Insulin Resistance and Cardiometabolic Health

Michael McPhaul, MD, is senior medical director of endocrinology at Quest Diagnostics and editor of the Journal of Investigative Medicine High Impact Case Reports. His work at Quest Diagnostics focuses on the development of new tests for biomarkers of metabolic health to improve prevention, early diagnosis, and monitoring for patients with cardiometabolic health conditions.

What do we know about insulin resistance?

Healthy individuals have normal levels of fasting glucose, hemoglobin A1C, and fasting insulin. With the onset of insulin resistance—the inability of cells and tissues to respond appropriately to insulin—these levels begin creeping up out of the reference ranges. Eventually, the body’s need to respond to insulin outpaces its capacity, leading to first prediabetes and then to type 2 diabetes mellitus. 

In the 1980s, Gerald Reaven, winner of the 1988 Banting Medal for Scientific Achievement, showed that insulin resistance could identify individuals at increased risk of not only diabetes, but also cardiovascular disease, cancer, and other chronic diseases.¹

Can you describe your research into cardiometabolic health biomarkers?

Insulin resistance can be difficult to measure accurately; the insulin suppression testing Reaven developed for research purposes² is too time- and labor-intensive for clinical use and the immunoassays used in the clinic typically show extreme variability between laboratories and platforms. That’s why my colleagues and I created an assay to measure intact insulin and C-peptide using mass spectrometry.³ This is a highly specific, reproducible technique that, unlike other approaches, is tethered to peptide content.

This innovation is seminal for two reasons: one, because it permits the standardization of both assays to peptide content,⁴ and two, because it allows us to examine whether we can use fasting insulin and C-peptide mass spectrometry to model the more complex—and more accurate—measurements of insulin resistance used in research settings. By performing this new assay on archived samples from Gerald Reaven’s study, we found that a single fasting measurement of insulin and C-peptide via mass spectrometry gave a very good approximation of the insulin resistance measurements obtained via insulin suppression testing.

In parallel, we looked at the impact of assessing insulin resistance in predicting future diabetes, cardiovascular disease, and all-cause mortality. Even in large cohorts tested via immunoassays, we were able to show that insulin resistance measurement allowed for reclassification well beyond what was possible using fasting glucose and HbA1C.⁵ This means that measures of insulin resistance and hyperglycemia can identify people at risk for not just type 2 diabetes, but also cardiovascular outcomes and chronic renal disease.⁶ Finally, we used mass spectrometry to assess insulin and C-peptide levels in existing samples from populations with outcomes available—and, in these populations, we showed that an insulin resistance risk over 80 percent leads to approximately 50 percent greater risk of cardiovascular disease or all-cause mortality.⁷

What are the current approaches to risk assessment and prognosis?

To evaluate a patient’s risk of cardiovascular or other chronic disease, we use tools like total cholesterol, low-density lipoprotein, or markers of inflammation. If the patient’s levels exceed a specific threshold, we identify them as “at risk.” Unfortunately, these tests are often used at a point when disease may already be well established.

However, insulin resistance measures show abnormalities long before any visible signs of disease. That allows us to identify individuals at risk before they need medical intervention. The best way to address metabolic risk is to normalize metabolic abnormalities—and earlier detection might make this an easier task. For instance, a person with high insulin resistance markers may have a shorter weight loss journey to normalize their metabolic health than one with overt type 2 diabetes. Right now, most diabetes prevention programs target individuals with abnormal fasting glucose or HbA1C levels, but if we can identify people at risk and implement these programs earlier, they might be even more effective.

How might that change the lives of patients—and clinical laboratory professionals?

If we begin screening people for insulin resistance at an earlier age, we could prevent metabolic changes instead of reacting to them. Using reproducible mass spectrometry assays tethered to unchangeable markers such as peptide content, we could also standardize the field and develop thresholds of disease. For example, my colleagues and I used our assay to examine non-diabetic patients and were able to establish that a fasting insulin threshold of 10.5 µIU/mL could accurately identify individuals with hepatic steatosis.⁸

So far, we’ve focused on conditions like type 2 diabetes, cardiovascular disease risk, and stroke risk—but other chronic diseases are also linked to the metabolic changes that occur with weight gain. Research suggests that insulin resistance may contribute to or accelerate the progression of many chronic diseases, including degenerative joint disease⁹ and Alzheimer’s disease.¹⁰ I think this warrants further exploration because, if removing insulin resistance as a contributing factor can delay the onset or progression of disease, that may open up new avenues for prevention and treatment.

References:

1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37(12):1595–1607. doi:10.2337/diab.37.12.1595.
2. Shen SW et al. Comparison of impedance to insulin-mediated glucose uptake in normal subjects and in subjects with latent diabetes. J Clin Invest. 1970;49(12):2151–2160. doi:10.1172/JCI106433.
3. Taylor SW et al. A high-throughput mass spectrometry assay to simultaneously measure intact insulin and C-peptide. Clin Chim Acta. 2016;455:202–208. doi:10.1016/j.cca.2016.01.019.
4. Taylor SW et al. Quantitative amino acid analysis in insulin and C-peptide assays. Clin Chem. 2016;62(8):1152–1153. doi:10.1373/clinchem.2016.256313.
5. Abbasi F et al. Insulin resistance probability scores for apparently healthy individuals. J Endocr Soc. 2018;2(9):1050–1057. doi:10.1210/js.2018-00107.
6. Rooney MR et al. Prognostic value of insulin resistance and hyperglycemia biomarkers for long-term risks of cardiometabolic outcomes. J Diabetes Complications. 2023;37(9):108583. doi:10.1016/j.jdiacomp.2023.108583.
7. Louie JZ et al. Insulin resistance probability score and incident cardiovascular disease. J Intern Med. 2023;294(4):531–535. doi:10.1111/joim.13687.
8. Bril F et al. Intact fasting insulin identifies nonalcoholic fatty liver disease in patients without diabetes. J Clin Endocrinol Metab. 2021;106(11): e4360–e4371. doi:10.1210/clinem/dgab417.
9. Courties A et al. Metabolic stress-induced joint inflammation and osteoarthritis. Osteoarthritis Cartilage. 2015;23(11):1955–1965. doi:10.1016/j.joca.2015.05.016.
10. Sędzikowska A, Szablewski L. Insulin and insulin resistance in Alzheimer’s disease. Int J Mol Sci. 2021;22(18):9987. doi:10.3390/ijms22189987.