Nanomath
Home/AOMM™
Automated Oral Minimal Model (AOMM™) Program · v1.4 · 2026

AOMM™ — OGTT, MMTT & IVGTT Analysis Software

For research teams running multi-sample glucose tests, AOMM™ is the easy and validated platform to have all metabolic metrics in one place, including the minimal models.

AOMM™ may help sponsors submit contextual, supportive, exploratory, mechanistic (why it works), or pharmacodynamic evidence. It does not make clinical decisions or replace trial endpoints.

Runs locally on Windows; data stay with you. Leave the local spreadsheet. One program for every index.

JDST paper ↓Request a research evaluation
→Science highlights
→Platform highlights →How to use →IVGTT
Powered by SAAM II™
Latest
OCT 2026 Alice Carr — AOMM™ at Breakthrough T1D CRC 2026 SEP 2026 EASD 2026 Milan — 3 oral presentations Oral presentations
  1. Measurement-driven variability in glycaemic staging may influence disease-modifying treatment eligibility in early stage type 1 diabetes
  2. Measuring the disease, not just risk: Phitotal as a continuous endpoint for stage 1 type 1 diabetes trials
  3. Early beta cell decline in preclinical type 1 diabetes: evidence from the oral minimal model
 JUN 2026 AOMM™ at ADA 2026 — TrialNet poster APR 2026 ATTD Barcelona — AOMM™ presented JUN 2025 ADA 2025 — AOMM™ markers in T2D diet response

How it works

One OGTT in.
48 validated indices out.

Run the test with whatever protocol's timepoints and glucose/CHO loads. AOMM™ converts OGTT/MTT data into research and drug development-ready metrics.

01

Run an OGTT

Standard oral glucose or mixed-meal test. Sample glucose, insulin and C-peptide as usual.

02

AOMM™ analyzes

Oral minimal models on the validated SAAM II engine, with quality control on every index.

03

48 validated indices

Insulin sensitivity, Phi-dynamic, ISR, insCL, iAUCs, index60, HOMAs etc.

Batch
no more one file at the time
100
tests run in ~2 min
25k+
tests run so far
Reproducible
results from one lab to another
Validated
drop your messy spredsheets

See all 48 indices ↓

AOMM Applications

CTR with Min Mod endpoints

Collabs

  • Claudio Cobelli — Padova
  • Colin Dayan — Birmingham
  • Alfonso Galderisi — Yale
  • Joseph Hedrick — C-Path
  • Alice Carr — Alberta
  • Peter Senior — Alberta
  • Breakthrough T1D
  • C-Path & TrialNET
Change from baseline in insulin sensitivity (Si) Mean change in Si was −0.08 with placebo and +1.11 with treatment; difference +1.19 (95% CI 0.10 to 2.28), p = 0.034. Figure 1 Change from baseline in insulin sensitivity (Si​) −2 −1 0 1 2 3 4 Change in Si​ from baseline 10−4​ dL·kg−1​·min−1​ per µU/mL Placebo: mean −0.08 (95% CI −0.86 to 0.70), n = 12 Treatment: mean +1.11 (95% CI 0.25 to 1.97), n = 12 Placebo n = 12 Treatment n = 12 Treatment − placeboDifference in mean change in Si​+1.1995% CI 0.10 to 2.28p = 0.034Mean change (95% CI)Placebo −0.08 (−0.86 to 0.70)Treatment +1.11 (0.25 to 1.97) Markers: mean with 95% CI; dots: individual subjects. Treatment difference by Welch two-sample t-test. Illustrative simulated data — not results from an actual clinical trial.
Change from baseline in Si
ArmnMean change95% CI
Placebo12−0.08−0.86 to 0.70
Treatment12+1.110.25 to 1.97
Difference (treatment − placebo)+1.190.10 to 2.28; p = 0.034
Treatment effect on AOMM indices Si +31% (95% CI +4% to +64%), p = 0.026; Φtotal +19% (95% CI −2% to +45%), p = 0.080; Φstatic +24% (95% CI +2% to +51%), p = 0.035; Φdynamic +79% (95% CI +30% to +146%), p = 0.001; DI +55% (95% CI +16% to +109%), p = 0.005. Figure 2 Treatment effect on AOMM™ indices AOMM™ index % difference vs placebo Estimate (95% CI) p −50%0+50%+100%+150% ← Favors placebo Favors treatment → Si​ Insulin sensitivity · n = 12/12 Si: +31% vs placebo (95% CI +4% to +64%), p = 0.026 +31% (+4% to +64%) 0.026 Φtotal​ Total β-cell responsivity · n = 12/12 Φtotal: +19% vs placebo (95% CI −2% to +45%), p = 0.080 +19% (−2% to +45%) 0.080 Φstatic​ Static β-cell responsivity · n = 12/12 Φstatic: +24% vs placebo (95% CI +2% to +51%), p = 0.035 +24% (+2% to +51%) 0.035 Φdynamic​ Dynamic β-cell responsivity · n = 12/12 Φdynamic: +79% vs placebo (95% CI +30% to +146%), p = 0.001 +79% (+30% to +146%) 0.001 DI Disposition index · n = 12/12 DI: +55% vs placebo (95% CI +16% to +109%), p = 0.005 +55% (+16% to +109%) 0.005 Squares: point estimate; lines: 95% CI. Welch t-test on log fold-change; DI = Si × Φtotal. No multiplicity adjustment. Illustrative simulated data — not results from an actual clinical trial.
Treatment effect on AOMM indices (% difference vs placebo)
IndexEstimate95% CIp
Si+31%+4% to +64%0.026
Φtotal+19%−2% to +45%0.080
Φstatic+24%+2% to +51%0.035
Φdynamic+79%+30% to +146%0.001
DI+55%+16% to +109%0.005
β-cell function (Φstatic) over time by progression to Stage 3 type 1 diabetes Φstatic stayed near 35.6 in non-progressors and declined to 15.1 in progressors by Month 36; difference −20.5 (95% CI −24.8 to −16.2), p < 0.001. Figure 3 β-cell function (Φstatic​) over time by progression to Stage 3 T1D Non-progressors (n = 18) Progressors (n = 12) 0 10 20 30 40 50 Φstatic​ 10−9​ min−1​ 061218243036 Months from baseline Non-progressors, Month 0: mean 36.1 (95% CI 34.0 to 38.2), n = 18 Non-progressors, Month 6: mean 35.6 (95% CI 33.3 to 37.8), n = 18 Non-progressors, Month 12: mean 37.2 (95% CI 34.3 to 40.0), n = 17 Non-progressors, Month 18: mean 36.3 (95% CI 34.0 to 38.7), n = 17 Non-progressors, Month 24: mean 36.9 (95% CI 34.6 to 39.2), n = 16 Non-progressors, Month 30: mean 36.0 (95% CI 33.8 to 38.1), n = 16 Non-progressors, Month 36: mean 35.6 (95% CI 33.0 to 38.1), n = 15 35.6 Progressors, Month 0: mean 36.8 (95% CI 34.2 to 39.5), n = 12 Progressors, Month 6: mean 35.1 (95% CI 33.1 to 37.1), n = 12 Progressors, Month 12: mean 32.1 (95% CI 29.9 to 34.3), n = 12 Progressors, Month 18: mean 29.1 (95% CI 26.8 to 31.4), n = 11 Progressors, Month 24: mean 26.4 (95% CI 24.1 to 28.8), n = 11 Progressors, Month 30: mean 20.6 (95% CI 17.7 to 23.5), n = 10 Progressors, Month 36: mean 15.1 (95% CI 11.3 to 18.9), n = 9 15.1 Number at risk Non-progressors 18181717161615 Progressors 1212121111109 Mean with 95% CI (shaded). Month 36 difference, progressors − non-progressors: −20.5 (95% CI −24.8 to −16.2), p < 0.001 (Welch). Illustrative simulated data — not results from an actual clinical trial.
Φstatic by visit (mean, 95% CI, number at risk)
MonthNon-progressorsProgressors
036.1 (34.0 to 38.2), n = 1836.8 (34.2 to 39.5), n = 12
635.6 (33.3 to 37.8), n = 1835.1 (33.1 to 37.1), n = 12
1237.2 (34.3 to 40.0), n = 1732.1 (29.9 to 34.3), n = 12
1836.3 (34.0 to 38.7), n = 1729.1 (26.8 to 31.4), n = 11
2436.9 (34.6 to 39.2), n = 1626.4 (24.1 to 28.8), n = 11
3036.0 (33.8 to 38.1), n = 1620.6 (17.7 to 23.5), n = 10
3635.6 (33.0 to 38.1), n = 1515.1 (11.3 to 18.9), n = 9
Dose-response: change in Si from baseline Mean change in Si: Placebo −0.11, Low dose +0.68, High dose +1.24. Low dose vs placebo +0.79 (p = 0.038); high dose vs placebo +1.35 (p = 0.003); dose trend p = 0.001. Figure 4 Dose-response: change in Si​ from baseline −2 −1 0 1 2 3 4 Change in Si​ from baseline 10−4​ dL·kg−1​·min−1​ per µU/mL Placebo: mean −0.11 (95% CI −0.60 to 0.39), n = 12 Placebo n = 12 Low dose: mean +0.68 (95% CI 0.08 to 1.28), n = 12 Low dose n = 12 High dose: mean +1.24 (95% CI 0.54 to 1.94), n = 12 High dose n = 12 Low dose − placebo+0.79 (0.05 to 1.52)p = 0.038High dose − placebo+1.35 (0.53 to 2.16)p = 0.003Linear dose trend+0.67 per dose stepp = 0.001 Markers: mean with 95% CI; dots: individual subjects. Contrasts vs placebo by Welch t-test; dose trend by linear regression. Illustrative simulated data — not results from an actual clinical trial.
Change from baseline in Si by dose
ArmnMean change95% CI
Placebo12−0.11−0.60 to 0.39
Low dose12+0.680.08 to 1.28
High dose12+1.240.54 to 1.94
Low dose − placebo+0.790.05 to 1.52; p = 0.038
High dose − placebo+1.350.53 to 2.16; p = 0.003
Linear dose trend (per step)+0.67p = 0.001

Tech specs

AOMM™ v1.3

Test

Oral glucose tolerance test or mixed-meal tolerance test.

Inputs

Glucose dose (fixed in grams, weight-based, or custom for each participant); sampling times (minutes - any schedule); glucose (mmol/L or mg/dL); insulin (pmol/L or µIU/mL); C-peptide (nmol/L or ng/mL); and visit demographics (age in years, BMI in kg/m2, and body weight in Kg).

Quality control

Precision reported for OMM each model index, per subject as coefficient of variation (CV).

Oral minimal model411 indices

  • Insulin sensitivity
  • Disposition index
  • Total beta-cell responsivity
  • Basal beta-cell responsivity
  • Static beta-cell responsivity
  • Dynamic beta-cell responsivity
  • Insulin mobilization delay
  • Bioavailable glucose dose
  • Insulin sensitivity with glucose effectiveness at zero insulin (GEZI - Basu/Cobelli)
  • Dynamic insulin sensitivity
  • Insulin sensitivity effectiveness

4.Cobelli C, et al. The oral minimal model method. Diabetes. 2014;63(4):1203–1213.

Insulin secretion9 indices

  • Basal insulin secretion
  • Peak insulin secretion
  • Time to peak secretion
  • Total insulin secretion
  • Incremental insulin secretion
  • Dynamic secretion responsivity
  • Early secretion responsivity
  • Secretion profile shape
  • Late insulin secretion

Hepatic extraction and clearance4 indices

  • Fasting hepatic insulin extraction
  • Two-hour hepatic insulin extraction
  • Fasting insulin clearance
  • OGTT insulin clearance
  • Minimal model hepatic extraction (HE)Coming soon

Fasting6 indices

  • Fasting glucose
  • Fasting insulin
  • Fasting C-peptide
  • HOMA-IR
  • HOMA-B
  • QUICKI
  • HOMA2Upon request

OGTT-derived8 indices

  • Matsuda insulin sensitivity index (Matsuda, 1999)
  • Insulinogenic index (Seltzer, 1967)
  • Matsuda disposition index
  • C-peptide index
  • ISSI-2 (Retnakaran, 2008)
  • Insulinogenic index to HOMA-IR ratio
  • Hepatic insulin resistance index (Abdul-Ghani, 2007)
  • Muscle insulin sensitivity index (Abdul-Ghani, 2007)

Glucose curve8 indices

  • Incremental glucose area
  • Incremental insulin area
  • Incremental C-peptide area
  • One-hour glucose
  • Two-hour glucose
  • Peak glucose
  • Time to glucose peak
  • Glucose excursion range

Type 1 diabetes risk2 indices

  • Index60 (Sosenko, 2015)
  • DPTRS (Sosenko, 2008)

Statistical test dashboardComing soon

Group comparisons and statistical testing on AOMM™ indices, built into the program.

Personalized metricsUpon request

Additional metrics tailored to your study, available on request.

↓Request a research evaluation