Background Temporal muscle thickness (TMT) was described as a surrogate marker of skeletal muscle mass. This study aimed to evaluate the prognostic relevance of TMT in patients with progressive glioblastoma. Methods TMT was analyzed on cranial MR images of 596 patients with progression of glioblastoma after radiochemotherapy enrolled in the European Organisation for Research and Treatment of Cancer 26101 trial. An optimal TMT cutoff for overall survival (OS) and progression-free survival (PFS) was defined in the training cohort (n = 260, phase II). Patients were grouped as “below” or “above” the TMT cutoff and associations with OS and PFS were tested using the Cox model adjusted for important risk factors. Findings were validated in a test cohort (n = 308, phase III). Results An optimal baseline TMT cutoff of 7.2 mm was obtained in the training cohort for both OS and PFS (area under the curve = 0.64). Univariate analyses estimated a hazard ratio (HR) of 0.54 (95% CI: 0.42, 0.70; P < 0.0001) for OS and an HR of 0.49 (95% CI: 0.38, 0.64; P < 0.0001) for PFS for the comparison of training cohort patients above versus below the TMT cutoff. Similar results were obtained in Cox models adjusted for important risk factors with relevance in the trial for OS (HR, 0.54; 95% CI: 0.41, 0.70; P < 0.0001) and PFS (HR, 0.47; 95% CI: 0.36, 0.61; P < 0.0001). Results were confirmed in the validation cohort. Conclusion Reduced TMT is an independent negative prognostic parameter in patients with progressive glioblastoma and may help to facilitate patient management by supporting patient stratification for therapeutic interventions or clinical trials.

PURPOSE The methylation status of the O6-methylguanine DNA methyltransferase (MGMT) gene promoter is predictive for benefit from temozolomide in glioblastoma. A clinically optimized cutoff was sought allowing patient selection for therapy without temozolomide, while avoiding to withhold it from patients who may potentially benefit. EXPERIMENTAL DESIGN Quantitative MGMT methylation-specific PCR data were obtained for newly diagnosed glioblastoma patients screened or treated with standard radiotherapy and temozolomide in four randomized trials. The pooled dataset was randomly split into a training and test dataset. The unsupervised cutoff was obtained at a 50% probability to be (un)methylated. Receiver operating characteristics (ROC) analysis identified an optimal cutoff supervised by overall survival (OS). RESULTS For 4041 patients valid MGMT results were obtained, whereof 1725 were randomized. The unsupervised cutoff in the training dataset was 1.27 (log2[1000x(MGMT+1)/ACTB]), separating unmethylated and methylated patients. The optimal supervised cutoff for unmethylated patients was -0.28 (AUC=0.61), classifying "truly unmethylated" (-0.28) and "grey zone" patients (>-0.28, 1.27), the latter comprising 10% of cases. In contrast, for MGMT methylated patients (>1.27) more methylation was not related to better outcome. Both methylated and grey zone patients performed significantly better for OS than truly unmethylated patients (HR=0.35, 95% CI: 0.27-0.45, p<0.0001; HR=0.58, 95% CI: 0.43-0.78, p<0.001), validated in the test dataset. The MGMT assay was highly reproducible upon retesting of 218 paired samples (R2=0.94). CONCLUSIONS Low MGMT methylation (grey zone) may confer some sensitivity to temozolomide treatment, hence the lower safety margin should be considered for selecting unmethylated glioblastoma patients into trials omitting temozolomide. AbstractPurpose: The methylation status of the O6-methylguanine DNA methyltransferase (MGMT) gene promoter is predictive for benefit from temozolomide in glioblastoma. A clinically optimized cutoff was sought allowing patient selection for therapy without temozolomide, while avoiding to withhold it from patients who may potentially benefit. Experimental Design: Quantitative MGMT methylation-specific PCR data were obtained for newly diagnosed glioblastoma patients screened or treated with standard radiotherapy and temozolomide in four randomized trials. The pooled dataset was randomly split into a training and test dataset. The unsupervised cutoff was obtained at a 50% probability to be (un)methylated. Receiver operating characteristics (ROC) analysis identified an optimal cutoff supervised by overall survival (OS). Results:For 4041 patients valid MGMT results were obtained, whereof 1725 were randomized. The unsupervised cutoff in the training dataset was 1.27 (log 2 [1000x(MGMT+1)/ACTB]), separating unmethylated and methylated patients. The optimal supervised cutoff for unmethylated patients was -0.28 (AUC=0.61), classifying "truly unmethylated" (≤-0.28) and "grey zone" ...

Metformin has been linked to improve survival of patients with various cancers. There is little information on survival of glioblastoma patients after use of metformin. We assessed the association between metformin use and survival in a pooled analysis of patient data from 1,731 individuals from the randomized AVAglio, CENTRIC and CORE trials. We performed multivariate Cox analyses for overall survival (OS) and progression‐free survival (PFS) comparing patients' use of metformin at baseline and/or during concomitant radiochemotherapy (TMZ/RT). Further exploratory analyses investigated the effect of metformin with a history of diabetes and nonfasting glucose levels in relation to OS or PFS of glioblastoma patients. Metformin alone or in any combination was not significantly associated with OS or PFS (at baseline, hazard ratio [HR] for OS = 0.87; 95% confidence interval [CI] = 0.65–1.16; HR for PFS = 0.84; 95% CI = 0.64–1.10; during TMZ/RT HR for OS = 0.97; 95% CI = 0.68–1.38; HR for PFS = 1.02; 95% CI = 0.74–1.41). We found a statistically nonsignificant association of metformin monotherapy with glioblastoma survival at baseline (HR for OS = 0.68; 95% CI = 0.42–1.10; HR for PFS = 0.57; 95% CI = 0.36–0.91), but not during the TMZ/RT period (HR for OS = 0.90; 95% CI = 0.51–1.56; HR for PFS = 1.05; 95% CI = 0.64–1.73). Diabetes mellitus or increased nonfasting glucose levels were not associated with a difference in OS or PFS in our selected study population. Metformin did not prolong survival of patients with newly diagnosed glioblastoma in our analysis. Additional studies may identify patients with specific tumor characteristics that are associated with potential benefit from treatment with metformin, possibly due to metabolic vulnerabilities.

Food policies for the prevention and management of diet-related non-communicable diseases (NCDs) have been increasingly relying on microsimulation models (MSMs) to assess effectiveness. Given the increased uptake of MSMs, this review aims to provide an overview of the characteristics of MSMs that link diets with NCDs. A comprehensive review was conducted in PubMed and Web of Knowledge. Inclusion criteria were: (i) findings from a MSM, (ii) diets, foods or nutrients as main exposure of interest, (iii) NCDs, such as overweight/obesity, type 2 diabetes, coronary heart disease, stroke or cancer as disease outcome for impact assessment. This review included information from 33 studies using MSM in analyzing diet and diverse food policies on NCDs. Hereby, most models employed stochastic, discrete-time, dynamic microsimulation techniques to calculate anticipated (cost-)effectiveness of strategies based on food pricing, food reformulation or dietary (lifestyle) interventions. Currently available models differ in the methodology used for quantifying the effect of the dietary changes on disease, and in the method for modelling disease incidence and mortality. However, all studies provided evidence that the models were sufficiently capturing the close-to-reality situation by justifying their choice of model parameters and validating externally their modelled disease incidence and mortality with observed or predicted event data. With the increasing use of various MSMs, between-model comparisons, facilitated by open access models and good reporting practices, would be important for judging model's accuracy, leading to continued improvement in the methodologies for developing and applying MSMs, and subsequently a better understanding of the results by policymakers. A statement of significance Given the advancement in the application of microsimulation modelling in evaluating food policies and measuring diet-related disease burdens, the present scoping review serves as an exercise to inform future modelling, hereby highlighting the need for transparency in model development, application and dissemination to advance and safeguard accuracy and relevance in modelling efforts.

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