Why Accurate Glycemic Assessment Matters More Than Ever

Diabetes mellitus now affects over 530 million adults worldwide, and that number continues to climb. For clinicians managing this complex metabolic disorder, accurate assessment of glycemic control is the cornerstone of effective treatment. Without reliable data on a patient’s blood glucose patterns, treatment adjustments become guesswork, exposing patients to risks of both hypoglycemia and long-term complications. The stakes are high: poor glycemic control accelerates microvascular damage leading to retinopathy, nephropathy, and neuropathy, while also increasing cardiovascular risk.

For decades, the Hemoglobin A1c test has served as the primary metric for gauging glycemic control. Its convenience—a single blood draw with no fasting required—and its ability to reflect average glucose levels over approximately three months made it an indispensable tool in endocrinology and primary care alike. Yet as clinical experience has grown and technology has advanced, the limitations of relying on any single biomarker have become increasingly apparent.

A growing consensus among diabetes specialists supports a more nuanced approach: combining A1c with other biomarkers to build a complete picture of a patient’s glycemic status. This multi-biomarker strategy addresses the blind spots inherent in A1c testing alone and enables truly personalized diabetes management. Understanding when and how to deploy these complementary tools is essential for any clinician or patient serious about optimizing glycemic outcomes.

The Strengths and Weaknesses of Hemoglobin A1c

What A1c Actually Measures

Hemoglobin A1c forms when glucose in the bloodstream binds non-enzymatically to the N-terminal valine of the beta-chain of hemoglobin A. This glycation process occurs continuously throughout the 120-day lifespan of red blood cells, so the A1c value represents a weighted average of blood glucose levels over the preceding two to three months, with the most recent 30 days contributing disproportionately. In controlled settings, A1c correlates reasonably well with mean plasma glucose, and large-scale clinical trials such as the Diabetes Control and Complications Trial (DCCT) and the United Kingdom Prospective Diabetes Study (UKPDS) established its utility as both a marker of glycemic control and a predictor of complications.

The American Diabetes Association currently recommends A1c targets of less than 7 percent for most nonpregnant adults, though these targets are individualized based on age, comorbidities, diabetes duration, and hypoglycemia risk. A1c testing is typically performed two to four times per year, making it a practical tool for routine monitoring in clinical practice.

Known Limitations and Potential Sources of Error

Despite its widespread adoption, A1c has well-documented limitations that can lead to inaccurate interpretation. Any condition that alters red blood cell lifespan will affect A1c values independently of actual glucose levels. For example, hemolytic anemias, recent blood transfusion, or treatment with erythropoietin-stimulating agents all lower A1c by increasing the proportion of young red blood cells with less accumulated glycation. Conversely, iron deficiency anemia, which is common in diabetic populations, can falsely elevate A1c by decreasing red blood cell turnover. Splenectomy and conditions associated with increased red blood cell lifespan also produce spuriously high results.

Hemoglobin variants present another significant challenge. Patients with sickle cell trait, hemoglobin C trait, or other structural hemoglobinopathies may have A1c values that do not accurately reflect their glycemic status, depending on which assay method is used. Some immunoassays and affinity chromatography methods handle these variants better than others, but many laboratories still use methods that can produce erroneous results in these populations. For this reason, the National Glycohemoglobin Standardization Program (NGSP) has worked to improve assay standardization, but point-of-care tests and older laboratory methods remain vulnerable.

Additional factors that can confound A1c interpretation include advanced chronic kidney disease where carbamylated hemoglobin interferes with certain assays, severe hypertriglyceridemia, and pregnancy where both hemodilution and altered red blood cell turnover affect results. Race and ethnicity also appear to influence the relationship between A1c and mean glucose, with studies suggesting that African American individuals may have slightly higher A1c levels than Caucasian individuals at equivalent mean glucose concentrations, though the clinical significance of this remains debated.

Perhaps most importantly, A1c alone tells clinicians nothing about glycemic variability. Two patients with identical A1c values of 7.5 percent can have profoundly different glucose profiles: one may experience wide swings between dangerous hypoglycemia and severe hyperglycemia, while the other maintains relatively stable glucose levels throughout the day. A1c cannot distinguish between these scenarios, yet the clinical implications are entirely different. High glycemic variability itself is increasingly recognized as an independent risk factor for oxidative stress, endothelial dysfunction, and cardiovascular complications.

Key Complementary Biomarkers for a Complete Glycemic Picture

Fasting Plasma Glucose (FPG) and Postprandial Glucose

Fasting plasma glucose remains one of the simplest and most accessible complementary tests. Measured after at least eight hours without caloric intake, FPG reflects hepatic glucose output and baseline insulin sensitivity. When FPG is discordant with A1c, it may signal conditions that interfere with A1c accuracy or indicate that the patient's hyperglycemia is predominantly postprandial. For example, a patient with an A1c of 7.2 percent but a normal FPG of 95 mg/dL likely experiences most hyperglycemia after meals, suggesting a different therapeutic approach than a patient with an elevated FPG and similar A1c. Paired with A1c, FPG helps clinicians distinguish between basal and prandial hyperglycemia and guides the selection of medications targeting fasting versus post-meal glucose excursions.

Self-monitored blood glucose (SMBG) readings, including both fasting and postprandial measurements, add further granularity. Though not a single biomarker, systematic glucose monitoring contributes essential data that A1c alone cannot provide. Postprandial glucose spikes, in particular, have been linked to increased cardiovascular risk and may require specific interventions such as prandial insulin or alpha-glucosidase inhibitors that would not be indicated based on A1c alone.

Fructosamine and Glycated Albumin: Shorter-Term Windows

For clinical situations where A1c is unreliable or where shorter-term monitoring is needed, fructosamine and glycated albumin offer valuable alternatives. Fructosamine measures the total concentration of glycated serum proteins, primarily albumin, reflecting glycemic control over the preceding two to three weeks. Glycated albumin specifically measures the fraction of albumin that has undergone glycation and provides a similar time window.

These tests are particularly useful in conditions that alter red blood cell turnover, such as hemolytic anemias, hemoglobinopathies, and end-stage renal disease where anemia is prevalent. They also have applications in pregnancy where the rapid physiologic changes and trimester-specific glycemic targets make A1c less reliable. In gestational diabetes management, glycated albumin may detect glucose excursions more rapidly than A1c, allowing for timelier adjustments to insulin therapy. Additionally, these markers can be used to monitor short-term responses to new treatment regimens, providing feedback within weeks rather than waiting for the three-month A1c window.

However, fructosamine and glycated albumin have their own limitations. Conditions that alter albumin concentration or turnover, such as nephrotic syndrome, liver disease, or thyroid dysfunction, will affect results. They also lack the extensive outcomes data that exist for A1c, making it difficult to establish clear treatment targets linked to long-term complication risk. Despite these drawbacks, they serve as indispensable tools in specific clinical scenarios.

1,5-Anhydroglucitol (1,5-AG): A Marker of Glycemic Excursions

1,5-anhydroglucitol is a monosaccharide found in relatively stable concentrations in the serum under normal conditions. It competes with glucose for renal tubular reabsorption, so when blood glucose exceeds the renal threshold of approximately 180 mg/dL, 1,5-AG is excreted in the urine and serum levels fall. Therefore, serum 1,5-AG concentration inversely correlates with the frequency and magnitude of hyperglycemic excursions over the preceding one to two weeks.

This biomarker provides a unique dimension of glycemic assessment that A1c cannot capture. While A1c reflects average glucose, 1,5-AG specifically detects periods of hyperglycemia above the renal threshold. In patients with well-controlled A1c values but persistent postprandial hyperglycemia, 1,5-AG will be low, alerting clinicians to the need for targeting post-meal glucose spikes. This is particularly relevant for patients on basal-only insulin regimens who may experience unrecognized prandial hyperglycemia that contributes to long-term complication risk without severely elevating A1c.

1,5-AG is most useful in patients with A1c values below 8.0 percent, as above this threshold the test floor effects limit its discriminating ability. It is also affected by advanced renal disease where the renal threshold for glucose excretion is altered, limiting its utility in patients with significant nephropathy. Because it reflects only hyperglycemia, it provides no information about hypoglycemia, which must be assessed through other means.

Continuous Glucose Monitoring (CGM) Data: A Paradigm Shift

Continuous glucose monitoring represents arguably the most significant advance in glycemic assessment since the introduction of A1c. CGM devices measure interstitial glucose levels every one to five minutes, generating a continuous stream of data that reveals the full complexity of a patient's glycemic patterns. The information obtained from CGM goes far beyond what any single laboratory biomarker can provide, offering insights into both hyperglycemia and hypoglycemia, glycemic variability, and the effects of meals, exercise, medications, and stress on glucose levels in daily life.

Standardized CGM metrics have now been established by international consensus, including time in range (TIR; glucose 70-180 mg/dL), time above range, time below range, mean glucose, glycemic variability measured as coefficient of variation, and the glucose management indicator (GMI), which estimates A1c from CGM data. These metrics provide actionable information for clinical decision-making that complements and enriches the understanding gained from A1c. For example, a patient with an A1c of 7.0 percent but only 50 percent time in range and 10 percent time below range requires an entirely different therapeutic strategy than a patient with the same A1c but 80 percent time in range and no hypoglycemia.

Importantly, CGM data can be used to identify causes of A1c discordance. A patient with an elevated A1c but normal mean glucose on CGM may have a condition that falsely elevates A1c, while a patient with a normal A1c but frequent hyperglycemic excursions on CGM may have a high A1c-independent risk of complications. CGM also provides immediate feedback to patients, enabling real-time adjustments to behavior and treatment that improve engagement and outcomes.

The adoption of CGM has accelerated dramatically as devices have become more accurate, less expensive, and easier to use. Current guidelines from professional organizations recommend CGM for patients with type 1 diabetes and those with type 2 diabetes on intensive insulin regimens, and its use is expanding to patients with non-insulin-treated type 2 diabetes and even prediabetes. As CGM technology continues to improve and reimbursement expands, it is increasingly becoming a standard component of comprehensive glycemic assessment rather than a specialized tool reserved for complex cases.

The Clinical Benefits of a Multi-Biomarker Approach

Improved Accuracy and Fewer Diagnostic Errors

Relying on a single biomarker inevitably misses cases where that biomarker is misleading. When A1c is combined with FPG, fructosamine, or CGM data, discrepancies become visible and can be investigated. Studies have estimated that between 10 and 20 percent of A1c results may be misleading due to interfering factors, and many of these cases go unrecognized when A1c is used in isolation. By routinely incorporating a second biomarker, clinicians can identify these cases and avoid inappropriate treatment intensification that could lead to hypoglycemia, or conversely, avoid undertreatment when A1c is falsely low.

In populations with high prevalence of hemoglobinopathies, iron deficiency, or chronic kidney disease, the value of complementary biomarkers is even greater. For example, a patient with sickle cell trait and diabetes might have a persistently low A1c despite clear evidence of hyperglycemia on glucose monitoring. Without a high index of suspicion and access to alternative biomarkers, this patient could be undertreated for years, accumulating diabetic complications. Multi-biomarker assessment provides a safety net against such diagnostic blind spots.

Better Detection of Hypoglycemia and Glycemic Variability

Hypoglycemia remains a major barrier to achieving glycemic targets and a significant cause of morbidity and mortality in diabetes. A1c alone provides no information about hypoglycemia, yet even a single episode of severe hypoglycemia can have devastating consequences. CGM data reveals hypoglycemia patterns that are frequently missed by self-monitoring of blood glucose, particularly nocturnal hypoglycemia and asymptomatic hypoglycemia. When combined with A1c, CGM metrics enable a balanced approach that simultaneously addresses hyperglycemia and hypoglycemia, rather than focusing on average glucose at the expense of safety.

Similarly, glycemic variability has emerged as an important independent predictor of complications and quality of life. Patients with high variability experience more symptoms, greater emotional burden, and potentially higher oxidative stress and inflammation. The coefficient of variation derived from CGM data provides a direct measure of variability that complements A1c-based assessment. By targeting both A1c and variability metrics, clinicians can work toward glycemic control that is not only adequate on average but also stable and predictable.

Personalized Treatment Strategies Based on Phenotype

Different patients achieve similar A1c values through different glucose patterns, and these patterns call for different therapeutic approaches. A patient with elevated FPG and relatively stable glucose throughout the day will respond well to basal insulin or medications targeting hepatic glucose output, such as metformin. Conversely, a patient with normal FPG but significant postprandial hyperglycemia and high glycemic variability may benefit more from prandial insulin, GLP-1 receptor agonists, or alpha-glucosidase inhibitors. Without detailed information about the pattern of hyperglycemia, clinicians must guess which strategy is most appropriate, leading to trial-and-error prescribing that delays achieving control and frustrates patients.

Multi-biomarker assessment enables phenotype-based medicine. By characterizing a patient's glycemic profile across multiple dimensions—fasting versus postprandial, average versus variability, hyperglycemia versus hypoglycemia risk—clinicians can match treatments to the specific physiology of each patient. This represents a move away from the one-size-fits-all approach to glycemic targets and toward truly individualized diabetes management.

Enhanced Patient Engagement and Shared Decision-Making

Patients who see their glucose data in real time, whether through CGM displays or structured SMBG records, tend to be more engaged in their own care. Multi-biomarker assessment provides richer, more detailed information that patients can understand and act on. A patient who sees that their time in range improves after dietary changes or specific medication adjustments gains confidence and motivation. The immediate feedback loop created by CGM, combined with periodic A1c testing that validates progress, reinforces positive behaviors and helps patients develop a sense of ownership over their glycemic control.

Furthermore, shared decision-making is enhanced when both clinician and patient have access to comprehensive data. Rather than discussing a single number, the conversation can focus on patterns, trends, and specific actionable adjustments. Patients feel heard when their experiences—such as symptoms of hypoglycemia or frustration with post-meal spikes—are validated by the data. This collaborative approach builds trust and adherence, which are essential for long-term diabetes management.

Practical Implementation in Clinical Practice

Choosing the Right Combinations for Each Patient

No single multi-biomarker protocol fits all patients. The choice of which biomarkers to combine depends on the clinical question, patient characteristics, and available resources. For a patient with type 2 diabetes on metformin alone with stable A1c in the 6.5 to 7.5 percent range, adding FPG and perhaps a periodic assessment of postprandial glucose may be sufficient. For a patient with type 1 diabetes using multiple daily injections or an insulin pump, CGM is nearly essential for safe management, and combining CGM metrics with A1c provides a comprehensive picture. For a pregnant patient with gestational diabetes, glycated albumin or fructosamine may be preferred over A1c, supplemented by regular glucose monitoring.

For patients with conditions that interfere with A1c accuracy, such as advanced CKD or known hemoglobinopathies, alternative biomarkers should be used as the primary metric rather than supplements. In these cases, glycated albumin or CGM-derived metrics may replace A1c as the main tool for longitudinal monitoring, with A1c used cautiously or abandoned entirely if unreliable.

Interpreting Discordant Results

When biomarkers give conflicting information, the disagreement itself is informative. A high A1c but normal CGM mean glucose suggests that the A1c may be falsely elevated due to red blood cell factors, iron deficiency, or assay interference. A normal A1c but low 1,5-AG indicates significant hyperglycemic excursions above the renal threshold, even if average glucose is acceptable. Discordance between A1c and FPG may signal that the patient experiences most hyperglycemia postprandially or that the A1c is being diluted by frequent hypoglycemic episodes that bring down the average.

Resolving discordance requires systematic investigation. Reviewing the patient's medication adherence, dietary patterns, physical activity, and recent acute illnesses can provide context. Repeating the A1c with a different assay method or at a different laboratory may help rule out interference. If discordance persists and clinical suspicion suggests that one biomarker is accurate and another is not, the decision of which to trust should be based on which best predicts outcomes and supports safe clinical decision-making.

Cost and Access Considerations

While multi-biomarker assessment offers clear clinical benefits, cost and access remain significant barriers in many healthcare settings. CGM devices, in particular, require substantial upfront investment and are not universally covered by insurance. Fructosamine and glycated albumin tests are less expensive but may not be available in all laboratories or familiar to all practitioners. Even FPG adds time and inconvenience for patients who must fast before blood draws.

Clinicians must therefore weigh benefits against costs for each patient. In resource-constrained settings, prioritizing the most impactful biomarker combinations for the patients who need them most is essential. For patients with type 1 diabetes or type 2 diabetes on intensive insulin therapy, CGM is increasingly considered standard of care and worth the investment given its ability to prevent hypoglycemia. For patients with well-controlled type 2 diabetes on oral agents alone, periodic FPG and A1c may be adequate, with CGM reserved for cases where treatment intensification is being considered or where A1c is unreliable.

Efforts to reduce costs and expand access to CGM and other advanced biomarkers continue. The development of integrated platforms that combine multiple biomarkers into single panels or algorithms that interpret combined data efficiently may help make multi-biomarker assessment more practical and affordable in the future. Professional organizations are also updating guidelines to reflect the growing evidence supporting multi-biomarker approaches, which in turn influences reimbursement policies.

Future Directions and Emerging Biomarkers

Research continues to identify new biomarkers and refine existing ones for glycemic assessment. Advanced glycation end products (AGEs) measured in skin or serum, markers of oxidative stress, and inflammatory mediators may eventually join the clinical toolkit for assessing glycemic control and complication risk. Wearable technology beyond current CGM devices is evolving to include noninvasive glucose monitoring, continuous ketone monitoring, and integration with other health metrics. These advances promise to make comprehensive, multi-dimensional glycemic assessment even more accessible and informative.

Artificial intelligence and machine learning algorithms are being developed to integrate data from multiple biomarkers and predict outcomes, identify patterns, and generate personalized treatment recommendations. As these tools mature, they will help clinicians make sense of the growing volume of glucose-related data and translate it into actionable clinical decisions. The future of glycemic assessment lies not in replacing A1c but in surrounding it with complementary data streams that together create a faithful representation of each patient's glycemic reality.

Conclusion

Hemoglobin A1c will remain an essential component of diabetes management for the foreseeable future. Its correlation with mean glucose and its robust association with long-term complications are supported by decades of evidence. However, the limitations of A1c are real and clinically significant. Anemia, hemoglobinopathies, kidney disease, pregnancy, and other conditions can distort A1c results, and the test provides no information about hypoglycemia, glycemic variability, or short-term changes in control.

Combining A1c with other biomarkers—particularly FPG, 1,5-AG, glycated albumin, and CGM metrics—addresses these limitations and provides a far more complete and accurate picture of a patient's glycemic status. This multi-biomarker approach improves diagnostic accuracy, enables personalized treatment strategies, enhances detection of both hyperglycemia and hypoglycemia, and empowers patients through better data and shared decision-making. The result is more effective diabetes management, fewer complications, and better quality of life for patients.

As the prevalence of diabetes continues to rise and the tools for monitoring glycemic control become more sophisticated, the importance of moving beyond a single-marker mindset will only grow. Clinicians who embrace a comprehensive, multi-biomarker approach to glycemic assessment will be best positioned to help their patients achieve safe, effective, and sustainable glycemic control in the years ahead.