Metabolic Syndrome and Mild Cognitive Impairment: A Case-Control Study among Elderly in a Shanghai Suburb

Abstract

Background:

Metabolic syndrome (MetS) maybe associated with mild cognitive impairment (MCI).

Objective:

To investigate the relationship between MetS, with its individual or combined components, and MCI among elderly.

Methods:

A case-control study was conducted among the elderly aged 65 years and over in a community located in the southwestern suburb of Shanghai, China. The Chinese version of the Mini-Mental Status Examination (C-MMSE) was used to screen subjects with MCI. Associations of MetS with its individual or combined components and MCI were analyzed using conditional regression analyses with or without adjustment for gender, education, current smoking, current drinking, and physical activities.

Results:

There were 379 subjects with MCI and 379 gender- and age-matched healthy controls in the study. Compared with healthy controls in univariate analyses, subjects with MCI were more likely to have less time spent on physical activity, lower C-MMSE score, heavier weight, larger waistline and hipline, higher diastolic blood pressure, higher body mass index, higher abdominal obesity index, higher serum glycated hemoglobin, higher serum triglycerides, higher serum cholesterol, higher serum uric acid, and higher serum alanine aminotransferase. After multivariable adjustment, MetS was significantly associated with an increased risk of MCI (OR = 2.277; 95% CI: 1.086–4.773). Among MetS components, abdominal obesity (OR = 2.101; 95% CI: 1.224–3.608) and hypertension (OR = 2.075; 95% CI: 1.170–3.678) showed a significant association with MCI, respectively; while these two components were combined, the association was stronger (OR = 2.459; 95% CI: 1.360–4.447).

Conclusion:

MetS and its components, particularly abdominal obesity and hypertension, were found to be significantly associated with the risk of MCI.

Keywords

Abdominal obesity case-control study hypertension metabolic syndrome mild cognitive impairment

INTRODUCTION

With rapid increases for the number and proportion of elderly in the world, dementia has constituted a major global health burden in aging societies [1], and Alzheimer’s disease (AD) is the most common form of dementia [2]. There is cumulative evidence that the most beneficial effects of treatment may only be achieved in the preclinical stage of dementia, so that the search for preventive strategies for cognitive decline and dementia appears to be of great importance [3]. In recent years, the term “predementia syndrome” has been applied to identify all conditions with age-related deficits in cognitive function from the literature, including a mild stage of cognitive impairment [4]. Among them, the term “mild cognitive impairment (MCI)” is most extensively used to refer to the symptomatic predementia phase of AD, which indicates non-demented aged persons with no significant disability and a mild memory or cognitive impairment that cannot be explained by any recognized medical or psychiatric condition [4 –7].

Metabolic syndrome (MetS) is an entity comprising a cluster of known vascular risk factors, such as abdominal obesity, impaired fasting glucose, hypertension, low high-density lipoprotein (HDL), and/or high triglycerides [8]. Several individual components of MetS have been linked to the risk of developing dementia and MCI [9 –15]. However, few studies have looked at the components of MetS as a whole [15]. Although several studies have investigated the relationship between MetS and MCI, findings are limited to draw any solid conclusion; and there are conflicting findings on the possible role of MetS in MCI [16 –21].

A recent study found an association between MetS with its number of components and the risk of developing cognitive impairment (clinically adjudicated dementia or MCI or cognitive impairment not clinically adjudicated) [22].

Furthermore, one study suggested that the association between MetS and accelerated cognitive decline disappeared at the age 85 and older [23]. Some investigators have suggested that inflammation should be added as a component in the definition of MetS because it is such an important part of the pathophysiology [24]. More recently, the Italian Longitudinal Study of Ageing (ILSA) reported no significant difference in overall risk of developing incident MCI in non-cognitively impaired individuals with MetS compared with those without MetS over 3.5-year follow-up [25].

In the present study, we examined whether MetS, with its individual or combined components, were associated with MCI in a case-control study. Measures of other risk factors for MetS, such as body mass index (BMI), were also included in the analyses to explore more specific indices for improving the recognition of high-risk subjects for MCI.

MATERIALS AND METHODS

Study subjects

Data were obtained from a population-based epidemiological study on cognitive impairment and dementia among elderly population. Study subjects were local residents aged ≥65 years from Sheshan town in the Songjiang district, located in the southwestern suburb of Shanghai, China. The interviews were conducted during the period of April to May in 2013. Face-to-face interviews, data collection, and relevant investigations were performed by trained medical staff, and the details were described elsewhere [26].

Study design

An individual-matching case-control study was designed.

Subjects with MCI and controls

The Chinese version of Mini-Mental Status Examination (C-MMSE) was used to screen subjects with cognitive impairment. The criteria of MCI were adjusted for education level with the following cutoff points: 17/18 for participants with <1 year of education (without formal education), 20/21 for those with 1–6 years of education (primary school), and 24/25 for those with >6 years of education (middle school or higher). Screened subjects with MCI were further examined for all-cause dementia by senior neurologists from the Department of Neurology, Ruijin Hospital according to DSM-IV criteria [27]. Detailed medical history, family history, results of neurological examinations, and neuropsychological tests from the survey were collected and analyzed. The diagnosis of AD was based on recommendations from the National Institute on Aging and Alzheimer’s association work group, 2011 (details of measurements and procedures were described in previous publications [26, 28].). All patients with dementia were excluded in the current study.

Subjects of the control group were also from the abovementioned survey, who were healthy inhabitants without cognitive impairment living in the same community and matched with MCI cases for gender and age (±3 years). Additionally, we excluded subjects with recent or ongoing infections, malignant diseases and other serious diseases, or taking corticosteroids or immunosuppressive drugs. Each of the subjects signed an informed consent, and for those who were illiterate or severely demented, the consent was signed by their legal guardian.

Diagnosis of MetS

MetS was diagnosed by any combination of three or more of the following components according to the 2005 revised Third Adults Treatment Panel of the National Cholesterol Education Program (NCEP-ATP-III) criteria as proposed by the AHA/NHLB: (1) abdominal obesity (waist circumstances≥90 cm for Asian men or≥80 cm for Asian women); (2) triglycerides≥150 mg/dl (≥1.7 mmol/L) or receiving specific drug treatment; (3) systolic/diastolic blood pressure≥130/85 mmHg or receiving drug treatment; and (4) fasting plasma glucose≥100 mg/dl (≥5.6 mmol/L) or previously diagnosed [29].

Covariates

The screening questionnaire collected information as following:

Demographic variables: Age; gender; education (<1 year, 1–6 years, >6 years)

Daily life variables (based on self-reports): Smoking (current-smoker versus non-current smoker) and alcohol drinking (current-drinker versus non-current drinker); physical activity (>60 min per day, >30 min per day, >15 min per day,≤15 min per day); a diagnosis of diabetes, hypertension, hyperglycemia, or taking relevant medications;

The serum biochemical indices were measured in all subjects by an available kit (Roche Biochemical Serum Immune Automatic Detection Module Cobas 6000). Blood samples were taken from all participants after an overnight fast. Serum was removed after centrifugation at 3000 rd for 10 min, and aliquots were stored at –80°C. All biochemical analyses on blood were carried out at our institution’s referral laboratory.

Evaluated items

Physical indices: Height, weight (electric scale: RGZ-120), waist circumstances, buttock circumstances, systolic blood pressure (SBP) and diastolic blood pressure (DBP), and BMI (weight/height², kg/m²). Abdominal obesity index (AOI) was calculated as waist circumstances/buttock circumstances (>0.9 for Asian men and >0.85 for Asian women).

Serum biochemical indices: Fasting blood glucose, glycated hemoglobin, triglycerides, total cholesterol, albumin, globulin, total protein, creatinine, uric acid, uric urea, total bilirubin, and alanine aminotransferase (ALT).

Statistical analysis

All data were analyzed by SPSS statistical software (version21.0). Characteristics of cases and controls were described with means (±SD) or medians, as well as percentages (P25 P75). Student’s t test was applied for continuous variables following a normal distribution, while Wilcoxon signed-rank test was conducted when appropriately or for ordinal variables. The Spearman rank order and Goodman-Kruskal Gamma were calculated to ascertain association and determination coefficient between variables, respectively. Chi-square test (Mcnemar-Bowker test) was used to compare categorical variables.

We assessed the association between MetS, with its individual or combined components, and MCI through conditional binomial logistic regression analysis; the diagnosis of MCI being the dependent variable and MetS, each individual component of MetS and the number of components as main independent variables. The strength of association was evaluated by the odds ratio (OR) and its 95% confidence interval (95% CI). To explore the effect of MetS risk factors, covariates modeled as continuous variables were examined by quartile analysis, using the lowest quartile as the reference group. OR was calculated for each interval. Models were adjusted for some covariates listed above (potential confounders including age, education, current smoking, current drinking, and physical activity). Unadjusted models were estimated first. Then in adjusted models, MetS was entered first, followed by age, education, current smoking, current drinking, and daily physical activities. Cox-regression Model was used to realize the utility of conditional logistic regression in paired subjects for studying the relationship between MCI and MetS when the original commands (logistic regression) could not be applied. All p-values were based on two-side tests at a significance level of 0.05.

RESULTS

Study population and demographic characteristics

After screening with C-MMSE, 516 subjects with MCI were identified from 4,399 eligible residents aged≥65 years. Subjects with MCI but without complete information from physical and laboratory examinations were excluded. Finally, 379 subjects with MCI and 379 healthy controls were included in the current study. Differences of age (78.64±5.84 versus 76.19±5.60, p = 0.01) and gender (male: 28.50% versus 26.40%, p = 0.67) were observed between excluded and included subjects with MCI. The difference of mean age was not statistically significant (p = 0.890) between subjects with MCI (76.19±5.60 years) and controls (75.87±5.63 years), and males accounted for 26.4% equally (Table 1).

MetS risk factors

Compared with healthy controls in univariate analyses, subjects with MCI were more likely to have less time spent on physical activity per day, lower C-MMSE score, heavier weight, larger waistline and hipline, higher DBP, higher BMI, higher AOI, higher serum glycated hemoglobin, higher serum triglycerides, higher serum total cholesterol, higher serum uric acid, and higher serum ALT (Tables 1 and 2).

Based on the results from multivariate analysis for the physical indices, having the first or second highest quartile of hipline, DBP, BMI, AOI, highest quartile of weight, SBP, and larger quartile of waistline were significantly associated with the increased risk of MCI. After adjusted for potential confounders, the estimates of odds with regard to all categories of blood pressure and the second highest quartile of waistline were no longer statistically significant while no significant alterations for the other variables (Supplementary Table 1). Concerning the serum biochemical indices, statistically significant associations were observed between the highest quartile of uric acid, ALT and increased risk of MCI while the highest quartile of albumin/globulin was associated with the decreased odds of MCI in both unadjusted and adjusted model (Supplementary Table 2).

MetS and its components

Results from conditional binomial logistic regression regarding associations of MetS and its individual components with MCI are presented in Fig. 1. In the unadjusted model, MetS was significantly associated with the increased risk of MCI (OR = 3.250; 95% CI: 1.905–5.209). After adjusted for demographic and clinical variables including age, education, current smoking, current drinking and daily physical activity, the estimate of the odds ratio was modestly reduced but remained statistically significant (OR = 2.277; 95% CI: 1.086–4.773).

For the individual components of MetS, abdominal obesity, raised triglycerides, hypertension, and raised fasting plasma glucose were significantly associated with MCI in the unadjusted model. The OR estimates for the associations between abdominal obesity and MCI, as well as between hypertension and MCI were weakened but remained statistically significant in the adjusted model (Fig. 1).

Increasing the number of MetS components was significantly associated with MCI in both unadjusted model (OR = 1.860; 95% CI: 1.549–2.232) and adjusted model (OR = 1.622; 95% CI: 1.216–2.163). Results from conditional binomial logistic regression regarding associations of various combinations of MetS components with MCI are listed in Table 3. The estimated OR for MCI was significantly increased for subjects with both abdominal obesity and hypertension (unadjusted model: OR = 3.083, 95% CI: 2.117–4.490; adjusted model: OR = 2.459, 95% CI: 1.360–4.447). However, when analyzed by gender, the statistically significant association was found only for females, although there was no significant alteration in MetS and its individual components (Fig. 2).

DISCUSSION

Our results indicate that all obesity related MetS risk factors are associated with the increased risk of MCI. Compared to healthy controls, subjects with MetS had a nearly three times higher risk with MCI. Two of the components of MetS, abdominal obesity and hypertension, were significantly associated with the increasing risk of MCI. In addition, uric acid, ALT, and total bilirubin in the highest quartile were found to have associations with the increasing risk of MCI, compared to the reference group.

Several studies did not find a positive relationship between MetS and MCI [25, 30], and it may be explained mainly by the difference of diagnosis criteria. In most studies, MetS is defined using the NCEP-ATP-III criteria, in which, the subjects with MetS were identified by any combination of three or more of the following components: Abdominal or central obesity (waist circumference >102 cm for men and >88 cm for women); elevated plasma triglycerides (≥150 mg/dl); low high-density lipoprotein (HDL) cholesterol (<40 mg/dl for men and <50 mg/dl for women); high blood pressure (≥130/85 mmHg) or being in hypertensive treatment; high fasting plasma glucose (≥110 mg/dl) or being in oral anti-diabetic treatment. However, we diagnosed MetS regardless of low HDL and adjusted the waist circumference >90 cm for Asian men and >80 cm for Asian women. The reasons are as following: (1) There is no universally accepted diagnostic criteria and the more obvious variance lies in the using of the parameter insulin resistance or HDL as essential components of MetS among the published definitions [8, 31]. (2) One cross-sectional study and all follow-up studies, performed in larger populations, have demonstrated no association between HDL cholesterol levels and MCI [32 –35]. We still accorded with the definition comprised of four main pillars that we focused on: Hypertension, impaired fasting glucose, visceral fat deposition, atherogenic mixed dyslipidemia and then reinforce the strength of associations in our study because of the concentration of components enhancing the sensitivity.

A study, which examined the potential interactive relations of central obesity and blood pressure, concluded that the combination of obesity and hypertension had led to diminished performance across various cognitive domains and individuals with greater waist circumstances (or BMI) and hypertension performed most poorly on these measures [36]. Similar to that report, we found hypertension and abdominal obesity as the main contributors to the increasing risk of MCI and the combination of these two components indicated a higher risk than any of the individual component. It might be noteworthy that none of the adjusted O.R. quartile differences for SBP or DBP were statistically significant (Supplementary Table 1), and the explanation could be due to the small numbers in each quartile after classification.

Our results not only support the previous report in which a 23.0% age-related increase for every unit increase in the number of MetS components was observed in the risk of MCI [22], but also indicate that the more components were involved in the combinations, the higher risk of developing MCI would be in unadjusted model.

Impaired glucose tolerance is another component of MetS, and individuals with prediabetes are defined as those presenting impaired fasting glucose and/or impaired glucose tolerance, which increase their risk of developing frank diabetes mellitus [37]. Since diabetes mellitus with chronic hyperglycemia eventually leads to the failure of several organs including the kidney, heart as well as damage to the micro- and macro-vasculature in brain, a possible link has been suggested with cognitive impairment [38]. A number of studies have shown that diabetes mellitus and prediabetes increases the risk of developing cognitive decline and MCI [39 –41]. However, there are different findings from several population-based studies examining the relationship between type 2 diabetes mellitus and predementia syndromes (MCI, aMCI, naMCI) [37, 42]. No significant association between impaired fasting glucose and MCI was found in our study. The similar result was reported in a Singapore Longitudinal Ageing Study, and the authors concluded that only central obesity among MetS components was associated with an elevated risk of aMCI [43]. The discrepancy of the findings from various reports could be due to differences in a few aspects, for example the duration of impaired glucose tolerance. In addition, longer duration of diabetes and lack of pharmacological treatment seemed to be associated with worse performance.

The strengths of the present study are its case-control design with an elevated statistical power, controlling the possible confounding factors (age and gender). And we were able to adjust for other possible confounders, such as education, current smoking, current drinking, and physical activities. Nonetheless, some limitations in the study have to be considered. For example, many excluded subjects with MCI without complete information in our study were due to their refusal for blood tests, and non-participants were significantly older than participants, which might have some impact on our results [23]. In addition, a few aspects should have been taken into consideration if data available, for example, more dietary soy consumption could increase the level of uric acid; fatty liver disease or low-grade viral hepatitis could be related to ALT and bilirubin. The relationship between MetS and MCI needs to be further examined.

Conclusions

In conclusion, MetS and its components, particularly abdominal obesity and hypertension, were found to be significantly associated with the increasing risk of MCI; while these two components were combined, the association was stronger. Abdominal obesity and hypertension appear to be the main contributors of the association between MetS and MCI.

Footnotes

ACKNOWLEDGMENTS

This study was supported by funds from Natural Science Foundation of China (30872179; (81070958; 81371218).

Authors’ disclosures available online ().

Appendix

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