Dataset Information

Association of a four-gene model with allergic diseases: Two-year follow-up of a birth cohort study.

ABSTRACT:

Background

Our previous study has developed a four-gene model involving IL13 rs20541, IL4 rs2243250, ADRB2 rs1042713, and FCER1B rs569108 associated with asthma and atopy in Chinese Han children. However, whether the gene model is associated with allergies in early life has yet to be determined. This study aimed to apply the gene model in a birth cohort to investigate its associations with the development of allergic diseases in Chinese Han toddlers.

Methods

Five hundred and ninety-seven children from a birth cohort completing 2-year follow-up were included. Epidemiologic information and cord blood were collected. Children were genotyped for the above polymorphisms and divided into high or low genetic risk groups based on the genotypes. Subjects were followed at 6, 12, and 24 months, with information on allergic diseases collected via standard questionnaires and assessed by specialists.

Results

Two hundred and eighty-four children were divided into a high-risk group and 313 into a low-risk group. Between the two groups, a significant difference was only found in delivery mode among all the subject characteristics (p?=?.025). After stratification for delivery mode, children at high risk were more likely to develop eczema (relative risk [RR]?=?1.46, p?=?.040) over 2 years of follow-up compared with those at low risk. No significant associations were found between genetic risk and food allergy, wheezing and allergic rhinitis (p?>?.05).

Conclusion

The gene model was significantly associated with the development of eczema in Chinese Han toddlers. Long-term follow-up along with functional and replication studies on the gene model are still needed in future.

SUBMITTER: Hua L

PROVIDER: S-EPMC7860525 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association of a four-gene model with allergic diseases: Two-year follow-up of a birth cohort study.

Hua Li L Yang Fen F Chen Qian Q Liu Quanhua Q Ji Ruoxu R Liu Haipei H Ye Jianmin J Zhang Jun J Zhang Jianhua J Bao Yixiao Y

Immunity, inflammation and disease 20201205 1

<h4>Background</h4>Our previous study has developed a four-gene model involving IL13 rs20541, IL4 rs2243250, ADRB2 rs1042713, and FCER1B rs569108 associated with asthma and atopy in Chinese Han children. However, whether the gene model is associated with allergies in early life has yet to be determined. This study aimed to apply the gene model in a birth cohort to investigate its associations with the development of allergic diseases in Chinese Han toddlers.<h4>Methods</h4>Five hundred and ninet ...[more]

PMID: 33277970

Similar Datasets

Project description:BackgroundAtopic diseases, obesity and neuropsychiatric disorders are lifestyle-related and environmental-related chronic inflammatory disorders, and the incidences have increased in the last years.ObjectiveTo outline the design of the 18-year follow-up of the Copenhagen Prospective Study on Asthma in Childhood (COPSAC2000) birth cohort, where risk factors of atopic diseases, obesity and neuropsychiatric disorders are identified through extensive characterisation of the environment, along with deep clinical phenotyping and biosampling for omics profiling.MethodsCOPSAC2000 is a Danish prospective clinical birth cohort study of 411 children born to mothers with asthma who were enrolled at 1 month of age and closely followed at the COPSAC clinical research unit through childhood for the development of atopic diseases. At the 18-year follow-up visit, biomaterial (hair, blood, urine, faeces, throat, and skin swabs, nasal lining fluid and scraping, and hypopharyngeal aspirates) and extensive information on environmental exposures and risk behaviours were collected along with deep metabolic characterisation and multiorgan investigations including anthropometrics, heart, lungs, kidneys, intestines, bones, muscles and skin. Neuropsychiatric diagnoses were captured from medical records and registers accompanied by electronic questionnaires on behavioural traits and psychopathology.ResultsA total of 370 (90%) of the 411 cohort participants completed the 18-year visit. Of these, 25.1% had asthma, 23.4% had a body mass index >25 kg/m2 and 16.8% had a psychiatric diagnosis in childhood. Of the 62 probands with a neuropsychiatric diagnosis in childhood, a total of 68.7% drank alcohol monthly, and when drinking, 22.2% drank >10 units. Of the participants, 31.4% were currently smoking, and of these, 24.1% smoked daily. A total of 23.8% had tried taking drugs, and 19.7% reported having done self-destructive behaviour. The mean screen time per day was 6.0 hours.ConclusionThis huge dataset on health and habits, exposures, metabolism, multiorgan assessments and biosamples from COPSAC2000 by age 18 provides a unique opportunity to explore risk factors and underlying mechanisms of atopic disease and other lifestyle-related, non-communicable diseases such as obesity and neuropsychiatric disorders, which are highly prevalent in the community and our cohort.

Project description:Background. Attrition is a major cause of potential bias in longitudinal studies and clinical trials. Attrition rate above 20% raises concern of the reliability of the results. Few studies have looked at the factors behind attrition in follow-ups spanning decades. Methods. We analyzed attrition and associated factors of a 30-year follow-up cohort of subjects who were born with perinatal risks for neurodevelopmental disorders. Attrition rates were calculated at different stages of follow-up and differences between responders and non-responders were tested. To find combinations of variables influencing attrition and investigate their relative importance at birth, 5, 9, 16 and 30 years of follow-up we used the random forest classification. Results. Initial loss of potential participants was 13%. Attrition was 16% at five, 24% at nine, 35% at 16 and 46% at 30 years. The only group difference that emerged between responders and non-responders was in socioeconomic status (SES). The variables identified by random forest classification analysis were classified into Birth related, Development related and SES related. Variables from all these categories contributed to attrition, but SES related variables were less important than birth and development associated variables. Classification accuracy ranged between 0.74 and 0.96 depending on age. Discussion. Lower SES is linked to attrition in many studies. Our results point to the importance of the growth and development related factors in a longitudinal study. Parents' decisions to participate depend on the characteristics of the child. The same association was also seen when the child, now grown up, decided to participate at 30 years. In addition, birth related medical variables are associated with the attrition still at the age of 30. Our results using a data mining approach suggest that attrition in longitudinal studies is influenced by complex interactions of a multitude of variables, which are not necessarily evident using other multivariate techniques.

Project description:BackgroundConsumer-oriented mobile self-diagnosis apps have been developed using undisclosed algorithms, presumably based on machine learning and other artificial intelligence (AI) technologies. The US Food and Drug Administration now discerns apps with learning AI algorithms from those with stable ones and treats the former as medical devices. To the author's knowledge, no self-diagnosis app testing has been performed in the field of ophthalmology so far.ObjectiveThe objective of this study was to test apps that were previously mentioned in the scientific literature on a set of diagnoses in a deliberate time interval, comparing the results and looking for differences that hint at "nonlocked" learning algorithms.MethodsFour apps from the literature were chosen (Ada, Babylon, Buoy, and Your.MD). A set of three ophthalmology diagnoses (glaucoma, retinal tear, dry eye syndrome) representing three levels of urgency was used to simultaneously test the apps' diagnostic efficiency and treatment recommendations in this specialty. Two years was the chosen time interval between the tests (2018 and 2020). Scores were awarded by one evaluating physician using a defined scheme.ResultsTwo apps (Ada and Your.MD) received significantly higher scores than the other two. All apps either worsened in their results between 2018 and 2020 or remained unchanged at a low level. The variation in the results over time indicates "nonlocked" learning algorithms using AI technologies. None of the apps provided correct diagnoses and treatment recommendations for all three diagnoses in 2020. Two apps (Babylon and Your.MD) asked significantly fewer questions than the other two (P<.001).Conclusions"Nonlocked" algorithms are used by self-diagnosis apps. The diagnostic efficiency of the tested apps seems to worsen over time, with some apps being more capable than others. Systematic studies on a wider scale are necessary for health care providers and patients to correctly assess the safety and efficacy of such apps and for correct classification by health care regulating authorities.

Dataset Information

Association of a four-gene model with allergic diseases: Two-year follow-up of a birth cohort study.

Background

Methods

Results

Conclusion

Publications

Association of a four-gene model with allergic diseases: Two-year follow-up of a birth cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets