Changes in ANA Positivity Following SARS‐CoV‐2 Outbreak in Lombardy Region, Italy

ABSTRACT

The Coronavirus Disease 2019 (COVID‐19) pandemic has raised concerns regarding its potential to induce autoimmune responses. Antinuclear antibodies (ANA) are hallmarks of systemic autoimmunity, and emerging evidence suggests their increased prevalence post‐infection. This study aimed to assess ANA positivity rates and patterns of distribution before and after the onset of the pandemic in Lombardy, Italy. We conducted a historical analysis of 1879 matched Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) – Reverse Transcription Polymerase Chain Reaction (RT‐PCR) and ANA records performed between March 2020 and December 2023. ANA positivity was assessed using indirect immunofluorescence (IIF) on Human Epithelial type 2 (HEp‐2) cells and classified according to the International Consensus on ANA Patterns (ICAP). The extracted data were compared to the pre‐pandemic period (2019). Cumulative risk analysis and Cox regression were used to evaluate associations among ANA and SARS‐CoV‐2 exposure, hospitalization, sex, and age. ANA positivity increased during the pandemic (42.4%) compared to 2019 (29.9%, p < 0.00001). Among SARS‐CoV‐2 positive individuals, ANA positivity was more frequent (12.9% vs 6%, OR: 2.31, p < 0.001). Cox regression confirmed that SARS‐CoV‐2 infection (HR:1.397), female sex (HR:1.458), hospitalization (HR 5.369) and age (HR:1.003) were independently associated with the risk of ANA positivity. Time‐to‐event analysis revealed that ANA positivity risk was higher in the first pandemic phases, following original and alfa variants of SARS‐CoV‐2 infections compared to delta and omicron variants. Nuclear anti‐topo I‐like [anti‐cell (AC)−29] pattern was more prevalent in SARS‐CoV‐2 positive individuals. SARS‐CoV‐2 infection is associated with an increased cumulative risk of ANA positivity in the Lombardy population, especially in the earlier phases of the pandemic.

1. Introduction

Since its emergence in late 2019, the Coronavirus Disease 2019 (COVID‐19) pandemic, caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2), has profoundly reshaped global health [1, 2]. Italy was one of the earliest countries to be affected in Europe, with the first locally acquired case identified in Lombardy Region on February 20, 2020 [3, 4].

Initially, much of the scientific effort was understandably focused on the acute phase of the disease, which was characterized by severe and life‐threatening cases, as well as an increased risk of reinfection, especially among high‐risk populations [5]. Furthermore, there has been growing recognition of long‐term consequences of the COVID‐19 pandemic and, in particular, the potential of SARS‐CoV‐2 itself to either trigger de novo or amplify autoimmune diseases [6, 7, 8, 9, 10, 11]. In this context, Gatti et al. demonstrated that coordinated cellular and humoral responses are detectable in SARS‐CoV‐2 convalescents up to 2 years after prior infection, and in light of this evidence, one hypothesized mechanism may involve the induction of autoantibody production, including antinuclear antibodies ANA [12, 13]. The presence of ANA precedes by several years the onset of several systemic autoimmune rheumatic diseases (SARDs) and enhances the diagnostic sensitivity even at early stages [14, 15, 16, 17, 18]. Moreover, ANA titer was associated with severe and critical forms of COVID‐19, and the kinetics of the immune system were investigated as useful predictors of SARS‐CoV‐2 outcomes in in‐hospital patients [19, 20, 21, 22, 23].

However, most of the studies worldwide concentrated on the incidence of new‐onset autoimmune diseases following SARS‐CoV‐2 infection, through the employment of administrative and healthcare databases. Especially, an increased risk of type 1 diabetes mellitus, IBD, psoriasis, vasculitis, RA, and alopecia areata, often correlating with COVID‐19 severity, has been demonstrated. Nonetheless, most of the aforementioned diseases were not ANA‐related, and the majority of the studies employed data collected between 2020 and 2022 [24, 25, 26, 27, 28]. Hileman CO et al. showed a higher incidence of positive ANA tests post‐infection, which strongly predicted subsequent autoimmune diagnoses [26]. Collectively, these findings pointed toward a plausible immunopathological link between SARS‐CoV‐2 infection and new‐onset of autoimmune diseases [24, 25, 27, 28]. However, few relatively small‐sampled studies expressly examined the consequences of SARS‐CoV‐2‐induced autoimmune dysregulation on related changes on autoantibodies detection, particularly ANA, in all pandemic phases, ultimately limiting insight into delayed autoimmune sequelae that occurred in later phases [29].

In the present study, we conducted a historical analysis of longitudinal changes in ANA positivity, ANA titers, and ANA pattern distribution across two main periods: the pre‐pandemic (2019) and pandemic periods (March 2020–May 2023). By analyzing matched data from a large healthcare network in Lombardy, including individuals who underwent both SARS‐CoV‐2 reverse transcription polymerase chain reaction (RT‐PCR) and ANA testing, this study aims to provide new insights into the evolution of ANA positivity, titers, and pattern distributions following SARS‐CoV‐2 infection.

2. Materials and Methods

2.1. Setting Definition, Study Design, and Data Acquisition

As the most densely populated region in the country, Lombardy rapidly evolved into the epicenter of the Italian outbreak, accounting for nearly 20% of the national pandemic burden by the end of 2023, with over 900,000 confirmed cases and 34,000 deaths [3, 4]. The region also recorded some of the highest in‐hospital frailty and case‐fatality rates during the initial waves, making it a relevant setting for investigating both the sequelae of SARS‐CoV‐2 infection [29, 30].

This study was designed as a historical longitudinal analysis of ANA positivity risk, related titer and pattern distribution across two distinct time periods: the pre‐pandemic period (from January 1, 2019, to December 31, 2019), the pandemic (March 1, 2020 to May 31, 2023). Accordingly, all ANA tests were performed when clinically indicated, that is, when the physician suspected the presence of SARDs, in accordance with good clinical practice [31, 32, 33].

Anonymized data were extracted from the automated laboratory information system (ORACLE Database 19c) of Azienda socio‐sanitaria territoriale (ASST) Ovest Milanese, incorporating records from both the centralized Microbiology and Virology Department and the Autoimmunity Laboratory for the pandemic period, and from the Autoimmunity Laboratory only for the pre‐pandemic period. No personally identifiable information was included in the dataset. Basic demographic information, specifically, date of birth and sex, was obtained anonymously through barcode‐encoded sample acceptance forms. Specifically, the data matching analysis was conducted on individuals aged between 12 and 95 years.

Only individuals with matched laboratory records (i.e., those who underwent both SARS‐CoV‐2 RT‐PCR testing via nasopharyngeal swab and ANA testing were included in the final analysis. Records without a matching barcode across the two laboratories, such as cases with only SARS‐CoV‐2 or only ANA testing, were excluded. ANA results, titer, and pattern data from 2019 were used as the pre‐pandemic control group and were included in the analysis for comparisons.

The present study was conducted in accordance with the Declaration of Helsinki and structured exclusively on anonymized data [Art. 4 (5) General Data Protection Regulation (GDPR)] with epidemiological purposes only, and no direct or indirect access to personal identifiers. In such a case, the General Data Protection Regulation (GDPR) does not apply, and no ethical committee approval was required, as confirmed by the 2023 Guidelines on scientific research and personal data.

2.2. ANA Detection by Indirect Immunofluorescence

ANA were detected using the gold‐standard indirect immunofluorescence (IIF) assay on Human epithelial‐2 (HEp‐2) cells. Automated processing was carried out using the Euroimmun Sprinter XL system, and slide interpretation was performed using the EURO Pattern fluorescence microscope. Serum samples were diluted and incubated on Mosaic Profile slides containing HEp‐2 cells. After incubation, the slides were washed and incubated with FITC‐labeled anti‐human IgG. Fluorescence was visualized, and an ANA titer greater than 1:160 was considered positive and included for the analysis, as well as patterns were interpreted and classified according to the international consensus of ANA patterns (ICAP) nomenclature [34]. The interpretation of the ANA positive result, and related titer and pattern, was performed by two experienced physicians (A.M.‐immunologist and S.F., autoimmunity pathologist).

For each period, the total number of ANA tests performed, the number of positive results, the corresponding positivity rate, and positive/negative ratios were reported.

2.3. SARS‐CoV‐2 Detection by RT‐PCR

Molecular detection of SARS‐CoV‐2 RNA was performed using the ELITe MGB® RT‐PCR kit on the ELITe InGenius® platform. This one‐step real‐time RT‐PCR method involves reverse transcription of viral RNA to cDNA, followed by PCR amplification. Detection is achieved using TaqMan® MGB® hydrolysis probes targeting two viral genomic regions: RdRp and ORF8. Fluorescent signals (FAM for ORF8, AP593 for RdRp) enable simultaneous detection of both targets. An endogenous human RNase P gene served as an internal control to verify sample integrity and amplification efficiency. The test was used in both qualitative and quantitative formats.

2.4. Exposure and Outcome Definition

For the pandemic period, the primary exposure of interest was SARS‐CoV‐2 infection, defined by at least one positive RT‐PCR result. The time frame of extraction of SARS‐CoV‐2 RT‐PCR results spanned from 10th March 2020 to 31st May 2023. Patients with confirmed SARS‐CoV‐2 positivity, the date of the first positive RT‐PCR result was defined as the index date. Subsequent positive or negative tests due to follow‐up or scheduled screenings were not considered in the analysis.

All ANA tests were performed within a defined time window of 4–26 weeks following the SARS‐CoV‐2 index date. The lower limit (≥ 29 days − 4 weeks) was chosen to minimize the influence of transient post‐infectious immune activation and to reduce potential reverse causality, such as pre‐existing ANA positivity influencing susceptibility to SARS‐CoV‐2 infection. The upper limit (26 weeks) was selected to reduce confounding due to unrelated autoimmune triggers that could emerge beyond this period.

Participants with multiple ANA tests not following SARS‐CoV‐2 testing within the observation period were excluded. Any ANA result and related titer and pattern following SARS‐CoV‐2 were recorded, and the time interval between the index date and the ANA analysis was calculated.

2.5. Statistical Analysis

Descriptive statistics were used to summarize the demographic characteristics of the study population. Continuous variables were expressed as mean ± standard deviation (SD) or median with interquartile range (IQR), depending on data distribution, which was assessed using the Shapiro–Wilk test.

For comparisons between groups, Student's t‐test was applied for normally distributed continuous variables, while the Mann–Whitney U test was used for non‐normally distributed data. Categorical variables were compared using the Chi‐square test or Fisher's exact test, as appropriate.

For the pandemic period, time‐dependent ANA positivity cumulative risk curves were generated using the Kaplan–Meier method. The time interval was set between 4 and 26.5 weeks (1 to 6 months). Cumulative risk curves were compared using the log‐rank test (Mantel‐Cox), by comparing patients according to SARS‐CoV‐2 exposure, in‐hospital and outpatients, and predominant SARS‐CoV‐2 variant across the pandemic period. SARS‐CoV‐2 variants were reported, according to epidemiological distribution in Lombardy region, as follows: (1) original SARS‐CoV‐2 variant from March 2020 to November 2020; (2) alfa variant, from December 2020 to June 2021; (3) delta variant, from July 2021 to December 2021, and (3) omicron variant, from January 2022 to May 2023.

To identify factors independently associated with ANA positivity over time, Cox proportional hazards regression models were employed, adjusting for potential confounders, including sex and age. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were reported.

All statistical tests were two‐tailed, and a p‐value < 0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics version 27 (IBM SPSS Software, Armonk, NY, USA).

3. Results

3.1. SARS‐CoV2 RT‐PCR and ANA Tests Matching Analysis

Across March 2020 and May 2023, a total of 927,740 RT‐PCR tests for SARS‐CoV‐2 were performed in the ASST West Milan, with 124,909 of these tests were positive (positivity rate of 13.46%). The matching analysis identified corresponding records for a total of 4267 individuals who had undergone both SARS‐CoV‐2 and ANA testing within the study period (Figure 1). After applying temporal criteria, a total of 1389 records were excluded because the ANA test had been performed prior to the index date. An additional 848 records were excluded because of the ANA test occurrence either on the same day as the index date or within 28 days of it. Furthermore, after applying age criteria (12–95 years), the final study cohort consisted of 1879 individuals. Among them, 163 tested positive for SARS‐CoV‐2 (positivity rate of 8.7%).

Figure 1.

Flow‐chart showing matching analysis steps during pandemics. ANA: antinuclear antibodies; SARS‐CoV‐2, severe acute respiratory syndrome Coronavirus 2.

3.2. Overall ANA Positivity Rates During Pre‐ and Pandemic Periods Independently of SARS‐CoV Exposure

A total of 40,327 ANA tests were performed, distributed between the pre‐pandemic year 2019 and the pandemic period (Supplementary Table S1). After excluding patients with repeated ANA detection (duplicate cases) and considering only the last assessment, a total of 32,529 ANA tests were considered in the analysis. In the pre‐pandemic period, 6530 tests were conducted, with 1,959 positive results, corresponding to a positivity rate of 29.98% and a positive‐to‐negative ratio of 0.43:1. During the pandemic period a total of 22,646 tests were performed, of which 9601 were positive, yielding a significantly higher positivity rate of 42.39% and a positive‐to‐negative ratio of 0.74:1. The comparison with pre‐pandemic and pandemic positivity rate revealed a statistic p < 0.00001.

3.3. ANA Results According to SARS‐CoV2 Exposure Within the Entire Period of Observation

For the pandemic period, among a total of 1879 individuals included in the analysis, 731 (38.9%) tested positive for ANA, while 1148 (61.1%) were ANA‐negative [Table 1]. The mean age was higher in the ANA‐positive group [61 years (IQR₁–IQR₃) 48–74] compared to the ANA‐negative group [58 years (IQR₁–IQR₃) 44–71, p < 0.001].

Table 1.

Comparative analysis between ANA positive vs. ANA‐negative individuals during the pandemic period.

Pandemic period	ANA positive N = 731	ANA negative N = 1148	OR (95% CI)	p value
Age (years), median (IQR 1 –IQR 3 )	61 (48–74)	58 (44–71)		< 0.001
Time interval (weeks) between SARS‐CoV‐2 and ANA tests, median (IQR 1 –IQR 3 )	6.7 (4.9–15.8)	8.3 (5–17.3)		0.051
SARS‐CoV‐2, n (%)
Positive	94 (12.9)	69 (6)	2.31 (1.67–3.19)	< 0.001
Negative	637 (87.1)	1079 (94)
Sex, n (%)
Male	195 (26.7)	458 (39.9)	1.82 (1.49–2.23)	< 0.001
Female	536 (73.3)	690 (60.1)

Abbreviations: %, percentage; ANA, antinuclear antibodies; CI, confidence interval; IQR, interqaurtile range; N, number; OR, Odds ratios; SARS‐CoV‐2, severe acute respiratory syndrome Corona virus 2; SD, standard deviation; y.o. = years old.

The median time interval between SARS‐CoV‐2 testing and ANA testing was shorter in ANA‐positive individuals [6.7 weeks (IQR₁–IQR₃) 4.9–15.8] than in ANA‐negative individuals [8.3 weeks (IQR₁–IQR₃) 5–17.3; p  = 0.0.051.

A percentage of 12.9% of ANA‐positive individuals tested positive for SARS‐CoV‐2, compared to 6% among ANA‐negative individuals (OR 2.31; 95% CI: 1.67–3.19; p < 0.001). ANA‐positivity was significantly more frequent among females than males (73.3% vs 26.7%, OR 1.82; 95% CI: 1.49–2.23; p < 0.001).

3.4. Cumulative ANA Positive Risk Curves and Cox‐Regression Model

Survival analysis using the Kaplan–Meier method was conducted to compare the time‐depending cumulative risk for ANA positivity between subjects with a negative and a positive SARS‐CoV‐2 swab result (Figure 2a) and between in‐hospital and outpatients (Figure 2b). The median estimated time interval to ANA positivity was 19.4 weeks (IQR 18.9–21.6) in the SARS‐CoV‐2 positive group, and 20.2 weeks (IQR 18.9–21.6) in the negative group. The two time‐dependent cumulative risk curves showed no significant Log‐rank (Mantel‐Cox) equal to 0.275. In‐hospital patients reported a median time interval to ANA positivity of 6.1 weeks (IQR 5.7–61) compared to a median time interval of 22.6 (IQR 21.6‐23.6) for outpatients, with a statistic log‐rank test < 0.001. A multivariable Cox hazards regression model showed that all covariates—sex, age, SARS‐CoV‐2 infection, and in‐hospital COVID forms—were risk factors for ANA positivity. Specifically, in‐hospital COVID forms exhibited the higher statistically significant effect (HR 5.369; p < 0.001), followed by female sex (1.458; p < 0.001), SARS‐CoV2 infection (HR 1.121, p = 0.003). Conversely, Age maintained positive HR but without statistical significance. (Table 2).

Figure 2.

Kaplan–Meier estimation curves of ANA positive cumulative risk according to the presence of SARS‐CoV‐2 exposure (a) and hospitalization (b). ANA, antinuclear antibodies; SARS‐CoV‐2, severe acute respiratory syndrome Coronavirus 2.

Table 2.

Cox‐regression analysis on the influence of female sex, age, and SARS‐CoV‐2 positivity on time‐dependent ANA cumulative risk across the pandemic period.

Pandemic period	Beta‐coefficient	Standard Error	Hazard Ratio	95% Confidence Interval	p value
Female sex	0.377	0.087	1.458	1.230–1.728	< 0.001
Age	0.003	0.002	1.003	0.998–1.007	0.244
SARS‐CoV‐2+	0.334	0.112	1.397	1.121–1.741	0.003
Hospitalization	1.681	0.093	5.369	4.471–6.448	< 0.001

Abbreviation: SARS‐CoV‐2, severe acute respiratory syndrome Coronavirus 2.

3.5. Comparative Analysis of 2019 ANA Positivity and SARS‐CoV‐2 Positive Individuals in Pandemic Period (2020–2023)

A comparative analysis was performed to evaluate the proportion of ANA‐positive individuals among SARS‐CoV‐2‐positive cases across the years (from March 2020 to May 2023), using 2019 as the reference baseline (pre‐pandemic year). (Figure 3). Accordingly, we divided the pandemic period based on the presence of each predominant SARS‐CoV‐2 variant, and we found statistical differences between the ANA positivity rate during the predominance of both alfa and delta variants (59.86% and 47.56%, respectively, with p < 0.00001). Conversely, the estimated ANA positive rates were comparable to those obtained in 2019 and the original variant and omicron variant timeframe in the late phases of the pandemic.

Figure 3.

Comparative analysis of ANA positivity between pre‐pandemic (2019) and pandemic period, divided by predominant variant prevalence in the SARS‐CoV positive population. ANA, antinuclear antibodies; Dec, December; Jan, January; Nov, November; SARS‐CoV‐2, severe acute respiratory syndrome Coronavirus 2.

Moreover, a Kaplan–Meier time‐to‐event analysis was performed to evaluate the time to ANA‐positivity cumulative risk, stratified by predominant variants of SARS‐CoV‐2 (Figure 4). Both original and alpha variants determined the shorter time interval to ANA positivity [18.6 weeks (IQR 16.7–20.4) and 14.5 weeks (IQR 11.6–17.4), respectively], compared to delta and omicron variants, which exhibited a longer time interval [23.3 weeks (IQR 21.6–25) and 23.9 weeks (21.5–26), respectively], with a log‐rank (Mantel‐Cox) < 0.0001.

Figure 4.

Cumulative ANA positivity risk curves divided by predominant SARS‐CoV‐2 variant during pandemic. ANA, antinuclear antibodies; SARS‐CoV‐2, severe acute respiratory syndrome Coronavirus 2.

3.6. ANA Patterns Distribution According to ICAP Definition

ANA pattern distribution according to SARS‐CoV‐2 exposure and ANA titer is reported in Table 3 and Supplementary Table S2, respectively. The AC‐29 pattern (nuclear anti‐topo I‐like) was significantly more common in SARS‐CoV‐2 positive individuals (3.1%, p = 0.002). No other ICAP patterns showed statistically significant differences between the groups. The AC‐1 (nuclear homogeneous) and AC‐8–10 pattern (nucleolar) appeared more frequently in low titer (1:160) individuals (p = 0.001 and p = 0.047), while AC‐2 (Dense Fine Speckled) pattern was more frequent at ANA titer of 1:320 and AC‐3 (centromere) at higher titers more than 1:640 (37.4%, p < 0.00001).

Table 3.

ANA pattern distribution according to SARS‐CoV‐2 exposure during the pandemic.

ICAP nomenclature	Total N = 731	SARS‐CoV‐2 negative N = 637	SARS‐CoV‐2 positive N = 94	p value
AC‐1 ‐ Homogeneous	273/37.3	238/37.4	35/37.2	0.981
AC‐2 ‐ Dense Fine Speckled	23/3.1	21/3.3	2/2.1	0.544
AC‐3 ‐ Centromere	21/2.9	20/3.1	1/1.1	0.261
AC‐4,5 ‐ Fine and Large Speckled	182/24.9	158/24.8	24/25.5	0.879
AC‐6,7 ‐ Discrete Nuclear Dots	7/1.0	5/0.8	2/2.1	0.212
AC‐8–10 ‐ Nucleolar	118/16.1	104/16.3	14/14.8	0.724
AC‐11,12 ‐ Nuclear envelope	2/0.3	2/0.3	0/0	1.0
AC‐15–17 ‐ Cytoplasmic Fibrillar	46/6.3	44/6.9	2/2.1	0.074
AC‐18 ‐ Discrete Cytoplasmatic Dots	1/0.1	1/0.2	0/0	1.0
AC‐19,20 ‐ Cytoplasmic Speckled	28/3.8	23/3.6	5/5.3	0.420
AC‐21 ‐ Coarse granular/filamentous cytoplasmic	6/0.8	4/0.6	2/2.1	0.132
AC‐22 ‐ Polar Golgi‐like	1/0.1	1/0.2	0/0	1.0
AC‐23 ‐ Rods and rings	1/0.1	1/0.2	0/0	1.0
AC‐24 ‐ Centrosome	3/0.4	3/0.5	0/0	1.0
AC‐25 ‐ Mitotic	2/0.3	1/0.3	1/1.1	0.116
AC‐26 ‐ Nuclear Mitotic Apparatus	1/0.1	0/0	1/1.1	0.129
AC‐27 ‐ Intercellular Bridge	13/1.8	11/1.7	2/2.1	0.783
AC‐29 ‐ Nuclear Anti‐Topo I‐like	3/0.3	0/0	3/3.1	0.002

Note: Bold highlights significant p values.

Abbreviations: AC, anti‐cell; ICAP, International consensus according to ANA pattern; SARS‐CoV‐2, severe acute respiratory syndrome Coronavirus2. 

Abbreviations: AC, anti‐cell; ICAP, International consensus according to ANA pattern; SARS‐CoV‐2, severe acute respiratory syndrome Coronavirus2. 

4. Discussion

Our study demonstrates a marked increase in ANA positivity following the outbreak of the COVID‐19 pandemic. This trend reflects a heightened detection capability by the autoimmunity laboratory and an enhanced diagnostic efficacy across healthcare services in the Lombardy region, which bore approximately 20% of Italy's total pandemic burden [3, 4]. In fact, across the region, autoimmunity laboratories operated in close coordination with intensive care units, infectious disease departments, and virology laboratories to satisfy the complex clinical demands posed by the pandemic, plausibly ensuring the clinical appropriateness of test requests and avoiding resource waste [8, 23].

Furthermore, SARS‐CoV‐2 infection, alongside hospitalization and sex, seemed to act as strong modifiers of ANA positivity. Women were nearly twice as likely as men to test positive for ANA, in line with the known female predominance in autoimmune diseases [35]. Additionally, ANA‐positive individuals were older than negative subjects, signifying that immune senescence or age‐related immune remodeling may increase susceptibility to post‐infectious autoimmunity [36]. This data was also confirmed by our time‐dependent analysis, revealing significant differences. In fact, an earlier and increased ANA positive cumulative risk was found in individuals positive for SARS‐CoV‐2. Accordingly, the Cox regression model also indicated greater hazard ratios in female, in‐hospital, and SARS‐CoV‐2 positive populations.

Two explanations may account for the increase in ANA positivity in such a population. On the one hand, individuals undergoing SARS‐CoV‐2 testing during the pandemic may have received greater clinical attention overall and may have been more frequently tested for autoantibodies, including ANA, compared to those not tested for SARS‐CoV‐2. As demonstrated by Karabey M et al., who analyzed the seroprevalence of autoantibodies in a large population over an established observational period from 2017 to 2022, ANA detection rates were higher during the pandemic, whereas the levels of antimitochondrial antibodies, anti‐liver–kidney microsoma antibodies, IgA anti‐gliadin antibodies, and anti‐endomysial antibodies were more elevated in the pre‐pandemic period [37].

Conversely, increased ANA detection may partly reflect intensified monitoring of high‐risk groups, such as individuals with autoimmune diseases, who may have been more susceptible toward SARS‐CoV‐2 infection and related complications, supporting the hypothesis of a non‐coincidental relationship between autoimmunity and COVID‐19 [38, 39].

In line with this reciprocal interchange, we observed a shorter median time to ANA positivity in the infected group, particularly during the first waves of the pandemic. In this context, we further compared the 2019 ANA positivity rate with the positive rate according to predominant SARS‐CoV‐2 variants. In the first 2 years of post‐pandemic, when the original and alfa variant, the ANA positivity rate rose to 60% with a significant statistical difference that progressively reduced to pre‐pandemic values in 2023. While this trend declined to near‐baseline levels in the subsequent years, the early spike suggests a strong temporal association with SARS‐CoV‐2 circulation, aligning with previous findings of Hileman CO et al., who reported a lower risk of autoimmune disease onset after infection with Omicron variants [26].

Furthermore, the present temporal pattern could reflect several biological and epidemiological dynamics. The first phase of the pandemic was marked by higher viral loads, a more severe clinical course due to more virulent variants, and a lack of population‐level immunity due to the absence of vaccines. The earliest pandemic phases encountered several life‐threatening conditions, such as hyperinflammatory syndrome, which determined significant concerns regarding the potential role of SARS‐CoV‐2 in triggering self‐maintaining hyperimmune responses [40]. Interestingly, several underlying mechanisms are shared between SARS‐CoV‐2–induced hyperinflammatory syndrome and autoimmune disorders. Among the most notable are the formation of autoantibodies via molecular mimicry, vascular injury secondary to immune complex deposition, and antibody‐dependent enhancement in Fc receptor–bearing cells [41]. Excessive activation of the immune response, including the overproduction of autoantibody and pro‐inflammatory cytokines, characterized by elevated levels of TNF, IL‐6, IL‐8, and IL‐10, closely resembles that observed in other inflammatory conditions, such as multiple sclerosis (MS), RA and Kawasaki disease [42].

This common scenario is also supported by evidence of the efficacy of biological agents at detaining hyperinflammation in COVID‐19. For instance, several studies and trials have shown that IL‐6 receptor blockers like tocilizumab can improve symptoms and outcomes in severe COVID‐19 patients [43].

Collectively, these observations together with ours are unlikely to be coincidental and support the growing body of evidence that links COVID‐19 to immune dysregulation and potential autoimmunity. The COVID‐19 pandemic has not only redefined the global approach to infectious diseases but has also brought to light potential immunological consequences with its long‐term implications.

Beyond the mere presence of ANA, our study also identified distinct immunofluorescence patterns more frequently observed in the SARS‐CoV‐2‐positive population. The International Consensus on ANA Patterns (ICAP) has provided a standardized classification system for interpreting ANA immunofluorescence patterns on HEp‐2 cells. ANA patterns can provide clues about potential clinical associations and help to distinguish between non‐specific reactivity and disease‐related autoimmunity [34]. Specifically, the AC‐29 (nuclear anti‐topo I‐like) pattern was significantly more prevalent in the SARS‐CoV‐2 population. This ANA pattern is typically associated with diffuse forms of Systemic Sclerosis, suggesting that COVID‐19 may activate disease‐specific immune pathways [44]. Although most ICAP‐classified patterns did not significantly differ between infected and uninfected individuals, the emergence of certain specific patterns in the post‐COVID group warrants further investigation for their diagnostic and prognostic value.

Despite the large dataset and rigorous analysis, certain limitations must be acknowledged. As an observational study, causality cannot be definitively established. While the temporal and statistical associations are strong, it remains unclear whether ANA positivity directly contributes to disease onset or serves as a marker of immune activation. Therefore, only demographic and administrative information (such as age, sex, and ward of origin) was available, while the lack of detailed clinical data, such as medication history and vaccination status, limits our ability to fully contextualize the ANA findings in terms of clinical outcomes. In this context, previous evidence has suggested the presence of a link between COVID‐19 severity and the excessive production of autoantibodies, specifically rheumatoid factor type Immunoglobulin M, or the presence of autoimmune manifestations or the onset of autoimmune diseases following unmasked SARS‐CoV‐2 infection. Consequently, the potential influence of long COVID‐19 or the development of autoimmune conditions in individual cases could not be fully assessed [44, 45]. Notably, recent literature also highlights the complex role of vaccination in autoimmunity. While mRNA‐based COVID‐19 vaccines are not associated with increased ANA production, inactivated whole‐virus vaccines have been linked to elevated ANA and anti‐dsDNA titers in selected cohorts [46, 47, 48, 49, 50]. Reports of new‐onset autoimmune diseases such as inflammatory myopathies, vasculitis or membranous nephropathy following vaccination remain rare, and the overwhelming consensus supports continued vaccination due to the clear benefits in reducing COVID‐19 severity [51, 52].

In conclusion, the evidence presented draws attention to a relevant increase in ANA positivity associated with SARS‐CoV‐2 infection, particularly during the early waves of the pandemic. While ANA positivity alone does not confirm autoimmune disease, its increased prevalence and specific pattern distributions suggest development or acceleration of autoimmune processes following SARS‐CoV‐2. Ongoing surveillance in long‐term cohort studies will be essential in understanding the entire spectrum of COVID‐19's immunological legacy.

Author Contributions

Eugenio Capparelli was responsible for conceptualization, software, methodology, validation, formal analysis, investigation, resources, data curation, and writing the original draft. Dennis Maggiolini was responsible for methodology, software, validation, formal analysis, investigation, resources, data curation, and writing the original draft. Massimo De Paschale and Claudia Pavia were responsible for formal investigation, resources, and data curation. Paola Faggioli, Miriam Colombo, and Maria Sole Chimenti were responsible for investigation, data curation, validation, and supervision. Sergio Finazzi and Antonino Mazzone were responsible for conceptualization, validation, resources, review and editing, supervision, and project administration.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File 1

Supporting File 2

Capparelli E., Maggiolini D., Paschale M. D., et al., “Changes in ANA Positivity Following SARS‐CoV‐2 Outbreak in Lombardy Region, Italy,” Journal of Medical Virology 98 (2026): e70927. 10.1002/jmv.70927.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.