Metabolic Network Measurement- Plausible Solution for Huntington's Disease

Huntington’s disease is an inherited neurodegenerative disorder characterized by progressive impairments in motor, cognitive, and executive functions (Tang et al., 2013). Since Huntington’s disease is by definition an autosomal dominant disorder, only one parent needs to have this abnormal gene in order to pass on the disease to an offspring, as indicated by Kaneishiro (2012). With the near 100% penetrance level of the autosomal dominance characteristic in this disease, individuals who potentially can be affected later on can be tested for the presence of unstable CAG (amino acid glutamine) expansion in the gene encoding the Huntington protein years before the onset of clinical symptoms (Tang et al., 2013). However, it has been postulated by Tang et al. that through the effective use of metabolic network measurements, and accurate determination of the preclinical progression rate, intervention can be designed to delay and/or prevent the onset of symptoms (2013).

Brain imaging, therefore, provides the answer to tracing the progression of the disease in individual before actual symptoms are apparent. This is due to the fact that imaging has the capability to tap into the networks that can identify and associate the biomarkers with the process of specific disease mutation carriers in each development stage through the analysis of imaging data. As such, Tang et al. proposed in 2013 that with metabolic network measurements as a means of evaluating disease progression in premanifest individuals of Huntington’s disease (both with adults and Juvenile Huntington's Disease), that it will help expedite the analysis of effective disease-modifying therapies for neurodegenerative disorders (Tang et al., 2013).

Tang et al. points out that at this period of time, while standards have been established to evaluate the severity level of Huntington’s disease, it is not an effective way to prevent and treat the disorder (2013). The Unified Huntington’s Disease Rating Scale is a clinical rating scale that is considered to be the gold standard in taking measurement of how far the disorder has progressed. However, while it documents the progression of the different disease stages, it is insensitive to pre-symptomatic indicators and as well as during symptom onset (Tang et al., 2013). Another type of imaging tool identified by Tang et. al, known as the C-raclopride with PET and volumetric MRI have been able to document disease-progression in at-risk individuals (2013). However, this type of in vivo imaging measurement provides limited information on the greater functional areas of the disease progression process.

After an examination of the imaging types and network analyses, it was found that longitudinal metabolic imaging data of premanifest Huntington’s disease carriers validate a specific spatial covariance pattern that can be used to quantify the rate of disease progression in the preclinical period (Tang et al., 2013). In an analysis conducted by Tang et al., they tested 12 premanifest individuals carrying the mutation gene for Huntington’s disease with longitudinal metabolic imaging data to determine its pattern identification for progression in disease network activity over time. The process for conducting the longitudinal metabolic imaging consisted of using a unique computational algorithm that detected patterns of brain networks through PET scans. The results were promising. The metabolic network was able to quantify subject scores in the individual scans that comprised the derivation sets, and was able to identify individual Huntington’s disease genes without other information such as age, disease burden indices, and etc. (Tang et al., 2013). Through the test, Tang et al. discovered that metabolic network activities was the most sensitive to disease progression and allow for the creation of a theoretical timeline that documents the anatomical/functional changes during the preclinical period of Huntington’s disease progression. Effectively, the metabolic network measurement proved that, in order to gain a complete understanding and take control of Huntington’s disease, it is absolutely necessary to know the progression of the disease so that a plausible solution may be applied to combat against any predictable changes in the disorder.

Reference

Tang, C. C., Feigin, A., Ma, Y., Habeck, C., Paulsen, J. S., Leenders, K. L., . . . Teune, L. K. (2013, September 3). Metabolic network as a progression biomarker of premanifest Huntington’s disease.123(9):4076–4088. Retrieved October 21, 2013, from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3754266/