Evidence map›Paper›PMID 36963840›Full record

ArticleNeurology2023

Association of Long-term Antiseizure Medication Use and Incident Type 2 Diabetes Mellitus.

Wei-En Johnny Tseng, Chun-Wei Chang, Jawl-Shan Hwang, Po-Chuan Ko, Chun-Jing Liu, Siew-Na Lim

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
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

6 authors at 2 institutions in 1 country.

Wei-En Johnny TsengFrom the Section of Epilepsy (W.-E.J.T., C.-W.C., C.-J.L., S.-N.L.), Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, and Chang Gung University College of Medicine; PhD Program in Biomedical Engineering (W.-E.J.T.), Chang Gung University; Division of Endocrinology and Metabolism (J.-S.H.), Department of Internal Medicine, Linkou Chang Gung Memorial Hospital; and Center for Big Data Analytics and Statistics (P.-C.K.), Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Chun-Wei ChangFrom the Section of Epilepsy (W.-E.J.T., C.-W.C., C.-J.L., S.-N.L.), Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, and Chang Gung University College of Medicine; PhD Program in Biomedical Engineering (W.-E.J.T.), Chang Gung University; Division of Endocrinology and Metabolism (J.-S.H.), Department of Internal Medicine, Linkou Chang Gung Memorial Hospital; and Center for Big Data Analytics and Statistics (P.-C.K.), Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Jawl-Shan HwangFrom the Section of Epilepsy (W.-E.J.T., C.-W.C., C.-J.L., S.-N.L.), Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, and Chang Gung University College of Medicine; PhD Program in Biomedical Engineering (W.-E.J.T.), Chang Gung University; Division of Endocrinology and Metabolism (J.-S.H.), Department of Internal Medicine, Linkou Chang Gung Memorial Hospital; and Center for Big Data Analytics and Statistics (P.-C.K.), Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Po-Chuan KoFrom the Section of Epilepsy (W.-E.J.T., C.-W.C., C.-J.L., S.-N.L.), Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, and Chang Gung University College of Medicine; PhD Program in Biomedical Engineering (W.-E.J.T.), Chang Gung University; Division of Endocrinology and Metabolism (J.-S.H.), Department of Internal Medicine, Linkou Chang Gung Memorial Hospital; and Center for Big Data Analytics and Statistics (P.-C.K.), Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Chun-Jing LiuFrom the Section of Epilepsy (W.-E.J.T., C.-W.C., C.-J.L., S.-N.L.), Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, and Chang Gung University College of Medicine; PhD Program in Biomedical Engineering (W.-E.J.T.), Chang Gung University; Division of Endocrinology and Metabolism (J.-S.H.), Department of Internal Medicine, Linkou Chang Gung Memorial Hospital; and Center for Big Data Analytics and Statistics (P.-C.K.), Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Siew-Na LimFrom the Section of Epilepsy (W.-E.J.T., C.-W.C., C.-J.L., S.-N.L.), Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center, and Chang Gung University College of Medicine; PhD Program in Biomedical Engineering (W.-E.J.T.), Chang Gung University; Division of Endocrinology and Metabolism (J.-S.H.), Department of Internal Medicine, Linkou Chang Gung Memorial Hospital; and Center for Big Data Analytics and Statistics (P.-C.K.), Chang Gung Memorial Hospital, Taoyuan, Taiwan. siewna@cgmh.org.tw.
Chang Gung University · TWLinkou Chang Gung Memorial Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesDiabetes mellitus (DM) contributes significantly to metabolic syndrome and cardiovascular events, and it may be a comorbidity of epilepsy. The objective of this study was to investigate whether long-term antiseizure medication (ASM) use is associated with the risk of developing type 2 diabetes.

methodsWe analyzed data from the Chang Gung Research Database. Patients aged ≥45 years who received ASM treatment from January 2001 to May 2019 were identified. Patients with DM-associated diseases and short-term ASM use were excluded. The patients were classified into nonenzyme interaction, enzyme-inducing, enzyme-inhibiting, and mixed ASM groups. The rate of incident diabetes associated with individual ASM was further analyzed. Propensity score weighting was performed to balance between-group differences. Analyses were conducted with Cox proportional regression models and stabilized inverse probability of treatment weighting (IPTW). Hazard ratios (HRs) were calculated at 3, 4, 6, and 9 years after the index date and the end of follow-up.

resultsA total of 5,103 patients were analyzed, of whom 474 took nonenzyme interaction ASMs, 1,156 took enzyme-inducing ASMs, 336 took enzyme-inhibiting ASMs, and 3,137 took mixed ASMs. During follow-up (39,248 person-years), 663 patients developed new-onset DM, and the prevalence was 13.0%. The incidence of DM plateaued at 6-9 years after ASM initiation. Enzyme-inhibiting ASMs were significantly associated with a higher HR starting at the third year and then throughout the study period. The HRs were 1.93 (95% CI 1.33-2.80), 1.85 (95% CI 1.24-2.75), and 2.08 (95% CI 1.43-3.03) in unadjusted, adjusted, and stabilized IPTW models, respectively, at the end of follow-up. The dosing of ASM did not increase the risk of DM, and none of the individual ASM analyses reached statistical significance. DISCUSSION: The long-term use of enzyme-inhibiting ASMs was associated with an increased risk of incident DM, and the risk increased with the duration of treatment. These findings may guide the choice of drugs in those requiring long-term ASM therapy, particularly in high-risk individuals. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that enzyme-inhibiting ASMs were associated with an increased risk of developing DM compared with nonenzyme interaction ASMs.

Indexed as

Diabetes Mellitus, Type 2Metabolic SyndromeAnticonvulsantsDatabases, FactualHumansPropensity ScoreAnticonvulsants

Identifiers

PMID36963840
PMCPMC10186244
OpenAlexW4360797538

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.