Why Early Metabolic Imbalance Is Missed in Routine Health Screening

In the prior post, I introduced the elephant in the room: early metabolic imbalance (EMI). The defining feature of EMI is early insulin resistance.

Today, we’ll address a critical question -- why is such an important metabolic risk factor so often overlooked?

Early insulin resistance plays a central role in how we metabolize food, regulate weight, and maintain long-term metabolic health. It is a major risk factor for obesity, prediabetes, type 2 diabetes, metabolic syndrome, and cardiovascular disease. Given its importance, you might reasonably expect routine blood testing to screen for it.

It doesn’t.

The reasons are somewhat complex, but they can be broken down. Some relate to the reactive nature of our healthcare (or “sick-care”) system and its financial disincentives -- topics we’ll explore in a future post. Here, the focus is on the blood tests themselves and how they are commonly used (or not used) in clinical practice.

7 Reasons Early Insulin Resistance Is Often Missed During Routine Health Screening

1) Insulin resistance is difficult to measure directly

There are well-established tests that directly measure insulin resistance. However, these tests are invasive, expensive, time-consuming, and impractical for routine outpatient care. As a result, clinicians rely on indirect markers instead.

Bottom line: Direct measurement exists, but it isn’t feasible for routine screening.

2) Commonly used indirect tests do not detect early insulin resistance

Markers such as fasting glucose, HbA1c, triglycerides, and HDL do not measure insulin resistance itself. They measure glucose tolerance and lipid transport, the downstream consequences of insulin resistance.

While these markers can eventually be affected by insulin resistance, that typically occurs late in the process, after years of compensation.

3) Practice guidelines do not distinguish early from late insulin resistance

Early insulin resistance/EMI is insidious because the body initially hides it. Insulin levels rise to compensate, allowing glucose and lipid values to remain within normal ranges.

Diagnostic criteria for prediabetes and diabetes rely on glucose-based tests—fasting glucose, HbA1c, and the 2-hour value on an oral glucose tolerance test (OGTT). These tests do not detect EMI because glucose tolerance is still normal at this stage. The same is true for fasting triglycerides and HDL.

As a result, patients in the Yellow Zone are often falsely reassured by “normal” labs.

4) Many providers lack training and awareness around EMI

Despite decades of research, metabolism and insulin resistance remain under-emphasized in medical education. Training focuses heavily on diagnosing and treating established disease, with far less attention paid to early diabetes risk detection and prevention.

This is not a failure of individual clinicians. It reflects medical inertia, the well-documented lag between scientific evidence and changes in clinical practice standards.

Addressing disease is essential. But ignoring early warning signs misses opportunities for lower-cost, lower-risk interventions -- when change is easiest and outcomes are best.

5) Practice standards discourage the use of insulin and c-peptide tests, despite workable solutions

Two of the most informative biomarkers for early insulin resistance—insulin and c-peptide—are often discouraged in routine practice.

One concern is that insulin assays are not fully standardized across laboratories. This is a valid issue, but it is not insurmountable.

Several practical workarounds exist:

• Percentile reporting: Insulin and c-peptide can be interpreted relative to population percentiles, a common and accepted approach in medical research. This approach adjusts for lab-to-lab differences.

• Pattern analysis: Tools such as the HOMA2 calculator use fasting insulin and fasting glucose to estimate insulin sensitivity and beta-cell activity. The compensation signature of EMI is readily apparent: low insulin sensitivity (%S << 100%) in the face of high beta cell activity (%B >> 100%). By contrast, average healthy balanced metabolism yields %S and %B values of ~100%.

• Dynamic testing: In expanded oral glucose tolerance testing (OGTT), the change in insulin or c-peptide after a glucose load matters more than absolute values. This approach yields a validated estimate of whole body insulin sensitivity.

• C-peptide measurement: Unlike insulin, c-peptide is not cleared by the liver so it is not confounded by individual-to-individual differences in liver clearance. Also, c-peptide is closer to assay standardization.

No biomarker is perfect. Rejecting useful tools because they are imperfect can result in the loss of important clinical insight.

As my grandmother used to say: “Don’t throw the baby out with the bath water.”

(And no—that isn’t a sketch of my grandmother.)

6) Cut points are imperfect, but that’s true for many biomarkers

Some argue that insulin and c-peptide lack well-defined cut points for normal versus abnormal. This is true -- but it is also true for many biomarkers in medicine.

Risk for future disease often increases continuously, not abruptly. Defining cut points involves a trade-off between precision and usability -- a trade-off clinicians make every day.

Long-term data suggest that insulin levels above approximately the 60th percentile (top half to top third), in individuals with normal glucose and lipids, identifies individuals with EMI -- at increased risk for future disease. This approach empowers individuals to benefit from early lifestyle changes and avoid the need for future medications.

In the CARDIA study, young adults with fasting insulin above the 60th percentile, but normal glucose and lipids, had nearly double the risk of developing diabetes or cardiovascular disease over the next 30 years compared with those below the 60th percentile.

That risk should not be ignored.

7) Insulin resistance is incorrectly equated with obesity

Obesity is often assumed to be a visible marker of insulin resistance, but population data tell a different story.

Studies that sample the whole U.S. (not just its less healthy sub-populations) show wide variation in insulin and HOMA levels among both obese and non-obese individuals. A surprising number of lean individuals are insulin resistant, while some individuals with obesity show good insulin sensitivity.

You cannot tell simply by looking or weighing.

Lean individuals with EMI are frequently overlooked, undertested, and undertreated -- another missed opportunity for chronic disease prevention.

Bottom Line

You cannot determine early insulin resistance and EMI by appearance alone. The only way to know is to test.

Unlike many conventional panels, our Zenova Grounded™ and Aventra BaseCamp™ panels include markers designed to detect insulin resistance, hyperinsulinemia, and EMI early -- when the maximum impact of meaningful change can be achieved.

There is a clear need for better biomarkers that are practical, inexpensive, sensitive, and standardized. Our T2 markers show promise and are undergoing ongoing research and validation.

But while we wait for better markers, we should make better use of the tools we already have.

Progress, not perfection.

In future posts, we will explore the other insidious features of EMI linked to early insulin resistance: oxidative stress, hypoxemia & hypoxia, impaired oxidative metabolism, and chronic low-grade inflammation.

References

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Cistola DP, Dwivedi AK. (2023) EMI Not Just BMI: Unveiling Hidden Diabetes Risk Among Apparently Healthy U.S. Young Adults. Metabolism 142(S), 155469, Elsevier; https://doi.org/10.1016/j.metabol.2023.155469.

Cistola DP, Dwivedi AK (2023) Early Metabolic Imbalance in Young Adults is a Hidden Risk Factor for Midlife Cardiovascular Disease: CARDIA 35-year Follow Up (2023) Circulation 147, AP336, Lippincott, Williams & Wilkins; https://www.ahajournals.org/doi/10.1161/circ.147.suppl_1.P336.

Cistola DP, Mendiola LI, Dwivedi AK. Hypoxia Response Markers are Associated with Early Metabolic Imbalance: The U.S. National Health and Nutrition Examination Survey. Circulation 151(Suppl_1), P1153. https://www.ahajournals.org/doi/10.1161/cir.151.suppl_1.P1153.

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Wallace TM, Levy JC, Matthews DR (2004) Use and Abuse of HOMA Modeling. Diabetes Care 27(6):1487-95. https://doi.org/10.2337/diacare.27.6.1487.

Matsuda M, DeFronzo RA (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22(9), 1462-1470. https://doi.org/10.2337/diacare.22.9.1462.

Sacks DB et al. (2023) Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Diabetes Care 46(10), e151-e199. https://doi.org/10.2337/dci23-0036.

Cistola DP, Dwivedi AK (2025) Plasma and Serum Water T2 are Strong Predictors of Cardiometabolic Health: Implications for Point-Of-Care Screening. Circulation 151(Suppl_1), P3004. Lippincott Williams & Wilkins. https://www.ahajournals.org/doi/10.1161/cir.151.suppl_1.P3004.

Cistola DP, Patel V, Deodhar S, Mishra I, Robinson MD, Dwivedi AK. Whole Blood T2P Links Hemoglobin Status to Cardiometabolic Health. (2023) American Journal for Preventative Cardiology 15S, 100576, Elsevier. https://doi.org/10.1016/j.ajpc.2023.100576

Mishra I, Jones C, Patel V, Deodhar S, Cistola DP. (2018) Early detection of metabolic dysregulation using water T(2) analysis of biobanked samples. Diabetes Metab Syndr Obes. 2018;11:807-818. doi: 10.2147/DMSO.S180655. eCollection 2018. PubMed PMID: 30538517; PubMed Central PMCID: PMC6260129.

Robinson MD, Mishra I, Deodhar S, Patel V, Gordon KV, Vintimilla R, Brown K, Johnson L, O’Bryant S, Cistola DP. (2017) Water T(2) as an early, global and practical biomarker for metabolic syndrome: an observational cross-sectional study. Journal of Translational Medicine 2017 Dec 19;15(1):258. doi: 10.1186/s12967-017-1359-5. PubMed PMID: 29258604; PubMed Central PMCID: PMC5738216.

© 2026 Dr. David P. Cistola/T2YourHealth LLC. All rights reserved.