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Nuguri SM, Hackshaw KV, Castellvi SDL, Wu Y, Gonzalez CM, Goetzman CM, Schultz ZD, Yu L, Aziz R, Osuna-Diaz MM, Sebastian KR, Brode WM, Giusti MM, Rodriguez-Saona L. Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes. Biomedicines 2024; 12:1447. [PMID: 39062021 DOI: 10.3390/biomedicines12071447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/15/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.
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Affiliation(s)
- Shreya Madhav Nuguri
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
| | - Kevin V Hackshaw
- Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Silvia de Lamo Castellvi
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
- Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain
| | - Yalan Wu
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
| | - Celeste Matos Gonzalez
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
| | - Chelsea M Goetzman
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA
- Savannah River National Laboratory, Jackson, SC 29831, USA
| | - Zachary D Schultz
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA
| | - Lianbo Yu
- Center of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH 43210, USA
| | - Rija Aziz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Michelle M Osuna-Diaz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Katherine R Sebastian
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - W Michael Brode
- Center of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH 43210, USA
| | - Monica M Giusti
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
| | - Luis Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
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Nuguri SM, Hackshaw KV, de Lamo Castellvi S, Bao H, Yao S, Aziz R, Selinger S, Mikulik Z, Yu L, Osuna-Diaz MM, Sebastian KR, Giusti MM, Rodriguez-Saona L. Portable Mid-Infrared Spectroscopy Combined with Chemometrics to Diagnose Fibromyalgia and Other Rheumatologic Syndromes Using Rapid Volumetric Absorptive Microsampling. Molecules 2024; 29:413. [PMID: 38257325 PMCID: PMC10821365 DOI: 10.3390/molecules29020413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
The diagnostic criteria for fibromyalgia (FM) have relied heavily on subjective reports of experienced symptoms coupled with examination-based evidence of diffuse tenderness due to the lack of reliable biomarkers. Rheumatic disorders that are common causes of chronic pain such as rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain are frequently found to be comorbid with FM. As a result, this can make the diagnosis of FM more challenging. We aim to develop a reliable classification algorithm using unique spectral profiles of portable FT-MIR that can be used as a real-time point-of-care device for the screening of FM. A novel volumetric absorptive microsampling (VAMS) technique ensured sample volume accuracies and minimized the variation introduced due to hematocrit-based bias. Blood samples from 337 subjects with different disorders (179 FM, 158 non-FM) collected with VAMS were analyzed. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. The OPLS-DA algorithm enabled the classification of the spectra into their corresponding classes with 84% accuracy, 83% sensitivity, and 85% specificity. The OPLS-DA regression plot indicated that spectral regions associated with amide bands and amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases.
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Affiliation(s)
- Shreya Madhav Nuguri
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.M.N.); (S.d.L.C.); (H.B.); (M.M.G.); (L.R.-S.)
| | - Kevin V. Hackshaw
- Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Silvia de Lamo Castellvi
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.M.N.); (S.d.L.C.); (H.B.); (M.M.G.); (L.R.-S.)
- Campus Sescelades, Departament d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain
| | - Haona Bao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.M.N.); (S.d.L.C.); (H.B.); (M.M.G.); (L.R.-S.)
| | - Siyu Yao
- Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, Nanjing 210019, China;
| | - Rija Aziz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (S.S.); (M.M.O.-D.); (K.R.S.)
| | - Scott Selinger
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (S.S.); (M.M.O.-D.); (K.R.S.)
| | - Zhanna Mikulik
- Department of Internal Medicine, Division of Immunology and Rheumatology, The Ohio State University, 480 Medical Center Dr, Columbus, OH 43210, USA;
| | - Lianbo Yu
- Center of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH 43210, USA;
| | - Michelle M. Osuna-Diaz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (S.S.); (M.M.O.-D.); (K.R.S.)
| | - Katherine R. Sebastian
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (S.S.); (M.M.O.-D.); (K.R.S.)
| | - M. Monica Giusti
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.M.N.); (S.d.L.C.); (H.B.); (M.M.G.); (L.R.-S.)
| | - Luis Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.M.N.); (S.d.L.C.); (H.B.); (M.M.G.); (L.R.-S.)
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Bao H, Hackshaw KV, Castellvi SDL, Wu Y, Gonzalez CM, Nuguri SM, Yao S, Goetzman CM, Schultz ZD, Yu L, Aziz R, Osuna-Diaz MM, Sebastian KR, Giusti MM, Rodriguez-Saona L. Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics. Biomedicines 2024; 12:133. [PMID: 38255238 PMCID: PMC10813180 DOI: 10.3390/biomedicines12010133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 12/28/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.
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Affiliation(s)
- Haona Bao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Kevin V. Hackshaw
- Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Silvia de Lamo Castellvi
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
- Departament d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain
| | - Yalan Wu
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Celeste Matos Gonzalez
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Shreya Madhav Nuguri
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Siyu Yao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
- Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, Nanjing 210009, China
| | - Chelsea M. Goetzman
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA; (C.M.G.); (Z.D.S.)
- Savannah River National Laboratory, Jackson, SC 29831, USA
| | - Zachary D. Schultz
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA; (C.M.G.); (Z.D.S.)
| | - Lianbo Yu
- Center of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH 43210, USA;
| | - Rija Aziz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Michelle M. Osuna-Diaz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Katherine R. Sebastian
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Monica M. Giusti
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Luis Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
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