Can We Detect Metabolic Dysfunction Before Prediabetes?
Introduction
Prediabetes is often described as an early warning sign for type 2 diabetes. But metabolically, it may not be particularly early.
By the time fasting glucose reaches 100 mg/dL, HbA1c reaches 5.7%, or a 2-hour oral glucose tolerance test becomes abnormal, the body may already have spent years adapting to declining insulin sensitivity. Pancreatic beta cells can compensate by increasing insulin secretion, the liver and skeletal muscle can become progressively less responsive to insulin, and changes in lipid, amino-acid, and energy metabolism may develop while glucose remains within conventional reference ranges. Hyperglycemia can therefore represent a relatively visible stage of a process that began much earlier.
This creates an important problem for metabolic screening. Fasting glucose and HbA1c primarily tell us when glucose regulation has become abnormal; they do not necessarily tell us when the metabolic process leading toward that abnormality began. A person can maintain apparently normal glucose through compensatory hyperinsulinemia for a considerable period, while another may develop post-meal glucose abnormalities that are poorly represented by fasting measurements.
A new prospective study raises the possibility that another blood marker could provide information during this earlier metabolic period. Researchers measured α-hydroxybutyrate (α-HB, also called 2-hydroxybutyrate or 2-HB) in participants from the Swedish PAROKRANK cohort and followed them for nearly a decade. α-HB concentrations increased across worsening categories of glucose regulation, and higher baseline levels predicted future type 2 diabetes even after adjustment for conventional risk factors including BMI and fasting HbA1c. Among the 1,224 participants with long-term follow-up, 134 developed type 2 diabetes over a median 9.95 years. Each log-unit increase in α-HB was associated with a 78% higher adjusted risk of incident type 2 diabetes.
That does not mean α-HB can diagnose diabetes ten years before it happens, nor does it establish the biomarker as a replacement for fasting glucose, HbA1c, insulin testing, or the oral glucose tolerance test. What makes the finding interesting is the biology behind it. α-HB is connected to amino-acid metabolism, hepatic glutathione production, cellular redox balance, and insulin resistance, potentially providing a biochemical signal of metabolic stress that is different from simply measuring circulating glucose.
The larger question is therefore more important than any single biomarker: How early can we detect metabolic dysfunction before glucose visibly begins to fail? Understanding that timeline may eventually allow metabolic risk to be recognized not only when someone crosses the threshold into prediabetes, but while the body is still compensating to keep glucose looking normal.
🎧 Listen to the Episode: The Biomarker That Sees Diabetes Coming
Fasting glucose and HbA1c tell us a great deal about glucose regulation—but they don't necessarily reveal how hard the body is working to keep those numbers normal. Years of compensatory hyperinsulinemia and metabolic stress can develop before conventional thresholds finally identify prediabetes or type 2 diabetes.
In this episode of The Health Pulse, we explore alpha-hydroxybutyrate (α-HB), an emerging metabolomic marker linked to insulin resistance, oxidative stress, glutathione metabolism, and cellular redox balance. We examine why researchers are interested in it, what the existing evidence suggests, and why promising early-detection biomarkers still require careful validation before becoming routine clinical tools.
▶️ Click play below to listen, or keep reading to discover what this small metabolite may reveal about the metabolic changes happening long before blood sugar finally sounds the alarm.
Type 2 Diabetes Usually Begins Before Blood Sugar Looks Diabetic
Type 2 diabetes rarely appears because glucose regulation suddenly fails. In most cases, it develops through a prolonged interaction between insulin resistance and pancreatic beta-cell compensation. As skeletal muscle, liver, and adipose tissue become less responsive to insulin, the pancreas can compensate by secreting more of it. As long as that response is sufficient, fasting glucose and HbA1c may remain normal or only mildly elevated despite substantial underlying metabolic dysfunction.
This creates a compensated phase in which normal glucose does not necessarily mean normal insulin physiology. Skeletal muscle may require progressively more insulin to dispose of glucose, hepatic insulin resistance can impair suppression of glucose production, and adipose insulin resistance can increase fatty-acid release into the circulation. Meanwhile, higher insulin secretion helps prevent those abnormalities from immediately appearing as overt hyperglycemia. Eventually, if beta-cell compensation becomes inadequate relative to the degree of insulin resistance, post-challenge glucose and later fasting glucose can begin to rise. The transition toward prediabetes therefore reflects not simply the appearance of insulin resistance, but an increasing inability to compensate for it.
Longitudinal human studies support this gradual trajectory. In the Whitehall II cohort, metabolic changes associated with diabetes could be detected years before diagnosis. Participants who eventually developed type 2 diabetes showed worsening insulin sensitivity and compensatory changes in insulin secretion well before the sharp deterioration in glucose regulation occurring closer to diagnosis. The precise trajectory differs among individuals, but the study illustrates why the date someone crosses a diagnostic glucose threshold should not be confused with the date their metabolic dysfunction began.
This also explains why different biomarkers can provide different information during the same disease process. Fasting insulin can provide evidence of compensatory insulin demand; triglycerides and HDL-C can reflect aspects of insulin-resistant lipid metabolism; an OGTT can expose abnormal glucose handling that fasting measurements miss; and HbA1c summarizes longer-term glycemic exposure. None is a perfect clock for diabetes progression, and they do not necessarily become abnormal in the same order in every person.
α-HB is interesting precisely because it may provide another window into this compensated period. Instead of measuring glucose itself, it reflects metabolic pathways associated with insulin resistance, amino-acid metabolism, and cellular redox balance. To understand why that might reveal risk before overt hyperglycemia, we first need to understand where α-HB comes from and why its concentration rises during metabolic stress.
What Is α-Hydroxybutyrate?
α-Hydroxybutyrate (α-HB), also called 2-hydroxybutyrate or 2-HB, is a small metabolite produced during normal intermediary metabolism. It is not a glucose molecule, an insulin measurement, or a ketone body like beta-hydroxybutyrate. Its concentration can rise when several metabolic pathways become more active, particularly pathways involving amino-acid metabolism, glutathione synthesis, and the cellular balance between oxidation and reduction.
One important source begins with the sulfur-containing amino acid methionine. Through the transsulfuration pathway, homocysteine can ultimately contribute to cysteine production. Cysteine is required for synthesis of glutathione, one of the body's major intracellular antioxidant systems. During this pathway, cystathionine is converted into cysteine while producing α-ketobutyrate (α-KB). When demand for glutathione synthesis increases—as can occur during greater oxidative or metabolic stress—flux through this pathway may increase, producing more α-KB.
α-KB can then be reduced to α-HB by lactate dehydrogenase, using NADH and producing NAD⁺. This reaction connects α-HB with another important aspect of metabolism: the cellular NADH/NAD⁺ redox state. When NADH availability is increased, conversion of α-KB toward α-HB can become more favorable. α-HB may therefore rise through a combination of increased α-KB production and changes in cellular redox metabolism rather than through a single disease-specific pathway.
That distinction is important because α-HB is not a diabetes-specific molecule. Elevated concentrations should not be interpreted as proof that someone has insulin resistance or will inevitably develop diabetes. Instead, metabolomic studies have repeatedly found α-HB associated with insulin resistance and impaired glucose regulation. Earlier work using hyperinsulinemic-euglycemic clamp measurements—the research reference method for assessing insulin sensitivity—identified α-HB among metabolites strongly associated with insulin resistance, including in individuals whose glucose had not yet reached diabetic levels.
This gives α-HB a different biological meaning from HbA1c. HbA1c asks what glucose exposure has been over recent months; α-HB may provide information about metabolic pathways that become disturbed as insulin resistance and oxidative/redox stress develop. That does not automatically make it a better biomarker, but it raises an intriguing possibility: metabolic changes associated with future diabetes may leave a measurable biochemical signal before conventional glucose markers clearly reveal the problem.
Why α-HB Rises With Insulin Resistance
The association between α-HB and insulin resistance appears to reflect several overlapping metabolic changes rather than one isolated pathway. As insulin sensitivity declines, the body does not simply develop higher glucose. Fuel handling changes across the liver, skeletal muscle, and adipose tissue, altering fatty-acid availability, amino-acid metabolism, mitochondrial substrate processing, and cellular redox balance. α-HB appears to sit at the intersection of several of these processes.
One proposed mechanism involves increased oxidative stress and glutathione demand. Glutathione helps maintain cellular redox balance by neutralizing reactive species and participating in detoxification reactions. Greater demand for glutathione can increase flux through the transsulfuration pathway, generating more α-ketobutyrate as cysteine is produced. α-ketobutyrate can subsequently be converted into α-HB. In this model, rising α-HB partly reflects an adaptive response to increased oxidative and metabolic pressure rather than being a molecule that directly causes insulin resistance.
A second mechanism involves the NADH/NAD⁺ redox couple. The conversion of α-ketobutyrate to α-HB consumes NADH and regenerates NAD⁺, making α-HB production sensitive to cellular redox conditions. Insulin-resistant states are often accompanied by increased fatty-acid delivery and oxidation, particularly in the liver. Processing these fuels generates reducing equivalents such as NADH. When NADH production exceeds the rate at which mitochondrial oxidative metabolism can efficiently reoxidize it, the redox environment shifts, potentially favoring reactions that regenerate NAD⁺—including formation of α-HB.
This helps explain why α-HB can become informative before fasting glucose becomes obviously abnormal. During compensated insulin resistance, the pancreas may still produce enough insulin to keep glucose within conventional limits, while hepatic fuel handling, amino-acid metabolism, oxidative stress, and redox physiology have already changed. A metabolite reflecting those processes could therefore carry information that glucose alone does not yet reveal.
Importantly, this remains a biomarker association rather than proof that α-HB drives diabetes. Elevated α-HB is better understood as a metabolic signal produced within pathways that become altered during insulin resistance. The clinically relevant question is therefore not whether α-HB causes type 2 diabetes, but whether measuring that signal can reliably identify people whose metabolism is already moving toward dysglycemia. That is the question the long-term PAROKRANK study was designed to explore.
What the 10-Year Study Found
The PAROKRANK study provides the strongest reason to pay attention to α-HB because it asked a prospective question: could a single baseline α-HB measurement identify people more likely to develop type 2 diabetes years later? Investigators analyzed 1,349 Swedish participants without previously known diabetes who underwent a standardized 2-hour OGTT and α-HB measurement at baseline. Participants were classified across the glycemic spectrum, from normoglycemia through impaired fasting glucose, impaired glucose tolerance, and diabetes. α-HB showed a stepwise increase as glucose regulation worsened, suggesting that higher concentrations tracked with progressively greater metabolic dysfunction. PubMed
Long-term outcome data showed why that association was more than a cross-sectional observation. Over a median 9.95 years of follow-up, 134 participants developed type 2 diabetes. In the unadjusted analysis, each one-unit increase in log-transformed α-HB was associated with nearly twice the risk of developing diabetes (HR 1.99; 95% CI 1.32–3.01). Importantly, the association persisted after adjustment for conventional clinical variables, including BMI and fasting HbA1c: the adjusted hazard ratio remained 1.78 (95% CI 1.19–2.67). In other words, α-HB appeared to contain information about future diabetes risk that was not completely captured by body size or baseline glycemia. PubMed
The investigators also tested whether adding α-HB actually improved the clinical model rather than merely producing a statistically significant association. Adding the biomarker resulted in a significant improvement in net reclassification (NRI 0.23; p=0.01), meaning α-HB helped reclassify some participants' long-term risk beyond the conventional variables included in the model. That is encouraging for a potential risk biomarker, although improved statistical prediction does not automatically mean that testing improves patient outcomes. PubMed
Another 2026 study provides an interesting complement to these long-term findings. In Diabetes Care, researchers measured enzymatic 2-HB in 772 individuals. Concentrations increased from a median 39.0 μmol/L in normoglycemia to 51.9 μmol/L in prediabetes and 57.0 μmol/L in type 2 diabetes. More strikingly, among 534 people whose fasting glucose was below 110 mg/dL and HbA1c below 5.7%, 2-HB still discriminated individuals with abnormal 2-hour glucose during an OGTT, producing an AUROC of 0.79. Each 1-SD increase in 2-HB was associated with more than twice the odds of dysglycemia (OR 2.23; p<0.001). Diabetes Journals
Together, these studies point toward two potentially useful roles for α-HB: identifying post-challenge dysglycemia that conventional fasting measurements may miss and contributing information about long-term diabetes risk. But neither establishes α-HB as a replacement for established testing. Current ADA diagnostic criteria remain based on HbA1c and plasma glucose measurements, including fasting glucose and the 2-hour OGTT. α-HB is not currently part of those diagnostic criteria. Diabetes Journals
The distinction is crucial. The evidence does not show that an elevated α-HB means someone will develop diabetes within ten years. It shows that α-HB behaves as a risk marker associated with metabolic dysfunction and future diabetes. Whether measuring it routinely—and intervening on the basis of that result—actually prevents diabetes remains a separate question that prospective intervention trials would need to answer.
How Does α-HB Compare With Fasting Insulin, HbA1c, and the OGTT?
α-HB becomes most useful conceptually when it is not treated as a competitor to established metabolic tests. These biomarkers measure different parts of the same evolving process. HbA1c and glucose describe glycemic regulation, insulin provides information about the compensatory response required to maintain that glucose, an OGTT tests the system dynamically, and α-HB reflects metabolic pathways associated with insulin resistance and altered redox metabolism. None provides a complete picture by itself.
Fasting insulin can reveal compensatory hyperinsulinemia while glucose is still relatively normal. When interpreted alongside fasting glucose, it can also be used to calculate HOMA-IR as a surrogate measure of insulin resistance. This makes fasting insulin particularly useful for understanding how much insulin appears to be required to maintain fasting glucose. Its limitations are equally important: insulin assays are not fully standardized, there is no universally accepted diagnostic cutoff for fasting insulin or HOMA-IR across populations, and a fasting measurement provides limited information about the response to a glucose challenge.
HbA1c answers a different question. Because hemoglobin becomes glycated in proportion to circulating glucose exposure, HbA1c provides an integrated estimate of glycemia over approximately the previous two to three months, with greater influence from more recent glucose exposure. It is convenient, does not require fasting, and is well validated for diagnosing and monitoring diabetes. But HbA1c becomes abnormal because glucose has become abnormal; it does not directly measure insulin resistance or the compensatory insulin secretion that may precede overt hyperglycemia.
The OGTT provides a dynamic stress test. After a standardized 75-g glucose load, glucose measurements—classically at two hours—can reveal impaired glucose tolerance that fasting glucose or HbA1c may miss. Measuring insulin during the OGTT can provide additional physiological information about insulin secretion and insulin sensitivity, although insulin-based interpretation is less standardized for routine diagnosis. The disadvantage is practicality: an OGTT requires preparation, fasting, a glucose drink, timed measurements, and substantially more time than a single blood draw.
α-HB potentially occupies another position. Rather than directly measuring glucose or insulin, it reflects metabolic pathways that appear to change alongside insulin resistance and dysglycemia. The recent studies are particularly interesting because α-HB added predictive information beyond conventional variables and identified post-challenge dysglycemia in some people whose fasting glucose and HbA1c remained below commonly used thresholds. That makes it a potentially useful complementary biomarker, not evidence that established tests should be abandoned.
The broader lesson is that metabolic dysfunction does not produce one universal laboratory sequence. Some people develop fasting hyperglycemia first, others predominantly develop postprandial abnormalities, and others may maintain apparently normal glucose through substantial compensatory insulin secretion. Triglycerides, HDL-C, liver enzymes, fasting insulin, α-HB, glucose, HbA1c, and OGTT results therefore represent different windows into an evolving metabolic phenotype rather than steps on a rigid timeline. The challenge for future research is determining which combination provides enough additional information to change clinical decisions and, ultimately, improve outcomes.
Could α-HB Become a Routine Metabolic Test?
Until recently, one practical limitation of α-HB was that measuring it generally required metabolomic platforms such as mass spectrometry, making it far less accessible than glucose, HbA1c, or a conventional lipid panel. That is beginning to change. Enzymatic assays have now been developed that can measure α-HB using routine clinical chemistry workflows, potentially making the biomarker easier and less expensive to deploy at scale.
That transition became more relevant in September 2026, when Precision Diabetes and DirectSens announced a U.S. commercialization partnership for the MetMark α-HB test, using DirectSens' XpressGT enzymatic technology. The development is important from an accessibility standpoint because it moves α-HB closer to the type of testing that could potentially be performed through conventional laboratory infrastructure rather than specialized metabolomic analysis. However, commercial availability should not be confused with established clinical utility. The announcement demonstrates that the assay can enter clinical laboratory workflows; it does not establish that routine α-HB screening improves diabetes prevention or patient outcomes. (prnewswire.com)
Several questions still need answers before α-HB could reasonably become part of routine metabolic screening. Researchers need to determine which populations benefit most from testing, what thresholds should trigger concern, how biological and analytical variability affect interpretation, whether α-HB meaningfully improves prediction over inexpensive existing markers, and—most importantly—whether acting on an elevated result changes outcomes. A biomarker can significantly improve a statistical prediction model without necessarily improving clinical decision-making.
There is also the question of what an elevated α-HB would actually change. If a person already has obesity, elevated fasting insulin, high triglycerides, impaired fasting glucose, or abnormal glucose tolerance, α-HB may add information without fundamentally changing the need to address metabolic risk. Its greatest potential value may therefore lie in people whose conventional glucose markers still appear reassuring but whose underlying metabolism is beginning to deteriorate. The recent finding that 2-HB identified post-challenge dysglycemia among some people with fasting glucose below 110 mg/dL and HbA1c below 5.7% makes this possibility particularly interesting.
For now, α-HB belongs in a promising but still developing category: a biomarker with prospective human evidence, plausible metabolic biology, and increasing laboratory accessibility, but without enough evidence to replace established screening strategies or become a universal recommendation. The technology is moving faster than the clinical guidelines, which makes α-HB worth watching—but also makes careful interpretation especially important.
How Lab Testing Can Detect Metabolic Dysfunction Earlier
The practical lesson from α-HB research is not that everyone should immediately add another biomarker to their laboratory panel. It is that metabolic dysfunction can be examined from several physiological angles before overt diabetes develops. Glucose-based tests remain essential, but glucose is only one part of the process. Insulin resistance, compensatory insulin secretion, lipid abnormalities, hepatic metabolism, post-challenge glucose handling, and eventually beta-cell dysfunction can all provide information about where someone sits along the metabolic continuum.
A basic assessment can begin with fasting glucose and HbA1c, interpreted alongside triglycerides, HDL-C, blood pressure, waist circumference, body composition, family history, and other clinical risk factors. Adding fasting insulin can sometimes reveal that apparently acceptable glucose is being maintained with a substantial insulin response, although fasting insulin and HOMA-IR do not have universally accepted diagnostic cutoffs. Liver enzymes and the broader lipid profile can provide additional context when metabolic dysfunction or fatty liver is suspected. These measurements are not substitutes for one another; they answer different questions.
When the clinical picture and routine laboratory results do not agree, dynamic testing can become particularly informative. A person may have fasting glucose and HbA1c below prediabetes thresholds yet experience substantial glucose elevations after a glucose challenge or meals. A 75-g OGTT can identify impaired glucose tolerance that fasting measurements miss, while insulin measurements during the test can provide additional information about the compensatory response. α-HB may eventually complement this approach by identifying metabolic alterations associated with insulin resistance and post-challenge dysglycemia without requiring a multi-hour glucose challenge, but the evidence is not yet strong enough to treat it as an equivalent replacement.
This is where laboratory testing should remain individualized. Someone with an HbA1c of 6.2%, elevated triglycerides, central adiposity, and hypertension already has substantial evidence of metabolic risk; discovering an elevated α-HB may not meaningfully change the immediate strategy. In contrast, a person with apparently normal fasting glucose and HbA1c but strong risk factors, unexplained post-meal glucose excursions, or other evidence suggesting insulin resistance may benefit from a more detailed evaluation. The value of additional testing depends on whether the result answers a meaningful clinical question and changes what happens next.
For patients in Miami, QuickLab Mobile provides at-home collection for many established metabolic markers, including glucose, HbA1c, insulin, C-peptide, lipid testing, liver markers, and other cardiometabolic measurements. As newer biomarkers such as α-HB move toward broader clinical availability, the same principle should apply: new technology is most useful when it adds meaningful information to established physiology—not simply because another number can be measured.
Conclusion
Prediabetes is considered an early stage of abnormal glucose regulation, but the metabolic changes that eventually produce it can begin much earlier. Insulin resistance, compensatory hyperinsulinemia, altered lipid and amino-acid metabolism, changes in hepatic fuel handling, and declining beta-cell compensation can develop while fasting glucose and HbA1c remain below diagnostic thresholds. By the time glucose becomes visibly abnormal, the underlying metabolic process may already have been developing for years.
α-HB is interesting because it approaches this problem from a different direction. Rather than measuring glucose itself, it reflects metabolic pathways connected with amino-acid metabolism, glutathione synthesis, redox balance, and insulin resistance. In the PAROKRANK cohort, higher baseline α-HB predicted incident type 2 diabetes over nearly ten years even after adjustment for conventional risk factors including BMI and fasting HbA1c. Separate 2026 research also found that 2-HB could identify post-challenge dysglycemia among some individuals whose fasting glucose and HbA1c were still below commonly used prediabetes thresholds.
Those findings are promising, but they do not mean α-HB can diagnose diabetes ten years in advance. Prediction is not diagnosis. α-HB is not currently part of standard ADA diagnostic criteria, and we still need evidence showing that routinely measuring the biomarker—and changing treatment based specifically on the result—improves clinical outcomes. Commercial availability of an enzymatic assay makes that research easier and potentially more relevant, but accessibility alone does not establish clinical utility.
The larger lesson extends beyond α-HB. Fasting glucose, HbA1c, fasting insulin, triglycerides and HDL-C, C-peptide, the OGTT, and emerging metabolites provide different windows into metabolic physiology. They should not be arranged into a rigid sequence or interpreted independently. The most useful testing strategy is one that combines appropriate biomarkers with clinical context to determine whether metabolic regulation is beginning to deteriorate and whether that information changes management.
If α-HB ultimately proves useful in routine practice, its greatest contribution may not be identifying diabetes earlier. It may be helping identify metabolic dysfunction while the body is still successfully preventing diabetes from becoming visible in the glucose numbers.
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