A causal relationship between antioxidants, minerals and vitamins and metabolic syndrome traits: a Mendelian randomization study

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Abstract

Background

The available evidence regarding the association of antioxidants, minerals, and vitamins with the risk of metabolic syndrome (MetS) traits is currently limited and inconsistent. Therefore, the purpose of this Mendelian randomization (MR) study was to investigate the potential causal relationship between genetically predicted antioxidants, minerals, and vitamins, and MetS.

Methods

In this study, we utilized genetic variation as instrumental variable (IV) to capture exposure data related to commonly consumed dietary nutrients, including antioxidants (β-carotene, lycopene, and uric acid), minerals (copper, calcium, iron, magnesium, phosphorus, zinc, and selenium), and vitamins (folate, vitamin A, B6, B12, C, D, E, and K1). The outcomes of interest, namely MetS (n = 291,107), waist circumference (n = 462,166), hypertension (n = 463,010), fasting blood glucose (FBG) (n = 281,416), triglycerides (n = 441,016), and high-density lipoprotein cholesterol (HDL-C) (n = 403,943), were assessed using pooled data obtained from the most comprehensive genome-wide association study (GWAS) available. Finally, we applied the inverse variance weighting method as the result and conducted a sensitivity analysis for further validation.

Results

Genetically predicted higher iron (OR = 1.070, 95% CI 1.037–1.105, P = 2.91E−05) and magnesium levels (OR = 1.130, 95% CI 1.058–1.208, P = 2.80E−04) were positively associated with increased risk of MetS. For each component of MetS, higher level of genetically predicted selenium (OR = 0.971, 95% CI 0.957–0.986, P = 1.09E−04) was negatively correlated with HDL-C levels, while vitamin K1 (OR = 1.023, 95% CI 1.012–1.033, P = 2.90E−05) was positively correlated with HDL-C levels. Moreover, genetically predicted vitamin D (OR = 0.985, 95% CI 0.978–0.992, P = 5.51E−5) had a protective effect on FBG levels. Genetically predicted iron level (OR = 1.043, 95% CI 1.022–1.064, P = 4.33E−05) had a risk effect on TG level.

Conclusions

Our study provides evidence that genetically predicted some specific, but not all, antioxidants, minerals, and vitamins may be causally related to the development of MetS traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13098-023-01174-y.

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Most cited references 66

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Jack Bowden, George Davey Smith, Philip C. Haycock … (2017)

ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

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Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

Marie Verbanck, Chia-Yen Chen, Benjamin M. Neale … (2018)

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that MR-PRESSO is best suited when horizontal pleiotropy occurs in <50% of instruments. Next, we applied MR-PRESSO, along with several other MR tests to complex traits and diseases, and found that horizontal pleiotropy: (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from −131% to 201%; (iii) induced false positive causal relationships in up to 10% of relationships; and (iv) can be corrected in some but not all instances.

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Author and article information

Contributors

Junxian Li: lijunxian0711@163.com

Fengju Song:

ORCID: https://orcid.org/0000-0003-1542-0828

songfengju@163.com

Journal

Journal ID (nlm-ta): Diabetol Metab Syndr

Journal ID (iso-abbrev): Diabetol Metab Syndr

Title: Diabetology & Metabolic Syndrome

Publisher: BioMed Central (London )

ISSN (Electronic): 1758-5996

Publication date (Electronic): 10 October 2023

Publication date PMC-release: 10 October 2023

Publication date Collection: 2023

Volume: 15

Electronic Location Identifier: 194

Affiliations

[1 ]Department of Blood Transfusion, Key Laboratory of Cancer Prevention and Therapy in Tianjin, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, ( https://ror.org/0152hn881) Tianjin, China

[2 ]Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology in Tianjin, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin, China

Author information

Fengju Song https://orcid.org/0000-0003-1542-0828

Article

Publisher ID: 1174

DOI: 10.1186/s13098-023-01174-y

PMC ID: 10563368

PubMed ID: 37817280

SO-VID: fa3c708b-90ff-4e17-8b60-199c1a8246bc

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 17 April 2023

Date accepted : 27 September 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 81974439

Award Recipient : Fengju Song

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ScienceOpen disciplines: Nutrition & Dietetics

Keywords: metabolic syndrome,antioxidant,mineral,vitamin,mendelian randomization

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ScienceOpen disciplines: Nutrition & Dietetics

Keywords: metabolic syndrome, antioxidant, mineral, vitamin, mendelian randomization

A causal relationship between antioxidants, minerals and vitamins and metabolic syndrome traits: a Mendelian randomization study

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

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Karger: Nutrition and Dietetics

Most cited references 66

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

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