Discussion
Using human data, we provide genetic evidence supporting the notion that
chronically elevated GDF15 levels increase BMI. There was no genetic
evidence to support bi-directional effects, or that chronically elevated
GDF15 levels directly affect liability to type 2 diabetes. Our results
contrast the BMI lowering effects of an acute increase in GDF15 levels
observed after metformin use2. One possible
explanation for this discrepancy is that chronic elevation of
circulating GDF15 levels leads to desensitization of the GDF15 receptor
and reduced signaling8.
The use of both colocalization and Mendelian randomization in this study
provide complementary evidence supporting causal effects of chronically
elevated GDF15 levels on BMI. As genetic variants are randomly allocated
at conception, the Mendelian randomization paradigm is less susceptible
to the confounding and reverse causation that that can hinder causal
inference in observational studies. As a limitation of this work, the
genetic associations were derived from individuals of European
ancestries, and therefore our results may not generalize to other ethnic
groups.
In conclusion, this genetic analysis found robust evidence to support
that, in contrast to acute elevations in GDF15 levels, chronically
elevated GDF15 levels increase BMI. These findings may be used to inform
the design of pharmacological strategies aimed at targeting GDF15 for
weight loss.
References
1. Tsai VWW, Husaini Y, Sainsbury A, Brown DA, Breit SN. The
MIC-1/GDF15-GFRAL Pathway in Energy Homeostasis: Implications for
Obesity, Cachexia, and Other Associated Diseases. Cell
Metabolism . 2018/09/04/ 2018;28(3):353-368.
doi:https://doi.org/10.1016/j.cmet.2018.07.018
2. Coll AP, Chen M, Taskar P, et al. GDF15 mediates the effects of
metformin on body weight and energy balance. Nature . 2020/02/01
2020;578(7795):444-448. doi:10.1038/s41586-019-1911-y
3. Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian Test for
Colocalisation between Pairs of Genetic Association Studies Using
Summary Statistics. PLOS Genetics . 2014;10(5):e1004383.
doi:10.1371/journal.pgen.1004383
4. Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic
epidemiology contribute to understanding environmental determinants of
disease?*. International Journal of Epidemiology .
2003;32(1):1-22. doi:10.1093/ije/dyg070
5. Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human
plasma proteome. Nature . 2018/06/01 2018;558(7708):73-79.
doi:10.1038/s41586-018-0175-2
6. Pulit SL, Stoneman C, Morris AP, et al. Meta-analysis of genome-wide
association studies for body fat distribution in 694 649 individuals of
European ancestry. Human Molecular Genetics . 2018;28(1):166-174.
doi:10.1093/hmg/ddy327
7. Mahajan A, Taliun D, Thurner M, et al. Fine-mapping type 2 diabetes
loci to single-variant resolution using high-density imputation and
islet-specific epigenome maps. Nature Genetics . 2018/11/01
2018;50(11):1505-1513. doi:10.1038/s41588-018-0241-6
8. Eddy AC, Trask AJ. Growth Differentiation Factor-15 and Its Role in
Diabetes and Cardiovascular Disease. Cytokine & Growth Factor
Reviews . 2020/12/01/
2020;doi:https://doi.org/10.1016/j.cytogfr.2020.11.002
Figure. Colocalization plot of genetic associations for
circulating growth differentiation factor 15 levels and body mass index
within ±10 kb of GDF15 gene. LD = linkage disequilibriumr2 with rs16982345, the variant identified as
the most likely shared causal variant.