Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia ..., 2023, Hackshaw et al.

SNT Gatchaman

SNT Gatchaman

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Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics
Hackshaw, Kevin V.; Yao, Siyu; Bao, Haona; de Lamo Castellvi, Silvia; Aziz, Rija; Nuguri, Shreya Madhav; Yu, Lianbo; Osuna-Diaz, Michelle M.; Brode, W. Michael; Sebastian, Katherine R.; Giusti, M. Monica; Rodriguez-Saona, Luis

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30–40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable.

The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM.

Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer.

Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm−1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm−1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity.

This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

Link | PDF (Biomedicines)
 
The authors are from Spain and the US.

There's a weird bit about opioid use:
This is vitally important to recognize since a large percentage of patients with chronic non-malignant pain regularly seek and obtain analgesics for the treatment of their global pain complaints. However, there is no evidence that opioids improve their status beyond standard care and may contribute to a less favorable outcome [28,29]. A prior survey of chronic pain patients has shown that 49% of patients taking opioids continued to report severe pain (>7/10), and 41% of those surveyed meet current criteria for FM [30]. Although this survey preceded the identification of LC, it is likely that similar numbers would be seen in a LC cohort since the clinical characteristics and level of pain seen in this group are comparable to those seen in FM and CFS/ME [10]. Over-reliance on opioids for chronic pain disorders may be due to the complexity of managing chronic pain conditions [31–33].
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There was no matching of subjects by gender and BMI:
LC: 18 males; 32 females....... mean BMI 29.5
Fibromyalgia: 50 females ........ mean BMI 31.6
Controls: 4 males, 2 females .......... mean BMI 25

And, only 6 controls.

Most of the disease participants were on medications, with quite a lot of them on a number of medications.

The authors acknowledge the resulting limitations:
The FM group was 100% female, while the LC group had 18 males and 32 females. An obvious limitation regards the statistical power and generalizability of our data due to the size of the cohort, gender differences, and medication usage, amongst others. The inclusion of a 100% female FM group versus a mixed-gender LC group presents potential biases and lacks gender representation, impacting the reliability of these results.
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With the relatively small n of 50 in each cohort, we are not able to definitively eliminate medications as a confounding variable.
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Our pilot results must be interpreted with caution lest we run the risk of overgeneral- ization. First of all, although our findings are intriguing, we will exercise caution rather than extrapolate our results to be indicative for all individuals affected with LC since this study is not powered to evaluate whether these signatures are characteristic of all affected with LC or only a subset of specific variant. Indeed, it is unclear at this time whether all LCs are the same or if it varies in severity between variants. Secondly, enrollment bias could exist due to differences in male/female ratios in groups and medication differences between groups amongst others. Future studies with much larger sample sizes should mitigate these types of concerns.
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Looking at the PubMed citations for this 2023 paper, the team have a recent related paper —

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics (2024)
Bao, Haona; Hackshaw, Kevin V.; Castellvi, Silvia de Lamo; Wu, Yalan; Gonzalez, Celeste Matos; Nuguri, Shreya Madhav; Yao, Siyu; Goetzman, Chelsea M.; Schultz, Zachary D.; Yu, Lianbo; Aziz, Rija; Osuna-Diaz, Michelle M.; Sebastian, Katherine R.; Giusti, Monica M.; Rodriguez-Saona, Luis

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.

Link | PDF (Biomedicines)
 
From my limited understanding of the approach, it seems fine. But, it's the replicability that matters, and identifying biochemicals in the spectral signatures that lead to biologically plausible mechanisms.

Yes, my impression was that this latest study didn't strongly replicate the findings from an earlier study by this team on fibromyalgia. And I'm not sure from that abstract in teh previous post that it is sounding as though that latest 2024 study has found the glutamate signal to be so strong either.
 
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