Patients diagnosed with MM at Shanghai Changzheng Hospital between January 1, 2010, and January 1, 2020, were included in this cohort study, with follow-up continuing until October 2023. Patients with monoclonal gammopathy of undetermined significance (MGUS), solitary plasmacytoma, or smoldering multiple myeloma (SMM) were excluded. The study received approval from the Clinical Ethics Committee of Shanghai Changzheng Hospital (approval number: 2023SLYS7) prior to its initiation. Informed consent was obtained from all participants prior to their inclusion in the study. A unique study ID was assigned to each patient to ensure data anonymization.

Data for this cohort study were collected using the Hospital Information System (HIS) and Laboratory Information System (LIS) at Shanghai Changzheng Hospital, offering comprehensive access to demographic, clinical, laboratory, and treatment information for all MM patients.

The primary laboratory and clinical data were gathered at the time of MM diagnosis, with missing data supplemented by records from within two months before or after the diagnosis. If data within this timeframe were unavailable, those laboratory values were treated as missing and excluded from the analysis. All laboratory data were collected prior to the initiation of the first chemotherapy.

Covariates were chosen based on known or potential impacts on MM progression and treatment outcomes, including age, sex, M protein type, light chain restriction, hemoglobin (Hb) level, serum β2-microglobulin (β2M), lactate dehydrogenase (LDH), serum calcium (Ca), and bone marrow plasma cells (BMPC), and the free light chain ratio (FLCR). High-risk cytogenetic abnormalities were defined per the Mayo myeloma stratification system, including t(4;14), t(14;16), t(14;20), del(17p), and 1q gains (12). Disease classification was based on the Durie-Salmon (DS) stage, International Staging System (ISS), and Revised ISS (R-ISS) (13–15). Additional variables included autologous stem cell transplantation (ASCT) status, the use of proteasome inhibitors (bortezomib, carfilzomib, ixazomib), immunomodulators (lenalidomide, thalidomide, pomalidomide), and monoclonal antibodies (daratumumab, isatuximab, elotuzumab, belantamab mafodotin). Renal disease, hypertension (16), diabetes (17), and autoimmune diseases were considered based on medical history.

Renal disease was defined as a documented history of chronic kidney disease (CKD), acute kidney injury (AKI), or any form of renal impairment, as diagnosed by a nephrologist (18). Autoimmune diseases were defined as a diagnosis of conditions such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren’s syndrome, or other autoimmune disorders based on established diagnostic criteria and confirmed by clinical records (19–21).

All data on patient comorbidities and clinical characteristics were extracted from electronic medical records. Staging classifications were determined based on international guidelines, with each patient’s staging independently evaluated by two experienced hematology clinicians and subsequently reviewed by a chief hematologist for accuracy.

Autoantibody results obtained during the study via LIS included ANAs and SSA, which were detected using the indirect immunofluorescence (IIF) method (Euroimmun, Lubeck, Germany) and the Euroline ANA Profile (Euroimmun, Lubeck, Germany), following the manufacturer’s protocols ( Supplementary Figure S1 ).

For ANA detection, we adopted a cutoff titer of ≥1:160, based on the International Consensus on ANA Patterns (ICAP) guidelines and American College of Rheumatology (ACR) recommendations (22).

For SSA detection, we used a semi-quantitative immunoblot assay, following the manufacturer’s classification criteria. Results were categorized as positive, negative, or borderline, and only definitively positive results were considered SSA-positive to ensure clinical relevance and reduce potential misclassification bias.

To ensure test reliability, all laboratory analyses were conducted in a single certified laboratory with strict internal quality controls, and all assays met the manufacturer’s specified quality criteria. Additionally, all positive ANA and SSA results were reviewed by experienced clinicians to verify their clinical relevance in the study context.

Autoantibody screening was performed at any time before the patient was diagnosed with MM or before the initiation of treatment following diagnosis.

In accordance with the International Myeloma Working Group (IMWG) criteria for treatment response, treatment response was evaluated after initial induction therapy (23). Patients were categorized into the following response groups: stringent complete response (sCR), complete response (CR), very good partial response (VGPR), partial response (PR), stable disease (SD), and progressive disease (PD). These response classifications were extracted from electronic medical records and cross-validated by two independent hematologists to ensure accuracy.

Baseline characteristics were compared between patients with different autoantibody results using chi-square tests for categorical variables and independent samples t-tests or Mann-Whitney U tests for continuous variables, depending on normality. A two-tailed significance level of α = 0.05 was used for all analyses. All analyses were performed using the statistical software packages R (http://www.R-project.org, The R Foundation and d Free Statistics software versions 1.6).

The primary objective of this study was to evaluate the relationship between autoantibody and prognosis in MM patients. To achieve this, we utilized Cox regression analysis, controlling for several important baseline MM prognostic factors including age, gender, DS stage, ISS stage, R-ISS stage, MM treatment, and comorbidities. An extended Cox model approach was applied to adjust the model for various covariates. Survival curves were plotted using Kaplan-Meier and log-rank analyses. Subgroup analyses were conducted, stratified by a number of relevant effect covariates.

In the sensitivity analysis, we employed both propensity score matching (PSM) and an inverse probability of treatment weighting (IPTW) model to assess the robustness of our results. PSM was conducted using a 1:1 nearest neighbor matching algorithm with a caliper width of 0.2, while IPTW was truncated at the 5th and 95th percentiles (24, 25). All covariates were included in the generation of the propensity scores. The quality of the PSM was evaluated using the standardized mean difference (SMD), with a threshold of less than 0.1 considered acceptable, indicating a well-balanced distribution of covariates between the matched groups.

In this study, we initially screened a comprehensive panel of autoantibodies, including ANA,SSA, SSB, anti-topoisomerase I (SCL-70), anti-histidyl-tRNA synthetase (Jo-1), anti-histone antibody (AHA), anti-U1 ribonucleoprotein (U1-RNP), anti-nucleolar antibody (ANuA), anti-RNA antibody (ARA), and anti-Smith antibody. Among the 304 patients, 51 (16.8%) were ANA-positive and 13 (4.3%) were SSA-positive. The remaining autoantibodies showed very low positivity rates ( Supplementary Figure S2 ). Due to their low prevalence, only ANA and SSA were included in the final analysis, as statistical power to detect meaningful associations was limited (24).

Table 1 summarizes the baseline characteristics of the study population, stratified by ANA and SSA status. A total of 304 patients were enrolled, with a mean age of 60 years. Among them, 170 were male (55.9%) and 134 were female (44.1%). The most common M protein type was IgG (48%). 14 patients were diagnosed with autoimmune diseases, within 8 ANA-positive and 2 SSA-positive. Baseline characteristics and results in 14 MM patients diagnosed with autoimmune disease are presented in Supplementary Table S1 . Statistically significant differences (p < 0.05) were observed in the ANA group for sex, M protein type, hemoglobin level, serum β2-microglobulin, serum creatinine, ISS stage, R-ISS stage, and autoimmune disease. In the SSA group, only hemoglobin level showed a significant difference.

Kaplan–Meier analysis revealed that ANA-positive MM patients had a significantly shorter PFS compared to ANA-negative patients ( Figure 1A ). The median PFS for ANA-positive patients was 32 months, whereas for ANA-negative patients, it was 43 months. This trend was even more pronounced among SSA-positive patients ( Figure 1B ), where the median PFS for SSA-positive patients was 17 months compared to 44 months for negative.

Table 2 presents the hazard ratios (HR) and 95% confidence intervals (CI) for PFS based on ANA and SSA status. In the crude model, ANA positivity was significantly associated with a higher risk of PFS (HR = 1.57, 95% CI 1.04-2.4, p = 0.034), while SSA positivity showed an even greater risk (HR = 2.93, 95% CI 1.53-5.64, p = 0.001). After adjusting for age, sex, DS stage, ISS, and R-ISS in Model 2, SSA positivity remained significantly associated with a higher risk of PFS (HR = 2.46, 95% CI 1.25-4.83, p = 0.009). In Model 3, which further adjusted for ASCT status, use of proteasome inhibitors, immunomodulators, daratumumab, monoclonal antibodies, and comorbidities, SSA-positive patients continued to exhibit a 112% higher risk of progression compared to negative (HR = 2.12, 95% CI 1.01-4.43, p = 0.047).

A chi-square test was conducted to compare treatment response distributions between groups. The overall p-value for the ANA-positive and ANA-negative groups was 0.309. In contrast, the p-value for the SSA-positive and SSA-negative groups was 0.047, suggesting a statistically significant difference in treatment response distribution between SSA groups. Specifically, within the SD/PD category, the proportion of patients in the SSA-negative group was 36 (12.4%), whereas in the SSA-positive group, it was 5 (38.5%) (p = 0.023) ( Table 3 ).

The median FLCR values were 0.6 (0.5, 1.6) in the ANA-negative group and 0.6 (0.6, 2.9) in the ANA-positive group (p = 0.156), indicating no significant difference. Similarly, the χ² test for FLCR category distribution (0.01–100 vs. ≤0.01 or ≥100) showed no significant difference between ANA groups (p = 0.583).In contrast, the SSA-positive group had significantly higher FLCR values (1.8 [1.0, 78.9] vs. 0.6 [0.6, 1.6], p = 0.002). However, the proportion of patients with extreme FLCR values (≤0.01 or ≥100) did not significantly differ between SSA groups (7.7% vs. 15.5%, p = 0.7)( Supplementary Table S3 ).

We conducted subgroup analyses stratified by age, sex, Hb, LDH, serum β2M, and plasma cells of BM to further explore the association between SSA positivity and PFS. The results demonstrated a consistent effect of SSA positivity on PFS across all subgroups, with no evidence of significant interaction effects ( Figure 2 ).

The baseline characteristics after PSM are presented in Supplementary Table S2 . The association between SSA positivity and PFS was evaluated using multiple models. After adjusting for the propensity score, this risk remained elevated (HR = 2.89, 95% CI: 1.27-6.57, p = 0.012). When PSM was applied, the HR increased to 3.66 (95% CI: 1.01-13.35, p = 0.049). Finally, inverse probability of treatment weighting (IPTW) yielded an even higher risk estimate (HR = 4.76, 95% CI: 1.76-12.9, p<0.001) ( Table 4 ).