Background Previous findings on the association of statins, plasma lipids, and Parkinson’s disease are confounded by the fact that statins also affect lipid profiles. We prospectively examined plasma lipids and statin use in relation to Parkinson’s disease in the Atherosclerosis Risk in Communities (ARIC) Study. Methods Statin use and plasma lipids were assessed at baseline (Visit 1, 1987–89) and at three triennial visits thereafter (Visits 2–4) until 1998. Potential Parkinson’s cases were identified from multiple sources and validated where possible. The primary analysis was limited to incident Parkinson’s cases diagnosed between 1998 and 2008. Odds ratios and 95% confidence intervals were derived from multivariate logistic regression models. Results Statin use was rare at baseline (0.57%) but increased to 11.2% at Visit 4. During this time frame, total-cholesterol levels decreased, particularly among statin users. Fifty-six Parkinson’s cases were identified after 1998. Statin use before 1998 was associated with significantly higher Parkinson’s risk after 1998 (Odds ratio = 2.39, 95% Confidence interval 1.11–5.13) after adjusting for total-cholesterol and other confounders. Conversely, higher total-cholesterol was associated with lower risk for Parkinson’s after adjustment for statin usage and confounders. Compared to the lowest tertile of average total-cholesterol, the odds ratios for Parkinson’s were 0.56 (0.30–1.04) for the second and 0.43 (0.22–0.87) for the third tertile (Ptrend=0.02). Conclusions Statin use may be associated with a higher Parkinson’s risk, whereas higher total cholesterol may be associated with lower risk. These data are inconsistent with the hypothesis that statins are protective against Parkinson’s disease.

Plasma low density lipoprotein (LDL) cholesterol has been associated both with risk of Parkinson’s disease (PD) and with age-related changes in cognitive function. This prospective study examined the relationship between baseline plasma LDL-cholesterol and cognitive changes in PD and matched Controls. Fasting plasma LDL-cholesterol levels were obtained at baseline from 64 non-demented PD subjects (62.7 ± 7.9 y) and 64 Controls (61.3 ± 6.8 y). Subjects underwent comprehensive neuropsychological testing at baseline, 18-, and 36-months. Linear mixed-effects modeling was used to assess the relationships between baseline LDL-cholesterol levels and longitudinal cognitive changes. At baseline, PD patients had lower scores of fine motor (p<0.0001), executive set shifting (p=0.018), and mental processing speed (p=0.049) compared to Controls. Longitudinally, Controls demonstrated improved fine motor and memory test scores (p=0.044, and p=0.003), whereas PD patients demonstrated significantly accelerated loss in fine motor skill (p=0.002) compared to Controls. Within the PD group, however, higher LDL-cholesterol levels were associated with improved executive set shifting (β=0.003, p<0.001) and fine motor scores (β=0.002, p=0.030) over time. These associations were absent in Controls (p>0.7). The cholesterol - executive set shifting association differed significantly between PDs and Controls (interaction p=0.005), whereas the cholesterol - fine motor association difference did not reach significance (interaction, p=0.104). In summary, higher plasma LDL-cholesterol levels were associated with better executive function and fine motor performance over time in PD, both of which may reflect an effect on nigrostriatal mediation. Confirmation of these results and elucidation of involved mechanisms are warranted, and might lead to feasible therapeutic strategies.

BACKGROUND AND PURPOSE: Total brain volume and total intracranial volume are important measures for assessing whole-brain atrophy in Alzheimer disease, dementia, and other neurodegenerative diseases. Unlike MR imaging, which has a number of well-validated fully-automated methods, only a handful of methods segment CT images. Available methods either use enhanced CT, do not estimate both volumes, or require formal validation. Reliable computation of total brain volume and total intracranial volume from CT is needed because head CTs are more widely used than head MRIs in the clinical setting. We present an automated head CT segmentation method (CTseg) to estimate total brain volume and total intracranial volume. MATERIALS AND METHODS: CTseg adapts a widely used brain MR imaging segmentation method from the Statistical Parametric Mapping toolbox using a CT-based template for initial registration. CTseg was tested and validated using head CT images from a clinical archive. RESULTS: CTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (P , .001) for total intracranial volume and 0.94 (P , .001) for total brain volume. When CTseg was applied to a cross-sectional Alzheimer disease dataset (58 with Alzheimer disease patients and 58 matched controls), CTseg detected a loss in percentage total brain volume (as a percentage of total intracranial volume) with age (P , .001) as well as a group difference between patients with Alzheimer disease and controls (P , .01). We observed similar results when total brain volume was modeled with total intracranial volume as a confounding variable. CONCLUSIONS: In current clinical practice, brain atrophy is assessed by inaccurate and subjective "eyeballing" of CT images. Manual segmentation of head CT images is prohibitively arduous and time-consuming. CTseg can potentially help clinicians to automatically measure total brain volume and detect and track atrophy in neurodegenerative diseases. In addition, CTseg can be applied to large clinical archives for a variety of research studies. ABBREVIATIONS: AD 4 Alzheimer disease; BET 4 Brain Extraction Tool; ICC 4 intraclass correlation coefficient; TBV 4 total brain volume; TIV 4 total intracranial volume; TPM 4 tissue probability map

BACKGROUND/OBJECTIVES: Although several approaches have been developed to provide comprehensive care for persons living with dementia (PWD) and their family or friend caregivers, the relative effectiveness and cost effectiveness of community-based dementia care (CBDC)