Significant correlation between plasma proteome profile and pain intensity, sensitivity, and psychological distress in women with fibromyalgia

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Abstract

Fibromyalgia (FM) is a complex pain condition where the pathophysiological and molecular mechanisms are not fully elucidated. The primary aim of this study was to investigate the plasma proteome profile in women with FM compared to controls. The secondary aim was to investigate if plasma protein patterns correlate with the clinical variables pain intensity, sensitivity, and psychological distress. Clinical variables/background data were retrieved through questionnaires. Pressure pain thresholds (PPT) were assessed using an algometer. The plasma proteome profile of FM (n = 30) and controls (n = 32) was analyzed using two-dimensional gel electrophoresis and mass spectrometry. Quantified proteins were analyzed regarding group differences, and correlations to clinical parameters in FM, using multivariate statistics. Clear significant differences between FM and controls were found in proteins involved in inflammatory, metabolic, and immunity processes. Pain intensity, PPT, and psychological distress in FM had associations with specific plasma proteins involved in blood coagulation, metabolic, inflammation and immunity processes. This study further confirms that systemic differences in protein expression exist in women with FM compared to controls and that altered levels of specific plasma proteins are associated with different clinical parameters.

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Hospital Anxiety and Depression Scale (HAD): some psychometric data for a Swedish sample.

J Lisspers, A. Nygren, E Söderman (1997)

The Hospital Anxiety and Depression Scale (HAD) was evaluated in a Swedish population sample. The purpose of the study was to compare the HAD with the Beck Depression Inventory (BDI) and Spielberger's State Trait Anxiety Inventory (STAI). A secondary aim was to examine the factor structure of the HAD. The results indicated that the factor structure was quite strong, consistently showing two factors in the whole sample as well as in different subsamples. The correlations between the total HAD scale and BDI and STAI, respectively, were stronger than those obtained using the different subscales of the HAD (the anxiety and depression subscales). As expected, there was also a stronger correlation between the HAD and the non-physical items of the BDI. It was somewhat surprising that the factor analyses were consistently extracting two factors, 'depression' and 'anxiety', while on the other hand both BDI and STAI tended to correlate more strongly with the total HAD score than with the specific depression and anxiety HAD subscales. Nevertheless, the HAD appeared to be (as was indeed originally intended) a useful clinical indicator of the possibility of depression and clinical anxiety.

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Acute-phase proteins: As diagnostic tool

Sachin Jain, Vidhi Gautam, Sania Naseem (2011)

The varied reactions of the host to infection, inflammation, or trauma are collectively known as the acute-phase response and encompass a wide range of pathophysiological responses such as pyrexia, leukocytosis, hormone alterations, and muscle protein depletion combining to minimize tissue damage while enhancing the repair process. The mechanism for stimulation of hepatic production of acute-phase proteins is by proinflammatory cytokines. The functions of positive acute-phase proteins (APP) are regarded as important in optimization and trapping of microorganism and their products, in activating the complement system, in binding cellular remnants like nuclear fractions, in neutralizing enzymes, scavenging free hemoglobin and radicals, and in modulating the host’s immune response. APP can be used as diagnostic tool in many diseases like bovine respiratory syncytial virus, prostate cancer, bronchopneumonia, multiple myeloma, mastitis, Streptococcus suis infection, starvation, or lymphatic neoplasia. Thus, acute-phase proteins may provide an alternative means of monitoring animal health.

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Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine.

Åsa M Wheelock, Craig Wheelock (2013)

Respiratory diseases are multifactorial heterogeneous diseases that have proved recalcitrant to understanding using focused molecular techniques. This trend has led to the rise of 'omics approaches (e.g., transcriptomics, proteomics) and subsequent acquisition of large-scale datasets consisting of multiple variables. In 'omics technology-based investigations, discrepancies between the number of variables analyzed (e.g., mRNA, proteins, metabolites) and the number of study subjects constitutes a major statistical challenge. The application of traditional univariate statistical methods (e.g., t-test) to these "short-and-wide" datasets may result in high numbers of false positives, while the predominant approach of p-value correction to account for these high false positive rates (e.g., FDR, Bonferroni) are associated with significant losses in statistical power. In other words, the benefit in decreased false positives must be counterbalanced with a concomitant loss in true positives. As an alternative, multivariate statistical analysis (MVA) is increasingly being employed to cope with 'omics-based data structures. When properly applied, MVA approaches can be powerful tools for integration and interpretation of complex 'omics-based datasets towards the goal of identifying biomarkers and/or subphenotypes. However, MVA methods are also prone to over-interpretation and misuse. A common software used in biomedical research to perform MVA-based analyses is the SIMCA package, which includes multiple MVA methods. In this opinion piece, we propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value). Examples of these applications in recent COPD and asthma studies are provided. It is expected that readers will gain an increased understanding of the power and utility of MVA methods for applications in biomedical research.

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Author and article information

Contributors

Karin Wåhlén: karin.wahlen@liu.se

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 27 July 2020

Publication date PMC-release: 27 July 2020

Publication date Collection: 2020

Volume: 10

Electronic Location Identifier: 12508

Affiliations

[1 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Pain and Rehabilitation Center, and Department of Health, Medicine and Caring Sciences, , Linköping University, ; Linköping, Sweden

[2 ]Department of Dental Medicine, Karolinska Institutet and Scandinavian Center for Orofacial Neurosciences (SCON), 141 04 Huddinge, Sweden

[3 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Clinical Neuroscience, ; Karolinska Institutet, 171 77 Stockholm, Sweden

[4 ]ISNI 0000 0000 9919 9582, GRID grid.8761.8, Department of Health and Rehabilitation/Physiotherapy, Institute of Neuroscience and Physiology, , Sahlgrenska Academy, Gothenburg University, ; Gothenburg, Sweden

Article

Publisher ID: 69422

DOI: 10.1038/s41598-020-69422-z

PMC ID: 7385654

PubMed ID: 32719459

SO-VID: 0cdf0e24-46e6-4b22-9c13-6d76d2ee548a

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 4 February 2020

Date accepted : 10 July 2020

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ScienceOpen disciplines: Uncategorized

Keywords: proteomics,pain,musculoskeletal system,inflammation,biomarkers,anxiety,depression,fibromyalgia

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ScienceOpen disciplines: Uncategorized

Keywords: proteomics, pain, musculoskeletal system, inflammation, biomarkers, anxiety, depression, fibromyalgia

Significant correlation between plasma proteome profile and pain intensity, sensitivity, and psychological distress in women with fibromyalgia

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Biomarkers for Inflammatory Lung Disease

Most cited references 55

Hospital Anxiety and Depression Scale (HAD): some psychometric data for a Swedish sample.

Acute-phase proteins: As diagnostic tool

Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine.

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