SARS-CoV-2 RNA血症による予後の予測


Gutmann, C., Takov, K., Burnap, S.A. et al. SARS-CoV-2 RNAemia and proteomic trajectories inform prognostication in COVID-19 patients admitted to intensive care. Nat Commun 12, 3406 (2021).


予後を特徴づけることによって,ICUに入院したCOVID-19患者におけるリスク層別化することができる.COVID-19入院患者(n= 123),非COVID-19の敗血症ICU入院患者(n= 25),健常対照者(n= 30)から血液サンプル(n 474)を採取した.COVID-19ICU入院患者の血漿または血清中に,中和抗体反応が低いときにSARS-CoV-2 RNAが検出された(RNA血症: RNAemiaRNA血症は,28ICU死亡率の上昇と関連していた(年齢と性別で調整したハザード比[HR], 1.84[95%CI], 1.22-2.77RNA血症は,最も優れたタンパク質予測因子と同等の性能を有している.自然免疫系の補体経路の活性化因子であるMannose結合lectin 2MBL2pentraxin-3PTX3は,死亡率と正の相関がある機械学習により,”年齢, RNAemia”と”年齢, PTX3”が,28ICU死亡率と関連する最適な二値シグネチャとして同定された.縦断的な比較では,COVID-19 ICU患者は,死亡率に関連した明確なプロテオミクスの軌跡を持っており,多くの肝臓由来タンパク質の回復(liver-derived protines)が生存を示していた.最後に,SARS-CoV-2スパイク糖タンパク質の相互作用パートナーとして,補体系のタンパク質とgalectin-3結合タンパク質(LGALS3BPが同定された.in vitroにおいて,LGALS3BPを過剰発現させると,スパイク糖タンパク質の取り込みやスパイクによる細胞間融合が阻害されることがわかった




Figure 2: SARS-CoV-2 RNAemia and the humoral immune response.

a Unadjusted hazard ratios with 95% confidence interval (CI) based on two ICU patient cohorts (n=60 survivors and n=18 non-survivors, KCH and GSTT). Green indicates P value<0.05, maroon indicates P value<0.001 and blue indicates P value>0.05. b, Hazard ratios with 95% CI after adjustment for age and sex (n=60 survivors and n=18 non-survivors, KCH and GSTT). c Association of SARS-CoV-2 RNAemia with binary variables (Cohens Kappa correlation) and continuous variables (point-biserial correlation). Red indicates positive and blue negative correlation with P value<0.05. Abbreviations: Alb albumin, ALP alkaline phosphatase, ALT alanine aminotransferase, Bil bilirubin, COPD chronic obstructive pulmonary disease, Crea creatinine, CRP C-reactive protein, DM diabetes, Hct hematocrit, Hb hemoglobin, HR heart rate, HTN hypertension, Lymphoc lymphocytes, MAP mean arterial pressure, Monoc monocytes, Neutroph neutrophils, K+ potassium, Resp. rate respiratory rate, Na+ sodium, Temp body temperature, WCC white cell count. d Anti-SARS-CoV-2 spike IgG and anti-SARS-CoV-2 neutralization response based on days post-onset of symptoms (POS) in patients who tested positive (red) or negative (blue) for plasma/serum SARS-CoV-2 RNA within the first 6 ICU days (261 samples from n=55 RNAemia negative and n=15 RNAemia positive patients). Lines show fitted generalized additive models (GAM) with gray bands indicating the 95% CI, correcting for age and sex. e Anti-SARS-CoV-2 spike IgG levels and anti-SARS-CoV-2 neutralization capacity in individual samples negative (232 samples) or positive (29 samples) for SARS-CoV-2 RNA (n=70 patients). Lines inside violin plots show median (continuous line) and interquartile range (dotted lines). Significance was determined through a Mann–Whitney U test. P values are corrected for age, sex, and days POS. All statistical analyses are two-tailed.



Fig. 2



Figure 5: SARS-CoV-2 mortality prediction using machine learning.

a Kaplan–Meier plot for age (using the median age of 54 years). b Kaplan–Meier plot for SARS-CoV-2 RNAemia. As a single predictor, RNAemia provides the best stratification for survival. c Kaplan–Meier plot for PTX3 using the median levels of serum or plasma. d–f Kaplan–Meier plots for “RNAemia, PTX3”, “Age, RNAemia”, and “Age, PTX3” combined using support vector machine with radial basis function kernel (SVM RBF), a non-linear machine learning model. The machine learning model selected binary combinations of “Age, RNAemia” and “Age, PTX3” as the best predictors. Kaplan–Meier analysis is two-tailed. Nonsurvivors: n=18; survivors: n=60.

Fig. 5