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Article

Associations Between Immune-Related Conditions and Lymphoid Disorders: An Analysis of the Diverse All of Us Research Program

by
Hanna Terhaar
1,
Mohammad Saleem
1,†,
Evan Liu
1,† and
Nabiha Yusuf
2,*
1
Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
2
Department of Dermatology and Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Lymphatics 2025, 3(1), 3; https://doi.org/10.3390/lymphatics3010003
Submission received: 6 November 2024 / Revised: 11 January 2025 / Accepted: 20 January 2025 / Published: 29 January 2025
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Abstract

:
Introduction: Studies on the association between immune-mediated disorders and lymphoid disorders have been very limited, especially in diverse populations. The objective of this study is to evaluate the relationship between a variety of immune diseases and lymphoid malignancies. Methods: The NIH “All of Us” database was utilized to perform a cross-sectional analysis between lymphoid disorders and various immune diseases. The adjusted multivariable logistic regression analysis was performed in R to examine the association between lymphoid disorders such as leukemia, lymphoma, and plasma cell neoplasms against a variety of autoimmune diseases. Results: In the study cohort of 316,044 patients, we found significant associations between lymphomas and the aforementioned immune-mediated diseases, with the exception of dermatomyositis and scleroderma. Lymphoid leukemias showed significant associations (p < 0.001) with several autoimmune conditions, including psoriasis, type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosus, ulcerative colitis, and hyperthyroidism. In plasma cell neoplasms, significant associations were found in all but dermatomyositis, scleroderma, vitiligo, and atopic dermatitis (p < 0.001). Conclusions: In this population-level analysis, the majority of immune-mediated diseases were found to be significantly correlated with an increased incidence of lymphoid malignancies. As such, patients diagnosed with immune-mediated diseases should undergo close surveillance and early screening with the goal of early identification and treatment of lymphoid malignancies.

1. Introduction

Autoimmune disorders are characterized by an atypical immune response against self-antigens that affects 1 in 10 individuals, equivalent to approximately 10% of the world’s population [1]. Autoimmune diseases can be broadly classified into two main categories: organ specific and systemic. Organ-specific autoimmune diseases target particular organs or tissues, such as type 1 diabetes (pancreas), Hashimoto’s thyroiditis (thyroid), and psoriasis (skin). In contrast, systemic autoimmune diseases, including systemic lupus erythematosus (SLE) and rheumatoid arthritis, affect multiple organs or body systems [2]. Among the most prevalent autoimmune diseases, rheumatoid arthritis affects approximately 1% of the global population [3], while psoriasis impacts about 2–3% of people worldwide [4]. Type 1 diabetes, although its prevalence varies by region, affects millions globally, with incidence rates increasing by about 3–4% annually [5]. SLE is estimated to affect 5 million people worldwide [6], and multiple sclerosis impacts more than 2.8 million individuals globally [7]. These common autoimmune diseases, along with others, have been associated with an increased risk of lymphoproliferative disorders, which forms the basis of our current investigation [2]. Multiple myeloma is an abnormal proliferation of plasma cells that can build up in bone marrow and form tumors in multiple locations in the body [8]. Abnormal amounts of plasma cells secrete an excess amount of immunoglobulins, and these immunoglobulin levels can range from monoclonal gammopathy of undetermined significance to multiple myeloma. When only one plasma tumor is present, it is labeled as plasmacytoma, whereas multiple tumors are labeled as multiple myeloma [8]. Patients with autoimmune disorders, such as rheumatoid arthritis and systemic lupus erythematosus (SLE), have a higher incidence of multiple myeloma. Chronic lymphocytic leukemia (CLL) is labeled the most common adult leukemia in the Western world and is caused by the proliferation of mature B lymphocytes in the peripheral blood, bone marrow, and secondary lymphoid organs [9]. Acute lymphoblastic leukemia (ALL) is more commonly seen in children and is labeled by the accumulation of immature B- and T-cell lymphoblasts that accumulate in the blood [10]. Hodgkin’s lymphoma is a B-cell lymphoma that is characterized by relatively few malignant cells known as Reed–Sternberg cells, surrounded by numerous immune effector cells in a cytokine-rich environment [11]. Non-Hodgkin’s lymphoma is a neoplasm of lymphoid tissues that can arise from either B- or T-cell precursors or mature B- or T-cells [12]. These lymphomas have been well documented in their associations with autoimmune diseases [13,14,15,16,17]. Epidemiological factors behind the development of hematologic diseases include genetic predispositions, environmental interactions, and immune dysregulation. Autoimmune disorders are characterized by chronic inflammation followed by immune system activation, which ultimately favors a pro-tumorigenic environment [18]. Limited current evidence displays mixed results on the association between various autoimmune disorders and plasma cell neoplasms, lymphoid leukemias, and lymphomas; however, this research is limited [16,19,20]. Furthermore, research on this association in population-level studies as well as underrepresented populations is limited. The All of Us database is a research program that aims to collect data from a diverse participant pool that includes members of groups who have been left out of research in the past. Therefore, the purpose of this research is to conduct an analysis on the association between autoimmune diseases and lymphoproliferative disorders.

2. Results

We identified a cohort of 316,044 patients in the registered tier v6 dataset. Of the patients utilized in the All of Us database, 224,936 patients were White (71.17%), 77,039 patients were Black/African American (24.37%), and 14,069 patients were Asian (4.45%). Table 1 depicts the patient demographics for each disease categorized by race, age, and sex at birth. Within the study cohort, we identified a total of 5385 patients diagnosed with lymphoproliferative disorders. Specifically, 3139 patients were diagnosed with lymphomas, 1000 patients were diagnosed with lymphoid leukemia, and 1246 patients were found to have plasma cell neoplasms (Table 1).
Bars display percentages of demographics and are in the order of male; female; age (18–44); age (45–64); age (65+); White; African American; and Asian. Colors depict green: gender, orange: age, and blue: race (Figure 1).

2.1. Lymphoma

In this cohort, we found significant associations between lymphomas and the aforementioned immune-mediated diseases, with the exception of dermatomyositis (OR 1.55, 95% Cl 0.68–3.02, p = 0.2) and scleroderma (OR 1.29, 95% Cl 0.85–1.89, p = 0.2). In comparison to the remaining the immune-mediated diseases, hyperthyroidism (OR 1.19, 95% Cl 1.00–1.41, p = 0.049) and multiple sclerosis (OR 1.43, 95% Cl 1.03–1.94, p = 0.026) exhibited significant associations with lymphomas, while the remaining diseases also displayed strong significant associations (p < 0.001) (Table 2, Figure 2).

2.2. Lymphoid Leukemias

For lymphoid leukemias, significant associations were identified in psoriasis (OR 1.49, 95% Cl 1.10–1.96, p = 0.007), type 1 diabetes (OR 1.67, 95% Cl 1.19–2.29, p = 0.002), and Sjogren’s syndrome (OR 1.68, 95% CI 1.14–2.40, p = 0.006). Rheumatoid arthritis, SLE, ulcerative colitis, and hyperthyroidism all exhibited strong associations (p < 0.001), while the remaining disease associations were insignificant (Table 2, Figure 2).

2.3. Plasma Cell Neoplasms

In plasma cell neoplasms, significant associations were found in all but dermatomyositis, scleroderma, vitiligo, and atopic dermatitis. With the exception of hyperthyroidism (OR 1.19, 95% Cl 1.00–1.41, p = 0.049) and Crohn’s disease (OR 1.48, 95% Cl 1.12–1.93, p = 0.005), the remaining immune-mediated diseases exhibited a significantly strong association with plasma cell neoplasms (p < 0.001) (Table 2, Figure 2).

3. Discussion

This population-level study demonstrates significant associations between various autoimmune disorders (AIDs) and lymphoproliferative diseases (LPDs). These findings align with prior research that suggests a relationship between AIDs and LPDs, providing new insights by leveraging the large and diverse cohort from the All of Us database. Notably, this is the first study to identify significant associations between alopecia areata and lymphomas, as well as multiple myeloma, within a U.S. nationwide cohort. Additionally, our study uniquely examines the associations of autoimmune disorders across diverse plasma cell neoplasms.
Several previous studies have explored the link between ADs and LDs. Söderberg et al.’s Swedish study found associations between psoriasis and Sjogren’s syndrome with non-Hodgkin’s lymphoma, but not with multiple sclerosis [19]. Our finding of a significant association between multiple sclerosis and lymphomas contrasts with this result but aligns with other studies linking multiple sclerosis to young adult non-Hodgkin’s lymphoma and a recent genome-wide association study linking a multiple sclerosis risk SNP to Hodgkin’s lymphoma [21,22]. This discrepancy is likely due to our combined analysis of Hodgkin’s and non-Hodgkin’s lymphoma. Anderson et al.’s analysis of the U.S. SEER database similarly found positive correlations between several ADs (rheumatoid arthritis, Sjogren’s syndrome, psoriasis, and SLE) and non-Hodgkin’s lymphoma, with SLE also correlated with Hodgkin’s lymphoma. Fallah et al.’s Swedish population study reported associations between non-Hodgkin’s lymphoma and several ADs, including Crohn’s disease, type 1 diabetes, hypothyroidism, rheumatoid arthritis, SLE, ulcerative colitis, Sjogren’s syndrome, and localized scleroderma [16]. Discrepancies regarding multiple sclerosis and hyperthyroidism are likely attributable to differences in lymphoma classification and the potential for lymphoma treatment to induce thyroid dysfunction [23,24,25]. While some studies have found no association between lymphomas and ADs [26,27], our findings generally support the existing literature.
Figure 3 highlights several novel associations identified in our study, including the link between alopecia areata and both lymphomas and plasma cell neoplasms. These findings warrant further investigation to explore potential causal relationships and underlying mechanisms. Our study also identified significant associations between alopecia areata and vitiligo with lymphomas. While limited to case reports and a Taiwanese cohort study, prior research suggests a link between alopecia areata and both Hodgkin’s and non-Hodgkin’s lymphoma [28,29,30]. Further research is needed to establish causality, particularly given the variable temporal relationship between alopecia areata and lymphoma onset. Similarly, the association between vitiligo and lymphoma, supported by only one recent retrospective cohort study, requires further investigation [31].
The observed associations likely stem from the complex interplay between genetic predisposition, environmental factors, and immune dysregulation. Chronic inflammation, a key feature of autoimmune diseases, creates a pro-tumorigenic microenvironment through the sustained production of cytokines such as TNF-α, IL-6, and IL-1β [18]. This chronic inflammation, coupled with dysregulated immune activation—also present in LPDs—leads to persistent lymphocyte activation, increasing the risk of oncogenic mutations [36,37]. Shared genetic susceptibilities, such as certain HLA haplotypes, further contribute to the overlapping risk profiles of these conditions. Furthermore, immunosuppressive therapies, while managing AIDs, can also increase LPD risk by reducing immune surveillance [38].
Our findings regarding lymphocytic leukemia include significant associations with several ADs, including psoriasis, rheumatoid arthritis, SLE, Sjogren’s syndrome, type 1 diabetes, ulcerative colitis, and hypothyroidism. This is consistent with some, but not all, prior studies, which have reported varying associations [19,32,38,39,40,41,42]. This inconsistency highlights the need for further research to clarify these relationships [43].
Regarding plasma cell neoplasms, we found significant associations with most ADs studied, except dermatomyositis, scleroderma, vitiligo, and atopic dermatitis. This represents the first large, diverse population analysis examining these associations. Previous studies, often limited to case reports or specific neoplasms, like multiple myeloma, have reported mixed results [14,33,34,35,44,45,46]. Notably, our finding of a significant association between alopecia areata and plasma cell neoplasms is novel and requires further validation.
The underlying pathophysiology likely involves complex interactions between genetic predisposition, environmental factors, and immune dysregulation. Chronic inflammation and immune stimulation are implicated in the development of both ADs and LDs [35,47,48]. The mechanisms linking alopecia areata to LDs and plasma cell neoplasms may involve T-lymphocyte-mediated inflammation and shared genetic factors [28,49]. Immunosuppressive therapies used to treat ADs may also contribute to increased LD risk [50].
Several limitations of this study should be acknowledged. The cross-sectional design precludes establishing causality or determining the temporal relationship between AIDs and LPDs. Reliance on ICD-9, ICD-10, and SNOMED codes may have introduced misclassification bias, potentially underestimating or overestimating the true associations. Additionally, the lack of detailed data on disease severity, treatment regimens, and genetic factors limits the scope of our analysis. Selection bias and surveillance bias may also have influenced the findings, as participants with AIDs often undergo more frequent medical evaluations.
To build on these findings, future longitudinal and molecular/genetic studies are crucial to elucidate the temporal relationship between AIDs and LPDs, explore shared pathways, and identify potential therapeutic targets. Molecular and genetic studies are needed to explore shared pathways and identify specific inflammatory mediators and immune cell populations that could serve as therapeutic targets. The development of risk prediction models incorporating genetic, clinical, and environmental factors could enable personalized screening and prevention strategies for patients with AIDs.

4. Materials and Methods

4.1. Patients

We identified men and women over the age of 18 diagnosed with the diseases of interest utilizing the “All of Us” Research Program registered tier v6 dataset, a United States database that prioritizes the inclusion of groups that are historically underrepresented in biomedical research.

4.2. Variables and Outcomes

Lymphoid diseases and autoimmune disorders were identified using the Systemized Nomenclature of Medicine (SNOMED), International Classification of Diseases ICD9, and ICD10 codes. Patients who were identified with the following immune-mediated diseases were included in the analysis: Crohn’s disease, ulcerative colitis, hyperthyroidism, hypothyroidism, type 1 diabetes, rheumatoid arthritis, scleroderma, Sjogren’s syndrome, systemic lupus erythematosus (SLE), dermatomyositis/polymyositis, multiple sclerosis, alopecia areata, atopic dermatitis, psoriasis, and vitiligo. Lymphoma included all types of lymphoma, including non-Hodgkin’s lymphoma and Hodgkin’s lymphoma

4.3. Statistical Analysis

A cross-sectional analysis was performed to evaluate the association between lymphoid disorders and immune-regulated diseases. Multivariable logistic regression analysis was performed in R 4.1.3 via jupyter notebook on the All of Us platform to examine the association between these diseases and the aforementioned lymphoid disorders. Multivariable logistic regression models included several key variables known to potentially influence the relationship between autoimmune diseases and lymphoid disorders. Age was incorporated as a continuous variable, measured in years at the time of data collection. Sex was included as a binary variable (male/female) based on sex assigned at birth. Race/ethnicity was categorized into White, Black/African American (AA), Asian, and other, with White as the reference category, based on self-reported data in the All of Us database. For each autoimmune disease–lymphoid disorder pair, we calculated adjusted odds ratios (ORs) with 95% confidence intervals (CIs), considering p-values less than 0.05 as statistically significant. Tables were then generated by formatting R output into LaTeX and then compiled using Overleaf, an online LaTeX editor. Tables were generated for each set of associations grouped by lymphoid disease.

5. Conclusions

This population-level study utilizing the diverse All of Us Research Program dataset reveals significant associations between various autoimmune disorders and lymphoproliferative diseases, with strong correlations observed between most studied autoimmune diseases and lymphomas, lymphoid leukemias, and plasma cell neoplasms. These findings underscore the complex interplay between autoimmune disorders and lymphoid malignancies, likely stemming from shared genetic susceptibilities, chronic inflammation, and dysregulated immune activation. Based on these results, we recommend implementing enhanced surveillance protocols for lymphoproliferative disorders in patients with autoimmune diseases, particularly those with conditions showing strong associations such as SLE, rheumatoid arthritis, and Sjögren’s syndrome. Clinicians should consider developing risk assessment tools that incorporate autoimmune disease status, educate patients about their potentially increased risk, and adopt a multidisciplinary approach involving rheumatologists, dermatologists, and hematologists–oncologists. Personalized follow-up schedules and screening intensity should be tailored based on the specific autoimmune disease and its associated risk level. Future research should focus on elucidating the temporal relationship between these conditions and investigating the underlying molecular mechanisms, which will be crucial for developing targeted prevention strategies and improving patient outcomes. This study highlights the importance of considering autoimmune disease history in the management and screening of patients for lymphoproliferative disorders, potentially leading to earlier detection and more effective treatment strategies.

Author Contributions

Conceptualization, H.T., M.S. and N.Y.; methodology H.T. and E.L.; software, H.T., M.S. and E.L.; validation, H.T., E.L. and M.S.; formal analysis, H.T. and E.L.; investigation, H.T.; resources, H.T.; data curation, H.T.; writing—original draft preparation, H.T.; writing—review and editing, H.T., M.S. and N.Y.; visualization, H.T. and M.S.; supervision, M.S. and N.Y.; project administration, N.Y.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created in the study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Lymphatics 03 00003 g001
Figure 1. Patient demographics with selected autoimmune diseases.
Figure 1. Patient demographics with selected autoimmune diseases.
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Figure 2. Multivariable-adjusted odds ratios.
Figure 2. Multivariable-adjusted odds ratios.
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Figure 3. Significance of associations between autoimmune diseases and cancer types [15,16,19,21,29,31,32,33,34,35].
Figure 3. Significance of associations between autoimmune diseases and cancer types [15,16,19,21,29,31,32,33,34,35].
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Table 1. Patient demographics with selected autoimmune diseases.
Table 1. Patient demographics with selected autoimmune diseases.
Sex (n, %)Age (n, %)Race (n, %)Total (n)
Male Female 18–4445–6465+White African American Asian
Controls124,135 (39.2)191,909 (60.7)96,306 (30.5)113,256 (35.8)106,482 (33.7)224,936 (71.2)77,039 (24.4)14,069 (4.5)316,044
Leukemia547 (54.7)453 (45.3)90
(9)		200
(20)		707 (70.7)862 (86.2)117
(11.7) 		21 (2.1)1000
Lymphoma1561 (49.7)1578 (50.3)273 (8.7)870 (27.7)1996 (63.6)2578 (82.1)496
(15.8)		65 (2.1)3139
Plasma Cell Neoplasm648
(52)		598
(48)		44 (3.53)349
(28)		853 (68.5)896 (71.9)322
(25.8)		28 (2.3)1246
Crohn’s Disease983
(39)		1532 (60.9)750 (29.8)842 (33.5)923 (36.7)2091 (83.1)380
(15.1)		44 (1.8)2515
Ulcerative Colitis880
(37)		1499 (63)542 (22.8)808
(34)		1029 (43.3)2016 (84.7)329
(13.8)		34 (1.4)2379
Hyperthyroidism1201 (20.9)4533 (79.1)892 (15.6)2144 (37.4)2698 (47.1)4036 (70.4)1530
(26.7)		168 (2.9)5734
Hypothyroidism6836 (22.9)23,061 (77.1)3680 (12.3)10,111 (33.8)16,106 (53.9)25,555 (85.5)3598
(12)		744 (2.5)29,897
Type 1 diabetes2114 (45.9)2492 (54.1)956 (20.8)1716 (37.3)1934 (42)2965 (64.4)1550
(33.7)		91
(2)		4606
Rheumatoid Arthritis1358 (22.3)4731 (77.7)644 (10.6)2335 (38.3)3110 (51.1)4445 (73)1520
(25)		124 (2.0)6089
Scleroderma69
(8.2)		772 (91.8)50 (5.9)227
(27)		564 (67.1)728 (86.6)95
(11.3)		18 (2.1)841
Sjogren’s Syndrome444 (14.6)2588 (85.4)340 (11.2)1096 (36.1)1596 (52.6)2395 (79)509
(16.8)		128 (4.2)3032
Systemic Lupus Erythematosus299 (10.9)2439 (89.1)634 (23.2)1275 (46.6)829 (30.3)1619 (59.1)1026
(37.5)		93 (3.4)2738
Dermatomyositis53 (23.2)175 (76.8)33 (14.5)102 (44.7)93
(40.8)		155
(68)		68
(29.8)		5
(2.2)		228
Multiple Sclerosis502 (22.7)1708 (77.3)500 (22.6)1052 (47.6)658 (29.8)1766 (80)414
(18.7)		30 (1.4)2210
Alopecia Areata144 (21.2)534 (78.8)161 (23.7)283 (41.7)234 (34.5)416 (61.4)215
(31.7)		47 (6.9)678
Atopic Dermatitis1700 (33.4)3396 (66.6)1279 (25.1)1761 (34.6)2056 (40.3)3436 (67.4)1388
(27.2)		272 (5.3)5096
Psoriasis2400 (40)3602 (60)947 (15.8)2062 (34.4)2993 (49.9)5287 (88.1)554
(9.2)		161 (2.7)6002
Vitiligo294 (38.7)465 (61.3)110 (14.5)250 (32.9)399 (52.6)526 (69.3)206
(27.1)		27 (3.6)759
Table 2. Multivariable-adjusted odds ratio (mOR) and 95% CI of each autoimmune disorder by lymphoid disease.
Table 2. Multivariable-adjusted odds ratio (mOR) and 95% CI of each autoimmune disorder by lymphoid disease.
Autoimmune DisorderLymphoma OR (95% CI)p-ValueLeukemia OR (95% CI)p-ValuePlasma Cell Neoplasms OR (95% CI)p-Value
Race (AA)1.14(0.88–1.49)0.4220.82(0.53–1.35) 1.64(1.13–2.47)0.422
Race (White)1.47(1.16–1.19)0.1211.45(0.96–2.31) 1.03(0.72–1.54)0.121
Sex (Male)1.62(1.50–1.74) **<0.0011.91(1.68–2.18) **<0.0011.46(1.30–1.64) **<0.001
Age1.04(1.04–1.04) **<0.0011.05(1.04–1.05) **<0.0011.06(1.05–1.06) **<0.001
Crohn’s Disease1.48 (1.12–1.93) **0.0051.37 (0.83–2.15)0.1561.79 (1.17–2.63) **0.003
Ulcerative Colitis1.64 (1.25–2.11) **<0.0012.13 (1.38–3.16) **<0.0011.73 (1.14–2.52) **0.008
Hyperthyroidism1.19 (1.00–1.41) *0.0491.14 (0.82–1.55)0.3411.51 (1.17–1.93) **0.001
Hypothyroidism2.64 (2.42–2.88) **<0.0012.46 (2.11–2.86) **<0.0012.13 (1.85–2.45) **<0.001
Type 1 Diabetes1.98 (1.65–2.34) **<0.0011.67 (1.19–2.29) **0.0021.62 (1.20–2.13) **0.005
Rheumatoid Arthritis1.52 (1.29–1.78) **<0.0011.67 (1.25–2.19) **0.0021.56 (1.22–1.98) **0.001
Scleroderma1.29 (0.85–1.89)0.2241.48 (0.70–2.73)0.2751.37 (0.72–2.36)0.326
Sjogren’s Syndrome1.93 (1.57–2.35) **<0.0011.68 (1.14–2.40) **0.0061.71 (1.24–2.31) **0.004
Systemic Lupus Erythematosus2.37 (1.89–2.95) **<0.0012.47 (1.63–3.61) **<0.0013.46 (2.55–4.60) **<0.001
Dermatomyositis1.55 (0.68–3.02)0.1580.64 (0.04–2.90)0.4320.77 (0.12–2.48)0.612
Multiple Sclerosis1.43 (1.03–1.94) **0.0361.17 (0.58–2.08)0.3423.11 (2.12–4.39) **<0.001
Alopecia Areata1.91 (1.17–2.94) **0.0062.09 (0.82–4.33)0.1213.02 (1.63–5.12) **<0.001
Atopic Dermatitis1.77 (1.47–2.12) **<0.0011.33 (0.91–1.87)0.1421.30 (0.94–1.76)0.184
Psoriasis1.73 (1.47–2.01) **<0.0011.49 (1.10–1.96) **0.0071.56 (1.19–2.00) **0.002
Vitiligo2.33 (1.60–3.28) **<0.0011.28 (0.51–2.64)0.4321.56 (0.77–2.78)0.212
** p-value < 0.05; * p-value = 0.05; OR = odds ratio; CI = confidence interval.
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Terhaar, H.; Saleem, M.; Liu, E.; Yusuf, N. Associations Between Immune-Related Conditions and Lymphoid Disorders: An Analysis of the Diverse All of Us Research Program. Lymphatics 2025, 3, 3. https://doi.org/10.3390/lymphatics3010003

AMA Style

Terhaar H, Saleem M, Liu E, Yusuf N. Associations Between Immune-Related Conditions and Lymphoid Disorders: An Analysis of the Diverse All of Us Research Program. Lymphatics. 2025; 3(1):3. https://doi.org/10.3390/lymphatics3010003

Chicago/Turabian Style

Terhaar, Hanna, Mohammad Saleem, Evan Liu, and Nabiha Yusuf. 2025. "Associations Between Immune-Related Conditions and Lymphoid Disorders: An Analysis of the Diverse All of Us Research Program" Lymphatics 3, no. 1: 3. https://doi.org/10.3390/lymphatics3010003

APA Style

Terhaar, H., Saleem, M., Liu, E., & Yusuf, N. (2025). Associations Between Immune-Related Conditions and Lymphoid Disorders: An Analysis of the Diverse All of Us Research Program. Lymphatics, 3(1), 3. https://doi.org/10.3390/lymphatics3010003

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