Diabetes mellitus and
hyperglycaemia as risk factors in cognitive impairment, dementia and
Alzheimer’s disease:
John Jeffrey, MSc. Psychology,
DHP(NC)
Abstract: Most research into whether Type-2 diabetes is
associated with cognitive impairment (CI) and Alzheimer’s disease (AD) is generally
accompanied with a preamble describing the inconsistent results in the
literature. This is true for both psychological testing and brain imaging
results. This review also finds inconsistent findings, but in addition attempts
to explain ‘between study’ variance by examining the
definitions of the independent variables themselves. This is achieved by
looking at the theoretical relevance and validity of the variables involved,
examining how these are operationalised.
The complications associated
with Type-2 Diabetes Mellitus (DM) are proportional to blood sugar levels, UKPDS (1998). The resulting hyperglycaemic
conditions have damaging effects on non-insulin dependent cells and the
vasculature of the body. It is proposed these effects may also occur in the
brain, causing cognitive impairment. In the glycation theory of AD sugar
concentrations and elapsed time are critical parameters, Takeuchi, (2004). In the vascular theory of AD, it
is the accumulated levels of vascular damage Zlokovic (2005). Elapsed time and glucose levels are
again critical.
Clearly how DM control and
elapsed time are operationalised may result in
different associations between DM and cognitive impairment. Diagnosis and
treatment type variables are less problematic than DM control and age of onset.
However DM sometimes goes undiagnosed or age of onset is not available, often
HbA1c figures are not available.
Studies will be examined to
see how these variables are treated and what conclusions were drawn. Generally,
in this research, it is concluded that more recent studies are better designed
and have a greater reliability but are systematically flawed if DM control and
age of onset are not included as independent variables.
Good long term Blood Glucose
(BG) control is important and is associated with long term health benefits. The
psychological needs of people with diabetes are also increasingly recognised,
as are the effects of various psychosocial stressors. The effects of
psychosocial stress on BG, compliance, diet and exercise are considered with a
view to evaluating therapeutic interventions including the use of hypnosis.
Index
Section
1: Introduction
Section
2: Theoretical bases for neural ageing and neurodegenerative disease:
protein
glycation and vascular diseases.
Section 3: Neuroscience: brain imaging
Section
4: Epidemiological results: an analysis of the treatment of the diabetes
variable.
Section 5: Neuropsychology: normal
ageing and psychopathology
Section
6: Glycaemic control in diabetes
Section 7:
Psychotherapeutic interventions and hypnosis
Section
8: Discussion
Section
9: Conclusions
Diabetes
mellitus and hyperglycaemia as risk factors in cognitive impairment, dementia
and Alzheimer’s disease
Section
1: Introduction
Alzheimer’s
disease (AD) is a common type of dementia characterised
by progressive forgetfulness and memory loss with end stage dependency and
death. Normal ageing is often associated with similar if less dramatic
cognitive deficits benignly associated with old age.
Type-2 diabetes mellitus (DM)
results from insufficient insulin or insulin resistance, reducing glucose
transport into insulin dependent cells such as muscle and fat. Uncontrolled DM
is associated with high blood glucose levels.
There are a number of
methodologies that describe pathological neural ageing and associations with DM. Neuropsychological testing maps the extent and location of
impairment to cognitive systems, this will include normal ageing. DM may be
associated with selective cognitive deficits including memory and executive
function, Yeung et al., (2009). Animal models of diabetes are
used to manipulate some of the effects and complications of controlled and
uncontrolled diabetes. Gispen and Biessels, (2000).
Epidemiological
studies look basic co-morbidity but also take into account the
definition and aetiology of DM and often consider mechanisms that will account
for associations between DM and neural ageing. DM is associated with increased prevalence of dementia and AD in old age, Ott et al., (1996);
Akomolafe et al., (2006). The incidence
of AD with age shows an increase after age 60 with a step change in the over
80’s.
Neuroscience:
The volumetrics and
functioning of ageing brains can be imaged using Magnetic Resonance Imaging (MRI)
and f-MRI. Comparisons can then be made between controls, Mild Cognitive
Impairment (MCI) and AD, Colliot et al., (2008), Jauhiainen et
al., (2008). This allows both diagnoses and prediction of conversion
rates. Similar measures of brain atrophy and lesions have been studied by Schmidt, et al., (2004) allowing comparisons
between DM subjects and controls.
AD is associated with the
presence of extracellular β-amyloid peptide (Aβ) deposits known as senile plaques (SP’s) and
particularly associated with intracellular neurofibrillary tangles (NFT’s)
comprising tau protein and other cellular debris, (Finch
& Cohen, 1997). SP’s and NFT’s are present in normal ageing but much
increased in AD.
Advanced
glycation end products (AGE’s)
are responsible for many of the vascular complications of diabetes and are
implicated in neuro-pathogenesis, (Brownlee 1995; Finch & Cohen, 1997; Gispen and Biessels 2000; Takeuchi et
al., 2007). AGE’s occur independently
of SP’s and NFT’s. They are present in the blood stream, Cerebrospinal Fluid
(CSF) and in cells and in the extracellular matrix. They are found in
association with SP’s and NFT’S (Finch).
AGE’s are reactive and modify cellular and extracellular proteins by covalent
bonding forming insoluble aggregates, damaging collagen and lipids, resulting
in narrowing of vasculature typically seen in diabetes. Similar consequences
are theorised for long lived neurons and the vascular units of the brain, Takeuchi et al., 2004; Zlokovic,
2005.
Most reviews carry caveats
highlighting inconsistent results in
associations of DM with mild cognitive impairment (MCI) or AD, Ott, (1996);Finch & Cohen,
(1997);Schmidt et al., (2004). Current contradictory results are
evidenced in Ott (Rotterdam study, 1996) and Akomolafe et al. (Framlington study, 2006). This may reflect the
multivariate contribution of predictors for AD and/or the individual
variability of neural ageing, dementia, AD and diabetic aetiology. However
there are inherent design weaknesses in many epidemiological studies set up to
monitor co-morbidity of diseases of the elderly. The benefits of control over
blood sugars in reducing diabetic complications has been extensively raised (Pirart, (1978); DCCT, (1993),
UKPDS, (1998)), yet many
epidemiological studies do not hold basic control markers such as HbA1c or age
of onset.
Hyperglycaemia is a major
risk factor in the vascular complications of diabetes and vascular disease is
associated with cognitive impairment, dementia and AD. The aim of the review is
not only to examine associations between DM, impairment and AD, but also to
determine if the diabetes variable is adequately and consistently defined.
Does diabetes per se have
significant associations with cognitive impairment and AD or is it moderated by
quality of glucose control, one of
the risk factors for diabetes complications?
2.
Theoretical bases for neural ageing and neurodegenerative disease: protein
glycation and vascular diseases.
The prevalence of all forms
of dementia in the elderly (>65yrs) in Europe is estimated at 6.4% of which
4.4% AD and 1.6% VaD, van
der Flier & Scheltens,
(2005). Vascular dementia (VaD) is
characterised by vascular disease resulting in large or small vessel infarcts,
commonly known as stroke. This causes areas of neuronal death and loss of function.
Alzheimer’s disease (AD) is a metabolic disease, associated with SP’s, NFT’s
and massive loss of neurons in the hippocampal area. Both VaD
and AD result in serious cognitive impairment, weight loss and end stage death.
The incidence of AD and VaD increases with increasing
old age.
Diabetes complications are
predominantly vascular in nature causing neuropathy, retinopathy, nephropathy,
CHD and stroke. UKPDS 1998 recommended
tighter control of blood sugars and diet to reduce the long term risk of complications.
Risk factors for complications include hypertension, BMI, plasma lipids and
HbA1c. HbA1c is a measure of glycated haemoglobin
averaged over the previous 120 days and is an independent measure of diabetes control. A clinical diagnosis
of Type-2 diabetes mellitus or impaired glucose tolerance (IGT) is associated
with blood sugars above standard limits of 4.0 –
7.0 mmol/l. Type-1 diabetes, previously known
as Insulin Dependent IDDM, results from destruction of pancreatic β cells
often from autoimmune disease. Cognitive impairment in DM is generally mild and
may be wholly associated with hyperglycaemia itself, (Nilsson
et al., 2002; Yeung, 2009)
The glycation theory of AD:
Brownlee,
1995
proposed a role for advance protein ‘glycosylation’
in neural ageing and DM. Adducts of sugar convert to stable ‘Amadori’ products in proportion to glucose concentration.
These reducing ‘Amadori’ products modify cellular
proteins including those in the epithelium and basement proteins, forming
Advanced Glucose End products (AGE’s). These insoluble products themselves
stimulate immune response and Reactive Oxygen Species (ROS) production which
can cause further oxidative damage.
Brownlee
proposed the Amadori glucose derived product
3-deoxyglucosone as a precursor to AGE production, see
AGE-6 in Fig.1 below, (Takeuchi, 2004). AGE-6 is normally broken down by
a reductase enzyme to harmless 3-deoxyfructose.
Brownlee also proposed possible genetic factors might be involved in the
ability to break down AGE-s and that this might account for the variation in
diabetes complications.
The accumulation of
AGE is proportionate to glucose concentration and elapsed time: a 10-45 times
increase in non-insulin gated cells was observed in diabetic rats after 5-20
weeks of diabetes. Glyceraldehyde derived from
glyceraldehyde-3-phosphate (G3P), see AGE-2 in Fig.
1 below is much more reactive than glucose, resulting faster rates of
intracellular AGE accumulation: a 13.8 fold increase was observed in glucose
rich cultured endothelial cells. Age inhibitors such as aminogaunidine
react with G3P to prevent AGE formation and complications in DM rodents.
AGE’s are involved in
the pathology of the complications of DM causing narrowing the vasculature and
ischemia. AGE production in hyperglycaemic diabetes can take place over
relatively short time periods. Brownlee
proposes the same mechanisms for long lived neurons and vasculature in the
brain on the basis that AGE modified Aβ,
precursor of SP’s, and AGE modified tau, precursor of NFT’S are present in AD
brains. Sasaki, N, (1998) cited by Takeuchi et al.,
(2004) has confirmed AGE’s are present in SP’s and NFT’s in patients
with AD and they are also present in primitive SP’s suggesting a role for
glycation in AD.
The rationale for
association between DM and AD is the propensity for AGE formation in
hyperglycaemic conditions and the damage caused to non-insulin dependent cells
in DM. AGE’s are present in SP’s and
NFT’s in AD brains. Takeuchi (2007), Fig. 1 below),
describes a family of AGE’s circulating in the serum of diabetic
patients on haemodialysis. Breakdown protein
products and AGE’s may contribute to ‘abnormal tau protein and the deposition
of Aβ’ in the brains of such patients.
However the results remain
inconclusive.
Glucose derived AGE, AGE
Receptors (RAGE) and Aβ have been found in the
hippocampal area of AD and DM patients. In AD brains 70-80% of astrocytes were AGE-1 and RAGE positive and 20-30% Aβ positive. Intracellular glucose derived AGE-1 was
‘very rare’ in DM and controls, suggesting a role for glycated
Aβ in AD, Takeuchi
et al., (2004).
Choie, Sasaki,Takeuchi et al., (2004) suggest that
glucose derived AGE although widespread in the AD brain has no pathological
role in AD. AGE-1 is found in SP’s and this may be responsible for Aβ glycation. Glycer-AGE
(AGE-2) and glycol-AGE (AGE-3) are both more toxic to rat neurons and AGE-2 has
been found in the cytostol of hippocampal neurons in
AD patients but not in SP’s or astrocytes suggesting
a limited role for glycated Aβ.
Only small amounts of AGE-3 were found in AD or control brains suggesting no
specific role in controls or AD.
It has also been proposed Finch & Cohen 1997;Takeuchi et al., (2004)
that the decreased metabolic activity of glyceraldehydes-3-phoshhate dehydrogenase (GAPDH) observed in AD patients, and in
normal ageing (Finch & Cohen, pp84) may
be sufficient to disrupt the glycolytic pathway in
favour of glyceraldehyde and AGE-2 production see Choie et al., (2004,
Fig.3 below). AGE-2 is present in the blood
of diabetic patients on haemodialysis contributing to damage to non-insulin
dependent cells such as kidney, neurons, retina and red blood cells. The
hyperglycaemic route to AGE-2 production may render DM patients particularly
susceptible to neuronal damage.
RAGE’s are also
implicated in AD. a) Double transgenic mice that over-express RAGE and mutant Amyloid Precursor Protein (APP) show spatial learning
deficits compared to m-APP only mice, Takeuchi,
(2007). b) Clearance of AGE’s depends on scavenger RAGE’s on the surface
of astrocytes. Synaptic plasticity may account for
increased levels of RAGE, responding to increased levels of AGE, Gispen 2000. Increased neuropathy may be due to the
affinity of AGE-2 for RAGE and the corresponding increase of these receptors.
c) RAGE is also a receptor for Aβ and increased
receptors and may contribute to increased Aβ
uptake into the cell maintaining a role for both AGE-2 and Aβ
in AD.
Taken
together these results show:
Glucose AGE (AGE-1) is
present in SP’s and NFT’s. Increased SP’s and NFT’s are present in the AD
brain. However AGE-1 may have no pathological role.
Production of neuro-toxic AGE-2 is considerably more likely in glycaemic conditions leading to a vicious circle of reduced
GADPH metabolism and the polypol pathway.
Increased AGE leads to
neuronal plasticity, more RAGE, greater uptake of AB and AGE including
AGE-2. AGE-2 damage to non-insulin
dependent cells, including peripheral neuropathy, occurs in hyperglycaemic
conditions such as diabetes. A similar, if delayed process is proposed for long
lived neurons in the brain.
AGE-2 damage to hippocampal neurons occurs in
AD. This may be associated with metabolic changes in GAPDH activity that
increase AGE-2 levels. However these changes in GAPDH activity occur in normal
ageing.
Only small amounts of AGE-3
were found in AD or control brains suggesting no specific role in controls or
AD.
Figure 1. Takeuchi et al., (2007, pp.1360)
Figure 3. Choie, Sasaki, Takeuchi et al., (2004, pp.192)
The vascular theory of AD:
Zlokovic (2005) proposes
that neuronal and vascular lesions define AD. Neuronal loss is the result of
vascular disease in the brain with the accumulated dysregulation
of the neurovascular units causing MCI, dementia and AD. The theory describes
the effects of vascular senescence in brain capillaries and cerebral arteries
leading to hypo-perfusion, stroke and cell death. In addition an increasingly
leaky BBB contributes to an imbalance of Aβ
aggregates and increased RAGE expression leading to further cell death.
The Blood Brain
Barrier (BBB) allows Aβ transport, however when
the vasculature is ‘leaky’ this leads to an imbalance of Aβ
in the brain, increased aggregation into Aβ oligomers and their deposition in SP’s. Increased Aβ in brain up-regulates RAGE (see
Gispen above) and low-density lipoprotein
receptor-related protein (LRP), involved in Aβ
clearance from the brain.
Neuro-vasculature
remodelling is part of normal ageing and occurs in degenerative disorders such
as stroke. However brain capillary endothelial cells do not work as a repair
system in presence of Aβ rich in β sheets
which is anti-angiogenic.
AD is co-morbid with cerebo-vascular disease and atherosclerosis and is
associated with reduced capillaries in the hippocampal area CA1 and increased dementia ratings. Diabetes
accelerates vascular ageing and has negative effects on the normal Blood Brain
Barrier (BBB).
In
summary the vascular theory of AD Fig.2
below has a specific role for DM vascular complications and deposition
of Aβ, as with the pathogenesis of AD. This is
not incompatible with the additional processes associated with Advanced Glucose
End products, see glycation model above.
Figure
2, Zlokovic (2005, pp.206)
3.
Neuroscience: brain imaging results
The pathology in Alzheimer’s
disease (AD) occurs, at least initially, mainly in the Hippocampus and related
cortical areas of the limbic system. This results in severe degeneration of the
hippocampus, entorhinal cortex and the neocortex especially the frontal
temporal lobes. Morris, (1999, pp.1172) reports
‘abundant’ Aβ
plaques in the neocortex in pre-clinical AD with 32% loss of neurons in the
entorhinal cortex.
This percentage loss has
been demonstrated using automated volumetrics and MR imaging. Colliot et al., (2008) found similar levels of
hippocampal atrophy, 32% in AD and 19% in MCI, concurrently demonstrating an
association between smaller baseline hippocampal volumes and progression to AD.
Lind, Larson
et al., 2006 demonstrate a possible confound with smaller hippocampal
volume in carriers of APOE e4, while Rodriguez et
al., 2002 in the Honolulu study
demonstrated a 3-fold increase in SP’s and NFT’’s in post mortem diabetic
carriers of this allele.
Schmidt et
al., (2004), in the CASCADE study, report associations between
diabetes and MRI volumes of the brain. The study has the hallmarks of a well
run study especially validity of design and methodology. It is a large study
(n=1252) randomly selected from ongoing community based studies with disease
data collected ≥5yrs previously. It includes cardiovascular disease (CVD)
data and DM risk factors such as BMI and hypertension. The diabetes variable
itself is well defined as having a ‘diagnosis by a physician’ with details of diabetes treatment, diet, tablets or insulin, although like many
studies it does not report duration of DM, as suggested by Finch & Cohen,
1997. Mini Mental Status Examination ( MMSE values < 15) excluded participants with probable
dementia or MCI.
There were no significant
differences in brain lesions between diabetes and the non diabetes control
group. Significant differences between groups were seen in severe cortical
atrophy (p=0.003) and the sub-cortical ventricle to brain ratio (p=0.03). The
added design value of the CASCADE study lies in the capture of biological and
medical predictor variables. Multiple
regression controlling for ‘sex, age, study, education and CVD risk factors
hypertension, CHD, smoking status, BMI and total cholesterol’ redefined group
differences to a non-significant trend towards more pronounced cortical
atrophy, ( β= 0.44, 95%CI:(- 0.4 to 0.92, p=0.07).
Additionally some
interesting interaction terms are included in the model, namely the interaction
of treatment type and hypertension as predictive of cortical atrophy. Table 4) below highlights the differences between
‘untreated’: no treatment or diet and ‘treated’ diabetics: tablets or
insulin. Overall, diabetics with
hypertension have a significant association with increased cortical atrophy
β = 1.19, 95%CI (0.22-2.15), p=0.02 but ‘treated’ diabetics with
hypertension showed no significant increased risk of cortical atrophy
β=0.10, 95%CI (1.24-1.44), p=ns.
The overall odds
ratios for DM versus non diabetic patients for severe cortical atrophy were
[OR] 1.73, 95%CI (1.06-2.81), reducing to [OR]: 1.25, 95% CI (0.64–2.45) for normotensive diabetics. Schmidt
cautions that brain atrophy does not necessarily map to dementia or AD but may
relate to the hydrating effects of glucose.
In
summary: the better designed CASCADE study does not find
significant effects after controlling for important risk factors including
treatment regime. Again the literature search revealed little consensus. Schmidt et al., found only 6 of 19 previous
population based imaging studies with positive associations between diabetes
and small vessel disease and 3 with positive associations to cortical atrophy.
The interaction between
hypertension and treatment should be matter of increasing interest.
4.
Epidemiological results: an analysis of the treatment of the diabetes variable.
It is common for studies to
make reference to the inconsistent findings in the literature. This section
proposes that this is due to the treatment of the diabetes variable. There is a
general trend towards a more comprehensive concept of the diabetes variable
that includes treatment, control/HbA1c, age of onset and medical diagnosis.
This section seeks to demonstrate a trend towards increased design validity
that will give increased validity to associations between DM and AD.
1)
Finch and Cohen 1997 raise the
problem of inconsistent correlation between DM and AD with a summary of studies
between 1983 and 1997 showing 5 without any association, 6 with significant
negative associations and only 2 with significant positive associations, namely
Ott et al., 1996; Leibson et al.,
in press (2009). More significantly Finch
points out that they are not aware of any studies with data on HbA1c.
The Leibson study
is well designed and uses a proper diagnosis of diabetes according to NDDG criteria. It reports a > 2-fold increased
(p<0.05) risk for males only. Risk of AD for Adult Onset DM was elevated (for men, RR =
2.27, 95% CI 1.55-3.31; for women, RR = 1.37, 95% CI 0.94-2.01). Ott’s diagnostic
procedures, sufficient for epidemiological studies, do not conform to clinical
standards for symptomless diabetes which require both the random and oral load
to exceed 11.1 mmol/l,
WHO 1999.
2)
Schmidt 2004 in the CASCADE study raises the inconsistency in previous
radiological studies. The population
validity of previous samples is also
questioned by Schmidt, subjects belonging to highly selective groups for
radiological procedures. Six out of 19 studies found associations with small
vessel disease and 3 with brain atrophy. The interaction of diabetes and
hypertension is known from the UKDPS and
this interaction is reported by Schmidt. In
the CASCADE study only 22 of the 114 diabetic participants relied on one random
or one oral load for a diagnosis of DM, more in line with current standards, WHO 1999.
No data is available for
HbA1c although the participants were dichotomised into ‘treated’ and
‘untreated’ diabetes demonstrating a powerful interaction with hypertension in
relation to brain atrophy.
3) Again in the
CASCADE study Schmidt et
al.2004 correctly excluded participants not clinically diagnosed with DM
and or without treatment information. This excluded 92 potential participants,
13 from the Rotterdam study and 21 from the
Whitehall II study.
In the Rotterdam blood
glucose measuring data did not start until July 1990.
Ott, et al., 1996, found an overall increased
association between DM and AD [OR] 1.3, 95%CI (1.0-1.9), treatment data was
available but no HbA1c or age of onset. Ott, et al.,1999, (abstract), found [OR] 1.9 95%CI (1.2-3.1).
These two studies associating DM and AD are much cited: Ott, et al.,1996 cited 229 times; Ott, et al.,
1999 cited 480 times, www.SCOPUS.com
27.09.08:18.45.
4) Akomolafe et al., 2006 in the Framlington
community based study account for the
variability in previous results by positing possible population differences and
confounders such as definitions of DM, dementia and AD. They found no significant
association between DM and AD and no effect from ‘treatment type’ but found
significant associations in a low risk sub-group.
The study includes both ‘treatment type’ and
risk factors for DM in its regression models, but still no mention of HbA1. The
finding of differential sub-groups raises doubts as to the design validity of previous studies.
5)
Nilsson (2006) describes the approach of the Betula
study combining longitudinal and cross-sectional design so controlling for age,
cohort and time of testing. The benefits of the Betula
design include multiple testing domains; multidisciplinary teams and the
indivisibility of cognitive testing in detecting early MCI leading to dementia.
HbA1c levels are part of the study and the results showed a main effect with HbA1C rather than DM per
se, Nilsson Nilsson, Wahlin, Fastbom, and Nilsson
(2002) and Nilsson, Wahlin, and Nilsson (2005). This
study incorporates good design features, a multi-domain approach to MCI and
dementia.
6) Finally, Yeung et al., 2009 in their literature search of almost
exclusively post 2000 papers again find no consistency in associations between
DM cognitive functioning. After controlling for hypertension Yeung found only two
significant measures (p< 0.05) out of 20 tested and no interaction between
diabetes and age groups 53-70yrs, m=72.5yrs and 71-90yrs, m=77.6yrs. This
differs from Hassing 2004 who found significant effects of diabetes on
nearly all measures in an elderly sub-group 80-93; m=82.8.
Both studies had diabetes
duration and hypertension data although only Yeung
controls for hypertension. Neither study included glucose control status as a
variable although Yeung tellingly refers to study
participants having ‘mild’ diabetes. However Yeung et al., (2009) highlight their Victoria
Longitudinal Study has sampling validity. It is taken from the wider community
rather than relying on selective samples from sheltered care and nursing homes.
Additionally epidemiological
studies often use Oral Glucose Tolerance Testing at 2hrs. This may not be
diagnostic particularly in the elderly WHO section
2.3.2.
In
summary: while more recent studies have better design many
studies still fail the test of good research set out by Sapsford, 2007.
The diabetes variable is
inconsistently diagnosed and ill defined in terms of treatment options failing validity of measurement. The populations
are often not randomly sampled and may be drawn from clinical or non community
based populations, failing the validity
of population test. The failure
to include pertinent variables such as diabetes control, treatment or known
risk factors such as hypertension means conclusions may fail the test of validity of design.
5.
Neuropsychology: normal ageing and psychopathology
Associations with memory
deficit and diabetes are widely reported but evidence is weak for progression
to AD.
Diabetes associations with
dementia, VaD and AD:
The aim of psychological
testing is to determine early signs of MCI and dementia. The memory impairments
reported in DM may be an effect of glycaemia, diabetic neural ageing or
diabetic neuropathy.
The increased risk of
vascular dementia in diabetes is reported by, Ott (1999), Akomolafe
et al., 2006. These results are subject to the same design caveats
raised in section 4 above. Given an
ostensibly non-demented group Hassing et al., 2002: (abstract) found increased relative
risk [RR] 2.54 95%CI (1.35-4.78) of VaD in the very
old (m=83yrs) but no association with AD.
The association
between diabetes and AD is then still unclear. Ott et al.,
(1996) found an overall positive association between diabetes and AD
with the strongest association in those using insulin therapy [OR] 3.2, 95%CI
(1.4-7.5). As above this study does not consider HbA1c or duration of
disease.
Akomolafe et al., 2006 Framlington study, fig.1
below report a small increased overall relative risk (RR) of 1.15, 95% CI
(0.65-2.05) compared to controls and conclude that ‘DM is a risk factor for AD
in the absence of other known major AD risk factors’.
Akomolafe et al., did not find any
interaction effects by treatment type although 26
of the 202 DM subjects were on ‘no treatment’. This is in contradiction to the
strong interaction effects found by Schmidt et al.,(2004). The interaction found between treatment
types as defined in the Rotterdam study, Ott et al.,
(1996), is instructive, with higher relative risks for dementia and AD
for those on insulin treatment. Since Type-2 diabetes may progress from ‘no
treatment’ to tablets to insulin therapy this would support a more complex
aetiological analysis than is served by treatment categories alone.
Diabetes and
cognitive decline:
Lower cognitive scores are
associated with normal ageing and onset of neurological disease such as dementia
or AD. The incidence of diabetes increases with age so it is important to
separate out the effects. The goal of psychological testing is earlier
diagnosis allowing earlier therapeutic intervention.
1) Verhaeghen et al (2003) Berlin Study
using 1990 baseline data performed a multivariate analysis of cognitive tests
controlling for age, sex, social educational status and dementia status. Of the
remaining major risk factors congestive heart failure, stroke, coronary heart
disease, and diabetes mellitus each contributed to overall cognitive
impairment. DM was the only risk factor that contributed to decline in memory,
however the presence of any one risk factor did not lead to an increased rate
of cognitive decline, see Fig.2 below
.
2)
Hassing, et al., (2004) report no differences
at baseline 1991 between diabetic and normal in elderly subjects (m=82.8yrs),
but shows a significant cognitive decline in MMSE and in 5 out of 10 other
cognitive measures over the following 6 years, concluding that diabetes is a
risk factor for cognitive decline in elderly subjects..
3) In contrast to many
studies Yeung et al., (2009) report no significant contribution
of diabetes to episodic memory, verbal fluency, reaction time or perceptual
speed. Diabetic impairment was observed in 1 out of 3 tests of executive
function and in 1 out of 2 measures of sentence verification, a measure of
semantic speed. No significant decline was seen in the other 16 tests. These
measures held over both groups Young-old (m=63.6yrs) and Old-old
(m=75.6yrs) .
In conclusion there is
evidence for selective effects of DM on cognition but little for long term
cognitive decline, except in the most elderly subjects
Hassing. A possible confound is the effect of
hyperglycaemia on cognitive testing Nilsson, Wahlin, and Nilsson (2005) find it is the presence
of elevated glucose, hyperglycaemia as measured by blood sugars, rather than
diabetes per se that affects memory.
6.
Glycaemic control in diabetes
Animal models:
Experimental animal studies
are not subject to random control regimes and can demonstrate analogous
associations between diabetes and cognition, neuropathy, insulin control and
deficits in old age.
STZ-diabetic rats with
glucose levels at 20-25mmol/l quickly develop diabetic complications. Young
adult rats show learning difficulties unless given insulin treatment. If
treatment is withheld until 10 weeks of onset learning is already impaired.
After around 12 weeks without insulin treatment, hippocampal areas show maximum
synaptic change that affects Long Term Potentiation
(LTP). The effects are dose dependent: at 15mmol/l glucose levels no LTP
deficits were observed. Gispen and Biessels 2000. Hyperglycaemia in diabetic rats favours the polypol pathway as evidenced by the levels of sorbitol and fructose: less in diabetic rat brains than in
the peripheral nerves. Takeuchi proposes
that hyperglycaemia and the polypol pathway are
associated with increased
AGE-2 production, a neurotoxin.
Control of diabetes, by
treatments lowering glucose levels, has positive effects in both rodent and
human subjects. Gispen (2000, pp.458) makes this argument citing studies
that correlate good metabolic control with enhanced cognitive function. Where diabetic complications are reduced and in
rats and cognitive deficits eliminated.
Current control strategies
Holland and Rabbitt (1991) raised the issues of age of
onset and diabetes control as important variables when studying cognitive
deficit in psychological testing. Although they are rarely collected
together in epidemiological studies. Schmidt ,(2004)
comments on the dearth of such figures, indeed Yeung et al.,
(2009) reports that no HbA1c figures, let alone treatment or age of
onset, available in their VLS, relying on self report for diabetes control
status. They describe their sample as having ‘mild diabetes’, somewhat of a
contradiction of terms.
1) The findings of the US
Diabetic Control and Complications Trial (DCCT), 1993 and The UK Prospective
Diabetes Study (UKPDS), 1998 have shown that
tight control of blood glucose levels reduce the long term complications of DM.
This is true of other risk factors BMI, hypertension and cholesterol levels.
Diabetic regimes aimed at tight control of blood glucose levels, measured by glycated haemoglobin (HbA1c), have been shown to change the
rate of progress in micro-vascular and macro-vascular complications in type-2
diabetes. HbA1c is used to assess blood glucose control, a range of
6.5 – 7.5% being the aim set by NICE, (2002).
2) There remains unexplained
variance with well controlled patients suffering severe complications and
poorly controlled patients suffering no ill-effects.
In general however, good
control will reduce glucose concentration and reduce the glycation of proteins
and production of AGE’s. According to the glycation theory AGE-2 is favoured by
hyperglycaemic conditions and is implicated in AD.
3) Using UK as a proxy
timetable for better glycaemic control, following on
from DCCT (1993), UKPDS (1998) and the UK
National Services Framework for Diabetes (NSF) 2003
a 10yr plan - we can assume that a reduction in undiagnosed diabetes and the
effects from good diabetic control would not begin to show in research much
before 2000. Regardless of this if the data are collected they can be controlled
for.
More recent results could
should show different effect . The Schmidt 2004 CASCADE study and the Akomolafe et al., 2006 Framlington
study apply more rigorous definitions of ‘diabetes’ and have established
interesting ‘treatment’ effects.
7. Psychotherapeutic
interventions and hypnosis: John Jeffrey, MSc. Psychology, DHP(NC)
The need for improved
emotional and psychological support is recognised in the Department of Health (2008) publication ‘5 Years On’: Delivering the Diabetes National Service Framework.
The proposed Improved Access to Psychological Therapies (IAPT) aimed at
improving support above the 38% level of primary care trusts providing
psychological support for adults with diabetes. The efficacy of psychological
interventions in the general population is not in doubt. However the literature
reveals a limited number of well controlled studies of psychological
interventions with diabetic patients, Snoek, F. J. & Skinner T. C., (2001).
In the absence of
emotional and psychological support people may seek to maximise self control
through alternative therapies.
Complementary and
Alternative medicine (CAM) is a life style choice for many United States
diabetic patients. This does not appear to be a barrier or alternative to
conventional medicine. In a sample of 2500 DM patients no negative impact was
observed on the take up of preventative care or primary care, Garrow, D, Egede, L
(2006a). Using
the same 2002 national household survey data sponsored by the “National Center for Health Statistics”, Garrow, D, Egede, L (2006b) confirmed a dramatic increase in CAM among DM patients in line
with the general population. 48% of DM patients used CAM of which 17% used
relaxation therapy. Prayer was significantly higher in diabetic patients (Odds
Ratio 1.19, 95% CI 1.05, 1.36). Hypnosis was grouped under ‘other’ but no
conclusions could be drawn given the small sub-sample size.
A review of the
CAMEOL project, Pilkington,
K., Stenhouse., E, Kirkwood., G, Richardson J (2007), identifies only one study of hypnosis and
its affect on Blood Glucose (BG). This same study is cited by Xu, Y and Cardeña, E (2008) in a review of hypnosis as an adjunct to
therapy in supporting patients manage their diabetes. Xu, et al., cite
one other case study (n=1) in their review covering BG control. Xu, et al., also review compliance, weight loss and
increased peripheral circulation discussed below.
Glucose Control
Pilkington et al., & Xu et al., cite
the study measuring
the affects of hypnotically induced stress by abreaction on three physiological
markers including blood glucose. However the actual study by Vandenbergh, Sussman, and Titus (1966) was poorly controlled and lacks both design and
methodological validity. It was a small non random opportunity sample using repeated
measures (n=6). It had uncontrolled variables such diabetes type, patient
profile, treatment type and level of abreaction. Post priori selection of
control day, uncontrolled order effects and simplistic student t-tests mean the
results have little relevance to the literature on glucose control or hypnosis.
As a proxy however DM
glucose control and stress management were examined by Surwit, S et al., (2002). A
controlled study using type-2 DM patients looked at the effects of stress
management on glucose control. Stress management training showed positive
effects in follow up assessments, 1 year after baseline, on glucose control as
measured by HbA1c. The training consisted of 1) Progressive Muscle Relaxation
PMR, 2) Cognitive Behavioural Techniques for recognising and dealing with
stress, including guided imagery, deep breathing and ‘thought stopping’ and 3)
health education on the effects of stress. Training was 5 half-hour sessions,
in small groups, the control group were given non
directive diabetes education. The treatment group continued to practice mini
stress busting techniques, incorporating these into their daily lives.
Compared to the control
group HbA1c was reduced by 0.5% in the treatment group. Although modest this is
associated with significant reductions in micro-vascular complications.
Compliance
Diabetes has multiple
psychological components and often the resultant effect of psychological stress
on BG is idiosyncratic and contingent. This is more so in IDDM than with NIDDM
when BG should theoretically rise in response to stress hormones.
Stress and DM can have
bi-directional effects. Stress can also affect BG indirectly through poor
compliance with BG testing and self- medication, similarly with diet and
exercise regimes. Psychological stress can be negative or positive.
Interventions aimed at
compliance need to be targeted. Cox, D., J., and
Linda Gonder-Frederick, L., (1992) review the
complexity of psychological behavioural research in diabetes. Inconsistent
results they conclude are due to the high level of uncontrolled diabetes
related variables and research would benefit from a more experimental
methodology.
Xu, Y et al. cite
the Ratner, Gross, Casas, and Castells (1990) with hypnosis leading to improved
compliance as evidenced in HbA1c results. However this is a study of 7 Insulin
Dependent DM (IDDM) adolescent patients (n=7) and does not necessarily
generalise beyond the study to the DM population in general.
Exercise & Diet
Weight reduction and
exercise are associated with both short term and long term health benefits for
both NIDDM and IDDM patients, UKPDS, (1998), ‘5
Years On’ (2008). There is a large amount of literature relating to
healthy exercise and diet regimes in the general population, including CAM and
psychological interventions. In this respect, subject to any caveats above,
there is no reason to assume any significant differences in the diabetic
population per se. Typically evidence on compliance and its positive effects is
variable Cox et al., cite Rubin et al. (1990), who found that ‘improvements
in insulin adjustment and SMBG were maintained after intervention, but changes
in diet and exercise were not’.
Increasing Peripheral
Circulation
There is an effect here. Xu, et al., cite Grabowska (1971), although the number of studies
since remains low.
A literature search of www.PsychINFO.com 07.08.10:11.15 using (ABS(vasodilation) AND ABS(hypnosis)) gave 10 hits including Grabowska, www.SCOPUS.com 07.08.10 9 hits and www. PubMED. com 07.08.10.18 hits. There is some overlap.
The range of studies are not
necessarily directly relevant to the issues of this research, namely glucose
control, compliance and lifestyle choices, however they would appear to be a
candidate for further analysis and research.
In summary there is evidence in
the literature to support psychotherapeutic interventions aimed at compliance,
exercise and diet. While there is a lack of controlled studies using hypnosis
the content of hypno-psychotherapy sessions is well
represented namely progressive relaxation, CBT techniques and self awareness.
8.
Discussion
1) Two themes of the epidemiological
research are diseases of the elderly and cognition. Epidemiological studies
have included in their remit the explication of the causes of disease, van der Fliers, 2005. Notwithstanding
genetic variation, the inconsistent associations between MCI, AD and diabetes
may be due to lack of study validity and confounds such as treatment, control
and age of onset.
The variables included in
studies are those considered valid at the time the studies are set up. These
may be amended in the light of new findings although the results may take time
to feed through. Treatment, control and age of onset of diabetes are such
predictor variables: defined by diagnosis, treatment and medical history.
Ideally diagnosis of should
be by a physician, especially when dealing with elderly subjects. Treatment
ranges from diet and exercise, tablets to insulin. There are different tablet
types and different insulin regimes. Age of onset and control are associated
with vascular complications and may be relevant.
2) To properly partial out
the effect of diabetes it is necessary to include these predictor variables in
multiple regression analyses, including appropriate interaction terms in the
model. An example is Schmidt (2004) who
included treatment as a dichotomised variable showing hypertensive diabetics
are at increased risk of brain atrophy ( β=1.19, p=0.02). But when
interaction between hypertension and treatment is included the contribution to overall risk for
treated diabetes is not significant (β=0.10, p=ns), while untreated
diabetes is at increased risk (β=2.2, p=0.001).
Nilsson 2002 included
HbA1c levels allowing the separation of diabetes from transitory glucose
effects on memory. Hyperglycaemia rather than diabetes is associated with
memory deficit. This again supports a role for these variables.
Ott 1996
included treatment details and found no increased risk of dementia with
diabetics on oral medication [OR] 1.0: CI 95%(0.6-1.9)
after controlling for risk factors including hypertension and treatment for
hypertension. Compared to the overall [OR] 1.2: CI 95%(0.8-1.9)
and insulin treatment ,[OR] 2.6: CI 95%(1.1- 6.2). The range of OR values for
insulin treatment (1.1- 6.2) shows a high level of variation. Unfortunately
there are no data available to subdivide this range by level of glucose control
( HbA1c).
Lack of appropriate data
meant that 13 participants from the Rotterdam and 21 from the Whitehall study
were excluded from the CASCADE study due to lack fasting or random glucose
levels, Schmidt 2006. Yet both the Rotterdam
and Whitehall studies are widely cited. Yeung 2009,
investigating the effects of diabetes on cognition included years since onset
but no HbA1c detail, which in light of Nilsson’s
conclusions above emphasises the need for such data.
3) Epidemiological studies
are not immune from the effects of International and National Health Policy
standards. Diabetes diagnosis, treatment and control in the UK, and elsewhere,
have been subject to a number of such changes, DCCT
1993; UKDPS 1998; WHO 1999; NSF for Diabetes
(2003).
If age of onset, treatment
and glucose control are important predictors of MCI then studies that do not
include them will have inherent confounds, secondly if these variables are
significant predictors more recent studies that include them should show a
change in effect size, e.g. Akalomafe, Yeung.
4) The glycation
theory of neural ageing supports the importance of data on treatment and
glucose control, Takeuchi, 2004. Glucose
concentration and elapsed time determine the production of AGE’s and
hyperglycaemia particularly favours the polypol
pathway and TAGE. AGE’s accumulate in the skin of diabetic animals according to
severity of complications but there is no correlation between HbA1c in mammals
and accumulation in the dura mater of the brain, Finch and Cohen, 1997.
This is taken to
imply different tissue thresholds for accumulation of AGE’s and could be a
factor in the much increased incidence rates of AD in the over 70’s.
The vascular theory of
neural ageing Zlokovic (2005) relies on same parameters of glucose
concentration and elapsed time as above. It
is the quality of prolonged control over blood sugar levels that mainly
determine vascular complications in diabetes, namely retinopathy nephropathy,
neuropathy, coronary heart disease and stroke. According to Zlokovic (2005)
vascular disease is responsible for an increase in Aβ
leaking across the BBB and for dysfunction of the neurovascular unit leading to
encephalopathy.
Clearly on theoretical
grounds treatment, control and age of onset shuld be
pertinent data in the analysis of associations between DM, MCI and AD.
5) Psycho-therapeutic
interventions aimed at compliance can have an effect on blood glucose control
and help to delay or avoid the complications of diabetes. Additionally
psychotherapy, counselling
and education enable the person with diabetes to assert personal
control over their condition. This can have the indirect effects on better
control and avoid the spiral of failure that can lead to depression often associated
with chronic conditions.
9.
Summary/conclusions.
Main effects for DM seen in
historic epidemiological studies may be significantly different from more
recent findings. This is due to poor design in operationalising
key diabetic variables such as treatment, control and duration of illness or
from other uncontrolled variables.
It is perhaps a subtle
distinction to argue that hyperglycemia rather than
diabetes is responsible for cognitive impairment in diabetic patients. But, the
distinction being made, it allows the limits of normal functioning to be
defined, less than 15mmol/l for rodents, much in the way that blood glucose
limits of 4-7mmol/l before meals are optimum levels to reduce diabetic
complications. Control within these limits can be assisted using
psychotherapeutic interventions.
Glucose control, treatment
and duration have theoretical links to neural ageing in both the glycation and
vascular theories. Glucose control, treatment and duration all have causative
links.
When all sources of variance
have been accounted for then results from different studies should converge.
Previous results showed little consensus. Policy changes on diabetes have
resulted in earlier diagnosis and better control. Glucose control and duration
will have different cumulative effects between different cohorts, although this
can be controlled for. Any associations between diabetes and AD that do not
include as variables glucose control and duration are likely to have
uncontrolled variation and results will remain inconsistent. It is also a
caution to the practice of citing previous studies in support of a research
hypothesis.
In summary the inclusion of
both HbA1c and duration of illness would add value to epidemiological studies.
Accounting for more variation will result in more consistent findings on the
association between diabetes, MCI and AD. The results considered here
(1996-2009) are still inconsistent. However more recent studies controlling for
glucose control and treatment type do show smaller or non-significant effect
sizes and admit to the possibility that together both variables might reduce
variability further. In any event psychotherapeutic interventions have their
own modest effects
John Jeffrey, MSc. Psychology, September 2010
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