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  pdftitle={ECG and Blood Glucose: A Meta-Analysis of the Signal},
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\begin{center}
  {\LARGE ECG and Blood Glucose: A Meta-Analysis of the Signal\par}
  \vskip 1.1em
  Ben Letchford\par
  \vskip 0.35em
  {\small\itshape\color{muted} 29 June 2026\par}
\end{center}

\vskip 1.6em
\begin{center}
  {\footnotesize\bfseries ABSTRACT}
\end{center}
\vskip -0.4em
\begin{center}
  \begin{minipage}{0.92\textwidth}
    \small A structured synthesis of the evidence linking ECG features to blood glucose, from QT changes to wearable machine-learning models.
  \end{minipage}
\end{center}

\vskip 2em

\begin{center}\itshape
A weak physiological echo is still a signal
\end{center}

\noindent Whatever else it is, the ECG is not a glucose sensor, and the field gets into
trouble the moment it forgets that.

A glucose meter measures chemistry directly. A continuous glucose monitor reads
an electrochemical reaction in the fluid between your cells. An ECG does neither.
It records the heart's electrical activity at the skin. There is no glucose
molecule in that trace, no spectral line reading 6.1 mmol/L, no hidden blood
sugar channel waiting for the right decoder. The quantity we want is simply not
the quantity we are recording.

The idea survives anyway, and not because it is wishful. It is indirect. Glucose
changes the body, and the body changes the heart. A falling glucose level
triggers counterregulatory physiology. A rising one shifts autonomic tone,
repolarization, vascular state, and electrolyte handling. Diabetes sits
underneath all of it and slowly rewires autonomic control and cardiac risk. None
of that is glucose, but all of it leaves marks the ECG can pick up: in heart
rate, in heart-rate variability, in the QT interval, in T-wave shape, in ST and
PR features, and in the higher-order patterns a neural network can pull out of
raw beats. The heart is a poor witness to glucose, but it is not a blank one.

That changes the question worth asking. Forget whether the ECG can measure
glucose the way a meter does, because it can't. Ask instead how much
glucose-related information actually reaches the ECG when glucose moves, and how
much of that information is still there once you test the model on someone it has
never seen.

My own read, up front so you can argue with the rest of it: the correlation is
biologically real, not a statistical artifact, and it is clearest during
hypoglycemia, acute glucose challenge, and some hyperglycemic states. It is also
not strong or specific enough to trust on its own, and most of the field's
overclaiming lives in the gap between those two facts. The role that holds up for
now is a supporting one. An alarm, a risk score, a bit of trend context, one
input among several in a wearable. Not the number you dose insulin against.

\section{What kind of meta-analysis this can honestly be}

You cannot pool this literature into a single effect size, and any paper that
tries should make you suspicious. The studies are barely measuring the same
thing. One quantifies QTc drift during clamp-induced hypoglycemia. The next
classifies hypoglycemic episodes as events. Another classifies hyperglycemia.
Another regresses continuous glucose against the waveform. The reference standard
wanders too, from plasma glucose to capillary to interstitial CGM, and one recent
line of work splits fasting blood glucose from HbA1c entirely, on the correct
grounds that the two live on different time scales and should not be averaged
together (\hyperlink{ref-azam-singh-2026}{Azam \& Singh, 2026}).

The populations differ as much as the endpoints: healthy volunteers, children
with type 1 diabetes, adults with type 1, adults with type 2, hospital-grade
protocols, nocturnal ambulatory recordings, oral glucose tolerance tests, scraped
public datasets. Validation ranges from a subject-specific model that knows one
person cold to a subject-independent network that has to generalize to people it
never trained on. Average across all of that and you get a number with three
decimal places and no meaning.

So this is a structured synthesis rather than a forest plot. I have stacked the
evidence by causal layer, working from physiology up to product:

\begin{itemize}
\item the physiological changes the ECG shows during glycemic excursions;
\item the hand-engineered features built to read glucose state off those changes;
\item the deep-learning and wearable models that learn the morphology themselves;
\item and the validation limits that explain why a decade of strong retrospective
scores still has not produced a clinical ECG glucose monitor.
\end{itemize}

The layering matters because a QT study and a neural-network study can both be
true while answering completely different questions, and a lot of the confusion
in this field comes from mistaking that for agreement.

\section{The signal is strongest for hypoglycemia}

Hypoglycemia detection is the oldest coherent branch of the field, and it is also
where the physiology is clearest. Falling glucose is an emergency, and the body
treats it as one. Sympathoadrenal activation kicks in, potassium shifts between
compartments, and ventricular repolarization bends under the strain. Those
pathways run close enough to the heart's electrical behavior to show up on the
trace, which is why the ECG reads glucose best at exactly the moment the body is
most alarmed by it.

The first clue was the QT interval. In 1998, Eckert and Agardh put healthy men
into experimental hypoglycaemia and watched the QT interval lengthen
(\hyperlink{ref-eckert-agardh-1998}{Eckert \& Agardh, 1998}). Robinson and colleagues followed with an insulin clamp and
confirmed QTc changes during hypoglycemia, alongside the expected
counterregulatory and electrolyte effects (\hyperlink{ref-robinson-2003}{Robinson et al., 2003}). Koivikko and
colleagues then complicated the tidy QT story in a useful way. In type 1 diabetes
and healthy controls alike, controlled hypoglycemia did not confine itself to a
single number. T-wave amplitude, T-wave area, the QRS-T angle, and the geometry
of the T-wave loop all moved as well, which means the signal was spread across
the whole shape of repolarization rather than sitting in QTc alone
(\hyperlink{ref-koivikko-2008}{Koivikko et al., 2008}).

This is worth dwelling on. A wearable that watches only QTc is reading one
descriptor, and not even a clean one, since QTc depends on its own correction
formula, on heart rate, and on whether you are standing or lying down. The ECG's
response to glucose is not a single value, and an algorithm that treats it as one
risks measuring its own assumptions.

The harder question is whether any of this survives outside the clamp lab. Some
of it does. Lee and colleagues used simultaneous Holter ECG and continuous
glucose monitoring and caught QT prolongation accompanying hypoglycemia in
insulin-treated type 1 and type 2 diabetes, in ordinary conditions rather than
under a protocol (\hyperlink{ref-lee-2016}{Lee et al., 2016}). That is the test most clean lab effects fail.
People sleep, move, digest, get stressed, and wear imperfect sensors, and crisp
physiology tends to blur. Here it held up well enough to be seen, though not well
enough to retire the glucose meter.

The same biology that produces the signal also explains its weakness. The people
at greatest risk of severe hypoglycemia are often the ones whose hearts can no
longer raise much of a response. Lipponen and colleagues found that
repolarization-based detection worked well in healthy subjects and in
otherwise-healthy type 1 diabetes, then weakened in type 1 diabetes with
complications, where the autonomic response was already blunted (\hyperlink{ref-lipponen-2011}{Lipponen et al., 2011}).
That is the central paradox of the whole approach. The signal is the body's
reaction to glucose, and the body's reaction is exactly what long-standing
diabetes erodes, so the detector tends to work best on the patients who need it
least.

\section{Hyperglycemia changes the ECG too, but less cleanly}

Run glucose the other way and the signal gets quieter and harder to trust. Acute
hyperglycaemia can prolong QTc even in healthy subjects, which Marfella and
colleagues showed with a glucose infusion, so the electrophysiology is real and
not just borrowed from the hypoglycemia story (\hyperlink{ref-marfella-2000}{Marfella et al., 2000}). There is a
foundation here. It is just a narrower one.

The trap is treating hyperglycemia as hypoglycemia in reverse. It isn't. High
glucose comes in too many forms to share one ECG signature: the post-meal spike,
the fasting plateau, the slow grind of an elevated HbA1c, and the conditions that
travel with it, like illness, dehydration, neuropathy, and cardiovascular
disease. Some of those states change HRV, some change repolarization, and most
sit behind a long list of confounders: age, medication, fitness, sleep, caffeine,
stress, infection, and how long the person has had diabetes. Hypoglycemia gives
you one alarmed body to read. Hyperglycemia gives you a crowd, and the ECG has to
pick out the relevant face.

The cleaner approach is to pin glucose down with an acute challenge. Tobore and
colleagues did this with 16 participants and a 75 g oral glucose tolerance test,
and found glucose-associated patterns across HRV, heart rate, PRQ, QT, and ST
segments (\hyperlink{ref-tobore-2019}{Tobore et al., 2019}). Ha and colleagues went further and handled a problem
most studies skip. Working with 30 adults, an OGTT, Holter ECG, and interstitial
glucose monitoring, they measured each participant's HRV response delay and
aligned to it before scoring. With the timing matched up, the HRV-glucose
association tightened and acute hyperglycemia detection reached about 0.89
accuracy and AUROC under temporal validation (\hyperlink{ref-ha-2026}{Ha et al., 2026}).

That delay is easy to skip past, but it quietly governs the whole field. The ECG
does not move at the same instant as glucose. Glucose rises first, and the heart
follows on a lag that varies from person to person. Ignore the lag and you smear
a real effect into noise and conclude the physiology is weak. Estimate the lag
from the whole dataset and you let the model use future information to align the
past, which produces a good-looking score built on leakage. The lag is both the
key to the signal and an easy way to fool yourself, and a serious study has to
deal with both sides of that.

\section{The engineered-feature era}

Before deep learning took over the field, researchers had to decide by hand which
parts of the ECG were worth measuring. Each chosen feature was a small bet about
where glucose information might be hiding.

The early HypoMon work bet on heart rate and corrected QT. In a 24-child study,
Nguyen, Ghevondian, and Jones found both rising during naturally occurring
nocturnal hypoglycemia, not induced episodes, and used a Bayesian neural network
to reconstruct glucose profiles with significant correlation on a held-out test
set (\hyperlink{ref-nguyen-2009}{Nguyen et al., 2009}). The question was older still. A 1995 note in Diabetes Care had
already asked whether an ECG recording could detect hypoglycemia at all
(\hyperlink{ref-heinemann-1995}{Heinemann et al., 1995}). The field has been circling the same idea for three decades.

The list of candidate features grew from there. Nuryani, Ling, and Nguyen used
swarm-tuned support vector machines for hypoglycemia detection (\hyperlink{ref-nuryani-2012}{Nuryani et al., 2012}).
Lipponen and colleagues used repolarization features, including QT and T-wave
flattening, to flag hypoglycemic events in clamp data (\hyperlink{ref-lipponen-2011}{Lipponen et al., 2011}). Nguyen,
Su, and Nguyen used a wider set at once, including heart rate, corrected QT, PR,
corrected RT, and corrected TpTe, applied to both hypo- and hyperglycemia in type
1 diabetes, and reported that the parameters moved in opposite directions for low
versus high glucose (\hyperlink{ref-nguyen-su-2012}{Nguyen et al., 2012}).

Underneath the different methods, this era left two findings that pull against
each other.

The encouraging one is that the glucose information really is in there. Different
groups, using different descriptors, populations, and protocols, kept finding it.
That consistency is hard to dismiss as coincidence.

The sobering one is that none of those features belong to glucose. Heart rate is
not glucose-specific, and neither are HRV, QT, or T-wave shape. Each of them also
responds to the protocol, the person, the sensor, the sleep stage, the meal
schedule, and the disease phenotype. So a classifier can post a strong score by
learning one of those instead, unless the validation is deliberately built to
remove those shortcuts. That problem only gets worse in the deep-learning era,
where the shortcuts become more tempting and much harder to spot.

\section{The deep-learning era}

Deep learning changed the central question from which feature to trust to whether
a model can find the relevant morphology itself, including the parts nobody
thought to name. It can. The same flexibility is also what makes these results so
easy to fool yourself with.

Porumb and colleagues built personalized detectors for nocturnal hypoglycemia
from short raw ECG segments recorded by wearables over 14 days in healthy people
(\hyperlink{ref-porumb-2020}{Porumb et al., 2020}). The word personalized is doing real work. They argued that people
differ enough from one another that a single cohort-level detector had been
unreliable, so they did not claim to have found one universal ECG-to-glucose
mapping. The claim was narrower and sturdier: a given person's heart has its own
signature, and a model trained on that person can read it.

Cordeiro, Karimian, and Park went the other way, toward scale. On ECG from 1119
subjects, their hyperglycemia classifier reported 94.53\% AUC, 87.57\%
sensitivity, and 85.04\% specificity (\hyperlink{ref-cordeiro-2021}{Cordeiro et al., 2021}). That result matters because
it points across people rather than within one, which suggests the hyperglycemia
signal is more than one person's baseline drift relabeled as prediction.

Then the field reached for the harder target of a continuous glucose number
rather than a yes-or-no, and the limits showed. Fellah Arbi and colleagues
attempted continuous glucose estimation from ECG using three patients and eight
days of the D1NAMO dataset, with a CNN to segment the beats and regression on the
extracted parameters. The best model performed well within patients, but a
three-person sample makes it a proof of concept rather than a usable estimator
(\hyperlink{ref-fellah-arbi-2023}{Fellah Arbi et al., 2023}). Zhang and colleagues returned to the same D1NAMO data in
2026 with a more mature design, a multi-expert SC-ResNet that splits segments by
glucose state, extracts and fuses deep features, and hands a random forest the
final prediction, and reported improved RMSE, MARD, and Clarke error grid results
(\hyperlink{ref-zhang-2026}{Zhang et al., 2026}). The architecture is genuinely more sophisticated than earlier
pipelines. The open question is the same one every paper in this section
inherits: how much of the performance carries over to a new person, a longer time
window, a different device, and an ordinary chaotic day.

This is why the most useful deep-learning papers are not the ones with the
highest score but the ones that show what the model is using. Porumb and
colleagues visualized which regions the model relied on, including T-wave and
ST-interval contributions, so the result could be checked against physiology
(\hyperlink{ref-porumb-2020}{Porumb et al., 2020}). Ha and colleagues paired saliency analysis with their delay
alignment to tie the model's decisions back to plausible ECG and HRV features
(\hyperlink{ref-ha-2026}{Ha et al., 2026}). Without that link, an ECG glucose model can look accurate while
quietly learning a shortcut, right up until it meets someone new.

\section{Multimodal wearables make the problem more honest}

If the ECG is a weak, nonspecific proxy, the better move may be to stop forcing it
to stand alone and give it some context.

Dave and colleagues paired ECG with an accelerometer over two weeks in five
healthy participants. Their best regression model detected both hypo- and
hyperglycemic excursions better with ECG plus accelerometer than with ECG alone,
reaching roughly mid-to-high 70\% sensitivity and specificity for both excursion
types (\hyperlink{ref-dave-2022}{Dave et al., 2022}).

Next to the 90-something AUCs in the previous section that looks unimpressive,
but it is one of the more honest results in this area. Activity, sleep, and
posture all change HRV, and exercise moves glucose and the ECG at the same time,
which is exactly the kind of confound that makes a lone ECG misleading. An
accelerometer is a cheap way to tell ``the heart rate rose because glucose fell''
apart from ``the heart rate rose because the person walked upstairs.'' The
single-sensor papers post the bigger numbers, but this is the more realistic
framing.

This is probably where the ECG belongs in an actual device. Not as the only
sensor, but as one input in a context model that can also include movement, skin
temperature, sleep state, time since the last meal, medication timing, the
previous glucose reading, and the occasional real glucose measurement to keep the
whole thing calibrated.

\section{Why validation is the whole story}

The most important paper here is not the one with the deepest network or the
highest score. It is the one that strips away the usual conveniences and checks
what is left. That is Azam and Singh's 2026 re-evaluation of HRV biomarkers for
glucose sensing (\hyperlink{ref-azam-singh-2026}{Azam \& Singh, 2026}), and it reads like a corrective to the rest of
the field.

They took 43 male type 2 diabetes patients, kept HbA1c and fasting blood glucose
as separate targets instead of merging them into one convenient measure of
``glucose,'' and validated strictly. They used leave-one-subject-out
cross-validation, with feature selection and standardization done inside each
fold so the model never saw the test subject in any form. Under that setup the
associations were still real, but the accuracy was not clinically useful. The
best results were modest: an R\textsuperscript{2} of 0.222 for HbA1c and 0.086 for fasting blood
glucose, with mean absolute errors of 1.18 percentage points and 2.27 mmol/L
(\hyperlink{ref-azam-singh-2026}{Azam \& Singh, 2026}). A 2.27 mmol/L error is not a glucose monitor.

The value of the paper is the standard it sets. It lays out the questions a
serious ECG glucose study has to answer before its score means anything:

\begin{itemize}
\item Are subjects separated between training and testing?
\item Are days separated, or can the model memorize a person's local baseline?
\item Are feature selection, scaling, lag estimation, and calibration done inside
the training fold only?
\item Is the target acute glucose, fasting glucose, interstitial CGM, capillary
glucose, plasma glucose, or HbA1c?
\item Is performance reported as event detection, regression error, Clarke error
grid, MARD, alarm latency, false alarm burden, or all of them?
\item Does the model survive sleep, meals, exercise, stress, illness, medications,
arrhythmias, autonomic neuropathy, sensor movement, and different ECG hardware?
\end{itemize}

Without those controls, an accuracy figure can be perfectly real for one dataset
and useless everywhere else.

\section{What the evidence says when you read it together}

Read end to end, the literature does not support the headline claim that an ECG
can replace glucose measurement. It never comes close.

What it does support is quieter and more interesting. The ECG carries measurable
information about glycemic state, because a glucose excursion drags autonomic and
cardiac electrophysiology along with it. The information is real, but it is
indirect, partial, and easily drowned out.

The firmest ground is hypoglycemia and repolarization, meaning QT, T-wave
morphology, and the related ventricular repolarization descriptors that keep
reappearing across labs (\hyperlink{ref-eckert-agardh-1998}{Eckert \& Agardh, 1998}; \hyperlink{ref-robinson-2003}{Robinson et al., 2003}; \hyperlink{ref-koivikko-2008}{Koivikko et al., 2008}; \hyperlink{ref-lee-2016}{Lee et al., 2016}). The best modern case for hyperglycemia rests on large-scale
classification and on delay-aware, challenge-controlled HRV work
(\hyperlink{ref-cordeiro-2021}{Cordeiro et al., 2021}; \hyperlink{ref-ha-2026}{Ha et al., 2026}). The sharpest warning comes from subject-independent
revalidation, where the association survives but the clinical accuracy does not
(\hyperlink{ref-azam-singh-2026}{Azam \& Singh, 2026}). Hold those three together and you have the real shape of the
field.

So the honest framing is proxy sensing rather than glucose sensing. The ECG never
sees glucose. It sees the cardiovascular response to glucose, mixed in with the
response to sleep, movement, stress, and disease. That response is sometimes
strong, sometimes delayed, sometimes absent, and usually sharing the trace with
something else.

\section{Where this should go next}

The field does not need another small retrospective model with an impressive
within-subject score, because there are already plenty of those. It needs the
slower, less exciting work:

\begin{itemize}
\item pre-registered protocols, so the hypothesis comes before the result;
\item simultaneous ECG, CGM, and reference blood glucose;
\item subject-independent and device-independent validation;
\item explicit handling of physiological lag;
\item alarm latency and false-alarm burden reported rather than buried;
\item subgroup analysis for autonomic neuropathy, diabetes type, medications,
arrhythmias, sleep, and exercise, since those are the cases where the signal is
known to weaken;
\item comparison against simple baselines such as time of day, recent meal, movement,
heart rate, and previous glucose, which have to be beaten first;
\item external replication on data the model's designers never touched.
\end{itemize}

It also needs honesty about what is being sold. ``This person may be entering a
risky glucose state'' is a useful statement even though it is not ``your glucose is
5.7 mmol/L.'' A carefully tuned supporting alarm and a non-invasive continuous
glucose monitor are different medical claims, and treating them as the same
product is how reasonable engineering turns into a regulatory and clinical
problem.

That is the real promise of this literature, and it is worth more than the magic
version. Not glucose read off the heartbeat, but a cheap, continuous signal that
people already wear, which, combined with context and checked against the
occasional real measurement, might warn them before glucose becomes dangerous.
The signal is real. The translation is not finished yet.


\vskip 2.2em
\pdfbookmark[1]{References}{references}%
{\centering\normalsize\bfseries References\par}
\vskip 0.2em
\begingroup\small
\setlength{\parindent}{-1.75em}\setlength{\leftskip}{1.75em}\setlength{\parskip}{0.45em}
\hypertarget{ref-azam-singh-2026}{}Azam, M. B., \& Singh, S. I. (2026). Re-evaluating heart rate variability biomarkers for glucose sensing: The impact of age normalisation and subject-independent validation. \emph{BMC Medical Informatics and Decision Making, 26}, Article 166. \url{https://doi.org/10.1186/s12911-026-03455-8}\par
\hypertarget{ref-cordeiro-2021}{}Cordeiro, R., Karimian, N., \& Park, Y. (2021). Hyperglycemia identification using ECG in deep learning era. \emph{Sensors, 21}(18), Article 6263. \url{https://doi.org/10.3390/s21186263}\par
\hypertarget{ref-coronado-reyes-2025}{}Coronado-Reyes, O. I., Tellez-Anguiano, A. C., Castro-Pimentel, L. A., \& Gutierrez-Gnecchi, J. A. (2025). Non-invasive hyperglycemia detection via electrocardiogram using discrete wavelet transform and machine learning. \emph{Cureus, 17}(3), Article e80548. \url{https://doi.org/10.7759/cureus.80548}\par
\hypertarget{ref-dave-2022}{}Dave, D., Vyas, K., Branan, K., McKay, S., DeSalvo, D. J., Gutierrez-Osuna, R., Cote, G. L., \& Erraguntla, M. (2022). Detection of hypoglycemia and hyperglycemia using noninvasive wearable sensors: Electrocardiograms and accelerometry. \emph{Journal of Diabetes Science and Technology, 18}(2), 351-362. \url{https://doi.org/10.1177/19322968221116393}\par
\hypertarget{ref-eckert-agardh-1998}{}Eckert, B., \& Agardh, C.-D. (1998). Hypoglycaemia leads to an increased QT interval in normal men. \emph{Clinical Physiology, 18}(6), 570-575. \url{https://doi.org/10.1046/j.1365-2281.1998.00138.x}\par
\hypertarget{ref-fellah-arbi-2023}{}Fellah Arbi, K., Soulimane, S., Saffih, F., Bechar, M. A., \& Azzoug, O. (2023). Blood glucose estimation based on ECG signal. \emph{Physical and Engineering Sciences in Medicine, 46}(1), 255-264. \url{https://doi.org/10.1007/s13246-022-01214-3}\par
\hypertarget{ref-ha-2026}{}Ha, J., Hwang, H. B., Kim, H., Lee, S., Lee, J., Park, J. H., Lee, J., \& Kim, I. Y. (2026). Noninvasive detection of acute hyperglycemia using signal from wearable ECG sensors considering individual HRV response delays to glucose. \emph{Biosensors, 16}(5), Article 251. \url{https://doi.org/10.3390/bios16050251}\par
\hypertarget{ref-heinemann-1995}{}Heinemann, L., Meinhold, J., \& Kunze, W. (1995). Hypoglycemia detection by ECG recording? \emph{Diabetes Care, 18}(1), 139-140. \url{https://doi.org/10.2337/diacare.18.1.139}\par
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