Association of systemic inflammatory and autoimmune manifestations with myelodysplastic syndromes: A systematic review and meta-analysis : Medicine

Journal Logo

Research Article: Systematic Review and Meta-Analysis

Association of systemic inflammatory and autoimmune manifestations with myelodysplastic syndromes: A systematic review and meta-analysis

Liang, Qian MD, PhDa,b; Zhao, Jingyu MPHa; Zhang, Lele MD, PhDa; Gao, Zhen MDa; Pan, Hong MDa; Fang, Liwei MDa; Shi, Jun MD, PhDa,*

Author Information
doi: 10.1097/MD.0000000000031427



  • This is the first meta-analysis to evaluate association of SIAMs with MDS.
  • SIAMs appeared to have an adverse effect on overall survival of MDS patients.
  • Our study may bring a new insight into prognostic assessment and potential treatment strategy in MDS.

1. Introduction

Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematologic stem cell diseases characterized by dysplasia in 1 or more myeloid cell lineages, ineffective hematopoiesis and a propensity for transformation to acute myeloid leukemia (AML). Systemic inflammatory and autoimmune manifestations (SIAMs) are frequently reported in MDS, and the prevalence is up to 30%.[1–6] Accumulating evidence suggests that dysregulation of innate immune and aberrant inflammatory signaling act as pivotal drivers of MDS pathogenesis.[7–9] More than 50% MDS patients were recorded overexpression of gene mutations involved in innate immune pathway.[10] In addition, research has shown MDS clone and innate immune pathway activation are critical to the development of SIAMs.[3,11] These findings demonstrate innate Immune dysregulation may be a potential common trigger force between MDS and SIAMs. Nevertheless, the association of SIAMs with the prognosis of MDS is still not well established.

Several observational studies revealed that MDS patients with SIAMs experienced the worse overall survival and higher mortality than those without SIAMs.[12–14] Conversely, some research indicated that SIAMs conferred a favorable impact on outcomes in MDS patients.[3,4] Moreover, a 4-year prospective cohort study and a large French multicenter retrospective study found SIAMs had no impact on the prognosis of MDS.[15,16] The heterogeneity and complexity of pathology, clinical manifestations and response to therapy make the prognosis of MDS with SIAMs a matter of debate.[17]

Currently, the association of SIAMs with MDS is not completely consistent, and the isolated observational studies were underpowered to answer this question. In addition, no research has been done specifically and quantitatively on their relationship. Hence, there is an urgent need for a pooled analysis to provide critical and clinically useful information concerning MDS prognosis.

2. Methods

2.1. Data source and search strategy

We followed the PRISMA guidelines to perform this meta-analysis. Literature search was conducted in 4 databases without any language restrictions, including PubMed, EMBASE, Medicine and Cochrane library (up to April 30, 2021). Additional search was conducted on Google Scholar for eligible studies. The main search terms were as follows: ((Myelodysplastic Syndrome) OR (Dysmyelopoietic Syndrome) OR (Hematopoietic Myelodysplasia) OR (MDS)) AND ((Autoimmune disease) OR (Autoimmunity) OR (Autoimmune disorder) OR (Autoimmune phenomena) OR (Autoimmune manifestations) OR (Immunologic abnormalities) OR (Autoimmune and inflammatory conditions) OR (Systemic and Immune Manifestations)). The review protocol has been registered in the PROSPERO International Prospective Register of Systematic Reviews (registration number: CRD42021229595).

2.2. Measured outcome and selection criteria

The primary outcome was to identify the impact of SIAMs on overall survival or median survival of MDS patients. The other 2 outcomes included AML transformation and mortality.

Studies were selected based on the following inclusion criteria: Comparative studies concentrated on the association of SIAMs with MDS patients; Studies reported sufficient data for at least 1 of the measured outcomes, including rate of AML transformation, mortality, median survival and overall survival. Literatures with overlapping data were excluded, only the latest or the highest quality 1 could be included in our meta-analysis.

2.3. Data extraction and quality assessment

Two reviewers (Qian Liang and Jingyu Zhao) independently extracted the data of interest from eligible studies. The extracted information for each study included: title and study details (name of the first author, study design, year of publications, country, MDS subtype, number of cases and controls), study population characteristics (gender, median or mean age, median follow-up, median survival, overall survival, rate of AML transformation and mortality) (Table 1). We also extracted more essential details of case and control arms in each study (WHO classification, blood cell counts, risk scoring, karyotype and the percentage of peripheral blood and bone marrow blasts) (Table 2). The quality of each study was evaluated based on the Newcastle-Ottawa Scale, and 7–9 scores was perceived as high quality (Table S1, Supplemental Digital Content,

Table 2 - Difference between MDS patients with or without SIAMs in the meta-analysis.
Characteristics MDS/SIAMs+ MDS/SIAMs- χ 2 /W P
Median age (range), years 70 (21–95) 74 (24–94) 68.0* .41
Gender (%)
 Male 642 (59.28) 2039 (61.68) 1.87 .17
 Female 441(40.72) 1267 (38.32)
WHO classification (%)
 MLD/RS-MLD 388 (29.24) 790 (23.24) 29.59 <.01
 SLD/RS-SLD 199 (17.21) 689 (20.26)
 EB 284 (24.57) 982 (28.88)
 CMML 78 (6.75) 197 (5.79)
 Del (5q) 36(3.11) 100 (2.94)
 MDS-U 33(2.85) 59 (1.74)
 Other 188 (16.26) 583 (17.15)
 Hemoglobin (range), g/L 92.5 (40–150) 92 (40–169) 29.00* .79
 Platelets (range), × 109 cells per L 124.5 (2–1780) 110.5 (1–897) 38.00* .56
 Neutrophils (range), × 109 cells per L 2.35 (0.02–23) 1.80 (0.07–23.67) 29.00* .61
IPSS (%)
 Low 173 (25.63) 576 (26.28) 7.35 .06
 INT-1 311 (46.07) 927 (42.29)
 INT-2 127 (18.82) 402 (18.34)
 High 64 (9.48) 287 (13.09)
IPSS-R (%)
 Very low 100 (18.38) 190 (15.35) 31.32 <.01
 Low 201 (36.95) 429 (34.65)
 INT 108 (19.85) 247 (19.95)
 High 72 (13.24) 193 (15.59)
 Very high 63 (11.58) 179 (14.46)
Karyotype (%)
 Favorable 466 (73.73) 1318 (74.55) 0.67 .41
 INT 106 (16.77) 330 (18.67)
 Poor 36 (5.70) 114 (6.45)
 Very poor 24 (3.80) 6 (0.34)
Blood blasts (range), % 0.55 (0–1.1) 0.60 (0–1.2) 1.50* 1.00
Bone marrow blasts (range), % 3.6 (0–29) 3.0 (0–32) 15.50* .60
*W derived from the Wilcoxon rank sum test.
χ2 derived from the Pearson’s chi-squared test.
χ2 derived from the Cochran–Mantel–Haenszel test.
CI = confidence interval, CMML = chronic myelomonocytic leukemia, Del(5q) = MDS associated with isolated del (5q), EB = excess blasts, MDS-U = MDS unclassifiable, MLD = multilineage dysplasia, RS-MLD = ring sideroblasts with multilineage dysplasia, RS-SLD = Ring sideroblasts with single lineage dysplasia, SLD = Single lineage dysplasia.

2.4. Statistical analysis

Overall survival and median survival were reported as hazard ratio (HR) and median survival ratio (MSR), respectively. The HR was extracted from the univariate cox-regression analysis provided by eligible studies. A MSR was the ratio of median survival time compared the SIAMs with non-SIAMs group. AML transformation and mortality were reported as odds ratio (OR). On condition that the result greater than 1.0, a HR or OR demonstrated SIAMs carried a poor prognosis in MDS, whereas a MSR favored a better survival outcome. The difference of the clinical features between MDS with and without SIAMs were detected by Pearson’s chi-squared test, Cochran–Mantel–Haenszel test and Wilcoxon rank sum test.

Heterogeneity across studies was assessed by the Cochran’s Q-test with a significance level at P = .1, and the I2 was performed to estimate the extent of heterogeneity. The impact of SIAMs on MDS throughout meta-analysis was combined using a fixed-effects model (P ≥ .1 and I2 ≤ 0.25) and a random- effects model (P < .1 or I2 > 0.25). We also conducted the sensitivity analysis by the sequential omission of individual studies to investigate the validity of the overall results. Potential publication bias was examined by visual inspection of asymmetry in the funnel plot and estimated quantitatively using the Begg’s and Egger’s test. A P-value < .05 indicated publication bias was significant across studies. All analysis was conducted by using R software, Version 4.0.2.

2.5. Ethical consideration

Institutional review board approval was not necessary because all the data were retrieved from public databases.

3. Results

3.1. Description of studies

A total of 3515 publications were identified through database searching, of which 18 studies were eligible for inclusion (Fig. 1). All studies were published between 2002 and 2021. Most of the studies were retrospective case-control studies except for 2 prospective studies. The 18 studies included a total of 4603 MDS patients, of which 1175 (25.5%) patients had SIAMs (Table 1).[1,3,4,12–16,18–28] The most common SIAMs were hypothyroidism (17.7%), vasculitis (16.2%) and the immune cytopenia or coagulation disorder (10.7%) (Table S2, Supplemental Digital Content,

Table 1 - Characteristics of the included studies in meta-analysis.
Author/design Year Country MDS subtype Patient no. MDS-SIAMs+
Male/Female Median/mean age, years (range) Median follow-up, months (range) Median survival, months (95% CI) Overall survival, years (95% CI) AML transformation (%) Death (%)
Yojiro A, et al[13]
Single-center retrospective study
61 12
NR NR NR 0 (0)
2 (16.7)
5 (47.1)
8 (16.3)
Abdulla W, et al[14]
Single-center retrospective study
134 62
NR 23*
NR 17 (27.4)
7 (9.7)
44 (71.0)
34 (47.2)
Anne LR, et al[19]
Multicenter retrospective study
2020 France MLD/ RS/ RS-SLD/ RS-MLD/5q-/EB1/EB2/ U
253 70
71.5 (21–90)
70 (38–79)
33.2 (1–162)
28.5 (0–174)
NR NR 9 (12.9)
59 (32.2)
20 (28.6)
47 (25.7)
Julie S, et al[3]
Single-center retrospective study
2019 France SLD/MLD/ RS-SLD/ RS-MLD/5q-/EB1/EB2/ U/CMML1/CMML2
216 89
10.3 (6.2–12.9)
4.8 (4.2–8.7),
13 (14.6)
35 (27.6)
36 (40.9)
66 (52.0)
B. Kipfer, et al[17]
Single-center retrospective study
2018 Switzerland RARS/RCMD/RAEB1/RAEB2/CMML/NC
93 30
71 (47–87)
75 (42–91)
21 (2–77)
18 (1–62)
NR 9 (30.0)
23 (76.1)
45 (71.4)
Julia M, et al[12]
Single-center retrospective study
2018 Spain SLD/MLD/ RS/5q-/EB1/EB2/ CMML0/CMML1/CMML2
142 68
77 (44–95)
76 (24–90)
NR 38.5
NR NR 21 (30.9)
9 (12.2)
Praveen, et al[20]
Single-center retrospective study
2017 India NR 117 52
NR NR NR NR 8 (16.3)
15 (28.6)
16 (25.0)
JM Gómez, et al[21]
Single-center retrospective study
241 92
77 (44–95)
76 (24–94)
22 (0–69) 23 (17–29)
45 (35-NA)
Rami S, et al[4]
Multicenter retrospective study
2016 United Kingdom RA/RARS/RCMD/5q-/RAEB1/RAEB2/U/CMML/ Other
1408 391
NR 74 (69–78) 60 (50–70)
45 (40–49)
NR 89 (22.8)
301 (29.6)
Arsene M, et al[16]
Multicenter retrospective study
788 123
25 (12–58)
25 (12–46)
72 (59–105)
75 (48–300)
NR 26 (21.1)
83 (12.5)
43 (35.0)
154 (23.2)
Y Takeoka, et al[22]
Single-center retrospective study
102 13
NR 20.4*
NR 6 (46.2)
18 (20.2)
Li B, et al[23]
Single-center retrospective study
2012 China RA/RARS/RCMD/5q-/RAEB1/RAEB2/U
173 16
46 (30–75)
53 (7–81)
14 (1–65) NR NR NR 4 (25.0)
66 (42.0)
Ki-Jo KIM, et al[24]
Single-center retrospective study
129 27
NR NR NR 60.5 (45.6–75.3)
53.6 (46.0–61.2)
NR NR 11 (40.7)
34 (33.3)
Hollanda, et al[25]
Multicenter retrospective study
2011 France RA&RARS/RCMD&RS/5q-/RAEB1/RAEB2
235 46
78 (68–93)
81 (65–89)
NR NR NR 8 (17.4)
32 (16.9)
31 (67.4)
130 (68.8)
Dragmir, et al[26]
Single-center retrospective study
2006 Serbia NR 284 32
NR 37
NR NR 16 (50.0)
170 (67.5)
F. Bouali, et al[27]
Single-center prospective study
40 20
NR NR NR 1 (5.0)
6 (30.0)
9 (45.0)
Giannouli, et al[15]
Single-center prospective study
2004 Greece NR 70 13
67 (51–80)
74 (43–89)
NR 39
NR 2 (15.4)
7 (12.3)
Zhao S, et al[28]
Single-center retrospective study
117 19
NR NR 18.5 (6.3–77.4)
29.7 (4.7–81.4)
NR 5 (26.3)
20 (21.5)
* Median survival was estimated from the Kaplan–Meier curve.
MDS subtype was determined using the French-American-British (FAB) classification system.
5q- = MDS associated with isolated del (5q), ACML = atypical chronic myeloid leukemia, CML = chronic myeloid leukemia, CMML = chronic myelomonocytic leukemia, MLD = multilineage dysplasia, NC = not classified, NR = not reported, RA = refractory anemia, RAEB1 = refractory anemia with excess blasts-1, RAEB2 = refractory anemia with excess blasts-2, RAEB-T = refractory anemia with excess blasts in transformation, RARS = refractory anemia with excess blasts, RCMD = refractory anemia with multilineage dysplasia, RS-MLD = ring sideroblasts with multilineage dysplasia, RS-SLD = Ring sideroblasts with single lineage dysplasia, SLD = Single lineage dysplasia., U = MDS unclassifiable.

Figure 1.:
Flow-chart of literature search.

3.2. Patient characteristics

According to the available data of included studies, clinical characteristics of MDS patients with or without SIAMs were detailed analyzed in Table 2. Median age of MDS with SIAMs were 70, with the majority of subjects being male (59.28%). Similarly, 61.68% of male patients in the non-SIAMs group, and the median age ranged between 24 and 94.

Concerning clinical subtype, most MDS patients with SIAMs had multilineage dysplasia or ring sideroblasts with multilineage dysplasia (n = 388, 29.24%), followed by excess blasts (EB) (n = 284, 24.57%), and single lineage dysplasia (SLD) or ring sideroblasts with SLD (n = 199, 17.21%). The most common subtypes in the non-SIAMs group were EB (n = 982, 28.88%), followed by multilineage dysplasia or multilineage dysplasia (n = 790, 23.24%) and SLD or ring sideroblasts with SLD (n = 689, 20.26%).

According to the IPSS, most patients with or without SIAMs had an intermediate prognosis, with 64.89% and 60.63% respectively. Depending on the IPSS-R, most patients had lower risk in both arms.

Among MDS patients with or without SIAMs, 466 (73.73%) and 1318 (74.55%) presented favorable karyotype, 106 (16.77%) and 330 (18.67%) presented intermediate karyotype. More details regarding other clinical characteristics were reported in Table 2, (Table S3–S4, Supplemental Digital Content ,

Both MDS subtypes and IPSS-R were statistically significant in patients with SIAMs compared with the non-SIAMs group (P < .01). No differences in median age, gender, median of blood cell counts, IPSS, karyotype and the percentage of peripheral blood and bone marrow blasts.

3.3. Siams and MDS

Three retrospective studies reported univariate cox regression hazard ratios. MDS patients with SIAMs had a statistically shorter overall survival compared with patient without SIAMs (HR, 2.43; 95% CI, 1.34–4.41; P < .01). Meta-analysis of 11 studies showed SIAMs did not have a statistically significant effect on median survival (MSR, 1.16; 95% confidence interval [CI], 0.91–1.47; P = .24) (Fig. 2).

Figure 2.:
Forest plots of association between SIAMs and overall survival (A) and median survival (B) of MDS. MDS = Myelodysplastic syndromes, SIAM = systemic inflammatory and autoimmune manifestations.

Of the 18 eligible publication, 12 and 13 studies reported data on AML transformation and death, respectively. Patients with SIAMs had similar rate of AML transformation and mortality compared with the non-SIAMs group (OR, 0.96; 95% CI, 0.63–1.45 and 1.20; 95% CI, 0.84–1.70, respectively) (Fig. 3).

Figure 3.:
Forest plots of association between SIAMs and rate of AML transformation (A) and mortality (B) of MDS. AML = acute myeloid leukemia, MDS = Myelodysplastic syndromes, SIAM = systemic inflammatory and autoimmune manifestations.

3.4. Heterogeneity and sensitivity analysis

Significant heterogeneity was observed among the analysis of 3 survival outcomes, varied from 48% to 98%. Sensitivity analysis was conducted with leave-one-out meta-analysis by sequential omission of individual studies (Figure S1–S2, Supplemental Digital Content, The changes in P-values and the heterogeneity of the combined results were shown in Table 3.

Table 3 - Overall meta-analysis of the impact of SIAMs on survival outcomes.
Survival outcomes Studies Number Type of Indicators 95% CI P Heterogeneity Publication bias
I2 (%) P H M P Begg P Egger
Overall survival 3 HR 2.43 (1.34,4,41) <.01 48.0 .15 R .60 .40
Median survival 11 MSR 1.16 (0.91, 1.47) .24 97.8 <.01 R .94 .68
Rate of AML transformation 12 OR 0.96 (0.63, 1.45) .83 70.2 <.01 R .27 .47
Mortality 13 OR 1.20 (0.84, 1.70) .31 62.5 <.01 R .81 .77
AML = acute myeloid leukemia, CI = confidence interval, HR = hazard ratio, OR = odds ratio; model for meta-analysis, PBegg = P-value for Begg’s test, PEgger = P-value for Egger’s test, PH = P-value for heterogeneity test, POR = P-value for OR test, MSR = median survival ratio, R, Random-effects model.

3.5. Publication bias

Visual inspection of the funnel plot did not identify substantial asymmetry (Fig. 4). Begg’s rank correlation, Egger’s weighted regression also indicated no evidence of publication bias.

Figure 4.:
Funnel plot analysis to examine publication bias.

4. Discussion

In this study, we demonstrated that MDS patients with SIAMs had inferior overall survival compared with the non-SIAMs group. Nevertheless, no strong evidence of the association of SIAMs with median survival, the rate of AML transformation or mortality.

Firstly, SIAMs negatively affected the overall survival of MDS. All 3 studies included in this meta reported the consistent result of the negative association of SIAMs and the survival of MDS. The heterogeneity was substantial due to the limited studies but was not explained by any individual study. It is worth mentioning that the multivariate survival analysis of these 3 studies suggested inconsistent results of the association of SIAMs with overall survival of MDS. Yojiro[13] and Julia[12] showed that patients with SIAMs had shorter overall survival. However, Abdulla[14] found SIAMs was not related to the survival of MDS after adjusting for age, gender, risk stratification and received treatment. Moreover, Lee’s study[29] using the univariate analysis reported no association between SIAMs and the overall survival of MDS, was not included in the meta because the detailed data was unavailable.

Secondly, SIAMs did not have a statistically significant effect on median survival. MSR is an important statistic for use in survival data and helps us to better understand the clinical interpretation of the HR.[30,31] In this meta-analysis, the pooling HR and MSR presented a divergence in results. That may include the following reasons. First, the result of HR was susceptible for selection bias due to limited data. Second, MSR is not an indicator for time-to-event outcomes and may not accurately represent the entire observation period.[32]

Thirdly, our results were most compatible with no effect of SIAMs on the rate of AML transformation and mortality. In selected studies of Anne,[18] Rami[4] and Julie,[3] MDS patients with SIAMs had a lower risk to develop AML. However, contrary results were represented in the other 2 studies.[22,23] The finding of the remaining studies was consistent with ours. Meanwhile, there were 3 studies reported the presence of SIAMs increased the mortality of MDS in the included 13 studies. The finding of other 9 studies were compatible with our result. The reason for these inconsistent results may potential due to the clinical heterogeneity of SIAMs and MDS.[5] SIAMs are not exactly certain type of autoimmune disorders. Hence, depends on types of SIAMs in each study, the impact on the survival outcomes could be different.

To the best of our knowledge, this is the first meta-analysis to evaluate the association of SIAMs with survival outcomes of MDS patients. The highlight of this study was that we comprehensively measured the relationship between SIAMs and MDS by using 4 survival outcomes with 3 indicators, including HR, MSR and OR. However, the findings of this systematic review and meta-analysis were limited by several factors. Firstly, the study design included only case-control studies that were susceptible to biases. Hence, the casual relationship between SIAMs and MDS was unclear. Secondly, both SIAMs and MDS consist of a group of diseases, and lead to clinical heterogeneity. That probably effects the accuracy of our result. Thirdly, we were unable to perform detailed subgroup analysis due to paucity of data. There was still a great need for large, prospective and high methodological quality publications to verify our findings.

5. Conclusion

In this systematic review and meta-analysis, SIAMs appeared to have an adverse effect on overall survival of MDS patients. This finding suggested that SIAMs may be a potential independent prognostic factor for MDS.

Author contributions

Conceptualization: Qian Liang, Jingyu Zhao, Lele Zhang, Zhen Gao, Hong Pan, Liwei Fang, Jun Shi.

Data curation: Qian Liang, Jingyu Zhao.

Formal analysis: Qian Liang, Jingyu Zhao.

Funding acquisition: Jun Shi.

Investigation: Jun Shi.

Methodology: Qian Liang, Jingyu Zhao.

Writing – original draft: Jingyu Zhao.

Writing – review & editing: Qian Liang, Jingyu Zhao, Lele Zhang, Zhen Gao, Hong Pan, Liwei Fang, Jun Shi.


    [1]. Ganan-Gomez I, Wei Y, Starczynowski DT, et al. Deregulation of innate immune and inflammatory signaling in myelodysplastic syndromes. Leukemia. 2015;29:1458–69.
    [2]. Al Ustwani O, Ford LA, Sait SJ, et al. Myelodysplastic syndromes and autoimmune diseases--case series and review of literature. Leuk Res. 2013;37:894–9.
    [3]. Seguier J, Gelsi-Boyer V, Ebbo M, et al. Autoimmune diseases in myelodysplastic syndrome favors patients survival: a case control study and literature review. Autoimmun Rev. 2019;18:36–42.
    [4]. Komrokji RS, Kulasekararaj A, Al Ali NH, et al. Autoimmune diseases and myelodysplastic syndromes. Am J Hematol. 2016;91:E280–3.
    [5]. Grignano E, Jachiet V, Fenaux P, et al. Autoimmune manifestations associated with myelodysplastic syndromes. Ann Hematol. 2018;97:2015–23.
    [6]. Williamson BT, Foltz L, Leitch HA. Autoimmune syndromes presenting as a paraneoplastic manifestation of myelodysplastic syndromes: clinical features, course, treatment and outcome. Hematol Rep. 2016;8:6480.
    [7]. Barreyro L, Chlon TM, Starczynowski DT. Chronic immune response dysregulation in MDS pathogenesis. Blood. 2018;132:1553–60.
    [8]. Sallman DA, List A. The central role of inflammatory signaling in the pathogenesis of myelodysplastic syndromes. Blood. 2019;133:1039–48.
    [9]. Kristinsson SY, Bjorkholm M, Hultcrantz M, et al. Chronic immune stimulation might act as a trigger for the development of acute myeloid leukemia or myelodysplastic syndromes. J Clin Oncol. 2011;29:2897–903.
    [10]. Muto T, Walker CS, Choi K, et al. Adaptive response to inflammation contributes to sustained myelopoiesis and confers a competitive advantage in myelodysplastic syndrome HSCs. Nat Immunol. 2020;21:535–45.
    [11]. Fozza C, Crobu V, Isoni MA, et al. The immune landscape of myelodysplastic syndromes. Crit Rev Oncol Hematol. 2016;107:90–9.
    [12]. Montoro J, Gallur L, Merchan B, et al. Autoimmune disorders are common in myelodysplastic syndrome patients and confer an adverse impact on outcomes. Ann Hematol. 2018;97:1349–56.
    [13]. Arinobu Y, Kashiwado Y, Miyawaki K, et al. Autoimmune manifestations associated with myelodysplastic syndrome predict a poor prognosis. Medicine (Baltim). 2021;100:e25406–e25406.
    [14]. Watad A, Kacar M, Bragazzi NL, et al. Somatic mutations and the risk of undifferentiated autoinflammatory disease in MDS: an under-recognized but prognostically important complication. Original Research. Front Immunol. 2021;12.
    [15]. Giannouli S, Voulgarelis M, Zintzaras E, et al. Autoimmune phenomena in myelodysplastic syndromes: a 4-yr prospective study. Rheumatology (Oxford). 2004;43:626–32.
    [16]. de Hollanda A, Beucher A, Henrion D, et al. Systemic and immune manifestations in myelodysplasia: a multicenter retrospective study. Arthritis Care Res (Hoboken). 2011;63:1188–94.
    [17]. Newman K, Akhtari M, Heidarian S, Fuchs O (Eds). Myelodysplastic syndrome and autoimmune disorders: causal relationship or coincidence? Internet. Intechopen. 2018. Available at:
    [18]. Roupie AL, Guedon A, Terrier B, et al. Vasculitis associated with myelodysplastic syndrome and chronic myelomonocytic leukemia: French multicenter case-control study. Sem Arthritis Rheum. 2020;50:879–84.
    [19]. Kipfer B, Daikeler T, Kuchen S, et al. Increased cardiovascular comorbidities in patients with myelodysplastic syndromes and chronic myelomonocytic leukemia presenting with systemic inflammatory and autoimmune manifestations. Semin Hematol. 2018;55:242–7.
    [20]. Malayath P, PK S. Association of myelodysplastic syndrome and autoimmune disorders in North Kerala. J Evid Based Med Healthcare. 2017;4:5821–4.
    [21]. Gomez JM. Immune deregulation in myelodysplastic syndromes. Leuk Res. 2017;55:S113–4.
    [22]. Mekinian A, Grignano E, Braun T, et al. Systemic inflammatory and autoimmune manifestations associated with myelodysplastic syndromes and chronic myelomonocytic leukaemia: a French multicentre retrospective study. Rheumatology (Oxford). 2016;55:291–300.
    [23]. Takeoka Y, Miura A, Nakamura K, et al. A detailed analysis of myelodysplastic syndrome complicated by autoimmune or inflammatory disorders: a possible efficacy of low-dose lenalidomide. Blood. 2014;124:3256–3256.
    [24]. Li B, Xu ZF, Cui R, et al. Clinical features and survival analysis in primary myelodysplastic syndromes patients with immunological abnormalities. Zhonghua Xue Ye Xue Za Zhi. 2012;33:527–31.
    [25]. Kim KJ, Kwok SK, Park YJ, et al. Low C3 levels is associated with neutropenia in a proportion of patients with myelodysplastic syndrome: retrospective analysis. Int J Rheum Dis. 2012;15:86–94.
    [26]. Marisavljević D, Kraguljac N, Rolović Z. Immunologic abnormalities in myelodysplastic syndromes: clinical features and characteristics of the lymphoid population. Med Oncol. 2006;23:385–91.
    [27]. Bouali F, Berrah A, Si Ahmed-Bouali D, et al. Immunological abnormalities in myelodysplastic syndromes. Prospective study (series of 40 patients). Rev Med Interne. 2005;26:777–83.
    [28]. Zhao S, Mao H, Wang H, et al. The relationship between myelodysplastic syndromes and autoimmune disorders. Zhonghua Xue Ye Xue Za Zhi. 2002;23:311–3.
    [29]. Lee SJ, Park JK, Lee EY, et al. Certain autoimmune manifestations are associated with distinctive karyotypes and outcomes in patients with myelodysplastic syndrome: a retrospective cohort study. Medicine (Baltim). 2016;95:e3091.
    [30]. Zang J, Xiang C, He J. Synthesis of median survival time in meta-analysis. Epidemiology. 2013;24:337–8.
    [31]. Pak K, Uno H, Kim DH, et al. Interpretability of cancer clinical trial results using restricted mean survival time as an alternative to the hazard ratio. JAMA Oncol. 2017;3:1692–6.
    [32]. Michiels S, Piedbois P, Burdett S, et al. Meta-analysis when only the median survival times are known: a comparison with individual patient data results. Int J Technol Assess Health Care. 2005;21:119–25.

    autoimmune manifestations; meta-analysis; myelodysplastic syndromes; prognosis; systemic inflammatory

    Supplemental Digital Content

    Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.