Skip to main content
SpringerLink
Log in

Association between animal source foods consumption and risk of hypertension: a cohort study

  • Original Contribution
  • Published:
European Journal of Nutrition Aims and scope Submit manuscript

Abstract

Purpose

This study assessed the association between animal source foods (ASF) consumption and hypertension, a recognised risk factor for cardiovascular disease. Adverse effects of red and processed meat (RPM) consumption and beneficial effects of the consumption of dairy products and other ASF have been discovered separately; however, the constrained nature of food intake has been typically ignored. We assessed the effects of substituting RPM and other ASF.

Methods

We followed-up 5394 Chinese adults (age 18–60 years) at baseline using the China Health and Nutrition Survey from 2004 to 2011. Food consumption was assessed using individual-based consecutive 24-h recall and household-based food weighing approaches. Both traditional substitution analysis and substitution analysis based on compositional transformation were used to assess substitution effects.

Results

In total, 1267 participants were newly diagnosed with hypertension during the median follow-up time of 6.81 years (range, 2.97–6.99 years). The traditional substitution analysis found that substituting eggs for RPM was associated with a lower risk of hypertension. The compositional transformation substitution analysis revealed that replacing RPM with any other ASF was associated with a lower risk of hypertension; it implemented substitutions of one or many ASF for RPM; it also revealed different substitution effects of RPM and dairy products, and substituting dairy products for RPM was associated with reduced hypertension risks.

Conclusion

The compositional transformation substitution analysis considers the constrained and relative nature of food consumption. It is a flexible approach to estimating substitution effects using different patterns to obtain personalised estimation effects and provide individualised dietary recommendations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the China Health and Nutrition Survey (CHNS) repository, [https://www.cpc.unc.edu/projects/china/data/datasets].

References

  1. WHO (2017) A Global Brief on Hypertension. https://apps.who.int/iris/bitstream/10665/79059/1/WHO_DCO_WHD_2013.2_eng.pdf?ua=1&bcsi_scan_43167910db6ab4d9=0&bcsi_scan_filename=WHO_DCO_WHD_2013.2_eng.pdf. Accessed 15 Feb 2017

  2. Rfc NCD (2017) Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants. Lancet 389:37–55. https://doi.org/10.1016/S0140-6736(16)31919-5

    Article  Google Scholar 

  3. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J (2005) Global burden of hypertension: analysis of worldwide data. Lancet 365:217–223. https://doi.org/10.1016/S0140-6736(05)17741-1

    Article  PubMed  Google Scholar 

  4. Appel LJ, Brands MW, Daniels SR, Karanja N, Elmer PJ, Sacks FM, American Heart A (2006) Dietary approaches to prevent and treat hypertension: a scientific statement from the American Heart Association. Hypertension 47:296–308. https://doi.org/10.1161/01.HYP.0000202568.01167.B6

    Article  CAS  PubMed  Google Scholar 

  5. Daniel CR, Cross AJ, Koebnick C, Sinha R (2011) Trends in meat consumption in the USA. Public Health Nutr 14:575–583. https://doi.org/10.1017/S1368980010002077

    Article  PubMed  Google Scholar 

  6. Janet L (2013) China's, World’s Leading Pork Producer. Earth policy institute. https://www.earth-policy.org/data_highlights/2013/highlights39. Accessed 6 June 2013

  7. He Y, Li Y, Yang X, Hemler EC, Fang Y, Zhao L, Zhang J, Yang Z, Wang Z, He L et al (2019) The dietary transition and its association with cardiometabolic mortality among Chinese adults, 1982–2012: a cross-sectional population-based study. Lancet Diabetes Endocrinol 7:540–548. https://doi.org/10.1016/s2213-8587(19)30152-4

    Article  PubMed  Google Scholar 

  8. Golzarand M, Bahadoran Z, Mirmiran P, Azizi F (2016) Protein foods group and 3-year incidence of hypertension: a prospective study from Tehran Lipid and Glucose Study. J Ren Nutr 26:219–225. https://doi.org/10.1053/j.jrn.2016.01.017

    Article  CAS  PubMed  Google Scholar 

  9. Lajous M, Bijon A, Fagherazzi G, Rossignol E, Boutron-Ruault MC, Clavel-Chapelon F (2014) Processed and unprocessed red meat consumption and hypertension in women. Am J Clin Nutr 100:948–952. https://doi.org/10.3945/ajcn.113.080598

    Article  CAS  PubMed  Google Scholar 

  10. Lelong H, Blacher J, Baudry J, Adriouch S, Galan P, Fezeu L, Hercberg S, Kesse-Guyot E (2017) Individual and combined effects of dietary factors on risk of incident hypertension: prospective analysis from the NutriNet-Sante Cohort. Hypertension 70:712–720. https://doi.org/10.1161/HYPERTENSIONAHA.117.09622

    Article  CAS  PubMed  Google Scholar 

  11. Wang L, Manson JE, Buring JE, Sesso HD (2008) Meat intake and the risk of hypertension in middle-aged and older women. J Hypertens 26:215–222. https://doi.org/10.1097/HJH.0b013e3282f283dc

    Article  CAS  PubMed  Google Scholar 

  12. Miguel M, Aleixandre A (2006) Antihypertensive peptides derived from egg proteins. J Nutr 136:1457–1460. https://doi.org/10.1093/jn/136.6.1457

    Article  CAS  PubMed  Google Scholar 

  13. Ruidavets JB, Bongard V, Simon C, Dallongeville J, Ducimetiere P, Arveiler D, Amouyel P, Bingham A, Ferrieres J (2006) Independent contribution of dairy products and calcium intake to blood pressure variations at a population level. J Hypertens 24:671–681. https://doi.org/10.1097/01.hjh.0000217849.10831.16

    Article  CAS  PubMed  Google Scholar 

  14. Schwingshackl L, Schwedhelm C, Hoffmann G, Knüppel S, Iqbal K, Andriolo V, Bechthold A, Schlesinger S, Boeing H (2017) Food groups and risk of hypertension: a systematic review and dose-response meta-analysis of prospective studies. Adv Nutr 8:793–803. https://doi.org/10.3945/an.117.017178

    Article  PubMed  Google Scholar 

  15. DeSalvo KB (2016) Public health 3.0: applying the 2015–2020 Dietary Guidelines for Americans. Public Health Rep 131:518–521. https://doi.org/10.1177/0033354916662207

    Article  PubMed  Google Scholar 

  16. Willett W (2012) Issues in analysis and presentation of dietary data. Oxford University Press, New York

    Book  Google Scholar 

  17. Aitchison J (2003) The statistical analysis of compositional data. Blackburn Press, London

    Google Scholar 

  18. Song M, Giovannucci E (2018) Substitution analysis in nutritional epidemiology: proceed with caution. Eur J Epidemiol 33:137–140. https://doi.org/10.1007/s10654-018-0371-2

    Article  PubMed  Google Scholar 

  19. Dumuid D, Stanford TE, Martin-Fernandez JA, Pedisic Z, Maher CA, Lewis LK, Hron K, Katzmarzyk PT, Chaput JP, Fogelholm M et al (2018) Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res 27:3726–3738. https://doi.org/10.1177/0962280217710835

    Article  PubMed  Google Scholar 

  20. Gupta N, Mathiassen SE, Mateu-Figueras G, Heiden M, Hallman DM, Jorgensen MB, Holtermann A (2018) A comparison of standard and compositional data analysis in studies addressing group differences in sedentary behavior and physical activity. Int J Behav Nutr Phys Act 15:53. https://doi.org/10.1186/s12966-018-0685-1

    Article  PubMed  PubMed Central  Google Scholar 

  21. Arnold KF, Berrie L, Tennant PWG, Gilthorpe MS (2020) A causal inference perspective on the analysis of compositional data. Int J Epidemiol. https://doi.org/10.1093/ije/dyaa021

    Article  PubMed  PubMed Central  Google Scholar 

  22. Solans M, Coenders G, Marcos-Gragera R, Castello A, Gracia-Lavedan E, Benavente Y, Moreno V, Perez-Gomez B, Amiano P, Fernandez-Villa T et al (2019) Compositional analysis of dietary patterns. Stat Methods Med Res 28:2834–2847. https://doi.org/10.1177/0962280218790110

    Article  CAS  PubMed  Google Scholar 

  23. Correa Leite ML (2019) Compositional data analysis as an alternative paradigm for nutritional studies. Clin Nutr ESPEN 33:207–212. https://doi.org/10.1016/j.clnesp.2019.05.011

    Article  PubMed  Google Scholar 

  24. Trinh HT, Morais J, Thomas-Agnan C, Simioni M (2019) Relations between socio-economic factors and nutritional diet in Vietnam from 2004 to 2014: New insights using compositional data analysis. Stat Methods Med Res 28:2305–2325. https://doi.org/10.1177/0962280218770223

    Article  PubMed  Google Scholar 

  25. Leite MLC, Prinelli F (2017) A compositional data perspective on studying the associations between macronutrient balances and diseases. Eur J Clin Nutr 71:1365–1369. https://doi.org/10.1038/ejcn.2017.126

    Article  CAS  PubMed  Google Scholar 

  26. Popkin BM, Du S, Zhai F, Zhang B (2010) Cohort Profile: The China Health and Nutrition Survey–monitoring and understanding socio-economic and health change in China, 1989–2011. Int J Epidemiol 39:1435–1440. https://doi.org/10.1093/ije/dyp322

    Article  PubMed  Google Scholar 

  27. Zhang B, Zhai FY, Du SF, Popkin BM (2014) The China Health and Nutrition Survey, 1989–2011. Obes Rev 15(Suppl 1):2–7. https://doi.org/10.1111/obr.12119

    Article  PubMed  Google Scholar 

  28. Zhang Y, Wang H, Wang X, Liu M, Wang Y, Wang Y, Zhou H (2019) The association between urbanization and child height: a multilevel study in China. BMC Public Health 19:569. https://doi.org/10.1186/s12889-019-6921-z

    Article  PubMed  Google Scholar 

  29. Batis C, Sotres-Alvarez D, Gordon-Larsen P, Mendez MA, Adair L, Popkin B (2014) Longitudinal analysis of dietary patterns in Chinese adults from 1991 to 2009. Br J Nutr 111:1441–1451. https://doi.org/10.1017/S0007114513003917

    Article  CAS  PubMed  Google Scholar 

  30. Xu X, Byles JE, Shi Z, Hall JJ (2015) Evaluation of older Chinese people’s macronutrient intake status: results from the China Health and Nutrition Survey. Br J Nutr 113:159–171. https://doi.org/10.1017/S0007114514003444

    Article  CAS  PubMed  Google Scholar 

  31. Yang Y (2005) China food composition 2004. Peking University Medical Press, Beijing

    Google Scholar 

  32. Liu J (2020) Highlights of the 2018 Chinese hypertension guidelines. Clin Hypertens 26:8. https://doi.org/10.1186/s40885-020-00141-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, Shao L, Tian Y, Dong Y, Zheng C et al (2018) Status of hypertension in China: results from the China Hypertension Survey, 2012–2015. Circulation 137:2344–2356. https://doi.org/10.1161/circulationaha.117.032380

    Article  PubMed  Google Scholar 

  34. U.S. Department of Health and Human Services (2015) Department of Agriculture. 2015–2020 Dietary Guidelines for Americans, 8th Edition. https://health.gov/dietaryguidelines/2015/guidelines. Accessed 14 Nov 2019

  35. Evert AB, Dennison M, Gardner CD, Garvey WT, Lau KHK, MacLeod J, Mitri J, Pereira RF, Rawlings K, Robinson S et al (2019) Nutrition Therapy for Adults With Diabetes or Prediabetes: A Consensus Report. Diabetes Care 42:731–754. https://doi.org/10.2337/dci19-0014

    Article  PubMed  PubMed Central  Google Scholar 

  36. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW et al (2019) 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: a Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 140:e596–e646. https://doi.org/10.1161/cir.0000000000000678

    Article  PubMed  PubMed Central  Google Scholar 

  37. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB (2008) Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med 168:713–720. https://doi.org/10.1001/archinte.168.7.713

    Article  PubMed  Google Scholar 

  38. Zhang Y, Zhang DZ (2018) Red meat, poultry, and egg consumption with the risk of hypertension: a meta-analysis of prospective cohort studies. J Hum Hypertens 32:507–517. https://doi.org/10.1038/s41371-018-0068-8

    Article  CAS  PubMed  Google Scholar 

  39. Aljefree N, Ahmed F (2015) Association between dietary pattern and risk of cardiovascular disease among adults in the Middle East and North Africa region: a systematic review. Food Nutr Res 59:27486. https://doi.org/10.3402/fnr.v59.27486

    Article  PubMed  Google Scholar 

  40. Drouin-Chartier JP, Brassard D, Tessier-Grenier M, Cote JA, Labonte ME, Desroches S, Couture P, Lamarche B (2016) Systematic Review of the Association between Dairy Product Consumption and Risk of Cardiovascular-Related Clinical Outcomes. Adv Nutr 7:1026–1040. https://doi.org/10.3945/an.115.011403

    Article  PubMed  Google Scholar 

  41. Soedamah-Muthu SS, Verberne LD, Ding EL, Engberink MF, Geleijnse JM (2012) Dairy consumption and incidence of hypertension: a dose-response meta-analysis of prospective cohort studies. Hypertension 60:1131–1137. https://doi.org/10.1161/HYPERTENSIONAHA.112.195206

    Article  CAS  PubMed  Google Scholar 

  42. Yang B, Shi MQ, Li ZH, Yang JJ, Li D (2016) Fish, long-chain n-3 PUFA and incidence of elevated blood pressure: a meta-analysis of prospective cohort studies. Nutrients. https://doi.org/10.3390/nu8010058

    Article  PubMed  Google Scholar 

  43. Zhuang P, Jiao J, Wu F, Mao L, Zhang Y (2020) Associations of meat consumption and changes with all-cause mortality in hypertensive patients during 114-year follow-up: Findings from a population-based nationwide cohort. Clin Nutr. https://doi.org/10.1016/j.clnu.2020.06.040

    Article  PubMed  Google Scholar 

  44. Greenacre M (2019) Compositional data analysis in practice. CRC Press, Taylor & Francis Group, Boca Raton

    Google Scholar 

  45. Kipnis V, Freedman LS, Brown CC, Hartman A, Schatzkin A, Wacholder S (1993) Interpretation of energy adjustment models for nutritional epidemiology. Am J Epidemiol 137:1376–1380. https://doi.org/10.1093/oxfordjournals.aje.a116647

    Article  CAS  PubMed  Google Scholar 

  46. Leite ML (2016) Applying compositional data methodology to nutritional epidemiology. Stat Methods Med Res 25:3057–3065. https://doi.org/10.1177/0962280214560047

    Article  PubMed  Google Scholar 

  47. Stamler J, Caggiula AW, Grandits GA (1997) Relation of body mass and alcohol, nutrient, fiber, and caffeine intakes to blood pressure in the special intervention and usual care groups in the Multiple Risk Factor Intervention Trial. Am J Clin Nutr 65:338S-365S. https://doi.org/10.1093/ajcn/65.1.338S

    Article  CAS  PubMed  Google Scholar 

  48. Stamler J, Liu K, Ruth KJ, Pryer J, Greenland P (2002) Eight-year blood pressure change in middle-aged men: relationship to multiple nutrients. Hypertension 39:1000–1006. https://doi.org/10.1161/01.hyp.0000016178.80811.d9

    Article  CAS  PubMed  Google Scholar 

  49. Hua NW, Stoohs RA, Facchini FS (2001) Low iron status and enhanced insulin sensitivity in lacto-ovo vegetarians. Br J Nutr 86:515–519. https://doi.org/10.1079/bjn2001421

    Article  CAS  PubMed  Google Scholar 

  50. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, Britt EB, Fu X, Wu Y, Li L et al (2013) Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med 19:576–585. https://doi.org/10.1038/nm.3145

    Article  CAS  PubMed  Google Scholar 

  51. Sacks FM, Campos H (2010) Dietary therapy in hypertension. N Engl J Med 362:2102–2112. https://doi.org/10.1056/NEJMct0911013

    Article  CAS  PubMed  Google Scholar 

  52. McCarron DA, Reusser ME (2001) Are low intakes of calcium and potassium important causes of cardiovascular disease? Am J Hypertens 14:206S-212S. https://doi.org/10.1016/s0895-7061(01)02090-8

    Article  CAS  PubMed  Google Scholar 

  53. Arjmand G, Shahraki M, Rahati S, Shahraki T (2016) food patterns, lifestyle, and hypertension. Zahedan J Res Med Sci 18:e7547. https://doi.org/10.17795/zjrms-7547

    Article  CAS  Google Scholar 

  54. Sun Z (2015) Aging, arterial stiffness, and hypertension. Hypertension 65:252–256. https://doi.org/10.1161/hypertensionaha.114.03617

    Article  CAS  PubMed  Google Scholar 

  55. Aguila MB, Mandarim-de-Lacerda CA (2003) Heart and blood pressure adaptations in Wistar rats fed with different high-fat diets for 18 months. Nutrition 19:347–352. https://doi.org/10.1016/s0899-9007(02)00934-6

    Article  CAS  PubMed  Google Scholar 

  56. Roza NA, Possignolo LF, Palanch AC, Gontijo JA (2016) Effect of long-term high-fat diet intake on peripheral insulin sensibility, blood pressure, and renal function in female rats. Food Nutr Res 60:28536. https://doi.org/10.3402/fnr.v60.28536

    Article  CAS  PubMed  Google Scholar 

  57. Staessen J, Fagard R, Amery A (1991) Isolated systolic hypertension in the elderly: implications of Systolic Hypertension in the Elderly Program (SHEP) for clinical practice and for the ongoing trials. J Hum Hypertens 5:469–474

    CAS  PubMed  Google Scholar 

  58. Van Vliet BN, Montani JP (2008) The time course of salt-induced hypertension, and why it matters. Int J Obes (Lond) 32(Suppl 6):S35-47. https://doi.org/10.1038/ijo.2008.205

    Article  Google Scholar 

Download references

Acknowledgements

This research uses data from the China Health and Nutrition Survey (CHNS). We thank the China National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, The University of North Carolina at Chapel Hill and the Fogarty International Center for providing the data use. We also thank the China-Japan Friendship Hospital and the Chinese Ministry of Health for support for the CHNS surveys.

Funding

This study was funded by the National Natural Science Foundation of China (Item number: 81872715) and the Innovation Foundation for Graduate Student of Shanxi Province (Grant number: 2018JD25). The data used in this study were from the China Health and Nutrition Survey (CHNS), 2004–2011, which is supported by the National Institutes of Health (Grant numbers: R01-HD30880, DK056350, and R01- HD38700).

Author information

Authors and Affiliations

  1. Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 Xinjiannanlu Street, Taiyuan, 030001, Shanxi, People’s Republic of China

    Jie Liang, Jun-Kang Zhao, Ju-Ping Wang & Tong Wang

Authors
  1. Jie Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun-Kang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ju-Ping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Author contributions to manuscript: J. Liang and T. Wang designed the research; J. Liang and J. Zhao contributed to acquisition of the data; J. Liang and J. Wang modelled the CoTSA; J. Liang and J. Wang analyzed data; J. Liang wrote the paper; T. Wang and J. Zhao revised the paper; T. Wang had primary responsibility for the final content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Tong Wang.

Ethics declarations

Conflicts of interests

The authors have no conflicts of interest.

Ethics approval

The China Health and Nutrition Survey (CHNS) study was conducted according to the Helsinki declaration and approved by the institutional review committees of the University of North Carolina at Chapel Hill and the Chinese Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention.

Consent to participate

All participants provided their written informed consent prior to participation.

Code availability

Compositional package in R software (version 3.6.1), [https://cran.r-project.org/web/packages/Compositional/index.html].

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 270 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, J., Zhao, JK., Wang, JP. et al. Association between animal source foods consumption and risk of hypertension: a cohort study. Eur J Nutr 60, 2469–2483 (2021). https://doi.org/10.1007/s00394-020-02423-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00394-020-02423-w

Keywords

Search

Navigation