空腹血糖与新冠病毒疾病严重程度：非线性关系至关重要。
Fasting Blood Glucose and COVID-19 Severity: Nonlinearity Matters.
作者信息
Alahmad Barrak, Al-Shammari Abdullah A, Bennakhi Abdullah, Al-Mulla Fahd, Ali Hamad
机构信息
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA.
Dasman Diabetes Institute (DDI), Dasman, Kuwait.
出版信息
Diabetes Care. 2020 Dec;43(12):3113-3116. doi: 10.2337/dc20-1941. Epub 2020 Oct 13.
OBJECTIVE
Fasting blood glucose (FBG) could be an independent predictor for coronavirus disease 2019 (COVID-19) morbidity and mortality. However, when included as a predictor in a model, it is conventionally modeled linearly, dichotomously, or categorically. We comprehensively examined different ways of modeling FBG to assess the risk of being admitted to the intensive care unit (ICU).
RESEARCH DESIGN AND METHODS
Utilizing COVID-19 data from Kuwait, we fitted conventional approaches to modeling FBG as well as a nonlinear estimation using penalized splines.
RESULTS
For 417 patients, the conventional linear, dichotomous, and categorical approaches to modeling FBG missed key trends in the exposure-response relationship. A nonlinear estimation showed a steep slope until about 10 mmol/L before flattening.
CONCLUSIONS
Our results argue for strict glucose management on admission. Even a small incremental increase within the normal range of FBG was associated with a substantial increase in risk of ICU admission for COVID-19 patients.
目的
空腹血糖（FBG）可能是2019冠状病毒病（COVID-19）发病和死亡的独立预测指标。然而，当将其作为模型中的预测指标时，传统上是采用线性、二分法或分类法进行建模。我们全面研究了对FBG进行建模的不同方法，以评估入住重症监护病房（ICU）的风险。
研究设计与方法
利用科威特的COVID-19数据，我们采用了对FBG进行建模的传统方法以及使用惩罚样条的非线性估计。
结果
对于417例患者，对FBG进行建模的传统线性、二分法和分类法均遗漏了暴露-反应关系中的关键趋势。非线性估计显示，在约10 mmol/L之前斜率较陡，之后趋于平缓。
结论
我们的结果支持入院时进行严格的血糖管理。即使FBG在正常范围内有小幅递增，也与COVID-19患者入住ICU的风险大幅增加相关。
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