Time-Lagged Effects of Hyperglycemia on Inflammation in Older Adults with Community-Acquired Pneumonia: Nutritional...

The dynamic interplay between glucose metabolism and systemic inflammation is increasingly recognized as a pivotal factor influencing outcomes in older adults with community-acquired pneumonia (CAP), yet its temporal patterns and the modifying role of nutritional status remain insufficiently understood.

In this retrospective cohort study, 507 older adults (≥65 years) hospitalized with CAP were included. Serial measurements of blood glucose (GLU), C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR) were obtained at admission, 24 hours, and 72 hours. Cross-correlation function (CCF) analysis was used to characterize lagged temporal relationships among biomarkers, while autoregressive integrated moving average (ARIMA) models predicted biomarker trends. Subgroup analyses were conducted according to 28-day survival, diabetes status, nutritional status, and age.

Patients who died within 28 days had higher CRP, NLR, and GLU levels across all time points compared to survivors, with pronounced delays in normalization of inflammatory markers and persistent hyperglycemia (all P<0.001). CCF analyses demonstrated that glucose elevations often preceded increases in CRP and NLR, particularly at a lag of -1, indicating early metabolic perturbations can foreshadow subsequent inflammatory surges. Survivors showed evidence of timely feedback regulation, with negative correlations at subsequent lags, while non-survivors, malnourished patients, and those aged ≥85 years exhibited disrupted, delayed, or reversed cross-correlation patterns. ARIMA models provided robust predictions, identifying critical intervention windows based on biomarker trends.

These findings reveal the systemic impact of hyperglycemia on inflammation and suggest potential benefits of nutritional interventions targeting glucose control to modulate inflammatory responses in elderly CAP patients. Overall, this study underscores the importance of integrating metabolic monitoring with nutritional strategies to improve outcomes in this vulnerable population.