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Leukocyte glucose index as a novel biomarker for COVID-19 severity
Wendy Marilú Ramos-Hernández1, Luis F Soto2, Marcos Del Rosario-Trinidad1
1Unidad Médica de Alta Especialidad, Hospital de Especialidades No. 14, Centro Médico Nacional "Adolfo Ruiz Cortines", Instituto Mexicano del Seguro Social (IMSS), 91897, Veracruz, México.
Insights
The leukocyte glucose index (LGI) can predict severe COVID-19 outcomes. This biomarker is particularly useful for patients with diabetes and shows improved accuracy when combined with respiratory rate.
Area of Science:
- Immunology
- Metabolic Medicine
- Infectious Diseases
Background:
- COVID-19 severity is linked to host immune response and metabolic changes.
- Identifying reliable biomarkers for COVID-19 severity is crucial for patient management.
Purpose of the Study:
- To investigate the leukocyte glucose index (LGI) as a potential biomarker for predicting severe COVID-19.
- To compare the predictive value of LGI against other established risk factors.
Main Methods:
- Analysis of 109 COVID-19 patients.
- Evaluation of LGI as an independent risk factor for COVID-19 severity.
- Comparison of LGI's predictive performance with other clinical and biochemical parameters.
Main Results:
- LGI was identified as an independent risk factor for severe COVID-19 (OR=1.727, P=0.041).
- LGI showed higher predictive value in female patients and those with diabetes (AUC=0.915).
- Combining LGI with respiratory rate (LGI+RR) significantly improved severity prediction (AUC=0.894).
Conclusions:
- LGI is a potential biomarker for assessing COVID-19 severity.
- LGI demonstrates particular utility in predicting severity among diabetic COVID-19 patients.
- The combination of LGI and respiratory rate offers enhanced predictive capability for severe COVID-19.
Abstract:
The severity of coronavirus disease 2019 (COVID-19) quickly progresses with unfavorable outcomes due to the host immune response and metabolism alteration. Hence, we hypothesized that leukocyte glucose index (LGI) is a biomarker for severe COVID-19. This study involved 109 patients and the usefulness of LGI was evaluated and compared with other risk factors to predict COVID 19 severity. LGI was identified as an independent risk factor (odds ratio [OR] = 1.727, 95% confidence interval [CI]: 1.026-3.048, P = 0.041), with an area under the curve (AUC) of 0.749 (95% CI: 0.642-0.857, P < 0.0001). Interestingly, LGI was a potential risk factor (OR = 2.694, 95% CI: 1.575-5.283, Pcorrected < 0.05) for severe COVID-19 in female but not in male patients. In addition, LGI proved to be a strong predictor of the severity in patients with diabetes (AUC = 0.915 (95% CI: 0.830-1), sensitivity = 0.833, and specificity = 0.931). The AUC of LGI, together with the respiratory rate (LGI + RR), showed a considerable improvement (AUC = 0.894, 95% CI: 0.835-0.954) compared to the other biochemical and respiratory parameters analyzed. Together, these findings indicate that LGI could potentially be used as a biomarker of severity in COVID-19 patients.
