Intracranial internal carotid artery calcification is not predictive of future cognitive decline

Table 7 Univariable linear regression model to explore the association of baseline imaging biomarkers on dementia with annual rates of change in cognitive scores

Baseline imaging biomarkers of dementia	Estimated annual rate of change in the MMSE score
Baseline imaging biomarkers of dementia	p-value ^§	Coefficient (SE)
WMH volume (1000 mm³)^a	<0.001	−0.011 (0.002)
Total hippocampal volume (100 mm³)^a	<0.001	0.036 (0.004)
AD cortical signature thickness (mm)	<0.001	2.36 (0.257)
Centiloid (5 unit)^a	<0.001	−0.040 (0.004)
Baseline imaging biomarkers of dementia	Estimated annual rate of change in the PACC score
Baseline imaging biomarkers of dementia	p-value ^§	Coefficient (SE)
WMH volume (1000 mm³)^a	0.169	−2.66e−4 (1.87e−4)
Total hippocampal volume (100 mm³)^a	<0.001	0.002 (0.0003)
AD cortical signature thickness (mm)	<0.001	0.099 (0.021)
Centiloid (5 unit)^a	<0.001	−1.969e−3 (3.45e−4)

Abbreviations: MMSE, Mini-Mental State Examination, CDR, Clinical Dementia Rating Scale, PACC, Preclinical Alzheimer Cognitive Composite, WMH volume, white matter hyperintensities volume, Centiloid, measure of global amyloid disposition based on conversion of PIB or AV45 PET SUVRs to a standardized scale, AD cortical signature thickness, cortical thickness in signature regions affected in Alzheimer disease (see Dincer et al. 2020), SE, standard error of beta coefficient
^aCoefficients are demonstrated for 1000-mm³ increment in WMH volume, 100-mm³ increment in total hippocampal volume, and 5-unit increment in Centiloid scale
^§ p-values were corrected for multiple comparisons through estimation of the false discovery rate via the Benjamini and Hochberg method. Tests with a p-value of 0.05 were considered statistically significant in both regression models (denoted in bold)

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