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Background Greater maternal adiposity before or during pregnancy is associated with greater offspring adiposity throughout childhood, but the extent to which this is due to causal intrauterine or periconceptional mechanisms remains unclear. Here, we use Mendelian randomisation (MR) with polygenic risk scores (PRS) to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal. Methods We undertook confounder adjusted multivariable (MV) regression and MR using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB). In ALSPAC and BiB, the outcomes were birthweight (BW; N = 9339) and BMI at age 1 and 4 years ( N = 8659 to 7575). In ALSPAC only we investigated BMI at 10 and 15 years ( N = 4476 to 4112) and dual-energy X-ray absorptiometry (DXA) determined fat mass index (FMI) from age 10–18 years ( N = 2659 to 3855). We compared MR results from several PRS, calculated from maternal non-transmitted alleles at between 29 and 80,939 single nucleotide polymorphisms (SNPs). Results MV and MR consistently showed a positive association between maternal BMI and BW, supporting a moderate causal effect. For adiposity at most older ages, although MV estimates indicated a strong positive association, MR estimates did not support a causal effect. For the PRS with few SNPs, MR estimates were statistically consistent with the null, but had wide confidence intervals so were often also statistically consistent with the MV estimates. In contrast, the largest PRS yielded MR estimates with narrower confidence intervals, providing strong evidence that the true causal effect on adolescent adiposity is smaller than the MV estimates ( P difference = 0.001 for 15-year BMI). This suggests that the MV estimates are affected by residual confounding, therefore do not provide an accurate indication of the causal effect size. Conclusions Our results suggest that higher maternal pre-/early-pregnancy BMI is not a key driver of higher adiposity in the next generation. Thus, they support interventions that target the whole population for reducing overweight and obesity, rather than a specific focus on women of reproductive age.

ObjectiveThis study aimed to understand hospital doctors’ priorities (target use cases and aetiologies) for the development of a new rapid diagnostic test for patients with fever.DesignA cross-sectional online survey.SettingEurope-wide.ParticipantsSecondary and tertiary care doctors involved in patient assessment and diagnosis across Europe.InterventionOnline survey from April to September 2024.Main outcome measuresImportance of developing a new test on a scale of 1–10 for up to 19 ‘use cases’ (types of febrile presentations in specific demographic groups): use case scores and ranks and differences across subgroups of respondents, with free text to capture additional suggestions; respondents’ preferences (multiple choice) regarding which aetiologies should be included in a new test.Results265 respondents from 30 European countries (out of 270 starting the survey) were included in the analysis. Top priorities included febrile immunocompromised patients and fever without a focus for both paediatric and adult use cases, and 1–3 months old febrile infants. Rankings were similar across clinician subgroups despite some differences in average scores. 92% (243/263), 95% CI 89% to 95%, of respondents would find a ‘generic’ test for bacterial aetiology useful, even if it does not differentiate between Gram-positive and Gram-negative aetiologies. 54% (63/116), 95% CI 45% to 63%, of respondents would find a ‘generic’ test for inflammatory aetiology useful when seeking to diagnose children for whom Kawasaki’s disease (KD) is on the differential, even in the absence of any KD-specific test, 83% (96/116), 95% CI 75% to 89%, would find such a ‘generic’ test useful if they could use it alongside a KD test when desired.ConclusionClinicians prioritise the most vulnerable patients (because of age or comorbidities) and unclear presentations (fever without a focus) for the development of a new fever diagnostic test. Even relatively simple (eg, bacterial, inflammatory) tests could provide added value to most clinicians.

The microbiome plays a key role in the health of the human body. Interest often lies in finding features of the microbiome, alongside other covariates, which are associated with a phenotype of interest. One important property of microbiome data, which is often overlooked, is its compositionality as it can only provide information about the relative abundance of its constituting components. Typically, these proportions vary by several orders of magnitude in datasets of high dimensions. To address these challenges we develop a Bayesian hierarchical linear log-contrast model which is estimated by mean field Monte-Carlo co-ordinate ascent variational inference (CAVI-MC) and easily scales to high dimensional data. We use novel priors which account for the large differences in scale and constrained parameter space associated with the compositional covariates. A reversible jump Monte Carlo Markov chain guided by the data through univariate approximations of the variational posterior probability of inclusion, with proposal parameters informed by approximating variational densities via auxiliary parameters, is used to estimate intractable marginal expectations. We demonstrate that our proposed Bayesian method performs favourably against existing frequentist state of the art compositional data analysis methods. We then apply the CAVI-MC to the analysis of real data exploring the relationship of the gut microbiome to body mass index.

Whitening, or sphering, is a common preprocessing step in statistical analysis to transform random variables to orthogonality. However, due to rotational freedom there are infinitely many possible whitening procedures. Consequently, there is a diverse range of sphering methods in use, for example, based on principal component analysis (PCA), Cholesky matrix decomposition, and zero-phase component analysis (ZCA), among others. Here, we provide an overview of the underlying theory and discuss five natural whitening procedures. Subsequently, we demonstrate that investigating the cross-covariance and the cross-correlation matrix between sphered and original variables allows to break the rotational invariance and to identify optimal whitening transformations. As a result we recommend two particular approaches: ZCA-cor whitening to produce sphered variables that are maximally similar to the original variables, and PCA-cor whitening to obtain sphered variables that maximally compress the original variables.

The clinical, radiological and pathological similarities between sarcoidosis and tuberculosis can make disease differentiation challenging. A complicating factor is that some cases of sarcoidosis may be initiated by mycobacteria. We hypothesised that immunological profiling might provide insight into a possible relationship between the diseases or allow us to distinguish between them. We analysed bronchoalveolar lavage (BAL) fluid in sarcoidosis (n = 18), tuberculosis (n = 12) and healthy volunteers (n = 16). We further investigated serum samples in the same groups; sarcoidosis (n = 40), tuberculosis (n = 15) and healthy volunteers (n = 40). A cross-sectional analysis of multiple cytokine profiles was performed and data used to discriminate between samples. We found that BAL profiles were indistinguishable between both diseases and significantly different from healthy volunteers. In sera, tuberculosis patients had significantly lower levels of the Th2 cytokine interleukin-4 (IL-4) than those with sarcoidosis (p = 0.004). Additional serum differences allowed us to create a linear regression model for disease differentiation (within-sample accuracy 91%, cross-validation accuracy 73%). These data warrant replication in independent cohorts to further develop and validate a serum cytokine signature that may be able to distinguish sarcoidosis from tuberculosis. Systemic Th2 cytokine differences between sarcoidosis and tuberculosis may also underly different disease outcomes to similar respiratory stimuli.

IntroductionUnderstanding causal risk factors that contribute to the development of multimorbidity is essential for designing and targeting effective preventive strategies. Despite a large body of research in this field, there has been little critical discussion about the appropriateness of the various analytical approaches used. This proposed scoping review aims to summarise and appraise the analytical approaches used in the published literature that evaluated risk factors of multimorbidity and to provide guidance for researchers conducting analyses in this field.Methods and analysisWe will systematically search three electronic databases—Embase, Global Health and MEDLINE, as well as the reference lists of identified relevant review articles, from inception to September 2024. We will screen titles and abstracts using the artificial intelligence-aided software ASReview, followed by screening for eligible articles in full text and extracting data. We will then categorise the analytical approaches used across studies, provide a comprehensive overview of the methodology and discuss the potential strengths and limitations of each analytical approach.Ethics and disseminationWe will undertake a secondary analysis of published literature; therefore, ethical approval is not required. The results will be disseminated through an open-access, peer-reviewed publication. This systematic scoping review will serve as a guide for researchers in selecting analytical approaches for aetiological multimorbidity research, thereby improving the quality and comparability of research in this field.

ObjectivesAn effectiveness and cost-effectiveness analyses of two-staged community sports interventions; taster sports sessions compared with portfolio of community sport sessions.DesignQuasi-experiment using an interrupted time series design.SettingCommunity sports projects delivered by eight lead partners in London Borough of Hounslow, UK.ParticipantsInactive people aged 14 plus years (n=246) were recruited between May 2013 and February 2014.InterventionsCommunity sports interventions delivered in two stages, 6-week programme of taster sport sessions (stage 1) and 6-week programme of portfolio of community sporting sessions delivered by trained coaches (stage 2).Outcome measures(a) Change in days with ≥30 min of self-reported vigorous intensity physical activity (PA), moderate intensity PA, walking and sport; and (b) change in subjective well-being and EQ5D5L quality-adjusted life-years (QALYs).MethodsInterrupted time series analysis evaluated the effectiveness of the two-staged sports programmes. Cost-effectiveness analysis compares stage 2 with stage 1 from a provider’s perspective, reporting outcomes of incremental cost per QALY (2015/2016 price year). Uncertainty was assessed using deterministic and probabilistic sensitivity analyses.ResultsCompared with stage 1, counterfactual change at 21 days in PA was lower for vigorous (log odds: −0.52; 95% CI −1 to –0.03), moderate PA (−0.50; 95% CI 0.94 to 0.05) and sport(−0.56; 95% CI −1.02 to –0.10). Stage 2 increased walking (0.28; 95% CI 0.3 to 0.52). Effect overtime was similar. Counterfactual change at 21 days in well-being was positive particularly for ‘happiness’ (0.29; 95% CI 0.06 to 0.51). Stage 2 was more expensive (£101 per participant) but increased QALYs (0.001; 95% CI −0.034 to 0.036). Cost per QALY for stage 2 was £50 000 and has 29% chance of being cost-effective (£30 000 threshold).ConclusionCommunity-based sport interventions could increase PA among inactive people. Less intensive sports sessions may be more effective and cost-effective.

Background : A major cause of disability in secondary progressive multiple sclerosis (SPMS) is progressive brain atrophy, whose pathogenesis is not fully understood. The objective of this study was to identify protein biomarkers of brain atrophy in SPMS. Methods : We used surface-enhanced laser desorption-ionization time-of-flight mass spectrometry to carry out an unbiased search for serum proteins whose concentration correlated with the rate of brain atrophy, measured by serial MRI scans over a 2-year period in a well-characterized cohort of 140 patients with SPMS.  Protein species were identified by liquid chromatography-electrospray ionization tandem mass spectrometry. Results : There was a significant (p<0.004) correlation between the rate of brain atrophy and a rise in the concentration of proteins at 15.1 kDa and 15.9 kDa in the serum.  Tandem mass spectrometry identified these proteins as alpha-haemoglobin and beta-haemoglobin, respectively.  The abnormal concentration of free serum haemoglobin was confirmed by ELISA (p<0.001).  The serum lactate dehydrogenase activity was also highly significantly raised (p<10 -12 ) in patients with secondary progressive multiple sclerosis.  Conclusions : An underlying low-grade chronic intravascular haemolysis is a potential source of the iron whose deposition along blood vessels in multiple sclerosis plaques contributes to the neurodegeneration and consequent brain atrophy seen in progressive disease. Chelators of free serum iron will be ineffective in preventing this neurodegeneration, because the iron (Fe 2+ ) is chelated by haemoglobin.

Background : A major cause of disability in secondary progressive multiple sclerosis (SPMS) is progressive brain atrophy, whose pathogenesis is not fully understood. The objective of this study was to identify protein biomarkers of brain atrophy in SPMS. Methods : We used surface-enhanced laser desorption-ionization time-of-flight mass spectrometry to carry out an unbiased search for serum proteins whose concentration correlated with the rate of brain atrophy, measured by serial MRI scans over a 2-year period in a well-characterized cohort of 140 patients with SPMS.  Protein species were identified by liquid chromatography-electrospray ionization tandem mass spectrometry. Results : There was a significant (p<0.004) correlation between the rate of brain atrophy and a rise in the concentration of proteins at 15.1 kDa and 15.9 kDa in the serum.  Tandem mass spectrometry identified these proteins as alpha-haemoglobin and beta-haemoglobin, respectively.  The abnormal concentration of free serum haemoglobin was confirmed by ELISA (p<0.001).  The serum lactate dehydrogenase activity was also highly significantly raised (p<10 -12 ) in patients with secondary progressive multiple sclerosis.  Conclusions : The results are consistent with the following hypothesis. In progressive multiple sclerosis, low-grade chronic intravascular haemolysis releases haemoglobin into the serum; the haemoglobin is subsequently translocated into the central nervous system (CNS) across the damaged blood-brain barrier.  In the CNS, the haemoglobin and its breakdown products, including haem and iron, contribute to the neurodegeneration and consequent brain atrophy seen in progressive disease. We postulate that haemoglobin is a source of the iron whose deposition along blood vessels in multiple sclerosis plaques is associated with neurodegeneration.  If so, then chelators of haemoglobin, rather than chelators of free serum iron, may be effective in preventing this neurodegeneration.

We present a Bayesian hierarchical model for detecting differentially expressing genes that includes simultaneous estimation of array effects, and show how to use the output for choosing lists of genes for further investigation. We give empirical evidence that expression‐level dependent array effects are needed, and explore different nonlinear functions as part of our model‐based approach to normalization. The model includes gene‐specific variances but imposes some necessary shrinkage through a hierarchical structure. Model criticism via posterior predictive checks is discussed. Modeling the array effects (normalization) simultaneously with differential expression gives fewer false positive results. To choose a list of genes, we propose to combine various criteria (for instance, fold change and overall expression) into a single indicator variable for each gene. The posterior distribution of these variables is used to pick the list of genes, thereby taking into account uncertainty in parameter estimates. In an application to mouse knockout data, Gene Ontology annotations over‐ and underrepresented among the genes on the chosen list are consistent with biological expectations.