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Historically, highly pathogenic beta-coronaviruses have been associated with severe respiratory diseases. According to the WHO, the severe acute respiratory syndrome coronavirus (SARS-CoV), and the Middle East respiratory syndrome coronavirus (MERS-CoV) were responsible for epidemics in 2002–2003 and 2015, respectively. During the SARS-CoV epidemic, the virus was reported in 29 countries with 8,437 cases and 813 fatalities

It has been widely described that some viruses lead to persistent physiological alterations even a decade after infection. The term “post-viral syndrome” has been in use for over a century

Long COVID (also known as post-COVID-19 syndrome or post-acute sequelae of COVID-19 (PACS)) is a condition characterized by long-term or persistent health problems appearing after the initial recovery from COVID-19 infection. The WHO has described long COVID as a condition “that occurs in individuals with a previous history of probable or confirmed SARS-CoV-2 infection, usually three months after the onset, with symptoms lasting at least two months that cannot be explained by an alternative diagnosis”

Similar to COVID-19, long COVID affects multiple organ systems, including the respiratory, cardiovascular, nervous, and gastrointestinal systems. More than 50 symptoms have been reported associated with long COVID

Both untargeted and targeted metabolomics have proven to be valuable tools for studying of long COVID. Our results, based on an untargeted lipidomics approach,

In the present work we used quantitative targeted metabolomics to evaluate the metabolic reversion of patients with persistent sequelae due to confirmed SARS-CoV-2 infection. Comparison with negative controls allowed us to identify those metabolites persistently dysregulated after two years of the initial infection. Number, type of symptoms as well as metabolic signatures were different in patients experiencing long COVID (arbitrarily defined by us as long COVID class A and class B patients) and recovered patients (non-long COVID). Besides, IL-17 level was increased in patients with the worst disease evolution (class B patients). To the best of our knowledge, this is the first targeted metabolomics study of long COVID patients conducted beyond twenty months post-infection.

Table

Baseline characteristics of participants in the study.

a: negative controls vs. COVID-19; b: COVID-19 vs. post-COVID-19; c: negative controls vs. post-COVID-19; d: COVID-19 vs. post-COVID-19

The questionnaire answered by the patients revealed the most persistent symptoms which were grouped into five broad categories: systemic, neurologic, psychiatric, cardiologic, and respiratory. The most predominant symptoms were loss of memory (73.3%), sleep disorders, arthralgia, fatigue, exercise intolerance, myalgia (66.7%), and anxiety (60.0%) (Fig. 

Most common symptoms remaining after 24 months in 48 post-COVID-19 patients. (

When paired samples (COVID-19/long COVID-19) from 15 patients were compared metabolically, 53 plasma metabolites were found to be significantly different (FDR < 0.05). The volcano plot (Fig. 

Multivariate analysis. (

In order to know if the altered metabolites (and those that remained unsignificant) were dysregulated with respect to normal values, a group of negative SARS-CoV-2 controls (i.e., healthy controls) collected from 2020 was added to the analysis. The heatmap (Fig. 

Multivariate analysis. (

In addition, lysoPC 14:0 (adjusted p = 2.8 × 10

Glucose, C10:2, C18:1, C10:1, lysoPC 14:0, lysoPC 16:1, lysoPC 18:1, PC ae 36:0, uric acid, C10, and pyruvic acid were found to be in similar concentration levels as for those in the COVID-19 phase.

Several other metabolites previously related with severity in COVID-19 tend towards normal or healthy levels (kynurenine/tryptophan ratio, C18:2, glutamic acid, glutamine, spermidine, kynurenine) in the long COVID-19 group. Of note, a group of sphingomyelins (SM(OH)14:0, SM16:0, SM(18:0), SM(OH)16:1, SM(OH)24:1, SM(18:1), SM(16:1)) were found to be normalized, as well as lysoPC 26:0, lysoPC 26:1, lysoPC 28:1, and lysoPC 28:0, phenylalanine, butyric acid, and propionic acid.

The multivariate analysis (PLS-DA) showed a clear separation between both classes (accuracy: 1; R

Differences were found within the post-COVID-19 group, both in the frequency of symptoms reported, and in the plasma levels of some metabolites such as lactic acid, with a bimodal distribution across the group. Therefore, these patients were subclassified according to our own scale as a surrogate for disease severity. 18 patients did not report any symptoms (recovered or non-long COVID). 18 patients reported less than five persistent symptoms (class A long COVID), while 12 reported more than five symptoms (class B long COVID).

We measured the levels of ammonia (in the form of plasmatic urea) in class B patients. The concentration of urea in COVID-19 phase was 43.8 ± 7.35 mg/dL, and 32.8 ± 3.1 mg/dL in long COVID phase. Although the urea levels were lower during the long COVID phase (falling within normal values), no significant differences were found between the two phases (t-test for paired samples, p = 0.146). Blood urea nitrogen (BUN) was similar for all post-COVID patients.

Figure 

Box plots for some significantly altered metabolites and ratios (p < 0.05) in plasma of class A patients (less than five symptoms), class B patients (more than five symptoms), and recovered patients. The bar plots show the original and normalized values (mean +/− one standard deviation). Medians are indicated by horizontal lines within each box.

For differentiating class B long COVID patients from all other post-COVID-19 patients, the lactate/pyruvate ratio had the best performance (AUC: 0.94 (0.85–0.99), sensitivity: 0.92 (0.87–0.97), specificity: 0.94 (0.91–0.98.

Our pathway enrichment analysis (Fig. 

Metabolic pathway analysis. Predicted metabolic pathways with p-value ≤ 0.05 are listed. (

Figure 

Concentrations of IL-17 and leptin measured by ELISA in post-COVID-19 patients. Graphs were constructed in GraphPad Prism v8.0. The ** p value < 0.01 was calculated using Kruskall-Wallis tests with Dunn´s post-tests.

Cumulative evidence from the last three years supports the dysregulation of metabolic and immune markers due to SARS-CoV-2 infection

Since well-defined classification or diagnostic criteria are not available for the long COVID assessment, there is an urgent need for molecular methods able to stratify patients according to the severity of the symptoms they are experiencing. Quantitative and validated scales, such as HAM-A, HAM-D, MoCA and mMRC are considered gold standards for neurocognitive impairment and for dyspnea assessment. However, their practical utility could be limited for complex conditions such as long COVID where a broader range of self-reported symptoms with different severity and duration are present. It has been reported that some long COVID-19 patients complain about extreme cognitive disorders (self-reported symptoms) but without any objective alterations, while others do not report symptoms but exhibit severe cognitive disorders after 6 to 9 months following SARS-CoV-2 infection

Our results revealed that 50% of analyzed plasma metabolites showed statistical differences between COVID-19 and long COVID-19 phases in patients with a more complicated evolution. One of the most dysregulated metabolites was glucose. Montefusco et al.

A number of other metabolites were also found to be dysregulated. Increased plasma pyruvate levels could be both a consequence of glycolytic dysregulation and protein degradation. The increase in putrescine levels in the long COVID phase may be an indicator of increased protein degradation to help fuel pyruvate metabolism.

Taurine and spermidine were found significantly decreased in the long COVID phase, although a trend towards normalization was observed when compared with controls. Decreased levels of serum taurine have been observed in patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Furthermore, we observed increased levels of kynurenine, and a trend towards normalization in tryptophan and the kynurenine/tryptophan ratio in long COVID-19 patients. This indicates that, although lower in magnitude, the inflammatory conditions attributable to the hyperactivation of this metabolic pathway are still present and may account for some persistent physiological symptoms in these patients. Increased levels of hippuric acid in the long COVID-19 phase could be associated with a residual intestinal dysbiosis. This metabolite has been found increased in patients with chronic kidney disease and several age-related conditions

Our study revealed increased levels of metabolites associated with collagen metabolism in long COVID patients. Among these metabolites, proline is particularly noteworthy due to its involvement in protein structure and function, as well as its role in maintaining cellular redox homeostasis through the generation of ATP and reactive oxygen species (ROS) during its catabolism. Proline can be synthesized from arginine through various enzymes, including arginase (both type I and type II), ornithine aminotransferase, and P5C reductase

The increase in glutamine (and decrease in glutamate levels) indicates a partial reestablishment of critical processes that took place during the COVID-19 infection phase, such as severe immunometabolic dysregulation. During COVID-19 phase, a decrease in circulating levels of glutamine has been widely described

Alterations in lipid metabolism are evident in most long COVID-19 patients. These patients exhibited significantly higher levels of carnitine and some short, medium, and long acylcarnitines. These alterations have been largely associated with altered fatty acid metabolism, dysfunctional mitochondria-dependent lipid catabolism, and immune processes or the lysis of white blood cells. Similar results have been reported by Guntur et al.

As positive findings for the metabolic state of long COVID patients, we found that 30 metabolites fell within normal levels. Phenylalanine, which has been widely associated with sepsis and COVID disease severity

In addition, sphingomyelins and long-chain monounsaturated and saturated LysoPCs were found to be within normal levels. We previously noted altered sphingolipids levels during COVID-19 infection

In a recent report, Holmes et al.

We believe that metabolic information may complement, and partially explain the phenotypic differences among long COVID-19 patients. Xu et al.

In our work, lactic acid levels were increased in patients with more than five symptoms and systemic disorders (class B patients). Ghali et al.

Increased level of the lactate/pyruvate ratio in class B patients is another important indicator of mitochondrial dysfunction. The lactate/pyruvate ratio has been proposed as a marker for mitochondrial disorders since it indirectly reflects the NADH/NAD + redox state

The increased ornithine/citrulline ratio level in class B patients reflects abnormal metabolic activity in the urea cycle. It is notable that Yamano et al.

In addition, class B patients had decreased levels of arginine in comparison with the other subgroups. The reduced bioavailability of arginine to produce adequate levels of nitric oxide in endothelial cells and vascular tissues leads to the impairment of multiple physiological functions of skeletal muscles, including contractile functions, and muscle repair

Previous studies have pointed to the persistent immune dysregulation following COVID-19 infection

We also measured IL-17 levels in post-COVID-19 patients since it is well known that this cytokine is persistently altered in several chronic inflammatory and autoimmune diseases

Metabolomics is not only useful in providing a snapshot of transient physiological or pathophysiological processes taking place in a living organism, but it has also proven to be a powerful tool for proposing and monitoring therapeutic interventions. In the case of long COVID, a common situation worldwide is that patients have reported an absence of adequate support and a poor recognition of their condition, initially attributed to psychiatric issues. People with long COVID have tried a vast range of self-prescribed medicines, supplements, remedies, and dietary changes to manage the disease and to overcome the effects it has on their quality of life and work capacity. Based on our findings, some interventions could be tested for treating long COVID patients: (1) supplementation of taurine (reducing musculoskeletal disorders); (2) supplementation of citrulline (enhancing ammonia clearance and reducing blood lactate, as well as increasing arginine bioavailability for adequate NO production); (3) supplementation of glutamine (primary source for neurotransmitters and immune function balancing); (4) supplementation of antioxidants such as N-acetylcysteine or NAD + (redox balance); (5) supplementation of arginine (targeting endothelial dysfunction in Long-COVID), as has been previously suggested by Tosato et al.

We need to acknowledge several limitations with this study. The small sample size was due to the limited number of patients who agreed to participate. While several objective measures of mood and cognition (HAM-A, HAM-D, MoCA mMRC) were used, the sample size did not allow for stratification of patients according to the different test scores obtained, and only self-reported symptoms were used for sub-group classification. Furthermore, we were unable to have a detailed tracking of treatments, medications or alternative therapies during the period evaluated. This limited our interpretation with regard to the impact of pharmacological interventions on the metabolome. Also, some compounds such as hexoses were measured by direct injection (DI); therefore, it was not possible to differentiate glucose (the most abundant sugar) from its epimers. We did not have access to Ct values from the electronic files of the patients indicating the initial viral load. All the patients were infected with the original strain, which was the predominant strain circulating in 2020. There are limited studies examining the association between the initial viral load, as determined by Ct values, and the various long COVID-19 effects

Patients from two different hospitals participated in our study: one private hospital located in Chihuahua city, and one public hospital located in Zacatecas city. In general, private hospital patients have relatively high incomes while public hospital patients have lower incomes. Logistic regression models showed no effects of sex, age, comorbidities, vaccination status or severity during the acute phase in the metabolomic profile associated with long COVID. However, patients from the public hospital reported more systemic symptoms in general, while patients from the private hospital reported principally neuropsychiatric symptoms. A recent study found that patients diagnosed with a post-COVID-19 condition were more likely to be unemployed or on public health insurance, illustrating racial and social disparities in access to and experience with healthcare, at least in the USA

At the moment of this study, most of the negative controls recruited in 2020 were tested positive for COVID-19 in subsequent waves in 2021 or 2022. Therefore, we could not compare the prevalence of symptoms in post-COVID-19 patients with a non-COVID-19 matched group. In a study conducted by Ballering et al.

To our knowledge, this study is the first describing quantitative metabolic perturbations two years after the initial acute COVID-19 infection using targeted metabolomics. The evolution of post-COVID-19 patients is different, and symptoms are associated to distinctive metabolic patterns resembling, to some extent, the ME/CSF condition. Moreover, the differences observed between the phenotypes of post-COVID-19 patients reveals potential biomarkers that, once validated in larger and heterogeneous populations, and integrated together with clinical and sociodemographic data, will enable a more accurate and precise classification of long COVID patients beyond classification via self-reported symptoms.

For the aims of this study, COVID-19 patient survivors (with confirmed diagnostic based on a positive PCR for SARS-CoV-2) who developed a mild, severe, or critical disease, and were admitted (or hospitalized) in the Instituto Mexicano de Seguridad Social (Zacatecas city, Mexico) and Christus Muguerza del Parque Hospital (Chihuahua city, Mexico) between March and November 2020, were recruited. Participants were contacted for a face-to face interview. They were invited to respond to a questionnaire and to donate a blood sample. Plasma was isolated from the donated blood. COVID-19 patients from the Instituto Mexicano de Seguridad Social were recruited from an initial set of 124 COVID-19 patients enrolled in a previous research study

Additionally, from a cohort of patients that were hospitalized in 2020 in Christus Muguerza del Parque Hospital, 33 were randomly selected by age stratification. For those patients, a basal blood sample was not available; however, all clinical information and chest computed tomography (CT) scans were recorded in the hospital archive.

For the neuropsychological assessment, the validated Hamilton Anxiety Rating Scale (HAM-A)

To assess for differences in the severity of long COVID patients, our own classification was made (arbitrarily) considering the frequency of concomitant symptoms. Recovered patients were classified as those who did not report persistent symptoms. Long COVID was considered if patients reported at least one persistent neurologic, psychiatric, gastrointestinal, cardiologic, respiratory, or systemic symptom. The class A long COVID patients were those reporting less than five persistent symptoms (17 patients), while class B long COVID patients were those reporting five or more persistent symptoms (13 patients). As negative controls and an indicator of normal population, stored plasma samples from 37 individuals who tested negative for SARS-CoV-2 in 2020 were used.

A combination of direct injection mass spectrometry with a reverse-phase LC–MS/MS custom assay was used, as previously described

The method combines the derivatization and extraction of analytes, and the selective mass-spectrometric detection using multiple reaction monitoring (MRM) pairs. Isotope-labeled internal standards and other internal standards were used for metabolite quantification. The custom assay uses a 96 deep-well plate with a filter plate attached via sealing tape, and reagents and solvents used to prepare the plate assay. The first 14 wells of the 96-well plate were used for calibration and quality control with one double blank, three zero samples, seven calibration standards and three quality control samples. To measure all metabolites except organic acids, samples were first thawed on ice and were vortexed. 10 µL of each sample was loaded onto the center of the filter on the upper 96-well plate and dried under a stream of nitrogen. Subsequently, phenyl-isothiocyanate (PITC) was added for derivatization. After incubation, the filter spots were dried again using an evaporator. Extraction of the metabolites was then achieved by adding 300 µL of extraction solvent. The extracts were obtained by centrifugation into the lower 96-deep well plate, followed by a dilution step with the mass spectrometry running solvent.

For organic acid analysis, 150 µL of ice-cold methanol and 10 µL of isotope-labeled internal standard mixture was added to 50 µL of each plasma sample for overnight protein precipitation. Each sample was then centrifuged at 13,000×

Mass spectrometric analysis for the PITC-derivatized and 3-NPH-derivatized samples was performed on an ABSciex 4000 Qtrap® tandem mass spectrometry instrument (Applied Biosystems/MDS Analytical Technologies, Foster City, CA) equipped with an Agilent 1260 series UHPLC system. Organic acids, biogenic amines, amino acids, and amino acid derivatives were detected and quantified via LC–MS, while lipids, acylcarnitines, and glucose were detected and quantified via a direct injection (DI) method.

Analyst 1.6.2 and MultiQuant 3.0.3 was used for quantitative analysis. An individual seven-point calibration curve was generated to quantify organic acids, amino acids, biogenic amines, and derivatives. Ratios for each analyte’s signal intensity to its corresponding isotope-labelled internal standard were plotted against the specific known concentrations using quadratic regression with a 1/x

ELISA kits were used for the quantification of IL-17 (Catalog Number RAB0262, Sigma-Aldrich, St. Louis, MO, USA) and leptin (catalog number ab108879, Abcam, Cambridge, UK), following manufacturer’s instructions. Briefly, standard solutions (or plasma samples), were added to each type of pre-coated 96-well plate and incubated overnight at 4 °C. The plates were then incubated with the corresponding detection antibodies (100 μL/well) for 1 h at room temperature. Streptavidin solution (100 μL) was then added to each well and the plates were incubated for 45 min. After the antibody-HPR incubation, TMB one-step substrate reagent (100 μL) was added to the wells and the plates were incubated for another 30 min before the addition of a stop solution (50 μL/well). Absorbance values (at 450 nm) were used for the calculation of the protein concentrations (pg/mL) by comparing the absorbance to an appropriate standard curve.

To describe baseline characteristics of negative controls (non-COVID-19), COVID-19 or post-COVID-19 patients, medians with interquartile ranges (IQRs) or means [with standard deviations (s.d.)] and frequencies (%) were used for continuous and categorical data, respectively. Normality was assessed using the D’Agostino-Pearson normality test. Student’s t-test or Mann–Whitney tests were used for continuous data. For categorical variables (e.g., sex, smoking, symptoms, and comorbidities) Pearson Chi

Metabolite analysis was performed with MetaboAnalyst 5.0

Pathway analysis was done using Metabolite Set Enrichment Analysis (MSEA) and Metabolomic Pathway Analysis (MetPA) modules as found in MetaboAnalyst 5.0

The metabolites with the highest VIP scores were used to create metabolite panels for predicting long COVID using multivariate logistic regression. Additionally, models were adjusted for relevant potential confounders such as sex, age, relevant comorbidities (i.e., DM-II, HTN, and obesity), so that only statistically significant variables (p < 0.05) remained in the final models. Logistic regression analysis was performed with the auto-scaled data. K-fold cross-validation (CV) was used to ensure that the logistic regression models were robust. To determine the performance of each generated model, the area under the receiver operating characteristics curve (AUROC or AUC) was calculated, as was sensitivity and specificity.

This study was conducted in accordance with the Declaration of Helsinki (1976). It was also revised and approved by the Research and Ethics Committees of the Instituto Mexicano de Seguridad Social, with the registration number R-2022-3301-038, and Christus Muguerza del Parque Hospital (HCMP-CEI-15042020-3, and HCMP-CEI-28022022-A01). Informed consent was obtained from all participants. All patients included in this study were informed in writing regarding the collection of their samples for research aims and were given the right to refuse participation.

Supplementary Information.