formula for SII was [neutrophil (103/mmc) × platelet (103/mmc)]/lymphocyte (103/mmc) count while PIV was calculated as following: [neutrophil (103/mmc) × platelet (103/mmc) × monocyte (103/mmc)]/lymphocyte (103/mmc) count.
Concomitant diseases (e.g. thyroid disorders, cancer, dyslipidemia, diabetes, vitiligo, atopy, depression) with records in the literature on altered hematological parameters (i.e. BC-count and derived formulas) were considered confounding factors in group sub-analysis of AA and therefore were considered as exclusion criteria.
Descriptive statistics were used to summarize the characteristics of the study population. For continuous variables, the significance of the difference between medians of the groups was investigated by using Mann-Whitney test. One-way ANOVA test for independent measures (plus Dunn’s post-test), was used to compare the means of ≥three independent samples simultaneously. Treatment effect was studied comparing medians of values at different time point either with Wilcoxon matched-pairs signed rank test or with Repeated measures ANOVA with Geisser-Greenhouse's epsilon correction for ≥three samples simultaneously. Categorical variables were analyzed with Pearson’s chi-square test. Fisher exact test was also used when appropriate. All P values cited are two-sided, and values of P less than .05 were considered statistically significant.

3. Results

Overall, we included 170 patients. Patients with hair diseases were in total 104 (57 AA, 29 TE, 18 AGA). Baseline features are reported in Table 1.
The number of female patients was significantly higher compared to the male proportion, with special regard to TE (p=0.03).
Median age at visit, and therefore at the moment of blood sampling, was almost similar across the three different group of patients with hair diseases, while AA patients had a significant earlier onset of the disease as compared to patients with TE (p=0.0341), but not versus AGA patients.
Family history of AGA was common in all patients, however was significantly higher in patients with AGA, while family history of AA was found only in patients with AA.
Patients with psoriasis were 41 (19 F, 22 M) while patients with HS were 25 (7 F, 18 M).
Psoriasis patients were generally older than all the other patients (Figure 1), except for patients with TE however no significant difference existed among patients with hair diseases.

3.1. Whole Blood Cells Count, Derived Parameters and Inflammatory Markers

3.1.1. Not Cicatricial Alopecia versus Psoriasis and HS

BC-count and derived parameters were assessed for all the patients. Results are shown in Figure 2.
We found that psoriasis and HS patients displayed a systemic inflammatory profile with significantly increase in SII (p=0.0015, p=0.0086, respectively) and PIV values (p=0.0002; p<0,0001, respectively), compared to patients with not cicatricial alopecia. Compared to hair disorders, psoriatic patients had significantly higher values also for neutrophils (p=0.0011), monocytes (p=0.0369), basophils (p=0.0010), NLR (p<0.0001) and MLR (p=0.0009).
HS patients had significant variation of total number of white blood cells (WBC; p<0.0001), neutrophils (p<0.0001) and monocytes (p<0.0001), compared to patients with alopecia.
Psoriasis had increased basophils (p= 0.0048) compared to HS, while HS had a higher number of WBC (p=0.034), monocytes.
Both psoriasis and HS had higher CRP values compared to hair diseases (p=0.0049, 0.0012, respectively), while ESR was increased only in patients with HS versus alopecia (p= 0.0024). For CRP and ESR, results were limited by a reduced number of available data and total number is shown in Figure 2 (n) and (o).

3.1.2. Alopecia Areata versus TE and AGA

A sub-analysis of whole BC-count and derived formulas was performed for each hair disease. No significant differences were found comparing values of AA versus TE versus AGA patients. Median values with IQR range are detailed in Table 2.
Furthermore, we were able to retrieve data on ESR, CRP, vitamin D serum levels and TSH values (Figure 3) for some patients (the exact number of patients with available data are described in the Figure 3). TE patients displayed higher values of ESR compared to AA (p= 0.0382) and AGA (p= 0.0194). However, ANOVA analysis of ESR across the three groups and of CRP, vitamin D and TSH showed no statistically differences (Kruskal-Wallis test with Dunn’s post- test).
  • A further sub-analysis comparing AA, TE; AGA, HS and psoriasis revealed that: i) neutrophils and NLR of TE patients were comparable to those of psoriasis patients; ii) basophils of AA patients did not differ from those of psoriatic patients; iii) MLR differences between psoriasis and hair disorders was due only to AGA patients; iv) TE patients had similar levels of SII and CRP compared to psoriasis and HS although not significantly higher than the other hair diseases; v) TE patients had similar levels of PIV compared to psoriasis although not significantly higher than the other hair diseases (data not shown)

3.1.3. AA Subgroups

We aimed to study whether disease severity in AA patients could be related to systemic inflammatory status.
We first analyzed all AA patients according to phenotype: uAA versus msAA versus pAA. (Table 3).
When we compared pAA versus more extended disease (uAA+msAA; u-msAA), the only relevant differences we found were related to RBC (p=0.0408). However, msAA patients displayed a pronounced inflammatory profile compared to the other patients especially to uAA with significantly higher levels of NLR (p=0.023), PLR (p=0.0370), SII (p=0.0090) and PIV (p=0.0370) and TSH (0.0204). Values of SII and PIV of msAA patients were overall comparable to those of psoriatic (versus msAA: p= >0,9999 SII and PIV; versus uAA p=0.0023, SII and p=0.0077 PIV; versus pAA p=0.0892 SII and 0.0404 PIV) and HS patients (versus msAA: p>0,9999 SII and PIV; versus uAA p=0.0054 SII and p=0,0013 PIV; versus pAA p=0.1274 SII and 0.0069 PIV) (Kruskal-Wallis test with Dunn’s post- test). Patients with uAA had higher MPV levels compared to msAA patients (p= 0.0435).
Then, as further step, possible confounding factors were removed in order to assess the direct influence of systemic inflammation on hair loss. Patients with reported alteration of whole BC-count or BC-count derived parameters were excluded during this analysis.
Nineteen (6 M, 13 F) patients were otherwise healthy, with the exception of AA (7 uAA, 2 msAA, 10 pAA) (Figure 4). Notably, of the 10 patients with pAA 9 had active, unstable, and of recent onset disease (<1 year) and 1 patient, despite the very long history of AA (25 years) had a recent (<3 months) worsening of pAA (median time of onset 0.5 years; IQR 0.3-0.6). U-msAA patients had long-standing disease (≥1 year; median time of onset 4 years; IQR 1-10.5) (p= 0.0029; Mann Whitney t-Test).
When comparing patchy with more severe AA, patients with localized disease had significant higher values of SII (417.4; IQR 220.9-545.7) compared to severe disease (ms-uAA) (p=0.0220), and almost significant higher PIV (p= 0.0535). SII and PIV in pAA patients were not statistically different from values observed in our patients with psoriasis and HS (psoriasis versus pAA, p=0.6892 SII and p=0.5456 PIV; HS versus pAA, p=0.6193 SII and p=0.1413 PIV) while they were significantly lower in patients with more severe disease (psoriasis versus u-msAA, p= 0.0025 SII; p= 0.0143 PIV; HS versus u-msAA p= 0.0031 SII and p=0.0026 PIV). No differences were observed with regard to ESR, CRP, vitamin D and TSH.

3.1.4. BC-Count and Derived Parameters over Treatment in AA

Data from blood sampling during systemic treatments (oral steroids, cyclosporin A and baricitinib) were retrieved for 8 patients at time 0 (T0) and time 12/24 of treatment (last observation, LO).
A direct comparison of whole BC and derived parameters was performed between T0, and LO. Treatment effect on hematologic parameters was associated significant increased over time in number of PLT and decreased in volume (MPV) (Table 4).

4. Discussion

The aim of our study was to investigate whether whole BC-count and derived parameters such as NLR, MLR, PLR, SII and PIV could be useful in common hair disorders either for differential diagnosis, staging or therapeutic monitoring.
Actually, in the recent years, pathogenesis of human diseases has been a relevant field of investigation in order to identify (and potentially to prevent) causative factors and therapeutic targets. Easy and reliable biomarkers are a highly desirable target of investigation since they can really help physicians in disease diagnosis, treatment, prognosis and monitoring [1,2]. Recently, a huge number of studies focused on very simple parameters such as whole BC-count and identified some formulas that could be reliably used as markers of general inflammation, treatment efficacy and diseases severity such as SII and PIV [12,13].
Combining different cell subtypes and the proportion among these cells helps to normalized physiologic individual variations, however it has to be considered the influence of biologic and pathologic factors on the overall results (such as age, gender, associated comorbidities).
In order to reduce confounding factors, we compared patients with hair diseases such as AA, TE and AGA with patients with other cutaneous diseases with well described alterations of BC-count and derived formulas such as psoriasis and HS. Moreover, sub-analysis excluding AA patients with systemic comorbidities was performed as well.
Baseline features of our trichology population, were in line with the literature with regard to age of onset, associated diseases and BMI, this improving the quality of our data, besides the predominance of female patients in the hair diseases group, due to either epidemiology for AA and TE and to the retrospective nature of the study [29,40,41,42,43,44,45,46,47]. Indeed, male patients tend to not refer to a second level center for hair disorders for AGA and blood examination are not routinary prescribed at first visit in AGA patients.
Family history of AGA was common in all patients, however as expected, was significantly higher in patients with AGA, with 17/18 patients reporting at least 1 person in the 1st degree familiar with AGA and 15/18 with ≥2 person. This is not surprising if we consider that we included mostly females in this population [48]. Family history of AA was found only in patients with AA, with a prevalence of 15.8% (9/57 patients) with at least 1st degree familiar, comparable with to the data in the literature [49,50,51,52].
When we analyzed whole BCs and derived parameters, we found that psoriasis and HS patients displayed a systemic inflammatory profile with significantly increase in CRP, neutrophils, monocytes, SII and PIV values compared to patients with not cicatricial alopecia, confirming data already known in the literature.
Recently Zhang et al.[2], suggested that NLR, MLR, and SII may be used as useful biomarkers for assessing the level of systemic inflammation and disease severity in psoriasis patients versus a healthy control population. Ye et al., performed a systematic review of the literature and a meta-analysis reaching the conclusion that NLR and PLR could be used as diagnostic but not severity biomarkers in psoriasis [13]. Another very recent (2024) systematic review and meta-analysis suggested that the novel BC-count derived parameters could represent potential targets for screening (NLR, PLR, SII, and MLR) and monitoring of psoriasis (NLR and PLR) [13]. To our knowledge, no data on PIV in psoriasis have been published so far.
Our results on HS fit with those reported by Gambichler et al., showing that PIV and SII were significantly higher in HS patients, while PLR and MLR were significantly lower in HS patients when compared to controls. PIV could be used as severity marker in HS [53] and associated metabolic syndrome [16].
Another finding of our study, was that in our cohort, psoriasis patients had higher baseline levels of basophils compared to hair disorders and HS. This data should be furtherly addressed, due to the role of basophils in pruritic skin diseases in order to clarify their effect in psoriasis as well [54].
NLR was found to be significantly higher in psoriasis patients compared to hair diseases. Several studies have reported the elevation of NLR versus healthy controls in psoriasis. It is known that NLR positively correlates with age of healthy individuals [55] and this could explain higher NLR levels of psoriasis versus HS and alopecia patients since psoriasis patients were generally older than the others, at the moment our visit. However, despite we confirmed the correlation NLR/age of the patients in our overall population (data not shown), when we performed the sub-analysis for each disease, the correlation for NLR in psoriasis patients by age was lost, confirming that psoriasis patients have higher NLR levels independently from the age at the moment of blood sampling.
Looking closely at not cicatricial hair disorders, we were not able to find differences among the three subgroups (namely AA, TE and AGA), however to our knowledge it is the first study investigating circulating BCs on TE and AGA. Our results need to be validated and controlled with general healthy population.
Interestingly, when we compared each hair disease to our control groups, we found that TE patients had a closer systemic inflammatory profile to psoriasis and HS with similar CRP, neutrophils, NLR and SII values and also similar PIV values compared to psoriasis. We also found that TE and AA patients had comparable MLR levels to psoriasis and HS and that basophils of AA patients did not differ to those of psoriatic patients.
Those data confirm that systemic inflammation plays a role in TE occurrence. Comparison with healthy controls and stratification for triggering factors and concomitant diseases are need to clarify the potential role of circulating cells in TE.
Then, we focused only on AA patients. We found that patients with pAA had a later onset of the disease and a shorter duration compared to patients with SALT≥50, thus confirming that duration correlates with AA severity [31,56,57]. Moreover, severe AA patients had lower levels of RBCs. We suggest that RBCs could be used as an indirect marker of oxidative stress in patients with AA. It has been demonstrated that patients with AA displayed reduced erythrocyte SOD and GSH-Px activities and enhanced plasma MDA levels [58]. Moreover structural perturbation in erythrocytes might play an active part in the progression of disease [59].
Significant differences in terms of systemic inflammation were observed when we compared universal versus moderate-to-severe AA, the latter displaying significantly higher levels of NLR, PLR, SII and PIV. These findings could be related to the fact that patients with uAA had chronic alopecia, while msAA patients presented to our attention at the moment of recurrences or worsening of the disease. Moreover, patients with msAA had higher TSH levels suggesting a role in general inflammation of concomitant thyroid status. A plenty of data have been reported so far on the alteration of hematologic data (i.e. NLR, PLR, SII) on thyroid diseases from Hashimoto thyroiditis [60,61,62] to Graves’ disease, thyrotoxicosis [63,64] and thyroid cancer [65,66,67].
It has also to be considered that NLR, SII, MPV, PLT have been studied and reported has possible diagnostic, severity therapeutic and prognostic factors in atopic status (including allergic rhinitis, atopic dermatitis, food allergies, asthma) [68,69,70,71,72,73,74]. Adults and adolescents with various psychiatric diseases, from depression to mood alteration and schizophrenia, presented variable of NLR, MLR and SII levels [75,76,77,78,79,80]. Cardiovascular diseases including hypertension, metabolic (e.g. diabetes, dyslipidemia, insulin resistance) disorders and obesity [81,82,83,84,85,86,87,88,89,90,91] have been extensively studied in terms of whole BC-count and hematological parameters since they are all strictly linked to general systemic inflammatory status, as well as celiac and gynecologic diseases.
For these reasons we performed a further sub-analysis of AA patients with no clinically evident concomitant disease and otherwise healthy AA patients in order to assess the real impact of systemic inflammation on hematologic parameters. Interestingly we found that patients with pAA had significantly higher SII levels, and almost significantly higher PIV values, compared to more diffuse and severe AA. This result fits with our observation that acute rather than chronic AA is associated to general inflammatory pattern. Indeed, patients with pAA had all active patches at the moment of blood sampling.
AA classification is challenging due to different clinical and dermoscopic manifestations, the number of recurrences and to treatment response, as well. Some authors proposed a differentiation between chronic and acute AA and our results could be relevant in order to identify patients with an active rather than quiescent disease [31].
Lastly, we wanted to assess whether therapy could modify the inflammatory status. Unfortunately, due to the retrospective nature of the study complete data were available for 8 patients with AA at baseline, and at the last observation during therapy (which occurred between week 12 and 24 weeks of treatment). Notably, we observed increase in number of peripheral PLTs and a concomitant reduction of their volume (MPV), consistently with recent findings published by Komurcugil and Karaosmanoglu [22].
PLTs play a relevant role in tissue regeneration, and their ability to release growth factors and cytokines such as platelet-derived growth factor (PGDF), vascular endothelial growth factor (VEGF), transforming growth factor (TGF), fibroblast growth factor (FGF), connective tissue growth factor, epidermal growth factor (EGF), and Insulin-like Growth Factor I (IGF-1) involved in hair growth and follicle regeneration has therapeutic implications, in AA as well [92,93,94,95]. Furthermore, autologous Platelet Rich Plasma (PRP) injections can exert an anti-inflammatory effect suppressing cytokine release and limiting local inflammation [96,97]. Moreover, MPV has been linked to platelet mobilization rather than to number of PLTs [98] and to inflammation [99], suggesting that systemic treatments could modulate circulating PLTs and theirs migration into pathologic tissue such as skin. Akpolat et al., suggested to use MPV as clinical indicator of activity in pediatric AA patients, but not as disease severity marker [100]. On the other side, neither Dere and Gündoğdu nor İslamoğlu and Demirbaş found differences in MPV values between adult AA patients and matched controls [23,24].

5. Conclusions

To our knowledge, this is the first study on novel hematological parameters on TE and AGA patients, and it is the first time that BC-count and derived formulas on AA blood samples is compared to other not-cicatricial alopecia and cutaneous diseases such as psoriasis and HS. Our study highlight the need of larger and prospective studies in order to validate our findings since BC-count and its derived parameters could represent potential tools in AA and TE.

Author Contributions

“Conceptualization, A.D.C, and F.P.; methodology, A.D.C.; software, A.D.C.; validation, A.D.C. and F.P.; formal analysis, A.D.C..; investigation, A.D.C., F.P.; data curation, A.D.C.; writing—original draft preparation, A.D.C..; writing—review and editing, A.D.C., F.P; visualization, A.D.C., E.R. I.S. and F.P; supervision, F.P.; project administration, A.D.C. All authors have read and agreed to the published version of the manuscript.”.

Funding

“This research received no external funding”.

Institutional Review Board Statement

“The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Azienda USL Toscana Centro, Firenze (CEAVC 19799).

Informed Consent Statement

“Informed consent was obtained from all subjects involved in the study.”.

Data Availability Statement

Dataset available on request from the authors.

Conflicts of Interest

“The authors declare no conflicts of interest.”.

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Figure 1. Median age (years) with IQR representation by each disease at the moment of the visit. Median values are reported for each the bar. Significant differences with ANOVA (Kruskal-Wallis test, Dunn’s post-test) are represented by * (*p=0.0472, **p=0.0029; *p=0.0006).
Figure 1. Median age (years) with IQR representation by each disease at the moment of the visit. Median values are reported for each the bar. Significant differences with ANOVA (Kruskal-Wallis test, Dunn’s post-test) are represented by * (*p=0.0472, **p=0.0029; *p=0.0006).
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Figure 2. Median values with IQR of whole blood cells and derived formulas in patients with hair disorders (not-cicatricial alopecia, i.e. AA, TE, AGA), psoriasis and HS. (a) white blood cells (WBC), (b) red blood cells (RBC), (c) platelets (PLT), (d) median platelet volume (MPV); (e) Neutrophils (Neu), (f) Lymphocytes (Ly), (g) Monocytes (Mono), (h) Eosinophils (Eo), (i) Basophils (Baso), (j) NLR, (k) PLR, (l) MLR, (m) SII, (n) PIV, (o) ESR and (p) CRP. Median values are reported within each bar. Significant differences across the different parameters were analyzed with One-way ANOVA (Kruskal-Wallis test, Dunns post-test; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001). Number of patients with available data correspond to patients enrolled in the study, if not otherwise stated as for ESR and CRP.
Figure 2. Median values with IQR of whole blood cells and derived formulas in patients with hair disorders (not-cicatricial alopecia, i.e. AA, TE, AGA), psoriasis and HS. (a) white blood cells (WBC), (b) red blood cells (RBC), (c) platelets (PLT), (d) median platelet volume (MPV); (e) Neutrophils (Neu), (f) Lymphocytes (Ly), (g) Monocytes (Mono), (h) Eosinophils (Eo), (i) Basophils (Baso), (j) NLR, (k) PLR, (l) MLR, (m) SII, (n) PIV, (o) ESR and (p) CRP. Median values are reported within each bar. Significant differences across the different parameters were analyzed with One-way ANOVA (Kruskal-Wallis test, Dunns post-test; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001). Number of patients with available data correspond to patients enrolled in the study, if not otherwise stated as for ESR and CRP.
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Figure 3. Median values with IQR of ESR (a), CRP (b), Vitamin D (c) and TSH (d)are reported in Figure 1. Subgroups and number of patients for whom we were able to retrieve data are reported in the legend of lower x axis. *Unpaired T-test analysis of ESR of AA versus TE (p= 0.0382) and TE versus AGA (p= 0.0194).
Figure 3. Median values with IQR of ESR (a), CRP (b), Vitamin D (c) and TSH (d)are reported in Figure 1. Subgroups and number of patients for whom we were able to retrieve data are reported in the legend of lower x axis. *Unpaired T-test analysis of ESR of AA versus TE (p= 0.0382) and TE versus AGA (p= 0.0194).
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Figure 4. Comparison between patients otherwise healthy with severe (ms-uAA) versus localized (pAA) AA. (a) whole BC-count: WBC [5.7 (IQR 4.9-8.6) vs 7.7 (IQR 5.4-8.9); p=0.3257], RBC [4.7 (IQR 4.7-4.9) vs 4.6 (IQR 4.4-5.2); p=0.7046], PLT [204 (IQR 179-234) vs 240 (IQR 200-289.5); p=0.1502], MPV [10.8 (IQR 10.6-11.6) vs 10.9 (IQR 10.5-11.8); p=0.7038], Neutrophils [3.2 (IQR 2.4-4.5) vs 4 (IQR 2.9-5.2); p=0.1502], Lymphocytes [2.5 (IQR 1.8-2.9) vs 2.4 (IQR 1.8-3.5); p=0.9048], Monocytes [0.6-0.4-0.6) vs 0.5 (IQR 0.4-0.7); p=0.8888], Eosinophils [0.2 IQR 0.1-0.5) vs 0.2 (IQR 0.08-0.3); p=0.7951], Basophils [0.04 (IQR 0.02-0.07) vs 0.03 (IQR 0.02-0.05); p=0.9830],. (b) BC-count derived parameters: NLR [1.4 (IQR 1.2-1.7) vs 1.6 (IQR 1.4-2.2); p=0.1564], PLR [95.6 (IQR 68.1-107.4) vs 92.1 (IQR 82.9-126.6); p=0.4967], MLR [0.2 (IQR 0.2-0.3) vs 0.2 (IQR 0.2-0.3); p=0.8421], SII [280.3 (IQR 239.9-357.5) vs 417.4 (IQR 327.3-545.7); p=0.0220 and PIV [139.1 (IQR 125.8-178.4) vs 215.4 (IQR 165.4-277.6); p=0.0535. *Significant values <0.05 (Mann-Whitney t Test).
Figure 4. Comparison between patients otherwise healthy with severe (ms-uAA) versus localized (pAA) AA. (a) whole BC-count: WBC [5.7 (IQR 4.9-8.6) vs 7.7 (IQR 5.4-8.9); p=0.3257], RBC [4.7 (IQR 4.7-4.9) vs 4.6 (IQR 4.4-5.2); p=0.7046], PLT [204 (IQR 179-234) vs 240 (IQR 200-289.5); p=0.1502], MPV [10.8 (IQR 10.6-11.6) vs 10.9 (IQR 10.5-11.8); p=0.7038], Neutrophils [3.2 (IQR 2.4-4.5) vs 4 (IQR 2.9-5.2); p=0.1502], Lymphocytes [2.5 (IQR 1.8-2.9) vs 2.4 (IQR 1.8-3.5); p=0.9048], Monocytes [0.6-0.4-0.6) vs 0.5 (IQR 0.4-0.7); p=0.8888], Eosinophils [0.2 IQR 0.1-0.5) vs 0.2 (IQR 0.08-0.3); p=0.7951], Basophils [0.04 (IQR 0.02-0.07) vs 0.03 (IQR 0.02-0.05); p=0.9830],. (b) BC-count derived parameters: NLR [1.4 (IQR 1.2-1.7) vs 1.6 (IQR 1.4-2.2); p=0.1564], PLR [95.6 (IQR 68.1-107.4) vs 92.1 (IQR 82.9-126.6); p=0.4967], MLR [0.2 (IQR 0.2-0.3) vs 0.2 (IQR 0.2-0.3); p=0.8421], SII [280.3 (IQR 239.9-357.5) vs 417.4 (IQR 327.3-545.7); p=0.0220 and PIV [139.1 (IQR 125.8-178.4) vs 215.4 (IQR 165.4-277.6); p=0.0535. *Significant values <0.05 (Mann-Whitney t Test).
Preprints 118687 g004
Table 1. Demographic and clinical features of patients affected with hair disease.
Table 1. Demographic and clinical features of patients affected with hair disease.
Baseline features AA (57) TE (29) AGA (18) p
Sex (M) (n) 16 1 4 0.026168@
Age (median, IQR) 41 (26.5-51) 42 (34-52.5) 37.5 (25-47.8) 0. 4945*
Age of onset (median, IQR) 32 (17-47)* 39 (33-52)* 34.5 (21-44.25) *0.0341 *AA vs TE
Disease duration (median, IQR) 4 (1-10)* 1 (0.5-3)* 2.5 (1-9.25) *0.01 *AA vs TE
Family history for (n)
AA 9 0 0 --NP
TE 0 1 0 --NP
AGA 13 13 17 < 0.00001@
BMI (median, IQR) 22.8 (19.2-25.9) 21.4 (19.7-22.5) 23.9 (21.3-28.2) 0. 1448*
Concomitant diseases (n)
Anemia
Thyroid
Cardiovascular
Atopy
Psychiatric
Metabolic alterations°
Celiac
Immune/autoimmune
Hormonal and gynecologic		 36
1
7
9
2
6
10
1
4
6		 17
7
7
4
2
0
0
0
1
6		 17
1
5
2
3
2
1
1
1
4		 0.130986@
--NP
--NP
--NP
--NP
--NP
--NP
--NP
--NP
--NP
Cancer history 2 0 0 --NP
*One-way ANOVA test for independent measures (plus Dunn’s post-test); @ chi-square test ° (diabetes, dyslipidemia, steatosis), --NP: not performed due to the low number of patients.
Table 2. Circulating BC-count and derived parameters across the three different sub groups of not-cicatricial hair disorders.
Table 2. Circulating BC-count and derived parameters across the three different sub groups of not-cicatricial hair disorders.
Hematologic parameters AA (57) TE (29) AGA (18) p*
WBC 103/mmc 6.6 (5.3-7.9) 6.5 (5.3-7.4) 6.2 (5.5-7.2) 0.8641
RBC 106/mmc 4.7 (4.5-5) 4.6 (4.3-4.9) 4.7 (4.4.-4.9) 0.5923
PLT 103/mmc 234 (207-274.3) 256 (205.5-302) 242 (209-299) 0.5471
MPV fl 10.9 (10.4-11.7) 10.9 (10-11.3) 10.6 (10.1-11.3) 0.3959
Neu 103/mmc 3.5 (2.9-4.3) 3.8 (2.8-4.3) 3.1 (2.8-4.0) 0.4670
Ly 103/mmc 2.2 (1.7-2.7) 2.1 (1.7-2.6) 2.2 (2-2.5) 0.8045
Mono 103/mmc 0.5 (0.4-0.6) 0.4 (0.35-0.6) 0.4 (0.3-0.6) 0.2705
Eo103/mmc 0.2 (0.1-0.3) 0.1 (0.1-0.3) 0.1 (0.1-0.2) 0.9707
Baso 103/mmc 0.04 (0.02-0.05) 0.03 (0.02-0.04) 0.03 (0.01-0.05) 0.2136
NLR 1.6 (1.4-2) 1.7 (1.5-2.1) 1.5 (1.2-1.8( 0.2314
PLR 110.5 (87.4-135.5) 113.1 (97.8-143.3) 110.8 (96.1-212-3) 0.6812
MLR 0.2 (0.2-0.3) 0.2 (0.2-0.3) 0.2 (0.2-0.3) 0.2132
SII 382.4 (291.8-516.5) 437 (332.2-548) 364.5 (269-420.5) 0.2114
PIV 172.4 (128.5-289.4) 220.7 (118-312.6) 150.6 (100.9-232.2) 0.5338
*values are reported as Median with IQR. Analysis was performed with not parametric one-way Anova (Kruskal-Wallis test, Dunn’s post-test).
Table 3. Demographic features, whole BC-count and derived parameters across AA patients.
Table 3. Demographic features, whole BC-count and derived parameters across AA patients.
SALT ≥50 e uAA (32) p pAA (25) p
uAA (23) msAA (9)
Sex (M) (n) 4 2 >0.9999# 10 0.1362#
Age 37 (23-47) 41 (30.5-48.5) 0.6133° 48 (28.5-52) 0.0745§
Age of onset 25 (15.5-35) 30 (13.5-40) 0.9557° 41 (25.8-51) 0.0065§
Disease duration 7 (3-12) 7 (2-17.8) 0.7478° 1 (0.5-5) 0.0004§
Family history of AA 4 2 >0.9999# 4 >0.9999#
BMI 20.4 (19-26.30) 22- (19.3-23.4) 0.9089° 23.6 (21-26.5) 0.1423§
Concomitant diseases (n)
Anemia
Thyroid
Cardiovascular
Athopy
Psychiatric
Metabolic alterations°
Celiac
Immune-mediated
Hormonal and gynecologic
Cancer		 17
2
1
4
4
3
4
1
2
5
1		 7
0
3
1
0
1
0
0
3
1		 >0.9999#
--NP
--NP
--NP
--NP
--NP
--NP
--NP
--NP
--NP
--NP 		15
0
3
2
2
1
5
0
0
2
0		 0.2618#
--NP
--NP
--NP
--NP
--NP
--NP
--NP
--NP
--NP
-NP
WBC 103/mmc* 6.1 (5.4-7.4) 7.2 (4.9-8.3) 0.6506° 7.1 (5.4-7.8) 0.6123§
RBC 106/mmc* 4.7 (4.3-4.9) 4.6 (4.3-4.9) 0.5159° 4.9 (4.6-5.3) 0.0408§
PLT 103/mmc* 233 (193.5-270) 257 (221-321) 0.1849° 234 (215.5-267) 0.8094§
MPV fl 11.2 (11-12) 10.6 (9-11.3) 0.0435° 11.1 (10.4-11.8) 0.5776§
Neu 103/mmc* 2.6 (1.6-4.3) 4.1 (2.9-5.8) 0.1565° 3.7 (3-4.3) 0.3398§
Ly 103/mmc* 2.2 (1.6-2.7) 1.7 (1.6-2.3) 0.2964° 2.3 (1.9-2.7) 0.1312§
Mono 103/mm*c 0.5 (0.4-0.6) 0.5 (0.4-0.7) 0.9404° 0.5 (0.4-0.7) 0.8862§
Eo103/mmc* 0.2 (0.1-0.3) 0.1 (0.8-0.2) 0.2761° 0.2 (0.08-0.3) 0.9380§
Baso 103/mm*c 0.03 (0.03-0.06) 0.04 (0.03-0.09) 0.5952° 0.03 (0.02-0.05) 0.1472§
NLR * 1.429 (1.232-1970) 2.3 (1.7-2.6) 0.0203° 1.5 (1.3-1.9) 0.7190§
PLR* 97.8 (82.6-143) 143.1 (116.2-157.9) 0.0370° 109.6 (85.1-121-5) 0.2039§
MLR* 0.2 (0.2-0.3) 0.3 (0.2-0.3) 0.3791° 0.2 (0.2-0-3) 0.1151§
SII* 317.5 (247.2-413.9) 586.8 (393.4-773.8) 0.0090° 394 (297.5-459.1) 0.6236§
PIV* 139.1 (122.2-185.6) 258.5 (152.4-436.5) 0.0370° 194.7 (144.6-265.7) 0.8419§
ESR* 5 (2-6) (n=7) 10 (8-12) (n=2) 0.1667° 5 (3.5-20) (n=9) 0.8444§
CRP* 00.6 (0.03-0.09) (n=8) 0.6 (0.1-1) (n=2) 0.0889° 0.08 (0.05-0.09) (n=8) 0.9132§
Vitamin D* 35.8 (22.6-48.6) (n=7) 21.6 (17.5-27.2) (n=7) 0.0728° 21.7 (13.6-26) (n=16) 0.1661§
TSH* 3.1 (2.1-4.2) (n=8) 1.2 (1.4-1.7) (n=8) 0.0104° 2.1 (1.4-2.9) (n=18) 0.8514§
* Values are reported as Median with IQR; ° Unpaired t test uAA versus msAA; § unpaired T test pAA versus uAA+msAA (u-msAA); #fisher exact test; --NP: not performed due to the low number of patients; Significant results are in bold.
Table 4. Comparison of whole BC-count and derived parameters at time 0 (T0) and last observation LO during systemic treatment for AA.
Table 4. Comparison of whole BC-count and derived parameters at time 0 (T0) and last observation LO during systemic treatment for AA.
Hematologic parameters* T0 (8) LO (8) P@
WBC 103/mmc 5.8 (4.8-7) 6.2 (5.3-7.3) 0.3828
RBC 106/mmc 4.6 (4.4-4.9) 4.7 (4-5) 0.8438
PLT 103/mmc 224 (170.5-258) 262 (207.8-287.5) 0.0469.
MPV fl 11 (10.4-11.9) 10.55 (10.1-10.9) 0.0469
Neu 103/mmc 2.5 (2.4-4.1) 3.3 (2.3-3.9) 0.4844
Ly 103/mmc 2.3 (1.7-3) 2.6 (1.9-2.7) 0.3359
Mono 103/mmc 0.5 (0.3-0.6) 0.5 (0.4-0.6) 0.6094
Eo 103/mmc 0.2 (0.06-0.6) 0.1 (0.06-0.3) 0.0781
Baso 103/mmc 0.03 (0.02-0.06) 0.04 (0.02-0.05) 0.6719
NLR 1.3 (1-1.4) 1.3 (0.8-1.5) 0.6406
PLR 94.2 (77.2-115.1) 103.1 (85.7-111.8) 0.6406
MLR 0.2 (0.2-0.3) 0.2 (0.2-0.2) 0.7422
SII 276.3 (221.4-342.2) 311.4 (186.2-409.5) 0.8438
PIV 131.9 (85.8-164.6) 129.2 (97.7-196.2) 0.3828
*Values are reported as median with IQR range @Wilcoxon paired t-test; Significant results are in bold.
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