Traffic Density Exposure, Oxidative Stress Biomarkers and Plasma Metabolomics in a Population-Based Sample: The Hortega Study

Exposure to traffic-related air pollution (TRAP) generates oxidative stress, with downstream effects at the metabolic level. Human studies of traffic density and metabolomic markers, however, are rare. The main objective of this study was to evaluate the cross-sectional association between traffic density in the street of residence with oxidative stress and metabolomic profiles measured in a population-based sample from Spain. We also explored in silico the potential biological implications of the findings. Secondarily, we assessed the contribution of oxidative stress to the association between exposure to traffic density and variation in plasma metabolite levels. Traffic density was defined as the average daily traffic volume over an entire year within a buffer of 50 m around the participants' residence. Plasma metabolomic profiles and urine oxidative stress biomarkers were measured in samples from 1181 Hortega Study participants by nuclear magnetic resonance spectroscopy and high-performance liquid chromatography, respectively. Traffic density was associated with 7 (out of 49) plasma metabolites, including amino acids, fatty acids, products of bacterial and energy metabolism and fluid balance metabolites. Regarding urine oxidative stress biomarkers, traffic associations were positive for GSSG/GSH% and negative for MDA. A total of 12 KEGG pathways were linked to traffic-related metabolites. In a protein network from genes included in over-represented pathways and 63 redox-related candidate genes, we observed relevant proteins from the glutathione cycle. GSSG/GSH% and MDA accounted for 14.6% and 12.2% of changes in isobutyrate and the CH2CH2CO fatty acid moiety, respectively, which is attributable to traffic exposure. At the population level, exposure to traffic density was associated with specific urine oxidative stress and plasma metabolites. Although our results support a role of oxidative stress as a biological intermediary of traffic-related metabolic alterations, with potential implications for the co-bacterial and lipid metabolism, additional mechanistic and prospective studies are needed to confirm our findings.

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

Effectiveness of a Second Dose of an mRNA Vaccine Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron Infection in Individuals Previously Infected by Other Variants

BACKGROUND

Single-dose vaccination was widely recommended in the pre-Omicron era for persons with previous SARS-CoV-2 infection. The effectiveness of a second vaccine dose in this group in the Omicron era is unknown.

METHODS

We linked nationwide population registries in Spain to identify community-dwelling individuals aged 18-64, with a positive SARS-CoV-2 test before single-dose mRNA vaccination (mRNA-1273 or BNT162b2). Every day between 3 January and 6 February 2022 we matched 1:1 individuals receiving a second mRNA vaccine dose and controls on sex, age, province, first dose type and time, month of primary infection, and number of previous tests. We then estimated Kaplan-Meier risks of confirmed SARS-CoV-2 reinfection. We performed a similar analysis in a Delta-dominant period, between 19 July and 30 November 2021.

RESULTS

In the Omicron period, estimated effectiveness (95% CI) of a second dose was 62.2% (58.2-66.4%) 7-34 days after administration, similar across groups defined by age, sex, type of first vaccine, and time since the first dose. Estimated effectiveness was 65.4% (61.1-69.9%) for mRNA-1273 and 52.0% (41.8-63.1%) for BNT162b2. Estimated effectiveness was 78.5% (67.4-89.9%), 66.1% (54.9-77.5%), and 60.2% (55.5-64.8%) when primary infection had occurred in the Delta, Alpha, and pre-Alpha periods, respectively. In the Delta period, the estimated effectiveness of a second dose was 8.8% (-55.3% to 81.1%).

CONCLUSIONS

Our results suggest that, over 1 month after administration, a second dose of mRNA vaccine increases protection against SARS-CoV-2 reinfection with the Omicron variant among individuals with single-dose vaccination and previously infected with another variant.

Effectiveness of mRNA vaccine boosters against infection with the SARS-CoV-2 omicron (B.1.1.529) variant in Spain: a nationwide cohort study

BACKGROUND

The omicron (B.1.1.529) variant of SARS-CoV-2 has increased capacity to elude immunity and cause breakthrough infections. The aim of this study was to estimate the effectiveness of mRNA-based vaccine boosters (third dose) against infection with the omicron variant by age, sex, time since complete vaccination, type of primary vaccine, and type of booster.

METHODS

In this nationwide cohort study, we linked data from three nationwide population registries in Spain (Vaccination Registry, Laboratory Results Registry, and National Health System registry) to select community-dwelling individuals aged 40 years or older, who completed their primary vaccine schedule at least 3 months before the start of follow-up, and had not tested positive for SARS-CoV-2 since the start of the pandemic. On each day between Jan 3, and Feb 6, 2022, we matched individuals who received a booster mRNA vaccine and controls of the same sex, age group, postal code, type of vaccine, time since primary vaccination, and number of previous tests. We estimated risk of laboratory-confirmed SARS-CoV-2 infection using the Kaplan-Meier method and compared groups using risk ratios (RR) and risk differences. Vaccine effectiveness was calculated as one minus RR.

FINDINGS

Between Jan 3, and Feb 6, 2022, 3 111 159 matched pairs were included in our study. Overall, the estimated effectiveness from day 7 to 34 after a booster was 51·3% (95% CI 50·2-52·4). Estimated effectiveness was 52·5% (51·3-53·7) for an mRNA-1273 booster and 46·2% (43·5-48·7) for a BNT162b2 booster. Effectiveness was 58·6% (55·5-61·6) if primary vaccination had been with ChAdOx1 nCoV-19 (Oxford-AstraZeneca), 55·3% (52·3-58·2) with mRNA-1273 (Moderna), 49·7% (48·3-51·1) with BNT162b2 (Pfizer-BioNTech), and 48·0% (42·5-53·7) with Ad26.COV2.S (Janssen). Estimated effectiveness was 43·6% (40·0-47·1) when the booster was administered between 151 days and 180 days after complete vaccination and 52·2% (51·0-53·3) if administered more than 180 days after primary scheduled completion.

INTERPRETATION

Booster mRNA vaccine-doses were moderately effective in preventing infection with the omicron variant of SARS-CoV-2 for over a month after administration, which indicates their suitability as a strategy to limit the health effects of COVID-19 in periods of omicron variant domination. Estimated effectiveness was higher for mRNA-1273 compared with BNT162b2 and increased with time between completed primary vaccination and booster.

FUNDING

None.

Assessing the effect of non-pharmaceutical interventions on COVID-19 transmission in Spain, 30 August 2020 to 31 January 2021

BackgroundAfter a national lockdown during the first wave of the COVID-19 pandemic in Spain, regional governments implemented different non-pharmaceutical interventions (NPIs) during the second wave.AimTo analyse which implemented NPIs significantly impacted effective reproduction number (R_t) in seven Spanish provinces during 30 August 2020-31 January 2021.MethodsWe coded each NPI and levels of stringency with a 'severity index' (SI) and computed a global SI (mean of SIs per six included interventions). We performed a Bayesian change point analysis on the R_t curve of each province to identify possible associations with global SI variations. We fitted and compared several generalised additive models using multimodel inference, to quantify the statistical effect on R_t of the global SI (stringency) and the individual SIs (separate effect of NPIs).ResultsThe global SI had a significant lowering effect on the R_t (mean: 0.16 ± 0.05 units for full stringency). Mandatory closing times for non-essential businesses, limited gatherings, and restricted outdoors seating capacities (negative) as well as curfews (positive) were the only NPIs with a significant effect. Regional mobility restrictions and limited indoors seating capacity showed no effect. Our results were consistent with a 1- to 3-week-delayed R_t as a response variable.ConclusionWhile response measures implemented during the second COVID-19 wave contributed substantially to a decreased reproduction number, the effectiveness of measures varied considerably. Our findings should be considered for future interventions, as social and economic consequences could be minimised by considering only measures proven effective.

Characterization of anaphylaxis reveals different metabolic changes depending on severity and triggers

BACKGROUND

Despite the increasing incidence of anaphylaxis, its underlying molecular mechanisms and biomarkers for appropriate diagnosis remain undetermined. The rapid onset and potentially fatal outcome in the absence of managed treatment prevent its study. Up today, there are still no known biomarkers that allow an unequivocal diagnosis. Therefore, the aim of this study was to explore metabolic changes in patients suffering anaphylactic reactions depending on the trigger (food and/or drug) and severity (moderate and severe) in a real-life set-up.

METHODS

Eighteen episodes of anaphylaxis, one per patient, were analysed. Sera were collected during the acute phase (T1), the recovery phase (T2) and around 2-3 months after the anaphylactic reaction (T0: basal state). Reactions were classified following an exhaustive allergological evaluation for severity and trigger. Sera samples were analysed using untargeted metabolomics combining liquid chromatography coupled to mass spectrometry (LC-MS) and proton nuclear magnetic resonance spectroscopy (¹ H-NMR).

RESULTS

'Food T1 vs T2' and 'moderate T1 vs T2' anaphylaxis comparisons showed clear metabolic patterns during the onset of an anaphylactic reaction, which differed from those induced by drugs, food + drug or severe anaphylaxis. Moreover, the model of food anaphylaxis was able to distinguish the well-characterized IgE (antibiotics) from non-IgE-mediated anaphylaxis (nonsteroidal anti-inflammatory drugs), suggesting a differential metabolic pathway associated with the mechanism of action. Metabolic differences between 'moderate vs severe' at the acute phase T1 and at basal state T0 were studied. Among the altered metabolites, glucose, lipids, cortisol, betaine and oleamide were observed altered.

CONCLUSIONS

The results of this exploratory study provide the first evidence that different anaphylactic triggers or severity induce differential metabolic changes along time or at specific time-point, respectively. Besides, the basal status T0 might identify high-risk patients, thus opening new ways to understand, diagnose and treat anaphylaxis.

Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain

BACKGROUND

On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks.

AIM

To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain.

METHODS

A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf's prospective space-time scan statistic (STSS) to detect daily emerging active clusters.

RESULTS

Analyses were performed daily during the summer 2020 (June 21st - August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August.

CONCLUSION

STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.

Polycythemia Vera and Essential Thrombocythemia Patients Exhibit Unique Serum Metabolic Profiles Compared to Healthy Individuals and Secondary Thrombocytosis Patients

Simple Summary

Current diagnosis of myeloproliferative neoplasms (MPNs), including polycythemia vera (PV) and essential thrombocythemia (ET), is controversial due to limitations associated with the lack of reproducibility, subjectivity and the presence of common somatic mutations in the driver genes. Metabolomics represents a powerful approach to identify altered metabolites that can differentiate between disease status at the time of diagnosis. The objective of this study was to characterize the serum metabolic profile of MPNs patients (PV and ET) and compare it with healthy controls (HC) and secondary thrombocytosis (ST) patients. The analysis revealed metabolites following similar trends between PV and ET patients, as well as unique significant differences in the serum metabolite levels of MNPs patients compared to HC and ST patients. These results could contribute to better differentiate patients with these diseases from HC and ST patients.

Abstract

Most common myeloproliferative neoplasms (MPNs) include polycythemia vera (PV) and essential thrombocythemia (ET). Accurate diagnosis of these disorders remains a clinical challenge due to the lack of specific clinical or molecular features in some patients enabling their discrimination. Metabolomics has been shown to be a powerful tool for the discrimination between different hematological diseases through the analysis of patients’ serum metabolic profiles. In this pilot study, the potential of NMR-based metabolomics to characterize the serum metabolic profile of MPNs patients (PV, ET), as well as its comparison with the metabolic profile of healthy controls (HC) and secondary thrombocytosis (ST) patients, was assessed. The metabolic profile of PV and ET patients, compared with HC, exhibited higher levels of lysine and decreased levels of acetoacetic acid, glutamate, polyunsaturated fatty acids (PUFAs), scyllo-inositol and 3-hydroxyisobutyrate. Furthermore, ET patients, compared with HC and ST patients, were characterized by decreased levels of formate, N-acetyl signals from glycoproteins (NAC) and phenylalanine, while the serum profile of PV patients, compared with HC, showed increased concentrations of lactate, isoleucine, creatine and glucose, as well as lower levels of choline-containing metabolites. The overall analysis revealed significant metabolic alterations mainly associated with energy metabolism, the TCA cycle, along with amino acid and lipid metabolism. These results underscore the potential of metabolomics for identifying metabolic alterations in the serum of MPNs patients that could contribute to improving the clinical management of these diseases.

Metabolomics Contributions to the Discovery of Prostate Cancer Biomarkers

Prostate cancer (PCa) is one of the most frequently diagnosed cancers and a leading cause of death among men worldwide. Despite extensive efforts in biomarker discovery during the last years, currently used clinical biomarkers are still lacking enough specificity and sensitivity for PCa early detection, patient prognosis, and monitoring. Therefore, more precise biomarkers are required to improve the clinical management of PCa patients. In this context, metabolomics has shown to be a promising and powerful tool to identify novel PCa biomarkers in biofluids. Thus, changes in polyamines, tricarboxylic acid (TCA) cycle, amino acids, and fatty acids metabolism have been reported in different studies analyzing PCa patients' biofluids. The review provides an up-to-date summary of the main metabolic alterations that have been described in biofluid-based studies of PCa patients, as well as a discussion regarding their potential to improve clinical PCa diagnosis and prognosis. Furthermore, a summary of the most significant findings reported in these studies and the connections and interactions between the different metabolic changes described has also been included, aiming to better describe the specific metabolic signature associated to PCa.