Associations of Eating Out and Dietary Diversity with Mild Cognitive Impairment among Community-Dwelling Older Adults

Eating Out, Dietary Diversity, and Cognition

Article information

Ann Geriatr Med Res. 2024;28(3):266-272

Publication date (electronic) : 2024 April 11

doi : https://doi.org/10.4235/agmr.23.0218

¹Graduate School of Health Sciences, Kagoshima University, Kagoshima, Japan

²Department of Preventive Gerontology, Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan

³Department of Physical Therapy, School of Health Sciences, Faculty of Medicine, Kagoshima University, Kagoshima, Japan

⁴Department of Mechanical Systems Engineering, Faculty of Engineering, Daiichi Institute of Technology, Kagoshima, Japan

⁵Department of Rehabilitation, Faculty of Nursing and Welfare, Kyushu University of Nursing and Social Welfare, Kumamoto, Japan

⁶Center for Health Promotion, International Life Sciences Institute, Tokyo, Japan

⁷Tarumizu Municipal Medical Center, Tarumizu Chuo Hospital, Kagoshima, Japan

⁸Department of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan

Corresponding Author Hyuma Makizako, PT, PhD Department of Physical Therapy, School of Health Sciences, Faculty of Medicine, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan E-mail: makizako@health.nop.kagoshima-u.ac.jp

Received 2023 December 27; Revised 2024 March 7; Accepted 2024 April 5.

Abstract

Background

Dementia is a critical late-life health issue that occurs among members of aging societies. This study examined the relationships between eating out, dietary diversity, and mild cognitive impairment (MCI) among community-dwelling older adults.

Methods

We analyzed data from 597 older adults (median age 73.0 years, interquartile range 69.0–78.0 years; 62.6% females). We applied the food frequency score to evaluate diet variety and the weekly consumption frequencies of ten food items were determined. The National Center for Geriatrics and Gerontology Functional Assessment Tool (NCGG-FAT) was used to evaluate MCI. Finally, we asked the participants how often they ate out each month; those who replied "none" were categorized into the "non-eating out" group.

Results

The overall prevalence of MCI was 122 (20.4%), with a higher prevalence in the low dietary diversity group than in the high dietary diversity group (28.6% vs. 18.6%). After adjusting for covariates, the participants who self-described as not eating out were independently associated with low dietary diversity (odds ratio [OR]=1.97, 95% confidence interval [CI] 1.20–3.20), while low dietary diversity was associated with MCI (OR=1.72; 95% CI 1.02–2.87). Structural equation models revealed that not eating out had no direct effect on MCI but was associated with MCI via low dietary diversity (root mean square error of approximation=0.030, goodness-of-fit index=0.999, and adjusted goodness-of-fit index=0.984).

Conclusions

Although non-eating out may not have a direct effect on MCI, an indirect relationship may exist between eating-out habits and MCI via dietary diversity status.

Keywords: Dementia; Aging; Eating behavior

INTRODUCTION

Dementia is a serious health issue that occurs in later life in the aging population. Global estimates indicate that the number of older adults suffering from dementia will rise1) and a decline in cognitive function attributes to adverse health outcomes.2) According to the 2020 report of the Lancet Commission on Dementia Prevention, Intervention, and Care, 40% of global dementia cases are attributable to modifiable risk factors. Therefore, the identification of modifiable risk factors for cognitive decline is critical.3) Mild cognitive impairment (MCI) is a condition that occurs between dementia and age-related cognitive decline. Interventions to prevent and postpone dementia onset aimed at older adults with MCI have been developed.4) Among the modifiable factors for dementia in the lifestyles of older adults are dietary patterns.5)

Several studies have examined the relationship between dietary factors and cognitive function. Otsuka et al.6) reported that ingesting a variety of foods was positively associated with cognitive function. The World Health Organization’s guidelines recommend healthy and balanced dietary patterns to prevent cognitive decline and enhance cognitive function.7) A prospective study of 11,179 Chinese community-dwelling older adults also showed that low dietary diversity increases the risk of cognitive impairment.8) Other studies have shown that ingesting a variety of foods contributes to the maintenance and improvement of cognitive function.

Dietary habits, such as eating out, eating prepared, and home-cooked food helps maintain dietary diversity.9) According to previous studies, individuals who eat meals at home are more likely to have a high-quality diet compared with those who eat meals outside the home.10) However, Lachat et al.11) reported that eating outside was positively associated with dietary diversity and better dietary intake in terms of energy supply from fat among Vietnamese adolescents. Thus, the association between eating out and dietary diversity remains controversial. Particularly, any association between eating out and dietary diversity among community-dwelling older adults remains unclear.

Therefore, this study examined the association between eating out and dietary diversity among community-dwelling older adults. We also investigated the direct or indirect associations of eating-out habits with MCI based on dietary diversity. Clarifying these relationships may provide a better understanding of the association between these factors to develop intervention strategies to prevent cognitive decline.

MATERIALS AND METHODS

Participants

This cross-sectional study examined data from the Tarumizu Study, which included a community-based health survey conducted since 2017 in partnership with Tarumizu Chuo Hospital, Tarumizu City Office, and Kagoshima University’s Faculty of Medicine.12) The participants were individually chosen from Tarumizu City, a residential subdivision of Kagoshima City in Japan. To recruit participants for the Tarumizu study in 2019, we issued invitations to every inhabitant in Tarumizu City aged 40 years or older. Additionally, we recruited locals by running community campaigns and placing advertisements in the local newspaper. Data from a health check survey performed between June and December of 2019 were used in this investigation and 1,024 people aged ≥40 years participated in the 2019 Tarumizu study. We excluded individuals under the age of 65, with a history of serious neurological illness such as dementia or stroke, depression, an inability to perform basic daily tasks; missing data were also excluded. Finally, we analyzed data from 597 older adult community-dwelling participants (median age 73.0 years; women 62.6%) (Fig. 1). The Ethics Committee of Kagoshima University's Faculty of Medicine approved this study (Ref No. 170351), and all participants provided written informed consent before being included in the study. This study complied the ethical guidelines for authorship and publishing in the Annals of Geriatric Medicine and Research.13)

Fig. 1.

Participant inclusion criteria flow diagram.

Assessment of Dietary Diversity

The food frequency score was used to evaluate diet variety.12) Ten food groups’ 1-week intake frequency was evaluated by the self-reported Food Frequency Survey (FFS). Meat, fish and shellfish, eggs, dairy products, green and yellow vegetables, potatoes, fruits, seaweed, fats and oils, and soy products are all included in the FFS. Based on the following answers, the FFS assigned a score of 0–3 points for each meal category: 3 points, almost every day; 2 points, 3–4 days per week; 1 point, 1–2 days per week; and 0 point, seldom eaten. The scores were added to the FFS, with lower scores (0–30) denoting less diet variety. Other studies have shown that the FFS is a simple and convenient tool for assessing dietary diversity.14) We defined older adults with an FFS of ≤16 points as having low dietary diversity, as previously described.15)

Assessment of Eating Out

During the in-person interviews, the participants were asked how frequently they went out to eat each month. The choices were (1) every day, (2) 10–20 times, (3) 1–5 times, and (4) never. We categorized those who responded "none" as non-eating out.

Assessment of MCI

The National Center for Geriatrics and Gerontology–Functional Assessment Tool (NCGG–FAT) was used to evaluate MCI status. The NCGG–FAT consists of the following domains: (1) memory (word list memory–I [immediate recognition] and word list memory–II [delayed recall]); (2) attention (a tablet version of the Trail Making Test (TMT)–Part A); (3) executive function (a tablet version of the TMT–Part B); and (4) processing speed (a tablet version of the Digit Symbol Substitution Test). Among older community-dwelling adults, the NCGG–FAT has moderate to high validity and high test-retest reliability.16) All tests in this study with a population-based cohort of older community-dwelling adults were conducted using established standardized thresholds to define impairment in the corresponding domain (scores > 1.5 standard deviations below age- and education-specific means). MCI was identified in participants who scored lower on one or more tests of the NCGG–FAT.16)

Covariates

During face-to-face interviews, the participants were asked about their age, sex, body mass index (BMI), educational attainment, number of current medications, and living arrangements (living alone or not).

Statistical Analysis

Statistical analyses were performed using AMOS 20.0 Graphics and IBM SPSS Statistics for Windows, version 26.0 (IBM Japan, Tokyo, Japan). We compared categorical values between the two dietary diversity statuses (high and low) using chi-square tests. We applied the Kolmogorov–Smirnov test to confirm the normal distributions of continuous variables and compared the two dietary diversity statuses using the Mann–Whitney U test and described median and interquartile range (IQR). The relationship between not eating out and low dietary diversity, and MCI was also investigated using binary logistic regression. Dietary diversity status (low dietary diversity) and MCI were set as the dependent variables for calculating the odds ratio (OR) with 95% confidence interval (CI). We adjusted for covariates including age, sex, education, medications, BMI, and living alone. Furthermore, we applied structural equation modeling (SEM), which can assess a priori models, identify mediators, and clarify direct and indirect paths between variables. Thus, this was the best statistical method for testing our hypotheses. The model examined whether eating out had a direct relationship with MCI, or whether it was a result of the indirect effect of dietary diversity. The model fit index included the goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), root mean square error of approximation (RMSEA), and Akaike Information Criterion (AIC). Conventional criteria state that GFI >0.95, AGFI >0.90, and RMSEA <0.05 indicate a "good fit," while GFI >0.90, AGFI >0.85, and RMSEA <0.08 indicate an "acceptable fit."17)

RESULTS

This study included data from 597 older adults with a median age of 73.0 years (IQR 69.0–78.0), 62.6% of whom were women. According to the Kolmogorov–Smirnov test, the continuous variables in the present study were not normally distributed (p<0.01). Table 1 shows the participants’ demographic characteristics. The participants were divided into high (n=485; 81.2%) and low (n=112; 18.8%) dietary diversity groups. Compared with participants with high dietary diversity, those with low dietary diversity were less likely to be women, have less education, and have a higher BMI (all p<0.05). The overall prevalence of MCI was 20.4 % (n=122). The prevalence was higher among participants with low dietary diversity than those with high dietary diversity (28.6% vs. 18.6%; p<0.05). Furthermore, we classified 23.8% (n=142) of participants as not eating out, with a higher proportion in the low dietary diversity group than in the high dietary diversity group (33.0% vs. 21.6%; p<0.05).

Table 1.

Participants’ characteristics

The results of the adjusted multiple logistic regression analysis, which examined the relationship among MCI, not eating out, and low dietary diversity, are presented in Table 2. After adjusting for confounding variables such as age, sex, education, medication, BMI, and single status, participants who did not eat out had low dietary diversity (OR=1.97; 95% CI 1.20–3.20). Second, we examined the association between not eating out and MCI using a logistic regression analysis. The proportion of participants who did not eat out was not significantly associated with MCI (OR=0.76; 95% CI 0.46–1.25). Multiple logistic regression analysis indicated an association between low dietary diversity and MCI. The results showed that low dietary diversity at baseline was associated with a higher odds ratio of MCI compared with participants with high dietary diversity (OR=1.72; 95% CI 1.02–2.87).

Table 2.

Association between eating-out habits, low dietary diversity, and MCI using the logistic regression model

Fig. 2 shows the results of the SEM, which examined whether the effect of not eating out on MCI was indirectly transmitted through dietary diversity status, or was directly related to MCI. This model indicated that not eating out was not directly associated with MCI (β=-0.037, p=0.36) but contributed significantly to low dietary diversity (β=0.108, p<0.01). Furthermore, low dietary diversity was significantly and directly associated with MCI (β=0.101 p<0.05). Moreover, not eating out was associated with MCI via low dietary diversity and provided an excellent fit to the data (GFI=0.999, AGFI=0.984, RMSEA=0.030, AIC=29.549).

Fig. 2.

Structural equation modelling for direct/indirect effect model between eating out habits, dietary diversity status, and mild cognitive impairment (MCI). GFI, goodness-of-fit index; RMSEA, root mean square error of approximation; AGFI, adjusted goodness-of-fit index; AIC, Akaike Information Criterion. ^*p<0.05, ^**p<0.01.

DISCUSSION

The results of the present study indicated that older adults with non-eating out habits had low dietary diversity patterns, and that low dietary diversity was associated with MCI. Analysis of the SEM showed that not eating out was not directly associated with MCI but was indirectly associated with MCI through low dietary diversity.

Multiple analyses in the present study revealed low dietary diversity among community-dwelling older adults who did not eat out. Food literacy might explain these findings. To define the term "food literacy," Vidgen and Gallegos18) identified an effect that was related to the areas of preparation, eating, planning and management, and selection. In a nationwide cross-sectional study in Japan, high food literacy was associated with high scores on the National Cancer Institute’s Healthy Eating Index 2015, which indicated high diet quality.19) In another study, individuals with a low frequency of eating out demonstrated lower food literacy compared with those with a higher frequency.20) Thus, older adults who do not eat out have lower food literacy, and therefore, may have lower dietary diversity.

The present cross-sectional study examined the association between low dietary diversity and MCI among community-dwelling older adults. The potential mechanisms behind the present findings may involve the complex effects of various nutrients on cognitive function due to dietary diversity. First, calcium and magnesium have favorable effects against vascular diseases by inhibiting free radical formation and platelet aggregation, improving dyslipidemia, and increasing insulin sensitivity.21-23) Calcium and magnesium are abundant in milk and dairy products. A prospective cohort study showed that ingesting milk and dairy products reduced the risk of developing dementia after a follow-up of >17 years.24) Second, the intake of diverse amino acids has protective effects on cognitive function. A longitudinal study showed that very low meat consumption increases the long-term risk of dementia25) and that the intake of amino acids, especially lysine, phenylalanine, threonine, and alanine, is positively associated with cognitive function in late life.26) Thus, the intake of foods containing proteins and amino acids, such as meat, is important to enhance cognitive function. One potential explanation for these effects is that the ingestion of amino acids contributes to neurotransmitter synthesis and growth hormone levels.27,28) A previous large prospective cohort study reported that high fish consumption was associated with a lower risk of dementia.29) Fish contain nutrients, including eicosapentaenoic acid,30) docosahexaenoic acid,31) vitamins B,32,33) and vitamin D.34) Furthermore, vitamins C35) and vitamin E,36) and carotenoids37) are also present in green and yellow vegetables and fruits. However, because food and nutrients are consumed in combination rather than separately, increasing and improving dietary diversity may be a more successful strategy for delaying cognitive decline.

In the current study, we used structural equation modeling to clarify whether not eating out was directly or indirectly associated with MCI. Our results indicated that not eating out was not directly associated with MCI. However, it did have an indirect effect owing to low dietary diversity. Nutritional knowledge is an important factor in maintaining dietary diversity and balance.38) A previous cross-sectional study reported that lower nutritional knowledge was associated with a lower frequency of eating out,39) in which older adults with a high frequency of eating out may be more nutritionally knowledgeable. Lee et al. reported higher dietary diversity and intake of protein, fat, calcium, phosphorus, iron, potassium, vitamin B₁, vitamin B₂, and niacin among adults who favored non-homemade meals, including eating out.9) Furthermore, social isolation, including eating alone, was associated with a decline in cognitive function in a 3-year prospective study.40) Moreover, eating outside the home was positively associated with dietary diversity and better dietary intake in terms of energy supply from fat among Vietnamese adolescents.11) Thus, eating out could be a key factor in ensuring a positive nutritional status and dietary diversity. A positive nutritional status and consumption of a variety of foods have favorable effects on cognitive function.5) According to these findings, eating-out behavior was associated with nutritional status and dietary diversity status and may be related to MCI through nutritional status.

The present study examined the association between participants who did not eat out and social relationships (going out less frequently than the previous year, visiting friends sometimes, and talking with someone every day)41) (Supplementary Table S1). The results suggest that non-eating out participants were less likely to visit friends and converse with others compared with older adults who ate out. A previous large cohort study showed that persistent loneliness in midlife is an independent risk factor for dementia and Alzheimer’s disease42) and that social relationships such as social networks, social activity participation, and social support were negatively related to incident dementia.43) Based on previous studies and our results, we believe that similar aspects may link eating habits to social relationships and loneliness. Future research should further clarify the relationship between eating habits, social engagement, and MCI among community-dwelling older adults.

This study has several limitations. First, although we evaluated the frequency of intake, we could not assess the quantity of each food item. Future studies should consider the quantity of food ingested. Second, because this was a cross-sectional study, we were unable to determine the causal relationships between MCI, eating-out practices, and dietary diversity. Third, we excluded socioeconomic and economic status domains from the multivariate analyses. Other possible covariates (e.g., lifestyle activity and socioeconomic status) should also be considered as they may be connected to eating out, dietary diversity, and MCI. Fourth, this cohort study used self-reported diet assessments. Although we excluded older adults with dementia and severe cognitive impairment, recall bias was still possible. Additional research using objective food records such as pictures is required. Fifth, while we asked participants about the frequency of their eating-out habits, we could not evaluate what they ate when eating out. Eating habits such as meal content, frequency of eating out, food environment, and the cost of meals vary by country and region. Our sample was obtained from a single country (Japan) and the target population was older adults living in Tarumizu City, Japan. Further investigation is required to clarify whether our findings can be generalized to other countries and regions.

In conclusion, the results of this cross-sectional study revealed that non-eating habits were not directly associated with MCI. In addition, we applied structural equation models to identify that non-eating habits were indirectly associated with MCI due to low dietary diversity. These findings suggest that promoting eating-out behaviors may lead to enhanced dietary diversity, resulting in positive effects on cognitive function.

Notes

We thank Tarumizu Chuo Hospital for their help in arranging the testers, and the medical doctors, nurses, physical therapists, occupational therapists, instructors, and testers at Tarumizu Chuo Hospital and Tarumizu City Office for their contributions to the study. We also thank all the study participants.

CONFLICT OF INTEREST

The researchers claim no conflicts of interest.

FUNDING

This work was supported by the Japan Agency for Medical Research and Development (AMED) (No. 18dk0207027h0003); Research Funding for Longevity Sciences (20–1) from the National Center for Geriatrics and Gerontology (NCGG) and Japan Society for the Promotion of Science (JSPS) Scientific Research [B] (No. 19H03978), Grant-in-Aid for Research Activity Start-up (No. 22K21230), and Grants-in-Aid from the 24th General Assembly of the Japanese Association of Medical Science.

AUTHOR CONTRIBUTIONS

Conceptualization: YK, HM; Data curation: YN, YT, MK, TT, TK, MO; Formal analysis: YK, HM, KT; Investigation: YN, YT, SA, MT, MK, TT, TK, MO; Writing–original draft: YK, HM; Writing–review and editing: HM, YN, YT, SA, MT, MK, TT, TK, KT, HS, MO.

SUPPLEMENTARY MATERIALS

Supplementary materials can be found via https://doi.org/10.4235/agmr.23.0218.

Supplementary Table S1.

Association between eating out habits and social relationships

agmr-23-0218-Supplementary-Table-S1.pdf

References

1. GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2022;7:e105–25.

2. Oh-Park M. Interplay between cognition and mobility in older adults. Ann Geriatr Med Res 2017;21:2–9.

3. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 2020;396:413–46.

4. Petersen RC. Clinical practice: mild cognitive impairment. N Engl J Med 2011;364:2227–34.

5. Scarmeas N, Anastasiou CA, Yannakoulia M. Nutrition and prevention of cognitive impairment. Lancet Neurol 2018;17:1006–15.

6. Otsuka R, Nishita Y, Tange C, Tomida M, Kato Y, Nakamoto M, et al. Dietary diversity decreases the risk of cognitive decline among Japanese older adults. Geriatr Gerontol Int 2017;17:937–44.

7. World Health Organization. Risk reduction of cognitive decline and dementia Geneva, Switzerland: World Health Organization; 2019.

8. Zheng J, Zhou R, Li F, Chen L, Wu K, Huang J, et al. Association between dietary diversity and cognitive impairment among the oldest-old: findings from a nationwide cohort study. Clin Nutr 2021;40:1452–62.

9. Lee KW, Song WO, Cho MS. Dietary quality differs by consumption of meals prepared at home vs. outside in Korean adults. Nutr Res Pract 2016;10:294–304.

10. Virudachalam S, Long JA, Harhay MO, Polsky DE, Feudtner C. Prevalence and patterns of cooking dinner at home in the USA: National Health and Nutrition Examination Survey (NHANES) 2007-2008. Public Health Nutr 2014;17:1022–30.

11. Lachat C, Khanh le NB, Khan NC, Dung NQ, Nguyen do VA, Roberfroid D, et al. Eating out of home in Vietnamese adolescents: socioeconomic factors and dietary associations. Am J Clin Nutr 2009;90:1648–55.

12. Makizako H, Kubozono T, Kiyama R, Takenaka T, Kuwahata S, Tabira T, et al. Associations of social frailty with loss of muscle mass and muscle weakness among community-dwelling older adults. Geriatr Gerontol Int 2019;19:76–80.

13. Noh JH, Jung HW, Ga H, Lim JY. Ethical guidelines for publishing in the Annals of Geriatric Medicine and Research. Ann Geriatr Med Res 2022;26:1–3.

14. Kimura M, Moriyasu A, Makizako H, Ihira H, Furuna T. Mail-based intervention for improvement of dietary habits among community-dwelling older adults living in heavy snowfall regions. Nihon Koshu Eisei Zasshi 2019;66:681–9.

15. Kiuchi Y, Makizako H, Nakai Y, Tomioka K, Taniguchi Y, Kimura M, et al. The association between dietary variety and physical frailty in community-dwelling older adults. Healthcare (Basel) 2021;9:32.

16. Makizako H, Shimada H, Park H, Doi T, Yoshida D, Uemura K, et al. Evaluation of multidimensional neurocognitive function using a tablet personal computer: test-retest reliability and validity in community-dwelling older adults. Geriatr Gerontol Int 2013;13:860–6.

17. Shermelleh-Engel K, Moosbrugger H, Muller H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness‐of‐fit measures. Methods Psychol Res Online 2003;8:23–74.

18. Vidgen HA, Gallegos D. Defining food literacy and its components. Appetite 2014;76:50–9.

19. Murakami K, Shinozaki N, Livingstone MB, Yuan X, Tajima R, Matsumoto M, et al. Associations of food choice values and food literacy with overall diet quality: a nationwide cross-sectional study in Japanese adults. Br J Nutr 2023;130:1795–805.

20. Takaizumi K. Influence of healthy eating literacy on awareness of the food environment and consumption of meals consisting of grain dishes, fish and meat dishes, and vegetable dishes: path analysis in men and women in their thirties. Jpn J Nutr Diet 2021;79:113–25.

21. Young DB, Lin H, McCabe RD. Potassium’s cardiovascular protective mechanisms. Am J Physiol 1995;268(4 Pt 2):R825–37.

22. Karanja N, Morris CD, Illingworth DR, McCarron DA. Plasma lipids and hypertension: response to calcium supplementation. Am J Clin Nutr 1987;45:60–5.

23. Mooren FC, Kruger K, Volker K, Golf SW, Wadepuhl M, Kraus A. Oral magnesium supplementation reduces insulin resistance in non-diabetic subjects: a double-blind, placebo-controlled, randomized trial. Diabetes Obes Metab 2011;13:281–4.

24. Ozawa M, Ninomiya T, Ohara T, Hirakawa Y, Doi Y, Hata J, et al. Self-reported dietary intake of potassium, calcium, and magnesium and risk of dementia in the Japanese: the Hisayama Study. J Am Geriatr Soc 2012;60:1515–20.

25. Ngabirano L, Samieri C, Feart C, Gabelle A, Artero S, Duflos C, et al. Intake of meat, fish, fruits, and vegetables and long-term risk of dementia and Alzheimer’s disease. J Alzheimers Dis 2019;68:711–22.

26. Kinoshita K, Otsuka R, Takada M, Tsukamoto-Yasui M, Nishita Y, Tange C, et al. The association between dietary amino acid intake and cognitive decline 8 years later in Japanese community-dwelling older adults. J Nutr Health Aging 2021;25:165–71.

27. Hersch EC, Merriam GR. Growth hormone (GH)-releasing hormone and GH secretagogues in normal aging: Fountain of Youth or Pool of Tantalus? Clin Interv Aging 2008;3:121–9.

28. Shimada H, Doi T, Lee S, Makizako H. Reversible predictors of reversion from mild cognitive impairment to normal cognition: a 4-year longitudinal study. Alzheimers Res Ther 2019;11:24.

29. Tsurumaki N, Zhang S, Tomata Y, Abe S, Sugawara Y, Matsuyama S, et al. Fish consumption and risk of incident dementia in elderly Japanese: the Ohsaki cohort 2006 study. Br J Nutr 2019;122:1182–91.

30. Morris MC, Evans DA, Bienias JL, Tangney CC, Bennett DA, Wilson RS, et al. Consumption of fish and n-3 fatty acids and risk of incident Alzheimer disease. Arch Neurol 2003;60:940–6.

31. Schaefer EJ, Bongard V, Beiser AS, Lamon-Fava S, Robins SJ, Au R, et al. Plasma phosphatidylcholine docosahexaenoic acid content and risk of dementia and Alzheimer disease: the Framingham Heart Study. Arch Neurol 2006;63:1545–50.

32. Moore E, Mander A, Ames D, Carne R, Sanders K, Watters D. Cognitive impairment and vitamin B12: a review. Int Psychogeriatr 2012;24:541–56.

33. Whyte EM, Mulsant BH, Butters MA, Qayyum M, Towers A, Sweet RA, et al. Cognitive and behavioral correlates of low vitamin B12 levels in elderly patients with progressive dementia. Am J Geriatr Psychiatry 2002;10:321–7.

34. Masoumi A, Goldenson B, Ghirmai S, Avagyan H, Zaghi J, Abel K, et al. 1alpha,25-dihydroxyvitamin D3 interacts with curcuminoids to stimulate amyloid-beta clearance by macrophages of Alzheimer’s disease patients. J Alzheimers Dis 2009;17:703–17.

35. Travica N, Ried K, Sali A, Scholey A, Hudson I, Pipingas A. Vitamin C status and cognitive function: a systematic review. Nutrients 2017;9:960.

36. La Fata G, Weber P, Mohajeri MH. Effects of vitamin E on cognitive performance during ageing and in Alzheimer’s disease. Nutrients 2014;6:5453–72.

37. Christensen K, Gleason CE, Mares JA. Dietary carotenoids and cognitive function among US adults, NHANES 2011-2014. Nutr Neurosci 2020;23:554–62.

38. Katenga-Kaunda LZ, Kamudoni PR, Holmboe-Ottesen G, Fjeld HE, Mdala I, Shi Z, et al. Enhancing nutrition knowledge and dietary diversity among rural pregnant women in Malawi: a randomized controlled trial. BMC Pregnancy Childbirth 2021;21:644.

39. Garcia S, Valencia C, Amaro H. Cognitive factors associated with frequency of eating out and eating takeout among Latinas. J Acad Nutr Diet 2021;121:520–8.

40. Noh HM, Han J, Roh YK, Song HJ. Eating alone and cognitive decline in Korean older adults: a 3-year prospective study. Ann Geriatr Med Res 2021;25:17–24.

41. Makizako H, Tsutsumimoto K, Shimada H, Arai H. Social frailty among community-dwelling older adults: recommended assessments and implications. Ann Geriatr Med Res 2018;22:3–8.

42. Akhter-Khan SC, Tao Q, Ang TF, Itchapurapu IS, Alosco ML, Mez J, et al. Associations of loneliness with risk of Alzheimer’s disease dementia in the Framingham Heart Study. Alzheimers Dement 2021;17:1619–27.

43. Saito T, Murata C, Saito M, Takeda T, Kondo K. Influence of social relationship domains and their combinations on incident dementia: a prospective cohort study. J Epidemiol Community Health 2018;72:7–12.

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Characteristic	Overall (n=597)	High dietary diversity (n=485)	Low dietary diversity (n=112)	p-value
Age (y)	73.0 (69.0–78.0)	73.0 (69.0–78.0)	72.0 (69.0–77.8)	0.383^a)
Sex, female	374 (62.6)	327 (67.4)	47 (42.0)	<0.001^b)
Education (y)	12.0 (9.0–12.0)	12.0 (9.0–12.0)	12.0 (9.0–12.0)	0.015^a)
Medications per day	3.0 (1.0–5.0)	3.0 (1.0–5.0)	2.5 (1.0–5.8)	0.107^a)
BMI (kg/m²)	22.6 (20.8–24.9)	22.6 (20.7–24.6)	23.4 (21.6–25.9)	0.005^a)
Living alone	168 (28.1)	131 (27.0)	37 (33.0)	0.201^b)
Non-eating out	142 (23.8)	105 (21.6)	37 (33.0)	0.011^b)
MCI	122 (20.4)	90 (18.6)	32 (28.6)	0.018^b)

	OR (95% CI)	p-value
Low dietary diversity as outcome
Non-eating out	1.97 (1.20–3.20)	0.007
MCI as outcome
Non-eating out	0.76 (0.46–1.25)	0.300
Low dietary diversity	1.72 (1.02–2.87)	0.038