Inflammation proteomics datasets in the ALSPAC cohort.
Neil J GouldingLucy J GoudswaardDavid A HughesLaura J CorbinAlix GroomSusan M RingNicholas John TimpsonAbigail FraserKate NorthstoneMatthew SudermanPublished in: Wellcome open research (2024)
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.
Keyphrases
- electronic health record
- oxidative stress
- label free
- body mass index
- high throughput
- pregnant women
- big data
- high fat diet
- quality control
- public health
- mass spectrometry
- healthcare
- magnetic resonance imaging
- machine learning
- physical activity
- artificial intelligence
- data analysis
- mental health
- hepatitis c virus
- weight gain
- climate change
- health information
- pregnancy outcomes
- drug discovery
- antiretroviral therapy
- quantum dots