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Table of Contents    
COMMENTARY
Year : 2019  |  Volume : 67  |  Issue : 5  |  Page : 1318-1319

Towards Improving Prediction of Progression to Dementia: Emerging Evidence for Role F-18 FDG PET in Developing Countries


Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

Date of Web Publication19-Nov-2019

Correspondence Address:
Dr. Suvarna Alladi
Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.271294

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How to cite this article:
Alladi S, Arshad F. Towards Improving Prediction of Progression to Dementia: Emerging Evidence for Role F-18 FDG PET in Developing Countries. Neurol India 2019;67:1318-9

How to cite this URL:
Alladi S, Arshad F. Towards Improving Prediction of Progression to Dementia: Emerging Evidence for Role F-18 FDG PET in Developing Countries. Neurol India [serial online] 2019 [cited 2019 Dec 7];67:1318-9. Available from: http://www.neurologyindia.com/text.asp?2019/67/5/1318/271294




Mild cognitive impairment (MCI) represents a stage of cognition between normal to dementia, and is considered to represent a higher risk of progression to Alzheimer's Disease Dementia (AD) at a rate that ranges from 10 to 30% per year.[1],[2],[3] With increasing awareness and rising burden of dementia in developing countries like India, persons with mild cognitive problems seeking help in clinical practice is rising, and there is a need to develop research evidence for identifying reliable predictive methods for the context of India. Advances in research suggest that several predictive factors for developing dementia are emerging, based on biomarkers. The incorporation of biomarkers into dementia research has not only enhanced the prediction of development of AD from MCI but has also helped in investigating disease modifying agents that will prevent the progression of MCI to dementia. Thus, differentiating patients who are likely to progress to dementia, from those who are unlikely to do so, is critical in the context of management and future therapies. Recently, the A/T/N biomarker classification in AD has been proposed[4] which advocates the use of plasma, CSF, and neuroimaging biomarkers for diagnosis. Although amyloid imaging is a considered to be an important biomarker in predicting future cognitive decline, its lack of availability and potential cost in Low- and Middle-income countries like India are major limiting factors. Therefore, the study of the utility of a more available and accessible alternative biomarker in the form of 18-fluorodeoxyglucose positron emission tomography (18 FDG PET) imaging of the brain is important for developing countries like India.

In the study published by Madhavi et al.,[5] in this issue of Neurology India, the authors prospectively evaluated the diagnostic accuracy of baseline 18 FDG-PET in amnestic MCI to predicting conversion to AD or other dementias. The authors evaluated a fairly large number (87 patients) with aMCI, and each patient underwent detailed and systematic neuropsychological evaluation and a FDG PET scan within three months of initial enrolment. Patients were classified as converters and non-converters, based on a follow up of a minimum period of 18 months. The conversion rate for all-types of dementia observed in this study was 26.4% (23/87) and for AD it was 21.8% (19/87). Sensitivity and specificity for FDG PET-based prediction of dementia conversion was 86.9% and 93.7% respectively, suggesting a higher specificity and comparable sensitivity to earlier studies.[6],[7] In a multicenter prospective cohort study by SEAD-J group, the annualized conversion rate over a 3-year follow up was 15.7% with low specificity and high sensitivity for detection of AD converters.[8] The prediction accuracy of MCI to AD conversion is reported as 91.9%, higher compared to the ADNI European network (79.6%) and the ADNI group (83.8%).[6],[9] Since there is a variable specificity and sensitivity reported in predicting MCI progression, a longer follow up of the cohort will also help in resolving differences in findings between studies. Adoption of standardized clinical diagnostic methods and possible recruitment of persons in later stages of MCI may have contributed to high rates of accuracy. An additional crucial finding in the current study was that, none of the FDG negative MCI group converted to AD, a finding with useful clinical implications.[10]

The study also reports the relevance of determining patterns of hypometabolism. Evidence from studies suggests that the pattern of AD-related neurodegeneration may help in prediction of MCI conversion to AD. Smaller hippocampal and amygdala volumes, involvement of inferior parietal, posterior cingulate and frontal cortices are classically affected by neurodegeneration.[11] Consistent to this, the authors in the present study found inferior parietal lobule hypometabolism as the most reliable indicator of progression from amnestic MCI to AD, consistent with previous studies.[12],[13] An interesting observation noted by the authors was a combination of at least unilateral hypometabolism of precuneus, posterior cingulate and temporo-parietal region, seen in 28 patients (32%) as a reliable pattern for predicting progression to AD. The limited role of the finding of hypometabolism of medial temporal lobe, is significant. As the authors suggest, a longer follow-up period is likely to have shed more insights into the eventual clinical outcome of the cohort.

While FDG PET studies have shown variable results in MCI to AD conversion prediction, this may to be due to variability in follow-up durations, selection of patients, and heterogeneity in the methodology used. However, like the current study, majority do support the crucial role of FDG PET in MCI conversion prediction. This study assumes particular significance in the context of India, where awareness about MCI is increasing and there is limited availability of advanced imaging and CSF biomarkers for clinical practice and research. FDG-PET is available now in many cities, and once methods are standardized, it may be considered as a good imaging biomarker to diagnose and predict conversion of MCI to dementia. This will have a significant impact on the management strategy and quality of life of affected persons. There is evidence that intensive control of blood pressure may reduce the risk of cognitive impairment; thus suggesting role of antihypertensives as disease modifying agents to prevent MCI conversion to AD.[14] Because individuals with MCI are at an intermediate stage between normal cognition and AD, and are associated with higher risk of cognitive decline than healthy older individuals, they are a target population for evaluating early treatment interventions like diet modification, cognitively stimulating activities, and exercise.[15] Lifestyle interventions can therefore be instituted in MCI patients that are likely to convert to AD, to delay progression to dementia.

However, this study does raise some important issues and stimulates the need for additional research in India. Can a single biomarker be used for prediction of MCI conversion? Should automated quantification of FGD PET be utilized to determine the metabolic pattern with high reliability? Will studies with longer follow up uncover more reliable information about risk of MCI conversion to AD? This study paves the way for more research in the field from India.

Overall, the study establishes some valuable points in supporting the role of FDG PET as a biomarker in predicting MCI to AD conversion. Study with larger number of subjects, use of CSF biomarkers, and a longer follow up will further aid in this endeavor.



 
  References Top

1.
Tripathi M, Tripathi M, Parida GK, Kumar R, Dwivedi S, Nehra A, et al. Biomarker-based prediction of progression to dementia: F-18 FDG-PET in amnestic MCI. Neurol India 2019;67:1310-17.  Back to cited text no. 1
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Rozzini L, Chilovi BV, Conti M, Bertoletti E, Delrio I, Trabucchi M, et al. Conversion of amnestic mild cognitive impairment to dementia of Alzheimer type is independent to memory deterioration. Int J Geriat Psychiatry 2007;22:1217-22.  Back to cited text no. 3
    
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Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 2016;87:539-47.  Back to cited text no. 4
    
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Ito K, Fukuyama H, Senda M, Ishii K, Maeda K, Yamamoto Y, et al. Prediction of outcomes in mild cognitive impairment by using 18F-FDG-PET: A multicenter study. J Alzheimer's Dis 2015;45:543-52.  Back to cited text no. 8
    
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Bailly M, Destrieux C, Hommet C, Mondon K, Cottier JP, Beaufils E, et al. Precuneus and cingulate cortex atrophy and hypometabolism in patients with Alzheimer's disease and mild cognitive impairment: MRI and 18F-FDG PET quantitative analysis using FreeSurfer. BioMed Res Int 2015.  Back to cited text no. 11
    
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