Correlations Between Inflammatory Biomarkers in Tuberculosis-Associated Obstructive Pulmonary Disease Patients With Anxiety and Depression

Introduction Tuberculosis-associated obstructive pulmonary disease (TOPD), anxiety, and depression are significant public health problems worldwide and their prevalence is common. These diseases interfere with physical, psychosocial, and economic well-being, resulting in unemployment, prolonged hospitalization, abstinence from working, and isolation. Subjects and methods This is a single-center, cross-sectional cohort, observational study conducted in a tertiary care hospital over six years to understand spirometry, laboratory profiles, as well as the impact on overall health, daily life, and perceived well-being in patients with TOPD. Result The sample size of the study was 73 patients. A total of 43 (58.5%) patients had depression with an average St. George's Respiratory Questionnaire for chronic obstructive pulmonary disease (SGRQ-C) score of 67.5, and 16 (21.9%) patients had anxiety with an average SGRQ-C score of 78.9. In the patients who scored higher on the Hamilton Depression Rating Scale (HAM-D), there was a significant correlation between Hamilton Anxiety Rating Scale (HAM-A) and HAM-D scores, as well as C-reactive protein (CRP) levels and WBC counts. In 16 (21.9%) of the patients with moderate to severe anxiety, there was a statistically significant negative correlation between higher HAM-A scores and lower WBC counts. Anxiety, depression, CRP level, WBC count, and serum fibrinogen did not show a significant correlation with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) groups-based assessment of TOPD severity. A high serum fibrinogen level did not correlate with a high HAM-D score, nor did a high CRP level correlate with a high HAM-A score. Conclusion Psychiatric comorbidities like depression are associated with increased inflammation in chronic diseases like TOPD, but no definitive biomarker has been identified and further studies are required to identify suitable biomarkers.