On the impact of residential history in the spatial analysis of diseases with a long latency period: A study of mesothelioma in Belgium

Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low-rank kriging multiple membership model

The exposome is an ideal in public health research that posits that individuals experience risk for adverse health outcomes from a wide variety of sources over their lifecourse. There have been increases in data collection in the various components of the exposome, but novel statistical methods are needed that capture multiple dimensions of risk at once. We introduce a Bayesian index low-rank kriging (LRK) multiple membership model (MMM) to simultaneously estimate the health effects of one or more groups of exposures, the relative importance of exposure components, and cumulative spatial risk over time using residential histories. The model employs an MMM to consider all residential locations for subjects weighted by duration and LRK to increase computational efficiency. We demonstrate the performance of the Bayesian index LRK-MMM through a simulation study, showing that the model accurately and consistently estimates the health effects of one or several group indices and has high power to identify a region of elevated spatial risk due to unmeasured environmental exposures. Finally, we apply our model to data from a multicenter case-control study of non-Hodgkin lymphoma (NHL), finding a significant positive association between one index of pesticides and risk for NHL in Iowa. Additionally, we find an area of significantly elevated spatial risk for NHL in Los Angeles. In conclusion, our Bayesian index LRK-MMM represents a step forward toward bringing the ideals of the exposome into practice for environmental risk analyzes.

Estimating cumulative spatial risk over time with low-rank kriging multiple membership models

Many health outcomes result from accumulated exposures to one or more environmental factors. Accordingly, spatial risk studies have begun to consider multiple residential locations of participants, acknowledging that participants move and thus are exposed to environmental factors in several places. However, novel methods are needed to estimate cumulative spatial risk for disease while accounting for other risk factors. To this end, we propose a Bayesian model (LRK-MMM) that embeds a multiple membership model (MMM) into a low-rank kriging (LRK) model in order to estimate cumulative spatial risk at the point level while allowing for multiple residential locations per subject. The LRK approach offers a more computationally efficient means to analyze spatial risk in case-control study data at the point level compared with a Bayesian generalized additive model, and as increased precision in spatial risk estimates by analyzing point locations instead of administrative areas. Through a simulation study, we demonstrate the efficacy of the model and its improvement upon an existing multiple membership model that uses area-level spatial random effects to estimate risk. The results show that our proposed method provides greater spatial sensitivity (improvements ranging from 0.12 to 0.54) and power (improvements ranging from 0.02 to 0.94) to detect regions of elevated risk for disease across a range of exposure scenarios. Finally, we apply our model to case-control data from the New England bladder cancer study to estimate cumulative spatial risk while adjusting for many covariates.

A novel PDX modeling strategy and its application in metabolomics study for malignant pleural mesothelioma

BACKGROUND

Malignant pleural mesothelioma (MPM) is a rare and aggressive carcinoma located in pleural cavity. Due to lack of effective diagnostic biomarkers and therapeutic targets in MPM, the prognosis is extremely poor. Because of difficulties in sample extraction, and the high rate of misdiagnosis, MPM is rarely studied. Therefore, novel modeling methodology is crucially needed to facilitate MPM research.

METHODS

A novel patient-derived xenograft (PDX) modeling strategy was designed, which included preliminary screening of patients with pleural thickening using computerized tomography (CT) scan, further reviewing history of disease and imaging by a senior sonographer as well as histopathological analysis by a senior pathologist, and PDX model construction using ultrasound-guided pleural biopsy from MPM patients. Gas chromatography-mass spectrometry-based metabolomics was further utilized for investigating circulating metabolic features of the PDX models. Univariate and multivariate analysis, and pathway analysis were performed to explore the differential metabolites, enriched metabolism pathways and potential metabolic targets.

RESULTS

After screening using our strategy, 5 out of 116 patients were confirmed to be MPM, and their specimens were used for modeling. Two PDX models were established successfully. Metabolomics analysis revealed significant metabolic shifts in PDX models, such as dysregulations in amino acid metabolism, TCA cycle and glycolysis, and nucleotide metabolism.

CONCLUSIONS

To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed.