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Introduction Meta-analyses implicate immune dysfunction in depression confirming increased levels of circulating immune proteins (e.g., cytokines) in depression cases compared to controls. White blood cells (WBC) both produce and are influenced by cytokines, and play key roles in orchestrating innate and adaptive immune responses, but their role in depression remains unclear. Therefore, a systematic review of studies of various WBC subsets in depression is required for a greater understanding of the nature of immune dysfunction in this illness. Methods We searched PubMed and PsycINFO databases (inception to 5 th April 2022) and conducted a systematic review and meta-analysis of identified studies comparing absolute count and/or relative percentage of flow cytometry-derived WBC subsets between depression cases and controls. Selected studies were quality assessed. Random-effect meta-analysis was performed. Results Thirty-three studies were included and 27 studies ( n  = 2277) were meta-analysed. We report an increase in mean absolute counts of WBC (seven studies; standardised mean difference [SMD] = 1.07; 95% CI, 0.61–1.53; P  < 0.01; I 2  = 64%), granulocytes (two studies; SMD = 2.07; 95% CI, 1.45–2.68; P  < 0.01; I 2  = 0%), neutrophils (four studies; SMD = 0.91; 95% CI, 0.23–1.58; P  < 0.01; I 2  = 82%), monocytes (seven studies; SMD = 0.60; 95% CI, 0.19–1.01; P  < 0.01; I 2  = 66%), CD4 + helper T cells (11 studies; SMD = 0.30; 95% CI, 0.15–0.45; P  < 0.01; I 2  = 0%), natural killer cells (11 studies; SMD = 1.23; 95% CI, 0.38–2.08; P  < 0.01; I 2  = 95%), B cells (10 studies; SMD = 0.30; 95% CI, 0.03–0.57; P  = 0.03; I 2  = 56%), and activated T cells (eight studies; SMD = 0.45; 95% CI, 0.24–0.66; P  < 0.01; I 2  = 0%) in depression, compared to controls. Fewer studies reported relative percentage, indicating increased neutrophils and decreased total lymphocytes, Th1, and Th2 cells in depression. Conclusions Depression is characterised by widespread alterations in circulating myeloid and lymphoid cells, consistent with dysfunction in both innate and adaptive immunity. Immune cells could be useful biomarkers for illness subtyping and patient stratification in future immunotherapy trials of depression, along with cytokines, other biomarkers, and clinical measures.

Vitamin D deficiency has been associated with type 1 diabetes in observational studies, but evidence from randomized controlled trials (RCTs) is lacking. The aim of this study was to test whether genetically decreased vitamin D levels are causally associated with type 1 diabetes using Mendelian randomization (MR). For our two-sample MR study, we selected as instruments single nucleotide polymorphisms (SNPs) that are strongly associated with 25-hydroxyvitamin D (25OHD) levels in a large vitamin D genome-wide association study (GWAS) on 443,734 Europeans and obtained their corresponding effect estimates on type 1 diabetes risk from a large meta-analysis of 12 type 1 diabetes GWAS studies (Ntot = 24,063, 9,358 cases, and 15,705 controls). In addition to the main analysis using inverse variance weighted MR, we applied 3 additional methods to control for pleiotropy (MR-Egger, weighted median, and mode-based estimate) and compared the respective MR estimates. We also undertook sensitivity analyses excluding SNPs with potential pleiotropic effects. We identified 69 lead independent common SNPs to be genome-wide significant for 25OHD, explaining 3.1% of the variance in 25OHD levels. MR analyses suggested that a 1 standard deviation (SD) decrease in standardized natural log-transformed 25OHD (corresponding to a 29-nmol/l change in 25OHD levels in vitamin D-insufficient individuals) was not associated with an increase in type 1 diabetes risk (inverse-variance weighted (IVW) MR odds ratio (OR) = 1.09, 95% CI: 0.86 to 1.40, p = 0.48). We obtained similar results using the 3 pleiotropy robust MR methods and in sensitivity analyses excluding SNPs associated with serum lipid levels, body composition, blood traits, and type 2 diabetes. Our findings indicate that decreased vitamin D levels did not have a substantial impact on risk of type 1 diabetes in the populations studied. Study limitations include an inability to exclude the existence of smaller associations and a lack of evidence from non-European populations. Our findings suggest that 25OHD levels are unlikely to have a large effect on risk of type 1 diabetes, but larger MR studies or RCTs are needed to investigate small effects.

While artificial intelligence (AI) offers promising solutions in healthcare, it also poses a number of threats to human health and well-being via social, political, economic and security-related determinants of health. We describe three such main ways misused narrow AI serves as a threat to human health: through increasing opportunities for control and manipulation of people; enhancing and dehumanising lethal weapon capacity and by rendering human labour increasingly obsolescent. We then examine self-improving ‘artificial general intelligence’ (AGI) and how this could pose an existential threat to humanity itself. Finally, we discuss the critical need for effective regulation, including the prohibition of certain types and applications of AI, and echo calls for a moratorium on the development of self-improving AGI. We ask the medical and public health community to engage in evidence-based advocacy for safe AI, rooted in the precautionary principle.

Non-native plant pests/pathogens are a mostly overlooked threat to biodiversity. Surveillance for plant pests and pathogens is key to early detection yet is rarely undertaken in natural habitats. Current methodologies to prioritise surveillance are pest-based, there is no methodology available to help managers identify 'at risk' hosts and habitats for targeted surveillance. This study compares four host-based methods. Prioritisation of: (1) plant genera known to host the pests/pathogens most likely to establish (Host-pest); (2) habitats known to host the greatest number of pests/pathogens most likely to establish (Habitat-pest); (3) plants classed as foundation species (those that drive ecosystem functioning and support populations of dependent biodiversity) (Foundation-species); (4) habitats with low plant species diversity and hence low resilience (Habitat-resilience). Twelve habitats and 22 heathland vegetation communities in the UK were used as a case-study. The Host-pest method gave 121 plant genera to monitor across all habitats and 14 within heathlands. The Habitat-pest and Habitat-resilience methods prioritised different habitats because the Habitat-pest method uses existing lists of pests which are biased towards those of commercial importance. The Foundation-species method gave 272 species for surveillance across all habitats and 14 within heathlands. Surveillance of habitats and plants prioritised on potential ecological impact (the Foundation-species and Habitat-resilience methods) is recommended rather than known pests/pathogens (the Host-pest and Habitat-pest methods) as this avoids biases within existing lists of pests/pathogens, removes the need for the prioritisation to be regularly updated as new pests/pathogens are identified and takes account of impacts on associated biodiversity and ecosystem functions.

Heme-oxygenase 1 (HMOX1) is a critical stress response gene that catalyzes the multistep oxidation of heme. A GT(n) repeat of variable length in the promoter in has been associated with a wide range of human diseases, including infections. This paper aims to summarise and systematically review associations between the length of the HMOX1 GT(n) promoter and infectious disease in humans. A search using relevant terms was performed in PubMED and EMBASE through to 15/01/21 identifying all research that studied an association between the HMOX1 GT(n) repeat polymorphism and the incidence and/or outcome of any human infectious disease. Citations were screened for additional studies. Potential studies were screened for inclusion by two authors. Data was extracted on allele frequency, genotype, strength of association, mechanism of genotyping, and potential biases. A narrative review was performed across each type of infection. 1,533 studies were identified in the search, and one via citation screening. Sixteen studies were ultimately included, seven in malaria, three in HIV, three in sepsis, and one each in pneumonia, hepatitis C, and acute respiratory distress syndrome (ARDS). Sample sizes for nearly all studies were small (biggest study, n = 1,646). Allelic definition was different across all included studies. All studies were at some risk of bias. In malaria, three studies suggested that longer alleles were associated with reduced risk of severe malaria, particularly malaria-induced renal dysfunction, with four studies identifying a null association. In sepsis, two studies suggested an association with longer alleles and better outcomes. Despite the importance of HMOX1 in survival from infection, and the association between repeat length and gene expression, the clinical data supporting an association between repeat length and incidence and/or outcome of infection remain inconclusive.

Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as “index event”) bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.’s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.

The causes of multiple sclerosis (MS) remain unknown. Smoking has been associated with MS in observational studies and is often thought of as an environmental risk factor. We used two-sample Mendelian randomization (MR) to examine whether this association is causal using genetic variants identified in genome-wide association studies (GWASs) as associated with smoking. We assessed both smoking initiation and lifetime smoking behaviour (which captures smoking duration, heaviness, and cessation). There was very limited evidence for a meaningful effect of smoking on MS susceptibility as measured using summary statistics from the International Multiple Sclerosis Genetics Consortium (IMSGC) meta-analysis, including 14,802 cases and 26,703 controls. There was no clear evidence for an effect of smoking on the risk of developing MS (smoking initiation: odds ratio [OR] 1.03, 95% confidence interval [CI] 0.92–1.61; lifetime smoking: OR 1.10, 95% CI 0.87–1.40). These findings suggest that smoking does not have a detrimental consequence on MS susceptibility. Further work is needed to determine the causal effect of smoking on MS progression.

IL-10 is an immunomodulatory cytokine with a critical role in limiting inflammation in immune-mediated pathologies. The mechanisms leading to IL-10 expression by CD4 + T cells are being elucidated, with several cytokines implicated. We explored the effect of IL-4 on the natural phenomenon of IL-10 production by a chronically stimulated antigen-specific population of differentiated Th1 cells. In vitro , IL-4 blockade inhibited while addition of exogenous IL-4 to Th1 cultures enhanced IL-10 production. In the in vivo setting of peptide immunotherapy leading to a chronically stimulated Th1 phenotype, lack of IL-4Rα inhibited the induction of IL-10. Exploring the interplay of Th1 and Th2 cells through co-culture, Th2-derived IL-4 promoted IL-10 expression by Th1 cultures, reducing their pathogenicity in vivo . Co-culture led to upregulated c-Maf expression with no decrease in the proportion of T-bet + cells in these cultures. Addition of IL-4 also reduced the encephalitogenic capacity of Th1 cultures. These data demonstrate that IL-4 contributes to IL-10 production and that Th2 cells modulate Th1 cultures towards a self-regulatory phenotype, contributing to the cross-regulation of Th1 and Th2 cells. These findings are important in the context of Th1 driven diseases since they reveal how the Th1 phenotype and function can be modulated by IL-4.

Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies. Population structure can bias the results of genetic and epidemiological analysis. Here, Haworth et al. report that fine-scale structure is detectable in apparently homogeneous samples such as ALSPAC when measured very precisely, and remains detectable in UK Biobank despite conventional approaches to account for it.