Assessing the combined effect of multiple metal exposures on pregnancy and birth outcomes: Methodological insights in systematic review research

This systematic review focused on pregnant women and aimed to evaluate how exposure to multiple metals impacts their pregnancy and birth outcomes. Previous research has predominantly focused on the effects of individual metal exposures on adverse birth outcomes. However, it is crucial to recognize that real-world scenarios often involve simultaneous exposure to multiple pollutants. Recent studies have emphasized the significance of considering exposure to mixtures of metals to gain a more comprehensive understanding of their collective health impacts. This article outlines the essential steps taken during the systematic review process, which involved synthesizing existing evidence and evaluating the strength and consistency of the relationship between metal mixtures and pregnancy and birth outcomes.

Associations of chronic exposure to a mixture of pesticides and type 2 diabetes mellitus in a Chinese elderly population

Epidemiological studies have related exposure to pesticides to increased risk of diabetes. However, few studies have evaluated the health effects of mixed pesticides exposure, especially in an elderly population. Here, we utilized gas chromatography-tandem mass spectrometry to quantify the levels of 39 pesticides in 4 categories in a Chinese elderly population. Then we used general linear models to explore the association between individual pesticide exposure and type 2 diabetes mellitus (T2DM). Restricted cubic spline (RCS) models were fitted to identify potential non-linearities between those associations. Furthermore, stratified analysis by gender was conducted to explore the gender-specific associations. Finally, we used weighted quantile sum (WQS) regression, quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR) to evaluate the effects of mixed exposure to 39 pesticides. The results showed that exposure to pesticides was associated with high risk of T2DM, with β-Hexachlorocyclohexane (β-BHC) and oxadiazon being the most significant independent contributors, which was pronounced among elderly women. Moreover, the association of β-BHC and oxadiazon with T2DM was linear. These indicated that it is an urgent need to take practical measures to control these harmful pesticides.

Prenatal exposures to phthalates and bisphenols in relation to oxidative stress: single pollutant and mixtures analyses

Prenatal exposures to phthalates and bisphenols have been shown to be linked with adverse birth outcomes. Oxidative stress (OS) is considered a potential mechanism. The objective of this study was to explore the individual and mixtures of prenatal exposures to phthalates and bisphenols in associations with OS biomarkers. We measured eight phthalate metabolites and three bisphenols in the urine samples from 105 pregnant women in Wuhan, China. Urinary 8-hydroxydeoxyguanosine (8-OHdG), 8-isoprostaglandin F2α (8-isoPGF2α), and 4-hydroxy-2-nonenal-mercapturic acid (HNE-MA) were determined as OS biomarkers. The OS biomarkers in associations with the individual chemicals were estimated by linear regression models and restricted cubic spline (RCS) models, and their associations with the chemical mixtures were explored by quantile g-computation (qg-comp) models. In single-pollutant analyses, five phthalate metabolites including monomethyl phthalate (MMP), monoethyl phthalate (MEP), mono-(2-ethylhexyl) phthalate (MEHP), (2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono (2-ethyl-5-oxohexyl) phthalate (MEOHP) were positively associated with urinary 8-OHdG levels (all FDR-adjusted P = 0.06). These associations were further confirmed by the RCS models and were linear (P for overall association ≤ 0.05 and P for non-linear association > 0.05). In mixture analyses, qg-comp models showed that a one-quartile increase in the chemical mixtures of phthalate metabolites and bisphenols was positively associated with urinary levels of 8-OHdG and 8-isoPGF2α, and bisphenol A (BPA) and bisphenol F (BPF) were the most contributing chemicals, respectively. Prenatal exposures to individual phthalates and mixtures of phthalates and bisphenols were associated with higher OS levels.

Phthalate metabolites in urine and follicular fluid in relation to menstrual cycle characteristics in women seeking fertility assistance

BACKGROUND

Phthalates have been shown to disrupt the estrous cycle in animal studies. However, epidemiological research investigating their associations with menstrual cycle characteristics is limited.

OBJECTIVE

To explore the relationships between phthalate exposure and menstrual cycle characteristics among women seeking fertility assistance.

METHODS

We determined the levels of eight phthalate metabolites in both follicular fluid (FF) and urine specimens collected from 441 women in the Tongji Reproductive and Environmental (TREE) cohort, using high-performance liquid chromatography and tandem mass spectrometry. Information about menstrual cycle parameters was obtained through a questionnaire. The impacts of individual and joint exposure to phthalates on menstrual cycle characteristics were assessed using multivariable linear regression, Poisson regression, and quantile g-computation approaches.

RESULTS

After adjusting for relevant covariates, we found that per log10-unit increase in mono(2-ethylhexyl) phthalate (MEHP) level in urine specimens was associated with a decrease of 0.20 days (95 % CI: -0.37, -0.03) in bleeding duration. We also observed that mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) and the sum of di(2-ethylhexyl) phthalate (DEHP) metabolites (∑DEHP) concentrations in FF samples were inversely related to cycle length [β = -1.92 (95 % CI: -3.10, -0.75) and -1.87 (95 % CI: -3.56, -0.19), respectively]. However, we generally observed null associations between phthalate metabolites and irregular cycle, dysmenorrhea, hypomenorrhea, or cycle length variation. Furthermore, we also found that phthalate metabolite mixtures in FF and urine were generally unrelated to menstrual cycle characteristics.

CONCLUSION

Our findings suggest that some DEHP metabolites in FF and urine are inversely associated with menstrual cycle length and menstrual bleeding duration in women attending a fertility center.

Co-exposure to organic UV filters and phthalates and their associations with oxidative stress levels in children: A prospective follow-up study in China

Children are highly vulnerable to environmental pollutants, especially endocrine-disrupting chemicals (EDCs). Previous research has linked both organic UV filters and phthalates exposure to adiposity and pubertal development in children. Nevertheless, the individual and collective effects of these chemicals on this population remain poorly understood. In this study, twelve organic UV filters and metabolites, six phthalate metabolites and two oxidative stress biomarkers were analyzed in a prospective follow-up study in Shanghai, China after a baseline study conducted 1.5 years earlier. Results revealed a positive association between exposure to individual organic UV filters or their mixture and levels of 8-OHdG (β ranging from 0.242 to 0.588, P < 0.05), a marker of oxidative DNA damage. BP-3 and OD-PABA made a greater contribution to oxidative DNA damage than other UV filters. Levels of 8-OHdG were also positively correlated with single phthalate metabolites and their mixture, with MnBP and MMP contributing the most. Stratified analysis found that these associations were mainly observed in girls. Our mixture analysis revealed cumulative risks of oxidative DNA damage when there was co-exposure to these two kinds of EDCs. These results underscore the importance of considering the risks associated with organic UV filters and the necessity of evaluating the effects of all these pollutants, both individually and in mixtures.

The Relationship Between Heavy Metals and Missed Abortion: Using Mediation of Serum Hormones

Accumulating evidence suggests that heavy metal exposure may have adverse effects on the fetal development. Furthermore, disruption of serum hormone homeostasis can result in the adverse pregnancy outcomes. Therefore, this study aimed to investigate the potential association between heavy metals and missed abortion, with a focus on whether serum hormones mediate this relationship. The concentrations of heavy metals and hormones in serum were measured in this case-control study. Statistical models including, logistic regression model, principal component analysis (PCA), and weighted quantile sum (WQS) regression model were employed to examine the relationship between heavy metals, serum hormones, and missed abortion. Furthermore, the mediation analysis was performed to assess the role of serum hormones as potential mediators in this relationship. This study revealed significant associations between heavy metal exposure and missed abortion. Notable, the WQS index weight, which was mainly influenced by copper (Cu) and zine (Zn), is associated with missed abortion. Moreover, heavy metals including manganese (Mn), nickel (Ni), Zn, arsenic (As), Cu, cadmium (Cd), and lead (Pb) were found to be associated with serum levels of β-human chorionic gonadotropin (β-hCG), progesterone (P), estradiol (E2), and lactogen (HPL). In addition, the mediation analysis indicated that β-hCG explained a portion of the association (ranging from 18.77 to 43.51%) of between Mn, Ni, Zn, and As exposure and missed abortion. Serum P levels explained 17.93 to 51.70% of the association between Ni, Cu, and As exposure and missed abortion. Serum E2 levels played a significant mediating role, explaining a portion of the association (ranging from 22.14 to 73.60%) between Mn, Ni, Cu, As, Cd, and Pb exposure and missed abortion. Our results suggested that β-hCG, P, and E2 are one of the potential mediators in the complex relationship between heavy metals exposure and missed abortion. These results highlight the importance of considering both heavy metal exposure and serum hormone levels in understanding the etiology of missed abortion.

Prenatal exposure to phthalates and child growth trajectories in the first 24 months of life

BACKGROUND

Phthalates are a class of environmental chemicals with endocrine-disrupting properties. Prenatal phthalate exposure has been associated with adverse developmental outcomes in childhood. However, data assessing the effects of prenatal phthalate exposure on postnatal infant growth trajectories are sparse.

OBJECTIVES

To evaluate the associations of prenatal phthalate exposure with child growth trajectories from birth to 24 months old.

METHODS

Within a Chinese birth cohort study, 1051 mother-offspring pairs were included. Seven phthalate metabolites were quantified in maternal urine collected between weeks 33 and 39 of gestation. The trajectories for weight-for-age z-score (WAZ), length-for-age z-score (LAZ), weight-for-length z-score (WLZ) and head-circumference-for-age z-score (HCZ) were determined by group-based trajectory modeling (GBTM). Multinomial logistic regression and the weighted quantile sum approach (WQS) were used to investigate the association between individual and phthalate mixture exposure and the growth trajectories of four anthropometric metrics.

RESULTS

Five trajectory groups were identified for each anthropometric measure using GBTM. Higher prenatal exposure to several phthalate metabolites (MEHP, MEHHP, MEOHP, MECCP, summed DEHP metabolites, as well as MBP) was associated with child growth trajectories, especially for WAZ and LAZ in the first 24 months of life. The associations were further confirmed by a mixture analysis of phthalate metabolites and a sex-specific effect was observed in the WAZ and LAZ trajectories.

CONCLUSION

Prenatal phthalate exposure had heterogeneous associations with postnatal growth trajectories. More studies are warranted to confirm and elucidate the meaning of our findings.

Modeling historic neighborhood deprivation and non-Hodgkin lymphoma risk

Many studies have identified associations between neighborhood deprivation and disease, emphasizing the importance of social determinants of health. However, when studying diseases with long latency periods such as cancers, considering the timing of exposures for deprivation becomes more important. In this study, we estimated the associations between neighborhood deprivation indices at several time points and risk of non-Hodgkin lymphoma (NHL) in a population-based case-control study at four study centers - Detroit, Iowa, Los Angeles County, and Seattle (1998-2000). We used the Bayesian index regression model and residential histories to estimate neighborhood deprivation index effects in crude models and adjusted for four chemical mixtures measured in house dust and individual-level covariates. We found that neighborhood deprivation in 1980, approximately twenty years before study entry, provided better model fit than did neighborhood deprivation at 1990 and 2000. We identified several statistically significant associations between neighborhood deprivation in 1980 and NHL risk in Iowa and among long-term (20+ years) residents of Detroit. The most important variables in these indices were median gross rent as a percentage of household income in Iowa and percent of single-parent households with at least one child and median household income in Detroit. Associations remained statistically significant after adjustment for individual-level covariates and chemical mixtures, providing evidence for historic neighborhood deprivation as a risk factor for NHL and motivating future research to uncover the specific carcinogens driving these associations in deprived areas.

Associations of phthalates, phthalate replacements, and their mixtures with eicosanoid biomarkers during pregnancy

Humans are exposed to complex mixtures of phthalates. Gestational exposure to phthalates has been linked to preeclampsia and preterm birth through potential pathways such as endocrine disruption, oxidative stress, and inflammation. Eicosanoids are bioactive signaling lipids that are related to a variety of homeostatic and inflammatory processes. We investigated associations between urinary phthalates and their mixtures with plasma eicosanoid levels during pregnancy using the PROTECT cohort in Puerto Rico (N = 655). After adjusting for covariates, we estimated pair-wise associations between the geometric mean of individual phthalate metabolite concentrations across pregnancy and eicosanoid biomarkers using multivariable linear regression. We used bootstrapping of adaptive elastic net regression (adENET) to evaluate phthalate mixtures associated with eicosanoids and subsequently create environmental risk scores (ERS) to represent weighted sums of phthalate exposure for each individual. After adjusting for false-discovery, in single-pollutant analysis, 14 of 20 phthalate metabolites or parent compound indices showed significant and primarily negative associations with multiple eicosanoids. In our mixture analysis, associations with several metabolites of low molecular weight phthalates - DEP, DBP, and DIBP - became prominent. Additionally, MEHHTP and MECPTP, metabolites of a new phthalate replacement, DEHTP, were selected as important predictors for determining the concentrations of multiple eicosanoids from different pathway groups. A unit increase in phthalate ERS derived from bootstrapping of adENET was positively associated with several eicosanoids mainly from Cytochrome P450 pathway. For example, an increase in ERS was associated with 11(S)-HETE (β = 1.6, 95% CI: 0.020, 3.180), (±)11,12-DHET (β = 2.045, 95% CI: 0.250, 3.840), 20(S)-HETE (β = 0.813, 95% CI: 0.147, 1.479), and 9 s-HODE (β = 2.381, 95% CI: 0.657, 4.104). Gestational exposure to phthalates and phthalate mixtures were associated with eicosanoid levels during pregnancy. Results from the mixture analyses underscore the complexity of physiological impacts of phthalate exposure and call for further in-depth studies to examine these relationships.

Firefighters and the liver: Exposure to PFAS and PAHs in relation to liver function and serum lipids (CELSPAC-FIREexpo study)

INTRODUCTION

Firefighting is one of the most hazardous occupations due to exposure to per- and polyfluoroalkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAHs). Such exposure is suspected to affect the cardiometabolic profile, e.g., liver function and serum lipids. However, only a few studies have investigated the impact of this specific exposure among firefighters.

METHODS

Men included in the CELSPAC-FIREexpo study were professional firefighters (n = 52), newly recruited firefighters in training (n = 58), and controls (n = 54). They completed exposure questionnaires and provided 1-3 samples of urine and blood during the 11-week study period to allow assessment of their exposure to PFAS (6 compounds) and PAHs (6 compounds), and to determine biomarkers of liver function (alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and total bilirubin (BIL)) and levels of serum lipids (total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL) and triglycerides (TG)). The associations between biomarkers were investigated both cross-sectionally using multiple linear regression (MLR) and Bayesian weighted quantile sum (BWQS) regression and prospectively using MLR. The models were adjusted for potential confounders and false discovery rate correction was applied to account for multiplicity.

RESULTS

A positive association between exposure to PFAS and PAH mixture and BIL (β = 28.6%, 95% CrI = 14.6-45.7%) was observed by the BWQS model. When the study population was stratified, in professional firefighters and controls the mixture showed a positive association with CHOL (β = 29.5%, CrI = 10.3-53.6%) and LDL (β = 26.7%, CrI = 8.3-48.5%). No statistically significant associations with individual compounds were detected using MLR.

CONCLUSIONS

This study investigated the associations between exposure to PFAS and PAHs and biomarkers of cardiometabolic health in the Czech men, including firefighters. The results suggest that higher exposure to a mixture of these compounds is associated with an increase in BIL and the alteration of serum lipids, which can result in an unfavourable cardiometabolic profile.

Air pollution weaken your muscle? Evidence from a cross-sectional study on sarcopenia in central China

BACKGROUND

As the world experiences a demographic shift towards aging populations, there will be a significant surge in the number of sarcopenia patients, along with an unprecedented expansion in the associated economic burden. The multitudinous risk factors for sarcopenia have been reported, but evidence for air pollution remains rare.

METHODS

This cross-sectional study employed multi-stage random sampling to select 1592 participants over 40 years of age from Hubei Province. Daily mean concentrations of air pollutants were collected ChinaHighAirPollutants dataset. Unconditional logistic regression models were utilized to investigate the associations between air pollution and sarcopenia.

RESULTS

For each 1 μg/m³ increase in PM_2.5, PM₁₀, SO₂ and O₃, there were corresponding elevations of 11.1% [95% confidence interval (CI): 4.9, 17.7], 4.3% (95% CI: 1.4, 7.2), 22.6% (95% CI: 7.2, 40.1) and 9.3% (95% CI: 0.7, 18.7) in the risk of sarcopenia, respectively. The associations of PM_2.5/PM₁₀/O₃-sarcopenia were more pronounced in females, with corresponding odds ratios (ORs) and 95% CIs of 1.179 (1.062, 1.310), 1.079 (1.027, 1.135) and 1.180 (1.026, 1.358), separately. Additionally, individuals residing in rural areas were more susceptible to the effects of PM_2.5 and PM₁₀. Current/ever smokers or drinkers were also at higher risk of developing sarcopenia caused by PM_2.5, PM₁₀ and O₃ exposure. Mixture analyses show a surge of 48.4% (95% CI: 3.6%, 112.5%) in the likelihood of suffering from sarcopenia, and the joint impacts of the air pollution were mainly driven by PM_2.5.

CONCLUSIONS

Our results produced evidence for a relationship between air pollution exposure and the increased prevalence of sarcopenia in China. Public health and relevant departments should make efforts to prevent sarcopenia, particularly in China experiencing rapid demographic aging.

Polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, pesticides, and diabetes in the Anniston Community Health Survey follow-up (ACHS II): single exposure and mixture analysis approaches

Dioxins and dioxin-like compounds measurements were added to polychlorinated biphenyls (PCBs) and organochlorine pesticides to expand the exposure profile in a follow-up to the Anniston Community Health Survey (ACHS II, 2014) and to study diabetes associations. Participants of ACHS I (2005-2007) still living within the study area were eligible to participate in ACHS II. Diabetes status (type-2) was determined by a doctor's diagnosis, fasting glucose ≥125 mg/dL, or being on any glycemic control medication. Incident diabetes cases were identified in ACHS II among those who did not have diabetes in ACHS I, using the same criteria. Thirty-five ortho-substituted PCBs, 6 pesticides, 7 polychlorinated dibenzo-p-dioxins (PCDD), 10 furans (PCDF), and 3 non-ortho PCBs were measured in 338 ACHS II participants. Dioxin toxic equivalents (TEQs) were calculated for all dioxin-like compounds. Main analyses used logistic regression models to calculate odds ratios (OR) and 95 % confidence intervals (CI). In models adjusted for age, race, sex, BMI, total lipids, family history of diabetes, and taking lipid lowering medication, the highest ORs for diabetes were observed for PCDD TEQ: 3.61 (95 % CI: 1.04, 12.46), dichloro-diphenyl dichloroethylene (p,p'-DDE): 2.07 (95 % CI 1.08, 3.97), and trans-Nonachlor: 2.55 (95 % CI 0.93, 7.02). The OR for sum 35 PCBs was 1.22 (95 % CI: 0.58-2.57). To complement the main analyses, we used BKMR and g-computation models to evaluate 12 mixture components including 4 TEQs, 2 PCB subsets and 6 pesticides; suggestive positive associations for the joint effect of the mixture analyses resulted in ORs of 1.40 (95% CI: -1.13, 3.93) for BKMR and 1.32 (95% CI: -1.12, 3.76) for g-computation. The mixture analyses provide further support to previously observed associations of trans-Nonachlor, p,p'- DDE, PCDD TEQ and some PCB groups with diabetes.

Prenatal polymetallic exposure and small for gestational age: A case-control study in Taiyuan, China

BACKGROUND

Studies focused on independent effects of metals on small for gestational age, failing to account for potential interdependence among metals.

METHODS

In this case-control study, we selected 187 pregnant women and 187 matched controls from the First Hospital of Shanxi Medical University. Determination of 12 elements in the venous blood of pregnant women before delivery by ICP-MS. Logistic regression, weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR) were used to estimate the overall effect and identify important mixture components that drive the associations with SGA.

RESULTS

An increased risk of SGA was associated with As (OR= 1.06,95%CI: 1.01,1.12), Cd (OR= 1.24,95%CI: 1.04,1.47) and Pb (OR= 1.05,95%CI: 1.02,1.08), while Zn (OR= 0.58,95%CI: 0.45,0.76) and Mn (OR= 0.97,95%CI: 0.94,0.99) were protective factors for SGA. In the WQSR positive model, the mixture of heavy metals has a positive combined effect on SGA (OR= 1.74,95%CI: 1.15, 2.62), with Sb and Cd having the highest weights. The BKMR models confirmed that the metal mixture was associated with decreased risk of SGA when the concentration of 12 metals was between the 30th percentile and the 65th percentile, and Zn and Cd had the greatest independent effect. Zn and SGA may not be linearly correlated, higher Zn level may reduce the effect of Cd on the risk of SGA.

CONCLUSIONS

Our study suggested that exposure to multiple metals was associated with risk of SGA, and the observed association with multiple metals was dominated by Zn, Cd. Sb exposure during pregnancy may also increase the risk of SGA.

Modeling historic environmental pollutant exposures and non-Hodgkin lymphoma risk

Environmental exposures to chemicals are suspected risk factors for non-Hodgkin lymphoma (NHL), but few studies have assessed historic environmental risk factors. In this study, we estimated the associations between NHL and 1) historic environmental pollutant emissions from the Risk Screening Environmental Indicators (RSEI) model, which uses a database from the Environmental Protection Agency of toxic release emissions to air, water, and land, and 2) chemical mixtures measured in house dust (groups of PCBs, PAHs, and two mixtures of pesticides) for study participants enrolled in the NCI-SEER population-based case-control study (1998-2000) at four SEER centers - Detroit, Iowa, Los Angeles County, and Seattle. We assigned 11 years of annual temporally-varying historic environmental exposure scores by intersecting residential locations from participants' residential histories with a fine grid from the RSEI model and by performing inverse distance weighting between facilities releasing specific carcinogenic chemicals and residential locations for spatially-precise exposure assignments. We used Bayesian index low-rank kriging multiple membership models to identify important lag times for RSEI scores, cumulative effects of RSEI scores, and specific carcinogenic chemical releases into the environment. We found a significant positive association between RSEI scores and NHL at the maximum time lag of 11 years (OR = 1.17, 95% CI (1.06, 1.32)) and a significant cumulative RSEI score effect (OR = 1.30, 95% CI (1.02, 1.84)) for long-term residents in Detroit, where benzene and trichloroethylene were the most important chemicals driving this association. Additionally, we identified significant inverse associations for two study centers and time lags that did not persist in cumulative exposure models. Large weights for dichloromethane and pentachlorophenol in models of cumulative exposure also support evidence for their association with NHL risk. These results underscore the importance of considering historic and cumulative environmental exposures and using residential histories for diseases with long latency periods such as NHL.

Association between endocrine-disrupting chemical mixture and metabolic indices among children, adolescents, and adults: A population-based study in Korea

Exposure to endocrine-disrupting chemicals (EDCs) play a role in the etiology of obesity and dyslipidemia. However, few studies have analyzed the combined effects of EDC mixtures. This study explored the association between concurrent exposure to EDCs and obesity or dyslipidemia in children, adolescents, and adults. A total of 1454 children, 891 adolescents, and 3758 (for BMI) and 3424 (for TG/HDL) adults from the Korean National Environmental Health Survey 2015 to 2017 were included in this cross-sectional study. Urinary concentrations of eight phthalate metabolites, three phenols, three parabens, and one pyrethroid pesticides metabolite were quantified. Body mass index (BMI) was measured for all participants, and triglyceride (TG) and high-density lipoprotein cholesterol (HDL-c) levels were measured for adolescents and adults. Associations between combined EDC mixtures with the BMI and TG to HDL-c ratio were evaluated using Bayesian Kernel Machine Regression (BKMR). In all age groups, most of the chemical exposures, with the exception of BPF and BPS, were detected in more than 90% of participants. There were significant moderate to high correlations within phthalate metabolites and a high correlation within parabens. The BKMR showed that EDC mixtures were associated with higher BMI in both adolescents and adults, with greater significance in adults compared with adolescents, and a higher TG/HDL in male adolescents. In adolescents, MEP and MCPP drove the main effects on BMI and TG/HDL, respectively. In adults, 3PBA and BPA drove the main effects on BMI. The findings of this study suggest that exposure to EDC mixtures is associated with higher BMI and TG/HDL, and adolescence may be a critical period for EDC mixture in terms of both outcomes. Further studies are needed, but strategies to reduce EDC exposure from early life stages may be necessary to lower the risk of metabolic disease.

Exploring the use of infrared absorption spectroscopy and two-trace two-dimensional correlation analysis for the resolution of multi-component drug mixtures

Community drug checking provides an essential service that responds to the unpredictable and variable supply of illicit drugs. Point of care detection of trace components using portable infrared spectrometers is a harm reduction measure to prevent overdose. This study investigates the ability of weighted subtraction and two-trace two-dimensional (2T2D) correlation analysis to reveal the presence of heroin in an opioid mixture that contains heroin and fentanyl mixed with caffeine as a cutting agent. In both methods, a spectral trace was identified that provided reasonably high correlation scores to heroin when compared to entries in drug libraries. The two-trace correlation analysis produced a higher match score, suggesting that future improvements in spectral unmixing methods may enhance the reliability of detecting trace components in drugs.

Urinary concentrations of polycyclic aromatic hydrocarbon and phthalate metabolite mixtures in relation to semen quality among men attending an infertility clinic

Previous studies have reported that exposure to phthalates and polycyclic aromatic hydrocarbons (PAHs) is individually associated with altered semen quality, but no human studies have evaluated their joint effects of exposure mixtures, a more real-world scenario. We aimed to explore urinary metabolite mixtures of phthalates and PAHs in associations with semen quality. Repeated spot-urine samples gathered from 695 men attending a fertility clinic were analyzed for urinary metabolites of eight phthalates and ten monohydroxylated-PAHs (OH-PAHs). Principal component analysis (PCA)-multivariable linear regression (MLR) model, quantile g-computation (qg-comp), and Bayesian kernel machine regression (BKMR) were applied to estimate the associations of urinary mixtures of phthalate and OH-PAH metabolites with semen quality. The overall effects of urinary mixtures of phthalate and PAH metabolites on semen quality were not statistically significant. However, hydroxynaphthalene (OHNa) factor identified from PCA was monotonically associated with decreased total sperm count and sperm concentration, whereas di(2-ethylhexyl) phthalate (DEHP) factor was non-monotonically related to increased progressive sperm motility and total sperm motility. Qg-comp and BKMR models confirmed these findings and identified 2-OHNa and 2-OHFlu as the primary negative contributors, whereas MEOHP and MEHP as the primary positive contributors. Our findings suggest that exposure to mixtures of naphthalene and DEHP is associated with altered semen quality. The finding is warranted to confirm in further well-designed epidemiological studies.

Exposure to perfluoroalkyl substances and neonatal immunoglobulin profiles in the upstate KIDS study (2008-2010)

Infant exposure to per/polyfluoroalkyl compounds is associated with immune disruption. We examined associations between neonatal concentrations of perflurooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) and immunoglobulin (Ig) isotype profiles in a prospective cohort of infants. We measured Ig isotypes, including IgA, IgE, IgM and the IgG subclasses IgG₁, IgG₂, IgG₃, and IgG_4, and PFOA and PFOS in newborn dried bloodspots from N = 3175 infants in the Upstate KIDS Study (2008-2010). We examined the association between newborn Ig isotype levels and individual PFOS and PFOA concentrations using mixed effects regression models with a random intercept to account for twins among study participants. We assessed the joint effect PFOA and PFOS with quantile-based g-computation on all singletons and one randomly selected twin (N = 2901), with Ig categorized as above or below median value. Models were adjusted for infant sex, and maternal pre-pregnancy body mass index, race, parity, age and infertility treatment. In adjusted models, PFOA was inversely associated with IgE (coefficient = -0.12 per unit increase in PFOA, 95% CI: -0.065, -0.17), whereas IgG₂, IgM, and IgA were positively associated with PFOA (coefficient for IgG₂ = 0.22, 95% CI: 0.15, 0.27; coefficient for IgM = 0.11, 95% CI: 0.08, 0.15; and coefficient for IgA = 0.15, 95% CI: 0.07, 0.18). There was no relation between PFOS and Ig isotypes. Analysis of the joint effect of PFOA and PFOS showed an OR of 1.2 (95% CI: 1.04, 1.36) for IgA and OR of 1.12 (95% CI: 1.00, 1.24) for IgG₂ levels above the median for every quartile increase. PFOA levels were significantly associated with elevated IgA, IgM, IgG₂, and reduced levels of IgE in single-pollutant models. A small but significant joint effect of PFOA and PFOS was observed. Our results suggest that early exposure to PFOA and PFOS may disrupt neonatal immunoglobulin levels.

Prenatal single and combined exposure to phthalates associated with girls' BMI trajectory in the first six years

Evidence of the influence of prenatal phthalate exposure on childhood longitudinal obesity markers is limited. Nested on the Ma'anshan birth cohort study, 990 mother-daughter pairs were included. Seven phthalate metabolites were determined in urine collected in each trimester. Each child underwent a physical examination from birth to 6 years of age twelve times. Latent class growth models were used to identify three trajectories of girls' body mass index (BMI). Logistic regression, quantile g-computation and Bayesian kernel machine regression models analyzed the relationships of prenatal exposure to individual and mixed phthalates with girls' body mass index (BMI) trajectory. Compared to the "lowest trajectory" class, prenatal average concentrations of mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP, OR_crude = 2.095, 95 % CI = 1.014-4.328) and di(2-ethylhexyl) phthalate (DEHP, OR_crude = 2.336, 95 % CI = 1.022-5.338) during pregnancy were associated with an increased probability of being in the "highest trajectory" class. The average concentration of DEHP (OR_crude = 1.879, 95 % CI = 1.002-3.522) was associated with an increased probability of being in the "moderate trajectory" class. Stratified analyses by trimester of pregnancy mainly showed that third-trimester exposure to monoethyl phthalate (MEP, OR_adjusted = 1.584, 95 % CI = 1.094-2.292), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP, OR_adjusted = 2.885, 95 % CI = 1.367-6.088), MEHHP (OR_adjusted = 2.425, 95 % CI = 1.335-4.407), DEHP (OR_adjusted = 2.632, 95 % CI = 1.334-5.193) and high molecular weight phthalate (OR_adjusted = 2.437, 95 % CI = 1.239-4.792) was associated with an increased probability of being in the "highest trajectory" class. However, the mixture of phthalates was not significantly related to the girl's BMI trajectory. In conclusion, in utero exposure to phthalates, including MEP and DEHP metabolites (MEHHP and MEOHP), was significantly associated with early childhood high BMI trajectories in girls. The third trimester of pregnancy seemed to be the window of vulnerability to phthalate exposure for girls' high BMI trajectory at periods of prenatal development. No evidence supported a significant relationship between combined exposure to phthalate metabolites and girls' high BMI trajectory.

Early pregnancy exposure to metal mixture and birth outcomes - A prospective study in Project Viva

BACKGROUND

Prenatal exposure to metals has been individually associated with birth outcomes. However, little is known about the effect of metal mixture, particularly at low exposure levels.

OBJECTIVES

To estimate individual and joint effects of metal mixture components on birth outcomes.

METHODS

We used data from 1,391 mother-infant pairs in Project Viva (1999-2002). We measured 11 metals in maternal 1st trimester erythrocyte; abstracted birth weight from medical records; calculated gestational age from last menstrual period or ultrasound; and obtained birth length (n = 729) and head circumference (n = 791) from research measurements. We estimated individual and joint effects of metals using multivariable linear and Bayesian kernel machine regressions.

RESULTS

In both single metal and metal mixture analyses, exposure to higher concentrations of arsenic was associated with lower birth weight in males, zinc with higher head circumference in females, and manganese with higher birth length in sex-combined analysis. We also observed sex-specific metal interactions with birth outcomes. Arsenic and manganese showed a synergistic association with birth weight in males, in whom an interquartile range (IQR) increase in arsenic was associated with 25.3 g (95% CI: -79.9, 29.3), 47.9 g (95% CI: -98.0, 2.1), and 72.2 g (95% CI: -129.8, -14.7) lower birth weight when manganese concentrations were at 25th, 50th, and 75th percentiles, respectively. Lead and zinc showed an antagonistic association with head circumference in males, where an IQR increase in lead was associated with 0.18 cm (95% CI: -0.35, -0.02), 0.10 cm (95% CI: -0.25, 0.04), 0.03 cm (95% CI: -0.2, 0.14) smaller head circumference when zinc concentrations were at 25th, 50th, and 75th percentiles, respectively. Exposure to higher concentrations of arsenic was also associated with lower gestational age in males when concentrations of manganese and lead were higher.

DISCUSSION

Maternal erythrocyte concentrations of arsenic, manganese, lead, and zinc were individually and interactively associated with birth outcomes. The associations varied by infant sex and exposure level of other mixture components.

Quantitative analysis of wine and other fermented beverages with benchtop NMR

We present a fully automated approach for quantitative compositional analysis of fermented beverages using benchtop nuclear magnetic resonance (NMR) spectroscopy. NMR spectroscopy, renowned for its applications in chemical structure elucidation, is gaining attention as a quantitative analytical technique due to its inherent linearity and the ability to obtain comprehensive quantitative information with a single simple experiment. Recently developed benchtop NMR spectrometers offer the quantitative capabilities of NMR to a wide range of potential users in industry, but their applicability has been limited by the reduced effective spectral resolution and the need for more advanced data processing. We address this problem with a model-based algorithm that hinges on the well-understood description of quantum mechanical phenomena in NMR spectroscopy. We demonstrate the effectiveness of our approach on a challenging problem of analysing the composition of wine and related fermented beverages - an important potential niche application of quantitative NMR. We successfully quantify more than 15 major components in the wine matrix and enable the quantification of species whose analysis is generally not possible with established methods. The average discrepancy of the obtained concentrations, when compared to the traditional methods of analysis, usually does not exceed 10% and is lower for the most abundant species (e.g. below 5% for ethanol).

Incorporated diffusion ordered heteronuclear multiple bond correlation spectroscopy, 3D iDOSY-HMBC. Merging of diffusion delay with long polarization transfer delay of HMBC

3D iDOSY-HMBC pulse sequences allow the simplification of HMBC data of mixtures via separation in the diffusion domain. The presented methods utilize incorporated DOSY approach, iDOSY, where the existing delays of the basic pulse sequence are utilized for diffusion attenuation. In the simplest form of the proposed 3D iDOSY-HMBC sequences, no extra delays or RF-pulses were required, only two diffusion gradients were added within HMBC polarization transfer delay.

Exposure to multiple metals and prevalence for preeclampsia in Taiyuan, China

BACKGROUND

Several studies with small sample size have reported inconsistent associations between single metal and preeclampsia (PE). Very few studies have investigated metal mixtures and PE.

METHODS

Blood concentrations of chromium (Cr), cadmium, mercury (Hg), arsenic (As), lead (Pb), nickel, cobalt, and antimony were measured using inductively coupled plasma-mass spectrometry among 427 PE women and 427 matched controls from Taiyuan, China. Multivariate logistic regression models, weighted quantile sum (WQS) regression, and principal component analysis were employed to examine exposure to single metals and metal mixtures in relation to PE.

RESULTS

An increased prevalence of PE was associated with Cr (OR = 1.76, 95% CI: 1.18, 2.62 and 1.90, 1.22, 2.93 for the middle and high vs. low), Hg (OR = 1.60, 95% CI: 1.08, 2.38 for the high vs. low) and As (OR = 1.64, 95% CI: 1.07, 2.52 for the middle vs. low). The WQS index, predominated by Cr, Hg, Pb, and As, was positively associated with PE. A principal component characterized by Cr and As also exhibited excessive association with PE. The highest PE prevalence was found among women who were overweight/obese before pregnancy and had high Cr levels compared to women who had pre-pregnancy normal body mass index (BMI) and low Cr levels.

CONCLUSIONS

Our study provided evidence that exposure to multiple metals was associated with increased prevalence of PE, and the observed association with multiple metals was dominated by Cr, As. Our study also suggested that pre-pregnancy BMI might modify the association between Cr and PE.

Quantitative analysis using external standards with a benchtop NMR spectrometer

This paper investigates the use of benchtop NMR spectrometers for quantitative analysis with external standards. Specifically, it focuses on the measurement of aqueous samples with analyte concentrations ranging from 30 mM to 1.7 M and electrical conductivity of up to 84mScm^-1 using a 43 MHz instrument. It is demonstrated that measurements using the PULCON method cannot achieve an average error in quantification of <4% with the benchtop NMR tested here unless the standard and analyte are very similar. Our analysis indicates that this comparatively large error arises from the fixed tuning and matching of the benchtop spectrometer. We confirm that for moderately dilute samples (less than 0.2 M), the integral area of the solvent peak is suitable for use as an internal standard to mitigate this error. Furthermore, a round robin study demonstrates that the second major source of uncertainty in these measurements arises from the manual processing of the spectra by different analysts. Here we propose heuristics for manual baseline and phase correction to reduce this analyst-dependent error to about 3 %. We also demonstrate that semi-automated quantification using qGSD is able to achieve similar accuracy of integration, but with reduced sensitivity to the processing of the operator.

Interaction between a Mixture of Heavy Metals (Lead, Mercury, Arsenic, Cadmium, Manganese, Aluminum) and GSTP1, GSTT1, and GSTM1 in Relation to Autism Spectrum Disorder

BACKGROUND

Exposure to many environmental chemicals, including metals, often does not occur in isolation, hence requires assessment of the associations between exposure to mixtures of chemicals and human health.

OBJECTIVES

To investigate associations of a metal mixture of lead (Pb), mercury (Hg), arsenic (As), cadmium (Cd), manganese (Mn), and aluminum (Al) in children with autism spectrum disorder (ASD), additively or interactively with each of three glutathione S-transferase (GST) genes (GSTP1, GSTT1, and GSTM1).

METHOD

Using data from 266 case-control pairs of Jamaican children (2-8 years old), we fitted negative and positive generalized weighted quantile sum (gWQS) regression models to assess the aforementioned associations.

RESULTS

Based on additive and interactive negative gWQS models adjusted for maternal age, parental education, child's parish, and seafood consumption, we found inverse associations of the overall mixture score with ASD [MOR (95% CI): 0.70 (0.49, 0.99); P < 0.05) and [MOR (95%CI): 0.46 (0.25, 0.84); P = 0.01], respectively. In an unadjusted negative gWQS model, we found a marginally significant interaction between GSTP1 and a mixture of three metals (Pb, Hg, and Mn) (P = 0.07) while the association was no longer significant after adjustment for the same covariates (P = 0.24).

CONCLUSIONS

Differences in diet between ASD and control groups may play a role in the inverse associations we found. The possible interactive association between Mn and GSTP1 in ASD based on gWQS is consistent with our previous reports. However, possible interaction of GSTP1 with Pb and Hg in ASD requires further investigation and replication.

Bayesian approach for automated quantitative analysis of benchtop NMR data

Low-cost, user-friendly benchtop NMR instruments are often touted as a "one-click" solution for data acquisition, however insufficient peak dispersion in their spectra often reduces the accuracy of quantification and requires user expertise with sophisticated processing tools. Our work aims to facilitate the wide acceptance of benchtop NMR instruments as a viable and effective substitute for cryogenic magnets. We propose an algorithmic approach that completely automates the routine analysis of sets of samples with similar compositions - the problem that often underlies many industrial applications concerned with reaction and process monitoring and quality control. Our solution is rooted in the idea of parametric modelling formulated in terms of Bayesian statistics, which effectively incorporates prior knowledge about the studied system (such as concentration-dependent chemical shift changes) that is usually available in industrial applications. Furthermore, the use of quantum mechanical models for chemical species makes our approach invariant to the spectrometer field strength - a necessary prerequisite for the successful analysis of benchtop data. We demonstrate the performance of our method with two representative sets of samples: mixtures of alcohols and acetates, and aqueous mixtures of biologically relevant species. In these examples, our fully automated analysis of benchtop spectra achieves average errors in concentrations of 0.01 mol/mol and 0.02 mol/mol respectively. Our method is competitive with the traditional processing approaches of well resolved high-field data and has the potential to bring the benefits of NMR even to a small chemistry laboratory.

Nanoparticle-assisted NMR spectroscopy: A chemosensing perspective

Sensing methodologies for the detection of target compounds in mixtures are important in many different contexts, ranging from medical diagnosis to environmental analysis and quality assessment. Ideally, such detection methods should allow for both identification and quantification of the targets, minimizing the possibility of false positives. With very few exceptions, most of the available sensing techniques rely on the selective interaction of the analyte with some detector, which in turn produces a signal as a result of the interaction. This approach hence provides indirect information on the targets, whose identity is generally ensured by comparison with known standards, if available, or by the selectivity of the sensor system itself. Pursuing a different approach, NMR chemosensing aims at generating signals directly from the analytes, in the form of a (complete) NMR spectrum. In this way, not only are the targets unequivocally identified, but it also becomes possible to identify and assign the structures of unknown species. In this review we show how relaxation- and diffusion-based NMR techniques, assisted by appropriate nanoparticles, can be used to edit the ¹H NMR spectrum of a mixture and extract the signals of specific target compounds. Monolayer-protected nanoparticles, in particular those made from gold, are well suited to this task because they provide a versatile, protein-size support to build or incorporate supramolecular receptors. Remarkably, the self-organized and multifunctional nature of the nanoparticle coating allows exploitation of different kinds of non-covalent interactions, to provide tailored binding sites for virtually any class of molecules. From the NMR standpoint, the reduced translational and rotational diffusion rates of bulky nanoparticles offer a way to manipulate the states of the monolayer spins and build a reservoir of magnetization that can be selectively transferred to the interacting analytes. In addition, the low correlation time and the enhanced rigidity of the coating molecules (due to their grafting and crowding on the particle surface) promote efficient spin diffusion, useful in saturation transfer experiments. The optimized combination of NMR experiments and nanoreceptors can ultimately allow the detection of relevant analytes in the micromolar concentration range, paving the way to applications in the diagnostic field and beyond.

A field-invariant method for quantitative analysis with benchtop NMR

Recently developed benchtop instruments have the potential of bringing the benefits of NMR spectroscopy to the wide variety of industrial applications. Unfortunately, their low spectral resolution poses significant challenges for traditional quantification approach. Here we present a novel model-based method designed to overcome these challenges. By defining our models in terms of quantum mechanical properties of the underlying spin system, we make our approach invariant to the spectrometer field strength and especially suitable for analyzing benchtop data. Our experimental results on prepared samples and natural fruit juices confirm the applicability of our method for quantitative analysis of medium-field ¹H NMR spectra. The developed method succeeds in accurately separating the spectra of glucose anomers and even monitoring their interconversion in non-deuterated water. Furthermore, the compositions of unbuffered natural fruit juices estimated using data from 43 MHz to 400 MHz spectrometers are in good agreement with each other and with the reference values from nutrition databases.

Age at onset of first suicide attempt: Exploring the utility of a potential candidate variable to subgroup attempters

PURPOSE

Our objective was to explore the utility of age at first suicide attempt in identifying subgroups of suicide attempters.

METHODS

In a retrospective study design, we collected information from the clinical charts of 895 patients assessed over a seven-year period. Admixture analysis was used to determine the best fitting theoretical model for distribution of age at first attempt that divided the sample. Subsequently, multivariate analysis was performed to identify variables that distinguished the subgroups identified.

RESULTS

The theoretical solution that best explained the observed distribution of age at first suicide attempt was a mixture of two Gaussian distributions with a cut-off of 31 years for the two subgroups. In logistic regression analysis, male gender (Odds ratios [OR] 3.047, 95% Confidence Interval (CI) 1.818-5.106), fewer years of formal schooling (OR 3.384, 95% CI 1.701-6.734) and being married (OR 23.36, 95% CI 10.753-50.000), were more commonly associated with the late onset subgroup (age at first attempt >31 years). Further, the late onset subgroup had poorer global functioning (OR 0.980, 95% CI 0.962 to 0.998).

CONCLUSION

Age at onset of first suicide attempt is a useful candidate marker to delineate an early and late onset subgroup among suicide attempters. These results are likely to inform customization of suicide prevention strategies.

Heterogeneity of sleep quality in relation to circadian preferences and depressive symptomatology among major depressive patients

BACKGROUND

The current study aimed at investigating the latent dimensional structure of sleep quality as indexed by the seven components of the Pittsburgh Sleep Quality Index (PSQI), as well as latent covariance structure between sleep quality, circadian preferences and depressive symptoms.

METHODS

Two hundred twenty-five patients with major depressive disorder (MDD), with an average age of 29.92 ± 10.49 years (aged between 17 and 63), participated in the study. The PSQI, Morningness-Eveningness Questionnaire (MEQ) and Beck Depression Inventory (BDI) were administered to participants. Four sets of latent class analyses were subsequently run to obtain optimal number of latent classes best fit to the data.

RESULTS

Mixture models revealed that sleep quality is multifaceted in MDD. The data best fit to four-latent-class model: Poor Habitual Sleep Quality (PHSQ), Poor Subjective Sleep Quality (PSSQ), Intermediate Sleep Quality (ISQ), and Good Sleep Quality (GSQ). MDD patients classified into GSQ latent class (23.6%) reported the lowest depressive symptoms and were more prone to morningness diurnal preferences compared to other three homogenous sub-groups. Finally, the significant association between eveningness diurnal preferences and depressive symptomatology was significantly mediated by poor sleep quality.

LIMITATION

The cross-sectional nature of the study and the lack of an objective measurement of sleep such as polysomnography recordings was the most striking limitation of the study.

CONCLUSIONS

We concluded sleep quality in relation to circadian preferences and depressive symptoms has a heterogeneous nature in MDD.

Heparin/heparan sulfate analysis by covalently modified reverse polarity capillary zone electrophoresis-mass spectrometry

Reverse polarity capillary zone electrophoresis coupled to negative ion mode mass spectrometry (CZE-MS) is shown to be an effective and sensitive tool for the analysis of glycosaminoglycan mixtures. Covalent modification of the inner wall of the separation capillary with neutral or cationic reagents produces a stable and durable surface that provides reproducible separations. By combining CZE-MS with a cation-coated capillary and a sheath flow interface, a rapid and reliable method has been developed for the analysis of sulfated oligosaccharides from dp4 to dp12. Several different mixtures have been separated and detected by mass spectrometry. The mixtures were selected to test the capability of this approach to resolve subtle differences in structure, such as sulfation position and epimeric variation of the uronic acid. The system was applied to a complex mixture of heparin/heparan sulfate oligosaccharides varying in chain length from dp3 to dp12 and more than 80 molecular compositions were identified by accurate mass measurement.

Quantitative Analysis of Single and Mix Food Antiseptics Basing on SERS Spectra with PLSR Method

Usage and dosage of food antiseptics are very concerned due to their decisive influence in food safety. Surface-enhanced Raman scattering (SERS) effect was employed in this research to realize trace potassium sorbate (PS) and sodium benzoate (SB) detection. HfO2 ultrathin film-coated Ag NR array was fabricated as SERS substrate. Protected by HfO2 film, the SERS substrate possesses good acid resistance, which enables it to be applicable in acidic environment where PS and SB work. Regression relationship between SERS spectra of 0.3~10 mg/L PS solution and their concentration was calibrated by partial least squares regression (PLSR) method, and the concentration prediction performance was quite satisfactory. Furthermore, mixture solution of PS and SB was also quantitatively analyzed by PLSR method. Spectrum data of characteristic peak sections corresponding to PS and SB was used to establish the regression models of these two solutes, respectively, and their concentrations were determined accurately despite their characteristic peak sections overlapping. It is possible that the unique modeling process of PLSR method prevented the overlapped Raman signal from reducing the model accuracy.

Diagonal designs for a multi-component calibration experiment

Modern spectroscopic and sensor technologies combined with multivariate modelling are increasingly used for the quantitative analysis of complex mixtures. Their performance depends directly on the data design chosen for model training and validation. A well-balanced calibration experiment with the fewest samples possible presents additional challenges when several mixture components (factors) need to be calibrated on the same dataset and subsequently quantified from the same multivariate measurement. This practically important problem stays poorly addressed by the theory of experimental design. This theoretical work systematically formulates the requirements to an optimal calibration/validation dataset and introduces a new family of calibration designs, where the samples are placed along the diagonals of an experimental space that is a hypercube. Such placement is appropriate due to reasonable assumptions about the linear nature of analytical response. Suggested filling schemes allow economical diagonal designs with intrinsic validation to be built for multiple factors presented in as many levels as the number of samples. The most important practical cases of two and three factors are considered in detail, and generalization to higher dimensions is outlined. Diagonal designs of any complexity can be generated using a simple geometrical scheme or with a supplied script.

Novel selective TOCSY method enables NMR spectral elucidation of metabolomic mixtures

Complex mixture analysis is routinely encountered in NMR-based investigations. With the aim of component identification, spectral complexity may be addressed chromatographically or spectroscopically, the latter being favored to reduce sample handling requirements. An attractive experiment is selective total correlation spectroscopy (sel-TOCSY), which is capable of providing tremendous spectral simplification and thereby enhancing assignment capability. Unfortunately, isolating a well resolved resonance is increasingly difficult as the complexity of the mixture increases and the assumption of single spin system excitation is no longer robust. We present TOCSY optimized mixture elucidation (TOOMIXED), a technique capable of performing spectral assignment particularly in the case where the assumption of single spin system excitation is relaxed. Key to the technique is the collection of a series of 1D sel-TOCSY experiments as a function of the isotropic mixing time (τ_m), resulting in a series of resonance intensities indicative of the underlying molecular structure. By comparing these τ_m-dependent intensity patterns with a library of pre-determined component spectra, one is able to regain assignment capability. After consideration of the technique's robustness, we tested TOOMIXED firstly on a model mixture. As a benchmark we were able to assign a molecule with high confidence in the case of selectively exciting an isolated resonance. Assignment confidence was not compromised when performing TOOMIXED on a resonance known to contain multiple overlapping signals, and in the worst case the method suggested a follow-up sel-TOCSY experiment to confirm an ambiguous assignment. TOOMIXED was then demonstrated on two realistic samples (whisky and urine), where under our conditions an approximate limit of detection of 0.6mM was determined. Taking into account literature reports for the sel-TOCSY limit of detection, the technique should reach on the order of 10μM sensitivity. We anticipate this technique will be highly attractive to various analytical fields facing mixture analysis, including metabolomics, foodstuff analysis, pharmaceutical analysis, and forensics.

A combination of dynamic measurement protocol and advanced data treatment to resolve the mixtures of chemically similar analytes with potentiometric multisensor system

Data processing techniques and measuring protocol are very important parts of the multisensor systems methodology. Complex analytical tasks like resolving the mixtures of two components with very similar chemical properties require special attention. We report on the application of non-linear (artificial neural networks, ANNs) and linear (projections on latent structures, PLS) regression techniques to the data obtained from the flow cell with potentiometric multisensor detection of neighouring lanthanides in the Periodic System of the elements (samarium, europium and gadolinium). Quantification of individual components in mixtures is possible with reasonable precision if dynamic components of the response are incorporated thanks to the use of an automated sequential injection analysis system. The average absolute error in prediction of lanthanides with PLS was around 1 × 10(-4)mol/L, while the use of ANNs allows the lowering of prediction errors down to 2 × 10(-5)mol/L in certain cases. The suggested protocol seems to be useful for other analytical applications where simultaneous determination of chemically similar analytes in mixtures is required.