Skip to main content

Dyslipidemia and colorectal cancer risk: a meta-analysis of prospective studies

Abstract

Purpose

The findings from epidemiologic studies of dyslipidemia and colorectal cancer risk have been conflicting. We performed a dose–response meta-analysis of published prospective studies to assess the aforementioned association.

Methods

Relevant studies that reported the association between the components of dyslipidemia (serum triglyceride, total cholesterol, and high-/low-density lipoprotein cholesterol) and colorectal cancer risk were identified by searching PubMed until the end of May 2014. We pooled the relative risks (RRs) from individual studies using a random- and fixed-effects models and performed dose–response, heterogeneity, and publication bias analyses.

Results

Seventeen prospective studies, including 1,987,753 individuals with 10,876 colorectal cancer events, were included in the meta-analysis. The overall pooled RR for high versus low concentrations for triglyceride (n = 9 studies) was 1.18 (95 % CI 1.04–1.34; I 2 = 47.8 %), for total cholesterol (n = 10 studies) was 1.11 (95 % CI 1.01–1.21; I 2 = 46.7 %), for high-density lipoprotein cholesterol (n = 6 studies) was 0.84 (95 % CI 0.69–1.02; I 2 = 42.5 %), and for low-density lipoprotein cholesterol (n = 3 studies) was 1.04 (95 % CI 0.60–1.81; I 2 = 82.7 %). In the dose–response analysis, the overall pooled RR was 1.01 (95 % CI 1.00–1.03; I 2 = 0 %) per 50 mg/dL of triglyceride and 1.01 (95 % CI 0.97–1.05; I 2 = 64.3 %) per 100 mg/dL of total cholesterol.

Conclusions

This meta-analysis of prospective studies suggests that dyslipidemia, especially high levels of serum triglyceride and total cholesterol, is associated with an increased risk of colorectal cancer, whereas high-density lipoprotein cholesterol might associate with a decreased risk of colorectal cancer. Further studies are warranted to determine whether altering the concentrations of these metabolic variables may reduce colorectal cancer risk.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

References

  1. 1.

    Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray, F. (2013) GLOBOCAN 2012 v1.0, Cancer incidence and mortality worldwide: IARC cancer base no. 11 [Internet]. International agency for research on cancer, Lyon, France. http://globocan.iarc.fr. Accessed 14 July 2014

  2. 2.

    World Cancer Resarch Fund/American Institue for Cancer Research (2007) Food, nutrition, physical activity and the prevention of cancer: a global perspective. AICR, Washington

  3. 3.

    Bardou M, Barkun AN, Martel M (2013) Obes colorectal Cancer. Gut 62:933–947

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Giovannucci E (2007) Metabolic syndrome, hyperinsulinemia, and colon cancer: a review. Am J Clin Nutr 86:s836–s842

    PubMed  Google Scholar 

  5. 5.

    Esteve E, Ricart W, Fernandez-Real JM (2005) Dyslipidemia and inflammation: an evolutionary conserved mechanism. Clin Nutr 24:16–31

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Kontush A, de Faria EC, Chantepie S, Chapman MJ (2005) A normotriglyceridemic, low HDL–cholesterol phenotype is characterised by elevated oxidative stress and HDL particles with attenuated antioxidative activity. Atherosclerosis 182:277–285

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Vekic J, Kotur-Stevuljevic J, Jelic-Ivanovic Z et al (2007) Association of oxidative stress and PON1 with LDL and HDL particle size in middle-aged subjects. Eur J Clin Invest 37:715–723

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Avramoglu RK, Basciano H, Adeli K (2006) Lipid and lipoprotein dysregulation in insulin resistant states. Clin Chim Acta 368:1–19

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Ghandehari H, Kamal-Bahl S, Wong ND (2008) Prevalence and extent of dyslipidemia and recommended lipid levels in US adults with and without cardiovascular comorbidities: the National Health and Nutrition Examination Survey 2003–2004. Am Heart J 156:112–119

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Rosamond W, Flegal K, Furie K et al (2008) Heart disease and stroke statistics—2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 117:e25–e146

    PubMed  Article  Google Scholar 

  11. 11.

    Ford ES, Mokdad AH, Giles WH, Mensah GA (2003) Serum total cholesterol concentrations and awareness, treatment, and control of hypercholesterolemia among US adults: findings from the National Health and Nutrition Examination Survey, 1999–2000. Circulation 107:2185–2189

    PubMed  Article  Google Scholar 

  12. 12.

    Borena W, Stocks T, Jonsson H et al (2011) Serum triglycerides and cancer risk in the metabolic syndrome and cancer (Me-Can) collaborative study. Cancer Causes Control 22:291–299

    PubMed  Article  Google Scholar 

  13. 13.

    Inoue M, Noda M, Kurahashi N et al (2009) Impact of metabolic factors on subsequent cancer risk: results from a large-scale population-based cohort study in Japan. Eur J Cancer Prev 18:240–247

    PubMed  Article  Google Scholar 

  14. 14.

    Tulinius H, Sigfusson N, Sigvaldason H et al (1997) Risk factors for malignant diseases: a cohort study on a population of 22,946 Icelanders. Cancer Epidemiol Biomarkers Prev 6:863–873

    CAS  PubMed  Google Scholar 

  15. 15.

    Agnoli C, Grioni S, Sieri S et al (2014) Colorectal cancer risk and dyslipidemia: a case–cohort study nested in an Italian multicentre cohort. Cancer Epidemiol 38:144–151

    PubMed  Article  Google Scholar 

  16. 16.

    Kitahara CM, Berrington DGA, Freedman ND et al (2011) Total cholesterol and cancer risk in a large prospective study in Korea. J Clin Oncol 29:1592–1598

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  17. 17.

    Tornberg SA, Holm LE, Carstensen JM, Eklund GA (1986) Risks of cancer of the colon and rectum in relation to serum cholesterol and beta-lipoprotein. N Engl J Med 315:1629–1633

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Strohmaier S, Edlinger M, Manjer J et al (2013) Total serum cholesterol and cancer incidence in the Metabolic syndrome and Cancer Project (Me-Can). PLoS One 8:e54242

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  19. 19.

    van Duijnhoven FJ, Bueno-De-Mesquita HB, Calligaro M et al (2011) Blood lipid and lipoprotein concentrations and colorectal cancer risk in the European prospective investigation into cancer and nutrition. Gut 60:1094–1102

    PubMed  Article  Google Scholar 

  20. 20.

    Ahn J, Lim U, Weinstein SJ et al (2009) Prediagnostic total and high-density lipoprotein cholesterol and risk of cancer. Cancer Epidemiol Biomarkers Prev 18:2814–2821

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  21. 21.

    Iso H, Ikeda A, Inoue M et al (2009) Serum cholesterol levels in relation to the incidence of cancer: the JPHC study cohorts. Int J Cancer 125:2679–2686

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Ahmed RL, Schmitz KH, Anderson KE et al (2006) The metabolic syndrome and risk of incident colorectal cancer. Cancer 107:28–36

    PubMed  Article  Google Scholar 

  23. 23.

    Bowers K, Albanes D, Limburg P et al (2006) A prospective study of anthropometric and clinical measurements associated with insulin resistance syndrome and colorectal cancer in male smokers. Am J Epidemiol 164:652–664

    PubMed  Article  Google Scholar 

  24. 24.

    Tsushima M, Nomura AM, Lee J, Stemmermann GN (2005) Prospective study of the association of serum triglyceride and glucose with colorectal cancer. Dig Dis Sci 50:499–505

    PubMed  Article  Google Scholar 

  25. 25.

    Saydah SH, Platz EA, Rifai N et al (2003) Association of markers of insulin and glucose control with subsequent colorectal cancer risk. Cancer Epidemiol Biomarkers Prev 12:412–418

    CAS  PubMed  Google Scholar 

  26. 26.

    Schoen RE, Tangen CM, Kuller LH et al (1999) Increased blood glucose and insulin, body size, and incident colorectal cancer. J Natl Cancer Inst 91:1147–1154

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Chyou PH, Nomura AM, Stemmermann GN (1996) A prospective study of colon and rectal cancer among Hawaii Japanese men. Ann Epidemiol 6:276–282

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Esposito K, Chiodini P, Capuano A et al (2013) Colorectal cancer association with metabolic syndrome and its components: a systematic review with meta-analysis. Endocrine 44:634–647

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Law MR, Thompson SG (1991) Low serum cholesterol and the risk of cancer: an analysis of the published prospective studies. Cancer Causes Control 2:253–261

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Giovannucci E (2007) Metabolic syndrome, hyperinsulinemia, and colon cancer: a review. Am J Clin Nutr 86:s836–s842

    PubMed  Google Scholar 

  31. 31.

    Kritchevsky SB, Kritchevsky D (1992) Serum cholesterol and cancer risk: an epidemiologic perspective. Annu Rev Nutr 12:391–416

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Sidney S, Farquhar JW (1983) Cholesterol, cancer, and public health policy. Am J Med 75:494–508

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 339:b2535

    Google Scholar 

  34. 34.

    Schatzkin A, Hoover RN, Taylor PR et al (1988) Site-specific analysis of total serum cholesterol and incident cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-up Study. Cancer Res 48:452–458

    CAS  PubMed  Google Scholar 

  35. 35.

    Wells GA, O’Connell D, Peterson J, et al. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed 15 June 2014

  36. 36.

    Wu QJ, Yang Y, Vogtmann E et al (2013) Cruciferous vegetables intake and the risk of colorectal cancer: a meta-analysis of observational studies. Ann Oncol 24:1079–1087

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  37. 37.

    Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539–1558

    PubMed  Article  Google Scholar 

  38. 38.

    DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7:177–188

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Hamling J, Lee P, Weitkunat R, Ambuhl M (2008) Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category. Stat Med 27:954–970

    PubMed  Article  Google Scholar 

  40. 40.

    Danesh J, Collins R, Appleby P, Peto R (1998) Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA 279:1477–1482

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Chene G, Thompson SG (1996) Methods for summarizing the risk associations of quantitative variables in epidemiologic studies in a consistent form. Am J Epidemiol 144:610–621

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Greenland S, Longnecker MP (1992) Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol 135:1301–1309

    CAS  PubMed  Google Scholar 

  43. 43.

    Orsini N, Li R, Wolk A et al (2012) Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. Am J Epidemiol 175:66–73

    PubMed Central  PubMed  Article  Google Scholar 

  44. 44.

    Royston P (2000) A strategy for modelling the effect of a continuous covariate in medicine and epidemiology. Stat Med 19:1831–1847

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Bagnardi V, Zambon A, Quatto P, Corrao G (2004) Flexible meta-regression functions for modeling aggregate dose-response data, with an application to alcohol and mortality. Am J Epidemiol 159:1077–1086

    PubMed  Article  Google Scholar 

  46. 46.

    Egger M, Davey SG, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  47. 47.

    Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50:1088–1101

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Esposito K, Chiodini P, Capuano A et al (2013) Metabolic syndrome and postmenopausal breast cancer: systematic review and meta-analysis. Menopause 20:1301–1309

    PubMed  Article  Google Scholar 

  49. 49.

    Esposito K, Chiodini P, Capuano A et al (2013) Effect of metabolic syndrome and its components on prostate cancer risk: meta-analysis. J Endocrinol Invest 36:132–139

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Wei EK, Giovannucci E, Wu K et al (2004) Comparison of risk factors for colon and rectal cancer. Int J Cancer 108:433–442

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  51. 51.

    Sugai T, Habano W, Jiao YF et al (2006) Analysis of molecular alterations in left- and right-sided colorectal carcinomas reveals distinct pathways of carcinogenesis: proposal for new molecular profile of colorectal carcinomas. J Mol Diagn 8:193–201

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  52. 52.

    van Stiphout WA, Hofman A, de Bruijn AM (1987) Serum lipids in young women before, during, and after pregnancy. Am J Epidemiol 126:922–928

    PubMed  Google Scholar 

  53. 53.

    Munzer T, Harman SM, Sorkin JD, Blackman MR (2009) Growth hormone and sex steroid effects on serum glucose, insulin, and lipid concentrations in healthy older women and men. J Clin Endocrinol Metab 94:3833–3841

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  54. 54.

    Kumagai S, Kai Y, Sasaki H (2001) Relationship between insulin resistance, sex hormones and sex hormone-binding globulin in the serum lipid and lipoprotein profiles of Japanese postmenopausal women. J Atheroscler Thromb 8:14–20

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    McKeown-Eyssen G (1994) Epidemiology of colorectal cancer revisited: are serum triglycerides and/or plasma glucose associated with risk? Cancer Epidemiol Biomarkers Prev 3:687–695

    CAS  PubMed  Google Scholar 

  56. 56.

    Esteve E, Ricart W, Fernandez-Real JM (2005) Dyslipidemia and inflammation: an evolutionary conserved mechanism. Clin Nutr 24:16–31

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Erdman SE, Poutahidis T (2010) Roles for inflammation and regulatory T cells in colon cancer. Toxicol Pathol 38:76–87

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  58. 58.

    Kim S, Keku TO, Martin C et al (2008) Circulating levels of inflammatory cytokines and risk of colorectal adenomas. Cancer Res 68:323–328

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  59. 59.

    Wu H, Jiang H, Lu D et al (2009) Effect of simvastatin on glioma cell proliferation, migration, and apoptosis. Neurosurgery 65:1087

    PubMed Central  PubMed  Article  Google Scholar 

  60. 60.

    van Exel E, Gussekloo J, de Craen AJ et al (2002) Low production capacity of interleukin-10 associates with the metabolic syndrome and type 2 diabetes : the Leiden 85-Plus Study. Diabetes 51:1088–1092

    PubMed  Article  Google Scholar 

  61. 61.

    Kontush A, de Faria EC, Chantepie S, Chapman MJ (2005) A normotriglyceridemic, low HDL–cholesterol phenotype is characterised by elevated oxidative stress and HDL particles with attenuated antioxidative activity. Atherosclerosis 182:277–285

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Vekic J, Kotur-Stevuljevic J, Jelic-Ivanovic Z et al (2007) Association of oxidative stress and PON1 with LDL and HDL particle size in middle-aged subjects. Eur J Clin Invest 37:715–723

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Clarke R, Shipley M, Lewington S et al (1999) Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am J Epidemiol 150:341–353

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Al-Delaimy WK, Jansen EH, Peeters PH et al (2006) Reliability of biomarkers of iron status, blood lipids, oxidative stress, vitamin D, C-reactive protein and fructosamine in two Dutch cohorts. Biomarkers 11:370–382

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Bonovas S, Filioussi K, Flordellis CS, Sitaras NM (2007) Statins and the risk of colorectal cancer: a meta-analysis of 18 studies involving more than 1.5 million patients. J Clin Oncol 25:3462–3468

    PubMed  Article  Google Scholar 

  66. 66.

    Cole BF, Logan RF, Halabi S et al (2009) Aspirin for the chemoprevention of colorectal adenomas: meta-analysis of the randomized trials. J Natl Cancer Inst 101:256–266

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgments

None of the grant supports this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Zhong Tian.

Electronic supplementary material

Below is the link to the electronic supplementary material.

10552_2014_507_MOESM1_ESM.tif

Supplementary material 1 Supplementary Figure S1. Forest plots (random effect model) of meta-analysis on the relationship between high-density lipoprotein cholesterol concentrations and colorectal cancer risk. Squares indicate study-specific relative risks (size of the square reflects the study-specific statistical weight); horizontal lines indicate 95 % CIs; and diamond indicates the summary relative risk estimate with its 95 % CI. CC: colon cancer; CI: confidence interval; CRC: colorectal cancer; F: female; M: male; RC: rectal cancer; and RR: relative risk. (TIFF 2,577 kb)

10552_2014_507_MOESM2_ESM.tif

Supplementary material 2 Supplementary Figure S2. Forest plots (random effect model) of meta-analysis on the relationship between low-density lipoprotein cholesterol concentrations and colorectal cancer risk. Squares indicate study-specific relative risks (size of the square reflects the study-specific statistical weight); horizontal lines indicate 95 % CIs; and diamond indicates the summary relative risk estimate with its 95 % CI. CC: colon cancer; CI: confidence interval; CRC: colorectal cancer; F: female; M: male; RC: rectal cancer; and RR: relative risk. (TIFF 1,823 kb)

Supplementary material 3 (DOCX 47 kb)

Supplementary material 4 (DOC 68 kb)

Supplementary material 5 (DOCX 19 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yao, X., Tian, Z. Dyslipidemia and colorectal cancer risk: a meta-analysis of prospective studies. Cancer Causes Control 26, 257–268 (2015). https://doi.org/10.1007/s10552-014-0507-y

Download citation

Keywords

  • Cancer prevention
  • Colorectal neoplasms
  • Dyslipidemia
  • Epidemiology