EASD 2025: The Technologies That Will Reshape Diabetes Care

TL;DR

  • CGM becomes infrastructure: inpatient trials show +15% Time-in-Range (TIR) and −24% complications; CGM variability maps reveal five new diabetes subtypes.
  • AID scales across populations: toddlers, pregnancy, type 2 diabetes (T2D) and chronic kidney disease (CKD) all show benefit; 24-month real-world durability is confirmed.
  • Pregnancy head-to-head: CamAPS FX and Control-IQ outperform MiniMed 780G on neonatal outcomes and glycaemia.
  • Algorithm-first insulin therapy emerges: smart multiple daily injections (smart MDI) and electronic glucose management systems (eGMS) push insulin decision-making beyond pumps.
  • Multi-analyte sensing arrives: Abbott’s dual glucose–ketone (DGK) sensor ecosystem enters pre-launch reality.
  • Regulation tightens: iCGM expectations rise and technology competency frameworks become essential; workforce skills, not devices, become the bottleneck.
  • Trust becomes a feature: AID + CGM + AI only work when psychological and clinical trust are deliberately engineered into the system.

What EASD 2025 Told Us About Diabetes Tech

At EASD 2025, diabetes technology stopped behaving like a set of separate gadgets and started to look like
a connected infrastructure for care. Continuous glucose monitoring (CGM) is now operating like
hospital telemetry. Automated insulin delivery (AID) systems are moving into almost every metabolic situation
– toddlers, pregnancy, T2D and kidney disease. Algorithms are stepping outside pumps and into both
hospital decision-support systems and smart injection regimens.

The message is simple: the biggest questions are no longer “does this work?” but “how do we scale this safely,
fairly, and intelligently?” For people living with diabetes and the clinicians supporting them, this means
more automation, more data, and more need for education, skills, and trust.

For an overview of how we think about CGM and technology at The Glucose Never Lies, you can explore our CGM hub and our AID systems guide.

The Big Stories in One Read

EASD 2025 in Vienna marked a clear shift for diabetes technology. Rather than isolated device launches, we saw
whole-system moves: CGM in hospitals, AID across age groups and diagnoses, algorithmic insulin
dosing in wards, and early signals of multi-analyte sensing.

On the CGM front, the DIETEC inpatient trial (n=166) showed that using CGM alongside structured titration and
dedicated diabetes teams increased TIR by around 15 percentage points (63 → 78%)
and reduced hospital complications by 24%, mainly infections. Day-to-day analyses showed patients
on CGM approaching 80–90% TIR by discharge, whilst those on traditional blood glucose monitoring plateaued
around 60% TIR. Accuracy remains strongest in non-ICU settings, but this is now credible evidence that
continuous data can change hard outcomes in hospital.

Another CGM theme was classification. An observational study in 527 people used CGM variability profiles to define
five metabolic subgroups: stable hyperglycaemia, postprandial spikes, brittle hypoglycaemia, dawn
phenomenon, and erratic fluctuators. Each pattern maps onto different treatment strategies. CGM is no longer just a
way to see glucose; it is becoming a way to re-draw the map of diabetes itself.

AID systems, meanwhile, continued their expansion. MiniMed 780G secured CE-Mark coverage for children aged
two years and older, pregnancy, insulin-requiring T2D, and advanced CKD. The LENNY trial in toddlers (n=101)
reported approximately a 10-point TIR gain and about a 0.6% A1c reduction, with
parents reporting better sleep and less fear of hypoglycaemia. Longer-term data from Tandem’s Control-IQ show
that improvements at 12 months are maintained at 24 months, and nationwide real-world data from France suggest that
AID benefits are durable at scale.

Crucially, we finally saw pregnancy head-to-head data: in women with type 1 diabetes, CamAPS FX and Control-IQ
delivered stronger neonatal and maternal outcomes compared with MiniMed 780G. This moves us from “all systems
are good” to a more nuanced view of which algorithms perform best in pregnancy physiology.

The frontier is not just pumps. Medtronic and others highlighted smart MDI and
electronic glucose management systems (eGMS) – computerised insulin-dosing algorithms used
in hospital wards. These tools push us towards algorithm-first insulin therapy, where the
software–decision layer is as important as the hardware delivering the insulin. At the same time, Abbott’s
Exhibit Hall presence emphasised ketone risk ahead of an upcoming dual glucose–ketone (DGK) sensor,
signalling the arrival of continuous metabolic monitoring beyond glucose alone.

Finally, there was a strong systems flavour. Regulatory expectations for iCGM are rising, demanding higher accuracy
and better interoperability. The University of Leicester’s four-level technology competency framework
acknowledges that the true bottleneck is skills and training, not sensors and pumps. Across
sessions, “trust” was the word behind the slides: trust in algorithms, alarms, automation, and the people
who interpret them.

Theme Breakdown

1. Continuous Glucose Monitoring (CGM)

1.1 CGM in Hospitals

The DIETEC trial placed CGM at the centre of an inpatient T2D protocol. With structured insulin titration and
diabetes-specialist input, CGM users increased TIR from around 63% to 78%, whilst complication rates fell by 24%.
Accuracy was strongest in non-ICU settings, and performance was more variable in ICU patients, reminding us that
critical care remains a frontier problem. Even so, the direction of travel is obvious: for many
inpatients, CGM can safely replace frequent point-of-care testing and deliver better outcomes.

1.2 Remote CGM Telemetry & EHR Integration

Speakers drew an explicit analogy between remote CGM telemetry and cardiac monitoring: nurses can watch multiple
patients in real time and respond to alarms. The future here is not new sensor chemistry, but
workflow and data integration – getting CGM data into hospital EHRs and building
actionable, trusted alarm pathways.

1.3 CGM-Derived Metabolic Phenotypes

Using CGM variability metrics in 527 people, researchers identified five reproducible glucose profiles:
stable hyperglycaemia, post-meal spikes, brittle hypoglycaemia, dawn phenomenon, and erratic fluctuators. Each
phenotype suggests distinct therapeutic priorities (e.g. bolus optimisation, basal fine-tuning, hypo prevention,
or behavioural focus). This work nudges the field away from a one-size-fits-all A1c or TIR model towards
pattern-based, CGM-informed subtypes.

1.4 Pregnancy and CGM

CGM continued to show benefits in pregnancy, including reduced risk of large-for-gestational-age infants and
better maternal TIR. The meeting tone suggested that CGM in pregnancy is moving from “recommended”
towards “expected standard of care” in high-resource settings.

1.5 Beyond Glucose: Dual Glucose–Ketone (DGK) Sensing

Abbott used its Exhibit Hall presence to spotlight unmonitored ketones and heavily trailed its upcoming DGK
sensor. This is less about a single product and more about a direction of travel:
multi-analyte metabolic monitoring. For people prone to DKA or using treatments that modulate
ketone metabolism, this could be a major safety upgrade.

For a structured dive into how to choose and use CGM in the real world, see Selecting a Continuous Glucose Monitor and Mastering CGM: 10 Top Lessons.

2. Automated Insulin Delivery (AID)

2.1 780G Across Toddlers, Pregnancy, T2D and CKD

MiniMed 780G secured CE-Mark coverage across four major groups: children aged two years and above, pregnancy,
insulin-requiring T2D, and advanced CKD. This reflects regulatory confidence that the algorithm can cope with very
different physiologies and risk profiles. It also underlines a wider trend: AID is no longer a niche
solution for “ideal” adult T1D users.

2.2 LENNY Trial: AID in Toddlers

In the LENNY study (n=101), MiniMed 780G increased TIR by about 10 percentage points and reduced A1c by around
0.6%, without new safety issues. Parents reported better sleep and less fear of hypos. From a lived-experience
perspective, those last two outcomes are arguably as important as the lab numbers.

2.3 Pregnancy: CamAPS FX and Control-IQ vs 780G

A key session directly compared AID systems in pregnant women with type 1 diabetes. CamAPS FX and Control-IQ both
delivered superior maternal glycaemia and neonatal outcomes compared with 780G. This begins to establish a
hierarchy of performance in pregnancy-specific physiology, moving beyond device-agnostic
enthusiasm.

2.4 Durability & Real-World Population Data

Tandem reported that glycaemic improvements with Control-IQ at 12 months are maintained at 24 months, and
nationwide French registry data showed durable AID benefits at scale. Together, these findings suggest that
algorithm–behaviour interactions are stable over time in the real world, not just in trials.

2.5 AID Beyond the “Perfect User”

Several presentations highlighted benefits in people who do not regularly bolus or who live with complex
co-morbidities. This challenges the assumption that AID is only for motivated, tech-savvy users and supports a
more inclusive view of who can realistically benefit.

For structured help choosing a system, see our Choosing an AID System and AID Systems series.

3. Algorithmic Insulin Therapy Beyond Pumps

3.1 Smart MDI

Medtronic showcased “smart MDI” – decision-support tools that help people using injections
make more informed dosing decisions. Conceptually, this exports AID thinking into multiple daily injections:
the algorithm starts to matter as much as the delivery method. For the many people worldwide
who will never have access to pumps, this is potentially more disruptive than any new pump launch.

3.2 Electronic Glucose Management Systems (eGMS)

eGMS platforms apply computerised algorithms to insulin dosing in hospital wards, often combined with CGM data.
Evidence is still mostly retrospective, but early results are promising. The key shift is that insulin
decisions are becoming software-mediated inside hospitals, not just at home on personal devices.

3.3 Software–Hardware Decoupling

Taken together, smart MDI and eGMS point towards a future where insulin therapy is governed primarily by
software logic, and pumps, pens, or syringes are just delivery channels. This aligns strongly
with the way we already teach dose-thinking and pattern recognition in our Mealtime Insulin and Dynamic Glucose Management modules.

4. Systems, Regulation, and Human Factors

4.1 Rising iCGM Standards

Regulatory discussions highlighted evolving expectations for iCGM designation: higher accuracy thresholds, better
interoperability, stronger data integrity, and clearer post-market surveillance. This will shape the next
generation of Libre, Dexcom, and emerging sensors, and it aligns closely with our ongoing work on CGM regulation.

For more context, see CGM Regulation and CGM Regulation: 2025 Update.

4.2 Competency Frameworks

The University of Leicester’s four-level technology competency framework treats CGM and AID as clinical
skillsets that must be trained, assessed, and maintained. This is a quiet but important shift:
technology is only as good as the team using it. Expect more structured curricula, credentialing,
and expectations around tech literacy for healthcare professionals.

4.3 Trust as a Core Design Parameter

Across multiple sessions, speakers returned to the idea of trust. It is not enough for algorithms to be accurate;
people need to understand and predict how systems will behave. That includes clear alarms, transparent logic,
robust safety nets, and realistic communication about limitations. This sits at the heart of the
Glucose Never Lies philosophy: data must correspond with lived experience to be believed.

Practical: What This Means Going Forward

  • For people with diabetes: CGM and AID are becoming more available and more capable, including in
    hospital and pregnancy. The key challenge is choosing systems that fit your life and learning to use them without
    burning out.
  • For clinicians: the priority is skills, not just prescribing. Interpreting CGM phenotypes,
    managing AID in complex populations, and understanding algorithm limitations will all become core competencies.
  • For regulators and payers: the evidence base is now strong enough to justify broader access,
    but only if implementation is matched with training, support, and realistic expectations.
  • For industry: multi-analyte sensors, interoperable algorithms, and trustworthy user experience
    will matter more than marginal gains in accuracy.

References & Further Resources

Key Trials To Be Published Soon

  • DIETEC trial – Inpatient CGM vs BGM in T2D (TIR and complications).
  • LENNY trial – MiniMed 780G in children aged 2–6 years.
  • Pregnancy AID trials – CamAPS FX and Control-IQ vs MiniMed 780G.
  • Control-IQ long-term real-world outcomes (12–24 months).
  • French nationwide AID registry – durability and population-level outcomes.
  • CGM variability phenotype study (five metabolic subgroups).

The Glucose Never Lies — Where to Go Next

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