Systematic review with meta-analysis of diagnostic test accuracy for COVID-19 by mass spectrometry

Use of the Ishii Test for screening sarcopenia in older adults: a systematic review with meta-analysis of diagnostic test accuracy (DTA) studies

BACKGROUND

The Ishii Test is recommended by the European Working Group on Sarcopenia in Older People (EWGSOP2), however the use of this technique is still little explored in the clinical context and the scientific literature.

OBJECTIVE

We aimed to verify the use of the Test of Ishii in screening for sarcopenia in older adults.

METHODS

We searched three electronic databases and two reviewers independently screened and assessed the studies. Studies with older adults (60 years or more) of both genders, no year or language restriction and which aimed to evaluate sarcopenia using the Ishii Test and another diagnostic criteria were selected. A summary of the ROC curve, sensitivity and specificity were performed using the MedCalc and SPSS software programs, respectively.

RESULTS

A total of 3,298 references were identified in the database, 278 by manually searching, and finally 11 studies were included for the review. The screening test showed good sensitivity and specificity in both genders. All studies showed values above the considered value for the Area Under the Curve (AUC) results, without discriminating power (0.500). Four studies used the original values, and five studies developed a new cut-off point. A summary of the AUC curve showed the diamond close to one, indicating that the Ishii test has good performance for screening sarcopenia (I2=83,66%; p<0.001; 95%CI: 69.38 to 91.28 for men; and I2=60.04%; p<0.001; 95%CI: 13.06 to 81.63 for women).

CONCLUSION

The Ishii Test can be considered a useful tool for the early identification of sarcopenia in older adults. However, further studies are still needed to understand the behavior of this screening tool.

TRIAL REGISTRATION

CRD42023424392.

A Targeted LC-MRM3 Proteomic Approach for the Diagnosis of SARS-CoV-2 Infection in Nasopharyngeal Swabs

Since its first appearance, severe acute respiratory syndrome coronavirus 2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein. A highly selective multiple reaction monitoring-cubed (MRM3) strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral nucleocapsid protein peptides from nasopharyngeal swabs samples presenting a cycle threshold value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immunopurification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with cycle threshold values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted MS.

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19

During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

Application of MALDI-TOF MS and machine learning for the detection of SARS-CoV-2 and non-SARS-CoV-2 respiratory infections

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) could aid the diagnosis of acute respiratory infections (ARIs) owing to its affordability and high-throughput capacity. MALDI-TOF MS has been proposed for use on commonly available respiratory samples, without specialized sample preparation, making this technology especially attractive for implementation in low-resource regions. Here, we assessed the utility of MALDI-TOF MS in differentiating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vs non-COVID acute respiratory infections (NCARIs) in a clinical lab setting in Kazakhstan. Nasopharyngeal swabs were collected from inpatients and outpatients with respiratory symptoms and from asymptomatic controls (ACs) in 2020-2022. PCR was used to differentiate SARS-CoV-2+ and NCARI cases. MALDI-TOF MS spectra were obtained for a total of 252 samples (115 SARS-CoV-2+, 98 NCARIs, and 39 ACs) without specialized sample preparation. In our first sub-analysis, we followed a published protocol for peak preprocessing and machine learning (ML), trained on publicly available spectra from South American SARS-CoV-2+ and NCARI samples. In our second sub-analysis, we trained ML models on a peak intensity matrix representative of both South American (SA) and Kazakhstan (Kaz) samples. Applying the established MALDI-TOF MS pipeline "as is" resulted in a high detection rate for SARS-CoV-2+ samples (91.0%), but low accuracy for NCARIs (48.0%) and ACs (67.0%) by the top-performing random forest model. After re-training of the ML algorithms on the SA-Kaz peak intensity matrix, the accuracy of detection by the top-performing support vector machine with radial basis function kernel model was at 88.0%, 95.0%, and 78% for the Kazakhstan SARS-CoV-2+, NCARI, and AC subjects, respectively, with a SARS-CoV-2 vs rest receiver operating characteristic area under the curve of 0.983 [0.958, 0.987]; a high differentiation accuracy was maintained for the South American SARS-CoV-2 and NCARIs. MALDI-TOF MS/ML is a feasible approach for the differentiation of ARI without specialized sample preparation. The implementation of MALDI-TOF MS/ML in a real clinical lab setting will necessitate continuous optimization to keep up with the rapidly evolving landscape of ARI.IMPORTANCEIn this proof-of-concept study, the authors used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning (ML) to identify and distinguish acute respiratory infections (ARI) caused by SARS-CoV-2 versus other pathogens in low-resource clinical settings, without the need for specialized sample preparation. The ML models were trained on a varied collection of MALDI-TOF MS spectra from studies conducted in Kazakhstan and South America. Initially, the MALDI-TOF MS/ML pipeline, trained exclusively on South American samples, exhibited diminished effectiveness in recognizing non-SARS-CoV-2 infections from Kazakhstan. Incorporation of spectral signatures from Kazakhstan substantially increased the accuracy of detection. These results underscore the potential of employing MALDI-TOF MS/ML in resource-constrained settings to augment current approaches for detecting and differentiating ARI.

Development of a High-Throughput Mass Spectrometry-Based SARS-CoV-2 Immunoassay

The serious impact of the Covid-19 pandemic underscores the need for rapid, reliable, and high-throughput diagnosis methods for infection. Current analytical methods, either point-of-care or centralized detection, are not able to satisfy the requirements of patient-friendly testing, high demand, and reliability of results. Here, we propose a two-point separation on-demand diagnostic strategy that uses laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) and adopts a stable yet cleavable ionic probe as a mass reporter. The use of this reporter enables ultrasensitive, interruptible, storable, restorable, and high-throughput on-demand detection. We describe a demonstration of the concept whereby we (i) design and synthesize a laser-cleavable reporter (DTPA), (ii) conjugate the reporter onto an antibody and verify the function of the conjugate, (iii) detect with good turnaround and high sensitivity the conjugated reporter, (iv) analyze quantitatively by using a laser-cleavable internal standard, and (v) identify negative and positive samples containing the spike protein. The protocol has excellent sensitivity (amol for the SARS-CoV-2 Spike S1 subunit antibody) without any amplification. This strategy is also applicable for the detection of other disease antigens besides SARS-CoV-2.

A magnetic control enrichment technique combined with terahertz metamaterial biosensor for detecting SARS-CoV-2 spike protein

The highly contagious SARS-CoV-2 virus, responsible for the COVID-19 pandemic continues to pose significant challenges to public health. Developing new methods for early detection and diagnosis is crucial in combatting the disease, mitigating its impact and be prepared for future challenges in pandemic diseases. In this study, we propose a terahertz (THz) biosensing technology that capitalizes on the properties of THz metamaterial in conjunction with magnetic nanoparticles. This approach can accurately identify the SARS-CoV-2 spike protein by pinpointing its location on the THz resonance sources grooved surface. The magnetic nanoparticles are employed to selectively bind with target molecules, and migrate towards the THz metamaterial unit cell when exposed to an applied magnetic field. The presence of target molecules in to the metamaterial variation in the frequency, amplitude, and phase of the resonance response, thus enabling swift, accurate and sensitive detection. To assess the effectiveness of the proposed technique, we have conducted a comparative analysis between real samples on platforms controlled by magnetic manipulation and those without the control. It was confirmed that the proposed THz sensing method demonstrated a linear detection range spanning from 0.005 ng mL-1 to 1000 ng mL-1 with a detection limit of 0.002 ng mL-1. Furthermore, it exhibited a frequency shift of 24 GHz and a stability index of 95%. The THz biosensing technique may pave a new avenue in identifying and preempting the spread of potential pandemic diseases.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Serum metabolomic profiling reveals potential biomarkers in systemic sclerosis

BACKGROUND

Systemic sclerosis (SSc) is a chronic and systemic autoimmune disease marked by the skin and visceral fibrosis. Metabolic alterations have been found in SSc patients; however, serum metabolomic profiling has not been thoroughly conducted. Our study aimed to identify alterations in the metabolic profile in both SSc patients before and during treatment, as well as in mouse models of fibrosis. Furthermore, the associations between metabolites and clinical parameters and disease progression were explored.

METHODS

High-performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS)/MS was performed in the serum of 326 human samples and 33 mouse samples. Human samples were collected from 142 healthy controls (HC), 127 newly diagnosed SSc patients without treatment (SSc baseline), and 57 treated SSc patients (SSc treatment). Mouse serum samples were collected from 11 control mice (NaCl), 11 mice with bleomycin (BLM)-induced fibrosis and 11 mice with hypochlorous acid (HOCl)-induced fibrosis. Both univariate analysis and multivariate analysis (orthogonal partial least-squares discriminate analysis (OPLS-DA)) were conducted to unravel differently expressed metabolites. KEGG pathway enrichment analysis was performed to characterize the dysregulated metabolic pathways in SSc. Associations between metabolites and clinical parameters of SSc patients were identified by Pearson's or Spearman's correlation analysis. Machine learning (ML) algorithms were applied to identify the important metabolites that have the potential to predict the progression of skin fibrosis.

RESULTS

The newly diagnosed SSc patients without treatment showed a unique serum metabolic profile compared to HC. Treatment partially corrected the metabolic changes in SSc. Some metabolites (phloretin 2'-O-glucuronide, retinoyl b-glucuronide, all-trans-retinoic acid, and betaine) and metabolic pathways (starch and sucrose metabolism, proline metabolism, androgen and estrogen metabolism, and tryptophan metabolism) were dysregulated in new-onset SSc, but restored upon treatment. Some metabolic changes were associated with treatment response in SSc patients. Metabolic changes observed in SSc patients were mimicked in murine models of SSc, indicating that they may reflect general metabolic changes associated with fibrotic tissue remodeling. Several metabolic changes were associated with SSc clinical parameters. The levels of allysine and all-trans-retinoic acid were negatively correlated, while D-glucuronic acid and hexanoyl carnitine were positively correlated with modified Rodnan skin score (mRSS). In addition, a panel of metabolites including proline betaine, phloretin 2'-O-glucuronide, gamma-linolenic acid and L-cystathionine were associated with the presence of interstitial lung disease (ILD) in SSc. Specific metabolites identified by ML algorithms, such as medicagenic acid 3-O-b-D-glucuronide, 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide, valproic acid glucuronide, have the potential to predict the progression of skin fibrosis.

CONCLUSIONS

Serum of SSc patients demonstrates profound metabolic changes. Treatment partially restored the metabolic changes in SSc. Moreover, certain metabolic changes were associated with clinical manifestations such as skin fibrosis and ILD, and could predict the progression of skin fibrosis.

A Systematic Review and Meta-Analysis Comparing the Diagnostic Accuracy Tests of COVID-19

In this paper, we present a systematic review and meta-analysis that aims to evaluate the reliability of coronavirus disease diagnostic tests in 2019 (COVID-19). This article seeks to describe the scientific discoveries made because of diagnostic tests conducted in recent years during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Between 2020 and 2021, searches for published papers on the COVID-19 diagnostic were made in the PubMed database. Ninety-nine scientific articles that satisfied the requirements were analyzed and included in the meta-analysis, and the specificity and sensitivity of the diagnostic accuracy were assessed. When compared to serological tests such as the enzyme-linked immunosorbent assay (ELISA), chemiluminescence immunoassay (CLIA), lateral flow immunoassay (LFIA), and chemiluminescent microparticle immunoassay (CMIA), molecular tests such as reverse transcription polymerase chain reaction (RT-PCR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR) performed better in terms of sensitivity and specificity. Additionally, the area under the curve restricted to the false-positive rates (AUC_FPR) of 0.984 obtained by the antiviral neutralization bioassay (ANB) diagnostic test revealed significant potential for the identification of COVID-19. It has been established that the various diagnostic tests have been effectively adapted for the detection of SARS-CoV-2; nevertheless, their performance still must be enhanced to contain potential COVID-19 outbreaks, which will also help contain potential infectious agent outbreaks in the future.

Risk of incident diabetes after COVID-19 infection: A systematic review and meta-analysis

BACKGROUND

COVID-19 might be a risk factor for various chronic diseases. However, the association between COVID-19 and the risk of incident diabetes remains unclear. We aimed to meta-analyze evidence on the relative risk of incident diabetes in patients with COVID-19.

METHODS

In this systematic review and meta-analysis, the Embase, PubMed, CENTRAL, and Web of Science databases were searched from December 2019 to June 8, 2022. We included cohort studies that provided data on the number, proportion, or relative risk of diabetes after confirming the COVID-19 diagnosis. Two reviewers independently screened studies for eligibility, extracted data, and assessed risk of bias. We used a random-effects meta-analysis to pool the relative risk with corresponding 95 % confidence intervals. Prespecified subgroup and meta-regression analyses were conducted to explore the potential influencing factors. We converted the relative risk to the absolute risk difference to present the evidence. This study was registered in advance (PROSPERO CRD42022337841).

MAIN FINDINGS

Ten articles involving 11 retrospective cohorts with a total of 47.1 million participants proved eligible. We found a 64 % greater risk (RR = 1.64, 95%CI: 1.51 to 1.79) of diabetes in patients with COVID-19 compared with non-COVID-19 controls, which could increase the number of diabetes events by 701 (558 more to 865 more) per 10,000 persons. We detected significant subgroup effects for type of diabetes and sex. Type 2 diabetes has a higher relative risk than type 1. Moreover, men may be at a higher risk of overall diabetes than women. Sensitivity analysis confirmed the robustness of the results. No evidence was found for publication bias.

CONCLUSIONS

COVID-19 is strongly associated with the risk of incident diabetes, including both type 1 and type 2 diabetes. We should be aware of the risk of developing diabetes after COVID-19 and prepare for the associated health problems, given the large and growing number of people infected with COVID-19. However, the body of evidence still needs to be strengthened.

Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK’s National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both ‘omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of ‘omics dysregulation caused by COVID-19 infections.

Metabolomics Markers of COVID-19 Are Dependent on Collection Wave

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2–7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.

An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics

The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.