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Lipidomics and pancreatic cancer risk in two prospective studies

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Abstract

Pancreatic ductal carcinoma (PDAC) is highly fatal with limited understanding of mechanisms underlying its carcinogenesis. We comprehensively investigated whether lipidomic measures were associated with PDAC in two prospective studies. We measured 904 lipid species and 252 fatty acids across 15 lipid classes in pre-diagnostic serum (up to 24 years) in a PDAC nested-case control study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, NCT00002540) with 332 matched case–control sets including 272 having serial blood samples and Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC, NCT00342992) with 374 matched case–control sets. Controls were matched to cases by cohort, age, sex, race, and date at blood draw. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) per one-standard deviation increase in log-lipid concentrations within each cohort, and combined ORs using fixed-effects meta-analyses. Forty-three lipid species were associated with PDAC (false discovery rate, FDR ≤ 0.10), including lysophosphatidylcholines (LPC, n = 2), phosphatidylethanolamines (PE, n = 17), triacylglycerols (n = 13), phosphatidylcholines (PC, n = 3), diacylglycerols (n = 4), monoacylglycerols (MAG, n = 2), cholesteryl esters (CE, n = 1), and sphingomyelins (n = 1). LPC(18:2) and PE(O-16:0/18:2) showed significant inverse associations with PDAC at the Bonferroni threshold (P value < 5.5 × 10–5). The fatty acids LPC[18:2], LPC[16:0], PC[15:0], MAG[18:1] and CE[22:0] were significantly associated with PDAC (FDR < 0.10). Similar associations were observed in both cohorts. There was no significant association for the differences between PLCO serial lipidomic measures or heterogeneity by follow-up time overall. Results support that the pre-diagnostic serum lipidome, including 43 lipid species from 8 lipid classes and 5 fatty acids, is associated with PDAC.

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Data availability

Ethical restrictions on human subjects’ data prevents our posting the data used for this analysis. Biomedical research scientists from recognized research institutions can contact us directly to request data as bona fide researchers by e-mailing corresponding author.

Abbreviations

ATBC:

Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study

AUC:

Area under the curve

BMI:

Body mass index

CE:

Cholesteryl ester

CI:

Confidence interval

DAG:

Diacylglycerol

DCER:

Dihydroceramide

LASSO:

Least absolute shrinkage and selection operator

LPC:

Lysophosphatidylcholine

MAG:

Monoacylglycerol

OR:

Odds ratio

PDAC:

Pancreatic ductal adenocarcinoma

PE:

Phosphatidylethanolamine

PLCO:

Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

SM:

Sphingomyelin

TAG:

Triacylglycerol

References

  1. National Cancer Institute, Bethesda, MD. SEER cancer stat facts: pancreatic cancer. https://seer.cancer.gov/statfacts/html/pancreas.html.

  2. Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Pineros M, et al. Global cancer observatory: cancer tomorrow [Internet]. 2018. https://gco.iarc.fr/tomorrow.

  3. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Article  PubMed  Google Scholar 

  4. Gordon-Dseagu VL, Devesa SS, Goggins M, Stolzenberg-Solomon R. Pancreatic cancer incidence trends: evidence from the Surveillance, Epidemiology and End Results (SEER) population-based data. Int J Epidemiol. 2018;47:427–39.

    Article  PubMed  Google Scholar 

  5. Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature. 2010;467:1114–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fahy E, Cotter D, Sud M, Subramaniam S. Lipid classification, structures and tools. Biochim Biophys Acta. 2011;1811:637–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang R, Li B, Lam SM, Shui G. Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression. J Genet Genom Yi Chuan Xue Bao. 2020;47:69–83.

    Article  Google Scholar 

  8. Heimerl S, Fischer M, Baessler A, Liebisch G, Sigruener A, Wallner S, et al. Alterations of plasma lysophosphatidylcholine species in obesity and weight loss. PLoS ONE. 2014;9: e111348.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Fernandez C, Surma MA, Klose C, Gerl MJ, Ottosson F, Ericson U, et al. Plasma lipidome and prediction of type 2 diabetes in the population-based Malmö diet and cancer cohort. Diabetes Care. 2020;43:366–73.

    Article  CAS  PubMed  Google Scholar 

  10. Eichelmann F, Sellem L, Wittenbecher C, Jäger S, Kuxhaus O, Prada M, et al. Deep lipidomics in human plasma—cardiometabolic disease risk and effect of dietary fat modulation. Circulation. 2022;146:21–35.

    Article  CAS  PubMed  Google Scholar 

  11. Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021;18:493–592.

    Article  PubMed  PubMed Central  Google Scholar 

  12. WCRF/AICR. World Cancer Research Fund/American Institute for Cancer Research. Continuous Update Project Expert Report 2018. Alcoholic drinks and the risk of cancer [Internet]. WCRF/AICR; 2018. dietandcancerreport.org.

  13. Thiébaut ACM, Jiao L, Silverman DT, Cross AJ, Thompson FE, Subar AF, et al. Dietary fatty acids and pancreatic cancer in the NIH-AARP diet and health study. J Natl Cancer Inst. 2009;101:1001–11.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhou D, Mu D, Cheng M, Dou Y, Zhang X, Feng Z, et al. Differences in lipidomics may be potential biomarkers for early diagnosis of pancreatic cancer. Acta Cir Bras. 2020;35: e202000508.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gaiser RA, Pessia A, Ateeb Z, Davanian H, Fernández Moro C, Alkharaan H, et al. Integrated targeted metabolomic and lipidomic analysis: a novel approach to classifying early cystic precursors to invasive pancreatic cancer. Sci Rep. 2019;9:10208.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Tao L, Zhou J, Yuan C, Zhang L, Li D, Si D, et al. Metabolomics identifies serum and exosomes metabolite markers of pancreatic cancer. Metabol Off J Metab Soc. 2019;15:86.

    Google Scholar 

  17. Wolrab D, Jirásko R, Cífková E, Höring M, Mei D, Chocholoušková M, et al. Lipidomic profiling of human serum enables detection of pancreatic cancer. Nat Commun. 2022;13:124.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. The alpha-tocopherol, beta-carotene lung cancer prevention study: design, methods, participant characteristics, and compliance. The ATBC Cancer Prevention Study Group. Ann Epidemiol. 1994;4:1–10.

  19. Prorok PC, Andriole GL, Bresalier RS, Buys SS, Chia D, Crawford ED, et al. Design of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Control Clin Trials. 2000;21:273S-309S.

    Article  CAS  PubMed  Google Scholar 

  20. Andriole GL, Crawford ED, Grubb RL, Buys SS, Chia D, Church TR, et al. Prostate cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: mortality results after 13 years of follow-up. J Natl Cancer Inst. 2012;104:125–32.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Korhonen P, Malila N, Pukkala E, Teppo L, Albanes D, Virtamo J. The Finnish Cancer Registry as follow-up source of a large trial cohort–accuracy and delay. Acta Oncol Stockh Swed. 2002;41:381–8.

    Article  Google Scholar 

  22. Löfgren L, Ståhlman M, Forsberg G-B, Saarinen S, Nilsson R, Hansson GI. The BUME method: a novel automated chloroform-free 96-well total lipid extraction method for blood plasma. J Lipid Res. 2012;53:1690–700.

    Article  PubMed Central  Google Scholar 

  23. Fay MP, Graubard BI, Freedman LS, Midthune DN. Conditional logistic regression with sandwich estimators: application to a meta-analysis. Biometrics. 1998;54:195–208.

    Article  CAS  PubMed  Google Scholar 

  24. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36:1–48. https://doi.org/10.18637/jss.v036.i03.

    Article  Google Scholar 

  25. Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001;125:279–84.

    Article  CAS  PubMed  Google Scholar 

  26. Bodenhofer U. PODKAT: an R package for association testing involving rare and private variants. 2021. http://www.bioinf.jku.at/software/podkat/.

  27. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B Methodol. 1996;58:267–88.

    Google Scholar 

  28. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B Stat Methodol. 2011;73:273–82. https://doi.org/10.1111/j.1467-9868.2011.00771.x.

    Article  Google Scholar 

  29. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):22.

    Article  Google Scholar 

  30. Yuan C, Kim J, Wang QL, Lee AA, Babic A, PanScan/PanC4 I-III Consortium, et al. The age-dependent association of risk factors with pancreatic cancer. Ann Oncol Off J Eur Soc Med Oncol. 2022;33:693–701.

    Article  CAS  Google Scholar 

  31. Klein AP, Wolpin BM, Risch HA, Stolzenberg-Solomon RZ, Mocci E, Zhang M, et al. Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer. Nat Commun. 2018;9:556.

    Article  PubMed  PubMed Central  Google Scholar 

  32. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020. https://www.R-project.org/.

  33. Martín-Blázquez A, Jiménez-Luna C, Díaz C, Martínez-Galán J, Prados J, Vicente F, et al. Discovery of pancreatic adenocarcinoma biomarkers by untargeted metabolomics. Cancers. 2020;12:1002.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ritchie SA, Akita H, Takemasa I, Eguchi H, Pastural E, Nagano H, et al. Metabolic system alterations in pancreatic cancer patient serum: potential for early detection. BMC Cancer. 2013;13:416.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Urayama S, Zou W, Brooks K, Tolstikov V. Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer. Rapid Commun Mass Spectrom RCM. 2010;24:613–20.

    Article  CAS  PubMed  Google Scholar 

  36. Mehta KY, Wu H-J, Menon SS, Fallah Y, Zhong X, Rizk N, et al. Metabolomic biomarkers of pancreatic cancer: a meta-analysis study. Oncotarget. 2017;8:68899–915.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Di Gangi IM, Mazza T, Fontana A, Copetti M, Fusilli C, Ippolito A, et al. Metabolomic profile in pancreatic cancer patients: a consensus-based approach to identify highly discriminating metabolites. Oncotarget. 2016;7:5815–29.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Liu P, Zhu W, Chen C, Yan B, Zhu L, Chen X, et al. The mechanisms of lysophosphatidylcholine in the development of diseases. Life Sci. 2020;247: 117443.

    Article  CAS  PubMed  Google Scholar 

  39. Breeur M, Ferrari P, Dossus L, Jenab M, Johansson M, Rinaldi S, et al. Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European prospective investigation into cancer and nutrition. BMC Med. 2022;20:351.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Barber MN, Risis S, Yang C, Meikle PJ, Staples M, Febbraio MA, et al. Plasma lysophosphatidylcholine levels are reduced in obesity and type 2 diabetes. PLoS ONE. 2012;7: e41456.

    Article  CAS  PubMed  Google Scholar 

  41. Wang Y, Jiang C-T, Song J-Y, Song Q-Y, Ma J, Wang H-J. Lipidomic profile revealed the association of plasma lysophosphatidylcholines with adolescent obesity. BioMed Res Int. 2019;2019:1382418.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost H-G, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62:639–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8:615.

    Article  PubMed  PubMed Central  Google Scholar 

  44. van der Veen JN, Kennelly JP, Wan S, Vance JE, Vance DE, Jacobs RL. The critical role of phosphatidylcholine and phosphatidylethanolamine metabolism in health and disease. Biochim Biophys Acta Biomembr. 2017;1859:1558–72.

    Article  PubMed  Google Scholar 

  45. Wang X, Wang S, Tang X, Zhang A, Grabinski T, Guo Z, et al. Development and evaluation of monoclonal antibodies against phosphatidylethanolamine binding protein 1 in pancreatic cancer patients. J Immunol Methods. 2010;362:151–60.

    Article  CAS  Google Scholar 

  46. Newsom SA, Brozinick JT, Kiseljak-Vassiliades K, Strauss AN, Bacon SD, Kerege AA, et al. Skeletal muscle phosphatidylcholine and phosphatidylethanolamine are related to insulin sensitivity and respond to acute exercise in humans. J Appl Physiol Bethesda Md. 1985;2016(120):1355–63.

    Google Scholar 

  47. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121:1402–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Yuan C, Babic A, Khalaf N, Nowak JA, Brais LK, Rubinson DA, et al. Diabetes, weight change, and pancreatic cancer risk. JAMA Oncol. 2020;6: e202948.

    Article  PubMed  Google Scholar 

  49. Naudin S, Sampson JN, Moore SC, Stolzenberg-Solomon R. Sources of variability in serum lipidomic measurements and implications for epidemiologic studies. Am J Epidemiol. 2022;191:1926–35.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mayfield J. Diagnosis and classification of diabetes mellitus: new criteria. Am Fam Physician. 1998;58(1355–62):1369–70.

    Google Scholar 

  51. Carrard J, Gallart-Ayala H, Infanger D, Teav T, Wagner J, Knaier R, et al. Metabolic view on human healthspan: a lipidome-wide association study. Metabolites. 2021;11:287.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the participants, the investigators, and support staff of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening cohort study without whom this research would not be possible.

Funding

Division of Cancer Epidemiology and Genetics intramural research program (National Cancer Institute, National Institute of Health, Bethesda, Maryland, United States of America).

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Authors and Affiliations

  1. Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, DHHS, 9609 Medical Center Drive, NCI Shady Grove, Room 6E420, Rockville, MD, 20850, USA

    Sabine Naudin, Steven C. Moore, Demetrius Albanes, Neal D. Freedman, Stephanie J. Weinstein & Rachael Stolzenberg-Solomon

  2. Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, USA

    Joshua N. Sampson

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  1. Sabine Naudin
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Contributions

All authors have directly participated in the different steps of the study and are responsible for its content. RSS was involved in the conceptualization, funding acquisition, resources mobilization, project administration, and supervision of the study. RSS, SW, NF and DA were involved in data curation. SN, JS, SM and RSS were responsible for leading the investigation. JS and RSS provided methodological expertise. SN and JS developed the software programs and were responsible of the formal analysis. SN, SM, RSS were involved in the visualization of the findings. SN was responsible for writing of the original draft. SN, JS, SM, RSS, SW, NF, DA were involved in writing, reviewing, and editing the manuscript.

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Correspondence to Rachael Stolzenberg-Solomon.

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Naudin, S., Sampson, J.N., Moore, S.C. et al. Lipidomics and pancreatic cancer risk in two prospective studies. Eur J Epidemiol 38, 783–793 (2023). https://doi.org/10.1007/s10654-023-01014-3

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