New clinical prediction model for early recognition of sepsis in adult primary care patients:a prospective diagnostic cohort study of development and external validation

Background Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs.Aim To develop and validate a sepsis prediction model for adult patients in primary care.Design and setting This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020.Method Adult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations.Results A total of 357 patients were included with a median age of 80 years (interquartile range 71–86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged >65 years; temperature >38°C; systolic blood pressure ≤110 mmHg; heart rate >110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation.Conclusion Based on this study’s GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters

Vitamin D and metabolic disturbances in polycystic ovary syndrome (PCOS): A cross-sectional study

Objective To compare vitamin D status in women with PCOS versus fertile women and subsequently evaluate the association between vitamin D status and metabolic disturbances in PCOS women. Methods We conducted a cross-sectional comparison study of 639 women with PCOS and 449 fertile women. Serum 25-hydroxyvitamin D (25(OH)D) was stratified into a severe deficient ( 75 nmol/l) status. The main outcome measures were the difference in vitamin D status between PCOS and fertile women, and the association between serum 25(OH)D and metabolic disturbances in PCOS women only. Results Serum 25(OH)D was significantly lower in PCOS women compared to fertile controls (mean 25(OH)D of 49.0 nmol/l versus 64.5 nmol/l). An adjusted significant difference was seen between serum 25(OH)D and homeostasis model assessment (HOMA-IR) (β = 0.76; 95% CI: 0.63–0.91; p < 0.01), HDL-cholesterol (β = 0.20; 95% CI: 0.05–0.60, p < 0.01) and apolipoprotein A1 (β = 26.2; 95% CI: 7.5–45.0, p < 0.01) between the highest vitamin D group compared to the lowest vitamin D group. Conclusions This study demonstrates that women

Impact of CYP3A4*22 on Pazopanib Pharmacokinetics in Cancer Patients

Background and Objective: As pazopanib plasma trough concentrations are correlated with treatment outcome, we explored whether single nucleotide polymorphisms in the elimination pathway of pazopanib affect systemic pazopanib concentrations. Methods: The decreased function alleles CYP3A4 15389 C > T (*22), ABCB1 3435 C >T, ABCG2 421 C >A, and ABCG2 34G >A were analyzed within a recently developed population-pharmacokinetic model. Results: Incorporation of CYP3A4*22 in the model resulted in a 35% lower clearance for variant carriers (0.18 vs. 0.27 L/h; difference in objective function value: − 9.7; p < 0.005). Simulated median trough concentrations of cancer patients with CYP3A4*22 with 600 mg once daily or 800 mg once daily were 31 and 35 mg/L, respectively. The simulated trough concentrations for the population excluding the CYP3A4*22 carriers after 600 mg once daily or 800 mg once daily were 18 and 20 mg/L, respectively. Conclusion: This analysis shows that CYP3A4*22 heterozygotes have a substantial lower pazopanib clearance and that dose adjustments based on CYP3A4*22 status could be considered

A genome-wide association study of early menopause and the combined impact of identified variants

Early menopause (EM) affects up to 10% of the female population, reducing reproductive lifespan considerably. Currently, it constitutes the leading cause of infertility in the western world, affecting mainly those women who postpone their first pregnancy beyond the age of 30 years. The genetic aetiology of EM is largely unknown in the majority of cases. We have undertaken a meta-analysis of genome-wide association studies (GWASs) in 3493 EM cases and 13 598 controls from 10 independent studies. No novel genetic variants were discovered, but the 17 variants previously associated with normal age at natural menopause as a quantitative trait (QT) were also associated with EM and primary ovarian insufficiency (POI). Thus, EM has a genetic aetiology which overlaps variation in normal age at menopause and is at least partly explained by the additive effects of the same polygenic variants. The combined effect of the common variants captured by the single nucleotide polymorphism arrays was estimated to account for ∼30% of the variance in EM. The association between the combined 17 variants and the risk of EM was greater than the best validated non-genetic risk factor, smokin

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project-imputed genotype data in up to similar to 370,000 women, we identify 389 independent signals (P <5 x 10(-8)) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain similar to 7.4% of the population variance in age at menarche, corresponding to similar to 25% of the estimated heritability. We implicate similar to 250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

The sociology of cancer: a decade of research

Biomedicine is often presented as the driving force behind improvements in cancer care, with genomics the latest innovation poised to change the meaning, diagnosis, treatment, prevention and lived experience of cancer. Reviewing sociological analyses of a diversity of patient and practitioner experiences and accounts of cancer during the last decade (2007–17), we explore the experiences of, approaches to and understandings of cancer in this period. We identify three key areas of focus: (i) cancer patient experiences and identities; (ii) cancer risk and responsibilities and (iii) bioclinical collectives. We explore these sociological studies of societal and biomedical developments and how sociologists have sought to influence developments in cancer identities, care and research. We end by suggesting that we extend our understanding of innovations in the fields of cancer research to take better account of these wider social and cultural innovations, together with patients, activists' and sociologists' contributions therein