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.

Zhou L ，Wu D ，Chen H ，Han J ，Liu W ... -  《Scientific Reports》

Association between volatile organic compounds exposure and infertility risk among American women aged 18-45 years from NHANES 2013-2020.

The risk of infertility is progressively escalating over the years, and it has been established that exposure to environmental pollutants is closely linked to infertility. As a prevalent environmental pollutant in daily life, there is still a lack of substantial evidence on the association between volatile organic compounds (VOCs) exposure and infertility risk. This study aimed to examine the association between VOCs exposure and the risk of female infertility in the United States. Participant data sets from three cycles (2013-2020) were collected and downloaded from the National Health and Nutrition Examination Survey (NHANES), including demographics, examination, laboratory and questionnaire data. The baseline characteristics of the included population were evaluated, and the weighted quartile logistic regression was used to analyze the association between the urinary metabolites of VOCs (mVOCs) levels and the risk of infertility. Further exploration of the relationship between mVOCs and infertility was conducted by using 35 and 25 as the cut-off points for age and BMI subgroup analyses, respectively. Restricted cubic spline (RCS) was employed to elucidate the nonlinear relationship between mVOCs and infertility risk. Additionally, the Bayesian kernel machine regression (BKMR) model with 20,000 iterations was applied to elucidate the link between mVOCs and the risk of infertility when exposed to mixed or individual mVOCs. A total of 1082 women aged 18 to 45 years were included in this study, with 133 in the infertility group and 949 in the control group. The analysis of baseline characteristics suggested that urinary 34MHA, AMCC and DHBMA levels were significantly higher in the infertility group compared to the control group (p < 0.05). Quartile logistic regression analysis indicated that AAMA (Q3), AMCC (Q4), CYMA (Q3) and HPMMA (Q3) were positively associated with infertility risk in all models (p < 0.05). Subgroup analysis revealed different risk factors for infertility among various subgroups, with CYMA consistently showing a positive correlation with infertility risk in two age subgroups (p < 0.05). Furthermore, the association between mVOCs and infertility was observed only in the subgroup with BMI ≥ 25 kg/m2. RCS analysis indicated that 2MHA, ATCA, BMA, BPMA, CYMA, 2HPMA, 3HPMA and PGA exhibited linear dose-response relationships with infertility (p > 0.05), while the remaining variables showed nonlinear relationships (p < 0.05). The BKMR model demonstrated that the risk of female infertility exhibited an increasing trend with the accumulation of mVOCs co-exposure. A positive association between the exposure to mVOCs represented by 34MHA and AMCC and the risk of infertility was observed in this research. However, the inherent limitations associated with the cross-sectional study design necessitate the pursuit of additional prospective and experimental research to further elucidate and validate the relationships between various mVOCs exposure and female infertility.

Yang Q ，Zhang J ，Fan Z 《Scientific Reports》

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

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. 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. 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. 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. 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.

Jiang W ，Wu W ，Zhang K ，Liu L ，Yan B ... -  《-》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》

Increased levels of urine volatile organic compounds are associated with hypertension risk.

Individuals are exposed to various volatile organic compounds (VOCs) in their surroundings. VOCs were associated with some cardiovascular and metabolic diseases, but the effects on blood pressure (BP) have not yet been clarified. This study aimed to ascertain the relationship between the urine levels of VOCs and the prevalence of hypertension (HTN) in the general population. This analysis utilized data from 4156 participants aged from 20 to 79 years in 2013-2018 National Health and Nutrition Examination Survey (NHANES). Exposure to VOCs was assessed through measurements of urinary VOC metabolites, with 16 VOCs selected for analysis. The relationships between VOCs and the risk of HTN in patients were examined through the weighted logistic regression and the weighted linear regression models. Generalized additive models were employed to analyze potential nonlinear associations between VOCs and the risk of HTN. Additionally, subgroup analyses and intergroup interaction tests were conducted. A total of 4156 participants with 16 VOCs were finally included for analysis. Multivariable logistic regression showed that ln-transformed urine levels of N -acetyl-S-(2-cyanoethyl)-L-cysteine (CYMA) [odds ratio (OR) 1.54; 95% confidence interval (CI) 1.18-2.02], N -acetyl-S-(3-hydroxypropyl)-L-cysteine (3HPMA; OR 1.33; 95% CI 1.03-1.74), N -acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine (MHBMA3; OR 1.68; 95% CI 1.29-2.20), and N -acetyl-S-(1-phenyl-2-hydroxyethyl)-L-cysteine + N -acetyl-S-(2-phenyl-2-hydroxyethyl)-L-cysteine (PHEMA; OR 1.55; 95% CI 1.19-2.00) were significantly associated with an increased risk of HTN in US general population. A nonlinear relationship and a threshold effect were only observed between ln ( N -acetyl-S-(2-hydroxypropyl)-L-cysteine or 2HPMA) and HTN. There was a significantly positive correlation between ln(2HPMA) and HTN when ln(2HPMA) at least 5.29. Sub-analysis revealed that there was a more pronounced association in the elderly group (age ≥60 years), the overweight group (BMI ≥25), and the alcohol consumption group. Our work presents novel epidemiological evidence supporting the establishment of the relationship between environmental pollutants and HTN, highlighting hitherto ignored positive correlations between nonoccupational VOC exposure and the entire population's risk of HTN.

Zheng X ，Zou P ，Zeng C ，Liu J ，He Y ... -  《-》