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SYSTEMATIC REVIEW article

Front. Aging Neurosci., 13 June 2025

Sec. Neuroinflammation and Neuropathy

Volume 17 - 2025 | https://doi.org/10.3389/fnagi.2025.1541779

Environmental risk factors, protective factors, and biomarkers for amyotrophic lateral sclerosis: an umbrella review

  • 1. Department of Neurology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China

  • 2. Key Laboratory of Cell Biology, Department of Cell Biology, National Health Commission of the People’s Republic of China, China Medical University, Shenyang, Liaoning, China

  • 3. Key Laboratory of Medical Cell Biology, Ministry of Education of the People’s Republic of China, Shenyang, China

  • 4. Key Laboratory of Neurological Disease Big Data of Liaoning Province, Shenyang, China

  • 5. Shenyang Clinical Medical Research Center for Difficult and Serious Diseases of the Nervous System, Shenyang, China

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Abstract

Introduction:

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the rapid loss of motor neurons. Given the significant global economic impact of ALS, effective preventive measures are urgently needed to reduce the incidence of this devastating disease. Recent meta-analyses have explored potential links between environmental factors, biomarkers, and ALS occurrence. However, the findings of these studies have been inconsistent and controversial. Therefore, we present a comprehensive umbrella review of recent meta-analyses to systematically summarize the available epidemiological evidence and evaluate its credibility.

Methods:

A systematic search was conducted in PubMed and Embase from inception until 01 October 2024, to identify meta-analyses of observational studies examining associations between environmental risk factors, protective factors, biomarkers, and ALS susceptibility. For each meta-analysis, summary effect estimates, 95% confidence intervals (CIs), 95% prediction intervals, study heterogeneity, small study effects, and excess significance biases were calculated independently by two investigators. The methodological quality was evaluated using the AMSTAR 2 criteria. The strength of the epidemiological evidence was categorized into five levels based on predefined criteria.

Results:

Out of 1,902 articles identified, 43 met the inclusion criteria, resulting in 103 included meta-analyses. These analyses covered 46 environmental risk and protective factors (344,597 cases, 71,415,574 population) and 57 cerebrospinal fluid (CSF) and serum biomarkers (30,941 cases, 2,180,797 population). The evidence was classified as convincing (Class I) for the regular use of antihypertensive drugs (OR: 0.85, 95% CI: 0.81–0.88) and highly suggestive (Class II) for premorbid body mass index (OR: 0.97, 95% CI: 0.95 to 0.98), trauma (OR: 1.51, 95% CI: 1.32 to 1.73), CSF NFL levels (SMD: 2.06, 95% CI: 1.61 to 2.51), serum NFL levels (SMD: 1.57, 95% CI: 1.29 to 1.85), ferritin levels (SMD: 0.66, 95% CI: 0.50 to 0.83), and uric acid levels (SMD: −0.72; 95% CI: −0.98 to −0.46).

Discussion:

This umbrella review offers new insights into the epidemiological evidence regarding the associations between environmental factors, biomarkers, and ALS susceptibility. We aim for our study to enhance the understanding of the roles of environmental factors and biomarkers in ALS occurrence and assist clinicians in developing evidence-based prevention and control strategies.

1 Introduction

Amyotrophic lateral sclerosis (ALS) is a deadly neurodegenerative disease resulting from the deterioration of motor neurons in the brain and spinal cord. It is typically characterized by progressive muscle weakness and atrophy in adults (Feldman et al., 2022). Globally, the incidence and prevalence rates of ALS were estimated to be 1.59 and 4.42 per 100,000, respectively (Xu et al., 2020). Recent studies have indicated that the national cost associated with ALS ranges from $149 million to $1,329 million, imposing a significant financial burden on families and society (Achtert and Kerkemeyer, 2021). Moreover, most ALS patients lack effective treatment, and the median survival time from clinical symptoms to death is 20–48 months (Paulukonis et al., 2015). Therefore, it is crucial to investigate and develop more efficient strategies for the early detection and prevention of this devastating disease.

To date, the causes of ALS have been suggested to be multifactorial, with various genetic predispositions and environmental factors intricately linked to its development. While significant progress has been achieved in investigating the genetic factors associated with ALS (Rosen et al., 1993; Savage et al., 2019), the connection between environmental factors and biomarkers of ALS susceptibility remains unclear. Consequently, numerous systematic reviews and meta-analyses of observational studies have examined potential associations between diverse environmental factors, biomarkers, and ALS incidence. However, the consistency and conclusiveness of the epidemiological evidence from these meta-analyses have not always been consistent and conclusive.

Umbrella reviews are increasingly popular for summarizing and evaluating evidence from published meta-analyses. They play a crucial role in investigating relationships between environmental factors, biomarkers, and diseases such as inflammatory bowel diseases, autism spectrum disorder, and multiple sclerosis (Belbasis et al., 2015; Kim et al., 2019; Piovani et al., 2019). In Belbasis et al. (2016)conducted an umbrella review on ALS susceptibility, consolidating eight meta-analyses to explore potential associations between environmental factors and ALS. Numerous meta-analyses on ALS have been published, prompting us to conduct a comprehensive umbrella review of all recently published meta-analyses. This review aims to systematically summarize the epidemiological evidence available and assess its credibility. We anticipate that our umbrella review will assist healthcare professionals and policymakers in devising effective strategies for the early diagnosis and prevention of ALS.

2 Materials and methods

2.1 Search strategy and eligibility criteria

The systematic literature search for our umbrella review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009). We systematically searched the PubMed and Embase databases from their inception to 01 October 2024, using the following search strategy: (“amyotrophic lateral sclerosis” OR “motor neuron disease”) AND (“systematic review” OR “meta-analysis”). Besides the database searches, we conducted manual screening of the reference lists of the retrieved articles to guarantee comprehensive coverage of relevant studies.

2.2 Inclusion and exclusion criteria

Our umbrella review comprised meta-analyses examining the relationships between environmental risk factors, protective factors, and biomarkers of susceptibility to ALS. We considered meta-analyses from observational studies (cohort, case-control, and cross-sectional) focusing on high versus low exposure or dose-response relationships. Eligible meta-analyses are needed to provide sufficient data for calculating summary effect estimates, 95% confidence intervals (CIs), and 95% prediction intervals (PIs), as well as information on heterogeneity, small-study effects, and excess significance bias for further analysis. Only studies published in English were included, while meta-analyses based on randomized controlled trials were excluded. Our study specifically excluded research on prognostic factors and biomarkers related to ALS survival, focusing instead on factors associated with ALS occurrence. Studies lacking raw data for calculating summary risk estimates, 95% CIs, or 95% PIs, such as systematic reviews without meta-analyses, were excluded. Publications categorized as reviews, editorials, letters, or conference abstracts were disregarded due to the absence of original data, and duplicate publications were likewise excluded.

2.3 Data extraction and methodological quality assessment

Two researchers independently extracted the following information for each eligible meta-analysis: name of the first author; journal and publication year; original article retrieval time; environmental risk factors; protective factors or biomarkers of interest (ALS); quality appraisal tool; funding information; conflicts of interest; number of studies included; number of participants and cases; and study design of the original study. Additionally, we gathered the most adjusted effect estimates (odds ratio [OR], relative risk [RR], hazard ratio [HR], weighted mean difference [WMD], and standardized mean difference [SMD]) along with the corresponding 95% CIs from the original studies. The most common adjustment factors comprised age, sex, body mass index (BMI), education, physical activity, smoking, alcohol consumption, and occupation.

The methodological quality of the included meta-analyses was evaluated using the robust and validated AMSTAR-2 tool (a measurement tool to assess systematic reviews). This tool rigorously evaluated the risk of bias, including ratings of search quality, reporting, analytics, and transparency. The overall methodological quality of each eligible meta-analysis was graded as high, moderate, low, or critical low (Shea et al., 2017).

2.4 Statistical analysis

First, we recalculated summary effect sizes and their 95% CIs for each eligible meta-analysis using random- and fixed-effect models to assess associations between environmental factors and biomarkers and ALS risk. When reporting weighted mean difference (WMD) in the included meta-analyses, we converted it to SMD according to an established formula (Carvalho et al., 2020). We also computed the 95% PIs, the probability range within which we predicted the effect size of the association would lie for 95% of similar future studies (Higgins et al., 2009). Subsequently, we performed Cochran’s Q test and calculated the I2 statistic to further assess statistical heterogeneity among the original studies. A p-value < 0.10 and I2 > 50% were considered to indicate significant heterogeneity. Additionally, Egger’s test was applied to evaluate publication bias and small-study effects, with a p-value less than 0.10, which was judged to be significant evidence of small-study effects. Finally, we applied an excess significance test to investigate whether the observed number of statistically significant studies was greater than expected (Ioannidis and Trikalinos, 2007). All statistical analyses were performed using STATA version 15.0. A p-value less than 0.05 was considered statistically significant for all tests, except for heterogeneity, small-study effects, and excess significant bias.

2.5 Credibility of epidemiologic evidence

In line with previously published umbrella reviews (Belbasis et al., 2015; Kim et al., 2019; Kim et al., 2020; Kim et al., 2022), we categorized the epidemiologic evidence regarding the relationship between environmental factors and biomarkers with ALS susceptibility into five strength levels based on specific assessment criteria (Supplementary Table 1). Convincing evidence (Class I) necessitated highly significant associations (P < 10–6 by random effects model), over 1,000 ALS cases, the largest study reporting a statistically significant result (P < 0.05), a 95% PI excluding the null value, no substantial heterogeneity (I2 < 50%), absence of excess significance bias (P > 0.10), and no significant small study effects (P > 0.10). Highly suggestive evidence (Class II) required statistically significant associations (P < 10–6 by random effects model) with more than 1,000 ALS cases, and the primary study component showed a statistically significant result (P < 0.05). Suggestive evidence (Class III) mandated only P < 10–3 by random effects model and over 1,000 cases. All other associations with nominal significance (P < 0.05) were classified as weak (Class IV). Lastly, evidence was deemed non-significant if no significance threshold was met (P > 0.05). Furthermore, for associations rated as convincing or highly suggestive, we conducted sensitivity analyses exclusively on nested case-control and cohort studies to evaluate any changes in the robustness of the epidemiologic evidence.

3 Results

3.1 Study identification

From database inception to 01 October 2024, we initially identified 1,902 articles by systematically searching PubMed and Embase. First, 890 repetitive and 859 irrelevant articles were excluded by reviewing the titles and abstracts. After full-text screening, 82 articles were excluded, comprising 38 non-meta-analyses of observational studies, 21 letters, conference abstracts, and reviews; 12 not focusing on ALS risk; 8 unrelated to environmental factors or cerebrospinal fluid (CSF)/serum biomarkers; and 3 not in English. To identify meta-analyses, the selection prioritized the meta-analysis with the most individual studies when multiple meta-analyses focused on the same association. In cases where multiple meta-analyses included the same largest number of studies, the most recently published meta-analysis was chosen. Subsequently, 28 articles were excluded due to the publication of a larger meta-analysis on the same topic. A list of all excluded publications is available in Supplementary Table 2. Therefore, 43 eligible articles published between 2013 and 2024 were included in this umbrella review. In total, 103 unique associations (46 potential environmental factors and 57 CSF/serum biomarkers) were extracted and are presented in Tables 1, 2 and Supplementary Tables 3, 4, respectively. The random-effects meta-analyses and corresponding funnel plots for all 103 associations are presented in Supplementary Datasheets 1, 2. The flow chart of the process of selecting eligible meta-analyses is presented in Figure 1.

TABLE 1

Environmental factors References Effect metrics Studies (n) Subjects (n) Cases (n) Random effects model The largest study
SMD (95% CI)		 Heterogeneity Egger
p-value		 Excess significance p-value 95% PI
OR/RR (95% CI) P-value P-value I 2
Class I
Anti-hypertensives Hu and Ji, 2022 OR 4 250,871 11,594 0.85 (0.81 to 0.88) < 10–6 0.85 (0.81 to 0.88) 0.580 0.00% 0.756 1.000 0.78 to 0.93
Class II
Trauma Gu et al., 2021 OR 29 6,537,781 18,390 1.51 (1.32 to 1.73) < 10–6 1.43 (1.33 to 1.54) < 0.001 78.10% 0.657 1.000 0.87 to 2.63
Premorbid body mass index Zeng et al., 2019 OR 11 5,314,782 5,673 0.97 (0.95 to 0.98) < 10–6 0.98 (0.97 to 0.99) 0.017 52.50% 0.447 < 0.001 0.93 to 1.00
Class III
Farming occupation Kang et al., 2014 OR 10 2,621,006 9,338 1.42 (1.17 to 1.73) < 0.001 1.20 (1.02 to 1.41) 0.080 41.70% 0.858 0.312 0.90 to 2.25
Pesticides Kang et al., 2014 OR 15 3,732,028 9,534 1.44 (1.22 to 1.70) < 0.001 1.20 (1.02 to 1.41) 0.048 41.30% 0.165 0.140 0.94 to 2.20
Head injury Watanabe and Watanabe, 2017 OR 16 510,802 11,692 1.46 (1.20 to 1.74) < 0.001 1.19 (0.88 to 1.61) 0.057 38.80% 0.933 0.790 0.88 to 2.39
Leisure time activity Zheng et al., 2023 OR 8 10,569 3,327 1.08 (1.04 to 1.12) < 0.001 1.07 (1.02 to 1.12) 0.519 0.00% 0.619 0.170 1.03 to 1.13
Anti-diabetes Duan et al., 2023 OR 3 5,180 1,248 0.56 (0.41 to 0.78) < 0.001 0.51 (0.43 to 0.60) 0.221 33.70% 0.525 0.043 0.02 to 12.68
Diabetes mellitus Wannarong and Ungprasert, 2020 OR 11 5,410,951 11,961 0.68 (0.55 to 0.84) < 0.001 0.98 (0.85 to 1.13) < 0.001 81.10% 0.310 0.016 0.34 to 1.36
Kidney diseases Duan et al., 2023 OR 3 136,375 11,735 0.84 (0.78 to 0.91) < 0.001 0.84 (0.78 to 0.91) 0.650 0.00% 0.639 0.138 0.51 to 1.38
Smoking Kim et al., 2024 OR 32 3,287,035 20,947 1.14 (1.06 to 1.23) < 0.001 1.18 (1.07 to 1.30) 0.005 44.10% 0.531 0.557 0.88 to 1.47
Metals Wang et al., 2014 OR 13 3,787 1,685 1.87 (1.51 to 2.33) < 0.001 1.52 (0.95 to 2.42) 0.339 10.60% 0.040 0.418 1.29 to 2.72
Class IV
Heavy metals Duan et al., 2023 OR 7 7,285 1,311 1.80 (1.28 to 2.52) 0.001 1.45 (1.00 to 2.10) 0.083 46.40% 0.977 1.000 0.75 to 4.32
Lead Meng et al., 2020 OR 12 25,872 4,246 1.46 (1.16 to 1.83) 0.001 1.07 (0.85 to 1.35) 0.019 51.80% 0.041 < 0.001 0.78 to 2.72
Annual PM2.5 exposure Gong et al., 2023 OR 2 387,737 6,486 1.83 (1.01 to 3.35) 0.048 1.79 (1.00 to 3.39) 0.650 0.00% NA < 0.001 NA
Competitive organized sports Blecher et al., 2019 RR 23 100,864 17,029 1.78 (1.11 to 2.86) 0.015 1.52 (1.03 to 2.25) < 0.001 84.00% 0.789 0.581 0.23 to 13.78
Vigorous physical activity Zheng et al., 2023 OR 17 9,088 2,849 1.26 (1.06 to 1.49) 0.008 1.03 (1.01 to 1.05) < 0.001 74.50% 0.059 < 0.001 0.71 to 2.22
Occupational-related activity Zheng et al., 2023 OR 12 12,277 4,025 1.14 (1.04 to 1.24) 0.005 1.06 (1.03 to 1.09) < 0.001 77.90% 0.052 < 0.001 0.91 to 1.42
Unclassified physical activity Zheng et al., 2023 OR 7 9,196 2,943 1.05 (1.02 to 1.09) 0.001 1.06 (1.04 to 1.09) 0.241 24.70% 0.517 0.016 0.99 to 1.12
Military personnel Tai et al., 2017 OR 9 2,116,690 10,492 1.27 (1.06 to 1.54) 0.006 1.31 (1.09 to 1.58) 0.014 55.10% 0.748 0.761 0.77 to 2.16
Heavy physical work Gunnarsson and Bodin, 2018 RR 9 494,435 2,097 1.89 (1.27 to 2.81) 0.002 0.95 (0.88 to 1.02) < 0.001 87.20% 0.006 0.020 0.51 to 7.04
Chemicals Gunnarsson and Bodin, 2018 RR 13 2,160,038 6,534 1.20 (1.06 to 1.35) 0.003 1.07 (0.93 to 1.23) 0.006 56.70% 0.060 0.002 0.85 to 1.68
Environmental and occupational solvents Zhang and Zhou, 2023 OR 13 179,686 6,365 1.29 (1.08 to 1.55) 0.001 0.92 (0.77 to 1.09) 0.002 59.70% 0.159 0.035 0.78 to 2.19
ELF-MF Jalilian et al., 2021 RR 27 11,207,625 22,673 1.20 (1.04 to 1.38) 0.008 1.09 (1.00 to 1.19) < 0.001 66.30% 0.034 0.030 0.73 to 1.99
Stroke Duan et al., 2023 OR 6 138,016 14,158 1.25 (1.06 to 1.47) 0.007 1.29 (1.15 to 1.44) 0.123 42.30% 0.782 0.057 0.67 to 2.35
ω-3 Polyunsaturated fatty acid intake Fitzgerald et al., 2014 RR 5 1,056,837 994 0.66 (0.54 to 0.82) < 0.001 0.64 (0.43 to 0.95) 0.923 0.00% 0.425 0.015 0.50 to 1.01
Carotenoids Fitzgerald et al., 2013 RR 5 1,053,575 1,093 0.92 (0.87 to 0.97) 0.002 0.93 (0.86 to 1.01) 0.708 0.00% 0.623 0.475 0.86 to 0.98
Acetaminophen Chang et al., 2020 OR 2 425,754 1,104 0.80 (0.67 to 0.96) 0.019 0.87 (0.71 to 1.07) 0.249 24.60% NA 1.000 NA
Living in urban Duan et al., 2023 OR 5 16,262 1,167 0.69 (0.48 to 1.00) 0.047 1.00 (0.83 to 1.20) 0.029 62.90% 0.121 0.023 0.01 to 63.81
NS
Occupation in industry Zhu et al., 2023 OR 3 866 264 1.24 (0.81 to 1.91) 0.328 1.48 (0.80 to 2.74) 0.665 0.00% 0.156 < 0.001 0.08 to 20.37
Annual PM10 exposure Gong et al., 2023 OR 2 3,763 969 3.51 (0.63 to 19.36) 0.150 7.09 (0.79 to 64.83) 0.321 0.00% NA < 0.001 NA
Rural residence Kang et al., 2014 OR 5 1,591 559 1.25 (0.84 to 1.87) 0.273 0.80 (0.54 to 1.18) 0.044 59.30% 0.635 0.219 0.35 to 4.51
Work with electricity Gunnarsson and Bodin, 2018 RR 10 5,766,217 14,752 1.16 (1.00 to 1.36) 0.053 0.99 (0.90 to 1.09) 0.001 65.70% 0.235 < 0.001 0.74 to 1.82
Alcohol consumption Duan et al., 2023 OR 11 12,110 4,430 1.02 (0.78 to 1.32) 0.895 0.72 (0.62 to 0.84) < 0.001 84.40% 0.175 0.066 0.41 to 2.52
hypertension Duan et al., 2023 OR 5 925,227 12,349 1.03 (0.98 to 1.08) 0.308 1.05 (1.01 to 1.10) 0.370 6.40% 0.290 1.000 0.88 to 1.19
NSAIDs Duan et al., 2023 OR 3 31,753 1,880 1.08 (0.82 to 1.42) 0.581 1.04 (0.87 to 1.25) 0.007 17.70% 0.290 0.040 0.44 to 2.68
Welding Gunnarsson and Bodin, 2018 RR 6 9,607,980 18,482 0.95 (0.70 to 1.29) 0.740 0.71 (0.57 to 0.89) < 0.001 82.40% 0.154 1.000 0.34 to 2.61
Electric shocks Jalilian et al., 2021 RR 8 2,273,307 14,036 0.97 (0.80 to 1.17) 0.754 0.73 (0.67 to 0.79) < 0.001 80.50% 0.237 < 0.001 0.56 to 1.69
Statins Hu and Ji, 2022 OR 10 3,588,732 27,698 0.91 (0.78 to 1.07) 0.166 0.87 (0.83 to 0.91) < 0.001 92.00% 0.967 1.000 0.60 to 1.42
Aspirin Chang et al., 2020 OR 3 793,588 1,548 0.94 (0.75 to 1.17) 0.583 1.04 (0.90 to 1.21) 0.077 61.10% 0.925 0.212 0.09 to 10.05
High vitamin diet Duan et al., 2023 OR 3 1,171 409 0.95 (0.72 to 1.27) 0.740 1.08 (0.70 to 1.66) 0.723 0.00% 0.417 1.000 0.15 to 5.99
Sport-related activity Zheng et al., 2023 OR 18 12,607 4,898 0.98 (0.76 to 1.26) 0.844 0.93 (0.72 to 1.20) < 0.001 77.80% 0.419 0.002 0.37 to 2.58
AMI/IS Duan et al., 2023 OR 6 162,136 16,371 0.96 (0.88 to 1.05) 0.412 0.96 (0.88 to 1.05) 0.606 0.00% 0.396 0.603 0.85 to 1.10
Cerebrovascular disease Zhu et al., 2023 OR 2 51,293 1,238 0.99 (0.75 to 1.29) 0.928 1.12 (1.04 to 1.19) 0.004 88.10% NA 0.513 NA
Occupation in service industry Zhu et al., 2023 OR 2 580 205 0.47 (0.19 to 1.17) 0.105 0.33 (0.19 to 0.56) 0.129 56.60% NA 0.005 NA
Coffee drinking Duan et al., 2023 OR 3 960,249 1,819 0.80 (0.58 to 1.10) 0.169 0.98 (0.85 to 1.11) 0.005 81.20% 0.285 0.162 0.02 to 34.32

Characteristics of included meta-analyses evaluating associations between environmental factors and ALS risk.

OR, odds ratio; RR, relative risk; CI, confidence intervals; PI, prediction interval; NA: not available; NS, not significant; ELF-MF, exposure to extremely-low frequency magnetic fields; NSAIDs, non-steroidal anti-inflammatory drugs; AMI/IS, acute myocardial infarction/ischemic stroke.

TABLE 2

Biomarkers References Effect metrics Studies (n) Subjects (n) Cases (n) Random effects model The largest study
SMD/OR (95% CI)		 Heterogeneity Egger
p-value		 Excess significance p-value 95% PI
SMD/OR (95% CI) P-value P value I 2
Class II
CSF NFL Sferruzza et al., 2022 SMD 23 2,887 1,901 2.06 (1.61 to 2.51) < 10–6 0.87 (0.67 to 1.08) < 0.001 94.80% < 0.001 0.717 −0.16 to 4.28
Serum NFL Sferruzza et al., 2022 SMD 15 1,781 1,074 1.57 (1.29 to 1.85) < 10–6 1.45 (1.13 to 1.78) < 0.001 81.80% 0.077 1.000 0.48 to 2.67
Serum ferritin Cheng et al., 2021 SMD 9 2,880 1,661 0.66 (0.50 to 0.83) < 10–6 0.46 (0.31 to 0.60) < 0.001 70.80% 0.504 0.013 0.16 to 1.17
Serum uric acid Zhang et al., 2018 SMD 8 2,559 1,168 −0.72 (−0.98 to −0.46) < 10–6 −1.07 (−1.20 to −0.94) < 0.001 87.90% 0.503 0.091 −1.58 to 0.14
Class III
Serum transferrin Wang et al., 2020 SMD 6 1,837 1,129 −0.27 (−0.39 to −0.16) < 0.001 −0.30 (−0.44 to −0.17) 0.347 10.80% 0.844 1.000 −0.47 to −0.08
Class IV
CSF CHIT1 Xu et al., 2024 SMD 6 798 580 1.92 (0.78 to 3.06) < 0.001 0.92 (0.59 to 1.26) < 0.001 96.50% 0.201 0.519 0.03 to 3.81
CSF cystatin C Zhu et al., 2018 SMD 6 396 246 −1.40 (−2.43 to −0.36) 0.008 −0.29 (−0.64 to 0.06) < 0.001 94.50% 0.079 0.018 −4.84 to 2.04
CSF TNF-α Chen et al., 2018 SMD 6 318 175 0.36 (0.04 to 0.67) 0.028 0.66 (0.20 to 1.12) 0.095 46.70% 0.211 0.002 −0.44 to 1.15
CSF MIP-1α Chen et al., 2018 SMD 6 490 292 0.90 (0.10 to 1.71) 0.028 2.09 (1.69 to 2.48) < 0.001 93.60% 0.365 < 0.001 −1.81 to 3.61
CSF MCP-1 Chen et al., 2018 SMD 7 509 283 0.58 (0.40 to 0.75) < 10–6 0.43 (0.10 to 0.75) 0.588 0.00% 0.745 0.450 0.35 to 0.80
CSF IL-17 Chen et al., 2018 SMD 5 267 151 0.74 (0.49 to 0.99) < 10–6 0.71 (0.25 to 1.17) 0.631 0.00% 0.863 0.665 0.39 to 1.10
CSF IL-15 Chen et al., 2018 SMD 4 241 120 0.46 (0.03 to 0.88) 0.035 0.00 (−0.45 to −0.45) 0.067 58.00% 0.734 0.014 −0.80 to 1.71
CSF G-CSF Chen et al., 2018 SMD 5 417 251 0.55 (0.35 to 0.76) < 0.001 0.30 (−0.03 to 0.62) 0.374 5.70% 0.269 0.107 0.22 to 0.89
CSF IL-2 Chen et al., 2018 SMD 5 417 251 0.34 (0.02 to 0.66) 0.039 0.32 (−0.01 to 0.64) 0.050 57.90% 0.678 1.000 −0.55 to 1.22
CSF NFH Xu et al., 2016 SMD 6 710 443 1.01 (0.54 to 1.49) < 0.001 0.36 (0.08 to 0.64) < 0.001 84.30% 0.007 0.006 −0.49 to 2.52
CSF TDP-43 Gambino et al., 2023 SMD 7 472 254 0.66 (0.23 to 1.10) 0.003 0.32 (−0.03 to 0.67) < 0.001 79.10% 0.227 0.012 −0.71 to 2.03
CSF homocysteine Hu and Wang, 2023 SMD 3 338 169 2.78 (0.61 to 4.95) 0.012 0.67 (0.17 to 1.17) < 0.001 97.90% 0.110 1.000 −6.67 to 12.23
CSF t-tau Thapa et al., 2023 SMD 7 1,100 634 1.76 (0.52 to 2.99) 0.005 −0.55 (−0.82 to −0.27) < 0.001 98.50% 0.026 0.020 −2.56 to 6.07
CSF CHI3L1 Xu et al., 2024 SMD 5 556 369 3.16 (1.25 to 5.06) 0.001 0.85 (0.41 to 1.29) < 0.001 97.40% 0.208 < 0.001 −3.37 to 9.68
Serum 8-OHdG Wang et al., 2019 SMD 2 79 18 2.20 (0.56 to 3.83) 0.009 3.02 (2.14 to 3.90) 0.012 84.20% NA 1.000 −15.22 to 19.61
Serum GSH Wang et al., 2019 SMD 2 79 18 2.20 (0.56 to 3.83) 0.009 3.02 (2.14 to 3.90) 0.012 84.20% NA 1.000 −15.22 to 19.61
Serum AOPP Wang et al., 2019 SMD 2 280 147 0.56 (0.32 to 0.79) < 0.001 0.54 (0.20 to 0.87) 0.876 0.00% NA 1.000 −0.99 to 2.10
Serum MDA Wang et al., 2019 SMD 5 243 123 1.17 (0.81 to 1.52) < 10–6 0.97 (0.56 to 1.38) 0.216 30.80% 0.411 1.000 0.37 to 1.97
Serum lead Farace et al., 2022 SMD 9 984 409 0.60 (0.12 to 1.07) 0.013 0.50 (0.30 to 0.70) < 0.001 88.90% 0.567 1.000 −0.99 to 2.18
Serum TNF-α Hu et al., 2017 SMD 12 822 456 0.66 (0.28 to 1.03) 0.001 0.50 (0.20 to 0.79) < 0.001 82.70% 0.328 0.781 −0.67 to 1.98
Serum TNFR1 Hu et al., 2017 SMD 3 246 155 0.74 (0.47 to 1.01) < 10–6 0.71 (0.33 to 1.09) 0.965 0.00% 0.995 1.000 0.15 to 1.33
Serum IL-1β Hu et al., 2017 SMD 4 304 161 0.29 (0.01 to 0.58) 0.038 0.50 (0.20 to 0.79) 0.283 21.20% 0.500 0.627 −0.33 to 0.92
Serum IL-6 Hu et al., 2017 SMD 7 509 267 0.25 (0.07 to 0.43) 0.006 0.40 (0.11 to 0.69) 0.512 0.00% 0.808 0.434 0.03 to 0.47
Serum IL-8 Hu et al., 2017 SMD 6 434 242 0.45 (0.26 to 0.64) < 0.001 0.50 (0.20 to 0.79) 0.471 0.00% 0.868 0.692 0.20 to 0.70
Serum IL-17 Gautam et al., 2023 SMD 2 79 50 0.64 (0.16 to 1.12) 0.009 0.78 (0.14 to 1.41) 0.517 0.00% NA 1.000 −2.46 to 3.75
Serum VEGF Hu et al., 2017 SMD 3 199 107 0.89 (0.29 to 1.49) 0.003 0.40 (0.01 to 0.79) 0.029 71.80% 0.368 0.044 −1.40 to 3.18
Serum FBG Cheng et al., 2021 SMD 10 542 301 0.20 (0.01 to 0.40) 0.040 −0.02 (−0.30 to 0.27) 0.337 11.50% 0.066 0.404 −0.13 to 0.53
Serum CK Cheng et al., 2021 SMD 5 2,072 229 0.74 (0.27 to 1.20) 0.002 0.72 (0.42 to 1.02) 0.034 61.70% 0.200 0.687 −0.53 to 2.00
Serum TSC Cheng et al., 2021 SMD 3 1,344 858 0.23 (0.11 to 0.34) < 0.001 0.20 (0.06 to 0.34) 0.758 0.00% 0.509 1.000 −0.03 to 0.48
Serum TIBC Cheng et al., 2021 SMD 3 540 236 −0.25 (−0.43 to −0.07) 0.006 −0.16 (−0.42 to 0.09) 0.350 4.70% 0.141 0.490 −0.67 to 0.17
Serum creatinine Liu et al., 2020b SMD 5 1,920 983 −0.78 (−0.97 to −0.60) < 10–6 −0.78 (−0.90 to −0.66) 0.031 62.40% 0.907 1.000 −1.30 to −0.27
Serum folic Hu and Wang, 2023 SMD 3 338 169 2.78 (0.61 to 4.95) 0.012 0.67 (0.17 to 1.17) < 0.001 97.90% 0.110 1.000 −6.67 to 12.23
Serum miR-206 Liu et al., 2023 SMD 5 211 110 0.76 (0.49 to 1.04) < 10–6 0.63 (0.18 to 1.08) 0.494 0.00% 0.029 0.022 0.37 to 1.15
Serum miR-338-3p Liu et al., 2023 SMD 3 252 139 0.47 (0.21 to 0.72) < 0.001 0.34 (0.001 to 0.69) 0.538 0.00% 0.29 0.250 −0.09 to 1.03
Serum miR-133b Liu et al., 2023 SMD 2 46 26 1.18 (0.56 to 1.79) < 0.001 1.04 (0.22 to 1.87) 0.624 0.00% NA 0.545 −2.81 to 5.17
Serum miR-133a Liu et al., 2023 SMD 2 46 26 0.77 (0.18 to 1.36) 0.010 0.68 (−0.12 to 1.47) 0.729 0.00% NA 1.000 −3.05 to 4.60
NS
CSF VEGF Chen et al., 2018 SMD 9 619 365 0.51 (−0.001 to 1.01) 0.051 1.93 (1.55 to 2.32) < 0.001 88.30% 0.043 < 0.001 −1.26 to 2.28
CSF lead Farace et al., 2022 SMD 6 244 114 0.51 (−0.01 to 1.03) 0.057 0.10 (−0.35 to 0.55) 0.002 72.90% 0.124 0.315 −1.06 to 2.08
CSF p-tau Thapa et al., 2023 SMD 6 933 577 0.12 (−0.38 to 0.62) 0.645 0.82 (0.72 to 0.94) < 0.001 94.80% 0.043 < 0.001 −1.55 to 1.78
Serum NFH Xu et al., 2016 SMD 2 195 117 1.07 (−0.08 to 2.21) 0.068 0.53 (0.20 to 0.86) 0.005 87.50% NA 0.531 −11.24 to 13.37
Serum iron Wang et al., 2020 SMD 7 1,796 1,055 0.48 (−0.07 to 1.03) 0.086 1.20 (1.05 to 1.35) < 0.001 95.40% 0.22 0.003 −1.38 to 2.34
Serum HDL Cheng et al., 2021 SMD 14 7,045 2,674 −0.01 (−0.19 to 0.17) 0.909 −0.02 (−0.13 to 0.09) < 0.001 87.70% 0.480 < 0.001 −0.66 to 0.63
Serum LDL Liu et al., 2020a SMD 6 2,990 1,495 −0.01 (−0.12 to 0.10) 0.907 0.08 (−0.03 to 0.18) 0.152 38.10% 0.987 0.602 −0.26 to 0.24
Serum TC Liu et al., 2020a SMD 6 2,981 1,495 −0.06 (−0.21 to 0.09) 0.453 0.06 (−0.05 to 0.17) 0.019 63.00% 0.952 0.121 −0.46 to 0.34
Serum TG Liu et al., 2020a SMD 8 3,727 1,918 −0.08 (−0.17 to 0.01) 0.080 −0.05 (−0.16 to 0.06) 0.194 29.30% 0.508 1.000 −0.27 to 0.11
Serum homocysteine Hu and Wang, 2023 SMD 11 3,444 812 0.50 (0.04 to 0.97) 0.034 −0.03 (−0.21 to 0.14) < 0.001 95.70% 0.223 < 0.001 −1.26 to 2.27
Serum vitamin B12 Hu and Wang, 2023 SMD 4 521 252 −0.00 (−0.20 to 0.19) 0.984 −0.16 (−0.39 to 0.08) 0.347 9.20% 0.345 1.000 −0.37 to 0.36
Serum galectin Ramos-Martínez et al., 2022 SMD 2 180 70 0.30 (−0.04 to 0.63) 0.085 0.17 (−0.21 to 0.54) 0.283 13.20% NA 1.000 −2.18 to 2.77
Serum selenium Zhou et al., 2023 SMD 10 995 425 −0.27 (−1.09 to 0.55) 0.525 −2.08 (−2.34 to −1.82) < 0.001 96.10% 0.281 < 0.001 −3.35 to 2.81
Serum vitamin D Lanznaster et al., 2020 SMD 4 439 163 −0.75 (−1.61 to 0.12) 0.090 −1.75 (−2.07 to −1.42) < 0.001 93.30% 0.598 0.450 −3.77 to 2.28
Serum ApoA1 Chalitsios et al., 2024 OR 2 1,062,073 1,514 0.77 (0.60 to 1.00) 0.053 0.82 (0.72 to 0.94) 0.250 24.40% NA 1.000 NA
Serum ApoB Chalitsios et al., 2024 OR 2 1,062,073 1,514 0.77 (0.60 to 1.00) 0.053 0.82 (0.72 to 0.94) 0.250 24.40% NA 1.000 NA

Characteristics of included meta-analyses evaluating associations between biomarkers and ALS risk.

CSF, cerebrospinal fluid; SMD, standardized mean difference; CI, confidence intervals; PI, prediction interval; NA, not available; NFL, neurofilaments light chain; TNF, tumor necrosis factor; MIP, macrophage inflammatory proteins; MCP, monocyte chemoattractant protein; IL, interleukin; G-CSF, granulocyte colony-stimulating factor; NFH, neurofilaments heavy chain; TDP-43, an RNA-binding protein; 8-OHdG, 8-hydroxyguanosine; GSH, glutathione; AOPP, Advanced Oxidation Protein Product; MDA, malondialdehyde; TNFR1, TNF receptor 1; VEGF, vascular endothelial growth factor; FBG, fasting blood glucose; CK, creatine kinase; TSC, transferrin saturation coefficient; TIBC, total iron binding capacity; miR, microRNA; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglyceride; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; t-tau, total tau; p-tau, phosphorylated-tau; CHIT1, chitotriosidase; CHI3L1, chitinase 3-like 1.

FIGURE 1

FIGURE 1

Flow diagram of literature search.

3.2 Methodological quality assessment of meta-analyses

The AMSTAR 2 quality assessment tool was utilized to evaluate all 43 meta-analysis articles in our umbrella review. Among the potential environmental factors, 2 out of 21 meta-analyses (10%) were deemed high quality, 1 (5%) was rated as moderate quality, 6 (29%) were considered low quality, and 12 (57%) were classified as critically low quality (Supplementary Table 5). Of the 22 meta-analysis articles focusing on potential biomarkers, only 3 (14%) were rated as moderate, 9 (41%) as low, and 10 (45%) as critically low (Supplementary Table 5). Overall, most meta-analysis researchers did not register protocols before conducting the review (30 studies, 70%), which significantly impacts methodological quality. Consequently, we performed a supplementary analysis that did not consider the absence of a registered protocol as a critical flaw when reassessing the methodological quality of the included studies. The results of the supplementary analysis showed that AMSTAR 2 ratings were reclassified as high in 3 studies (7%), moderate in 10 studies (23%), and low (18 studies, 42%) or critically low (12 studies, 28%) in 43 studies (Supplementary Table 6).

3.3 Environmental risk and protective factors

The 46 associations between environmental factors and ALS susceptibility were based on 344,597 ALS cases, a total population of 71,415,574, a median of 4,664 ALS cases per meta-analysis (interquartile range 1,370–11,904, range 205–27,698), and a median of 215,279 subjects per meta-analysis (interquartile range 12,152–2,149,201, range 580–11,207,625). Among these meta-analyses, 42 were case-control studies, with 26 including cohort studies. The median number of study estimates was eight (interquartile range 3–12, range 2–29). The effect metrics used to evaluate the relationships between environmental factors and ALS risk were RR and OR. Twenty-nine of the 46 associations (63%) were statistically significant under the random effect model, with 13 (45%) having P < 10–3 and 3 (10%) having P < 10–6. Of these 29 statistically significant associations, 28 (97%) included more than 1,000 ALS cases, and 13 (45%) exhibited substantial heterogeneity (I2 > 50%). Additionally, 12 associations (41%) were statistically significant without small study effects or excess significance bias, and the 95% PI excluded the null value in 5 (17%) of the 29 associations.

As shown in Figure 2, the summary effect size, along with its corresponding 95% CI, was calculated to assess the relationships between various environmental factors and ALS risk. Out of 46 associations of environmental risk/protective factors, the sole environmental protective factor classified as convincing evidence (Class I) was the regular use of antihypertensive drugs (OR: 0.85, 95% CI: 0.81 to 0.88) (Donohue et al., 2022). Furthermore, premorbid body mass index (OR: 0.97, 95% CI: 0.95 to 0.98) (Zeng et al., 2019) and trauma (OR: 1.51, 95% CI: 1.32 to 1.73) (Gu et al., 2021) were graded as highly suggestive evidence (class II) for environmental protective and risk factors, respectively. Nine environmental risk/protective factors were categorized as suggestive evidence (Class III), among which farming occupation (OR: 1.42, 95% CI: 1.17 to 1.73) (Kang et al., 2014), pesticides exposure (OR: 1.44, 95% CI: 1.22 to 1.70) (Kang et al., 2014), head injuries (OR: 1.46, 95% CI: 1.20 to 1.74) (Watanabe and Watanabe, 2017), leisure time activity (OR: 1.08, 95% CI: 1.04 to 1.12) (Zheng et al., 2023), and metals exposure (OR: 1.87, 95% CI: 1.51 to 2.33) (Wang et al., 2014) were identified as environmental risk factors. Conversely, anti-diabetes (OR: 0.56, 95% CI: 0.41 to 0.78) (Duan et al., 2023), diabetes mellitus (OR: 0.68, 95% CI: 0.55 to 0.84) (Wannarong and Ungprasert, 2020), smoking (OR: 1.14, 95% CI: 1.06 to 1.23) (Kim et al., 2024), and kidney diseases (OR: 0.84, 95% CI: 0.78 to 0.91) (Duan et al., 2023) were recognized as environmental protective factors. Additionally, 13 other risk factors and four protective factors were statistically significant but with weak evidence certainty (Figure 2). Ultimately, it was determined that the remaining 17 environmental factors had no significant impact on ALS (P > 0.05).

FIGURE 2

FIGURE 2

Summary estimates of environmental risk and protective factors for amyotrophic lateral sclerosis.

3.4 CSF and serum biomarkers

A total of 57 associations focusing on CSF/serum biomarkers with ALS susceptibility were based on 30,941 ALS cases, 2,180,797 individuals in the total population, a median of 254 ALS cases per meta-analysis (interquartile range 151–812, range 18–2,674), and a median of 509 subjects per meta-analysis (interquartile range 267–1,781, range 46–7,045). All these studies were cohort, case-control, or cross-sectional studies. The median number of study estimates in each meta-analysis was five (interquartile range 3–7, range 2–23). The effect metrics used to assess the association between various biomarkers and ALS risk were SMD and OR. Among 57 associations, 41 (72%) associations were nominally statistically significant at P < 0.05, 19 of 57 (33%) at P < 10–3, and 10 of 57 (18%) at P < 10–6. Among the 41 statistically significant associations, five (12%) enrolled more than 1,000 ALS cases, and 22 (54%) exhibited large heterogeneity (I2 > 50%). Additionally, 15 (37%) statistically significant associations suggested hints for small study effects or excessive significance bias. Lastly, in 13 (32%) associations with P < 0.05, the 95% PI excluded the null value.

Among the 57 CSF/serum biomarker associations, 15 CSF and 26 serum biomarkers were significantly associated with ALS (Figure 3). Unfortunately, none of these biomarkers received a grade of convincing evidence (Class I). Three serums and one CSF biomarker exhibited highly suggestive evidence (Class II). Specifically, serum levels of neurofilament light chains (NFL) (SMD: 1.57, 95% CI: 1.29 to 1.85) (Sferruzza et al., 2022), CSF NFL levels (SMD: 2.06, 95% CI: 1.61 to 2.51) (Sferruzza et al., 2022), and serum ferritin levels (SMD: 0.66, 95% CI: 0.50 to 0.83) (Cheng et al., 2021) were significantly higher in ALS patients, while uric acid levels (SMD: −0.72; 95% CI: −0.98 to −0.46) (Wang et al., 2019) were lower in ALS patients compared to controls. Serum transferrin levels (SMD: −0.27; 95% CI: −0.39 to −0.16) (Wang et al., 2020) were classified as suggestive evidence (Class III). In contrast, 14 other CSF biomarkers were categorized as weak evidence (Class IV), including CSF NFH, TDP-43, TNF-α, MIP-1α, MCP-1, G-CSF, IL-2, IL-15, IL-17, cystatin C, CHIT1, CHI3L1, t-tau, and homocysteine levels. Similarly, 22 serum biomarkers were also considered weak evidence, encompassing serum 8-OHdG, GSH, AOPP, MDA, TNF-α, TNFR1, IL-1β, IL-6, IL-8, IL-17, VEGF, FBG, CK, TSC, TIBC, lead, creatinine, folic, miR-133a, miR-133b, miR-206, and miR-338–3p levels. Furthermore, no significant impact of the remaining 16 biomarkers on ALS was detected (P > 0.05).

FIGURE 3

FIGURE 3

Summary estimates of cerebrospinal fluid and serum biomarkers for amyotrophic lateral sclerosis.

3.5 Results of sensitivity analysis

TTo evaluate the robustness of the seven associations categorized as convincing or highly suggestive, we conducted sensitivity analyses exclusively utilizing cohort and nested case-control studies. The evidence supporting the link between trauma history and ALS risk, comprising four cohort studies and one nested case-control study, remained consistent (Class II, highly suggestive). Conversely, the association between premorbid body mass index and the onset of ALS was reclassified to suggestive evidence. Regrettably, the meta-analysis on antihypertensive drug usage incorporated solely one cohort study. Notably, investigations on CSF NFL, serum NFL, ferritin, and uric acid levels did not encompass any cohort or nested case-control studies. Consequently, a sensitivity analysis for these associations was infeasible.

4 Discussion

4.1 Principal findings

ALS is a fatal disease characterized by neuronal degeneration that has garnered significant attention from numerous scholars. An increasing number of meta-analyses have aimed to evaluate the reliability and certainty of epidemiological evidence regarding the association of various environmental factors and biomarkers with ALS incidence. However, the published data often present inconsistent or conflicting findings. Therefore, we conducted an umbrella review that systematically assessed all recently published meta-analyses investigating 103 potential associations between ALS and different environmental factors and biomarkers. We applied stringent criteria to evaluate the credibility of eligible meta-analyses. Seven factors or biomarkers were identified as providing convincing or highly suggestive evidence (trauma, use of antihypertensive drugs, premorbid body mass index, CSF NFL levels, serum NFL levels, ferritin levels, and uric acid levels), indicating their potential significance in the development of ALS. Among these factors, the use of antihypertensive drugs and premorbid body mass index were associated with a decreased risk of ALS, while trauma was linked to increased susceptibility. Moreover, elevated CSF NFL levels, serum NFL levels, and ferritin levels, as well as decreased serum uric acid levels, were observed in ALS patients compared to controls, suggesting their potential utility as reliable biomarkers for the onset and progression of ALS. Belbasis et al. (2016) conducted a similar umbrella review in 2016, consolidating eight meta-analyses to explore 11 associations between various environmental factors and ALS incidence. The previous study identified long-term occupational exposure to lead as a compelling environmental risk factor for ALS occurrence, with head injury being considered highly suggestive. In contrast, our study encompassed 43 meta-analyses involving 344,597 ALS cases and 71 million participants. Following a meticulous evaluation using robust methods, we reclassified the association of lead exposure and head injury with ALS risk as weak and suggestive evidence in our umbrella review.

4.2 Possible explanations

Antihypertensive medications can significantly decrease the incidence of ALS, which may be attributed to multiple mechanisms. First, hypertension has been linked to maladaptation of cerebral circulation, resulting in dysregulation of cerebral blood flow and disruption of the blood–brain barrier (BBB) (Ungvari et al., 2021). BBB disruption permits the entry of neurotoxic blood-derived debris, cells, and microbial pathogens into the brain, triggering inflammation and immune responses that activate various ALS pathways. BBB breakdown has been observed to directly cause early motor neuron impairment and dysfunction in ALS mouse models, and early preservation of BBB integrity can postpone the onset of motor neuron injury and degeneration (Winkler et al., 2014). Additionally, neuroimaging studies of living human brains and postmortem tissue analyses have confirmed BBB disruption in the initial states of ALS (Sweeney et al., 2018; Mirian et al., 2022). Hence, antihypertensive medications might lower the incidence of ALS by averting BBB dysfunction (Kucuk et al., 2002; Katsi et al., 2020). Second, the most commonly prescribed antihypertensive drugs, ACEIs, β-blockers, and CCBs, could reduce the risk of ALS through distinct neuroprotective mechanisms (Donohue et al., 2022). Specifically, ACEI might promote neuronal survival by scavenging free radicals and providing protection against glutamate-induced neurotoxicity (Ravati et al., 1999; Sengul et al., 2011). β-blockers could diminish neuroinflammation by modulating macrophages and microglia from pro-inflammatory to anti-inflammatory phenotypes, thereby contributing to neuroprotective effects (Lin et al., 2020). Moreover, calcium dysregulation can induce motor neuron degeneration by directly or indirectly impacting crucial proteins involved in ALS neurodegeneration, such as VAP-B, Matrin 3, and alsin (Leal and Gomes, 2015). Therefore, individuals with ALS and chronic hypertension may derive additional benefits beyond blood pressure reduction from regular use of antihypertensive medications, which should be recommended in forthcoming clinical practice.

In our umbrella review, a history of trauma, graded as highly suggestive, was linked to an increased risk of ALS. Trauma, particularly repeated trauma, contributes to a sustained low-level pro-inflammatory state in the body, resulting in the overexpression of various circulating inflammatory factors such as IL-1, IL-6, and TNF-α (Lenz et al., 2007). These pro-inflammatory cytokines can lead to excessive activation of microglia in the central nervous system (CNS), causing chronic neuroinflammation (Potgieter et al., 2015; Ritzel et al., 2015). Activated microglia also release significant amounts of pro-inflammatory factors, attracting more peripheral immune cells to migrate to the CNS (Huang et al., 2021). Given that neuroinflammation plays a crucial role in ALS pathogenesis, the close association between trauma and ALS susceptibility is understandable.

Traumatic brain injury (TBI), a prevalent form of trauma, could also increase the risk of developing ALS through various mechanisms. Anomalous phosphorylation and translocation of TDP-43 to the cytoplasm play crucial roles in ALS pathogenesis (Dewey et al., 2011; Correia et al., 2015). A prior autopsy study revealed TDP-43 inclusions in 61 out of 71 TBI cases across multiple brain regions such as the brainstem, basal ganglia, and diencephalon (Smith et al., 2013). Recent research has validated that TBI can trigger ALS-related TDP-43 pathological alterations in mouse models, possibly due to TBI-induced inflammation promoting NF-κB-mediated TDP-43 overexpression (McKee et al., 2009; McKee et al., 2013; Wiesner et al., 2018; Gao et al., 2022). Additionally, a study using an ALS fly model indicated that TBI may prompt stress granule formation in the brain, potentially leading to motor neuron degeneration (Anderson et al., 2018). In certain instances, TBI can directly compromise the BBB breakdown (Shlosberg et al., 2010). Specifically, localized head trauma can disrupt BBB regulation by damaging the endothelium of small blood vessels (Rodríguez-Baeza et al., 2003). It is widely recognized that the exacerbation of motor neuron damage resulting from BBB impairment is a significant factor in ALS pathogenesis (Garbuzova-Davis et al., 2011). Therefore, TBI could increase ALS susceptibility by affecting several crucial pathophysiological processes involved in ALS onset. Notably, our umbrella review encompassing a meta-analysis of 11,692 ALS cases has substantiated a statistically significant link between a history of head injury and ALS risk (OR 1.45, 95% CI 1.21 to 1.74). Unfortunately, the evidence level for this correlation was deemed suggestive, as the p-value of 8.0 × 10–5 was very close but did not reach 10–6.

In our study, highly suggestive evidence demonstrated that premorbid BMI was inversely associated with the risk of ALS. One possible explanation for this is that individuals susceptible to ALS are more likely to be in a hypermetabolic state (low BMI) (O’Reilly et al., 2013). Previous studies have shown that mice with ALS exhibit increased energy expenditure, skeletal muscle hypermetabolism, and reduced adipose tissue levels prior to symptom onset (Dupuis et al., 2004). A recent observational study showed that approximately 50% of patients were hypermetabolic at diagnosis, and up to 80% had no change in metabolic status during 2 years of follow-up, suggesting that in most cases, hypermetabolism may occur early during ALS (Bouteloup et al., 2009). Moreover, Mark et al. suggested that low-energy diets might render motor neurons vulnerable to degeneration. In contrast, high-energy diets could induce adaptive responses in neuronal populations, activate signaling pathways that promote plasticity and disease resistance, and initiate a neuroprotective response to energy stress (Mattson et al., 2007). These findings suggest that metabolic dysfunction plays a key role in the pathogenesis of ALS (Batulan et al., 2003; Sinclair, 2005; Perera and Turner, 2016). Another explanation is that BMI is closely related to type II diabetes (T2D), and T2D is involved in ALS occurrence, indicating that BMI may indirectly affect ALS susceptibility through the T2D pathway (Corbin et al., 2016; D’Ovidio et al., 2018). Mechanistically, higher blood glucose levels may act as unintentional compensation to meet the higher energy expenditure of damaged motor neurons (Fergani et al., 2007; Dupuis et al., 2011; Zhang et al., 2020). Additionally, the potential anti-inflammatory effects of metformin and sulphonylureas may suppress neuroinflammation in patients with ALS (Han et al., 2018). However, the exact mechanisms underlying the relationship between BMI and ALS are complex and should be interpreted cautiously.

Regarding robust serum biomarkers, circulating ferritin levels were significantly higher in patients with ALS than in healthy controls. One possible explanation is that the disruption of iron homeostasis may cause neuronal death, which plays an important role in the pathogenesis of ALS (Rathore et al., 2012; Kim and Connor, 2020; Obrador et al., 2020). The ferritin complex releases the stored iron by triggering autophagy (Gao et al., 2016; Hou et al., 2016). Subsequently, excessive iron may increase the generation of reactive oxygen species through the Fenton reaction, thus inducing ferroptosis and apoptosis due to the failure of redox control (Dixon et al., 2012; Conrad and Pratt, 2019). Interestingly, serum uric acid levels were reduced in patients with ALS compared with controls, suggesting a protective effect of uric acid on ALS. This can be explained by the following mechanism. First, uric acid can assist in scavenging superoxides by inhibiting the degradation of superoxide dismutase to reduce neurotoxicity, ultimately exerting neuroprotective effects (Kutzing and Firestein, 2008). Second, uric acid can also chelate iron, preventing increased free radical production to further reduce oxidative damage (Davies et al., 1986). Finally, uric acid has been demonstrated to protect neurons from damage by reducing glutamate toxicity (Du et al., 2007).

Similarly, CSF and serum NFL levels were significantly increased in patients with ALS compared to controls, which was rated as highly suggestive evidence. In multiple animal models of ALS, axonal damage precedes motor neuron death and symptom onset (Fischer and Glass, 2007). As a crucial structural component of axons, NFL is vital for maintaining normal axonal diameter and conduction velocity (Brettschneider et al., 2006). Additionally, NFL can be released into CSF and serum through axonal degeneration (Brettschneider et al., 2006). In our umbrella review, patients with ALS exhibited elevated CSF and serum NFL levels, reflecting extensive damage to motor neurons and axons and serving as important diagnostic markers (Bjornevik et al., 2021; Falzone et al., 2021). While both CSF and serum NFL levels offer potential diagnostic accuracy, serial lumbar puncture for monitoring NFL levels is significantly less practical than blood collection, making blood-borne NFL a more favorable surrogate marker for ALS patients (Puentes et al., 2014; McCombe et al., 2015).

4.3 Strengths and limitations

To the best of our knowledge, our umbrella review offers the most comprehensive and systematic evaluation of all published meta-analyses on environmental risk factors, protective factors, and biomarkers of ALS susceptibility. We applied stringent criteria to assess the methodological quality and strength of evidence in each eligible meta-analysis. Furthermore, we emphasized sensitivity analysis and biological plausibility to enhance the accuracy of evaluating each environmental factor and biomarker. However, it is important to note several limitations of this study. First, we focused solely on associations synthesized by published meta-analyses, potentially overlooking important relationships not yet evaluated using meta-analytic methods. Second, it is worth acknowledging that biomarker studies included relatively small sample sizes, ranging from 46 to 7,045 cases without longitudinal data. Therefore, there is a need for more multicenter, large-sample, prospective biomarker studies. Third, in our umbrella review, the AMSTAR 2 criteria revealed that among the 43 included meta-analyses, two (5%) were rated as high quality, four (9%) as moderate, 15 (35%) as low, and 22 (51%) as critically low quality. A significant factor contributing to these lower-quality ratings was the absence of protocol registration, noted in 30 (70%) of the included meta-analyses. While this methodological concern did not substantially alter our overall findings, future researchers are strongly encouraged to prospectively register study protocols and adhere closely to standardized reporting guidelines such as PRISMA and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) to enhance transparency and reliability in this research field. Fourth, the original studies were susceptible to confounding biases in observational meta-analyses. To mitigate this issue, most original studies adjusted for known confounding factors to minimize their impact on the results. Our umbrella review extracted the fully adjusted effect estimates for further analysis. However, due to variations in the adjustment models across the original studies, we cannot completely rule out the possibility of residual confounding in some effect estimates, which could potentially distort the true effect sizes. Finally, out of the 70 statistically significant associations, 35 (50%) exhibited heterogeneity, which could compromise the robustness of our results despite most estimates demonstrating significant effects consistently. Therefore, it is advisable to exercise caution when interpreting these results.

5 Conclusion

In summary, we provide a comprehensive overview of 103 potential environmental risk factors, protective factors, and biomarkers for ALS susceptibility. Following rigorous criteria to evaluate the epidemiological evidence, seven factors and biomarkers show convincing or highly suggestive evidence, including trauma, use of antihypertensive drugs, premorbid body mass index, circulating NFL, ferritin, and uric acid levels, and CSF NFL levels. Further research is needed to enhance understanding of the complex mechanisms through which these factors impact ALS development.

Statements

Data availability statement

The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

QW: Writing – original draft, Writing – review and editing, Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Visualization. JY: Writing – original draft, Data curation, Formal Analysis, Investigation, Methodology. YD: Investigation, Methodology, Software, Writing – original draft. YM: Investigation, Writing – original draft. YZ: Investigation, Writing – original draft. ST: Investigation, Writing – original draft. JW: Formal Analysis, Writing – original draft. YW: Formal Analysis, Writing – original draft. BL: Investigation, Writing – original draft. JZ: Writing – review and editing, Resources, Funding acquisition, Supervision. XL: Supervision, Methodology, Conceptualization, Writing – review and editing, Funding acquisition, Resources, Project administration.

Funding

The authors declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Grant No. 81400950) and Natural Science Foundation of Liaoning Province (Grant No. 2019-MS-365).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The authors declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2025.1541779/full#supplementary-material

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Summary

Keywords

amyotrophic lateral sclerosis, environmental risk factors, biomarkers, meta-analysis, umbrella review

Citation

Wu Q, Yang J, Duan Y, Ma Y, Zhang Y, Tan S, Wang J, Wang Y, Liu B, Zhang J and Liu X (2025) Environmental risk factors, protective factors, and biomarkers for amyotrophic lateral sclerosis: an umbrella review. Front. Aging Neurosci. 17:1541779. doi: 10.3389/fnagi.2025.1541779

Received

08 December 2024

Accepted

13 May 2025

Published

13 June 2025

Volume

17 - 2025

Edited by

Robert Bucelli, Washington University in St. Louis, United States

Reviewed by

Dongwei Sun, University of California, Riverside, United States

Hongfei Tai, Capital Medical University, China

Updates

Copyright

© 2025 Wu, Yang, Duan, Ma, Zhang, Tan, Wang, Wang, Liu, Zhang and Liu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xu Liu, Jian Zhang,

†These authors have contributed equally to this work and share first authorship

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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