Skills Module: Nutrition

Malnutrition in a Sample of Community-Dwelling People with Parkinson’s Disease Jamie M. Sheard1,2*, Susan Ash1, George D. Mellick3, Peter A. Silburn4, Graham K. Kerr1,2

1 School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Queensland, Australia, 2 Movement Neuroscience Program, Institute of Health

and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia, 3 Australia Eskitis Institute for Cell and Molecular Therapies, Griffith

University, Brisbane, Queensland, Australia, 4 University of Queensland Centre for Clinical Research, Brisbane, Queensland, Australia

Abstract

Objective: Malnutrition results in poor health outcomes, and people with Parkinson’s disease may be more at risk of malnutrition. However, the prevalence of malnutrition in Parkinson’s disease is not yet well defined. The aim of this study is to provide an estimate of the extent of malnutrition in community-dwelling people with Parkinson’s disease.

Methods: This is a cross-sectional study of people with Parkinson’s disease residing within a 2 hour driving radius of Brisbane, Australia. The Subjective Global Assessment (SGA) and scored Patient Generated Subjective Global Assessment (PG-SGA) were used to assess nutritional status. Body weight, standing or knee height, mid-arm circumference and waist circumference were measured.

Results: Nineteen (15%) of the participants were moderately malnourished (SGA-B). The median PG-SGA score of the SGA-B group was 8 (4–15), significantly higher than the SGA-A group, U = 1860.5, p,.05. The symptoms most influencing intake were loss of appetite, constipation, early satiety and problems swallowing.

Conclusions: As with other populations, malnutrition remains under-recognised and undiagnosed in people with Parkinson’s disease. Regular screening of nutritional status in people with Parkinson’s disease by health professionals with whom they have regular contact should occur to identify those who may benefit from further nutrition assessment and intervention.

Citation: Sheard JM, Ash S, Mellick GD, Silburn PA, Kerr GK (2013) Malnutrition in a Sample of Community-Dwelling People with Parkinson’s Disease. PLoS ONE 8(1): e53290. doi:10.1371/journal.pone.0053290

Editor: Mathias Toft, Oslo University Hospital, Norway

Received August 13, 2012; Accepted November 30, 2012; Published January 9, 2013

Copyright: � 2013 Sheard et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: Parkinson’s Queensland, Inc (http://parkinsons-qld.org.au/) provided funding for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders did advertise the study in their newsletter and on their website for the purpose of recruiting participants.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

The frequency of malnutrition, defined as protein-energy

undernutrition, in people with Parkinson’s disease (PWP) is

reported to be 0–2% using the Mini-Nutritional Assessment

(MNA) [1,2] with a further 20234% at risk of malnutrition [1,2].

Similarly, Jaafar, et al [3] reported that 24% of community-

dwelling PWP were at medium to high risk of malnutrition using

the Malnutrition Universal Screening Tool (MUST). This

reported frequency of malnutrition is lower than may be expected

given that significant amounts of weight loss have previously been

reported in PWP [4–6]. Due to limitations such as sampling bias

towards more mobile participants without cognitive impairment

[7], the frequency of malnutrition in PWP reported to date is likely

to be under-reported.

Malnutrition results from inadequate calories, protein or other

nutrients that are required for maintenance and repair of body

tissues. Appropriate identification of and interventions for

malnutrition should be a priority in order to prevent or delay

the associated poor outcomes such as decreased quality of life,

longer recovery from illness, higher likelihood of falls, increased

risk for osteoporosis and increased risk of hospitalisation and

admittance to aged care facilities [8–13]. The etiology of

malnutrition is multi-factorial, and nutrition assessment tools,

such as the MNA and the Subjective Global Assessment (SGA),

incorporate anthropometry, weight history, dietary intake and

physical signs of malnutrition in the criteria for a diagnosis of

malnutrition. The scored Patient Generated Subjective Global

Assessment (PG-SGA), which incorporates the SGA, also includes

assessment of nutrition impact symptoms, such as poor appetite,

nausea, constipation, problems swallowing, early satiety and

depression. These symptoms commonly occur in Parkinson’s

disease and can provide information about appropriate interven-

tion strategies. The recording of nutrition impact symptoms also

allows for monitoring and evaluation of the effectiveness of both

medical and nutritional interventions. Therefore, the use of the

SGA and the scored PG-SGA in this population appears to be the

most appropriate choice particularly as the SGA is a valid tool for

use in the community setting [14].

The current study aims to use a nutrition assessment tool, the

SGA to assess nutritional status to better estimate the frequency of

PLOS ONE | www.plosone.org 1 January 2013 | Volume 8 | Issue 1 | e53290

malnutrition in community-dwelling people with Parkinson’s

disease in Australia.

Subjects and Methods

Community-dwelling people with PD, aged .18 years were

recruited using a variety of methods between February and August

2011: invitations to previous research participants who had

expressed an interest in involvement in future studies, appearances

at community Parkinson’s disease support groups, advertisement

in Queensland-wide Parkinson’s disease organisation newsletter

and media coverage in local and state newspapers. Participants

were excluded if they resided in an assisted living facility (Figure 1).

Geographical location was limited to areas within ,2 hour driving distance from Brisbane, Queensland, Australia.

Ethics Statement Informed written consent was obtained as per protocol

approved by the Queensland University of Technology Human

Research Ethics Committee which also approved the study

(#1000001150). Data collection was completed either at Queensland University

of Technology Institute of Health and Biomedical Innovation

(n = 15) or at participants’ homes (n = 110). During the data

collection visit, disease duration was obtained from the participant

and/or their spouse.

Nutritional status was measured by a dietitian using the

Subjective Global Assessment (SGA) [15]. Nutrition assessment

using the SGA is based on a medical history (recent changes in

weight, dietary intake, gastrointestinal symptoms, functional

capacity and disease status) and a physical examination of fat

stores, muscle status and fluid status. The assessment results in

a categorisation of nutrition status: SGA-A (well nourished), SGA-

B (moderately malnourished) or SGA-C (severely malnourished).

The SGA has been used extensively for nutrition assessment and is

validated for use in many different patient groups [16]. The scored

Patient-Generated Subjective Global Assessment (PG-SGA) was

also completed, which uses the same global nutrition assessment

rating as the SGA but also provides a total score from four

worksheets with a higher score indicating poorer nutritional status

[17].

PG-SGA Worksheet 1, which is completed by the participant,

provides a score for recent changes in weight, food intake,

nutrition impact symptoms and functional capacity. Worksheet 2

provides a score for medical conditions and age. Worksheet 3

provides a score for components of metabolic stress, and finally

Worksheet 4 consists of the physical examination score. Work-

sheets 2, 3 and 4 are completed by the assessor. The scores from

the worksheets are totaled to provide the PG-SGA score.

Anthropometric and body composition data were collected

while fasting. Body weight was measured to the nearest 0.1 kg

(Tanita HD-316, Japan) in light clothing, without shoes. For those

participants able to stand independently, height was measured to

the nearest 0.5 cm, without shoes. For those participants unable to

stand independently or with marked stooped posture, knee height

was used to estimate height. Knee height was measured to the

nearest 0.1 cm using a knee caliper on the left leg with the knee

and ankle at a 90u angle. The equation used for men was height (cm) = 78.31+(1.946knee height) 2 (0.146age), and the equation used for women was height (cm) = 82.21+(1.856knee heigh-

Figure 1. Recruitment process. doi:10.1371/journal.pone.0053290.g001

Malnutrition in Parkinson’s Disease

PLOS ONE | www.plosone.org 2 January 2013 | Volume 8 | Issue 1 | e53290

t)2(0.216age). [18] BMI was calculated (body weight(kg)/(height in metres)

2 ). Mid-arm circumference (MAC) was measured to the

nearest 0.1 cm at the mid-point between the acromion and the

olecranon process on the non-dominant arm [19]. Calf circum-

ference was measured to the nearest 0.1 cm at the widest part of

the calf with the knee at 90u [20]. Waist circumference was measured at the mid-point between the iliac crest and the lower rib

[19]. Some participants presented with visible peripheral oedema

but no adjustments were made to the weight or calf circumference

measurements to account for this.

The Hoehn and Yahr (H&Y) scale, which is a five point scale

(1–5) with a higher rating on the scale indicating a greater amount

of disability and impairment, was also measured. The H&Y

classifications were split into 2 groups (less severe PD H&Y 0–3,

severe PD H&Y 4–5).

Statistical analysis was completed using SPSS Version 19 (SPSS

Inc., Chicago, IL, USA). Missing data were not included in the

analysis. Variables were compared between SGA-A and SGA-B

classifications only as no one was classified as SGA-C. The only

variable of interest that was normally distributed in the SGA-A

group was height. The only variable that was normally distributed

for both males and females was also height. Therefore, non-

parametric Mann-Whitney U tests were conducted to compare the

scores between the groups. Although within the SGA-B group, the

variables age, height, weight, MAC, calf circumference, PD

duration and PG-SGA score were all normally distributed for both

males and females, non-parametric tests were all conducted for

consistency in reporting. Pearson’s Chi square tests were used to

evaluate the differences in categorical variables. Statistical

significance was set at p,0.05.

Results

One hundred twenty five community dwelling adults over the

age of 18 years (74 M, 51 F) agreed to participate. Characteristics

of those who did not participate were not known. Of the 27 that

provided a reason for not participating, four replied that they were

too ill, one stated immobility as the reason, two replied that

participation was too stressful, seven were not interested in the

research project, and the remaining 13 were not available or too

busy to participate. Median age of the participants was 70.0 years

(range: 35–92), and 72.8% (n = 91) were over the age of 65 years.

Median age at diagnosis was 63.0 years (range: 28.0–84.0), and

self-reported median disease duration was 6.0 years (range: 0–31).

The median H&Y score was 2 with 92.8% of participants in the

less severe category and 7.2% in the more severe category. Median

BMI was 25.1 kg/m 2 (range: 17.0–49.5) with 4 participants (3.2%)

falling within the World Health Organisation (WHO) underweight

classification (,18.5 kg/m 2 ). One participant could not recall

when diagnosis had occurred. Five participants did not have

a waist circumference measurement, and one participant did not

have a calf circumference measurement.

Nineteen (15.2%) participants were moderately malnourished

(SGA-B) while none were severely malnourished (SGA-C). In the

SGA-B category, 3 (15.8%) of the participants were ,65 years old.

Of the 125 participants, 54 (43%) (28 M, 26 F) reported previous

unintentional weight loss at some point following diagnosis. The

well-nourished participants had significantly higher anthropomet-

ric measures (BMI, U = 367.0, z = 24.40, p = .000; MAC,

U = 482.5, z = 23.61, p = .000; calf circumference, U = 355.5,

z = 24.46, p = .000; and waist circumference, U = 410.5,

z = 23.47, p = .001) than the malnourished participants. Signifi-

cantly lower PG-SGA scores, U = 1860.5,z = 5.94,p = .000 were

found for the well-nourished participants (Table 1). This difference

in total score was determined by the significant differences in the

scores for Worksheet 1 (SGA A Mdn = 1, SGA B Mdn = 6),

U = 1811.5, z = 5.64, p = .000, and Worksheet 4 (SGA A Mdn = 0,

SGA B Mdn = 1), U = 1783.0, z = 6.37, p = .000. The scores for

Worksheets 2 and 3 were not significantly different between the

two groups. The score for Worksheet 3 was 0 for all participants.

‘No appetite or not feeling like eating’ was the symptom

reported most frequently on the PG-SGA for both SGA-A (13%)

and SGA-B (53%) groups, X 2 (1, n = 125) = 14.91,p,.05 (Table 2).

In addition to ‘no appetite’, the differences between groups

reached significance for constipation, X 2 (1, n = 125) = 8.11, p,.05,

diarrhea, X 2 (1, n = 125) = 6.32, p,.05, problems swallowing, X

2 (1,

n = 125) = 7.08, p,.05, and feel full quickly, X 2 (1,

n = 125) = 16.81, p,.05.

The PG-SGA score for 53% of the SGA B participants placed

them into the triage category requiring intervention by a dietitian

(scores 4–8), and the remaining 47% fell into the triage category

indicating a critical need for improved symptom management

and/or nutrient intervention options (scores $9). Comparatively,

21% and 9% of the SGA A participants were placed into these

categories, respectively. The remaining SGA A participants were

evenly split between the category requiring no intervention (scores

0–1) and the category requiring patient and family education

(scores 2–3). This categorisation was significantly different between

the groups, X 2 (3, n = 125) = 34.91, p,.05.

Of the 19 malnourished participants, 9 were female (47%). A

similar proportion of females (18%) were malnourished than males

(14%) as assessed using Chi square analysis. A greater proportion

of females (50%) also experienced unintentional weight loss when

compared to males (38%). Females who were malnourished had

significantly lower BMIs, U = 71.5, z = 2.17, p = .028, smaller mid-

arm circumferences, U = 80.0, z = 2.86, p = .003, and waist

circumferences, U = 64.0, z = 2.83, p = .003, than the malnour-

ished males. This was reflected in the overall sample with

significantly lower BMIs, U = 2352.5, z = 2.34, p = .019, smaller

mid-arm circumference, U = 2526.5, z = 3.22, p = .001, and waist

circumferences, U = 2291.5, z = 3.34, p = .001, as well as smaller

calf circumferences, U = 2295.0, z = 2.27, p = .023, in the females

than the males (Table 3).

Discussion

The current study resulted in a malnutrition diagnosis in 15% of

the sample of community-dwelling adults with Parkinson’s disease.

This result is higher than previous studies where 0–2% of PWP

were diagnosed with malnutrition. The discrepancy could be

a result of the different assessment tools used. It could also be

related to the fact that the samples in the studies by Wang, et al [1]

and Barichella, et al [2], included participants that were recruited

from a Movement Disorders outpatient clinic and physiotherapy

participants, respectively. As a result, the results may not be

generalizable to the wider community-dwelling population of

PWP in which the rates of malnutrition may be higher. This may

have also been the case in the present study. The most socially

isolated and those with higher disease severity are also the least

likely to participate in research, regardless of setting. This is

reflected in the current sample where only 1 participant was

classified as having a Hoehn & Yahr classification equal to 5 and

only eight were classified as having a Hoehn & Yahr classification

equal to 4.

Comparatively, estimates of malnutrition in community-dwell-

ing adults without PD ranges from 3211% with the risk of

malnutrition between 15% and 38% [12,21–24]. The disparity in

results again arises from the differences in tool used, the age range

Malnutrition in Parkinson’s Disease

PLOS ONE | www.plosone.org 3 January 2013 | Volume 8 | Issue 1 | e53290

of the samples and the sample sources. The frequency in the

current sample of PWP is also higher than that reported in the

general population. This is not wholly unexpected given the

presence of factors in PD that increase the risk of malnutrition

such as lack of appetite, altered taste and smell senses, poor eating

skills and potentially higher energy expenditures. Disturbance of

autonomic function of the gastrointestinal tract in PD including

dysphagia, delayed gastric emptying and constipation is well-

documented [25] and it is has been suggested that these

disturbances, especially constipation, precede the motor symptoms

[26]. The proportion of the malnourished with reduced intake due

to loss of appetite, constipation, diarrhea, problems swallowing

and early satiety was significantly higher than the proportion of

well-nourished in the current study. The total PG-SGA score

particularly reflects the presence of these nutrition impact

symptoms. The 30% of the well-nourished participants with

a PG-SGA score placing them in the triage categories requiring

intervention are potentially at risk of becoming malnourished if

those symptoms are not appropriately managed.

Significant unintentional weight loss is not uncommon in the

elderly population with between 17235% of community-dwelling

older adults losing at least 5% of their baseline weight over a 3

year period [13] resulting in significantly lower BMIs when

compared to non-weight losers [27]. A greater number of PWP

(60%) reportedly lose weight unintentionally following diagnosis

resulting in significantly lower body BMIs than those who do not

lose weight [4]. Lower BMIs are also found in malnourished PWP

compared to well-nourished [1]. Similarly, the current study found

a higher percentage of weight losers (43%) than that reported in

the general population, and BMIs were significantly lower in the

malnourished group compared to the well-nourished group. The

physical examination in the PG-SGA also resulted in significantly

Table 1. Characteristics of the participants compared between nutritional status categories using Mann-Whitney analysis for continuous variables and Chi square analysis for categorical variables and reported as median (range).

SGA Classification

A (Well-nourished) (n = 116) B (Moderately malnourished) (n = 19)

Age (years) 69.0 (35.0–92.0) 74.0 (61.0–87.0)

Height (cm) 167.25 (148.0–186.0) 164.8 (140.5–174.0)

Weight (kg) 73.2 (46.5–145.0)* 55.4 (40.7–100.6)*

BMI (kg/m 2 ) 25.9 (17.0–49.5)* 20.0 (17.7–33.2)*

MAC (cm) 29.1 (21.0–43.0)* 25.5 (21.0–35.0)*

Waist circumference (cm) 95.5 (67.5–149.5)* 82.5 (65.5–116.0)*

Calf Circumference (cm) 36.5 (31.0–48.5)* 32.5 (28.0–39.0)*

PG-SGA score 2 (0–15)* 8 (4–15)*

*Statistically significant difference between SGA classifications (p,0.05). {Reported as frequencies (percentages). Abbreviations: BMI = body mass index, MAC = mid-arm circumference, PD = Parkinson’s disease, PG-SGA = Patient-Generated Subjective Global Assessment, SGA = Subjective Global Assessment. doi:10.1371/journal.pone.0053290.t001

Table 2. Nutrition impact symptoms as reported on the PG-SGA compared between SGA groups using Chi-square analysis.

SGA Classification

A (Well-nourished) (n = 116) B (Moderately malnourished) (n = 19)

No appetite, just did not feel like eating 15 (13%)* 10 (53%)*

Nausea 4 (3%) 1 (5%)

Vomiting 1 (1%) 0

Constipation 2 (2%)* 3 (16%)*

Diarrhea 1 (1%)* 1 (5%)*

Mouth sores 2 (2%) 0

Dry mouth 6 (5%) 1 (5%)

Things taste funny or have no taste 9 (8%) 4 (21%)

Smells bother me 1 (1%) 0

Problems swallowing 10 (9%)* 6 (32%)*

Feel full quickly 7 (6%)* 8 (42%)*

Pain 2 (2%) 1 (5%)

Other 5 (4%) 0

*Statistically significant difference between SGA classifications (p,0.05). Abbreviations: PG-SGA = Patient-Generated Subjective Global Assessment, SGA = Subjective Global Assessment. doi:10.1371/journal.pone.0053290.t002

Malnutrition in Parkinson’s Disease

PLOS ONE | www.plosone.org 4 January 2013 | Volume 8 | Issue 1 | e53290

reduced fat stores and muscle status in the malnourished group in

this study when compared to the well-nourished. Therefore, the

current results may more accurately reflect the extent of

malnutrition in PWP.

Females generally are at greater risk of unintentional weight loss

and malnutrition than males [22,28]. This appears to also be true

in PWP with previous studies [1,2] and the current study

supporting this finding. The significantly larger average values

for BMI and waist circumference (an indicator of adiposity [29])

coupled with the evidence of muscle loss in the PG-SGA

assessment in the malnourished males may indicate that males

in this group tend towards sarcopenic obesity while the females

exhibit both fat and muscle loss.

This study is the largest to report the extent of malnutrition in

community dwelling PWP and the first to do so in Australia. In

Australia, an estimated 20% of PWP are under the age of 65 with

over 2,000 aged in their 30 s and 40 s [30]. Therefore, the age

range in the current sample is representative of the Australian PD

population. While older age is a risk factor for malnutrition [31],

Parkinson’s disease has also been reported as an independent risk

factor for malnutrition [32]. Therefore it is important to un-

derstand the extent of malnutrition in PWP of any age, and there

were malnourished participants in the current sample (15.8%) who

were under the age of 65 years. Future studies in a larger sample

could attempt to further classify the extent of malnutrition in

younger PWP vs older PWP and to identify the risk factors of

importance in each age group.

Another strength of this study is the use of a nutrition assessment

tool applicable to any age that includes, in addition to recent

weight changes and a physical examination, the presence of

symptoms that are likely to influence intake in PWP. This study

also highlights the fact that anthropometric measures alone would

not have identified the malnourished in this population. Only 3

participants had BMI values in the underweight range (,18.5 kg/

m 2 ), no one had a mid-arm circumference below 21 cm, and two

had calf circumferences less than 31 cm. This could be explained

by the fact that a higher BMI range to detect underweight may be

more appropriate in the older participants ($65 years) [33].

However, a multi-factorial approach to the diagnosis including

recent weight loss, intake, nutrition impact symptoms and

a thorough assessment of muscle and fat status resulted in a higher

proportion of participants classified as malnourished.

Limitations in the current study include the limited reach of the

sampling process to the frailest and most vulnerable of the

community-dwelling PD population. Although the current study

involved data collection at participants’ homes, some potential

Table 3. Characteristics of the total sample and of the malnourished participants compared between genders using Mann- Whitney analysis and reported as median (range).

Total sample (n = 125) (74 M, 51 F) Malnourished participants (n = 19) (10 M, 9 F)

Age (years) Male 70.5 (35.0–86.0) 74.5 (66.0–81.0)

Female 68.0 (42.0–92.0) 72.0 (61.0–87.0)

PD Duration (years) Male 6.0 (0.3–31.0) 9.0 (1.0–26.0)

Female 6.0 (0.0–25.5) 5.0 (1.0–11.0)

Height (cm) Male 172.75 (156.0–186.0)* 169.5 (158.5–174.0)*

Female 160.0 (140.5–173.5)* 154.5 (140.5–171.5)*

Weight (kg) Male 75.5 (49.8–145.0)* 63.1 (49.8–100.6)*

Female 60.8 (40.7–128.8)* 46.7 (40.7–58.7)*

BMI (kg/m 2 ) Male 26.1 (19.1–46.3)* 21.3 (19.5–33.2)*

Female 23.6 (17.0–49.5)* 19.5 (17.7–21.6)*

MAC (cm) Male 30.5 (23.0–41.0)* 27.6 (23.0–35.0)*

Female 27.0 (21.0–43.0)* 22.0 (21.0–28.0)*

Waist circumference (cm) Male 96.5 (74.0–149.5)* 86.75 (78.0–116.0)*

Female 86.5 (65.5–117.0)* 73.5 (65.5–88.0)*

Calf circumference (cm) Male 36.75 (28.0–48.5)* 33.75 (28.0–39.0)

Female 35.0 (29.0–43.0)* 32.0 (29.0–36.0)

PG-SGA score Male 3 (0–15) 8 (4–15)

Female 3 (0–13) 10 (8–13)

Male Female Male Female

H&Y{ 0 0 (0%) 2 (4%) 0 (0%) 0 (0%)

1 17 (23%) 11 (22%) 1 (10%) 0 (0%)

2 26 (35%) 18 (35%) 4 (40%) 3 (33%)

3 27 (37%) 15 (29%) 3 (30%) 4 (45%)

4 3 (4%) 5 (10%) 1 (10%) 2 (22%)

5 1 (1%) 0 (0%) 1 (10%) 0 (0%)

*Statistically significant difference between genders (p,0.05). {Reported as frequencies (percentages). Abbreviations: BMI = body mass index, H&Y = Hoehn & Yahr, MAC = mid-arm circumference, PD = Parkinson’s disease, PG- SGA = Patient-Generated Subjective Global Assessment. doi:10.1371/journal.pone.0053290.t003

Malnutrition in Parkinson’s Disease

PLOS ONE | www.plosone.org 5 January 2013 | Volume 8 | Issue 1 | e53290

participants declined the invitation to participate stating that they

were not well enough or their Parkinson’s disease symptoms were

too advanced to participate. The majority of the sample also lived

within the urban area of Brisbane, Queensland, Australia where

access to services and food supplies is not as limited as it might be

in a more rural setting. Despite the high frequency of malnutrition,

the small sample size limited the number of malnourished

participants with whom comparisons could be made and

potentially limits the ability to generalise the results. However,

the current sample size exceeds that of other studies reporting the

rate of malnutrition in community-dwelling people with Parkin-

son’s disease (n = 61 [2] and n = 117 [1]).

Another potential limitation in this study is the lack of validation

of the available nutrition assessment tools for the Parkinson’s

disease population. Therefore, the tool itself may influence the

validity of the results. Worksheets 2 and 3 of the PG-SGA also did

not significantly contribute to the overall score nor were they

different between groups. These boxes may be of particular value

in acutely ill populations, for which the tool was created and has

since been validated, rather than in a chronic condition such as

Parkinson’s disease. Therefore, the triage categories may not be

appropriate in this population. However, the SGA is a robust

nutrition assessment tool that has been widely used [16] and is

considered a valid tool in community populations, regardless of

disease state. Further exploration of appropriate nutrition assess-

ment tools within this population is still warranted.

The first step to addressing malnutrition, the loss of lean body

mass and the associated poor outcomes is identifying the problem.

This study highlights the fact that malnutrition is prevalent in this

sample of PWP. However, as with malnutrition in other

populations, it remains under-recognised and undiagnosed. The

health professionals who have the most contact with these

individuals in the community setting should screen for malnutri-

tion at regular intervals and provide appropriate referrals for

nutrition-related care for those who are at risk of malnutrition.

Acknowledgments

The authors would like to thank the participants of the study for their time.

The authors would also like to thank Parkinson’s Queensland, Inc for their

assistance with recruiting participants.

Author Contributions

Critically revised manuscript: SA GDM PAS GKK. Conceived and

designed the experiments: JMS SA GDM PAS GKK. Performed the

experiments: JMS. Analyzed the data: JMS. Wrote the paper: JMS.

References

1. Wang G, Wan Y, Cheng Q, Wang Y, Zhang J, et al. (2010) Malnutrition and

associated factors in Chinese patients with Parkinson’s disease: results from a pilot investigation. Parkinsonism Relat Disord 16: 119–123.

2. Barichella M, Villa MC, Massarotto A, Cordara SE, Marczewska A, et al. (2008) Mini Nutritional Assessment in patients with Parkinson’s disease: Correlation

between worsening of the malnutrition and increasing number of disease years. Nutr Neurosci 11: 128–134.

3. Jaafar AF, Gray WK, Porter B, Turnbull EJ, Walker RW (2010) A cross-

sectional study of the nutritional status of community-dwelling people with idiopathic Parkinson’s disease. BMC Neurol 10: 124–129. doi:10.1186/1471-

2377-10-124. 4. Beyer PL, Palarino MY, Michalek D, Busenbark K, Koller WC (1995) Weight

change and body composition in patients with Parkinson’s disease. J Am Diet

Assoc 95: 979–983. 5. Uc EY, Struck LK, Rodnitzky RL, Zimmerman B, Dobson J, et al. (2006)

Predictors of weight loss in Parkinson’s disease. Mov Disord 21: 930–936. 6. Chen H, Zhang SM, Hernán MA, Willett WC, Ascherio A (2003) Weight loss in

Parkinson’s disease. Ann Neurol 53: 676–679. 7. Sheard JM, Ash S, Silburn PA, Kerr GK (2011) Prevalence of malnutrition in

Parkinson’s disease: a systematic review. Nutr Rev 69: 520–532. doi:10.1111/

j.1753-4887.2011.00413.x. 8. Correia MITD, Waitzberg DL (2003) The impact of malnutrition on morbidity,

mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clin Nutr 22: 235–239. doi:Doi: 10.1016/s0261-5614(02)00215-

7.

9. Crogan NL, Pasvogel A (2003) The influence of protein-calorie malnutrition on quality of life in nursing homes. J Gerontol A Biol Sci Med Sci 58: 159–164.

10. Banks M, Bauer J, Graves N, Ash S (2010) Malnutrition and pressure ulcer risk in adults in Australian health care facilities. Nutrition 26: 896–901.

11. Wikby K, Ek A-C, Christensson L (2006) Nutritional status in elderly people admitted to community residential homes: comparisons between two cohorts.

J Nutr Health Aging 10: 232–238.

12. Visvanathan R, Macintosh C, Callary M, Penhall R, Horowitz M, et al. (2003) The nutritional status of 250 older Australian recipients of domiciliary care

services and its association with outcomes at 12 months. J Am Geriatr Soc 51: 1007–1011.

13. Newman AB, Yanez D, Harris T, Duxbury A, Enright PL, et al. (2001) Weight

change in old age and its association with mortality. J Am Geriatr Soc 49: 1309– 1318.

14. Watterson C, Fraser A, Banks M, Isenring E, Miller M, et al. (2009) Evidence based practice guidelines for the nutritional management of malnutrition in

adult patients across the continuum of care. Nutr Diet 66: S1–S34. 15. Detsky AS, McLaughlin JR, Baker JP, Johnston N, Whittaker S, et al. (1987)

What is subjective global assessment of nutritional status? JPEN J Parenter

Enteral Nutr 11: 8–13. doi:10.1177/014860718701100108. 16. Barbosa-Silva MCG, Barros AJD (2006) Indications and limitations of the use of

subjective global assessment in clinical practice: an update. Curr Opin Clin Nutr Metab Care 9: 263–269. doi:10.1097/01.mco.0000222109.53665.ed.

17. Ottery F (2000) Patient-Generated Subjective Global Assessment. In: McCallum

PD, editor. The Clinical Guide to Oncology Nutrition. Chicago: American

Dietetic Association. 11–23.

18. Chumlea WC, Guo SS, Wholihan K, Cockram D, Kuczmarksi RJ, et al. (1998)

Stature prediction equations for elderly non-Hispanic white, non-Hispanic

black, and Mexican-American persons developed from NHANES III data. J Am

Diet Assoc 98: 137–142.

19. Gibson RS (2005) Principles of Nutritional Assessment. 2nd ed. New York, New

York: Oxford University Press, Inc. p.

20. Lohman TG, Roche AF, Martorell R (1988) Anthropometric Standardization

Manual. Champaign: Human Kinetic Books. p.

21. Davidson J, Getz M (2008) Nutritional risk and body composition in free-living

elderly participating in congregate meal-site programs. J Nutr Elder 24: 53–68.

22. Johansson Y, Bachrach-Lindström M, Carstensen J, Ek A (2009) Malnutrition in

a home-living older population: prevalence, incidence and risk factors. A

prospective study. J Clin Nurs 18: 1354–1364.

23. Leggo M, Banks M, Isenring E, Stewart L, Tweeddale M (2008) A quality

improvement nutrition screening and intervention program available to Home

and Community Care eligible clients. Nutr Diet 65: 162–167. doi:10.1111/

j.1747-0080.2008.00239.x.

24. Pai MK (2011) Comparative study of nutritional status of elderly population

living in the home for aged vs those living in the community. Biomed Res 22:

120–126.

25. Pfeiffer RF (2003) Gastrointestinal dysfunction in Parkinson’s disease. Lancet

Neurol 2: 107–116.

26. Chaudhuri KR, Healy DG, Schapira AHV (2006) Non-motor symptoms of

Parkinson’s disease: Diagnosis and management. Lancet Neurol 5: 235–245.

27. Lee JS, Kritchevsky SB, Harris TB, Tylavsky F, Rubin SM, et al. (2005) Short-

term weight changes in community-dwelling older adults: The Health, Aging,

and Body Composition Weight Change Substudy. Am J Clin Nutr 82: 644–650.

28. Alho Letra Martins CP, Correia JR, Freitas Do Amaral T (2006) Undernutrition

risk screening and length of stay of hospitalized elderly. J Nutr Elder 25: 5–21.

doi:10.1300/J052v25n02_02.

29. Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R (2002) Body mass

index and waist circumference independently contribute to the prediction of

nonabdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr 75:

683–688.

30. Deloitte Access Economics (2011) Living with Parkinson’s Disease – update.

31. Morley JE (1997) Anorexia of aging: Physiologic and pathologic. The American

Journal of Clinical Nutrition 66: 760–773.

32. Mamhidir AG, Ljunggren G, Kihlgren M, Kihlgren A, Wimo A (2006)

Underweight, weight loss and related risk factors among older adults in sheltered

housing - a Swedish follow-up study. J Nutr Health Aging 10: 255–263.

33. Donini LM, Savina C, Gennaro E, De Felice MR, Rosano A, et al. (2012) A

systematic review of the literature concerning the relationship between obesity

and mortality in the elderly. J Nutr Health Aging 16: 89–98.

Malnutrition in Parkinson’s Disease

PLOS ONE | www.plosone.org 6 January 2013 | Volume 8 | Issue 1 | e53290