Screening Cognition in the Elderly with Metabolic Syndrome

Abstract

Background:

Metabolic syndrome reaches its highest prevalence in the elderly, and evidence suggests that metabolic syndrome could be an independent risk factor for cognitive impairment. The aims of this study were to detect whether patients with metabolic syndrome have lower cognition and to investigate whether there is a relationship with cognition and single metabolic syndrome components.

Methods:

We assessed fasting blood glucose (FBG), high-density lipoprotein cholesterol (HDL-C), triglycerides, high-sensitivity C-reactive protein (hsCRP), and anthropometric measurements. Metabolic syndrome was diagnosed according to National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The population sample was divided into two groups according to the presence of metabolic syndrome. Cognitive function was investigated through the Mini-Mental State Examination (MMSE).

Results:

We enrolled 159 elderly subjects (mean age, 69.8±4.8 years). Seventy had metabolic syndrome. Metabolic syndrome subjects had higher hsCRP values (P<0.0001) and lower MMSE scores (P<0.0001) than those without metabolic syndrome. MMSE scores were significantly correlated with body mass index (BMI), hsCRP, metabolic syndrome, the number of metabolic syndrome components, and each of them. However, at multivariate regression analysis, only fasting blood glucose [FBG; B=−0.046; 95% confidence interval (CI) −0.066 to −0.028; P<0.0001] and the number of metabolic syndrome components (B=−0.317; 95% CI −0.572 to −0.010; P=0.042) were found to be independent predictors of lower MMSE scores.

Conclusion:

We found that subjects with metabolic syndrome have lower MMSE scores than those without, even without symptomatic cognitive impairment, and that the number of metabolic abnormalities is independently associated to lower MMSE scores. We suggest that these patients should always undergo cognitive screening to prevent more severe outcomes.

Introduction

W estern countries are experiencing a pandemic of metabolic syndrome. It is estimated that 25% of adults and 40% of subjects aged 65 and older have the condition according to National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria.^1

–5 Metabolic syndrome is a cluster of cardiovascular risk factors that includes visceral obesity, hypertension, low high-density lipoprotein cholesterol (HDL-C), and high levels of triglycerides and fasting blood glucose (FBG). Metabolic syndrome is closely related to type 2 diabetes mellitus (T2DM), and it steadily increases the risk of cardiovascular morbidity and mortality.^3,4

Recent studies have shown that metabolic syndrome independently impairs cognitive functioning, even in the subjects without vascular brain lesions, and it has been suggested that metabolic syndrome–associated microinflammation could be the underlying mechanism.^6
–8 Some authors have revealed that metabolic syndrome is associated with cognitive decline, even after adjustment for silent brain lesions,^6,9 and others have demonstrated that the amount of white matter lesions, especially in the frontal lobe, could be the real promoter of cognitive impairment¹⁰ in the patients with metabolic syndrome. Despite these findings, some authors have denied the association between metabolic syndrome and cognitive impairment.^11–12 Such disparities could be due to methodological differences in the cognitive evaluation across these investigations.

The role of the individual components in metabolic syndrome on cognition has been investigated. Increased FBG seems to be consistently and independently associated with cognitive disturbances,^{9,11,13
–15} and it has been observed that T2DM and its duration are strongly associated with the poorest cognitive performances.^14,16 Furthermore, a relationship has been found with low levels of HDL-C and high plasma triglyceride levels.^17,18 The role of blood hypertension is still under debate,¹⁹ and those who found that hypertensive individuals were more likely to have worse cognition did not investigate the association with other metabolic syndrome features.^20,21

We hypothesize that it would be important to screen cognition in the subjects with metabolic abnormalities, even in those who have not cognitive complaints. The aims of this study are to detect whether metabolic syndrome patients without cognitive symptoms have lower cognitive functioning by using a screening test such as the Mini-Mental State Examination (MMSE), and to determine if there is a relationship between metabolic syndrome traits and lower MMSE scores.

Methods

Study population

Elderly patients attending the Ambulatory of our Department in the period between April, 2011, and February, 2012, were considered for this study. We excluded subjects who met diagnostic criteria for dementia according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) and mild cognitive impairment (Petersen, 2005) or those who reported cognitive complaints; subjects with current mood disorders; history of coronary heart disease, angina, stroke, transient ischemic attack, peripheral artery disease, and other symptomatic vascular events; and other chronic diseases, except blood hypertension, diabetes/impaired fasting blood glucose (FBG), obesity, and dyslipidemia.

Measurements

On the same morning of the visit, each patient underwent venous blood sampling. Standard laboratory techniques were used to assess plasmatic HDL-C, triglycerides, and FBG. We also assessed high-sensitivity C-reactive protein (hsCRP) using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (R&D Systems, Inc., Minneapolis, MI). The analytical range extended from 5 to 5000 pg/mL.

A clinical interview investigated the education, medical history, home therapy, and general habits of the patients. Waist circumference was measured at the level of the iliac crest with the patients standing. Body mass index (BMI) was measured by dividing the weight (kilograms) by the square of the height (meters). Blood pressure was measured three times during the same morning of the visit using a mercury sphygmomanometer after the subjects had rested for 10 min. Patients were classified as hypertensive if they were on antihypertensive therapy or if they had systolic blood pressure (SBP) ≥130 mmHg and/or diastolic blood pressure (DBP) ≥85 mmHg.

Metabolic syndrome was diagnosed according to the NCEP ATP III 2001 criteria by the presence of three or more of the following features: Waist circumference ≥88 cm in females and ≥102 cm in males; HDL-C <50 mg/dL in females and <40 mg/dL in males; FBG ≥100 mg/dL or antidiabetic medications; fasting triglycerides ≥150 mg/dL or relevant drug treatment; and blood pressure ≥130/85 mmHg or antihypertensive medication.

The MMSE was administered to each patient to assess cognitive functioning. The score obtained from each patient was adjusted for age and education. The MMSE score ranged from 0 to 30. Adjusted scores within the range of 24–30 are considered normal.

Statistical analysis

Statistical analysis was performed via Statistical Package for Social Sciences (SPSS) software, version 17.0, for Windows. Data are presented as mean±standard deviation (SD) for ordinal variables and as frequencies for nominal variables for the total study sample and according to the presence of metabolic syndrome. The chi-squared test was used to compare categorical variables, and the Student t-test was used to compare continuous variables. Bivariate regression analysis was performed to assess the correlation of MMSE score with the other variables. Multivariate regression analysis was performed to identify the variables independently associated with MMSE score. hsCRP values are presented as the median, and the values were normalized through logarithmic transformation (log₁₀). Log₁₀ hsCRP was used in both regression analyses. A P value <0.05 was considered statistically significant.

Results

The study sample consisted of 159 subjects (63.5% women), mean age 69.8±4.8 years. All subjects had good performance status and were able to complete all elements of the study assessment. In all, 88% of the hypertensive subjects (60.4% of the entire sample) were on antihypertensive therapy. A total of 40 subjects were taking antidiabetic medications; 29 had T2DM and 11 had impaired fasting glucose.

As shown in Table 1, the comparative analysis between the two groups showed no differences in sex, age, education, current smoking, and prevalence of diabetes and medications, except for antidiabetic (P=0.011) and antihypertensive (P<0.0001) drugs. Obviously, the two groups highly differed in the components of metabolic syndrome presented by each subject, in each feature of metabolic syndrome, and in BMI. As expected, metabolic syndrome patients had significantly higher levels of hsCRP (P<0.0001). Moreover, we observed significantly lower MMSE scores (P<0.0001) in metabolic syndrome subjects, even though all of the MMSE scores obtained were still within the “normal range” and there were no clinically relevant cognitive disparities between the two groups. As shown in Table 2, in the bivariate analysis, MMSE scores showed a strong linear correlation (P<0.0001) with metabolic syndrome (r=−0.438), waist circumference (r=−0.337), FBG (r=−0.553), number of metabolic syndrome components (r=−0.515), BMI (r=−0.313), and log₁₀ hsCRP (r=−0.308). In addition, MMSE score largely correlated with HDL-C (r=0.186; P=0.009) and hypertension (r=−0.237; P=0.001). No correlations were found with triglycerides, diabetes, sex, age, education, and current smoking.

Table 1.

Characteristics of the Sample According to the Presence of Metabolic Syndrome and Comparative Analysis

Variables	Total (n=159)	With MetS (n=70)	Without MetS (n=89)	p^a
Females (%)	63.5	67.1	60.6	0.500
Age (years)	69.8±4.8	70.5±5.1	69.1±4.5	0.068
Education (years)	6.5±2.9	6.1±2.2	6.8±3.3	0.129
Current smokers (%)	26.8	26.7	25.8	0.870
BMI (kg/m²)	28±3.5	29.8±3.4	26.6±2.9	<0.0001
Metabolic syndrome components (n)	2.2±1.3	3.3±0.8	1.3±0.9	<0.0001
High waist circumference (%)	64.1	85.7	47.2	<0.0001
Low HDL-C (%)	35.8	62.8	14.6	<0.0001
Triglycerides (mg/dL)	121.5±46.2	139.3±47.8	107.6±40	<0.0001
FBG (mg/dL)	95.9±12.3	102±13.3	91.1±9	<0.0001
Hypertension (%)	60.4	75.7	48.3	<0.0001
Diabetes (%)	18.2	21.4	15.7	0.437
Medications
Antihypertensive (%)	52.8	68.6	40.4	<0.0001
Antidiabetic (%)	25.1	35.7	16.8	0.011
Lipid-lowering(%)	13.8	17.1	11.2	0.401
Antiplatelet (%)	24.9	27.1	21.3	0.507
hsCRP (μg/L)	1430^b	2100^b	1235^b	<0.0001
MMSE score (n)	26.8±1.5	26.1±1.1	27.4±1.5	<0.0001

Data are given as means±standard deviations or as frequencies.

Chi-squared test (categorical variables) or Student t-test (continuous variables).

hsCRP values are presented as median.

MetS, metabolic syndrome; BMI, body mass index; WC, waist circumference; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; hsCRP, high-sensitivity C-reactive protein.

Table 2.

Bivariate Regression Analysis

	MMSE score
	r	p
Sex	−0.009	NS
Age	−0.010	NS
Education	−0.041	NS
Current smoking	−0.042	NS
BMI	−0.313	<0.0001
Metabolic syndrome	−0.438	<0.0001
Metabolic syndrome components	−0.515	<0.0001
Waist circumference	−0.337	<0.0001
HDL-C	0.186	0.009
Triglycerides	−0.129	NS
FBG	−0.553	<0.0001
Hypertension	−0.237	0.001
Diabetes	−0.121	NS
Log₁₀ hsCRP	−0.308	<0.0001

MMSE, Mini-Mental State Examination; r, Pearson correlation coefficient; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; log₁₀ hsCRP, log₁₀ high-sensitivity C-reactive protein.

In the multivariate model that included sex, age, education, smoke, metabolic syndrome and its single components, diabetes, medications, and log₁₀ hsCRP, as shown in Table 3, we found that only FBG [B=−0.046; 95% confidence interval (CI) −0.066 to −0.028; P<0.0001) and the number of metabolic syndrome components (B=−0.317; 95% CI −0.572 to −0.010; P=0.042) were independently associated with MMSE score.

Table 3.

Multivariate Regression Analysis: Mini-Mental State Examination Score

	B	95% CI	p
Metabolic syndrome components	−0.317	−0.572 to –0.010	0.042
FBG	−0.046	−0.066 to –0.028	<0.0001

B, regression coefficient; 95% CI, 95% confidence intervals; FBG, fasting blood glucose.

Discussion

We found that “cognitively normal” subjects with metabolic syndrome have lower MMSE scores. Despite this, we have not found clinically relevant differences between the two groups, as expected by the adopted exclusion criteria. This finding could indicate that metabolic syndrome subjects are at high risk of latent cognitive impairment, and this should be investigated further with proper tools.

Metabolic syndrome has been demonstrated to be independently associated with worsened memory and executive function tests in men but not in women,⁶ whereas other authors showed that the presence of metabolic syndrome was independently associated with a higher likelihood to develop cognitive decline in time overall when higher CRP values were associated, without gender differences.⁷ Both of these investigations revealed that the number of metabolic syndrome components was strongly associated with cognitive decline.

Despite these findings, in our study we found that metabolic syndrome itself was not associated with lower MMSE scores. These discrepancies may be due to several factors. First of all, we excluded patients with cognitive impairment and cognitive complaints to detect a latent cognitive decline. The aforementioned studies^6
–8,9 were aimed at investigating the association between metabolic syndrome and clinical global or domain-specific cognitive impairment; also, they used different tools to investigate cognition. However, we cannot exclude that this finding could be related to differences in the study population. Our sample was constituted of relatively healthy subjects, free of cardiovascular and other severe chronic diseases, whereas a high prevalence of clinical cardiovascular diseases was observed in the aforementioned investigations.^6,8 Moreover, our mean sample age was not very advanced.

The number of metabolic syndrome components was associated in an inverse linear trend with MMSE score. This would suggest that metabolic abnormalities may exert a cumulative effect on cognition. FBG was found to be the only trait of metabolic syndrome independently associated with MMSE score, as suggested by previous reports.^{9,11,13
–15} Several hypotheses may explain this finding. The insulin resistance and the accumulation of advanced glycation end products may play a role in neuronal functioning, and high levels of FBG may lead to neuronal overoxidation and impaired stress response, triggering microvascular brain damage. A limitation of our investigation is that we did not assess plasma insulin, so that we were unable to determine whether the association between FBG and MMSE scores could be partly mediated by insulin levels. Further investigations on this are certainly needed.

We found that diabetes was not associated with MMSE score, perhaps because all diabetic subjects of our sample were on antidiabetic therapy and no one else had newly diagnosed diabetes. A definite limitation of our investigation is that our subjects did not undergo brain imaging, so that the impact of subclinical vascular brain lesions on cognitive performance could not be evaluated.

We suppose that the number of metabolic abnormalities could independently impair cognitive function by exerting a cumulative effect of neuronal function impairment. It is probably that high levels of FBG, even within the normal range, could represent the leading culprit of neuronal dysfunction, possibly by determining chronic oxidative stress. Clustering of the metabolic abnormalities could induce neuronal dysfunction in the same way, but further investigations are needed to explore this question. Certainly the vascular damage is of importance in the patients with metabolic syndrome, even in the subclinical extent.

The association with inflammation was not confirmed by our results. As expected, hsCRP levels were found to be higher in metabolic syndrome subjects, and they were closely related to MMSE score in the bivariate analysis. However, no association was found in the multivariate analysis. In metabolic syndrome patients, the inflammation may originate from the increased visceral adipose tissue, but probably hsCRP is much less specific in the elderly, so that its elevation may be explained by several unmeasured confounding factors.

We adopted the MMSE to screen global cognition. In our study, the MMSE was found to be a highly sensitive tool in detecting lower cognitive level in “cognitively normal” subjects with metabolic syndrome. In our opinion, MMSE may be a good screening tool in patients with metabolic abnormalities to detect those individuals who require more in-depth cognitive assessment.

The healthy status of our study population could represent a possible limitation, so that our findings cannot be completely transferred to the general elderly population. However, these are the typical outpatients coming to medical attention for which cognitive function and independency should be always preserved.

In conclusion, we suggest that all subjects with metabolic abnormalities should undergo cognitive screening. Further longitudinal investigations are needed to confirm the relationship between metabolic abnormalities and latent cognitive impairment and to understand whether the treatment of metabolic abnormalities may prevent and/or improve the cognitive impairment. Moreover it would be interesting to investigate whether metabolic abnormalities may affect specific cognitive domains.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

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