Demographic and clinical characteristics of subjects with ADHD and HC.

<p>ADHD: Attention-Deficit/Hyperactivity Disorder; HC: Healthy Controls; SD: Standard Deviation; ASRS-18: Adult ADHD Self-Report Scale.</p><p>Demographic and clinical characteristics of subjects with ADHD and HC.</p

An Alzheimer’s Disease-Derived Biomarker Signature Identifies Parkinson’s Disease Patients with Dementia

<div><p>Biomarkers from multiple modalities have been shown to correlate with cognition in Parkinson’s disease (PD) and in Alzheimer’s disease (AD). However, the relationships of these markers with each other, and the use of multiple markers in concert to predict an outcome of interest, are areas that are much less explored. Our objectives in this study were (1) to evaluate relationships among 17 biomarkers previously reported to associate with cognition in PD or AD and (2) to test performance of a five-biomarker classifier trained to recognize AD in identifying PD with dementia (PDD). To do this, we evaluated a cross-sectional cohort of PD patients (n = 75) across a spectrum of cognitive abilities. All PD participants had 17 baseline biomarkers from clinical, genetic, biochemical, and imaging modalities measured, and correlations among biomarkers were assessed by Spearman’s rho and by hierarchical clustering. We found that internal correlation among all 17 candidate biomarkers was modest, showing a maximum pairwise correlation coefficient of 0.51. However, a five-marker subset panel derived from AD (CSF total tau, CSF phosphorylated tau, CSF amyloid beta 42, <i>APOE</i> genotype, and SPARE-AD imaging score) discriminated cognitively normal PD patients vs. PDD patients with 80% accuracy, when employed in a classifier originally trained to recognize AD. Thus, an AD-derived biomarker signature may identify PDD patients with moderately high accuracy, suggesting mechanisms shared with AD in some PDD patients. Based on five measures readily obtained during life, this AD-derived signature may prove useful in identifying PDD patients most likely to respond to AD-based crossover therapies.</p></div

Voxel clusters of relatively increased fractional anisotropy in attention-deficit/hyperactivity disorder subjects compared to healthy controls are shown in red, overlaid on transaxial sections from a reference brain spatially normalized to the Montreal Neurological Institute stereotactic space, in neurological convention.

<p>Statistical maps are displayed with a statistical threshold of p<0.05, corrected for multiple comparisons (false-discovery rate). 1) Middle temporal gyrus white matter (WM) (right); 2) Middle temporal gyrus WM (left); 3) Superior frontal gyrus WM (right); 4) Middle frontal gyrus (right); 5) Cingulate gyrus (right); 6) Superior frontal gyrus (left); 7) Cingulate gyrus (left); 8) Postcentral gyrus (left). R: right.</p

Comparison of GM volumes between ADHD and HC.

<p>GM: Gray Matter; ADHD: Attention-Deficit/Hyperactivity Disorder; HC: Healthy Controls; FDR: False Discovery Rate; nonCom: comparison excluding the comorbidities AHD patients; <i>N</i>: number of significant voxels in each anatomical region; <i>t</i>: value calculated based on the means of Regional Analysis of Volumes Examined in Normalized Space (RAVENS) values of the significant voxels.</p><p>*Talairach coordinates represent center-of-mass obtained with the significance level of <i>p</i><0.001.</p><p>Comparison of GM volumes between ADHD and HC.</p

Cohort characteristics and bivariate cross-sectional analyses.

<p><b>(A)</b> By consensus clinical determination, 47/75 patients were classified as PD-CN, 20/75 PD-MCI, and 8/75 as PDD. <b>(B)</b> Histogram and kernel density plot of age-adjusted DRS scores for the study cohort. Dashed vertical lines represent separations between PDD vs. PD-MCI vs. PD-CN ranges for DRS performance. <b>(C)</b> A bivariate analysis of eight candidate markers previously reported to associate cross-sectionally with cognition confirms associations between PDD and two candidate markers in the present study: Unified Parkinson’s Disease Rating Scale Motor score (UPDRS III, corrected p = 0.002) and Spatial Pattern of Atrophy for Recognition of AD score (SPARE-AD, corrected p = 0.031). An additional marker–CSF measures of Aβ42 –trended towards association with PDD (corrected p = 0.062). Vertical line indicates corrected p<0.05. EGF = Epidermal Growth Factor. MODHY = Modified Hoehn and Yahr. The other nine candidate markers assessed in this study have previously been reported to associate with longitudinal decline in cognition. <b>(D)–(F)</b> Boxplots of the distribution of UPDRS-III score, CSF Aβ42 levels, and SPARE-AD score among the three cognitive classes. Median and interquartile range are shown.</p

Age and memory impairment in PD patients classified as AD-like.

<p><b>(A)</b> Histogram depicting age frequencies for all 75 PD patients in the study (grey bars) vs. subset of PD patients classified as having an AD-like biomarker profile (black bars). AD-like individuals represent a range of ages. <b>(B)</b> DRS-memory domain scores for cognitively impaired (PDD and PD-MCI) PD patients classified as AD-like (black) vs. not AD-like (grey). Age-adjusted DRS memory domain scores are shown. *Two-tailed Mann-Whitney p <0.05. <b>(C-D)</b> Standardized scores (T-scores, where 50 is the mean and each 10-point change from 50 represents one standard deviation) for total immediate free recall <b>(C)</b> and recognition discrimination <b>(D)</b> from the Hopkins Verbal Learning Test-Revised (HVLT-R) for cognitively impaired (PDD and PD-MCI) PD patients classified as AD-like (black) vs. not AD-like (grey). Of note, 3/13 PD patients classified as AD-like were unable to complete testing due to severity of dementia; these individuals are denoted by a square symbol and assigned a T-score of 20. All PD patients classified as not AD-like were able to complete testing. *Two-tailed Mann-Whitney p <0.05.</p

Correlations among candidate biomarkers for cognition.

<p><b>(A)</b> Pairwise Spearman correlation coefficients were calculated for the candidate biomarkers for cognition in PD across the entire cohort. With a few exceptions (<i>e</i>.<i>g</i>. MODHY and UPDRS-III scores (ρ = 0.51), candidate biomarkers did not show high correlations. Shades of red indicate a positive correlation coefficient, white indicates a correlation coefficient of zero, and shades of blue indicate a negative correlation coefficient. The correlation coefficient for each pairwise comparison is reported in the corresponding box. Only 12 candidate biomarkers are shown because five markers are categorical variables with relatively few categories. <b>(B)</b> Hierarchical clustering of biomarker candidates does not suggest a high degree of internal correlation among the 17 markers assessed. Both patients and biomarkers were clustered by Euclidean distance using average linkage, with the patient dendrogram shown to the left of the heatmap and the biomarker dendrogram shown above the heatmap. Each column represents one of 17 biomarkers, and each row represents a patient, with PD-CN (white), PD-MCI (black), and PDD (red) individuals indicated by color. On the heatmap, darker red indicates higher marker levels, and darker blue indicates lower marker levels relative to the mean. A branch that captured all the PDD subjects is highlighted in yellow.</p

Voxel clusters of relatively reduced gray matter (A) and increased gray matter (B) in attention-deficit/hyperactivity disorder subjects compared to healthy controls are shown in red, overlaid on transaxial sections from a reference brain spatially normalized to the Montreal Neurological Institute stereotactic space, in neurological convention.

<p>Statistical maps are displayed with a statistical threshold of <i>p</i><0.001, uncorrected for multiple comparisons. A)1) Angular gyrus (Right); B)1) Supplementary motor area (right); B)2) Superior frontal gyrus (right). R: Right.</p

Voxel clusters of relatively decreased Trace in attention-deficit/hyperactivity disorder subjects compared to healthy controls (HC) are shown in red, overlaid on transaxial sections from a reference brain spatially normalized to the Montreal Neurological Institute stereotactic space, in neurological convention.

<p>Statistical maps are displayed with a statistical threshold of p<0.05, corrected for multiple comparisons (false-discovery rate). 1) Middle occipital gyrus white matter (WM) (left); 2) Caudate nucleus (right); 3) Splenium of corpus callosum (right); 4) Superior fronto-occipital fasciculus (right); 5) Body of the corpus callosum WM (left); 6) Superior corona radiata (right); 7) Superior longitudinal fasciculus (right); 8) Body of the corpus callosum (right); 9) Cingulate gyrus WM (right); 10) Precentral gyrus WM (right); 11) Middle frontal gyrus WM (left); 12) Superior frontal gyrus WM (right); 13) Middle frontal gyrus WM (right); 14) Cingulate gyrus WM (left). R: right.</p

An Alzheimer’s Disease-derived classifier for PDD.

<p><b>(A)</b> Hierarchical clustering of five AD-derived biomarkers (CSF Aβ42, CSF t-tau, CSF p-tau, SPARE-AD score, and <i>APOE</i> genotype) using data from AD and normal controls (NC) in the ADNI cohort. In AD, these five markers are highly correlated with each other. Moreover, clustering of individuals using these five markers produces a branch highly enriched in AD (yellow highlight). <b>(B)</b> Hierarchical clustering of the same five biomarkers using data from the full UPenn Udall cohort (n = 75). In PD, these five markers demonstrate less internal correlation. <b>(C)</b> Hierarchical clustering of the five AD-derived biomarkers using data from only PD-CN and PDD patients (n = 55). Even when only the extreme ends of the PD cognitive spectrum are included, less internal correlation is seen among these five markers in PD than in AD. (<b>D)</b> A logistic regression classifier (black curve) using the five AD-derived biomarkers discriminates AD from NC in the ADNI cohort with high accuracy. Accuracy, area under the curve, sensitivity, and specificity were assessed by ten-fold cross-validation, using the training cohort of ADNI subjects. Applying the exact same AD-derived classifier to the UPenn Udall cohort discriminates PD-CN from PDD patients with 80% accuracy as well (red curve).</p