Neutrophilia and its correlation with increased inflammatory response in COVID-19 in diabetic and pre-diabetic patients

Abstract

Background: Hyperglycemic patients are at a high risk of COVID-19 severity. Neutrophils have been considered critical effector cells in COVID-19 development. Vitamin D deficiency is prevalent in hyperglycemic patients and was found to adversely associate with the neutrophil count.

Aim: The goal of this work was to evaluate the characteristics of diabetic and pre-diabetic COVID-19 patients and discovered changes in neutrophils and their correlation, if any, with disease clinical presentation.

Patients and Methods: The study included total of (514) Covid-19 positive patients confirmed by PCR and recruited from the Prince Mohammad Bin Abdulaziz Hospital in Riyadh, Saudi Arabia. Patient’s clinical characteristics were collected for all patients. Laboratory tests include HbA1c, neutrophil count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), ferritin, D- dimer, 25 hydroxy vitamin D (25(OH)D), and folate.

Results: The results found that 286 patients (55.6%) were diabetic, 77 patients (15%) were pre-diabetic and 151 (29.4%) were normoglycaemic. A significant difference was exhibited regarding the neutrophil count and inflammatory factors of COVID-19 severity. Furthermore, the neutrophil count was found to be directly correlated with the severity monitoring biochemical markers for Covid-19: CRP, ESR, ferritin, and D-dimer and inversely associated with vitamin D levels in diabetic and pre-diabetic patients.

Conclusion: Our findings highlight the change of neutrophils in COVID-19 diabetic and pre-diabetic patients that was found to correlate positively with CRP, ESR, ferritin, and D-dimer, and negatively with 25(OH)D, but their correlation with the clinical presentation of the disease need further large investigations.

Keywords

diabetes pre-diabetes neutrophil neutropenia neutrophilia vitamin D- 25-hydroxy

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel pathogen that caused an outbreak of coronavirus disease (COVID-19) in 2019, spreading rapidly throughout the world.¹ Diabetes mellitus (DM) is one of the most common comorbidities in COVID-19 patients, and it is linked to catastrophic outcomes such as intensive care unit (ICU) admission, invasive ventilation and mortality.² COVID-19 individuals with diabetes have a 10% mortality rate, according to a recent study from the United Arab Emirates.³ In newly diagnosed diabetic individuals, rates of mortality and the need for mechanical ventilation were also found to be substantially higher than for those with pre-existing diabetes. Accordingly, COVID-19-infected patients are negatively affected by uncontrolled hyperglycaemia.⁴ Pre-diabetes is a long-lasting state in which glycaemic levels vary. Pre-diabetes are at a high risk for developing type 2.⁵ Several studies have established the association between poor outcomes of COVID-19 and pre-diabetes.^6,7 ICU admissions and longer hospital stays were also found to be more frequent in COVID-19 patients with pre-diabetes.⁸

Neutrophils have prognostic value for COVID-19 severity. The activation and quantity of neutrophils were reported to correlate with the severity of COVID-19 outcomes.⁹ A positive correlation was also established between neutrophil count, C-reactive protein (CRP) and D-dimer with disease progression.¹⁰ More recently, we assessed the association of vitamin D deficiency with clinical presentation of COVID-19 and we found that inflammatory markers were negatively correlated with vitamin D level in COVID-19 patients.¹¹ Vitamin D deficiency was found to be prevalent in hyperglycaemic patients.¹² Accordingly, the current study aimed to evaluate the correlation between neutrophilia and inflammatory response markers and 25(OH)D level in hyperglycaemic COVID-19 patients.

Subjects and Methods

Study design and participants

This is retrospective observational study included 514 COVID-19 patients, aged 23–88 years, who were admitted to Prince Mohammed Bin Abdulaziz Hospital in Riyadh, Saudi Arabia, between January 2021 and October 2021. They were diagnosed by reverse transcription polymerase chain reaction (RT-PCR), and further assessment was done by computed tomography (CT) scans of the chest.

All patients received the same treatment protocol (i.e., oseltamivir, azithromycin, hydroxychloroquine and paracetamol), without corticosteroid therapy, and their regular treatment for diabetes and hypertension.

Inclusion criteria

For the control groups, COVID-19 patients with no clinical or laboratory evidence of DM or any other disease were included. For the hyperglycaemic patients, COVID-19 patients with type 2 diabetes were included. Hyperglycaemia was defined as haemoglobin A1C (HbA1c) levels of 5.7–6.4% for pre-diabetes and HbA1c levels of ≥6.5% for DM.¹³

Exclusion criteria

The exclusion criteria for both the control and patient groups included the following: 1) patients with malabsorptive states, metabolic syndrome, gestational diabetes or type 1 diabetes, and 2) patients with other diseases such as hepatic diseases, chronic illness, malignancy, renal diseases or any blood diseases. In addition, to ensure the true measurement of serum 25(OH)D and cytokine levels, patients who received vitamin D or calcium supplementation and patients who were undergoing anti-inflammatory treatment were excluded from the study.

Data collection

The demographic and clinical data of all included patients were obtained from hospital electronic records and databases and analysed independently by two researchers. The procedures were carried out after receiving prior approval from the research ethics committee of Taif University (42-0010).

Laboratory analysis

The Bio-Rad VARIANTTM HbA1c Program (Bio-Rad Laboratories, Inc., Hercules, CA, United States) was used to evaluate HbA1c level, which was measured using high-performance liquid chromatography (HPLC). Haematological analysis—including the measurement of complete blood count (CBC), erythrocyte sedimentation rate (ESR) and D-dimer—and serum biochemistry assays—including the measurement of CRP, ferritin, folate and 25-hydroxy vitamin D (25(OH)D)—were performed. Blood samples were obtained in ethylenediaminetetraacetic acid (EDTA) tubes for CBC, following the protocol outlined by the manufacturer (i.e., Beckman Coulter). The total amount of neutrophils in the white blood cell (WBC) count is known as the absolute neutrophil count (ANC). This is usually done as part of a differential CBC. The ANC is calculated by multiplying the total number of WBCs by the neutrophil percentage and dividing by 100.¹⁴ Neutrophilia and neutropenia were considered present if the ANC was above 7.5 × 10⁹/L or less than 1500 cells/mm³, respectively.¹⁴ Sodium ESR was measured by collecting blood in a blacktop ESR vacuum tube using the Westergren method.

According to the manufacturer protocol, 25(OH)D serum level was measured using an Abcam human vitamin D enzyme-linked immunosorbent assay (ELISA) kit. The assay’s sensitivity was 1.98 ng/mL, and the measurement range was 0.5–1010 ng/mL. Vitamin D deficiency was considered present when the serum level of vitamin D was < 20 ng/mL. Serum CRP was measured with an autoanalyser and immunoturbidimetric analysis (CRP II Latex X2; Denka Seiken). Serum-ferritin levels were evaluated using an ELISA kit (RCD012 R, BioVendor), as was plasma D-dimer (NB 110–8376, Novus Biologicals). Folate was measured using a human folic acid ELISA kit (Colorimetric; NBP2-59966, Novus Biologicals) with a detection range of 12.5–200 pg/mL. Vitamin B12 was determined using a human vitamin B12 ELISA kit (Cat. No. VB369B, Calbiotech, El Cajon), and the detection range was 100–2000 pg/mL.

Statistical analyses

The data were analysed using appropriate statistical tests within SPSS version 20.0 (SPSS Inc., Chicago, IL, United States). Quantitative data were expressed as mean ± standard deviation (SD) and median (interquartile range; IQR), and they were tested using an analysis of variance (ANOVA) followed by Tukey’s honestly significant difference (HSD) and a Kruskal–Wallis test. Multiple comparisons between categorical groups were performed through post-hoc tests. Qualitative data were expressed as frequency and percentage, and a chi-squared (χ²) test was applied. The association between NAC and the studied parameters was assessed using the Pearson correlation coefficient. The confidence interval was set to 95%, and the significance level was set at p < 0.05.

Results

A total of 514 COVID-19 patients were included in this study, of which 286 (55.6%) were diabetic, 77 (15%) were pre-diabetic and 151 (29.4%) were non-diabetic, according to the diagnostic criteria of the American Diabetes¹³ Association.¹³ Baseline data for the enrolled patients are presented in Table 1. In the diabetic, pre-diabetic and non-diabetic groups, the mean HbA1c percentages were 10.62 ± 5.40%, 7.02 ± 2.30% and 4.21 ± 4.65%, respectively (p < 0.001). Compared with non-diabetic patients, pre-diabetic and diabetic patients were older (55.93 ± 15.49 and 56.77 ± 13.90 years, respectively, vs. 48.24 ± 16.10 years; p < 0.001). There were no differences in the admission symptoms—including fever, headache, anorexia and fatigue—between the categorical groups. The parameter of ICU admission was significantly difference between the three subgroups of patients (p = 0.025). In terms of laboratory results upon admission, pre-diabetic and diabetic patients had a higher level of CRP (10.26 ± 4.70 mg/L and 9.20 ± 5.80 mg/L, respectively, vs. 7.07 ± 5.20 mg/L; p = 0.002), a higher ESR (84.25 ± 30.72 mm/h and 76.08 ± 31.82 mm/h, respectively, vs. 62.71 ± 33.58 mm/h; p < 0.001) and a higher D-dimer level (2.82 ± 7.53 ng/mL and 1.93 ± 2.60 ng/mL, respectively, vs 1.01 ± 3.03 ng/mL; p = 0.04).

Table 1.

Demographic, clinical and laboratory characteristics of the study groups.

Variables	All patients (n = 514)	Non-diabetic patients (n = 151)	Pre-diabetic patients (n = 77)	Diabetic patients (n = 286)	p value
Age (years)
Mean ± SD	53.80 ± 15.83	48.24 ± 16.10	56.77 ± 13.90^a	55.93 ± 15.49^a,b	<0.001**
Sex
Female	186 (36.2%)	55 (36.4%)	27 (35.1%)	104 (36.4%)	0.976
Male	328 (63.8%)	96 (63.6%)	50 (64.9%)	182 (63.6%)	0.976
Admission symptoms
Fever	473 (87.4%)	134 (88.7%)	67 (87.0%)	272 (95.1%)	0.57
Headache	420 (81.7%)	122 (80.8%)	63 (81.8%)	235 (82.2%)
Anorexia	344 (66.9%)	112 (74.2%)	56 (72.7%)	176 (61.5%)
Fatigue	475 (92.4%)	145 (96.0%)	61 (79.2%)	269 (94.1%)
ICU admission	23 (4.47%)	1 (0.66%)	5 (6.5%)	17 (5.9%)	0.025*
HbA1c %
Median (IQR)	6.90 (5.70: 8.40)	5.50 (5.10:5.70)	6.10 (6.10:6.30)^a	8.20 (7.50:10.30)^a,b	<0.001**
Vitamin B12 level pg/mL
Median (IQR)	364.00 (264.00:492.00)	354.00 (249.00:441.00)	396.50 (310.25:515.75)	387.50 (264.50:531.75)	0.238
25(OH)D level ng/mL
Median (IQR)	25.00 (18.00:34.00)	27.00 (18.00:35.00)	21.00 (16.50:27.50)	25.00 (18.75:34.00)	0.087
Folate level pg/mL
Median (IQR)	19.00 (12.90:29.00)	19.00 (12.90:29.80)	19.20 (13.00:23.00)	19.00 (12.00:29.53)	0.97
CRP (mg/L)
Median (IQR)	8. 7.97 (3.40:15.02)	6.40 (3.00:11.00)	10.53 (5.50:14.89)^a	8.16 (3.47:16.00)^a	0.002*
ESR (mm/h)
Median (IQR)	75.00 (47.00:101.00)	61.00 (33.00:87.00)	85.00 (65.75:118.75)^a	77.00 (50.00:105.00)^a,b	<0.001**
D-dimer level (ng/mL)
Median (IQR)	0.89 (0.51:1.79)	0.79 (0.48:1.46)	1.10 (0.58:1.76)^a,b	0.93 (0.51:2.14)^a	0.04**
Ferritin level (ng/mL)
Median (IQR)	634.40 (275.20:1331.00)	643.10 (246.80:1184.75)	572.50 (336.00:1140.35)	634.40 (279.00:1519.00)	0.685

*p < 0.05, **p < 0.01.

^aStatistically significant difference with the non-diabetic group.

^bStatistically significant difference with the pre-diabetic group.

Table 2 presents the laboratory findings related to the patients’ ANC. It shows that the ANC was significantly higher in diabetic and pre-diabetic COVID-19 patients (20.74 ± 8.29 and 14.59 ± 9.39, respectively) than it was in non-diabetic patients (10.44 ± 11.51; p = 0.01). There was also a significant difference in relative percentage among the three groups (p = 0.04). Neutrophilia was found in 54.9% and 62.3% of the patients in the diabetic and pre-diabetic groups, respectively, while only 43% of the non-diabetic patients had neutrophilia.

Table 2.

Laboratory findings related to neutrophil count in the study’s patients.

	All patients (n = 514)	Non-diabetic patients (n = 151)	Pre-diabetic patients (n = 77)	Diabetic patients (n = 286)	p value
Neutrophil auto absolute (#)
Mean ± SD	11.78 ± 9.49	10.44 ± 11.51	14.59 ± 9.39	20.74 ± 8.29	0.01*
Median (IQR)	8.77 (5.63:13.03)	7.84 (5.08:11.70)	9.45 (6.82:14.35)	8.90 (5.66:13.13)	0.01*
Neutrophil level count
Neutropenia	4 (0.6%)	2 (1.3%)	1 (1.3%)	1 (0.3%)	0.034*
Neutrophilia	270 (54.1%)	65 (43%)	48 (62.3%)	157 (54.9%)
Normal	240 (45.3%)	84 (55.6%)	28 (36.4%)	128 (44.8%)
Neutrophil auto relative (%)
Mean ± SD	80.43 ± 12.52	78.82 ± 12.22	82.76 ± 8.80	80.65 ± 13.42	0.04*
Median (IQR)	83.50 (74.78:89.50)	80.70 (73.80:87.20)	83.80 (78.95:90.00)	84.45 (74.48:89.73)	0.04*

*p < 0.05, **p < 0.01.

A comparison between the neutrophil counts and other parameters (i.e., age, HbA1c, 25(OH)D, vitamin B12, folate, CRP, ESR, D-dimer and ferritin) in diabetic and pre-diabetic patients is shown in Table 3. There was a statistically significant difference between the three categorical groups (i.e., patients who have neutropenia, neutrophilia or a normal count) according to age, 25(OH)D, CRP, ESR, D-dimer and ferritin. Patients with neutrophilia were older (57.10 ± 15.63; p < 0.001) and had a lower level of 25(OH)D (25.26 ± 13.17 ng/mL; p = 0.012). In addition, higher levels of CRP (10.10 ± 5.46 mg/L), ESR (80.55 ± 30.69), D-dimer (3.35 ± 7.95 ng/mL) and ferritin (982.94 ± 709.33 ng/mL) were found in patients with neutrophilia (p < 0.001).

Table 3.

Comparison between neutrophil count and studied variables.

Parameters	Neutropenia		Neutrophilia		Normal count		Test	p value
Parameters	Mean	±SD	Mean	±SD	Mean	±SD	Test	p value
Age (years)	40.67^a	18.01	60.10^a,b	15.63	50.03	15.17	14.405	<0.001**
HbA1c %	6.60	1.49	7.60	2.34	7.47	2.48	0.423	0.656
25(OH)D (ng/mL)	28.33	2.52	25.26^a,b	13.17	28.68	12.63	4.452	0.012*
Vitamin B12 level	257.00	179.61	470.01	338.83	430.41	252.16	0.772	0.464
Folate level (pg/mL)	21.15	13.93	20.31	8.31	21.92	11.09	0.506	0.604
CRP (mg/L)	3.20^a	2.55	10.10^a,b	5.46	6.90	5.21	13.725	<0.001**
ESR (mm/h)	20.00^a	1.41	80.55^a,b	30.69	63.90	33.35	17.395	<0.001**
D-dimer level (ng/mL)	0.44^a	0.16	3.35^a,b	7.95	1.01	1.03	8.898	<0.001**
Ferritin level (ng/mL)	227.10^a	260.36	982.94^a,b	709.33	622.64	571.58	12.190	<0.001**

*p < 0.05, **p < 0.01.

^aStatistically significant difference with normal count.

^bStatistically significant difference of neutrophilia with neutropenia.

The correlation between neutrophil count and the other variables in the studied groups was then further analysed. Figure 1 demonstrates that the ANC were positively correlated with age (r = 0.201), CRP (r = 0.239), ESR (r = 0.194), D-dimer (r = 0.153) and ferritin (r = 0.214; p < 0.001). In contrast, 25(OH)D showed a significant negative correlation with ANC (r = −0.027; p < 0.001).

Figure 1.

Scatter plot of comparison between neutrophil count and age (a), 25(OH)D (b), CRP (c), ESR (d), D-dimer (e) and ferritin (f). The ANC showed a significant positive correlation with age, CRP, ESR, D-dimer and ferritin, and a significant negative correlation with 25(OH)D (p ≤ 0.001).

Discussion

In the current study, the majority of enrolled Coronavirus disease (COVID-19) patients (70.6%) were with hyperglycaemia. Also, we found that the pre-diabetic and diabetic groups included elderly patients and had a higher level of C - reactive protein (CRP), a higher erythrocyte sedimentation rate (ESR) and a significant elevation in D-dimer.

In accordance with our findings, Alguwaihes, Al-Sofiani¹⁵ reported a higher incidence of Diabetes Mellitus (DM) (300 out of 439) among hospitalised COVID-19 patients. A nationwide study in China reported that patients with severe COVID-19 were associated with a higher prevalence of DM than were those with a non-severe form of the disease (16.2% vs. 5.7%).¹⁶

Age and elevated inflammatory markers predict COVID-19 progression.¹⁷ It has been reported that elderly diabetic individuals are at a higher risk of developing complications from COVID-19, resulting in a higher proportion of intensive care unit (ICU) admission and a greater mortality rate.^18–20 Further, Hui et al.²¹ found that diabetic patients had a higher median age (p = 0.001) than did non-diabetic patients.

Inflammatory markers such as ESR, CRP, and ferritin are significantly correlated with COVID-19 severity and outcomes.^22–24 CRP is a partial mediator of the correlation between the severity of COVID-19 and DM, confirming the importance of inflammation in the pathogenesis of COVID-19 severity in diabetic patients.²⁵ Similar to our findings, Guo et al.²⁶ found that diabetic patients had a higher level of CRP (44.8 mg/L vs. 25.8 mg/L; p = 0.003) compared with non-diabetic patients with COVID-19. Moreover, it has been established that COVID-19 patients with DM have higher levels of inflammatory biomarkers such as CRP and ESR than do non-diabetic patients, reflecting the severity of infection in this group.^3,27 D‐dimer is a thrombotic biomarker and one of the main psychopathological aspects of COVID-19.²⁸ Consistent with our results, Miri et al.²⁹ reported a higher level of D-dimer in diabetic patients compared to non-diabetic patients. Moreover, it has been reported that the high D-dimer level in diabetic patients is significantly associated with COVID-19 severity.^30,31

Regarding neutrophil level, our results found a significant difference among the categorical groups (i.e., patients with a normal neutrophil count, neutropenia and neutrophilia) in terms of age (p < 0.001), 25(OH)D (p = 0.012), CRP (p < 0.001), ESR (p < 0.001), D-dimer (p < 0.001) and ferritin (p < 0.001). Thus, we then investigated whether there is a relationship between change in neutrophil count and age, inflammatory markers (i.e., CRP, ESR and ferritin), thrombotic marker (i.e., D-dimer) and 25(OH)D. Expectedly, it was found that age, inflammatory markers (i.e., CRP, ESR and ferritin) and thrombotic marker (i.e., D-dimer) were all significantly and positively correlated with the change in ANC (p < 0.001).

Neutrophils have prognostic value for COVID-19 severity. It has been reported that neutrophilia predicts poor outcomes in patients with COVID-19 and severe respiratory failure.³² An elevated neutrophil count is an immunological phenotype of severe COVID-19.³³ The activation of neutrophils in the lungs causes cytotoxicity, inflammation and overall lung damage. Based on the fact that collateral lung damage leads to hypoxemic respiratory failure, neutrophils have recently been suggested to have a role in acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), both of which are associated with COVID-19.³³ In a retrospective study of COVID-19 patients’ initial laboratory indices, 34.5% were found to have neutrophilia, and patients with ARDS had more neutrophils than did those without ARDS (p < 0.001).³⁰

The function of circulating neutrophils in COVID-19 patients is significantly impacted in a variety of ways, including increased neutrophil extracellular trap (NET) formation, increased reactive oxygen species generation (i.e., oxidative burst) and enhanced phagocytosis. Numerous studies have identified the changes in NETosis and oxidative burst in COVID-19.³³ Some researchers believe that NETs may play a role in the abnormal immunological response seen in patients with high neutrophil counts and SARS-CoV-2 infections.^33,34

A higher neutrophil count is an infection-related biomarker that predicts a poor prognosis for COVID-19. It has been reported that NETs can contribute to inflammation-associated lung damage, fibrosis and thrombosis.³⁵ Previously, a positive correlation has been established between neutrophil count, CRP and D-dimer.³⁶

Much evidence has demonstrated the association between DM and vitamin D deficiency.^37–39 Vitamin D deficiency was also found to be linked with the positivity and severity of COVID-19.⁴⁰ Further, similar to our study, the level of serum 25(OH)D was found to be adversely linked with neutrophil count and CRP level.⁴¹

There are several limitations to our study. First, power analysis for sample size calculation was not done. Second, the design of the study was retrospective in nature, as participants were enrolled through hospital-based cohort groups; thus, the findings and data are limited to the accuracy of the hospital’s record keeping. In addition, the neutrophil level before the start of SARS-CoV-2 infection was not known for the included hyperglycaemic patients, and thus, we cannot define the causal relationship between neutrophil count and hyperglycaemia and its clinical implications as a marker of poor outcomes in hyperglycaemic COVID-19 patients. Information about other outcome variables related to disease severity, including in-hospital mortality and medication data, could not be obtained and analysed in this study. Last, the small sample size may not be a representative distribution of hyperglycaemic patients. Despite the study’s limitations, we believe that the findings of the current study are a valuable contribution to the limited literature on hyperglycaemic COVID-19 patients. The study may also provide endocrinologists and other clinicians with evidence-based recommendations to use in COVID-19 management and treatment.

Conclusions

We assessed neutrophil level and its association with the inflammatory response in hyperglycaemic patients with COVID-19. Our results suggest that neutrophilia is proportionally associated with the inflammatory markers of COVID-19 infection and adversely associated with 25(OH)D level in in pre-diabetic and diabetic patients.

Footnotes

Acknowledgments

The authors would like to thank Taif University, Taif, Saudi Arabia, for their support (Taif University Researchers Supporting Project number: TURSP-2020/80), Taif University, Taif, Saudi Arabia, the authors gratefully acknowledge the support of the Deanship of Scientific Research, Taif University.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work was funded by Taif University, Taif, Saudi Arabia. (Taif University Researchers Supporting Project number: TURSP- 2020/80).

ORCID iDs

Hayaa M. Alhuthali

Mazen Almehmadi

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