Individuals who are obese and/or with alterations of insulin homeostasis, including diabetes, are at an increased risk of developing dementia and Alzheimer’s disease. The risk of vascular dementia is increased 2 to 2.5 fold in people with type 2 diabetes, that of developing Alzheimer’s disease is 1.5 to 2 fold. The association between type 2 diabetes and Alzheimer’s disease is particularly pronounced in carriers of the apolipoprotein E epsilon 4 allele. Carriers of this allele who also have type 2 diabetes have a two-fold increased risk of developing Alzheimer’s disease compared to those who have the allele but not diabetes.

The neurocognitive effects of diabetes are most clearly visible in children and the elderly. In people with type 1 diabetes, changes are seen in the first five to seven years of life when the brain is developing. The elderly, over 65 years of age in whom the brain undergoes neurodegenerative changes are also particularly vulnerable to the neurocognitive effects of diabetes. These neurodegenerative changes include generalized brain atrophy, with larger lesions than those found in controls without diabetes, often in subcortical areas of the brain. Leukocaryosis, also known as white matter hyperintensive lesions are usually seen in people of the age of 80, but appear earlier and are more extensive in the brains of people with diabetes. MRIs often shows atrophy of the amygdala in people with diabetes. Finally patients with diabetes often have extensive amyloid plaques that are otherwise characteristic of Alzheimer’s disease.

The occurrence of amyloid plaques in the brains of people with diabetes points to a link between the pathophysiology of Alzheimer’s disease and diabetes. In patients with insulin resistance, too much insulin enters the brain. Both insulin and amyloid are metabolized by the insulin degrading enzyme (IDE). Since IDE has a much higher specificity for insulin than amyloid, the overabundance of insulin in the brain effectively blocks the clearance of amyloid and so promotes plaque formation. In summary, diabetes appears to accelerate the aging process of the brain by increasing atrophy and reducing the cognitive reserve.

So, do these pathophysiological changes in the brain lead to cognitive impairment? In people with type 1 diabetes, the reduced cognitive performance becomes apparent in childhood in the form of reduced psychomotor ability or speed, attention, memory and verbal IQ scores. The factors that most affect the intelligence of people with type 1 diabetes are age at diagnosis and glycemic control. Diagnosis before the age of 4 is associated with impaired executive skills, attention, and processing speed, most likely because the development of the brain is disrupted by the metabolic disturbance caused by diabetes. Notably, academic performance improves with better glycemic control.

In people with type 2 diabetes, the neurocognitive deficits are decreased psychomotor speed complex motor function, executive functions, memory skills, immediate and delayed recall, verbal fluency, attention, visuospatial ability. These deficits were recently assessed by Whitehead and colleagues, who tested whether neurocognitive speed or inconsistency was the better clinical marker of type 2 diabetes. Patients with type 2 diabetes performed slower and more inconsistently than the non-diabetic control subjects. In a longitudinal study, Espeland and colleagues observed the decline of cognitive function and fine motor speed. The study assessed women between the ages of 65 and 80 years, 179 with type 2 diabetes and 1984 non-diabetics. The study found a significantly accelerated of decline for verbal knowledge and verbal memory, but the use of oral diabetes medications was associated with relatively better cognitive function.

References

S Roriz-Filho J, Sá-Roriz TM, Rosset I, Camozzato AL, Santos AC, Chaves ML, Moriguti JC, & Roriz-Cruz M (2009). (Pre)diabetes, brain aging, and cognition. Biochimica et biophysica acta, 1792 (5), 432-43 PMID: 19135149

Whitehead BP, Dixon RA, Hultsch DF, & MacDonald SW (2011). Are neurocognitive speed and inconsistency similarly affected in type 2 diabetes? Journal of clinical and experimental neuropsychology, 33 (6), 647-57 PMID: 21416426

Espeland MA, Miller ME, Goveas JS, Hogan PE, Coker LH, Williamson J, Naughton M, Resnick SM, & Whisca Study Group (2011). Cognitive function and fine motor speed in older women with diabetes mellitus: results from the women’s health initiative study of cognitive aging. Journal of women’s health (2002), 20 (10), 1435-43 PMID: 21819251

Image via MARKBZ / Shutterstock.

Since depression is so frequent it is not surprising that there are a wide range of treatments available. Options include antidepressant medication, cognitive behavioral therapy, and elctroconvulsive therapy in relatively rare and severe cases. Now, how treatable is depression? The answer to that is complex. There appears to be no one best treatment for major depression, and not all treatments work for everybody. Moreover, the treatments do not work quickly. So if a depressed patient sees their physician today, the prescription they get may not really take effect for several weeks, if at all. So, now what?

For the patient who did not get better, this means there are other treatments available. Should the next treatment be from the same class drug class or a different one? Should the first medication continue, or should a second one be added? Maybe cognitive behavioral therapy would help? Questions like these were explored in the largest prospective randomized treatment study to date, the Sequenced Treatments to Relieve Depression Trial (STAR*D). Unlike most previous trials, STAR*D allowed patients with comorbidities, and was conducted under real world conditions. It followed an initial sample of over 4000 patients over four treatment steps. A new treatment was tried whenever the previous treatment did not lead to remission of the patient’s depressive symptoms. Everyone started on an SSRI in step 1. In step 2, several other drugs and cognitive behavioral therapy became available. Step 3 added more drugs, among them the tricyclic antidepressants. And finally in step 4 electroconvulsive therapy and yet another drug class, monoamine oxidase inhibitors were tried.

And what was learned? Some patients reached remission at every step, meaning they had no depressive symptoms. At the first step, it was 37%, at the second step, 31%, 14% at the third step and finally 13% at the last step. At the end of the trial over 70% of patients had remitted. So, mostly depression is treatable, although it can take time, each of the treatment trials could take 12 weeks, and four of them might be needed.

But the knowledge gained does not stop there. STAR*D yielded a huge set of raw data that will be valuable for a long time to come. Let’s look at two examples.

First, the placebo effect has long been known and describes the sum of all treatment effects that are not directly attributable to the medication under study. In a placebo controlled trial, a patient in the placebo group will be treated exactly like one on active, medication. That is, they will see the physician, the nurses, will receive some positive attention, and all that may help with their illness. The effects of the medication are over and above the placebo effect. We know from placebo controlled phase III registration trials of antidepressants that large placebo effects are common. If applied to the first steap in the STAR*D trial, this consideration of the placebo effect then tells us this: 37% of the patients remitted from the combined effects of citalopram and all the non-medication effects of treatments. So not only did 63% of the patients not remit, but a lot of the remissions may not have been due to the medication at all. So, the contribution of citalopram may to the patient’s recovery may have been relatively small. In a recent column in the Journal of Psychiatric Practice, Dr. Sheldon Preskorn estimated that the drug specific response rate for modern antidepressants is about one in four patients.

Another example of how knowledge from STAR*D remains useful after the trial comes from a study assessing the interaction of anti inflammatory drugs and antidepressants. Antidepressants raise the level of certain cytokines in the cortex, anti inflammatory drugs lower them. Is this interaction clinically relevant? Looking back to the data from STAR*D confirms that in clinical practice under real world conditions antidepressants and anti inflammatory drugs have antagonistic effects. In this way STAR*D is a a large repository of information that will remain useful into the future.

References

Kessler, R. (2003). The Epidemiology of Major Depressive Disorder: Results From the National Comorbidity Survey Replication (NCS-R) JAMA: The Journal of the American Medical Association, 289 (23), 3095-3105 DOI: 10.1001/jama.289.23.3095

Stafford, R., Ausiello, J., Misra, B., & Saglam, D. (2000). National Patterns of Depression Treatment in Primary Care The Primary Care Companion to The Journal of Clinical Psychiatry, 02 (06), 211-216 DOI: 10.4088/PCC.v02n0603

Warden, D., Rush, A., Trivedi, M., Fava, M., & Wisniewski, S. (2007). The STAR*D project results: A comprehensive review of findings Current Psychiatry Reports, 9 (6), 449-459 DOI: 10.1007/s11920-007-0061-3

Preskorn SH (2011). What Do the Terms “Drug-Specific Response/Remission Rate” and “Placebo” Really Mean? Journal of psychiatric practice, 17 (6), 420-424 PMID: 22108399

Warner-Schmidt, J., Vanover, K., Chen, E., Marshall, J., & Greengard, P. (2011). From the Cover: Antidepressant effects of selective serotonin reuptake inhibitors (SSRIs) are attenuated by antiinflammatory drugs in mice and humans Proceedings of the National Academy of Sciences, 108 (22), 9262-9267 DOI: 10.1073/pnas.1104836108

Image via Nomad_Soul / Shutterstock.