Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis

Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.

The Impact of Maternal Asthma on the Preterm Infants' Gut Metabolome and Microbiome - The Microbiome, Atopic Disease, and Prematurity (MAP) Study

Preterm infants are at a greater risk for the development of asthma and atopic disease, which can lead to lifelong negative health consequences. This may, in part, be due to alterations that occur in the gut microbiome and metabolome during their stay in the Neonatal Intensive Care Unit (NICU). To explore the differential roles of family history (i.e., predisposition due to maternal asthma diagnosis) and hospital-related environmental and clinical factors that alter microbial exposures early in life, we looked at a unique cohort of preterm infants born £ 34 weeks gestational age from two local level III NICUs, as part of the MAP (Microbiome, Atopic disease, and Prematurity) Study. Weekly stool, milk feeds, and saliva were collected until hospital discharge, and monthly stool and milk samples were collected at home until one year of age. We also chose a sub-cohort of infants whose mothers had a history of asthma and matched gestational age and sex to infants of mothers without a history of asthma (control). We performed a prospective, paired metagenomic and metabolomic analysis of stool and milk feed samples collected at birth, 2 weeks, and 6 weeks postnatal age. Although there were clinical factors associated with shifts in the diversity and composition of stool-associated bacterial communities, maternal asthma diagnosis did not play an observable role in shaping the infant gut microbiome in the study period. There were significant differences, however, in the metabolite profile between the maternal asthma and control groups at 6 weeks postnatal age. The most notable changes occurred in the linoleic acid spectral network, which plays a role in inflammatory and immune pathways, suggesting early metabolomic changes in the gut of preterm infants born to mothers with a history of asthma. Our pilot analysis suggests that a history of maternal asthma alters a preterm infants’ metabolomic pathways in the gut as early as the first 6 weeks of life.

The Identification of Electrical Properties of Acupuncture Points for Asthma Diagnosis

The aim of this research is to identify the electrical potential profile on the acupoint betwen healthy people and the patient of asthma. The raw data has taken by recording the electrical potential profile on the acupoints: Feishu, Pishu, and Shenshu from 10 healthy women and the 10 women with asthma attain the age of 20-30 years old based on the second data observation at the Local Government Clinic Kalijudan, Surabaya. Potential profile of the organs were the electrical signals form. It was achieved by the result of electrical potential which was based time recording. Recording time was done for 180 second. The results couldn't be differentiated significantly, so it needs the other signals processing with FFT analyze method with cutting as the data frames. It was done every 5 second. Based on the result of analyzing the amplitude of each frequency group, the significant differences are on the acupoint Shenshu : 0-5 Hz with p= 0.001, on the acupoint Phishu 148-152 Hz with p= 0.010, on the acupoint Feishu for frequency 198-203 Hz with p= 0.004 and on the acuponit Phishu p=0.011, for frequency 348-352 Hz on the acupoint Feishu and Shenshu have both value is p= 0,004 and 398-402 Hz with p=0,009 on the acupoint Phishu. According to the preference, it was found that the electrical potential profile on the acupoints of the healthy people has lower amplitude than the people with asthma. Then, the analyze of electrical potential profile on the acupoints can be used for asthma diagnose.

Early life blood lead levels and asthma diagnosis at age 4–6 years

The USA has a high burden of childhood asthma. Previous studies have observed associations between higher blood lead levels and greater hypersensitivity in children. The objective of the present study was to estimate the association between blood lead concentrations during early childhood and an asthma diagnosis between 48 and 72 months of age amongst a cohort with well-characterized blood lead concentrations. Blood lead concentrations were measured at 6, 12, 18, 24, 36, and 48 months of age in 222 children. The presence of an asthma diagnosis between 48 and 72 months was assessed using a questionnaire which asked parents or guardians whether they had been told by a physician, in the past 12 months, that their child had asthma. Crude and adjusted risk ratios (RR) of an asthma diagnosis were estimated for several parameterizations of blood lead exposure including lifetime average (6 to 48 months) and infancy average (6 to 24 months) concentrations. After adjustment for child sex, birthweight, daycare attendance, maternal race, education, parity, breastfeeding, income, and household smoking, age-specific or composite measures of blood lead were not associated with asthma diagnosis by 72 months of age in this cohort.

Identification of asthma control factor in clinical notes using a hybrid deep learning model

Background There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician’s documentation is not feasible using only structured data but requires labor-intensive chart review of electronic health records (EHRs). A certain guideline element in asthma control factors, such as review inhaler techniques, requires context understanding to correctly capture from EHR free text. Methods The study data consist of two sets: (1) manual chart reviewed data—1039 clinical notes of 300 patients with asthma diagnosis, and (2) weakly labeled data (distant supervision)—27,363 clinical notes from 800 patients with asthma diagnosis. A context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) was developed to identify inhaler techniques in EHR free text. Both original BERT and clinical BioBERT (cBERT) were applied with a cost-sensitivity to deal with imbalanced data. The distant supervision using weak labels by rules was also incorporated to augment the training set and alleviate a costly manual labeling process in the development of a deep learning algorithm. A hybrid approach using post-hoc rules was also explored to fix BERT model errors. The performance of BERT with/without distant supervision, hybrid, and rule-based models were compared in precision, recall, F-score, and accuracy. Results The BERT models on the original data performed similar to a rule-based model in F1-score (0.837, 0.845, and 0.838 for rules, BERT, and cBERT, respectively). The BERT models with distant supervision produced higher performance (0.853 and 0.880 for BERT and cBERT, respectively) than without distant supervision and a rule-based model. The hybrid models performed best in F1-score of 0.877 and 0.904 over the distant supervision on BERT and cBERT. Conclusions The proposed BERT models with distant supervision demonstrated its capability to identify inhaler techniques in EHR free text, and outperformed both the rule-based model and BERT models trained on the original data. With a distant supervision approach, we may alleviate costly manual chart review to generate the large training data required in most deep learning-based models. A hybrid model was able to fix BERT model errors and further improve the performance.