Association of body mass index and the depletion of nigrostriatal dopamine in Parkinson's disease

Accepted Manuscript Association of body mass index and the depletion of nigrostriatal dopamine in Parkinson’s disease Jae Jung Lee, MD, Jungsu S. Oh, PhD, Jee H. Ham, MD, Dong H. Lee, MD, Injoo Lee, MS, Young H. Sohn, MD, PhD, Jae S. Kim, MD, PhD, Phil Hyu Lee, MD, PhD PII:

S0197-4580(15)00576-X

DOI:

10.1016/j.neurobiolaging.2015.11.009

Reference:

NBA 9449

To appear in:

Neurobiology of Aging

Received Date: 30 April 2015 Revised Date:

17 November 2015

Accepted Date: 17 November 2015

Please cite this article as: Lee, J.J., Oh, J.S., Ham, J.H., Lee, D.H., Lee, I., Sohn, Y.H., Kim, J.S., Lee, P.H., Association of body mass index and the depletion of nigrostriatal dopamine in Parkinson’s disease, Neurobiology of Aging (2015), doi: 10.1016/j.neurobiolaging.2015.11.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Association of body mass index and the depletion of nigrostriatal dopamine in Parkinson’s disease

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Jae Jung Lee,1 MD, Jungsu S. Oh,2 PhD, Jee H. Ham,1 MD, Dong H. Lee,1 MD,

Injoo Lee,2 MS, Young H. Sohn,1 MD, PhD, Jae S. Kim,2 MD, PhD, Phil Hyu Lee,1,3 MD,

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Department of Neurology, Yonsei University College of Medicine, Seoul, Korea

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Department of Nuclear Medicine, Asan Medical Center, College of Medicine, University of

Ulsan, Seoul, Korea

Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea

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A word count: 168 in the abstract, 3026 in the main text, and 40 references.

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This article includes 1 figure, 4 tables, and supplementary data.

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Key words: Parkinson’s disease, Body mass index, Dopamine transporter activity

Correspondence: Phil Hyu Lee, MD, PhD. Department of Neurology, Yonsei University Medical College, 250 Seongsanno, Seodaemun-gu, Seoul, 120-752, South Korea. Tel: +82-2-2228-1608, Fax: +82-2-393-0705. E-mail: [email protected]

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ACCEPTED MANUSCRIPT Author contributions Lee JJ designed and conducted the study, analyzed clinical data, interpreted data, and drafted the manuscript. Oh JS and Lee I analyzed imaging data. Ham JH and Lee DH conducted the

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study and collected clinical data. Sohn YH and Kim JS conducted the study and analyzed imaging data. Lee PH designed and conducted the study, interpreted data, drafted the

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manuscript, and supervised the study.

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Abstract Several antecedent studies had reported close relationship between low body weight and Parkinson’s disease (PD). However, there have been few investigations about the role of body

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weight to nigrostriatal dopaminergic neurodegeneration. This study enrolled 398 de novo patients with PD whom underwent [18F] N-(3-Fluoropropyl)-2β-carbon ethoxy-3β-(4-

iodophenyl) nortropane positron emission tomography scan and body mass index (BMI)

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measurement. The relationships between BMI and dopamine transporter (DAT) activity was analyzed using linear regression analysis. A multivariate analysis adjusted for age, gender,

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disease duration, smoking status, coffee and tea consumption and residence area revealed that BMI remained independently and significantly associated with DAT activity in all striatal subregions. Moreover, multiple logistic regression analyses showed that BMI was a significant predictor for the lowest quartile of DAT activity in the anterior putamen, ventral

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striatum, caudate nucleus, and total striatum. The present findings suggest that a low BMI might be closely associated with low density of nigrostriatal dopaminergic neurons in PD,

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which could support the evidence for the role of low body weight to PD-related pathologies.

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ACCEPTED MANUSCRIPT Nutrition is the ingestion and assimilation of food by living organisms for the production of energy that is used to enhance growth and replace existing tissues. In humans, nutrition is indispensable for health and the maintenance of homeostasis in body systems. Thus, the

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unsteadiness or deterioration of a harmonious nutritional balance can lead to a variety of disorders (Flegal, et al., 2007).

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Low body weight is frequently associated with old age and poverty and is highly correlated with negative health outcomes, including mortality and morbidity (Harrington, et al., 2009).

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On the other hand, the obesity a well-known representative risk factor against overall cardiovascular disorders, even extends to diverse categories of human disease, is responsible for increased all-cause mortality, either (Flegal, et al., 2013). In addition, an inverse association between BMI and mortality has been reported in patients with cardiovascular

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diseases, such as coronary heart disease, heart failure, and stroke (Morse, et al., 2010; Vemmos, et al., 2011). Regarding the association of body weight and PD, several studies showed that PD patients had a lower body mass index (BMI) than control subjects, and this

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pattern seemed to be established 10 years prior to the diagnosis of PD (Chen, et al., 2003). Moreover, a low body weight in PD was more pronounced in patients with greater disease

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severity (van der Marck, et al., 2012). These observations may indicate that a low BMI may be closely coupled with PD-related pathologies, although it is possible that PD itself may influence body morphology. In the present study, we explored the association of BMI with nigrostriatal dopamine in patients with de novo PD to identify the role of BMI in dopaminergic neuronal loss by quantitatively analyzing [18F] N-(3-Fluoropropyl)-2β-carbon ethoxy-3β-(4-iodophenyl) nortropane positron emission tomography (FP-CIT PET) scans.

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Patients and Methods Subjects This cohort study recruited consecutive PD patients attended the Movement Disorders Clinic

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at the Yonsei University Severance Hospital from March 2009 to June 2013. All subjects were drug-naïve de novo PD patients who completed an 18F-FP-CIT PET scan at their initial diagnosis, which was performed according to the clinical criteria of the UK PD brain bank

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(Gibb and Lees, 1988). Patients with focal lesions or destructive etiologies in the basal

ganglia confirmed by brain magnetic resonance imaging (MRI) were excluded from the

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present study. During the follow-up period, patients whose initial diagnosis of PD was changed to atypical parkinsonian syndrome were also excluded. Motor symptoms were assessed using the Unified PD Rating Scale (UPDRS) motor score (part III) at the time of initial PD diagnosis (during the off status). The BMI and Mini Mental State Examination

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(MMSE) were also evaluated at the time of PD. Other variables including smoking status (non-smoker, ex-smoker, or current smoker), coffee and tea consumption (cups/day), and

their families.

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residence area (rural or urban) were determined via telephone interview with the subjects or

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Standard protocol approvals, registrations, and patient consents We received approval from the Yonsei University Severance Hospital ethical standards committee on human experimentation for experiments using human subjects.

Assessment of BMI Subjects’ height and weight were assessed using anthropometric measurement at their initial visit to the outpatient clinic, and BMI was calculated using the standard formula: weight

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ACCEPTED MANUSCRIPT (kg)/height (m2). The BMI assessment and 18F-FP-CIT PET scans were performed within 3

months of each other for all participants. Based on the distribution of the BMI data for the PD patients, BMI level was categorized into quintiles: Q1 (BMI < 20.0, n=54), Q2 (20.0 ≤ BMI <

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23.0, n=109), Q3 (23.0 ≤ BMI < 24.0, n=64), Q4 (24.0 ≤ BMI < 26.0, n=94), and Q5 (26.0 ≤ BMI, n=77). In addition, the study subjects were re-classified according to the conventional Asian BMI classification criteria by World Health Organization (Organization, 2000): BMI <

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18.5, Underweight; 18.5 – 22.9, Normal range; 23 – 24.9, Overweight at risk; 25 – 29.9,

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Obese I; > 30, Obese II.

MRI acquisition and volumetric analysis of striatum

A high resolution 3D T1 weighted MRI were used in current analyses configured with the following acquisition parameters: axial acquisition with a 224×256 matrix; 256×256

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reconstructed matrix with 182 slices; 220 mm field of view; 0.98×0.98×1.2 mm3 voxels; TE 4.6 ms; TR 9.6 ms; flip angle 8°; and slice gap 0 mm. All image process of striatal volumetry was automatically performed using CIVET pipeline software (Montreal Neurological Institue,

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Canada). Skull stripping was performed by using a Brain Extraction Tool algorithm. The extracted volumes were classified as white and gray matter, cerebrospinal fluid (CSF), and

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background. The segmentation results of the CSF were masked out using a CSF classification map to exclude false positive results. We employed the automatic anatomical labeling (Tzourio-Mazoyer, et al., 2002) and ITK-SNAP 3.4.0 (www.itksnap.org) (Yushkevich, et al., 2006) to parcellate the subcortical deep nuclei into the anterior and posterior putamen, ventral striatum, and caudate nucleus. The total striatal volume was calculated as sum of all the above subregions. In order to exclude confounders regarding individual brain size, acquired volume data were analyzed under controlled age, sex, disease duration, and intracranial volume.

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F-FP-CIT acquisition

To assess the degeneration of nigrostriatal dopaminergic neurons via an analysis of dopamine

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transporter (DAT) activity, 18F-FP-CIT PET scans were performed using a GE Discovery STe (DSTE) PET-CT scanner (GE Healthcare Technologies, Milwaukee, WI), which obtains

images with three-dimensional resolution of 2.3 mm full width at half-maximum. All subjects

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fasted for at least 6 hours before PET scanning. After fasting, 5mCi (185 MBq) of 18F-FP-CIT was injected intravenously, and images were acquired in a three-dimensional mode at 120

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KVp and 380 mAs during a 20-minutes session that occurred 90 minutes following injection.

Quantitative analyses of the 18F-FP-CIT image data

The quantitative analyses of the 18F-FP-CIT PET images were carried out according to a

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previously published procedure (Oh, et al., 2012). Image processing was performed using SPM8 (Wellcome Department of Imaging Neuroscience, Institute of Neurology, UCL, London, UK) under MATLAB 2013a for Windows (MathWorks, Natick, MA). Quantitative

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analyses were based on volumes of interests (VOIs), which were defined based on a template in standard space. All reconstructed PET images were spatially normalized to the Montreal

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Neurology Institute (MNI) template space using a standard 18F-FP-CIT PET template, which was made using the 18F-FP-CIT PET and T1 MRI scans of 13 normal controls to remove inter-subject anatomical variability. Twelve VOIs of bilateral striatal subregions and one occipital VOI were drawn on a coregistered spatially normalized single T1 MRI and 18F-FPCIT PET template image on MRIcro version 1.37 (Chris Rorden, Columbia, SC), based on a previous study (Oh, et al., 2012). These VOIs were adjusted by a minor translation in our inhouse VOI editing software called ANTIQUE (Oh, et al., 2014). Each unilateral striatum was divided into 6 subregions, comprised with 2 caudate subregions (anterior and posterior),

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the ventral striatum was determined from the anterior boundary of the striatum to the level of the anterior commissure coronal plane. The reference line that demarcate between anterior

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and posterior part of the respective caudate and putamen was the anterior commissure at coronal plane in common, and anterior–posterior commissure transaxial plane for posterior part of putamen from ventral part of it. The outer boundaries of the striatal subregions were

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visually determined from the characteristic of increased striatal activity, which readily

distinguished these subregions from extrastriatal structures. Through combination of these

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subregions, the unilateral total striatum was redefined as the sum of all 6 unilateral striatal subregions, and the unilateral total caudate was sum of unilateral anterior and posterior caudate subregions. Then, using the DAT activity concentration in each VOI, we estimated the surrogate of non-displaceable binding potential (BP), defined as (mean standardized

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uptake value [SUV] of the striatal subregions VOI - mean SUV of the occipital VOI)/mean SUV of the occipital VOI (Innis, et al., 2007). Finally, the hemispheres in which withinsubject DAT activity in the posterior putamen of one hemisphere was lower relative to the

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other hemisphere were selected for analysis.

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Statistical analysis

To assess the demographic characteristics among BMI subgroups, an analysis of variance was used to compare mean values of continuous variables, the Kruskal–Wallis test was used to compare continuous variables that did not follow a normal distribution, and χ2 test was used for categorical variables. The normality of the continuous variables was evaluated using the Kolmogorov–Smirnov test. To determine whether there was a relationship between BMI and DAT uptake in the striatal subregions, partial correlation coefficients were calculated for each striatal subregion after adjusting for age at onset and gender. A multiple linear regression

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analysis was adopted to identify significant contributing factors to DAT activity in the striatal subregions. This analysis was constructed using two models. Model 1 included age at onset, gender, interval of BMI to PET, and BMI as independent variables. Model 2 included the

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variables from Model 1 as well as disease duration, smoking status, coffee and tea consumption, and residence areas that were known to be associated with dopaminergic

density. The Kruskal–Wallis test in conjunction with a nonparametric Bonferroni method for

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multiple comparisons was used to compare the DAT activity in each striatal subregion among the BMI subgroups. Finally, the DAT activity in each striatal subregion was divided into

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quartiles, and a logistic regression analysis in a forward step-wise manner was used to estimate independent predictors of the highest and lowest quartiles of DAT activity. The independent variables included BMI, age at onset, gender, interval of BMI to PET, disease duration, smoking, coffee and tea consumption, and residence area. A two-tailed p-value

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<0.05 was considered to indicate statistical significance, and all statistical analyses were performed using SPSS for Windows 18.0 (SPSS, Inc., Chicago, IL).

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Results

Demographic characteristics

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A total of 398 patients were enrolled in the present study. The mean age at onset was 64.4 ± 10.1 years, 217 patients (54.4%) were female, the mean disease duration was 1.4 ± 1.1 years, the mean UPDRS III score was 24.2 ± 11.3, and the mean MMSE score was 26.1 ± 3.0. The demographic characteristics according to BMI quintile and the conventional Asian BMI classification criteria are summarized in Table 1 and Supplementary Table e-1, respectively. There were no significant differences in overall clinical characteristics including volume of

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striatal subregions among BMI subgroups (Table 1). The UPDRS III score was higher in the lowest BMI quintile group than in the other groups (p = 0.003).

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Correlation analyses of BMI and DAT activities in striatal subregions The correlation analysis revealed that age was negatively correlated with DAT activity in the anterior putamen (r = - 0.116, p = 0.022), ventral striatum (r = - 0.183, p < 0.001), caudate

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nucleus (r = - 0.35, p < 0.001), and total striatum (r = - 0.176, p < 0.001), but not in the posterior putamen. The DAT activity of each striatal subregion had significant inverse

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correlation with UPDRS motor score as follows: anterior putamen (r = - 0.215, p < 0.001), posterior putamen (r = - 0.157, p = 0.005), ventral striatum (r = - 0.164, p = 0.003), caudate nucleus (r = - 0.188, p = 0.001), and total striatum (r = - 0.199, p < 0.001). With MMSE scores, a strong positive correlation with DAT activity was evident in caudate nucleus (r = -

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0.147, p = 0.005), but not in elsewhere (anterior putamen, r = 0.04, p = 0.449; posterior putamen, r = -0.087, p > 0.999; ventral striatum, r = 0.072, p = 0.169; total striatum, r = 0.055, p = 0.301). In addition, BMI level showed significant positive correlation with DAT

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activities in all striatal subregions; anterior putamen (r =0.159, p = 0.001), posterior putamen (r = 0.126, p = 0.012), ventral striatum (r = 0.136, p = 0.007), caudate nucleus (r = 0.15, p =

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0.003), and total striatum (r = 0.161, p = 0.001). After adjusting for age at onset and gender, these positive associations remained being significant in the anterior putamen (r = 0.162, p = 0.001; Figure 1A), posterior putamen (r =0.133, p = 0.009; Figure 1B), ventral striatum (r = 0.134, p = 0.008; Figure 1C), caudate nucleus (r =0.159, p = 0.002; Figure 1D), and total striatum (r =0.164, p = 0.001; Figure 1E).

Independent contribution of BMI to DAT activity in striatal subregions The results of a multiple linear regression analysis are presented in Table 2. In Model 1 of the

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multiple linear regression analysis, positive associations were found between BMI and DAT activity in the anterior putamen (ß = 0.036, p = 0.001), posterior putamen (ß = 0.022, p = 0.009), ventral striatum (ß = 0.027, p = 0.006), caudate nucleus (ß = 0.032, p = 0.002), and

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total striatum (ß = 0.029, p = 0.001). In Model 2, which was a stricter analysis that incorporated additional independent variables including disease duration, smoking status, coffee and tea consumption, and residence area, BMI remained independently and

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significantly associated with DAT activity in the anterior putamen (ß = 0.036, p = 0.004), posterior putamen (ß = 0.02, p = 0.017), ventral striatum (ß = 0.029, p = 0.011), caudate

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nucleus (ß = 0.028, p = 0.015), and total striatum (ß = 0.027, p = 0.005). In addition, we further assessed correlation between BMI and volume of each striatal subregion by means of partial correlation analysis. A BMI was not relevant to volume of any striatal subregions and

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total striatum (Supplementary Table e-2).

Association of the lowest BMI and striatal DAT activities The comparisons of DAT activity in each striatal subregion among the BMI subgroups are

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presented in Table 3. The lowest BMI quintile group (Q1) exhibited the greatest decrease in DAT activity compared with other BMI quintile subgroups. These differences were

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significant in the anterior putamen (1.58 ± 0.73, p = 0.006), posterior putamen (0.87 ± 0.47, p = 0.028), ventral striatum (1.82 ± 0.67, p = 0.001), caudate nucleus (1.41 ± 0.73, p = 0.003), and total striatum (1.38 ± 0.59, p = 0.002). Furthermore, seventeen patients whose BMI was less than 18.5 which is conventionally known threshold value as an underweight were included in this group, and their striatal DAT acitivities were found to be even lower: anterior putamen, 1.41 ± 0.49; posterior putamen, 0.77 ± 0.26; ventral striatum, 1.55 ± 0.39; caudate nucleus, 1.32 ± 0.44; total striatum, 1.24 ± 0.33 (Supplementary Table e-3). Additionally, we

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ACCEPTED MANUSCRIPT performed comparative analysis of DAT activity in each striatal subregion among BMI subgroups based the conventional Asian BMI classification criteria to evaluate the effect of obesity on nigrostriatal dopamine density. As expected, the greatest DAT decrease were

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observed in Underweight subgroup, while the DAT activities of the Obese II subgroup did not differ from any other subgroups except for Underweight in all striatal subregions

(Supplementary Table e-3). The results of the multiple logistic regression analysis adjusted

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for age at onset, gender, interval of BMI to PET, disease duration, smoking, coffee and tea consumption, and residence area are presented in Table 4. BMI was a significant independent

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predictor of the lowest quartile of DAT activity at anterior putamen (odds ratio [OR]: 0.888; p = 0.012), ventral striatum (OR: 0.835; p < 0.001), caudate nucleus (OR: 0.899; p = 0.028), and total striatum (OR: 0.857; p = 0.002). Meanwhile, BMI did not significantly predict the

Discussion

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highest quartile of DAT activity in any of the striatal subregions.

Based on analysis of the association between BMI and DAT activity using quantitative

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analyses of 18F-FP-CIT scans, the present study demonstrated that BMI level exhibited a significant linear correlation with the severity of nigrostriatal depletion in all of the

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investigated striatal subregions and dopaminergic depletion was more prominent in the lowest BMI quintile group compared to other quintile groups. After adjusting for confounding factors that may have influenced on loss of nigrostriatal dopaminergic neurons in PD, a BMI remained as an independent predictor of a decrease in DAT activity. The present data suggest that a low body weight might have an intimate association with lower density of nigrostriatal dopamines in PD, which could provide the evidence for the possible negative implication of low body weight to PD pathologies.

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Several studies showed that patients with PD had reduced BMI and other indexes of relative to healthy controls (Markus, et al., 1993; van der Marck, et al., 2012). In general, both energy intake and energy expenditure contribute to the development of weight loss during the course

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of PD. PD-related difficulties in chewing and swallowing (Cereda, et al., 2014), decreased hand-to-mouth coordination (Bachmann and Trenkwalder, 2006), loss of taste and smell

(Hawkes, 2006), alterations in gastrointestinal motility (Pfeiffer, 2003), or loss of appetite

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could lead to decreases in energy intake. On the other hand, PD-related tremor and rigidity or abnormalities in the neuroendocrine regulation of growth hormone-, orexin-, or leptin-related

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signaling pathways may be associated with increased energy expenditure (Hommel, et al., 2006; Markus, et al., 1992; Sirtori, et al., 1972; Willie, et al., 2001). The prevalence of undernutrition in community dwelling PD patients is approximately 15% (Jaafar, et al., 2010) and a meta-analysis found that 3 – 60 % of PD patients are at risk of malnutrition (Sheard, et

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al., 2011). In terms of the prognosis for a PD patient, undernutrition can, in and of itself, influence quality of life and the length of hospital stay, raise the cost of care, delay wound healing, and increase infectious complications that may in turn contribute to increased

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morbidity and mortality (Norman, et al., 2008). In patient with PD, the effect of weight loss on neuropathological changes and disease progression has yet to be fully characterized.

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However, weight loss is known to be associated with a higher burden of Alzheimer’s disease biomarkers of β-amyloid and tau (Vidoni, et al., 2011), and accelerates clinical progression of Alzheimer’s disease (Guerin, et al., 2005). In the present study, we found that there was a considerable attenuation of DAT activity in PD patients with the lowest BMI quintile compared to the other BMI subgroups. Irrespective of the issues related to the prognosis for PD, this finding provides an additional clue that undernutrition, even in the early stage of PD, may result in insufficient or limited capacity for the neuroprotection that is needed to overcome neurodegenerative changes in the substantia nigra of the midbrain. Thus, PD

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patients with low body weight likely undergo an extraordinary depletion of the dopaminergic system compared with PD patients with well-nourished status.

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Several functional neuroimaging studies have demonstrated that dopaminergic denervation in PD patients is most evident in the putamen, especially the posterodorsal area (Morrish, et al., 1996; Nandhagopal, et al., 2009), and that subregional differences of dopamine depletion are

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more pronounced in the early stages of PD (Nandhagopal, et al., 2009). This pattern reflects the neuropathological characteristics of PD, where dopaminergic neuronal loss or the density

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of α-synuclein is more severe in the ventrolateral portion of the substantia nigra, which projects to the posterior part of the putamen. Meanwhile, neuronal loss in the medial portion of the substantia nigra projecting into anterior putamen or caudate is less prominent. Interestingly, the present study showed that decreased DAT activity in patients with the

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lowest BMI quintile relative to other quintile subgroups was consistently observed across the striatal subregions, including the posterior putamen, anterior putamen, ventral striuatum and caudate. This finding may suggest that detrimental effect of undernutrition on dopaminergic

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neurons is not region specific within the substantia nigra. Thus, undernutrition may not act as an initiation factor of PD producing regional selectivity, but rather may act as non-specific

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mechanisms of dopaminergic neuronal degeneration, such as oxidative stress, excitotoxicity, mitochondrial dysfunction, and neuroinflammation (Dexter and Jenner, 2013). In fact, some evidence suggests that adipose tissue produces soluble tumor necrosis factor-α (TNF-α) receptor, which could have protective properties by counteracting TNF-α related inflammatory process (Ferrari, et al., 1995; Mohamed-Ali, et al., 1999). In addition, underweight status is associated with neurohormonal dysfunction and cytokine activation in patients with chronic heart failure (Anker, et al., 1999). Therefore, a decrease in adiposity may leave an individual prone to exaggerated systemic inflammation, which in turn, may

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exert a further negative influence on nigral dopaminergic neurons that are already undergoing the neurodegenerative process of PD.

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Alternatively, it is possible that the greater weight loss in patients with PD may be a consequence of relatively severe striatal dopamine depletion, as dopamine signaling plays an important role in the regulation of food intake (Ravussin and Bogardus, 2000). Dopamine is

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closely coupled with motivation and reward, and food intake induces a dopamine release in the striatum thereby mediating the reward effect (Volkow, et al., 2012). Indeed, several

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human studies have demonstrated a decrease in available striatal dopamine D2 receptors in obese (de Weijer, et al., 2011; Wang, et al., 2001) and that BMI is negatively correlated with D2 receptor availability (Wang, et al., 2001); however, the striatal DAT activity of healthy subjects does not appear to be associated with BMI (van de Giessen, et al., 2013). In addition,

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dysfunction of serotonergic system which has been proposed to regulate the appetite appears to contribute to weight loss in PD patients because abnormal BMI changes are linked with serotonin transporter availability in PD (Politis, et al., 2011). Moreover, experimental

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research has demonstrated that insulin and leptin receptors are expressed not only in the hypothalamus but also in the ventral tegmental area and substantia nigra, where they act as

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bioactive molecules responsible for nutritional balance (Figlewicz, et al., 2003). Accordingly, striatal dopamine depletion in PD patients, which is more pronounced compared with that in healthy controls, may have a detrimental influence on the neuroendocrine system responsible for nutritional balance, which could lead to undernourishment. Nevertheless, further study is required to examine the cause and effect relationship between BMI and striatal dopamine depletion in patients with PD.

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With regard to the initiation of the weight loss process, it is known that PD patients begin to lose weight several years before the disease is diagnosed. It is likely that the administration of nutritional interventions during the presymptomatic or premotor stages of PD may have a

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beneficial effect on dopaminergic neurons via correction of systemic conditions that are related to being underweight. On the other hand, some epidemiological evidence has

suggested that similar to chronic systemic diseases, weight gain or obesity is associated with

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increased risk of PD (Hu, et al., 2006; Ragonese, et al., 2008; Vikdahl, et al., 2014). In an analysis of relationship between obesity and nigrostriatal dopaminergic density, a DAT

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activity in the Obese II subgroup (i.e., BMI > 30) did not differ from the other subgroups based on the conventional Asian BMI classification criteria. Taken together, we consider that the relationship between BMI and DAT activity in the present data was not sufficient to exhibit an inverted U pattern which might reflect detrimental effect of obesity against

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dopaminergic neurons in PD. However, given that a sample size of the obese II subgroup in this study is small, a larger sample size and clarifying distinct risk factors in terms of under-

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and overweight against PD should be required to draw a confirmative conclusion on this issue.

There are several strengths and limitations to this study. The present study is the first to

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evaluate the relationship between BMI and nigrostriatal dopaminergic density using a detailed quantification of DAT activity and a relatively large number of de novo PD patients. However, the possible involvement of patients with atypical parkinsonian syndrome could not be completely excluded due to the enrollment of early-stage PD patients and a relatively short follow-up period; this may have biased the results. Second, several known confounding factors that greatly influence to nigrostriatal dopaminergic density were controlled in the present study, but it is possible that other lifestyle-related confounding factors may have altered the results. Third, the present study calculated BMI using objective anthropometric

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ACCEPTED MANUSCRIPT measures of height and weight rather than the self-report of patients. Even though BMI is a widely used indicator of obesity, limitations of BMI as a precise indicator of the nutritional

status of an individual are weak points associated with its use in the current study (Freeman,

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et al., 1995; Romero-Corral, et al., 2008).

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ACCEPTED MANUSCRIPT Disclosure: The authors report no disclosures relevant to the manuscript.

Study funding

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This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health

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& Welfare, Republic of Korea (grant number: HI14C0093).

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ACCEPTED MANUSCRIPT Figure Legend Figure 1. Correlation analyses between body mass index (BMI) and dopamine transporter (DAT) activities in striatal subregions. The BMI in patients with Parkinson’s disease was

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positively correlated with the DAT activity in the anterior putamen (r = 0.162, p = 0.001; A), posterior putamen (r = 0.133, p = 0.009; B), ventral striatum (r = 0.134, p = 0.008; C),

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caudate nucleus (r = 0.159, p = 0.002; D), and total striatum (r = 0.164, p = 0.001; E).

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Table 1. Demographic characteristics of patients with Parkinson’s disease according to the level of body mass index (BMI).

Q2

Q3

Q4

Q5

(BMI < 20.0)

(20.0 ≤ BMI< 23.0)

(23.0 ≤ BMI < 24.0)

(24.0 ≤ BMI < 26.0)

(26.0 ≤ BMI )

(n=54)

(n=109)

(n=64)

(n=94)

(n=77)

p

Age at diagnosis, yr

67.1 ± 11.9

65.1 ± 9.5

67.6 ± 9.9

65.3 ± 10.7

65.4 ± 8.8

ns

Onset age

65.4 ± 12.1

63.4 ± 9.4

66.1 ± 9.7

64.0 ± 10.7

63.9 ± 9.0

ns

Gender, ratio (M/F)

0.93 (26/28)

0.70 (45/64)

0.88 (30/34)

0.96 (46/48)

0.79 (34/43)

ns

Disease duration, yr

1.7 ± 1.3

1.0 ± 1.2

1.4 ± 1.1

1.2 ± 0.9

1.5 ± 1.3

ns

UPDRS III

28.9 ± 12.7

25.0 ± 10.7

25.6 ± 11.2

21.0 ± 10.7

22.6 ± 10.8

0.003

MMSE

25.4 ± 4.0

26.0 ± 3.8

25.7 ± 3.5

26.7 ± 2.8

26.4 ± 3.4

ns

Education, yr

9.9 ± 5.3

8.9 ± 5.3

9.5 ± 4.8

9.4 ± 5.1

9.8 ± 5.4

ns

0.18 (7/39)

0.26 (19/72)

0.16 (7/43)

0.10 (7/71)

0.30 (14/46)

ns

30.4

21.5

23.5

42.1

44.1

ns

1.5 ± 0.8

1.4 ± 0.8

1.8 ± 1.1

1.6 ± 1.0

ns

1.3 ± 0.6

1.2 ± 0.5

1.4 ± 0.7

ns

(rural/urban) Smoking, % Coffee consumption (cups/day) Tea consumption

1.6 ± 0.9 1.1 ± 0.4

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Residence, ratio

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Characteristics

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Q1

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BMI

1.2 ± 0.6

856.1 ± 404.1

793.7 ± 359.3

791.9 ± 345.9

853.7 ± 399.4

869.7 ± 414.3

ns

Posterior putamen

677.3 ± 288.4

623.2 ± 266.4

633.8 ± 272.9

673.7 ± 331.4

672.9 ± 330.2

ns

Ventral striatum

326.5 ± 54.1

319.6 ± 49.5

320.1 ± 49.4

323.1 ± 46.0

325.2 ± 69.7

ns

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(cups/day) Volume of striatal subregion (mm3) Anterior putamen

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3288.8 ± 469.4

3392.8 ± 583.8

3284.8 ± 386.13

3353.5 ± 428.3

3369.5 ± 704.0

ns

Total striatum

5148.7 ± 1040.7

5129.4 ± 1082.3

5030.6 ± 851.1

5203.9 ± 957.5

5237.4 ± 1208.3

ns

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Caudate nucleus

Values are expressed as mean (standard deviation) or ratio or number.

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UPDRS = unified Parkinson’s disease rating scale; MMSE = Mini-Mental State Examination; ns = not significant

ACCEPTED MANUSCRIPT Table 2. Factors associated with dopamine transporter activities in each striatal subregion by a multiple linear regression analysis. Model 1a

Model 2b

Variables

ß

p

R2

Variables

ß

p

R2

Anterior putamen

BMI onset age

0.036 -0.008

0.001 0.016

0.065

BMI onset age

0.036 -0.007

0.004 0.048

0.088

gender

0.257

< 0.001

gender coffee

0.271 0.081

0.001 0.04

tea ex-smoker

0.135 0.205

0.04 0.044

BMI onset age

0.02 0.006

0.017 0.019

gender coffee ex-smoker 0.006 0.101 BMI < 0.001 onset age < 0.001 gender ex-smoker 0.002 0.207 BMI < 0.001 onset age < 0.001 gender ex-smoker 0.001 0.102 BMI < 0.001 onset age < 0.001 gender

0.166 0.067 0.178 0.029 -0.011 0.237 0.195 0.028 -0.023 0.385 0.221 0.027 -0.009 0.269

0.004 0.014 0.011 0.011 0.001 0.002 0.034 0.015 < 0.001 < 0.001 0.02 0.005 0.002 < 0.001

ex-smoker

0.199

0.013

0.009 0.029

gender

0.142

0.005

Ventral striatum

BMI onset age gender

0.027 -0.012 0.247

Caudate nucleus

BMI onset age gender

0.032 -0.024 0.366

Total striatum

BMI onset age gender

0.029 -0.01 0.257

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0.091

0.087

0.189

0.111

Model 1 was adjusted for onset age and gender, and interval of BMI to PET.

b

Model 2 was adjusted for onset age, gender, interval of BMI to PET, disease duration, smoking (non-

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a

0.04

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0.022 0.005

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BMI onset age

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Posterior putamen

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Striatal subgroup

smoker, ex-smoker, current smoker), coffee (cups/day), tea (cups/day), and residence (rural or urban).

ACCEPTED MANUSCRIPT Table 3. DAT activities in each striatal subregion according to the level of body mass index (BMI). BMI subgroup Q2

Q3

Q4

Q5

p

,a

1.93 ± 0.63

1.93 ± 0.57

1.93 ± 0.58

2.05 ± 0.91

0.006

Posterior putamen

,b

0.87 ± 0.47*

1.03 ± 0.44

1.04 ± 0.41

1.02 ± 0.37

1.13 ± 0.75

0.028

Ventral striatum

1.82 ± 0.67*,c

2.27 ± 0.61

2.23 ± 0.57

2.16 ± 0.53

2.27 ± 0.72

0.001

Caudate nucleus

,d

1.79 ± 0.63

1.70 ± 0.66

1.73 ± 0.61

1.89 ± 0.78

0.003

,e

1.70 ± 0.50

1.66 ± 0.50

1.66 ± 0.45

1.79 ± 0.72

0.002

Anterior putamen

Total striatum

Q1 1.58 ± 0.73*

1.41 ± 0.73*

1.38 ± 0.59*

Values are expressed as mean (standard deviation).

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DAT = dopamine transporter

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Striatal subregion

*Statistical comparisons were conducted by Kruskal-Wallis test followed by Bonferroni multiple corrections.

From Q2 at p < 0.01, Q3 at p < 0.01, Q4 at p = 0.01, Q5 at p < 0.01

d

From Q2 at p = 0.01, Q3 at p = 0.15, Q4 at p = 0.02, Q5 at p < 0.01

From Q2 at p < 0.01, Q3 at p = 0.02, Q4 at p = 0.01, Q5 at p < 0.01

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e

From Q2 at p = 0.06, Q3 at p = 0.06, Q4 at p = 0.03, Q5 at p = 0.2

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c

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b

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ACCEPTED MANUSCRIPT Table 4. Logistic regression to estimate independent predictors of the highest or the lowest quartile of dopamine transporter (DAT) activities in striatal subregions.

Striatal subregions

Highest quartile of

DAT activities

DAT activities

Odds ratio,* (95% CI)

p

Odds ratio,* (95% CI)

p

Anterior putamen Posterior putamen Ventral striatum Caudate nucleus

0.888 (0.808-0.975) 0.929 (0.847-1.018) 0.835 (0.756-0.922) 0.899 (0.818-0.989)

0.012 0.116 < 0.001 0.028

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Variable

Lowest quartile of

0.63 0.417 0.848 0.589

Total striatum

0.857 (0.778-0.945)

0.002

1.022 (0.936-1.115) 1.038 (0.949-1.134) 1.009 (0.923-1.102) 1.027 (0.933-1.129)

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BMI

1.008 (0.923-1.102)

*Adjusted for onset age, gender, interval of BMI to PET, disease duration, smoking (non-smoker, ex-

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smoker, current smoker), coffee (cups/day), tea (cups/day), and residence (rural or urban).

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BMI = body mass index.

0.852

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ACCEPTED MANUSCRIPT We explored the relationship between BMI and striatal dopamine in Parkinson’s disease. BMI was well correlated with dopaminergic density in striatum. Dopaminergic density was significantly lower in lower BMI group.

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A low BMI might have intimate association with low striatal dopamine in Parkinson’s disease.