Next Article in Journal
The Protective and Long-Lasting Effects of Human Milk Oligosaccharides on Cognition in Mammals
Previous Article in Journal
Congenital, Intrapartum and Postnatal Maternal-Fetal-Neonatal SARS-CoV-2 Infections: A Narrative Review
Previous Article in Special Issue
Cross-Talk between Diet-Associated Dysbiosis and Hand Osteoarthritis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 12
Issue 11
10.3390/nu12113571
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of Nutritional Status and Methods to Identify Nutritional Risk in Rheumatoid Arthritis and Spondyloarthritis

by
Marie Njerve Olsen
1,2,*,
Randi J. Tangvik
1,3 and
Anne-Kristine Halse
2,4
1
Mohn Nutrition Research Laboratory, Department of Clinical Medicine, Faculty of Medicine, University of Bergen, P.O. Box 7804, 5020 Bergen, Norway
2
Department of Rheumatology, Haukeland University Hospital, 5021 Bergen, Norway
3
Department of Research and Development, Haukeland University Hospital, P.O. Box 1400, 5021 Bergen, Norway
4
Department of Clinical Science, Faculty of Medicine, University of Bergen, P.O. Box 7804, 5020 Bergen, Norway
*
Author to whom correspondence should be addressed.
Nutrients 2020, 12(11), 3571; https://0-doi-org.brum.beds.ac.uk/10.3390/nu12113571
Submission received: 30 October 2020 / Revised: 19 November 2020 / Accepted: 19 November 2020 / Published: 21 November 2020
(This article belongs to the Special Issue Nutrition and Metabolism in Rheumatic Diseases)
Versions Notes

Abstract

:
Patients with rheumatoid arthritis (RA) and spondyloarthritis (SpA) experience several nutritional challenges and are prone to develop malnutrition. This observational study aimed to perform a comprehensive nutritional assessment of outpatients diagnosed with RA and SpA, as well as to evaluate methods to identify nutritional risk. Nutritional status was investigated by anthropometric measures, body composition (DXA, dual energy X-ray absorptiometry), and handgrip strength (HGS). Nutritional risk was classified by Nutritional Risk Screening 2002 (NRS2002) and malnutrition was defined by the Global Leadership Initiative on Malnutrition (GLIM) criteria and fat-free mass index (FFMI; kg/m2, <16.7 (M), <14.6 (F)). Out of 71 included patients, 46 (66%) were abdominally obese, 28 (39%) were obese in terms of body mass index (BMI), and 33 (52%) were obese in terms of the fat mass index (FMI; kg/m2, ≥8.3 (M), ≥11.8 (F)). Malnutrition was identified according to FFMI in 12 (19%) patients, according to GLIM criteria in 5 (8%) patients, and on the basis of BMI (<18.5 kg/m2) in 1 (1%) patient. None were identified by NRS2002 to be at nutritional risk. Our study revealed high prevalence of abdominal obesity and low FFMI. Waist circumference was a good indicator of FMI. BMI, NRS2002, and HGS did not capture patients with malnutrition identified by DXA.

1. Introduction

Disease-related malnutrition is a severe life-threatening condition receiving considerable and increasing attention in healthcare. Malnutrition has been shown to reduce patients’ resistance to infections and quality of life, and increase the risk of morbidity, hospitalization, and even mortality [1,2]. National and international nutrition guidelines, as well as patient safety campaigns, emphasize procedures to improve nutritional status in hospitalized patients, starting with nutritional risk screening [3]. In cases of nutritional risk, nutritional assessment is recommended, and, in most cases, an individualized nutritional plan should be made. As many as one in three hospitalized patients are at nutritional risk [2,4]. An individualized nutritional plan has been shown to improve clinical outcome and survival for these at-risk patients [5,6].
Patients with rheumatoid arthritis (RA) and spondyloarthritis (SpA) are prone to develop malnutrition as their medication often affects appetite, nutrient absorption, and digestion [7,8,9,10,11]. Due to joint pain and fatigue, patients can experience challenges in food shopping and preparation [8,12]. In addition, the increased level of inflammation may increase nutritional needs [13]. Nutritional status is commonly assessed by body mass index (BMI; kg/m2) in a clinical setting [14]. However, reduced fat-free mass (FFM) and increased fat mass (FM) have been reported in rheumatic patients, due to cytokine-driven hyper-metabolism and decreased activity levels [15]. Rheumatoid cachexia (RC) is a term used to describe this phenomenon [16]. An increase in FM often masks a reduction in FFM, which results in no change in BMI. Measurement of body composition (fat-free mass index (FFMI; kg/m2) and fat mass index (FMI; kg/m2)) provides more precise information, and dual energy X-ray absorptiometry (DXA) is considered the gold standard for this purpose [14]. FFMI and FMI are conceptually similar to BMI (BMI = FFMI + FMI). There is no clear consensus on the cut-off levels used to define RC, but FFMI <10th percentile and FMI >25th percentile have been proposed [17].
Although new drugs have revolutionized treatment, some patients still suffer from pain, stiffness, or fatigue, or experience side effects of their drugs. Patients with rheumatic diseases are therefore likely to experiment with “dietary therapies” as a complementary treatment. However, the scientific evidence regarding the effect of specific diets on RA and SpA is limited [18,19]. It is a cause for concern that dietary therapies, leading to restrictions that are not based on scientific evidence, can lead to reduced intake of food or nutrients [20].
As treatment of inflammatory rheumatic diseases in hospitals increasingly takes place in outpatient clinics, it is necessary to validate the nutritional screening tools and guidelines for this purpose. To our knowledge, no such guidelines exist for patients with RA or SpA [21]. Further, it is unclear to what extent Nutritional Risk Screening 2002 (NRS2002) identifies nutritional risk in this patient group [22,23]. It is worrying that little attention is given to these patients’ dietary intake and nutritional status [24,25]. The Global Leadership Initiative on Malnutrition (GLIM) has developed a two-step approach for diagnosing malnutrition: (i) a nutritional risk screening using a validated screening tool (e.g., NRS2002) and (ii) a diagnostic assessment that considers both phenotypic and etiological aspects of malnutrition [26]. It is unclear whether these criteria are appropriate to identify malnutrition in patients with RA and SpA. The majority of previous studies addressing nutrition and rheumatic disease have applied to patients with RA, while studies on SpA are rare [19].
Therefore, this study aimed to evaluate nutritional status and dietary habits in outpatients with RA and SpA in order to describe nutritional challenges for these patients. We also wanted to investigate whether nutritional risk screening and the GLIM criteria are valid to evaluate nutritional status of outpatients with RA and SpA by comparing these with more comprehensive methods.

2. Materials and Methods

2.1. Study Design and Setting

This was an observational study conducted in the Rheumatology Department of Haukeland University Hospital (HUH) from August 2018 to December 2019. HUH is the second largest hospital in Norway with about 1000 beds. All data were collected by master students in clinical nutrition (Marie Njerve Olsen and Mathilde Skogseide), unless otherwise stated.

2.2. Study Population and Recruitment

Outpatients taking part in a rehabilitation program and outpatients receiving infusions with biological disease-modifying anti-rheumatic drugs (bDMARDs) were eligible for inclusion. Patients in the rehabilitation program were invited to participate upon arrival, while those receiving bDMARDs were invited prior to hospitalization.
Inclusion criteria were adult patients diagnosed with one of the following diagnoses, according to the 10th version of the International Classification of Diseases: rheumatoid arthritis (RA) (M05.8, M05.9, M06.0), polyarthritis (M13.0), psoriatic arthritis and enteropathic arthritis (M07.0, M07.3, M07.4, M07.5), ankylosing spondylitis (M45), sacroiliitis (M46.1), and unspecified spondyloarthritis (M46.8, M46.9). For practical reasons, patients with RA and polyarthritis were categorized as RA. Patients with psoriatic arthritis, ankylosing spondylitis, sacroiliitis, and unspecified spondyloarthritis were categorized as SpA. We excluded patients with severe cardiovascular diseases, such as heart failure (NYHA class II-III), neurological diseases, severe mental illnesses, pregnant and lactating women, and non-Norwegian-speaking patients.

2.3. Data Collected

2.3.1. Demographics

Information about socioeconomic status, level of physical activity, and the need for help with housekeeping were collected through a questionnaire. Diagnosis, disease duration, and use of medication were recorded in the data program “GoTreatITRheuma" by the physician. The medications were categorized into the following four groups: disease-modifying anti-rheumatic drugs (DMARDs), non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids (GC), or none.

2.3.2. Nutritional Assessment

The anthropometric measurements height (m), weight (kg), and waist circumference (WC; cm) were obtained according to guidelines [27,28,29]. Height was measured by a wall-mounted stadiometer, without shoes with heels placed against the wall and the head positioned in the Frankfort plane [27]. Weight was measured in light clothing with TANITA MC-180MA, Tanita Corporation, Tokyo, Japan. One kilogram, was subtracted from the weight for the clothes. WC was measured by a non-stretchable measuring tape at the mid-point between the lowest palpable rib and the iliac crest.
Body composition included FM (kg) and FFM (kg), measured with dual-energy X-ray absorptiometry (DXA) in 63 patients, standardized by qualified personnel (Lunar iDXA Scanner, GE Medical Systems Lunar, Madison WI, USA). In addition, body mass index (BMI; kg/m2), fat-free mass index (FFMI; FFM in kg/m2), and fat mass index (FMI; FM in kg/m2) were calculated. Underweight, overweight, and obesity were classified according to the BMI categories of the World Health Organization [30]. Levels of FMI and FFMI (DXA) were compared with levels in a healthy European population [31]. Levels were categorized as low, normal, high, or very high, depending on the matching level reported in the BMI categories as previously described by Kyle et al. [31].
Blood samples for analysis of nutritional and inflammatory biomarkers were collected by a study nurse, analyzed at the laboratory of HUH, and compared with levels in healthy age- and sex-matched adults [32].
Handgrip strength (HGS) was measured by a dynamometer (maximum weight capacity of 80 kg) (KERN MAP 80K1, Kern and Sohn, Balingen, Germany), according to guidelines [33]. The mean value of 3 results was compared with levels in healthy age- and sex-matched adults. A level below the 5th percentile was considered to be low HGS [33].

2.3.3. Malnutrition and Nutritional Risk Screening

Malnutrition was defined by muscle mass (FFMI <16.7 (M) and <14.6 (F), measured by DXA) and the GLIM criteria [26,31]. In order to receive a malnutrition diagnosis according to the GLIM criteria, patients must fulfil 1 phenotypic and 1 etiological criterion. The phenotypic criteria are involuntary weight loss, BMI <20 or <22 if ≥70 years, and reduced muscle mass, FFMI <15 for males or <17 for females. The etiological criteria are reduced food intake or assimilation, and inflammation [26].
Nutritional risk was defined as a score of 3 or more points on NRS 2002, while rheumatoid cachexia (RC) was defined as FFMI below the 10th percentile combined with FMI above the 25th percentile in a healthy reference population [26,34].

2.3.4. Dietary Habits

Information about dietary knowledge, dietary restrictions, and use of supplements was collected through a validated questionnaire [35]. In addition, questions about dietary habits were included.

2.3.5. Ethics

The study was approved by the Norwegian “Regional Committees for Medical and Health Research Ethics”, 2018/1063 (07/06/18), and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from the participants. The participants could withdraw at any time without consequences for their further treatment.

2.4. Statistical Analysis

Statistical analysis was performed in SPSS version 25 (SPSS Inc. Chicago, IL, USA). Statistical power was not calculated due to the study design. The normal distribution of the continuous variables was tested by the Shapiro–Wilk test. Continuous variables are reported as mean with standard deviation, minimum and maximum values. Categorical variables are reported as frequencies and percentages.
A chi-squared test with Yates’ continuity correction was performed to compare the groups.
We calculated sensitivity, specificity, positive predictive value, and negative predictive value for NRS2002, categories of BMI, WC, and HGS in order to identify malnutrition, defined as low FFMI (<16.7 (M) or <14.6 (F)), and obesity, defined as very high FMI (≥8.3 (M) or ≥11.8 (F)), measured by DXA [31]. p-values < 0.05 were considered significant.

3. Results

3.1. Description of the Study Population

A total of 71 outpatients fulfilled the inclusion criteria and participated in the study: 53 (75%) females, mean age 52 ± 12 years and mean BMI 28.0 ± 5.5 kg/m2. The mean disease duration was 14 ± 12 years. Table 1 shows the characteristics of the study population.
Eleven (15%) patients had a gastric condition: celiac disease (n = 1), previous bariatric surgery (n = 3), or inflammatory bowel disease (n = 7). A total of 34 (48%) patients were diagnosed with RA, and 37 (52%) patients had SpA. Fifty-seven (80%) patients used one or more DMARDs, and the rest used either NSAIDs or GC.

3.2. Nutritional Assessment

Table 2 shows data on nutritional status.
Abdominal obesity was present in 46 (66%) patients, and 46 (65%) were overweight or obese, on the basis of BMI.
A very high FMI by DXA was seen in 33 (52%) of the 63 patients, measured by DXA (Table 3). High or very high FMI was found in 12 (52%) of the 23 patients with normal BMI (Table 3). The mean value of FMI was higher than the reference values for males and females (Table 2).
Vitamin D deficiency (<50 nmol/L) was observed in 13 (19%) patients, and 12 (17%) patients had a low level of hemoglobin (<13.4 g/dL (M), <11.7 g/dL (F)).
Compared with the reference levels of healthy adults, 19 (30%) patients had lower HGS in the dominant hand, of whom 13 patients (42%) had RA and 6 (18%) had SpA. Low HGS in the non-dominant hand was observed in eight (13%) patients.

3.3. Malnutrition and Nutritional Risk Screening

Low FFMI, by DXA, was observed in 12 (19%) patients, of which 7 had RA and 5 SpA. None of the malnourished participants were identified to be at nutritional risk according to NRS 2002, and five (8%) patients were moderately malnourished according to GLIM criteria. One (2%) patient was underweight in terms of BMI. Furthermore, nine (14%) patients fulfilled the criteria for RC (FFMI <10th percentile and FMI >25th percentile), of which four were also malnourished according to GLIM criteria.
Levels of FMI and FFMI varied greatly within the BMI categories, and low FFMI was found in 11 of the 23 (36%) patients with a normal BMI (Table 3).
No significant differences between RA and SpA in terms of low FFMI, low HGS, obesity, or abdominal obesity were observed.

3.4. Evaluation of Methods to Detect Malnutrition and Obesity

Compared with DXA, low HGS had higher sensitivity in detecting malnutrition (63%) than low BMI, defined by GLIM criteria (42%), BMI < 18.5 (8%), and NRS2002 (0%) (Table 4).
Abdominal obesity had the highest sensitivity in detecting a very high level of FMI, but had lower specificity than the BMI categories ≥28 and ≥30 (67%) (Table 5).

3.5. Dietary Habits

The questionnaire revealed that dietary supplements were used by 50 (70%) patients; fish oil or omega-3 supplements were most common, used by 32 (45%). Dietary restrictions were reported by 29 (41%) patients. Most frequently excluded or reduced were pork (10 patients, 34%), wheat (10 patients, 34%), and dairy products (9 patients, 31%). In addition, eight (28%) patients reported avoiding sugar.
Lack of knowledge of nutrition and diet was reported by 24 (34%) patients, while 32 (46%) patients reported a dietary change to reduce their disease activity. Out of 37 patients with SpA, 20 (54%) reported a dietary change.

4. Discussion

This study provides a comprehensive nutritional assessment of outpatients diagnosed with RA and SpA. Most of the patients were overweight or obese with high occurrence of abdominal fat mass. Importantly, one in five had a low FFMI. Even patients with normal BMI had a low FFMI, classified as malnourished. Notably, none of the patients were identified to be at nutritional risk on the basis of NRS2002. Low HGS showed highest sensitivity in detecting malnutrition, but also the lowest specificity. Patients reported poor nutritional knowledge.
In this study, the prevalence of abdominal obesity (WC) and obesity (BMI) was higher than in the general Norwegian population (66% vs. 45% and 39% vs. 20–25%, respectively) [36,37]. We found a higher percentage of obesity (BMI) than in other studies of RA and SpA patients (39% vs. 13–22%) [22,38]. The prevalence of obesity tends to increase during the progression of the disease [39]. Nikiphorou et al. reported an annual increase in BMI of 0.27 units in newly diagnosed RA subjects in a five-year follow-up study [25]. Investigations of body composition with DXA revealed that the WHO categories of BMI were not a sufficient indicator of overweight, as almost half of the patients with normal BMI had high FMI. New BMI cut-offs have been proposed to define overweight (>23 kg/m2) and obesity (>28 kg/m2) in patients with inflammatory rheumatic diseases (IRD) [40]. Using the suggested cut-off values, half of our patients would be categorized as obese. However, WC predicted high fat mass even more precisely than BMI. WC is easy to perform in a clinical setting and is also an important risk factor for cardiovascular disease. Our results concur with those of previous studies reporting altered body composition in patients with IRD [41]. Both the disease and medication might affect body composition. In particular, glucocorticosteroids increase appetite and influence body composition by increasing WC and reducing muscle mass [42]. Since obesity is associated with higher disease activity, lower probability of remission, and poorer treatment response to medication, management of obesity is of utmost importance [25,43].
Only one patient in the entire study population had low BMI, but interestingly, one-fifth of the patients had a low FFMI and one in seven had RC. In contrast to other studies, none of our patients had experienced involuntary weight loss within the last 6 months [44]. Thus, screening tools including BMI as one of the criteria for nutritional risk are not reliable.
We found a lower prevalence of RC than previous studies—14% vs. 20–38% [44,45]. A possible explanation might be the selection of patients, due to close follow-up at the outpatient clinic, and they may therefore have had lower disease activity than reported in older studies [44].
The key finding that half of our patients with normal BMI had low FFMI has previously been reported in a study by Konijn et al., where 38% of patients with normal BMI had low FFMI, measured by bioelectrical impedance analysis [41]. Neither screening tools nor BMI are suitable for detecting malnutrition and RC in patients with RA [17,22]. Thus, BMI < 18.5 may be too low to identify nutritional risk in patients with RA and SpA. Our results indicate that the cut-off values for low BMI (<20/22), based on the GLIM criteria, are more sensitive in identifying nutritional risk.
Low HGS was sensitive in detecting low FFMI, although PPV was low. HGS has been found to correlate with functional status and muscle mass in hospitalized patients [46]. On the other hand, HGS can probably not be used as a functional marker of nutritional status in this patient group due to joint pain and reduced hand function [47]. This is confirmed by the low PPV of HGS, meaning that a low HGS does not indicate a high probability of malnourishment.
Dietary supplements were more frequently used in our patients than in the general Norwegian population (70% vs. 53%), and also more often than in IRD in previous studies [48,49]. Female gender and high education are associated with the use of dietary supplements and might explain the frequent use of supplements in our study population [50].
Dietary restrictions might lead to insufficient intake of important nutrients [20]. Half of the patients reported having altered their diet to reduce disease symptoms, mostly limiting foods high in calcium and proteins. Proteins are important to preserve muscle mass, and calcium to prevent osteoporosis, both of relevance for patients with IRD. However, one in three patients reported a lack of relevant nutritional knowledge. Thus, individual dietary guidance might help patients to ensure the quality of their diet, achieve weight control, and prevent comorbidities.

4.1. Strengths and Limitations

This was a cross-sectional observational study, and our results have certain limitations. Bowel inflammation is more common in patients diagnosed with SpA, and might have affected the absorption of nutrients [51,52]. We did not exclude any patients with conditions affecting nutritional status. Patients with an interest in nutrition and health might have been over-represented among those who consented to study participation. A clear weakness is that we did not focus on previous use of medication, which can affect nutritional status. It is also a limitation that the questionnaire used in this study was not tested in a pilot.
A major strength is that DXA was used to measure FM and FFM [17,53]. The comprehensive assessment of nutritional status strengthens internal validity, as several parameters were used to judge nutritional status. The majority of questions were based on previously validated questionnaires.

4.2. Clinical Implications

In this study, the nutritional situation of patients with RA and SpA was complex. Both malnutrition and obesity were present, and low nutritional knowledge was reported. Hence, these patients may benefit from dietary counselling. Early nutritional risk screening and assessment of nutritional status are important. However, existing screening tools are not appropriate for these patients with altered body composition.
The GLIM involves a two-step approach for diagnosing malnutrition [26]. It is a concern that patients identified as malnourished in step 2 were not identified in step 1, as 20% were malnourished on the basis of low muscle mass, and none according to NRS2002. We therefore suggest excluding the first screening step in the GLIM criteria for this patient group.

4.3. Further Research

Due to the complex nutritional status of these patients, they may be expected to benefit from optimization of their diet. No specific dietary recommendations for patients with IRD exist, and existing screening tools for identifying nutritional risk are not appropriate. Many patients may receive information from unvalidated sources. Further studies should address these issues. The effect of diet on disease activity remains unclear, and we are currently conducting an intervention study to examine this.

5. Conclusions

In summary, pronounced nutritional characteristics of patients with RA and SpA were abdominal obesity and low muscle mass. We suggest that WC is a reliable indicator of FMI. Neither BMI, NRS2002, nor HGS captured patients with malnutrition, identified by DXA. Studies to investigate other screening tools to identify risk of malnutrition in this patient population are needed.

Author Contributions

Conceptualization, R.J.T. and A.-K.H.; formal analysis, M.N.O.; investigation, M.N.O. and A.-K.H.; project administration, M.N.O. and A.-K.H.; resources, R.J.T. and A.-K.H.; supervision, R.J.T. and A.-K.H.; validation, R.J.T. and A.-K.H.; writing—original draft, M.N.O.; writing—review and editing, R.J.T. and A.-K.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We would like to thank the staff of the Department of Rheumatology, Haukeland University Hospital; the study nurses Signe Lise Hjertaker and Tove Hatletveit who were flexible with their schedule; Mathilde Skogseide for her contribution to the data collection; and the patients participating in this study. We are also grateful to Anne Marie Beck and Jutta Dierkes for critical reading and valuable advice.

Conflicts of Interest

M.N.O. has received funding from the G.C. Rieber Foundation; otherwise, there is no conflict of interest to declare. The funders had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Norman, K.; Pichard, C.; Lochs, H.; Pirlich, M. Prognostic impact of disease-related malnutrition. Clin. Nutr. 2008, 27, 5–15. [Google Scholar] [CrossRef]
  2. Tangvik, R.J.; Tell, G.S.; Eisman, J.A.; Guttormsen, A.B.; Henriksen, A.; Nilsen, R.M.; Øyen, J.; Ranhoff, A.H. The nutritional strategy: four questions predict morbidity, mortality and health care costs. Clin. Nutr. 2014, 33, 634–641. [Google Scholar] [CrossRef] [Green Version]
  3. Cederholm, T.; Barazzoni, R.; Austin, P.; Ballmer, P.; Biolo, G.; Bischoff, S.C.; Compher, C.; Correia, I.; Higashiguchi, T.; Holst, M.; et al. ESPEN guidelines on definitions and terminology of clinical nutrition. Clin. Nutr. 2017, 36, 49–64. [Google Scholar] [CrossRef]
  4. Tangvik, R.J.; Tell, G.S.; Guttormsen, A.B.; Eisman, J.A.; Henriksen, A.; Nilsen, R.M.; Ranhoff, A.H. Nutritional risk profile in a university hospital population. Clin. Nutr. 2015, 34, 705–711. [Google Scholar] [CrossRef] [Green Version]
  5. Schuetz, P.; Fehr, R.; Baechli, V.; Geiser, M.; Deiss, M.; Gomes, F.; Kutz, A.; Tribolet, P.; Bregenzer, T.; Braun, N.; et al. Individualised nutritional support in medical inpatients at nutritional risk: a randomised clinical trial. Lancet 2019, 393, 2312–2321. [Google Scholar] [CrossRef]
  6. Rivelsrud, M.; Paur, I.; Sygnestveit, K.; Nilsen, R.M.; Tangvik, R.J. Nutritional treatment is associated with longer survival in patients with pancreatic disease and concomitant risk of malnutrition. Clin. Nutr. 2020. [Google Scholar] [CrossRef] [PubMed]
  7. Giles, J.T.; Ling, S.M.; Ferrucci, L.; Bartlett, S.J.; Andersen, R.E.; Towns, M.; Muller, D.; Fontaine, K.R.; Bathon, J.M. Abnormal body composition phenotypes in older rheumatoid arthritis patients: association with disease characteristics and pharmacotherapies. Arthritis Rheum. 2008, 59, 807–815. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Gómez-Vaquero, C.; Nolla, J.M.; Fiter, J.; Ramon, J.M.; Concustell, R.; Valverde, J.; Roig-Escofet, D. Nutritional status in patients with rheumatoid arthritis. Joint Bone Spine 2001, 68, 403–409. [Google Scholar] [CrossRef]
  9. Chang, H.Y.; Tang, F.Y.; Chen, D.Y.; Chih, H.M.; Huang, S.T.; Cheng, H.D.; Lan, J.L.; Chiang, E.P. Clinical use of cyclooxygenase inhibitors impairs vitamin B-6 metabolism. Am. J. Clin. Nutr. 2013, 98, 1440–1449. [Google Scholar] [CrossRef] [Green Version]
  10. Curtis, J.R.; Westfall, A.O.; Allison, J.; Bijlsma, J.W.; Freeman, A.; George, V.; Kovac, S.H.; Spettell, C.M.; Saag, K.G. Population-based assessment of adverse events associated with long-term glucocorticoid use. Arthritis Rheum. 2006, 55, 420–426. [Google Scholar] [CrossRef] [PubMed]
  11. Whittle, S.L.; Hughes, R.A. Folate supplementation and methotrexate treatment in rheumatoid arthritis: A review. Rheumatology 2004, 43, 267–271. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Ahlstrand, I.; Björk, M.; Thyberg, I.; Falkmer, T. Pain and difficulties performing valued life activities in women and men with rheumatoid arthritis. Clin. Rheumatol. 2015, 34, 1353–1362. [Google Scholar] [CrossRef] [PubMed]
  13. Guadagni, M.; Biolo, G. Effects of inflammation and/or inactivity on the need for dietary protein. Curr. Opin. Clin. Nutr. Metab. Care 2009, 12, 617–622. [Google Scholar] [CrossRef] [PubMed]
  14. Cederholm, T.; Bosaeus, I.; Barazzoni, R.; Bauer, J.; Van Gossum, A.; Klek, S.; Muscaritoli, M.; Nyulasi, I.; Ockenga, J.; Schneider, S.M.; et al. Diagnostic criteria for malnutrition—An ESPEN Consensus Statement. Clin. Nutr. 2015, 34, 335–340. [Google Scholar] [CrossRef]
  15. Roubenoff, R.; Roubenoff, R.A.; Cannon, J.G.; Kehayias, J.J.; Zhuang, H.; Dawson-Hughes, B.; Dinarello, C.A.; Rosenberg, I.H. Rheumatoid cachexia: cytokine-driven hypermetabolism accompanying reduced body cell mass in chronic inflammation. J. Clin. Investig. 1994, 93, 2379–2386. [Google Scholar] [CrossRef]
  16. Santo, R.C.E.; Fernandes, K.Z.; Lora, P.S.; Filippin, L.I.; Xavier, R.M. Prevalence of rheumatoid cachexia in rheumatoid arthritis: a systematic review and meta-analysis. J. Cachexia Sarcopenia Muscle 2018, 9, 816–825. [Google Scholar] [CrossRef] [Green Version]
  17. Elkan, A.C.; Engvall, I.L.; Cederholm, T.; Hafstrom, I. Rheumatoid cachexia, central obesity and malnutrition in patients with low-active rheumatoid arthritis: feasibility of anthropometry, Mini Nutritional Assessment and body composition techniques. Eur. J. Nutr. 2009, 48, 315–322. [Google Scholar] [CrossRef]
  18. Gioia, C.; Lucchino, B.; Tarsitano, M.G.; Iannuccelli, C.; Di Franco, M. Dietary Habits and Nutrition in Rheumatoid Arthritis: Can Diet Influence Disease Development and Clinical Manifestations? Nutrients 2020, 12, 1456. [Google Scholar] [CrossRef]
  19. Macfarlane, T.V.; Abbood, H.M.; Pathan, E.; Gordon, K.; Hinz, J.; Macfarlane, G.J. Relationship between diet and ankylosing spondylitis: A systematic review. Eur. J. Rheumatol. 2018, 5, 45–52. [Google Scholar] [CrossRef] [Green Version]
  20. Smedslund, G.; Byfuglien, M.G.; Olsen, S.U.; Hagen, K.B. Effectiveness and safety of dietary interventions for rheumatoid arthritis: a systematic review of randomized controlled trials. J. Am. Diet. Assoc. 2010, 110, 727–735. [Google Scholar] [CrossRef]
  21. Van Venrooij, L.M.W.; de Vos, R.; Borgmeijer-Hoelen, A.M.M.J.; Kruizenga, H.M.; Jonkers-Schuitema, C.F.; de Mol, B.A.M.J. Quick-and-easy nutritional screening tools to detect disease-related undernutrition in hospital in- and outpatient settings: A systematic review of sensitivity and specificity. e-SPEN Eur. e-J. Clin. Nutr. Metab. 2007, 2, 21–37. [Google Scholar] [CrossRef] [Green Version]
  22. Elkan, A.C.; Engvall, I.L.; Tengstrand, B.; Cederholm, T.; Hafstrom, I. Malnutrition in women with rheumatoid arthritis is not revealed by clinical anthropometrical measurements or nutritional evaluation tools. Eur. J. Clin. Nutr. 2008, 62, 1239–1247. [Google Scholar] [CrossRef] [PubMed]
  23. Kondrup, J.; Rasmussen, H.H.; Hamberg, O.; Stanga, Z. Nutritional risk screening (NRS 2002): A new method based on an analysis of controlled clinical trials. Clin. Nutr. 2003, 22, 321–336. [Google Scholar] [CrossRef]
  24. Barebring, L.; Winkvist, A.; Gjertsson, I.; Lindqvist, H.M. Poor Dietary Quality Is Associated with Increased Inflammation in Swedish Patients with Rheumatoid Arthritis. Nutrients 2018, 10, 1535. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Nikiphorou, E.; Norton, S.; Young, A.; Dixey, J.; Walsh, D.; Helliwell, H.; Kiely, P.; Early Rheumatoid Arthritis Study; Early Rheumatoid Arthritis Network. The association of obesity with disease activity, functional ability and quality of life in early rheumatoid arthritis: Data from the Early Rheumatoid Arthritis Study/Early Rheumatoid Arthritis Network UK prospective cohorts. Rheumatology 2018, 57, 1194–1202. [Google Scholar] [CrossRef] [Green Version]
  26. Cederholm, T.; Jensen, G.L.; Correia, M.; Gonzalez, M.C.; Fukushima, R.; Higashiguchi, T.; Baptista, G.; Barazzoni, R.; Blaauw, R.; Coats, A.; et al. GLIM criteria for the diagnosis of malnutrition—A consensus report from the global clinical nutrition community. Clin. Nutr. 2019, 38, 1–9. [Google Scholar] [CrossRef] [Green Version]
  27. Gibson, R.S. Anthropometric assessment of body size. In Principles of Nutritional Assessment, 2nd ed.; Oxford University Press: Oxford, UK, 2005. [Google Scholar]
  28. World Health Organization. Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation, Geneva, 8–11 December; WHO: Geneva, Switzerland, 2011; p. 39. [Google Scholar]
  29. Alberti, K.G.M.M.; Zimmet, P.; Shaw, J. Metabolic syndrome-a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabetic Med. 2006, 23, 469–480. [Google Scholar] [CrossRef]
  30. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech. Rep. Ser. 2000, 894, i–xii, 1–253. [Google Scholar]
  31. Kyle, U.G.; Schutz, Y.; Dupertuis, Y.M.; Pichard, C. Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index. Nutrition 2003, 19, 597–604. [Google Scholar] [CrossRef]
  32. Haukeland University Hospital. Analyseoversikten [Overview of Analysis]. Available online: https://analyseoversikten.no/analyser (accessed on 15 February 2020).
  33. Budziareck, M.B.; Pureza Duarte, R.R.; Barbosa-Silva, M.C. Reference values and determinants for handgrip strength in healthy subjects. Clin. Nutr. 2008, 27, 357–362. [Google Scholar] [CrossRef]
  34. Schutz, Y.; Kyle, U.U.; Pichard, C. Fat-free mass index and fat mass index percentiles in Caucasians aged 18–98 y. Int. J. Obes. Relat. Metab. Disord. 2002, 26, 953–960. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Norwegian University of Science and Technology. Questionnaires from the HUNT studies. Available online: https://www.ntnu.no/hunt/skjema (accessed on 19 October 2019).
  36. Midthjell, K.; Lee, C.M.; Langhammer, A.; Krokstad, S.; Holmen, T.L.; Hveem, K.; Colagiuri, S.; Holmen, J. Trends in overweight and obesity over 22 years in a large adult population: the HUNT Study, Norway. Clin. Obes. 2013, 3, 12–20. [Google Scholar] [CrossRef] [PubMed]
  37. Meyer, H.E.; Bøhler, L.; Vollrath, M. Overweight and Obesity in Norway; Norwegian Public Health Institute: Oslo, Norway, 2017. [Google Scholar]
  38. Maas, F.; Arends, S.; van der Veer, E.; Wink, F.; Efde, M.; Bootsma, H.; Brouwer, E.; Spoorenberg, A. Obesity Is Common in Axial Spondyloarthritis and Is Associated with Poor Clinical Outcome. J. Rheumatol. 2016, 43, 383–387. [Google Scholar] [CrossRef] [PubMed]
  39. Gremese, E.; Tolusso, B.; Gigante, M.R.; Ferraccioli, G. Obesity as a risk and severity factor in rheumatic diseases (autoimmune chronic inflammatory diseases). Fron. Immun. 2014, 5, 576. [Google Scholar] [CrossRef] [Green Version]
  40. Stavropoulos-Kalinoglou, A.; Metsios, G.S.; Koutedakis, Y.; Nevill, A.M.; Douglas, K.M.; Jamurtas, A.; van Zanten, J.J.C.S.V.; Labib, M.; Kitas, G.D. Redefining overweight and obesity in rheumatoid arthritis patients. Ann. Rheum. Dis. 2007, 66, 1316–1321. [Google Scholar] [CrossRef] [Green Version]
  41. Konijn, N.P.; van Tuyl, L.H.; Bultink, I.E.; Lems, W.F.; Earthman, C.P.; van Bokhorst-de van der Schueren, M.A. Making the invisible visible: bioelectrical impedance analysis demonstrates unfavourable body composition in rheumatoid arthritis patients in clinical practice. Scand. J. Rheumatol. 2014, 43, 273–278. [Google Scholar] [CrossRef]
  42. Reina, D.; Gómez-Vaquero, C.; Díaz-Torné, C.; Solé, J.M.N. Assessment of nutritional status by dual X-Ray absorptiometry in women with rheumatoid arthritis: A case-control study. Medicine 2019, 98, e14361. [Google Scholar] [CrossRef]
  43. Ottaviani, S.; Allanore, Y.; Tubach, F.; Forien, M.; Gardette, A.; Pasquet, B.; Palazzo, E.; Meunier, M.; Hayem, G.; Job-Deslandre, C.; et al. Body mass index influences the response to infliximab in ankylosing spondylitis. Arthritis Res. Ther. 2012, 14, R115. [Google Scholar] [CrossRef] [Green Version]
  44. Engvall, I.L.; Elkan, A.C.; Tengstrand, B.; Cederholm, T.; Brismar, K.; Hafstrom, I. Cachexia in rheumatoid arthritis is associated with inflammatory activity, physical disability, and low bioavailable insulin-like growth factor. Scand. J. Rheumatol. 2008, 37, 321–328. [Google Scholar] [CrossRef]
  45. Elkan, A.C.; Hakansson, N.; Frostegard, J.; Cederholm, T.; Hafstrom, I. Rheumatoid cachexia is associated with dyslipidemia and low levels of atheroprotective natural antibodies against phosphorylcholine but not with dietary fat in patients with rheumatoid arthritis: a cross-sectional study. Arthritis Res. Ther. 2009, 11, R37. [Google Scholar] [CrossRef] [Green Version]
  46. Humphreys, J.; de la Maza, P.; Hirsch, S.; Barrera, G.; Gattas, V.; Bunout, D. Muscle strength as a predictor of loss of functional status in hospitalized patients. Nutrition 2002, 18, 616–620. [Google Scholar] [CrossRef]
  47. Higgins, S.C.; Adams, J.; Hughes, R. Measuring hand grip strength in rheumatoid arthritis. Rheumatol. Int. 2018, 38, 707–714. [Google Scholar] [CrossRef] [PubMed]
  48. Kłak, A.; Borowicz, J.; Mańczak, M.; Grygielska, J.; Samel-Kowalik, P.; Raciborski, F. Current nutritional status of patients with rheumatic diseases in the population of Poland. Reumatologia 2015, 53, 26–33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Totland, T.H.; Melnæs, B.K.; Lundberg-Hallén, N.; Helland-Kigen, K.M.; Lund-Blix, N.A.; Myhre, J.B.; Johansen, A.M.W.; Løken, E.B.; Andersen, L.F. Norkost 3 En landsomfattende kostholdsundersøkelse blant menn og kvinner i Norge i alderen 18-70 år, 2010–201; [Norkost-3- a Nationwide Dietary Survey of Men and Women in Norway aged 18–70 years]; Norwegian Directorate of Health: Oslo, Norway, 2012; p. 70. [Google Scholar]
  50. Kofoed, C.L.; Christensen, J.; Dragsted, L.O.; Tjønneland, A.; Roswall, N. Determinants of dietary supplement use--healthy individuals use dietary supplements. Br. J. Nutr. 2015, 113, 1993–2000. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Cojocaru, M.; Cojocaru, I.M.; Silosi, I.; Vrabie, C.D. Gastrointestinal manifestations in systemic autoimmune diseases. Maedica 2011, 6, 45–51. [Google Scholar] [PubMed]
  52. Rudwaleit, M.; Baeten, D. Ankylosing spondylitis and bowel disease. Best Pract. Res. Clin. Rheumatol. 2006, 20, 451–471. [Google Scholar] [CrossRef]
  53. De Koning, L.; Merchant, A.T.; Pogue, J.; Anand, S.S. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur. Heart. J. 2007, 28, 850–856. [Google Scholar] [CrossRef]
Table
Table 1. General and socioeconomic characteristics of the study population; N = 70.
Table 1. General and socioeconomic characteristics of the study population; N = 70.
n (%)
Education
Primary or high school, or certificate of apprenticeship23 (33)
College/university47 (67)
Work situation
Paid work45 (64)
Tobacco use
Current smoker12 (17)
Former smoker29 (41)
Uses snuff6 (9)
Physical activity level
≤3 times/week47 (67)
Almost every day23 (33)
Needs help with housekeeping
Sometimes28 (39) a
Not at all43 (61) a
an = 71.
Table
Table 2. Anthropometric measurements, body composition, biochemical assessments, and handgrip strength; N = 71.
Table 2. Anthropometric measurements, body composition, biochemical assessments, and handgrip strength; N = 71.
Variable NMean ± SDMin–MaxReference ValueBelow Reference
n (%)		Above Reference
n (%)
Anthropometry
BMI (kg/m2)All7128.0 ± 5.518.2–39.918.5–24.91 (1)46 (65)
WC (cm)M18103.8 ± 14.082.0–134.0<943 (17)15 (83)
F5298.6 ± 15.567.5–130.5<805 (10)47 (90)
Body composition
FFMI (kg/m2)M1719.8 ± 1.914.8–22.716.7–19.81 (6)8 (47)
F4616.2 ± 1.912.2–20.714.6–16.811 (24)15 (33)
FMI (kg/m2)M179.0 ± 3.34.0–15.01.8–5.2015 (88)
F4611.8 ± 4.44.5–21.73.9–8.2037 (80)
Biochemical parameters
Hemoglobin (g/dL)M1814.4 ± 1.113.0–16.213.4–17.05 (28)0
F5313.0 ± 0.910.9–15.311.7–15.37 (13)0
Ferritin (µg/L)M18140.4 ± 83.243.0–321.034–30001 (6)
F52102.4 ± 56.18.0–244.018–2402 (4)1 (2)
Homocysteine (µmol/L)All7010.9 ± 3.05.2–19.7<15-8 (11)
Methylmalonic acid (µmol/L)All700.16 ± 0.10.1–0.4<0.26-6 (9)
Vitamin D (nmol/L)All7066.8 ± 19.619.0–111.050–11313 (19)0
Zinc (µmol/L)All7012.2 ± 1.88.5–16.710.1–16.69 (13)1 (1)
Erythrocyte sedimentation rate (mm/t)M1811.3 ± 11.21.0–44.01–20 a-3 (17)
F5113.5 ± 11.61.0–43.01–30 a-5 (10)
Handgrip strength
Dominant handM1635.8 ± 10.618.5–59.0>5pct b2 (13)14 (87)
F4817.6 ± 7.04.9–40.1>5pct b17 (35)31 (65)
Non-dominant handM1632.4 ± 10.119.2–53.3>5pct b3 (19)13 (81)
F4417.3 ± 6.46.8–41.5>5pct b5 (11)39 (89)
a Age-specific references exist. b Handgrip strength below 5th percentile in healthy age- and sex-matched adults was considered to be below reference [33].
Table
Table 3. Distribution of low, normal, high, and very high levels of fat mass index and fat-free mass index within the BMI categories; N = 63.
Table 3. Distribution of low, normal, high, and very high levels of fat mass index and fat-free mass index within the BMI categories; N = 63.
VariableUnderweightNormal WeightOverweightObeseTotal
BMI < 18.5 aBMI 18.5–24.9 aBMI 25–29.9 aBMI ≥ 30 a
n (%)n (%)n (%)n (%)n (%)
Low FMI a-----
Normal FMI a 1 (2)11 (17)--12 (19)
High FMI a-11 (17)7 (11)-18 (29)
Very high FMI a-1 (2)7 (11)25 (40)33 (52)
Low FFMI a1 (2)11 (17)--12 (19)
Normal FFMI a-10 (16)12 (19)7 (11)29 (46)
High FFMI a-2 (3)2 (3)8 (13)12 (19)
Very high FFMI a---10 (16)10 (16)
Total n (%)1 (2)23 (36)14 (22)25 (40)63 (100)
a kg/m2. Body composition measured by dual-energy X-ray absorptiometry. Low, normal, high, and very high fat mass index (FMI; kg/m2), and fat-free mass index (FFMI; kg/m2) defined as levels reported in a healthy European population with body mass index (BMI) of <18.5 kg/m2, 18.5–24.9 kg/m2, 25–29.9 kg/m2, and ≥30 kg/m2, respectively [31]. Low FMI: <1.8 (M), <3.9 (F). Normal FMI: 1.8–5.2 (M), 3.9–8.2 (F). High FMI: 5.2–8.3 (M), 8.2–11.8 (F). Very high FMI: ≥8.3 (M), ≥11.8 (F). Low FFMI: <16.7 (M), <14.6 (F). Normal FFMI: 16.7–19.8 (M), 14.6–16.8 (F). High FFMI: 19.8–21.7 (M), 16.8–18.2 (F). Very high FFMI ≥21.7 (M), ≥18.2 (F).
Table
Table 4. Sensitivity, specificity, positive predictive value, and negative predictive value of malnutrition identified by BMI, Nutritional Risk Screening 2002 (NRS2002), and handgrip strength (HGS); N = 63.
Table 4. Sensitivity, specificity, positive predictive value, and negative predictive value of malnutrition identified by BMI, Nutritional Risk Screening 2002 (NRS2002), and handgrip strength (HGS); N = 63.
Malnourished aWell-NourishedSensitivitySpecificityPPVNPV
(n)(n)(%)(%)(%)(%)
BMI <18.5 b 810010082
Yes (n)10
No (n)1151
BMI <20, or <22 if ≥70 y c 4210010088
Yes (n)50
No (n)751
At nutritional risk d 0100081
Yes (n)00
No (n)1251
Low HGS 63732892
Yes (n)513
No (n)336
PPV: positive predictive value. NPV: negative predictive value. a Malnourished patients defined as fat-free mass index (FFMI; kg/m2) < 16.7 (M), <14.6 (F), measured by DXA. b kg/m2. c Defined as low BMI by the Global Leadership Initiative on Malnutrition. d NRS2002: Nutritional Risk Screening 2002 ≥3. Low handgrip strength (HGS) <5 pct in age- and sex-matched healthy adults [33].
Table
Table 5. Sensitivity, specificity, positive predictive value, and negative predictive value of obesity identified by BMI and waist circumference (WC); N = 63.
Table 5. Sensitivity, specificity, positive predictive value, and negative predictive value of obesity identified by BMI and waist circumference (WC); N = 63.
Obese aNot ObeseSensitivitySpecificityPPVNPV
(n)(n)(%)(%)(%)(%)
BMI ≥ 30 b 7610010079
Yes (n)25 0
No (n)8 30
BMI ≥ 28 b 91979790
Yes (n)301
No (n)329
Abdominal obesity c 97677695
Yes (n)3110
No (n)120
PPV: positive predictive value. NPV: negative predictive value. a Obese patients defined as very high fat mass index (FMI; kg/m2): ≥8.3 (M), ≥11.8 (F), measured by DXA. b kg/m2. c Abdominal obesity: waist circumference ≥102 cm (M), ≥88 cm (F).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Olsen, M.N.; Tangvik, R.J.; Halse, A.-K. Evaluation of Nutritional Status and Methods to Identify Nutritional Risk in Rheumatoid Arthritis and Spondyloarthritis. Nutrients 2020, 12, 3571. https://0-doi-org.brum.beds.ac.uk/10.3390/nu12113571

AMA Style

Olsen MN, Tangvik RJ, Halse A-K. Evaluation of Nutritional Status and Methods to Identify Nutritional Risk in Rheumatoid Arthritis and Spondyloarthritis. Nutrients. 2020; 12(11):3571. https://0-doi-org.brum.beds.ac.uk/10.3390/nu12113571

Chicago/Turabian Style

Olsen, Marie Njerve, Randi J. Tangvik, and Anne-Kristine Halse. 2020. "Evaluation of Nutritional Status and Methods to Identify Nutritional Risk in Rheumatoid Arthritis and Spondyloarthritis" Nutrients 12, no. 11: 3571. https://0-doi-org.brum.beds.ac.uk/10.3390/nu12113571

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop