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Meta-Analysis
. 2021 Jun 28;21(1):246.
doi: 10.1186/s12883-021-02267-9.

The global prevalence of familial multiple sclerosis: an updated systematic review and meta-analysis

Naeim Ehtesham  1   2 , Maryam Zare Rafie  3 , Meysam Mosallaei  4
Affiliations
Meta-Analysis

The global prevalence of familial multiple sclerosis: an updated systematic review and meta-analysis

Naeim Ehtesham et al. BMC Neurol. .

Abstract

Background: Considering that many recent studies have reported the prevalence of familial multiple sclerosis (FMS), we performed an updated meta-analysis of the worldwide prevalence of FMS by the addition of recent publications.

Methods: A search in PubMed, Scopus, the ISI Web of Science, and Google Scholar was undertaken up to 20 December 2020. The inclusion criteria were based on the CoCoPop approach (condition, context, and population). Meta-analysis of the qualified studies was conducted by comprehensive meta-analysis ver. 2 software.

Results: The pooled prevalence of MS in relatives of 16,179 FMS cases was estimated to be 11.8% (95% CI: 10.7-13) based on a random-effects model. The pooled mean age of disease onset in adult probands was calculated to be 28.7 years (95% CI: 27.2 ± 30.2). Regarding 13 studies that reported the data of FMS in pediatrics (n = 877) and adults (n = 6636), the FMS prevalence in pediatrics and adults was 15.5% (95% CI: 13.8-17.4) and 10.8% (95% CI: 8.1-14.2), respectively. The prevalence of FMS in affected males (n = 5243) and females (n = 11,503) was calculated to be 13.7% (95% CI: 10.1-18.2) and 15.4% (95% CI: 10.3-22.4), respectively. The odds ratio of male/female in FMS cases was not statistically significant (OR = 0.9; 95% CI: 0.6-1.2, P = 0.55). Subgroup analysis demonstrated a significant difference in the prevalence of FMS between the geographical areas (P = 0.007). The meta-regression model indicated that the prevalence of FMS is lower with higher latitude and higher MS prevalence (P < 0.001). In contrast, meta-regression based on prevalence day was not statistically significant (P = 0.29).

Conclusions: The prevalence of FMS is higher in the pediatric group than that of adults, distinct between geographical areas, and diminishes with the increment of MS prevalence and latitude. Also, the symptoms initiate relatively at younger ages in the FMS cases. Interestingly, our analysis unveiled that FMS is not more prevalent in men than women and the risk of MS development in relatives is not higher when the affected proband is male.

Keywords: Familial multiple sclerosis; Meta-analysis; Pediatric-onset multiple sclerosis; Systematic review.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of study selection through the systematic search
Fig. 2
Fig. 2
The prevalence of FMS (Heterogeneity: I2 = 97.112%, P < 0.001, Random effects)
Fig. 3
Fig. 3
Forest plot of sensitivity analysis
Fig. 4
Fig. 4
Mean age of disease onset in FMS cases
Fig. 5
Fig. 5
The prevalence of FMS in POMS and AOMS cases
Fig. 6
Fig. 6
The prevalence of FMS in men (A) and women (B)
Fig. 7
Fig. 7
The OR of male/female among FMS cases
Fig. 8
Fig. 8
The prevalence of FMS in different geographical areas
Fig. 9
Fig. 9
Meta-regression analysis of FMS prevalence in terms of latitude (meta-regression coefficient: -0.025, 95% CI: -0.027 to -0.023, P< 0.001) (A), MS prevalence (meta-regression coefficient: -0.0018, 95% CI: -0.0021 to -0.0016, P< 0.001) (B), and prevalence day (meta-regression coefficient: -0.002, 95% CI: -0.005 to 0.001, P=0.29) (C)
Fig. 10
Fig. 10
Funnel plot of publication bias
See this image and copyright information in PMC

References

    1. Ehtesham N, Mosallaei M, Karimzadeh MR, Moradikazerouni H, Sharifi M. microRNAs: key modulators of disease-modifying therapies in multiple sclerosis. Int Rev Immunol. 2020;39(6):264–279. doi: 10.1080/08830185.2020.1779712. - DOI - PubMed
    1. Moutsianas L, Jostins L, Beecham AH, Dilthey AT, Xifara DK, Ban M, Shah TS, Patsopoulos NA, Alfredsson L, Anderson CA, Attfield KE, Baranzini SE, Barrett J, Binder TMC, Booth D, Buck D, Celius EG, Cotsapas C, D'Alfonso S, Dendrou CA, Donnelly P, Dubois B, Fontaine B, Fugger L, Goris A, Gourraud PA, Graetz C, Hemmer B, Hillert J, International IBD Genetics Consortium (IIBDGC) Kockum I, Leslie S, Lill CM, Martinelli-Boneschi F, Oksenberg JR, Olsson T, Oturai A, Saarela J, Søndergaard HB, Spurkland A, Taylor B, Winkelmann J, Zipp F, Haines JL, Pericak-Vance MA, Spencer CCA, Stewart G, Hafler DA, Ivinson AJ, Harbo HF, Hauser SL, de Jager PL, Compston A, McCauley J, Sawcer S, McVean G. Class II HLA interactions modulate genetic risk for multiple sclerosis. Nat Genet. 2015;47(10):1107–1113. doi: 10.1038/ng.3395. - DOI - PMC - PubMed
    1. Patsopoulos N, Baranzini S, Santaniello A, Shoostari P, Cotsapas C, Wong G, et al. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science. 2019;365:1–10. 10.1126/science.aav7188. - PMC - PubMed
    1. Nourbakhsh B, Mowry EM. Multiple sclerosis risk factors and pathogenesis. Continuum (Minneap Minn) 2019;25(3):596–610. doi: 10.1212/con.0000000000000725. - DOI - PubMed
    1. Koch-Henriksen N, Sørensen PS. The changing demographic pattern of multiple sclerosis epidemiology. Lancet Neurol. 2010;9(5):520–532. doi: 10.1016/s1474-4422(10)70064-8. - DOI - PubMed

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