Perhaps one of the most difficult aspects of diagnosing Neurological disorders is the actual difficulty in making an accurate diagnosis or prognosis. There’s no other system in the body as complex as the nervous system, and many neurological diseases share common features. We have existed in a realm of empiric therapy based on scant objective data for a very long time. Physician intuition has guided the process, but that certainly is fallible and subject to bias. So the promise of Precision Medicine has held that better tools will yield better outcomes.
But Precision Medicine requires the use of diagnostic tools with a high degree of accuracy. In part, this entails the use of biomarkers. Unfortunately, some of our most damaging and difficult to diagnose diseases remarkably have had little to nothing in the way of accurate biomarkers. So it is with delight, this week, that I focus on the development of biomarkers in three different neurological conditions that should help to increase the degree of precision and potentially improving outcomes. In summary, they are:
1. Distinguishing between Multiple Sclerosis and Neuromyelitis Optica.1
It has been suspected for some time that Multiple Sclerosis (MS) is not a simple disorder, and that many similar conditions exist. Until recently, we have not had the ability to sort out the different types of autoimmune demyelinating conditions: Neuromyelitis optica spectrum disorder (NMOSD) is a condition that causes damage to the optic nerves and spinal cord predominantly, but it can also affect brain tissue. The presentation, evolution, symptom pattern, prognosis and treatment have a distinct pattern, and a critical aspect of this disease is that conventional MS disease modifying drugs often worsen the symptoms of NMOSD. Therefore, separating out the two disorders is important. This can be challenging because almost two thirds of patients with NMOSD have brain lesions.
- The Aquaporin P4 (AQP4) antibody has a high degree of accuracy in making the diagnosis of NMOSD. However, it is not perfect, and borderline cases now can benefit from a neuroimaging biomarker: MRI Lesion probability maps (FNIRT). This type of MRI sequence detects important differences between MS and NMOSD with a high degree of accuracy.
- The bottom line is that the pattern of brain MRI changes seen in this type of MRI imaging can help improve the precision of treatment that is delivered, reducing the likelihood of erroneous therapy and improving outcomes.
2. Making the correct diagnosis in the case of idiopathic normal pressure hydrocephalus (iNPH).2
This is a condition that causes dementia and oftentimes is misdiagnosed as Alzheimer’s disease or Parkinson’s disease. Researches have just discovered a pattern a biomarkers in the cerebrospinal fluid that gets us a step closer to diagnostic accuracy. Further investigations are necessary to confirm the findings in this new study, but it appears as though for the first time we may have a fairly a more simple path to properly diagnosing this disabling disorder, which is critical because iNPH ultimately may be reversible in some patients. Biomarkers that look useful are neurofilament light (NFL) protein; Aβ38, Aβ40 and Aβ42; Amyloid precursor protein (APP); tau (τ) protein; and Myelin Basic Protein (MBP).
3. Predicting outcomes after stroke with Copeptin.3
The journal Neurology just published the the study “Copeptin adds prognostic information after ischemic stroke: Results from the CoRisk study.” This investigation revealed that copeptin is a blood biomarker with strong characteristics to accurately predict:
- The risk of mortality within 3 months after a stroke. Higher copeptin levels independently predicted higher likelihood of mortality.
- The likelihood of complications. Higher copeptin levels again were associated with a greater likelihood of in-hospital complications.
- The patient’s long-term prognosis, regardless of the method of acute stroke treatment (thrombolysis vs. conservative treatment).
Copeptin is a stress biomarker that may help the treatment team to decide how aggressive to be with a patient following a stroke. Such information may have implications for the level of intensity of acute care, decisions regarding disposition planning, and long term care or end of life decisions as well. Copeptin itself was more useful when it was when included in models of prediction investigating functional outcome and mortality. And even though the area under the curve (AUC) of the receiver operator characteristic (ROC) curves was at best 0.87, which is not great, it’s still better than anything that has come before it.
For as long as Neurology has been a medical specialty, it has been plagued by uncertainty and inaccuracy. Of all the medical specialties, it is one of the last to come to fruition, and there is still much catching up to do. For comparison sake, think of the patient who presents to the emergency department with chest pain. One of the first things to occur is blood testing looking for biomarkers of heart attack. The severity of damage can be predicted by these tests, and long-term prognosis can be as well. The same could not have been said of the patient with stroke until now.
- Distinction of seropositive NMO spectrum disorder and MS brain lesion distribution. Matthews L, et. al. Neurology 2013;80:1330-1337.
- Idiopathic normal-pressure hydrocephalus. Jeppsson A, et. al. Neurology 2013; 80:1385-1392.
- Copeptin adds prognostic information after ischemic stroke: Results from the CoRisk Study. De Marchis GM, et. al. Neurology 2013; 80:1278-1286.