Are Excessive Daytime Sleepiness and Lower Urinary Tract Symptoms the Triggering Link for Mental Imbalance? An Exploratory Post Hoc Analysis

Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome

Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO, and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. A three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier for non-ulcerative BPS. The ML-based framework used to define BPS classifiers establishes a solid foundation for comprehending the gene expression changes in the bladder during BPS and serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.

Lower urinary tract symptoms in young-middle aged males with a diagnosis of obstructive sleep apnea syndrome

BACKGROUND

The aim of the current study is to measure the prevalence and the potential role of International Prostate Symptom Score (IPSS) score as a predictor of obstructive sleep apnea syndrome (OSAS) in male experienced lower urinary tract symptoms (LUTS).

METHODS

A cross-sectional web-based Italian survey was administered via Google Forms between July 17 and October 31, 2022. The urinary functioning was measured through the IPSS questionnaire. Specifically, we considered symptoms occurring more than "about half the time" (score ≥ 3) as bothering symptoms. Multivariable logistic regression models (LRMs) adjusting for age, body mass index (BMI), International Index of Erectile Function-5, IPSS, and hypertension were fitted to predict OSAS in the cohort of men responding to the survey and experiencing LUTS.

RESULTS

Overall, 58 (24.4%) patients had a confirmed diagnosis of OSAS. The overall median IPSS was 5 (inter quartile range [IQR]: 3-8), respectively. According to IPSS items, 24 (10%), 44 (18.4%), 12 (5%), 12 (5%), 12 (5%), 11 (4.6%), 63 (26.4%) patients exhibit incomplete bladder emptying, urinary frequency, intermittency, urgency, weak stream, straining, nocturia with a score ≥ 3, respectively. After multivariable LRMs predicting the developing OSAS, age (odds ratio [OR]: 1.09, p < 0.001), BMI (OR:1.12, p < 0.001) and IPSS total score (OR:1.08, p = 0.02) were independent predicting factors.

CONCLUSION

This analysis revealed that the IPSS total score, age, and BMI are independent predictors of OSAS in males. In this context, the use of IPSS in daily practice could be helpful in assessing the LUTS presence and in supporting physicians to identify a hidden sleep apnea condition.

The Role of Illness Perceptions in Dyspnoea-Related Fear in Chronic Obstructive Pulmonary Disease

Dyspnoea is often the most distressing symptom described by people with a chronic respiratory condition. The traditional biomedical model of neuromechanical uncoupling that explains the physiological basis for dyspnoea is well accepted. However, in people with chronic obstructive pulmonary disease (COPD), measures that are linked with neuromechanical uncoupling are poorly related to the restriction in activity during daily life attributed to dyspnoea. This suggests that activity restriction that results from dyspnoea is influenced by factors other than expiratory airflow limitation and dynamic pulmonary hyperinflation, such as the ways people perceive, interpret and respond to this sensation. This review introduces the common-sense model as a framework to understand the way an individual's lay beliefs surrounding sensations can lead to these sensations being perceived as a health threat and how this impacts their emotional and behavioural responses. The aim is to provide insight into the nuances that can shape an individual's personal construct of dyspnoea and offer practical suggestions to challenge unhelpful beliefs and facilitate cognitive re-structuring as a pathway to reduce distress and optimise health behaviours and outcomes.