Skip to content
Frontiers
Search

Original Research

Frontiers in Virology ·

Volume 6 - 2026 | https://doi.org/10.3389/fviro.2026.1756837

Distinguishing the clinical types and laboratory markers of the ancestral and Omicron variants of COVID-19: a multicenter retrospective study

  • Xiongwen Tu1
  • Huijuan Li2
  • Zhenjun Liu3
  • Yusheng Cheng1,4,5
  • Lingling Li1*corresponding author
  1. 1Department of Respiratory Medicine, Yijishan Hospital, Wannan Medical College, Wuhu, China
  2. 2Department of Infectious Diseases, The Third People’s Hospital of Wuhu, Wuhu, China
  3. 3Department of Infectious Diseases, Anqing Hospital of Anhui Medical University, Anqing, China
  4. 4Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
  5. 5Anhui Public Health Clinical Center, Hefei, China
Article metrics0 viewsView details
PDFCiteMetrics

Abstract

Omicron has become a dominant variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide. A majority of individuals infected with Omicron SARS-CoV-2 experience no or mild symptoms, but exhibit excessively higher transmissibility than those infected with the ancestral variant in 2020. This study aimed to identify the distinctive clinical types and laboratory markers of coronavirus disease 2019 (COVID-19). This retrospective study was performed in multiple medical centers that treated patients infected with COVID-19. Demographic characteristics and laboratory data, such as the viral shedding time, blood routine test results, and biochemical indicators, were initially recorded after admission. A total of 59 patients with ancestral SARS-CoV-2 infection and 112 patients with Omicron SARS-CoV-2 infection were eventually included. Patients with Omicron SARS-CoV-2 infection were asymptomatic, whereas the ancestral variant was a common type. Patients with Omicron SARS-CoV-2 infection had markedly higher white blood cell (WBC), eosinophil, and platelet (PLT) counts, but remarkably lower lymphocyte counts. The concentration of D-dimer was lower in the Omicron group than in the ancestral variant group. The Omicron variant showed a notably shorter viral shedding time than the ancestral variant, with median durations of 11 and 16 days, respectively (p < 0.001). Multivariate analysis revealed that WBC count, lymphocyte count, eosinopenia, D-dimer, alanine transaminase (ALT), and viral shedding time were independent risk factors for predicting Omicron SARS-CoV-2 infection. The receiver operating characteristic (ROC) curves showed that viral shedding time, ALT, D-dimer, and lymphocyte count favored differential diagnosis of ancestral SARS-CoV-2 infection, whereas WBC and eosinophil counts favored differential diagnosis of Omicron SARS-CoV-2 infection. A majority of patients with Omicron SARS-CoV-2 infection are asymptomatic. WBC count, eosinopenia, and viral shedding time were identified as independent factors for predicting the risk of Omicron SARS-CoV-2 infection. Viral shedding time and eosinophil count were identified as biomarkers for differential diagnosis of ancestral and Omicron SARS-CoV-2 infections, respectively. Clinical features and various laboratory tests may serve as auxiliary reference indicators to complement viral nucleic acid testing (the diagnostic gold standard) for preliminary stratification of suspected COVID-19 cases, particularly in retrospective analysis of variant-related characteristics.

Keywords

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection poses a threat to public health worldwide owing to the progressively increasing transmissibility of its variants, including the wild-type, D614G, Alpha, Beta, Delta, and Omicron variants. Notably, Omicron (BA.1 and BA.2) has replaced the Delta variant and has become a dominant infective strain of SARS-CoV-2 worldwide, replicating faster than other variants of concern. Omicron is characterized by a higher replication capability in the bronchi, but a lower replication capability in the lung parenchyma, as it enters cells through a different route compared with the other variants (1). Vaccination is an important prevention strategy against SARS-CoV-2 infection and has effectively reduced the hospitalization rates of patients with coronavirus disease 2019 (COVID-19) (2). Suzuki et al. found that, despite its rapid growth and higher transmissibility, Omicron showed lower pathogenicity than the Delta and ancestral SARS-CoV-2 variants (3). Remarkably, there were increased reinfection rates with the Omicron variant, but mainly associated with moderate infections (4). Omicron infection is associated with a higher prevalence of sore throat and a markedly lower prevalence of loss of taste or smell. This symptom profile makes Omicron more complicated to detect using clinical symptom-based testing algorithms (5). Runny nose, sore throat, body aches, and headache are more common in patients with Omicron SARS-CoV-2 infection (6). However, the Omicron variant mainly causes asymptomatic infection, with a lower proportion of non-severe disease in elderly patients (7). Gu et al. reported that, compared with the outbreak of the COVID-19 epidemic in 2020 (ancestral variant) in China, the subsequent outbreak of Omicron infection caused more non-symptomatic and non-severe cases. Radiologically, ground-glass opacities and crazy paving patterns are relatively less common in patients with Omicron SARS-CoV-2 infection (8). In this study, we identify the distinctive clinical features and laboratory markers of ancestral and Omicron SARS-CoV-2 infections.

Patients and methods

This retrospective study was performed in multiple medical centers that treated patients infected with COVID-19 in Wuhu, Tongling, Maanshan, and Anqing in Anhui Province. From January to May 2020, all patients with ancestral SARS-CoV-2 infection admitted to Yijishan Hospital were reviewed. Patients with Omicron SARS-CoV-2 infection were recruited from multiple medical centers from March to May 2022. In all patients, the presence of SARS-COV-2 was confirmed using reverse transcription polymerase chain reaction (RT-PCR). We included only hospitalized patients in both cohorts to ensure comparable disease severity at enrollment. The two cohorts were recruited during different phases of the COVID-19 pandemic (2020 vs. 2022), which may have been associated with different admission criteria and case management policies. However, all patients were hospitalized with confirmed COVID-19, providing a comparable baseline for laboratory parameter analysis.

Demographic characteristics, such as sex and age, and clinical characteristics were recorded. Laboratory indices, including throat swab viral shedding time, routine blood cell counts [white blood cell (WBC), lymphocyte, eosinophil, and platelet (PLT) counts], and the plasma D-dimer, alanine transaminase (ALT), aspartate transaminase (AST), and creatine kinase (CK) levels, were initially recorded after admission. RT-PCR was performed using commercially available assay kits approved by the National Medical Products Administration of China. Throat swab samples were collected at intervals of 24–48 h until two consecutive negative results (ancestral cohort) or Ct values ≥35 (Omicron cohort) were obtained. All participating hospitals followed standardized testing protocols according to national guidelines. All participating hospital laboratories used standardized testing methodologies and participated in national external quality assessment programs. Hematological parameters were measured using automated hematology analyzers (Sysmex series, Kobe, Japan), and biochemical markers were measured using automated chemistry analyzers [Roche (Basel, Switzerland) or Beckman Coulter (Brea, CA, USA) series] using standardized protocols. For the ancestral variant, the end of viral shedding was defined as at least two consecutive negative RT-PCR results (at intervals of at least 24 h) (9). For the Omicron variant, the end of viral shedding was defined as at least two consecutive real-time PCR results (at intervals of 24 h) showing Ct values >35 for both the ORF1ab and N genes of SARS-COV-2. Viral shedding time was defined as the duration from the first confirmed positive RT-PCR result to the end of viral shedding. During the ancestral variant wave (from January to May 2020), the national guidelines in China defined viral clearance as two consecutive negative RT-PCR results (undetectable viral RNA), as the Ct value-based criteria had not yet been established or widely adopted in clinical practice. However, during the Omicron wave (from March to May 2022), the updated national guidelines specified a Ct value ≥35 for both the ORF1ab and N genes as the criterion for viral clearance, which has now become the standard practice. We acknowledge that different criteria were used to define the end of viral shedding between the two cohorts owing to evolving national guidelines during the different phases of the pandemic. Although this difference may have introduced potential measurement bias, the consistency of our findings with published literature supports the biological validity of the observed differences. We performed sensitivity analysis using the consistent criterion of a Ct value ≥35 for both cohorts (applying the Omicron criterion retrospectively to the ancestral cohort data where Ct values were available). The results remained consistent with our primary findings, with the Omicron group showing a significantly shorter viral shedding duration (data not shown, but available upon request).

Statistical analysis

Continuous data were expressed as the median (interquartile range, 25th–75th), and categorical data were expressed as a number (n). The Mann–Whitney U test was used to analyze continuous variables, while the chi-square test was used to analyze categorical variables. A multivariate logistic regression model was constructed to identify independent risk factors for Omicron infection. The variables included in the multivariate model were selected based on a predefined two-step criterion: 1) clinical relevance: variables with established biological or clinical associations with SARS-CoV-2 variant pathogenicity or host immune responses (i.e., the WBC, lymphocyte, eosinophil, and PLT counts; the D-dimer, ALT, and CK levels; and the viral shedding time) were prioritized for inclusion based on previous studies and expert consensus on the laboratory biomarkers for COVID-19 and 2) univariate statistical significance: variables showing a trend toward association with the outcome (omicron vs. ancestral variant infection) in the univariate analysis (p < 0.10) were retained to ensure that potential predictive factors were not overlooked. For the multivariate model, eosinopenia was included as a categorical variable based on a predefined cutoff (eosinophil count <0.05 × 10 (9)/L). No collinearity between variables was detected using the variance inflation factor (VIF < 3 for all included variables), confirming the validity of the model. Receiver operating characteristic (ROC) curves were plotted to identify factors for predicting the variants of SARS-CoV-2. All statistical analyses were performed using SPSS for Windows (version 22.0; IBM Corporation, Armonk, NY, USA). A p-value <0.05 was considered statistically significant.

Results

In this multicenter retrospective study, 59 patients with ancestral SARS-CoV-2 infection and 112 patients with Omicron SARS-CoV-2 infection were eventually included. As shown in Table 1, no significant differences were observed in the sex or age between the two groups. Notably, the clinical characteristics were significantly different between the two groups (p < 0.001), with the Omicron variant causing asymptomatic and mild infection and the ancestral variant being the ordinary type.

Table 1. Univariate analysis for comparisons of the clinical features in the ancestral variant and the Omicron variant of coronavirus disease 2019 (COVID-19).

CharacteristicsAncestral variant (n = 59)Omicron variant (n = 112)p-value
Sex, Men/women, n32/2768/440.42
Age (years), median (range)39 (30–54)45 (32–54)0.651
Vaccination status, n (%)0 (0.0)106 (94.6)–
Underlying disease, n (%)
Hypertension12 (20.3)28 (25.0)0.456
Diabetes8 (13.6)19 (16.9)0.583
Chronic airway diseases5 (8.5)11 (9.8)0.789
Chronic kidney disease2 (3.4)3 (2.7)0.821
Clinical classification, n (%)<0.001
Asymptomatic infection0 (0.0)101 (90.2)
Mild type7 (11.9)11 (9.8)
Ordinary type46 (78.0)0 (0.0)
Severe type6 (10.2)0 (0.0)

The WBC, eosinophil, and PLT counts were markedly higher in the Omicron group than in the ancestral variant group. However, the lymphocyte counts were remarkably lower in the Omicron group (Table 2). The D-dimer levels were notably lower in the Omicron group than in the ancestral variant group, with median levels of 280 and 500 μg/L, respectively (p < 0.001). The Omicron group showed a notably shorter viral shedding time than the ancestral variant group, with median durations of 11 and 16 days, respectively (p < 0.001) (Table 2). In addition, the Omicron group showed lower plasma ALT levels and higher CK levels than the ancestral variant group.

Table 2. Univariate analysis for comparisons of the laboratory tests in the ancestral variant and the Omicron variant of coronavirus disease 2019 (COVID-19).

Laboratory parametersAncestral variant (n = 59)Omicron variant (n = 112)p-value
Number59112–
WBC (×109/L)5.00 (4.06–6.50)5.94 (4.71–7.64)0.003
Lymphocyte (×109/L)1.27 (0.90–1.70)1.00 (0.74–1.46)0.042
Eosinophil (×109/L)0.01 (0.01–0.05)0.05 (0.03–0.11)<0.001
PLT (×109/L)164 (138–204)186.50 (155.00–219.50)0.025
D-dimer (μg/L)500 (190–910)280 (192.50–390.00)<0.001
ALT (U/L)27 (17–40)19.50 (14.00–27.75)0.001
AST (U/L)23 (20–32)23.00 (19.00–31.75)0.545
CK (U/L)55 (40–66)69.50 (41.25–98.75)0.019
Viral shedding time (days)16 (12–19)11 (9–13)<0.001

WBC, white blood count; PLT, platelets; ALT, alanine transaminase; AST, aspartate transaminase; CK, creatine kinase.

Multivariate analysis revealed that WBC count [odds ratio (OR) = 1.685, 95% confidence interval (95%CI) = 1.261–2.251, p < 0.001], lymphocyte count (OR = 0.249, 95%CI = 0.107–0.578, p = 0.001), eosinopenia (OR = 0.304, 95%CI = 0.114–0.812, p = 0.017), ALT level (OR = 0.937, 95%CI = 0.905–0.971, p < 0.001), and viral shedding time (OR = 0.768, 95%CI = 0.674–0.875, p < 0.001) were independent predictive factors for Omicron infection. The D-dimer level (OR = 0.999, 95%CI = 0.998–1.000, p = 0.036) showed statistical significance in the multivariate analysis; however, its OR approached 1.0, which limits its clinical utility as an independent predictor. With an OR of 0.999, D-dimer was analyzed as a continuous variable expressed in micrograms per liter, with values ranging from 190 to 910 µg/L in the ancestral variant group and from 192.5 to 390 µg/L in the Omicron group. The narrow 95%CI (0.998–1.000) and the OR approaching 1.0 indicate that D-dimer has negligible potential in distinguishing between SARS-CoV-2 variants at the individual level (Table 3).

Table 3. Multivariate analysis revealing the independent risk factors of the laboratory examination for predicting the Omicron variant.

Risk factorOdds ratio (OR)95% Confidence interval (CI)p-value
WBC (×109/L)1.6851.261–2.251<0.001
Lymphocyte (×109/L)0.2490.107–0.5780.001
Eosinopenia (×109/L)0.3040.114–0.8120.017
PLT (×109/L)0.9980.989–1.0070.629
D-dimer (μg/L)0.9990.998–1.0000.036
ALT (U/L)0.9370.905–0.971<0.001
CK (U/L)1.0090.999–1.0190.92
Viral shedding time (days)0.7680.674–0.875<0.001

Although the D-dimer was statistically significant (p = 0.036), the odds ratio approaching 1.0 suggests limited clinical utility for variant discrimination.

WBC, white blood cell count; PLT, platelets; ALT, alanine transaminase; CK, creatine kinase.

As shown in Figure 1, the area under the curve (AUC) values for viral shedding time, ALT level, D-dimer level, and lymphocyte count were 0.755 (95%CI = 0.673–0.837, p < 0.001), 0.652 (95%CI = 0.563–0.741, p = 0.001), 0.693 (95%CI = 0.599–0.787, p < 0.001), and 0.595 (95%CI = 0.506–0.683, p = 0.042), respectively (Table 4).

Receiver operating characteristic (ROC) curves of viral shedding time, alanine transaminase (ALT), D-dimer, and lymphocytes for the diagnosis of the ancestral variant. The optimal cutoff values, as well as the corresponding sensitivity and specificity, for each indicator are as follows: viral shedding time, >13 days (sensitivity = 74.6%, specificity = 70.5%); ALT, >23 U/L (sensitivity = 62.7%, specificity = 65.2%); D-dimer, >350 μg/L (sensitivity = 67.8%, specificity = 64.3%); and lymphocytes, >1.15 × 10<sup>9</sup>/L (sensitivity = 59.3%, specificity = 57.1%).
Figure 1. Receiver operating characteristic (ROC) curves of viral shedding time, alanine transaminase (ALT), D-dimer, and lymphocytes for the diagnosis of the ancestral variant. The optimal cutoff values, as well as the corresponding sensitivity and specificity, for each indicator are as follows: viral shedding time, >13 days (sensitivity = 74.6%, specificity = 70.5%); ALT, >23 U/L (sensitivity = 62.7%, specificity = 65.2%); D-dimer, >350 μg/L (sensitivity = 67.8%, specificity = 64.3%); and lymphocytes, >1.15 × 109/L (sensitivity = 59.3%, specificity = 57.1%).

Table 4. Diagnostic factors for the ancestral variant.

IndicatorAUC value95%CIp-value
Viral shedding time (days)0.7550.673–0.837<0.001
ALT (U/L)0.6520.563–0.7410.001
D-dimer (μg/L)0.6930.599–0.787<0.001
Lymphocyte (×109/L)0.5950.506–0.6830.042

ALT, alanine transaminase.

Figure 2 shows the ROC curves of the eosinophil and WBC counts for the diagnosis of Omicron infection. The AUC values for the eosinophil and WBC counts were 0.726 (95%CI = 0.641–0.811, p < 0.001) and 0.641 (95%CI = 0.555–0.727, p = 0.003), respectively (Table 5).

Receiver operating characteristic (ROC) curves of the eosinophil and white blood cell (WBC) counts for the diagnosis of Omicron. The optimal cutoff values, as well as the corresponding sensitivity and specificity, for each indicator are as follows: eosinophil, >0.03 × 10<sup>9</sup>/L (sensitivity = 71.4%, specificity = 68.6%) and WBC >5.5 × 10<sup>9</sup>/L (sensitivity = 63.4%, specificity = 60.7%).
Figure 2. Receiver operating characteristic (ROC) curves of the eosinophil and white blood cell (WBC) counts for the diagnosis of Omicron. The optimal cutoff values, as well as the corresponding sensitivity and specificity, for each indicator are as follows: eosinophil, >0.03 × 109/L (sensitivity = 71.4%, specificity = 68.6%) and WBC >5.5 × 109/L (sensitivity = 63.4%, specificity = 60.7%).

Table 5. Diagnostic factors for Omicron.

IndicatorAUC value95%CIp-value
Eosinophil (×109/L)0.7260.641–0.811<0.001
WBC (×109/L)0.6410.555–0.7270.003

WBC, white blood cell count.

Discussion

The main findings of this study are as follows: firstly, a significant difference was observed between the clinical classification of the Omicron and ancestral variants of SARS-CoV-2. Specifically, the Omicron variant mainly causes asymptomatic infection, whereas the ancestral variant is more likely to cause severe infection. Secondly, laboratory tests revealed remarkable differences in the blood cell distributions and the viral shedding between the Omicron and ancestral variant groups. WBC count, eosinopenia, and viral shedding time were identified as independent predictive factors for Omicron infection. Thirdly, viral shedding time was identified as a promising biomarker for the diagnosis of ancestral infection, whereas eosinophil count exhibited modest utility in identifying the Omicron variant.

In December 2019, a cluster of patients with pneumonia of unknown cause emerged in Wuhan city, and the disease spread rapidly throughout China. Subsequently, a novel coronavirus, originally named 2019-nCoV and later designated as SARS-CoV-2, was isolated (10, 11). Piroth et al. found that patients with COVID-19 between March 1 and April 30, 2020, showed stronger respiratory pathogenicity, a higher prevalence of respiratory complications, and higher mortality than those with influenza (12). A majority of patients with Omicron SARS-CoV-2 infection experienced non-symptomatic and mild disease, but showed excessively higher transmissibility than those with ancestral infection in 2020 (4). Consistent with previous studies, this study revealed that the Omicron variant mainly causes asymptomatic infection, whereas the ancestral variant mainly causes severe infection. However, more challenges persist in distinguishing Omicron infection from common cold or routine respiratory viral infections (13).

The Omicron variant of SARS-COV-2 is substantially more likely to evade the immune system, with an increased ability to reinfect human hosts. However, differences in the objective laboratory indices, such as the blood cell distributions and the biochemical indicators, should be clarified between the 2020 ancestral variant and the 2022 Omicron variant in order to improve the diagnosis and treatment of COVID-19. Paul et al. found that the WBC and PLT counts were lower in patients with ancestral SARS-CoV-2 infection. However, lymphopenia was not identified as a distinguishing feature of the ancestral variant (14). Neutrophil and lymphocyte counts may serve as predictors of asymptomatic ancestral SARS-CoV-2 infection (15). Lymphocyte count multiplied by the neutrophil count has been suggested as a promising diagnostic biomarker for Omicron infection (16). In addition, increased ALT levels and lymphopenia provide evidence for the diagnosis of COVID-19 (2020) (17). In this study, WBC count, lymphocyte count, and ALT level were identified as independent predictors of Omicron infection. Furthermore, lymphocyte count and ALT level were found to be useful in the diagnosis of ancestral infection, whereas WBC count was found to be useful in the diagnosis of Omicron infection.

Accumulating data suggest that eosinophils play a crucial role in the pathogenesis of COVID-19 (18). Patients with low eosinophil counts at admission were more likely to have apparent syndrome including fever, fatigue, and shortness of breath. At the same time, the low eosinophil group exhibited more lesions in chest CT and longer length of hospital stay and course of disease than the normal count group (19). Notably, this study revealed that the eosinophil counts were higher in the Omicron group than in the ancestral variant group. This may be due to several reasons. The Omicron cohort predominantly consisted of asymptomatic (90.2%) or mild cases, whereas the ancestral variant cohort primarily consisted of ordinary (78.0%) or severe (10.2%) cases. Eosinopenia in COVID-19 is closely associated with severe inflammation and disease severity; therefore, the milder clinical course of Omicron infection may have mitigated the suppression of eosinophils (20). Corticosteroids can reduce eosinophils and are more commonly used in the treatment of severe ancestral SARS-CoV-2 infection (21). Although our study did not systematically collect data on corticosteroid use, variations in adjunctive therapy use between the two cohorts might have impacted the eosinophil counts. However, the exact underlying mechanisms remain unclear. The high vaccination rate in the Omicron cohort (94.6%) versus the ancestral variant cohort (0%) represents a significant difference in baseline immunity. Vaccination can reduce the disease severity and influence the laboratory indices, such as the inflammatory markers and viral shedding dynamics. Therefore, some of the observed differences between the two variants can be attributed to vaccination status rather than intrinsic viral characteristics. Furthermore, the higher eosinophil counts observed in the Omicron group can be partially attributed to vaccination, as vaccines can induce eosinophil accumulation. Future studies should ideally compare vaccinated cohorts or adjust for vaccination status in the analysis. This important limitation should be considered when interpreting our findings. Notably, we found that eosinophil count exhibited modest utility in distinguishing Omicron from ancestral SARS-CoV-2 infection.

SARS-COV-2 infection leads to an excessive inflammatory burden and subsequent thrombotic complications that are partially mediated by platelet activation (22). The platelet-to-lymphocyte ratio (PLR) reflected systemic inflammation and predicted COVID-19 mortality (23). D-dimer levels above 0.5 μg/ml increased the odds of progression to severe or critical disease in previous waves of COVID-19 before Omicron (24). Zeng et al. reported that only 0.2% of patients with Omicron infection had abnormally high D-dimer levels on admission (25). In this study, we found that patients with Omicron infection had lower D-dimer levels and higher PLT counts than those with ancestral SARS-CoV-2 infection. Although the D-dimer concentration showed statistical significance in the multivariate analysis, the OR of 0.999 indicates its limited clinical value as a predictive factor. The significantly lower D-dimer levels in the Omicron group (median = 280 vs. 500 μg/L) likely reflect the generally milder disease severity rather than serving as a useful diagnostic biomarker. Therefore, we emphasize that the AUC-based analysis (Table 4) provides a more meaningful assessment of the diagnostic utility of the D-dimer level for COVID-19 (AUC = 0.693). Furthermore, the meta-analysis indicated that COVID-19 survivors had lower proportions of abnormal increases of AST and ALT (26). Our work showed that an increased ALT, but not AST, in the ancestral variant could be used to distinguish this variant.

The average viral shedding time of the Omicron variant (12.58 days) is shorter than that of the ancestral variant (15 days) (9, 27). Similarly, in this study, the Omicron group showed a notably shorter viral shedding time (median time = 11 days) than the ancestral variant group (median = 16 days). Therefore, although Omicron exhibits higher transmissibility than the ancestral variant, its viral shedding time is shorter.

Several limitations of this study should be acknowledged. Firstly, the sample size of this study is small, which may have led to interpretation bias in the results. Future studies should include larger sample sizes and multiple centers. Secondly, the two cohorts were recruited during different phases (2020 vs. 2022) of the COVID-19 pandemic with different vaccination statuses (0% vs. 94.6%), which may have affected the comparability of the findings. The higher vaccination rate in the Omicron cohort represents a significant difference in baseline immunity, and some of the observed differences can be attributed to the vaccination status rather than to intrinsic viral characteristics. Thirdly, different criteria were used to define the viral shedding endpoints in the two cohorts owing to evolving national guidelines during the pandemic. Although this difference may have introduced potential measurement bias, the consistency of our findings with the published literature supports the biological validity of the observed differences. Fourthly, although all laboratories followed standardized protocols and participated in national quality assessment programs, some inter-laboratory variations in the test results cannot be completely excluded. Fifthly, the biological mechanisms responsible for the differences in the laboratory indices between the ancestral and Omicron SARS-CoV-2 variants remain unclear. These mechanisms should be comprehensively investigated to identify potential targets for the prevention and treatment of COVID-19. Finally, owing to the retrospective nature of this study, data on numerous indicators, such as serum creatinine, incubation period, and mental scales, were incomplete during collection.

Conclusions

A majority of the patients infected with Omicron SARS-CoV-2 included in this study were asymptomatic. In contrast to the ancestral variant, WBC count, eosinopenia, and viral shedding time are the independent risk factors with meaningful clinical utility for the prediction of the Omicron variant. Viral shedding time was identified as a diagnostic biomarker for ancestral SARS-CoV-2 infection, whereas eosinophil count was identified as a diagnostic biomarker for Omicron infection. Clinical features and several laboratory indices are relatively convenient for the differential diagnosis of Omicron SARS-CoV-2 infection, independent of the viral nucleic acid test.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Patricia A. Barril, National Scientific and Technical Research Council (CONICET), Argentina

Reviewed by: Jianliang Huang, Central South University, China

Santenna Chenchula, All India Institute of Medical Sciences, India

Statements

Conflict of interest

The authors state that the study design, results, interpretation of the findings, or any other subject discussed in the submitted manuscript were dependent on financial support.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The design of the study and data collection, analysis, and interpretation was supported by the Anhui Provincial Key projects of the Natural Science Foundation for Colleges and Universities (2023AH051738), Foundation for talents of Wannan medical college (YR202218, YR202417), and Anhui Provincial Key projects of Research and Development (2022e07020083).Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data availability

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Ethics approval

The studies involving humans were approved by the Medical Ethical Committee of the First Affiliated Hospital of Wannan Medical College. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

XT: Writing – original draft, Project administration. HL: Formal analysis, Writing – original draft. ZL: Formal analysis, Writing – original draft. YC: Visualization, Writing – review & editing. LL: Funding acquisition, Writing – review & editing.

References

  1. 1 Hui KPY, Ho JCW, Cheung MC, Ng KC, Ching RHH, Lai KL. SARS-CoV-2 Omicron variant replication in human bronchus and lung ex vivo. Nature. (2022) 603: 715–20. doi:, PMID: doi:10.1038/s41586-022-04479-6
  2. 2 Gram MA, Emborg HD, Schelde AB, Friis NU, Nielsen KF, Moustsen-Helms IR. Vaccine effectiveness against SARS-CoV-2 infection or COVID-19 hospitalization with the Alpha, Delta, or Omicron SARS-CoV-2 variant: A nationwide Danish cohort study. PloS Med. (2022) 19: e1003992. doi:, PMID: doi:10.1371/journal.pmed.1003992
  3. 3 Suzuki R, Yamasoba D, Kimura I, Wang L, Kishimoto M, Ito J. Attenuated fusogenicity and pathogenicity of SARS-CoV-2 Omicron variant. Nature. (2022) 603: 700–5. doi:, PMID: doi:10.1038/s41586-022-04462-1
  4. 4 Morris CP, Eldesouki RE, Fall A, Gaston DC, Norton JM, Gallagher ND. SARS-CoV-2 reinfections during the Delta and Omicron waves. JCI Insight. (2022) 7: e162007. doi:, PMID: doi:10.1172/jci.insight.162007
  5. 5 Vihta KD, Pouwels KB, Peto TEA, Pritchard E, House T, Studley R. Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom. Clin Infect Dis. (2022) 76: e133–41. doi:, PMID: doi:10.1101/2022.01.18.22269082
  6. 6 Yang N, Wang C, Huang J, Dong J, Ye J, Fu Y. Clinical and pulmonary CT characteristics of patients infected with the SARS-coV-2 omicron variant compared with those of patients infected with the alpha viral strain. Front Public Health. (2022) 10: 931480. doi:, PMID: doi:10.3389/fpubh.2022.931480
  7. 7 Yu W, Guo Y, Zhang S, Kong Y, Shen Z, Zhang J. Proportion of asymptomatic infection and nonsevere disease caused by SARS-CoV-2 Omicron variant: A systematic review and analysis. J Med Virol. (2022) 94: 5790–801. doi:, PMID: doi:10.1002/jmv.28066
  8. 8 Gu B, Yao L, Zhu XY, Zou T, Feng YJ, Yan JY. Comparison of initial clinic characteristics of hospitalized patients in Suzhou City during the COVID-19 Omicron wave with ancestral variant wave. Ther Adv Respir Dis. (2022) 17534666221110346. doi:, PMID: doi:10.1177/17534666221110346
  9. 9 Hao SR, Yan R, Zhang SY, Lian JS, Cai H, Zhang XL. Interferon-alpha2b spray inhalation did not shorten virus shedding time of SARS-CoV-2 in hospitalized patients: a preliminary matched case-control study. J Zhejiang Univ Sci B. (2020) 21: 628–36. doi:, PMID: doi:10.1631/jzus.B2000211
  10. 10 Zhu N, Zhang D, Wang W, Li X, Yang B, Song J. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. (2020) 382: 727–33. doi:, PMID: doi:10.1056/NEJMoa2001017
  11. 11 Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. (2020) 382: 1708–20. doi:, PMID: doi:10.1056/NEJMoa2002032
  12. 12 Piroth L, Cottenet J, Mariet AS, Bonniaud P, Blot M, Tubert-Bitter P. Comparison of the characteristics, morbidity, and mortality of COVID-19 and seasonal influenza: a nationwide, population-based retrospective cohort study. Lancet Respir Med. (2021) 9: 251–9. doi:, PMID: doi:10.1016/S2213-2600(20)30527-0
  13. 13 Balloux F, Tan C, Swadling L, Richard D, Jenner C, Maini M. The past, current and future epidemiological dynamic of SARS-CoV-2. Oxf Open Immunol. (2022) 3: iqac003. doi:, PMID: doi:10.1093/oxfimm/iqac003
  14. 14 Hengeveld PJ, Khader AO, de Bruin LHA, Geelen IGP, van Baalen EA, Jansen E. Blood cell counts and lymphocyte subsets of patients admitted during the COVID-19 pandemic: a prospective cohort study. Br J Haematol. (2020) 190: e201–4. doi:, PMID: doi:10.1111/bjh.16983
  15. 15 Gu X, Sha L, Zhang S, Shen D, Zhao W, Yi Y. Neutrophils and lymphocytes can help distinguish asymptomatic COVID-19 from moderate COVID-19. Front Cell Infect Microbiol. (2021) 15: 654272. doi:, PMID: doi:10.3389/fcimb.2021.654272
  16. 16 Chang H, Li J. Lymphocyte * Neutrophil” count decreased in SARS-CoV-2 Omicron patients in Shanghai with no significant change in CRP and SAA. J Clin Lab Anal. (2022) 36: e24671. doi:, PMID: doi:10.1002/jcla.24671
  17. 17 Tu H, Zhao H, Su J, Wu W, Xu K, Hu J. Predictors of COVID-19 infection: A prevalence study of hospitalized patients. Can J Infect Dis Med Microbiol. (2021) 2021: 6213450. doi:, PMID: doi:10.1155/2021/6213450
  18. 18 Cortés-Vieyra R, Gutiérrez-Castellanos S, Álvarez-Aguilar C, Baizabal-Aguirre VM, Nuñez-Anita RE, Rocha-López AG. Behavior of eosinophil counts in recovered and deceased COVID-19 patients over the course of the disease. Viruses. (2021) 13: 1675. doi:, PMID: doi:10.3390/v13091675
  19. 19 Xie G, Ding F, Han L, Yin D, Lu H, Zhang M. The role of peripheral blood eosinophil counts in COVID-19 patients. Allergy. (2021) 76: 471–82. doi:, PMID: doi:10.1111/all.14465
  20. 20 Rosenberg HF, Foster PS. The role of peripheral blood eosinophil counts in COVID-19 patients. Allergy. (2021) 76: 471–82. doi:, PMID: doi:10.1111/all.14465
  21. 21 Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. (2020) 395: 1054–62. doi:, PMID: doi:10.1016/S0140-6736(20)30566-3
  22. 22 Theofilis P, Sagris M, Antonopoulos AS, Oikonomou E, Tsioufis C, Tousoulis D. Inflammatory mediators of platelet activation: focus on atherosclerosis and COVID-19. Int J Mol Sci. (2021) 16. doi:, PMID: doi:10.3390/ijms222011170
  23. 23 Karaaslan T, Karaaslan E. Predictive value of systemic immune-inflammation index in determining mortality in COVID-19 patients. J Crit Care Med (Targu Mures). (2022) 8: 156–64. doi:, PMID: doi:10.2478/jccm-2022-0013
  24. 24 Shi Q, Wang Z, Liu J, Wang X, Zhou Q, Li Q. Risk factors for poor prognosis in children and adolescents with COVID-19: A systematic review and meta-analysis. EClinicalMedicine. (2021) 41: 101155. doi:, PMID: doi:10.1016/j.eclinm.2021.101155
  25. 25 Zeng QL, Lv YJ, Liu XJ, Jiang ZY, Huang S, Li WZ. Clinical characteristics of omicron SARS-coV-2 variant infection after non-mRNA-based vaccination in China. Front Microbiol. (2022) 13: 901826. doi:, PMID: doi:10.3389/fmicb.2022.901826
  26. 26 Ye L, Chen B, Wang Y, Yang Y, Zeng J, Deng G. Prognostic value of liver biochemical parameters for COVID-19 mortality. Ann Hepatol. (2021) 21: 100279. doi:, PMID: doi:10.1016/j.aohep.2020.10.007
  27. 27 Xu N, Pan J, Sun L, Zhou C, Huang S, Chen M. Interferon alpha-2b spray shortened viral shedding time of SARS-CoV-2 Omicron variant: An open prospective cohort study. Front Immunol. (2022) 13: 967716. doi:, PMID: doi:10.3389/fimmu.2022.967716

Citation

Tu, X., et al. (2026). Distinguishing the clinical types and laboratory markers of the ancestral and Omicron variants of COVID-19: a multicenter retrospective study. Frontiers in Virology, Volume 6. https://doi.org/
Received
Accepted
Published
Volume
6 - 2026

Copyright

Copyright © 2026 Tu, Li, Liu, Cheng and Li.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Lingling Li

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0

Related articles

ReviewGlobal emergence of begomoviruses in cucurbits: diversity, epidemiology, and management strategiesM. S. Gambo, Manjunath S. Hurakadli, Man Mohan Baghel, S. A. Suleiman, Nitin Jangra, Aditya DagarReviewInflammation, immune dysfunction and viral disease: could micro- and nanoplastics be the missing link?Diana Maryory Gómez-Gallego, Juan C. HernandezOriginal ResearchSubcellular mapping of ToBRFV movement protein in Nicotiana glutinosa via confocal laser scanning microscopy and agroinfiltrationRitika Dwivedi, Tanu Vishwakarma, Archana Rathore, Dilip Kumar Maurya, Prabhat Kumar Maurya, Vijayanandraj Selvaraj, Praveen Chandra Verma, Susheel Kumar