
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-08871
00026
10.1097/MD.0000000000039587
3
5300
Research Article
Observational Study
Prevalence of cognitive impairment and its associated factors in middle-aged and elderly people in Anhui Province, China: An observational study
Cui Can MD 1473061285@qq.com
a
Yu Tianyun MD yty19981025@163.com
a
Zhai Yujia MD 798027287@qq.com
a
Zhang Shan MD 815872256@qq.com
a
https://orcid.org/0000-0002-5833-0083
Su Zengfeng PhD a*
a Department of General Medicine, Chaohu Hospital affiliated with Anhui Medical University, Chaohu, China.
* Correspondence: Zengfeng Su, Department of General Medicine, Chaohu Hospital affiliated with Anhui Medical University, Chaohu, China (e-mail: suzengfeng@163.com).
06 9 2024
06 9 2024
103 36 e3958710 10 2023
13 7 2024
15 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

To understand the prevalence of cognitive impairment (CI) in middle-aged and elderly individuals in Anhui Province and to develop a CI risk prediction model. From May to June 2022, a multistage, stratified cluster-sampling method was used to select 3200 middle-aged and elderly people over 45 years old in Anhui Province for a questionnaire survey, and the Chinese version of the Mini-Mental State Examination (MMSE) was used to assess cognitive function. SPSS 25.0 was used for univariate and multivariate analyses, and R software was used to establish and validate the nomogram. A total of 3059 valid questionnaires were included, of which 384 were from participants who were diagnosed with CI, and the prevalence rate was 12.6%. Multivariate logistic analysis showed that female sex, advanced age, family history, etc., were closely related to the occurrence of CI. The area under curve (AUC) values in the modeling and validation groups were 0.845 (95% CI: 0.822–0.868) and 0.868 (95% CI: 0.835–0.902), respectively, indicating that the predictive ability of the model was good. The Hosmer–Lemeshow test suggested that the model had good goodness-of-fit, and the decision-curve evaluation nomogram had a high benefit within the threshold, which had a certain clinical importance. The prevalence rate of CI among middle-aged and elderly individuals in Anhui Province was 12.6%. Female sex, elderly age, family history, low educational status, current smoking status, sleep disorders, hypertension, stroke, and diabetes were shown to be risk factors for CI, while exercise was shown to be a protective factor.

cognitive impairment
Mini-Mental State Examination (MMSE)
nomogram
prevalence
risk factors
The Scientific Research Foundation of the Education Department of Anhui Province of ChinaKJ2020A0189 Zengfeng SuOPEN-ACCESSTRUE
==== Body
pmc1. Introduction

Cognitive impairment (CI) refers to abnormalities in the higher intellectual processing of the brain related to learning, memory, thinking, and judgment in the process of recognition and acquisition of knowledge by the organism, resulting in memory impairment and accompanied by changes such as aphasia, dysfunction, and dyscognition, and it includes 2 main aspects: mild cognitive impairment (MCI) and dementia.[1] MCI is a transitional state between normal aging and dementia that is characterized by decreased cognitive function, but the individual’s daily life is mainly unaffected.[2] Alzheimer’s disease and vascular dementia are the most common subtypes of dementia, with an unknown and irreversible etiology, and there are no curative drugs available at home or abroad.[3–6] According to relevant reports in the literature, approximately 50 million people have dementia worldwide. This number is expected to increase to 152 million by 2050, rising among those living in low- and middle-income countries.[7,8] A recent study by Jia et al[9] noted that the prevalence of dementia in adults over 60 in China is 6.04%, with 15.07 million people. At the same time, the majority of MCI cases are 15.54%, with a total of 38.77 million people. Meanwhile, Alzheimer’s Disease Facts and Figures 2022 state that the emergence of COVID-19 has now led to tens of thousands of hospitalizations, increasing the number of cases of postcritical illness CI and exacerbating the trend of death in CI patients.[10,11] There is a relative lack of epidemiological studies on CI in Anhui Province. To understand the epidemiological status of CI in the middle-aged and elderly population in Anhui Province, this study used a multistage stratified random-sampling method to recruit middle-aged and elderly individuals over 45 years of age in Anhui Province from May to June 2022 to conduct an epidemiological survey, screen risk factors associated with CI, and establish a nomogram graph score model to identify the population with a high prevalence of CI and provide possibilities for early intervention.

2. Methods

2.1. Study design and population

From May to June 2022, this research team adopted a multistage stratified random-sampling method to conduct this survey. In the 1st stage, we divided Anhui Province into southern and northern Anhui using the Yangtze River as the boundary and randomly selected 2 to 3 cities or counties from each. In the 2nd stage, we randomly selected 2 districts or townships from the selected cities or counties. In the 3rd stage, 4 communities or villages were randomly selected from the sampled districts or townships.

The inclusion criteria for study participants were as follows: permanent residence in the survey area and lived in the survey area at the time of the survey; age ≥ 45 years old; normal hearing, vision, and pronunciation; and agreement to participate in this survey and sign the informed consent form. The exclusion criteria were as follows: having permanent residence in the survey area but temporarily residing outside of the survey area for an extended period of time (separation of people and residence); suffering from severe schizophrenia; having an intellectual disability; experiencing blindness, deafness, muteness, or vegetative state; and refusal to participate in this survey.

2.2. Sample size estimation

A recent study by Jia indicated that the prevalence of dementia and MCI in Chinese adults over 60 years of age were 6.04% and 15.54%, respectively, while the estimated prevalence of dementia combined with other studies ranged from 2% to 13%, while the estimated prevalence of MCI ranged from 9.7% to 23.3%.9 Combining the above minimum prevalence rates for dementia and MCI, this study’s proposed CI prevalence rate is 11.7%. Combined with the sample size formula,[12] the sample size was calculated to be 2899, but considering the invalid questionnaires in the research survey, 3200 people were proposed to be surveyed to ensure the accuracy of the research results.

2.3. Research methods

All questionnaires will be standardized and include the interviewees’ demographic information, lifestyle, and medical history. Neuropsychological tests and physical examinations will also be carried out, with the neuropsychological tests completed by professionals using the MMSE scale to assess their cognitive function. Due to the broad scope of the survey and many respondents, the relevant professionals mainly comprise general practitioners from community health service centers. The appropriate professionals received the necessary knowledge and skills training to reduce inconsistencies in the assessment process and were assessed using a standardized questionnaire and survey terms. Informed consent was obtained from all participants, and the Medical Ethics Committee of our hospital approved the survey.

2.4. Ascertainment of CI

The MMSE is the most widely used scale for CI assessment at home and abroad, with simple operation, straightforward generalization, high sensitivity and specificity in identifying dementia, and a close relationship with the respondents’ educational status.[13,14] It mainly includes orientation, memory, attention and orientation, memory, language ability, etc. There are 30 items, with a total score of 30 points. Each correct answer is 1 point, and a wrong answer is 0 points. CI refers to the diagnostic criteria (DSM-III-R) formulated by Zhang Mingyuan, which is classified as illiteracy: MMSE ≤ 17 points; primary school: MMSE ≤ 20 points; and junior high school education and above: MMSE ≤ 24 points.

2.5. Statistical analysis

SPSS version 25.0 (SPSS Inc., Chicago) and R version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria) were applied to analyze the data, and a 2-sided P < .05 indicated statistical significance. R software was used to randomly divide the study participants into a modeling group and a validation group at a ratio of 7:3. The modeling group was used to screen the variables and construct the model. The validation group was used to verify the results obtained using the modeling group. For the univariate analysis, measures that were normally distributed were expressed as x ± s, t tests were used for comparison between 2 groups, and ANOVA was used for comparison between multiple groups; actions that were not normally distributed were expressed as median and quartiles, and nonparametric Wilcoxon tests were used to complete the comparison between 2 groups; count data were mainly described statistically as frequencies and percentages, and chi-square tests were used to complete the comparison between groups. Factors that were statistically significant for single factor analysis continued to be analyzed using multifactor logistic regression to obtain independent influences. Nomogram line plots were created using R software, and the model was evaluated using the receiver-operating characteristic curve (ROC) and its area under the curve (AUC) and calibration curve. The Hosmer–Lemeshow goodness-of-fit test was used to evaluate the differentiation and calibration of the model in the model and validation groups and for the logistic regression model, and decision curve analysis (DCA) was used to evaluate the benefit of the model at different threshold probabilities to measure the net clinical benefit.[15,16]

3. Results

3.1. Subjects and CI events

A total of 3059 study subjects were included in this study, of whom 384 were diagnosed with CI, yielding a CI prevalence of 12.6% in the middle-aged and elderly population in Anhui Province. A total of 265 people in the modeling group were diagnosed with CI, while 119 in the validation group had CI. The prevalence of CI in the modeling and validation groups was 12.4% and 13.0%, respectively. There were no statistically significant differences in demographic and clinical features between the modeling and validation groups (P > .05; see Table 1).

Table 1 Demographic and clinical characteristics of middle-aged and elderly individuals in Anhui Province.

Characteristic	Whole population [cases (%)]	Training cohort [cases (%)]	Validation cohort [cases (%)]	P value	
Total	3059	2141	918		
Sex					
 Male	1537 (50.2)	1100 (51.4)	437 (47.6)	.056	
 Female	1522 (49.8)	1041 (48.6)	481 (52.4)		
Age group (yr)				.297	
 45–59	1483 (48.5)	1048 (48.9)	435 (47.4)		
 60–74	1129 (36.9)	772 (36.1)	357 (38.9)		
 ≥75	447 (14.6)	321 (15.0)	126 (13.7)		
Marital status				.997	
 With spouse (married)	2799 (91.5)	1959 (91.5)	840 (91.5)		
 No spouse (widowed, divorced or separated)	260 (8.5)	182 (8.5)	78 (8.5)		
Educational status				.859	
 Primary school and below	1323 (43.2)	931 (43.5)	392 (42.7)		
 Junior high school	922 (30.1)	648 (30.3)	274 (29.8)		
 High school and junior college	532 (17.4)	364 (17.0)	168 (18.3)		
 University and above	282 (9.2)	198 (9.2)	84 (9.2)		
Place of residence				.590	
 Urban	526 (17.2)	363 (17.0)	163 (17.8)		
 Rural	2533 (82.8)	1778 (83.0)	755 (82.2)		
Family history of CI				.502	
 No	1855 (60.6)	1290 (60.3)	565 (61.5)		
 Yes	1204 (39.4)	851 (39.7)	353 (38.5)		
BMI				.063	
 Underweight	76 (2.5)	61 (2.8)	15 (1.6)		
 Normal	1667 (54.5)	1172 (54.7)	495 (53.9)		
 Overweight	1144 (37.4)	780 (36.4)	364 (39.7)		
 Obese	172 (5.6)	128 (6.0)	44 (4.8)		
Smoking status				.749	
 No	2167 (70.8)	1545 (72.2)	654 (71.2)		
 Yes	892 (29.2)	596 (27.8)	264 (28.8)		
Drinking status				.555	
 No	2217 (72.5)	1545 (72.2)	672 (73.2)		
 Yes	842 (27.5)	596 (27.8)	246 (26.8)		
Sleeping conditions				.183	
 Normal sleep	2131 (69.7)	1507 (70.4)	624 (68.0)		
 Sleep disorders	928 (30.3)	634 (29.6)	294 (32.0)		
Exercise status				.071	
 Yes	1887 (61.7)	1343 (62.7)	544 (59.3)		
 No	1172 (38.3)	798 (37.3)	374 (40.7)		
Hypertension status				.475	
 No	1616 (52.8)	1122 (52.4)	494 (53.8)		
 Yes	1443 (47.2)	1019 (47.6)	424 (46.2)		
Head trauma status				.348	
 No	2900 (94.8)	2035 (95.0)	865 (94.2)		
 Yes	159 (5.2)	106 (5.0)	53 (5.8)		
Coronary heart disease status				.481	
 No	2915 (95.3)	2044 (95.5)	871 (94.9)		
 Yes	144 (4.7)	97 (4.5)	47 (5.1)		
Heart failure status				.764	
 No	3025 (98.9)	2118 (98.9)	907 (98.8)		
 Yes	34 (1.1)	23 (1.1)	11 (1.2)		
Stroke status				.252	
 No	2828 (92.4)	1987 (92.8)	841 (91.6)		
 Yes	231 (7.6)	154 (7.2)	77 (8.4)		
History of thyroid disease				.650	
 No	2990 (97.7)	2091 (97.7)	899 (97.9)		
 Hyperthyroidism or hypothyroidism	69 (2.3)	50 (2.3)	19 (2.1)		
Diabetes status				.066	
 No	2625 (85.8)	1821 (85.1)	804 (87.6)		
 Yes	434 (14.2)	320 (14.9)	114 (12.4)		
Hyperlipidemia status				.450	
 No	2826 (92.4)	1983 (92.6)	843 (91.8)		
 Yes	233 (7.6)	158 (7.4)	75 (8.2)		
BMI = body mass index, CI = cognitive impairment.

3.2. Univariate analysis

Univariate analysis revealed that age, sex, family history, education, smoking, exercise, sleep status, hypertension, heart failure, stroke and diabetes were closely associated with the prevalence of CI, with statistically significant differences (P < .05; see Table 2). In contrast, there was no significant difference between marital status, place of residence, body mass index (BMI), alcohol consumption, hyperlipidemia, history of head trauma, coronary heart disease, hyperthyroidism or hypothyroidism and the prevalence of CI (P > .05).

Table 2 Univariate analysis of risk factors for cognitive impairment among middle-aged and elderly individuals in Anhui Province.

Characteristic	Non-CI groups (n=)	CI Group (n=)	χ2	P value	
Sex			5.071	.024	
 Male	981 (52.3)	119 (44.9)			
 Female	895 (47.7)	146 (55.1)			
Age group (yr)			218.670	<.001	
 45–59	1005 (53.6)	43 (16.2)			
 60–74	662 (35.3)	110 (41.5)			
 ≥75	209 (11.1)	112 (42.3)			
Marital status			1.108	.293	
 With spouse (married)	1721 (91.7)	238 (89.8)			
 No spouse (widowed, divorced or separated)	155 (8.3)	27 (10.2)			
Educational status			86.035	<.001	
 Primary school and below	758 (40.4)	173 (65.3)			
 Junior high school	569 (30.3)	79 (29.8)			
 High school and junior college	353 (18.8)	11 (4.2)			
 University and above	196 (10.4)	2 (0.8)			
Place of residence			0.288	.591	
 Urban	315 (16.8)	48 (18.1)			
 Rural	1561 (83.2)	217 (81.9)			
Family history of CI			79.927	<.001	
 No	1197 (63.8)	93 (35.1)			
 Yes	679 (36.2)	172 (64.9)			
BMI			2.989	.393	
 Underweight	54 (2.9)	7 (2.6)			
 Normal	1014 (54.1)	158 (59.6)			
 Overweight	695 (37.0)	85 (32.1)			
 Obese	113 (6.0)	15 (5.7)			
Smoking status			6.197	.013	
 No	1343 (71.6)	170 (64.2)			
 Yes	533 (28.4)	95 (35.8)			
Drinking status			2.486	.115	
 No	1343 (71.6)	202 (76.2)			
 Yes	533 (28.4)	63 (23.8)			
Sleeping conditions			59.200	<.001	
 Normal sleep	1374 (73.2)	133 (50.2)			
 Sleep disorders	502 (26.8)	132 (49.8)			
Exercise status			56.185	<.001	
 Yes	1232 (65.7)	111 (41.9)			
 No	644 (34.3)	154 (58.1)			
Hypertension status			74.927	<.001	
 No	1049 (55.9)	73 (27.5)			
 Yes	827 (44.1)	192 (72.5)			
Head trauma status			1.378	.240	
 No	89 (4.7)	17 (6.4)			
 Yes	1787 (95.3)	248 (93.6)			
Coronary heart disease status			1.588	.208	
 No	1795 (95.7)	249 (94.0)			
 Yes	81 (4.3)	16 (6.0)			
Heart failure status			20.736	<.001	
 No	1863 (99.3)	255 (96.2)			
 Yes	13 (0.7)	10 (3.8)			
Stroke status			61.753	<.001	
 No	1772 (94.5)	215 (81.1)			
 Yes	104 (5.5)	50 (18.9)			
History of thyroid disease			2.743	.098	
 No	1836 (97.9)	255 (96.2)			
 Hyperthyroidism or hypothyroidism	40 (2.1)	10 (3.8)			
Diabetes status			33.386	<.001	
 No	1627 (86.7)	194 (73.2)			
 Yes	249 (13.3)	71 (26.8)			
Hyperlipidemia status			1.867	.172	
 No	1743 (92.9)	240 (90.6)			
 Yes	133 (7.1)	25 (9.4)			
BMI = body mass index, CI = cognitive impairment.

3.3. Multivariate analysis

Multivariate analysis showed that female sex, advanced age, family history, low literacy, smoking, sleep disturbance, hypertension, stroke and diabetes were independent risk factors for CI prevalence (P < .05; see Table 3). At the same time, exercise was an independent protective factor.

Table 3 Multivariate analysis of cognitive impairment among middle-aged and elderly individuals in Anhui Province.

Risk factors	B	SE	Wald χ2	P value	OR (95% CI)	
Sex						
 Male					1.000	
 Female	0.816	0.208	15.355	0	2.262 (1.504–3.403)	
Age group (yr)						
 45–59					1.000	
 60–74	1.021	0.205	24.693	0	2.776 (1.856–4.152)	
 ≥75	1.907	0.230	68.744	0	6.735 (4.291–10.572)	
Family history of CI						
 No					1.000	
 Yes	1.119	0.156	51.721	0	3.062 (2.257–4.155)	
Educational status						
 Primary school and below					1.000	
 Junior high school	0.003	0.173	0	.987	1.003 (0.715–1.407)	
 High school and junior college	−1.216	0.351	12.000	.001	0.296 (0.149–0.590)	
 University and above	−1.465	0.735	3.972	.046	0.231 (0.055–0.976)	
Smoking status						
 No					1.000	
 Yes	0.709	0.215	10.844	.001	2.032 (1.333–3.100)	
Sleeping conditions						
 Normal sleep					1.000	
 Sleep disorders	0.590	0.157	14.095	0	1.993 (1.473–2.696)	
Exercise status						
 Yes					1.000	
 No	0.690	0.154	19.976	0	1.993 (1.473–2.696)	
Hypertension status						
 No					1.000	
 Yes	0.716	0.164	19.064	0	2.047 (1.484–2.824)	
Heart failure status						
 No					1.000	
 Yes	0.566	0.524	1.165	.281	1.761 (0.630–4.923)	
Stroke status						
 No					1.000	
 Yes	0.519	0.222	5.476	.019	1.680 (1.088–2.593)	
Diabetes status						
 No					1.000	
 Yes	0.464	0.178	6.791	.009	1.590 (1.122–2.254)	
Constant	−5.035	0.321	245.340	–	–	
CI = cognitive impairment.

3.4. Establishment and verification of the nomogram

Based on the above multivariate analysis results, a nomogram of CI in the middle-aged and elderly population in Anhui Province was created and plotted using R software (see Fig. 1). The nomogram allowed each predictor variable to be scored according to the uppermost scale. The total score was calculated by adding the scores of all predictor variables, and the predicted prevalence of CI was obtained by plotting the total score downward. The model’s discrimination was assessed for internal and external validation by calculating the C-index and plotting the ROC curve. The AUCs were 0.845 (95% CI: 0.822–0.868) and 0.868 (95% CI: 0.835–0.902) for the modeling and validation groups, respectively, indicating that the model has good predictive power (see Fig. 2). The Hosmer–Lemeshow goodness-of-fit test and calibration curve were used for calibration, in which the P values for the modeling and validation groups were 0.325 and 0.722, respectively, indicating that the differences were not statistically significant, suggesting that the model had good goodness-of-fit (see Fig. 3). The DCA curves of the column line graphs for the modeling and validation groups were used to assess that the column line graphs had high benefit within the threshold and had some clinical significance (see Fig. 4).

Figure 1. Nomogram of the CI risk prediction model for middle-aged and elderly individuals in Anhui Province. CI = cognitive impairment.

Figure 2. ROC curve of the CI risk prediction model for middle-aged and elderly individuals in Anhui Province. AUC = area under curve, CI = cognitive impairment, ROC = receiver operating characteristic curve.

Figure 3. The calibration curve of the CI risk prediction model for middle-aged and elderly individuals in Anhui Province. CI = cognitive impairment.

Figure 4. Decision curve of the CI risk prediction model for middle-aged and elderly individuals in Anhui Province. CI = cognitive impairment.

4. Discussion

With population aging, the prevalence of CI is increasing, posing a severe burden on global health care systems.[17] There are relative differences in the majority of CI in different regions, and the reasons for such differences may be related to the local environment, educational status, level of medical development, and lifestyle of the population.[18] The estimated prevalence of MCI in people aged 60 years or older in 4 major cities in Hebei Province in 2014 was 21.3%, compared with 20.1% in urban communities in China.[19] The prevalence of dementia in people aged 60 years or older in communities in Chongqing City in 2018 was 10.44%, compared with 2.8% in Jiangxi Province.[12,20] Meanwhile, a recent study pointed out that the standardized prevalence of dementia in people over 65 in Hubei, Hebei, and Tianjin was 8.93%, 12.03%, and 7.78%, respectively.[21] At present, relatively few epidemiological surveys on CI have been conducted in Anhui Province, China, and understanding the prevalence of CI and related influencing factors is the key to CI prevention. In the present study, the prevalence of CI in the middle-aged and elderly populations in Anhui Province was 12.6%, which is lower than the prevalence of CI in Jinan City and other regions.[19,22]

The results of the multifactorial logistic analysis in this study concluded that female sex, older age, family history, low literacy, smoking, sleep disorders, hypertension, stroke, and diabetes mellitus were independent risk factors for CI. At the same time, exercise was an independent protective factor. Advanced age is a significant risk factor for CI prevalence, and the prevalence of CI increases with increasing age.[23] Additionally, sex is closely related to CI. The present study showed that the prevalence of CI is higher in women than in men and that women are a risk factor affecting CI, which is consistent with the results of numerous studies thus far, and there are also related studies that point out that the prevalence of CI is higher in women than in men at all ages.[12,24–26] These results may be related to longer life expectancy in women, differences in hormone levels, a decrease in protective hormones in women after menopause, and brain development factors.[25–30] Most studies have concluded that dementia is closely associated with genetic factors such as the Apoprotein E gene and that those with a family history of dementia have a higher risk of developing dementia than the normal population, which may also be due to a combination of common living environment and dietary and exercise habits in addition to genetic factors.[30–34] In the present study, the prevalence of CI was considerably lower in those with higher education compared to those with elementary school and below. Higher literacy and increased cognitive reserve can reduce the prevalence of CI and are protective factors for CI.[35–38] Increased literacy may reduce the risk of dementia by increasing cognitive reserve, while numerous factors, such as reading, growing hobbies, and participation in geriatric education, also improve the brain’s knowledge reserve and delay cognitive decline.[35,39] Meanwhile, poorer health care systems and individual financial status may increase the risk of developing CI. At the same time, having high educational status may reduce the risk.[40,41] Less educated individuals may also face restricted medical care and limited financial conditions, leading to an increased prevalence of CI.[42] Smoking is not only strongly associated with many respiratory and cardiovascular diseases but can also increase the risk of dementia through different mechanisms.[43–45] In addition, poor lung health and cardiovascular system diseases caused by smoking may further increase the risk of developing CI.[46,47] The amount of tobacco and total years of smoking were also associated with CI risk in a dose–response relationship.[48] The earlier the time of smoking initiation and the more cigarettes smoked, the greater the risk of CI in smokers. It has also been suggested that quitting smoking at any time is beneficial, especially in high-risk groups. Early smoking cessation in early middle age may reduce the risk of dementia to a greater extent.[49] Some studies have explored the relationship between sleep disorders and CI, noting that sleep disorders lead to increased neurodegenerative changes associated with dementia and that insufficient (<4 hours per night or < 4 hours total sleep per day) or excessive (>10 hours per night and > 12.5 hours total sleep per day) sleep may increase the risk of CI, and the presence of sleep disorders may serve as an early marker of CI.[50–53] The present study concluded that insufficient or excessive sleep duration is a risk factor for the development of CI, which is consistent with most studies.[54–56]

The present findings show that a medical history of hypertension, stroke, and diabetes mellitus are independent risk factors for CI. In a series of studies on blood pressure and cognitive impairment in several countries, including China, the United States, and India, it was similarly concluded that hypertension is a modifiable risk factor affecting CI.[57–62] The management of blood pressure in middle-aged and elderly individuals is closely related to maintaining the vitality of cognitive function.[57,58] Enhancing knowledge and standardized management related to hypertension not only helps to reduce the occurrence of cardiovascular diseases associated with it but also helps to reduce the prevalence of CI, which is of great significance in promoting population health.[63] The occurrence of stroke is also positively correlated with the prevalence of CI, in which the risk of recurrent stroke after 1 year in stroke patients is increased approximately 3-fold. In contrast, the risk of dementia is increased approximately 2-fold.[64–67] Meanwhile, Koton et al[68] suggested that the risk of CI increases with the severity and number of strokes. A cross-sectional study conducted in a Beijing community in China showed that patients with impaired fasting glucose or diabetes achieved significantly lower scores on the MMSE test than subjects with average glucose.[69] Meanwhile, metformin, which can be used as a first-line treatment for diabetes, is effective in slowing the rate of cognitive decline and leading to a lower risk of dementia.[70–73] In contrast, regular treatment with glucose-lowering drugs or insulin to maintain stable and good levels can help reduce the risk of CI and delay cognitive decline.[73,74]

Exercise is a protective factor for CI disease, and physical activity is an essential protective mechanism for modulating brain function. It can be an effective prevention and treatment strategy for age-related memory loss and neurodegeneration.[75] Additionally, numerous studies have identified the benefits of physical activity in people with diagnosed MCI and dementia. Exercise can improve cognitive function, delay its progression and reduce the probability of dementia in people with MCI.[76–79]

The effects of alcohol consumption on cognitive function may still be controversial, and there was no significant correlation between alcohol consumption and CI prevalence in this study. Some researchers have argued that light-to-moderate alcohol consumption can reduce the risk of CI and that heavy and frequent alcohol consumption can increase the risk of CI.[80] However, several studies point to the small likelihood of a protective effect of moderate alcohol consumption on cognitive function, and the authors suggest that any amount of alcohol increases the risk of CI.[81–83] Until the impact of alcohol consumption on the prevalence of CI is clarified, alcohol abuse should still be discouraged.

This study investigated the prevalence of CI and related influencing factors in middle-aged and elderly individuals in Anhui Province and constructed a nomogram to assess the prevalence of CI in middle-aged and elderly individuals in Anhui Province based on the results of multifactorial logistic regression analysis, which has the advantages of simplicity, intuition, and readability. In addition, the model was internally and externally validated, with a high degree of differentiation and calibration. The DCA curves also showed some clinical significance and practicality, which can intuitively understand the results of multifactor analysis and the relationship between different variables and achieve individualized prediction of the risk of CI occurrence, which has guiding significance for early screening and timely intervention of CI in the middle-aged and elderly population in Anhui Province.

At the same time, there are limitations in this study. First, some of the participants in this study had less knowledge about CI, their medical history, and other conditions. The influence of other confounding factors that were not collected could not be excluded, such as income, socioeconomic status, employment status and nature of employment, depression, and respiratory history. When the questionnaire was conducted, the results of the cognitive function assessment using the MMSE scale referred more to screening than to clinical diagnosis. There was no further distinction between MCI and dementia types, which is another limitation. Finally, this study is a cross-sectional survey and lacks follow-up observations of middle-aged and elderly individuals with diagnosed CI. Longitudinal surveys are needed to further understand disease regression. In addition, more in-depth studies are required to add other influencing factors related to CI to understand the relevant mechanisms of action and the effectiveness of related interventions.

5. Conclusion

This survey showed that the prevalence of CI in the middle-aged and elderly populations in Anhui Province was 12.6%. Among those individuals, female sex, elderly age, and family history were irreversible risk factors associated with CI; low educational level, current smoking status, sleep disorders, hypertension, stroke, and diabetes were controllable risk factors for CI; and exercise had a protective effect on CI. This study established and validated a nomogram of CI risk in middle-aged and elderly individuals in Anhui Province which can be used to screen the population with a high prevalence of CI and individualize the diagnosis and treatment, improve the awareness of medical personnel for early diagnosis and treatment of CI, and provide meaningful guidance for early screening and early intervention of CI in middle-aged and elderly individuals in Anhui Province.

Acknowledgments

This study was supported by a grant from the Scientific Research Foundation of the Education Department of Anhui Province of China (grant number: KJ2020A0189). The funding source had no role in the study design, data collection, data analysis, data interpretation, writing of the manuscript, or decision to submit it for publication.

Author contributions

Conceptualization: Can Cui.

Data curation: Tianyun Yu.

Funding acquisition: Zengfeng Su.

Investigation: Tianyun Yu, Yujia Zhai, Shan Zhang.

Methodology: Can Cui, Yujia Zhai, Shan Zhang.

Software: Can Cui.

Supervision: Zengfeng Su.

Validation: Yujia Zhai.

Visualization: Tianyun Yu.

Writing – original draft: Can Cui.

Writing – review & editing: Zengfeng Su.

Abbreviations:

AUC area under curve

BMI body mass index

CI cognitive impairment

MCI mild cognitive impairment

MMSE Mini-Mental State Examination

ROC receiver operating characteristic curve.

The Scientific Research Foundation of the Education Department of Anhui Province of China (grant number: KJ2020A0189) grant was awarded to Zengfeng Su.

The Ethics Committee of Chaohu Hospital Affiliated to Anhui Medical University in China gave ethical approval for this work (ethical code: KYXM-202212-024).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

How to cite this article: Cui C, Yu T, Zhai Y, Zhang S, Su Z. Prevalence of cognitive impairment and its associated factors in middle-aged and elderly people in Anhui Province, China: An observational study. Medicine 2024;103:36(e39587).
==== Refs
References

[1] Stephan BCM Siervo M . Determining risk of dementia: a look at China and beyond. Age Ageing. 2020;49 :727–8.32756934
[2] Petersen RC Caracciolo B Brayne C Gauthier S Jelic V Fratiglioni L . Mild cognitive impairment: a concept in evolution. J Intern Med. 2014;275 :214–28.24605806
[3] van der Flier WM Skoog I Schneider JA . Vascular cognitive impairment. Nat Rev Dis Primers. 2018;4 :18003.29446769
[4] Scheltens P Blennow K Breteler MM . Alzheimer’s disease. Lancet. 2016;388 :505–17.26921134
[5] Zhang XX Tian Y Wang ZT Ma YH Tan L Yu JT . The epidemiology of Alzheimer’s disease modifiable risk factors and prevention. J Prev Alzheimers Dis. 2021;8 :313–21.34101789
[6] Ferrari C Sorbi S . The complexity of Alzheimer’s disease: an evolving puzzle. Physiol Rev. 2021;101 :1047–81.33475022
[7] Tiwari S Atluri V Kaushik A Yndart A Nair M . Alzheimer’s disease: pathogenesis, diagnostics, and therapeutics. Int J Nanomedicine. 2019;14 :5541–54.31410002
[8] Livingston G Huntley J Sommerlad A . Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396 :413–46.32738937
[9] Jia L Du Y Chu L . COAST Group. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5 :e661–71.33271079
[10] 2022 Alzheimer’s disease facts and figures. Alzheimers Dement. 2022;18 :700–89.35289055
[11] Mok VCT Pendlebury S Wong A . Tackling challenges in care of Alzheimer’s disease and other dementias amid the COVID-19 pandemic, now and in the future [published correction appears in Alzheimers Dement. 2021 May;17(5): 906-907]. Alzheimers Dement. 2020;16 :1571–81.32789951
[12] Wu Y Zheng H Xu F . Population attributable fractions for risk factors and disability burden of dementia in Jiangxi Province, China: a cross-sectional study. BMC Geriatr. 2022;22 :811.36271341
[13] Arevalo-Rodriguez I Smailagic N Roqué-Figuls M . Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2021;7 :CD010783.34313331
[14] Jia X Wang Z Huang F . A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study. BMC Psychiatry. 2021;21 :485.34607584
[15] Jehi L Ji X Milinovich A . Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19. PLoS One. 2020;15 :e0237419.32780765
[16] Fitzgerald M Saville BR Lewis RJ . Decision curve analysis. JAMA. 2015;313 :409–10.25626037
[17] Wang JT Xu G Ren RJ . The impacts of health insurance and resource on the burden of Alzheimer’s disease and related dementias in the world population. Alzheimers Dement. 2022;19 :967–79.35820032
[18] Ding D Zhao Q Wu W . Prevalence and incidence of dementia in an older Chinese population over two decades: the role of education. Alzheimers Dement. 2020;16 :1650–62.32886438
[19] Jiang F Kong F Li S . The association between social support and cognitive impairment among the Urban Elderly in Jinan, China. Healthcare (Basel). 2021;9 :1443.34828488
[20] Deng J Cao C Jiang Y . Prevalence and effect factors of dementia among the community elderly in Chongqing, China. Psychogeriatrics. 2018;18 :412–20.29761589
[21] Hu FF Cheng GR Liu D . Population-attributable fractions of risk factors for all-cause dementia in China rural and urban areas: a cross-sectional study. J Neurol. 2022;269 :3147–58.34839456
[22] Lv YB Zhu PF Yin ZX . A U-shaped association between blood pressure and cognitive impairment in Chinese Elderly. J Am Med Dir Assoc. 2017;18 :193.e7–13.
[23] Leung Y Barzilai N Batko-Szwaczka A . Cognition, function, and prevalent dementia in centenarians and near-centenarians: AN individual participant data (IPD) meta-analysis of 18 studies. Alzheimers Dement. 2022;19 :2265–75.36453627
[24] Oveisgharan S Arvanitakis Z Yu L Farfel J Schneider JA Bennett DA . Sex differences in Alzheimer’s disease and common neuropathologies of aging. Acta Neuropathol. 2018;136 :887–900.30334074
[25] Udeh-Momoh C Watermeyer T ; Female Brain Health and Endocrine Research (FEMBER) consortium. Female Brain Health and Endocrine Research (FEMBER) consortium. Female specific risk factors for the development of Alzheimer’s disease neuropathology and cognitive impairment: call for a precision medicine approach. Ageing Res Rev. 2021;71 :101459.34508876
[26] Xiong J Kang SS Wang Z . FSH blockade improves cognition in mice with Alzheimer’s disease. Nature. 2022;603 :470–6.35236988
[27] Villanueva MT . Hooking FSH as a potential target for Alzheimer disease. Nat Rev Drug Discov. 2022;21 :259.35277675
[28] Lemprière S . FSH provides link between menopause and AD. Nat Rev Neurol. 2022;18 :251.
[29] GBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18 :459–80.30879893
[30] Yagi S Galea LAM . Sex differences in hippocampal cognition and neurogenesis. Neuropsychopharmacology. 2019;44 :200–13.30214058
[31] Lee JS Lee H Park S . Association between APOE ε2 and Aβ burden in patients with Alzheimer- and vascular-type cognitive impairment. Neurology. 2020;95 :e2354–65.32928967
[32] Qian J Betensky RA Hyman BT Serrano-Pozo A . Association of APOE genotype with heterogeneity of cognitive decline rate in Alzheimer disease. Neurology. 2021;96 :e2414–28.33771840
[33] Reiman EM Arboleda-Velasquez JF Quiroz YT . Alzheimer’s Disease Genetics Consortium. Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5,000-person neuropathological study. Nat Commun. 2020;11 :667.32015339
[34] Wolters FJ van der Lee SJ Koudstaal PJ . Parental family history of dementia in relation to subclinical brain disease and dementia risk. Neurology. 2017;88 :1642–9.28356461
[35] Langa KM Larson EB Crimmins EM . A comparison of the prevalence of dementia in the United States in 2000 and 2012. JAMA Intern Med. 2017;177 :51–8.27893041
[36] Hendrie HC Smith-Gamble V Lane KA Purnell C Clark DO Gao S . The association of early life factors and declining incidence rates of dementia in an elderly population of African Americans. J Gerontol B Psychol Sci Soc Sci. 2018;73 (suppl_1 ):S82–9.29669098
[37] Dekhtyar S Marseglia A Xu W Darin-Mattsson A Wang H-X Fratiglioni L . Genetic risk of dementia mitigated by cognitive reserve: a cohort study. Ann Neurol. 2019;86 :68–78.31066941
[38] Hu Y Zhang Y Zhang H . Cognitive performance protects against Alzheimer’s disease independently of educational attainment and intelligence. Mol Psychiatry. 2022;27 :4297–306.35840796
[39] Wang Y Wang S Zhu W . Reading activities compensate for low education-related cognitive deficits. Alzheimers Res Ther. 2022;14 :156.36242017
[40] Han SD Boyle PA James BD Yu L Bennett DA . Mild cognitive impairment is associated with poorer decision-making in community-based older persons [published correction appears in J Am Geriatr Soc. 2015 Jun;63(6):1286]. J Am Geriatr Soc. 2015;63 :676–83.25850350
[41] Stewart CC Yu L Wilson RS Bennett DA Boyle PA . Healthcare and financial decision making and incident adverse cognitive outcomes among older adults. J Am Geriatr Soc. 2019;67 :1590–5.30882910
[42] Reitz C den Heijer T van Duijn C Hofman A Breteler MM . Relation between smoking and risk of dementia and Alzheimer disease: the Rotterdam Study. Neurology. 2007;69 :998–1005.17785668
[43] Anstey KJ von Sanden C Salim A O’Kearney R . Smoking as a risk factor for dementia and cognitive decline: a meta-analysis of prospective studies. Am J Epidemiol. 2007;166 :367–78.17573335
[44] Hoevenaar-Blom MP Richard E Moll van Charante EP . Targeting vascular risk factors to reduce dementia incidence in old age: extended Follow-up of the Prevention of Dementia by Intensive Vascular Care (preDIVA) Randomized Clinical Trial. JAMA Neurol. 2021;78 :1527–8.34633434
[45] Lutsey PL Chen N Mirabelli MC . Impaired lung function, lung disease, and risk of incident dementia. Am J Respir Crit Care Med. 2019;199 :1385–96.30433810
[46] Verlato G Olivieri M . Reduced lung function in midlife and cognitive impairment in the elderly. Am J Respir Crit Care Med. 2019;199 :1304–5.30566840
[47] Mons U Schöttker B Müller H Kliegel M Brenner H . History of lifetime smoking, smoking cessation and cognitive function in the elderly population. Eur J Epidemiol. 2013;28 :823–31.23990211
[48] Deal JA Power MC Palta P . Relationship of cigarette smoking and time of quitting with incident dementia and cognitive decline. J Am Geriatr Soc. 2020;68 :337–45.31675113
[49] Xu G Liu X Yin Q Zhu W Zhang R Fan X . Alcohol consumption and transition of mild cognitive impairment to dementia. Psychiatry Clin Neurosci. 2009;63 :43–9.19154211
[50] Sabia S Fayosse A Dumurgier J . Association of sleep duration in middle and old age with incidence of dementia. Nat Commun. 2021;12 :2289.33879784
[51] Xu W Tan CC Zou JJ Cao XP Tan L . Sleep problems and risk of all-cause cognitive decline or dementia: an updated systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2020;91 :236–44.31879285
[52] Kent BA Feldman HH Nygaard HB . Sleep and its regulation: AN emerging pathogenic and treatment frontier in Alzheimer’s disease. Prog Neurobiol. 2021;197 :101902.32877742
[53] Liguori C Placidi F Izzi F Spanetta M Mercuri NB Di Pucchio A . Sleep dysregulation, memory impairment, and CSF biomarkers during different levels of neurocognitive functioning in Alzheimer’s disease course [published correction appears in Alzheimers Res Ther. 2020 May 8;12(1):53]. Alzheimers Res Ther. 2020;12 :5.31901236
[54] Liang Y Qu LB Liu H . Non-linear associations between sleep duration and the risks of mild cognitive impairment/dementia and cognitive decline: a dose-response meta-analysis of observational studies. Aging Clin Exp Res. 2019;31 :309–20.30039452
[55] Devore EE Grodstein F Duffy JF Stampfer MJ Czeisler CA Schernhammer ES . Sleep duration in midlife and later life in relation to cognition. J Am Geriatr Soc. 2014;62 :1073–81.24786726
[56] Suh SW Han JW Lee JR . Sleep and cognitive decline: a prospective nondemented elderly cohort study. Ann Neurol. 2018;83 :472–82.29394505
[57] Carey A Fossati S . Hypertension and hyperhomocysteinemia as modifiable risk factors for Alzheimer’s disease and dementia: new evidence, potential therapeutic strategies, and biomarkers. Alzheimers Dement. 2023;19 :671–95.36401868
[58] Liang X Shan Y Ding D . Hypertension and high blood pressure are associated with dementia among Chinese dwelling elderly: the Shanghai aging study. Front Neurol. 2018;9 :664.30233479
[59] Shang S Li P Deng M Jiang Y Chen C Qu Q . The age-dependent relationship between blood pressure and cognitive impairment: a cross-sectional study in a rural area of Xi’an, China. PLoS One. 2016;11 :e0159485.27438476
[60] Abell JG Kivimäki M Dugravot A . Association between systolic blood pressure and dementia in the Whitehall II cohort study: role of age, duration, and threshold used to define hypertension. Eur Heart J. 2018;39 :3119–25.29901708
[61] Huang CQ Dong BR Zhang YL Wu HM Liu QX Flaherty JH . Cognitive impairment and hypertension among Chinese nonagenarians and centenarians. Hypertens Res. 2009;32 :554–8.19478816
[62] Gao S Jin Y Unverzagt FW . Hypertension and cognitive decline in rural elderly Chinese. J Am Geriatr Soc. 2009;57 :1051–7.19507297
[63] Palta P Albert MS Gottesman RF . Heart health meets cognitive health: evidence on the role of blood pressure. Lancet Neurol. 2021;20 :854–67.34536406
[64] Leys D Hénon H Mackowiak-Cordoliani MA Pasquier F . Poststroke dementia. Lancet Neurol. 2005;4 :752–9.16239182
[65] Becker CJ Heeringa SG Chang W . Differential impact of stroke on cognitive impairment in Mexican Americans and non-hispanic white Americans. Stroke. 2022;53 :3394–400.35959679
[66] Portegies ML Wolters FJ Hofman A Ikram MK Koudstaal PJ Ikram MA . Prestroke vascular pathology and the risk of recurrent stroke and poststroke dementia. Stroke. 2016;47 :2119–22.27418596
[67] Béjot Y Aboa-Eboulé C Durier J . Prevalence of early dementia after first-ever stroke: a 24-year population-based study. Stroke. 2011;42 :607–12.21233464
[68] Koton S Pike JR Johansen M . Association of ischemic stroke incidence, severity, and recurrence with dementia in the atherosclerosis risk in communities cohort study. JAMA Neurol. 2022;79 :271–80.35072712
[69] Xiu S Zheng Z Liao Q Chan P . Different risk factors for cognitive impairment among community-dwelling elderly, with impaired fasting glucose or diabetes. Diabetes Metab Syndr Obes. 2019;12 :121–30.30666140
[70] Campbell JM Stephenson MD de Courten B Chapman I Bellman SM Aromataris E . Metformin use associated with reduced risk of dementia in patients with diabetes: a systematic review and meta-analysis. J Alzheimers Dis. 2018;65 :1225–36.30149446
[71] Samaras K Makkar S Crawford JD . Metformin use is associated with slowed cognitive decline and reduced incident dementia in older adults with type 2 Diabetes: the Sydney Memory and Ageing Study. Diabetes Care. 2020;43 :2691–701.32967921
[72] Tumminia A Vinciguerra F Parisi M Frittitta L . Type 2 Diabetes Mellitus and Alzheimer’s disease: role of insulin signalling and therapeutic implications. Int J Mol Sci . 2018;19 :3306.30355995
[73] Li Z Li S Xiao Y Zhong T Yu X Wang L . Nutritional intervention for diabetes mellitus with Alzheimer’s disease. Front Nutr. 2022;9 :1046726.36458172
[74] Andrews SJ Fulton-Howard B O’Reilly P Marcora E Goate AM ; collaborators of the Alzheimer's Disease Genetics Consortium. Causal associations between modifiable risk factors and the Alzheimer’s Phenome. Ann Neurol. 2021;89 :54–65.32996171
[75] De la Rosa A Olaso-Gonzalez G Arc-Chagnaud C . Physical exercise in the prevention and treatment of Alzheimer’s disease. J Sport Health Sci. 2020;9 :394–404.32780691
[76] Northey JM Cherbuin N Pumpa KL Smee DJ Rattray B . Exercise interventions for cognitive function in adults older than 50: a systematic review with meta-analysis. Br J Sports Med. 2018;52 :154–60.28438770
[77] Wei L Chai Q Chen J . The impact of Tai Chi on cognitive rehabilitation of elder adults with mild cognitive impairment: a systematic review and meta-analysis. Disabil Rehabil. 2022;44 :2197–206.33043709
[78] Abe K . Total daily physical activity and the risk of AD and cognitive decline in older adults. Neurology. 2012;79 :1071; author reply 1071.
[79] Sanders LMJ Hortobágyi T Karssemeijer EGA Van der Zee EA Scherder EJA van Heuvelen MJG . Effects of low- and high-intensity physical exercise on physical and cognitive function in older persons with dementia: a randomized controlled trial. Alzheimers Res Ther. 2020;12 :28.32192537
[80] Andrews SJ Goate A Anstey KJ . Association between alcohol consumption and Alzheimer’s disease: a Mendelian randomization study. Alzheimers Dement. 2020;16 :345–53.31786126
[81] Au Yeung SL Jiang CQ Cheng KK . Evaluation of moderate alcohol use and cognitive function among men using a Mendelian randomization design in the Guangzhou biobank cohort study. Am J Epidemiol. 2012;175 :1021–8.22302076
[82] Cui Y Si W Zhu C Zhao Q . Alcohol consumption and mild cognitive impairment: a Mendelian randomization study from rural China. Nutrients. 2022;14 :3596.36079852
[83] Zhou H Deng J Li J Wang Y Zhang M He H . Study of the relationship between cigarette smoking, alcohol drinking and cognitive impairment among elderly people in China. Age Ageing. 2003;32 :205–10.12615566
