Three Routine Blood Tests May Help Tell IgG4-Related Disease Apart from Lymphoma

A painless swelling of the lymph nodes is one of medicine’s more unnerving presentations, and for good reason: it can signal anything from a benign immune condition to an aggressive cancer. Among the most treacherous diagnostic pairings is the one between IgG4-related disease, a chronic fibroinflammatory disorder driven by immune dysregulation, and diffuse large B-cell lymphoma, the most common subtype of non-Hodgkin lymphoma worldwide. Both can begin with enlarged glands, both can mimic malignancy on scans, and both demand entirely different treatments. A new retrospective study from the Affiliated Hospital of Xuzhou Medical University in China, published in Immunity, Inflammation and Disease, suggests that three routinely measured laboratory values may help clinicians separate the two conditions long before a biopsy result arrives.

The stakes of this distinction are considerable. IgG4-related disease generally responds to glucocorticoids and other immunomodulatory therapies, whereas diffuse large B-cell lymphoma, often abbreviated DLBCL, requires chemotherapy, radiotherapy, or other oncological interventions. If the autoimmune condition is mistaken for cancer, patients may undergo unnecessary and potentially harmful treatment, including organ resection, which has been reported in this setting. If the lymphoma is missed, effective therapy is delayed, and DLBCL’s aggressive biology leaves little room for hesitation. Around 40 percent of DLBCL patients present with disease outside the lymph nodes, and population data from China reported an overall five-year survival of roughly 38.3 percent for lymphoma in 2018, underscoring how much depends on early recognition.

The research team, led by Yongkang Chen, Weimiao Li, and colleagues, assembled a cohort of 56 newly diagnosed patients treated between January 2012 and October 2025: 19 with IgG4-related disease and 37 with DLBCL, all of whom presented with lymphadenopathy. IgG4-related disease cases were classified using the 2019 American College of Rheumatology and European League Against Rheumatism criteria or the 2011 Japanese comprehensive diagnostic criteria, while every DLBCL diagnosis was confirmed by experienced hematopathologists according to the World Health Organization classification. Within the first 24 hours of admission, the researchers extracted 62 variables from each patient, spanning demographics, complete blood counts, biochemistry, coagulation panels, and selected immunological tests.

The initial comparison revealed 20 variables that differed significantly between the two groups, including disease duration, white blood cell count, eosinophil percentages, red cell indices, prothrombin time, serum total protein, globulin, the albumin-to-globulin ratio, creatinine, sodium, bicarbonate, lactate dehydrogenase, and both complement C3 and C4. Many of these differences fit the underlying biology. IgG4-related disease is characterized by chronic immune activation, expansion of circulating plasmablasts, and hypergammaglobulinemia, which explains the markedly higher serum globulin and total protein concentrations in that group. Complement levels, too, were substantially lower in the IgG4-related disease patients, a finding consistent with the complement consumption that has been described in this disorder.

To distill these signals into something clinically usable, the team ran the five most promising candidates through univariate and then multivariable logistic regression. Three variables survived simultaneous adjustment for the others: lymphocyte count, serum total protein, and prothrombin time, or PT. Lymphocyte counts were higher in IgG4-related disease than in DLBCL, averaging 1.80 versus 1.17 billion cells per liter, a pattern the authors interpret as reflecting the contrasting systemic immune states of the two illnesses. DLBCL is associated with a peripheral immunosuppressive profile, even though higher infiltration of T cells and non-malignant B cells within lymphoma tissue is linked to better survival. IgG4-related disease, by contrast, features expansion of CD4-positive T helper 2 and follicular helper T cell subsets, in keeping with the dense lymphoplasmacytic infiltrates seen in affected tissues.

Serum total protein tells a complementary story. In IgG4-related disease, sustained polyclonal B-cell activation drives the production of immunoglobulins across multiple subclasses, not just IgG4, and proteomic studies have documented elevations of IgG1, IgG2, and acute-phase proteins such as alpha-1-antitrypsin. This flood of circulating antibodies raises the measured total protein concentration. DLBCL, on the other hand, is associated with tumor-related alterations of the serum proteome, and increased catabolism or impaired nutritional status can push total protein toward normal or reduced values. The authors are careful to note that total protein is an integrated signal rather than a disease-specific marker, capturing inflammation, nutrition, and tumor metabolism all at once.

The third predictor, prothrombin time, is the most unexpected. PT assesses the extrinsic coagulation pathway and is ordinarily used to monitor anticoagulation or liver function, yet it was longer in IgG4-related disease patients than in those with DLBCL, 11.61 versus 10.91 seconds. The authors offer several speculative explanations: tumor-associated tissue factor and inflammatory signaling may promote coagulation activation in lymphoma, shortening PT, while hepatobiliary involvement or IgG4 autoantibodies against complement factor H, described in a case of complement-mediated thrombotic microangiopathy, might influence coagulation in the autoimmune condition. Because many variables were screened without formal correction for multiple testing, a chance association cannot be excluded, and the absolute difference was modest. Still, PT remained significant both in the multivariable model and in a matched sensitivity analysis restricted to biopsy-proven cases, marking it as a reproducible signal that warrants independent investigation.

Combining the three predictors, the researchers constructed a nomogram, a graphical scoring tool that converts a patient’s laboratory values into a probability of having one disease versus the other. Within the development cohort, the model achieved an area under the receiver operating characteristic curve of 0.91, with a confidence interval of 0.82 to 0.99, along with 88 percent accuracy, 84 percent sensitivity, and 89 percent specificity at the chosen cutoff. Calibration analysis produced a mean absolute error of just 0.032, indicating close agreement between predicted and observed probabilities, and decision curve analysis suggested a potential net benefit over both treat-all and treat-none strategies across a wide range of threshold probabilities.

The authors are admirably candid about the limits of these numbers. The sensitivity analysis, which matched 12 biopsy-confirmed IgG4-related disease patients to 12 age- and sex-matched DLBCL patients, reproduced the PT difference and showed similar directional trends for the other predictors, but the reduced sample size meant several variables lost statistical significance. A post-hoc power calculation based on PT estimated statistical power at only 53.8 percent, a sobering reminder of the constraints imposed by a cohort of 56 patients. The retrospective, single-center design, the absence of external validation, and the omission of disease-specific markers such as serum IgG4 and the IgG4-to-IgG ratio all temper the conclusions. The favorable AUC obtained in a small development cohort may well overstate the performance that would be observed in new patients.

Even with those caveats, the study addresses a genuine and underexplored clinical gap. Previous publications involving both diseases have mainly described their coexistence or sequential development in individual patients, and lymphoma has been reported as the most frequent malignancy occurring in association with IgG4-related disease, making the differential diagnosis a recurring practical problem. The nomogram is explicitly positioned as a supplement to, not a substitute for, pathological biopsy, which remains the diagnostic gold standard. Its most plausible role is at the initial assessment, when lymphadenopathy has overlapping features in both conditions and tissue confirmation is not yet available, giving clinicians an early, non-invasive data point to guide the urgency and direction of further work-up. Larger, independent, multicenter cohorts will be needed before the model can be considered for routine use, but the underlying message is an appealing one: sometimes the blood draw that is already being done may hold the first clue to which of two very different diseases is hiding behind an enlarged lymph node.

Subject of Research: Differentiating IgG4-related disease from diffuse large B-cell lymphoma using routine laboratory biomarkers and a predictive nomogram

Article Title: Identification of Discriminatory Factors and Construction of a Nomogram for Differentiating IgG4‐RD and DLBCL

Article References: Identification of Discriminatory Factors and Construction of a Nomogram for Differentiating IgG4‐RD and DLBCL. (n.d.). https://doi.org/10.1002/iid3.70529

Image Credits: AI Generated

DOI: 10.1002/iid3.70529

Keywords: IgG4-related disease, diffuse large B-cell lymphoma, nomogram, lymphocyte count, serum total protein, prothrombin time, lymphadenopathy, biomarkers, logistic regression, diagnostic model, autoimmune disease, hematology