Burnout is a growing problem in high-performance sport and has negative consequences for athletes (e.g., mental ill-health). It is therefore important to effectively monitor athlete burnout to aid intervention efforts. While self-report measures are available (e.g., athlete burnout questionnaire), the limitations associated with these measures (e.g., social desirability bias) means that objective physiological markers may also be useful. Thus, this article critically discusses potential biomarkers of athlete burnout, drawing on research inside and outside of sport to offer an overview of the current state-of-the-art in this research area. First, it outlines what athlete burnout is, its deleterious consequences, and discusses existing psychological assessments. The article then critically discusses literature on hypothalamic-pituitary-adrenal axis (e.g., salivary cortisol) and autonomic nervous system (e.g., heart rate variability) indices of burnout, highlighting some promising biomarkers for future research (e.g., salivary cortisol at bedtime, vagally-mediated heart rate variability at rest). Finally, the article concludes by highlighting key considerations and offering recommendations for future research (e.g., use of more homogenous methods in assessing burnout and physiological parameters). As a result, the intention of this article is to spark more higher quality research on the psychophysiology of athlete burnout, thereby helping tackle this prominent issue in high-performance sport.

The ubiquitin C (UBC) network in bladder cancer.

Supplementary Figure 1: Electrophoretic mobility shift assays (EMSA) for CCNE1 SNPs rs8102137 and rs7257330. Supplementary Figure 2: Alignment of cyclin E protein isoforms - WT1, WT2 and ES and ET. Supplementary Figure 3: Functional analysis of cyclin E isoforms.Supplementary Table 1: Description of sub-studies included in NCI-GWAS1 and GWAS2 of bladder cancer. Supplementary Table 2: Characteristics of bladder tissue samples used for mRNA expression analysis. Supplementary Table 3: PCR primers, genotyping and gene expression assays, EMSA probes and antibodies. Supplementary Table 4: Bladder cancer stage and grade information for patients in the combined GWAS1+2 set. Supplementary Table 6: Association with bladder cancer risk with mutual adjustment for CCNE1 variants. Supplementary Table 7: Association with bladder cancer risk for CCNE1 variants previously associated with other cancers and for two non-synonymous coding variants. Supplementary Table 8: Association between cyclin E protein expression (IHC scores), bladder cancer patient characteristics and CCNE1 variants.

Supplementary Table 1 from Can Lactase Persistence Genotype Be Used to Reassess the Relationship between Renal Cell Carcinoma and Milk Drinking? Potentials and Problems in the Application of Mendelian Randomization

Perspectives on This Article from A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer

ObjectiveThe aim of this study was to investigate whether risk estimates for childhood acute lymphoblastic leukemia change when restricting model comparison groups to "nonpesticide exposure" (NPE10) households.MethodsCases (n = 1810) 15 years or younger were identified through Children's Cancer Group institutions between 1989 and 1993 and age-/sex-matched to controls (n = 1951). Household pesticide use during pregnancy/month prior was collected via telephone. NPE10 comparison group reporting no parental exposure to 10 pesticide classes was identified.ResultsAdjusted odds ratios increased from 15% to 49% when limiting the comparison to NPE10. Maternal termite insecticide exposure was associated with greatest risk (adjusted odds ratio, 4.21; 95% confidence interval, 2.00-8.88). There was minimal evidence of interaction by child sex or occupational pesticide exposure, and no monotonic dose-response pattern with frequency of use (times per year).ConclusionsElevated risks are consistent with published pooled-/meta-analyses and DNA damage. The consistency and magnitude of these associations warrant product labeling, exposure reduction interventions, or both.

Bladder cancer network containing debiquitinating enzymes (DUBs), chromosome remodeling genes and genes associated with VEGF signaling.

Supplementary Figure 1 from Apolipoprotein E/C1 Locus Variants Modify Renal Cell Carcinoma Risk

PDF - 115K, Table S1. Comparison of subjects included in this analysis to subjects excluded because they had high exposures both in early life and adulthood. Table S2. Comparison of residences of the Chile case control study controls at the time of ascertainment to the 2002 Chile census. Table S3. Cancer odds ratios in subjects exposed in utero and childhood using different age categories to define early-life exposure.

Supplementary Materials from A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer

Supplementary Tables and Figure. Supplementary Table S1. Cancer susceptibility SNPs at chromosome 8q24, sorted by coordinate. Supplementary Table S2. DNA methylation levels and QC results for the 60 CpG sites with a CV less than 25. Supplementary Figure S1. Boxplots displaying percent DNA methylation by genotype for the CpG site - SNP correlations (panels A-J) meeting a False Discovery Rate less than 0.05.

PDF file - 31K - Bladder and lung cancer odds ratios including only cases with histologic confirmation, in non-proxy subjects, and in males and females comparing subjects in the upper to lower quartiles of average lifetime arsenic concentration prior to 1971 (the end of the high exposure period in Antofagasta)

<p>Association between CCNE1 variants and bladder cancer</p>

PDF file - 26K - Arsenic concentration and intake metrics in bladder and lung cancer cases and controls

Supplementary Table S1 presents the clinical characteristics of patients and tumors analyzed in the study. Supplementary Table S2 contains the details about next generation sequencing of the tumor exomes, mapped reads, target depth, and target coverage. Supplementary Table S3 shows the somatic variants that were confirmed by PCR and Sanger sequencing of tumor-normal DNA. Supplementary Table S4 shows the germline variants that were confirmed by PCR and Sanger sequencing of tumor-normal DNA. Supplementary Table S5 includes details about bladder tumor samples with somatic BAP1 mutations. Supplementary Table S6 includes details about the somatic and germline variants that were characterized in the proximal TERT promoter. Supplementary Table S7 examines associations between altered genes to determine if the mutations in different cancer genes were correlated. Supplementary Table S8 shows the genes and biological functions that are associated with interaction networks that include the frequently altered bladder cancer genes. Supplementary Table S9 presents details about the microarray gene expression datasets that were used for the network and gene expression analyses.

Determinants of leukocyte telomere length among controls

Supplementary Figure 1 from Occupational Trichloroethylene Exposure and Renal Carcinoma Risk: Evidence of Genetic Susceptibility by Reductive Metabolism Gene Variants

Background: In ASCENT, patients with mTNBC refractory to or relapsing after ≥2 prior chemotherapies (at least one in the metastatic setting) were randomized 1:1 to receive sacituzumab govitecan (SG) or single-agent treatment of physician’s choice (TPC) (capecitabine, eribulin, vinorelbine, or gemcitabine). Primary endpoint was progression free survival. Secondary endpoints included overall survival, objective response rate, clinical benefit rate, and safety. Here we examined whether health-related quality of life (HRQoL) differed by clinical response. Methods: The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQC30) version 3 was used to assess HRQoL at baseline and on day 1 of each treatment cycle. This analysis included intention-to-treat patients who had a completed at least one of the 15 domains/scales at baseline and at least one evaluable assessment at post-baseline visits based on EORTC QLQ-C30. Patients were classified as responders (partial or complete disease response) or non-responders (stable or progressive disease or not evaluable) based on best overall response (RECIST). A mixed-effect model for repeated measures (MMRM) was used to estimate leastsquare (LS) mean EORTC QLQC30 score changes from baseline using all HRQoL data assessed during Cycle 2 Day 1 (C2D1) to C6D1 (where n was ≥25 in both treatment arms) for responders and nonresponders within each treatment group. Results: Mean QLQ-C30 subscale scores at baseline were similar between treatment arms. The analysis included 236 patients in the SG arm of the full trial population, of whom 82 (35%) were clinical responders; and 183 in the TPC arm, of whom 11 (6%) were clinical responders. Due to the small number of TPC responders, inferential statistical testing to compare between-group difference was not performed.Irrespective of their clinical response status, patients treated with SG showed more favorable LS mean changes than patients who received TPC in all EORTC QLQ-C30 domains, except for nausea/vomiting and diarrhea (Table). Overall, LS mean changes in EORTC QLQ-C30 scores in SG nonresponders were less favorable than those in SG responders, but more favorable than those in TPC responders and TPC nonresponders for most EORTC QLQ-C30 domains. Conclusions: The analysis demonstrates that regardless of response status, SG responders and non-responders showed a better trend in HRQoL changes than TPC. Patients who achieved a tumor response to SG may benefit most in HRQoL. Although patients treated with SG reported higher rates of diarrhea, this did not generate a negative impact on their overall quality of life or functioning. Table: Mixed effects model least-square mean EORTC QLQ-C30 score changes from baselineLeast-square mean change from baseline (95% confidence interval)SG responders(N=82)SG nonresponders(N=154)TPC responders(N=11)TPC non-responders(N=172)Global health status/QoL2.46 (-1.52, 6.43)-0.57 (-3.68, 2.54)-1.64 (-10.22, 6.95)-2.29 (-5.63, 1.05)FunctioningPhysical2.93 (-0.92, 6.79)0.22 (-2.71, 3.15)-3.47 (-11.93, 4.99)-3.75 (-6.87, -0.63)Role-0.35 (-5.74, 5.04)-3.23 (-7.45, 0.99)-8.40 (-19.93, 3.13)-7.33 (-11.88, -2.78)Emotional6.20 (2.23, 10.18)1.97 (-1.12, 5.06)4.87 (-3.70, 13.44)0.08 (-3.24, 3.40)Cognitive0.90 (-2.99, 4.79)-2.25 (-5.26, 0.76)-4.46 (-12.87, 3.95)-1.26 (-4.49, 1.98)Social2.06 (-3.50, 7.61)-3.35 (-7.65, 0.95)-5.79 (-18.29, 6.72)-4.36 (-8.99, 0.27)SymptomsFatigue0.90 (-3.49, 5.28)2.84 (-0.60, 6.29)4.15 (-5.34, 13.65)6.65 (2.93, 10.38)Nausea/vomiting4.68 (1.42, 7.95)4.03 (1.42, 6.64)1.38 (-5.53, 8.29)2.62 (-0.21, 5.45)Pain-11.40 (-16.43, -6.36)-8.57 (-12.48, -4.66)-11.99 (-22.85, -1.13)-0.24 (-4.47, 3.99)Dyspnea-7.88 (-13.09, -2.67)-1.90 (-5.93, 2.13)1.97 (-9.33, 13.27)3.86 (-0.47, 8.18)Insomnia-6.12 (-11.99, -0.26)-3.51 (-8.04, 1.02)4.83 (-7.85, 17.51)-0.98 (-5.86, 3.90)Appetite loss0.22 (-5.25, 5.70)5.45 (1.15, 9.75)8.67 (-3.05, 20.40)4.60 (-0.05, 9.26)Constipation0.93 (-4.57, 6.43)2.20 (-2.09, 6.49)3.87 (-7.96, 15.70)3.52 (-1.12, 8.16)Diarrhea16.03 (10.32, 21.74)13.65 (9.19, 18.11)2.46 (-9.88, 14.80)-1.53 (-6.34, 3.29)Financial difficulties-3.57 (-8.54, 1.39)-2.44 (-6.21, 1.34)-4.41 (-15.27, 6.46)0.61 (-3.42, 4.64)A higher score for a functional domain represents a higher or healthier level of functioning; a higher score for the global health status/QoL represents a higher overall HRQoL; but a higher score for a symptom domain represents a higher level of symptomatology or problems Citation Format: Sibylle Loibl, Sara M. Tolaney, Kevin Punie, Mafalda Oliveira, Hope S. Rugo, Aditya Bardia, Sara A. Hurvitz, Adam Brufsky, Kevin M Kalinsky, Javier Cortes, Joyce O’Shaughnessy, Lisa A. Carey, Luca Gianni, Véronique Diéras, Ling Shi, Mahdi Gharaibeh, Luciana Preger, Lee Moore, See Phan, Martine Piccart. Assessment of health-related quality of life by clinical response from the phase 3 ASCENT study in metastatic triple-negative breast cancer (mTNBC) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-16-01.

Background/Aim Household pesticide exposure is associated with increased risk of childhood acute lymphoblastic leukemia (ALL). We sought to investigate whether ALL risk estimates for pest-specific use categories (PSUC) change when using an analysis method (AM) versus a design method (DM) of multiple-PSUC confounding adjustment. Methods Cases (N=1810) ages ≤15 were identified through Children's Cancer Group institutions between 1989-1993 and age-/sex-matched to community controls (N=1951). Household pesticide use during pregnancy (including the month prior) was collected via telephone interview. We used unconditional logistic regression to estimate ALL odds ratios (OR) for parental (mother/father/both) exposure to ten household PSUC (ant-cockroach-fly-bees, moths, spiders-mites, rodents, fleas-ticks, termites, slugs-snails, weeds, plants, or commercial exterminator). For AM, we assessed risk by including all PSUC covariates. For DM, each PSUC was assessed as a contrast with a zero-use comparison group (all ten-categories=zero) in adjusted models. All models were adjusted for known ALL risk factors which remained significant in final models (income, maternal age, and prenatal-vitamin use). Results Pesticide use during pregnancy was prevalent (54% reported exposure to 2+ PSUC). PSUC bivariate correlations were all less than 0.25. Using AM, elevated ORAM (range: 1.41-1.55) were observed in three maternal and one paternal PSUC (p<0.05). All ORs increased when using DM (mother's range: 22.84%-62.50%; father's range: 22.34-60.50%). Risks were highest for termite (ORDM=3.98, 95% CI: 1.24-12.75, mothers) and spider-mite pesticides (ORDM=2.72, 95% CI: 1.29-5.75, fathers). Conclusions ALL risk estimates for exposure to ten pesticide use categories increased among both mothers and fathers when restricting exposure contrasts to a zero-use comparison group. These results are consistent with other studies reporting highest risks for termite pesticide exposure. This may reflect either a true risk difference or other characteristics among zero-use households which reduce risk. Investigation of potential mediation among pesticide classes appears warranted. Keywords Acute Lymphoblastic Leukemia; childhood; pesticides; prenatal; case-control