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Plasma Carotenoid Levels and Cognitive Performance in an Elderly Population: Results of the EVA Study

¹ INSERM U888 and Université Montpellier 1, France.
² Département de Biologie Intégrée, CHU de Grenoble, France.
³ INSERM U708 and Université Paris 6, France.

Address correspondence to N. Tasnime Akabaraly, PhD, INSERM U888, Hôpital La Colombière, 39 Avenue Charles Flahault, BP 34493, 34093 Montpellier, Cedex 5, France. E-mail: akbaraly{at}montp.inserm.fr

	Abstract

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Background. The hypothesis of carotenoids having a preventive role in cognitive impairment is suggested by their antioxidant properties.

Methods. We examined, in a cross-sectional analysis, the relationship between cognitive performance (assessed by the Mini-Mental State Examination, Trail Making Test Part B, Digit Symbol Substitution, Finger Tapping Test, and Word Fluency Test) and different plasma carotenoids (lutein, zeaxanthin, ß-cryptoxanthin, lycopene, -carotene, and trans-ß-carotene and cis-ß-carotene) in a healthy elderly population (the EVA,"Etude du Vieillissement Artériel," study; n = 589, age = 73.5 ± 3 years).

Results. Logistic regression showed that participants with the lowest cognitive functioning (<25th percentile) had a higher probability of having low levels of specific plasma carotenoids (<1st quartile): lycopene and zeaxanthin. For zeaxanthin, odds ratios (ORs) were as follows: OR_DSS = 1.97 (95% confidence interval [CI] = 1.21–3.20), OR_FTT = 1.70 (CI = 1.05–2.74), and OR_WFT = 1.82 (CI = 1.08–3.07); for lycopene, OR_DSS = 1.93 (CI = 1.20–3.12) and OR_TMTB = 1.64 (CI = 1.04–2.59).

Conclusion. Even if it is not possible to affirm if these low levels of carotenoids precede or are the consequence of cognitive impairment, our results suggest that low carotenoid levels could play a role in cognitive impairment. The biological significance of our findings needs further research.

	MATERIALS AND METHODS

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Study Population
The EVA ("Etude du Vieillissement Artériel") study is a 9-year longitudinal study with six waves of follow-up (14). During the first 2 years (1991–1993; EVA0), 1389 volunteers (574 men and 815 women) born between 1922 and 1932 (mean age = 65 years) residing in the town of Nantes (Western France) were recruited from electoral rolls, and to a lesser extent, via information campaigns. The sixth and last follow-up of the EVA study (EVA6) was conducted between June 2000 and December 2001. During the 9-year follow-up, 101 deaths occurred. The first leading cause of death was cancer (n = 45, 44.5%); the second was cardiovascular diseases (n = 22, 21.8%). The main factors related to mortality were found to be, as reported in the literature: male gender, smoking (current and prior), alcohol intake, medication use, obesity, diabetes, hypertension, and cardiovascular diseases (15). At EVA6, blood samples after a 12-hour fast were obtained from 773 participants. Those participants who did not complete the whole study (n = 616, 44.3%) were significantly more frequently men (those who did not complete: 44.3% vs those who completed: 38.9%, p =.04), obese or overweight (57.9% vs 47.3%, p =.0004), or hypertensive (52.6% vs 47.2%, p =.05). They were statistically more frequently participants in the lowest cognitive performance class (<25th percentile of the distribution) for the Mini-Mental State Examination (MMSE) (25.2% vs 18.2%, p =.002), Digit Symbol Substitution (DSS) (28.3% vs 20.3%, p =.0006), Trail Making Test Part B (TMTB) (30.5% vs 21.4%, p =.0002), and Word Fluency Test (WFT) (25.1% vs 19.3%, p =.009), but we did not observe that for the Finger Tapping Test (FTT) (p =.37).

The present analysis was restricted to the 589 participants who underwent at EVA6 a cognitive evaluation and blood sampling. The study protocol was approved by the Ethical Committee of the University Center Hospital of Kremlin-Bicêtre, Paris. Signed informed consent was obtained from all participants at enrollment.

Data Collection
Cognitive evaluation and depressive symptoms.-- Trained neuropsychologists evaluated cognition with a neuropsychological battery of tests including a global test, the MMSE (16), and an assessment of a range of cognitive domains. Visual conceptual and visuomotor tracking were assessed by TMTA and TMTB (17). Involving motor speed and attention functions, the TMT is highly vulnerable to the effects of brain injury (18). Part A is considered as exploring motor speed, control, and working memory, whereas Part B assesses executive functioning such as set shifting. The variables of interest are the time in seconds (19). These tests (Parts A and B) were performed with a maximum allotted time to perform the test of 180 and 240 seconds, respectively. When participants exceeded the time allotted for each part, the maximum allotted time was imputed. The DSS from the Wechsler Adult Intelligence Scale-Revised (WAIS-R) measured sustained attention and logical reasoning (20). Manual dexterity and psychomotor speed were evaluated with the FTT. Verbal fluency was evaluated with the WFT. Depression symptoms were assessed by the Center for Epidemiological Studies-Depression (CESD) scale using score (17 for men and 23 for women) for high risk of depression to define depressive symptomatology (21).

Carotenoid measurement.-- Retetinol and carotenoids were measured with a Biotek-Kontron high-performance liquid chromatography (HPLC) system (UVK Lab, Trappes, France), which consists of a 525 dual pump, a 465 autosampler, and a 540 diode array detector. Retinol and ß-carotene were purchased from Fluka (Sigma-France, L'Isle d'Abeau), and other carotenoids were provided by Hoffman-La Roche (Hoffman-La Roche, Bâle, Switzerland). The LC separation was run with an Alltech Adsorbosphere C18 column (150 x 4.5 mm ID, 3 µm particle size; Templemars, France), which was thermostated at 28°C with a 402 column oven. Carotenoids and retinol were measured by HPLC after two extractions with a hexane–tetrahydrofurane mixture. For quantification, we used the method of Steghens and colleagues (22) with minor modifications. Indeed, we used a single 150 mm-long column instead of two, and we added 10 ppm water in mobile phase A to improve the separation of retinol, lutein, and zeaxanthin. The laboratory participates in the NIST (National Institute of Standards and Technology, New York, NY) and in the French Society for Vitamins and Biofactors (SFVB) external quality assurance programs, and ChromSystems (Munchen, Germany) internal controls were analyzed in every series of measurements (one control every 10 unknown serums). The limits of detection were calculated as 5-fold the maximum baseline noise in the region of the peaks.

Hence we found limits of detection of 0.05 µM for retinol, and 0.02 µM for the carotenoids. All concentrations of retinol, lycopene, and ß-carotene were above these respective limits, and only 5% of lutein, 8% of zeaxanthin, and 2% of ß-cryptoxanthin were under. Total plasma carotenoid level was obtained by summing levels of lutein, zeaxanthin, ß-cryptoxanthin, lycopene, and - and ß-carotenes.

Questionnaire and medical examination.-- The general questionnaire allowed us to obtain information on sociodemographic factors such as sex, age, and educational achievement plus consumption habits such as smoking status, alcohol consumption (which was determined from the participant's estimated average amount of alcoholic beverages ingested weekly), and medication use. In addition, height and weight were measured. Two independent measures of systolic and diastolic blood pressure were taken with a digital electronic tensiometer after a 10-minute rest. Total plasma cholesterol and plasma glucose levels were also measured using routine methods. The apolipoprotein E genotype of participants was determined on DNA samples.

Statistical Methods
The characteristics of the 589 participants included in the analysis were described compared to the 184 participants who had blood sampling but not the cognitive evaluation at EVA6; results were expressed as percentages and means with their standard deviation (SD). To test the differences between these two groups, the Chi-square test and the Student t test were used. These characteristics comprised sex, age, educational achievement ( primary school vs high school), smoking status (current or ex-smokers vs nonsmokers), alcohol consumption (< 20 mL/d vs 20 mL/d), and medicine use (< 3/d vs 3/d). Health characteristics were body mass index (BMI) classes [underweight: BMI < 21 kg/m² (23); "normal weight": 21 BMI < 25; overweight: 25 BMI < 30 (24); obesity 30 kg/m² (24)], diabetes (plasma glucose level 7.80 mmol/L or use of antidiabetic drugs or diabetes medical history), dyslipidemia (total cholesterol 6.20 mmol/L or use of lipid-lowering drugs or dyslipidemia medical history), hypertension (systolic or diastolic blood pressure 140 or 90 mmHg, respectively, or use of hypertensive drugs or hypertension medical history), history of vascular diseases (self-reported history of myocardial infarction, angina pectoris, stroke, or use of vascular drugs), depressive symptomatology (yes/no), and apolipoprotein E genotype (4 allele +/–). To calculate the correlation between carotenoids, we calculated Pearson correlation coefficients on log-transformed carotenoids.

The graphic representation of the percentage of participants with low cognitive functioning for the different carotenoid levels showed that the relationship between cognition and these biological variables was not linear, so we considered them to be categorical variables. We compared the characteristics of participants in the different levels of carotenoids (< 25th percentile vs 25th percentile) by using the Student t test or the Chi-square test for both continuous and categorical variables.

To define participants with the lowest cognitive performances in this well-educated cohort, we chose two cutoffs: participants who had cognitive test scores below the 25th percentile (and above the 75th percentile for the TMTB), and participants who had cognitive test scores below the 10th percentile (and above the 90th percentile for the TMTB). Values for these 25th and 10th percentile cutoffs for each test are presented in Table 2.

	RESULTS

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Characteristics of the 589 EVA Participants
The 589 participants in the EVA study (361 women and 228 men, age = 73.5 ± 2.9 years), were well educated (52.1% had a high school or superior degree). Among them, 39.0% were smokers or ex-smokers, 24% consumed alcohol regularly ( 2 glasses/day). In dividing participants among BMI classes, we observed 13.8% underweight, 34.0% overweight, and 8.5% obese. Concerning health status, 8.3% showed depressive symptomatology, 8.1% were diabetics, 65.0 % were dyslipidemic, 78.8% had hypertension, 19.7% had a history of cardiovascular disease, and 20.5% carried at least one allele 4 of the apolipoprotein E. For the different carotenoids and scores on neuropsychological tests, concentrations and ranges are described in Table 1, and percentile distributions in Table 2.

Description of Carotenoids: Correlation and Associated Factors
Plasma carotenoids were highly and significantly intercorrelated. The highest correlations were found among the following carotenes: -carotene/trans-ß-carotene (r = 0.78), trans-ß-carotene/cis-ß-carotene (r = 0.61), -carotene/lycopene (r = 0.60) and among lutein/zeaxanthin (r = 0.58). The other correlation coefficients ranged from 0.16 for lycopene/ß-cryptoxanthin to 0.50 for trans-ß-carotene/lycopene.

In Table 3 and Table 4, we describe the characteristics of participants according to their total plasma carotenoid, the different xanthophylls, and carotenes levels (< 25th percentile vs 25th percentile). We observe that the profiles of factors associated with -carotene, trans-ß-carotene, and cis-ß-carotene were identical. Between the other carotenoids, however, the associated factors can differ. Gender was associated with all carotenoids, which (with the exception of zeaxanthin) reach higher levels in women. Smoking status and alcohol consumption were significantly associated with lower levels of total plasma carotenoids, ß-cryptoxanthin, -carotene, and trans- and cis-ß-carotene. Diabetes was associated with low levels of all carotenoids, and hypertension was significantly associated with low levels of lutein, -carotene, and trans- and cis-ß-carotene. Being obesity or overweight was associated with low levels of all carotenoids except for zeaxanthin and ß-cryptoxanthin. Age, education, depressive symptomatology, dyslipidemia, history of cardiovascular disease, and apolipoprotein E genotype were not associated with any plasma carotenoids.

View this table: [in this window] [in a new window]   Table 3. Characteristics of Participants According to Their Level of Plasma Total Carotenoids and Xanthophylls (<25th Percentile vs 25th Percentile).

View this table: [in this window] [in a new window]   Table 4. Characteristics of Participants According to Level of Plasma Carotenes (<25th Percentile vs 25th Percentile).

We also repeated analyses between cognitive performances and carotenoids levels by defining participants with the lowest cognitive performances as having cognitive test scores below the 10th percentile. Only associations between zeaxanthin and FTT (p =.003) and TMTB (p =.06), and between lycopene and DSS (p =.07) remained marginally statistically significant (data not shown). In these analyses, the number of participants who had the lowest cognitive performance scores was 2.2 to 2.8 smaller than for the preceding analyses (n = 54 for the MMSE, 49 for the DSS, 55 for the TMTA, 53 for the TMTB, 52 for the FTT, and 38 for the TEL). Lack of statistical power could explain the drop in statistically significant results.

All of our analyses were conducted with dichotomized variables for both the plasma carotenoids and the cognitive outcomes; in supplemental analyses, we tested whether similar findings were observed when a continuous measure for cognition was used. We calculated Spearman correlation coefficients between cognitive variables and log-transformed carotenoids. Results showed a significant association for zeaxanthin and lycopene and DSS (r = 0.08, p =.01 and r = 0.11, p =.006 for zeaxanthin and lycopene, respectively), TMTA (r = –0.11, p =.01 and r = –0.08, p =.05), TMTB (r = –0.08, p =.05 and r = –0.12, p =.004), and FTT (r = 0.09, p =.04 and r = 0.09, p =.04). These correlations confirm that our findings were not driven by the chosen dichotomous classifications for cognition and carotenoid levels.

	DISCUSSION

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To our knowledge, this study is the first that investigated, in a healthy elderly population, the relationship between cognitive performance measured by five neuropsychological tests and the different plasma carotenoids: xanthophylls (lutein, zeaxanthin, ß-cryptoxanthin) and carotenes (lycopene, -carotene, trans-ß-carotene, and cis-ß-carotene).

The EVA study included volunteers with higher educational status, higher incomes, and greater cognitive function than the average elderly French population. Despite this selection, plasma carotenoid concentrations in the EVA study population were in the same ranges as those in different European or American populations (4). In this present study, low levels of specific plasma carotenoids—lycopene and zeaxanthin—were associated to poor cognitive functioning in a highly educated, community-dwelling elderly population. Carotenoids are found in fruits and vegetables. Some studies have shown that plasma carotenoid levels could be related to dietary fruit and vegetable intake (25,26). More specifically, it seems that mangos, papayas, peaches, prunes, squash, oranges, and green fruits and vegetables are source of zeaxanthin, whereas tomatoes, pink grapefruit, and watermelon are sources of lycopene (3). Two cross-sectional studies (27,28) showed an association between greater intake of fruits and vegetables and better cognitive performance. In a large prospective study of older women (n = 13,388) (29), the authors reported a relationship between low vegetable intake and cognitive decline but no relationship with fruits (the strongest association being with greater intake of green leafy vegetables and cruciferous vegetables). Even if some studies show that intake of foods with high carotenoid contents are correlated with their corresponding plasma concentrations (26), fruits and vegetables are sources of many other nutrients [vitamin E (30), folates (31,32), flavonoids (33)] that have been associated with cognitive function. It is now impossible to know if the association between carotenoids and cognitive function is the result of a specific effect of carotenoids or if it is the result of combined effects of the different fruit and vegetable compounds.

For each cognitive test, we tested an eight-association hypothesis (Ho) between cognitive function and carotenoids. To ensure that significant observed associations were not hazard related (alpha risk = 5%), specific multiple test (Bonferroni, Sidak) corrections were applied to the data. Considering, however, that the tested hypotheses are not independent, submitting them to these corrections entails an overcorrection of threshold significance thus not necessarily leading to rejection of the Ho hypothesis, which we know to be false. In this exploratory analysis, the fact that one specific carotenoid was associated with more than one cognitive function, and that these associations remained statistically significant after controlling for potential confounding factors or after removing some participants (those with depressive symptomatology, those with an MMSE score < 25, or undernourished participants) seems to us more likely to ensure that our significant observed associations were not hazard related.

Although there were important correlations among all carotenoids, we observed significant associations between low cognitive performances and some (but not all) carotenoids. More specifically, we found no associations between ß-carotenes (trans or cis) or -carotene, which are the most studied carotenoids in epidemiological literature on cognitive impairment (5–11). These studies give conflicting results. Previously, in the EVA study, we showed that a low level of baseline total plasma carotenoid (< 25th percentile) was not significantly associated with a 4-year cognitive decline (5). In a cross-sectional study, Perrig and colleagues (9) showed that a higher ß-carotene plasma level was associated with better memory performances (free recall, recognition, and vocabulary) in 442 healthy persons aged 65–94 years. Studying dietary intake of ß-carotene, Morris and colleagues (8) found no association between carotene intake and cognitive decline, whereas the Rotterdam Study showed that a lower intake of ß-carotene was associated with impaired cognitive function measured by the MMSE (7). Three studies investigated the link between cognitive performance and supplementation of antioxidants including ß-carotene (6,10,11). All, except the work of the Age-Related Eye Diseases Study Research Group (11), found that use of these supplements reduced the risk of cognitive decline. In these studies with multiantioxidant supplementation it is, however, impossible to isolate the specific effect of ß-carotene on cognitive impairment. Only one study, with 1769 participants, focused on the association between a large spectrum of carotenoids and cognitive performance in elderly participants without neuropsychiatric disease, but it found no association (34). Discrepancies can be explained by methodological differences (e.g., neuropsychological tests to assess cognitive performances, number of tests used, choice of modelization of carotenoids). Finally, we found two clinical epidemiological studies (35,36) that focused on the comparison of plasma carotenoids and retinol levels in elderly patients with or without Alzheimer's disease (AD). One study showed that levels of vitamin A, lutein, zeaxanthin, ß-cryptoxanthin, and -carotene were lower in AD patients (n = 63) than in controls (n = 56). However, the researchers found no difference for lycopene and ß-carotene (36). In the second study, levels of carotenoids were lower in AD patients (n = 40) than in controls (n = 39) for zeaxanthin, ß-cryptoxanthin, lycopene, and ß-carotene but not for lutein and -carotene (35). These conflicting results could be explained by the limited sample size of these studies. The major problem in interpreting these two studies is that they are studying AD cases for which nutritional habits, and consequently, biological status, can be modified, as a consequence of the disease progression. The different measurement methods and the bioavailability of carotenoids, which is influenced by several factors [such as characteristics of the food sources, interactions with other dietary factors, and various participant characteristics (3)] could also explain the differences between studies.

Many epidemiological studies have associated high carotenoid status with a decrease in the incidence of chronic diseases (heart diseases and cancer); the biological mechanism for such protection is, however, currently unclear (3). Multiple possibilities exist. Among them, certain carotenoids can be converted to retinoids and have a provitamin A activity. To assess if the relationship we showed could be explained partly by this hypothesis, we adjusted our models for retinol concentration, but results remained unchanged.

Our results are supported by those of a biological study, led by Woodall and colleagues (37), on oxidation of carotenoids by free radicals. The authors found that lycopene, lutein, and zeaxanthin all reacted rapidly with oxidizing agents, and must also be considered as potential dietary antioxidants. A possible explanation for low levels of plasma carotenoids in AD or cognitive impairment is that they might be consumed because of a higher rate of free radical production in the brain.

Our results on the relationship between carotenoids and cognitive performance were not modified when we excluded the participants with BMI < 21 kg/m² for which the hypothesis of undernutrition is probable. This finding allows us to say that this relationship could not be limited by the influence of undernourished participants. However, in this cross-sectional framework, it is not possible to affirm whether these low levels of carotenoids preceded or were the consequence of cognitive impairment in a context in which poor cognitive status may be a risk factor for poor nutrition. In this study, we used various tests to explore cognitive performance; this approach is more powerful than using only MMSE, which is not a very sensitive measurement of cognitive impairment. With the other tests, the psychometric scores ranges are large and more powerful to study cognitive impairment. In addition, because the scores were not normally distributed, a percent cut-off is appropriate. The observed association will probably have no functional significance yet, because participants had only a subtle impairment. Moreover, we have no basis to expect specific association between carotenoids and psychometric evaluation. However, it is well known that low plasma lutein and zeaxanthin concentrations were implicated in age-related macular degeneration (38). Although the retina is a puzzle (the ultimate solution of which lies on the other side of the optic nerve in its connection with the brain), a highly specific accumulation of lutein and zeaxanthin in the retina and in the macula is described (39). Could other areas of the brain have the same affinity for some specific carotenoids? The biological significance of our findings needs further research by biological studies, longitudinal epidemiological studies, and by specific clinical trials with carotenoid supplementation.

	Acknowledgments

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N. Tasnime Akbaraly was supported by a grant from the French Alzheimer's Disease Association. The EVA study was carried out under an agreement between INSERM and the Merck, Sharp and Dohme-Chibret Laboratories (West Point, PA) and was supported by EISAI Laboratory, France.

	Footnotes

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Decision Editor: Luigi Ferrucci, MD, PhD

Received January 11, 2006

Accepted May 26, 2006

	References

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1. Finkel T, Holbrook NJ. Oxidants, oxidative stress and the biology of ageing. Nature. 2000;408:239-247.[Medline]
2. Coyle JT, Puttfarcken P. Oxidative stress, glutamate, and neurodegenerative disorders. Science. 1993;262:689-695.[Abstract/Free Full Text]
3. Paiva SA, Russell RM. Beta-carotene and other carotenoids as antioxidants. J Am Coll Nutr. 1999;18:426-433.[Abstract/Free Full Text]
4. Stahl W, Sies H. Lycopene: a biologically important carotenoid for humans? Arch Biochem Biophys. 1996;336:1-9.[Medline]
5. Berr C, Balansard B, Arnaud J, Roussel AM, Alperovitch A. Cognitive decline is associated with systemic oxidative stress: the EVA study. Etude du Vieillissement Arteriel. J Am Geriatr Soc. 2000;48:1285-1291.[Medline]
6. Gray SL, Hanlon JT, Landerman LR, Artz M, Schmader KE, Fillenbaum GG. Is antioxidant use protective of cognitive function in the community-dwelling elderly? Am J Geriatr Pharmacother. 2003;1:3-10.[Medline]
7. Jama JW, Launer LJ, Witteman JC, et al. Dietary antioxidants and cognitive function in a population-based sample of older persons. The Rotterdam Study. Am J Epidemiol. 1996;144:275-280.[Abstract/Free Full Text]
8. Morris MC, Evans DA, Bienias JL, Tangney CC, Wilson RS. Vitamin E and cognitive decline in older persons. Arch Neurol. 2002;59:1125-1132.[Abstract/Free Full Text]
9. Perrig WJ, Perrig P, Stahelin HB. The relation between antioxidants and memory performance in the old and very old. J Am Geriatr Soc. 1997;45:718-724.[Medline]
10. Smith A, Clark R, Nutt D, Haller J, Hayward S, Perry K. Anti-oxidant vitamins and mental performance of the elderly. Hum Psychopharmacol. 1999;14:459-471.
11. Yaffe K, Clemons TE, McBee WL, Lindblad AS. Impact of antioxidants, zinc, and copper on cognition in the elderly: a randomized, controlled trial. Neurology. 2004;63:1705-1707.[Abstract/Free Full Text]
12. Stahl W, Junghans A, de Boer B, Driomina ES, Briviba K, Sies H. Carotenoid mixtures protect multilamellar liposomes against oxidative damage: synergistic effects of lycopene and lutein. FEBS Lett. 1998;427:305-308.[Medline]
13. Woodall AA, Britton G, Jackson MJ. Carotenoids and protection of phospholipids in solution or in liposomes against oxidation by peroxyl radicals: relationship between carotenoid structure and protective ability. Biochim Biophys Acta. 1997;1336:575-586.[Medline]
14. Berr C, Coudray C, Bonithon-Kopp C, Roussel AM, Mainard F, Alperovitch A. Demographic and cardiovascular risk factors in relation to antioxidant status: the EVA Study. Int J Vitam Nutr Res. 1998;68:26-35.[Medline]
15. Akbaraly NT, Arnaud J, Hininger-Favier I, Gourlet V, Roussel AM, Berr C. Selenium and mortality in the elderly: results from the EVA study. Clin Chem. 2005;51:2117-2123.[Abstract/Free Full Text]
16. Folstein M, Anthony JC, Parhad I, Duffy B, Gruenberg EM. The meaning of cognitive impairment in the elderly. J Am Geriatr Soc. 1985;33:228-235.[Medline]
17. Robins Wahlin TB, Backman L, Wahlin A, Winblad B. Trail Making Test performance in a community-based sample of healthy very old adults: effects of age on completion time, but not on accuracy. Arch Gerontol Geriatr. 1996;22:87-102.[Medline]
18. Deutsch Lezak M, ed. Neuropsychological Assessment. New York: Oxford University Press; 1983:768 pp.
19. Reitan R. Validity of the Trail Making test as an indicator of organic brain damage. Percept Mot Skills. 1958;8:271-276.[Medline]
20. Wechsler D. The Wechsler Adult Intelligence Scale-Revised. New York: The Psychological Corporation; 1981.
21. Fuhrer R, Rouillon F. La version française de l'échelle CES-D. Psychiat Psychobiol. 1989;4:163-166.
22. Steghens JP, van Kappel AL, Riboli E, Collombel C. Simultaneous measurement of seven carotenoids, retinol and alpha-tocopherol in serum by high-performance liquid chromatography. J Chromatogr B Biomed Sci Appl. 1997;694:71-81.[Medline]
23. Évaluation diagnostique de la dénutrition protéino-énergétique des adultes hospitalisés. Service des recommendations professionnelles. Report of the ANAES (Agence nationale d'accréditation et d'évaluation en santé). Septembre 2003.
24. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i-xii, 1–253.[Medline]
25. Al-Delaimy WK, Ferrari P, Slimani N, et al. Plasma carotenoids as biomarkers of intake of fruits and vegetables: individual-level correlations in the European Prospective Investigation into Cancer and Nutrition (EPIC). Eur J Clin Nutr. 2005;59:1387-1396.[Medline]
26. Campbell DR, Gross MD, Martini MC, Grandits GA, Slavin JL, Potter JD. Plasma carotenoids as biomarkers of vegetable and fruit intake. Cancer Epidemiol Biomarkers Prev. 1994;3:493-500.[Abstract]
27. Lee L, Kang SA, Lee HO, et al. Relationships between dietary intake and cognitive function level in Korean elderly people. Public Health. 2001;115:133-138.[Medline]
28. Ortega RM, Requejo AM, Andres P, et al. Dietary intake and cognitive function in a group of elderly people. Am J Clin Nutr. 1997;66:803-809.[Abstract/Free Full Text]
29. Kang JH, Ascherio A, Grodstein F. Fruit and vegetable consumption and cognitive decline in aging women. Ann Neurol. 2005;57:713-720.[Medline]
30. Masaki KH, Losonczy KG, Izmirlian G, et al. Association of vitamin E and C supplement use with cognitive function and dementia in elderly men. Neurology. 2000;54:1265-1272.[Abstract/Free Full Text]
31. Ramos MI, Allen LH, Mungas DM, et al. Low folate status is associated with impaired cognitive function and dementia in the Sacramento Area Latino Study on Aging. Am J Clin Nutr. 2005;82:1346-1352.[Abstract/Free Full Text]
32. Ravaglia G, Forti P, Maioli F, et al. Homocysteine and folate as risk factors for dementia and Alzheimer disease. Am J Clin Nutr. 2005;82:636-643.[Abstract/Free Full Text]
33. Commenges D, Scotet V, Renaud S, Jacqmin-Gadda H, Barberger-Gateau P, Dartigues JF. Intake of flavonoids and risk of dementia. Eur J Epidemiol. 2000;16:357-363.[Medline]
34. Schmidt R, Hayn M, Reinhart B, et al. Plasma antioxidants and cognitive performance in middle-aged and older adults: results of the Austrian Stroke Prevention Study. J Am Geriatr Soc. 1998;46:1407-1410.[Medline]
35. Mecocci P, Polidori MC, Cherubini A, et al. Lymphocyte oxidative DNA damage and plasma antioxidants in Alzheimer disease. Arch Neurol. 2002;59:794-798.[Abstract/Free Full Text]
36. Rinaldi P, Polidori MC, Metastasio A, et al. Plasma antioxidants are similarly depleted in mild cognitive impairment and in Alzheimer's disease. Neurobiol Aging. 2003;24:915-919.[Medline]
37. Woodall AA, Lee SW, Weesie RJ, Jackson MJ, Britton G. Oxidation of carotenoids by free radicals: relationship between structure and reactivity. Biochim Biophys Acta. 1997;1336:33-42.[Medline]
38. Delcourt C, Carrière I, Delage M, Barberger-Gateau P, Schalch W, Group PS. Plasma lutein and zeaxanthin and other carotenoids as modifiable risk factors for age-related maculopathy and cataract; The POLA study. Invest Ophthalmol Vis Sci. 2006;47:2329-2335.[Abstract/Free Full Text]
39. Bone RA, Landrum JT, Tarsis SL. Preliminary identification of the human macular pigment. Vision Res. 1985;25:1531-1535.[Medline]

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