Search

Proteomics is the identification, detection and quantification of proteins within a biological sample. The complete set of proteins expressed by an organism is known as the proteome. The availability of new high-throughput proteomic technologies, such as Olink Proteomic Proximity Extension Assay (PEA) technology has enabled detailed investigation of the circulating proteome in large-scale epidemiological studies. In particular, the Olink® Target 96 inflammatory panel allows the measurement of 92 circulating inflammatory proteins. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based cohort study which recruited pregnant women in 1991-1992 and has followed these women, their partners, and their offspring ever since. In this data note, we describe the newly-released proteomic data available in ALSPAC. Ninety-two proteins were analysed in 9000 blood plasma samples using the Olink® Target 96 inflammatory panel. Samples were derived from 2968 fasted mothers (mean age 47.5; Focus on Mothers 1 (FOM1)), 3005 non-fasted offspring at age 9 (Focus@9) and 3027 fasted offspring at age 24 (Focus@24). Post sample filtering, 1834 offspring have data at both timepoints and 1119 of those have data from their mother available. We performed quality control analyses using a standardised data processing workflow (metaboprep) to produce a filtered dataset of 8983 samples for researchers to use in future analyses. Initial validation analyses indicate that IL-6 measured using the Olink® Target 96 inflammatory panel is highly correlated with IL-6 previously measured by clinical chemistry (Pearson’s correlation = 0.77) and we are able to reproduce the reported positive correlation between body mass index (BMI) and IL-6. The pre-processing and validation analyses indicate a rich proteomic dataset to further characterise the role of inflammation in health and disease.

Mendelian randomization (MR) is increasingly used for generating estimates of the causal impact of exposures on outcomes. Evidence suggests a causal role of excess adipose tissue (adiposity) on many health outcomes. However, this body of work has not been systematically appraised. We systematically reviewed and meta-analysed results from MR studies investigating the association between adiposity and health outcomes prior to the SARS-CoV-2/COVID-19 pandemic (PROSPERO: CRD42018096684). We searched Medline, EMBASE, and bioRxiv up to February 2019 and obtained data on 2,214 MR analyses from 173 included articles. 29 meta-analyses were conducted using data from 34 articles (including 66 MR analyses) and results not able to be meta-analysed were narratively synthesised. Body mass index (BMI) was the predominant exposure used and was primarily associated with an increase in investigated outcomes; the largest effect in the meta-analyses was observed for the association between BMI and polycystic ovary syndrome (estimates reflect odds ratios (OR) per standard deviation change in each adiposity measure): OR = 2.55; 95% confidence interval (CI) = 1.22–5.33. Only colorectal cancer was investigated with two exposures in the meta-analysis: BMI (OR = 1.18; 95% CI = 1.01–1.37) and waist-hip ratio (WHR; OR = 1.48; 95% CI = 1.08–2.03). Broadly, results were consistent across the meta-analyses and narrative synthesis. Consistent with many observational studies, this work highlights the impact of adiposity across a broad spectrum of health outcomes, enabling targeted follow-up analyses. However, missing and incomplete data mean results should be interpreted with caution.