Insulin resistance is associated with an unfavorable outcome among non-diabetic patients with isolated moderate-to-severe traumatic brain injury – A propensity score-matched study

The Interaction and Implication of Stress-Induced Hyperglycemia and Cytokine Release Following Traumatic Injury: A Structured Scoping Review

INTRODUCTION

This is a structured scoping review to assess whether there is a relationship between stress-induced hyperglycemia (SIH), cytokine interactions, and mortality in trauma patients in comparison to non-diabetic normoglycemia [NDN], diabetic normoglycemia [DN], and diabetic hyperglycemia [DH].

METHODS

We conducted a literature search of MEDLINE (PubMed) databases from 2000 to 2022 using a search strategy to identify observational studies. Initially, 2879 articles were retrieved. Of these, 2869 were excluded due to insufficient variables, and non-trauma focuses.

RESULTS

Nine studies on the interaction between SIH and proinflammatory cytokines were analyzed. SIH was associated with the highest mortality rate (21.3%), followed by DH (5.4%), DN (2.8%), and NDN (2.3%) (p < 0.001). Furthermore, SIH patients exhibited an 11.28-fold higher likelihood of mortality compared to NDN patients (95% CI [9.13-13.93]; p < 0.001) and a 4.72-fold higher likelihood compared to DH patients (OR 4.72; 95% CI [3.55-6.27]; p < 0.001).

CONCLUSIONS

SIH patients had elevated IL-6 concentrations relative to NDN, DN, and DH patients. SIH is linked to higher mortality in trauma, with greater odds than NDN. However, the robustness of this association is still being determined due to statistical and clinical variability. Uncertainties about injury severity and IL-6 level similarities between SIH and DH patients require further investigation.

Association of glycemic variability and prognosis in patients with traumatic brain injury: A retrospective study from the MIMIC-IV database

BACKGROUND

Elevated glycemic variability (GV) often occurs in intensive care unit (ICU) patients and is associated with patient prognosis. However, the association between GV and prognosis in ICU patients with traumatic brain injury (TBI) remains unclear.

METHOD

Clinical data of ICU patients with TBI were obtained from the Medical Information Mart for Intensive Care (MIMIC) -IV database. The coefficient of variation (CV) was utilized to quantify GV, while the Glasgow Coma Scale (GCS) was employed to evaluate the consciousness status of TBI patients. Pearson linear correlation analysis, linear regression, COX regression and restricted cubic spline (RCS) were used to investigate the relationship between CV and consciousness impairment, as well as the risk of in-hospital mortality.

RESULT

A total of 1641 ICU patients with TBI were included in the study from the MIMIC-IV database. Pearson linear correlation and restricted cubic spline (RCS) analysis results showed a negative linear relationship between CV and the last GCS (P = 0.002) with no evidence of nonlinearity (P for nonlinear = 0.733). Multivariable linear regression suggested a higher CV was associated with a lower discharge GCS [β (95 %CI) = -1.86 (-3.08 ∼ -0.65), P = 0.003]. Furthermore, multivariable COX regression indicated that CV ≥ 0.3 was a risk factor for in-hospital death in TBI patients [HR (95 %CI) = 1.74 (1.15-2.62), P = 0.003], and this result was also consistent across sensitivity and subgroup analyses.

CONCLUSION

Higher GV is related to poorer consciousness outcomes and increased risk of in-hospital death in ICU patients with TBI. Additional research is needed to understand the logical relationship between GV and TBI progression.

Postoperative hyperglycemia in patients with traumatic brain injury: development of a prediction model

Introduction

Blood glucose monitoring and management are very important for the prognosis of patients with traumatic brain injury (TBI). It is necessary to evaluate the status and influencing factors of hyperglycemia within 48 h after the operation in patients with TBI.

Material and methods

Patients with TBI who received craniocerebral surgery between March 1, 2022, and October 31, 2023, were enrolled. We assessed the clinical characteristics of TBI patients with and without the development of postoperative hyperglycemia. To identify potential risk factors associated with postoperative hyperglycemia, we performed both univariate and multivariate logistic regression analyses. Utilizing the regression coefficients derived from each significant risk factor, we subsequently constructed a predictive model aimed at forecasting postoperative hyperglycemia.

Results

A total of 216 TBI patients were included. The incidence of postoperative hyperglycemia was 31.48%. Correlation analysis indicated that age (r = 0.415), body mass index (BMI) (r = 0.441), diabetes (r = 0.513), Glasgow Coma Scale (GCS) score (r = 0.545) and length of hospital stay (r = 0.456) were all correlated with the postoperative hyperglycemia in TBI patients (all p < 0.05). Age ≥ 60 years (OR = 2.556, 95% CI: 1.831-3.641), BMI ≥ 24 kg/m2 (OR = 2.793, 95% CI: 2.305-3.679), diabetes (OR = 3.081, 95% CI: 2.326-3.811) and GCS score ≤ 8 (OR = 3.603, 95% CI: 1.956-4.086) were the independent factors influencing postoperative hyperglycemia in TBI patients (all p < 0.05). The area under the receiver operating characteristic curve and 95% CI were 0.795 (0.712, 0.849). The model had good discriminative ability to distinguish the occurrence of postoperative hyperglycemia in TBI patients (all p < 0.05).

Conclusions

Postoperative hyperglycemia in patients with TBI is common. For TBI patients with a total score ≥ 6 in the prediction model, early interventions and care are needed to reduce the postoperative hyperglycemia.

Metabolic syndrome associated with higher glycemic variability in type 1 diabetes: A multicenter cross-sectional study in china

AIMS

The comorbidity of metabolic syndrome (MetS) and type 1 diabetes mellitus (T1DM) is an obstacle to glucose control in patients with T1DM. We compared glycemic profiles using continuous glucose monitoring (CGM) systems in patients with T1DM with or without MetS.

METHODS

This was a multicenter cross-sectional study of patients with T1DM (N = 207) with or without MetS. CGM data were collected from study enrollment until discharge during a 1-week study session. We analyzed baseline HbA1c, average glucose, estimated HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), coefficient of variation (CV), postprandial glucose excursions (PPGE) and other glycemic variability (GV) metrics. Logistic regression was developed to investigate the association between MetS and CGM metrics.

RESULTS

The results showed higher average baseline HbA1c levels, and a higher percentage of patients with baseline HbA1c levels ≥7.5%, in the T1DM with MetS group. Furthermore, MetS was associated with GV, which indicated a higher CV in patients with T1DM with MetS. However, our results showed that TAR, TIR, TBR and other GV metrics were comparable between the two groups. The T1DM with MetS group also had a higher proportion of patients with high CV (≥ 36%) than the group without MetS. In multivariable logistic regression analysis, the presence of MetS was a risk factor for high CV (≥ 36%) in our study participants.

CONCLUSIONS

T1DM patients with MetS in our study had better β-cell function. However, MetS was associated with worse glycemic control characterized by higher GV and HbA1c levels. Efforts should be expanded to improve treatment of MetS in patients with T1DM to achieve better glycemic control.