Type 2 diabetes mellitus (DM) is one of the most prevalent noncommunicable diseases in the world, with India in particular suffering from 77 million cases. With the current trend of sedentary lifestyle pointing to a dramatic rise in numbers by at least 72% by 2030.
As is well established, diabetes is a chronic health disorder which is characterized by the high levels of glucose in the blood because the body cannot produce any or enough insulin.
Type 2 DM is characterized by resistance to insulin or decreased response by the body’s cells to insulin, with symptoms ranging from increased thirst, increased frequency of urination, fatigue, and tingling and numbness in the peripheries.
DM with neurocognitive impairment often develops microvascular, neuropathic, and macrovascular complications. The elevated sugar levels are not only responsible for causing brain dysfunction but are also responsible for the formation of sorbitol, which damages blood vessels and nerves. The oxidative stress, microvasculopathy, inflammation, and dyslipidemia are other key mediators in the neuropathology that can lead to dementia or cognitive impairment, which further impede the patient’s ability to understand and apply a strict diet and nutrition plan, which further results in increased sugar levels and complications related to hyperglycemia.
Management of such patients is, therefore, complicated and requires a lot of close monitoring and counseling. The main hurdle, however, is that cognitive assessment is not a part of the initial assessment for DM. Several tools are available for cognitive assessment for these patients. Similarly, with this study, we are incorporating the use of the Montreal Cognitive Assessment (MoCA) tool.
MoCA was shown to be a better tool for assessment for mild cognitive impairement (MCI) than mini mental state examination (MMSE) in a pilot study conducted in Canada. The aim of this study is to evaluate neurocognitive disorder in patients with type 2 DM.
- To study and compare characteristics between patients with and without diabetes and the relationship with cognitive impairment.
- To study the effects of poor glycemic control on cognitive function.
MATERIALS AND METHODS
This cross-sectional study was conducted in our hospital between October 2018 and September 2020; 67 patients with type 2 DM and 66 controls attending out patient department (OPD) were included in the study. The control group was matched with test group for age, sex, and educational class. Only people who were willing to give informed consent were included in the study.
The data compiled were age, sex, demographic data, comorbidity, history with particular emphasis on diet, physical activity, and family history. Also, smoking habits in terms of how many packs per year and alcohol intake in terms of units were recorded. Current medication use (antipsychotics, antidepressant drugs, and benzodiazepines) was noted.
Complete physical examination, with emphasis on anthropometric measurements like height, weight, body mass index (BMI), and waist circumference, was done.
Relevant investigations such as fasting blood sugar (FBS), post prandial blood sugar (PPBS), and hemoglobin A1C (HbA1c) were done and those with MoCA score ≤ 22.1 were recommended to undergo thyroid stimulating hormone (TSH) and vitamin B12 estimation in order to rule out hypothyroidism and vitamin B12 deficiency as a cause for cognitive impairment.
Diagnosis of DM: DM was diagnosed as per the criteria of American Diabetes Association.
Recognition of cognitive impairment: MoCA was done for all patients. MoCA scale was used as a tool to assess cognitive decline. Scores on the MoCA range from zero to 30, with a score of ≥26 considered normal. A score of ≤22.1 is considered as MCI.
Patients with HbA1c > 6.5 and MoCA score < 22 were considered to have neurocognitive disorder with type 2 DM.
Study design: Cross-sectional study.
Inclusion criteria: All patients > 18 years old, diagnosed as a type 2 DM for a minimum period of ≥6 months without history of diabetic ketoacidosis were included in this study.
Exclusion criteria: Type 2 DM patients with history of cerebrovascular accidents, psychiatric illness, or patients on antipsychotics, antidepressants, or benzodiazepines were excluded from the study.
Type 2 DM patients with hypertension, patients previously diagnosed and treated for vitamin B12 deficiency, and hypothyroidism were also excluded.
Sample size calculation
Sample size was calculated based on a previous study conducted by Kannayiram et al., in which the sensitivity of MoCA was 67% in identifying individuals with mild non communicable diseases (NCD). In the present study, expecting a similar result considering precision of 8% and desired confidence level of 95%, total sample size was estimated to be 133.
where, p = sensitivity of the new test (67%),
D = precision (8), and
= desired confidence level (1.96).
Data were entered into Microsoft excel datasheet and were analyzed using SPSS 22 version software (IBM SPSS Statistics, Somers, NY). Categorical data were represented in the form of frequencies and proportions. Chi-square test or Fischer exact test (for 2 × 2 tables only) was used as test of significance for qualitative data.
Continuous data were represented as mean and standard deviation. Independent t test was used as test of significance to identify the mean difference between two quantitative variables.
Correlations were performed with Pearson correlation coefficient.
For graphical representation of data, MS Excel and MS Word were used to obtain various types of graphs.
P-value (probability that the result is true) of <0.05 was considered as statistically significant after assuming all the rules of statistical tests.
Statistical software: MS Excel, SPSS version 22 was used to analyze data.
Table 1 shows mean age of control and cases in this study. Mean age among cases was 56.36 ± 10.29 years and among controls was 53.36 ± 10.90 years. There was no statistically significant difference found between cases and controls with respect to age. Table 2 shows sex distribution among both groups. There was no statistically significant difference found between cases and controls with respect to sex. Among cases, 17.9% of the subjects had a smoking history and among controls, 9% of the subjects had a smoking history. The P-value was 0.240; there was no statistically significant difference found between cases and controls with respect to smoking [Table 3]. Among cases, 14.9% of the subjects had alcohol history, while 11.9% of controls had alcohol history. P-value was 0.801; no statistically significant difference was found between cases and controls with respect to alcohol [Table 4]. But, a statistically significant difference was found between cases and controls with respect to BMI [Table 5]. [Table 6] shows the mean waist circumference among cases and controls. A statistically significant difference was found between the cases and controls with respect to waist circumference. Mean HbA1c among cases was 9.123 ± 1.81 and among controls was 5.685 ± 0.50. There was a statistically significant difference found between cases and controls with respect to HbA1c [Table 7]. Table 8 summarizes the mean MoCA scores among cases and controls as 25.67 ± 1.408 and 26.69 ± 1.351, respectively. This was a statistically significant. Table 9 shows that there was no statistically significant difference found between alcohol units and MoCA score. Table 10 shows a comparison between other studies and this study. Age correlated positively with MoCA score, but it was not statistically significant. BMI and waist circumference of diabetics had a negative correlation with MoCA score. The duration of DM, HbA1c levels had a positive correlation with MoCA score as they were statistically significant. Smoking and had negative relations with MoCA score. Alcohol, however, had a positive correlation with the MoCA scores, which was statistically significant.
Our results confirm previous evidence stating that DM leads to cognitive impairment. With the poor glycemic control as measured by HbA1c values, the duration of diabetes plays a major role in the onset of cognitive dysfunction in established diabetics. Our study also showed that age has positive correlation for development of cognitive impairment. Among the cases 33 had diabetis for 1 to 5 years, 28 cases had it for 6 to 10 years, and six cases had diabetes for >10 years; the duration of DM also showed a positive correlation with MCI as assessed by MoCA.
The exact mechanism of cognitive impairment due to diabetes is still a topic of debate, common being hypoglycemia, insulin resistance, and vascular changes. Hypoglycemia causes increased inflammation. Many literatures on diabetes have studied the effect of hypoglycemia on brain, suggesting that increased inflammation, oxidative stress, advanced glycation end products, and decreased neuronal repairs are the main causes of brain atrophy. All these are causative factors for the cognitive impairment individually or in combination. The main parameters of cognitive function that were assessed included psychomotor speed, memory, visuospatial functions, frontal executive functions, processing speed, verbal fluency, attention, and complex motor functions. Several authors reported verbal memory and processing speed being the most commonly affected parameters with the others being unaffected. In a recent study, Bruce et al. showed that about 17% of elderly diabetic patients had moderate to severe impairments in daily living activities, 11% were cognitively impaired, and 14% were depressed.
Our results were similar to a study by Yaffe et al., which investigated and analyzed the data from a 4-year randomized trial among 7027 patients at 178 sites, and the outcome of this trial showed clinically significant cognitive impairment among elderly group diabetics. Yerrapragada et al. found a statistically significant relation between age and cognitive impairment with a P-value of 0.009.
Our study also showed a positive correlation between the duration of diabetes and cognitive impairment, which is similar to the other studies by Yerrapragada et al. and Lalithambika et al. Both the studies concluded that the longer duration of the disease is related to cognitive dysfunction. This is also consistent with other studies, such as a study done by Gregg et al. Also, it was evident that each 5-year increment between diabetes diagnosis and cognitive assessment was associated with lower scores on tests of logical memory, word fluency, and similarities.
Our study showed a positive correlation between poor glycemic control and cognitive impairment. This was similar to the study by Yaffe et al., which also showed that cases with HbA1c level ≥ 7%, the risk for development of MCI was four-fold. The risk for development of dementia was three-fold. This is in concordance with that of Lalithambika et al. who found a correlation between HbA1c that was statistically significant with a P-value of 0.016.
A study by Cox et al. in diabetics showed that hyperglycemia was associated with slowing of all cognitive performance tests and more mental subtraction errors for both type 1 and type 2 subjects with diabetes. The effects of hyperglycemia were highly individualized, impacting >50% of the subjects.
Lalithambika et al. achieved P-values of 0.016 and 0.240, respectively, for correlation between HbA1c and duration of diabetes compared to our study, which had P-values of 0.004 and 0.042, respectively.
This study also included smoking history, with 17.9% of the cases and 9% of controls having smoking history. However, the P-value had no statistical correlation as the number of smokers in the study was low and there were difficulties in eliciting data regarding the history of smoking.
But a study by Lin et al. showed that smoking differed significantly between type 2 DM patients with cognitive impairment and those with normal cognitive function. Our study found a negative correlation between the BMI, waist circumference, and MoCA scores between controls and cases; however, this was limited due to small variability in the BMI and waist circumference of the sample size and time constraints.
However, there have been studies conducted using another anthropological parameter waist-to-hip ratio and cognitive impairment by Kerwin et al. in postmenopausal women showing that increased BMI was associated with poorer cognitive function in women with a smaller waist-to-hip ratio. Yerrapragada et al. found a similar result in the P-values of 0.353 and 0.945 for BMI and waist circumference, respectively. The authors recommend further research into this particular relationship as it may help predict the onset of cognitive changes.
On evaluation, the relationship betweenalcohol intake and cognitive impairment in type 2 DM was statistically significant. This is also in accordance with study done by Hudetz et al. which concluded that present results support the hypothesis that a history of alcohol abuse coupled with type 2 diabetes in older subjects contributes to neurocognitive impairment more than a history of alcohol abuse or diabetes alone. The pattern of cognitive findings does not suggest overall impairment of cognitive function in all domains but is characterized by specific deficits in diminished verbal and visuospatial memory.
This was a cross-sectional study conducted in type 2 DM patients to compare the MoCA scores between cases and controls with regard to age, duration of diabetes that had a positive correlation, BMI and waist circumference that had a negative correlation, and HbA1c correlated negative with statistical significance. Patients were diagnosed as diabetics on the basis of American diabetic association (ADA) criteria, and cognitive impairment was assessed using MoCA scores. Statistical analysis was performed and the MoCA scores between cases and controls were compared with regard to age, duration of diabetes, which had a positive correlation, BMI and waist circumference, which had a negative correlation, and HbA1c which correlated negative with statistical significance. This reinforces the need for assessment of cognitive impairment in diabetics using MoCA scores, to stop further progression into dementia and further complications.
The evaluation of the relationship between alcohol intake and cognitive impairment in type 2 DM was statistically significant. This is also in accordance with the study by Hudetz et al., who concluded that present results support the hypothesis that a history of alcohol abuse coupled with type 2 diabetes in older subjects contributes to neurocognitive impairment more than a history of alcohol abuse or diabetes alone. The pattern of cognitive findings does not suggest overall impairment of cognitive function in all domains but is characterized by specific deficits in diminished verbal and visuospatial memory.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
We thank Dr Anil Kumar, HOD, Department of Medicine for his help in this article.
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