Abstract
Aim:
There are currently no established, clinically relevant, non-invasive markers of cognitive impairment, except for age and APOE genotype.
Methods:
A cross-sectional study of 1,296 participants from Nanchang, China, has been conducted. We collected data from Mini–Mental State Examination (MMSE) scores, internal lipid profiles and body lipid profiles, age and other factors that may have an effect on cognitive impairment.
Results:
Internal lipid profiles (OR = 1.03 [95%CI, 1.00-1.06], P = 0.024), body lipid profiles (OR = 1.05 [95%CI, 1.01-1.09], P = 0.014), and age (OR = 1.03 [95%CI, 1.01-1.05], P < 0.001) were all positively correlated with cognitive impairment.
Conclusions:
Cognitive impairment was more frequent in female patients with high internal lipid profiles or body lipid profiles, and these characteristics were related to age and education.
Keywords: cognition, body lipid profiles, internal lipid profiles, education, age
Introduction
With the increasing aging of the population, the prevalence of dementia has increased significantly. The updated estimated number of people with dementia aged 60 years or above is 9.5 million in China, 1 the dementia in China is for cause specific pathologies, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD), and the prevalence of dementia in rural areas is significantly higher than in urban areas. 2 The fact that China has already become an aging society, together with the accompanying decline in cognition, has caused a series of social problems.
At the same time, the concept of “cognitive reserve” has been raised many times, involving the protective effect of education against cognitive decline. 3,4 The reliability of conclusions is decreased because of differences in inclusion criteria, exclusion criteria, cut-off values, research design, and data collection. 5
A recent study using data based on 1,570 older British men, it investigated the relationships between total and regional body composition measures and cognitive functioning. The results showed that increased levels of peripheral fat mass, visceral fat, and body mass index (BMI) are associated with severe cognitive impairment among older people. 6 In addition, lipids are key regulators of brain function and have been increasingly implicated in neurodegenerative disorders.
Moreover, in previous studies, the data were collected from white subjects in Europe and the United States; data from Chinese subjects are lacking. 7 Chinese aging populations exhibit unique characteristics, such as low education levels and poor health awareness. Therefore, we analyzed the impact of factors, especially internal lipid profiles and body lipid profiles, that may have an effect on cognitive impairment.
Materials and Methods
Ethics Statement
Ethical approval was obtained from the ethics review boards of the Second Affiliated Hospital of Nanchang University and the Fuwai Cardiovascular Hospital (Beijing, China). Written informed consent was obtained from each participant and from guardians on behalf of minors/children under the age of 18 years enrolled in the study. If guardians were unable to write, then fingerprinting was used. The ethics committee approved the procedure.
Study Design
A community-based study was conducted from November 2013 to March 2014 in Nanchang, Jiangxi Province, China. Residents in 4 communities were reached by door-to-door visits and invited to participate this study. 8 Two districts were selected using the probability proportional to size method. Then 2 communities were chosen within each district respectively, using the simple random sampling (SRS) method. 9 Finally, a given number of participants from each of the gender/age strata were chosen using the SRS method according to the national demographic composition; participants were chosen from communities using the lists compiled from the local government registers of households.
All subjects over 15 years and reside in Jiangxi Province for more than 3 months, those who lacking self-care ability were excluded. Subjects with self-reported dementia were examined by a neurologist; dementia was confirmed or ruled out based on the DSM-IV criteria (American Psychiatric Association (1994) Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), American Psychiatric Association, Washington DC). Exclusion criteria also included dementia, other severe psychiatric disorders (including schizophrenia, major depression, and bipolar disorders), neurological disorders (including stroke, Parkinson’s disease, epilepsy), brain tumor, previous history of brain trauma, and severe diseases affecting cognition (such as hepatic failure). A total of 1,298 eligible residents agreed to participate, 2 of whom were missing data on educational level. Thus, 1,296 residents were selected for this study (Figure 1). An interview was conducted for each participant during a visit to a local health care setting.
Figure 1.
Flow chart. Legends: 1296 included in the analysis.
Measurement
The Chinese version of the MMSE was administered to all participants. The MMSE is a commonly used 30-point scale for assessing cognitive function in orientation, registration, attention and calculation, recall, language, and praxis. The total score is the sum of correct responses to all questions, with a maximum score of 30 points representing the highest level of cognitive function. Due to Chinese populations with linguistic and cultural diversity as well as high illiteracy rates, the Chinese version of the MMSE 10 was administered to all participants; specifically, the cut-off value was 17 for the illiterate, 20 for the primary school-educated, and 24 for those educated at the middle school level or higher.
At each study visit, blood pressure (BP) was measured using the Omron HBP-1300 Professional Portable Blood Pressure Monitor (Kyoto, Japan) by trained medical students and research staff according to protocols. The participants’ BP was measured for approximately 10 minutes while the participant was resting and sitting in an upright position with legs uncrossed and feet flat on the floor. In addition, no vigorous exercise was allowed during the 30 minutes before each measurement. Two blood pressure readings were taken, with 30-second intervals between each measurement. The mean of the 2 measurements was used as the blood pressure for each participant.
Biochemical test parameters (low density lipoprotein, high density lipoprotein, total cholesterol, triglyceride, glucose) were tested by the biochemical laboratory of the Second Affiliated Hospital of Nanchang University. Internal lipid profiles, body lipid profiles, basic metabolism and body weight without heavy clothing were measured using an Omron body fat and weight measurement device (V-BODY HBF-701, Omron, Kyoto, Japan). The measurement method of basic metabolism is the energy metabolism rate when the human body is awake and extremely quiet, without being affected by muscle activity, ambient temperature, food and mental stress. Height was measured without shoes using a standard right-angle device and a fixed measurement tape (to the nearest 0.5 cm).
Definitions
Education was categorized into 3 groups: 1 (illiteracy), 2 (elementary school), and 3 (higher levels). Internal lipid profile, body lipid profile and pulse pressure (PP) were categorized into 3 groups by percentile, the precise cut off are 5 and 9, 28.7 and 34.2, 45 and 55.5. Using 55 years old as the cut-off value, age was divided into 2 groups. BMI was calculated as the weight in kilograms divided by the height in meters squared (kg/m2). PP was defined as the difference between systolic pressure and diastolic pressure.
Statistical Analysis
The Statistical Package for the Social Sciences 21.0 (SPSS, IL, USA) was used for all analyses. We used chi-square tests for categorical variables and Pearson tests for continuous variables. Descriptive analysis was used to describe the population characteristics of the sample in terms of demographic variables and scores on the MMSE. Univariate analysis and multivariate logistic regression analysis was performed to examine the extent to which exposures influenced MMSE total scores. P value less than or equal to 0.05 is significant. Demographic variables included age, gender and educational levels (illiteracy, elementary school and higher levels). Physical health conditions include systolic blood pressure (SBP), diastolic blood pressure (DBP), PP, BMI, basic metabolism, internal lipid profile, and body lipid profile.
Results
The baseline characteristics of the study participants, stratified by MMSE scores, are shown in Table 1. Of course, 1,217 participants (93.9%) had a normal MMSE score, and 79 participants (6.1%) were diagnosed as having cognitive impairment according to the criteria described above. As shown in Table 1, the non-cognitive impairment group and cognitive impairment group were different in terms of age (P < 0.001), body lipid profile (P = 0.014) and internal lipid profile (P = 0.024); members of the cognitive impairment group tended to be older in age (P = 0.011) and higher in education (P = 0.000).
Table 1.
Population Characteristics of Demographic Variables and Scores on the MMSE.
|
All N (%)
(mean±SD) |
MMSE
(mean±SD) |
P value | ||
|---|---|---|---|---|
| NCI | CI | |||
| N | 1296 | 1217 | 79 | |
| MMSE | 28.3 ± 3.5 | 29.0 ± 2.0 | 17.8 ± 4.9 | <0.001 |
| Age, y | 52.0 ± 15.7 | 51.6 ± 15.7 | 58.1 ± 14.3 | <0.001 |
| Sex | 0.717 | |||
| Male | 401(30.9%) | 378(31.1%) | 23(29.1%) | |
| Female | 895(69.1%) | 839 (68.9%) | 56 (70.9%) | |
| Body lipid profile | 31.1 ± 6.4 | 31.0 ± 6.3 | 32.8 ± 6.6 | 0.014 |
| Internal lipid profile | 8.5 ± 5.9 | 8.5 ± 5.5 | 10.0 ± 10.2 | 0.024 |
| BMI | 24.2 ± 3.6 | 24.2 ± 3.6 | 24.8 ± 3.8 | 0.135 |
| Basic metabolic | 1316.4 ± 198.9 | 1318.0 ± 199.6 | 1291.1 ± 186.8 | 0.250 |
| SBP, mmHg | 127.3 ± 17.6 | 127.2 ± 17.5 | 128.9 ± 19.2 | 0.517 |
| DBP, mmHg | 74.2 ± 10.4 | 74.2 ± 10.3 | 74.4 ± 11.3 | 0.919 |
| PP, mmHg | 53.1 ± 14.0 | 53.0 ± 14.0 | 54.5 ± 14.6 | 0.460 |
| LDL, mmol/L | 3.0 ± 0.8 | 3.0 ± 0.8 | 3.1 ± 0.8 | 0.613 |
| HDL, mmol/L | 1.2 ± 0.3 | 1.2 ± 0.3 | 1.2 ± 0.3 | 0.760 |
| CHOL, mmol/L | 5.2 ± 1.0 | 5.2 ± 1.0 | 5.3 ± 1.0 | 0.656 |
| TG, mmol/L | 1.4 ± 1.0 | 1.4 ± 1.0 | 1.2 ± 0.6 | 0.184 |
| GLU, mmol/L | 5.5 ± 1.2 | 5.5 ± 1.3 | 5.2 ± 0.6 | 0.155 |
NCI: non cognitive impairment; CI: cognitive impairment; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; PP: pulse pressure; LDL: low density lipoprotein; HDL: high density lipoprotein; CHOL: total cholesterol; TG: triglyceride; GLU: glucose.
As shown in Table 2, no significant correlations were found between sex or PP and cognitive impairment. Body lipid profile was positively correlated with cognitive impairment (OR = 1.05 [95%CI, 1.01-1.09], P = 0.014), such that ORs significantly increased in parallel to body lipid profile levels. In general, the relationships between internal lipid profiles and cognitive impairment (P = 0.032) and between age and cognitive impairment (P = 0.000) were similar to the relationship between body lipid profile and cognitive impairment. We can also see that education played a protective role, such that individuals with stage 3 education (OR = 0.24 [95%CI, 0.12-0.51], P = 0.000) had a much smaller OR than subjects with stage 2 education (OR = 0.75 [95%CI, 0.34-1.66], P = 0.48); in particular, the former association was statistically significant.
Table 2.
Univariate Analysis of Variables on Cognitive Impairment.
| Variables | (mean±SD) / N(%) | OR (95%CI) P value |
|---|---|---|
| Age | 52.02 ± 15.67 | 1.03 (1.01, 1.05) 0.000 |
| <55 | 610 (50.57%) | 1 |
| ≥55 | 602 (49.43%) | 1.86 (1.15, 3.00) 0.011 |
| Sex | ||
| Male | 401 (30.94%) | 1 |
| Female | 895 (69.06%) | 1.10 (0.67, 1.81) 0.717 |
| Body lipid profile | 31.07 ± 6.37 | 1.05 (1.01, 1.09) 0.014 |
| Low | 422 (33.07%) | 1 |
| Middle | 422 (33.07%) | 1.05 (0.56, 1.97) 0.873 |
| High | 432 (33.86%) | 1.83 (1.04, 3.21) 0.036 |
| Internal lipid profile | 8.55 ± 5.93 | 1.03 (1.00, 1.06) 0.032 |
| Low | 381 (30.12%) | 1 |
| Middle | 455 (35.97%) | 1.78 (0.93, 3.43) 0.082 |
| High | 429 (33.91%) | 2.18 (1.15, 4.15) 0.017 |
| PP | 53.11 ± 14.02 | 1.01 (0.99, 1.03) 0.460 |
| Low | 257 (31.69%) | 1 |
| Middle | 282 (34.77%) | 0.74 (0.36, 1.53) 0.413 |
| High | 272 (33.54%) | 1.18 (0.61, 2.29) 0.623 |
| Education | ||
| 1 | 65 (5.02%) | 1 |
| 2 | 216 (16.67%) | 0.75 (0.34, 1.66) 0.480 |
| 3 | 1015 (78.32%) | 0.24 (0.12, 0.51) 0.000 |
To better understand the relationship between body lipid profiles, internal lipid profiles, PP and cognitive impairment, we established multivariate logistic regression models for the total population (Table 3). After adjusting for sex, age and educational levels, the estimated associations between body lipid profile or internal lipid profile and cognitive impairment were attenuated but maintained the previously described trend. We also found a significant increase in ORs with an increase in body lipid profiles. The results for internal lipid profiles are similar to those for body lipid profiles. PP was negatively correlated with cognitive impairment in most situations, and a tendency toward the opposite relationship between high PP and cognitive impairment in model I was observed.
Table 3.
Multivariate Logistic Regression Analysis of Variables on Cognitive Impairment.
| Variables |
Model I
OR (95%CI) Pvalue |
Model II
OR (95%CI) Pvalue |
|---|---|---|
| Body lipid profile | (1.0, 1.1) 0.0141 | 1.0 (1.0, 1.1) 0.2677 |
| Low | 1 | 1 |
| Middle | 1.1 (0.6, 2.0) 0.8728 | 1.0 (0.5, 2.0) 0.9785 |
| High | 1.8 (1.0, 3.2) 0.0362 | 1.4 (0.6, 3.0) 0.4549 |
| Internal lipid profile | 1.0 (1.0, 1.1) 0.0324 | 1.0 (1.0, 1.1) 0.1393 |
| Low | 1 | 1 |
| Middle | 1.8 (0.9, 3.4) 0.0821 | 1.4 (0.7, 2.7) 0.3708 |
| High | 2.2 (1.2, 4.1) 0.0169 | 1.6 (0.7, 3.2) 0.2392 |
| PP | 1.0 (1.0, 1.0) 0.4600 | 1.0 (1.0, 1.0) 0.4104 |
| Low | 1 | 1 |
| Middle | 0.7 (0.4, 1.5) 0.4126 | 0.6 (0.3, 1.4) 0.2455 |
| High | 1.2 (0.6, 2.3) 0.6228 | 0.7 (0.3, 1.5) 0.3803 |
Adjust model I adjust for: None.
Adjust model II adjust for: Age; Sex; Educational levels.
To further understand the relationship between internal lipid profiles, body lipid profiles and cognitive impairment, we utilized stratified logistic regression (Figure 2A and B). The stratified logistic regression analysis showed that there was an interaction effect between internal lipid profiles and sex on cognitive impairment in the total population (Figure 2A). Specifically, the higher the female’s internal lipid profile, the easier it was to have cognitive impairment. We also examined the relationship between body lipid profiles and cognitive impairment (Figure 2B). It was similar to the relationship between internal lipid profiles and cognitive impairment; however, the significant differences were not as prominent in the former relationship as in the latter relationship.
Figure 2.
A and B, Stratified analysis of variables on cognitive impairment. Legends: subgroup includes overall, age, sex, education.
Discussion
Our society is aging at an unprecedented pace, mainly due to longer life spans and the aging of the baby boomer generation. Aging itself remains the strongest risk factor for all of the most prevalent chronic diseases, including neurodegenerative disorders. Thus, the increasing aging population will inevitably have a large impact on health care systems and national economies, along with an emotional and financial impact on patients and their families. Consequently, therapeutic interventions aimed to increase quality of life at advanced ages are in high demand, at the level of both the individual and society. In the past 20 years, much progress has been made on understanding the symptoms, etiology and pathogenic mechanisms of AD, PD and HD. It is clear that cognitive impairment occurs in all of these disorders. However, to date, there is no effective prevention or treatment for these debilitating diseases.
Aging is associated with changes in the total and regional distribution of body fat. 11 These age-related changes in body fat distribution might not be reflected in simple anthropometric measures, such BMI and waist circumference. Older individuals will likely have higher internal lipid profiles than younger individuals despite having the same BMI or waist circumference. It is well known that internal lipid profiles have been considered pathogenic adipose tissue compartments. Although BMI is a convenient and economical means of estimating adiposity, it is imprecise. Furthermore, its relationship with adiposity is affected by sex and race differences. 12 -14 Critically, BMI fails to account for the distribution of the excess adipose tissue. Waist circumference focuses on abdominal fat and conflates subcutaneous adipose tissue and internal lipid profiles.
Since few studies have examined whether body lipid profiles and internal lipid profiles are associated with cognition, these results are fairly novel and suggest that both were positively correlated with cognitive impairment. This finding is consistent with the growing evidence showing that low BMI or weight loss among older adults predicts cognitive decline or dementia. 15 -19 Even though some research reports a U-shaped relationship between body composition assessed by BMI and cognitive impairment, both underweight and overweight/obesity were associated with an elevated risk of cognitive impairment compared with normal weight status. 20,21 A previous longitudinal study with 2,268 amnestic mild cognitive impairment (aMCI) participants and 1,506 AD participants found that high BMI (versus moderate BMI) was associated with slower progression and weight loss (versus no weight change) was associated with faster progression in aMCI, but no significant differences were observed in clinical progression by BMI or weight change in AD. 22
The biological mechanisms behind body lipid profiles or internal lipid profiles and cognition are not clear. Several plausible explanations have been suggested. Dysregulation in hormone secretion or genetic susceptibility (e.g. APOE ε4 genotype) may lead to weight gain. Obesity will increase leptin and suppress appetite in the healthy condition. Leptin also appears to play important neuroprotective and developmental roles. 23,24 It may be dysregulated in aging and in AD. 25,26 A few studies have demonstrated significantly different plasma leptin levels by sex, 27 suggesting that sex-based interactions could be related to differences in energy-regulating hormones associated with neurodegeneration. 28
The associations between plasma lipids and cognition and the underlying mechanisms are complex and currently not fully understood. Low triglycerides values were generally beneficial to maintaining cognitive abilities, but findings on the relationship between cholesterol levels and cognitive impairment are inconsistent. 29 High cholesterol levels were reported to be associated with AD or cognitive impairment. 30,31 However, other studies found that low cholesterol levels were associated with AD or cognitive decline, 32,33 although some studies reported no association between cholesterol levels and dementia risk. 34 The inconsistency in results has often been attributed to the age-related cholesterol decline. 35,36 It also may result from the fact that cholesterol can be anti-inflammatory or pro-inflammatory. Moreover, the level of plasma cholesterol does not always represent the level and function of cholesterol. Still, other studies have found that the relationship between cholesterol levels and cognitive function is homocysteine dependent. 37 The most important thing, as we all know, all serum lipid concentrations were significantly higher in female than in male, 38 this underpins the findings from our study about the relationship with female and cognitive impairment.
Our findings support the concept of “cognitive reserves” involving the protective effect of education against cognitive decline. 4,39 Consistent with this view, persons who received higher education (elementary school or higher) in our study obtained higher cognitive test scores than those with a few years of education or those who were illiterate. The stratified logistic regression analysis showed that there was an interaction effect between internal lipid profiles and sex on cognitive impairment in the total population. The gender differences in lipid-cognitive change relationships found in the current study are consistent with the findings reported by others. 40
Our study has many strengths: 1) the study cohort was relatively large, allowing for the investigation of potential interactions among biomarker measures; 2) the cohort was unusual because few studies with cognitive and lipid profile measures have been conducted in elderly Chinese populations; and 3) body lipid profiles could distinguish between body fat and lean body mass, and they can serve as accurate predictors of body fat in older adults as well as in their younger and middle-aged counterparts; moreover, internal lipid profiles are a precise index of regional fat.
A few limitations of this study should be noted: 1) our analysis is a cross-sectional study, and the observed association could be subject to reverse causation; 2) classified research can be taken to study participants with hypertension or diabetes, APOE ε4 alleles or no APOE ε4 alleles, and aMCI or AD; 3) body lipid profiles or internal lipid profiles may have differential impacts on alternative domains of cognition; and 4) our study was conducted in a population with a low level of education. It remains to be seen whether our results can be confirmed in other populations.
Conclusions
The present study suggests that cognitive impairment was more frequent in female patients with high internal lipid profiles or body lipid profiles, and these characteristics were related to age and education. Since few studies have examined whether body lipid profiles and internal lipid profiles are associated with cognition, these results are fairly novel. Future studies are needed to replicate our findings and investigate the underlying physiological mechanisms in the relationship between body composition and cognitive functioning to elucidate the cognitive consequences of unhealthy body lipid profiles or internal lipid profiles.
Acknowledgments
The authors acknowledge the contribution of all the staff who participated in this study as well as the study participants who shared their time with us. All authors read and approved the final manuscript.
Footnotes
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethics approval and consent to participate: Ethical approval was obtained from the ethics review boards of the Second Affiliated Hospital of Nanchang University and the Fuwai Cardiovascular Hospital (Beijing, China). NO.2011BAI11B01.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Technology and National Natural Science Foundation of China [No.81500233].
ORCID iD: Lian Liu 
https://orcid.org/0000-0001-6627-0853
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