Novel use of menstrual blood for monitoring glycaemic control in patients with diabetes: a proof-of-concept study.

BACKGROUND: Glycated haemoglobin (HbA1c) is the diagnostic and prognostic standard for clinical management of diabetes mellitus (DM). Unfortunately, patient adherence to guidelines for routine testing can be poor and there are significant gender-based disparities in DM management and outcomes. Recent evidence suggests that menstrual blood may be comparable to systemic blood for monitoring of common biomarkers. The objective of the present study was to assess the concordance of HbA1c levels between menstrual and systemic blood in healthy women and women with diabetes of reproductive age. METHODS: In this prospective, observational cohort study, we enrolled healthy and diabetic (type 1 and type 2 DM) reproductive-age women (aged ≥18 and ≤45 years). Menstrual blood and venous systemic blood specimens were simultaneously obtained at time of menstruation, and analysed for HbA1c levels. Participants self-collected menstrual blood using a QPad, a novel, modified menstrual pad with an embedded dried blood spot strip. RESULTS: Among 172 participants, 57.6% were healthy and 42.4% had a diagnosis of either type 1 or type 2 DM. There were no significant differences in mean HbA1c values in menstrual and systemic blood across the overall cohort or within the diabetic subgroup. Furthermore, HbA1c levels between blood sources were robustly correlated and demonstrated a significant linear relationship. CONCLUSIONS: There is a strong concordance in HbA1c levels between menstrual and systemic blood. Empowered by self-collection technologies, these findings suggest that menstrual blood may serve as a reliable, non-invasive and potentially cost-effective alternative to serum for HbA1c monitoring among reproductive-age women with DM.

Feasibility, safety, and acceptability of a remotely monitored exercise pilot CHAMP: A Clinical trial of High-intensity Aerobic and resistance exercise for Metastatic castrate-resistant Prostate cancer.

BACKGROUND: Exercise may improve clinical and quality of life outcomes for men with prostate cancer. No randomized controlled trials (RCTs) have examined the feasibility, safety, and acceptability of remote exercise training in men with metastatic castrate-resistant prostate cancer (mCRPC). METHODS: We conducted a pilot RCT (1:1:1 aerobic or resistance exercise 3x/week or usual care) to determine the feasibility, safety, and acceptability of remotely monitored exercise over 12 weeks in 25 men with mCRPC. A prescribed exercise program was based on baseline testing including high- and moderate-intensity aerobic exercise or resistance exercise completed at a local exercise facility. Feasibility was based on attendance, adherence, and tolerance; safety on adverse events; and acceptability on participant interviews. RESULTS: Between March 2016 and March 2020, 25 patients were randomized (8 aerobic, 7 resistance, and 10 control). Twenty-three men (82%) completed the 12-week study. Men who completed the remote intervention attempted 90% and 96% of prescribed aerobic and resistance training sessions, respectively, and 86% and 88% of attempted sessions were completed as or more than prescribed. We observed changes in performance tests that corresponded with the exercise prescription. No safety concerns were identified. Ninety percent of participants interviewed were satisfied with the program and would recommend it to others. CONCLUSIONS: Remotely monitored exercise training is feasible, safe, and acceptable in men with mCRPC; there was no difference in these outcomes by mode of exercise. Through this research, we provide direction and rationale for future studies of exercise and clinical outcomes in patients with metastatic prostate cancer.

Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms.

OBJECTIVE: Development of a risk-stratification model to predict severe Covid-19 related illness, using only presenting symptoms, comorbidities and demographic data. MATERIALS AND METHODS: We performed a case-control study with cases being those with severe disease, defined as ICU admission, mechanical ventilation, death or discharge to hospice, and controls being those with non-severe disease. Predictor variables included patient demographics, symptoms and past medical history. Participants were 556 patients with laboratory confirmed Covid-19 and were included consecutively after presenting to the emergency department at a tertiary care center from March 1, 2020 to April 21, 2020 RESULTS: Most common symptoms included cough (82%), dyspnea (75%), and fever/chills (77%), with 96% reporting at least one of these. Multivariable logistic regression analysis found that increasing age (adjusted odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03-1.06), dyspnea (OR, 2.56; 95% CI: 1.51-4.33), male sex (OR, 1.70; 95% CI: 1.10-2.64), immunocompromised status (OR, 2.22; 95% CI: 1.17-4.16) and CKD (OR, 1.76; 95% CI: 1.01-3.06) were significant predictors of severe Covid-19 infection. Hyperlipidemia was found to be negatively associated with severe disease (OR, 0.54; 95% CI: 0.33-0.90). A predictive equation based on these variables demonstrated fair ability to discriminate severe vs non-severe outcomes using only this historical information (AUC: 0.76). CONCLUSIONS: Severe Covid-19 illness can be predicted using data that could be obtained from a remote screening. With validation, this model could possibly be used for remote triage to prioritize evaluation based on susceptibility to severe disease while avoiding unnecessary waiting room exposure.