Role and mechanism of PVN-sympathetic-adipose circuit in depression and insulin resistance induced by chronic stress.

Chronic stress induces depression and insulin resistance, between which there is a bidirectional relationship. However, the mechanisms underlying this comorbidity remain unclear. White adipose tissue (WAT), innervated by sympathetic nerves, serves as a central node in the interorgan crosstalk through adipokines. Abnormal secretion of adipokines is involved in mood disorders and metabolic morbidities. We describe here a brain-sympathetic nerve-adipose circuit originating in the hypothalamic paraventricular nucleus (PVN) with a role in depression and insulin resistance induced by chronic stress. PVN neurons are labelled after inoculation of pseudorabies virus (PRV) into WAT and are activated under restraint stress. Chemogenetic manipulations suggest a role for the PVN in depression and insulin resistance. Chronic stress increases the sympathetic innervation of WAT and downregulates several antidepressant and insulin-sensitizing adipokines, including leptin, adiponectin, Angptl4 and Sfrp5. Chronic activation of the PVN has similar effects. ß-adrenergic receptors translate sympathetic tone into an adipose response, inducing downregulation of those adipokines and depressive-like behaviours and insulin resistance. We finally show that AP-1 has a role in the regulation of adipokine expression under chronic stress.

The Eotaxin-1/CCR3 Axis and Matrix Metalloproteinase-9 Are Critical in Anti-NC16A IgE-Induced Bullous Pemphigoid.

Bullous pemphigoid (BP) is the most common autoimmune bullous skin disease of humans and is characterized by eosinophilic inflammation and circulating and tissue-bound IgG and IgE autoantibodies directed against two hemidesmosomal proteins: BP180 and BP230. The noncollagenous 16A domain (NC16A) of BP180 has been found to contain major epitopes recognized by autoantibodies in BP. We recently established the pathogenicity of anti-NC16A IgE through passive transfer of patient-derived autoantibodies to double-humanized mice that express the human high-affinity IgE receptor, FcεRI, and human NC16A domain (FcεRI/NC16A). In this model, anti-NC16A IgEs recruit eosinophils to mediate tissue injury and clinical disease in FcεRI/NC16A mice. The objective of this study was to characterize the molecular and cellular events that underlie eosinophil recruitment and eosinophil-dependent tissue injury in anti-NC16A IgE-induced BP. We show that anti-NC16A IgEs significantly increase levels of key eosinophil chemoattractants, eotaxin-1 and eotaxin-2, as well as the proteolytic enzyme matrix metalloproteinase-9 (MMP-9) in the lesional skin of FcεRI/NC16A mice. Importantly, neutralization of eotaxin-1, but not eotaxin-2, and blockade of the main eotaxin receptor, CCR3, drastically reduce anti-NC16A IgE-induced disease activity. We further show that anti-NC16A IgE/NC16A immune complexes induce the release of MMP-9 from eosinophils, and that MMP-9-deficient mice are resistant to anti-NC16A IgE-induced BP. Lastly, we find significantly increased levels of eotaxin-1, eotaxin-2, and MMP-9 in blister fluids of BP patients. Taken together, this study establishes the eotaxin-1/CCR3 axis and MMP-9 as key players in anti-NC16A IgE-induced BP and candidate therapeutic targets for future drug development and testing.

Two-phase stratified sampling and analysis for predicting binary outcomes.

The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a sub-sample of subjects. To date guidance on how best to allocate resources within the design has assumed that primary interest lies in estimating association parameters. When primary interest lies in the development and evaluation of a risk prediction tool, however, such guidance may, in fact, be detrimental. To resolve this, we propose a novel strategy for resource allocation based on oversampling cases and subjects who have more extreme risk estimates according to a preliminary model developed using fully observed predictors. Key to the proposed strategy is that it focuses on enhancing efficiency regarding estimation of measures of predictive accuracy, rather than on efficiency regarding association parameters which is the standard paradigm. Towards valid estimation and inference for accuracy measures using the resultant data, we extend an existing semiparametric maximum likelihood ethod for estimating odds ratio association parameters to accommodate the biased sampling scheme and data incompleteness. Motivated by our sampling design, we additionally propose a general post-stratification scheme for analyzing general two-phase data for estimating predictive accuracy measures. Through theoretical calculations and simulation studies, we show that the proposed sampling strategy and post-stratification scheme achieve the promised efficiency improvement. Finally, we apply the proposed methods to develop and evaluate a preliminary model for predicting the risk of hospital readmission after cardiac surgery using data from the Pennsylvania Health Care Cost Containment Council.

Feasibility of risk assessment for breast cancer molecular subtypes.

PURPOSE: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination. METHODS: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype. RESULTS: There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI. CONCLUSION: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.

Hsa_circ_0000520 suppresses vasculogenic mimicry formation and metastasis in bladder cancer through Lin28a/PTEN/PI3K signaling.

BACKGROUND: Vasculogenic mimicry (VM) is a potential cause of resistance to antiangiogenic therapy and is closely related to the malignant progression of tumors. It has been shown that noncoding RNAs play an important role in the formation of VM in malignant tumors. However, the role of circRNAs in VM of bladder cancer and the regulatory mechanisms are unclear. METHODS: Firstly, hsa_circ_0000520 was identified to have circular character by Sanger sequencing and Rnase R assays. Secondly, the potential clinical value of hsa_circ_0000520 was explored by quantitative real-time polymerase chain reaction (qRT-PCR) and fluorescence in situ hybridization (FISH) of clinical specimens. Thirdly, the role of hsa_circ_0000520 in bladder cancer invasion, migration, and VM formation was examined by in vivo and in vitro experiments. Finally, the regulatory mechanisms of hsa_circ_0000520 in the malignant progression of bladder cancer were elucidated by RNA binding protein immunoprecipitation (RIP), RNA pulldown, co-immunoprecipitation (co-IP), qRT-PCR, Western blot (WB), and fluorescence co-localization. RESULTS: Hsa_circ_0000520 was characterized as a circular RNA and was lowly expressed in bladder cancer compared with the paracancer. Bladder cancer patients with high expression of hsa_circ_0000520 had better survival prognosis. Functionally, hsa_circ_0000520 inhibited bladder cancer invasion, migration, and VM formation. Mechanistically, hsa_circ_0000520 acted as a scaffold to promote binding of UBE2V1/UBC13 to Lin28a, further promoting the ubiquitous degradation of Lin28a, improving PTEN mRNA stability, and inhibiting the phosphorylation of the PI3K/AKT pathway. The formation of hsa_circ_0000520 in bladder cancer was regulated by RNA binding protein QKI. CONCLUSIONS: Hsa_circ_0000520 inhibits metastasis and VM formation in bladder cancer and is a potential target for bladder cancer diagnosis and treatment.

Effective alleviation of depressive and anxious symptoms and sleep disorders in benzodiazepine-dependent patients through repetitive transcranial magnetic stimulation.

Benzodiazepine (BZD) dependence poses a significant challenge in mental health, prompting the exploration of treatments like repetitive transcranial magnetic stimulation (rTMS). This research aims to assess the impact of rTMS on alleviating symptoms of BZD dependence. A randomized control trial was employed to study 40 BZD-dependent inpatients. Their symptoms were quantified using the Hamilton Anxiety Rating Scale (HAMA), Montgomery-Åsberg Depression Rating Scale (MADRS) and Pittsburgh Sleep Quality Index (PSQI). Participants were divided into a conventional treatment group (daily diazepam with gradual tapering) with supportive psychotherapy and another group receiving the same treatment supplemented with rTMS (five weekly sessions for 2 weeks). Significant improvements were observed in both groups over baseline in MADRS, HAMA and PSQI scores at the 2nd, 4th, 8th and 12th week assessments (p < 0.05). The group receiving rTMS in addition to conventional treatment exhibited superior improvements in all measures at the 8th and 12th weeks. The addition of rTMS to conventional treatment methods for BZD dependence significantly betters the recovery in terms of depression, anxiety and sleep quality, highlighting the role of rTMS as an effective adjunct therapy.

Methods and Software to Analyze Gene-Environment Interactions under a Case-Mother-Control-Mother Design with Partially Missing Child Genotype.

INTRODUCTION: The case-mother-control-mother design allows to study fetal and maternal genetic factors together with environmental exposures on early life outcomes. Mendelian constraints and conditional independence between child genotype and environmental factors enabled semiparametric likelihood methods to estimate logistic models with greater efficiency than standard logistic regression. Difficulties in child genotype collection require methods handling missing child genotype. METHODS: We review a stratified retrospective likelihood and two semiparametric likelihood approaches: a prospective one and a modified retrospective one, the latter either modeling the maternal genotype as a function of covariates or leaving their joint distribution unspecified (robust version). We also review software implementing these modeling alternatives, compare their statistical properties in a simulation study, and illustrate their application, focusing on gene-environment interactions and partially missing child genotype. RESULTS: The robust retrospective likelihood provides generally unbiased estimates, with standard errors only slightly larger than when modeling maternal genotype based on exposure. The prospective likelihood encounters maximization problems. In the application to the association of small-for-gestational-age babies with CYP2E1 and drinking water disinfection by-products, the retrospective likelihood allowed a full array of covariates, while the prospective likelihood was limited to few covariates. CONCLUSION: We recommend the robust version of the modified retrospective likelihood.

Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

Efficient inference of parent-of-origin effect using case-control mother-child genotype data.

Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother-child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation-maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-likelihood. This algorithm differs from the conventional expectation-maximization algorithm in the sense that it is based on a modified instead of the original profile log-likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation-maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.

A constrained maximum likelihood approach to developing well-calibrated models for predicting binary outcomes.

The added value of candidate predictors for risk modeling is routinely evaluated by comparing the performance of models with or without including candidate predictors. Such comparison is most meaningful when the estimated risk by the two models are both unbiased in the target population. Very often data for candidate predictors are sourced from nonrepresentative convenience samples. Updating the base model using the study data without acknowledging the discrepancy between the underlying distribution of the study data and that in the target population can lead to biased risk estimates and therefore an unfair evaluation of candidate predictors. To address this issue assuming access to a well-calibrated base model, we propose a semiparametric method for model fitting that enforces good calibration. The central idea is to calibrate the fitted model against the base model by enforcing suitable constraints in maximizing the likelihood function. This approach enables unbiased assessment of model improvement offered by candidate predictors without requiring a representative sample from the target population, thus overcoming a significant practical challenge. We study theoretical properties for model parameter estimates, and demonstrate improvement in model calibration via extensive simulation studies. Finally, we apply the proposed method to data extracted from Penn Medicine Biobank to inform the added value of breast density for breast cancer risk assessment in the Caucasian woman population.