Whole-body aging mediates the association between exposure to volatile organic compounds and osteoarthritis among U.S. middle-to-old-aged adults

Associations of exposure to volatile organic compounds with biological aging: a cross-sectional study

BACKGROUND

Volatile organic compounds (VOCs) are recognized as potentially linked to various health damages. However, the effects of VOCs exposure on biological aging remain unknown. This study aimed to investigate this potential association through cross-sectional analyses.

METHODS

This study analyzed data from the National Health and Nutrition Examination Surveys (2011-2016). There was a total of 29,902 participants and 3,205 participants were finally included. Biological aging was represented by PhenoAge acceleration calculated through multiple indicators including albumin and alkaline phosphatase and so on. We employed weighted multivariate logistic regression to examine the correlation between individual VOC exposure and biological aging. The least absolute shrinkage and selection operator regression was utilized to identify key VOCs for the weighted quantile sum (WQS) regression, which assessed the association between mixed exposure to VOCs and biological aging. In addition, subgroup analyses were conducted using data from the same database on individuals' daily behaviors, such as physical activity levels, smoking, and alcohol consumption, to explore the influence of daily behaviors on the above associations.

RESULTS

Logistic regression analysis indicated that exposure to various individual VOCs was associated with biological aging. The WQS results revealed a significant positive association between mixed exposure to VOCs and biological aging (P < 0.001, OR = 1.523). Additionally, we found that participants with drinking, smoking, and lower levels of physical activity were more affected by exposure to individual VOCs. Mixed VOCs exposures differed only between smoking (P < 0.001, OR = 1.422) and non-smoking populations (P = 0.216, OR = 1.158).

CONCLUSION

VOCs exposure was associated with biological aging, and daily behaviors may influence an individual's susceptibility to such exposure. This discovery provided a new way of thinking about slowing down the aging process and improving overall health.

The effect of healthy eating index-2015 in the associations of biological aging and non-alcoholic fatty liver disease: an interaction and mediation analysis

BACKGROUND

The present study explored the association between biological aging (BA), healthy eating index-2015 (HEI-2015) and non-alcoholic fatty liver disease (NAFLD) in the general population of the United States.

METHODS

We used data from the NHANES database between 2017-2018 years to conduct the study. Weighted multivariable logistic regression analysis, restricted cubic spline (RCS), and subgroup analysis were performed to analyze the association of BA and HEI-2015 with prevalence of NAFLD and the mediation effect of HEI-2015 was also discussed. Additionally, generalized additive model was conducted to investigate the association of BA and HEI-2015 with ZJU index, BARD score, and NAFLD fibrosis score.

RESULTS

There was a total of 2,421 individuals. RCS shown that BA was positively correlated with prevalence of NAFLD, while HEI-2015 was negative correlated with NAFLD risk. After adjusting for interfering factors, compared with the lowest quartiles of BA and HEI-2015, the odds ratios with 95% confidence intervals for NAFLD across the quartiles were (1.24 (0.84, 1.84), 2.07 (1.15, 3.73) and 2.49 (1.16, 5.38)) and (0.89 (0.66, 1.18), 0.87 (0.65, 1.16) and 0.64 (0.46, 0.87)), respectively. The BA was linear positive with ZJU index, BARD score and NAFLD fibrosis score. However, the linear negative correlation existed between HEI-2015 and ZJU index, BARD score and NAFLD fibrosis score. Mediation analysis showed that the positive correlation between BA and NAFLD could be mediated and weakened by HEI-2015.

CONCLUSIONS

The prevalence of NAFLD gradually increases with BA, but this positive association can be weakened by the healthy diet.

Phthalates exposure, biological aging, and increased risks of insulin resistance, prediabetes, and diabetes in adults with metabolic dysfunction-associated steatotic liver disease

AIM

To estimate the individual and combined influences of phthalates and biological aging on insulin resistance (IR), prediabetes, and diabetes in population with metabolic dysfunction-associated steatotic liver disease (MASLD).

METHODS

Data on 3,045 US adults with MASLD were collected to outline the individual and mixed effects of urinary phthalate metabolites on prevalent IR, prediabetes, and diabetes by survey-weighted logistic regression and weighted quantile sum (WQS) regression, as well as the interaction effects between phthalates and biological aging.

RESULTS

We discovered positive relationships - odds ratio (OR) and 95 % confidence interval [CI] - of mono-2-ethyl-5-carboxypentyl phthalate 1.147 [1.041;1.264], mono-(2-ethyl-5-hydroxyhexyl) phthalate 1.175 [1.073;1.288], and mono-(2-ethyl-5-oxohexyl) phthalate 1.140 [1.040;1.250] with IR, and of mono-isobutyl phthalate with prediabetes 1.216 [1.064;1.390] (all FDR-adjusted P < 0.05). WQS analyses indicated significantly mixed effects of phthalate metabolites on the elevated risks of IR 1.166 [1.034;1.315], prediabetes 1.194 [1.006;1.416], and diabetes 1.214 [1.026;1.437]. Biological age (BA) and phenotypic age (PA) were positively associated with IR, prediabetes, and diabetes and further significantly interacted with phthalates on the outcomes; typically, compared to participants with low levels of phthalates mixture and PA, those with high levels of phthalates mixture and PA had the highest risks of IR 2.468 [1.474;4.133] (Pinteraction = 0.031), prediabetes 1.975 [1.189;3.278] (Pinteraction = 0.009), and diabetes 6.065 [3.210;11.460] (Pinteraction = 0.013).

CONCLUSION

Phthalates exposure of MASLD adults was related to increased risks of IR, prediabetes, and diabetes, which were interactively aggravated by biological aging. Controlling phthalates exposure and biological aging probably hold significant relevance for the prevention of diabetes in the MASLD population.

Association between urinary volatile organic compounds metabolites and rheumatoid arthritis among the adults from NHANES 2011-2018

Rheumatoid arthritis (RA) is closely associated with environmental factors. Volatile organic compounds (VOCs) are a common environment pollutant which can induce autoimmune diseases. However, studies on the relationship between VOCs and RA are still unclear. This study aimed to evaluate the potential associations between exposure to urinary VOCs and RA risk among adults. Data was analyzed from the National Health and Nutrition Examination Survey (NHANES) 2011-2018. We used logistics regression, restricted cubic splines (RCS) model, (Weighted Quantile Sum) WQS, qgcomp and (Bayesian Kernel Machine Regression) BKMR models to assess single and mixed relationships between VOCs and RA. A total of 3390 participants and 15 urinary VOCs included in this study. The results showed that AMCC, CEMC, DHBC, MB3C, PHGA, and PMMC were significantly higher than in RA compare to the participants without RA. Logistic regression model reveals that AAMC, AMCC, CEMC, CYMC, DHBC, HPMC, and MB3C were positive correlation with RA which age between 20 and 50. Then the WQS, qgcomp, and BKMR model suggest a positive association between mixed urinary VOCs and RA, with WQS and qgcomp model highlighting CYMC and CEMC as the major contributors in age 20-59 group. In BKMR analysis, the overall effects of co-exposure displayed CYMC, CEMC, and AMCC has significant positive with RA in age 20-59. Furthermore, RCS regression proved the positive linear relationship between CYMC, AMCC, and CEMC with RA. According to our study results, we demonstrated that exposure to certain urinary VOCs (CYMC, CEMC, and AMCC) is associated with an increased prevalence of RA among adults that age 20-59.

Mediating role of immune cells in association between volatile organic compounds and periodontitis: NHANES 2011-2014

BACKGROUND

The relationship between humans and volatile organic compounds (VOCs) is a persistent concern due to their widespread sources and high evaporation rates. However, there is currently limited direct evidence linking VOC exposure to the development of periodontitis.

METHODS

This cross-sectional study analyzed 1525 participants and 21 urinary VOCs in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014, aiming to investigate the relationship between periodontitis risk, assessed by attachment loss (AL) and probing depth (PD) and individual VOCs using logistic regression, quantile regression, and subgroup analysis. Weighted quantile sum analysis (WQS) and subgroup analysis were utilized to evaluate whether VOC mixtures were associated with periodontitis risk. Multiple linear regression models were used to examine the association between VOC co-exposure and peripheral immune cell counts. A mediation analysis was performed to evaluate whether peripheral immune cells are involved in the effect of VOC co-exposure on periodontitis prevalence.

RESULTS

Urinary levels of 2-aminothiazoline-4-carboxylic acid, mandelic acid, and N-acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine were positively associated with the risk of periodontitis after adjusting for all covariates. The WQS models demonstrated a positive correlation between the mixture of VOCs and the risk of periodontitis, wherein 2-aminothiazoline-4-carboxylic acid emerged as the most important contributor. The mediation analysis suggested that monocytes may play a role in the observed association between VOC co-exposure and the prevalence of periodontitis.

CONCLUSIONS

Exposure to VOCs is associated with a greater prevalence of periodontitis. Monocytes' mediating role plays a crucial function in the association between the risk of periodontitis and co-exposure to VOCs.

PLAIN LANGUAGE SUMMARY

Volatile organic compounds (VOCs) are chemicals that evaporate quickly and are found all around us-from paints to cleaning products. Understanding how these compounds affect our health is crucial, especially regarding conditions like periodontitis, a common oral chronic inflammatory disease. In our study, we looked at urine samples from 1525 people who participated in a national health survey between 2011 and 2014 to find out if there is an association between VOC exposure and the risk of developing periodontitis. We found that certain chemicals in the urine, which show VOC exposure, were indeed associated with a greater risk of the disease. We further investigated the collective impact of these VOCs on the risk of periodontitis, revealing that certain chemicals exert a more significant influence than their counterparts. Additionally, our research hints at a potential role for monocytes in the interplay between VOCs and the risk of periodontitis. Our data suggest that exposure to VOCs could be associated with a greater likelihood of periodontitis, with monocytes potentially playing a role in this relationship. This study helps us better understand the potential health impacts of daily chemical exposure and underscores the importance of investigating further how our environment affects our health.

Relationship between triglyceride glucose-body mass index and coronary artery calcium score in maintenance hemodialysis patients

Background

This study investigated the association between coronary artery calcification (CAC) and triglyceride glucose-body mass index (TyG-BMI) in patients receiving maintenance hemodialysis (MHD).

Methods

We used computed tomography (CT) to assess coronary artery calcification score (CACS) using the Agatston method. The TyG index was multiplied by BMI to derive the TyG-BMI index. Ordinal logistic regression models were used to analyze the relationship between TyG-BMI and CAC. The dose-response relationship was evaluated using restricted cubic spline regression. Weighted Quantile Sum regression was used to explore the weight of the TyG-BMI index components.

Results

Based on the TyG-BMI, 219 patients with MHD were stratified into three groups. The TyG-BMI index was shown to be an independent risk factor for CACS by multivariate ordinal logistic regression analysis (odds ratio, 1.011; [95% confidence interval, 1.002-1.021]; P = 0.021). The relationship between TyG-BMI and lg (CACS + 10) was linear (P-overall = 0.023, P-non-linear = 0.412). Body mass index (BMI) had the highest weight (0.566) when weights were assigned to the three components of TyG-BMI. In the non-diabetes and diabetes subgroups, TyG-BMI and lg (CACS + 10) did not exhibit a significant non-linear relationship.

Conclusion

TyG-BMI and CAC were independently positively correlated in patients undergoing MHD. These findings suggest that assessing TyG-BMI as a valuable tool for identifying the risk of CAC in patients with MHD.

AcidAGE: a biological age determination neural network based on urine organic acids

Organic acids reflect the course of all important metabolic processes and the effects of diet, nutrient deficiency, lifestyle, and microbiota composition. In present work, we focused on identifying age-related changes in organic acids in urine, and creating a neural network model based on them to determine biological age. The investigation involves data on concentrations of 60 organic acids in urine of 863 samples. Due to data analysis we found these acids could be used to determine human biological age. Two models were created for calculating biological age: a comprehensive AcidAGE model and a concise AcidAGE model based on 10 indicators. Both models demonstrate high accuracy. The presented models are useful for dynamically assessing the impact of medical interventions, lifestyle and diet amendments, and taking nutraceuticals on overall health and the risk of disease occurrence or progression. Their advantage lies in their ability to quickly update estimates as the corresponding biological processes change.

Volatile organic compounds (VOC) metabolites in urine are associated with increased systemic inflammation levels, and smokers are identified as a vulnerable population

BACKGROUND

Previous studies indicated that exposure to VOCs was linked to increased systemic inflammation levels. However, the dose-response relationships between urine VOCs metabolites and systemic inflammation have not been established, and the key metabolite of the toxic compounds has not been identified.

METHODS

We used data in 7007 US adults in the NHANES cycles (2011-2018) across 8 years. Urinary VOC metabolites were measured using ultra-performance liquid chromatography and electrospray tandem mass spectrometry (UPLC-ESI/MSMS). VOC metabolites were adjusted by urinary creatinine level before analysis. Systemic inflammation was assessed by systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) indices. Generalized linear models, restricted cubic splines (RCS), and weighted quantile sum (WQS) regression were applied to evaluate the associations, exposure-response (E-R) curve and identify the key contributor compound, adjusting for gender, age, race, BMI, marital condition, education level, smoking level, alcohol consumption and physical activity. Smoking status was assessed as an effect modifier.

RESULTS

Significant and robust positive correlations were found between 8 VOC metabolites and both SII and SIRI. They were N-Acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA), N-Acetyl-S-(2-cyanoethyl)-L-cysteine (CYMA), N-Acetyl-S-(3,4-dihydroxybutyl)-L-cysteine (DHBMA), N-Acetyl-S-(3-hydroxypropyl)-L-cysteine (3HPMA), mandelic acid (MA), N-Acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine (MHBMA3), phenylglyoxylic acid (PGA), and N-Acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine (HPMMA). The RCS curves showed J-shaped or exponential shaped E-R relationships for most VOC metabolites. WQS regression found that exposure to the mixture of VOC metabolites was related to increased systemic inflammation, and MA was the key VOC metabolite contributing most to systemic inflammation levels. Smokers exhibited higher levels of urinary VOCs and larger susceptibility to VOC-related increases in SII and SIRI compared to non-smokers.

CONCLUSION

This study demonstrated a strong link between urinary VOC metabolites and increased systemic inflammation, and smokers were more susceptible. Our findings highlighted the significance of reducing VOC exposure to mitigate the inflammation levels, particularly for smokers.

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology

BACKGROUND

Cardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine learning (ML) model to predict CVD risk based on VOC exposure and demographic data using SHapley Additive exPlanations (SHAP) for interpretability.

METHODS

We utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018, comprising 5098 participants. VOC exposure was assessed through 15 urinary metabolite metrics. The dataset was split into a training set (70 %) and a test set (30 %). Six ML models were developed, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM). Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC), accuracy, balanced accuracy, F1 score, J-index, kappa, Matthew's correlation coefficient (MCC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (sens), specificity (spec) and SHAP was applied to interpret the best-performing model.

RESULTS

The RF model exhibited the highest predictive performance with an ROC of 0.8143. SHAP analysis identified age and ATCA as the most significant predictors, with ATCA showing a protective effect against CVD, particularly in older adults and those with hypertension. The study found a significant interaction between ATCA levels and age, indicating that the protective effect of ATCA is more pronounced in older individuals due to increased oxidative stress and inflammatory responses associated with aging. E-values analysis suggested robustness to unmeasured confounders.

CONCLUSIONS

This study is the first to utilize VOC exposure data to construct an ML model for predicting CVD risk. The findings highlight the potential of combining environmental exposure data with demographic information to enhance CVD risk prediction, supporting the development of personalized prevention and intervention strategies.

Biological aging mediates the association between volatile organic compounds and cardiovascular disease

BACKGROUND

Evidence for the relationship between individual and combined volatile organic compounds (VOCs) and cardiovascular disease (CVD) is limited. Besides, the mediating role of biological aging (BA) has not been studied. Therefore, this study aimed to examine the association between VOCs and CVD risk and to explore the mediating effects of BA.

METHODS

Logistic regression models were used to investigate the relationships of metabolites of volatile organic compounds (mVOCs) and BA with CVD. In addition, weighted quantile sum (WQS) regression, adaptive elastic networks, and Environmental Risk Score (AENET-ERS) were utilized to assess overall associations of mixed VOCs co-exposure with CVD. Mediation analyses were used to identify potential mediating effects of BA.

RESULTS

In the single-pollutant model, CYMA was shown to be associated with an increased risk of CVD. Additionally, we identified significantly positive associations between the WQS index and CVD (odds ratio (OR) = 1.292, 95% confidence interval (CI): 1.006, 1.660), and DHBMA had the greatest contribution for CVD (0.246). Furthermore, the AENET-ERS results showed that 8 mVOCs were significantly associated with CVD, and ERS was related to an elevated risk of CVD (OR = 1.538, 95%CI: 1.255, 1.884). Three BA indicators mediated the association of the mVOCs mixture with CVD, with mediating effect proportions of 11.32%, 34.34%, and 7.92%, respectively.

CONCLUSION

The risk of CVD was found to increase with both individual and combined exposure to VOCs. BA mediates the positive effects of VOCs on CVD, suggesting that this pathway may be one of the mechanisms of CVD.

Association between per- and polyfluoroalkyl substances exposure and prevalence of chronic obstructive pulmonary disease: The mediating role of serum albumin

BACKGROUND

No study has examined the association between per- and polyfluoroalkyl substances (PFAS) exposure and chronic obstructive pulmonary disease (COPD) risk. This study aims to explore this relationship.

METHODS

This study enrolled 4541 individuals who had available data on PFAS, COPD, and covariates from NHANES 2007-2018. Serum PFAS including perfluorohexane sulfonate (PFHxS), perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS) were analyzed, because of high detective rates. Considering the skew distribution of PFAS levels, the natural logarithm-transformed PFAS (Ln-PFAS) was used. Logistic regression analysis, restricted cubic spline (RCS), and weighted quantile sum (WQS) regression were performed to explore the single, nonlinear, and mixed effects. A mediating analysis was used to evaluate the mediated effects of albumin.

RESULTS

Individuals with COPD had higher levels of PFHxS, PFNA, PFOA, and PFOS compared to those without COPD. Ln-PFNA (OR males: 1.92, 95 % CI:1.31 to 2.80, P: <0.001; OR females: 1.07, 95 % CI: 0.81 to 1.40, P: 0.636) and ln-PFOA (OR males: 2.17, 95 % CI:1.38 to 3.41, P: <0.001; OR females: 1.49, 95 % CI: 1.08 to 2.05, P: 0.016) were associated with COPD risk especially in males. The interaction between PFNA exposure and sex on COPD risk was significant (P interaction: <0.001). The RCS curve demonstrated the nonlinear relationship between the ln-PFOA (P nonlinear:0.001), ln-PFNA (P nonlinear:0.045), and COPD risk in males. WQS analysis showed mixed PFAS exposure was correlated with COPD risk in males (OR: 1.44, 95 % CI:1.18 to 1.75, P: <0.001). Albumin mediated the relationship between PFOA and COPD (mediated proportion: -17.94 %).

CONCLUSION

This study concludes PFOA and PFNA are linked to a higher COPD risk in males, and serum albumin plays a mediating role in the relationship between PFOA and COPD. Thess findings are beneficial for the prevention of COPD. Further studies are required to explore potential mechanisms.