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Relationships between metabolic syndrome and cognitive functions in South Korea
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Developing Topics
Table Baseline Signal Intensity
AD/HC pMCI/HC AD/sMCI pMCI/sMCI
Month-12 Signal Intensity
Month-12 Signal Intensity & Percentage Change Over 12 Months
Accuracy (%)
Sensitivity (%)
Specificity (%)
Accuracy (%) Sensitivity (%) Specificity (%)
Accuracy (%) Sensitivity (%) Specificity (%)
80.9 (6.7) 70.7 (7.6) 72.7 (7.8) 58.4 (7.9)
79.6 (10.6) 70.5 (13.0) 65.6 (13.2) 51.5 (13.1)
82.3 (11.1) 71.0 (12.3) 78.5 (10.8) 64.8 (13.4)
86.1 (6.3) 79.2 (7.3) 79.3 (6.7) 62.3 (7.8)
88.4 (6.2) 81.3 (6.8) 83.5 (7.1) 63.1 (8.1)
81.2 (10.3) 77.2 (11.9) 77.9 (11.2) 53.2 (13.1)
91.0 (8.2) 81.4 (11.0) 80.5 (9.3) 70.8 (12.4)
83.2 (10.4) 79.8 (11.1) 79.9 (11.5) 52.2 (13.5)
93.6 (7.4) 82.9 (10.7) 86.4 (8.9) 73.2 (12.3)
Classification accuracy, sensitivity and specificity, expressed as mean (standard deviation).
assessed using bootstrap resampling. Results: Highly significant increases in classification accuracy are achieved when using month-12 signal intensities compared to using baseline signal intensities. Accuracies increased from 81% to 86% for AD/HC, 71% to 79% for pMCI/HC, 73% to 79% for AD/sMCI, and 58% to 62% for pMCI/sMCI. More interestingly, further significant increases in accuracy may be achieved by combining month-12 signal intensities with the percentage changes over 12 months. Using this feature combination yields accuracies of 88% for AD/HC, 81% for pMCI/HC, 84% for AD/sMCI, and 63% for pMCI/sMCI. Conclusions: These results surpass many state-of-the-art image-based classification methods. This study demonstrates that information extracted from serial FDG-PET through regional analysis can accurately discriminate diagnostic groups, a finding that may be usefully applied in the diagnosis of AD, predicting disease course in individuals with MCI, and in the selection of participants for clinical trials.
P4-305
VISUAL PRESENTATION OF REGIONAL VOLUMETRY DATA FOR DIAGNOSTIC DECISION SUPPORT IN ALZHEIMER’S DISEASE MR IMAGING
Rolf Heckemann1, Shiva Keihaninejad2, Katherine Gray3, Daniel Rueckert4, Joseph Hajnal4, Alexander Hammers1, 1The Neurodis Foundation, Lyon, France; 2University College London, London, United Kingdom; 3Imperial College London, Hook, Hampshire, United Kingdom; 4 Imperial College London, London, United Kingdom. Background: Our previous work shows that confidence in an image reader’s diagnosis of Alzheimer’s disease versus normal ageing is favourably influenced by superimposing the grey-scale MR images with colour overlays representing a statistical expression of each anatomical region’s volume compared to a reference group. Combining recent advances in image segmentation software with high-performance computing and access to a large cohort
of images of well-characterized subjects, we re-assessed the potential of this approach. Methods: A robust and accurate automatic anatomical segmentation method, MAPER (multi-atlas propagation with enhanced registration), was applied to all T1-weighted screening MR images provided by the ADNI (Alzheimer’s disease neuroimaging initiative). From each diagnostic group (HS: healthy subjects; sMCI, pMCI: mild cognitive impairment without and with progression within the observation period; AD), three test subjects were randomly chosen. A colour overlay showing regional volumetric z-score as compared to a coarsely age-matched reference group of healthy subjects was generated for each test subject and reviewed in conjunction with the grey-scale images. Results: Onvisual review of the overlayed images, a typical pattern emerges. Normal subjects show predominantly green hues (z-score near zero) and few volumetric outliers. AD subjects show strongly negative z-scores for various regions predominantly located in the temporal lobe and strongly positive z-scores for ventricles, especially the temporal horn of the lateral ventricle. MCI subjects show intermediate patterns, with pMCI cases resembling the findings in AD more closely than cases of sMCI. Sample cases are shown in the figure. Conclusions: The findings encourage us to pursue a reader study to assess classification performance. After a short training phase, readers will be shown images with regional volumetric overlays and asked to assign an individual diagnostic label.
P4-306
RELATIONSHIPS BETWEEN METABOLIC SYNDROME AND COGNITIVE FUNCTIONS IN SOUTH KOREA
Sunggoo Kang, St. Vincent’s Hospital, the Catholic University of Korea, Suwon, South Korea. Background: Several studies suggested that metabolic syndrome may be important in cognitive decline. The objective of our study was to determine the relationship between the metabolic syndrome and cognitive function in Korean elderly above 60 years old. Methods: We examined elderly participants who visited the health promotion center of one college hospital and the Health department. The metabolic syndrome was defined according to the National Cholesterol Education Program guidelines and we categorized the two groups by the presence of the metabolic syndrome. We used the Korean versions of the Consortium to Establish a Registry for Alzheimer’ Disease (CERAD-K) to check the cognitive functions and Short Form of Geriatric Depression Scale (SGDS) to check the depression. And we compared scores between these two groups. Results: There were a total of 93 subjects. Compared with those without the metabolic syndrome, elders with the metabolic syndrome had lower mean total CERAD-K scores (64.2 6 11.1, 69.8 6 9.2, P ¼ 0.010). In all subcategories of CERAD-K except word list learning and recall, trail Making A, the average scores were lower in the metabolic syndrome group than in the control group. After controlling for age, sex, education, smoking, alcohol, physical activity and SGDS-K, multiple regression yielded the metabolic syndrome to be independent associated factor in cognitive function (P ¼ 0.014, R2 ¼ 0.394). Alcohol intake (P ¼ 0.002) and education years (P ¼ 0.001) were also attributing factors to cognitive function. Conclusions: We found a significant relationship between decreased cognitive function and the metabolic syndrome. There are needs for prospective study about probable prevention of dementia when cardiovascular risk factors in those are modulated.
Developing Topics
Table Baseline Signal Intensity
AD/HC pMCI/HC AD/sMCI pMCI/sMCI
Month-12 Signal Intensity
Month-12 Signal Intensity & Percentage Change Over 12 Months
Accuracy (%)
Sensitivity (%)
Specificity (%)
Accuracy (%) Sensitivity (%) Specificity (%)
Accuracy (%) Sensitivity (%) Specificity (%)
80.9 (6.7) 70.7 (7.6) 72.7 (7.8) 58.4 (7.9)
79.6 (10.6) 70.5 (13.0) 65.6 (13.2) 51.5 (13.1)
82.3 (11.1) 71.0 (12.3) 78.5 (10.8) 64.8 (13.4)
86.1 (6.3) 79.2 (7.3) 79.3 (6.7) 62.3 (7.8)
88.4 (6.2) 81.3 (6.8) 83.5 (7.1) 63.1 (8.1)
81.2 (10.3) 77.2 (11.9) 77.9 (11.2) 53.2 (13.1)
91.0 (8.2) 81.4 (11.0) 80.5 (9.3) 70.8 (12.4)
83.2 (10.4) 79.8 (11.1) 79.9 (11.5) 52.2 (13.5)
93.6 (7.4) 82.9 (10.7) 86.4 (8.9) 73.2 (12.3)
Classification accuracy, sensitivity and specificity, expressed as mean (standard deviation).
assessed using bootstrap resampling. Results: Highly significant increases in classification accuracy are achieved when using month-12 signal intensities compared to using baseline signal intensities. Accuracies increased from 81% to 86% for AD/HC, 71% to 79% for pMCI/HC, 73% to 79% for AD/sMCI, and 58% to 62% for pMCI/sMCI. More interestingly, further significant increases in accuracy may be achieved by combining month-12 signal intensities with the percentage changes over 12 months. Using this feature combination yields accuracies of 88% for AD/HC, 81% for pMCI/HC, 84% for AD/sMCI, and 63% for pMCI/sMCI. Conclusions: These results surpass many state-of-the-art image-based classification methods. This study demonstrates that information extracted from serial FDG-PET through regional analysis can accurately discriminate diagnostic groups, a finding that may be usefully applied in the diagnosis of AD, predicting disease course in individuals with MCI, and in the selection of participants for clinical trials.
P4-305
VISUAL PRESENTATION OF REGIONAL VOLUMETRY DATA FOR DIAGNOSTIC DECISION SUPPORT IN ALZHEIMER’S DISEASE MR IMAGING
Rolf Heckemann1, Shiva Keihaninejad2, Katherine Gray3, Daniel Rueckert4, Joseph Hajnal4, Alexander Hammers1, 1The Neurodis Foundation, Lyon, France; 2University College London, London, United Kingdom; 3Imperial College London, Hook, Hampshire, United Kingdom; 4 Imperial College London, London, United Kingdom. Background: Our previous work shows that confidence in an image reader’s diagnosis of Alzheimer’s disease versus normal ageing is favourably influenced by superimposing the grey-scale MR images with colour overlays representing a statistical expression of each anatomical region’s volume compared to a reference group. Combining recent advances in image segmentation software with high-performance computing and access to a large cohort
of images of well-characterized subjects, we re-assessed the potential of this approach. Methods: A robust and accurate automatic anatomical segmentation method, MAPER (multi-atlas propagation with enhanced registration), was applied to all T1-weighted screening MR images provided by the ADNI (Alzheimer’s disease neuroimaging initiative). From each diagnostic group (HS: healthy subjects; sMCI, pMCI: mild cognitive impairment without and with progression within the observation period; AD), three test subjects were randomly chosen. A colour overlay showing regional volumetric z-score as compared to a coarsely age-matched reference group of healthy subjects was generated for each test subject and reviewed in conjunction with the grey-scale images. Results: Onvisual review of the overlayed images, a typical pattern emerges. Normal subjects show predominantly green hues (z-score near zero) and few volumetric outliers. AD subjects show strongly negative z-scores for various regions predominantly located in the temporal lobe and strongly positive z-scores for ventricles, especially the temporal horn of the lateral ventricle. MCI subjects show intermediate patterns, with pMCI cases resembling the findings in AD more closely than cases of sMCI. Sample cases are shown in the figure. Conclusions: The findings encourage us to pursue a reader study to assess classification performance. After a short training phase, readers will be shown images with regional volumetric overlays and asked to assign an individual diagnostic label.
P4-306
RELATIONSHIPS BETWEEN METABOLIC SYNDROME AND COGNITIVE FUNCTIONS IN SOUTH KOREA
Sunggoo Kang, St. Vincent’s Hospital, the Catholic University of Korea, Suwon, South Korea. Background: Several studies suggested that metabolic syndrome may be important in cognitive decline. The objective of our study was to determine the relationship between the metabolic syndrome and cognitive function in Korean elderly above 60 years old. Methods: We examined elderly participants who visited the health promotion center of one college hospital and the Health department. The metabolic syndrome was defined according to the National Cholesterol Education Program guidelines and we categorized the two groups by the presence of the metabolic syndrome. We used the Korean versions of the Consortium to Establish a Registry for Alzheimer’ Disease (CERAD-K) to check the cognitive functions and Short Form of Geriatric Depression Scale (SGDS) to check the depression. And we compared scores between these two groups. Results: There were a total of 93 subjects. Compared with those without the metabolic syndrome, elders with the metabolic syndrome had lower mean total CERAD-K scores (64.2 6 11.1, 69.8 6 9.2, P ¼ 0.010). In all subcategories of CERAD-K except word list learning and recall, trail Making A, the average scores were lower in the metabolic syndrome group than in the control group. After controlling for age, sex, education, smoking, alcohol, physical activity and SGDS-K, multiple regression yielded the metabolic syndrome to be independent associated factor in cognitive function (P ¼ 0.014, R2 ¼ 0.394). Alcohol intake (P ¼ 0.002) and education years (P ¼ 0.001) were also attributing factors to cognitive function. Conclusions: We found a significant relationship between decreased cognitive function and the metabolic syndrome. There are needs for prospective study about probable prevention of dementia when cardiovascular risk factors in those are modulated.