GDF15 Human

What is Growth Differentiation Factor 15 (GDF15)?

Growth Differentiation Factor 15 (GDF15) is a stress-regulated hormone belonging to the transforming growth factor-β superfamily. First identified in the late 1990s as 'macrophage inhibitory cytokine-1,' GDF15 is synthesized as a larger propeptide that undergoes processing into a bioactive homodimer found in circulation. In healthy individuals, circulating levels typically range between 200 and 1,200pg/ml, with levels increasing with chronological age. The identification of its cognate receptor, GFRAL, in discrete areas of the brainstem in 2017 revealed GDF15's potent effects on appetite, body weight, and sickness behavior, significantly advancing understanding of its biological functions .

What experimental models are most effective for studying GDF15 function?

When designing experiments to study GDF15 function, researchers should consider models that can distinguish between its local and systemic effects. Cellular models can reveal tissue-specific GDF15 expression patterns under various stressors, while animal models with intact GDF15-GFRAL signaling are essential for studying systemic effects. Human cohort studies provide valuable correlative data, but caution must be exercised when interpreting results due to potential confounding factors. Transgenic models with tissue-specific GDF15 overexpression or knockout can help elucidate its tissue-specific roles, while pharmacological studies using recombinant GDF15 or GFRAL antagonists can demonstrate direct physiological effects .

What are the primary methods for measuring GDF15 in human samples?

Several methodologies are currently employed to measure GDF15 in human samples, each with distinct advantages and limitations:

• Immunoluminometric Assay (ILMA): This technique uses an antibody labeled with acridinium ester that binds over a sequence of Ala197-Ile308 in the GDF15 protein, with results quantified in a luminometer. ILMA measurements closely correlate with Immunoradiometric Assay (IRMA) results, with approximately 97.8% ± 1.3% concordance .

• Aptamer-based multiplex protein assays (SomaScan): This technology allows simultaneous measurement of multiple proteins including GDF15, offering high-throughput capabilities for large-scale studies .

• Proximity extension-based antibody assay (Olink): Another approach for protein quantification commonly used in large-scale biomarker studies .

Importantly, different assay methods may yield varying results, particularly in the presence of common genetic variants like rs1058587 (p.H202D) that can affect epitope binding. Researchers should carefully consider assay selection and potential epitope artifacts when designing GDF15 studies and interpreting results across different measurement platforms .

What are the challenges in accurately measuring GDF15 levels?

Accurate measurement of GDF15 presents several methodological challenges that researchers must address:

What genetic variants influence GDF15 plasma levels?

Genome-wide association studies (GWAS) have identified several genetic variants associated with GDF15 plasma levels. A meta-analysis of GWAS performed in two independent cohorts (FINRISK and INTERVAL) across three different assay platforms identified four genetic variants (rs1059369, rs1054221, rs1227734, rs189593084) at the GDF15 locus consistently associated with GDF15 plasma levels .

Additionally, the common missense variant rs1058587 (p.H202D) is particularly noteworthy as it may affect epitope binding in different assays, potentially leading to measurement artifacts rather than true biological effects. This variant has been previously associated with hyperemesis gravidarum, but its presence within this locus raises the possibility of epitope artifacts affecting study results .

How do researchers account for genetic confounders in GDF15 studies?

To account for genetic confounders in GDF15 studies, researchers employ several sophisticated approaches:

What diseases and conditions are associated with altered GDF15 levels?

GDF15 plasma levels have been associated with numerous disease phenotypes in comprehensive studies. In an analysis examining 676 disease endpoints defined by the FinnGen consortium, GDF15 was significantly associated with 80 disease endpoints after multiple-testing correction using false discovery rate (FDR, p < 0.05) .

The associations span various disease categories, with particularly strong links to conditions involving cellular stress and inflammatory processes. The identification of GFRAL as GDF15's receptor in the brainstem has provided mechanistic insights into how GDF15 might influence physiological responses to these disease states, particularly through effects on appetite, body weight, and sickness behavior .

What is the relationship between GDF15 and metabolic conditions like obesity and diabetes?

The relationship between GDF15 and metabolic conditions involves complex bidirectional interactions:

• BMI and GDF15 levels: Mendelian randomization analyses have found a significant association between higher genetically predicted BMI and higher GDF15 plasma levels (Inverse Variance Weighted estimate = 0.097, pFDR = 0.0040), suggesting that increased GDF15 plasma levels may be a consequence of higher BMI rather than a cause .

• Limited causal evidence: Genetic analyses have not supported a causal association between normal human GDF15 plasma levels and obesity and diabetes, though there was a nominal finding of a causal association with waist-to-hip ratio (WHR) that may warrant further investigation .

• Stress response mechanism: GDF15 may act primarily as a stress-induced biomarker in these conditions rather than a causal factor. This is consistent with its biological role as a stress-regulated hormone that increases in response to cellular stress .

• Physiological effects: Despite limited causal evidence at normal plasma levels, GDF15 does have potent effects on appetite and body weight through its interaction with GFRAL in the brainstem, suggesting potential therapeutic applications .

It's important to note that at normal human plasma levels, studies may be underpowered to detect GDF15's impact on BMI, and elevated levels may induce different or more pronounced effects. Future studies with unbiased GDF15 measurements should reassess these relationships .

How can researchers distinguish between correlation and causation in GDF15 studies?

Distinguishing between correlation and causation in GDF15 research requires sophisticated methodological approaches:

• Mendelian Randomization (MR): This technique uses genetic variants as instrumental variables to assess causal relationships. For example, researchers have used MR to investigate whether GDF15 causally influences BMI or whether BMI causally influences GDF15 levels, finding evidence for the latter but not the former .

• Bidirectional MR analyses: By conducting MR in both directions (e.g., GDF15→BMI and BMI→GDF15), researchers can assess the direction of causality, which has been valuable in clarifying the relationship between GDF15 and metabolic parameters .

• Sensitivity analyses for pleiotropy: Techniques such as MR-Egger and MR-PRESSO have been used to detect horizontal pleiotropy in GDF15 studies. For instance, horizontal pleiotropy was identified in diabetes with MR-Egger and MR-PRESSO, and MR-PRESSO additionally identified horizontal pleiotropy with waist-to-hip ratio, HDL cholesterol, and estimated bone mineral density .

• Assessment of protein-truncating variants: Studying the effects of rare protein-truncating variants (PTVs) in the GDF15 gene can provide insights into causality, though current data based solely on heterozygous carriers requires careful interpretation .

• Conditioning on potential confounders: Statistical approaches that condition on known genetic confounders, such as the rs1058587 variant that may affect assay binding, help isolate true biological relationships from measurement artifacts .

How should researchers interpret contradictory findings in GDF15 literature?

When facing contradictory findings in GDF15 literature, researchers should systematically evaluate several factors:

• Measurement methodology differences: The striking heterogeneity between genetic variants across studies using different assay platforms (ILMA, SomaScan, Olink) is a major source of apparent contradictions. For example, the common missense variant rs1058587 (p.H202D) affects epitope binding differently across assays .

• Population differences: Genetic background, age distribution, health status, and environmental factors can influence GDF15 levels and associations. Studies should clearly report demographic characteristics and consider subgroup analyses .

• Study design variations: Cross-sectional versus longitudinal designs, sample sizes, and statistical approaches all contribute to varying results. For instance, some studies use Cox proportional hazard regression for longitudinal analyses, while others employ Wilcoxon rank sum tests for cross-sectional comparisons .

• Adjustment strategies: Different approaches to controlling for factors like age, sex, BMI, and smoking can significantly affect reported associations. Studies commonly report results from multiple adjustment models: (1) age and sex, (2) age, sex, and BMI, and (3) age, sex, and smoking-adjusted models .

• Causality versus correlation: Many contradictions arise from confusing association with causation. Current evidence suggests GDF15 may be primarily a stress marker rather than a causal factor in many conditions, though it has direct physiological effects through GFRAL binding .

How can GDF15 be used as a biomarker in clinical research studies?

GDF15 has several applications as a biomarker in clinical research:

• Disease prognosis assessment: Elevated circulating GDF15 has been associated with poorer prognosis and disease severity across multiple conditions. Cox proportional hazard regression models have demonstrated that GDF15 levels can predict outcomes during 10-year follow-up periods .

• Risk stratification: Quartiles of GDF15 levels can be used to illustrate the timing of death, type 2 diabetes, and cardiovascular disease events, allowing for risk stratification in research cohorts. Statistical assessment between upper quartile and other quartiles can be performed using Cox proportional hazard regression models .

• Longitudinal monitoring: Changes in GDF15 levels over time may provide insights into disease progression or response to interventions. The test of the proportional hazards assumption for Cox regression models can be used to verify the validity of such longitudinal analyses .

• Model calibration: When developing predictive models incorporating GDF15, Hosmer-Lemeshow goodness of fit tests can determine whether models are well-calibrated with similar expected and observed event rates in both low- and high-risk individuals .

When using GDF15 as a biomarker, researchers should adjust for known confounding factors. Commonly used adjustment models include: (1) age and sex, (2) age, sex, and BMI, and (3) age, sex, and smoking status, as these factors can influence GDF15 levels independently of the condition being studied .

What statistical approaches are most appropriate for analyzing GDF15 data in complex datasets?

Analyzing GDF15 data in complex datasets requires tailored statistical approaches:

• Distribution normalization: Due to right-skewed distribution, inverse variance transformed GDF15 levels are typically used in statistical analyses. For non-normally distributed data, non-parametric methods such as Wilcoxon rank sum test (Mann-Whitney U test) may be appropriate .

• Multivariable adjustment: Multiple regression models adjusted for age, sex, BMI, and smoking status are commonly employed to control for known confounders. These adjustments are critical as GDF15 levels are influenced by these factors independently of disease states .

• Multiple testing correction: When examining associations with multiple outcomes, false discovery rate (FDR) correction should be applied. This approach has been used to identify significant associations between GDF15 and disease endpoints in large-scale studies .

• Survival analysis techniques: Cox proportional hazard regression models are appropriate for time-to-event analyses, such as predicting mortality, diabetes incidence, or cardiovascular events. The validity of these models should be verified using proportional hazards assumption tests .

• Genome-wide association approaches: For genetic studies, methods like SNPTEST employing frequentist tests with "expected" methods and assuming additive genetic models are commonly used. Results should be adjusted for principal components of genetic data to account for population stratification .

• Meta-analysis methods: When combining results across studies or assay platforms, inverse variance weighted (IVW) meta-analysis approaches can be used, with careful attention to heterogeneity assessment using metrics such as I² values .

What are the most promising avenues for future GDF15 research?

Several promising research avenues could advance our understanding of GDF15:

• Unbiased measurement approaches: Developing and implementing methods that do not involve binding to epitopes, such as mass spectrometry, could avoid potential artifacts due to protein-altering variants and provide more consistent measurements across studies .

• Functional characterization of genetic variants: Further investigation of variants like rs1058587 (p.H202D) could clarify whether their associations with conditions such as hyperemesis gravidarum reflect true biological effects or measurement artifacts .

• Therapeutic targeting: As a stress-regulated hormone with potent effects on appetite, body weight, and sickness behavior, reagents that influence the GDF15-GFRAL axis have real potential to enter the clinical arena. Understanding the mechanistic basis of these effects could lead to novel therapeutic approaches .

• Tissue-specific GDF15 expression: Exploring the regulation and consequences of GDF15 expression in specific tissues under various stress conditions could reveal specialized functions beyond its systemic effects and identify tissue-specific therapeutic targets .

• Longitudinal studies of dynamic regulation: Investigating how GDF15 levels change over time in response to different physiological and pathological states could provide insights into its role in disease progression and recovery processes .

• Integration with multi-omics data: Combining GDF15 measurements with genomics, transcriptomics, proteomics, and metabolomics data could provide a more comprehensive understanding of its biological context and regulatory networks .

What methodological innovations are needed to advance GDF15 research?

Addressing current methodological limitations in GDF15 research requires several innovations:

• Standardized measurement protocols: Adopting consistent methods for measuring GDF15 levels would enhance comparability across studies. The current inconsistency in GWAS results across this locus is likely driven by differences in the properties of the GDF15 assays used in each study .

• Assay performance characterization: Generating functional data quantifying assay performance across different genetic backgrounds, particularly in the presence of the common missense variant rs1058587, is essential for understanding potential measurement biases .

• Advanced genetic analysis approaches: Techniques that can account for epitope artifacts in genetic studies, such as conditioning on potential confounding variants while avoiding overcorrection, would improve the reliability of genetic findings .

• Improved phenotyping: More detailed characterization of disease phenotypes and physiological states in relation to GDF15 levels could help clarify its specific roles in different contexts .

• Mechanistic studies of GDF15-GFRAL signaling: Deeper investigation of the downstream effects of GDF15 binding to GFRAL in the brainstem could reveal new therapeutic targets and clarify its physiological functions .

• Functional validation systems: Developing cellular and animal models that accurately reflect human GDF15 biology would facilitate more translatable preclinical research .

What are the key takeaways for researchers working with GDF15?

Researchers working with GDF15 should consider several key points: