Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data.

BACKGROUND: Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics. METHODS: The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results. RESULTS: Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification. CONCLUSION: We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.

Effect of exposures to multiple metals on blood pressure and hypertension in the elderly: a community-based study.

A chronic disease, hypertension (HTN) is prevalent among the elderly. Exploring the factors that influence HTN and blood pressure (BP) changes is of great public health significance. However, mixed exposure to multiple serum metals has had less research on the effects on BP and HTN for the elderly. From April to August 2019, 2372 people participated in the community physical examination program for the elderly in Tongling City, Anhui Province. We measured BP and serum levels of 10 metals and collected basic demographic information. We analyzed the relationship between metal levels and changes in BP and HTN by the least absolute shrinkage and selection operator regression, Bayesian kernel machine regression model, and generalized linear model. In multiple models, lead (Pb) and cadmium (Cd) were still significantly associated with HTN occurrence after adjusting for potential confounders (Pb: ORquartile 4 VS quartile 1 = 1.20, 95% CI 1.01-1.43; Cd: ORquartile 4 VS quartile 1 = 1.37, 95% CI 1.16-1.62). In the male subgroup, results were similar to those of the general population. In the female group, Cd was positively correlated with HTN and systolic blood pressure, while Pb was not. According to this study, Pb and Cd were correlated with BP and HTN positively, and there was a certain joint effect. To some extent, our findings provide clues for the prevention of hypertension in the elderly.

Targeted metabolomics reveals the association between central carbon metabolism and pulmonary nodules.

With the widespread application of low-dose computed tomography (LDCT) technology, pulmonary nodules have aroused more attention. Significant alteration in plasma metabolite levels, mainly amino acid and lipid, have been observed in patients of PNs. However, evidence on the association between central carbon metabolism and PNs are largely unknown. The aim of this study was to investigate the underlying association of PNs and plasma central carbon metabolites. We measured the levels of 16 plasma central carbon metabolites in 1954 participants who gained LDCT screening in MALSC cohort. The inverse probability weighting (IPW) technique was used to control for bias due to self-selection for LDCT in the assessed high-risk population. The least absolute shrinkage and selection operator (LASSO) penalized regression was used to deal with the problem of multicollinearity among metabolites and the combined association of central carbon metabolites with PNs was estimated by using quantile g-computation (QgC) models. A quartile increase in 3-hydroxybutyric acid, gluconic acid, succinic acid and hippuric acid was positively associated with the PNs risk, whereas a quartile increase in 2-oxadipic acid and fumaric acid was negatively associated with the risk of PNs in multiple-metabolite models. A positive but insignificant joint associations of the mixture of 16 metabolites with PNs was observed by using QgC models analyses. Further studies are warranted to clarify the association between circulating metabolites and PNs and the biological mechanisms.

The impact of multiple metals exposure on the risk of developing proliferative diabetic retinopathy in Anhui, China: a case-control study.

Through multiple different pathways, the environmental multiple metals make their ways to the human bodies, where they induce different levels of the oxidative stress response. This study further investigated the impact of multiple-metal exposure on the risk of developing proliferative diabetic retinopathy (PDR). We designed a case-control study with type 2 diabetic patients (T2D), in which the case group was the proliferative diabetic retinopathy group (PDR group), while the control group was the non-diabetic retinopathy group (NDR group). Graphite furnace atomic absorption spectrometry (GFAAS) and inductively coupled plasma optical emission spectrometry (ICP-OES) were used to detect the metal levels in our participants' urine samples. The least absolute shrinkage and selection operator (LASSO) regression approach was used to include these representative trace elements in a multiple exposure model. Following that, logistic regression models and Bayesian kernel machine regression (BKMR) models were used to describe the effect of different elements and also analyze their combined effect. In the single-element model, we discovered that lithium (Li), cadmium (Cd), and strontium (Sr) were all positively related to PDR. The multiple-exposure model revealed a positive relationship between Li and PDR risk, with a maximum quartile OR of 2.80 (95% CI: 1.10-7.16). The BKMR model also revealed that selenium (Se) might act as a protective agent, whereas magnesium (Mg), Li, and Cd may raise the risk of PDR. In conclusion, our study not only revealed an association between exposure to multiple metals and PDR risk but it also implied that urine samples might be a useful tool to assess PDR risk.

Exposure to multiple trace elements and thyroid cancer risk in Chinese adults: A case-control study.

The incidence of thyroid cancer (TC) has increased rapidly in last decades. Multiple trace elements in the external environment have important effects with thyroid function. However, the evidence for these on TC risk were rarely reported. A total of 585 newly diagnosed TC patients and 585 healthy controls were included in this study, and 14 urinary elements were measured to explain the fixed-exposure effect on TC risk. Conditional logistic regressions were used to reflect the multi-element associations, and Bayesian kernel machine regression (BKMR) was applied to show the tendency of mixed effects. Furthermore, the interaction effects were examined by Generalized linear model (GLM). The levels of lithium (Li), cobalt (Co), strontium (Sr), zinc (Zn) and copper (Cu) had negative effects with TC risk, nevertheless lead (Pb), arsenic (As) and chromide (Cr) showed positive effects. The BKMR and GLM models reflected the effect fluctuations of different elements, and there was a slight interaction effects between Li and Cr, Co, Zn and Pb. Further study is required to confirm these results in the future.

Associations between environmental exposure to polybrominated diphenyl ethers and nodular goiter risk: A case-control study.

Polybrominated diphenyl ethers (PBDEs) are widespread and persistent environmental contaminants, but their association with nodular goiter (NG) remains unknown. The present case-control study of 179 NG cases and 358 matched normal controls aimed to investigate the association between PBDEs and risk of NG. The plasma concentrations of 8 PBDEs congeners (BDE-28, -47, -99, -100, -153, -154, -183, and -209) were determined by gas chromatograph-mass spectrometer. Conditional logistic regression model was used to evaluate the odds ratio (OR) and 95% confidence interval (CI) for the association between each PBDEs congener and NG. Bayesian kernel machine regression (BKMR) was used to evaluate the association between overall levels of 8 PBDEs mixture and NG. The results of logistic model suggested that increased risk of NG was associated with elevated concentrations of all PBDEs congeners, except for BDE-209. In BKMR model, the risk of NG increased with the increase in overall exposure level of 8 PBDEs mixture. Compared to when all PBDEs mixture were at their median value, the risk of exposure-response function for NG increased by 0.34 units when all PBDEs were at their 75th percentile. In women, the results showed similar trends after additional adjustment for age at menarche and menopausal status. These findings provide novel epidemiological evidence for the prevention of NG. However, larger prospective studies are required to address the associations between PBDEs exposure and NG risk.