Multi-omics profiling after different macronutrient loads reveals that GLP-1 and GIP are exclusively explained postprandially, hormone responses vary dramatically by food type, and protein loads show the strongest link to insulin resistance.
GLP-1 and GIP exclusively postprandialThese critical gut peptide hormones can only be understood from after-meal measurements — fasting data misses them entirely, highlighting the importance of dynamic metabolic profiling
What the researchers found
The study revealed several important findings about peptide hormone dynamics:
- GLP-1 and GIP secretion patterns are exclusively explained by postprandial (after-eating) data — fasting measurements miss them entirely
- Postprandial multi-omics data significantly improved the ability to explain insulin and glucagon secretion patterns compared to fasting data alone
- Hormone secretion and molecular responses showed substantial heterogeneity among macronutrient types (mixed meals vs. four distinct macronutrient loads)
- Protein load showed the strongest association with both hepatic (liver) and muscular insulin resistance
- Butter load connected most strongly with systemic insulin resistance
- Postprandial multi-omics better predicted insulin resistance than fasting data, with hepatic enrichment
- Several molecules were identified that mediate interactions between insulin resistance and islet α-cell (glucagon) and β-cell (insulin) function
Why it matters
GLP-1 and GIP are the targets of the most successful new drugs in metabolic medicine (semaglutide, tirzepatide). Understanding how these peptide hormones naturally respond to different foods — and how their responses connect to insulin resistance — is critical for both drug development and nutrition-based interventions. This study provides the most comprehensive postprandial peptide hormone map to date, potentially enabling precision nutrition approaches for diabetes prevention.
How the study worked
This was a human multi-omics study where participants consumed mixed meals and four distinct macronutrient loads. Dynamic postprandial measurements included islet hormones (insulin, glucagon), gut hormones (GLP-1, GIP), and comprehensive multi-omics profiling (likely proteomics, metabolomics, and/or lipidomics). Data were analyzed to characterize hormone secretion patterns, identify responsive molecular networks, and assess relationships with peripheral insulin resistance (hepatic, muscular, and systemic).
What this study cannot tell us
The abstract does not specify the number of participants or their characteristics (healthy vs. metabolic disease). The multi-omics approach generates large datasets that require careful statistical handling to avoid false discoveries. The macronutrient loads were individual nutrients, which may not reflect how hormones respond to real-world mixed meals. Cross-sectional associations between hormone patterns and insulin resistance don't prove causation.
How to read the evidence
This is a human multi-omics study published in Cell Reports Medicine — a high-impact translational journal. The comprehensive approach using multiple macronutrient challenges strengthens the findings, though the abstract doesn't detail participant numbers or validation cohorts.
When this study was published
Published in 2025, this is a very recent study that leverages cutting-edge multi-omics technology to provide the most detailed postprandial peptide hormone map to date.
The bigger picture
The incretin hormones GLP-1 and GIP are at the center of a therapeutic revolution — drugs mimicking them are among the most prescribed medications globally. Yet our understanding of how they're naturally regulated by diet has been surprisingly incomplete. This study fills that gap by mapping the full postprandial peptide hormone landscape across different macronutrients, providing the biological foundation for precision nutrition approaches that could complement or even reduce the need for pharmacological incretin therapy.
Questions still open
- Could personalized meal compositions based on individual GLP-1/GIP response patterns improve metabolic health as effectively as pharmacological GLP-1 agonists?
- Why does protein load show the strongest association with hepatic insulin resistance — and could protein timing or type be optimized to reduce this effect?
- Do the identified mediator molecules between insulin resistance and islet cell function represent new drug targets?
Common questions
Why can't you measure GLP-1 and GIP from a fasting blood test?
How could this research change how we eat for metabolic health?
Read the original research
Dynamic multi-omics profiling of islet and gut hormonal secretion and peripheral crosstalk in response to various nutrient loads.
Cell reports. Medicine, 6(9), 102327
Citation
Wang, Jiachen; Liu, Ling; Liu, Hechun; Qian, Yu; Zhang, Sijie; Zheng, Shuai; Jiang, Hemin; Zhou, Yue; Cheng, Xiaoliang; Fu, Qi; Dai, Hao; Yang, Tao. (2025). Dynamic multi-omics profiling of islet and gut hormonal secretion and peripheral crosstalk in response to various nutrient loads.. Cell reports. Medicine, 6(9), 102327. https://doi.org/10.1016/j.xcrm.2025.102327