Apr 07 2011

Predicting Alzheimer’s Disease

As our population ages, diseases of old age are increasing, including Alzheimer’s disease (AD) and other dementias (brain disorders that impair overall memory and cognition). One challenge facing patients and practitioners is distinguishing mild cognitive symptoms of either aging or of a functional process, like poor sleep or depression, from the early stages of a progressive degenerative disease like AD.

Patients with mild symptoms are considered to have mild cognitive impairment (MCI), and patients with MCI progress to the clinical diagnosis of Alzheimer’s type dementia at a  rate of about 20% per year, compared to 1-2% per year for the age-matched baseline population. What this likely means is that some people with MCI do not have AD, but either have a mild vascular dementia or some other process that is not relentlessly progressive or degenerative. While others are in the early stages of AD – passing through MCI on their way to clinical AD.

What we would like are better methods for distinguishing these two populations – in essence to predict who will go on to develop AD and who has stable MCI.  We are pretty good at doing this now just based on clinical criteria – patients with certain deficits on neurological exam (as opposed to just symptoms of poor memory and concentration) are more likely to have AD. If we add to our clinical exam an MRI scan of the brain to look for atrophy, that gives us more predictive power still. And we also do an EEG (electroencephalogram), which tends to show slowing in AD.

But even with these methods, we often need to follow patients over time to see who is actually progressing and who seems stable. We use part of the neurological exam called the mini-mental status exam to do this. We can also do formal neuropsychological testing – a much more elaborate and quantitative test of cognition, to follow patients to see who is progressing.

There continues to be a lot of research into biomarkers for AD to more easily and definitively predict who actually has this specific disease, vs other causes of early or mild dementia symptoms. It seems every few weeks I see a study claiming to do just that, but very few find their way into clinical practice. Most provide marginal information when added to established methods described above, or are simply not cost effective.

A recent study caught my eye, however, and seems to be promising. Researchers looked at MRI scans of healthy controls, patients with AD, and those with MCI at baseline and at one year. Not surprisingly the baseline MRI scan was able to increase the predictability of progression to AD.

They focused specifically on the thickness of the cortex, and found am odds ratio of 7.2 when comparing the best with the worst. In other words, for those with the most atrophy on MRI, they were 7.2 times more likely to develop AD than those with the least amount of atrophy.

They also did a second MRI scan at 1 year, and found an odds ratio of 12.0 for the worst compared to best MRI scans. Therefore cortical atrophy, as measured by these MRI scans which looked at the thickness of the cortical gray matter – there was high predictability both at baseline and at one year follow up.

At present it is not clear how much this will affect clinical practice, because there are no specific treatments that alter the course of AD. All treatments are for either underlying treatable conditions that may predispose or exacerbate dementia, or are symptomatic – they boost memory but are not specific to AD. Therefore, from a clinical perspective, we will look for those things that are treatable and treat what we find. If someone has a low vitamin B12 level, we will supplement, regardless of the causal relationship to AD (since we cannot determine this anyway).

But there are two potential benefits to improved predictability, the first being just giving the patient and their family a better idea of what they can expect and to plan accordingly. Studies like this will probably, however, find their greatest utility as part of clinical trials. This looks like a very quantitative and validated marker to both establish that the study population really has AD and to follow their progress over time. It therefore may give patients with clinical MCI but who are at high risk for progressing to AD access to study treatments by allowing them entry into clinical trials based on their MRI findings.

While potentially useful, this technique is certainly not the holy grail of AD diagnosis. We are still looking for a pathological marker that is both highly specific and sensitive to AD. Right now the only test with these features is a brain biopsy (or examining the brain at autopsy). AD remains a pathological diagnosis, only firmly established by looking at tissue. But we don’t do brain biopsies because there is no advantage to the patient – because there are no specific treatments. What we would like is something that is the equivalent of doing a biopsy without having to do a biopsy. A blood test would be nice. I don’t know if this is possible, but researchers continue to search.

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