Skip to main content

Advertisement

SpringerLink
Log in

Dietary patterns and cognitive function in older New Zealand adults: the REACH study

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

Abstract

Purpose

The global population is ageing. Evidence show dietary patterns may be associated with cognitive status in older adults. This cross-sectional study investigated associations between dietary patterns and cognitive function in older adults in New Zealand.

Methods

The REACH study (Researching Eating, Activity, and Cognitive Health) included 371 participants (65–74 years, 36% male) living independently in Auckland, New Zealand. Valid and reproducible dietary patterns were derived, using principal component analysis, from dietary data collected by a 109-item validated food frequency questionnaire. Six cognitive domains (global cognition, attention and vigilance, executive function, episodic memory, working memory, and spatial memory) were tested using COMPASS (Computerised Mental Performance Assessment System). Associations between dietary patterns and cognitive scores, adjusted for age, sex, education, physical activity, energy, and Apolipoprotein E-ε4 status were analysed using multiple linear regression analysis.

Results

Three dietary patterns explained 18% of dietary intake variation—‘Mediterranean style’ (comprising: salad vegetables, leafy cruciferous vegetables, other vegetables, avocados and olives, alliums, nuts and seeds, white fish and shellfish, oily fish, and berries); ‘Western’ (comprising: processed meats, sauces and condiments, cakes, biscuits and puddings, meat pies and chips, and processed fish); and ‘Prudent’ (comprising: dried legumes, soy-based foods, fresh and frozen legumes, whole grains, and carrots). No associations between any cognitive domain and dietary pattern scores were observed. Global cognitive function was associated with being younger and having a university education.

Conclusion

In this cohort of community-dwelling, older adults in New Zealand, current dietary patterns were not associated with cognitive function.

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

Similar content being viewed by others

Availability of data and material and code availability

Data described in the manuscript, code book, and analytic code will not be made available because study participants did not provide informed consent for this to occur.

Abbreviations

APOE-ε4:

Apolipoprotein E-ε4

COMPASS:

Computerised Mental Performance Assessment System

FFQ:

Food frequency questionnaire

KMO:

Kaiser–Meyer–Olkin

MoCA:

Montreal Cognitive Assessment

REACH:

Researching Eating, Activity, and Cognitive Health

References

  1. Horr T, Messinger-Rapport B, Pillai JA (2015) Systematic review of strengths and limitations of randomized controlled trials for non-pharmacological interventions in mild cognitive impairment: focus on Alzheimer’s disease. J Nutr Health Aging 19:141–153. https://doi.org/10.1007/s12603-014-0565-6

    Article  CAS  PubMed  Google Scholar 

  2. Winblad B, Amouyel P, Andrieu S, Ballard C, Brayne C, Brodaty H, Cedazo-Minguez A et al (2016) Defeating Alzheimer’s disease and other dementias: a priority for European science and society. Lancet Neurol 15:455–532. https://doi.org/10.1016/s1474-4422(16)00062-4

    Article  PubMed  Google Scholar 

  3. Small BJ, Rosnick CB, Fratiglioni L, Backman L (2004) Apolipoprotein E and cognitive performance: a meta-analysis. Psychol Aging 19:592–600. https://doi.org/10.1037/0882-7974.19.4.592

    Article  PubMed  Google Scholar 

  4. Wisdom NM, Callahan JL, Hawkins KA (2011) The effects of apolipoprotein E on non-impaired cognitive functioning: a meta-analysis. Neurobiol Aging 32:63–74. https://doi.org/10.1016/j.neurobiolaging.2009.02.003

    Article  CAS  PubMed  Google Scholar 

  5. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C (2014) Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol 13:788–794. https://doi.org/10.1016/s1474-4422(14)70136-x

    Article  PubMed  Google Scholar 

  6. Stephan BCM, Brayne C (2014) Risk factors and screening methods for detecting dementia: a narrative review. J Alzheimers Dis 42:S329–S338. https://doi.org/10.3233/JAD-141413

    Article  PubMed  Google Scholar 

  7. Solfrizzi V, Custodero C, Lozupone M, Imbimbo BP, Valiani V, Agosti P, Schilardi A et al (2017) Relationships of dietary patterns, foods, and micro- and macronutrients with Alzheimer’s disease and late-life cognitive disorders: a systematic review. J Alzheimers Dis 59:815–849. https://doi.org/10.3233/jad-170248

    Article  CAS  PubMed  Google Scholar 

  8. Cao L, Tan L, Wang HF, Jiang T, Zhu XC, Lu H, Tan MS, Yu JT (2016) Dietary patterns and risk of dementia: a systematic review and meta-analysis of cohort studies. Mol Neurobiol 53:6144–6154. https://doi.org/10.1007/s12035-015-9516-4

    Article  CAS  PubMed  Google Scholar 

  9. Francis H, Stevenson R (2013) The longer-term impacts of Western diet on human cognition and the brain. Appetite 63:119–128. https://doi.org/10.1016/j.appet.2012.12.018

    Article  PubMed  Google Scholar 

  10. Wu L, Sun D, Tan Y (2017) Intake of fruit and vegetables and the incident risk of cognitive disorders: a systematic review and meta-analysis of cohort studies. J Nutr Health Aging 21:1284–1290. https://doi.org/10.1007/s12603-017-0875-6

    Article  CAS  PubMed  Google Scholar 

  11. Barbaresko J, Lellmann AW, Schmidt A, Lehmann A, Amini AM, Egert S, Schlesinger S, Nöthlings U (2020) Dietary factors and neurodegenerative disorders: an umbrella review of meta-analyses of prospective studies. Adv Nutr 11:1161–1173. https://doi.org/10.1093/advances/nmaa053

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hu FB (2002) Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 13:3–9. https://doi.org/10.1097/00041433-200202000-00002

    Article  CAS  PubMed  Google Scholar 

  13. Milte CM, McNaughton SA (2016) Dietary patterns and successful ageing: a systematic review. Eur J Nutr 55:423–450. https://doi.org/10.1007/s00394-015-1123-7

    Article  CAS  PubMed  Google Scholar 

  14. Newby PK, Tucker KL (2004) Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 62:177–203. https://doi.org/10.1301/nr.2004.may.177-203

    Article  CAS  PubMed  Google Scholar 

  15. Chen X, Maguire B, Brodaty H, O’Leary F (2018) Dietary patterns and cognitive health in older adults: a systematic review. J Alzheimers Dis 67:583–619. https://doi.org/10.3233/JAD-180468

    Article  Google Scholar 

  16. Kesse-Guyot E, Andreeva VA, Jeandel C, Ferry M, Hercberg S, Galan P (2012) A healthy dietary pattern at midlife is associated with subsequent cognitive performance. J Nutr 142:909–915. https://doi.org/10.3945/jn.111.156257

    Article  CAS  PubMed  Google Scholar 

  17. Samieri C, Jutand MA, Feart C, Capuron L, Letenneur L, Barberger-Gateau P (2008) Dietary patterns derived by hybrid clustering method in older people: association with cognition, mood, and self-rated health. J Am Diet Assoc 108:1461–1471. https://doi.org/10.1016/j.jada.2008.06.437

    Article  PubMed  Google Scholar 

  18. Parrott MD, Shatenstein B, Ferland G, Payette H, Morais JA, Belleville S, Kergoat MJ et al (2013) Relationship between diet quality and cognition depends on socioeconomic position in healthy older adults. J Nutr 143:1767–1773. https://doi.org/10.3945/jn.113.181115

    Article  CAS  PubMed  Google Scholar 

  19. Corley J, Deary IJ (2020) Dietary patterns and trajectories of global and domain-specific cognitive decline in the Lothian Birth Cohort 1936. Br J Nutr. https://doi.org/10.1017/S0007114520005139

    Article  PubMed  Google Scholar 

  20. Kesse-Guyot E, Andreeva VA, Ducros V, Jeandel C, Julia C, Hercberg S, Galan P (2014) Carotenoid-rich dietary patterns during midlife and subsequent cognitive function. Br J Nutr 111:915–923. https://doi.org/10.1017/s0007114513003188

    Article  CAS  PubMed  Google Scholar 

  21. Ashby-Mitchell K, Peeters A, Anstey KJ (2015) Role of dietary pattern analysis in determining cognitive status in elderly Australian adults. Nutrients 7:1052–1067. https://doi.org/10.3390/nu7021052

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chen X, Liu Z, Sachdev PS, Kochan NA, O’Leary F, Brodaty H (2020) Dietary patterns and cognitive health in older adults: findings from the Sydney Memory and ageing study. J Nutr Health Aging 25:255–262. https://doi.org/10.1007/s12603-020-1536-8

    Article  Google Scholar 

  23. Yin ZX, Chen J, Zhang J, Ren ZP, Dong K, Kraus VB, Wang ZQ et al (2018) Dietary patterns associated with cognitive function among the older people in underdeveloped regions: finding from the NCDFaC study. Nutrients 10:12. https://doi.org/10.3390/nu10040464

    Article  CAS  Google Scholar 

  24. Yu FN, Hu NQ, Huang XL, Shi YX, Zhao HZ, Cheng HY (2018) Dietary patterns derived by factor analysis are associated with cognitive function among a middle-aged and elder Chinese population. Psychiatry Res 269:640–645. https://doi.org/10.1016/j.psychres.2018.09.004

    Article  PubMed  Google Scholar 

  25. Okubo H, Inagaki H, Gondo Y, Kamide K, Ikebe K, Masui Y, Arai Y et al (2017) Association between dietary patterns and cognitive function among 70-year-old Japanese elderly: a cross-sectional analysis of the SONIC study. Nutr J 16:56. https://doi.org/10.1186/s12937-017-0273-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Corley J, Starr JM, McNeill G, Deary IJ (2013) Do dietary patterns influence cognitive function in old age? Int Psychogeriatr 25:1393–1407. https://doi.org/10.1017/s1041610213000793

    Article  PubMed  Google Scholar 

  27. Qin B, Adair LS, Plassman BL, Batis C, Edwards LJ, Popkin BM, Mendez MA (2015) Dietary patterns and cognitive decline among Chinese older adults. Epidemiology 26:758–768. https://doi.org/10.1097/ede.0000000000000338

    Article  PubMed  PubMed Central  Google Scholar 

  28. Allès B, Samieri C, Feart C, Jutand MA, Laurin D, Barberger-Gateau P (2012) Dietary patterns: a novel approach to examine the link between nutrition and cognitive function in older individuals. Nutr Res Rev 25:207–222. https://doi.org/10.1017/s0954422412000133

    Article  PubMed  Google Scholar 

  29. Thompson JMD, Wall C, Becroft DMO, Robinson E, Wild CJ, Mitchell EA (2010) Maternal dietary patterns in pregnancy and the association with small-for-gestational-age infants. Br J Nutr 103:1665–1673. https://doi.org/10.1017/S0007114509993606

    Article  CAS  PubMed  Google Scholar 

  30. Wall CR, Gammon CS, Bandara DK, Grant CC, Carr PEA, Morton SMB (2016) Dietary patterns in pregnancy in New Zealand—influence of maternal socio-demographic, health and lifestyle factors. Nutrients 8:16. https://doi.org/10.3390/nu8050300

    Article  CAS  Google Scholar 

  31. Beck KL, Kruger R, Conlon CA, Heath A-LM, Matthys C, Coad J, Stonehouse W (2013) Suboptimal iron status and associated dietary patterns and practices in premenopausal women living in Auckland, New Zealand. Eur J Nutr 52:467–476. https://doi.org/10.1007/s00394-012-0348-y

    Article  CAS  PubMed  Google Scholar 

  32. Schrijvers JK, McNaughton SA, Beck KL, Kruger R (2016) Exploring the dietary patterns of young New Zealand women and associations with BMI and body fat. Nutrients 8:15. https://doi.org/10.3390/nu8080450

    Article  Google Scholar 

  33. Beck KL, Jones B, Ullah I, McNaughton SA, Haslett SJ, Stonehouse W (2018) Associations between dietary patterns, socio-demographic factors and anthropometric measurements in adult New Zealanders: an analysis of data from the 2008/09 New Zealand Adult Nutrition Survey. Eur J Nutr 57:1421–1433. https://doi.org/10.1007/s00394-017-1421-3

    Article  CAS  PubMed  Google Scholar 

  34. Saeedi P, Black K, Haszard J, Skeaff S, Stoner L, Davidson B, Harrex H et al (2018) Dietary patterns, cardiorespiratory and muscular fitness in 9–11-year-old children from Dunedin, New Zealand. Nutrients 10:887. https://doi.org/10.3390/nu10070887

    Article  PubMed Central  Google Scholar 

  35. Mumme K, von Hurst PR, Conlon CA, Jones B, Haskell-Ramsay CF, Stonehouse W, Heath A-LM et al (2019) Study protocol: associations between dietary patterns, cognitive function and metabolic syndrome in older adults—a cross-sectional study. BMC Public Health 19:535. https://doi.org/10.1186/s12889-019-6900-4

    Article  PubMed  PubMed Central  Google Scholar 

  36. Galioto R, Spitznagel MB (2016) The effects of breakfast and breakfast composition on cognition in adults. Adv Nutr 7:576S-589S. https://doi.org/10.3945/an.115.010231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Nasreddine ZS, Phillips NA, Bedirian V, Charbonneau S, Whitehead V, Collin I, Cummings JL, Chertkow H (2005) The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53:695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x

    Article  PubMed  Google Scholar 

  38. Kennedy DO, Wightman EL, Forster J, Khan J, Haskell-Ramsay CF, Jackson PA (2017) Cognitive and mood effects of a nutrient enriched breakfast bar in healthy adults: a randomised, double-blind, placebo-controlled, parallel groups study. Nutrients 9:21. https://doi.org/10.3390/nu9121332

    Article  CAS  Google Scholar 

  39. Veasey RC, Gonzalez JT, Kennedy DO, Haskell CF, Stevenson EJ (2013) Breakfast consumption and exercise interact to affect cognitive performance and mood later in the day. A randomized controlled trial. Appetite 68:38–44. https://doi.org/10.1016/j.appet.2013.04.011

    Article  CAS  PubMed  Google Scholar 

  40. Kennedy DO, Jackson PA, Forster J, Khan J, Grothe T, Perrinjaquet-Moccetti T, Haskell-Ramsay CF (2017) Acute effects of a wild green-oat (Avena sativa) extract on cognitive function in middle-aged adults: a double-blind, placebo-controlled, within-subjects trial. Nutr Neurosci 20:135–151. https://doi.org/10.1080/1028415X.2015.1101304

    Article  PubMed  Google Scholar 

  41. Jackson PA, Wightman EL, Veasey R, Forster J, Khan J, Saunders C, Mitchell S et al (2020) A randomized, crossover study of the acute cognitive and cerebral blood flow effects of phenolic, nitrate and botanical beverages in young, healthy humans. Nutrients 12:2254. https://doi.org/10.3390/nu12082254

    Article  CAS  PubMed Central  Google Scholar 

  42. Haskell-Ramsay CF, Jackson PA, Forster JS, Dodd FL, Bowerbank SL, Kennedy DO (2018) The acute effects of caffeinated black coffee on cognition and mood in healthy young and older adults. Nutrients 10:1386. https://doi.org/10.3390/nu10101386

    Article  CAS  PubMed Central  Google Scholar 

  43. Stonehouse W, Conlon CA, Podd J, Hill SR, Minihane AM, Haskell C, Kennedy D (2013) DHA supplementation improved both memory and reaction time in healthy young adults: A randomized controlled trial. Am J Clin Nutr 97:1134–1143. https://doi.org/10.3945/ajcn.112.053371

    Article  CAS  PubMed  Google Scholar 

  44. Mumme K, Conlon C, von Hurst PR, Jones B, de Seymour J, Heath A-L, Stonehouse W et al (2021) Relative validity and reproducibility of a food frequency questionnaire for assessing dietary patterns and food group intake in older New Zealand adults: the REACH study. J Acad Nutr Diet. https://doi.org/10.1016/j.jand.2021.05.022

    Article  PubMed  Google Scholar 

  45. van Buuren S, Groothuis-Oudshoorn K (2011) Mice: multivariate imputation by chained equations in R. J Stat Softw 45:1–67

    Article  Google Scholar 

  46. The New Zealand Institute for Plant & Food Research Limited, Ministry of Health (2016) New Zealand Food Composition Database 2017: New Zealand FOODfiles(TM) 2016 Version 01. https://www.foodcomposition.co.nz/foodfiles. Accessed 2 May 2021.

  47. Willett W (2012) Issues in analysis and presentation of dietary data. Nutritional epidemiology, 3rd edn. Oxford University Press, New York, pp 306–333

    Chapter  Google Scholar 

  48. McCann SE, Marshall JR, Brasure JR, Graham S, Freudenheim JL (2001) Analysis of patterns of food intake in nutritional epidemiology: food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer. Public Health Nutr 4:989–997. https://doi.org/10.1079/phn2001168

    Article  CAS  PubMed  Google Scholar 

  49. R Core Team (2019) R: a language and environment for statistical computing. Vienna, Austria, R version 3.6.1. https://www.R-project.org. Accessed 21 Jan 2021

  50. Revelle W (2020) psych: Procedures for personality and psychological research. Northwestern University, Evanston, Illinois, USA, R package version 2.0.8. https://CRAN.r-project.org/package=psych. Accessed 2 May 2021

  51. Stevens JP (2009) Exploratory and confirmatory factor analysis. Applied multivariate statistics for the social sciences, 5th edn. Routledge, New York, p 332

    Google Scholar 

  52. Agena Bioscience Inc. Single nucleotide polymorphism detection with iPLEX(R) Assay and the MassARRAY(R) system: efficient, scalable, and cost-effective SNP genotyping and somatic mutation analysis. https://agenabio.com/wp-content/uploads/2016/02/51-20061R3.0_iPLEX_Chemistry_App_Note_0216_WEB.pdf. Accessed 7 May 2021.

  53. Exeter DJ, Zhao J, Crengle S, Lee A, Browne M (2017) The New Zealand Indices of Multiple Deprivation (IMD): a new suite of indicators for social and health research in Aotearoa, New Zealand. PLoS ONE 12:1–19. https://doi.org/10.1371/journal.pone.0181260

    Article  CAS  Google Scholar 

  54. Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE (2017) What is polypharmacy? A systematic review of definitions. BMC Geriatr 17:1–10. https://doi.org/10.1186/s12877-017-0621-2

    Article  Google Scholar 

  55. Willett W (2012) Implications of total energy intake for epidemiologic analyses. Nutritional epidemiology, 3rd edn. Oxford Scholarship Online, New York, pp 261–287

    Chapter  Google Scholar 

  56. Craig C, Marshall A, Sjöström M, Bauman A, Booth M, Ainsworth B, Pratt M et al (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35:1381–1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB

    Article  PubMed  Google Scholar 

  57. Mumme K, Conlon C, von Hurst P, Jones B, de Seymour J, Stonehouse W, Heath AL, Coad J, Haskell-Ramsay C, Mugridge O, Slade C, Beck K (2021) Associations between dietary patterns and metabolic syndrome in older adults in New Zealand: the REACH study. Br J Nutr. https://doi.org/10.1017/S0007114521004682

    Article  PubMed  Google Scholar 

  58. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ et al (2005) Diagnosis and management of the metabolic syndrome—an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation 112:2735–2752. https://doi.org/10.1161/circulationaha.105.169404

    Article  PubMed  Google Scholar 

  59. Fox J, Sanford W (2019) An R companion to applied regression, 3rd edn. Sage, Thousand Oaks

    Google Scholar 

  60. Harrell FEJ et al. (2020) Hmisc: Harrell miscellaneous. R package version 4.4–1. https://CRAN.R-project.org/package=Hmisc. Accessed 7 May 2021

  61. Champely S (2020) pwr: Basic functions for power analysis. R package version 1.3–0, https://CRAN.R-project.org/package=pwr. Accessed 7 May 2021

  62. Balemi A, Chandra D, Curran J, Deppa B, Forster M, McArdle B, Stevenson B, Wild C (2020) s20x: functions for University of Auckland course STATS 201/208 data analysis. R package version 3.1–29. https://CRAN.R-project.org/package=s20x. Accessed 2 May 2021

  63. Wickham H, Averick M, Bryan J, Chang W, D’Agostino McGowan L, François R, Grolemund G et al (2019) Welcome to the tidyverse. J Open Source Softw 4:1686. https://doi.org/10.21105/joss.01686

    Article  Google Scholar 

  64. Lopez ME, Turrero A, Delgado ML, Rodriguez-Rojo IC, Arrazola J, Barabash A, Maestu F, Fernandez A (2017) APOE epsilon4 genotype and cognitive reserve effects on the cognitive functioning of healthy elders. Dement Geriatr Cogn Disord 44:328–342. https://doi.org/10.1159/000481852

    Article  CAS  PubMed  Google Scholar 

  65. Smith T, Gildeh N, Holmes C (2007) The Montreal Cognitive Assessment: validity and utility in a memory clinic setting. Can J Psychiat 52:329–332. https://doi.org/10.1177/070674370705200508

    Article  Google Scholar 

  66. Osawa Y, Arai Y, Takayama M, Hirata T, Kawasaki M, Abe Y, Iinuma T et al (2017) Identification of dietary patterns and their relationships with general and oral health in the very old. Asia Pac J Clin Nutr 26:262–270. https://doi.org/10.6133/apjcn.022016.02

    Article  PubMed  Google Scholar 

  67. Allès B, Samieri C, Jutand M-A, Carmichael P-H, Shatenstein B, Gaudreau P, Ferland G et al (2019) Nutrient patterns, cognitive function, and decline in older persons: results from the Three-City and NuAge studies. Nutrients 11:1808. https://doi.org/10.3390/nu11081808

    Article  CAS  PubMed Central  Google Scholar 

  68. Sugawara N, Yasui-Furukori N, Umeda T, Tsuchimine S, Kaneda A, Tsuruga K, Iwane K et al (2015) Relationship between dietary patterns and cognitive function in a community-dwelling population in Japan. Asia Pac J Public Health 27:NP2651–NP2660. https://doi.org/10.1177/1010539513490194

    Article  PubMed  Google Scholar 

  69. Benton D (2010) Neurodevelopment and neurodegeneration: are there critical stages for nutritional intervention? Nutr Rev 68:S6–S10. https://doi.org/10.1111/j.1753-4887.2010.00324.x

    Article  PubMed  Google Scholar 

  70. Hosking DE, Nettelbeck T, Wilson C, Danthiir V (2014) Retrospective lifetime dietary patterns predict cognitive performance in community-dwelling older Australians. Br J Nutr 112:228–237. https://doi.org/10.1017/s0007114514000646

    Article  CAS  PubMed  Google Scholar 

  71. Richard E, Laughlin G, Kritz-Silverstein D, Reas E, Barrett-Connor E, McEvoy L (2018) Dietary patterns and cognitive function among older community-dwelling adults. Nutrients 10:1088. https://doi.org/10.3390/nu10081088

    Article  CAS  PubMed Central  Google Scholar 

  72. Dearborn-Tomazos JL, Wu AZ, Steffen LM, Anderson CAM, Hu EA, Knopman D, Mosley TH, Gottesman RF (2019) Association of dietary patterns in midlife and cognitive function in later life in US adults without dementia. JAMA Netw Open 2:11. https://doi.org/10.1001/jamanetworkopen.2019.16641

    Article  Google Scholar 

  73. Akbaraly TN, Singh-Manoux A, Marmot MG, Brunner EJ (2009) Education attenuates the association between dietary patterns and cognition. Dement Geriatr Cogn Disord 27:147–154. https://doi.org/10.1159/000199235

    Article  PubMed  PubMed Central  Google Scholar 

  74. Debette S, Seshadri S, Beiser A, Au R, Himali JJ, Palumbo C, Wolf PA, DeCarli C (2011) Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77:461–468. https://doi.org/10.1212/WNL.0b013e318227b227

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Salthouse TA (2009) When does age-related cognitive decline begin? Neurobiol Aging 30:507–514. https://doi.org/10.1016/j.neurobiolaging.2008.09.023

    Article  PubMed  PubMed Central  Google Scholar 

  76. Clare L, Wu YT, Teale JC, MacLeod C, Matthews F, Brayne C, Woods B, Team CF-WS (2017) Potentially modifiable lifestyle factors, cognitive reserve, and cognitive function in later life: a cross-sectional study. PLoS Med 14:14. https://doi.org/10.1371/journal.pmed.1002259

    Article  Google Scholar 

  77. Richards M, Deary IJ (2005) A life course approach to cognitive reserve: a model for cognitive aging and development? Ann Neurol 58:617–622. https://doi.org/10.1002/ana.20637

    Article  PubMed  Google Scholar 

  78. Pettigrew C, Soldan A (2019) Defining cognitive reserve and implications for cognitive aging. Curr Neurol Neurosci Rep 19:12. https://doi.org/10.1007/s11910-019-0917-z

    Article  Google Scholar 

  79. Parr CL, Veierod MB, Laake P, Lund E, Hjartaker A (2006) Test–retest reproducibility of a food frequency questionnaire (FFQ) and estimated effects on disease risk in the Norwegian Women and Cancer Study (NOWAC). Nutr J 5:10. https://doi.org/10.1186/1475-2891-5-4

    Article  Google Scholar 

  80. Edefonti V, Bravi F, Ferraroni M (2017) Breakfast and behavior in morning tasks: facts or fads? J Affect Disord 224:16–26. https://doi.org/10.1016/j.jad.2016.12.028

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the REACH participants and the REACH team, including Nicola Gillies, Harriet Guy, Anne Hiol and Angela Yu for assistance with data collection. Permission has been received from those named in these Acknowledgements.

Funding

Funding was provided by a Health Research Council of New Zealand Emerging Researcher Grant 17/566—Beck: optimising cognitive function: the role of dietary and lifestyle patterns. The funders have no role in the design of the study; collection, analysis, and interpretation of the data; writing manuscripts or publishing results. Lottery Health New Zealand funded Jamie de Seymour’s postdoctoral fellowship. Karen Mumme is funded by a Massey University Doctoral scholarship.

Author information

Authors and Affiliations

  1. College of Health, Massey University, Private Bag 102904, Auckland, 0632, New Zealand

    Karen D. Mumme, Cathryn A. Conlon, Pamela R. von Hurst, Jamie V. de Seymour, Owen Mugridge, Cassandra Slade, Cheryl S. Gammon & Kathryn L. Beck

  2. Department of Statistics, University of Auckland, Auckland, 1010, New Zealand

    Beatrix Jones

  3. Department of Psychology, Northumbria University, Newcastle, NE1 8ST, UK

    Crystal F. Haskell-Ramsay

  4. Health and Biosecurity Business Unit, Commonwealth Scientific Industrial Research Organisation (CSIRO), Adelaide, SA, 5000, Australia

    Welma Stonehouse

  5. Department of Human Nutrition, University of Otago, Dunedin, 9016, New Zealand

    Anne-Louise M. Heath

  6. College of Sciences, Massey University, Palmerston North, 4474, New Zealand

    Jane Coad

Authors
  1. Karen D. Mumme
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cathryn A. Conlon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pamela R. von Hurst
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Beatrix Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Crystal F. Haskell-Ramsay
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jamie V. de Seymour
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Welma Stonehouse
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anne-Louise M. Heath
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jane Coad
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Owen Mugridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Cassandra Slade
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Cheryl S. Gammon
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Kathryn L. Beck
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors’ contributions were as follows: KLB, CAC, PRvH, BJ, CFH-R, WS, A-LMH, and JC designed the research; CSG advised on medication use and APOE-ε4 analysis; KLB, CAC, PRvH, KDM, CS, and OM conducted the research; KDM, KLB, JdS, and BJ analysed the data or performed statistical analysis; KDM wrote the paper; KLB, CAC, PRvH, and KDM had primary responsibility for final content. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Kathryn L. Beck.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Ethics approval

Massey University Human Ethics Committee granted ethical approval: Southern A, Application 17/69.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 158 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mumme, K.D., Conlon, C.A., von Hurst, P.R. et al. Dietary patterns and cognitive function in older New Zealand adults: the REACH study. Eur J Nutr 61, 1943–1956 (2022). https://doi.org/10.1007/s00394-021-02775-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00394-021-02775-x

Keywords

Search

Navigation