Background/Objectives: Oral frailty (OFr) and gut dysbiosis have each been associated with depressive symptoms (DSs); however, their interactive effects on DSs remain unclear. This study aimed to investigate the combined association of OFr and gut microbiota composition with DSs. Methods: A cross-sectional study was conducted among 277 adults in Shika Town, Ishikawa Prefecture, Japan. OFr was assessed based on number of remaining teeth, difficulty chewing and swallowing, oral dryness, and frequency of tooth brushing. DSs were evaluated using the Center for Epidemiologic Studies Depression Scale (CES-D). Gut microbiota were analyzed using 16S rRNA gene sequencing. Binary logistic regression analysis was performed with DSs as the dependent variable, the interaction term between gut microbiota and OFr as the independent variable, and age, sex, BMI, and drinking and smoking status as covariates. Results: The interaction terms between OFr and Bacteroides (odds ratio [OR] = 1.212; 95% confidence interval [CI] = 1.076–1.366; q = 0.023), Faecalibacterium (OR = 1.191; 95% CI = 1.046–1.357; q = 0.040), Blautia (OR = 1.229; 95% CI = 1.068–1.413; q = 0.030), and Subdoligranulum (OR = 1.310; 95% CI = 1.109–1.547; q = 0.023) remained statistically significant predictors of DSs after FDR correction. Conclusions: Significant interactions between OFr and specific gut microbial taxa were observed even after adjustment for potential confounders and FDR correction. These findings suggest that OFr may be an important factor to consider when investigating the relationship between gut microbiota and DSs.
Background: Parasomnias are abnormal behaviours in sleep. Disorders that can cause microarousals, such as obstructive sleep apnoea (OSA) and periodic limb movements in sleep (PLMS) are often considered to trigger parasomnias. However, the prevalence and impact of OSA, and its specific clinical phenotypes, such as REM-related OSA (ROSA) and positional OSA (POSA), as well as PLMS in parasomnias are not fully explored. Methods: We analysed the polysomnography reports of 155 patients who were referred to our tertiary service with symptoms of parasomnia. The prevalence of OSA, ROSA, POSA, and PLMS, as well as their severity indices were correlated with the prevalence of specific parasomnias using logistic regression. Results: The prevalence of OSA and PLMS was 48% and 23%, respectively. Whilst OSA and PLMS were inversely related to NREM parasomnias, PLMS was directly related to RBD (p < 0.05). Moreover, there was a significant inverse correlation between the severity of PLMS and the prevalence of NREM parasomnias (β = −0.12, p = 0.03) and a direct correlation with RBD (β = 0.02, p = 0.04). However, following adjustments for age, BMI, and gender, only the inverse relationships between OSA and sleep terrors as well as POSA and sleep-related eating disorder remained significant. Conclusions: The prevalence of OSA and PLMS is high among patients with parasomnia. Due to the exploratory nature of the study, conclusions on the associations need to be validated in interventional trials.
Background: Extreme temperatures, severe weather events, and air pollution pose important challenges to cardiovascular health. However, patients’ perceptions of environmental risks, symptom-related experiences, adaptive behaviours, and information needs remain insufficiently characterized. This study aims to develop and describe the methodology of a patient-centred questionnaire assessing these dimensions in adults with cardiovascular disease. Methods: An anonymous, self-administered, 37-item questionnaire was developed for adult patients with at least one cardiovascular diagnosis. The instrument comprises five sections addressing sociodemographic and clinical characteristics; perceived health effects of heat, cold, and air pollution; psychological and quality-of-life impacts; protective behaviours and healthcare utilization; and knowledge and information sources. Most closed-ended items use five-point Likert-type or frequency scales, alongside one open-ended question. Content validity was assessed by an independent panel of three experts in cardiology, psychology, and meteorology/climatology. A pilot study involving 39 patients with cardiovascular disease recruited from a clinical research database was conducted to assess questionnaire feasibility and internal consistency. Pilot participants were excluded from the main study. The target sample size for the main study was calculated as 385 participants, increased to 428 to account for non-submission. Eligible patients will be recruited consecutively during hospitalization in our cardiology department. Conclusions: This questionnaire provides a structured framework for investigating perceived environmental cardiovascular vulnerability, psychological impact, adaptive responses, healthcare-related consequences, and information needs. Its application may help identify gaps in patient awareness and protective practices and inform the development of targeted educational and preventive interventions addressing climate-related cardiovascular risks.
Background/Objectives: Hospitalization frequently exacerbates muscle wasting and functional decline in vulnerable older adults, yet comprehensive geriatric assessments are often too time-consuming. This study evaluated the clinical utility of baseline and longitudinal bilateral handgrip strength (HGS), measured via a Squegg® digital dynamometer, as a rapid bedside marker of frailty and functional trajectory in hospitalized older adults. Methods: This retrospective cohort study included 131 older adults (mean age 74.9 ± 9.7 years, 75.6% female) admitted to an acute geriatric unit. Bilateral HGS was measured at admission and discharge using the Squegg® digital dynamometer. Participants were also evaluated using the Edmonton Frail Scale (EFS) for frailty, the Timed Up and Go (TUG) test for functional mobility, and the Mini-Mental State Examination (MMSE) for cognitive performance. Results: Bilateral mean HGS improved significantly from admission (18.5 ± 8.7 kg) to discharge (20.7 ± 9.1 kg), representing a mean within-participant increase of 2.16 ± 2.94 kg (p < 0.001), with 77.9% of the cohort exhibiting a positive trajectory. Admission HGS was strongly and inversely correlated with baseline EFS scores and TUG. In multivariable linear regression, higher EFS scores were associated with lower baseline HGS after adjustment for the included covariates. Exploratory models provided no statistically significant evidence of an association between baseline HGS or longitudinal HGS change and length of hospital stay (mean: 11.9 ± 1.8 days). Conclusions: Bedside digital handgrip strength may provide a complementary marker of frailty and functional mobility in hospitalized older adults. Measurement reliability and screening accuracy were not assessed in this study.
Background/Objectives:AI-assisted documentation and coding tools add diagnoses to health records, and self-updating AI agents may soon learn from the codes they generate. The objective was to develop a framework for asking whether such systems could distort disease prevalence at population scale, and a measurement approach for detecting it. Methods: We specify a partially observable Markov decision process in which a coding agent observes the administrative record exactly but clinical state only with delay and noise, derives the conditions under which policy-gradient learning follows the administrative reward, and states the general claim as a falsifiable hypothesis. We define the artificial epidemiology divergence D(C) = Pa(C) − Pc(C) relative to an explicit clinical reference standard, relate it to sensitivity and specificity of administrative case definitions, and propose five markers with falsification conditions. A pre-study on nine synthetic datasets (Synthea; four US states; eight distinct populations, one of 11,475 and seven of about 3400 patients, and one repeated generation with the same seed) measured D(C) for five metabolic conditions without any AI coding process, with bootstrap intervals and sensitivity analyses, and tested whether injected coding changes were detectable. Results: Under the primary reference definition, D(C) was +0.041 (95% CI 0.036 to 0.045) for type 2 diabetes, −0.125 for hypertension, −0.191 for hyperlipidaemia and +0.314 for obesity, with consistent signs across all populations. The magnitude, and for hypertension, hyperlipidaemia and obesity the sign, depended on the reference definition. An injected upcoding of 5% per year was detected as a change in the slope of D(C) in all populations, and an undercoding of 10% per year in all populations. Conclusions: The baseline value of D(C) reflects the measurement definition, and the operationally relevant quantity is its change under a fixed definition. The synthetic results establish a measurement approach and its detection limits, not evidence that AI causes prevalence distortion; computing D(C) on linked administrative and clinical records around AI deployment is the necessary next step.
