Elevated Neopterin Levels Predict Early Death in Older Hip-fracture Patients

Abstract

Our society faces a major challenge concerning management of the health and socio-economic burden caused by acute physical stress in the older population (+75years). In particular, hip-fracture surgery (HFS) represents a major health care preoccupation, affecting 1.6 million patients worldwide, resulting in a significant drop in life quality and autonomy. The trauma is associated with 20-30% one-year mortality in the elderly. In the present study, we aim to identify factors, which influence and/or predict the outcome of elderly hip- fracture patients (HFP) post-surgery. Our objective was to identify biomarkers with a prognostic capacity of one-year mortality. We employed an observational cohort of HFP (n=60) followed-up longitudinally during the first year post fracture. Clinical and biological data (n=136), collected at arrival to hospital, were then compared to healthy controls (n=42) and analyzed using a regularized logistic regression model with lasso penalty followed by 10-fold cross-validation of variables. We show that plasmatic neopterin levels, a molecule released by IFN-γ-activated macrophages, is predictive of mortality in HFP (ROC-AUC=0.859). Moreover, neopterin measured at arrival to the hospital correlated negatively with the time of survival after HFS. Neopterin therefore represents a biomarker, which enables better follow-up of patients at risk of early death.

Keywords: Aging; Biomarker; Clinical outcome; Hip-fracture; Inflammation.

Fig. 1

Design of the study.

Fig. 2

Predictive score of mortality post hip-fracture. (a) Variables measurable prior to surgery capable of predicting death were identified using a lasso-regularized logistic regression model with 10-fold cross validation. To render the analysis more robust we repeated the model fitting (n = 500). Bar diagram depicts the frequency of models identifying a given variable. Only variables being identified in 2/3 of the models were selected for further analysis (dashed line). (b) Scatter plot of a two components of a Partial Least Square Discriminant Analysis (PLS-DA) identifying the degree of variance of the predicted variable (death) explained by the two predictive variables identified by regularized logistic regression (neopterin concentration in nmol/L and % CD16⁺ CD56dimNKG2C⁺ NK cells). The explained variance is 41.2% and 0.2% for component 1 and 2 (PLS-C1 & PLS-C2), respectively. Symbols discriminate between alive (white symbols) and dead (black symbols) hip-fracture patients one-year post-surgery. (c) Scatter plot depicting a mortality score mathematically derived from the PLS component 1 stratified according to survival status. The mortality score is calculated as the sum of neopterin concentration (nmol/L) and the % CD16⁺ CD56dimNKG2C⁺ NK cells. A proposed cut-off value (24.7) is indicated with a dotted line; this gives a test with 100% sensitivity and 70% specificity. Statistical significance is calculated with a non-parametric Mann-Whitney test. (d) Receiver operating characteristic (ROC) curves of logistic regression models of death occurrence predicted by the two predictive variables in combination or individually (blue line for % CD16⁺ CD56dimNKG2C⁺ NK cells at D0; redline for plasmatic concentration of neopterin (nmol/L) at D0 and black line for the 2 variables in combination). Area under curve (AUC) for each curve is indicated.

Fig. 3

Neopterin levels in elderly. (a) The concentration of neopterin at arrival to hospital (in nmol/L) was measured for the control group of healthy elderly > 75 years old (black circles, n = 42) and was analyzed for the hip-fracture patients according to 2 clinical outcomes at one-year post fracture: survivors (black squares, n = 50) or non-survivors (black triangles, n = 10). p values are calculate with a non-parametric Mann-Whitney test. (b) Distribution of the number of patients within each clinical subgroup (control healthy elderly/survivors post hip-fracture/non-survivors post hip-fracture), according to the upper 95% confidence interval of the mean of the neopterin concentration obtained from the elderly control cohort included in our study (n = 42). This value of 18.7 nmol/L separates individuals in two groups according to their neopterin level; low (white bar) vs high (black bar).

Fig. 4

One-year post fracture survival. (a) Kaplan-Meier curves showing the percent of survival one-year post fracture according to the level of plasmatic neopterin measured at D0, which corresponds to the time of arrival to the hospital. Statistical differences are assessed with Mantel-Cox test. (b) Correlation between the level of neopterin measured at D0 (nmol/L) and the time of survival (days). Correlation statistics was assessed with a Spearman test. The coefficient of correlation is r = − 0.67; p = 0.039.

Fig. 5

Predictive value of clinical, biological and immunological biomarkers. (a) Scatters plots showing the distribution of clinical and biological parameters at D0 obtained from survivors (n = 50) vs non survivors (n = 10) post hip-fracture. From left to right: level of plasmatic neopterin, concentration of creatinine, comorbidity score (CIRS) and the frequency of NK CD56⁺ NKG2C⁺ cells. Dotted line represents threshold values for each individual variable. (b) Histograms depicting odds ratios calculated for each variable based on threshold values indicated in (A): level of plasmatic neopterin (cut-off > 18.7 nmol/L), concentration of creatinine (cut-off > 100 μmol/L), comorbidity score (CIRS, cut-off > 11) and the frequency of NK CD56⁺ NKG2C⁺ cells (cut-off > 6%). Respective p-values and OR 95% confidence intervals of the individual logistic regression models are indicated on the graph. (c) Stacked bars representing the sensitivity (black) and specificity (white) calculated from the variables distribution based on the threshold values employed in (B).