Associations between the urban exposome and type 2 diabetes: Results from penalised regression by least absolute shrinkage and selection operator and random forest models

Effects of individual characteristics and seasonality and their interaction on ectoparasite load of Daurian ground squirrels in Inner Mongolia, China

Understanding the drivers of parasite distribution is vital for ecosystem health, disease management, and vector monitoring. While studies note the impact of host sex, size, behavior, and season on parasite load, concurrent assessments of these factors and their interactions are limited. During the spring, summer and autumn seasons from 2021 to 2023, we trapped Daurian ground squirrel (Spermophilus dauricus), a small rodent species that inhabits eastern Asian grasslands in Inner Mongolia and collected their ectoparasites. Using machine learning Lasso regression, we pinpointed factors affecting tick and flea abundance on S. dauricus. We then analyzed these factors and their seasonal interactions with a mixed negative binomial generalized linear model. Our study revealed significant but inconsistent seasonal effects on the load of ectoparasites. The tick load was significantly higher in spring and summer compared to autumn, while the flea load was higher in summer and autumn but lacked statistical significance. Furthermore, individual factors that influence the flea and tick load were moderated by seasonal effects, with a male bias in flea parasitism observed in spring. Significant interactions were also found among seasonality, sex, and body weight. The load of male squirrel fleas was positively correlated with body weight, with the highest increase observed in spring. On the contrary, the flea load of female squirrels showed a negative correlation with body weight, significantly decreasing in the autumn with increasing weight. Significant interactions were observed between season and survival status, with hosts exhibiting higher tick load during autumn survival. Our findings underscore the importance of considering seasonal variation in parasitism and the interactions between seasonal dynamics and host biological traits in shaping parasite distributions.

Reconceptualizing and Defining Exposomics within Environmental Health: Expanding the Scope of Health Research

BACKGROUND

Exposomics, the study of the exposome, is flourishing, but the field is not well defined. The term "exposome" refers to all environmental influences and associated biological responses throughout the lifespan. However, this definition is very similar to that of the term "environment"-the external elements and conditions that surround and affect the life and development of an organism. Consequently, the exposome seems to be nothing more than a synonym for the environment, and exposomics a synonym for environmental research. As a result, some have rebranded their "standard" environmental health research with the neologistic exposome term, whereas others ignore or seek to abandon the seemingly redundant concept of the exposome.

OBJECTIVES

We argue that exposomics needs to sharpen its mission focus to counteract this apparent redundancy. Exposomics should be defined as a research program in environmental health aimed at enabling a comprehensive and discovery-driven approach to identifying environmental determinants of human health. Similar to the aim of the Human Genome Project, exposomics aims to analyze the complete complexity of exposures and their corresponding biological responses. Exposomics' primary premise is that the existence of undiscovered, potentially interconnected, nongenetic (environmental) risk factors for health necessitates a comprehensive discovery-driven analysis approach.

DISCUSSION

We argue that exposomics researchers should adopt our reconceptualization of exposomics and focus on the productiveness and integrity of their research program: its purpose and principles. We suggest that exposomics researchers should coordinate the writing of reviews that assess the program's productiveness and integrity, as well as provide a platform for exposomics researchers to define their vision for the field. https://doi.org/10.1289/EHP14509.

The association between urban land use and depressive symptoms in young adulthood: a FinnTwin12 cohort study

BACKGROUND

Depressive symptoms lead to a serious public health burden and are considerably affected by the environment. Land use, describing the urban living environment, influences mental health, but complex relationship assessment is rare.

OBJECTIVE

We aimed to examine the complicated association between urban land use and depressive symptoms among young adults with differential land use environments, by applying multiple models.

METHODS

We included 1804 individual twins from the FinnTwin12 cohort, living in urban areas in 2012. There were eight types of land use exposures in three buffer radii. The depressive symptoms were assessed through the General Behavior Inventory (GBI) in young adulthood (mean age: 24.1). First, K-means clustering was performed to distinguish participants with differential land use environments. Then, linear elastic net penalized regression and eXtreme Gradient Boosting (XGBoost) were used to reduce dimensions or prioritize for importance and examine the linear and nonlinear relationships.

RESULTS

Two clusters were identified: one is more typical of city centers and another of suburban areas. A heterogeneous pattern in results was detected from the linear elastic net penalized regression model among the overall sample and the two separated clusters. Agricultural residential land use in a 100 m buffer contributed to GBI most (coefficient: 0.097) in the "suburban" cluster among 11 selected exposures after adjustment with demographic covariates. In the "city center" cluster, none of the land use exposures was associated with GBI, even after further adjustment with social indicators. From the XGBoost models, we observed that ranks of the importance of land use exposures on GBI and their nonlinear relationships are also heterogeneous in the two clusters.

IMPACT

This study examined the complex relationship between urban land use and depressive symptoms among young adults in Finland. Based on the FinnTwin12 cohort, two distinct clusters of participants were identified with different urban land use environments at first. We then employed two pluralistic models, elastic net penalized regression and XGBoost, and revealed both linear and nonlinear relationships between urban land use and depressive symptoms, which also varied in the two clusters. The findings suggest that analyses, involving land use and the broader environmental profile, should consider aspects such as population heterogeneity and linearity for comprehensive assessment in the future.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database

BACKGROUND AND AIM

In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression.

METHODS

In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model.

RESULTS

In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival.

CONCLUSIONS

Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory

BACKGROUND

Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments.

OBJECTIVES

We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods.

METHODS

The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome.

RESULTS

The OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%.

DISCUSSION

The index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.

Greenness, air pollution, and temperature exposure effects in predicting premature mortality and morbidity: A small-area study using spatial random forest model

BACKGROUND

Although studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework.

METHODS

Years of potential life lost reflecting premature mortality and comparative illness and disability ratio reflecting chronic morbidity from 1673 small areas covering Greater Manchester for the year 2008-2013 obtained. Average annual levels of NO2 concentration, normalized difference vegetation index (NDVI) representing greenness, and annual average air temperature were utilized to assess exposure in each area. These exposures were linked to health outcomes using non-spatial and spatial random forest (RF) models while accounting for spatial autocorrelation.

RESULTS

Spatial-RF models provided the best predictive accuracy when accounted for spatial autocorrelation. Among the exposures considered, air pollution emerged as the most influential in predicting mortality and morbidity, followed by NDVI and temperature exposure. Nonlinear exposure-response relations were observed, and interactions between exposures illustrated specific ranges or sweet and sour spots of exposure thresholds where combined effects either exacerbate or moderate health conditions.

CONCLUSION

Air pollution exposure had a greater negative impact on health compared to greenness and temperature exposure. Combined exposure effects may indicate the highest influence of premature mortality and morbidity burden.

Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models

BACKGROUND

Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors.

OBJECTIVES

Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies.

METHODS

Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies.

RESULTS

Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5 -km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to € 300,000 . The directions of associations were less consistent for walkability and share of single residents.

DISCUSSION

Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Integrating the social environment with an equity perspective into the exposome paradigm: A new conceptual framework of the Social Exposome

The importance of the social environment and social inequalities in disease etiology is well-known due to the profound research and conceptual framework on social determinants of health. For a long period, in exposome research with its classical orientation towards detrimental health effects of biological, chemical, and physical exposures, this knowledge remained underrepresented. But currently it gains great awareness and calls for innovations in rethinking the role of social environmental health determinants. To fill this gap that exists in terms of the social domain within exposome research, we propose a novel conceptual framework of the Social Exposome, to integrate the social environment in conjunction with the physical environment into the exposome concept. The iterative development process of the Social Exposome was based on a systematic compilation of social exposures in order to achieve a holistic portrayal of the human social environment - including social, psychosocial, socioeconomic, sociodemographic, local, regional, and cultural aspects, at individual and contextual levels. In order to move the Social Exposome beyond a mere compilation of exposures, three core principles are emphasized that underly the interplay of the multitude of exposures: Multidimensionality, Reciprocity, and Timing and continuity. The key focus of the conceptual framework of the Social Exposome is on understanding the underlying mechanisms that translate social exposures into health outcomes. In particular, insights from research on health equity and environmental justice have been incorporated to uncover how social inequalities in health emerge, are maintained, and systematically drive health outcomes. Three transmission pathways are presented: Embodiment, Resilience and Susceptibility or Vulnerability, and Empowerment. The Social Exposome conceptual framework may serve as a strategic map for, both, research and intervention planning, aiming to further explore the impact of the complex social environment and to alter transmission pathways to minimize health risks and health inequalities and to foster equity in health.

Identification and validation of ferroptosis-related prognostic risk model and immune landscape in hepatocellular carcinoma

BACKGROUND

Ferroptosis has been paid much more attention on account of the correlation withtumorigenesis and development. However, the molecular characteristics and immune landscape of ferroptosis regulators in hepatocellular carcinoma (HCC) have not been fully elucidated.

METHODS

RNA-sequencing data and matched clinical data were collected from The Cancer Genome Atlas (TCGA) database, then underwent gene expression, genetic variations, prognostic risk model, and immune characterization analyses. An independent cohort from Gene Expression Omnibus (GEO) database was utilized to validate ferroptosis-related prognostic risk model.

RESULTS

We first identified the differentially expressed ferroptosis regulators between the tumor tissues and normal controls in HCC. Furthermore, the prognostic risk model based on ferroptosis regulators was constructed, of which the high risk group presented poor clinical outcomes compared to the low risk group. Importantly, the ferroptosis-related prognostic risk model consistently presented excellent prediction ability in recognizing the high and low risk patients according to the validation from an independent cohort. Subsequently, immune landscape analysis uncovered that most of ferroptosis regulatory genes were significantly associated with the infiltration of multiple immune cells and the expression of immune checkpoints in HCC. Moreover, the correlations of risk score with immune cells infiltration and immune checkpoints were determined in HCC.

CONCLUSION

Our study developed a prognostic risk model based on ferroptosis regulatory genes, which could accurately predict the patients' prognosis. Immune characteristics analysisrevealed that ferroptosis regulatory genes were associated with immune cells infiltration and immune checkpoints in HCC.