Medical Research Council

BackgroundThe clinical and prognostic implications of asymptomatic tuberculosis remain poorly understood.MethodsWe conducted a multicentre prospective cohort study to evaluate the association between asymptomatic tuberculosis (TB) and treatment outcomes. Individuals with rifampicin-susceptible pulmonary TB were enrolled from the Cohort Study of Pulmonary Tuberculosis. Asymptomatic TB was defined as the absence of any TB-related symptoms at diagnosis. The primary outcome was a favourable outcome, defined as treatment success without recurrence. Multivariable logistic regression models were used to assess associations between asymptomatic TB and favourable outcomes. The Cox proportional hazards model was applied to evaluate effect of asymptomatic TB on failure to complete treatment within 1 year. Stratified analyses by symptom status and mode of detection were performed to examine stratum-specific effects.ResultsOf 1071 individuals with pulmonary TB, 32.7% were asymptomatic. Compared to symptomatic patients, asymptomatic individuals were younger, less likely to be underweight and more often diagnosed through population health screening rather than clinical presentation or opportunistic testing. Asymptomatic TB was associated with higher likelihood of favourable outcome in multivariable models (adjusted odds ratio (aOR) 1.50, 95% CI 1.04–2.20) and a reduced risk of failing to complete treatment within one year in survival analyses (adjusted hazard ratio 0.66, 95% CI 0.45–0.95). Asymptomatic TB detected through health screening had the most favourable outcomes (aOR 2.41, 95% CI 1.34–4.66).ConclusionAsymptomatic TB was significantly associated with treatment success without recurrence and particularly in patients identified through health screening. Our results support symptom-agnostic screening in TB control programmes.

Abstract Non-invasive measurement of neurotransmitter-specific glucose metabolism in the human brain remains a major challenge, limiting mechanistic insight into excitatory–inhibitory imbalance across neurological and psychiatric disorders. Current methods lack the ability to selectively and precisely resolve neurotransmitter-specific metabolic pathways, particularly GABA, while remaining compatible with clinically feasible acquisitions. Here, we introduce a clinically compatible approach which enables targeted and non-invasive detection of glutamate, GABA, and lactate metabolism in the human brain. Called proton-observed proton-edited ¹³C-magnetic resonance spectroscopy (POPE-¹³C-MRS), the method uses an exogenous ¹³C-glucose probe combined with standard proton radiofrequency hardware and widely available MR pulse sequences. We use a cross-species validation framework to first calibrate POPE-¹³C-MRS in mice and then demonstrate its feasibility in humans at ultra-high field. While in vivo GABA labelling has been previously reported, POPE-¹³C-MRS provides, for the first time, robust access to GABAergic metabolism using standard MRI hardware, feasible within clinical constraints, including applicability to deep brain regions. By refining existing indirect ¹H-¹³C-MRS strategies and enabling targeted probing of excitatory and inhibitory metabolic pathways, POPE-¹³C-MRS opens new opportunities for studying neurometabolic coupling and excitatory–inhibitory balance in vivo , with broad implications for translational and clinical neuroscience.

173 Background: Sarcopenia is common in advanced prostate cancer and worsened by androgen deprivation therapy (ADT). Routine staging CT scans provide an opportunity to screen for sarcopenia. The STAMPEDE trials have demonstrated treatment intensification beyond ADT improve cancer outcomes; however, benefits are heterogeneous. This study investigates CT-derived skeletal muscle index (SMI), a validated marker of total muscle mass, as a prognostic and predictive biomarker in the STAMPEDE docetaxel and ARPI trials. Methods: Men with newly diagnosed non-metastatic high-risk (M0) and metastatic (M1) hormone-sensitive prostate cancer (HSPC) with available staging CT imaging in the STAMPEDE docetaxel or ARPI trials were included. These trials compared standard of care (SOC) with addition of docetaxel ± zoledronic acid (ZA) or abiraterone acetate with prednisolone (AAP) ± enzalutamide (Enz). SMI (cm²/m²) was calculated as mean muscle area divided by height squared. Outcomes were overall survival (OS) in M1 patients and metastasis-free survival (MFS) in M0. Prognostic utility of SMI was evaluated in Kaplan–Meier analyses and Cox regression models. Predictive value was assessed by comparing hazard ratios of the treatment effect in Cox models for high vs low SMI cohorts. Likelihood ratio tests were used to identify treatment-SMI interactions. Continuous predictive effects were examined using multivariable fractional polynomial interaction (MFPI) models. Results: 2,267 patients (1,578 M1, 689 M0) met inclusion criteria. The median SMI was 47.2cm²/m² (IQR 42-52) in M1 patients and 48.2cm²/m² (IQR 44-54) in M0. SMI and CHAARTED burden were identified as independent prognostic biomarkers in M1 patients, with a 10 cm²/m² increase in SMI associated with a 15% reduction in risk of death (HR 0.85, 95% CI 0.79–0.92, p<0.001). SMI was not an independent prognostic biomarker in M0 patients. SMI was identified as an independent predictive biomarker of MFS benefit upon addition of AAP±Enz in M0 patients; high SMI cohorts had greater MFS benefit from addition of AAP±Enz compared with the low SMI cohort (HR 0.44 [0.3-0.66] vs 0.59 [0.38-0.91]). Likelihood ratio tests confirmed that adding a treatment-SMI interaction improved prediction of treatment benefit (χ²: 4.67, p=0.03). MFPI modelling demonstrated a significant MFS benefit with addition of AAP±Enz in M0 patients (χ²=9.95, p=0.006) with increasing SMI, however this benefit was observed in the range of 41-63 cm 2 /m 2 only. 20% of our cohort lay outside this range (13% lower, 7% higher) and did not observe MFS benefit from addition of AAP±Enz to SOC. Conclusions: SMI is an independent prognostic biomarker in trial patients with de novo metastatic HSPC. SMI is predictive of improved MFS with addition of AAP±Enz to SOC in M0 disease. Further research is required to validate our findings in real-world cohorts.

Advances in backbone modifications are driving the development of nucleic acid therapeutics, yet there is still a need to establish compounds that avoid the potential dose-limiting toxicity of phosphorothioate internucleosidic linkages, particularly outside the central nervous system. We have developed a novel 7',5'-α-bc-DNA (abcDNA) scaffold, and here we benchmark the biophysical and invivo gene knockdown efficacy of gapmer antisense oligonucleotides (ASOs) containing abcDNA nucleotides. Melting curve analyses show gapmers with abcDNA bases in both wings maintain a good affinity for complementary RNA and demonstrate stable mismatch discrimination, in addition to a high degree of serum biostability. To assess invivo on-target activity and tolerability, mice were dosed systemically with abcDNA ASOs targeting Malat-1, with or without conjugation to palmitic acid. Multiple tissues were assessed for on-target knockdown efficiency by RT-PCR and in situ hybridization alongside biodistribution analysis by immunohistochemistry. abcDNA ASOs were well tolerated and performed at a comparable level to an equivalent 2'-O-methoxyethylribose gapmer. We also present the first invivo pharmacokinetic data generated using an enzyme-free nucleic acid nanorobotics method. Together, these data support the utility of abcDNA as a valuable addition to ASO technology that maintains a natural phosphodiester backbone, providing a combination of preferred biostability and on-target affinity with acceptable invivo tolerability.

Abstract Highlights We introduce the TMS-RAT, a reporting (assessment) tool for TMS studies Developed within a community-informed, iterative process rating 333 TMS studies Empirically evaluated for usability, inter-rater, and test-retest reliability A validated subset enables reliable retrospective assessment of reporting The modular structure enables use across a wide range of TMS study designs Background A standardised tool for comprehensive reporting can improve transparency, support consistent documentation, and enable comparison across transcranial magnetic stimulation (TMS) studies. The most used reporting checklist lacks clear definitions of full reporting and was not initially evaluated for usability or inter-rater reliability. A scoping review of studies using this checklist shows that its items are reported only 50% of the time, suggesting that method descriptions are often incomplete. Methods We developed the TMS Reporting Assessment Tool (TMS-RAT), a comprehensive reporting framework that provides clear definitions and examples for its items, covering a wide range of TMS protocols. We tested the usability and reliability of the TMS-RAT by rating all studies published between 1991 and 2025 using afferent conditioning (n = 333), a protocol encompassing many reporting categories identified during tool development. Seventeen independent raters contributed across three development phases, a validation phase, and a retest phase, with naïve raters introduced in each phase. Iterative refinements of the tool were informed by inter-rater reliability, qualitative rater feedback, and consultation with external TMS experts. Results We present two versions of the tool: the 72-item TMS-RAT v1.0, designed to guide comprehensive reporting, and the TMS-RAT v1.1, a subset of 50 items with the highest inter-rater (overall AC1 = 0.78, range = [0.60–0.99]) and test-retest reliability (overall AC1 = 0.82, range = [0.65–1.0]), intended for retrospective evaluation of reporting in systematic reviews, meta-analyses. Conclusion The TMS-RAT is a comprehensive, reliable tool that seeks to improve transparency and reproducibility in TMS research.

HDAC inhibition shows promise in cancer treatment but pan-HDAC inhibitors cause gastrointestinal issues in 48% of patients. Understanding HDAC activation mechanisms is crucial to treating diverse diseases beyond cancer. Our study reveals that inositol polyphosphate multikinase (IPMK) and inositol hexakisphosphate (InsP6 or phytic acid), enriched in vegan diets, play essential roles in activating the HDAC3 epigenetic axis and maintaining intestinal barrier integrity. IPMK binds to HDAC3 and drives InsP6 synthesis, which selectively activates HDAC3 at a 10 nM concentration by recruiting the DAD domain of its corepressor protein. IPMK deletion diminishes HDAC3 activation, leading to histone hyperacetylation and MMP gene transcription that compromise intestinal barrier integrity. InsP6 treatment is sufficient to rescue these effects. In inflammatory bowel disease, diminished IPMK levels exacerbate intestinal permeability, while oral InsP6 treatment mitigates leaky gut effects by restoring the HDAC3 epigenetic axis, highlighting the clinical significance of the IPMK-HDAC3 pathway and the therapeutic potential of phytic acid.

Cardiometabolic risk factors and conditions are the leading contributors to morbidity and mortality, yet quantifying their causal effects on socioeconomic outcomes using observational data is challenging due to endogeneity. Using genetic variants as instrumental variables, Mendelian randomization (MR) offers a unique approach to strengthen causal inference in this context and has also gained popularity in health economic literature. This study aimed to: i) map the current landscape of MR studies evaluating the impact of cardiometabolic exposures on healthcare and socioeconomic outcomes; ii) describe how core MR assumptions were tested and reported; iii) summarize how additional assumptions underlying causal interpretation were discussed. We searched MEDLINE and EMBASE for studies applying MR to examine the impact of cardiometabolic risk factors or conditions (e.g., obesity, blood pressure, cholesterol, coronary artery disease, type 2 diabetes) on socioeconomic and healthcare outcomes (e.g., education, income, occupational status, social deprivation, healthcare use and costs, health-related quality of life). Study characteristics, MR design choices, and reporting of assumption testing and causal interpretation were extracted and narratively summarized. Sixteen studies were included, covering 79 exposure-outcome pairs. Most studies examined the effects of body mass index on employment or healthcare costs. Only one study assessed home ownership, social income transfers, resource utilization, and quality-adjusted life years as outcomes, respectively. Effects of childhood cardiometabolic exposures were rarely examined beyond educational outcomes. UK Biobank was the predominant data source. None of the core MR assumptions were mentioned across all studies. While weak instrument bias was frequently tested, less than 40% of studies assessed associations between instruments and observable confounders as falsification tests. Only few studies discussed monotonicity or homogeneity assumptions. Although MR is a promising identification strategy for assessing causal effects of cardiometabolic risk on healthcare and socioeconomic outcomes, reporting practices for assumption testing and causal interpretation vary widely. This review highlights opportunities to strengthen transparency and coherence in future MR applications. With increasing data availability and clearer methodological guidance, MR could complement conventional observational approaches in supporting policy decisions.

Identifying and tackling inequities in long-lasting insecticidal bed net (LLIN) coverage and usage is key in reducing malaria burden. This baseline study, prior to an LLIN trial, describes factors associated with LLIN usage and malaria infection prevalence, two years after a mass distribution in Côte d’Ivoire. In July 2023, cross-sectional data were obtained from randomly selected individuals of all ages in each of 33 study clusters, capturing information on socio-economic status, LLIN ownership, usage and results of malaria rapid diagnostic tests. Random-effects multivariable logistic regression analyses were used to assess factors associated with LLIN usage and malaria infection. A total of 1,672 participants were recruited. LLIN ownership and access were 66.3% (95% CI: 59.4-73.2) and 27.8% (95%CI: 22.6-33.1), respectively. LLIN usage was 50.0% (adjusted odds ratio [aOR]: 0.50; 95%CI: 0.34-0.75), 44.3% (aOR: 0.41; 95%CI: 0.26-0.62) and 56.9% (aOR: 0.72; 95%CI: 0.53-0.98) in participants aged, 5–9, 10–14 and ≥15years, respectively, compared to 63.2% in under-fives. LLIN usage was lowest in females aged 10–14 years (41.0%) and highest in under-five males (68.9%). The odds of LLIN usage were lowest in the second (aOR: 0.63; 95%CI: 0.44-0.90) and middle wealth quintile (aOR: 0.69; 95%CI: 0.47-1.00) compared to the poorest quintile (58.2%). Malaria infection prevalence was 41.1% (95%CI: 37.2-45.0). When compared to under-fives with a malaria prevalence of 61.3%, 5–9, 10–14 and ≥15years had 75.0% (aOR: 1.86; 95%CI: 1.24-2.78), 67.0% (aOR:1.18; 95%CI: 0.78-1.80) and 23.0% (aOR: 0.17; 95%CI: 0.12-0.23) malaria prevalence, respectively. Males (75.8%) and females (74.1%) aged 5–9 years had the highest malaria risk. LLIN users had an infection prevalence of 38.5% (aOR: 0.74; 95%CI: 0.58-0.94), compared to 44.3% in non-users. School-based net distributions, malaria education, routine screening and treatment and chemoprophylaxis in schools alongside community sensitization campaigns are recommended to improve protection and reduce infection risk among school-aged children.

This study aimed to investigate the decline over time in the proportion of total daily insulin delivered as boluses in newly diagnosed youth with type 1 diabetes using a hybrid closed-loop system. A secondary analysis was conducted using data from the CLOuD study, an open-label, multicenter, randomized, parallel hybrid closed-loop trial to investigate bolus patterns in youth with newly diagnosed type 1 diabetes. Over the 48-month trial period, the proportion of total daily insulin delivered as carbohydrate-related boluses decreased from 58% to 34%. There was a decreasing trend in the median (interquartile range) amount of carbohydrates entered per day from 236 (204, 253) g to 184 (127, 232) g, and the number of carbohydrate-related boluses per day from 5.5 (4.6, 6.5) to 3.7 (2.9, 5.2) over the 48 months. Mean ± SD daily carbohydrate-related bolus insulin increased from 15.1 ± 6.6 to 22.0 ± 9.0 units/d, and the amount of insulin delivered per 10 g of carbohydrate more than doubled from 0.6 (0.5, 0.8) units to 1.3 (0.9, 1.5) units. The postprandial change in glucose (measured as the difference between peak glucose 30 to 180 minutes post carbohydrate-related bolus and glucose on carbohydrate-related bolus delivery) changed from 49 (45, 54) to 59 (53, 66) mg/dL. The decline in the proportion of total daily insulin delivered for as bolus is likely attributable to a combination of missed boluses and under-bolusing, while the closed-loop algorithm compensates for the missed or insufficient carbohydrate-related insulin delivery by increasing basal insulin delivery.

Abstract Despite nearly 20% of the global population experiencing hearing loss, there remains limited insight into the underlying subtypes of its most prevalent cause, sensorineural hearing loss (SNHL). This understanding is crucial for effective therapeutic and preventative strategies. A recent study using a Gaussian Mixture Model (GMM) identified 10 distinct SNHL phenotypes in a large US cohort, highlighting the potential of unsupervised machine learning (UML) to provide a data-driven solution to this task. Rigorous validation of these models is essential; however, it is limited due to several factors, including the absence of ground truth labels for model evaluation, restricted data access, and the lack of a standardized reporting framework for comparing results across clustering studies. Here, we apply a GMM to a UK database of 109,854 audiograms, revealing 9 phenotypes, partly overlapping with prior findings. Notably, our study cohort is characterized by advanced age, a higher proportion of female participants, and more severe hearing impairments. We observed instability in the GMM model when subjected to variations in the original dataset. To enhance practices, we propose a Clustering Replicability Framework, ensuring robustness in UML driven health research for safe clinical translation.