Weight change and body composition in patients with Parkinson's disease

Objective: To compare reports of weight loss and actual measures of body composition to predict nutritional risk in patients with Parkinson's disease and matched control subjects. Design: Patients and control subjects were asked to record prior changes in weight and activity. Body composition was then compared in both groups using percentage ideal body weight (IBW), body mass index (BMI), triceps skinfold (TSF) thickness, midarm muscle circumference, and percentage body fat (BF) as determined by bioelectrical impedance. Subjects: Fifty-one free-living patients with Parkinson's disease and 49 matched control subjects were recruited from the neurology clinic and the surrounding area. Main outcome measures: We anticipated that reported weight loss would be greater and actual measures of body composition would show greater nutritional risk in the patients with Parkinson's disease. Statistical analyses performed: X2 Analysis was used to determine differences in the ratio of patients and control subjects who lost weight. Paired t tests were used to compare amount of weight change and measures of body composition. (Correlations were performed among measures of weight change, body composition, and associated disease factors. Results: Patients with Parkinson's disease were four times more likely to report weight loss greater than 10 lb than the matched control subjects (odds ratio>4.2). Patients reported a mean (+/-standard deviation) weight loss of 7.2+/-2.9 lb and control subjects reported a mean weight gain of 2.1+/-1.6 lb (P<.01). Percentage IBW (P<.02), BMI (P<.009), TSF thickness (P<.005), and percentage BF (P<.022) were lower in patients. Significant correlations (P<.01) were found between reported weight change and percentage IBW, BMI, TSF percentage BF, and stage of the disease. Conclusions/application: Patients with Parkinson's disease appear to be at greater nutritional risk than a matched population. Simple screening and assessment tools can be used to detect nutritional risk.