Cited by 130Amyotrophic Lateral Sclerosis ResearchLipoproteins and Cardiovascular HealthGenetic Associations and Epidemiology

Causal effects of blood lipids on amyotrophic lateral sclerosis: a Mendelian randomization study

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Zeng, Ping, Zhou, Xiang·2019·Human Molecular Genetics

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Abstract

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disorder that is predicted to increase across the globe by ~70% in the following decades. Understanding the disease causal mechanism underlying ALS and identifying modifiable risks factors for ALS hold the key for the development of effective preventative and treatment strategies. Here, we investigate the causal effects of four blood lipid traits that include high-density lipoprotein, low-density lipoprotein (LDL), total cholesterol and triglycerides on the risk of ALS. By leveraging instrument variables from multiple large-scale genome-wide association studies in both European and East Asian populations, we carry out one of the largest and most comprehensive Mendelian randomization analyses performed to date on the causal relationship between lipids and ALS. Among the four lipids, we found that only LDL is causally associated with ALS and that higher LDL level increases the risk of ALS in both the European and East Asian populations. Specifically, the odds ratio of ALS per 1 standard deviation (i.e. 39.0 mg/dL) increase of LDL is estimated to be 1.14 [95% confidence interval (CI), 1.05-1.24; P = 1.38E-3] in the European population and 1.06 (95% CI, 1.00-1.12; P = 0.044) in the East Asian population. The identified causal relationship between LDL and ALS is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Our study provides important evidence supporting the causal role of higher LDL on increasing the risk of ALS, paving ways for the development of preventative strategies for reducing the disease burden of ALS across multiple nations.

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  • Altered cholesterol, HDL and LDL metabolism, gene variants of APOA1, APOB, and APOE4, and decreased ApoA1 levels have been implicated in other neurologic disorders.

    “Altered cholesterol, HDL and LDL metabolism, gene variants of APOA1, APOB, and APOE4, and decreased ApoA1 levels have been implicated in other neurologic disorders [21–35].”

    From Sterol and lipid analyses identifies hypolipidemia and apolipoprotein disorders in autism associated with adaptive functioning deficits (2021) · cites this paper as [26]

    The Introduction (page unavailable) describes abnormal HDL, LDL, and total-cholesterol levels in people with ALS, and the Results (page unavailable) associate higher LDL with increased ALS risk. The paper does not establish an HDL effect or implicate APOA1, APOB, APOE4 variants, or decreased ApoA1 levels; it addresses ALS rather than neurologic disorders generally. (LLM, conf=0.86)

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  1. [2]Riva N., Agosta F., Lunetta C., Filippi M. and Quattrini A. (2016) Recent advances in amyotrophic lateral sclerosis. J. Neurol., 263, 1241– 1254. 27025851 PMC4893385
  2. [3]Chiò A., Logroscino G., Hardiman O., Swingler R., Mitchell D., Beghi E., Traynor B.G. and Eurals Consortium (2009) Prognostic factors in ALS: a critical review. Amyotroph. Lateral Scler., 10, 310– 323. 19922118 PMC3515205
  3. [4]Benyamin B., He J., Zhao Q., Gratten J., Garton F., Leo P.J., Liu Z., Mangelsdorf M., Al-Chalabi A. and Anderson L. (2017) Cross-ethnic meta-analysis identifies association of the GPX3-TNIP1 locus with amyotrophic lateral sclerosis. Nat. Commun., 8, 611. 28931804 PMC5606989
  4. [5]Arthur K.C., Calvo A., Price T.R., Geiger J.T., Chiò A. and Traynor B.J. (2016) Projected increase in amyotrophic lateral sclerosis from 2015 to 2040. Nat. Commun., 7, 12408. 27510634 PMC4987527
  5. [6]MacArthur J., Bowler E., Cerezo M., Gil L., Hall P., Hastings E., Junkins H., McMahon A., Milano A., Morales J. et al. (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res., 45, D896– D901. 27899670 PMC5210590
  6. [7]Rheenen W., Shatunov A., Dekker A.M., McLaughlin R.L., Diekstra F.P., Pulit S.L., Spek R.A. van der, Võsa U., Jong S. de Robinson M.R. et al. (2016) Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet., 48, 1043– 1048. 27455348 PMC5556360
  7. [8]Dupuis L., Corcia P., Fergani A., Gonzalez De Aguilar J.L., Bonnefont-Rousselot D., Bittar R., Seilhean D., Hauw J.J., Lacomblez L., Loeffler J.P. et al. (2008) Dyslipidemia is a protective factor in amyotrophic lateral sclerosis. Neurology, 70, 1004– 1009. 18199832
  8. [9]Chiò A., Calvo A., Ilardi A., Cavallo E., Moglia C., Mutani R., Palmo A., Galletti R., Marinou K., Papetti L. et al. (2009) Lower serum lipid levels are related to respiratory impairment in patients with ALS. Neurology, 73, 1681– 1685. 19917991
  9. [10]Mitchell J.D. and Borasio G.D. (2007) Amyotrophic lateral sclerosis. Lancet, 369, 2031– 2041. 17574095
  10. [11]Corcia P. and Meininger V. (2008) Management of amyotrophic lateral sclerosis. Drugs, 68, 1037– 1048. 18484797
  11. [12]Malek A.M., Barchowsky A., Bowser R., Heiman-Patterson T., Lacomis D., Rana S., Youk A., Stickler D., Lackland D.T. and Talbott E.O. (2014) Environmental and occupational risk factors for amyotrophic lateral sclerosis: a case-control study. Neurodegener. Dis., 14, 31– 38. 24246552
  12. [13]Andrew A.S., Caller T.A., Tandan R., Duell E.J., Henegan P.L., Field N., Bradley W.G. and Stommel E.W. (2017) Environmental and occupational exposures and amyotrophic lateral sclerosis (ALS) in New England. Neurodegener. Dis., 17, 110– 116. 28122372 PMC5383428
  13. [14]Alonso A., Logroscino G. and Hernán M.A. (2010) Smoking and the risk of amyotrophic lateral sclerosis: a systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry, 81, 1249– 1252. 20639382
  14. [15]Calvo A., Canosa A., Bertuzzo D., Cugnasco P., Solero L., Clerico M., De Mercanti S., Bersano E., Cammarosano S., Ilardi A. et al. (2016) Influence of cigarette smoking on ALS outcome: a population-based study. J. Neurol. Neurosurg. Psychiatry, 87, 1229– 1233. 27656044
  15. [16]Dupuis L., Pradat P.F., Ludolph A.C. and Loeffler J.P. (2011) Energy metabolism in amyotrophic lateral sclerosis. Lancet Neurol., 10, 75– 82. 21035400
  16. [17]Funalot B., Desport J.C., Sturtz F., Camu W. and Couratier P. (2009) High metabolic level in patients with familial amyotrophic lateral sclerosis. Amyotroph. Lateral Scler., 10, 113– 117. 18792852
  17. [18]Desport J.C., Preux P.M., Bouteloup-Demange C., Clavelou P., Beaufrère B., Bonnet C. and Couratier P.P. (2003) Validation of bioelectrical impedance analysis in patients with amyotrophic lateral sclerosis. Am. J. Clin. Nutr., 77, 1179– 1185. 12716669
  18. [19]Schmitt F., Hussain G., Dupuis L., Loeffler J.P. and Henriques A. (2014) A plural role for lipids in motor neuron diseases: energy, signaling and structure. Front. Cell. Neurosci., 8 25. 24600344 PMC3929843
  19. [20]Hamadeh M.J., Rodriguez M.C., Kaczor J.J. and Tarnopolsky M.A. (2005) Caloric restriction transiently improves motor performance but hastens clinical onset of disease in the Cu/Zn-superoxide dismutase mutant G93A mouse. Muscle Nerve, 31, 214– 220. 15625688
  20. [21]Kim S.M., Kim H., Kim J.E., Park K.S., Sung J.J., Kim S.H. and Lee K.W. (2011) Amyotrophic lateral sclerosis is associated with hypolipidemia at the presymptomatic stage in mice. PLoS One, 6, e17985. 21464953 PMC3064597
  21. [22]Paganoni S., Deng J., Jaffa M., Cudkowicz M.E. and Wills A.M. (2011) Body mass index, not dyslipidemia, is an independent predictor of survival in amyotrophic lateral sclerosis. Muscle Nerve, 44, 20– 24. 21607987 PMC4441750
  22. [23]Huang R., Guo X., Chen X., Zheng Z., Wei Q., Cao B., Zeng Y. and Shang H. (2015) The serum lipid profiles of amyotrophic lateral sclerosis patients: a study from south-west China and a meta-analysis. Amyotroph. Lateral Scler. Frontotemporal Degener., 16, 359– 365. 26121273
  23. [24]Rafiq M.K., Lee E., Bradburn M., McDermott C.J. and Shaw P.J. (2015) Effect of lipid profile on prognosis in the patients with amyotrophic lateral sclerosis: insights from the olesoxime clinical trial. Amyotroph. Lateral Scler. Frontotemporal Degener., 16, 478– 484. 26161993
  24. [25]Sutedja N.A., Schouw Y.T. van der, Fischer K., Sizoo E.M., Huisman M.H., Veldink J.H. and Van den Berg L.H. (2011) Beneficial vascular risk profile is associated with amyotrophic lateral sclerosis. J. Neurol. Neurosurg. Psychiatry, 82, 638– 642. 21471184
  25. [26]Yang J.W., Kim S.M., Kim H.J., Kim J.E., Park K.S., Kim S.H., Lee K.W. and Sung J.J. (2013) Hypolipidemia in patients with amyotrophic lateral sclerosis: a possible gender difference? J. Clin. Neurol., 9, 125– 129. 23626651 PMC3633190
  26. [27]Armon C. (2003) An evidence-based medicine approach to the evaluation of the role of exogenous risk factors in sporadic amyotrophic lateral sclerosis. Neuroepidemiology, 22, 217– 228. 12792141
  27. [29]Greenland S. (2000) An introduction to instrumental variables for epidemiologists. Int. J. Epidemiol., 29, 722– 729. 10922351
  28. [30]Sheehan N.A., Didelez V., Burton P.R. and Tobin M.D. (2008) Mendelian randomisation and causal inference in observational epidemiology. PLoS Med., 5, e177. 18752343 PMC2522255
  29. [31]Haycock P.C., Burgess S., Wade K.H., Bowden J., Relton C. and Smith G.D. (2016) Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am. J. Clin. Nutr., 103, 965– 978. 26961927 PMC4807699
  30. [33]Sleiman P.M. and Grant S.F. (2010) Mendelian randomization in the era of genomewide association studies. Clin. Chem., 56, 723– 728. 20224045
  31. [34]Deng M., Wei L., Zuo X., Tian Y., Xie F., Hu P., Zhu C., Yu F., Meng Y., Wang H. et al. (2013) Genome-wide association analyses in Han Chinese identify two new susceptibility loci for amyotrophic lateral sclerosis. Nat. Genet., 45, 697– 700. 23624525
  32. [35]Chiò A., Logroscino G., Traynor B.J., Collins J., Simeone J.C., Goldstein L.A. and White L.A. (2013) Global epidemiology of amyotrophic lateral sclerosis: a systematic review of the published literature. Neuroepidemiology, 41, 118– 130. 23860588 PMC4049265
  33. [36]Doi Y., Atsuta N., Sobue G., Morita M. and Nakano I. (2014) Prevalence and incidence of amyotrophic lateral sclerosis in Japan. J. Epidemiol., 24, 494– 499. 25373461 PMC4213224
  34. [37]Willer C.J., Schmidt E.M., Sengupta S., Peloso G.M., Gustafsson S., Kanoni S., Ganna A., Chen J., Buchkovich M.L., Mora S. et al. (2013) Discovery and refinement of loci associated with lipid levels. Nat. Genet., 45, 1274– 1283. 24097068 PMC3838666
  35. [38]Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A., Bender D., Maller J., Sklar P., Bakker P.I. de, Daly M.J. et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 81, 559– 575. 17701901 PMC1950838
  36. [39]Noyce A.J., Kia D.A., Hemani G., Nicolas A., Price T.R., De Pablo-Fernandez E., Haycock P.C., Lewis P.A., Foltynie T., Davey Smith G. et al. (2017) Estimating the causal influence of body mass index on risk of Parkinson disease: a Mendelian randomisation study. PLoS Med., 14, e1002314. 28609445 PMC5469450
  37. [41]Burgess S., Small D.S. and Thompson S.G. (2017) A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res., 26, 2333– 2355. 26282889 PMC5642006
  38. [42]Brion M.J., Shakhbazov K. and Visscher P.M. (2013) Calculating statistical power in Mendelian randomization studies. Int. J. Epidemiol., 42, 1497– 1501. 24159078 PMC3807619
  39. [43]Kanai M., Akiyama M., Takahashi A., Matoba N., Momozawa Y., Ikeda M., Iwata N., Ikegawa S., Hirata M., Matsuda K. et al. (2018) Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet., 50, 390– 400. 29403010
  40. [44]Burgess S., Thompson D.J., Rees J.M.B., Day F.R., Perry J.R. and Ong K.K. (2017) Dissecting causal pathways using Mendelian randomization with summarized genetic data: application to age at menarche and risk of breast cancer. Genetics, 207, 481– 487. 28835472 PMC5629317
  41. [45]Do R., Willer C.J., Schmidt E.M., Sengupta S., Gao C., Peloso G.M., Gustafsson S., Kanoni S., Ganna A., Chen J. et al. (2013) Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet., 45, 1345– 1352. 24097064 PMC3904346
  42. [46]Martin S.S., Blaha M.J., Elshazly M.B., Toth P.P., Kwiterovich P.O., Blumenthal R.S. and Jones S.R. (2013) Comparison of a novel method vs the Friedewald equation for estimating low-density lipoprotein cholesterol levels from the standard lipid profile. JAMA, 310, 2061– 2068. 24240933 PMC4226221
  43. [48]Bowden J., Del Greco M.F., Minelli C., Davey Smith G., Sheehan N.A. and Thompson J.R. (2016) Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int. J. Epidemiol., 45, 1961– 1974. 27616674 PMC5446088
  44. [49]Burgess S. and Thompson S.G. (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol., 32, 377– 389. 28527048 PMC5506233
  45. [50]Burgess S. and Thompson S.G. (2015) Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol., 181, 251– 260. 25632051 PMC4325677
  46. [52]Brown L., Rosner B., Willett W.W. and Sacks F.M. (1999) Cholesterol-lowering effects of dietary fiber: a meta-analysis. Am. J. Clin. Nutr., 69, 30– 42. 9925120
  47. [53]Williams P.T., Krauss R.M., Kindel-Joyce S., Dreon D.M., Vranizan K.M. and Wood P.D. (1986) Relationship of dietary fat, protein, cholesterol, and fiber intake to atherogenic lipoproteins in men. Am. J. Clin. Nutr., 44, 788– 797. 3024477
  48. [54]Cholesterol Treatment Trialists’ (CTT) Collaborators, Mihaylova B., Emberson J., Blackwell L., Keech A., Simes J., Barnes E.H., Voysey M., Gray A., Collins R. and Baigent C. (2012) The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet, 380, 581– 590. 22607822 PMC3437972
  49. [56]Lawlor D.A., Harbord R.M., Sterne J.A., Timpson N. and Davey Smith G. (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med., 27, 1133– 1163. 17886233
  50. [58]Burgess S., Bowden J., Fall T., Ingelsson E. and Thompson S.G. (2017) Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology, 28, 30– 42. 27749700 PMC5133381
  51. [59]The HUGO Pan-Asian SNP Consortium, Abdulla M.A., Ahmed I., Assawamakin A., Bhak J., Brahmachari S.K., Calacal G.C., Chaurasia A., Chen C.H., Chen J. et al. (2009) Mapping human genetic diversity in Asia. Science, 326, 1541– 1545. 20007900
  52. [60]Wang Y., Lu D., Chung Y.J. and Xu S. (2018) Genetic structure, divergence and admixture of Han Chinese, Japanese and Korean populations. Hereditas, 155, 19. 29636655 PMC5889524
  53. [61]Ingman M., Kaessmann H., Pääbo S. and Gyllensten U. (2000) Mitochondrial genome variation and the origin of modern humans. Nature, 408, 708– 713. 11130070
  54. [62]Wen W., Zheng W., Okada Y., Takeuchi F., Tabara Y., Hwang J.Y., Dorajoo R., Li H., Tsai F.J., Yang X. et al. (2014) Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum. Mol. Genet., 23, 5492– 5504. 24861553 PMC4168820
  55. [63]Foo J.N., Tan L.C., Irwan I.D., Au W.L., Low H.Q., Prakash K.M., Ahmad-Annuar A., Bei J., Chan A.Y. and Chen C.M. (2016) Genome-wide association study of Parkinson’s disease in East Asians. Hum. Mol. Genet., 26, 226– 232. 28011712
  56. [64]He M., Xu M., Zhang B., Liang J., Chen P., Lee J.Y., Johnson T.A., Li H., Yang X., Dai J. et al. (2015) Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci. Hum. Mol. Genet., 24, 1791– 1800. 25429064 PMC4351379
  57. [65]Okada Y., Sim X., Go M.J., Wu J.Y., Gu D., Takeuchi F., Takahashi A., Maeda S., Tsunoda T., Chen P. et al. (2012) Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat. Genet., 44, 904– 909. 22797727 PMC4737645
  58. [66]Wen W., Cho Y.S., Zheng W., Dorajoo R., Kato N., Qi L., Chen C.H., Delahanty R.J., Okada Y., Tabara Y. et al. (2012) Meta-analysis identifies common variants associated with body mass index in east Asians. Nat. Genet., 44, 307– 311. 22344219 PMC3288728
  59. [67]Lu X., Huang J., Mo Z., He J., Wang L., Yang X., Tan A., Chen S., Chen J., Gu C.C. et al. (2016) Genetic susceptibility to lipid levels and lipid change over time and risk of incident hyperlipidemia in Chinese populations. Circ. Cardiovasc. Genet., 9, 37– 44. 26582766
  60. [68]VanderWeele T.J., Tchetgen Tchetgen E.J., Cornelis M. and Kraft P. (2014) Methodological challenges in Mendelian randomization. Epidemiology, 25, 427– 435. 24681576 PMC3981897
  61. [69]Nitsch D., Molokhia M., Smeeth L., DeStavola B.L., Whittaker J.C. and Leon D.A. (2006) Limits to causal inference based on Mendelian randomization: a comparison with randomized controlled trials. Am. J. Epidemiol., 163, 397– 403. 16410347
  62. [70]Shim H., Chasman D.I., Smith J.D., Mora S., Ridker P.M., Nickerson D.A., Krauss R.M. and Stephens M. (2015) A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS One, 10, e0120758. 25898129 PMC4405269
  63. [71]Yavorska O.O. and Burgess S. (2017) MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol., 46, 1734– 1739. 28398548 PMC5510723
  64. [73]Thompson S.G. and Sharp S.J. (1999) Explaining heterogeneity in meta-analysis: a comparison of methods. Stat. Med., 18, 2693– 2708. 10521860

Cited by

Sorted by citations

Authors
Zeng, P., & Zhou, X.
Affiliations
  • Xuzhou Medical College (CN)
  • University of Michigan (US)
Corresponding author
  • Zeng, Ping
  • Zhou, Xiang
Venue
Human Molecular Genetics
Published
2018-11-16
Volume
28
Issue
4
Pages
688-697
ISSN
  • 0964-6906 (ISSN-L)
  • 1460-2083
OpenAlex
W2952420414
Cited by
130
References
8
Citations by year
2026: 6 · 2025: 11 · 2024: 15 · 2023: 24 · 2022: 19 · 2021: 28 · 2020: 18 · 2019: 9
FWCI
9.34
Type
journal article
Language
en
Open access
Locations
  • Human Molecular Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (R01HG009124)
  • National Science Foundation (DMS1712933)
  • Natural Science Foundation of Jiangsu Province (BK20181472)
  • Youth Foundation of Humanity and Social Science funded by Ministry
  • Youth Foundation
  • Ministry of Education of China (18YJC910002)
  • China Postdoctoral Science Foundation (2018M630607)
  • Jiangsu QingLan Research Project for Outstanding Young Teachers
  • National Natural Science Foundation of China (81402765)
  • National Bureau of Statistics of China (2014LY112)
  • Priority Academic Program Development of Jiangsu Higher Education Institutions for Xuzhou Medical University
Topics
  • Health Sciences › Medicine › Neurology › Amyotrophic Lateral Sclerosis Research
  • Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
amyotrophic lateral sclerosis, serum lipids, low-density lipoprotein, high-density lipoprotein, total cholesterol, triglycerides, causal risk factors, neurodegenerative diseases, modifiable risk factors
MeSH terms
Amyotrophic Lateral Sclerosis, Cholesterol, Female, Humans, Lipids, Lipoproteins, HDL, Lipoproteins, LDL, Male, Triglycerides, Polymorphism, Single Nucleotide, Mendelian Randomization Analysis

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