Evidence map›Paper›PMID 42035352›Full record

ArticleJournal of diabetes science and technology2026

MetaboNet: The Largest Publicly Available Consolidated Data Set for Type 1 Diabetes Management.

Miriam Kopperstad Wolff, Peter Calhoun, Eleonora Maria Aiello, Yao Qin, Sam Fox Royston

Abstract read
In one paragraph

Article in Journal of diabetes science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Miriam Kopperstad WolffReplica Health Co., Brooklyn, NY, USA.ORCID 0009-0000-6919-8164
Peter CalhounJaeb Center for Health Research, Tampa, FL, USA.ORCID 0000-0002-5325-7200
Eleonora Maria AielloDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.ORCID 0000-0001-5129-8829
Yao QinUniversity of California, Santa Barbara, CA, USA.ORCID 0000-0002-5779-1224
Sam Fox RoystonReplica Health Co., Brooklyn, NY, USA.ORCID 0009-0003-8623-4042

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProgress in type 1 diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management data sets. Current data sets differ substantially in structure and are time-consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D algorithm development.

methodMultiple publicly available T1D data sets were harmonized into a unified resource, termed the MetaboNet data set. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. In addition, auxiliary information such as reported carbohydrate intake and physical activity was retained when present.

resultsThe MetaboNet data set comprises 3135 subjects and 1228 patient-years of overlapping CGM and insulin data, making it substantially larger than existing standalone benchmark data sets. The resource is distributed as a fully public subset available for immediate download at https://metabo-net.org/, and with a data use agreement (DUA)-restricted subset accessible through their respective application processes. For the data sets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format.

conclusionsA harmonized public data set for T1D research is presented, and the access pathways for both its unrestricted and DUA-governed components are described. The resulting data set covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual data sets.

Indexed as

data setglucose predictionmachine learningtype 1 diabetes

Identifiers

PMID42035352
PMCPMC13111534

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.