Growth differentiation factor 15 (GDF15) is a stress response cytokine that is elevated in several cardiometabolic diseases and has attracted interest as a potential therapeutic target. To further explore the association of GDF15 with human disease, we conducted a broad study into the phenotypic and genetic correlates of GDF15 concentration in up to 14,099 individuals. Assessment of 772 traits across 6,610 participants in FINRISK identified associations of GDF15 concentration with a range of phenotypes including all-cause mortality, cardiometabolic disease, respiratory diseases and psychiatric disorders as well as inflammatory markers. A meta-analysis of genome-wide association studies (GWAS) of GDF15 concentration across 3 different assay platforms (n=14,099) confirmed significant heterogeneity due to a common missense variant rs1058587 in GDF15, potentially due to epitope-binding artefacts. After conditioning on rs1058587, statistical fine-mapping identified 4 independent putative causal signals at the locus. Mendelian randomisation (MR) analysis found evidence of a causal relationship between GDF15 concentration and high-density lipoprotein (HDL) but not body mass index (BMI). Using reverse MR, we identified a potential causal association of BMI on GDF15 (IVW pFDR=0.0040). Taken together, our data do not support a role for elevated GDF15 concentrations as a causal factor in human cardiometabolic disease but support its role as a biomarker of metabolic stress.
Participant-level genotype and phenotype data from UK Biobank are available by application: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access.Participant-level genotype and phenotype data (as part of the FinnGen consortium) are available by application: https://www.finngen.fi/en/access_results.INTERVAL-SomaScan participant-level genotype and protein data, and full summary association results from the genetic analysis are available through the European Genotype Archive (accession number EGA00001002555). Summary association results are also publically available at http://www.phpc.cam.ac.uk/ceu/proteins/, through PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk) and from the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics).INTERVAL-Olink summary association results are publically available at http://www.phpc.cam.ac.uk/ceu/proteins/.
UK Biobank Data: Come and Get ItUK Biobank.
- Rachel MY Ong
- Veikko Salomaa
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: FINRISK study was approved by the Ethics Committee of Helsinki and Uusimaa Hospital District.Informed consent was obtained from all participants and the INTERVAL study was approved by the National Research Ethics Service (11/EE/0538).All study participants provided informed consent and the UK Biobank has approval from the North-West Multi-centre Research Ethics Committee (MREC; 11/NW/0382).
- Edward D Janus, University of Melbourne, Australia
- Received: December 10, 2021
- Accepted: August 1, 2022
- Accepted Manuscript published: August 2, 2022 (version 1)
© 2022, Lemmelä et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) have been key drivers of new coronavirus disease 2019 (COVID-19) pandemic waves. To better understand variant epidemiologic characteristics, here we apply a model-inference system to reconstruct SARS-CoV-2 transmission dynamics in South Africa, a country that has experienced three VOC pandemic waves (i.e. Beta, Delta, and Omicron BA.1) by February 2022. We estimate key epidemiologic quantities in each of the nine South African provinces during March 2020 to February 2022, while accounting for changing detection rates, infection seasonality, nonpharmaceutical interventions, and vaccination. Model validation shows that estimated underlying infection rates and key parameters (e.g. infection-detection rate and infection-fatality risk) are in line with independent epidemiological data and investigations. In addition, retrospective predictions capture pandemic trajectories beyond the model training period. These detailed, validated model-inference estimates thus enable quantification of both the immune erosion potential and transmissibility of three major SARS-CoV-2 VOCs, that is, Beta, Delta, and Omicron BA.1. These findings help elucidate changing COVID-19 dynamics and inform future public health planning.
Vitamin D supplements are widely prescribed to help reduce disease risk. However, this strategy is based on findings using conventional epidemiological methods which are prone to confounding and reverse causation.
In this short report, we leveraged genetic variants which differentially influence body size during childhood and adulthood within a multivariable Mendelian randomization (MR) framework, allowing us to separate the genetically predicted effects of adiposity at these two timepoints in the lifecourse.
Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), there was strong evidence that higher childhood body size has a direct effect on lower vitamin D levels in early life (mean age: 9.9 years, range = 8.9–11.5 years) after accounting for the effect of the adult body size genetic score (beta = −0.32, 95% CI = −0.54 to –0.10, p=0.004). Conversely, we found evidence that the effect of childhood body size on vitamin D levels in midlife (mean age: 56.5 years, range = 40–69 years) is putatively mediated along the causal pathway involving adulthood adiposity (beta = −0.17, 95% CI = −0.21 to –0.13, p=4.6 × 10-17).
Our findings have important implications in terms of the causal influence of vitamin D deficiency on disease risk. Furthermore, they serve as a compelling proof of concept that the timepoints across the lifecourse at which exposures and outcomes are measured can meaningfully impact overall conclusions drawn by MR studies.
This work was supported by the Integrative Epidemiology Unit which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/1).