Evidence map›Paper›PMID 38517926›Full record

ArticlePloS one2024

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework.

Alan J M Brnabic, Sarah E Curtis, Joseph A Johnston, Albert Lo, Anthony J Zagar, Ilya Lipkovich, Zbigniew Kadziola, Megan H Murray, Timothy Ryan

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 29% of its field
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. 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

9 authors at 1 institution in 1 country.

Alan J M BrnabicEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.ORCID 0000-0003-4303-2515
Sarah E CurtisEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.
Joseph A JohnstonEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.ORCID 0000-0003-3913-9256
Albert LoEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.
Anthony J ZagarEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.
Ilya LipkovichEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.
Zbigniew KadziolaEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.
Megan H MurrayEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.
Timothy RyanEli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States of America.
Eli Lilly (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis.

Indexed as

Cardiovascular DiseasesCrotonatesDiabetes Mellitus, Type 2HydroxybutyratesMultiple SclerosisMultiple Sclerosis, Relapsing-RemittingNitrilesRenal Insufficiency, ChronicToluidinesAdultDimethyl FumarateFingolimod HydrochlorideHumansImmunosuppressive AgentsIncidenceNF-E2-Related Factor 2CrotonatesDimethyl FumarateFingolimod HydrochlorideHydroxybutyratesImmunosuppressive AgentsNF-E2-Related Factor 2NitrilesteriflunomideToluidines

Identifiers

PMID38517926
PMCPMC10959335
OpenAlexW4393079688

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.