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Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
Energy deficit is common during prolonged periods of strenuous physical activity and limited sleep, but the extent to which appetite suppression contributes is unclear. The aim of this randomised crossover study was to determine the effects of energy balance on appetite and physiological mediators of appetite during a 72-h period of high physical activity energy expenditure (about 9·6 MJ/d (2300 kcal/d)) and limited sleep designed to simulate military operations (SUSOPS). Ten men consumed an energy-balanced diet while sedentary for 1 d (REST) followed by energy-balanced (BAL) and energy-deficient (DEF) controlled diets during SUSOPS. Appetite ratings, gastric emptying time (GET) and appetite-mediating hormone concentrations were measured. Energy balance was positive during BAL (18 (sd 20) %) and negative during DEF (–43 (sd 9) %). Relative to REST, hunger, desire to eat and prospective consumption ratings were all higher during DEF (26 (sd 40) %, 56 (sd 71) %, 28 (sd 34) %, respectively) and lower during BAL (–55 (sd 25) %, −52 (sd 27) %, −54 (sd 21) %, respectively; Pcondition < 0·05). Fullness ratings did not differ from REST during DEF, but were 65 (sd 61) % higher during BAL (Pcondition < 0·05). Regression analyses predicted hunger and prospective consumption would be reduced and fullness increased if energy balance was maintained during SUSOPS, and energy deficits of ≥25 % would be required to elicit increases in appetite. Between-condition differences in GET and appetite-mediating hormones identified slowed gastric emptying, increased anorexigenic hormone concentrations and decreased fasting acylated ghrelin concentrations as potential mechanisms of appetite suppression. Findings suggest that physiological responses that suppress appetite may deter energy balance from being achieved during prolonged periods of strenuous activity and limited sleep.
X-ray micro-computed tomography (μCT) is a technique which can obtain three-dimensional images of a sample, including its internal structure, without the need for destructive sectioning. Here, we review the capability of the technique and examine its potential to provide novel insights into the lifestyles of parasites embedded within host tissue. The current capabilities and limitations of the technology in producing contrast in soft tissues are discussed, as well as the potential solutions for parasitologists looking to apply this technique. We present example images of the mouse whipworm Trichuris muris and discuss the application of μCT to provide unique insights into parasite behaviour and pathology, which are inaccessible to other imaging modalities.
To examine the quantitative relationship between sugar intake and the progressive development of dental caries.
A critical in-depth review of international studies was conducted. Methods included reassessing relevant studies from the most recent systematic review on the relationship between levels of sugars and dental caries. Reanalysis of dose–response relationships between dietary sugars and caries incidence in teeth with different levels of caries susceptibility in children was done using data from Japanese studies conducted by Takeuchi and co-workers.
Global, with emphasis on marked differences in both national sugar intake and fluoride use and preferably where one factor such as sugar intake changed progressively without changes in other factors over a decade or more.
Children aged 6 years or more and adults.
Caries occurred in both resistant and susceptible teeth of children when sugar intakes were only 2–3 % of energy intake, provided that the teeth had been exposed to sugars for >3 years. Despite increased enamel resistance after tooth eruption, there was a progressive linear increase in caries throughout life, explaining the higher rates of caries in adults than in children. Fluoride affects progression of caries development but there still is a pandemic prevalence of caries in populations worldwide.
Previous analyses based on children have misled public health analyses on sugars. The recommendation that sugar intakes should be ≤10 % of energy intake is no longer acceptable. The much greater adult burden of dental caries highlights the need for very low sugar intakes throughout life, e.g. 2–3 % of energy intake, whether or not fluoride intake is optimum.