Evidence map›Paper›PMID 38817245›Full record

ArticleNeurology. Genetics2024

Adaptive and Innate Immunity Are Key Drivers of Age at Onset of Multiple Sclerosis.

Elina Misicka, Yunfeng Huang, Stephanie Loomis, Nilanjana Sadhu, Elizabeth Fisher, Arie Gafson, Heiko Runz, Ellen Tsai, Xiaoming Jia, Ann Herman and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology. Genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Elina MisickaFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.ORCID https://orcid.org/0000-0003-2794-1225
Yunfeng HuangFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.ORCID https://orcid.org/0000-0003-2120-3328
Stephanie LoomisFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.ORCID https://orcid.org/0000-0002-7478-1538
Nilanjana SadhuFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.
Elizabeth FisherFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.ORCID https://orcid.org/0000-0002-3384-3704
Arie GafsonFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.
Heiko RunzFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.
Ellen TsaiFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.
Xiaoming JiaFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.ORCID https://orcid.org/0000-0002-5104-5431
Ann HermanFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.
Paola G BronsonFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.
Tushar BhangaleFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.ORCID https://orcid.org/0000-0002-8233-9977
Farren B BriggsFrom the Department of Population and Quantitative Health Sciences (E.M.), Case Western Reserve University, Cleveland, OH; Biogen (Y.H., S.L., N.S., E.F., A.G., H.R., E.T., P.G.B.), Cambridge, MA; Human Genetics and Bioinformatics (X.J., A.H., T.B.), Genentech, San Francisco, CA; and Department of Public Health Sciences (F.B.B.), University of Miami, FL.ORCID https://orcid.org/0000-0003-0903-1359

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Multiple sclerosis (MS) age at onset (AAO) is a clinical predictor of long-term disease outcomes, independent of disease duration. Little is known about the genetic and biological mechanisms underlying age of first symptoms. We conducted a genome-wide association study (GWAS) to investigate associations between individual genetic variation and the MS AAO phenotype. Methods: The study population was comprised participants with MS in 6 clinical trials: ADVANCE (N = 655; relapsing-remitting [RR] MS), ASCEND (N = 555; secondary-progressive [SP] MS), DECIDE (N = 1,017; RRMS), OPERA1 (N = 581; RRMS), OPERA2 (N = 577; RRMS), and ORATORIO (N = 529; primary-progressive [PP] MS). Altogether, 3,905 persons with MS of European ancestry were analyzed. GWAS were conducted for MS AAO in each trial using linear additive models controlling for sex and 10 principal components. Resultant summary statistics across the 6 trials were then meta-analyzed, for a total of 8.3 × 10 Results: Four lead SNPs within 2 loci were identified ( Discussion: Two genetic loci associated with MS AAO were identified, and functional annotation demonstrated an enrichment of genes involved in adaptive and complement immunity. There was also evidence supporting a link with age at puberty and telomere length. The findings suggest that AAO in MS is multifactorial, and the factors driving onset of symptoms overlap with those influencing MS risk.

Identifiers

PMID38817245
PMCPMC11139017

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