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Background Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine. Methods We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18–75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4–5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS. Results MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N -oxide, and p -cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome. Conclusions Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome.

Background Metabolic syndrome (MetS) is a cluster of medical conditions and risk factors correlating with insulin resistance that increase the risk of developing cardiometabolic health problems. The specific criteria for diagnosing MetS vary among different medical organizations but are typically based on the evaluation of abdominal obesity, high blood pressure, hyperglycemia, and dyslipidemia. A unique, quantitative and independent estimation of the risk of MetS based only on quantitative biomarkers is highly desirable for the comparison between patients and to study the individual progression of the disease in a quantitative manner. Methods We used NMR-based metabolomics on a large cohort of donors (n = 21,323; 37.5% female) to investigate the diagnostic value of serum or serum combined with urine to estimate the MetS risk. Specifically, we have determined 41 circulating metabolites and 112 lipoprotein classes and subclasses in serum samples and this information has been integrated with metabolic profiles extracted from urine samples. Results We have developed MetSCORE, a metabolic model of MetS that combines serum lipoprotein and metabolite information. MetSCORE discriminate patients with MetS (independently identified using the WHO criterium) from general population, with an AUROC of 0.94 (95% CI 0.920–0.952, p < 0.001). MetSCORE is also able to discriminate the intermediate phenotypes, identifying the early risk of MetS in a quantitative way and ranking individuals according to their risk of undergoing MetS (for general population) or according to the severity of the syndrome (for MetS patients). Conclusions We believe that MetSCORE may be an insightful tool for early intervention and lifestyle modifications, potentially preventing the aggravation of metabolic syndrome.

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts.

Purpose Down syndrome (DS) and STAT1 gain-of-function (GOF) share clinical and molecular features, including persistent inflammation. We aimed to investigate whether the coexistence of DS and a STAT1 GOF mutation in a patient synergistically enhances interferon (IFN) signaling and exacerbates inflammatory responses, posing additional management challenges. Two patients (P1 and P2) were studied: P1, with DS and a heterozygous p.P326S STAT1 variant, and P2, with the STAT1 p.P326S variant only. Individuals with isolated DS or STAT1 GOF served as controls. IFN receptor subunits (IFNγR1/R2 and IFNαR1/R2) and responses to IFNα/γ stimulation were analyzed using flow cytometry and RT-PCR. Whole blood type I IFN signature and serum cytokines were evaluated using NanoString and Luminex assays. P1 experienced recurrent infections, chronic mucocutaneous candidiasis, interstitial pneumonitis, and pulmonary hypertension. P2 presented with esophageal candidiasis, dysphagia, and stenosis. The p.P326S variant led to increased STAT1/pSTAT1 levels in response to IFNα/γ. Both patients showed significant clinical improvement with the Janus kinase (JAK) inhibitor ruxolitinib. However, P1’s key biomarkers (STAT1 levels, IFN signature, TNFα, IL-6) remained altered, indicating persistent inflammation despite clinical improvement. This first report of a STAT1 GOF variant in DS provides a unique “experiment of nature”, offering insights into the interplay between trisomy 21 and STAT1-mediated immune dysregulation. Although ruxolitinib demonstrated clinical benefits, the persistent inflammation observed in P1 highlights the need for further strategies to achieve complete immune resolution. These findings emphasize the importance of comprehensive genetic and immunological assessments in individuals with DS, particularly when immune dysfunction is suspected.

Necesitaba poner unas cartas en el correo, de modo que asistí a la sucursal de Bosques del Valle, ¡y me quedé espantada de ver lo sucia...

  BEATRIZ GONZÁLEZ ARÉCHIGA / Col. Del Valle, San Pedro

We thank the European Research Council (ERC Starting Grant; Strp-diferentiation 280304) and the Spanish “Ministerio de Economía, Industria y Competitividad” (MINECO; BIO2015-65709-R and CTQ2016-80069-C2-1R) for fnancial support. Nathaly Gonzalez-Quiñonez was funded by a Severo Ochoa fellowship (FICYT, Consejería de Educación y Ciencia, Asturias, Spain). Roberto Álvarez-Fernández García was funded by “Fundación para la Investigación Biosanitaria de Asturias” (FINBA, Asturias, Spain). Tanks to Beatriz Gutierrez Magan (Universidad de Oviedo, Dpto. Biología Funcional, Área de Microbiología) for her laboratory assistance, Paul Dyson and Meirwyn Evans (Swansea University) for providing the disrupted cosmids, Wouter de Jong (University of Groningen, the Netherlands) for providing the S. coelicolor NepA mutant strain, Proof-ReadingService.com and Nature Publishing Group Language Editing service for proofreading the text.

This study was funded by the “Ministerio de Ciencia, Innovación Universidades/Agencia Estatal de Investigación/Fondo Europeo de Desarrollo Regional” (RTI2018-093978-B-I00) and the “Consejería de Empleo, Industria y Turismo del Principado de Asturias” (SV-PA-21-AYUD/2021/51399). Gemma Fernández-García was funded by a predoctoral grant from the “Universidad de Oviedo”. Sergio Alonso was funded by a “Severo Ochoa” predoctoral grant from “Consejería de Ciencia, Innovación y Universidad del Principado de Asturias”. J.F. was funded by ISPA via a “Contrato Intramural Postdoctoral” 2021.

We thank the European Research Council (ERC Starting Grant; Strp-differentiation 280304), and the Spanish ‘Ministerio de Economía y Competitividad’ (MINECO; BIO2015-65709-R) for financial support. Support for R.K. from the European Union was obtained through the H2020 Programme grant iNEXT (grant No. 653706). N.G.Q. was funded by a Severo Ochoa fellowship (FICYT, Consejería de Educación y Ciencia, Spain). We thank Joseph R McCormick and Stuart Cantlay (Duquesne University) for providing the ftsZ mutant HU133; Michael S. VanNieuwenhze (Indiana University) for providing a sample of HADA; Sébastien Rigali (University of Liège) and Katherine Celler (Leiden University) for stimulating discussions; Ermelinda Tinetti (Invitrogen) for providing a sample of YOPRO-1; Ana Salas Bustamante, Angel Martinez Nistal, Marta Alonso Guervos and Tania Iglesias (Servicios Científico-Técnicos de la Universidad de Oviedo) for their support with flow cytometry, confocal microscopy and statistics; and Beatriz Gutierrez Magan (Universidad de Oviedo, Dpto. Biología Funcional, Área de Microbiología) for her laboratory assistance.

This research was funded by an ERC Starting Grant (Strp-differentiation 280304). Our thanks to Beatriz Gutiérrez Magán (Universidad de Oviedo, Dpto. Biología Funcional, Área de Microbiología) for laboratory assistance, Paul Dyson and Meirwyn Evans (Swansea University) for providing the disrupted cosmids, Carlos Barreiro and Mar Calonge (INBIOTEC, Leon) for the mass spectrometry and amino acid analyses, Maggie Smith (University of York) for providing the pMS82 plasmid, and Nature Publishing Group Language Editing service for proof-reading the text. B.R., P.Y., M.T.L.G., N.G.Q. and E.B. performed the experiments; B.R., P.Y., E.B., F.M. and A.M. assisted with data analysis; all authors contributed to the critical discussion of the manuscript; B.R., F.M. and A.M. conceived, designed the work and wrote the manuscript. All authors read and approved the final manuscript.