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.

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.

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.

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.