Defining and Phenotyping Gastric Abnormalities in Long-Term Type 1 Diabetes Using a Novel Body Surface Gastric Mapping Device
Bibliographic record
Abstract
Background and Aims Diabetic gastroenteropathy is associated with poor glycemic control and morbidity in people with type 1 diabetes (T1D). There is a lack of noninvasive techniques to assess and monitor gastric abnormalities. We aimed to define phenotypes of gastric myoelectrical abnormalities in people with longstanding T1D with and without symptoms using a novel noninvasive body surface gastric mapping (BSGM) device. Methods BSGM was performed on people with T1D of >10 years duration and matched controls, employing Gastric Alimetry (Alimetry, New Zealand), comprising of a high-resolution 64-channel array, validated symptom-logging App, and wearable reader. Results Thirty-two people with T1D were recruited (15 with a high symptom burden), and 32 controls. Those with symptoms showed more unstable gastric myoelectrical activity (Gastric Alimetry Rhythm Index 0.39 vs 0.51, P = .017; and lower average spatial covariance 0.48 vs 0.51, P = .009) compared with controls. Symptomatic patients also had a higher prevalence of peripheral neuropathy (67% vs 6%, P = .001), anxiety/depression diagnoses (27% vs 0%, P = .001), and higher mean hemoglobin A1C levels (76 vs 56 mmol/mol, P < .001). BSGM defined distinct phenotypes in T1D participants including those with markedly unstable gastric rhythms (4/32, 12.5%) and abnormally high gastric frequencies (9/32, 28%). Deviation in gastric frequency was positively correlated with symptoms of bloating, upper gut pain, nausea and vomiting, and fullness (R > 0.35, P < .05). Conclusion Gastric symptoms in people with longstanding T1D correlate with myoelectrical abnormalities on BSGM evaluation, in addition to glycemic control, psychological comorbidities, and peripheral neuropathy. BSGM using Gastric Alimetry identified a range of myoelectrical phenotypes, presenting targets for diagnosis, monitoring, and therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".