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Dietary lutein and zeaxanthin are associated with working memory in an older population

Published online by Cambridge University Press:  30 April 2020

Krystle E Zuniga*
Affiliation:
Department of Oncology, Dell Medical School, University of Texas at Austin, Austin, TX78712, USA
Nicholas J Bishop
Affiliation:
Family and Child Development, Texas State University, San Marcos, TX78666, USA
Alexandria S Turner
Affiliation:
Nutrition and Foods, Texas State University, San Marcos, TX78666, USA
*
*Corresponding author: Email krystle.zuniga@austin.utexas.edu
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Abstract

Objective:

The purpose of the study was to examine the association between dietary lutein and zeaxanthin (L + Z) intake and immediate word recall (IWR) and delayed word recall (DWR), and to identify the major contributors to dietary L + Z intake in a recent and representative sample of the older US population.

Design:

In this cross-sectional analysis, multivariate path analytic models estimated the association between L + Z consumption and cognitive performance while adjusting for covariates.

Setting:

Observations were drawn from the 2014 Health and Retirement Study, a nationally representative panel study of older US adults, and the 2013 Health Care and Nutrition Study, which assessed dietary intake via FFQ in a subsample of respondents.

Participants:

The analytic sample included 6390 respondents aged ≥50 years.

Results:

L + Z intake was 2·44 ± 2·32 mg/d on average, and L + Z intake differed significantly across quartiles (P < 0·001). For example, average L + Z intake in Q1 was 0·74 ± 0·23 mg/d and in Q4 was 5·46 ± 2·88 mg/d. In covariate adjusted models, older adults in the highest quartiles of L + Z intake had significantly greater IWR and DWR scores than those in the lowest quartile. Leafy vegetables, cruciferous vegetables, dark yellow vegetables, fish and seafood, legumes, eggs and fruit were significant and meaningful predictors of dietary L + Z intake.

Conclusion:

A high consumption of vegetables, fish and seafood, legumes, eggs and fruit is associated with a higher intake of L + Z and greater word recall among older adults.

Type
Research paper
Copyright
© The Authors 2020

Lutein and zeaxanthin (L + Z) belong to the xanthophyll family of carotenoids, commonly found in yellow, orange and dark green fruits and vegetables, including kale, spinach and carrots. L + Z are commonly associated with eye health, as they are the main dietary carotenoids found in the retina of humans, and protect the macula from blue light damage and enhance visual acuity(Reference Abdel-Aal, Akhtar and Zaheer1). Additionally, L + Z have been found to be the predominant carotenoids in the brain of both infants and older adults, making up approximately two-thirds of the overall carotenoid concentrations found in the brain(Reference Tanprasertsuk, Li and Bernstein2Reference Craft, Haitema and Garnett4). Emerging evidence suggests that L + Z may play a critical role in the development and preservation of cognitive function across the lifespan(Reference Johnson5,Reference Johnson6) .

Higher serum L + Z levels have been correlated with a lower likelihood of developing dementia and Alzheimer’s disease, as well as a reduced risk of Alzheimer’s disease-related mortality in older adults(Reference Feart, Letenneur and Helmer7,Reference Min and Min8) . A recent cross-sectional analysis of older US adults identified higher dietary intakes of L + Z to be associated with greater cognitive performance across multiple cognitive domains(Reference Christensen, Gleason and Mares9). Macular pigment optical density, a biomarker of L + Z status, was positively associated with cognitive performance in both healthy, community-dwelling older adults(Reference Ajana, Weber and Helmer10) and individuals with mild cognitive impairment(Reference Renzi, Dengler and Puente11). The few randomised controlled trials exploring L + Z supplementation in older adults have demonstrated cognitive benefits of these carotenoids(Reference Johnson, McDonald and Caldarella12Reference Hammond, Miller and Bello14). Four months of L + Z supplementation (12 mg/d), with or without DHA, resulted in a significant improvement in verbal fluency scores of older women(Reference Johnson, McDonald and Caldarella12). More recently, 12 months of supplementation in community-dwelling older adults significantly improved complex attention and cognitive flexibility(Reference Hammond, Miller and Bello14), and those with higher serum and retinal L + Z levels demonstrated enhanced neural efficiency(Reference Mewborn, Lindbergh and Robinson15). Previous L + Z supplementation trials have utilised approximately 10–12 mg/d of L + Z to elicit cognitive benefits, yet the average US adult consumes approximately 1–2 mg/d of L + Z(Reference Ranard, Jeon and Mohn16). Therefore, it is important to identify the dietary L + Z intakes that elicit cognitive benefits and the most significant dietary sources of L + Z in older US adults.

Most of the available observational research on cognitive benefits of L + Z has been limited to identifying the relationship between L + Z intake and risk of neurodegenerative diseases rather than current cognitive performance. Evaluating the maintenance of cognitive health is important in understanding L + Z’s role in the delay of cognitive impairment. In addition, only two of the few population-based studies available were conducted in a sample from the US population(Reference Min and Min8,Reference Christensen, Gleason and Mares9) . The purpose of the current study was to examine the association between dietary L + Z intake and cognitive function in a recent and nationally representative sample of the older US population. Additionally, we aimed to identify the major contributors to dietary L + Z intake in older adults.

Experimental methods

Observations were drawn from the Health and Retirement Study (HRS), a nationally representative panel study of older US adults beginning in 1992 with biennial follow-up. The HRS uses a dual-modality interview approach with around 43 % of interviews in 2014 administered face-to-face and 57 % of interviews administered through telephone. The 2013 Health Care and Nutrition Study (HCNS) is a supplemental off-year mail-out study measuring food consumption in a subsample of HRS respondents and is based on the Harvard FFQ developed by Willett and colleagues(Reference Willet, Sampson and Stampfer17) and utilises nutrient tables developed by the Harvard School of Public Health(18). The HRS is funded by the National Institute on Aging (NIA) and the Survey Research Center at the University of Michigan, and the HCNS was funded by the NIA (U01 AG009740) and conducted by the Survey Research Center of the Institute for Social Research at the University of Michigan(19). The study was deemed to be eligible for exemption by the Texas State University IRB.

In late 2013, questionnaires were mailed to a subsample of HRS respondents (n 12 418) with 8073 respondents completing the HCNS (simple response rate = 65 %). The final data contained 8035 respondents, with 97 % of them answering ≥90 % of the food consumption questions. Of the 8035 HCNS respondents with complete food consumption data, 238 were removed from the analytic sample due to reported age being <50 years, 581 were removed due to daily energy intakes falling outside the commonly used allowable range of 2092–14644 kJ/d for women and 3347–16736 kJ/d for men(Reference Willett20), 643 were removed with missing word recall scores in 2014, as were 162 respondents reporting a previously diagnosed memory-related disease. Finally, twenty-one cases with invalid population weights were removed, resulting in an analytic sample size of 6390.

L + Z intake and food groups: The HCNS FFQ asked respondents to report average number of servings of 164 unique food items over the past 12 months, which were converted to daily portion sizes. Missing data was imputed based on six food items with the least missing data as well as respondents’ race/ethnicity, gender, years of education and BMI. A list of other food items eaten at least once per week was mapped to the nutrient dataset, then totals for each nutrient were calculated(19). The measure of L + Z examined was a sum of both L + Z intake reported in milligrams. For models estimating the association between L + Z and word recall, quartiles of L + Z were calculated, and the lowest quartile was used as the reference category.

For the analyses identifying the foods that contributed the most to L + Z intake, food items were grouped, and five food items were excluded from analysis based on recommendations provided by the Food Patterns Equivalence Database by the United States Department of Agriculture(Reference Bowman, Clemens and Friday21). The remaining 159 food items were grouped based on nutritional similarity, then summed to represent daily intake of thirty-five separate food groups. Table 4 describes the food groups and excluded food items. For clarity, we use the term ‘food groups’ to describe the thirty-five separate foods and food groups used to identify dietary profiles. Due to non-normality in certain food groups, scores were log-transformed with an offset of 0·01 to improve normality and allow inclusion of individuals reporting non-intake of a given food group.

Cognitive function: The association between L + Z intake and cognitive function was evaluated on the cognitive domain of working memory. Episodic memory is particularly useful in measuring subclinical changes in cognitive performance among aging adults(Reference Runge, Craig and Jim22). Working memory and fluid processing ability measures were assessed using immediate word recall (IWR) and delayed word recall (DWR) scores. These two tests (IWR and DWR) measure episodic verbal memory via free recall and have been demonstrated to be sensitive to changes in fluid cognitive abilities(Reference Small, Stern and Tang23). The IWR score was measured by giving ten words from a list of common nouns and asking the respondent to recall them immediately after hearing the list, with the number of words correctly recalled providing the IWR score(Reference Ofstedal, Fisher and Herzog24). After an estimated 5 min of additional questioning had passed, the interviewer asked the participant to recall the words again, and the number of words correctly recalled was the DWR score.

Covariates: Measures taken from the 2014 HRS were included as covariates to adjust for risk factors of cognitive decline and to reduce the likelihood of alternative explanations when interpreting our results. Indicators of respondents’ demographic characteristics included age, gender (1 = female, 0 = male), race/ethnicity (White, Black, Hispanic, other), marital status (1 = partnered or married, 0 = single, divorced or widowed) and retirement status (1 = retired, 0 = not retired). Measures of socioeconomic context included education (<12 years of education, 12 years of education, >12 years of education), longest occupational tenure (white-collar, blue-collar, female homemaker, other occupational tenure) and log-transformed household income and assets. Measures of obesity and health behaviours included BMI (underweight: BMI < 18·5 kg/m2; normal weight: 18·5 kg/m2 ≤ BMI < 25 kg/m2; overweight: 25 kg/m2 ≤ BMI < 30 kg/m2; obese: BMI ≥ 30 kg/m2), vigorous physical activity (participation in activities such as sports, heavy housework or a job that involves physical labour, classifiable into no vigorous physical activity, vigorous physical activity less than once per week and vigorous physical activity more than once per week), current smoking status (1 = current smoker, 0 = no current smoker) and alcohol consumption (non-drinkers; moderate drinkers – men: 1–14 drinks per week, females: 1–7 drinks per week; and heavy drinkers – men: >14 drinks per week, females: >7 drinks per week). Health status was measured as a sum of doctor-diagnosed chronic conditions (high blood pressure, cancer, diabetes, lung disease, heart problems, stroke, psychiatric problems and arthritis), and disability was measured as the sum of eleven indicators of limitation in physical mobility (difficulty in any of the following activities: stooping or crouching, climbing one flight of stairs without resting, climbing several flights of stairs without resting, moving large objects, sitting in a chair for 2 hours, getting up from a chair after sitting for a long period, lifting weights >10 pounds, raising arms above shoulder level, walking one block, walking several blocks, picking up a dime from a table). Mobility limitations were log-transformed with an offset of 0·1 to adjust for non-normality (excluding descriptive statistics).

Statistics

Multivariate path analytic models with traditional multiple regression assumptions for normally distributed outcomes were used to test whether estimated L + Z intake in 2013 was associated with immediate and delayed word recall status in 2014. The models were jointly estimated, meaning that IWR and DWR were regressed on L + Z quartiles and covariates in a single statistical model, allowing an adjustment for the correlation between IWR and DWR outcome measures. To identify which food groups were most strongly associated with estimated L + Z intake, L + Z intake was regressed on the thirty-five food groups in a multiple regression model. Standardised regression estimates were produced to identify which food groups were most strongly associated with L + Z levels. Mplus (version 8.1) was used to estimate both the multivariate path analytic models and the L + Z food group regression while adjusting for the complex sampling design of HRS and multicollinearity between independent variables, as well as addressing missing data through maximum likelihood(Reference Muthén and Muthén25). Bivariate associations among L + Z intake, word recall and participant characteristics were estimated using SAS (version 9.4)(26). For L + Z intake and cognitive outcome measures, overall differences across L + Z intake quartiles were estimated using ANOVA with bivariate follow-up through least squares mean differences. To reduce the likelihood of type 1 error due to multiple comparisons, the significance level for each follow-up test was adjusted using Bonferroni correction. All statistical estimates were adjusted for complex survey design.

Results

Table 1 displays the demographic characteristics of study participants across quartiles of L + Z intake. The average age of participants at baseline was 65·3 years, and the majority of the sample was female (55·5 %) and White (79·8 %).

Table 1 Descriptive statistics for participant characteristics for analytic sample by lutein and zeaxanthin quartiles, Health Care and Nutrition Study 2013/Health and Retirement Study 2014

Means and percentages adjusted for complex survey design and may not match percentages based on non-weighted sample sizes.

* Household income and household assets reported on $10 000s; statistical tests based on log-transformed household income and assets.

As shown in Table 2, mean L + Z intake in the sample was 2·4 mg/d. Mean IWR and DWR significantly differed by L + Z quartile. Those in the highest quartile of L + Z intake had significantly greater immediate and delayed word recall scores than all other quartiles, and those in Q3 had significantly greater IWR and DWR compared to those in Q1. Generally, those above the median of L + Z intake appeared to have better working memory at baseline than those in the lowest L + Z intake quartile.

Table 2 Descriptive statistics for lutein and zeaxanthin consumption and cognitive measures, Health Care and Nutrition Study 2013/Health and Retirement Study 2014

Means adjusted for complex survey design.

a,bMatching superscript letters denote non-significant pairwise comparison.

Table 3 includes regression estimates from the multivariate path analytic models regressing immediate and delayed word recall scores on L + Z quartiles while adjusting for covariates. When examining differences by L + Z quartile, L + Z intake appeared to be positively associated with baseline IWR and DWR. For example, respondents in Q4 had IWR scores that were 0·25 unit greater (se = 0·07, P < 0·001) and DWR scores that were 0·28 unit greater (se = 0·08, P = 0·001) than respondents in the lowest quartile of L + Z intake.

Table 3 Regression estimates from models regressing immediate and delayed word recall on lutein and zeaxanthin intake quartiles, 2013 Health Care and Nutrition Study/2014 Health and Retirement Study

n 6390; all models adjusted for the following covariates: age, log-transformed household income, log-transformed household assets, doctor-diagnosed chronic conditions, log-transformed mobility limitations, gender, race/ethnicity, marital status, retirement status, education, longest occupational tenure, BMI, vigorous physical activity, smoking status and alcohol consumption.

Table 4 lists the food items that were categorised in the thirty-five food groups used to predict L + Z intake as shown in Table 5. Table 5 is sorted by the direction and magnitude of standardised regression estimates, indicating which food groups were the strongest contributors to L + Z intake. Leafy vegetables, cruciferous vegetables, dark yellow vegetables, fish and seafood, legumes, eggs, fruit, whole grain and fruit juice were significantly positively associated with L + Z intake, and red meat and mayonnaise and creamy dressing were negatively associated with L + Z intake, at the significance level of P < 0·001. The r-squared for the regression was 0·737, indicating that 73·7 % of the variation in L + Z intake was attributable to the food groups included in the model.

Table 4 Food groupings used to estimate lutein + zeaxanthin intake, 2013 Health Care and Nutrition Study

Food items excluded from analysis: Splenda, artificial sweetener, garlic, low-carbohydrate bars, plain water.

Some individual food items were not collapsed into groups due to the combination of multiple food items used to produce the food (i.e. pizza) or having varying preparation techniques (i.e. potatoes).

Table 5 Standardised regression estimates identifying food groups as primary contributors to lutein and zeaxanthin intake, 2013 Health Care and Nutrition Study

B, standardised regression estimate; r-squared = 0·737.

Discussion

In a nationally representative sample of older US adults, individuals with higher L + Z intakes had better episodic memory performance. Episodic memory – the ability to recall and elicit an event from a specific time and place – declines with age, especially after 60 years of age(Reference Hasselmo27Reference Nyberg29). Lower episodic memory scores have previously been demonstrated to be a significant predictor of mild cognitive impairment progressing into Alzheimer’s disease(Reference Blacker, Lee and Muzikansky30). Brain regions such as the hippocampus, neocortex and temporal lobe play a crucial role in episodic memory. Interestingly, the hippocampus accumulates more L + Z relative to other carotenoids(Reference Nyberg29,Reference Mohn, Erdman and Kuchan31) . A recent cross-sectional analysis of older US adults also identified dietary intakes of L + Z to be positively associated with a measure of immediate and delayed word recall(Reference Christensen, Gleason and Mares9). Thus, L + Z may play an important role in preserving episodic memory during aging. The mechanisms by which L + Z elicits cognitive benefits remain poorly understood, but there is evidence to suggest that L + Z function as dietary antioxidants. Reducing oxidative stress by acting as ROS scavengers may prevent subsequent neuroinflammation, attenuating cognitive decline(Reference Johnson5,Reference Erdman, Smith and Kuchan32) .

The average US adult consumes approximately 1–2 mg/d of L + Z(Reference Ranard, Jeon and Mohn16). Previous L + Z supplementation trials have utilised approximately 10–12 mg/d of L + Z to elicit cognitive benefits(Reference Johnson, McDonald and Caldarella12Reference Mewborn, Lindbergh and Robinson15). In our sample, cognitive performance was significantly higher in quartiles 3 and 4, which had a mean daily L + Z intake of 2·4 and 5·5 mg, respectively, which is well below that used in supplementation studies but higher than average US intake. Therefore, a supply of L + Z needed to elicit cognitive benefits may be achievable through dietary intake. In our sample, leafy vegetables, cruciferous vegetables, dark yellow vegetables, other vegetables, eggs and fruits were significantly associated with total L + Z intake. Similarly, in a sample of healthy adults, 75 % of L + Z intake was from vegetables, while egg and fruit consumption provided substantially low L + Z to the diet, at only 10 and 4 %, respectively(Reference Estévez-Santiago, Olmedilla-Alonso and Beltrán-de-Miguel33). Furthermore, green vegetables were the primary contributors, followed by white/yellow and red/orange vegetables(Reference Estévez-Santiago, Olmedilla-Alonso and Beltrán-de-Miguel33). Longitudinal studies have demonstrated a relationship between higher vegetable consumption and a reduced risk of cognitive decline in older adults(Reference Kang, Ascherio and Grodstein34Reference Morris, Wang and Barnes36). While these studies did not examine L + Z specifically, vegetables are a significant contributor to dietary L + Z intake and, therefore, may play a role in cognitive performance. Fish and seafood intake was also associated with L + Z accumulation in our sample. Although not a significant source of L + Z(37), fish and seafood are rich sources of the carotenoid astaxanthin and EPA and DHA – n-3 fatty acids known for their neuroprotective properties(Reference Fotuhi, Mohassel and Yaffe38,Reference López-Cervantes, Sánchez-Machado, Nabavi and Silva39) .

Strengths of this study include the analysis of a recent and representative sample of the older US population. A limitation was reliance on self-reported dietary L + Z intake, which may not have accurately reflected the bioavailability and bioactivity of these xanthophylls. Evidence is mixed on the association between intakes of specific L + Z-containing foods and serum and retinal concentrations(Reference Estévez-Santiago, Olmedilla-Alonso and Beltrán-de-Miguel33,Reference Olmedilla-Alonso, Beltrán-de-Miguel and Estévez-Santiago40Reference Sommerburg, Keunen and Bird42) . Further, we cannot discount the possibility that the cognitive benefits associated with L + Z intake have been due to an overall healthy dietary pattern. In our analysis, associating L + Z intake with food groups suggested that L + Z could be a possible marker of a plant-based dietary pattern that is high in fruits, vegetables, whole grains and lean proteins and low in red meat and saturated fat. Similar dietary patterns, such as the Mediterranean diet and DASH diet, have been demonstrated to reduce the risk of cognitive decline and/or dementia(Reference van de Rest, Berendsen and Haveman-Nies43). Although a high L + Z intake was reflective of a healthier diet pattern, L + Z may have a significant and unique role in cognitive health. Our study supports the growing body of observational, interventional and mechanistic evidence on the neuroprotective properties of L + Z. However, cognitive benefits directly attributable to L + Z as part of an overall healthy dietary pattern requires further study. Additionally, this sample of community-dwelling older adults limits the generalisability of our findings by excluding institutionalised older adults who are at a greater risk of cognitive impairment or decline. Although a cross-sectional analysis was conducted, the HRS is a prospective follow-up study, and additional data will be available in the future to examine the association between L + Z intake and cognitive decline or development of neurodegenerative disease. Further research is needed to examine the association between L + Z intake and cognitive domains other than working memory. The HRS administers all cognitive assessments only to adults ≥65 years of age, preventing us from examining the role of L + Z intake in global cognitive function and alternate cognitive subdomains when analysing the complete HCNS sample.

Conclusion

As more evidence suggests that a healthy dietary pattern can benefit cognitive function, observing the role that specific nutrients have on improving cognitive health is imperative for determining the potential to delay cognitive impairment. L + Z is a prevalent carotenoid in the adult brain(Reference Vishwanathan, Kuchan and Sen3,Reference Johnson, Vishwanathan and Johnson44) , and our findings contribute to the growing literature on the positive relationship between L + Z intake and cognitive health in older adults(Reference Lindbergh, Renzi-Hammond and Hammond13,Reference Power, Coen and Beatty28,Reference Vishwanathan, Iannaccone and Scott45) . Older adults may benefit from a higher intake of assorted vegetables, fruits and eggs, as L + Z may play a role in delaying cognitive decline, specifically protecting episodic memory. Further research is needed to better understand the mechanism and relationship of L + Z with cognitive function.

Acknowledgements

Acknowledgements: None. Financial support: This work was supported by the American Egg Board/Egg Nutrition Center. The American Egg Board/Egg Nutrition Center had no role in the design, analysis or writing of this article. Conflicts of interest: The authors declare no conflicts of interest. Authorship: Conceptualisation: K.Z. and N.B.; methodology: K.Z and N.B.; formal analysis: N.B.; data curation: N.B.; writing – original draft: A.T., K.Z., N.B.; writing – review and editing: A.T., K.Z., N.B.; funding acquisition: K.Z. and N.B. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving study participants were approved by the University of Michigan Institutional Review Board. Written informed consent was obtained from all subjects.

References

Abdel-Aal, ESM, Akhtar, H, Zaheer, K et al. (2013) Dietary sources of lutein and zeaxanthin carotenoids and their role in eye health. Nutrients 5, 11691185.CrossRefGoogle Scholar
Tanprasertsuk, J, Li, B, Bernstein, PS et al. (2016) Relationship between concentrations of lutein and StARD3 among pediatric and geriatric human brain tissue. PLoS One 11, e0155488.10.1371/journal.pone.0155488CrossRefGoogle ScholarPubMed
Vishwanathan, R, Kuchan, MJ, Sen, S et al. (2014) Lutein and preterm infants with decreased concentrations of brain carotenoids. J Pediatr Gastroenterol Nutr 59, 659665.CrossRefGoogle ScholarPubMed
Craft, NE, Haitema, TB, Garnett, KM et al. (2004) Carotenoid, tocopherol, and retinol concentrations in elderly human brain. J Nutr Health Aging 8, 156162.Google ScholarPubMed
Johnson, EJ (2014) Role of lutein and zeaxanthin in visual and cognitive function throughout the lifespan. Nutr Rev 72, 605612.10.1111/nure.12133CrossRefGoogle ScholarPubMed
Johnson, EJ (2012) A possible role for lutein and zeaxanthin in cognitive function in the elderly. Am J Clin Nutr 96, 1161S1165S.CrossRefGoogle ScholarPubMed
Feart, C, Letenneur, L, Helmer, C et al. (2016) Plasma carotenoids are inversely associated with dementia risk in an elderly French cohort. J Gerontol Ser A 71, 683688.CrossRefGoogle Scholar
Min, J & Min, K (2014) Serum lycopene, lutein and zeaxanthin, and the risk of Alzheimer’s disease mortality in older adults. Dement Geriatr Cogn Disord 37, 246256.CrossRefGoogle ScholarPubMed
Christensen, K, Gleason, CE & Mares, JA (2018) Dietary carotenoids and cognitive function among US adults, NHANES 2011–2014. Nutr Neurosci, 19. doi: 10.1080/1028415X.2018.1533199.Google Scholar
Ajana, S, Weber, D, Helmer, C et al. (2018) Plasma concentrations of lutein and zeaxanthin, macular pigment optical density, and their associations with cognitive performances among older adults. Investig Ophthalmol Vis Sci 59, 18281835.10.1167/iovs.17-22656CrossRefGoogle ScholarPubMed
Renzi, LM, Dengler, MJ, Puente, A et al. (2014) Relationships between macular pigment optical density and cognitive function in unimpaired and mildly cognitively impaired older adults. Neurobiol Aging 35, 16951699.10.1016/j.neurobiolaging.2013.12.024CrossRefGoogle ScholarPubMed
Johnson, E, McDonald, K, Caldarella, S et al. (2008) Cognitive findings of an exploratory trial of docosahexaenoic acid and lutein supplementation in older women. Nutr Neurosci 11, 7583.10.1179/147683008X301450CrossRefGoogle ScholarPubMed
Lindbergh, CA, Renzi-Hammond, LM, Hammond, BR et al. (2018) Lutein and zeaxanthin influence brain function in older adults: a randomized controlled trial. J Int Neuropsychol Soc 24, 114.CrossRefGoogle ScholarPubMed
Hammond, BR, Miller, LS, Bello, MO et al. (2017) Effects of lutein/zeaxanthin supplementation on the cognitive function of community dwelling older adults: a randomized, double-masked, placebo-controlled trial. Front Aging Neurosci 9, 254.CrossRefGoogle ScholarPubMed
Mewborn, CM, Lindbergh, CA, Robinson, TL et al. (2018) Lutein and zeaxanthin are positively associated with visual-spatial functioning in older adults: an fMRI study. Nutrients 10, E458.CrossRefGoogle ScholarPubMed
Ranard, KM, Jeon, S, Mohn, ES et al. (2017) Dietary guidance for lutein: consideration for intake recommendations is scientifically supported. Eur J Nutr 56, 3742.10.1007/s00394-017-1580-2CrossRefGoogle ScholarPubMed
Willet, WC, Sampson, L, Stampfer, MJ et al. (2017) Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 185, 11091123.Google Scholar
Harvard TH. Chan School of Public Health Nutrition Department’s File Download Site [cited 26 April 2019]. Available at: https://regepi-bwh-harvard-edu.ezproxy.lib.utexas.edu/health/nutrition.html.Google Scholar
2013 Health Care and Nutrition Study V4.0 Nutrient Totals Data Description. 2016 [cited 26 April 2019]. Available at: http://hrsonline.isr.umich.edu.ezproxy.lib.utexas.edu/modules/meta/2013/hcns/desc/2013HCNS_data_descr.Google Scholar
Willett, W (2013) Nutritional Epidemiology – Monographs in Epidemiology and Biostatistics, 3rd ed. New York: Oxford University Press.Google Scholar
Bowman, SA, Clemens, JC, Friday, JE et al. (2017) Food Patterns Equivalents Database 2013–14: Methodology and User Guide. Beltsville, MD: U.S. Department of Agriculture.Google Scholar
Runge, SK, Craig, BM & Jim, HS (2015) Word recall: cognitive performance within internet surveys. JMIR Ment Heal 2, e20.CrossRefGoogle ScholarPubMed
Small, SA, Stern, Y, Tang, M et al. (1999) Selective decline in memory function among healthy elderly. Neurology 52, 13921396.CrossRefGoogle ScholarPubMed
Ofstedal, M, Fisher, G & Herzog, A (2005) Documentation of Cognitive Functioning Measures in the Health and Retirement Study (HRS/AHEAD Documentation Report No. DR-006). Ann Arbor, MI: University of Michigan.CrossRefGoogle Scholar
Muthén, LK & Muthén, BO (2015) Mplus User’s Guide, 7th ed. Los Angeles, CA: Muthén and Muthén.Google Scholar
SAS Institute Inc (2015) SAS/STAT 14.1 User’s Guide. Cary, NC: SAS Institute Inc.Google Scholar
Hasselmo, ME (2012) How We Remember: Brain Mechanisms of Episodic Memory. Cambridge, MA: MIT Press.Google Scholar
Power, R, Coen, RF, Beatty, S et al. (2018) Supplemental retinal carotenoids enhance memory in healthy individuals with low levels of macular pigment in a randomized, double-blind, placebo-controlled clinical trial. J Alzheimer’s Dis 61, 947961.CrossRefGoogle Scholar
Nyberg, L (2017) Functional brain imaging of episodic memory decline in ageing. J Intern Med 1, 6574.10.1111/joim.12533CrossRefGoogle Scholar
Blacker, D, Lee, H, Muzikansky, A et al. (2007) Neuropsychological measures in normal individuals that predict subsequent cognitive decline. Arch Neurol 64, 862871.10.1001/archneur.64.6.862CrossRefGoogle ScholarPubMed
Mohn, ES, Erdman, JW, Kuchan, MJ et al. (2017) Lutein accumulates in subcellular membranes of brain regions in adult rhesus macaques: relationship to DHA oxidation products. PLoS One 12, e0186767.10.1371/journal.pone.0186767CrossRefGoogle ScholarPubMed
Erdman, J, Smith, J, Kuchan, M et al. (2015) Lutein and brain function. Foods 4, 547564.10.3390/foods4040547CrossRefGoogle ScholarPubMed
Estévez-Santiago, R, Olmedilla-Alonso, B, Beltrán-de-Miguel, B et al. (2016) Lutein and zeaxanthin supplied by red/orange foods and fruits are more closely associated with macular pigment optical density than those from green vegetables in Spanish subjects. Nutr Res 36, 12101221.CrossRefGoogle ScholarPubMed
Kang, JH, Ascherio, A & Grodstein, F (2005) Fruit and vegetable consumption and cognitive decline in aging women. Ann Neurol 57, 713720.10.1002/ana.20476CrossRefGoogle ScholarPubMed
Morris, MC, Evans, DA, Tangney, CC et al. (2006) Associations of vegetable and fruit consumption with age-related cognitive change. Neurology 67, 13701376.CrossRefGoogle ScholarPubMed
Morris, MC, Wang, Y, Barnes, LL et al. (2018) Nutrients and bioactives in green leafy vegetables and cognitive decline. Neurology 90, e214e222.CrossRefGoogle ScholarPubMed
U.S. Department of Agriculture Agricultural Research Service (2019) FoodData Central. Beltsville, MD: U.S. Department of Agriculture Agricultural Research Service.Google Scholar
Fotuhi, M, Mohassel, P & Yaffe, K (2009) Fish consumption, long-chain omega-3 fatty acids and risk of cognitive decline or Alzheimer disease: a complex association. Nat Clin Pract Neurol 5, 140152.Google ScholarPubMed
López-Cervantes, J & Sánchez-Machado, DI (2019) Astaxanthin, lutein, and zeaxanthin. In Nonvitamin Nonmineral Nutritional Supplements, 1st ed., pp. 1925 [Nabavi, SM & Silva, AS, editors]. London: Academic Press.10.1016/B978-0-12-812491-8.00003-5CrossRefGoogle Scholar
Olmedilla-Alonso, B, Beltrán-de-Miguel, B, Estévez-Santiago, R et al. (2014) Markers of lutein and zeaxanthin status in two age groups of men and women: dietary intake, serum concentrations, lipid profile and macular pigment optical density. Nutr J 13, 52.10.1186/1475-2891-13-52CrossRefGoogle ScholarPubMed
Eisenhauer, B, Natoli, S, Liew, G et al. (2017) Lutein and zeaxanthin – food sources, bioavailability and dietary variety in age-related macular degeneration protection. Nutrients 9, 120.CrossRefGoogle ScholarPubMed
Sommerburg, O, Keunen, JEE, Bird, AC et al. (1998) Fruits and vegetables that are sources for lutein and zeaxanthin: the macular pigment in human eyes. Br J Ophthalmol 82, 907910.10.1136/bjo.82.8.907CrossRefGoogle ScholarPubMed
van de Rest, O, Berendsen, AA, Haveman-Nies, A et al. (2015) Dietary patterns, cognitive decline, and dementia: a systematic review. Adv Nutr 6, 154168.10.3945/an.114.007617CrossRefGoogle ScholarPubMed
Johnson, EJ, Vishwanathan, R, Johnson, MA et al. (2013) Relationship between serum and brain carotenoids, α-tocopherol, and retinol concentrations and cognitive performance in the oldest old from the Georgia Centenarian Study. J Aging Res 2013, 951786.10.1155/2013/951786CrossRefGoogle ScholarPubMed
Vishwanathan, R, Iannaccone, A, Scott, TM et al. (2014) Macular pigment optical density is related to cognitive function in older people. Age Aging 43, 271275.10.1093/ageing/aft210CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Descriptive statistics for participant characteristics for analytic sample by lutein and zeaxanthin quartiles, Health Care and Nutrition Study 2013/Health and Retirement Study 2014

Figure 1

Table 2 Descriptive statistics for lutein and zeaxanthin consumption and cognitive measures, Health Care and Nutrition Study 2013/Health and Retirement Study 2014

Figure 2

Table 3 Regression estimates from models regressing immediate and delayed word recall on lutein and zeaxanthin intake quartiles, 2013 Health Care and Nutrition Study/2014 Health and Retirement Study

Figure 3

Table 4 Food groupings used to estimate lutein + zeaxanthin intake, 2013 Health Care and Nutrition Study

Figure 4

Table 5 Standardised regression estimates identifying food groups as primary contributors to lutein and zeaxanthin intake, 2013 Health Care and Nutrition Study