Accurate assessment of insulin resistance, sensitivity and β-cell function is essential for early detection and management of metabolic disorders. However, reference intervals (RIs) commonly used in Nepal have been adapted from Western populations, which may not accurately reflect local physiological characteristics. Thus, this study aimed to establish population-specific RIs for fasting insulin and key insulin-related indices using a direct priori method in healthy adults from Gandaki Province, Nepal.

This cross-sectional study recruited 135 healthy adults (20–69 years, body mass index 18.5–24.9 kg/m²) representing different districts of Gandaki Province, Nepal. Fasting blood samples were analysed for glucose, insulin and lipids using standardised assays. Insulin was measured using the chemiluminescence immunoassay method. Nineteen different insulin-derived indices (Homeostasis Model Assessment 1 of Insulin Resistance (HOMA1-IR), Homeostasis Model Assessment 2 of Insulin Resistance (HOMA2-IR), Homeostasis Model Assessment for Triglycerides, Fasting Insulin to Glucose Ratio, Fasting Insulin Resistance Index, Metabolic Score for Insulin Resistance (METS-IR), InsuTAG, HOMA1-%S, HOMA2-%S, Quantitative Insulin Sensitivity Check Index (QUICKI), McAuley, Bennett, Raynaud, Glucose-to-Insulin Ratio, Fasting Insulin Sensitivity Index, Single Point Insulin Sensitivity Estimator (SPISE), reciprocal insulin, HOMA1-%B and HOMA2-%B) were calculated. Non-parametric 95% double-sided RIs (2.5th–97.5th percentiles) were established following outlier removal per Clinical and Laboratory Standards Institute-International Federation of Clinical Chemistry and Laboratory Medicine EP28-A3c guidelines.

The RI for fasting insulin was 2.63–14.56 µIU/mL (median 7.69 µIU/mL). Among the 19 mathematically correlated insulin-derived indices which are calculated from core measurements (fasting serum insulin and glucose), consistent patterns emerged across functional categories. Insulin resistance indices (HOMA1-IR: 0.56–3.50; HOMA2-IR: 0.30–1.70; METS-IR: 25.14–38.94) exhibited concordant right-skewed distributions with elevated upper limits. Conversely, insulin sensitivity indices (QUICKI: 0.32–0.42; HOMA2-%S: 58.83–233.20; SPISE: 5.75–10.86) demonstrated inverse, left-skewed patterns. Beta-cell function indices (HOMA1-%B: 0.54–322.21; HOMA2-%β: 40.74–159.52) also exhibited right skewed characteristics and revealed wide interindividual variability, reflecting preserved pancreatic reserve despite varying insulin resistance. Composite indices incorporating lipid parameters showed broader ranges, capturing additional metabolic heterogeneity.

This is the first study to define the RIs of fasting insulin and a spectrum of insulin derived indices in a Nepalese population. These findings offer a valuable framework for early detection and management of metabolic disorders in South Asian populations.

This study aimed to assess the coronavirus disease 2019 (COVID-19) hospitalisation costs and its associated factors on Nepalese households during the second wave of the pandemic, within the context of Nepal’s COVID-19 response.

A cost-descriptive cross-sectional study.

Kathmandu Metropolitan City, Nepal.

We enrolled 306 hospitalised patients.

Telephonic interviews were conducted with COVID-19 patients between May and July 2022. Cost was assessed from a patient’s perspective. We assessed factors associated with the medical cost of COVID-19 treatment services using a generalised linear model with gamma distribution and log link in both bivariable and multivariable models for estimating coefficients and confidence intervals. Data were analysed using STATA version 13, adjusting for the potential confounders: socio-demographic characteristics, type of hospital, intensive care unit (ICU) requirement, lead time to hospital admission and number of days at hospital stay.

The total median cost for hospitalisation was US$ 754.9. The median direct medical, direct non-medical and indirect costs were US$ 624.4, US$ 49.3 and US$ 493.02, respectively. After adjusting for potential confounders, the cost of COVID-19 treatment was 6.9 times higher among those admitted to private hospital (95% CI 5.72 to 8.32, p

The cost of the COVID-19 treatment was beyond the average monthly income of Nepalese, indicating adverse consequences from the financial burden of a household. The direct medical cost was associated with the type of hospital, requirement of ICU, lead time to hospital admission, and length of hospital stay. Therefore, it is urgent to address the issue of high medical expenses, particularly to strengthen the health system’s resilience against unforeseen crises and pandemics.