Spectral fatigue

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  • Published: Jul 1, 2006
  • Author: David Bradley
  • Channels: Infrared Spectroscopy
thumbnail image: Spectral fatigue

Visible-near infra-red NIR spectroscopy can be combined with chemometrics analysis as a promising diagnostic tool for detecting chronic fatigue syndrome, without the subjectivity of patient profile for this disorder.

The cause of CFS, also known as myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS), and various other names, including the rather derogatory 'yuppie flu', is not yet known and its symptoms are broad, ranging from fatigue, pain, muscle weakness, and depression to digestive disturbances, immune system weakness, and breathing problems. Moreover, there is no simple diagnostic test for the disease and patients often rely on a sympathetic physician to recognise the problem, but who, nevertheless, does not necessarily have the tools to offer a definitive answer nor an effective treatment. An objective clinical diagnostic test would make understanding and treating this disease far easier.

Such a diagnostic could emerge from spectroscopy, thanks to Kazuyoshi Ikuta of the Department of Virology at Osaka University, Japan, and colleagues there and at the Kansai University of Welfare Science, Osaka City University Graduate School of Medicine, and Sakai Bio-Clinical Laboratory, Inc. Aware of a lack of a single definitive biochemical marker for CFS Ikuta and his colleagues reasoned that the non-destructive technique of vis-NIR spectroscopy might provide new insights if a comparison of patient sera could be compared with individuals without the disorder.

The researchers obtained vis-NIR spectra at 600-1100 nm for sera from 77 CFS patients and 71 healthy donors. Principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) were applied to the spectra to develop multivariate models that could discriminate between CFS and healthy samples. The team assessed the predictive power of the resulting model on 54 masked determinations in the healthy group and 45 in the CFS group. PCA was successful for all masked samples, while the SIMCA model was 100% accurate for healthy donors and showed 93.3% accuracy for CFS samples.

'These results suggest that vis-NIR spectroscopy for sera combined with chemometrics analysis could provide a promising tool to objectively diagnose CFS,' Ikuta and colleagues report. They add that their results suggest that an unknown factor or factors present in the serum of all CFS patients could provide important clues as to the causative agent in this debilitating disease. 'Further studies will be necessary to accurately perform band assignment for the important peaks of PCA loadings and SIMCA discriminating power,' they explain.

'In this study, we used serum samples for Vis-NIR spectroscopy,' team member Akikazu Sakudo, told SpectroscopyNOW.com, 'Therefore, this method is invasive but non-destructive. Vis-NIR spectroscopy can also be applied for non-invasive analysis and we are now approaching non-invasive diagnosis of CFS.'

The researchers concede that statistically their results are not robust enough for clinical use at this time. 'The PCA and SIMCA model developed from Vis-NIR spectra of 148 individuals including 77 CFS patients and 71 healthy donors are experimentally enough, but not sufficient number for practical use in clinic,' says Sakudo, 'The influence of sex and race, etc. on the results of this diagnostic method remains unclear. Hopefully, after resolving these matters, this diagnostic method might be adopted in the clinic.'

The next step in terms of research into the disease, as opposed to diagnosis, is to use this approach together with other evidence to try and identify the specific biochemical markers common to CFS. 'This is the best way to understand the cause of CFS,' Sakudo explains, 'Our experimental system of Vis-NIR spectroscopy coupled with chemometrics analysis may also contribute to the issue.'

Ikuta team (Ikuta front right)
Spectroscopy team detects CFS

Non-invasion plans for CFS diagnosis by Vis-NIR

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