Changes in the incidence of type 2 diabetes in Australia, 2005-2019, overall and by socio-demographic characteristics: a population-based study.

OBJECTIVES: To estimate changes in the incidence of clinically diagnosed type 2 diabetes in Australia, overall and by age, sex, socio-economic disadvantage, geographic remoteness, and country of birth. STUDY DESIGN: Population-based study; analysis of National Diabetes Services Scheme (NDSS) data (age-period-cohort models). SETTING, PARTICIPANTS: Data were extracted for incident cases of type 2 diabetes, 1 January 2005 to 31 December 2019, in residents of the Australian Capital Territory, New South Wales, Queensland, and Victoria aged 20 years or older registered with the NDSS. The numbers of people at risk were obtained from the Australian Bureau of Statistics. MAIN OUTCOME MEASURES: Changes in the incidence of type 2 diabetes, 2005-2019, by age, postcode-level socio-economic disadvantage (Index of Relative Socioeconomic Disadvantage) and remoteness (major city, inner regional, outer regional/remote/very remote), and country of birth, stratified by sex. RESULTS: During 2005-2019, 741 535 people aged 20 years or older with incident type 2 diabetes were registered with the NDSS; 421 190 were men (56.8%). Overall, the incidence of type 2 diabetes increased with age (until about age 70 years) and socio-economic disadvantage for both sexes; it was higher in inner regional areas than in major cities or outer regional/remote/very remote areas during 2005-2015, but highest among people in major cities after 2015. The age-standardised incidence of type 2 diabetes increased during 2005-2010, both among men (annual percentage change [APC], 4.4%; 95% confidence interval [CI], 3.6-5.2%) and women (APC, 2.9%; 95% CI, 2.2-3.7%); it declined during 2010-2019 among both men (APC, -5.2%; 95% CI, -5.4% to -4.9%) and women (APC, -6.5%; 95% CI, -6.8% to -6.2%). In general, similar patterns (but of differing magnitude) applied to all age, sex, socio-economic disadvantage, and remoteness groups. However, the incidence of type 2 diabetes increased during 2011-2019 among people born in Asia, North Africa and the Middle East, and the Pacific Islands. CONCLUSIONS: The incidence of type 2 diabetes in Australian adults declined during 2010-2019 across all age, sex, socio-economic disadvantage, and remoteness groups, but increased among people from Asia, North Africa and the Middle East, and the Pacific Islands.

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model.

BACKGROUND: Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts. METHODS: We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL. RESULTS: In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes). CONCLUSIONS: STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.

Impact of type 2 diabetes and its duration on incidence rates of dementia death and medication prescription in the Australian population during 2003-2016.

AIMS: To quantify rates of dementia treatment and death among Australians with type 2 diabetes relative to those without diabetes using linked national registries of Australia. METHODS: The study included 891,418 people with type 2 diabetes registered on the National Diabetes Services Scheme and a randomly sampled, population-based comparison group (n = 1,131,369). Outcomes included dementia death (all-cause dementia, Alzheimer's disease (AD) or vascular dementia), and first prescription of cholinesterase inhibitors or memantine. RESULTS: Excess dementia risk was observed in the diabetes group for the composite outcome of all-cause dementia death or dementia medication prescription but varied with age at diabetes diagnosis and its duration. At age 70, the rate of dementia death/medication prescription was ∼1.3 (95% CI 1.2, 1.3) and 1.1 (95% CI 1.1, 1.2) times higher in people with ten and five years of diabetes duration, respectively. Individual outcomes showed that diabetes was associated with a higher incidence of vascular dementia death, whereas an increased risk of AD death was only observed beyond âˆ¼10 years of diabetes duration. Further, the incidence of dementia medication prescription was lower among people with diabetes. CONCLUSIONS: A higher incidence of AD death in the setting of 10+ years of diabetes duration coupled with a lower incidence of AD treatment suggests an under-recognition of this dementia phenotype among people with type 2 diabetes.

Research elevation of bank lending and technological innovation in the excess liquidity countries.

This study examines the intellectual framework research in bank lending and technological innovation relationships in countries with high banking system liquidity. This study employs bibliometrics with R-studio tools and procedures to analyze documents regarding productions, collaborations, keyword occurrences, conceptual structure, and density and centrality occurrence's network. Combining data from the Web of Science and Scopus databases, this study obtained 939 documents from 527 sources with a significant opportunity for further elevation through combination with other themes. The development analysis based on the most related countries indicates that researchers from other countries have also conducted studies identified as having significant banking liquidity. Topic development and thematic evolution show that research on the role of bank lending on technological innovation evolves to environmental issues, with green credit as the most recent and emerging elaboration. For further direction, keywords in investment clusters can help elevate education, commerce, and impact clusters by combining them with research on government taxation, credit provision, sustainable development, and emission control themes.

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention.

BACKGROUND: Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies. OBJECTIVES: This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS. METHODS: The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)-based outcomes was independently validated in the BioHEART cohort (n = 994). RESULTS: LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS. CONCLUSIONS: LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management.

Excess Risk of Injury in Individuals With Type 1 or Type 2 Diabetes Compared With the General Population.

OBJECTIVE: To estimate the relative risk (RR) and excess hospitalization rate for injury in individuals with diabetes compared with the general population. RESEARCH DESIGN AND METHODS: Data were obtained from the Australian National Diabetes Services Scheme, hospitalization data sets, the Australian Pharmaceutical Benefits Scheme, the National Death Index, and the census spanning from 2011 to 2017. Hospitalizations for injury were coded as head and neck, lower-extremity, upper-extremity, or abdominal and thoracic injury; burns; or other injury. Poisson regression was used to estimate the age- and sex-adjusted RR of hospitalization for injury. RESULTS: The total number of hospitalizations for any injury was 117,705 in people with diabetes and 3,463,173 in the general population. Compared with that in the general population, an elevated adjusted risk of admission was observed for any injury (RR 1.22; 95% CI 1.21, 1.22), head and neck (1.28; 1.26, 1.30), lower extremity (1.24; 1.23, 1.26), abdominal and thoracic (1.29; 1.27, 1.30), upper extremity (1.03; 1.02, 1.05), burns (1.52; 1.44, 1.61), and other injury (1.37; 1.33, 1.40). The adjusted RR of any injury was 1.62 (1.58, 1.66) in individuals with type 1 diabetes, 1.65 (1.63, 1.66) in those with type 2 diabetes who were taking insulin, and 1.07 (1.06, 1.08) in individuals with type 2 diabetes not using insulin. Falls were the primary cause of injury in individuals with diabetes. CONCLUSIONS: Individuals with diabetes, especially those using insulin, had a higher risk of hospitalization for injury compared with the general population.

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age.

BACKGROUND: Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health. METHODS: Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status. FINDINGS: Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group. INTERPRETATION: Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases. FUNDING: The specific funding of this article is provided in the acknowledgements section.

Library size confounds biology in spatial transcriptomics data.

Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.

Urine Albumin-Creatinine Ratio Variability in People With Type 2 Diabetes: Clinical and Research Implications.

RATIONALE & OBJECTIVE: Evidence has demonstrated that albuminuria is a key diagnostic and prognostic marker of diabetic chronic kidney disease, but the impact of its day-to-day variability has not been adequately considered. This study quantified within-individual variability of albuminuria in people with type 2 diabetes to inform clinical albuminuria monitoring. STUDY DESIGN: Descriptive cross-sectional analysis. SETTING & PARTICIPANTS: People with type 2 diabetes (n=826, 67.1 [IQR, 60.3-72.4] years, 64.9% male) participating in the Progression of Diabetic Complications (PREDICT) cohort study. EXPOSURE: Four spot urine collections for measurement of urinary albumin-creatinine ratio (UACR) within 4 weeks. OUTCOME: Variability of UACR. ANALYTICAL APPROACH: We characterized within-individual variability (coefficient of variation [CV], 95% limits of random variation, intraclass correlation coefficient), developed a calculator displaying probabilities that any observed difference between a pair of UACR values truly exceeded a 30% difference, and estimated the ranges of diagnostic uncertainty to inform a need for additional UACR collections to exclude or confirm albuminuria. Multiple linear regression examined factors influencing UACR variability. RESULTS: We observed high within-individual variability (CV 48.8%; 95% limits of random variation showed a repeated UACR to be as high/low as 3.78/0.26 times the first). If a single-collection UACR increased from 2 to 5mg/mmol, the probability that UACR actually increased by at least 30% was only 50%, rising to 97% when 2 collections were obtained at each time point. The ranges of diagnostic uncertainty were 2.0-4.0mg/mmol after an initial UACR test, narrowing to 2.4-3.2 and 2.7-2.9mg/mmol for the mean of 2 and 3 collections, respectively. Some factors correlated with higher (female sex; moderately increased albuminuria) or lower (reduced estimated glomerular filtration rate and sodium-glucose cotransporter 2 inhibitor/angiotensin-converting enzyme inhibitor/angiotensin receptor blocker treatment) within-individual UACR variability. LIMITATIONS: Reliance on the mean of 4 UACR collections as the reference standard for albuminuria. CONCLUSIONS: UACR demonstrates a high degree of within-individual variability among individuals with type 2 diabetes. Multiple urine collections for UACR may improve capacity to monitor changes over time in clinical and research settings but may not be necessary for the diagnosis of albuminuria. PLAIN-LANGUAGE SUMMARY: Albuminuria (albumin in urine) is a diagnostic and prognostic marker of diabetic chronic kidney disease. However, albuminuria can vary within an individual from day to day. We compared 4 random spot urinary albumin-creatinine ratio (UACR) samples from 826 participants. We found that a second UACR collection may be as small as a fourth or as large as almost 4 times the first sample's UACR level. This high degree of variability presents a challenge to our ability to interpret changes in albuminuria. Multiple collections have been suggested as a solution. We have constructed tools that may aid clinicians in deciding how many urine collections are required to monitor and diagnose albuminuria. Multiple urine collections may be required for individual monitoring but not necessarily for diagnosis.

Liver disease mortality and hospitalisations among people with type 2 diabetes mellitus: A population-based study.

BACKGROUND AND AIMS: The burden of liver disease among people with diabetes at a population level is unknown. We explored the burden and trends of liver disease mortality and hospitalisations among Australians with diabetes. METHODS: We linked Australians with type 2 diabetes on the National Diabetes Services Scheme to the National Death Index for 2002-2019 to determine trends in the proportion of deaths due to liver disease, overall and by subcategory. We also determined the leading reasons and risk factors for liver disease hospitalisations in those with diabetes over this period. Finally, we compared the burden of liver disease hospitalisations among those with diabetes to the general population using excess hospitalisations per 100 000 person-years. RESULTS: Among Australians with type 2 diabetes (n = 1 122 431) liver diseases accounted for between 1.5% and 1.9% of deaths between 2002 and 2019, roughly one-third of the proportion of deaths caused by kidney disease. The proportion of deaths due to inflammatory liver diseases among those with diabetes increased from .08% in 2002 to .27% in 2019. Alcohol-related liver disease accounted for the greatest share (22.7%) of liver disease hospitalisation in those with diabetes, but the number of hospitalisations for this condition declined over time. Compared to the general population, men (RR 3.63, 95% CI 3.44-3.84) and women (RR 4.49, 4.21-4.78) with diabetes were at higher risk of hospitalisation for fibrosis and cirrhosis; however, this did not translate to a substantial excess risk per 100 000 population. CONCLUSIONS: Better screening methods for liver disease among people with diabetes should be developed and implemented into practice.

Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.

Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.

Late fetal hematopoietic failure results from ZBTB11 deficiency despite abundant HSC specification.

Hematopoiesis produces diverse blood cell lineages to meet the basal needs and sudden demands of injury or infection. A rapid response to such challenges requires the expansion of specific lineages and a prompt return to balanced steady-state levels, necessitating tightly coordinated regulation. Previously we identified a requirement for the zinc finger and broad complex, tramtrak, bric-a-brac domain-containing 11 (ZBTB11) transcription factor in definitive hematopoiesis using a forward genetic screen for zebrafish myeloid mutants. To understand its relevance to mammalian systems, we extended these studies to mice. When Zbtb11 was deleted in the hematopoietic compartment, embryos died at embryonic day (E) 18.5 with hematopoietic failure. Zbtb11 hematopoietic knockout (Zbtb11hKO) hematopoietic stem cells (HSCs) were overabundantly specified from E14.5 to E17.5 compared with those in controls. Overspecification was accompanied by loss of stemness, inability to differentiate into committed progenitors and mature lineages in the fetal liver, failure to seed fetal bone marrow, and total hematopoietic failure. The Zbtb11hKO HSCs did not proliferate in vitro and were constrained in cell cycle progression, demonstrating the cell-intrinsic role of Zbtb11 in proliferation and cell cycle regulation in mammalian HSCs. Single-cell RNA sequencing analysis identified that Zbtb11-deficient HSCs were underrepresented in an erythroid-primed subpopulation and showed downregulation of oxidative phosphorylation pathways and dysregulation of genes associated with the hematopoietic niche. We identified a cell-intrinsic requirement for Zbtb11-mediated gene regulatory networks in sustaining a pool of maturation-capable HSCs and progenitor cells.

Direct inference and control of genetic population structure from RNA sequencing data.

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

Investigating the relationship between monetary policy, macro-prudential policy and credit risk in Indonesia banking industry.

Using a novel panel data set we study the influence of monetary and macro-prudential policies on non-performing loans as a measure of credit risk in Indonesian banking industry from Q1 2010 to Q4 2022. The panel homogeneity assumption was verified through the utilization of the Chow and Roy-Zellner tests. The findings showed that the model was not homogenous, necessitating the use of the Pooled Mean Group (PMG) estimator. The results indicated that monetary and macro-prudential policies significantly impacted credit risk. Furthermore, tight monetary and macro-prudential policies increased and reduced credit risk in the long run, respectively. The findings also showed that a loosening monetary policy reduced credit risk in the short run. Therefore, higher authorities must establish effective monetary and macro-prudential policies to reduce the non-performing loan ratio and maintain credit risk in Indonesia's banking industry.

Body image profiles combining body shame, body appreciation and body mass index differentiate dietary restraint and exercise amount in women.

This project aimed to establish whether meaningful body image profiles (BIPs) could be identified across measures of body shame, body appreciation, and body mass index (BMI), and whether these profiles could differentiate key health behaviours. Data came from 1200 adult women who responded to an online body image survey. Latent profile analysis was used to identify BIPs based on relative levels of body shame, body appreciation and BMI. Differences in dietary restraint and weekly exercise amount were investigated according to BIP membership. Latent profile analysis revealed four unique BIPs; 1. Appreciative BIP (AP-BIP); 2. Medium Shame BIP (MS-BIP); 3. High Shame BIP (HS-BIP) and 4. Average BIP (AV-BIP). Dietary restraint and exercise amount differed significantly according to BIP in most comparisons. Women in the High Shame BIP exhibited the highest dietary restraint and lowest exercise. Women in the Appreciative BIP exhibited the lowest dietary restraint and highest exercise. Body shame and body appreciation intersect with BMI to form unique profiles (BIPs) that differentiate dietary restraint and exercise. Using BIPs to tailor interventions designed to promote healthful diet and exercise should be considered in public health initiatives.

Quantitative proteomic landscape of unstable atherosclerosis identifies molecular signatures and therapeutic targets for plaque stabilization.

Atherosclerotic plaque rupture leading to myocardial infarction is a major global health burden. Applying the tandem stenosis (TS) mouse model, which distinctively exhibits the characteristics of human plaque instability/rupture, we use quantitative proteomics to understand and directly compare unstable and stable atherosclerosis. Our data highlight the disparate natures and define unique protein signatures of unstable and stable atherosclerosis. Key proteins and pathway networks are identified such as the innate immune system, and neutrophil degranulation. The latter includes calprotectin S100A8/A9, which we validate in mouse and human unstable plaques, and we demonstrate the plaque-stabilizing effects of its inhibition. Overall, we provide critical insights into the unique proteomic landscape of unstable atherosclerosis (as distinct from stable atherosclerosis and vascular tissue). We further establish the TS model as a reliable preclinical tool for the discovery and testing of plaque-stabilizing drugs. Finally, we provide a knowledge resource defining unstable atherosclerosis that will facilitate the identification and validation of long-sought-after therapeutic targets and drugs for plaque stabilization.

Hospitalisation for mental health disorders in Australians with type 1 or type 2 diabetes.

AIMS: To determine the burden and leading reasons for mental health hospitalisation among Australians with diabetes. METHODS: We determined the incidence of hospitalisation for all mental health disorders in people with type 1 or type 2 diabetes of all ages by linking the National Diabetes Services Scheme to hospital admission datasets from 2010 to 2017. We compared those with type 2 diabetes aged 15 and above to the general population using excess hospitalisations per 100,000 person-years associated with diabetes. RESULTS: Depressive disorders were the leading reason for mental health admission in Australians with diabetes, responsible for 6.09 (95% CI 5.78-6.42) and 7.05 (6.95-7.14) admissions per 1,000 person-years in those with type 1 and type 2 diabetes, respectively. When considering only one admission per person, mental health admission rates were up to 90% lower. Among males with type 2 diabetes, stress and adjustment disorders were the leading cause of excess admissions compared to the general population, while depressive disorders were the leading cause in females. CONCLUSIONS: We found a substantial burden of psychiatric hospitalisations among Australians with diabetes, reinforcing the importance of mental health awareness among diabetes clinicians, and support by psychiatric teams for those with diabetes to prevent readmission.

The scaffold RhoGAP protein ARHGAP8/BPGAP1 synchronizes Rac and Rho signaling to facilitate cell migration.

Rho GTPases regulate cell morphogenesis and motility under the tight control of guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs). However, the underlying mechanism(s) that coordinate their spatiotemporal activities, whether separately or together, remain unclear. We show that a prometastatic RhoGAP, ARHGAP8/BPGAP1, binds to inactive Rac1 and localizes to lamellipodia. BPGAP1 recruits the RacGEF Vav1 under epidermal growth factor (EGF) stimulation and activates Rac1, leading to polarized cell motility, spreading, invadopodium formation, and cell extravasation and promotes cancer cell migration. Importantly, BPGAP1 down-regulates local RhoA activity, which influences Rac1 binding to BPGAP1 and its subsequent activation by Vav1. Our results highlight the importance of BPGAP1 in recruiting Vav1 and Rac1 to promote Rac1 activation for cell motility. BPGAP1 also serves to control the timing of Rac1 activation with RhoA inactivation via its RhoGAP activity. BPGAP1, therefore, acts as a dual-function scaffold that recruits Vav1 to activate Rac1 while inactivating RhoA to synchronize both Rho and Rac signaling in cell motility. As epidermal growth factor receptor (EGFR), Vav1, RhoA, Rac1, and BPGAP1 are all associated with cancer metastasis, BPGAP1 could provide a crucial checkpoint for the EGFR-BPGAP1-Vav1-Rac1-RhoA signaling axis for cancer intervention.

Exercise for the Prevention of Anthracycline-Induced Functional Disability and Cardiac Dysfunction: The BREXIT Study.

BACKGROUND: Breast cancer survivors treated with anthracycline-based chemotherapy (AC) have increased risk of functional limitation and cardiac dysfunction. We conducted a 12-month randomized controlled trial in 104 patients with early-stage breast cancer scheduled for AC to determine whether 12 months of exercise training (ExT) could attenuate functional disability (primary end point), improve cardiorespiratory fitness (VO2peak), and prevent cardiac dysfunction. METHODS: Women 40 to 75 years of age with stage I to III breast cancer scheduled for AC were randomized to 3 to 4 days per week aerobic and resistance ExT for 12 months (n=52) or usual care (UC; n=52). Functional measures were performed at baseline, at 4 weeks after AC (4 months), and at 12 months, comprising: (1) cardiopulmonary exercise testing to quantify VO2peak and functional disability (VO2peak ≤18.0 mL·kg-1·min-1); (2) cardiac reserve (response from rest to peak exercise), quantified with exercise cardiac magnetic resonance measures to determine changes in left and right ventricular ejection fraction, cardiac output, and stroke volume; (3) standard-of-care echocardiography-derived resting left ventricular ejection fraction and global longitudinal strain; and (4) biochemistry (troponin and BNP [B-type natriuretic peptide]). RESULTS: Among 104 participants randomized, greater study attrition was observed among UC participants (P=0.031), with 93 women assessed at 4 months (ExT, n=49; UC, n=44) and 87 women assessed at 12 months (ExT, n=49; UC, n=38). ExT attenuated functional disability at 4 months (odds ratio, 0.32 [95% CI, 0.11-0.94]; P=0.03) but not at 12 months (odds ratio, 0.27 [95% CI, 0.06-1.12]; P=0.07). In a per-protocol analysis, functional disability was prevented entirely at 12 months among participants adherent to ExT (ExT, 0% versus UC, 20%; P=0.005). Compared with UC at 12 months, ExT was associated with a net 3.5-mL·kg-1·min-1 improvement in VO2peak that coincided with greater cardiac output, stroke volume, and left and right ventricular ejection fraction reserve (P<0.001 for all). There was no effect of ExT on resting measures of left ventricular function. Postchemotherapy troponin increased less in ExT than in UC (8-fold versus 16-fold increase; P=0.002). There were no changes in BNP in either group. CONCLUSIONS: In women with early-stage breast cancer undergoing AC, 12 months of ExT did not attenuate functional disability, but provided large, clinically meaningful benefits on VO2peak and cardiac reserve. REGISTRATION: URL: https://www.anzctr.org.au/; Unique identifier: ACTRN12617001408370.

Assessing and removing the effect of unwanted technical variations in microbiome data.

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how in-silico approaches can minimize their impact. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. Using the novel Removing Unwanted Variations-III-Negative Binomial (RUV-III-NB), we identified several known experimental factors, specifically storage conditions and freeze-thaw cycles, as likely major sources of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performances of different correction methods, including ComBat, ComBat-seq, RUVg, RUVs, and RUV-III-NB. While RUV-III-NB performed consistently robust across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally. Our analyses suggest that a careful consideration of possible technical confounders is critical during experimental design of microbiome studies, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.