Diabetes and cognitive impairment pdf
File Name: diabetes and cognitive impairment .zip
- Risk Factors for Cognitive Impairment in Patients with Type 2 Diabetes
- Cognitive dysfunction in diabetes: how to implement emerging guidelines
- Relation of Diabetes to Mild Cognitive Impairment
Risk Factors for Cognitive Impairment in Patients with Type 2 Diabetes
Metrics details. Diabetes is a risk factor for cognitive impairment, but whether there is also a link between pre-diabetes and cognitive dysfunction is not yet fully established. Regression analyses were performed to investigate associations between: a categories of normal or impaired glucose metabolism, and b OGTT measurements, respectively, as exposure variables and cognitive test results as outcomes.
Adjustments were made for demographics, lifestyle factors and cardiovascular risk factors. Participants with pre-diabetes and diabetes scored slightly worse cognitive test results compared to the control group. Associations were stronger for older or less physically active individuals. When adjusting for cardiovascular risk factors, most correlations were non-significant.
Pre-diabetes and diabetes are associated with minor deficits in global cognitive function, processing speed and executive functioning. Long-standing diabetes is associated with bigger deficits. There appears to be a continuous inverse correlation between glucose levels and cognitive test results, also for people without diabetes.
Associations are stronger in older and less physically active individuals. Cardiovascular factors are important mediating factors in the pathway between diabetes and cognitive dysfunction. Peer Review reports. There is a growing body of evidence supporting that diabetes is a risk factor for cognitive impairment. Throughout the life course, mild cognitive decrements may develop as a consequence of long-term exposure to diabetes [ 1 ].
Type 2 diabetes also approximately doubles the risk of dementia [ 2 ]. The duration of diabetes has often been described as an important factor for these risk associations [ 3 , 4 , 5 ].
Neuroimaging studies have shown that diabetes-related cognitive impairment is characterized by similar pathological features as for vascular dementia, but also global brain atrophy [ 1 ]. Multiple cognitive domains are therefore often affected [ 6 ].
It is yet unclear whether pre-diabetes is a risk factor for cognitive decline, or not. Studies have been inconclusive, some showing that pre-diabetes is associated with worse cognitive performance [ 7 , 8 ], particularly in domains such as processing speed [ 9 ], whereas others do not support these findings [ 10 ].
If this risk association becomes more firmly established, this could motivate interventions at earlier stages than today to prevent cognitive decline in people at risk of diabetes. Studies have so far mainly focused on interventions in manifest diabetes [ 11 ].
Furthermore, studies with refined methods of identifying early stages of impaired glucose metabolism, such as Oral Glucose Tolerance Testing OGTT , are needed. Markers of glycaemic control such as HbA 1c , fasting glucose and 2-h post-OGTT glucose have previously been shown to be negatively correlated with cognitive test results, but studies have been inconsistent [ 12 ]. A reason may be that other cardiometabolic risk factors associated with diabetes have a more prominent effect on cognition than the actual glucose levels themselves.
However, hyperglycaemia is associated with negative effects on nerve cells and on vascular tissue in mechanistic studies [ 13 ]. Some studies have shown that glucose levels correlate with cognitive test results also in individuals without diabetes [ 14 , 15 , 16 ], suggesting that there may be a continuous relationship between such markers and cognitive function also in the general population, but more studies are needed. The mechanisms underlying cognitive decline and brain structural changes in people with diabetes are not well understood [ 17 ].
As diabetes is closely related to the metabolic syndrome, arterial stiffness [ 18 , 19 ] and other cardiovascular risk factors, which also can act as risk factors for cognitive decline [ 20 , 21 ], it is unclear to what extent these factors mediate the risk association. In this study, we aimed to investigate associations between different stages of impaired glucose metabolism, but also glucose levels during the OGTT, and results of cognitive testing.
We also investigated possible mediating and moderating effects of factors such as demographic, lifestyle-related and cardiovascular factors. Enrolment details, reasons of loss to follow-up and information about potential health selection bias in the cohort have previously been described [ 22 , 23 ]. The present study analyses cross-sectional data from the follow-up examination. The final study population comprised participants excluded.
The MMSE is a global cognitive screening test in which orientation, memory, naming ability, ability to follow instructions, attention and copying of pentagons is tested [ 24 ]. It is widely used internationally and has been validated in Swedish populations [ 25 ]. The AQT test [ 26 ], a reliable screening test to detect early dementia [ 27 ], is a test of processing speed, attention and executive function.
The test is timed, and involves naming first the colour, then the shape of 40 geometrical figures and then co-ordinating these activities. At baseline, fasting glucose and HbA 1c were measured. Participants also reported in a questionnaire whether they had diabetes and whether they were medically treated for the disease both at baseline and at follow-up. Data on exact duration of diabetes was not available. After that, participants that reported a diagnosis or drug treatment for diabetes at follow-up were added to the diabetes group, so as to re-classify those with treated diabetes and therefore normal glucose measurements into the diabetes category.
For the post-hoc analysis the diabetes category was then sub-divided into long-term diabetes, i. Blood samples, a health questionnaire and clinical measures were administered. A p -value less than 0. Missing data in covariates were imputed using multiple imputation with five consecutive imputations.
Exposure and outcome variables were not imputed. In all other variables less than 12 cases were imputed. Continuous variables were calculated into natural logarithmic values when needed to achieve normal distribution among residuals. To minimize ceiling effects that are usually present when analysing MMSE data in population-based studies, we used a normalizing transformation method that has been validated, creating a scale 0— instead of 0—30 as normally used in the test [ 28 ]. To compare differences in cognitive test results and covariates between glucometabolic categories, one-way between-groups analyses of variance ANOVA for continuous variables and Chi-square tests for categorical variables were carried out.
Two adjustment models were used for the main analyses that follow. In Model 1 , we adjusted for age, sex and education and also lifestyle factors physical activity level, smoking habits and alcohol consumption, as these factors could contribute to vascular cognitive impairment and therefore act as confounding factors.
In Model 2 , we additionally adjusted for cardiovascular risk factors: systolic blood pressure, heart rate, c-f PWV, waist circumference, total cholesterol, and anti-hypertensive, lipid-lowering and diabetes drug treatment. Although the relationship between diabetes and cognitive dysfunction is partly mediated by cardiovascular factors according to previous research, there are also non-vascular factors of importance [ 1 ].
We therefore made this model to see whether associations between diabetes and cognitive function are present independently of variations in cardiovascular factors. Some covariates were chosen over related variables based on their correlation with fasting glucose: i. Adjusted mean cognitive test results were compared between NGT reference to pre-diabetes and diabetes respectively in General linear model GLM analyses.
Linear trends in cognitive test results across these three glucometabolic categories were also investigated in regression analyses. Post-hoc analyses were performed in the same way but with diabetes sub-divided into short-term and long-term diabetes. As cognitive impairment caused by stroke can be regarded as a confounding factor, we excluded participants with a history of stroke in a sensitivity analysis, repeating the analyses mentioned data not shown.
However, it could be argued that stroke is a disease that is in the pathway between diabetes and cognitive impairment, which is why we did not exclude these participants in the main analyses. Multiple regression analyses were carried out to define whether interactions were present between covariates from the analyses possible confounding factors and the relationship between glucometabolic categories as described above and cognitive test results.
When such interactions were significant, GLM analyses were performed to investigate the relationship between glucometabolic categories and cognitive function stratified for these variables. Linear relationships between fasting and 2-h glucose respectively, and cognitive outcomes, were then investigated in multiple regression analyses. We also performed these analysis including only participants without diabetes.
To explore the data on fasting glucose longitudinally, as this was measured both during the baseline and follow-up examinations, we compared cognitive test results at follow-up between those that had IFG at baseline, at follow-up or both, with those who had NFG during both examinations reference , in GLM analyses. Our hypothesis was that longer disease duration i.
IFG only at baseline or during both examinations would be associated with worse cognitive outcomes because of organ damage accumulated over time.
Finally, we compared cognitive test results between the different classification methods for diabetes that were used during the baseline and follow-up examinations in further GLM analyses. The mean age was Participants with pre-diabetes or diabetes were on average 1. Proportions of low physical activity and high, but also no alcohol consumption, were higher among participants with diabetes.
There were no significant differences between the groups in educational level or smoking status. In general, cognitive test results were worse and cardiovascular risk factors more prevalent in the categories of pre-diabetes and diabetes compared to NGT. Total cholesterol levels were lower in the participants with diabetes, due to concomitant lipid-lowering treatment.
P -values representing differences between the NGT category reference and pre-diabetes and diabetes respectively, as well as p -values for trends in cognitive test results across the three categories are also shown. There were very small differences in cognitive test results between the groups.
The group with pre-diabetes had a mean difference in MMSE points p of 1. Those with pre-diabetes were on average 3. Differences in scores of cognitive sub-domains between NGT and pre-diabetes and diabetes, respectively, were also significant but very small around 0. There were significant linear trends in cognitive test results across the categories, with negative trends for MMSE and the domain memory measured in points and positive trends for AQT and the domains processing speed and executive functioning measured in time.
When additionally adjusting for cardiovascular factors in Model 2 , differences in cognitive test results between the categories and trends across the categories were in general non-significant.
In these analyses, the differences between results of participants with NGT and long-term diabetes were greater. The difference in MMSE total score was 5. When adjusting for cardiovascular factors in Model 2 there were also significant differences in cognitive test results between participants with NGT and long-term diabetes apart from when analysing the MMSE total score. Trends of cognitive test results across the categories were also significant in this model, apart from when analysing the MMSE total score.
When excluding participants with a history of stroke in an equivalent sensitivity analysis, results were essentially unchanged data not shown. All correlations were significant in Model 1 adjusted for demographics and lifestyle factors , with negative associations for MMSE results measured in points and positive associations for AQT results measured in test time.
In Model 2 additionally adjusted for cardiovascular factors , correlations were only significant between MMSE and 2-h glucose for the whole study population. In the sub-analysis with people without diabetes, all correlations were significant in this model apart from the relationship between fasting glucose and AQT. Correlations between glucose measurements and cognitive domains memory, processing speed, executive functioning were also significant in Model 1 , but not consistently in Model 2 Additional file 1 : Table S2.
Having NFG both at baseline and at follow-up was associated with the best mean cognitive test results. Results were marginally poorer for those with IFG only at follow-up newly diagnosed diabetes. The mean AQT test time for both these groups of participants was In Additional file 1 : Table S3, different classification methods of diabetes are compared as regards cognitive test results.
The results indicate that having diabetes is associated with worse mean cognitive test results compared to not having diabetes, irrespective of which classification method was used, although these differences were not always significant. There was one exception, i.
Cognitive dysfunction in diabetes: how to implement emerging guidelines
Some patients with type 1 and type 2 diabetes mellitus DM present with cognitive dysfunctions. The pathophysiology underlying this complication is not well understood. Type 1 DM has been associated with a decrease in the speed of information processing, psychomotor efficiency, attention, mental flexibility, and visual perception. Longitudinal epidemiological studies of type 1 DM have indicated that chronic hyperglycemia and microvascular disease, rather than repeated severe hypoglycemia, are associated with the pathogenesis of DM-related cognitive dysfunction. However, severe hypoglycemic episodes may contribute to cognitive dysfunction in high-risk patients with DM.
Relation of Diabetes to Mild Cognitive Impairment
Metrics details. Diabetes is a risk factor for cognitive impairment, but whether there is also a link between pre-diabetes and cognitive dysfunction is not yet fully established. Regression analyses were performed to investigate associations between: a categories of normal or impaired glucose metabolism, and b OGTT measurements, respectively, as exposure variables and cognitive test results as outcomes. Adjustments were made for demographics, lifestyle factors and cardiovascular risk factors.
With the aging of the population the prevalence of two common disorders is expected to rise: diabetes and dementia. It has been shown that people with diabetes are approximately 1. This appears to be due to a higher prevalence of both vascular dementia and Alzheimer's disease. The aim of this review is to describe the importance of this relationship, the evidence supporting it, possible explanations, and the implications of this relationship for physicians caring for people with diabetes.. This overview will describe the growing importance of diabetes, dementia and cognitive dysfunction in an aging society.
Recent guidelines therefore recommend screening for cognitive impairment in older individuals with diabetes.