Maternal obesity and childhood asthma risk: Exploring mediating pathways.

BACKGROUND: Growing evidence for the effect of maternal obesity on childhood asthma motivates investigation of mediating pathways. OBJECTIVE: To investigate if childhood body mass index (BMI), gestational weight gain (GWG) and preterm birth mediate the association of maternal obesity on childhood asthma risk. METHODS: We used electronic medical records from mother-child pairs enrolled in Kaiser Permanente Northern California integrated healthcare system. Children were followed from their birth (2005-2014) until at least age 4 (n = 95,723), age 6 (n = 59,230) or age 8 (n = 25,261). Childhood asthma diagnosis at each age was determined using ICD-9/10 codes and medication dispensings. Prepregnancy BMI (underweight [<18.5], normal [18.5-24.9], overweight [25-29.9], obese [≥30] kg/m2 ) were defined using height and weight measurements close to the last menstrual period date. Child's BMI (Centers for Disease Control and Prevention BMI-for-age percentiles: underweight [<5th], normal [5th-85th], overweight [85th-95th], obese [>95th]) were obtained using anthropometric measurements taken the year preceding each follow-up age. GWG (delivery weight-prepregnancy weight) was categorised based on Institutes of Medicine recommendations (inadequate, adequate, excessive). Implementing first causal inference test (CIT) then causal mediator models (to decompose the natural direct and indirect effects), we examined the potential mediating effect of childhood BMI, GWG, and preterm birth on the association between prepregnancy BMI (continuous and categorical) and childhood asthma. RESULTS: Overall, risk of childhood asthma increased as prepregnancy BMI increased (age 4 risk ratio: 1.07, 95% confidence interval: 1.04, 1.09, per 5 kg/m2 increase in BMI; similar for age 6 and 8). CIT identified childhood BMI and preterm birth, but not GWG as potential mediators. Causal mediation models confirmed childhood BMI, but not preterm birth, as having a partial mediating effect. Results were similar for age 6 and 8, and when continuous mediators (instead of binary) were assessed. CONCLUSIONS: Childhood overweight/obesity has a modest mediating effect on the association between prepregnancy BMI and childhood asthma.

Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD.

MetaMSD is a proteomic software that integrates multiple quantitative mass spectrometry data analysis results using statistical summary combination approaches. By utilizing this software, scientists can combine results from their pilot and main studies to maximize their biomarker discovery while effectively controlling false discovery rates. It also works for combining proteomic datasets generated by different labeling techniques and/or different types of mass spectrometry instruments. With these advantages, MetaMSD enables biological researchers to explore various proteomic datasets in public repositories to discover new biomarkers and generate interesting hypotheses for future studies. In this protocol, we provide a step-by-step procedure on how to install and perform a meta-analysis for quantitative proteomics using MetaMSD.

Prepregnancy body mass index and risk of childhood asthma.

BACKGROUND: Growing evidence suggests that maternal obesity may affect the intrauterine environment and increase a child's risk of developing asthma. We aim to investigate the relationship between prepregnancy obesity and childhood asthma risk. METHODS: Cohorts of children enrolled in Kaiser Permanente Northern California integrated healthcare system were followed from birth (2005-2014) to age 4 (n = 104,467), 6 (n = 63,084), or 8 (n = 31,006) using electronic medical records. Child's asthma was defined using ICD codes and asthma-related prescription medication dispensing. Risk ratios (RR) and 95% confidence intervals (95% CIs) for child's asthma were estimated using Poisson regression with robust error variance for (1) prepregnancy BMI categories (underweight [<18.5], normal [18.5-24.9], overweight [25-29.9], obese 1 [30-34.9], and obese 2/3 [≥35]) and (2) continuous prepregnancy BMI modeled using cubic splines with knots at BMI category boundaries. Models were adjusted for maternal age, education, race, asthma, allergies, smoking, gestational weight gain, child's birth year, parity, infant sex, gestational age, and child's BMI. RESULTS: Relative to normal BMI, RRs (95%CIs) for asthma at ages 4, 6, and 8 were 0.91 (0.75, 1.11), 0.95 (0.78, 1.16), and 0.97 (0.75, 1.27) for underweight, 1.06 (0.99, 1.14), 1.08 (1.01, 1.16), and 1.03 (0.94, 1.14) for overweight, 1.09 (1.00, 1.19), 1.12 (1.03, 1.23), 1.03 (0.91, 1.17) for obese 1, and 1.10 (0.99, 1.21), 1.13 (1.02, 1.25), 1.14 (0.99, 1.31) for obese 2/3. When continuous prepregnancy BMI was modeled with splines, child's asthma risk generally increased linearly with increasing prepregnancy BMI. CONCLUSIONS: Higher prepregnancy BMI is associated with modestly increased childhood asthma risk.

Marijuana knowledge, confidence in knowledge, and information efficacy as the protective and risk factors of marijuana use among college students.

Objective This study explored the relationships between marijuana knowledge, confidence in knowledge, and information efficacy and marijuana use. Furthermore, the effects of the knowledge-related variables were examined on intention to use, resistance efficacy, and intention to vote for legalization. Participants: Undergraduate students (N = 215) were surveyed in Fall 2018. Methods: Data were collected online and analyzed through a series of regression analyses. Results: Higher knowledge was related to less use via higher perceived risk whereas higher confidence in knowledge was related to more use. Marijuana use was related to higher future intention to use, lower resistance self-efficacy, and intention to vote for legalization. Information efficacy was related to intention to vote for legalization only. Conclusions: Students with more knowledge were less likely to use marijuana, whereas students who considered themselves well-informed were more likely to use it. Future intervention efforts will benefit from counteracting students' misplaced confidence in their knowledge.

Gender differences in the risk and protective factors of marijuana use among U.S. College students.

Gender differences in the risk and protective factors of marijuana use among college students were explored by analyzing online survey responses from 464 undergraduates. Women perceived higher risk and used marijuana less than men, with no gender difference in peer disapproval. In addition, women had higher objective knowledge regarding the health effects of marijuana, although they exhibited lower confidence in their knowledge. In subsequent regression analyses, health knowledge, confidence in knowledge, perceived risk, and peer disapproval predicted women's marijuana use, whereas only confidence in knowledge and perceived risk predicted men's use. These findings can help devise effective intervention strategies.

US direct-to-consumer medical service advertisements fail to provide adequate information on quality and cost of care.

BACKGROUND: In the 1970s, the Federal Trade Commission declared that allowing medical providers to advertise directly to consumers would be "providing the public with truthful information about the price, quality or other aspects of their service." However, our understanding of the advertising content is highly limited. OBJECTIVE: To assess whether direct-to-consumer medical service advertisements provide relevant information on access, quality and cost of care, a content analysis was conducted. METHOD: Television and online advertisements for medical services directly targeting consumers were collected in two major urban centres in Nevada, USA, identifying 313 television advertisements and 200 non-duplicate online advertisements. RESULTS: Both television and online advertisements reliably conveyed information about the services provided and how to make an appointment. At the same time, less than half of the advertisements featured insurance information and hours of operation and less than a quarter of them contained information regarding the quality and price of care. The claims of quality were substantiated in even fewer advertisements. The scarcity of quality and cost information was more severe in television advertisements. CONCLUSION: There is little evidence that medical service advertising, in its current form, would contribute to lower prices or improved quality of care by providing valuable information to consumers.

MGMS2: Membrane glycolipid mass spectrum simulator for polymicrobial samples.

RATIONALE: Polymicrobial samples present unique challenges for mass spectrometric identification. A recently developed glycolipid technology has the potential to accurately identify individual bacterial species from polymicrobial samples. In order to develop and validate bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, generating a large number of various polymicrobial samples can be beneficial, but it is costly and labor-intensive. Here, we propose an alternative cost-effective approach that generates realistic in silico polymicrobial glycolipid mass spectra. METHODS: We introduce MGMS2 (membrane glycolipid mass spectrum simulator) as a simulation software package that generates in silico polymicrobial membrane glycolipid matrix-assisted laser desorption/ionization time-of-flight mass spectra. Unlike currently available simulation algorithms for polymicrobial mass spectra, the proposed algorithm considers errors in m/z values and variances of intensity values, occasions of missing signature ions, and noise peaks. To our knowledge, this is the first stand-alone bacterial membrane glycolipid mass spectral simulator. MGMS2 software and its manual are freely available as an R package. An interactive MGSM2 app that helps users explore various simulation parameter options is also available. RESULTS: We demonstrated the performance of MGSM2 using six microbes. The software generated in silico glycolipid mass spectra that are similar to real polymicrobial glycolipid mass spectra. The maximum correlation between in silico mass spectra generated by MGMS2 and the real polymicrobial mass spectrum was about 87%. CONCLUSIONS: We anticipate that MGMS2, which considers spectrum-to-spectrum variation, will advance the bacterial algorithm development for polymicrobial samples.

Acute associations between PM2.5 and ozone concentrations and asthma exacerbations among patients with and without allergic comorbidities.

Acute effects of outdoor air pollution on asthma exacerbations may vary by asthma phenotype (allergic vs nonallergic). Associations of ambient PM2.5 and ozone concentrations with acute asthma visits (office, urgent, emergency, and hospitalization) were investigated using electronic medical records. International Classification of Disease codes were used to identify asthmatics, and classify them based on the presence or absence of an allergic comorbidity in their medical records. Daily 24-h average PM2.5, 8-h maximum ozone, and mean temperature were obtained from a centralized monitor. Using a time-stratified case-crossover approach, pollutant concentrations were modeled using moving averages and distributed lag nonlinear models (lag 0-6) to examine lag associations and nonlinear concentration-response. The adjusted odds ratios for a 10 µg/m3 increase in 3-day moving average (lag 0-2) PM2.5 in the two-pollutant models among patients with and without allergic comorbidities were 1.10 (95% confidence interval [CI]: 1.07, 1.13) and 1.05 (95% CI: 1.02, 1.09), respectively; and for a 20 ppb increase in 3-day moving average (lag 0-2) ozone were 1.08 (95% CI: 1.02, 1.14) and 1.00 (95% CI: 0.95, 1.05), respectively. Estimated odds ratios among patients with allergic comorbidities were consistently higher across age, sex, and temperature categories. Asthmatics with an allergic comorbidity may be more susceptible to ambient PM2.5 and ozone.

Simultaneous Determination of Twenty Mycotoxins in the Korean Soybean Paste Doenjang by LC-MS/MS with Immunoaffinity Cleanup.

Doenjang, a Korean fermented soybean paste, is vulnerable to contamination by mycotoxins because it is directly exposed to environmental microbiota during fermentation. A method that simultaneously determines 20 mycotoxins in doenjang, including aflatoxins (AFs), ochratoxin A (OTA), zearalenone (ZEN), and fumonisins (FBs) with an immunoaffinity column cleanup was optimized and validated in doenjang using LC-MS/MS. The method showed good performance in the analysis of 20 mycotoxins in doenjang with good linearity (R2 > 0.999), intra- and inter-day precision (<16%), recovery (72-112%), matrix effect (87-104%), and measurement uncertainty (<42%). The validated method was applied to investigate mycotoxin contamination levels in commercial and homemade doenjang. The mycotoxins that frequently contaminated doenjang were AFs, OTA, ZEN, and FBs and the average contamination level and number of co-occurring mycotoxins in homemade doenjang were higher than those in commercially produced doenjang.

Model-Based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids.

By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.

MetaMSD: meta analysis for mass spectrometry data.

Mass spectrometry-based proteomics facilitate disease understanding by providing protein abundance information about disease progression. For the same type of disease studies, multiple mass spectrometry datasets may be generated. Integrating multiple mass spectrometry datasets can provide valuable information that a single dataset analysis cannot provide. In this article, we introduce a meta-analysis software, MetaMSD (Meta Analysis for Mass Spectrometry Data) that is specifically designed for mass spectrometry data. Using Stouffer's or Pearson's test, MetaMSD detects significantly more differential proteins than the analysis based on the single best experiment. We demonstrate the performance of MetaMSD using simulated data, urinary proteomic data of kidney transplant patients, and breast cancer proteomic data. Noting the common practice of performing a pilot study prior to a main study, this software will help proteomics researchers fully utilize the benefit of multiple studies (or datasets), thus optimizing biomarker discovery. MetaMSD is a command line tool that automatically outputs various graphs and differential proteins with confidence scores. It is implemented in R and is freely available for public use at https://github.com/soyoungryu/MetaMSD. The user manual and data are available at the site. The user manual is written in such a way that scientists who are not familiar with R software can use MetaMSD.

Detecting differential protein expression in large-scale population proteomics.

MOTIVATION: Mass spectrometry (MS)-based high-throughput quantitative proteomics shows great potential in large-scale clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, there are unique challenges in analyzing the quantitative proteomics data. One issue is that the quantification of a given peptide is often missing in a subset of the experiments, especially for less abundant peptides. Another issue is that different MS experiments of the same study have significantly varying numbers of peptides quantified, which can result in more missing peptide abundances in an experiment that has a smaller total number of quantified peptides. To detect as many biomarker proteins as possible, it is necessary to develop bioinformatics methods that appropriately handle these challenges. RESULTS: We propose a Significance Analysis for Large-scale Proteomics Studies (SALPS) that handles missing peptide intensity values caused by the two mechanisms mentioned above. Our model has a robust performance in both simulated data and proteomics data from a large clinical study. Because varying patients' sample qualities and deviating instrument performances are not avoidable for clinical studies performed over the course of several years, we believe that our approach will be useful to analyze large-scale clinical proteomics data. AVAILABILITY AND IMPLEMENTATION: R codes for SALPS are available at http://www.stanford.edu/%7eclairesr/software.html.

Bioinformatics tools to identify and quantify proteins using mass spectrometry data.

Proteomics tries to understand biological function of an organism by studying its protein expressions. Mass spectrometry is used in the field of shotgun proteomics, and it generates mass spectra that are used to identify and quantify proteins in biological samples. In this chapter, we discuss the bioinformatics algorithms to analyze mass spectrometry data. After briefly describing how mass spectrometry generates data, we illustrate the bioinformatics algorithms and software for protein identification such as de novo approach and database-searching approach. We also discuss the bioinformatics algorithms and software to quantify proteins and detect the differential proteins using isotope-coded affinity tags and label-free mass spectrometry data.

Brief report: genome sequence and construction of an infectious cDNA clone of Ribgrass mosaic virus from Chinese cabbage in Korea.

Ribgrass mosaic virus (RMV) has severely decreased the production and lowered quality of Chinese cabbage co-infected with Turnip mosaic virus (63.4%) in Korea. The complete genome sequence of RMV isolated from Brassica rapa ssp. pekinensis was determined. The full genome consisted of 6,304 nucleotides and showed sequence identities of 91.5-94.2% with the corresponding genome of other RMV strains. Full-length cDNA of RMV-Br was amplified by RT-PCR with a 5'-end primer harboring a T7 promoter sequence and a 3'-end RMV specific primer. Subsequently, the full-length cDNA was cloned into plasmid vectors. Capped transcripts synthesized from the cDNA clone were highly infectious and caused characteristic symptoms in B. rapa ssp. pekinensis and several indicator plants, similar to wild type RMV. Since there has not been found RMV resistant Chinese cabbage yet and the virus has been prevalent already throughout the natural fields of Korea, the identification of full sequence and development of infectious clone would help developing breeding program for RMV resistant crops.