Purpose: The aim of the study was to compare lower urinary tract symptoms (LUTS) in women with and without type 2 diabetes mellitus (DM).
Subjects And Setting: The sample was drawn from community-dwelling women in the province of Istanbul who were cared for in the diabetes outpatient clinic of Istanbul Medical School between January and June 2012. Two hundred forty-nine women with DM were compared to 255 women without DM cared for in the obstetrics and gynecology department of the same university hospital. The mean ages of the groups were 55.1 and 53.7 years, respectively.
Methods: Participants completed a questionnaire that queried sociodemographic and clinical characteristics; the Bristol Female Lower Urinary Tract Symptoms-Short Form (BFLUTS-SF) was used to evaluate LUTS. The questionnaire required 10 to 15 minutes to complete; participants completed the questionnaire in a private room of each of the respective outpatient clinics.
Results: No statistically significant differences were found when groups (women with and without DM) were compared based on age and cigarette smoking (P > .05). In contrast, BMI scores were significantly higher in the women with DM (P < .001). The cumulative BFLUTS scores and the filling and incontinence symptoms subscale sores (P < .001) were significantly higher in women with DM. No differences were observed in voiding symptoms (P = .347), sexual function (P = .380), and health-related quality of life (P = .142) subscale scores. The prevalence of storage symptoms nocturia, voiding frequency, urge incontinence, stress incontinence, frequency of incontinent episodes were higher among women with DM. In addition, women with DM were more likely to report the need to change clothing because of urinary leakage, effect of incontinence on daily tasks, and overall interference with daily activities of living.
Conclusions: Women with type 2 DM are more likely to experience LUTS as compared to women without DM. Women with type 2 DM should routinely be assessed for LUTS.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/WON.0000000000000259 | DOI Listing |
Cancer Med
January 2025
The Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah, USA.
Introduction: The purpose of this study was to evaluate the association between body composition, overall survival, odds of receiving treatment, and patient-reported outcomes (PROs) in individuals living with metastatic non-small-cell lung cancer (mNSCLC).
Methods: This retrospective analysis was conducted in newly diagnosed patients with mNSCLC who had computed-tomography (CT) scans and completed PRO questionnaires close to metastatic diagnosis date. Cox proportional hazard models and logistic regression evaluated overall survival and odds of receiving treatment, respectively.
Wound Repair Regen
January 2025
Research Unit for Plastic Surgery, University of Southern Denmark, Odense, Denmark.
The WOUND-Q is a patient-reported outcome measure for individuals with any type of chronic wound. This study aimed to identify patient and wound factors associated with the four WOUND-Q health-related quality of life (HRQL) scales: Life impact, Psychological, Sleep, and Social. Adults with a chronic wound were recruited internationally through clinical settings between August 2018 and May 2020, and through an online platform (i.
View Article and Find Full Text PDFWorld J Clin Cases
January 2025
Department of Neurology, The Third Affiliated Hospital of Guizhou Medical University, Duyun 558099, Guizhou Province, China.
Gestational diabetes mellitus (GDM) refers to varying degrees of abnormal glucose metabolism that occur during pregnancy and excludes patients previously diagnosed with diabetes. GDM is a unique among the four subtypes of diabetes classified by the international World Health Organization standards. Although GDM patients constitute a small proportion of the total number of diabetes cases, the incidence of GDM has risen significantly over the past decade, posing substantial risk to pregnant women and infants.
View Article and Find Full Text PDFWorld J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
View Article and Find Full Text PDFJ Diabetes Metab Disord
June 2025
Department of Traditional Medicine, School of Persian Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Objectives: This study was designed to characterize the prevalence, pattern of herbal use, and related factors among diabetic patients in Tabriz, Iran.
Methods: A descriptive cross-sectional study was carried out on 322 diabetic patients with random cluster sampling of specialized and subspecialized clinics in Tabriz, Iran. Binary logistic regression analysis was performed to evaluate the association between predictor variables (sociodemographic and disease-related characteristics and patient preference for treatment type) with herb use Interviews were conducted using a structured questionnaire from October 1, 2022, to April 23, 2023.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!