The omic with the potential to provide a holistic view of patients and their disease

The musculoskeletal system is a highly active metabolic system. Energy consumption is driven by the combined demands of skeletal muscle, turnover and remodelling of bone, cartilage and other structural components in response to changing levels of loading. The high-energy respiratory chain is estimated to result in the turnover of ∼65 kg/day of adenosine triphosphate for the whole body, increasing during periods of activity [1]. With skeletal muscle accounting for 30% of body mass in a post-menopausal woman (and more in men), it is perhaps not surprising then that changes in metabolic activity are observed in musculoskeletal disease. Overall resting energy use is increased by ∼8% in RA patients and, interestingly, a similar increase in metabolic activity is seen in patients who smoke, a well-established risk factor for the development of RA [2]. The mechanism underlying this increase in energy metabolism is unclear, but the active role of the immune system in the inflammatory processes in RA suggests that immune cell activation and turnover may contribute. There are also significant changes in liver metabolism as a result of the acute phase response and large shifts in systemic metabolism as a result of rheumatoid cachexia, where muscle degradation occurs along with the related increase in the mass of body fat. Many of these metabolic changes can pre-date obvious joint symptoms, and metabolic pathways may change in response to therapy. It is not surprising therefore that there is increasing interest in using altered metabolites as biomarkers of disease activity and response to therapy. These studies may also provide novel insights into the pathological processes driving complex musculoskeletal diseases.

RA was first associated with altered levels of individual metabolites in a report from 1962 describing changes in the metabolism of tryptophan [3]. In recent years a more broad-ranging approach, metabolomics, has been applied to assessment of the disease. Metabolomics is the new kid on the omics block and comparable in scope to genomics, transcriptomics and proteomics. Through the study of small molecules (<1500 Da) within specific or multiple compartments (blood, urine, joints, saliva, eyes, tears, cerebrospinal fluid, intact cells), metabolite profiles or fingerprints, each containing thousands of metabolites, can be identified. In individuals at risk due to genetics, their environment or both, disruptive pathological processes can result in altered metabolite profiles long before overt signs and symptoms of disease appear. The metabolites themselves can be identified and quantified using NMR spectroscopy or mass spectrometry.

Metabolite identification is done by reference to metabolite databases or by direct metabolite assay. The Human Metabolome Database lists >41 000 metabolite entries in the latest version, however, just 3000 have been linked with diseases to date [4]. In common with other omics approaches, metabolomics experiments generate prodigious amounts of data, but this is amenable to multivariate analysis techniques, including principal components analysis, partial least squares discriminant analysis, regression methods and genetic algorithms.

Metabolomics studies have been reported for virtually all of the main rheumatologic diseases, although notably none yet for SSc [5]. Our own group has demonstrated the use of urinary metabolic fingerprint analysis to predict responses to anti-TNF [6], and we have suggested that metabolites resulting from TNF-driven cachexia are among the useful predictive biomarkers. Serum metabolic profiling can differentiate four types of human arthritis [7], and we have shown the predictive value of the serum metabolite profile in early synovitis patients, with differences between those with self-limiting disease and those who went on to develop persistent RA [8]. The value of combining omics approaches has been demonstrated in a study using proteomics and metabolomics to show alterations in both vitamin D3 metabolites and proteins in patients with AS [9]. The combination of genetic and metabolomic data [10] has shown the potential to identify genotype-influenced metabotypes in a number of chronic diseases.

Different cells types vary in their energy and metabolic requirements, with cells undergoing proliferation being very different from those in a stable steady state. This applies to tissue cells such as synovial stroma and to immune cells including macrophages and T lymphocytes. Thus the state of immune activation and tissue hyperplasia may be strongly reflected in the metabolic profiles observed in tissues and in biofluids from patients, providing useful insights into the state of the disease and its aetiopathology.

Comprehensive clinical assessment approaches used by BILAG and SLEDAI offer a clinical approach to individualized systems medicine by objectively quantifying components in a disease. Transcriptomics, proteomics and metabolomics are the biological equivalents of these clinical assessments. However, the sole analysis of tissues from the specific site of disease will miss the systemic changes commonly associated with complex diseases that may be responsible for driving the persistence and much of the associated co-morbidity. Metabolism in different sites, such as the liver, vasculature, muscles and synovium, are significantly altered in chronic inflammation and there is a widespread redistribution of body mass between muscle and fat driven by the inflammatory process. Environmental factors such as the microbiome, age, diet, gender and smoking, which are all significant risk factors for chronic inflammatory disease, also have profound effects on metabolism. Metabolomics therefore offers a unique approach to the assessment of disease, contributing to personalised medicine and the optimisation of diagnosis and therapy for these complex conditions.

Acknowledgements

S.R.K. was supported by an Arthritis Research UK Clinical PhD studentship (grant number 18552) and M.A.F. was supported by a Wellcome Trust PhD Studentship (grant number 14844).

Funding: No specific funding was received from any funding bodies in the public, commercial or not-for-profit sectors to carry out the work described in this article.

Disclosure statement: C.M.M. reports that during 1997–2005 she was employed by BCM (Alliance Boots) and Wyeth Biopharma (Pfizer) and with whom she retains preserved pensions. All other authors have declared no conflicts of interest.

References

1
Rich
P
.
Chemiosmotic coupling: the cost of living
.
Nature
 
2003
;
421
:
583
.
2
Metsios
GS
Stavropoulos-Kalinoglou
A
Nevill
AM
et al
.
Cigarette smoking significantly increases basal metabolic rate in patients with rheumatoid arthritis
.
Ann Rheum Dis
 
2008
;
67
:
70
3
.
3
Bett
IM
.
Metabolism of tryptophan in rheumatoid arthritis
.
Ann Rheum Dis
 
1962
;
21
:
63
9
.
4
Wishart
DS
Jewison
T
Guo
AC
et al
.
HMDB 3.0—the Human Metabolome Database in 2013
.
Nucleic Acids Res
 
2013
;
41
(
Database issue
):
D801
7
.
5
Priori
R
Scrivo
R
Brandt
J
et al
.
Metabolomics in rheumatic diseases: the potential of an emerging methodology for improved patient diagnosis, prognosis, and treatment efficacy
.
Autoimmun Rev
 
2013
;
12
:
1022
33
.
6
Kapoor
SR
Filer
A
Fitzpatrick
M
et al
.
Metabolic profiling predicts response to anti-tumor necrosis factor α therapy in patients with rheumatoid arthritis
.
Arthritis Rheum
 
2013
;
65
:
1448
56
.
7
Jiang
M
Chen
TL
Feng
H
et al
.
Serum metabolic signatures of four types of human arthritis
.
J Proteome Res
 
2013
;
12
:
3769
79
.
8
Young
SP
Kapoor
SR
Viant
MR
et al
.
The impact of inflammation on metabolomic profiles in patients with arthritis
.
Arthritis Rheum
 
2013
;
65
:
2015
23
.
9
Fischer
R
Trudgian
DC
Wright
C
et al
.
Discovery of candidate serum proteomic and metabolomic biomarkers in ankylosing spondylitis
.
Mol Cell Proteomics
 
2012
;
11
:
M111.013904
.
10
Suhre
K
Shin
SY
Petersen
AK
et al
.
Human metabolic individuality in biomedical and pharmaceutical research
.
Nature
 
2011
;
477
:
54
60
.

Comments

0 Comments