AgRP Neuron and Blood Glucose Time Series for Oral Glucose Response

2026 research by Anna Bowen's lab provides processed data from recording sessions linking AgRP neuron activity to glycemic responses. The dataset contains session-level bundles with aligned timestamps, processed continuous glucose monitoring (CGM) data, and processed fiber photometry (FP) data. It supports the manuscript 'AgRP Neuron Activity Predicts and Tracks the Glycemic Response to Oral Glucose'.

Use Cases

• Predict glycemic response metrics from processed fiber photometry (FP) data using time-series models.
• Analyze correlations between aligned timestamps of AgRP neuron activity and processed continuous glucose monitoring (CGM) data.
• Cluster session-level bundles based on subject/session metadata to identify experimental subgroups.
• Reproduce published analyses by linking retained raw values and processing metadata to specific figures.

Strengths

• Data is recent, published in 2026.
• Includes aligned, processed time-series for both neural activity (FP) and physiology (CGM).
• Associated code for full analysis reproduction is available on GitHub.

Limitations

• Small dataset size of 8.4 MB suggests a limited number of recording sessions.
• Data structure is complex (list of dictionaries in a pickle file), requiring specific Python tools to load.
• Unknown row/session count and specific temporal coverage of experiments.

Provenance

Source

Anna Bowen, likely from The Bowen Lab.

Collection Method

Experimental data from fiber photometry and continuous glucose monitoring during oral glucose tests in a research setting.

Freshness

Last updated April 2026.