Organic contaminants in human breast milk identified by non-targeted analysis

Understanding the health implications of human exposure to mixtures of chemical contaminants is aided by analytical methods that can screen for a broad range of both expected and unexpected compounds. We performed a proof-of-concept analysis combining human breast milk, a biomonitoring matrix for determining contaminant exposure to mothers and infants, with a non-targeted method based on comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOF-MS). A total of 172 presumably anthropogenic halogenated compounds and non-halogenated cyclic and aromatic compounds were tentatively identified in breast milk from San Diego, California through mass spectral database searches. Forty of the compounds were prioritized for confirmation based on halogenation or 100% frequency of detection, and the identities of 30 were verified using authentic standards. Thirty-four (85%) of the prioritized contaminants are not typically monitored in breast milk surveys, and 31 (77%) are regulated in at least one market worldwide, indicating breast milk may be a useful biomonitoring matrix for non-targeted analysis and the assessment of human exposure to future emerging or undiscovered contaminants.

Association between serum heavy metals and prostate cancer risk - A multiple metal analysis

BACKGROUND

Prostate cancer is one of the most prevalent cancers in men. Exposure to heavy metals and their association with prostate cancer risk has been studied extensively, but combined effects remain largely inconclusive.

OBJECTIVES

To elucidate the association between serum concentrations of heavy metals and prostate cancer risk.

METHODS

Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the concentrations of a panel of 10 heavy metals (Mn, Cu, Zn, As, Se, Sb, Co, Cu, Cd and Pb) in serum samples of 141 cases and 114 controls in the Singapore Prostate Cancer Study. Linear probit regression models were used to estimate risk differences (RDs) and 95% confidence intervals (CIs) for the associations between log-centered serum metal concentrations and prostate cancer risk with adjustment for potential confounders. Bayesian kernel machine regression (BKMR) models were used to account for nonlinear, interactive, and joint metal effects.

RESULTS

Using probit regression, four heavy metals (As, Zn, Mn, Sb) were significantly and positively associated with prostate cancer risk in the unadjusted models. Using BKMR analysis, both As and Zn had positive risk differences on prostate cancer risk when all other metals were held fixed at the 25th and 50th percentiles (RD, 25th percentile: As: 0.15, Zn: 0.19, RD, 50th percentile: As: 0.45, Zn: 0.37). In addition, the overall mixture risk difference was positive and the 95% credible intervals did not include 0 when all metals in the mixture were jointly above their 55th percentile, as compared to when all metals were below their median values.

CONCLUSIONS

In summary, we found positive associations between the serum levels of As and Zn and prostate cancer risk on the risk difference scale using BKMR models. The overall mixture effect was also associated with increased prostate cancer risk. Future studies are warranted to validate these findings in prospective studies.

Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis

The potencial of Coffea arabica leaves as bioindicators of atmospheric carbon dioxide (CO₂) was evaluated in a free-air carbon dioxide enrichment (FACE) experiment by using near-infrared reflectance (NIR) spectroscopy for direct analysis and partial least squares discriminant analysis (PLS-DA). A supervised classification model was built and validated from the spectra of coffee leaves grown under elevated and current CO₂ levels. PLS-DA allowed correct test set classification of 92% of the elevated-CO₂ level leaves and 100% of the current-CO₂ level leaves. The spectral bands accounting for the discrimination of the elevated-CO₂ leaves were at 1657 and 1698 nm, as indicated by the variable importance in the projection (VIP) score together with the regression coefficients. Seven months after suspension of enriched CO₂, returning to current-CO₂ levels, new spectral measurements were made and subjected to PLS-DA analysis. The predictive model correctly classified all leaves as grown under current-CO₂ levels. The fingerprints suggest that after suspension of elevated-CO₂, the spectral changes observed previously disappeared. The recovery could be triggered by two reasons: the relief of the stress stimulus or the perception of a return of favorable conditions. In addition, the results demonstrate that NIR spectroscopy can provide a rapid, nondestructive, and environmentally friendly method for biomonitoring leaves suffering environmental modification. Finally, C. arabica leaves associated with NIR and mathematical models have the potential to become a good biomonitoring system.

Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease

Importance

The American Heart Association 2020 Strategic Impact Goals target an improvement in overall cardiovascular health, as assessed by 7 health metrics (smoking, body weight, physical activity, diet, plasma glucose level, plasma cholesterol level, and blood pressure).

Objective

To examine whether trajectories of overall cardiovascular health over time, as assessed by the cardiovascular health score (CHS) in 2006, 2008, and 2010, are associated with subsequent risk of CVD.

Design, Setting, and Participants

The Kailuan study is a prospective, population-based study that began in 2006. The cohort included 74 701 Chinese adults free of myocardial infarction, stroke, and cancer in or before 2010. In the present study, CHS trajectories were developed from 2006 to 2010 to predict CVD risk from 2010 to 2015. Data analysis was performed from January 1, 2006, to December 31, 2015.

Exposures

The CHS trajectories during 2006-2010 were identified using latent mixture models.

Main Outcomes and Measures

Incident CVD events (myocardial infarction and stroke) during 2010-2015 were confirmed by review of medical records. The CHS trajectories were determined using 7 cardiovascular health metrics scored as poor (0 points), intermediate (1 point), and ideal (2 points); total score ranges from 0 (worst) to 14 (best). Based on the baseline CHS and patterns over time, 5 trajectories were categorized (low-stable, moderate-increasing, moderate-decreasing, high-stable I, and high-stable II).

Results

Of the 74 701 adults included in the study (mean [SD] age at baseline, 49.6 [11.8] years), 58 216 (77.9%) were men and 16 485 (22.1%) were women. Five CHS trajectories were identified from 2006 to 2010: low-stable (n = 4393; range, 4.6-5.2), moderate-increasing (n = 4643; mean increase from 5.4 to 7.8), moderate-decreasing (n = 14 853; mean decrease from 7.4 to 6.3), high-stable I (n = 36 352; range, 8.8-9.0), and high-stable II (n = 14 461; range, 10.9-11.0). During 5 years of follow-up, 1852 incident CVD cases were identified. Relative to the low-stable trajectory, the high-stable II trajectory was associated with a lower subsequent risk of CVD (adjusted hazard ratio, 0.21; 95% CI, 0.16-0.26, after adjusting for age, sex, educational level, income, occupation, alcohol intake, and serum high-sensitivity C-reactive protein concentration at baseline).

Conclusions and Relevance

Cardiovascular health trajectories may be associated with subsequent CVD risk.