Age and Ageing Advance Access originally published online on March 12, 2008
Age and Ageing 2008 37(4):458-461; doi:10.1093/ageing/afn051
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Glucose control levels, ischaemic brain lesions, and hyperinsulinaemia were associated with cognitive dysfunction in diabetic elderly
SIR—Type 2 diabetes mellitus (DM) in the elderly is associated with impaired cognitive functioning and an increased risk of dementia [1–4]. The pathogenesis of the impairment, however, remains unclear.Our previous study indicated that the performance of a cognitive functional test was positively correlated with glycohaemoglobin, HbA1c, which is an index of glucose control [2]. Recently, several studies suggested that use of anti-diabetic medication was associated with improved cognitive function [5], or prevented a decline in cognitive functioning [6].
Several reports have indicated that hyperinsulinaemia is associated with cognitive dysfunction and dementia in the general population [7, 8]. A small preliminary study reported that insulin sensitivity measured by the euglycemic insulin clamp method was inversely correlated with the cognitive functional test score [9].
Many studies have reported that the diabetic elderly have ischaemic brain lesions such as lacunae infarctions, white matter lesions and paraventricular lesions even without neurological symptoms [10]. Several reports have also suggested that these ischaemic lesions were associated with cognitive dysfunction in the diabetic elderly [11, 12].
In this study, we analysed the association of HbA1c, hyperinsulinaemia and ischaemic brain changes to DM-related cognitive dysfunction by performing an assessment of subjects' profiles including a brain imaging assessment by MRI.
Subjects
For the present study, we recruited consecutively 77 patients with type 2 DM from the Chubu Rosai Hospital's Diabetic Center. They ranged in age from 65 to 85 years. The exclusion criteria were as follows: malignancy, inflammatory disease (such as collagen disease, thyroid disease and viral hepatitis), severe microvascular complications (such as renal failure) and severe cardiovascular disease (such as myocardial infarction and unstable angina). None of the subjects had audio-visual deficiencies that would prevent them from participating in the cognitive functional assessment.
An ethical committee approved the study and all patients gave their written informed consent prior to the investigation. After giving informed consent, the cognitive functional tests were administered individually to each subject. On the day of the assessment, the subjects had breakfast as usual and the assessment was performed in the morning. Doctors performed a general physical check-up of the subjects before the assessment.
Regarding complications of DM, neuropathy, retinopathy and nephropathy were diagnosed as below: neuropathy, elevated vibratory perception thresholds or symptomatic neuropathy; retinopathy, simple retinopathy and more advanced; nephropathy, microalbuminuria and more advanced.
Cognitive tests
Cognitive function was assessed by structured performance tests that were selected to represent a broad range of cognitive domains.
(i) Mental status: the Mini-Mental State Examination (MMSE) [13]. (ii) Verbal memory (Word Recall): the Word List (a subtest of the Alzheimer's Disease Assessment Scale (ADAS) [14]; with a score range of 0–10. (iii) Complex psychomotor skill: the Digit Symbol Substitution (DSS) Test, a subtest of The Wechsler Adult Intelligence Scale-Revised [15], with a score range of 0–93. (iv) Attention: The Stroop Color Word Test [16] (Japanese version); the seconds to completion are recorded and the difference between the time required to read the word card and that required to read the dot card is calculated. The wider time difference generally means a lower cognitive performance.
Well-trained psychological testers administered all four tests in the same order for all subjects.
Evaluation of subjects
Haemoglobin A1c (HbA1c; reference range, 4.3–5.8%) was measured with a TOSHO Automated Glycohemoglobin Analyzer HLC-723G7 (TOSHO Co. Tokyo, Japan). Plasma insulin was assayed by radioimmunoassay.
Low-density cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides (TG) and fasting blood glucose (FBG) were measured by an autoanalyser using routine enzymatic techniques. Systolic blood pressure (sBP) and diastolic blood pressure (dBP), and the body weight and height were measured before the patients took the cognitive functioning examinations.
Assessment of brain lesions
Silent cerebral infarction (SCI) was diagnosed using MRI (GE Signa Horizon, 1.5 T, Milwaukee, WI), and the criteria used for diagnosis were irregular areas of high signal intensity larger than 3 mm in diameter detected on T2-weighted images, low signal intensity areas on T1-weighted images and areas of higher intensity than that of the cerebrospinal fluid in proton density images or fluid-attenuated inversion recovery images. Lesions less than 3 mm in diameter or lesions with signal intensities similar to cerebrospinal fluid in proton images and fluid-attenuated inversion recovery images were excluded because of the high possibility that they were enlarged perivascular spaces, even if they demonstrated high signals on T2-weighted images and low signals on T1-weighted images.
A modified rating scale was used to describe the different types of hyperintense signal abnormalities surrounding the ventricules and in the deep white matter [17]. Briefly, periventricular hyperintensity (PVH) was graded as 0 = absence, 1 = ‘caps’ or pencil-thin lining, 2 = smooth ‘halo’ and 3 = irregular PVH extending into the deep white matter. Separate deep white matter hyperintense lesions (WML) were rated as 0 = absence, 1 = punctate foci, 2 = beginning confluence of foci and 3 = large confluent areas.
Data analysis
All data are presented as the mean ± standard deviation (SD). Stepwise multiple regression analysis was performed to search for the association between the performance of cognitive functional testing and the covariants. The explanatory variables used were age, sex, years of education, HbA1c, serum insulin (log), number of SCI, WML and PVH incidents.
The characteristics of the subjects are shown in Table 1. The mean BMI was 22.5 ± 5.5 kg/m2, and mean HbA1c, LDL and TG were 6.7 ± 0.8%, 123.9 ± 26.0 mg/dl and 128.8 ± 86.0 mg/dl respectively. The subjects involved in this study were not obese and were relatively tightly controlled in terms of glucose and lipid metabolism.
|
|
Stepwise multiple regression analysis indicated that the covariants that predicted the scores of the four cognitive assessments were: years of education, HbA1c, number of SCI and WMH for DSS scores; number of SCI, HbA1c, years of education and sex for Stroop test scores; years of education, age and HbA1c for Word Recall counts; years of education and fasting serum insulin levels for MMSE scores (Table 2).
In this study, we assessed comprehensive variables in elderly diabetic subjects and demonstrated that several factors were associated with cognitive dysfunctioning; higher HbA1c was associated with worse performance in DSS, Word Recall and the Stroop test; silent cerebral infarctions were associated with worse performance in DSS and the Stroop test, and hyperinsulinaemia was associated with worse performance in MMSE.
Cognitive dysfunction in the diabetic elderly has recently attracted a lot of interest, but the pathogenesis of DM-associated cognitive dysfunction remains to be elucidated.
Hyperglycaemia, a striking feature of DM, is known to affect the peripheral nervous system and induce diabetic neuropathy [18]. The pathological mechanisms of diabetic neuropathy include oxidative stress mediated by free radicals and the formation of advanced glycaemia end products. Similar mechanism may contribute to the CNS pathology.
Some population-based studies have reported that hyperinsulinaemia was associated with cognitive decline or dementia [4, 6, 7]. Our previous study also showed that the insulin sensitivity measured by the euglycemic glucose clamp method was correlated with MMSE scores [9]. The mechanism of the involvement of hyperinsulinaemia in cognitive impairment is unknown to date, but several hypotheses including accelerating atherosclerosis, involvement of associated hypertension [19–21] and the alteration of amyloid β-metabolism, which plays a critical role in the pathogenesis of Alzheimer's type dementia [22, 23], have been proposed.
DM is reportedly a risk factor for SCI [24]. The association of asymptomatic brain infarctions with cognitive impairment was previously reported in some studies, including one in the diabetic elderly [13, 24, 25]. SCI may play a major role in diabetes-associated cognitive dysfunction.
This study had several limitations. First, the current analysis does not necessarily mean causal mechanism because the study design was cross-sectional. Second, the current study was performed in a single institute. Because exercise or medication can ameliorate serum glucose levels and reduce serum levels of insulin, HbA1c and hyperinsulinaemia are potentially modifiable factors. An interventional longitudinal study may be warranted with more subjects in multiple institutes to investigate whether treatment to reduce serum glucose and insulin levels, such as exercise and/or the use of anti-diabetic medication, can ameliorate the cognitive decline.
In conclusion, we demonstrated that higher serum insulin levels were associated with cognitive dysfunction in elderly diabetic Japanese.
- To analyse the factors associated with DM-related cognitive dysfunction, we performed an assessment of subject profiles including brain imaging by MRI.
- The scores of the Digit Symbol Substitution were associated with years of education, HbA1c, number of silent cerebral infarctions, white matter lesions and age.
- The performance of Stroop test was associated with the number of silent cerebral infarctions, HbA1c, years of education and sex.
- Word Recall was associated with years of education, age and HbA1c.
- The Mini-Mental State Examination was associated with years of education and serum insulin levels.
No conflicts of interest.
1 Department of Geriatrics, Nagoya University Graduate School of Medicine, 65 Tsuruma-cho, Showa-ku, Nagoya, Aichi, Japan
2 Department of Metabolism and Endocrine Internal Medicine, Chubu Rosai Hospital, Nagoya, Aichi, Japan
3 Department of Neurology, Chubu Rosai Hospital, Nagoya, Aichi, Japan
* To whom correspondence should be addressed E-mail: umegaki{at}med.nagoya-u.ac.jp
References
- Cukierman T, Gerstein HC, Williamson JD. Cognitive decline and dementia in DM-systematic overview of prospective observational studies. Diabetologia (2005) 48:2460–9.[CrossRef][Web of Science][Medline]
- Mogi N, Umegaki H, Hattori A, et al. Cognitive function in Japanese Elderly with type 2 diabetes mellitus. J Diabetes Complications (2004) 18:42–6.[CrossRef][Web of Science][Medline]
- Strachen MW, Ewing FM, Deary IJ, et al. Is type 2 diabetes associated with an increased risk of cognitive dysfunction? A critical review of published studies. Diabetes Care (1997) 20:438–45.[Abstract]
- Croxson SC, Jagger C. Diabetes and cognitive impairment: a community-based study of elderly subjects. Age Ageing (1995) 24:421–4.
[Abstract/Free Full Text] - Ryan CM, Freed MI, Rood JA, et al. Improving metabolic control leads to better working memory in adults with type 2 diabetes. Diabetes Care (2006) 29:345–51.
[Abstract/Free Full Text] - Wu JH, Haan MN, Liang J, et al. Impact of diabetes on cognitive function among older Latinos: a population-based cohort study. J Clin Epidemiol (2003) 56:686–93.[CrossRef][Web of Science][Medline]
- Geroldi C, Frisoni GB, Paolisso G, et al. Insulin resistance in cognitive impairment: the InCHIANTI study. Arch Neurol (2005) 62:1067–72.
[Abstract/Free Full Text] - Kalmijn S, Feskens EJ, Launer LJ, et al. Glucose intolerance, hyperinsulinaemia and cognitive function in a general population of elderly men. Diabetologia (1995) 38:1096–102.[Web of Science][Medline]
- Suzuki M, Umegaki H, Uno T, et al. Association between insulin resistance and cognitive function in elderly diabetic patients. Geriatr Gerotol Int (2006) 6:254–9.[CrossRef]
- Van Harten B, de Leeuw FE, Weinstein HC, et al. Brain imaging in patients with diabetes: a systematic review. Diabetes Care (2006) 29:2539–48.
[Free Full Text] - Akisaki T, Sakurai T, Takata T, et al. Cognitive dysfunction associates with white matter hyperintensities and subcortical atrophy on magnetic resonance imaging of the elderly diabetes mellitus Japanese elderly diabetes intervention trial (J-EDIT). Diabetes Metab Res Rev (2006) 22:376–84.[CrossRef][Web of Science][Medline]
- Araki A, Ito H. Asymptomatic cerebral infarction on brain MRI images and cognitive function in elderly diabetic patients. Geriatr Gerotol Int (2002) 2:206–14.[CrossRef]
- Folstein MF, Folstein SE, McHigh PR. ‘Mini-Mental State’: a practical method of grading the cognitive function of patients for the clinician. J Psychiatr Res (1978) 12:189–98.[CrossRef]
- Mohs RC, Rosen WG, Davis KL. The Alzheimer's disease assessment scale: an instrument for assessing treatment efficacy. Psychopharmacol Bull (1983) 19:448–50.[Web of Science][Medline]
- Shinagawa F, Kobayashi S, Fujita K, et al. Japanese Manual for the Wechsler Adult Intelligence Scale-Revised (1990) Tokyo: Nihon-bunka-kagaku-sya. 115–8.
- Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol (1935) 18:643–62.[CrossRef][Web of Science]
- Fazekas F, Chawluk JB, Alavi A, et al. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol (1987) 149:351.
[Abstract/Free Full Text] - Greene DA, Sima AA, Stevens MJ, et al. Complications: neuropathy, pathogenetic considerations. Diabetes Care (1992) 15:1902–25.[Abstract]
- Mediratta S, Fozailoff A, Frishman WH. Insulin resistance in systemic hypertension: pharmacotherapeutic implications. J Clin Pharmacol (1995) 35:943–56.[Abstract]
- Hassing LB, Hofer SM, Nilsson SE, et al. Comorbid type 2 diabetes mellitus and hypertension exacerbates cognitive decline: evidence from a longitudinal study. Age Ageing (2004) 33:355–61.
[Abstract/Free Full Text] - Schwartz MW, Figlewicz DP, Baskin DG, et al. Insulin in the brain: a hormonal regulator of energy balance. Endocr Rev (1992) 13:387–41.
[Abstract/Free Full Text] - Yu Y, Kastin AJ, Pan W. Reciprocal interactions of insulin and insulin-like growth factor I in receptor-mediated transport across the blood-brain barrier. Endocrinology (2006) 147:2611–5.
[Abstract/Free Full Text] - Qiu WQ, Folstein MF. Insulin, insulin-degrading enzyme and amyloid-beta peptide in Alzheimer's disease: review and hypothesis. Neurobiol Aging (2006) 27:190–8.[CrossRef][Web of Science][Medline]
- Vermeer SE, Prins ND, Den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med (2003) 27:1215–22.
- Gold G, Kovari E, Herrmann FR, et al. Cognitive consequences of thalamic, basal ganglia, and deep white matter lacunes in brain aging and dementia. Stroke (2005) 36:1184–8.
[Abstract/Free Full Text]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  H. Umegaki Pathophysiology of cognitive dysfunction in older people with type 2 diabetes: vascular changes or neurodegeneration? Age Ageing, January 1, 2010; 39(1): 8 - 10. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 American Association of Diabetes Educators Special Considerations in the Management and Education of Older Persons With Diabetes The Diabetes Educator, November 1, 2009; 35(3_suppl): 60S - 63S. [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||