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Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82–83) for Cluster 1, 77 years (IQR 75–77) for Cluster 2, 69 years (IQR 66–71) for Cluster 3 and 57 years (IQR 54–59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.

The use of psychedelic substances at sub-sensorium ‘ microdoses’, has gained popular academic interest for reported positive effects on wellness and cognition. The present study describes microdosing practices, motivations and mental health among a sample of self-selected microdosers ( n  = 4050) and non-microdosers ( n  = 4653) via a mobile application. Psilocybin was the most commonly used microdose substances in our sample (85%) and we identified diverse microdose practices with regard to dosage, frequency, and the practice of stacking which involves combining psilocybin with non-psychedelic substances such as Lion’s Mane mushrooms, chocolate, and niacin. Microdosers were generally similar to non-microdosing controls with regard to demographics, but were more likely to report a history of mental health concerns. Among individuals reporting mental health concerns, microdosers exhibited lower levels of depression, anxiety, and stress across gender. Health and wellness-related motives were the most prominent motives across microdosers in general, and were more prominent among females and among individuals who reported mental health concerns. Our results indicate health and wellness motives and perceived mental health benefits among microdosers, and highlight the need for further research into the mental health consequences of microdosing including studies with rigorous longitudinal designs.

Noncommunicable diseases (NCDs) are a leading cause of premature death globally and have common preventable risk factors. In Norway, the NCDNOR-project aims at establishing new knowledge in the prevention of NCDs by combining information from national registries with data from population-based health studies. In the present study, we aimed to harmonize data on key NCD risk factors from the health studies, describe clustering of risk factors using intersection diagrams and latent class analysis, and identify long-term risk factor trajectories using latent class mixed models. The harmonized study sample consisted of 808,732 individuals (1,197,158 participations). Two-thirds were exposed to ≥ 1 NCD risk factor (daily smoking, physical inactivity, obesity, hypertension, hypercholesterolaemia or hypertriglyceridaemia). In individuals exposed to ≥ 2 risk factors (24%), we identified five distinct clusters, all characterized by fewer years of education and lower income compared to individuals exposed to < 2 risk factors. We identified distinct long-term trajectories of smoking intensity, leisure-time physical activity, body mass index, blood pressure, and blood lipids. Individuals in the trajectories tended to differ across sex, education, and body mass index. This provides important insights into the mechanisms by which NCD risk factors can occur and may help the development of interventions aimed at preventing NCDs.

The worldwide pooled prevalence of psychiatric disorders in children is 13.4%. Studying the prevalence of childhood psychiatric disorders across radically different economic systems and social structures could indicate universal factors leading to their development. The prevalence of childhood psychiatric disorders in a mixed-subsistence foraging society has not been studied. The Strengths and Difficulties Questionnaire and the Development and Well-Being Assessment were used to compare the prevalence of behavioural symptoms and psychiatric disorders in Hadza children aged 5–16 years (n = 113) to a nationally representative sample from England (n = 18,029) using a cross-sectional study design. Emotional problems, conduct problems and hyperactivity were lower in the Hadza children. Prosocial behaviour and peer problems were higher in Hadza children. 3.6% of Hadza children met the criteria for a psychiatric disorder compared to 11.8% of English children. All psychiatric disorders in Hadza children were co-morbid with autism spectrum disorder. No child from the Hadza group met the criteria for an emotional, behaviour or eating disorder. Further work should study the factors which lead to the different prevalence of psychiatric disorders in Hadza children.

Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering (IF) are largely implemented for representing a signal as superposition of simpler well-behaved components called Intrinsic Mode Functions (IMFs). Although they are more suitable than traditional methods for the analysis of nonlinear and nonstationary signals, they could be easily misused if their known limitations, together with the assumptions they rely on, are not carefully considered. In this work, we examine the main pitfalls and provide caveats for the proper use of the EMD- and IF-based algorithms. Specifically, we address the problems related to boundary errors, to the presence of spikes or jumps in the signal and to the decomposition of highly-stochastic signals. The consequences of an improper usage of these techniques are discussed and clarified also by analysing real data and performing numerical simulations. Finally, we provide the reader with the best practices to maximize the quality and meaningfulness of the decomposition produced by these techniques. In particular, a technique for the extension of signal to reduce the boundary effects is proposed; a careful handling of spikes and jumps in the signal is suggested; the concept of multi-scale statistical analysis is presented to treat highly stochastic signals.

Climate change is already affecting vector-borne disease transmission and spread, and its impacts are likely to worsen. In the face of ongoing climate change, we must intensify efforts to prevent and control vector-borne diseases.

Non-invasive brain stimulation (NIBS), including transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS), is a potentially effective treatment strategy for a number of mental conditions. However, no quantitative evidence synthesis of randomized controlled trials (RCTs) of TMS or tDCS using the same criteria including several mental conditions is available. Based on 208 RCTs identified in a systematic review, we conducted a series of random effects meta-analyses to assess the efficacy of NIBS, compared to sham, for core symptoms and cognitive functioning within a broad range of mental conditions. Outcomes included changes in core symptom severity and cognitive functioning from pre- to post-treatment. We found significant positive effects for several outcomes without significant heterogeneity including TMS for symptoms of generalized anxiety disorder (SMD = −1.8 (95% CI: −2.6 to −1), and tDCS for symptoms of substance use disorder (−0.73, −1.00 to −0.46). There was also significant effects for TMS in obsessive-compulsive disorder (−0.66, −0.91 to −0.41) and unipolar depression symptoms (−0.60, −0.78 to −0.42) but with significant heterogeneity. However, subgroup analyses based on stimulation site and number of treatment sessions revealed evidence of positive effects, without significant heterogeneity, for specific TMS stimulation protocols. For neurocognitive outcomes, there was only significant evidence, without significant heterogeneity, for tDCS for improving attention (−0.3, −0.55 to −0.05) and working memory (−0.38, −0.74 to −0.03) in individuals with schizophrenia. We concluded that TMS and tDCS can benefit individuals with a variety of mental conditions, significantly improving clinical dimensions, including cognitive deficits in schizophrenia which are poorly responsive to pharmacotherapy.

Accurate and reproducible phenotyping is essential for large-scale biomedical research. However, developing robust phenotype definitions in biobanks is challenging due to diverse data sources and varying medical ontologies. As a result, the current phenotyping landscape is fragmented. We developed a computational framework to harmonize electronic health record (EHR) data, participant questionnaires, and clinical registry information, defining 313 disease phenotypes among 502,356 UK Biobank (UKB) participants. Our method integrated four medical ontologies (Read v2, CTV3, ICD-10, OPCS-4) across seven data sources, including primary care, hospital admissions, cancer and death registries, and self-reported data on diseases, procedures, and medication. Phenotypes underwent multi-layered validation, assessing data source concordance, age-sex incidence and prevalence patterns, external comparison to a representative UK EHR dataset, modifiable risk factor associations, and genetic correlations with external genome-wide association studies (GWAS). Results indicated consistent disease distributions by age and sex, high correlation with non-selected general population data prevalence estimates, confirmed risk factor associations, and significant genetic correlations with external GWAS for nine of ten evaluated diseases. Our approach establishes comprehensive disease validation profiles, improving phenotype generalizability despite inherent UKB demographic biases. The modular, reproducible framework can be extended to additional diseases and populations, supporting federated analyses across diverse biobanks, and facilitating research in underrepresented populations.

Immunotherapy has emerged as a major therapeutic modality in oncology. Currently, however, the majority of patients with cancer do not derive benefit from these treatments. Vascular abnormalities are a hallmark of most solid tumours and facilitate immune evasion. These abnormalities stem from elevated levels of proangiogenic factors, such as VEGF and angiopoietin 2 (ANG2); judicious use of drugs targeting these molecules can improve therapeutic responsiveness, partially owing to normalization of the abnormal tumour vasculature that can, in turn, increase the infiltration of immune effector cells into tumours and convert the intrinsically immunosuppressive tumour microenvironment (TME) to an immunosupportive one. Immunotherapy relies on the accumulation and activity of immune effector cells within the TME, and immune responses and vascular normalization seem to be reciprocally regulated. Thus, combining antiangiogenic therapies and immunotherapies might increase the effectiveness of immunotherapy and diminish the risk of immune-related adverse effects. In this Perspective, we outline the roles of VEGF and ANG2 in tumour immune evasion and progression, and discuss the evidence indicating that antiangiogenic agents can normalize the TME. We also suggest ways that antiangiogenic agents can be combined with immune-checkpoint inhibitors to potentially improve patient outcomes, and highlight avenues of future research.

Tebotelimab, a bispecific PD-1×LAG-3 DART molecule that blocks both PD-1 and LAG-3, was investigated for clinical safety and activity in a phase 1 dose-escalation and cohort-expansion clinical trial in patients with solid tumors or hematologic malignancies and disease progression on previous treatment. Primary endpoints were safety and maximum tolerated dose of tebotelimab when administered as a single agent ( n  = 269) or in combination with the anti-HER2 antibody margetuximab ( n  = 84). Secondary endpoints included anti-tumor activity. In patients with advanced cancer treated with tebotelimab monotherapy, 68% (184/269) experienced treatment-related adverse events (TRAEs; 22% were grade ≥3). No maximum tolerated dose was defined; the recommended phase 2 dose (RP2D) was 600 mg once every 2 weeks. There were tumor decreases in 34% (59/172) of response-evaluable patients in the dose-escalation cohorts, with objective responses in multiple solid tumor types, including PD-1-refractory disease, and in LAG-3 + non-Hodgkin lymphomas, including CAR-T refractory disease. To enhance potential anti-tumor responses, we tested margetuximab plus tebotelimab. In patients with HER2 + tumors treated with tebotelimab plus margetuximab, 74% (62/84) had TRAEs (17% were grade ≥3). The RP2D was 600 mg once every 3 weeks. The confirmed objective response rate in these patients was 19% (14/72), including responses in patients typically not responsive to anti-HER2/anti-PD-1 combination therapy. ClinicalTrials.gov identifier: NCT03219268 . The bispecific molecule tebotelimab, which blocks both PD-1 and LAG-3, is well tolerated as a monotherapy and in combination with the anti-HER-2 antibody margetuximab and elicits encouraging clinical activity in solid tumors with high LAG-3 levels and/or expression of IFN-γ-regulated genes.