IMR Press / JOMH / Volume 17 / Issue 4 / DOI: 10.31083/jomh.2021.092
Open Access Original Research
Risks and subgroups of cognitive impairment under different marital status among older adults: a latent profile analysis
Show Less
1 Andrology Laboratory, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China
2 Department of Urology, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China
3 Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, 400016 Chongqing, China

These authors contributed equally.

J. Mens. Health 2021, 17(4), 234–242;
Submitted: 25 May 2021 | Accepted: 7 July 2021 | Published: 30 September 2021

Background and objective: With aging, cognitive impairment is severe in the aging society. This paper aimed to investigate the association between cognitive impairment and marital status and the empirical typology of cognitive impairment in Chinese aging population.

Methods: Descriptive statistics were performed by retrieving data from the China Health and Retirement Longitudinal Study (CHARLS) to test the relation between cognitive impairment and marital status with univariate linear regression and multivariate linear regression. The subgroups of cognitive impairment for included older subjects were identified with latent profile analysis (LPA).

Results: The sample included 13,149 participants aged 40 years or older. Both unmarried males and females suffer lower cognitive function than married males and females (p < 0.001). The declining trend remained consistent (p < 0.05) after adjustment for covariates. The participants who were illiterate or older or agricultural hukou had lower cognitive functions than their counterparts. LPA results showed that the cognitive function of participants could be divided into three subgroups.

Conclusions: Unmarried males and females had lower cognitive function than that of married counterparts due to the declined percentage of class two, who have high mental intactness and episodic memory.

Cognitive impairment
Latent profile analysis
Marital status
Older adults
1. Introduction

The older adults account for the highest proportion of the population with cognitive impairment, such as dementia, attributed to a pivotal risk factor: age. An estimated 74.7 million people worldwide will suffer from dementia by 2030 [1]. The prevalence of dementia fluctuates in different studies concerning diagnostic criteria, races, regions, etc. [2-4]. In China, a weighted prevalence of dementia in people aged 65 years or older was 5.6% [5]. Furthermore, mild cognitive impairment (MCI), a symptomatic pre-dementia phase, still affects 10%–20% of human beings aged 65 years or older [6]. Treating cognitive impairment has become one of the greatest challenges. The high prevalence of cognitive impairment places a heavy burden on the aging society in China [7,8]. Therefore, it is urgent to identify the relevant factors for cognitive impairment and its subgroups, which may control it more accurately in a simple and low-cost way.

Multiple risk factors for cognitive impairment involved age, genes, diabetes, smoking, and other risk factors like sleep [9-15]. However, surveys of marital status and cognitive impairment are limited for Chinese aging population. Liu et al. [16] claimed that participants with marriage-like relationships had lower odds of cognitive impairment than participants with non-marriage relationships. Other studies also reported that the single males had higher odds of cognitive impairment than the married males, not in females [17]. Among unmarried participants, the risks of dementia were detected in both males and females but in men the risks were greater than in women [18]. These studies disclosed that the risks of cognitive impairment vary with gender, which reflected internal differences in the population. What’s more, previous studies mentioned above were principally performed in participants aged 55 years or older. There is no national investigation targeting Chinese aging population with different lifestyles and risk factors. Whether this kind of association still exists and the sources of internal differences are unclear.

In this study, the dataset from China Health and Retirement Longitudinal Study (CHARLS) was downloaded. CHARLS aimed at Chinese aging population to investigate the association between marital status and cognitive impairment. Moreover, latent profile analysis (LPA), an individual-centered algorithm, was adopted to identify the empirical typology of cognitive impairment. LPA can confirm the internal association with indiscrete manifest variables and classify individuals into common profiles [19,20], which may assist controlling cognitive impairment in Chinese aging populations more accurately.

2. Methods
2.1 Data and study samples

CHARLS possessed a set of high-quality datasets representing the whole aging population across China [21]. The baseline dataset collected the information of 17,705 participants. After four steps to clear unqualified samples (Supplemental Fig. 1), a total of 13,149 participants comprising 6272 males and 6877 females were enrolled into further analyses. The participants were divided into the married group and the unmarried group. The married group referred to married couples living together. The unmarried group referred to married but not living with spouses, separated, divorced, widowed, and never-married. The “married but not leaving with spouses” means that the most of the time for these couples were not stayed together for reasons such as work. This study was reviewed and approved by the ethics committee of Peking University (IRB 00001052-11014). Written and oral informed consent was obtained from all participants prior to their enrollment in this study.

2.2 Covariates

Seventeen covariates were collected, including age (40–50, 50–60, 60–70 and 70), educational levels (illiteracy, elementary school, middle school, high school, and college degree or above), sleeping time (0–6 hours, 6–8 hours, and 8 hours), afternoon napping (yes or no), smoking (yes, no, and smoked but quitted), alcohol consumption (never drank, drank but less than once a month, and drank more than once a month), Body mass index (BMI, <18.5 kg/m2; 18.5–24 kg/m2; 24–28 kg/m2; 28 kg/m2) [22], abdominal obesity (waistline 90 cm for males and 85 cm for females) [23], hypertension, low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), triglyceride (TG), uric acid, blood urea nitrogen (BUN), glycosylated hemoglobin (Hba1c) and hukou types. Hukou is a household registration system used in China. Participants with agricultural hukou mainly settle in rural areas, while participants with non-agricultural hukou mainly live in urban areas. Hypertension was defined as systolic pressure 140 mmHg or diastolic pressure 90 mmHg [24]. Hyperuricemia was defined as the concentration of uric acid >420 mmol/L for males and >360 mmol/L for females [25].

2.3 Cognitive assessment

In CHARLS, cognition was evaluated from three cognitive domains, including the dimensions of orientation and attention, visuospatial abilities, and word recall, which were consistent with the American Health and Retirement Study [26]. First, orientation, attention, and visuospatial abilities were aggregated as mental intactness (MI), and assessed by some mental status questions of the Telephone Interview of Cognitive Status (TICS). TICS was a well-designed measure to capture one’s MI [27] and it was also used elsewhere to describe one’s orientation/attention abilities and visuospatial ability [28]. Participants were asked ten items, including interview date (year, month, day), day of the week, current season, and serial-7 number subtraction questions (up to five times). Then, participants were shown a picture of two pentagons overlapping each other and asked to draw the picture. The scores of MI ranged from 0 to 11 points. Second, word recall representing episodic memory (EM) was performed. EM indicates the memory for autobiographical events [29]. A list of 10 Chinese nouns was read to participants. Then, participants were asked to repeat these ten words (immediate recall), and five minutes later, the participants were asked again (delayed recall) [30]. Each item was assigned as 0 points (answered in error) or 1 point (answered accurately). The mean scores in immediate and delayed word recall were adopted as the final scores of EM (0–10 points). Finally, the scores of MI and EM were aggregated together to form the overall cognitive function scores (0–21 points). The questionnaire was also adopted in previous publications [27,28,31].

2.4 Statistical analysis

The continuous data were described using mean ± standard deviation (SD), and the categories of data were proportioned (%). Discrepancies between married and unmarried groups were measured by t-test or chi-squared test according to types of the data. The association between marital status and cognitive impairment was assessed by univariate linear regression and multivariate linear regression adjusted for covariates. After this, the latent subgroups of cognitive impairment in participants were identified with LPA. p < 0.05 (two-sided) was seen as an indicator of statistical significance. LPA was conducted using Mplus 7.4 (Muthén & Muthén Inc., Los Angeles, CA, USA). Analyses were performed using Stata 15.0 (Stata Corporation, College Station, TX, USA). Figures were made using GraphPad Prism 8.0 version (GraphPad Software Inc., San Diego, CA, USA).

3. Results
3.1 Baseline characteristics of participants

The study included 13149 participants, 52.30% of whom were females, and 11.72% males were unmarried, and 19.56% females were unmarried. The average age of male participants was 59.00 ± 9.28 years old, and that of female participants was 57.55 ± 9.56 years old. More specific descriptions are shown in Table 1.

Table 1.Baseline characteristics of participants.
Characteristics Males Females
Married Unmarried χ2/t P Married Unmarried χ2/t P
Cases (n%) Cases (n%) Cases (n%) Cases (n%)
Total 5537 (88.28) 735 (11.72) - - 5532 (80.44) 1345 (19.56) - -
Age group −11.70 <0.001 23.33 <0.001
40–50 1271 (22.95) 111 (15.10) 1723 (31.15) 250 (18.59)
50–60 2153 (38.88) 224 (30.48) 2207 (39.90) 361 (26.84)
60–70 1504 (27.16) 223 (30.34) 1247 (22.54) 364 (27.06)
>70 609 (11.00) 177 (24.08) 355 (6.42) 370 (27.51)
Hukou 18.19 <0.001 1.43 0.488
Agricultural Hukou 4129 (74.57) 601 (81.77) 4292 (77.61) 1045 (77.70)
Non-Agricultural Hukou 1371 (24.76) 131 (17.82) 1202 (21.74) 295 (21.93)
Others 37 (0.67) 3 (0.41) 36 (0.65) 5 (0.37)
Educational levels 104.71 <0.001 93.93 <0.001
Illiterate 1422 (25.69) 310 (42.18) 2811 (50.82) 873 (64.91)
Elementary school 1479 (26.72) 194 (26.39) 1063 (19.22) 219 (16.28)
Middle school 1598 (28.87) 157 (21.36) 1039 (18.79) 155 (11.52)
High school 591 (10.68) 45 (6.12) 408 (7.38) 68 (5.06)
College degree or above 446 (8.06) 29 (3.95) 210 (3.80) 30 (2.23)
Sleeping time 5.31 <0.001 3.09 0.002
0–6 h 2615 (47.38) 399 (54.73) 2785 (50.74) 701 (53.11)
6–8 h 2476 (44.86) 269 (36.90) 2290 (41.72) 497 (37.65)
>8 h 428 (7.76) 61 (8.37) 414 (7.54) 122 (9.24)
Afternoon napping 0.87 <0.001 0.23 0.8166
Yes 3336 (60.25) 419 (57.01) 2682 (48.48) 638 (47.43)
No 2201 (39.75) 316 (42.99) 2850 (51.52) 707 (52.57)
Smoking 0.81 0.666 23.41 <0.001
Yes 3185 (57.52) 428 (58.23) 312 (5.64) 107 (7.96)
No 1452 (26.22) 182 (24.76) 5128 (92.71) 1196 (88.92)
Quitted 900 (16.25) 125 (17.01) 91 (1.65) 42 (3.12)
Alcohol consumption 13.67 0.001 3.25 0.196
Never 2381 (43.00) 369 (50.20) 4895 (88.49) 1168 (86.84)
Less than once a month 602 (10.87) 70 (9.52) 280 (5.06) 73 (5.43)
More than once a month 2554 (46.13) 296 (40.27) 357 (6.45) 104 (7.73)
BMI (kg/m2) 35.39 <0.001 36.70 <0.001
<18.5 276 (5.96) 55 (8.89) 261 (5.63) 89 (7.85)
18.5–24 2717 (58.63) 416 (67.21) 2088 (45.00) 594 (52.38)
24–28 1239 (26.74) 111 (17.93) 1581 (34.07) 322 (28.40)
28 402 (8.68) 37 (5.98) 710 (15.30) 129 (11.38)
Abdominal obesity 16.59 <0.001 6.40 0.011
Yes 1447 (30.91) 143 (77.05) 1568 (33.55) 340 (29.64)
No 3234 (69.09) 480 (22.95) 3105 (66.45) 807 (70.36)
Hypertension 27.08 <0.001 48.45 <0.001
Yes 1818 (37.80) 309 (48.51) 1830 (38.11) 577 (49.23)
No 2991 (62.20) 328 (51.49) 2972 (61.89) 595 (50.77)
Low density lipoprotein (LDL) 0.32 0.572 0.12 0.732
130 mg/dL 2710 (72.44) 347 (73.67) 2578 (65.41) 569 (64.81)
>130 mg/dL 1031 (27.56) 124 (26.33) 1363 (34.59) 309 (35.19)
Total cholesterol (TC) 2.07 0.151 1.32 0.251
200 mg/dL 2448 (65.31) 322 (68.66) 2252 (57.07) 483 (54.95)
>200 mg/dL 1300 (34.69) 147 (31.34) 1694 (42.93) 396 (45.05)
High density lipoprotein (HDL) 1.84 0.175 0.43 0.511
40 mg/dL 1059 (28.04) 119 (25.27) 862 (21.83) 183 (20.82)
>40 mg/dL 2691 (71.76) 352 (74.73) 3087 (78.17) 696 (79.18)
Triglyceride (Tg) 4.93 0.026 0.85 0.356
<150 mg/dL 2806 (74.85) 373 (79.53) 2789 (70.68) 635 (72.24)
150 mg/dL 943 (25.15) 96 (20.47) 1157 (29.32) 244 (27.76)
Uric acid 0.28 0.595 7.29 0.007
Non-hyperuricemia 3503 (93.41) 443 (94.06) 3782 (95.75) 823 (93.63)
Hyperuricemia 247 (6.59) 28 (5.94) 168 (4.25) 56 (6.37)
Blood urea nitrogen (BUN) 0.76 0.384 2.15 0.142
<21 mg/dL 3214 (85.75) 396 (84.26) 3629 (91.85) 794 (90.33)
21 mg/dL 534 (14.25) 74 (15.74) 322 (8.15) 85 (9.67)
Glycosylated hemoglobin (Hba1c) 7.73 0.005 0.08 0.784
6% 252 (6.70) 16 (3.39) 303 (7.64) 65 (7.37)
<6% 3512 (93.30) 456 (96.61) 3663 (92.36) 817 (92.63)
3.2 Scores of cognitive functions and its components under different marital status

Scores of cognitive functions and its components in different groups are shown in Fig. 1. Fig. 1A shows the scores after adjustment for demographic characteristics, individual behaviours, and blood biomarkers. No declining trend was found in EM of married males and unmarried males (p < 0.05). In Fig. 1B, after adjustment for all the covariates except age, cognitive functions between the married and the unmarried groups declined in all four age groups. In Fig. 1C, after adjustment for all the covariates except educational status, the declining trend of the married and unmarried groups is the same as age. Illiterate participants suffered lower cognitive functions in this study. In Fig. 1D, after adjustment for all the covariates except hukou types, participants with agricultural hukou have lower cognitive functions than people with non-agricultural hukou. Besides, in these two hukou types, a declining trend of cognitive functions still exists in males and females. The cognitive scores of married participants under different ages, education levels, and hukou types are higher than unmarried participants.

Fig. 1.

Adjusted scores of cognitive functions and its constituents in different age, educational levels and hukou status.Notes: The light colour area meant the 95% confidence interval of cognitive function scores. (A) shows the scores after adjustment for demographic characteristics, individual behaviors, and blood biomarkers. (B) shows the scores after adjustment for all the covariates except age. (C) shows the scores after adjustment for all the covariates except educational status. (D) shows the scores after adjustment for all the covariates except hukou types.

3.3 Association between marital status and cognitive functions

Four models were built using linear regression to assess the relationship between marital status and cognitive functions (Table 2). Univariate linear regression was performed in Model 0, displaying a significant association (β = –1.42, p < 0.001 for males & β = –1.56, p < 0.001 for females). In Model 1, results remained the same after adjustment for age and education (p < 0.001 for males and females). In Model 2, the discrepancy of EM between married and unmarried males was not found after adjustment for demographic characteristics and individual behaviors (p > 0.05), while for married and unmarried females, the difference still existed (β = –0.20, p 0.001). In Model 3, all demographic characteristics, individual behaviors, and blood biomarkers were adjusted. However, the difference of EM was not found in males, and the discrepancy regarding the cognitive function and its other components was still statistically significant both in males and females (p < 0.05).

Table 2.Associations between marital status and cognitive functions.
Males Females
Variables β (95% confidence interval) P Variables β (95% confidence interval) P
Model 0 Model 0
Mental intactness −0.99 (−1.19 to −0.78) <0.001 Mental intactness −1.03 (−1.21 to −0.85) <0.001
Episodic memory −0.43 (−0.57 to −0.29) <0.001 Episodic memory −0.53 (−0.64 to −0.41) <0.001
Cognitive functions −1.42 (−1.70 to −1.13) <0.001 Cognitive functions −1.56 (−1.81 to −1.30) <0.001
Model 1 Model 1
Mental intactness −0.55 (−0.74 to −0.36) <0.001 Mental intactness −0.43 (−0.59 to −0.27) <0.001
Episodic memory −0.15 (−0.29 to −0.02) <0.05 Episodic memory −0.19 (–0.34 to –0.25) <0.01
Cognitive functions −0.70 (−0.96 to −0.44) <0.001 Cognitive functions −0.62 (−0.84 to −0.40) <0.001
Model 2 Model 2
Mental intactness −0.41 (−0.61 to −0.20) <0.001 Mental intactness −0.34 (−0.52 to −0.17) <0.001
Episodic memory −0.01 (−0.15 to −0.13) 0.903 Episodic memory −0.20 (−0.32 to −0.08) <0.01
Cognitive functions −0.41 (−0.70 to −0.13) <0.01 Cognitive functions −0.54 (−0.79 to −0.30) <0.001
Model 3 Model 3
Mental intactness −0.37 (−0.62 to −0.11) <0.01 Mental intactness −0.27 (−0.48 to −0.06) <0.05
Episodic memory −0.00 (−0.18 to −0.17) 0.969 Episodic memory −0.15 (−0.29 to −0.01) <0.05
Cognitive functions −0.37 (−0.72 to −0.03) <0.05 Cognitive functions −0.42 (−0.71 to −0.13) <0.01
3.4 Fit indices of LPA for the subgroups of cognitive function in all the participants

The fit indices are shown in Table 3. Akaike information criterion (AIC), Bayesian information criterion (BIC), and adjusted BIC (aBIC) declined with the growing subgroups, indicating a better model. Lo-Mendell-Rubin likelihood ratio test (LMR) and bootstrap likelihood ratio test (BLRT) were all less than 0.0001. It indicated that the more subgroups in the model, the better the model fit; however, three subgroups of participants had the highest entropy index: 0.971, showing an optimal fitting with three class-model and the highest classification quality. Therefore, 3-class model was considered to be the best. Fig. 2 shows the properties of three subgroups. Item 1 to 11 was adopted to assess MI, and item 12 to 31 was adopted to assess EM. Class 2 is divided into high MI and high EM due to the highest mean of item response. Class 3 was divided as low MI and moderate EM due to the low mean of item response in items 8, 9, 10, and 11. Class 1 was divided into middle MI and low EM.

Table 3.Indices of latent class analysis for cognitive classes in all the participants.
Class k AIC BIC aBIC Entropy LMR BLRT Class probability (%)
1 31 491470.74 491702.75 491604.23 - - - -
2 63 447923.28 448394.78 448194.57 0.957 <0.0001 <0.0001 50.70/49.30
3 95 433161.43 433872.42 433570.52 0.971 <0.0001 <0.0001 11.41/45.98/42.61
4 127 427565.84 428516.32 428112.72 0.921 <0.0001 <0.0001 44.27/24.11/20.45/11.17
5 159 422486.45 423676.42 423171.13 0.883 <0.0001 <0.0001 20.04/15.51/33.10/23.44/7.92
k, number of free parameters; AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, adjusted Bayesian information criterion; LMR, Lo-Mendell-Rubin likelihood ratio; BLRT, parametric bootstrapped likelihood ratio test.
Fig. 2.

Item response mean of the three different cognitive function classes.

3.5 Alteration for percentages of males and females in three subgroups

Fig. 3 shows that the declining trend of cognitive functions between the married and unmarried groups results from the decreased percentage of class 2 (high MI and high EM), which remains the same trend both in males and females. The percentages of class 1 and class 3 are increased.

Fig. 3.

Alteration for percentages of males and females in three subgroups.

4. Discussion

Aging-related diseases like dementia and mild cognitive impairment are hitting high notes due to high prevalence and negative impact. CHARLS is a project aimed at investigating the aged population. It provides us an opportunity to examine the association between cognitive impairment and marital status, and the empirical typology of cognitive impairment in Chinese aging population.

Unmarried groups, including married couples but not living together, the separated, the divorced, the widowed, and the never-married in our study, were not separated into different groups mainly due to its small sample size. The previous study also showed that all unmarried groups share the same higher odds of dementia than the married counterparts [16]. This phenomenon may associate with the characteristics of the unmarried, who have more possibilities of loneliness at home, leading to cognitive impairment [32,33]. Furthermore, reduced sexual intercourse may contribute to cognitive impairment. Marriage-like relationships, including the married and the cohabitated, shared similar lower risks of cognitive impairment due to more sexual intercourse possibly [34]. L Scheunemann et al. [35] showed that Sperm peptides could enhance long-term memory by stimulating neurons in the uterus of female Drosophila, indicating that sexual intercourse could be a barrier to cognitive impairment.

Our study reveals that unmarried males and unmarried females have lower cognitive function than married counterparts, consistent with a previous study [16]. However, Feng et al. [17] found that only men had higher odds of cognitive impairment statistically but not women in Singapore. Social engagement may explain this difference. A previous study indicated that women might have higher intimate relationships and social engagement with their friends than men [36]. Loneliness could contribute to the increased risk of dementia in Chinese citizens aged 65 years or older [37]. Further prospective cohort investigation in Chinese should be performed to evaluate the discrepancy.

Gender differences should be further highlighted in the current study. Hormones, such as estrogen, progesterone and androgen, are crucial factors in initial cognitive impairment with gender differences [38,39]. The decline of estrogen level in menopausal women will arouse the higher prevalence of cognitive impairment and Alzheimer’s Disease (AD), but the change of hormones is more moderate in men amid this process [40]. In the clinical report, women have shown an overall and rapid decline of cognition after bilateral ovariectomy with the decrease of estrogen in pathological conditions [41]. In the rat experiment, the relationship between hypothalamus pituitary-gonad axis (HPGA) and cognition has been attached to neuroscience [42]. It was found that the female mice perform the worse spatial memory ability when they were in the concentration of high luteinizing hormone [42]. From the perspective of sociology, it has been showed that females have a positive association with high press, depression, poor education and exercise, as well as more chronic disease accounted for cognitive decline than males [43-46]. However, smoking habit is a major risk for cognition in males [47]. Therefore, gender difference and gender modification should be paid more attention between marital status and cognitive impairment based on previous neuroscience researches.

Declining trends of cognitive functions were also observed in different literature status, and hukou types in this study. Literate participants showed higher scores of cognitive functions than illiteracy as previous surveys performed in Chinese aging population [48]. This may be attributed to better lifestyles, compliance with doctors, and more social resources possibly. In our study, participants with agricultural hukou mainly living in rural areas have lower scores of cognitive functions than participants with non-agricultural hukou, who mainly settle in urban areas. A study performed in Chinese aged 65 years old and above indicated that inadequate access to healthcare in rural areas might be the cause why rural settlers had worse cognitive impairment [49]. More active healthcare for the older adults, improved education, and balanced medical resources should be considered to reduce cognitive impairment.

This study still has some limitations. First, the percentage of different marital status in different age groups is unevenly distributed, which may bias the final results. Additionally, it should be noted that this cross-sectional study cannot examine the causality between marital status and cognitive impairment. In future studies, longitudinal cohort studies and endeavors targeting the high-risk subgroups in Chinese older adults should be made. Moreover, due to the small size of these subgroups like the divorced, the widowed and etc. in the unmarried group, subgroups are not taken into analyses separately, which limits a further understanding of full spectrum.

This study firstly identifies the latent subgroups of cognitive impairment in Chinese aging population with a representative sample dataset. It provides a novel perspective to decrease the high prevalence of cognitive impairment in the aged. Targeting the identified high-risk subgroups may facilitate to contain the high prevalence of cognitive impairment.

5. Conclusions

For Chinese aging population, unmarried males and females had lower cognitive functions than the married counterparts. A comprehensive cognitive assessment of the high-risk subgroup who have high mental intactness and episodic memory should be evaluated earlier and more frequent than other subgroups. Additionally, more active prevention endeavors including intelligent exercises, nutrition support etc., before and/or after admitted to hospital should be considered.

Author contributions

YX and YCZ carried out the data analyses and drafted the manuscript; FXZ, CJW, XYZH and FQ helped to revise the manuscript; JHY conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.

Ethics approval and consent to participate

This study was reviewed and approved by the ethics committee of Peking University (IRB 00001052-11014). Written and oral informed consent was obtained from all participants prior to their enrollment in this study.


The authors express thanks to the office of CHARLS for performing and sharing the data of CHARLS project.


This work was supported by the Natural Science Foundation of China (No. 81871147 & 81671453) and Sichuan Science and Technology Program (No.2018SZ0019 & 2018TJPT0018).

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

Supplementary material associated with this article can be found, in the online version, at

Prince MJ, Wimo A, Guerchet MM, Ali GC, Prina M, Wu Y. World Alzheimer Report 2015—The Global Impact of Dementia: An analysis of prevalence, incidence, cost and trends. London: Alzheimer’s Disease International. 2015.
Langa KM, Larson EB, Crimmins EM, Faul JD, Levine DA, Kabeto MU, et al. A Comparison of the Prevalence of Dementia in the United States in 2000 and 2012. JAMA Internal Medicine. 2017; 177: 51.
Dominguez J, Fe de Guzman M, Reandelar M, Thi Phung TK. Prevalence of Dementia and Associated Risk Factors: a Population-Based Study in the Philippines. Journal of Alzheimer’s Disease. 2018; 63: 1065–1073.
Phung KTT, Chaaya M, Prince M, Atweh S, El Asmar K, Karam G, et al. Dementia prevalence, care arrangement, and access to care in Lebanon: a pilot study. Alzheimer’s & Dementia. 2017; 13: 1317–1326.
Huang Y, Wang Y, Wang H, Liu Z, Yu X, Yan J, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry. 2019; 6: 211–224.
Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. Journal of the American Medical Association. 2014; 312: 2551–2561.
Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, et al. Dementia prevention, intervention, and care. Lancet. 2017; 390: 2673–2734.
Fang EF, Scheibye-Knudsen M, Jahn HJ, Li J, Ling L, Guo H, et al. A research agenda for aging in China in the 21st century. Ageing Research Reviews. 2015; 24: 197–205.
Hebert LE, Bienias JL, Aggarwal NT, Wilson RS, Bennett DA, Shah RC, et al. Change in risk of Alzheimer disease over time. Neurology. 2010; 75: 786–791.
Green RC, Cupples LA, Go R, Benke KS, Edeki T, Griffith PA, et al. Risk of dementia among white and African American relatives of patients with Alzheimer disease. Journal of the American Medical Association. 2002; 287: 329–336.
Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, Mayeux R, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. a meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. Journal of the American Medical Association. 1997; 278: 1349–1356.
Biessels GJ, Despa F. Cognitive decline and dementia in diabetes mellitus: mechanisms and clinical implications. Nature Reviews Endocrinology. 2018; 14: 591–604.
Iadecola C. Hypertension and Dementia. Hypertension. 2014; 64: 3–5.
Appleton J, Scutt P, Sprigg N, Bath P. Hypercholesterolaemia and vascular dementia. Clinical Science. 2017; 131: 1561–1578.
Baumgart M, Snyder HM, Carrillo MC, Fazio S, Kim H, Johns H. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimer’s & Dementia. 2015; 11: 718–726.
Liu H, Zhang Y, Burgard SA, Needham BL. Marital status and cognitive impairment in the United States: evidence from the National Health and Aging Trends Study. Annals of Epidemiology. 2019; 38: 28–34.e2.
Feng L, Ng X, Yap P, Li J, Lee T, Håkansson K, et al. Marital Status and Cognitive Impairment among Community-Dwelling Chinese Older Adults: the Role of Gender and Social Engagement. Dementia and Geriatric Cognitive Disorders Extra. 2014; 4: 375–384.
Liu H, Zhang Z, Choi SW, Langa KM. Marital Status and Dementia: Evidence from the Health and Retirement Study. Journals of Gerontology. 2020; 75: 1783–1795
Carragher N, Adamson G, Bunting B, McCann S. Subtypes of depression in a nationally representative sample. Journal of Affective Disorders. 2009; 113: 88–99.
Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevention Science. 2013; 14: 157–168.
Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). International Journal of Epidemiology. 2014; 43: 61–68.
Xiong Y, Zhang Y, Li X, Qin F, Yuan J. The prevalence and associated factors of lower urinary tract symptoms suggestive of benign prostatic hyperplasia in aging males. Aging Male. 2020; 23: 1432–1439.
Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition. Lancet. 2005; 366: 1059–1062.
Poulter NR, Prabhakaran D, Caulfield M. Hypertension. Lancet. 2015; 386: 801–812.
Li Q, Li X, Wang J, Liu H, Kwong JS, Chen H, et al. Diagnosis and treatment for hyperuricemia and gout: a systematic review of clinical practice guidelines and consensus statements. BMJ Open. 2019; 9: e026677.
Mcardle JJ, Smith JP, Willis R. Cognition and Economic Outcomes in the Health and Retirement Survey. In Wise D.A. (ed.) Explorations in the Economics of Aging (pp: 209–233). Chicago: University of Chicago Press. 2011.
Lei X, Hu Y, McArdle JJ, Smith JP, Zhao Y. Gender Differences in Cognition among Older Adults in China. Journal of Human Resources. 2012; 47: 951–971.
Wang T, Wu Y, Sun Y, Zhai L, Zhang D. A Prospective Study on the Association between Uric Acid and Cognitive Function among Middle-Aged and Older Chinese. Journal of Alzheimer’s Disease. 2017; 58: 79–86.
McArdle JJ, Fisher GG, Kadlec KM. Latent variable analyses of age trends of cognition in the Health and Retirement Study, 1992–2004. Psychology and Aging. 2007; 22: 525–545.
McArdle JJ, Ferrer-Caja E, Hamagami F, Woodcock RW. Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology. 2002; 38: 115–142.
Qin T, Yan M, Fu Z, Song Y, Lu W, Fu A, et al. Association between anemia and cognitive decline among Chinese middle-aged and elderly: evidence from the China health and retirement longitudinal study. BMC Geriatrics. 2019; 19: 305.
Abdin E, Chong SA, Peh CX, Vaingankar JA, Chua BY, Verma S, et al. The mediational role of physical activity, social contact and stroke on the association between age, education, employment and dementia in an Asian older adult population. BMC Psychiatry. 2017; 17: 98.
Lara E, Martín-María N, De la Torre-Luque A, Koyanagi A, Vancampfort D, Izquierdo A, et al. Does loneliness contribute to mild cognitive impairment and dementia? a systematic review and meta-analysis of longitudinal studies. Ageing Research Reviews. 2019; 52: 7–16.
Hakansson K, Rovio S, Helkala EL, Vilska AR, Winblad B, Soininen H, et al. Association between mid-life marital status and cognitive function in later life: population-based cohort study. British Medical Journal. 2009; 339: b2462–b2462.
Scheunemann L, Lampin-Saint-Amaux A, Schor J, Preat T. A sperm peptide enhances long-term memory in female Drosophila. Science Advances. 2019; 5: eaax3432.
Lee GR, DeMaris A, Bavin S, Sullivan R. Gender differences in the depressive effect of widowhood in later life. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 2001; 56: S56–S61.
Zhou Z, Wang P, Fang Y. Loneliness and the risk of dementia among older Chinese adults: gender differences. Aging and Mental Health. 2018; 22: 519–525.
Mehta K, Pandey KK, Kaur B, Dhar P, Kaler S. Resveratrol attenuates arsenic-induced cognitive deficits via modulation of Estrogen-NMDAR-BDNF signalling pathway in female mouse hippocampus. Psychopharmacology. 2021. (in press)
Adu-Nti F, Gao X, Wu JM, Li J, Iqbal J, Ahmad R, et al. Osthole Ameliorates Estrogen Deficiency-Induced Cognitive Impairment in Female Mice. Frontiers in Pharmacology. 2021; 12: 641909.
Szoeke C, Downie SJ, Parker AF, Phillips S. Sex hormones, vascular factors and cognition. Frontiers in Neuroendocrinology. 2021; 62: 100927.
Echeverria V, Echeverria F, Barreto GE, Echeverría J, Mendoza C. Estrogenic Plants: to Prevent Neurodegeneration and Memory Loss and Other Symptoms in Women After Menopause. Frontiers in Pharmacology. 2021; 12: 644103.
McConnell SEA, Alla J, Wheat E, Romeo RD, McEwen B, Thornton JE. The role of testicular hormones and luteinizing hormone in spatial memory in adult male rats. Hormones and Behavior. 2012; 61: 479–486.
Kessler RC, McGonagle KA, Nelson CB, Hughes M, Swartz M, Blazer DG. Sex and depression in the National Comorbidity Survey. II: Cohort effects. Journal of Affective Disorders. 1994; 30: 15–26.
Gale CR, Cooper R, Craig L, Elliott J, Kuh D, Richards M, et al. Cognitive function in childhood and lifetime cognitive change in relation to mental wellbeing in four cohorts of older people. PLoS ONE. 2012; 7: e44860.
Garibotto V, Borroni B, Kalbe E, Herholz K, Salmon E, Holtoff V, et al. Education and occupation as proxies for reserve in aMCI converters and AD: FDG-PET evidence. Neurology. 2008; 71: 1342–1349.
Holmes C, Butchart J. Systemic inflammation and Alzheimer’s disease. Biochemical Society Transactions. 2011; 39: 898–901.
Rezvani AH, Levin ED. Cognitive effects of nicotine. Biological Psychiatry. 2001; 49: 258–267.
Zhu X, Qiu C, Zeng Y, Li J. Leisure activities, education, and cognitive impairment in Chinese older adults: a population-based longitudinal study. International Psychogeriatrics. 2017; 29: 727–739.
Zhang X, Dupre ME, Qiu L, Zhou W, Zhao Y, Gu D. Urban-rural differences in the association between access to healthcare and health outcomes among older adults in China. BMC Geriatrics. 2017; 17: 151.
Publisher’s Note: IMR Press stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Back to top