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Figure 1.
Change in Unified Parkinson’s Disease Rating Scale (UPDRS) by Body Mass Index (BMI) Trajectory Groups
Change in Unified Parkinson’s Disease Rating Scale (UPDRS) by Body Mass Index (BMI) Trajectory Groups

A, After adjusting for covariates, we found that the change in the motor UPDRS score between decreasing BMI and stable BMI was P < .001 and that the change in the motor UPDRS score between stable BMI and increasing BMI was P = .03. B, After adjusting for covariates, we found that the change in the total UPDRS score between decreasing BMI and stable BMI was P < .001 and that the change in the total UPDRS score between stable BMI and increasing BMI was P = .02. Error bars indicate 95% CIs.

Figure 2.
Kaplan-Meier Survival Curves by Body Mass Index (BMI) Trajectory Groups
Kaplan-Meier Survival Curves by Body Mass Index (BMI) Trajectory Groups

Of 1673 participants, 76 (4.5%) died.

Table 1.  
Characteristics of the Subset of 1673 Participants From the NET-PD LS-1 at Randomization
Characteristics of the Subset of 1673 Participants From the NET-PD LS-1 at Randomization
Table 2.  
Multivariable Mixed-Effects Linear Models for Motor and Total UPDRS Scores
Multivariable Mixed-Effects Linear Models for Motor and Total UPDRS Scores
Table 3.  
Multivariable Cox Proportional Hazards Model for Time to Deatha
Multivariable Cox Proportional Hazards Model for Time to Deatha
1.
Paganoni  S, Deng  J, Jaffa  M, Cudkowicz  ME, Wills  AM.  Body mass index, not dyslipidemia, is an independent predictor of survival in amyotrophic lateral sclerosis.  Muscle Nerve. 2011;44(1):20-24.PubMedArticle
2.
Kasarskis  EJ, Berryman  S, Vanderleest  JG, Schneider  AR, McClain  CJ.  Nutritional status of patients with amyotrophic lateral sclerosis: relation to the proximity of death.  Am J Clin Nutr. 1996;63(1):130-137.PubMed
3.
Desport  JC, Preux  PM, Truong  CT, Courat  L, Vallat  JM, Couratier  P.  Nutritional assessment and survival in ALS patients.  Amyotroph Lateral Scler Other Motor Neuron Disord. 2000;1(2):91-96.PubMedArticle
4.
Myers  RH, Sax  DS, Koroshetz  WJ,  et al.  Factors associated with slow progression in Huntington’s disease.  Arch Neurol. 1991;48(8):800-804.PubMedArticle
5.
Gambassi  G, Landi  F, Lapane  KL, Sgadari  A, Mor  V, Bernabei  R.  Predictors of mortality in patients with Alzheimer’s disease living in nursing homes.  J Neurol Neurosurg Psychiatry. 1999;67(1):59-65.PubMedArticle
6.
Hu  G, Jousilahti  P, Nissinen  A, Antikainen  R, Kivipelto  M, Tuomilehto  J.  Body mass index and the risk of Parkinson disease.  Neurology. 2006;67(11):1955-1959.PubMedArticle
7.
Abbott  RD, Ross  GW, White  LR,  et al.  Midlife adiposity and the future risk of Parkinson’s disease.  Neurology. 2002;59(7):1051-1057.PubMedArticle
8.
Chen  H, Zhang  SM, Schwarzschild  MA, Hernán  MA, Willett  WC, Ascherio  A.  Obesity and the risk of Parkinson’s disease.  Am J Epidemiol. 2004;159(6):547-555.PubMedArticle
9.
Logroscino  G, Sesso  HD, Paffenbarger  RS  Jr, Lee  IM.  Body mass index and risk of Parkinson’s disease: a prospective cohort study.  Am J Epidemiol. 2007;166(10):1186-1190.PubMedArticle
10.
van der Marck  MA, Dicke  HC, Uc  EY,  et al.  Body mass index in Parkinson’s disease: a meta-analysis.  Parkinsonism Relat Disord. 2012;18(3):263-267.PubMedArticle
11.
Chen  H, Zhang  SM, Hernán  MA, Willett  WC, Ascherio  A.  Weight loss in Parkinson’s disease.  Ann Neurol. 2003;53(5):676-679.PubMedArticle
12.
Wang  GJ, Volkow  ND, Logan  J,  et al.  Brain dopamine and obesity.  Lancet. 2001;357(9253):354-357.PubMedArticle
13.
Bachmann  CG, Trenkwalder  C.  Body weight in patients with Parkinson’s disease.  Mov Disord. 2006;21(11):1824-1830.PubMedArticle
14.
Sato  Y, Kaji  M, Tsuru  T, Oizumi  K.  Risk factors for hip fracture among elderly patients with Parkinson’s disease.  J Neurol Sci. 2001;182(2):89-93.PubMedArticle
15.
Pouwels  S, Bazelier  MT, de Boer  A,  et al.  Risk of fracture in patients with Parkinson’s disease.  Osteoporos Int. 2013;24(8):2283-2290.PubMedArticle
16.
Akbar  U, He  Y, Dai  Y,  et al.  Weight loss and impact on quality of life in Parkinson’s disease.  PLoS One. 2015;10(5):e0124541.PubMedArticle
17.
Elm  JJ; NINDS NET-PD Investigators.  Design innovations and baseline findings in a long-term Parkinson’s trial: the National Institute of Neurological Disorders and Stroke Exploratory Trials in Parkinson’s Disease Long-Term Study-1.  Mov Disord. 2012;27(12):1513-1521.PubMedArticle
18.
Kieburtz  K, Tilley  BC, Elm  JJ,  et al; Writing Group for the NINDS Exploratory Trials in Parkinson Disease (NET-PD) Investigators.  Effect of creatine monohydrate on clinical progression in patients with Parkinson disease: a randomized clinical trial.  JAMA. 2015;313(6):584-593.PubMedArticle
19.
Pérez  A, Tilley  BC. Conduct of stroke-related clinical trials. In: Grotta  JC, Albers  GW, Broderick  JP,  et al, eds.  Stroke Pathophysiology, Diagnosis, and Management.6th ed. Amsterdam, Netherlands: Elsevier; 2015:1030-1042.
20.
Jones  BL, Nagin  DS.  Advances in group-based trajectory modeling and an SAS procedure for estimating them.  Sociol Methods Res. 2007;35(4):542-571. doi:10.1177/0049124106292364.Article
21.
Jones  BL, Nagin  DS, Roeder  K.  A SAS procedure based on mixture models for estimating developmental trajectories.  Sociol Methods Res. 2001;29(3):374-393. doi:10.1177/0049124101029003005.Article
22.
Panel on Handling Missing Data in Clinical Trials; Committee on National Statistics; Division of Behavioral and Social Sciences and Education; National Research Council of the National Academies.  The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: National Academies Press; 2010:21-37.
23.
Toth  MJ, Fishman  PS, Poehlman  ET.  Free-living daily energy expenditure in patients with Parkinson’s disease.  Neurology. 1997;48(1):88-91.PubMedArticle
24.
Delikanaki-Skaribas  E, Trail  M, Wong  WW, Lai  EC.  Daily energy expenditure, physical activity, and weight loss in Parkinson’s disease patients.  Mov Disord. 2009;24(5):667-671.PubMedArticle
25.
Montaurier  C, Morio  B, Bannier  S,  et al.  Mechanisms of body weight gain in patients with Parkinson's disease after subthalamic stimulation.  Brain. 2007;130(pt 7):1808-1818. PubMedArticle
26.
Davies  KN, King  D, Davies  H.  A study of the nutritional status of elderly patients with Parkinson’s disease.  Age Ageing. 1994;23(2):142-145.PubMedArticle
27.
Schwarzschild  MA, Schwid  SR, Marek  K,  et al; Parkinson Study Group PRECEPT Investigators.  Serum urate as a predictor of clinical and radiographic progression in Parkinson disease.  Arch Neurol. 2008;65(6):716-723.PubMedArticle
28.
Ascherio  A, LeWitt  PA, Xu  K,  et al; Parkinson Study Group DATATOP Investigators.  Urate as a predictor of the rate of clinical decline in Parkinson disease.  Arch Neurol. 2009;66(12):1460-1468.PubMedArticle
Original Investigation
March 2016

Association Between Change in Body Mass Index, Unified Parkinson’s Disease Rating Scale Scores, and Survival Among Persons With Parkinson DiseaseSecondary Analysis of Longitudinal Data From NINDS Exploratory Trials in Parkinson Disease Long-term Study 1

Author Affiliations
  • 1Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston
  • 2Department of Biostatistics, The University of Texas Health Science Center at Houston UTHealth, School of Public Health, Austin
  • 3UTHealth, The University of Texas School of Public Health, Houston
  • 4Department of Neurology, Medical College of Georgia, Georgia Regents University, Augusta
  • 5Department of Management, Policy and Community Health, The University of Texas Health Science Center at Houston UTHealth, School of Public Health, Houston.
  • 6Department of Neurology, University of Colorado Hospital and University of Colorado School of Medicine, Aurora
  • 7Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
  • 8Departments of Neurology and Neurosurgery, University of Michigan, Ann Arbor
  • 9Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
  • 10Department of Neurology, Johns Hopkins University, Baltimore, Maryland
  • 11Department of Neurological Sciences, University of Vermont College of Medicine, Burlington
JAMA Neurol. 2016;73(3):321-328. doi:10.1001/jamaneurol.2015.4265
Abstract

Importance  Greater body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) is associated with improved survival among persons with Huntington disease or amyotrophic lateral sclerosis. Weight loss is common among persons with Parkinson disease (PD) and is associated with worse quality of life.

Objective  To explore the association between change in BMI, Unified Parkinson’s Disease Rating Scale (UPDRS) motor and total scores, and survival among persons with PD and to test whether there is a positive association between BMI at randomization and survival.

Design, Setting, and Participants  Secondary analysis (from May 27, 2014, to October 13, 2015) of longitudinal data (3-6 years) from 1673 participants who started the National Institute of Neurological Disorders and Stroke Exploratory Trials in PD Long-term Study-1 (NET-PD LS-1). This was a double-blind randomized placebo-controlled clinical trial of creatine monohydrate (10 g/d) that was performed at 45 sites throughout the United States and Canada. Participants with early (within 5 years of diagnosis) and treated (receiving dopaminergic therapy) PD were enrolled from March 2007 to May 2010 and followed up until September 2013.

Main Outcomes and Measures  Change across time in motor UPDRS score, change across time in total UPDRS score, and time to death. Generalized linear mixed models were used to estimate the effect of BMI on the change in motor and total UPDRS scores after controlling for covariates. Survival was analyzed using Cox proportional hazards models of time to death. A participant’s BMI was measured at randomization, and BMI trajectory groups were classified according to whether participants experienced weight loss (“decreasing BMI”), weight stability (“stable BMI”), or weight gain (“increasing BMI”) during the study.

Results  Of the 1673 participants (mean [SD] age, 61.7 [9.6] years; 1074 [64.2%] were male), 158 (9.4%) experienced weight loss (decreasing BMI), whereas 233 (13.9%) experienced weight gain (increasing BMI). After adjusting for covariates, we found that the weight-loss group’s mean (SE) motor UPDRS score increased by 1.48 (0.28) (P < .001) more points per visit than the weight-stable group’s mean (SE) motor UPDRS score. The weight-gain group’s mean (SE) motor UPDRS score decreased by −0.51 (0.24) (P = .03) points per visit, relative to the weight-stable group. While there was an unadjusted difference in survival between the 3 BMI trajectory groups (log-rank P < .001), this was not significant after adjusting for covariates.

Conclusions and Relevance  Change in BMI was inversely associated with change in motor and total UPDRS scores in the NET-PD LS-1. Change in BMI was not associated with survival; however, these results were limited by the low number of deaths in the NET-PD LS-1.

Trial Registration  clinicaltrials.gov Identifier: NCT00449865

Introduction

Greater body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) has been found to be associated with improved survival in several neurodegenerative diseases including Huntington disease and amyotrophic lateral sclerosis.15 However, to our knowledge, its effect on Parkinson disease (PD) progression and survival has not been previously examined. Midlife obesity has been associated with later PD risk in several studies6,7 although not others.8,9 At the same time, patients with PD are consistently reported to be underweight compared with healthy controls.10 This discrepancy is likely due to premorbid weight loss, which begins years prior to the diagnosis of PD.9,11 Weight loss is common among persons with PD and may be related to the effects of dopaminergic medications on appetite12 or to a combination of other factors, including hyposmia, difficulty self-feeding, dysphagia, intestinal hypomotility, depression, anorexia, nausea, and increased energy requirements due to rigidity, dyskinesia, and tremors.13 Although an abnormally low BMI (<20) has been shown to be a risk factor for fractures among persons with PD,14,15 to our knowledge, the effects of changes in body mass on changes in the Unified Parkinson’s Disease Rating Scale (UPDRS) score and survival have not previously been studied. A recent study16 using the National Parkinson Foundation Quality Improvement Initiative database found that weight loss was associated with a more rapid decline in health-related quality of life. In this secondary analysis of data from the National Institute of Neurological Disorders and Stroke (NINDS) Exploratory Trials in PD Long-term Study-1 (NET-PD LS-1), we examined the effects of BMI at randomization and change in BMI across time in 3 outcomes: (1) change in the UPDRS part III (hereafter referred to as the motor UPDRS score), (2) change in the UPDRS parts I, II, and III (hereafter referred to as the total UPDRS score), and (3) PD survival.

Box Section Ref ID

Key Points

  • Question: What is the association of body mass index (BMI) with change in Unified Parkinson’s Disease Rating Scale (UPDRS) scores and survival?

  • Findings: In this secondary analysis of longitudinal data from participants with early Parkinson disease (PD) in the National Institute of Neurological Disorders and Stroke Exploratory Trials in PD Long-term Study-1 (NET-PD LS-1), participants with a decreasing BMI had higher motor and total UPDRS scores over time, whereas participants with an increasing BMI had lower motor and total UPDRS scores, compared with participants with a stable BMI. Change in BMI was not associated with survival; however, there were few deaths in the NET-PD LS-1.

  • Meaning: Change in BMI is an important clinical feature that should be followed even in early PD.

Methods
Participants

The NET-PD LS-1 was a large, randomized, multicenter, double-blind, placebo-controlled trial of creatine monohydrate (10 g/d). Participants had early, treated PD (within 5 years of diagnosis and between 90 days to a maximum 2 years of starting dopaminergic therapy). The study design and characteristics at randomization of this clinical trial have been previously published.17 A total of 1741 participants were enrolled (from March 2007 to May 2010 and followed up until September 2013). Participants were scheduled for measurements at 3, 12, 24, 36, 48, 60, 72, and 84 months after randomization.17 Follow-up time was defined as the time between the randomization study visit and the loss to follow-up, death, or July 17, 2013, when the clinical trial was terminated early.18 The institutional review boards of the institutions that participated in the NET-PD LS-1 approved the study, the study protocol, and the informed consent process and documentation. All patients provided written informed consent.

Of the 1741 participants, 1673 were included in this analysis. The reasons for exclusion were as follows: 38 participants reported their height and weight at randomization and not across time (no trajectory feasible to estimate); 4 participants had no height and weight recorded; 2 participants did not have a height and weight recorded at randomization; 9 participants did not have the UPDRS scores measured at randomization; 7 participants were missing uric acid levels; 5 participants were missing their Beck Depression Inventory II score; 1 participant was missing data on the history of cardiovascular disease status at randomization; 1 participant was missing data on his or her history of dyskinesia at randomization; and 1 participant was missing data on his or her history of dysphagia or bulbar dysfunction at randomization. Given the minimal amount of data that was missing (from 68 of 1741 participants [3.9%]), the reduced sample was considered to be missing at random and to be representative of the observed data.19

Outcome Measures

This analysis focused on 3 separate outcome measures: (1) change across time from randomization in the motor UPDRS score, (2) change across time in the total UPDRS score, and (3) time to death.

Exposure

Body weight was measured at randomization and at every follow-up visit for each participant. Participants’ heights were measured at randomization and assumed to be constant across time to calculate BMI. The primary exposure variables were BMI at randomization and the classification of the change in BMI across time. Participants were classified into 3 trajectory groups according to their change in BMI. That is, the change in BMI from each visit to randomization, longitudinally, was used to fit a semiparametric group-based model with continuous multivariate density functions under a censored normal model.20,21 We sought to identify participants who had experienced weight loss (“decreasing BMI”), weight stability (“stable BMI”), or weight gain (“increasing BMI”) during the study.

Covariate Variables

Because randomization took place blocked by site, both site and treatment assignments (creatine vs placebo) were included as covariates. For outcomes 1 and 2, the following covariates were included based on a hypothesized association between the outcomes and the exposure: age, the interaction between visit and age, disease duration, and sex, and the following variables were included at randomization: UPDRS score, presence of monoamine oxidase B inhibitors, presence of catechol O-methyltransferase inhibitors, use of amantadine hydrochloride, use of anticholinergics, use of other adjunctive medications, uric acid levels, Beck Depression Inventory II total score, and presence of dysphagia or bulbar dysfunction reported in the diagnostic features form. A dyskinesia score (sum of questions 1-3 from UPDRS part IV) at randomization was calculated. The total levodopa equivalent daily dose and the type of symptomatic PD medications (ie, levodopa, dopamine agonist, or >1 type of dopaminergic therapy) at randomization were also included as covariates. For outcome 3, given the low number of deaths (n = 76) included in this secondary analysis, we limited our analysis to the following variables at randomization: age, sex, total UPDRS score at randomization, history of cardiovascular disease, and disease duration.

Statistical Methods

Descriptive statistics (mean, standard deviation, and percentages) were used to summarize the demographic and clinical characteristics of participants at randomization. The Bayesian information criterion was used to determine that 3 trajectory groups with a change in BMI across time had a better fit than 4 groups (data not shown). These BMI trajectory groups were tested for constant, linear, quadratic, and cubic trends, and the cubic trend was preferred (P < .001). The sample size for each group and the unadjusted mean values (and their corresponding 95% CIs) of the change in the motor and total UPDRS scores from each visit to the randomization visit were reported for each BMI trajectory group. The average weight change was estimated using a generalized linear mixed model, via the maximum likelihood method, for each trajectory group, controlling for age, visit, the interaction between visit and age, treatment assignment, site, and the interaction between BMI trajectory group and visit. Treatment assignment and site were included as random effects.

Following the recommendations of the National Research Council of the National Academies for analyzing clinical trials,22 we imputed the worst motor and total UPDRS scores for any visit after the death of participants in the trial to be 108 and 176, respectively. Generalized linear mixed-effects models using the residual maximum likelihood method for the change in the motor UPDRS score were performed for each potential covariate separately, as well as with age, the interaction between visit and age, treatment assignment, and site. The interaction between uric acid level and sex was also evaluated in univariate models. Covariates with P < .20 were included in the final multivariable model for the motor UPDRS score. Given that BMI at randomization and the trajectory groups were our exposure of interest, they were included in the multivariable model regardless of their P values. Finally, to evaluate the effect of the BMI trajectory groups by visit, the interaction term was included in the multivariable model. We applied the same modeling strategy to the total UPDRS score.

Survival was analyzed using Cox proportional hazards models, including BMI, BMI trajectory groups, and the previously mentioned covariates. The log-rank test was used to evaluate differences between the BMI trajectory groups without adjustment for covariates. The α level is the size of the type I error, which was defined as .05 to evaluate significance. All statistical analyses were conducted using SAS statistical software (version 9.3; SAS Institute Inc).

Results

Table 1 shows the overall demographic characteristics at randomization of the subset of participants used in this analysis and by the BMI trajectory groups, including the percentages of participants distributed by World Health Organization BMI criteria. Few participants reported dysphagia or were underweight at randomization (defined as BMI < 18.5). Surprisingly, 65.3% of participants reported a history of cardiovascular disease at randomization. However, based on the predetermined categorization of medical history, this included participants with hypertension or hyperlipidemia. Very few participants reported dysphagia symptoms at randomization.

In the trajectory analysis, of the 1673 participants, 158 (9.4%) experienced weight loss (decreasing BMI), 1282 (76.6%) experienced weight stability (stable BMI), and 233 (13.9%) experienced weight gain (increasing BMI). There was no imbalance in treatment assignment (creatine vs placebo) between the 3 trajectory groups. The decreasing-BMI group had more participants classified as obese at randomization, had a higher percentage of participants who received levodopa only, and had a higher mean levodopa daily dose equivalence at randomization. There was no imbalance in the other baseline characteristics.

Figure 1 shows the unadjusted mean change in the motor and total UPDRS scores and their corresponding 95% CIs and sample size by BMI trajectory group across visits. After adjusting for all of the previously mentioned covariates, we found that the participants with a decreasing BMI experienced a mean (SE) increase of 1.48 (0.28) more points per visit in the motor UPDRS score than participants with a stable BMI (Table 2; P < .001). Similarly, participants with a decreasing BMI experienced a mean (SE) increase of 2.40 (0.43) more points per visit in the total UPDRS score than participants with a stable BMI (P < .001). Participants with an increasing BMI experienced a mean (SE) decrease of −0.51 (0.24) (P = .03) points in the motor UPDRS and a mean (SE) decrease of −0.89 (0.36) (P = .02) points in the total UPDRS compared with participants with a stable BMI. By contrast, BMI at randomization was not significantly associated with the change in motor or total UPDRS scores.

We then explored the effect of BMI at randomization and the change in BMI on survival. There were no differences in the causes of the reported deaths between this subset of participants (1673 participants) and the total number of participants in the NET-PD LS-118 (1741 participants): 24 (31.6%) of the 76 deaths were due to cardiac issues, 15 (19.7%) were due to an unknown cause, 11 (14.5%) were due to cancer, 9 (11.8%) were due to infections, and 17 (22.4%) were due to other causes. The Kaplan-Meier curve for survival stratified by the BMI trajectory groups is shown in Figure 2. There were statistically significant differences in survival among these groups (log-rank P < .001). However, in the multivariable analysis, the effect of the BMI trajectory groups was no longer significant. Table 3 displays the hazard ratios, 95% CIs, and corresponding P values for the Cox proportional hazards model.

Discussion

In summary, classification in the decreasing-BMI trajectory group was associated with higher (worse) scores over time in both the motor and total UPDRS, whereas classification in the increasing-BMI trajectory group was associated with lower (better) UPDRS scores in the NET-PD LS-1. This was observed in early PD despite theoretically optimal treatment with dopaminergic therapy. Although this does not imply causation, our results suggest that weight and BMI are important clinical biomarkers and that data on weight and BMI should be collected even in early PD. Our analysis of the BMI trajectory groups appears to have identified a subtype of PD associated with both decreasing BMI and higher increases in motor and total UPDRS scores. This subtype might represent a more severe, hypermetabolic form of PD,2326 or it might be a subtype of PD in which the patient has greater gastrointestinal autonomic symptoms, reduced appetite, and reduced oral intake. Dietary intake was not measured in the NET-PD LS1, and therefore we do not know whether weight gain or weight loss was due to changes in oral intake. Because the increasing-BMI group also experienced a relative decrease in UPDRS scores over time compared with the stable-BMI group, the association of change in BMI with change in UPDRS score cannot be fully explained by other comorbid conditions associated with weight loss.

While more of the decreasing-BMI group were taking levodopa only at randomization and were prescribed a higher mean levodopa daily dose equivalence at randomization, our models adjusted for these variables. One caveat to our study is that we only adjusted for total levodopa daily dose equivalence at randomization; thus, participants in the decreasing-BMI group may have been less able to tolerate increases in total levodopa daily dose equivalence across time.

We were surprised to find that most participants were in the stable-BMI group and that more participants were in the increasing-BMI group than in the decreasing-BMI group. This reflects the early stage of participants in the NET-PD LS1. These unexpected results also suggest the need for further study of weight changes in PD. We were also surprised that uric acid levels at randomization were not associated with a change in motor or total UPDRS scores, despite its strong association in other studies27,28 (data not shown).

While not significant, the direction of the hazard ratios for the increasing-BMI and decreasing-BMI groups in the survival analysis were also consistent with the UPDRS results, which suggests that increasing BMI is associated with better disease outcomes. Our study was limited by the fact that there were very few deaths in this clinical trial, decreasing our power in the proportional hazards model. This was due to the design of the NET-PD LS-1, which enrolled participants at an early stage of the disease. Future analyses using clinical trials of longer duration may be helpful in elucidating the association of BMI with survival.

Conclusions

Change in BMI was inversely associated with change in motor and total UPDRS scores in the NET-PD LS-1, with the decreasing-BMI trajectory group experiencing increased motor and total UPDRS scores over time compared with the stable-BMI group, despite optimal treatment. Participants in the increasing-BMI group also experienced a relative decrease in motor and total UPDRS scores. Change in BMI was not associated with survival; however, our results were limited by the low number of deaths in the NET-PD LS-1.

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Article Information

Accepted for Publication: November 5, 2015.

Corresponding Author: Anne-Marie A. Wills, MD, MPH, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Wang Ambulatory Care Center, Room 715, Boston, MA 02114 (awills@partners.org).

Published Online: January 11, 2016. doi:10.1001/jamaneurol.2015.4265.

Author Contributions: Drs Wills and Pérez had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Wills, Pérez, Morgan, Rajan, Leehey, Mari, Boyd.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Wills, Pérez, Wang, Mari, Boyd.

Critical revision of the manuscript for important intellectual content: Pérez, Wang, Su, Morgan, Rajan, Leehey, Pontone, Chou, Umeh, Boyd.

Statistical analysis: Wills, Pérez, Wang, Su, Rajan.

Administrative, technical, or material support: Leehey.

Conflict of Interest Disclosures: Dr Wills has received research support from the National Institutes of Health (NIH), the Muscular Dystrophy Association, and the Amyotrophic Lateral Sclerosis Association; has consulting agreements with Accordant, a CVS/Caremark disease management company, and with Asubio Pharmaceuticals; and has participated in clinical trials funded by Schering-Plough/Merck and Pfizer. Dr Pérez reports grants from NIH during the conduct of the study. Dr Morgan has received consulting fees from Impax, Lundbeck, the National Parkinson Foundation, Teva, and Veloxis. He is a speaker for Impax and Teva. He has received research support from the NIH and the National Parkinson Foundation. He has also served as an expert witness/consultant in various neurological legal cases. Dr Rajan has received grant support from the NIH, the Agency for Healthcare Research and Quality, the Department of Veterans Affairs, and the Texas Department of Insurance. Dr Leehey has received grant support in the last 12 months from the NIH, the Michael J. Fox Foundation, Allergan, Medtronic, US WorldMeds, LLC, Adamas Pharmaceuticals, and Pharma Two B; consulting fees from the Neurologic Movement Disorders Market Research Team, Guidepoint Global, Scientae Inc, the Health Practices Research Institute, and the Gerson Lehman Group; and travel support from AbbVie. Dr Pontone has received grant funding from the NIH, the Michael J Fox Foundation, the Parkinson Disease Foundation and has been the site principal investigator for clinical trials sponsored by Acadia Pharmaceuticals Inc and EMD Serono/Merck. He does medical-legal consulting for Allergan Inc. Dr Chou has received research support from the NIH (grants NS44504-08 and 5R44NS070438) and the Michael J. Fox Foundation, has participated as a site principal investigator in clinical trials sponsored by the Huntington Study Group (2CARE), receives royalties from UpToDate and Demos Health Publishing, and serves as a consultant for Medtronic Inc and Accordant. Dr Mari received grant support in the last 12 months from the NIH, the Michael J. Fox Foundation, the National Parkinson Foundation, AVID, and AbbVie and received honorarium from Ipsen, Impax, and Navidea. Dr Boyd received grant support in the last 12 months from the NIH, the Michael J. Fox Foundation, and the Binter Center for Parkinson’s Disease and Other Movement Disorders at the University of Vermont; served as a site principal investigator for AbbVie, Auspex, and Biotie; and received personal compensation/honoraria from AbbVie, Auspex, and Lundbeck. No other disclosures are reported.

Funding/Support: The NET-PD Investigators were supported, in part, by grants U01NS043127, U01NS043128, and U10NS44415-44555 from the NINDS. The following additional NINDS grants supported the NET-PD LS-1: U10 NS044547, U10 NS044425 U10 NS044462, U10 NS053379, U10 NS044483, U10 NS044479, U10 NS 044474, U10 NS 044545, U10 NS044460, U10 NS053381, U10 NS044453, U10 NS053370, U10 NS053380, U10 NS044475, U10 NS044431, U10 NS044466, U10 NS044451, U10 NS044465, U10 NS044482, U10 NS044484, U10 NS044450, U10 NS044504, U10 NS053369, U10 NS044437, U10 NS053372, U10 NS044448, U10 NS044426, U10 NS044455, U10 NS044446, U10 NS044501, U10 NS053377, U10 NS044469, U10 NS053368, U10 NS044471, U10 NS044454, U10 NS044481, U10 NS044441, U10 NS044464, U10 NS044505, U10 NS053387, U10 NS044427, U10 NS044555, U10 NS044458, U10 NS044415, and U10 NS044472.

Role of the Funder/Sponsor: The NINDS had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information:NET-PD Steering Committee: Karl Kieburtz, MD, MPH (principal investigator, coordination center), University of Rochester, Rochester, New York; Barbara Tilley, PhD (principal investigator, statistical center), University of Texas, Houston; Debra Babcock, PhD, MD, and Wendy Galpern, MD, PhD, NIH, Bethesda, Maryland; Robert Hauser, MD, University of South Florida, Tampa; Connie Kawai, RN, BSN, CCRC, University of Southern California, Los Angeles; Brad A. Racette, MD, Washington University School of Medicine, St Louis, Missouri; Bernard Ravina, MD, MSCE, Voyager Therapeutics Inc, Cambridge, Massachusetts; Sue Reichwein, CCRC, University of Pennsylvania, Philadelphia; G. Webster Ross, MD, Pacific Health Research and Education Institute, Honolulu, Hawaii; Kathleen M. Shannon, MD, Rush University Medical Center, Chicago, Illinois; Oksana Suchowersky, MD, University of Calgary, Alberta, Canada; Caroline M. Tanner, MD, PhD, The Parkinson’s Institute, Sunnyvale, California; Jessie Tatsuno Roth, RN, BSN, University of California San Francisco. NET-PD Statistical Center (University of Texas Health Science Center at Houston): Keith Burau, PhD; Jordan Elm, PhD; Rong Ye, MS; and Adriana Pérez, MS, PhD. NET-PD Clinical Trials Coordination Center Staff (University of Rochester, Rochester, New York): Debbie Baker, AAS; Liana Baker, MPH; Susan Bennett, AAS; Lisa DeBlieck, MPA, CCRC; Debbie Frasier, BS; Irenita Gardiner, RN; Jennifer Harman, PhD, CCRP, CCRC; Cornelia Kamp, MBA; Laith Khadim, MD; Gina Lau, BS; Beverly Olsen, BA; Saloni Sharma, MD; David Shprecher DO; Ann Stoutenburg, CCRC; Christine Weaver, CCRP; and Renee Wilson, MA. NET-PD Consultants: Christopher Goetz, MD, Rush University Medical Center, Chicago, Illinois; David Ploth, MD, Medical University of South Carolina, Charleston. Data and Safety Monitoring Board: Cynthia R. Gross, PhD (chair), University of Minnesota, Minneapolis; Karen L. Bell, MD, Columbia University, New York, New York; Donna T. Chen, MD, MPH, University of Virginia Health System, Charlottesville; Robert Foley, MD, United States Renal Data System Coordinating Center, Minneapolis, Minnesota; David E. Levy, MD, Weill Cornell Medical College, New York, New York; Robert L. Rodnitzky, MD, University of Iowa College of Medicine, Iowa City. Oversight Board: K. Michael Welch, MD (chair), Rosalind Franklin University of Medicine and Science, North Chicago, Illinois; M. Flint Beal, MD, Weill Medical College of Cornell University, New York, New York; Jeffrey L. Cummings, MD, University of California, Los Angeles, Alzheimer Disease Center; Diane DiEuliis, PhD, Health and Human Services, Washington, DC; David J. Edwards, PharmD, Wayne State University, Detroit, Michigan; Stanley Fahn, MD, and Bruce Levin, PhD, Columbia University, New York, New York; Russell G. Katz, MD, US Food and Drug Administration, Rockville, Maryland; Deborah B. Marin, MD, and C. Warren Olanow, MD, Mount Sinai School of Medicine, New York, New York; Jeffrey C. Martin, Esq, Goodwin Proctor LLP, Rockville, Maryland; Steven Piantadosi, MD, PhD, Cedars-Sinai Medical Center, Los Angeles, California; William J. Powers, MD, University of North Carolina School of Medicine, Chapel Hill; Alison Wichman, MD, NIH, Bethesda, Maryland. NIH (NINDS, Bethesda, Maryland): Debra Babcock, PhD, MD; Wendy Galpern, MD, PhD; John Marler, MD; Claudia Moy, PhD; Joanne Odenkirchen, MPH.

Additional Contributions: We thank the patients and families who participated in the NET-PD LS-1.

References
1.
Paganoni  S, Deng  J, Jaffa  M, Cudkowicz  ME, Wills  AM.  Body mass index, not dyslipidemia, is an independent predictor of survival in amyotrophic lateral sclerosis.  Muscle Nerve. 2011;44(1):20-24.PubMedArticle
2.
Kasarskis  EJ, Berryman  S, Vanderleest  JG, Schneider  AR, McClain  CJ.  Nutritional status of patients with amyotrophic lateral sclerosis: relation to the proximity of death.  Am J Clin Nutr. 1996;63(1):130-137.PubMed
3.
Desport  JC, Preux  PM, Truong  CT, Courat  L, Vallat  JM, Couratier  P.  Nutritional assessment and survival in ALS patients.  Amyotroph Lateral Scler Other Motor Neuron Disord. 2000;1(2):91-96.PubMedArticle
4.
Myers  RH, Sax  DS, Koroshetz  WJ,  et al.  Factors associated with slow progression in Huntington’s disease.  Arch Neurol. 1991;48(8):800-804.PubMedArticle
5.
Gambassi  G, Landi  F, Lapane  KL, Sgadari  A, Mor  V, Bernabei  R.  Predictors of mortality in patients with Alzheimer’s disease living in nursing homes.  J Neurol Neurosurg Psychiatry. 1999;67(1):59-65.PubMedArticle
6.
Hu  G, Jousilahti  P, Nissinen  A, Antikainen  R, Kivipelto  M, Tuomilehto  J.  Body mass index and the risk of Parkinson disease.  Neurology. 2006;67(11):1955-1959.PubMedArticle
7.
Abbott  RD, Ross  GW, White  LR,  et al.  Midlife adiposity and the future risk of Parkinson’s disease.  Neurology. 2002;59(7):1051-1057.PubMedArticle
8.
Chen  H, Zhang  SM, Schwarzschild  MA, Hernán  MA, Willett  WC, Ascherio  A.  Obesity and the risk of Parkinson’s disease.  Am J Epidemiol. 2004;159(6):547-555.PubMedArticle
9.
Logroscino  G, Sesso  HD, Paffenbarger  RS  Jr, Lee  IM.  Body mass index and risk of Parkinson’s disease: a prospective cohort study.  Am J Epidemiol. 2007;166(10):1186-1190.PubMedArticle
10.
van der Marck  MA, Dicke  HC, Uc  EY,  et al.  Body mass index in Parkinson’s disease: a meta-analysis.  Parkinsonism Relat Disord. 2012;18(3):263-267.PubMedArticle
11.
Chen  H, Zhang  SM, Hernán  MA, Willett  WC, Ascherio  A.  Weight loss in Parkinson’s disease.  Ann Neurol. 2003;53(5):676-679.PubMedArticle
12.
Wang  GJ, Volkow  ND, Logan  J,  et al.  Brain dopamine and obesity.  Lancet. 2001;357(9253):354-357.PubMedArticle
13.
Bachmann  CG, Trenkwalder  C.  Body weight in patients with Parkinson’s disease.  Mov Disord. 2006;21(11):1824-1830.PubMedArticle
14.
Sato  Y, Kaji  M, Tsuru  T, Oizumi  K.  Risk factors for hip fracture among elderly patients with Parkinson’s disease.  J Neurol Sci. 2001;182(2):89-93.PubMedArticle
15.
Pouwels  S, Bazelier  MT, de Boer  A,  et al.  Risk of fracture in patients with Parkinson’s disease.  Osteoporos Int. 2013;24(8):2283-2290.PubMedArticle
16.
Akbar  U, He  Y, Dai  Y,  et al.  Weight loss and impact on quality of life in Parkinson’s disease.  PLoS One. 2015;10(5):e0124541.PubMedArticle
17.
Elm  JJ; NINDS NET-PD Investigators.  Design innovations and baseline findings in a long-term Parkinson’s trial: the National Institute of Neurological Disorders and Stroke Exploratory Trials in Parkinson’s Disease Long-Term Study-1.  Mov Disord. 2012;27(12):1513-1521.PubMedArticle
18.
Kieburtz  K, Tilley  BC, Elm  JJ,  et al; Writing Group for the NINDS Exploratory Trials in Parkinson Disease (NET-PD) Investigators.  Effect of creatine monohydrate on clinical progression in patients with Parkinson disease: a randomized clinical trial.  JAMA. 2015;313(6):584-593.PubMedArticle
19.
Pérez  A, Tilley  BC. Conduct of stroke-related clinical trials. In: Grotta  JC, Albers  GW, Broderick  JP,  et al, eds.  Stroke Pathophysiology, Diagnosis, and Management.6th ed. Amsterdam, Netherlands: Elsevier; 2015:1030-1042.
20.
Jones  BL, Nagin  DS.  Advances in group-based trajectory modeling and an SAS procedure for estimating them.  Sociol Methods Res. 2007;35(4):542-571. doi:10.1177/0049124106292364.Article
21.
Jones  BL, Nagin  DS, Roeder  K.  A SAS procedure based on mixture models for estimating developmental trajectories.  Sociol Methods Res. 2001;29(3):374-393. doi:10.1177/0049124101029003005.Article
22.
Panel on Handling Missing Data in Clinical Trials; Committee on National Statistics; Division of Behavioral and Social Sciences and Education; National Research Council of the National Academies.  The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: National Academies Press; 2010:21-37.
23.
Toth  MJ, Fishman  PS, Poehlman  ET.  Free-living daily energy expenditure in patients with Parkinson’s disease.  Neurology. 1997;48(1):88-91.PubMedArticle
24.
Delikanaki-Skaribas  E, Trail  M, Wong  WW, Lai  EC.  Daily energy expenditure, physical activity, and weight loss in Parkinson’s disease patients.  Mov Disord. 2009;24(5):667-671.PubMedArticle
25.
Montaurier  C, Morio  B, Bannier  S,  et al.  Mechanisms of body weight gain in patients with Parkinson's disease after subthalamic stimulation.  Brain. 2007;130(pt 7):1808-1818. PubMedArticle
26.
Davies  KN, King  D, Davies  H.  A study of the nutritional status of elderly patients with Parkinson’s disease.  Age Ageing. 1994;23(2):142-145.PubMedArticle
27.
Schwarzschild  MA, Schwid  SR, Marek  K,  et al; Parkinson Study Group PRECEPT Investigators.  Serum urate as a predictor of clinical and radiographic progression in Parkinson disease.  Arch Neurol. 2008;65(6):716-723.PubMedArticle
28.
Ascherio  A, LeWitt  PA, Xu  K,  et al; Parkinson Study Group DATATOP Investigators.  Urate as a predictor of the rate of clinical decline in Parkinson disease.  Arch Neurol. 2009;66(12):1460-1468.PubMedArticle
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