Data Resource Profile: The ALSPAC birth cohort as a platform to study the relationship of environment and health and social factors

Boyd, Andy; Thomas, Richard; Hansell, Anna; Gulliver, John; Hicks, Lucy Mary; Griggs, Rebecca L.; Hey, Joshua Vande; Taylor, Caroline M; Morris, Tim; Golding, Jean; Doerner, Rita; Fecht, Daniela; Henderson, John; Lawlor, Debbie A.; Timpson, Nicholas J.; Macleod, John

doi:10.1093/ije/dyz063

Cited by 35 publications

(32 citation statements)

References 41 publications

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“…In addition, information about health, wellbeing and educational outcomes is collected by data linkage to routinely collected health data from hospitals, GP practices and local government systems recording educational progress. A sense of place is important in such studies and research investigating the relationship between the environment and health is facilitated by geospatial data linkages (Boyd et al, 2019 ). Study data can be linked with data on the physical and social environment using geocoded records of participants’ residential location across the life course.…”

Section: Overview Of Collaborative Data Modelsmentioning

confidence: 99%

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

Oldroyd

Hobbs

Campbell

et al. 2021

Appl. Spatial Analysis

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Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand’s Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.

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Section: Overview Of Collaborative Data Modelsmentioning

confidence: 99%

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

Oldroyd

Hobbs

Campbell

et al. 2021

Appl. Spatial Analysis

View full text Add to dashboard Cite

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“…This was undertaken using measured BMI data from the Young Finns Study (Raitakari et al, 2008), which suggested that the childhood genetic score is a stronger predictor of childhood BMI in this cohort compared to the adult score, whereas the adult score was a stronger predictor of adult BMI. These analyses provide further validation on top of analyses based on genetic correlation analyses and also validation analyses within the Avon Longitudinal Study of Parents and Children (ALSPAC)(Boyd et al, 2019) (Supplementary Figure 2) . Taken together, these findings suggest that our genetic instruments can reliably separate childhood and adult body size as distinct exposures.…”

Section: Discussionmentioning

confidence: 95%

Evaluating the direct effects of childhood adiposity on adult systemic-metabolism: A multivariable Mendelian randomization analysis

Richardson

Mykkänen

Pahkala

et al. 2020

Preprint

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Background: Individuals who are obese in childhood have an elevated risk of cardiometabolic disease in adulthood. However, whether childhood adiposity directly impacts intermediate markers of this risk, independent of adult adiposity, is unclear. Methods and Results: We conducted a multivariable Mendelian randomization (MR) study to simultaneously evaluate the effects of childhood and adulthood body size on over 100 systemic molecular biomarkers representing multiple metabolic pathways. We first validated UK Biobank-derived genetic risk scores using data on body mass index (BMI) measured during childhood (n=2,427, age: 3-18 years) and adulthood (n=1,762, age: 34-49 years) from the Young Finns Study (YFS). Results indicated that the childhood score is a stronger predictor of childhood BMI (0.74 vs 0.62 area under the curve (AUC) for the childhood and adult scores respectively), whereas the adult score was a stronger predictor of adulthood BMI (0.57 vs 0.62 AUC). Two-sample MR analyses in a univariable setting using summary genome-wide association study (GWAS) data in up to 24,925 adults provided evidence of an effect of childhood body size on 42 of the 123 metabolic markers assessed (based on P<4.07x10-04). Undertaking multivariable MR analyses suggested that the effects for the majority of these metabolic biomarkers (35/42) substantially attenuated when accounting for adult body size. In further analyses, the biomarkers with the strongest evidence of mediating a long-term effect of adiposity on coronary artery disease (CAD) risk were those related to triglyceride-rich very-low-density lipoprotein particles. In contrast, the biomarkers which showed the strongest evidence of being directly influenced by childhood body size (amino acids leucine, isoleucine and tyrosine) provided little evidence that they mediate this effect on adult disease risk. Conclusions: The effects of childhood adiposity on the majority of biomarkers investigated in this study were greatly attenuated when accounting for adult body size. This suggests that the detrimental impact of genetically predicted childhood adiposity on systemic metabolism, as well as subsequent later life risk of CAD, can likely be mitigated through lifestyle modifications during adolescence and early adulthood.

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“…We assigned exposure estimates based on home address locations and averaged address-time-weighted PM 10 exposure estimates in each trimester and the whole pregnancy (WP) [ 27 , 31 ]. The date of conception was estimated from 2 weeks after the last menstrual period (LMP).…”

Section: Methodsmentioning

confidence: 99%

Trimester effects of source-specific PM10 on birth weight outcomes in the Avon Longitudinal Study of Parents and Children (ALSPAC)

et al. 2021

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Background Evidence suggests that exposure to particulate matter with aerodynamic diameter less than 10 μm (PM10) is associated with reduced birth weight, but information is limited on the sources of PM10 and exposure misclassification from assigning exposures to place of residence at birth. Methods Trimester and source-specific PM10 exposures (PM10 from road source, local non-road source, and total source) in pregnancy were estimated using dispersion models and a full maternal residential history for 12,020 births from the Avon longitudinal study of parents and children (ALSPAC) cohort in 1990–1992 in the Bristol area. Information on birth outcomes were obtained from birth records. Maternal sociodemographic and lifestyle factors were obtained from questionnaires. We used linear regression models for continuous outcomes (birth weight, head circumference (HC), and birth length (BL) and logistic regression models for binary outcomes (preterm birth (PTB), term low birth weight (TLBW) and small for gestational age (SGA)). Sensitivity analysis was performed using multiple imputation for missing covariate data. Results After adjustment, interquartile range increases in source specific PM10 from traffic were associated with 17 to 18% increased odds of TLBW in all pregnancy periods. We also found odds of TLBW increased by 40% (OR: 1.40, 95%CI: 1.12, 1.75) and odds of SGA increased by 18% (OR: 1.18, 95%CI: 1.05, 1.32) per IQR (6.54 μg/m3) increase of total PM10 exposure in the third trimester. Conclusion This study adds to evidence that maternal PM10 exposures affect birth weight, with particular concern in relation to exposures to PM10 from road transport sources; results for total PM10 suggest greatest effect in the third trimester. Effect size estimates relate to exposures in the 1990s and are higher than those for recent studies – this may relate to reduced exposure misclassification through use of full residential history information, changes in air pollution toxicity over time and/or residual confounding.

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Data Resource Profile: The ALSPAC birth cohort as a platform to study the relationship of environment and health and social factors

Cited by 35 publications

References 41 publications

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

Evaluating the direct effects of childhood adiposity on adult systemic-metabolism: A multivariable Mendelian randomization analysis

Trimester effects of source-specific PM10 on birth weight outcomes in the Avon Longitudinal Study of Parents and Children (ALSPAC)

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