Autonomic Neural Circuit and Intervention for Comorbidity Anxiety and Cardiovascular Disease

Determinants of generalized anxiety and construction of a predictive model in patients with chronic obstructive pulmonary disease

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) frequently experience exacerbations requiring multiple hospitalizations over prolonged disease courses, which predispose them to generalized anxiety disorder (GAD). This comorbidity exacerbates breathing difficulties, activity limitations, and social isolation. While previous studies predominantly employed the GAD 7-item scale for screening, this approach is somewhat subjective. The current literature on predictive models for GAD risk in patients with COPD is limited.

AIM

To construct and validate a GAD risk prediction model to aid healthcare professionals in preventing the onset of GAD.

METHODS

This retrospective analysis encompassed patients with COPD treated at our institution from July 2021 to February 2024. The patients were categorized into a modeling (MO) group and a validation (VA) group in a 7:3 ratio on the basis of the occurrence of GAD. Univariate and multivariate logistic regression analyses were utilized to construct the risk prediction model, which was visualized using forest plots. The model's performance was evaluated using Hosmer-Lemeshow (H-L) goodness-of-fit test and receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 271 subjects were included, with 190 in the MO group and 81 in the VA group. GAD was identified in 67 patients with COPD, resulting in a prevalence rate of 24.72% (67/271), with 49 cases (18.08%) in the MO group and 18 cases (22.22%) in the VA group. Significant differences were observed between patients with and without GAD in terms of educational level, average household income, smoking history, smoking index, number of exacerbations in the past year, cardiovascular comorbidities, disease knowledge, and personality traits (P < 0.05). Multivariate logistic regression analysis revealed that lower education levels, household income < 3000 China yuan, smoking history, smoking index ≥ 400 cigarettes/year, ≥ two exacerbations in the past year, cardiovascular comorbidities, complete lack of disease information, and introverted personality were significant risk factors for GAD in the MO group (P < 0.05). ROC analysis indicated that the area under the curve for predicting GAD in the MO and VA groups was 0.978 and 0.960. The H-L test yielded χ 2 values of 6.511 and 5.179, with P = 0.275 and 0.274. Calibration curves demonstrated good agreement between predicted and actual GAD occurrence risks.

CONCLUSION

The developed predictive model includes eight independent risk factors: Educational level, household income, smoking history, smoking index, number of exacerbations in the past year, presence of cardiovascular comorbidities, level of disease knowledge, and personality traits. This model effectively predicts the onset of GAD in patients with COPD, enabling early identification of high-risk individuals and providing a basis for early preventive interventions by nursing staff.

A Mendelian randomization study of the effect of mental disorders on cardiovascular disease

Objective

The effect of mental disorders (MD) on cardiovascular disease (CVD) remains controversial, and this study aims to analyze the causal relationship between eight MD and CVD by Mendelian randomization (MR).

Methods

Single nucleotide polymorphisms of attention-deficit/hyperactivity disorder (ADHD), anorexia nervosa (AN), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), depression, obsessive-compulsive disorder (OCD), schizophrenia (SCZ), and CVD were obtained from UK Biobank and FinnGen. Exposure-outcome causality was tested using inverse variance weighted (IVW), MR-Egger, and weighted median. Horizontal pleiotropy and heterogeneity were assessed by MR-Egger intercept and Cochran's Q, respectively, while stability of results was assessed by leave-one-out sensitivity analysis.

Results

MR analysis showed that ANX (IVW [odds ratio (OR) 1.11, 95% confidence intervals (CI) 1.07-1.15, p < 0.001]; MR-Egger [OR 1.03, 95% CI 0.92-1.14, p = 0.652]; weighted median [OR 1.09, 95% CI 1.03-1.14, p = 0.001]), ASD (IVW [OR 1.05, 95% CI 1.00-1.09, p = 0.039]; MR-Egger [OR 0.95, 95% CI 0.84-1.07, p = 0.411]; weighted median [OR 1.01, 95% CI 0.96-1.06, p = 0.805]), depression (IVW [OR 1.15, 95% CI 1.10-1.19, p < 0.001]; MR-Egger [OR 1.10, 95% CI 0.96-1.26, p = 0.169]; weighted median [OR 1.13, 95% CI 1.08-1.19, p < 0.001]) were significantly associated with increased risk of CVD, whereas ADHD, AN, BD, OCD, and SCZ were not significantly associated with CVD (p > 0.05). Intercept analysis showed no horizontal pleiotropy (p > 0.05). Cochran's Q showed no heterogeneity except for BD (p = 0.035). Sensitivity analysis suggested that these results were robust.

Conclusions

ANX, ASD, and depression are associated with an increased risk of CVD, whereas AN, ADHD, BD, OCD, and SCZ are not causally associated with CVD. Active prevention and treatment of ANX, ASD, and depression may help reduce the risk of CVD.

Prevalence and predictors of preoperative cardiac autonomic dysfunction among elective neurosurgical patients: A prospective observational study

Background and Aims

The autonomic nervous system (ANS) is cardinal for systemic homeostasis. Autonomic dysfunction is prevalent in as high as 65% of patients presenting for cardiac surgery in the Indian scenario. Pre-existing cardiac autonomic dysfunction (CAD) in surgical patients can accentuate perioperative haemodynamic fluctuations during stressful intraoperative events, predispose to adverse cardiac events, and contribute to morbidity and mortality. The prevalence and predictors of CAD in the elective neurosurgical population are unknown in the Indian scenario. The current study was conducted to bridge this knowledge gap.

Methods

In this single-centre prospective observational study conducted at a tertiary care neurosciences centre, among 400 consenting adult patients of either gender, between 18 and 80 years of age, undergoing elective neurosurgery, the preoperative ANS function at the bedside was assessed as the primary outcome measure. The ANS status was evaluated using ANSiscope™-derived indices of heart rate variability. The diagnosis of CAD was made when the ANS index exceeded a threshold of 13.5. Data regarding predictors of CAD were collected from patient records as the secondary outcome measure. Statistical analysis was done using the R software. A P-value of <0.05 was considered statistically significant.

Results

The prevalence of preoperative CAD in our study population was 79.7% (319/400 patients). None of the demographic and baseline clinical characteristics we studied predicted CAD in our study.

Conclusion

We observed a significant prevalence of preoperative CAD among elective neurosurgical patients. None of the parameters we evaluated predicted CAD in our study.