Association of smoking with amyotrophic lateral sclerosis: A systematic review, meta-analysis, and dose-response analysis

Tob Induc Dis. 2024 Jan 18:22. doi: 10.18332/tid/175731. eCollection 2024.

Abstract

Introduction: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder primarily affecting the voluntary motor nervous system. Several observational studies have provided conflicting results regarding the association between smoking and ALS. Therefore, our objective was to investigate this association through a systematic review, meta-analysis, and dose-response analysis.

Methods: On 16 January 2023, we initially extracted records from medical databases, which included Medline, Embase, Web of Science, Scopus, and ScienceDirect. We included case-control and cohort studies as eligible studies. Subgroup analyses were performed based on sex, study design, and current smoking. Restricted cubic-spline analysis was utilized to assess the dose-response relationship between smoking (pack-years) and ALS.

Results: Twenty-eight case-control and four cohort studies met the inclusion criteria. The unadjusted OR for the overall association between smoking and ALS was 1.14 (95% CI: 1.06-1.22, I2=44%, p<0.001), and the adjusted OR (AOR) was 1.12 (95% CI: 1.03-1.21, I2=49%, p=0.009). Subgroup analysis revealed a more pronounced association among current smokers, with an AOR of 1.28 (95% CI: 1.10-1.49, I2=66%, p<0.001) and AOR of 1.28 (95% CI: 1.10-1.48, I2=58%, p=0.001). In the dose-response analysis, the non-linear model revealed an inverted U-shaped curve.

Conclusions: Our study provides evidence of a positive relationship between smoking and the risk of ALS. To mitigate the risk of developing ALS, discontinuing smoking, which is a modifiable risk factor, may be crucial.TRIAL REGISTRATION: The study was registered in PROSPERO.IDENTIFIER: CRD42023388822.

Keywords: amyotrophic lateral sclerosis; dose-response analysis; meta-analysis; smoking; systematic review.

Grants and funding

FUNDING This work was supported by the Medical Research Center program (NRF-2018R1A5A2023879, and RS-2023-00207946) through the National Research Foundation of Korea and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) (HI22C1377, and HI22C073600) funded by the Ministry of Health & Welfare, Republic of Korea. This work was supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00621 to TJS, Development of artificial intelligence technology that provides dialog-based multi-modal explainability). This work was supported by KREONET.