Abstract
Purpose
While crudely quantified lipoproteins have been reported to affect the risk of breast cancer, the effects of subclass lipoproteins characterized by particle size, particle number, and lipidomes remain unknown.
Methods
Utilizing nuclear magnetic resonance-based GWAS of 85 lipoprotein traits, we performed two-sample univariable Mendelian randomization (MR) to evaluate the causal relationship between each trait with breast cancer (Ncase/control = 133,384/113,789) and with its estrogen receptor (ER) subtypes. Then, we applied multivariable MR to investigate the independent effects considering both general and central obesity.
Results
In univariable MR, a heterogeneous effect of subclass high-density lipoproteins (HDL) was observed, in which small HDL traits (ORs ranged from 0.89 to 0.94) were associated with a decreased risk of breast cancer while non-small HDLs traits (OR ranged from 1.04 to 1.08) were associated with an increased risk of breast cancer. Very-low-density lipoproteins (VLDL) traits and serum total triglycerides (TG) were associated with a decreased risk of breast cancer (ORs ranged from 0.88 to 0.94). Similar association patterns were found for ER + subtype. In multivariable MR, only the protective effects of small HDL, VLDL and TG on ER + subtype remained significant.
Conclusion
We identified a heterogeneous effect of subclass HDLs and a consistent protective effect of VLDL on breast cancer. Only the effects of small HDL and VLDL on ER + subtype remained robust after controlling for obesity. These findings provide new insight into the causal pathway underlying lipoproteins and breast cancer.
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Data availability
All data used in this study are publicly available summary-level data, with the relevant studies cited.
Abbreviations
- BC:
-
Breast cancer
- BMI:
-
Body mass index
- CI:
-
Confidence interval
- ER:
-
Estrogen-receptor
- FDR:
-
False discovery rate
- GWAS:
-
Genome-wide association study
- HDL:
-
High-density lipoproteins
- HR:
-
Hazard ratio
- IDL:
-
Intermediate-density lipoproteins
- IVs:
-
Instrumental variables
- IVW:
-
Inverse-variance-weighted
- LD:
-
Linkage disequilibrium
- LDL:
-
Low-density lipoproteins
- MR:
-
Mendelian randomization
- MVMR:
-
Multivariable Mendelian randomization
- NMR:
-
Nuclear magnetic resonance
- OR:
-
Odds ratio
- SNP:
-
Single nucleotide polymorphisms
- TG:
-
Serum total triglycerides
- VLDL:
-
Very-low-density lipoproteins
- WHRadjBMI:
-
BMI-adjusted waist-to-hip ratio
References
Feingold KR. Introduction to Lipids and Lipoproteins. In: Feingold KR, Anawalt B, Boyce A, Chrousos G, de Herder WW, Dhatariya K, et al., editors. Endotext. South Dartmouth (MA): Copyright © 2000–2022, MDText.com, Inc.; 2000.
Cedó L, Reddy ST, Mato E, Blanco-Vaca F, Escolà-Gil JC (2019) HDL and LDL: potential new players in breast cancer development. J Clin Med. https://doi.org/10.3390/jcm8060853
Revilla G, Cedó L, Tondo M, Moral A, Pérez JI, Corcoy R et al (2021) LDL, HDL and endocrine-related cancer: from pathogenic mechanisms to therapies. Semin Cancer Biol 73:134–157. https://doi.org/10.1016/j.semcancer.2020.11.012
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin 71(3):209–49. https://doi.org/10.3322/caac.21660
Pedersen KM, Çolak Y, Bojesen SE, Nordestgaard BG (2020) Low high-density lipoprotein and increased risk of several cancers: 2 population-based cohort studies including 116,728 individuals. J Hematol Oncol 13(1):129. https://doi.org/10.1186/s13045-020-00963-6
Ni H, Liu H, Gao R (2015) Serum lipids and breast cancer risk: a meta-analysis of prospective cohort studies. PLoS One 10(11):e0142669. https://doi.org/10.1371/journal.pone.0142669
Borgquist S, Butt T, Almgren P, Shiffman D, Stocks T, Orho-Melander M et al (2016) Apolipoproteins, lipids and risk of cancer. Int J Cancer 138(11):2648–2656. https://doi.org/10.1002/ijc.30013
Urbina EM, McCoy CE, Gao Z, Khoury PR, Shah AS, Dolan LM et al (2017) Lipoprotein particle number and size predict vascular structure and function better than traditional lipids in adolescents and young adults. J Clin Lipidol 11(4):1023–1031. https://doi.org/10.1016/j.jacl.2017.05.011
Flote VG, Vettukattil R, Bathen TF, Egeland T, McTiernan A, Frydenberg H et al (2016) Lipoprotein subfractions by nuclear magnetic resonance are associated with tumor characteristics in breast cancer. Lipids Health Dis 15:56. https://doi.org/10.1186/s12944-016-0225-4
Smith GD, Ebrahim S (2003) “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32(1):1–22. https://doi.org/10.1093/ije/dyg070
Nowak C, Ärnlöv J (2018) A Mendelian randomization study of the effects of blood lipids on breast cancer risk. Nat Commun 9(1):3957. https://doi.org/10.1038/s41467-018-06467-9
Johnson KE, Siewert KM, Klarin D, Damrauer SM, Chang KM, Tsao PS et al (2020) The relationship between circulating lipids and breast cancer risk: a Mendelian randomization study. PLoS Med 17(9):e1003302. https://doi.org/10.1371/journal.pmed.1003302
Tan VY, Bull CJ, Biernacka KM, Teumer A, Richardson TG, Sanderson E et al (2021) Investigation of the interplay between circulating lipids and IGF-I and relevance to breast cancer risk: an observational and mendelian randomization study. Cancer Epidemiol Biomarkers Prev https://doi.org/10.1158/1055-9965.Epi-21-0315
His M, Dartois L, Fagherazzi G, Boutten A, Dupré T, Mesrine S et al (2017) Associations between serum lipids and breast cancer incidence and survival in the E3N prospective cohort study. Cancer causes & control : CCC 28(1):77–88. https://doi.org/10.1007/s10552-016-0832-4
Moreno-Gordaliza E, van der Lee SJ, Demirkan A, van Duijn CM, Kuiper J, Lindenburg PW et al (2016) A novel method for serum lipoprotein profiling using high performance capillary isotachophoresis. Anal Chim Acta 944:57–69. https://doi.org/10.1016/j.aca.2016.09.038
Suna T, Salminen A, Soininen P, Laatikainen R, Ingman P, Mäkelä S et al (2007) 1H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organising maps. NMR Biomed 20(7):658–672. https://doi.org/10.1002/nbm.1123
Kettunen J, Demirkan A, Würtz P, Draisma HH, Haller T, Rawal R et al (2016) Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun 7:11122. https://doi.org/10.1038/ncomms11122
Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikäinen LP et al (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44(3):269–276. https://doi.org/10.1038/ng.1073
Gallois A, Mefford J, Ko A, Vaysse A, Julienne H, Ala-Korpela M et al (2019) A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context. Nat Commun 10(1):4788. https://doi.org/10.1038/s41467-019-12703-7
Chasman DI, Paré G, Mora S, Hopewell JC, Peloso G, Clarke R et al (2009) Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet 5(11):e1000730. https://doi.org/10.1371/journal.pgen.1000730
Zhang H, Ahearn TU, Lecarpentier J, Barnes D, Beesley J, Qi G et al (2020) Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat Genet 52(6):572–581. https://doi.org/10.1038/s41588-020-0609-2
Michailidou K, Lindström S, Dennis J, Beesley J, Hui S, Kar S et al (2017) Association analysis identifies 65 new breast cancer risk loci. Nature 551(7678):92–94. https://doi.org/10.1038/nature24284
Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J et al (2019) Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 28(1):166–174. https://doi.org/10.1093/hmg/ddy327
Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N et al (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47(3):291–295. https://doi.org/10.1038/ng.3211
Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE et al (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422):56–65. https://doi.org/10.1038/nature11632
Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, Peltonen L et al (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467(7311):52–58. https://doi.org/10.1038/nature09298
Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37(7):658–665. https://doi.org/10.1002/gepi.21758
Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2):512–525. https://doi.org/10.1093/ije/dyv080
Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40(4):304–314. https://doi.org/10.1002/gepi.21965
Burgess S, Thompson SG (2015) Multivariable mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol 181(4):251–260. https://doi.org/10.1093/aje/kwu283
Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR et al (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47(11):1228–1235. https://doi.org/10.1038/ng.3404
Debik J, Schäfer H, Andreassen T, Wang F, Fang F, Cannet C et al (2022) Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study. Br J Cancer. https://doi.org/10.1038/s41416-022-01924-1
Mora S, Otvos JD, Rifai N, Rosenson RS, Buring JE, Ridker PM (2009) Lipoprotein particle profiles by nuclear magnetic resonance compared with standard lipids and apolipoproteins in predicting incident cardiovascular disease in women. Circulation 119(7):931–939. https://doi.org/10.1161/circulationaha.108.816181
Liu J, van Klinken JB, Semiz S, van Dijk KW, Verhoeven A, Hankemeier T et al (2017) A Mendelian randomization study of metabolite profiles, fasting glucose, and type 2 diabetes. Diabetes 66(11):2915–2926. https://doi.org/10.2337/db17-0199
Camont L, Chapman MJ, Kontush A (2011) Biological activities of HDL subpopulations and their relevance to cardiovascular disease. Trends Mol Med 17(10):594–603. https://doi.org/10.1016/j.molmed.2011.05.013
Du XM, Kim MJ, Hou L, Le Goff W, Chapman MJ, Van Eck M et al (2015) HDL particle size is a critical determinant of ABCA1-mediated macrophage cellular cholesterol export. Circ Res 116(7):1133–1142. https://doi.org/10.1161/circresaha.116.305485
Nazih H, Bard JM (2020) Cholesterol, oxysterols and LXRs in breast cancer pathophysiology. Int J Mole Sci. https://doi.org/10.3390/ijms21041356
Huang JK, Lee HC (2022) Emerging evidence of pathological roles of very-low-density lipoprotein (VLDL). Int J Mole Sci. https://doi.org/10.3390/ijms23084300
Bendinelli B, Vignoli A, Palli D, Assedi M, Ambrogetti D, Luchinat C et al (2021) Prediagnostic circulating metabolites in female breast cancer cases with low and high mammographic breast density. Sci Rep 11(1):13025. https://doi.org/10.1038/s41598-021-92508-1
Key TJ, Appleby PN, Reeves GK, Travis RC, Alberg AJ, Barricarte A et al (2013) Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol 14(10):1009–1019. https://doi.org/10.1016/s1470-2045(13)70301-2
Mesalić L, Tupković E, Kendić S, Balić D (2008) Correlation between hormonal and lipid status in women in menopause. Bosn J Basic Med Sci 8(2):188–192. https://doi.org/10.17305/bjbms.2008.2980
Palmisano BT, Zhu L, Stafford JM (2017) Role of estrogens in the regulation of liver lipid metabolism. Adv Exp Med Biol 1043:227–256. https://doi.org/10.1007/978-3-319-70178-3_12
Vandeweyer E, Hertens D (2002) Quantification of glands and fat in breast tissue: an experimental determination. Ann Anat - Anatomischer Anzeiger 184(2):181–184. https://doi.org/10.1016/S0940-9602(02)80016-4
Choi J, Cha YJ, Koo JS (2018) Adipocyte biology in breast cancer: from silent bystander to active facilitator. Prog Lipid Res 69:11–20. https://doi.org/10.1016/j.plipres.2017.11.002
Sturtz LA, Deyarmin B, van Laar R, Yarina W, Shriver CD, Ellsworth RE (2014) Gene expression differences in adipose tissue associated with breast tumorigenesis. Adipocyte 3(2):107–114. https://doi.org/10.4161/adip.28250
Iyengar P, Combs TP, Shah SJ, Gouon-Evans V, Pollard JW, Albanese C et al (2003) Adipocyte-secreted factors synergistically promote mammary tumorigenesis through induction of anti-apoptotic transcriptional programs and proto-oncogene stabilization. Oncogene 22(41):6408–6423. https://doi.org/10.1038/sj.onc.1206737
Chiba T, Nakazawa T, Yui K, Kaneko E, Shimokado K (2003) VLDL induces adipocyte differentiation in ApoE-dependent manner. Arterioscler Thromb Vasc Biol 23(8):1423–1429. https://doi.org/10.1161/01.Atv.0000085040.58340.36
Qiao L, Zou C, van der Westhuyzen DR, Shao J (2008) Adiponectin reduces plasma triglyceride by increasing VLDL triglyceride catabolism. Diabetes 57(7):1824–1833. https://doi.org/10.2337/db07-0435
Gao Y, Zhang J, Zhao H, Guan F, Zeng P (2021) Instrumental heterogeneity in sex-specific two-sample mendelian randomization: empirical results from the relationship between anthropometric traits and breast/prostate cancer. Front Genet 12:651332. https://doi.org/10.3389/fgene.2021.651332
Funding
This study was supported by funds from the National Natural Science Foundation of China (81874282, U22A20359, 81874283, 81673255), the National Key R&D Program of China (2020YFC2006505, 2022YFC3600600, 2022YFC3600604), the Health Commission of Sichuan Province (20PJ093), the Key R&D Program of Sichuan, China (2022YFS0055), the Recruitment Program for Young Professionals of China, the Promotion Plan for Basic Medical Sciences, the Development Plan for Cutting-Edge Disciplines, Sichuan University, and other Projects from West China School of Public Health and West China Fourth Hospital, Sichuan University. The sponsors of this study had no role in study design, data collection, analysis, interpretation, writing of the report, or the decision for submission.
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BZ, XJ, and JL conceived and supervised the study. JX did the analyses. JX and XJ drafted the manuscript with significant contributions from, YH, XW, XZ, CX, ZW, LZ, HX, CY, PY, MT, YW, LC, YL, YZ, CY and YY. All authors contributed to the interpretations of the findings, critically revised the paper, and had final responsibility for the decision to submit for publication.
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Xiao, J., Hao, Y., Wu, X. et al. Nuclear magnetic resonance-determined lipoprotein profile and risk of breast cancer: a Mendelian randomization study. Breast Cancer Res Treat 200, 115–126 (2023). https://doi.org/10.1007/s10549-023-06930-2
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DOI: https://doi.org/10.1007/s10549-023-06930-2