The “Pre-Pre-Diabetes” Zone: Are We Overdiagnosing Glycemic Risk?

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The Evolution of Prediabetes Diagnostic Criteria

Historical Context and Changing Thresholds

The diagnostic criteria for prediabetes have undergone multiple revisions, reflecting both advancing scientific understanding and changing clinical philosophies. The American Diabetes Association (ADA) has revised the system of classification and criteria for diagnosis of diabetes to help remedy the problem of undiagnosed diabetes, as well as to move away from a system of diagnosis based on treatment used toward a system based on disease etiology. The ADA report identifies 4 major categories of diabetes: (1) type 1 (absolute insulin deficiency); (2) type 2 (insulin resistance with an insulin secretory defect); (3) other specific types; and (4) gestational diabetes mellitus. [7]

The introduction of HbA1c as a diagnostic criterion represents a vital shift in the field. The ADA subsequently recommended HbA1c levels for diagnosing prediabetes/intermediate hyperglycemia of 39–47 mmol/mol (5.7–6.4%) based on a model that utilized the composite risk of developing diabetes and CVD. [8] However, this expansion has created considerable debate within the medical community. The evidence that the intermediate hyperglycemia that defines prediabetes is independently associated with CVD is weak. Rather, the other risk factors for CVD in the metabolic syndrome are responsible. [9]

International Variations in Diagnostic Approaches

The lack of global consensus on prediabetes diagnostic criteria has created a complex landscape where different organizations recommend different thresholds. The WHO opined that prediabetes/intermediate hyperglycemia could not be diagnosed by HbA1c levels but the Canadians and Europeans recommended its diagnosis by values of 42–47 mmol/mol (6.0–6.4%). [10] This variation in diagnostic criteria has profound implications for prevalence estimates and clinical practice.

There are different definitions of prediabetes/intermediate hyperglycemia recommended by 4 organizations, the American Diabetes Association (ADA), World Health Organization (WHO), the Diabetes Canada Clinical Practice Guidelines (DCCPG) and the National Institute for Health Care Excellence (NICE). Since the WHO uses the term “intermediate hyperglycemia” and discourages the term “prediabetes”, terminology varies across organizations. [11]

Recent comparative studies have highlighted the impact of these different criteria on diagnosis patterns. Overall, 3,729 subjects were diagnosed consistently under ADA/IDF criteria; while 941 and 717 subjects exhibited inconsistencies in the diagnostic classification for diabetes and IH, respectively. [12] This inconsistency raises important questions about the validity and clinical utility of current diagnostic approaches.

The Phenomenon of Regression to Normoglycemia

High Rates of Spontaneous Reversal

One of the most compelling arguments for reconsidering current diagnostic approaches is the high rate of regression to normoglycemia observed in individuals diagnosed with prediabetes. The cumulative incidences of normoglycemia and diabetes were 43.7% (95%CI 40.9–46.4) and 40.1% (37.3–42.7), respectively. [13] This finding suggests that progression to diabetes and regression to normoglycemia occur at nearly equal rates, challenging the conventional view of prediabetes as a unidirectional pathway to diabetes.

During a 10-year median follow-up, the overall rate of progression to diabetes in our population with pre-diabetes (40.1%) and the overall rate of returning to normoglycemia (43.7%) were roughly equal. [14] These data from the Tehran Lipid and Glucose Study provide compelling evidence that prediabetes may represent a more dynamic state than previously appreciated.

Population-Specific Patterns

The regression patterns appear to vary across different populations and age groups. In this community-based cohort study of older adults, the prevalence of prediabetes was high; however, during the study period, regression to normoglycemia or death was more frequent than progression to diabetes. These findings suggest that prediabetes may not be a robust diagnostic entity in older age. [15]

In younger populations, the patterns may differ. Among those with prediabetes at baseline, 70.6% of the individuals converted to NGT and the remaining 29.4% either got converted to diabetes or remained as prediabetes. [16] This suggests that age may be a crucial factor in determining the clinical importance of prediabetes diagnosis.

Predictors of Regression

Understanding the factors that predict regression to normoglycemia is crucial for developing more precise diagnostic and treatment approaches. Regression to normoglycemia was associated with younger age, female sex, lower BMI, no familial history of diabetes, higher HDL-C, and ex-smoking. [17] Importantly, the modifiable predictors of regression to normoglycemia and progression to diabetes are roughly the same. [18]

Females and younger people are more likely to reverse to normoglycemia. [19] [20] This demographic pattern suggests that diagnostic criteria may need to be tailored to different population groups rather than applied universally.

 

Measurement Variability and Overdiagnosis

The Impact of Biological and Analytical Variation

One of the most notable challenges in prediabetes diagnosis is the inherent variability in glucose measurements. For both FPG and HbA1c, measurement variation may result in diagnostic classification errors: biological variation in particular, and to a smaller extent analytical variation, may cause the observed glucose value to differ from the true value in the individual. Measurement variation around diagnostic thresholds may lead to either underdiagnosis or overdiagnosis of prediabetes and diabetes. [21]

The clinical implications of this variability are substantial. For 100,000 people assessed according to the guidelines, between 1,602 and 2,233 people with true values in the normal range would be over diagnosed with prediabetes after 3 re-screens. [22] This represents a major burden of false-positive diagnoses that could lead to unnecessary anxiety, medical interventions, and healthcare costs.

Simulation Studies and Real-World Implications

Recent simulation studies have provided quantitative estimates of the magnitude of overdiagnosis due to measurement variability. A further 627 to 1,672 people with true values in the prediabetes range would be over diagnosed with diabetes after 5 re-screens. [23] These findings suggest that current diagnostic approaches may be creating a cascade of overdiagnosis that extends beyond prediabetes into frank diabetes.

The problem is particularly acute given the current emphasis on single-test diagnosis in many clinical settings. Type 2 diabetes was historically a symptomatic disease with a high mortality risk. However, these days most diagnoses are made in asymptomatic individuals, meaning the condition may be better conceptualized as a risk factor than a disease. [24]

 

 

Glycemic Variability and the “Pre-Pre-Diabetes” Zone

Normal Glucose Regulation and Physiological Variation

The concept of normoglycemia itself requires careful examination, as under physiological conditions, blood glucose is maintained within a given range, “normoglycemia,” by remarkable regulatory mechanisms. [25] Research has shown that in 30 healthy volunteers, the mean of all 498 blood samples was 4.2 ± 0.8 mmol/L. The lowest mean blood glucose of the day was 3.9 ± 0.6 mmol/L and was found in samples collected at 17:00 h. The highest level was found in samples collected at 14:00 h and averaged only 4.9 ± 1.0 mmol/L. [26]

Glycemic Variability in Apparently Healthy Individuals

Continuous glucose monitoring studies have revealed significant glycemic variability even in individuals with normal glucose tolerance. This study demonstrates that glycemic variability is increased in abdominally obese men with NGT. [27] Furthermore, subjects with normal glucose regulation (NGR), whose 1-h postload plasma glucose is ≥8.6 mmol/L (155 mg/dL, NGR 1 h ≥ 8.6) during 75-g oral glucose tolerance test (OGTT), have an increased risk of type 2 diabetes and subclinical organ damage. [28] [29]

These findings suggest that there may be a spectrum of glycemic dysfunction that extends into the supposedly normal range, potentially creating a “pre-pre-diabetes” zone where individuals may be at risk despite having normal standard glucose tests.

Normative Ranges and Clinical Implications

The establishment of normative ranges for glycemic variability has become increasingly important for clinical practice. We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research. [30] However, the clinical value of elevated glycemic variability in the absence of overt glucose intolerance remains unclear.

Studies with continuous glucose monitoring (CGM) have shown increased GV in individuals with subclinical disorders of glucose metabolism, such as impaired glucose tolerance and elevated post-load plasma glucose. Increased GV can be the result of various abnormalities in hormonal regulation, among which disorders of insulin secretion and sensitivity play a pivotal role. [31]

 

Clinical Implications and Intervention Evidence

The Evidence Base for Prediabetes Intervention

The clinical rationale for identifying prediabetes rests on the assumption that early intervention can prevent progression to diabetes. There is good evidence to implement intensive, structured lifestyle interventions for individuals with impaired glucose tolerance. [32] However, the evidence for those with impaired fasting glucose or elevated HbA1c is less clear, but individuals should still be provided with generalised healthy lifestyle strategies. [33]

The landmark Diabetes Prevention Program provides some of the strongest evidence for intervention in prediabetes. Restoration of normal glucose regulation (NGR) in people with prediabetes decreases the risk of future diabetes. We sought to examine whether regression to NGR is also associated with a long-term decrease in cardiovascular disease (CVD) risk. [34]

Differential Effectiveness Across Diagnostic Categories

The effectiveness of interventions may vary across different prediabetes diagnostic categories. Very limited evidence exists on preventing progression of prediabetes. Some evidence suggests that a major proportion of obese youth with prediabetes will revert to normoglycemia without pharmacological management. [35] This finding is particularly important for pediatric populations, where the natural history of prediabetes may differ from adults.

However, only lifestyle modification interventions provide strong evidence of effectiveness, which supports the current expert statements recommending this as the first-line approach for treating prediabetes. [36] The evidence for pharmacological interventions remains limited, with pharmacological therapy with GLP-1 receptor agonists (GLP-1 RAs), pioglitazone, metformin, acarbose, and orlistat shown to decrease the risk of T2D in persons with prediabetes for the duration of the medication’s use. [37]

Weight Loss and Normoglycemia Outcomes

Recent meta-analyses have provided important insights into the relationship between weight loss and glycemic outcomes. Lifestyle weight loss interventions increased regression to normoglycemia by 11/100 participants (95% confidence interval [CI]: 8 more, 17 more; risk ratio: 1.51; 95% CI: 1.27, 1.80). [38] However, regression to normoglycemia is more important than remaining in a prediabetes state. [39]

The dose-response relationship appears to be linear, with the relationship between weight loss and the progression to type 2 diabetes, as well as the regression to normoglycemia, following a linear pattern over a median duration of 24 months, with weight loss ranging from 1% to 9%. [40] This suggests that even modest weight loss may have clinically meaningful effects on glycemic status.

 

 

Cardiovascular Risk and the Metabolic Syndrome Connection

Independent Risk Factor or Marker?

A critical question in the prediabetes debate is whether glycemic abnormalities represent an independent cardiovascular risk factor or simply a marker of other metabolic disturbances. Pre-diabetes carries some predictive power for macrovascular disease, but most of this association appears to be mediated through the metabolic syndrome. [41] [42]

The increased risk for cardiovascular disease in prediabetes is multifactorial, with etiologies including insulin resistance, hyperglycemia, dyslipidemia, hypertension, systemic inflammation, and oxidative stress. [43] This multifactorial nature makes it challenging to determine the independent contribution of glycemic abnormalities to cardiovascular risk.

Meta-Analysis Evidence

Recent meta-analyses have attempted to quantify the cardiovascular risk associated with prediabetes. In the general population, prediabetes was associated with an increased risk of all cause mortality (relative risk 1.13, 95% confidence interval 1.10 to 1.17), composite cardiovascular disease (1.15, 1.11 to 1.18), coronary heart disease (1.16, 1.11 to 1.21), and stroke (1.14, 1.08 to 1.20). However, the absolute risk differences were relatively modest, with the absolute risk difference in prediabetes for all cause mortality, composite cardiovascular disease, coronary heart disease, and stroke being 7.36, 8.75, 6.59, and 3.68 per 10,000 person years, respectively.

Implications for Clinical Practice

The clinical implications of these findings are complex. The preferred clinical approach to cardiovascular prevention is to treat all the metabolic risk factors. [44] This suggests that focusing solely on glycemic abnormalities may miss the broader picture of cardiovascular risk assessment and management.

Our study suggests that prediabetes is associated with a higher odds of major cardiovascular events. Further prospective studies should be conducted to identify prediabetes as an independent causative factor for these events. [45] The distinction between association and causation remains a critical gap in our understanding.

 

Psychological and Social Implications of Diagnosis

The Burden of Labeling

The psychological impact of a prediabetes diagnosis represents an often-overlooked aspect of the overdiagnosis debate. While this analysis does not include direct psychological studies, the implications of false-positive diagnoses are well-recognized in medical literature. The process of labeling individuals as “prediabetic” may create anxiety, affect self-perception, and lead to unnecessary lifestyle restrictions.

Healthcare System Implications

From a health systems perspective, expanding the diagnostic criteria for prediabetes has far-reaching consequences. In 2021, the U.S. Preventive Services Task Force lowered the recommended age for diabetes screening from 40 to 35 years. This change resulted in approximately 13.9 million additional U.S. adults becoming newly eligible for screening. The greatest relative increase in eligibility occurred among Hispanic adults, a population already facing disproportionate burdens related to diabetes and healthcare access.[46]

This expansion increases the number of individuals requiring screening, follow-up testing, counseling, and potentially preventive interventions. While early identification of at-risk individuals offers the potential to prevent or delay the onset of diabetes, it also demands considerable healthcare resources. The associated costs are particularly notable given that individuals with impaired glucose tolerance incur higher healthcare expenses than those with normal glucose metabolism, even before developing diabetes.

Preventing the progression from prediabetes to diabetes can yield long-term cost savings, especially considering the markedly higher medical expenditures associated with diabetes management and its complications. However, these benefits must be weighed against the economic and psychological costs of labeling large segments of the population with a condition that may never progress and for which the clinical course remains variable. [47]

In sum, the broadened use of the prediabetes label introduces complex trade-offs. It underscores the need for careful consideration of diagnostic thresholds, clear communication with patients, and targeted public health strategies that balance early intervention with the avoidance of unnecessary harm.

 

 

Contemporary Screening Approaches and Their Limitations

Current Screening Guidelines

Current screening approaches vary across different healthcare systems and organizations. Following the 2021 USPSTF screening criteria will identify a greater proportion of adults with prediabetes and undiagnosed diabetes who are now eligible for screening, compared with following the 2015 criteria. Gains in sensitivity associated with the new criteria were greater among Black, Hispanic, and Asian individuals than among White adults. [48]

However, the expansion of screening criteria raises important questions about specificity and the potential for overdiagnosis. The balance between sensitivity and specificity becomes particularly important when considering the high rates of regression to normoglycemia observed in clinical practice.

Challenges in Implementation

The implementation of prediabetes screening faces several practical challenges. Evidence to guide management of prediabetes in children is limited. Current practice patterns of pediatric weight management programs show areas of variability in practice, reflecting the limited evidence base. [49] This limitation is particularly concerning given the potential for overdiagnosis in pediatric populations.

The variability in clinical practice extends beyond pediatric care. A variety of definitions and diagnostic cutpoints have been promulgated for prediabetes without universal agreement. Professional organizations agree that current scientific evidence justifies intervention in high-risk populations for the delay or prevention of progression to diabetes. [50]

 

Toward a More Nuanced Approach

Risk Stratification and Personalized Medicine

The evidence suggests that a more nuanced approach to prediabetes diagnosis and management may be needed. We conclude that prediabetes is a high-risk state for diabetes, especially in patients who remain with prediabetes despite intensive lifestyle intervention. [51] This finding suggests that persistence of prediabetes despite intervention may be a more meaningful clinical marker than initial diagnosis alone.

The concept of personalized medicine becomes particularly relevant in this context. The importance of BMI attenuates in elderly subjects. [52] This age-related variation in risk factors suggests that diagnostic criteria may need to be tailored to different demographic groups.

Dynamic Assessment Over Time

Rather than relying on single-point-in-time diagnoses, a more dynamic approach to glycemic risk assessment may be warranted. Regression from pre-diabetes back to euglycemia was much more common than progression to diabetes. [53] This pattern suggests that serial monitoring and assessment of glycemic trajectory may be more clinically meaningful than isolated measurements.

Given that most of the attention on the clinical side appears to focus on progression towards disease, the findings of regression to normal glucose levels is equally or more important to signal avenues for prevention and feasible targets to sustain public health efforts. [54]

Integration of Multiple Risk Factors

The evidence strongly supports an approach that integrates multiple risk factors rather than focusing solely on glycemic parameters. The metabolic syndrome comprises an array of cardiovascular disease (CVD) risk factors such as abdominal obesity, dyslipidemia, hypertension, and glucose intolerance. Insulin resistance and/or increased abdominal (visceral) obesity have been suggested as potential etiological factors. [55]

This multifactorial approach aligns with the finding that the metabolic syndrome is associated with a 2-fold increase in cardiovascular outcomes and a 1.5-fold increase in all-cause mortality. Studies are needed to investigate whether or not the prognostic importance of the metabolic syndrome exceeds the risk associated with the sum of its individual components.

 

 

Recommendations for Clinical Practice

Refined Diagnostic Criteria

Based on the evidence reviewed, several recommendations emerge for clinical practice. First, diagnostic criteria should account for the high rate of regression to normoglycemia and the measurement variability inherent in glucose testing. Individuals with prediabetes should generally be evaluated annually for their diabetes status. [56] This recommendation supports a longitudinal approach to risk assessment rather than relying on single measurements.

Targeted Intervention Strategies

The evidence suggests that interventions should be targeted to those most likely to benefit. Lifestyle intervention is universally accepted as the primary intervention strategy. [57] However, the intensity and type of intervention may need to be tailored based on individual risk factors and the likelihood of progression versus regression.

Compared with a strategy of general health education, a lifestyle intervention strategy could reverse glucose levels to normoglycemia in individuals with prediabetes. [58] This finding supports the effectiveness of targeted interventions while highlighting the importance of appropriate patient selection.

Monitoring and Follow-up

The dynamic nature of prediabetes requires careful monitoring and follow-up strategies. Regression to NGR is associated with a lower prevalence of aggregate MVD, nephropathy, and retinopathy, primarily due to lower glycemic exposure over time. Differential risk for the MVD subtypes begins in the prediabetes A1C range. [59] This finding suggests that achieving regression to normoglycemia should be a primary therapeutic goal.

 

 

Implications for Healthcare Policy

Screening Program Design

The evidence has important implications for the design of screening programs. This is the first study examining the health equity implications of the recent USPSTF recommendation for prediabetes and diabetes screening by quantifying its clinical performance characteristics. [60] The expansion of screening criteria must be carefully balanced against the potential for overdiagnosis and the associated costs and psychological burden.

Resource Allocation

The high rates of regression to normoglycemia raise important questions about resource allocation. Although lifestyle intervention is effective in delaying or preventing T2D onset in persons with prediabetes, long-term adherence can be challenging for many. Pharmacological therapy with GLP-1 receptor agonists (GLP-1 RAs), pioglitazone, metformin, acarbose, and orlistat has been shown to decrease the risk of T2D in persons with prediabetes for the duration of the medication’s use. [61]

The cost-effectiveness of different approaches to prediabetes management requires careful consideration, particularly given the proportion of individuals who will spontaneously revert to normoglycemia without intervention.

 

Future Research Directions

Biomarkers and Risk Prediction

Future research should focus on developing more precise biomarkers and risk prediction models that can better distinguish between individuals who will progress to diabetes and those who will regress to normoglycemia. Previous achievement of normal glucose regulation (odds ratio [OR] 3·18, 95% CI 2·71-3·72, p<0·0001), increased β-cell function (OR 1·28; 95% CI 1·18-1·39, p<0·0001), and insulin sensitivity (OR 1·16, 95% CI 1·08-1·25, p<0·0001) were associated with normal glucose regulation. [62]

Longitudinal Studies

Long-term longitudinal studies are needed to better understand the natural history of prediabetes and the factors that influence progression versus regression. Overall, 5–10% of people with pre-diabetes progress to diabetes annually, and the regression rate is roughly similar. [63] This finding highlights the need for studies that follow individuals over extended periods to understand the true clinical value of prediabetes diagnosis.

Intervention Optimization

Research is needed to optimize interventions for different prediabetes phenotypes. Any form of lifestyle weight loss intervention, including diet, exercise, or a combination of both, can have beneficial impacts on participants with prediabetes. [64] However, the optimal approach may vary based on individual characteristics and risk factors.

 

Limitations and Considerations

Methodological Considerations

This analysis has several limitations that should be acknowledged. The heterogeneity of diagnostic criteria across studies makes direct comparisons challenging. Additionally, the follow-up periods in many studies may not be sufficient to capture the full natural history of prediabetes.

Population Generalizability

Many of the studies included in this analysis were conducted in specific populations or geographic regions, which may limit the generalizability of findings to other populations. Cultural, genetic, and environmental factors may influence the natural history of prediabetes and the effectiveness of interventions.

Evolving Diagnostic Landscape

The rapidly evolving landscape of diabetes diagnosis and management means that findings from earlier studies may not fully reflect current clinical practice. The introduction of new diagnostic technologies and biomarkers may change the clinical approach to prediabetes in the future.

 

 

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Conclusion

The evidence reviewed in this analysis suggests that the current approach to prediabetes diagnosis may indeed be creating a “pre-pre-diabetes” zone characterized by overdiagnosis of glycemic risk. Several key findings support this conclusion:

1. High Regression Rates: The substantial rates of regression to normoglycemia observed across multiple studies challenge the conventional view of prediabetes as a progressive condition. The overall rate of progression to diabetes (40.1%) and the overall rate of returning to normoglycemia (43.7%) were roughly equal. [65]
2. Measurement Variability: The inherent variability in glucose measurements leads to major diagnostic misclassification, with between 1,602 and 2,233 people with true values in the normal range overdiagnosed with prediabetes after 3 re-screens per 100,000 people assessed. [66]
3. Modest Cardiovascular Risk: While prediabetes is associated with increased cardiovascular risk, the absolute risk differences are relatively modest, and much of this risk appears to be mediated through other components of the metabolic syndrome.
4. Limited Intervention Evidence: The evidence for intervention effectiveness varies across different prediabetes diagnostic categories, with the strongest evidence limited to lifestyle interventions in specific populations.

The implications of these findings are valuable for clinical practice, healthcare policy, and patient outcomes. A more nuanced approach to prediabetes diagnosis and management is needed—one that considers the dynamic nature of glucose regulation, the notable rates of regression to normoglycemia, and the multifactorial nature of metabolic risk.

Rather than expanding diagnostic criteria further, the focus should shift toward:

• Developing more precise risk stratification tools
• Implementing longitudinal assessment strategies
• Targeting interventions to those most likely to benefit
• Addressing the broader spectrum of metabolic risk factors
• Avoiding the psychological and social burden of unnecessary labeling

The goal should be to identify and treat individuals who will truly benefit from intervention while avoiding the overdiagnosis and overtreatment of those who represent normal physiological variation or transient glycemic fluctuations. This balanced approach will require continued research, clinical judgment, and careful consideration of the individual patient’s overall risk profile and preferences.

Future research should focus on developing personalized approaches to prediabetes management that account for individual risk factors, likelihood of progression versus regression, and patient preferences. Only through such a nuanced approach can we hope to maximize the benefits of early detection while minimizing the harms of overdiagnosis in the evolving landscape of glycemic risk assessment.

 

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