Metabolic Syndrome and Progression of Mild Cognitive Impairment Eric Peters, MD

Conflicts of Interest The presenters and contributors have no financial conflicts of interest.

Background  19 % of people older than 65 develop mild cognitive impairment (MCI), and 46% of MCI develop to dementia within 3 years. There are not many prognostic indicators on predicting prognosis or effective treatment. Metabolic syndrome has been suggested as a risk factor for dementia. Metabolic syndrome defined by HTN, hypercholesterolemia, BMI/waist circumference, Impaired fasting glucose.

Hypothesis Metabolic syndrome in a cohort of mildly cognitively impaired (MCI) subjects is associated with MCI progression and the prevalence of MRI findings related to dementia

Methods Cohort of 103 subjects including MCI and normal controls. 7 excluded for incomplete records or unusable data  -> 96 subjects remained All cohort had axial FLAIR images Cohort followed for 5 years to determine which subjects progressed to Alzheimers Disease (AD) Overall this leads to 3 groups: normal, MCI without progression, and MCI with progression. A previously analyzed cohort of 96 people including patients categorized with MCI and matched normal controls was analyzed using a standardized MRI sequence including FLAIR axial imaging. The cohort was followed and a portion of the MCI group was found to progress to Alzheimer's Dementia (AD).  

Methods The earliest FLAIR axial MRI images available for each subject was retrospectively analyzed by a blinded observer using Olea Sphere * software  Volumes collected included: Total brain parenchymal volume White Matter Hyperintensity (WMH) volume. The earliest FLAIR axial MRI images available for each MCI patient and their matched control was retrospectively analyzed using a combination of Olea Sphere software (Olea Medical Solutions INC, Cambridge, MA), and a blinded observers measurements.  Data collected included total brain parenchymal volume, white matter hyperintensity (WMH) volume, and pattern of distribution of WMH.  *Olea Medical Solutions INC, Cambridge, MA

WMH Native Total volume

Methods Additionally, on the same set of images, a categorization of the distribution of the white matter hyperintensity was assigned. Assignments were labeled: Central Peripheral Mixed The earliest FLAIR axial MRI images available for each MCI patient and their matched control was retrospectively analyzed using a combination of Olea Sphere software (Olea Medical Solutions INC, Cambridge, MA), and a blinded observers measurements.  Data collected included total brain parenchymal volume, white matter hyperintensity (WMH) volume, and pattern of distribution of WMH. 

Peripheral Mixed Central

RESULTS

Data expressed values of WMH (mL), total brain volume (mL), and ratio of WMH to total volume, arranged in ascending order of WMH  Absolute disease volume and ratio seem to correlate roughly, as expected. There is a range of data collected across an entire order of magnitude.  Total brain volume seems fairly consistent May consider grouping patients into categories of volumes rather than absolute value, since there is a lot of subjectivity to the numbers.

Number of patients assigned each pattern of distribution Pearson Chi-Square = 12.55 (df = 4, P = .014)

WMH volume versus Disease Group Kruskal-Wallis Procedure (P = .094)​ Greater burden of white matter disease volume was observed for AD subjects as compared to the MCI and Normal subjects. However, the effect was weak, and not statistically significant.  Kruskal wallace compares multiple groups.  P 0.094 leans towards there being a difference, but not significant.

WMH volume versus Pattern Kruskal-Wallis Procedure (P < .001) P = .050 P < .001 Greater burden of white matter disease was noted for the peripheral and mixed pattern when compared to the central pattern. No statistically significant difference in white matter disease burden was noted between the mixed and peripheral patterns.

Pattern of WMH volume within Disease Group Statistically significant differences in white matter disease volume among the patterns were noted only for MCI and Normal group. Specifically, the mixed pattern presented with greater burden of white matter disease than the central pattern. Although this pattern was also true for the AD subjects, the difference was not statistically significant. In addition, the variability in the white matter disease burden was wider for the central and mixed pattern.

Association of WMH volume with Disease Group and/or Pattern Analysis of Covariance model was conducted to determine whether the burden of white matter disease was independently associated with cognitive disease (AD/MCI/Normal) and pattern of white matter disease. Only the pattern of white matter disease was significantly associated with white matter disease burden (P = .020). Cognitive disease (group) appears to have a weak effect on white matter burden when adjusting for the pattern of white matter disease (P= .221). No interaction between cognitive disease and pattern of white matter burden was observed (P=.994).

Association of WMH volume with Disease Group and/or Pattern After correcting for Total Brain Volume However, after adjusting for whole brain volume, the effect of cognitive disease on white matter burden strengthens, though it is still statistically weak (P = .131). The effect of pattern of white matter disease on burden also strengthened (P = .008) after adjusting for whole brain volume. There was also a statistically significant increase in the R-square (accounting for total variance of white matter disease volume) after adjusting for whole brain volume. Still, no interaction between cognitive disease and pattern of white matter burden was observed (P=.980).

Conclusions There are significant differences of white matter disease burden with respect to pattern of distribution. Within each disease group there are also significant differences of white matter disease burden, when grouped for pattern of distribution. However, we do not know what is the cause.

Discussion Further characterization (including accounting for presence or absence of metabolic syndrome) could yield the distinguishing factor that accounts for the significant difference. This may then allow for prediction of progression of MCI to AD. We think the cause is Metabolic syndrome

Future direction Clinical data collection is continuing with goal to further characterize the cohorts metabolic status.  Further analysis will follow.   Additional blinded reader to review the data independently to confirm results. Long term: Future research including prospectively collected data with full metabolic analysis, larger number of patients to identify the source of statistical variance in the data.

Acknowledgments Adina Achiriloaie, MD Udochukwu Oyoyo, MPH Daniel Kido, MD Brenda Bartnik, PhD Wolff Kirsch, MD. Neurosurgery Center for Research Training and Education Loma Linda University Health Department of Radiology