arxiv: 2604.13141 · v1 · submitted 2026-04-14 · 🧬 q-bio.OT

Recognition: unknown

Baseline glycemia exhibits non-random, history-dependent variation across repeated meals

Arturo Tozzi

Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:58 UTC · model grok-4.3

classification 🧬 q-bio.OT

keywords glycemic regulationbaseline glucosehistory-dependent variationpostprandial responsecontinuous glucose monitoringrepeated mealsnormoglycemic subjectstemporal dependence

0 comments

The pith

Pre-meal glucose baselines shift systematically across repeated identical meals, with each displacement proportional to the size of the prior post-meal response.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tracks pre-meal glucose levels in people eating the same meal multiple times and finds that these starting points do not stay fixed but move in a consistent pattern. Larger blood sugar rises after one meal are linked to bigger changes in the baseline before the next meal. This pattern holds even after testing against random reordering of the data, suggesting the shifts are not mere noise or chance. The work matters because current descriptions of glucose control treat baselines as stable set points that the body returns to after each meal, yet the results point to built-in memory of recent events that alters the next starting level.

Core claim

In a public dataset of normoglycemic subjects undergoing repeated identical meal challenges, pre-meal glucose baselines—defined as the median glucose in a pre-meal window—displayed systematic displacements across repetitions that exceeded within-interval fluctuations. These displacements were positively related to the magnitude of the postprandial glucose response from the immediately preceding meal, and the relationship survived permutation testing that destroyed any true temporal order. The findings indicate that glycemic regulation cannot be captured as independent fluctuations around a fixed baseline; instead, each perturbation produces history-dependent adjustments that alter subsequent

What carries the argument

History-dependent baseline displacement, computed from successive changes in pre-meal median glucose and scaled by the size of the prior postprandial excursion.

If this is right

Glycemic trajectories contain temporal dependence in which each meal response sets the baseline for the next exposure.
Continuous glucose monitoring records must be interpreted with evolving rather than fixed baselines to separate systematic shifts from noise.
Mathematical models of glucose homeostasis require explicit terms for history-dependent adjustments instead of assuming return to a constant set point.
Intra-individual variability in repeated-meal studies reflects ordered system memory rather than purely stochastic processes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Personalized glucose prediction tools could use the size of one meal's response to forecast the next baseline and adjust advice accordingly.
The same history-dependent pattern might appear in other repeated physiological challenges such as exercise or stress tests.
Closed-loop insulin systems might improve performance by incorporating a memory term that updates the target baseline after each detected excursion.

Load-bearing premise

The observed baseline shifts arise from intrinsic history-dependent dynamics of the glucose regulation system rather than unmeasured external influences, sensor effects, or incomplete experimental controls.

What would settle it

A controlled experiment in which the order of meals is fully randomized while holding all other variables constant, after which the correlation between baseline shifts and preceding response sizes disappears.

read the original abstract

Glycemic regulation is often described as maintaining glucose levels near a stable baseline. However, continuous glucose monitoring after meals displays intra-individual variability even under controlled conditions, suggesting intrinsic system dynamics beyond sensor noise, measurement error or short-term variability around a fixed set point. Therefore, we estimated pre-meal glucose baselines, tracking their changes across repeated identical meal challenges within individuals. The baseline was defined as the median glucose level in a pre-meal window, while successive displacements were computed between consecutive repetitions. Using a publicly available dataset of normoglycemic subjects, we observed systematic changes in baseline levels across repeated exposures. These displacements exceeded short-term fluctuations within the same pre-meal interval and were robust to alternative baseline definitions. Moreover, the magnitude of each baseline shifted is positively related to the size of the preceding postprandial response. This association persisted under permutation testing, indicating that it cannot be explained by random temporal ordering. Overall, these findings suggest that glycemic dynamics cannot be fully described as independent fluctuations around a fixed baseline. Instead, baseline levels evolve across repeated perturbations through history-dependent adjustments, such that each perturbation influences subsequent system states. Potential applications include refined interpretation of continuous glucose monitoring data and development of models that incorporate temporal dependence in glucose dynamics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows pre-meal glucose baselines shift across repeated identical meals with displacement size tied positively to the prior postprandial response, backed by permutation testing on public CGM data.

read the letter

The main point is that pre-meal glucose baselines aren't fixed across repeated identical meals but shift in a history-dependent manner, with the magnitude of the shift positively associated with the size of the preceding postprandial response, and this holds after permutation testing. They work with a public dataset of normoglycemic subjects doing the same meal multiple times. Baseline is the median glucose in a pre-meal window, displacements are tracked between repeats, and they find the link to the last response. The result survives different ways of defining the baseline and is larger than short-term variation inside one window. The permutation check is a reasonable way to show the ordering isn't arbitrary. That's the solid empirical core. The weaker part is handling of possible confounders. Permutation testing addresses random temporal order but leaves open whether external variables like inter-meal activity, stress levels, minor differences in actual meal content, or sensor drift could jointly influence the postprandial size and the next baseline without any internal system memory. The paper notes controlled conditions, yet the abstract gives no specifics on measured covariates or adjustments, so the claim for intrinsic history dependence needs more support. The work stays limited to this narrow setup of repeated same meals in healthy adults. Readers working on glucose dynamics models or CGM data interpretation would find the quantified association useful for thinking about temporal dependence. It's not a broad theory paper but a targeted observation that could inform how we view baseline stability. I would send this to peer review. The specific finding and the use of permutation testing make it worth referees' time to check the full methods and data processing for robustness against those alternative explanations.

Referee Report

1 major / 2 minor

Summary. The manuscript analyzes continuous glucose monitoring (CGM) data from normoglycemic subjects in a public dataset undergoing repeated identical meal challenges. Pre-meal baselines are defined as the median glucose in a pre-meal window; successive displacements are computed across repetitions. The authors report that these displacements exceed short-term within-interval fluctuations, are robust to alternative baseline definitions, and that the magnitude of each baseline shift correlates positively with the size of the preceding postprandial response. This association persists under permutation testing, which the authors interpret as evidence that glycemic baselines exhibit non-random, history-dependent variation rather than independent fluctuations around a fixed set point.

Significance. If the reported association and its interpretation hold after addressing potential confounders, the work would challenge the standard fixed-baseline model of glycemic regulation and support the incorporation of temporal memory effects into physiological models of glucose homeostasis. This has potential implications for CGM data interpretation in research and for the design of dynamic predictive models. The study benefits from use of public data, explicit robustness checks to alternative baseline definitions, and permutation testing to assess non-randomness; these elements provide a transparent empirical foundation.

major comments (1)

[Methods (permutation testing)] The permutation testing procedure (described in the methods and referenced in the abstract) rules out the null of random temporal ordering of postprandial responses within subjects. However, it does not address systematic external confounders that could jointly enlarge a postprandial excursion and shift the subsequent pre-meal median (e.g., unmeasured inter-meal physical activity, stress, minor meal-composition differences, or CGM sensor drift). Because the positive association between baseline displacement and preceding response size is the primary evidence for the claim of intrinsic history-dependent dynamics, this limitation is load-bearing and requires either additional covariate adjustment, sensitivity analyses, or expanded discussion of why such factors are unlikely to explain the result.

minor comments (2)

[Abstract and Methods] The abstract and methods would benefit from explicit reporting of the number of subjects, number of meal repetitions per subject, exact pre-meal window parameters (length and placement), and inclusion/exclusion criteria for the public dataset to allow readers to evaluate statistical power and generalizability.
[Results] Notation for baseline displacement and postprandial response size should be defined consistently with symbols or equations in the main text rather than only in prose, to improve clarity for quantitative readers.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and insightful review. The major comment on the scope of the permutation testing is well-taken and highlights a key interpretive nuance. We address it point by point below and will revise the manuscript to incorporate an expanded discussion of potential limitations.

read point-by-point responses

Referee: [Methods (permutation testing)] The permutation testing procedure (described in the methods and referenced in the abstract) rules out the null of random temporal ordering of postprandial responses within subjects. However, it does not address systematic external confounders that could jointly enlarge a postprandial excursion and shift the subsequent pre-meal median (e.g., unmeasured inter-meal physical activity, stress, minor meal-composition differences, or CGM sensor drift). Because the positive association between baseline displacement and preceding response size is the primary evidence for the claim of intrinsic history-dependent dynamics, this limitation is load-bearing and requires either additional covariate adjustment, sensitivity analyses, or expanded discussion of why such factors are unlikely to explain the result.

Authors: We agree that the permutation procedure specifically tests against random temporal ordering of the postprandial responses and thereby supports the non-random character of the observed association. It does not, however, exclude the possibility that unmeasured systematic factors could jointly affect both the preceding postprandial excursion and the subsequent baseline displacement. The study design relies on repeated identical meal challenges drawn from a public dataset collected under controlled conditions, which substantially reduces variability attributable to meal composition or timing. Nevertheless, factors such as inter-meal physical activity, stress, or minor sensor drift remain possible. In the revised manuscript we will expand the Discussion to explicitly acknowledge this limitation and to articulate why the pattern is more consistent with intrinsic history-dependent glycemic dynamics than with sporadic external influences. Supporting points include the persistence of the association across multiple subjects, its robustness to alternative baseline definitions, and the observation that baseline displacements exceed short-term within-interval fluctuations. We will also note that datasets containing additional covariates would enable more direct sensitivity analyses in future work. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical data analysis with permutation testing

full rationale

The paper reports observational results from a public dataset of normoglycemic subjects. Baselines are defined directly as median glucose in pre-meal windows, displacements are computed between repetitions, and the key association with preceding postprandial response size is assessed via permutation testing to rule out random ordering. No equations, derivations, fitted parameters presented as predictions, self-citations, or ansatzes appear in the provided text. The findings are computed from the data without any reduction to inputs by construction, satisfying the criteria for a self-contained empirical study.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The claim rests on statistical processing of a public CGM dataset rather than theoretical derivation; key assumptions concern data quality and the adequacy of permutation testing to rule out ordering artifacts.

free parameters (1)

pre-meal window length and placement
The choice of time window used to compute the median baseline glucose is a parameter that directly affects measured displacements.

axioms (1)

domain assumption The public dataset of normoglycemic subjects under controlled repeated-meal conditions accurately captures intrinsic glycemic dynamics without major unaccounted confounders.
Invoked to support generalization of the observed history dependence.

pith-pipeline@v0.9.0 · 5511 in / 1313 out tokens · 52380 ms · 2026-05-10T13:58:50.823770+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

21 extracted references · 19 canonical work pages

[1]

Continuous Glucose Monitoring for the Routine Care of Type 2 Diabetes Mellitus

Ajjan, R. A., T. Battelino , X. Cos, S. Del Prato, J. C. Philips, L. Meyer, J. Seufert, and S. Seidu. 2024. “Continuous Glucose Monitoring for the Routine Care of Type 2 Diabetes Mellitus.” Nature Reviews Endocrinology 20 (7): 426–440. https://doi.org/10.1038/s41574-024-00973-1

work page doi:10.1038/s41574-024-00973-1 2024
[2]

Continuous Glucose Monitoring in Gestational Diabetes Mellitus: Hope or Hype?

Chai, T. Y ., S. Leathwick, M. M. Agarwal, D. B. Sacks, and D. Simmons. 2025. “Continuous Glucose Monitoring in Gestational Diabetes Mellitus: Hope or Hype?” Diabetes Research and Clinical Practice 227: 112389. https://doi.org/10.1016/j.diabres.2025.112389

work page doi:10.1016/j.diabres.2025.112389 2025
[3]

Optical Nanosensors for Real-Time Feedback on Insulin Secretion by Beta -Cells

Ehrlich, R., A. Hendler-Neumark, V . Wulf, D. Amir, and G. Bisker. 2021. “Optical Nanosensors for Real-Time Feedback on Insulin Secretion by Beta -Cells.” Small 17 (30): e2101660. https://doi.org/10.1002/smll.202101660

work page doi:10.1002/smll.202101660 2021
[4]

Continuous Glucose Monitoring Systems in Noninsulin-Treated People with Type 2 Diabetes: A Systematic Review and Meta -Analysis of Randomized Controlled Trials

Ferreira, R. O. M., T. Trevisan, E. Pasqualotto, M. P. Chavez, B. F. Marques, R. N. Lamounier, and S. van de Sande-Lee. 2024. “Continuous Glucose Monitoring Systems in Noninsulin-Treated People with Type 2 Diabetes: A Systematic Review and Meta -Analysis of Randomized Controlled Trials.” Diabetes Technology & Therapeutics 26 (4): 252–262. https://doi.org/...

work page doi:10.1089/dia.2023.0390 2024
[5]

Estrogen and Glycemic Homeostasis: The Fundamental Role of Nuclear Estrogen Receptors ESR1/ESR2 in Glucose Transporter GLUT4 Regulation

Gregorio, K. C. R., C. P. Laurindo, and U. F. Machado. 2021. “Estrogen and Glycemic Homeostasis: The Fundamental Role of Nuclear Estrogen Receptors ESR1/ESR2 in Glucose Transporter GLUT4 Regulation.” Cells 10 (1): 99. https://doi.org/10.3390/cells10010099

work page doi:10.3390/cells10010099 2021
[6]

Type 2 Diabetes and the Multifaceted Gut -X Axes

Guo, H., L. Pan, Q. Wu, L. Wang, Z. Huang, J. Wang, L. Wang, X. Fang, S. Dong, Y . Zhu, and Z. Liao. 2025. “Type 2 Diabetes and the Multifaceted Gut -X Axes.” Nutrients 17 (16): 2708. https://doi.org/10.3390/nu17162708

work page doi:10.3390/nu17162708 2025
[7]

Glucotypes Reveal New Patterns of Glucose Dysregulation

Hall, H., D. Perelman, A. Breschi, P. Limcaoco, R. Kellogg, T. McLaughlin, and M. Snyder. 2018. “Glucotypes Reveal New Patterns of Glucose Dysregulation.” PLoS Biology 16 (7): e2005143. https://doi.org/10.1371/journal.pbio.2005143

work page doi:10.1371/journal.pbio.2005143 2018
[8]

Continuous Glucose Monitoring in Pregnancy

Horgan, R., Y . Hage Diab, M. Fishel Bartal, B. M. Sibai, and G. Saade. 2024. “Continuous Glucose Monitoring in Pregnancy.” Obstetrics & Gynecology 143 (2): 195–203. https://doi.org/10.1097/AOG.0000000000005374

work page doi:10.1097/aog.0000000000005374 2024
[9]

IPMK Modulates Hepatic Glucose Production and Insulin Signaling

Jung, I. R., F. Anokye -Danso, S. Jin, R. S. Ahima, and S. F. Kim. 2022. “IPMK Modulates Hepatic Glucose Production and Insulin Signaling.” Journal of Cellular Physiology 237 (8): 3421 –3432. https://doi.org/10.1002/jcp.30827

work page doi:10.1002/jcp.30827 2022
[10]

Direct and Indirect Control of Hepatic Glucose Production by Insulin

Lewis, G. F., A. C. Carpentier, S. Pereira, M. Hahn, and A. Giacca. 2021. “Direct and Indirect Control of Hepatic Glucose Production by Insulin.” Cell Metabolism 33 (4): 709–720. https://doi.org/10.1016/j.cmet.2021.03.007

work page doi:10.1016/j.cmet.2021.03.007 2021
[11]

Effects of Continuous Glucose Monitoring on Glycaemic Control in Type 2 Diabetes: A Systematic Review and Network Meta -Analysis of Randomized Controlled Trials

Lu, J., Z. Ying, P. Wang, M. Fu, C. Han, and M. Zhang. 2024. “Effects of Continuous Glucose Monitoring on Glycaemic Control in Type 2 Diabetes: A Systematic Review and Network Meta -Analysis of Randomized Controlled Trials.” Diabetes, Obesity and Metabolism 26 (1): 362–372. https://doi.org/10.1111/dom.15328

work page doi:10.1111/dom.15328 2024
[12]

Insulin: The Master Regulator of Glucose Metabolism

Norton, L., C. Shannon, A. Gastaldelli, and R. A. DeFronzo. 2022. “Insulin: The Master Regulator of Glucose Metabolism.” Metabolism 129: 155142. https://doi.org/10.1016/j.metabol.2022.155142

work page doi:10.1016/j.metabol.2022.155142 2022
[13]

Efficacy and Safety of HIMABERB Berberine on Glycemic Control in Patients with Prediabetes: Double -Blind, Placebo -Controlled, and Randomized Pilot Trial

Panigrahi, A., and S. Mohanty. 2023. “Efficacy and Safety of HIMABERB Berberine on Glycemic Control in Patients with Prediabetes: Double -Blind, Placebo -Controlled, and Randomized Pilot Trial.” BMC Endocrine Disorders 23 (1): 190. https://doi.org/10.1186/s12902-023-01442-y

work page doi:10.1186/s12902-023-01442-y 2023
[14]

Hepatic Glucose Metabolism in the Steatotic Liver

Scoditti, E., S. Sabatini, F. Carli, and A. Gastaldelli. 2024. “Hepatic Glucose Metabolism in the Steatotic Liver.” Nature Reviews Gastroenterology & Hepatology 21 (5): 319–334. https://doi.org/10.1038/s41575-023-00888-8

work page doi:10.1038/s41575-023-00888-8 2024
[15]

Adipose Tissue Macrophages in Remote Modulation of Hepatic Glucose Production

Tao, Y ., Q. Jiang, and Q. Wang. 2022. “Adipose Tissue Macrophages in Remote Modulation of Hepatic Glucose Production.” Frontiers in Immunology 13: 998947. https://doi.org/10.3389/fimmu.2022.998947

work page doi:10.3389/fimmu.2022.998947 2022
[16]

Variability, Stability and the Law of Effect

Wasserman, Edward A., Odysseus R. P. Orr, and Sophia Li. 2026. “Variability, Stability and the Law of Effect.” Journal of Experimental Psychology: Animal Learning and Cognition. Published April 6, 2026

2026
[17]

Individual Variations in Glycemic Responses to Carbohydrates and Underlying Metabolic Physiology

Wu, Y ., B. Ehlert, A. A. Metwally, et al. 2025. “Individual Variations in Glycemic Responses to Carbohydrates and Underlying Metabolic Physiology.” Nature Medicine 31: 2232–2243. https://doi.org/10.1038/s41591-025- 03719-2

work page doi:10.1038/s41591-025- 2025
[18]

Feedback Regulation of Insulin Secretion by Extended Synaptotagmin-1

Xie, B., P. M. Nguyen, and O. Idevall -Hagren. 2019. “Feedback Regulation of Insulin Secretion by Extended Synaptotagmin-1.” F ASEB Journal 33 (4): 4716–4728. https://doi.org/10.1096/fj.201801878R

work page doi:10.1096/fj.201801878r 2019
[19]

Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?

Zahalka, S. J., R. J. Galindo, V . N. Shah, and C. C. Low Wang. 2024. “Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?” Journal of Diabetes Science and Technology 18 (4): 835 –846. https://doi.org/10.1177/19322968241242487

work page doi:10.1177/19322968241242487 2024
[20]

Vitamin K2 Supplementation Improves Impaired Glycemic Homeostasis and Insulin Sensitivity for Type 2 Diabetes through Gut Microbiome and Fecal Metabolites

Zhang, Y ., L. Liu, C. Wei, X. Wang, R. Li, X. Xu, Y . Zhang, G. Geng, K. Dang, Z. Ming, X. Tao, H. Xu, X. Yan, J. Zhang, J. Hu, and Y . Li. 2023. “Vitamin K2 Supplementation Improves Impaired Glycemic Homeostasis and Insulin Sensitivity for Type 2 Diabetes through Gut Microbiome and Fecal Metabolites.” BMC Medicine 21 (1):

2023
[21]

https://doi.org/10.1186/s12916-023-02880-0

work page doi:10.1186/s12916-023-02880-0