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BackgroundSince the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation.MethodsWe customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls.ResultsEpigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively.ConclusionsIn summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Background: Evidence of a long-period biological rhythm present in mammalian hard tissue relates to species average body mass. Studies have just begun to investigate the role of this biorhythm in human physiology., Methods: The biorhythm is calculated from naturally exfoliated primary molars for 61 adolescents. We determine if the timing relates to longitudinal measures of their weight, height, lower leg length and body mass collected over 14 months between September 2019 to October 2020. We use univariate and multivariate statistical analyses to isolate and identify relationships with the biorhythm., Results: Participants with a faster biorhythm typically weigh less each month and gain significantly less weight and mass over 14-months, relative to those with a slower biorhythm. The biorhythm relates to sex differences in weight gain., Conclusions: We identify a previously unknown factor that associates with the rapid change in body size that accompanies human adolescence. Our findings provide a basis from which to explore novel relationships between the biorhythm and weight-related health risks., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2022.)

Background: Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors., Methods: Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs., Results: The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79., Conclusion: This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2022.)

Background: Over five million joint replacements are performed across the world each year. Cobalt chrome (CoCr) components are used in most of these procedures. Some patients develop delayed-type hypersensitivity (DTH) responses to CoCr implants, resulting in tissue damage and revision surgery. DTH is unpredictable and genetic links have yet to be definitively established., Methods: At a single site, we carried out an initial investigation to identify HLA alleles associated with development of DTH following metal-on-metal hip arthroplasty. We then recruited patients from other centres to train and validate an algorithm incorporating patient age, gender, HLA genotype, and blood metal concentrations to predict the development of DTH. Accuracy of the modelling was assessed using performance metrics including time-dependent receiver operator curves., Results: Using next-generation sequencing, here we determine the HLA genotypes of 606 patients. 176 of these patients had experienced failure of their prostheses; the remaining 430 remain asymptomatic at a mean follow up of twelve years. We demonstrate that the development of DTH is associated with patient age, gender, the magnitude of metal exposure, and the presence of certain HLA class II alleles. We show that the predictive algorithm developed from this investigation performs to an accuracy suitable for clinical use, with weighted mean survival probability errors of 1.8% and 3.1% for pre-operative and post-operative models respectively., Conclusions: The development of DTH following joint replacement appears to be determined by the interaction between implant wear and a patient's genotype. The algorithm described in this paper may improve implant selection and help direct patient surveillance following surgery. Further consideration should be given towards understanding patient-specific responses to different biomaterials., Competing Interests: Competing interestsThe authors declare the following competing interests: the algorithm described in this study has been developed into software to be used as a commercial medical device (Orthotype). Orthotype has been patented, and is owned by the company PXD Ltd, trading as ExplantLab. David Langton, the lead author, is director of this company. Matthew Nargol is an employee of ExplantLab. All other authors have no competing interests to declare., (© The Author(s) 2022.)

Background: With the rising number of chimeric antigen receptor (CAR) T cell treated patients, it is increasingly important to understand the treatment's impact on patient-reported outcomes (PROs) and, ideally, identify biomarkers of central nervous system (CNS) adverse effects., Methods: The purpose of this exploratory study was to assess short-term PROs and serum kynurenine metabolites for associated neurotoxicity among patients treated in an anti-CD20, anti-CD19 (LV20.19) CAR T cell phase I clinical trial (NCT03019055). Fifteen CAR T treated patients from the parent trial provided serum samples and self-report surveys 15 days before and 14, 28, and 90 days after treatment., Results: Blood kynurenine concentrations increased over time in patients with evidence of neurotoxicity ( p  = 0.004) and were increased in self-reported depression ( r  = 0.52, p  = 0.002). Depression improved after CAR T infusion ( p  = 0.035). Elevated 3-hydroxyanthranilic acid (3HAA) concentrations prior to cell infusion were also predictive of neurotoxicity onset ( p  = 0.031), suggesting it is a biomarker of neurotoxicity following CAR T cell therapy., Conclusions: Elevated levels of kynurenine pathway metabolites among CAR T cell recipients are associated with depressed mood and neurotoxicity. Findings from this exploratory study are preliminary and warrant validation in a larger cohort., Competing Interests: Competing interestsThe authors declare the following competing interests. P.H. reports receiving honoraria from Incyte, BMS, Legend, Jannsen, Takeda, Amgen, Karyopharm, GSK, Pfizer. C.J.H. is a member of the Scientific Advisory Boards of Phytecs, Inc, and has equity in Formulate Biosciences. B.J. reports receiving research support and honoraria and travel support from Miltenyi Biotec. D.S. and B.D. are authors on a patent for the 20.19 CAR. N.N.S. reports receiving honoraria and/or travel support from Incyte, Celgene, Lily, and Miltenyi Biotec; serving on scientific advisory boards for Lily, Kite, Celgene, Legend, Epizyme, Seattle Genetics, and TG therapeutics; equity ownership in Exelixis, Geron; receiving institutional research support for clinical trials from Miltenyi Biotec. The remaining authors declare no competing interests., (© The Author(s) 2022.)

Background: Evaluation of susceptibility to emerging SARS-CoV-2 variants of concern (VOC) requires rapid screening tests for neutralising antibodies which provide protection., Methods: Firstly, we developed a receptor-binding domain-specific haemagglutination test (HAT) to Wuhan and VOC (alpha, beta, gamma and delta) and compared to pseudotype, microneutralisation and virus neutralisation assays in 835 convalescent sera. Secondly, we investigated the antibody response using the HAT after two doses of mRNA (BNT162b2) vaccination. Sera were collected at baseline, three weeks after the first and second vaccinations from older (80-99 years, n  = 89) and younger adults (23-77 years, n  = 310) and compared to convalescent sera from naturally infected individuals (1-89 years, n  = 307)., Results: Here we show that HAT antibodies highly correlated with neutralising antibodies (R = 0.72-0.88) in convalescent sera. Home-dwelling older individuals have significantly lower antibodies to the Wuhan strain after one and two doses of BNT162b2 vaccine than younger adult vaccinees and naturally infected individuals. Moverover, a second vaccine dose boosts and broadens the antibody repertoire to VOC in naïve, not previously infected older and younger adults. Most (72-76%) older adults respond after two vaccinations to alpha and delta, but only 58-62% to beta and gamma, compared to 96-97% of younger vaccinees and 68-76% of infected individuals. Previously infected older individuals have, similarly to younger adults, high antibody titres after one vaccination., Conclusions: Overall, HAT provides a surrogate marker for neutralising antibodies, which can be used as a simple inexpensive, rapid test. HAT can be rapidly adaptable to emerging VOC for large-scale evaluation of potentially decreasing vaccine effectiveness., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2022.)

Background: Tumor mutational burden (TMB) has been proposed as a predictive biomarker of response to immunotherapy. Efforts to standardize TMB scores for use in the clinic and to identify the factors that could impact TMB scores are of high importance. However, the biopsy collection site has not been assessed as a factor that may influence TMB scores., Methods: We examine a real-world cohort comprising 137,771 specimens across 47 tissues in 12 indications profiled by the FoundationOne assay (Foundation Medicine, Cambridge, MA) to assess the prevalence of biopsy sites for each indication and their TMB scores distribution., Results: We observe a wide variety of biopsy sites from which specimens are sent for genomic testing and show that TMB scores differ in a cancer- and tissue-specific manner. For example, brain or adrenal gland specimens from NSCLC patients show higher TMB scores than local lung specimens (mean difference 3.31 mut/Mb; p  < 0.01, 3.90 mut/Mb; p  < 0.01, respectively), whereas bone specimens show no difference., Conclusions: Our data shed light on the biopsied tissue as a driver of TMB measurement variability in clinical practice., Competing Interests: Competing interestsS.P.-C. and A.M.W. are employees of Bristol Myers Squibb. J.F.H., S.H.R. and L.A.A. are employees of Foundation Medicine., (© The Author(s) 2021.)

Background: Therapeutic management of epilepsy remains a challenge, since optimal systemic antiseizure medication (ASM) concentrations do not always correlate with improved clinical outcome and minimal side effects. We tested the feasibility of noninvasive real-time breath metabolomics as an extension of traditional therapeutic drug monitoring for patient stratification by simultaneously monitoring drug-related and drug-modulated metabolites., Methods: This proof-of-principle observational study involved 93 breath measurements of 54 paediatric patients monitored over a period of 2.5 years, along with an adult's cohort of 37 patients measured in two different hospitals. Exhaled breath metabolome of epileptic patients was measured in real time using secondary electrospray ionisation-high-resolution mass spectrometry (SESI-HRMS)., Results: We show that systemic ASM concentrations could be predicted by the breath test. Total and free valproic acid (VPA, an ASM) is predicted with concordance correlation coefficient (CCC) of 0.63 and 0.66, respectively. We also find (i) high between- and within-subject heterogeneity in VPA metabolism; (ii) several amino acid metabolic pathways are significantly enriched ( p  < 0.01) in patients suffering from side effects; (iii) tyrosine metabolism is significantly enriched ( p  < 0.001), with downregulated pathway compounds in non-responders., Conclusions: These results show that real-time breath analysis of epileptic patients provides reliable estimations of systemic drug concentrations along with risk estimates for drug response and side effects., Competing Interests: Competing interestsPS and MK are cofounders of Deep Breath Intelligence AG (Switzerland), which develops breath-based diagnostic tools. KS is consultant for Deep Breath Intelligence AG (Switzerland). All other authors declare no competing interests. The breath-analysis concept of predicting drug concentrations and risk scores, and the data-processing pipeline discussed here have been incorporated in the European patents 20186274.5, filed on July 16th 2020 and 21185400.5, filed on July 13th 2021, respectively., (© The Author(s) 2021.)