Education attainment and nutritional status in the prevention of cognitive impairment in the hospitalized Chinese elderly

Abstract

Background:

Effect of education attainment and nutritional status on the development of cognitive impairment in Chinese elderly has not been reported.

Objective:

To investigate the role of education and nutrition in preventing cognitive impairment in the hospitalized Chinese elderly.

Methods:

Cognitive function was examined using the scoring system of Mini-Mental State Examination (MMSE) domains performed under instruction of Physicians of Geriatrics. Generalized linear mixed-effect regression was used for analyzing the association of demographic factors (age and gender), socioeconomic factors (education attainment and monthly income), as well as health-related factors (nutritional status, comorbidity, anxiety, and depression) and MMSE scores.

Results:

Total 246 hospitalized Chinese elders were enrolled into this study. Of them, 96 participants were 60–70 years old, 65 participants were 71–80 years old, and 85 of them were 81 years or older. Of the examined factors, we found that age, education attainment, and nutritional status were significantly associated with the outcome of MMSE scores, while monthly income and health condition (comorbidity, anxiety, and depression) were not significantly associated with MMSE score. Furthermore, education attainment was significantly associated with majority of the MMSE domains, including orientation, registration, attention and calculation, recall, and most of language sub-domains.

Conclusion:

Education attainment and nutritional status were significantly associated with MMSE scores in the hospitalized Chinese elderly. Higher education and better nutritional status are protective factors for the development of cognitive impairment in the hospitalized elderly Chinese population.

Keywords

Alzheimer's disease cognitive functioning education attainment Mini-Mental State Examination nutritional status

Introduction

Age-related cognitive decline or impairment has become one of the major health issues in the elderly population worldwide. It has been estimated that approximately forty-seven million people have dementia worldwide and that nearly eight million dementia were newly diagnosed every year.^1,2 The World Health Organization estimated that, globally by 2050, 115.4 million people could be living with dementia.³

Risk factors for the development of cognitive impairment are diverse. Genetic factor (apolipoprotein E4),^4,5 demographic factors such as older age, sex, and ethnicity,^6–10 as well as health-related biological factors such as hypertension, type 2 diabetes, and insulin resistance^11–13 have been reported to be associated with an increased risk of cognitive decline or impairment. In contrast, evidence has also shown that protective factors such as education attainment and healthy nutrition or dietary pattern are associated with lowing the risk of developing cognitive impairment in the process of aging.^14–21

Since its development in 1975,²² the Mini-Mental State Examination (MMSE) is one of the most widely used scoring system in the field of dementia research and clinic. It covers various cognitive domains including orientation, memory or registration, visual construction, and language. The MMSE score system has a specificity of 85–93% and sensitivity of 85–92% with cut points of 24 or less for the diagnosis of cognitive impairment.²³

The interest on nutritional components as potential factor for preventing or postponing the onset of age-related cognitive dysfunction has been growing.²⁴ However, limited studies on the protective factors, especially nutritional status and educational attainment, in the Chinese elderly population have not been reported. In the current study, therefore, we compared and studied MMSE scores in a group of Chinese elderly, who were grouped by their age, gender, level of education attainment, amount of monthly income, nutritional status, as well as whether had anxiety, depression, or comorbidities of other diseases.

Materials and methods

Participants of the study

This was a cross-sectional study. All participants were elderly Chinese who were hospitalized in our hospital, from January 2019 through October 2022.

Inclusion criteria: 1) ≥ 60 years old; 2) Were conscious and could cooperate to complete the evaluation; 3) Willing to participate the study and signed an informed consent form.

Exclusion criteria: 1) Was suffering from acute heart, liver, brain, or kidney disease; 2) Had comorbidities of aphasia, blindness, severe hearing impairment, or physical disability; 3) Had impaired consciousness or unconsciousness.

The protocol of this study was approved by the Ethic Committee of our Hospital (#: 2018066).

Cognitive test and assessment of nutritional status, anxiety, and depression

The cognitive function was assessed in all participants by the Physicians of Geriatrics using the MMSE questionnaire in Chinese, which was adapted from the MMSE in English.^22,25 Specifically, the scale of MMSE ranged from 0 to 30 pints, which included orientation to time (5 points), orientation to place (5 points), registration or memory (3 points), attention and calculation (5 points), recall (3 points), and language (9 points).²²

Nutritional status was assessed by nursing staff within 24 h after hospitalization using the Nutritional Risk Screening 2002 (NRS-2002) scoring questionnaire.²⁶ Anxiety was assessed using the Hamilton Anxiety Scale.^27,28 Depression was assessed using the Geriatric Depression Scale.²⁹

Statistical analyses

Frequency with percentage (%) was used for describing categorical variables and continuous variables were expressed by means and SDs. Generalized linear mixed-effects regression was used to analyze total scores of MMSE as well as score of each MMSE domain, and their association with the demographic factors (age and gender), socioeconomic factors (levels of monthly income and education attainment), and health-related factors (nutritional status, anxiety, depression, and comorbidities). All statistical analyses were performed using SAS 9.4 software (SAS, USA) with two-tails examination. p < 0.05 was considered as significant.

Results

Demographic characteristics of the participants

As shown in Table 1, total 246 hospitalized Chinese elders were enrolled into this study. Of them, 96 participants were between 60 to 70 years old, 65 participants aged between 70 to 80 years old, and 85 of them were over 80 years old; nearly equal distribution in gender ratio (M/F: 120/126, 48.8%/51.2%); majority of the participants have attained education of high school (163, 66.2%) or higher (54, 22.0% had college or higher education attainment); majority of them were in normal nutritional status (190, 77.3%); and approximately one tenth of them had anxiety (25, 10.2%) or depression (29, 11.8%). In addition, all participants had various comorbidities of health-related problems including digestive system diseases (71, 28.9%), cardiovascular diseases (87, 35.4%), cerebrovascular diseases (29, 11.8%), and others (59, 23.9%); and majority of the participants were in the middle (175, 71.1%) or high (43, 17.5%) income range.

Table 1.

Demographic characteristics of the participants.

Variable	Level of variable	Number (%)
Age (y)
	60–70	96 (39.0)
	70–80	65 (26.4)
	≥ 81	85 (34.6)
Gender
	Male	120 (48.8)
	Female	126 (51.2)
Monthly income (yuan)
	> 10,000	43 (17.5)
	5000–10,000	175 (71.1)
	< 5000	28 (11.4)
Education
	≥ 13 years	54 (22.0)
	7–12 years	163 (66.2)
	0–6 years	29 (11.8)
Comorbidity
	Digestive system disease	71 (28.9)
	Cerebrovascular disease	29 (11.8)
	Cardiovascular disease	87 (35.4)
	Others	59 (23.9)
Nutrition status
	Malnutrition	56 (22.8)
	Normal	190 (77.3)
Anxiety
	Yes	25 (10.2)
	Maybe	93 (37.8)
	No	128 (52.0)
Depression
	Yes	29 (11.8)
	No	217 (88.2)
Walking speed
	Slow	134 (54.5)
	Normal	112 (45.5)

Association of the socioeconomic factors and score of MMSE domains

To investigate potential association of socioeconomic factors with MMSE scores, univariate analysis (Wilcoxon two sample test or Kruskal-Wallis test) as well as multivariate analysis (generalized linear mixed-effects regression) were performed. Of the 8 analyzed factors (age, gender, level of monthly income, education attainment, comorbidity, nutrition status, anxiety, and depression), age, educational level, and nutritional status were significantly associated with the outcome of MMSE score (age: p < 0.001 by univariate analysis, and p = 0.003 by multivariate analysis; education: p < 0.001 by either univariate or multivariate analyses; nutrition: p = 0.002 by univariate analysis, and p = 0.019 by multivariate analysis, Table 2).

Table 2.

Association of the sociodemographic factors and MMSE score.

	Univariate		Multivariate
Effect factor	Statistic value	p*	Numeratordegrees of freedom	Denominatordegrees of freedom	Statistic value	p**
Age	22.824	<0.001	2	230	6.09	0.003
Gender	0.93	0.352	1	230	0.09	0.762
Monthly income	0.832	0.660	2	230	0.3	0.7415
Education	32.106	<0.001	2	230	20.36	<0.001
Comorbidity	3.143	0.370	3	230	0.1	0.902
Nutrition	−3.153	0.002	1	230	5.56	0.019
Anxiety	0.134	0.935	2	230	0.01	0.991
Depression	−1.647	0.100	1	230	0.05	0.817

*By univariate analysis with Wilcoxon two sample test for gender, anxiety, and depression, and with Kruskal-Wallis test for the rest. **By multivariate analysis with generalized linear mixed-effects regression; MMSE, Mini-Mental State Examination.

Next, association of the participants’ educational level, age, and nutritional status and scores of total MMSE as well as each MMSE domain was further analyzed. As expected, the higher level of education was attained, the better total MMSE score was achieved, that is, 27.98 ± 3.75 for the participants with college or higher institute graduation (≥ 13 years of education), 25.80 ± 4.08 for middle or high school graduation (7–12 years of education), and 21.86 ± 6.04 for primary school graduation (0–6 years of education), p < 0.001 by either univariate or multivariate analyses. Specifically, education attainment was significantly associated with majority of MMSE domains including orientation (p = 0.002 by either univariate or multivariate analyses), registration (memory, p < 0.001 by either univariate or multivariate analyses), attention and calculation (p = 0.001 by univariate analysis, and p = 0.006 by multivariate analysis), recall (p = 0.024 by univariate analysis, and p = 0.002 by multivariate analysis), and most of language sub-domains (repeat: p = 0.012 by univariate analysis, and p = 0.023 by multivariate analysis; reading: p < 0.001 by either univariate or multivariate analyses; writing: p < 0.001 by either univariate or multivariate analyses; and drawing: p < 0.001 by either univariate or multivariate analyses) except for naming ability (p = 0.316) and 3-stage command performance (p = 0.116, Table 3).

Table 3.

Association of education attainment and score of MMSE domains.

MMSE	≥ 13 years(N = 54)	7–12 years(N = 163)	0–6 years(N = 29)	p*	p**
Total MMSE	27.98 ± 3.75	25.80 ± 4.08	21.86 ± 6.04	<0.001	<0.001
Orientation	9.69 ± 0.70	9.15 ± 1.71	8.10 ± 2.74	0.002	0.002
Registration	2.91 ± 0.29	2.82 ± 0.57	2.34 ± 1.01	0.001	<0.001
Attention & calculation	4.09 ± 1.62	3.82 ± 1.52	2.72 ± 1.89	0.001	0.006
Recall	2.24 ± 0.89	2.06 ± 1.11	1.48 ± 1.24	0.024	0.002
Language
Naming	2.00 ± 0.34	1.94 ± 0.31	1.93 ± 0.46	0.250	0.316
Repeat	0.94 ± 0.23	0.89 ± 0.44	0.69 ± 0.47^ab	0.012	0.023
3-stage command	2.89 ± 0.42	2.76 ± 0.62	2.69 ± 0.85	0.300	0.116
Reading	1.00 ± 0.00	0.91 ± 0.33	0.62 ± 0.49^ab	<0.001	<0.001
Writing	0.98 ± 0.14	0.82 ± 0.39^a	0.48 ± 0.51^ab	<0.001	<0.001
Drawing	0.91 ± 0.29	0.72 ± 0.45	0.31 ± 0.47	<0.001	<0.001

*By univariate analysis with Kruskal-Wallis test. **By multivariate analysis with generalized linear mixed-effects regression. ^aCompared with college or higher (≥ 13 years) education; ^bCompared with Middle and High School (7–12 years) education; MMSE, Mini-Mental State Examination.

As anticipated, total MMSE score was significantly different between the three age groups, that is, 27.19 ± 3.21 of the group aged from 60 to 70 years old; 25.97 ± 3.72 of the group aged between 71 to 80 years old; and 24.14 ± 5.86 of ≥ 81 years old group (p = 0.003). However, only two MMSE domains (attention and calculation, and recall) were significantly affected by age. Score of attention and calculation in the age of ≥ 81 group (3.39 ± 1.88) was significantly lower than that of the group aged from 60 to 70 (4.06 ± 1.41, p = 0.006), but was not significantly different compared to the age group of 71–80 (3.75 ± 1.49, Table 4). Score of recall ability in the age ≥ 81 group (1.67 ± 1.20) was significantly lower compared to either group of 60–70 (2.33 ± 0.96) or 71–80 (2.05 ± 1.02, p = 0.001, Table 4). Scores of other MMSE domains were not significantly different among the three age groups.

Table 4.

Association of age and score of MMSE domains.

MMSE	60–70 (N = 96)	71–80 (N = 65)	≥ 81 (N = 85)	p*	p**
Total MMSE	27.19 ± 3.21	25.97 ± 3.72	24.14 ± 5.86	0.003	0.003
Orientation	9.58 ± 0.93	9.14 ± 1.94	8.65 ± 2.18	0.002	0.102
Registration	2.90 ± 0.51	2.83 ± 0.52	2.62 ± 0.74	0.006	0.089
Attention & Calculation	4.06 ± 1.41	3.75 ± 1.49	3.39 ± 1.88 ^a	0.039	0.006
Recall	2.33 ± 0.96	2.05 ± 1.02	1.67 ± 1.20^ab	0.001	0.001
Language
Naming	1.99 ± 0.18	1.97 ± 0.25	1.89 ± 0.49	0.406	0.720
Retelling	0.94 ± 0.35	0.92 ± 0.48	0.78 ± 0.42	0.020	0.097
3-stage command	2.88 ± 0.42	2.72 ± 0.67	2.72 ± 0.73	0.242	0.455
Reading	0.92 ± 0.28	0.91 ± 0.34	0.86 ± 0.38	0.457	0.247
Writing	0.86 ± 0.34	0.80 ± 0.40	0.76 ± 0.43	0.218	0.485
Drawing	0.78 ± 0.42	0.74 ± 0.44	0.61 ± 0.49	0.037	0.150

By univariate analysis with Kruskal-Wallis test. ** By multivariate analysis generalized linear mixed-effects regression. ^aCompared with the group of 60–70 years old; ^bCompared with the group of 71–80 years old; MMSE, Mini-Mental State Examination.

The total score as well as scores of each MMSE domain in the participants with malnourishment was then analyzed in comparison with the participants in a good nutritional status. Participants with malnourishment had significantly lower total MMSE scored compared to that of participants at normal nutritional status (24.11 ± 6.18 versus 26.32 ± 3.89, p = 0.003). In addition, orientation, registration (memory), and attention and calculation abilities were significantly lower in the participants with malnutrition than that of the participants in a good nutritional status (orientation: 8.48 ± 2.30 versus 9.34 ± 1.52, p = 0.013; registration: 2.61 ± 0.73 versus 2.84 ± 0.56, p = 0.026; attention and calculation: 3.27 ± 1.82 versus 3.89 ± 1.54, p = 0.036, Table 5). Scores of other MMSE domains were not significantly different between the participants with malnutrition and normal nutritional status.

Table 5.

Association of nutrition status and score of MMSE domains.

MMSE	Malnutrition	Normal	p*	p**
Total MMSE	24.11 ± 6.18	26.32 ± 3.89	0.003	0.003
Orientation	8.48 ± 2.30	9.34 ± 1.52	<0.001	0.013
Registration	2.61 ± 0.73	2.84 ± 0.56	0.004	0.026
Attention & Calculation	3.27 ± 1.82	3.89 ± 1.54	0.014	0.036
Recall	1.80 ± 1.18	2.09 ± 1.06	0.132	0.419
Language
Naming	1.88 ± 0.57	1.97 ± 0.22	0.422	0.418
Retelling	0.79 ± 0.41	0.91 ± 0.41	0.064	0.290
3-stage command	2.77 ± 0.69	2.78 ± 0.59	0.803	0.661
Reading	0.82 ± 0.39	0.92 ± 0.31	0.064	0.148
Writing	0.75 ± 0.44	0.83 ± 0.38	0.170	0.308
Drawing	0.61 ± 0.49	0.74 ± 0.44	0.051	0.080

By univariate analysis with Kruskal-Wallis test. **By multivariate analysis generalized linear mixed-effects regression; MMSE, Mini-Mental State Examination.

Discussion

In the current study, role of social and economic factors as well as demographic factors on severity of cognitive impairment was analyzed in a hospitalized Chinese population aged over 60 years old. Among the examined factors including age, gender, monthly income, education attainment, comorbidity, nutritional status, anxiety, and depression, we found that age, educational level, and nutritional status were the factors that significantly associated with the severity of cognitive impairment evaluated by the score of MMSE. Furthermore, educational level was significantly associated with all MMSE domains except the abilities of naming and 3-stage command, age was significantly associated with the MMSE domains of attention and calculation as well as recall ability, and nutritional status was significantly associated with domains of orientation, registration, as well as attention and calculation.

Cognitive abilities of attention and memory declines with increasing of age as a normal and physiological process, which could manifest as reduced MMSE scores.^30,31 Physiological process of aging, however, could potentially deteriorate into cognitive impairment or dementia, especially, when he/she has impairment of multiple MMSE domains. In this regard, compared to single-domain impairment, elderly people with multi-domain impairment are more likely to progress to dementia.^7,32 We found that score of attention and calculation was significantly lower in the group of ≥ 81 years old compared to that of ≤ 70 years old group; and that score of recall was significantly lower in the group of ≥ 81 years old compared to that of 71–80 years old or ≤ 70 years old groups, indicating that cognitive function change pathologically with increasing of age.

While there is no evidence of clinically available pharmacological therapies to prevent or reverse cognitive function deterioration in elderly population, a multi-strategic intervention including cognitive training, physical activity, and diet has been reported to improve cognitive functions or delay deterioration of cognitive functioning.^24,33 In this regard, the interest on dietary and nutritional components, especially on Mediterranean diet, Nordic diet, and Japanese diet in association of preventing or postponing cognitive decline has been growing.^18–21 In fact, recent studies of nutrition research indicated that unhealthy diet not only increases the risk of cardiovascular diseases, but also seems to be a risk factor for the cognitive deterioration; and that a multi-nutrient intervention seems better than single-nutrient intervention in the outcomes of preserving cognitive function.^34–38 Consistently, we found that nutritional status of the participants was significantly associated with not only total MMSE score, but also with domains of orientation, registration, as well as attention and calculation, that is, elderly people with malnutrition had poorer MMSE score performance compared to the same age group with normal nutritional status, suggesting that a good nutritional status may help with preventing or delaying the development of cognitive dysfunction.

Studies on the association of sociodemographic factors including education attainment and living with family members have been reported.^14–17 Outcomes of these studies clearly demonstrated that education attainment is positively associated with MMSE score and performance in the MMSE domains.^14–17 In the current study, sociodemographic factors including education attainment and monthly income, as well as heal-related factors including comorbidity, anxiety, and depression were also analyzed by generalized linear mixed-effects regression. Consistent with the previous reports, we also found that education attainment was significantly associated with not only the total MMSE score, but also with the performance in majority of the domains including orientation, registration, attention and calculation, recall, and language (except Naming ability and 3-stage command). These findings indicated that people could benefit from higher education attainment in their mid- or later life by reserving cognitive function and by delaying or reducing the potential development of dementia.

The current study has some limitations. First, the sample size of this study was small, and further study with larger sample size maybe required to validate our findings. Second, this was a cross-sectional study in a single hospital, and we could not provide follow-up data. Longitudinal study design on the association of participants’ nutritional status and MMSE scores in the future study could be more convincing. Third, studies have reported that active lifestyle such as physical activity and dietary pattern could strength cognitive function two times and decrease risk of MMSE decline by 30%.³⁹ These potential factors of lifestyle were not collected and analyzed in the current study. Fourth, this study was carried out in an ethnically homogeneous population (Chinese of Han nationality). Lastly, this study was carried out only in the hospitalized patients and thus, they were not representative for the general population of Chinese Elderly. Future studies addressing these findings in generalized elderly populations of minorities in China or with different ethnic/cultural backgrounds are warranted.

Taken together, the current study added evidence that higher education attainment can protect elderly person from cognitive function decline and impairment of each MMSE domain; that nutritional status is associated with higher MMSE score as well as domains of orientation, memory, and calculation. Findings of the current study suggested that attaining higher education and maintaining good nutritional status could reduce the cognitive decline occurred in the process of aging.

Footnotes

Acknowledgments

The authors have no acknowledgements to report.

ORCID iD

Weifeng Guo

Author contributions

Zhao Gao (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Tianjiong Luo (Data curation; Formal analysis; Investigation; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Chenyu Ye (Data curation; Formal analysis; Investigation; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Kun Cheng (Data curation; Formal analysis; Investigation; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Lichao Qian (Data curation; Formal analysis; Funding acquisition; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Qingqing Cai (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Qiong Zhou (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Hui Fang (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Guancheng Zhang (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Shenyan Cai (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Ming Shi (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Ye Ji (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Letian Zhao (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Yilin Zhu (Data curation; Formal analysis; Investigation; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing); Weifeng Guo (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing).

Funding

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This study was funded by the fourth batch of TCM clinical outstanding talents project in Jiangsu Province; the Natural Science Foundation of Nanjing University of Chinese Medicine (Grant No. XZR2021050) for Lichao Qian and Project of National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province, China (Grant No. JD2023SZ16) for Lichao Qian.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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