Insulin Resistance Markers: What New Studies Show

What are insulin resistance markers and why do researchers measure them?

Insulin resistance markers are measurable signals in blood that tell researchers how well the body's cells are responding to insulin. Researchers track them because changes in those signals can appear years before a formal diabetes diagnosis, making them useful early indicators in clinical studies.

Insulin is a hormone made by the pancreas. Its job is to move glucose (sugar) from the bloodstream into cells for energy. When cells stop responding well to insulin, the pancreas compensates by making more. That extra insulin, and the glucose that stays elevated in the blood, are what researchers measure.

The most common markers used in studies include:

• Fasting glucose — blood sugar measured after an overnight fast; a straightforward snapshot of baseline glucose control
• Fasting insulin — the amount of insulin circulating when no food has been eaten recently; high levels suggest the pancreas is working harder than expected
• HOMA-IR (Homeostatic Model Assessment of Insulin Resistance) — a calculated score using fasting glucose and fasting insulin together; a higher number points toward greater insulin resistance
• HbA1c (glycated hemoglobin) — reflects average blood glucose over roughly three months; one prospective study used HbA1c alongside time-in-range data to track glucose control changes in people using insulin therapy
• C-peptide — a byproduct released when the pancreas makes insulin; researchers use it to estimate how much insulin the body is still producing on its own, as seen in a trial measuring beta-cell function in people with type 1 diabetes

Researchers also study newer, less familiar proteins. A case-control study on gestational diabetes measured serum isthmin-1 — a protein secreted by fat tissue — and found elevated levels in women in that study who had gestational diabetes, with a statistically significant association with HOMA-IR scores. The researchers described this as preliminary evidence, not a confirmed clinical tool.

Why does any of this matter for reading peptide research? Peptide medicines that affect glucose metabolism are often evaluated using exactly these markers. Knowing what each one measures — and what it cannot tell you — helps you read a study's conclusions accurately rather than taking a headline at face value.

One honest caveat: a marker improving in a trial does not automatically mean a treatment is safe or effective for any individual. Populations in studies vary by age, reproductive status, metabolic history, and other factors. The trial did not establish outcomes for people outside its specific enrollment criteria.

This content is for educational purposes only and does not constitute medical advice. Consult a qualified healthcare provider before making any decisions about your care.

What did the isthmin-1 study find in women with gestational diabetes?

A 2025 case-control study found that women in this study who had gestational diabetes mellitus showed significantly higher serum isthmin-1 levels than pregnant women without the condition, and those elevated levels correlated with insulin resistance markers including HOMA-IR, fasting insulin, and fasting glucose. One study. One snapshot. But it raises a concrete question worth understanding.

What isthmin-1 is

Isthmin-1 is a protein your body produces naturally. Researchers have been studying it because it appears to interact with fat tissue and glucose regulation. It is not a medicine you take; it is something the body makes, and scientists are trying to understand what changing levels might signal.

What the study actually measured

The case-control study compared serum (blood) isthmin-1 in two groups of pregnant women: those diagnosed with gestational diabetes mellitus and those with normal glucose tolerance during pregnancy. Women in this study with gestational diabetes had higher circulating isthmin-1. The researchers also found a positive correlation between isthmin-1 and HOMA-IR — a calculated score that estimates insulin resistance — as well as with fasting insulin and fasting glucose levels.

What the study cannot tell us

Correlation is not causation. The study cannot establish whether elevated isthmin-1 drives insulin resistance, responds to it, or simply travels alongside it. The case-control design captures a moment; it does not follow women over time. The study did not establish whether isthmin-1 levels predict gestational diabetes before diagnosis, nor whether they normalize after delivery. Sample size and population characteristics matter here, and the authors describe their findings as preliminary evidence — their words, not a softening of certainty.

Why this matters for context

Gestational diabetes affects glucose regulation during pregnancy and carries implications for both the pregnant person and the pregnancy. Researchers are actively looking for biological signals — proteins, peptides, hormones — that might one day improve early detection or clarify underlying mechanisms. Isthmin-1 is one candidate among many at an early stage of investigation.

No clinical use for isthmin-1 measurement in gestational diabetes has been established. The study calls for larger, longitudinal research before any clinical conclusions can be drawn.

This section is for general health education only and is not medical advice. Speak with your obstetric or endocrine care team about your individual situation.

What happened to beta-cell function one year after stopping baricitinib?

Beta-cell function one year after stopping baricitinib declined meaningfully for most participants. The trial tracked insulin resistance markers alongside beta-cell output to understand the full metabolic picture. The short answer: the cells that make insulin lost ground once treatment stopped, but the degree of loss varied across individuals.

The trial (PMID 42627334) followed people with type 1 diabetes who had taken oral baricitinib — a JAK inhibitor, meaning a drug that dials down a specific immune signaling pathway — for two years, then stopped. One year after stopping, researchers measured how well beta cells (the insulin-producing cells in the pancreas) were still working.

Key findings from that follow-up year:

• C-peptide levels — a direct measure of how much insulin the body is making on its own — fell after baricitinib stopped. C-peptide is the clearest signal researchers have for beta-cell survival.
• The rate of decline after stopping was faster than the decline seen in the untreated comparison group during the original trial period, suggesting the drug had been actively slowing beta-cell loss while people took it.
• Participants who had higher C-peptide at the start of the trial tended to retain more function one year out, though the trial did not establish that baseline levels predict individual outcomes reliably.
• HbA1c — a measure of average blood sugar over roughly three months — worsened for many participants in this study after stopping, meaning blood sugar control became harder to maintain.

The trial did not establish whether restarting baricitinib would restore the function that was lost during the off-treatment year. That question remains open.

This was a specific population enrolled in a clinical trial; the results describe what happened to women and men in this study, not to everyone with type 1 diabetes. The published summary does not break out beta-cell outcomes separately by sex or by reproductive life stage, so sex-specific conclusions cannot be drawn from this data alone. Age, disease duration, and baseline beta-cell reserve all shaped individual trajectories more than any single factor.

The gap between stopping a drug and watching function decline is not unique to baricitinib — it reflects how immunotherapy works. The drug suppresses the immune attack on beta cells; when the drug leaves, the attack can resume. How fast that happens differs from person to person.

This content is for educational purposes only and is not medical advice. Speak with a qualified clinician before making any decisions about your care.

Does improving insulin injection timing change blood sugar outcomes?

Yes, improving insulin injection timing does change blood sugar outcomes — a prospective study found that people who used a connected insulin cap device to track and correct their injection timing saw meaningful improvements in several insulin resistance markers and glucometrics over 12 weeks.

The study, published in 2024 and cited here as the Insulclock v2.0 prospective study, enrolled adults with type 1 and type 2 diabetes who used a smart cap fitted to their existing insulin pen. The cap recorded the exact time of each injection and sent reminders when doses were late or missed. Researchers measured whether correcting those timing errors changed blood sugar control.

The trial found:

• Participants reduced the number of late or missed injections over the 12-week follow-up period, according to the Insulclock v2.0 prospective study.
• Time in range — the share of hours each day that blood glucose stayed within a target window — improved in the study group.
• HbA1c (a three-month average of blood sugar levels, sometimes called "the A1c") improved in participants who used the device consistently.

The trial did not establish that these effects apply equally across all life stages, sexes, or diabetes types. Women in this study were not analyzed as a separate subgroup in the published results, so the data cannot tell us whether the timing benefit was larger or smaller for women specifically, or whether it differed by reproductive stage, hormonal status, or age.

Insulin timing matters because the body's blood sugar rises after eating, and insulin is designed to meet that rise. When an injection comes too late, glucose peaks higher and stays elevated longer. The device closes the gap between when a dose should happen and when it actually does — a simple mechanism with measurable downstream effects on the numbers clinicians track.

One honest limitation: the Insulclock v2.0 prospective study was a single-arm prospective study, not a randomized controlled trial. That design cannot rule out other explanations for improvement, such as participants paying more attention to their diabetes management overall because they were being observed. Larger, controlled trials would clarify the picture.

This section is for general health education only and is not medical advice. Talk with your own clinician about insulin management, timing, and any devices that may be appropriate for your situation.

What are the biggest evidence gaps across these studies?

The biggest evidence gaps across these studies cluster around insulin resistance markers, sex-disaggregated data, and long-term follow-up — and they matter because gaps in research translate directly into gaps in clinical guidance for women at different life stages.

Start with what the studies measured and who they measured it in. The isthmin-1 case-control study found elevated serum isthmin-1 levels associated with insulin resistance markers in women with gestational diabetes, but the authors describe this as preliminary evidence from a single-center design (PMID 42629968). The finding needs replication in larger, more diverse populations before clinicians can act on it confidently. Small. One center. One pregnancy complication. That combination limits how far the result travels.

Several studies enrolled mixed-sex or pregnancy-specific populations without reporting outcomes separately by sex or reproductive status. The thyroid abnormality study in pregnant women examined ABO and RhD blood type associations but did not establish whether those patterns hold outside of pregnancy (PMID 42570668). The ELABELA peptide study measured maternal serum levels in threatened miscarriage between 8 and 14 weeks of gestation — a narrow window that tells us nothing about ELABELA's behavior in non-pregnant people or across trimesters (PMID 42595351).

Long-term data is thin across the board. The baricitinib immunotherapy trial tracked beta-cell function one year after stopping treatment (PMID 42627334). One year is a short window for a condition people manage across decades. The prolactinoma surgery cohort covered 20 years, which sounds long, but the authors note it is a single-center retrospective study, so selection effects can skew what looks like a clean outcome picture (PMID 42622769).

Age and life stage are underrepresented as variables. The resistance exercise and BDNF study focused on older adults but did not report whether sex or hormonal status modified the brain-derived neurotrophic factor response to training intensity (PMID 42572691). That gap matters because estrogen levels influence BDNF, and a postmenopausal woman and a woman in her reproductive years may respond differently to the same exercise dose.

The ropeginterferon alfa-2b meta-analysis pooled data across studies, which increases statistical power, but pooling can also obscure subgroup differences — including differences by sex, age, or disease duration — if the original trials did not collect or report those variables (PMID 42566639).

The connected insulin cap study improved glucometrics through better injection timing, but it did not isolate whether the benefit differed by sex, body composition, or insulin type (PMID 42528822).

The pattern across all eight sources is the same: promising signals, narrow populations, short follow-up, and limited sex- and life-stage-disaggregated reporting. That is not a reason to dismiss the findings — it is a reason to read them as early chapters, not conclusions.

This content is for general health education only and does not constitute medical advice. Consult a qualified healthcare provider before making any decisions about your care.