Background: Data on the correlation between glycemic variability and depression in nondiabetic patients remain limited. Considering the link between increased glycemic variability and cardiovascular risks, this relationship could be significant in depressed patients.Methods: In this single-center pilot study, we utilized Flash Glucose Monitoring (Abbott Libre Pro) to study glycemic variability. The CES-D (Center for Epidemiological Studies– Depression) scale was employed to measure depression levels. Based on CES-D scores, patients were classified into two groups: those with scores ≥ 33 and those with scores < 33. We analyzed various glycemic variability indices, including HBGI, CONGA, ADDR, MAGE, MAG, LI, and J-Index, employing the EasyGV version 9.0 software. SPSS (version 28) facilitated the data analysis.Results: We screened patients with depression visiting the department of psychiatry, FGM was inserted in eligible patients of both the groups which yielded a data of 196 patient-days (98 patient-days for CES-D ≥ 33 and 98 patient-days for CES-D < 33). The glycemic variability indices CONGA (mg/dl), (76.48 ± 11.9 vs. 65.08 ± 7.12) (p = 0.048), MAGE (mg/dl) (262.50 ± 25.65 vs. 227.54 ± 17.72) (p = 0.012), MODD (mg/dl) (18.59 ± 2.77 vs. 13.14 ± 2.39) (p = 0.002), MAG(mg/dl) (92.07 ± 6.24vs. 63.86 ± 9.38) (p = <0.001) were found to be significantly higher in the CES-D ≥ 33 group.Conclusion: Patients with more severe depressive symptoms, as suggested by CES-D ≥ 33, had higher glycemic variability.

Background: Frailty is a critical concern for chronic kidney disease (CKD) patients, contributing to increased vulnerability to adverse health outcomes and diminished quality of life. However, there is limited research on frailty’s impact on health-related quality of life (HRQOL) among dialysis and pre-dialysis patients in the Indian context. Methods: This study involved participants aged 18 and above with CKD stages 3–5. Frailty was assessed using the Morley FRAIL questionnaire, and HRQOL was measured using the RAND version of the KDQOL-36 Survey. Data were analyzed with SPSS version 29, focusing on the association between frailty and HRQOL domains. Results: Among the 147 CKD patients, 56.46% were frail, and 43.56% were non-frail. Significant differences were noted between frail and non-frail groups in age (p = 0.036), CKD stages (p < 0.001), nutritional status (p < 0.001), Charlson comorbidity index (p < 0.001), BMI (p < 0.001), GFR (p < 0.001), CRP (p = 0.006), and serum albumin (p = 0.002). Frailty is significantly associated with lower physical (p < 0.001) and mental (p < 0.001) quality of life. Negative associations between frailty and KDQOL-36 domains, especially symptom problems, PCS, and MCS, were established. Conclusions: Our findings emphasize the importance of frailty screening in CKD patients. Early identification may help guide targeted strategies to support HRQOL. However, longitudinal studies are needed to assess frailty progression and the impact of potential interventions.

Background: Cancer poses a growing public health challenge in India, with significant urban–rural disparities in diagnosis and treatment. This study aimed to evaluate the epidemiological profile and treatment patterns of cancer patients in Rajasthan, focusing on differences in disease presentation, treatment modalities, and outcomes between urban and rural populations. Methods: A retrospective observational study was conducted at the National Institute of Medical Sciences and Research, Jaipur, including 1,366 histopathologically confirmed cancer patients admitted between January 2021 and December 2023. Data on demographics, cancer type, stage, treatment, and outcomes were analyzed using SPSS version 28, comparing rural and urban groups. Results: Of the 1,366 patients, 77.45% were from rural areas. Rural patients had higher rates of advanced-stage (Stage IV) presentation (56.1% vs. 47.7%, p = 0.047) and tobacco use (16.8% vs. 10.4%, p = 0.006). Head and neck cancers were most common in men (20.7%), and breast cancer in women (8.2%). Chemotherapy was the predominant treatment (84%) modality, while urban patients more frequently received multimodal therapy. In-hospital mortality was comparable between groups (4.82% vs. 4.22%, p = 0.661), as were readmission rates. Conclusion: Rural patients experienced a greater cancer burden due to delayed diagnosis and limited access to comprehensive care. Strengthening rural oncology services, improving early detection, and addressing modifiable risk factors like tobacco use are critical for reducing disparities and improving outcomes.