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We describe the evolution of the nonprofit Nepal Ambulance Service (NAS) in a narrative of its 10-y history, presenting geographical, social, cultural, and financial considerations that permeated the development of NAS. We gathered narrative information from the NAS leadership and partners to detail key organizational considerations regarding the implementation and maintenance of the prehospital system in Nepal. We describe the response of NAS to the 2015 earthquake and summarize transport data for 6 mo before and 6 mo after the event. The data collected included the date and time of calls received, time to ambulance dispatch, on-scene time, time to arrival at the hospital, time until the ambulance crew was back in service, patient age and sex, chief complaints, and work shift time of the ambulance crew. To characterize the time to response and transport after the 2015 earthquake, we present the means and standard deviations of the time intervals. There was an overall increase in calls and, specifically, trauma-related calls after the 2015 earthquake. The time from a call placed to dispatch was stable, approximately 2 min, throughout the period, whereas the time from dispatch to the scene and arrival at the scene varied widely. We discuss the response to coronavirus disease 2019 (COVID-19). The NAS provided care to 1230 patients with COVID-19. The descriptive data show how well NAS responded to a major national disaster and the recent pandemic.

Supplementary Table 7: S-PrediXcan statistics at each brain tissue for the 31 identified gene associations.

Supplementary Table 6: Genes and their TWAS P-values for association with (a) GBM glioma (b) non-GBM glioma. s.d., standard deviation. Detailed are the S-MultiXcan P-value for association between gene expression and GBM/non-GBM risk and the corresponding Z-scores quantifying this relationship (e.g. a positive score indicates increased gene expression increases risk of GBM or non-GBM glioma). N and Nindep indicate the total number of single-tissue results used for S-MultiXcan analysis and the number of independent components after singular value decomposition, respectively. "p_i_best" and "t_i_best" correspond to the best single-tissue P-value and tissue model respectively, accordingly "p_i_worst" and "t_i_worset" correspond to the worst. "eigen_max" and "eigen_min" correspond to the maximum and minimum eigenvalue in the covariance matrix respectively, with "eigen_min_kept" corresponding to the minimum eigenvalue that survived thresholding.

age at allergy onset

PDF file, 102K, Linkage locations suggested by Loki on chromosome 17 by batches of 100K iterations and over all 500K iterations.

Background: Several previous studies have found inverse associations between glioma susceptibility and a history of allergies or other atopic conditions. Some evidence indicates that respiratory allergies are likely to be particularly relevant with regard to glioma risk. Using data from the Glioma International Case-Control Study (GICC), we examined the effects of respiratory allergies and other atopic conditions on glioma risk.Methods: The GICC contains detailed information on history of atopic conditions for 4,533 cases and 4,171 controls, recruited from 14 study sites across five countries. Using two-stage random-effects restricted maximum likelihood modeling to calculate meta-analysis ORs, we examined the associations between glioma and allergy status, respiratory allergy status, asthma, and eczema.Results: Having a history of respiratory allergies was associated with an approximately 30% lower glioma risk, compared with not having respiratory allergies (mOR, 0.72; 95% confidence interval, 0.58–0.90). This association was similar when restricting to high-grade glioma cases. Asthma and eczema were also significantly protective against glioma.Conclusion: A substantial amount of data on the inverse association between atopic conditions and glioma has accumulated, and findings from the GICC study further strengthen the existing evidence that the relationship between atopy and glioma is unlikely to be coincidental.Impact: As the literature approaches a consensus on the impact of allergies in glioma risk, future research can begin to shift focus to what the underlying biologic mechanism behind this association may be, which could, in turn, yield new opportunities for immunotherapy or cancer prevention. Cancer Epidemiol Biomarkers Prev; 25(2); 282–90. ©2016 AACR.

PDF file, 212K, Genome-wide multipoint model-based linkage under the three trait locus segregation models. Multipoint LOD scores have been truncated at -3. Some scores are below -3, such as for the whole of chromosome 15.

Genome-wide association studies (GWAS) have so far identified 25 loci associated with glioma risk, with most showing specificity for either glioblastoma (GBM) or non-GBM tumors. The majority of these GWAS susceptibility variants reside in noncoding regions and the causal genes underlying the associations are largely unknown. Here we performed a transcriptome-wide association study to search for novel risk loci and candidate causal genes at known GWAS loci using Genotype-Tissue Expression Project (GTEx) data to predict cis-predicted gene expression in relation to GBM and non-GBM risk in conjunction with GWAS summary statistics on 12,488 glioma cases (6,183 GBM and 5,820 non-GBM) and 18,169 controls. Imposing a Bonferroni-corrected significance level of P < 5.69 × 10−6, we identified 31 genes, including GALNT6 at 12q13.33, as a candidate novel risk locus for GBM (mean Z = 4.43; P = 5.68 × 10−6). GALNT6 resides at least 55 Mb away from any previously identified glioma risk variant, while all other 30 significantly associated genes were located within 1 Mb of known GWAS-identified loci and were not significant after conditioning on the known GWAS-identified variants. These data identify a novel locus (GALNT6 at 12q13.33) and 30 genes at 12 known glioma risk loci associated with glioma risk, providing further insights into glioma tumorigenesis.Significance:This study identifies new genes associated with glioma risk, increasing understanding of how these tumors develop.

PDF file, 302K, Scatter plot of the visits at possible linked QTL locations on chromosome 17 vs. iteration.

Supplementary Figure 1: Quantile-Quantile Plots of -log10 (P-value) associations. (a) TWAS for GBM glioma; (b) TWAS for GBM glioma (lower 90% of associations); (c) TWAS for non-GBM glioma; (d) TWAS for non-GBM glioma (lower 90% of associations); (e) GWAS meta-analysis for GBM glioma; (f) GWAS meta-analysis for GBM glioma (lower 90% of associations); (g) GWAS meta-analysis for non-GBM glioma; (h) GWAS meta-analysis for non-GBM glioma (lower 90% of associations). Supplementary Figure 2: Manhattan Plots of SNP genomic co-ordinates against GWAS meta-analysis -log10 (P-value). (a) GBM glioma; (b) Non-GBM glioma. The red line represents the Bonferroni-corrected threshold of P{less than or equal to}5Ã-10-8. Supplementary Figure 3: Dendograms to display gene module clustering in brain tissues. Clustering was performed using the WGCNA (11) package for each tissue. (a) Amygdala; (b) Anterior cingulate cortex (BA24); (c) Caudate (basal ganglia); (d) Cerebellar Hemisphere; (e) Cerebellum; (f) Cortex; (g) Frontal Cortex (BA9); (h) Hippocampus; (i) Hypothalamus; (j) Nucleus accumbens (basal ganglia); (k) Putamen (basal ganglia); (l) Spinal cord (cervical c-1); (m) Substantia nigra. Supplementary Figure 4: Heatmaps illustrating gene module correlations in brain tissues. Darker colours indicate stronger associations. (a) Amygdala; (b) Anterior cingulate cortex (BA24); (c) Caudate (basal ganglia); (d) Cerebellar Hemisphere; (e) Cerebellum; (f) Cortex; (g) Frontal Cortex (BA9); (h) Hippocampus; (i) Hypothalamus; (j) Nucleus accumbens (basal ganglia); (k) Putamen (basal ganglia); (l) Spinal cord (cervical c-1); (m) Substantia nigra. Supplementary Figure 5: Module clustering of brain tissues with amygdala. Modules were created by clustering according to the amygdala tissue using WGCNA (11). These modules were applied to each tissue in turn and the Z statistic is a quantitative measure of module preservation. (a) Amygdala (reference); (b) Anterior cingulate cortex (BA24); (c) Caudate (basal ganglia); (d) Cerebellar Hemisphere; (e) Cerebellum; (f) Cortex; (g) Frontal Cortex (BA9); (h) Hippocampus; (i) Hypothalamus; (j) Nucleus accumbens (basal ganglia); (k) Putamen (basal ganglia); (l) Spinal cord (cervical c-1); (m) Substantia nigra. Supplementary Figure 6: TWAS power plots. Simulation analysis based on 12,488 glioma cases (6,183 GBM, 5,820 non-GBM) and 18,169 controls. Gene expression was generated from the distribution of gene expression levels from the respective brain tissue. Statistical power was calculated at P 0.4, case and control MAF > 0.01, PHWE in controls > 1x10-8. For the meta-analysis we considered all SNPs meeting the above criteria in all eight studies as well as having heterogeneity I2 < 75 in GBM/non-GBM glioma. a For the UCSF/Mayo study an imputation threshold of > 0.8 was applied. Supplementary Table 2: Prediction models trained on GTEx v7 data and covariance files obtained from http://predictdb.org/. Genes were retained if nested cross-validated correlation between predicted and actual levels > 0.10 (R2>1%) and P-value of the correlation test < 0.05. Supplementary Table 3: Previously reported glioma risk SNPs. Supplementary Table 4: Brain tissue, soft power threshold and number of independent genes. Soft power thresholds were chosen at the 90% threshold unless otherwise indicated. + For Cerebellar Hemisphere and Cerebellum, the 90% threshold was not achieved and 80% was imposed. For this reason such tissues were excluded from median calculations for soft power and number of independent genes. Supplementary Table 8: S-PrediXcan results for 31 genes in 922 whole-blood samples from the Depression Genes and Networks (DGN) study. Supplementary Table 9: Annotation of identified genes by presence within COSMIC cancer gene census and overlap with tumour copy number changes. *Cosmic cancer gene census annotation for CDKN2A was used.

PDF file, 133K, Additional methods describing the segregation-linkage analysis approach with variable age at onset.

PDF file, 89K, L-scores for three prior settings, after discarding the beginning 5K iterations

PDF file, 34K, Typical assumptions in model-based vs. model-free multipoint linkage analysis.

Supplementary Table 5: Table of module color with module size and associated Z-statistics. Modules were created by clustering according to the Amygdala tissue using WGCNA11. These modules of gene number "moduleSize" were applied to each tissue in turn and the Z-statistic ("Zsummary.pres") is a quantitive measure of module preservation relative to Amygdala. (a) Anterior cingulate cortex (BA24); (b) Caudate (basal ganglia); (c) Cerebellar Hemisphere; (d) Cerebellum; (e) Cortex; (f) Frontal Cortex (BA9); (g) Hippocampus; (h) Hypothalamus; (i) Nucleus accumbens (basal ganglia); (j) Putamen (basal ganglia); (k) Spinal cord (cervical c-1); (l) Substantia nigra.

This methodology article proposes a basic framework for assessing confidence in residential address through attribute sets of the tumor record that enable or modify spatiotemporal relationships in cancer surveillance data. A first step in assessing confidence for a statutory downstream data steward, like the Central Cancer Registry (CCR), is identifying sets of attributes whose domains are independently controlled by data stewards outside of the CCR. These include attribute sets that comprise the digital entities of person, time, and place. In this article, we describe the uncertainty in the geolocation of a cancer patient at the time of diagnosis, focusing on multiple stewardship of the cancer surveillance data. We also propose an approach to account for this uncertainty that is practical within the framework of existing cancer registry data coding, processing conventions, and legislative mandates for cancer surveillance.

Background The etiological basis of glioma is poorly understood. We have used genetic markers in a Mendelian Randomisation (MR) framework to examine if lifestyle, cardiometabolic and inflammatory factors influence the risk of glioma. This methodology reduces bias from confounding and is not affected by reverse causation. Methods We identified genetic instruments for 37 potentially modifiable risk factors and evaluated their association with glioma risk using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. We used the estimated odds ratio of glioma associated with each of the genetically defined traits to infer evidence for a causal relationship with the following exposures: lifestyle and dietary factors (height, plasma IGF-1, blood carnitine, blood methionine, blood selenium, blood zinc, circulating adiponectin, circulating carotenoids, iron status, serum calcium, vitamin [A1, B12, B6, E and 25-hydroxyvitamin D], fatty acids levels [mono-unsaturated, omega-3 and omega-6] and circulating fetuin-A); cardiometabolic factors (birth weight, HDL cholesterol, LDL cholesterol, total cholesterol, total triglycerides, basal metabolic rate, body fat percentage, body mass index, fasting glucose, fasting proinsulin, HbA1C levels, diastolic and systolic blood pressure, waist circumference, waist-to-hip ratio) were included; inflammatory factors (C-reactive protein (CRP), plasma IL-6 sRa and serum IgE). Results After correction for the testing of multiple potential risk factors and excluding associations driven by one single nucleotide polymorphism (SNP) no significant association with glioma risk was observed (i.e. PCorrected > 0.05). Conclusions This study did not provide evidence supporting any of the 37 factors examined as having a significant influence on glioma risk.

Glioma incidence is highest in non-Hispanic Whites, and to date, glioma genome-wide association studies (GWAS) to date have only included European ancestry (EA) populations. African Americans and Hispanics in the US have varying proportions of EA, African (AA) and Native American ancestries (NAA). It is unknown if identified GWAS loci or increased EA is associated with increased glioma risk. We assessed whether EA was associated with glioma in African Americans and Hispanics. Data were obtained for 832 cases and 675 controls from the Glioma International Case-Control Study and GliomaSE Case-Control Study previously estimated to have

Background Short-term markers of successful visceral adipose tissue (VAT) loss are needed. Urinary F2-isoprostanes might serve as a marker for intensified lipid metabolism, whereas circulating IL-6 might stimulate fat oxidation and enhance mobilization of VAT. Objectives This pilot study was designed to explore the hypotheses that 1) reduction in VAT is associated with increase in IL-6, and 2) that increases in urinary F2-isoprostanes are associated with increases in IL-6 and reduction in VAT. Methods Eighteen participants (aged 60–75 y, BMI 30–40 kg/m2) were randomly assigned to either a very-low-carbohydrate diet (VLCD; 65% energy from carbohydrate:protein:fat) or a low-fat diet (LFD; 55:25:20%) for 8 wk. Changes in fat distribution were assessed by MRI. Four urinary F2-isoprostane isomers were quantified in 24-h urine collection using LC-MS/MS analyses. Changes in 4 F2-isoprostane isomers were summarized using factor analysis (Δ-F2-isoprostane factor). Statistical significance was set at P

BackgroundThe functions of most glioma risk alleles are unknown. Very few studies had evaluated expression quantitative trait loci (eQTL), and insights of susceptibility genes were limited due to scarcity of available brain tissues. Moreover, no prior study had examined the effect of glioma risk alleles on alternative RNA splicing.ObjectiveThis study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both cis eQTL and sQTL.MethodsWe first evaluated eQTLs and sQTLs of the CommonMind Consortium (CMC) and Genotype-Tissue Expression Project (GTEx) using genotyping, or whole-genome sequencing and RNA-seq data. Alternative splicing events were characterized using an annotation-free method that detected intron excision events. Then, we conducted meta-analyses by pooling the eQTL and sQTL results of CMC and GTEx using the inverse variance-weighted model. Afterward, we integrated QTL meta-analysis results (Q < 0.05) with the Glioma International Case Control Study (GICC) GWAS meta-analysis (case:12,496, control:18,190), using a summary statistics-based mendelian randomization (SMR) method.ResultsBetween CMC and GTEx, we combined the QTL data of 354 unique individuals of European ancestry. SMR analyses revealed 15 eQTLs in 11 loci and 32 sQTLs in 9 loci relevant to glioma risk. Two loci only harbored sQTLs (1q44 and 16p13.3). In seven loci, both eQTL and sQTL coexisted (2q33.3, 7p11.2, 11q23.3 15q24.2, 16p12.1, 20q13.33, and 22q13.1), but the target genes were different for five of these seven loci. Three eQTL loci (9p21.3, 20q13.33, and 22q13.1) and 4 sQTL loci (11q23.3, 16p13.3, 16q12.1, and 20q13.33) harbored multiple target genes. Eight target genes of sQTLs (C2orf80, SEC61G, TMEM25, PHLDB1, RP11-161M6.2, HEATR3, RTEL1-TNFRSF6B, and LIME1) had multiple alternatively spliced transcripts.ConclusionOur study revealed that the regulation of transcriptome by glioma risk alleles is complex, with the potential for eQTL and sQTL jointly affecting gliomagenesis in risk loci. QTLs of many loci involved multiple target genes, some of which were specific to alternative splicing. Therefore, quantitative trait loci that evaluate only total gene expression will miss many important target genes.

State- and county-level reports suggest that the COVID-19 pandemic exacerbated the opioid crisis. We examined US national trends of nonfatal opioid overdose in 2020 in comparison to pre-COVID years 2018-2019.We used National Emergency Medical Services Information System (NEMSIS) data to conduct a temporal analysis from 2018 to 2020. Opioid-related EMS run was defined using five scenarios of naloxone administration. To determine annual patterns and slope inflection points, we used the Prophet model of the time series analysis. Linear slopes and their 95% confidence intervals (CIs) were calculated for pre-stay-at-home (pre-SaH) and SaH periods in 2020 and compared to the slopes during the same time in 2018-2019. Three cut-points for SaH start were considered: March 19, 24, and 29.We identified 91,065, 144,802, and 242,904 opioid-related EMS runs in 2018-2020, respectively. In 2020, opioid-related runs increased in January-June, with a pronounced acceleration in March, which coincides with the stay-at-home (SaH) orders. In both 2018 and 2019, opioid-related runs increased in January-August without the spring acceleration. In 2020, weekly increases (95% CI) during SaH for all examined cut-points were significantly greater than in pre-SaH: 18.09 (16.03-20.16) vs. 6.44 (3.42-9.47) for March 19, 17.77 (15.57-19.98) vs. 4.85 (2.07-7.64) for March 24, 18.03 (15.68-20.39) vs. 4.97(2.4-7.54) for March 29. No significant difference was found between these periods in 2018-2019.The acceleration of opioid-related EMS runs during the SaH period of 2020 suggests that EMS data may serve as an early warning system for local health jurisdictions to deploy harm reduction/prevention resources.

This review focuses on racial differences in systemic levels of lipid peroxidation markers F(2)-isoprostanes as metabolic characteristics predisposing to obesity and type 2 diabetes. Elevated levels F(2)-isoprostanes were found in obesity, type 2 diabetes and their comorbidities. It was hypothesized that increased F(2)-isoprostane levels reflect the obesity-induced oxidative stress that promotes the development of type 2 diabetes. However, African Americans have lower levels of systemic F(2)-isoprostane levels despite their predisposition to obesity and type 2 diabetes. The review summarizes new findings from epidemiological studies and a novel interpretation of metabolic determinants of systemic F(2)-isoprostane levels as a favorable phenotype. Multiple observations indicate that systemic F(2)-isoprostane levels reflect intensity of oxidative metabolism, a major endogenous source of reactive oxygen species, and specifically, the intensity of fat utilization. Evidence from multiple human studies proposes that targeting fat metabolism can be a productive race-specific strategy to address the existing racial health disparities. Urinary F(2)-isoprostanes may provide the basis for targeted interventions to prevent obesity and type 2 diabetes among populations of African descent.

Glioblastoma (GBM) is the most common malignant brain tumor in the United States. Incidence of GBM increases with age, and younger age-at-diagnosis is significantly associated with improved prognosis. While the relationship between candidate GBM risk SNPs and age-at-diagnosis has been explored, genome-wide association studies (GWAS) have not previously been stratified by age. Potential age-specific genetic effects were assessed in autosomal SNPs for GBM patients using data from four previous GWAS. Using age distribution tertiles (18–53, 54–64, 65+) datasets were analyzed using age-stratified logistic regression to generate p values, odds ratios (OR), and 95% confidence intervals (95%CI), and then combined using meta-analysis. There were 4,512 total GBM cases, and 10,582 controls used for analysis. Significant associations were detected at two previously identified SNPs in 7p11.2 (rs723527 [p(54–63)=1.50×10(−9), OR(54–63)=1.28, 95%CI(54–63)=1.18–1.39; p(64+)=2.14×10(−11), OR(64+)=1.32, 95%CI(64+)=1.21–1.43] and rs11979158 [p(54–63)=6.13×10(−8), OR(54–63)=1.35, 95%CI(54–63)=1.21–1.50; p(64+)=2.18×10(−10), OR(64+)=1.42, 95%CI(64+)=1.27–1.58]) but only in persons >54. There was also a significant association at the previously identified lower grade glioma (LGG) risk locus at 8q24.21 (rs55705857) in persons ages 18–53 (p(18–53)=9.30×10(−11), OR(18–53)=1.76, 95%CI(18–53)=1.49–2.10). Within The Cancer Genome Atlas (TCGA) there was higher prevalence of ‘LGG’-like tumor characteristics in GBM samples in those 18–53, with IDH1/2 mutation frequency of 15%, as compared to 2.1% [54–63] and 0.8% [64+] (p=0.0005). Age-specific differences in cancer susceptibility can provide important clues to etiology. The association of a SNP known to confer risk for IDH1/2 mutant glioma and higher prevalence of IDH1/2 mutation within younger individuals 18–53 suggests that more younger individuals may present initially with ‘secondary glioblastoma.’

Background: The purpose of this study was to evaluate the distribution of glioma-related seizures and seizure control at the time of tumor diagnosis with respect to tumor histologic subtypes, tumor treatment and patient characteristics, and to compare seizure history preceding tumor diagnosis (or study enrollment) between glioma patients and healthy controls. Methods: The Glioma International Case Control study (GICC) risk factor questionnaire collected information on demographics, past medical/medication history, and occupational history. Cases from eight centers were also asked detailed questions on seizures in relation to glioma diagnosis; cases (n = 4533) and controls (n = 4171) were also asked about seizures less than 2 years from diagnosis and previous seizure history more than 2 years prior to tumor diagnosis, including childhood seizures. Results: Low-grade gliomas (LGGs), particularly oligodendrogliomas/oligoastrocytomas, had the highest proportion of glioma-related seizures. Patients with low-grade astrocytoma demonstrated the most medically refractory seizures. A total of 83% of patients were using only one antiepileptic drug (AED), which was levetiracetam in 71% of cases. Gross total resection was strongly associated with reduced seizure frequency (p < 0.009). No significant difference was found between glioma cases and controls in terms of seizure occurring more than 2 years before diagnosis or during childhood. Conclusions: Our study showed that glioma-related seizures were most common in low-grade gliomas. Gross total resection was associated with lower seizure frequency. Additionally, having a history of childhood seizures is not a risk factor for developing glioma-related seizures or glioma.

Graphical abstract, Highlights • MSA is an appropriate molecular epidemiology tool in case control studies. • Case-control status/exposure categories are not associated with the number of breaks. • Cell lines of glioma patients did not show reduced DNA repair capacity in response to acrylamide in the MSA assay., Few risk factors for glioma have been identified other than ionizing radiation. The alkylating agent acrylamide is a compound found in both occupational and the general environment and identified as one of the forty known or suspected neurocarcinogens in animal models. The mutagen sensitivity assay (MSA) has been used to indirectly show reduced DNA repair capacity upon exposure to ionizing radiation in those with glioma compared to controls. In this study, MSA was used to assess its applicability to a glioma case-control study and to test the hypothesis that subjects with glioma may have lower DNA repair capacity after exposure to selected potential human neurocarcinogens (i.e. acrylamide), compared to controls. Approximately 50 case and 50 control subjects were identified from a clinic-based study that investigated environmental risk factors for glioma, who completed an exposure survey, and had frozen immortalized lymphocytes available. A total of 50 metaphase spreads were read and reported for each participant. The association of case-control status with MSA for acrylamide, i.e. breaks per spread, was examined by multivariable logistic regression models. The mean number of breaks per slide was similar between hospital-based controls and cases. In addition, case-control status or exposure categories were not associated with the number of breaks per spread. Although the MSA has been shown as a useful molecular epidemiology tool for identifying individuals at higher risk for cancer, our data do not support the hypothesis that glioma patients have reduced DNA repair capacity in response to exposure to acrylamide. Further research is needed before the MSA is utilized in large-scale epidemiological investigations of alkylating agents.

Background Short-term markers of successful visceral adipose tissue (VAT) loss are needed. Urinary F2-isoprostanes might serve as a marker for intensified lipid metabolism, whereas circulating IL-6 might stimulate fat oxidation and enhance mobilization of VAT. Objectives This pilot study was designed to explore the hypotheses that 1) reduction in VAT is associated with increase in IL-6, and 2) that increases in urinary F2-isoprostanes are associated with increases in IL-6 and reduction in VAT. Methods Eighteen participants (aged 60-75 y, BMI 30-40 kg/m2) were randomly assigned to either a very-low-carbohydrate diet (VLCD; 65% energy from carbohydrate:protein:fat) or a low-fat diet (LFD; 55:25:20%) for 8 wk. Changes in fat distribution were assessed by MRI. Four urinary F2-isoprostane isomers were quantified in 24-h urine collection using LC-MS/MS analyses. Changes in 4 F2-isoprostane isomers were summarized using factor analysis (Δ-F2-isoprostane factor). Statistical significance was set at P Results Within the VLCD group, change in VAT was inversely associated with change in IL-6 (r = -0.778, P = 0.069) and Δ-F2-isoprostane factor (r = -0.690, P = 0.086), demonstrating that participants who maintained higher concentrations of F2-isoprostane factor across the intervention showed greater decreases in VAT. A positive relation between Δ-F2-isoprostane factor and change in IL-6 was observed (r = 0.642, P = 0.062). In the LFD group, no significant associations between changes in VAT, F2-isoprostane factor, or IL-6 were observed. Conclusions Results from this exploratory study in older adults with obesity suggest that, in the context of a VLCD, IL-6 could be involved in VAT mobilization, and urinary F2-isoprostanes could reflect intensified oxidation of mobilized fatty acids.Trial registration: This study is registered at clinicaltrials.gov as NCT02760641.

BACKGROUND Triple-negative breast cancer (TNBC) has been associated with a more aggressive histology, poorer prognosis, and nonresponsiveness to hormone therapy. It is imperative that cancer research identify factors that drive disparities and focus on prevention. METHODS Using the United States Cancer Statistics database, the authors examined differences between TNBCs compared with all other breast cancers with regard to age, race/ethnicity, and stage at diagnosis. RESULTS A total of 1,151,724 cases of breast cancer were identified from 2010 through 2014, with the triple-negative phenotype accounting for approximately 8.4% of all cases. In unadjusted analyses, non-Hispanic black women (odds ratio [OR], 2.27; 95% CI, 2.23-2.31) and Hispanic women (OR, 1.22; 95% CI, 1.19-1.25) had higher odds of diagnosis when compared with non-Hispanic white women. Women aged

To examine for a causal relationship between vitamin D and glioma risk we performed an analysis of genetic variants associated with serum 25-hydroxyvitamin D (25(OH)D) levels using Mendelian randomisation (MR), an approach unaffected by biases from confounding. Two-sample MR was undertaken using genome-wide association study data. Single nucleotide polymorphisms (SNPs) associated with 25(OH)D levels were used as instrumental variables (IVs). We calculated MR estimates for the odds ratio (OR) for 25(OH)D levels with glioma using SNP-glioma estimates from 12,488 cases and 18,169 controls, using inverse-variance weighted (IVW) and maximum likelihood estimation (MLE) methods. A non-significant association between 25(OH)D levels and glioma risk was shown using both the IVW (OR = 1.21, 95% confidence interval [CI] = 0.90-1.62, P = 0.201) and MLE (OR = 1.20, 95% CI = 0.98-1.48, P = 0.083) methods. In an exploratory analysis of tumour subtype, an inverse relationship between 25(OH)D levels and glioblastoma (GBM) risk was identified using the MLE method (OR = 0.62, 95% CI = 0.43-0.89, P = 0.010), but not the IVW method (OR = 0.62, 95% CI = 0.37-1.04, P = 0.070). No statistically significant association was shown between 25(OH)D levels and non-GBM glioma. Our results do not provide evidence for a causal relationship between 25(OH)D levels and all forms of glioma risk. More evidence is required to explore the relationship between 25(OH)D levels and risk of GBM.

There is increased interest in using high throughput in vitro assays to characterize human population variability in response to toxicants and drugs. Utilizing primary human endothelial colony-forming cells (ECFCs) isolated from blood would be highly useful for this purpose because these cells are involved in neonatal and adult vasculogenesis. We characterized the cytotoxicity of four known toxic chemicals (NaAsO2, CdCl2, tributyltin [TBT], and menadione) and their four relatively nontoxic counterparts (Na2HAsO4, ZnCl2, SnCl2, and phytonadione, respectively) in eight ECFC clones representing four neonatal donors (2 male and 2 female donors, 2 clones per donor). ECFCs were exposed to 9 concentrations of each chemical in duplicate; cell viability was evaluated 48 h later using the fluorescent vital dye fluorescent dye 5-Carboxyfluorescein Diacetate (CFDA), yielding concentration-effect curves from each experiment. Technical (day-to-day) variability of the assay, assessed from three independent experiments, was low: p-values for the differences of results were 0.74 and 0.64 for the comparison of day 2 vs. day 1 and day 3 vs. day 1, respectively. The statistical analysis used to compare the entire concentration-effect curves has revealed significant differences in levels of cytotoxicity induced by the toxic and relatively nontoxic chemical counterparts, demonstrating that donor-specific ECFCs can clearly differentiate between these two groups of chemicals. Partitioning of the total variance in the nested design assessed the contributions of between-clone and between-donor variability for different levels of cytotoxicity. Individual ECFC clones demonstrated highly reproducible responses to the chemicals. The most toxic chemical was TBT, followed by NaAsO2, CdCl2, and Menadione. Nontoxic counterparts exhibited low cytotoxicity at the higher end of concentration ranges tested. Low variability was observed between ECFC clones obtained from the same donor or different donors for CdCl2, NaAsO2, and TBT, but for menadione, the between-donor variability was much greater than the between-clone variability. The low between-clone variability indicates that an ECFC clone may represent an individual donor in cell-based assays, although this finding must be confirmed using a larger number of donors. Such confirmation would demonstrate that an in vitro ECFC-based testing platform can be used to characterize the inter-individual variability of neonatal ECFCs exposed to drugs and/or environmental toxicants.

Background There is a projected rapid increase in cancer survivors in the US population, from 15.5 million in 2016 to 26.1 million by 2040. Improvements in treatment and detection have led to increased survival, however, there is now a risk of developing new cancers as a result of environment toxins, behavioral risk factors, genetic predisposition, and late-term effects of radiation and chemotherapeutic treatments. This study takes a geospatial approach to examining the place of occurrence of multiple cancers originating in the population of four screenable cancers-female breast, colorectal, prostate, and cervical cancers-among the US population. Methods During 2004-2014, 6,523,532 primary cancer patients with one of these four screenable cancers were examined, and subsequent primary cancers (multiple cancers of any type) were noted. Individual level analyses estimated the odds of diagnosis with multiple cancers controlling for age, sex, and race-ethnicity. Change in effects on odds of multiple cancer diagnoses with age, sex, and race-ethnicity were evaluated controlling separately for late-stage diagnosis of the primary cancer or each primary cancer diagnosis type. County-level spatial cluster analysis was employed to identify and visualize higher than average multiple cancer rates. Results Over half of the study population were female and almost 30% of the study population were diagnosed at late-stage for their first cancer. Multiple occurrences of all cancers increased during the time period for patients with initial breast or colorectal cancers. Among BC primary cancer cases, subsequent multiple cancers were mostly new breast cancers. By contrast, for CRC primary cancer cases, subsequent multiple cancers were about equally likely to be new CRC cases or other cancer types. Sex, age and race-ethnicity were all significantly associated with multiple cancers. In the model controlling for CRC as the primary type, the age and race-ethnicity effects were somewhat different than for all the other models. Thus, there was something distinctly different about the multiple cancer incidence among patients with CRC as their primary cancer as compared to patients with BC, CVC, or PC primaries. In subsequent analyses by county, there were distinct geospatial patterns in multiple cancer rates with most high-rate clusters occurring in the north- and mid-west US. Conclusions There were distinct individual level and geospatial disparities in multiple cancer diagnoses for the study population of all primary breast, colorectal, cervical, or prostate cancer patients during the decade studied. It is importance to emphasize continued screening for cancer survivors and research on personal and environmental drivers of multiple primary cancers.

OBJECTIVE Recent studies using untargeted metabolomics approaches have suggested that plasma branched-chain amino acids (BCAAs) are associated with incident diabetes. However, little is known about the role of plasma BCAAs in metabolic abnormalities underlying diabetes and whether these relationships are consistent across ethnic populations at high risk for diabetes. We investigated the associations of BCAAs with insulin sensitivity (SI), acute insulin response (AIR), and metabolic clearance of insulin (MCRI) in a multiethnic cohort. RESEARCH DESIGN AND METHODS In 685 participants without diabetes of the Insulin Resistance Atherosclerosis Study (IRAS) (290 Caucasians, 165 African Americans, and 230 Hispanics), we measured plasma BCAAs (sum of valine, leucine, and isoleucine) by mass spectrometry and SI, AIR, and MCRI by frequently sampled intravenous glucose tolerance tests. RESULTS Elevated plasma BCAAs were inversely associated with SI and MCRI and positively associated with fasting insulin in regression models adjusted for potential confounders (β = −0.0012 [95% CI −0.0018, −0.00059], P < 0.001 for SI; β = −0.0013 [95% CI −0.0018, −0.00082], P < 0.001 for MCRI; and β = 0.0015 [95% CI 0.0008, 0.0023], P < 0.001 for fasting insulin). The association of BCAA with SI was significantly modified by ethnicity, with the association only being significant in Caucasians and Hispanics. Elevated plasma BCAAs were associated with incident diabetes in Caucasians and Hispanics (multivariable-adjusted odds ratio per 1-SD increase in plasma BCAAs: 1.67 [95% CI 1.21, 2.29], P = 0.002) but not in African Americans. Plasma BCAAs were not associated with SI-adjusted AIR. CONCLUSIONS Plasma BCAAs are associated with incident diabetes and underlying metabolic abnormalities, although the associations were generally stronger in Caucasians and Hispanics.

Rare cancers, affecting 1 in 5 cancer patients, disproportionally contribute to cancer mortality. This research focuses on liposarcoma, an understudied rare cancer with unknown risk factors and limited treatment options. Liposarcoma incident cases were identified from the U.S. Surveillance, Epidemiology, and End Result (SEER) program and the combined SEER-National Program of Cancer Registries (CNPCR) between 2001&ndash, 2016. Incidence rates (age-adjusted and age-specific), 5-year survival, and the time trends were determined using SEER*stat software. Three-dimensional visualization of age&ndash, time curves was conducted for males and females. SEER liposarcoma cases represented ~30% (n = 11,162) of the nationwide pool (N = 37,499). Both sources of data showed males accounting for ~60% of the cases, 82%&ndash, 86% cases were identified among whites. Age-adjusted incidence was greater among males vs. females and whites vs. blacks, whereas survival did not differ by sex and race. The dedifferentiated (57.2%), pleomorphic (64.1%), and retroperitoneal (63.9%) tumors had the worse survival. Nationwide, liposarcoma rates increased by 19%, with the annual percent increase (APC) of 1.43% (95% confidence interval (CI): 1.12&ndash, 1.74). The APC was greater for males vs. females (1.67% vs. 0.89%) and retroperitoneal vs. extremity tumors (1.94% vs. 0.58%). Thus, incidence increased faster in the high-risk subgroup (males), and for retroperitoneal tumors, the low-survival subtype. The SEER generally over-estimated the rates and time trends compared to nationwide data but under-estimated time trends for retroperitoneal tumors. The time trends suggest an interaction between genetic and non-genetic modifiable risk factors may play a role in the etiology of this malignancy. Differences between SEER and CNCPR findings emphasize the need for nationwide cancer surveillance.

Background: There have been few studies of sufficient size to address the relationship between glioma risk and the use of aspirin or NSAIDs, and results have been conflicting. The purpose of this study was to examine the associations between glioma and aspirin/NSAID use, and to aggregate these findings with prior published studies using meta-analysis. Methods: The Glioma International Case-Control Study (GICC) consists of 4,533 glioma cases and 4,171 controls recruited from 2010 to 2013. Interviews were conducted using a standardized questionnaire to obtain information on aspirin/NSAID use. We examined history of regular use for ≥6 months and duration-response. Restricted maximum likelihood meta-regression models were used to aggregate site-specific estimates, and to combine GICC estimates with previously published studies. Results: A history of daily aspirin use for ≥6 months was associated with a 38% lower glioma risk, compared with not having a history of daily use [adjusted meta-OR = 0.62; 95% confidence interval (CI), 0.54–0.70]. There was a significant duration-response trend (P = 1.67 × 10−17), with lower ORs for increasing duration of aspirin use. Duration-response trends were not observed for NSAID use. In the meta-analysis aggregating GICC data with five previous studies, there was a marginally significant association between use of aspirin and glioma (mOR = 0.84; 95% CI, 0.70–1.02), but no association for NSAID use. Conclusions: Our study suggests that aspirin may be associated with a reduced risk of glioma. Impact: These results imply that aspirin use may be associated with decreased glioma risk. Further research examining the association between aspirin use and glioma risk is warranted.

Summary Calorie restriction (CR) is a dietary intervention with potential benefits for healthspan improvement and lifespan extension. In 53 (34 CR and 19 control) non-obese adults, we tested the hypothesis that energy expenditure (EE) and its endocrine mediators are reduced with a CR diet over 2 years. Approximately 15% CR was achieved over 2 years, resulting in an average 8.7 kg weight loss, whereas controls gained 1.8 kg. In the CR group, EE measured over 24 hr or during sleep was approximately 80–120 kcal/day lower than expected on the basis of weight loss, indicating sustained metabolic adaptation over 2 years. This metabolic adaptation was accompanied by significantly reduced thyroid axis activity and reactive oxygen species (F2-isoprostane) production. Findings from this 2-year CR trial in healthy, non-obese humans provide new evidence of persistent metabolic slowing accompanied by reduced oxidative stress, which supports the rate of living and oxidative damage theories of mammalian aging.

Summary Calorie restriction (CR) without malnutrition slows aging in animal models. Oxidative stress reduction was proposed to mediate CR effects. CR effect on urinary F2-isoprostanes, validated oxidative stress markers, was assessed in CALERIE, a two-year randomized controlled trial. Healthy volunteers (n = 218) were randomized to prescribed 25% CR (n = 143) or ad libitum control (AL, n = 75) stratifying the randomization schedule by site, sex, and BMI. F2-isoprostanes were quantified using LC-MS/MS in morning, fasted urine specimens at baseline, at 12 and 24 months. The primary measure of oxidative status was creatinine-adjusted 2,3-dinor-iPF(2α)-III concentration, additional measured included iPF(2α)-III, iPF2a-VI, and 8,12-iso-iPF2a-VI. Intention-to-treat analyses assessed change in 2,3-dinor-iPF(2α)-III using mixed models assessing treatment, time, and treatment-by-time interaction effects, adjusted for blocking variables and baseline F2-isoprostane value. Exploratory analyses examined changes in iPF(2α)-III, iPF(2α)-VI, and 8,12-iso-iPF(2α)-VI. A factor analysis used aggregate information on F2-isoprostane values. In CR group, 2,3-dinor-iPF(2α)-III concentrations were reduced from baseline by 17% and 13% at 12 and 24 months, respectively; these changes were significantly different from AL group (p

BACKGROUND: Obesity and related factors have been implicated as possible aetiological factors for the development of glioma in epidemiological observation studies. We used genetic markers in a Mendelian randomisation framework to examine whether obesity-related traits influence glioma risk. This methodology reduces bias from confounding and is not affected by reverse causation.METHODS: Genetic instruments were identified for 10 key obesity-related risk factors, and their association with glioma risk was evaluated using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. The estimated odds ratio of glioma associated with each of the genetically defined obesity-related traits was used to infer evidence for a causal relationship.RESULTS: No convincing association with glioma risk was seen for genetic instruments for body mass index, waist-to-hip ratio, lipids, type-2 diabetes, hyperglycaemia or insulin resistance. Similarly, we found no evidence to support a relationship between obesity-related traits with subtypes of glioma-glioblastoma (GBM) or non-GBM tumours.CONCLUSIONS: This study provides no evidence to implicate obesity-related factors as causes of glioma.

Background An inverse relationship between allergies with glioma risk has been reported in several but not all epidemiological observational studies. We performed an analysis of genetic variants associated with atopy to assess the relationship with glioma risk using Mendelian randomisation (MR), an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations. Methods Two-sample MR was undertaken using genome-wide association study data. We used single nucleotide polymorphisms (SNPs) associated with atopic dermatitis, asthma and hay fever, IgE levels, and self-reported allergy as instrumental variables. We calculated MR estimates for the odds ratio (OR) for each risk factor with glioma using SNP-glioma estimates from 12,488 cases and 18,169 controls, using inverse-variance weighting (IVW), maximum likelihood estimation (MLE), weighted median estimate (WME) and mode-based estimate (MBE) methods. Violation of MR assumptions due to directional pleiotropy were sought using MR-Egger regression and HEIDI-outlier analysis. Results Under IVW, MLE, WME and MBE methods, associations between glioma risk with asthma and hay fever, self-reported allergy and IgE levels were non-significant. An inverse relationship between atopic dermatitis and glioma risk was found by IVW (OR 0.96, 95% confidence interval (CI) 0.93–1.00, P = 0.041) and MLE (OR 0.96, 95% CI 0.94–0.99, P = 0.003), but not by WME (OR 0.96, 95% CI 0.91–1.01, P = 0.114) or MBE (OR 0.97, 95% CI 0.92–1.02, P = 0.194). Conclusions Our investigation does not provide strong evidence for relationship between atopy and the risk of developing glioma, but findings do not preclude a small effect in relation to atopic dermatitis. Our analysis also serves to illustrate the value of using several MR methods to derive robust conclusions. Electronic supplementary material The online version of this article (10.1186/s12916-018-1027-5) contains supplementary material, which is available to authorized users.

BackgroundGenome-wide association studies (GWAS) have identified 25 risk variants for glioma, which explain ~30% of heritable risk. Most glioma histologies occur with significantly higher incidence in males. A sex-stratified analysis ide7ntified sex-specific glioma risk variants, and further analyses using gene- and pathway-based approaches may further elucidate risk variation by sex.MethodsResults from the Glioma International Case-Control Study were used as a testing set, and results from three GWAS were combined via meta-analysis and used as a validation set. Using summary statistics for autosomal SNPs found to be nominally significant (p−6in ⅔ algorithms.Results25 genes within five regions and 19 genes within six regions reached the set significance threshold in at least 2/3 algorithms in males and females, respectively.EGFRandRTEL1-TNFRSF6Bwere significantly associated with all glioma and glioblastoma in males only, and a female-specific association inTERT, all of which remained nominally significant after conditioning on known risk loci. There were nominal associations with the Telomeres, Telomerase, Cellular Aging, and Immortality pathway in both males and females.ConclusionsThese results suggest that there may be biologically relevant significant differences by sex in genetic risk for glioma. Additional gene- and pathway-based analyses may further elucidate the biological processes through which this risk is conferred.