Predicting risk of substantial weight gain in German adults-a multi-center cohort approach

Weight Gain after Interferon-Free Treatment of Chronic Hepatitis C-Results from the German Hepatitis C-Registry (DHC-R)

Chronic hepatitis C can be treated very effectively with direct-acting antivirals (DAA) with only minor side effects compared to an interferon-containing treatment regimen. The significance of metabolic comorbidities after HCV cure is not well defined. This study aims to investigate short- and long-term weight change of patients receiving interferon-free antiviral treatment for chronic hepatitis C. The German Hepatitis C-registry (DHC-R) is a national multicenter real-world cohort. A total of 5111 patients were followed prospectively after DAA treatment for up to 3 years. Weight change compared to baseline was analyzed at end of treatment and at years 1, 2, and 3 after completion of antiviral therapy. Regression analysis was performed to identify baseline predictors for weight change. While there was no relevant mean weight change (−0.2 kg, SD 4.3 kg) at the end of antiviral treatment, weight started to increase during long-term follow-up reaching +1.7 kg (SD 8.0 kg, p < 0.001) compared to baseline at 3 years (follow-up year 3, FU3) after completion of antiviral therapy. 48%, 31%, and 22% of patients had a weight gain greater than 1, 3, and 5 kg at FU3, respectively. During follow-up, a body mass index (BMI) <30 proved to be the only consistent predictor for weight gain. DAA treatment is followed by a substantial weight gain (+3 kg or more) in one-third of the patients during long-term follow-up. Non-obese patients seemed to be most vulnerable to weight gain. The body compartment involved in weight gain as well as the mechanism of weight gain remain to be elucidated.

Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain

Background

Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches.

Methods and results

Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m²) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10⁻⁸) and 5 suggestively (p < 1 × 10⁻⁶) significant loci, several of which have been previously linked to obesity-related phenotypes.

Conclusions

We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

A Multi-Center Competing Risks Model and Its Absolute Risk Calculation Approach

In the competing risks frame, the cause-specific hazard model (CSHM) can be used to test the effects of some covariates on one particular cause of failure. Sometimes, however, the observed covariates cannot explain the large proportion of variation in the time-to-event data coming from different areas such as in a multi-center clinical trial or a multi-center cohort study. In this study, a multi-center competing risks model (MCCRM) is proposed to deal with multi-center survival data, then this model is compared with the CSHM by simulation. A center parameter is set in the MCCRM to solve the spatial heterogeneity problem caused by the latent factors, hence eliminating the need to develop different models for each area. Additionally, the effects of the exposure factors in the MCCRM are kept consistent for each individual, regardless of the area they inhabit. Therefore, the coefficient of the MCCRM model can be easily explained using the scenario of each model for each area. Moreover, the calculating approach of the absolute risk is given. Based on a simulation study, we show that the estimate of coefficients of the MCCRM is unbiased and precise, and the area under the curve (AUC) is larger than that of the CSHM when the heterogeneity cannot be ignored. Furthermore, the disparity of the AUC increases progressively as the standard deviation of the center parameter (SDCP) rises. In order to test the calibration, the expected number (E) of strokes is calculated and then compared with the corresponding observed number (O). The result is promising, so the SDCP can be used to select the most appropriate model. When the SDCP is less than 0.1, the performance of the MCCRM and CSHM is analogous, but when the SDCP is equal to or greater than 0.1, the performance of the MCCRM is significantly superior to the CSHM. This suggests that the MCCRM should be selected as the appropriate model.

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Background

Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke.

Objective

This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke.

Methods

WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method.

Results

This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes.

Conclusions

This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.