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Research ArticleNeurology

Principal Components Analysis of Brain Metabolism Predicts Development of Alzheimer Dementia

Ganna Blazhenets, Yilong Ma, Arnd Sörensen, Gerta Rücker, Florian Schiller, David Eidelberg, Lars Frings and Philipp T. Meyer; for the Alzheimer’s Disease Neuroimaging Initiative
Journal of Nuclear Medicine June 2019, 60 (6) 837-843; DOI: https://doi.org/10.2967/jnumed.118.219097
Ganna Blazhenets
1Department of Nuclear Medicine, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Yilong Ma
2Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
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Arnd Sörensen
1Department of Nuclear Medicine, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Gerta Rücker
3Institute of Medical Biometry and Statistics, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and
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Florian Schiller
1Department of Nuclear Medicine, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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David Eidelberg
2Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
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Lars Frings
1Department of Nuclear Medicine, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
4Center for Geriatrics and Gerontology Freiburg, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Philipp T. Meyer
1Department of Nuclear Medicine, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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  • FIGURE 1.
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    FIGURE 1.

    Patterns of regional brain metabolism. (A) ADCRP derived by PCA (P < 0.05) and (B) significant regions derived by SPM t test (familywise error–corrected P < 0.05), overlaid on MRI template image. Voxels with negative region weights and hypometabolism are shown in “cool” colors, and regions with positive region weights and hypermetabolism are depicted in “hot” colors. Data are presented in neurologic orientation.

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    FIGURE 2.

    Hazard ratios for different predictors, penalized by ridge regression to suppress effects of multicollinearity among them. Normalized 18F-FDG uptake stems from linear combination of normalized 18F-FDG uptake in VOIs with significant hypometabolism and that in VOIs with significant hypermetabolism. APOE reference: APOE positive; sex reference: female. FAQ = FAQ total score; N = number of subjects; n. = normalized. All continuous variables were z transformed.

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    FIGURE 3.

    Kaplan–Meier curves for test dataset. (A) Risk strata determined using PES values alone. (B) Risk strata determined using PI derived from combined model (including PES values). Med. = medium.

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    TABLE 1

    Clinical and Demographic Characteristics of Training and Test Datasets

    Training dataset (n = 272)Test dataset (n = 272)
    CharacteristicMCI-c (n = 87)MCI-nc (n = 185)MCI-c (n = 94)MCI-nc (n = 178)
    Age in y (mean ± SD)75 ± 774 ± 873 ± 773 ± 8
    Sex (no. of men/no. of women)56/31116/6954/40108/70
    MMSE score (mean ± SD)27 ± 228 ± 227 ± 228 ± 2
    APOE ε4 positive (%)63437042
    FAQ score (mean ± SD)1.74 ± 3.14.44 ± 4.71.70 ± 3.04.97 ± 4.7
    Follow-up time (mo)
    Median48474747
    95% CI47–4947–4946–4846–48
    Interquartile range21.326.529.018.0
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    TABLE 2

    Characteristics of Cox Regression Models

    Harrell C index for:
    ModelPredictorHazard ratioP valueAICTraining datasetTest dataset
    ImagingPES2.962 × 10−16783.3*,†0.760.73
    Age1.010.87
    Sex0.980.93
    ClinicalAge1.000.95797.2†,‡0.800.77
    Sex1.180.48
    FAQ1.662.0 × 10−10
    APOE1.850.007
    MMSE1.543.5 × 10−5
    CombinedPES2.467.1 × 10−13749.6*,‡0.840.81
    Age1.010.90
    Sex1.080.72
    FAQ1.492.2 × 10−6
    APOE1.360.18
    MMSE1.510.0001
    • ↵* Imaging vs. combined model: P = 4 × 10E−10.

    • ↵† Imaging vs. clinical model: P = 0.007.

    • ↵‡ Clinical vs. combined model: P = 8 × 10E−9.

    • All continuous variables were z transformed.

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    TABLE 3

    Separation of Risk Strata by Different Models

    Hazard ratioMedian time to conversion (mo)Pairwise log-rank P value
    ParameterLow riskMedium riskHigh riskLow riskMedium riskHigh riskLow vs. mediumMedium vs. highLow vs. high
    PES values alone14.629.7012068361.2 × 10−50.00073.0 × 10−13
    PI derived from combined model (including PES values)14.7515.9212096328.3 × 10−81.2 × 10−52.0 × 10−16

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Journal of Nuclear Medicine: 60 (6)
Journal of Nuclear Medicine
Vol. 60, Issue 6
June 1, 2019
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Principal Components Analysis of Brain Metabolism Predicts Development of Alzheimer Dementia
Ganna Blazhenets, Yilong Ma, Arnd Sörensen, Gerta Rücker, Florian Schiller, David Eidelberg, Lars Frings, Philipp T. Meyer
Journal of Nuclear Medicine Jun 2019, 60 (6) 837-843; DOI: 10.2967/jnumed.118.219097

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Principal Components Analysis of Brain Metabolism Predicts Development of Alzheimer Dementia
Ganna Blazhenets, Yilong Ma, Arnd Sörensen, Gerta Rücker, Florian Schiller, David Eidelberg, Lars Frings, Philipp T. Meyer
Journal of Nuclear Medicine Jun 2019, 60 (6) 837-843; DOI: 10.2967/jnumed.118.219097
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Keywords

  • Alzheimer Dementia
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  • 18F-FDG PET
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