Insulin Resistance and Continuous Glucose Monitors (CGMs)

June 15th, 2022

According to the CDC, more than 37.3 million Americans have type II diabetes, and roughly 96 million people – or 1 in 3 adults – have pre-diabetes. Diabetes is the most expensive health crisis in the United States, costing $327 billion (yes, billion!) dollars per year. (1) Insane, right?

The good news – nutrition and lifestyle interventions are the foundation for prevention and reversing pre-diabetes and insulin resistance before progressing to full-blown diabetes.

The bad news – 80% of people with pre-diabetes are entirely unaware.

More good news, though – we can do a lot as functional nutrition practitioners to identify glucose dysregulation and insulin resistance early on, like assessing various lab metrics, utilizing continuous glucose monitors (CGMs), and leveraging personalized nutrition and supplement interventions.

Before getting into the nitty-gritty, let’s focus on some important information first – what is insulin resistance, and how do we measure it?

What is insulin resistance?

As a quick refresher, insulin is an anabolic hormone stored in the beta cells of the pancreas. It’s secreted in response to an increase in blood glucose following a meal and is vital for the transportation and storage of glucose. It also plays a role in lipid metabolism by inhibiting lipolysis and lowering plasma fatty acid concentration. (2)

According to a review in the National Library of Medicine, “insulin resistance is identified as an impaired biologic response to insulin stimulation of target tissues. Insulin resistance impairs glucose disposal, resulting in a compensatory increase in beta-cell insulin production and hyperinsulinemia. (3)

In simpler terms, when cells cannot respond to insulin, glucose remains in the blood. The body compensates by producing and releasing more insulin, which results in higher levels of insulin in the blood. Over time, this mismatched relationship between glucose and insulin can lead to various health complications, the most obvious being type II diabetes. Other health complications include metabolic syndrome, metabolic associated fatty liver disease (MAFLD), gout, and cardiovascular disease. (5,6,7)

Insulin resistance occurs primarily in muscle, the liver, and adipose tissue. Muscle tissue, in particular, is the primary site for glucose disposal – it’s responsible for a whopping 70% of glucose uptake. However, when muscles reach max glucose capacity, the excess glucose returns to the liver and undergoes de novo lipogenesis (DNL). DNL increases serum triglycerides, free fatty acid production, inflammation, and visceral fat, which further contributes to insulin resistance. (7)

 

What causes insulin resistance?

Lifestyle and genetic factors influence the risk of insulin resistance, including

  • obesity
  • a history of gestational diabetes
  • a family history of diabetes
  • high blood pressure
  • high cholesterol
  • polycystic ovarian syndrome (PCOS)
  • metabolic syndrome
  • a sedentary lifestyle
  • overconsumption of calories
  • excessive alcohol intake
  • nutrient imbalances
  • smoking
  • working nightshift (3,8,9,10)

 

How do we measure insulin resistance?

Interestingly, insulin resistance can occur 10-15 years before developing type II diabetes, confirming the need for testing and early intervention. (3) The gold standard for assessing insulin resistance is a technique called the hyperinsulinemic-euglycemic glucose clamp, but it has limited clinical use because of its complexity. However, looking at other blood metrics can help assess an individual’s risk of insulin resistance:

Fasting glucose

The American Diabetes Association recommends measuring fasting glucose in anybody over 45 with or without risk factors. In Track 1, Module 4, Dr. Dedhia explains that a fasting glucose between 95-99mg/dL increases a person’s risk of diabetes by 2.33. (11)

 

Standard Fasting Glucose Measurements Finding
70-99 mg/dL “Normal” Fasting glucose
100-125 mg/dL Prediabetes or impaired fasting glucose
>126 on more than 1 occasion Diabetes

 

For optimal health and disease prevention, fasting glucose between 75-86 mg/dL is ideal. (29)

 

Fasting insulin

Fasting insulin is an excellent metric to parallel with fasting glucose. In the case of insulin resistance, fasting glucose may present as normal, but fasting insulin could be high. This combination is a sign of insulin resistance.

Fasting insulin is a particularly valuable metric in conditions like PCOS, metabolic syndrome, and people with acanthosis nigricans.

 

What? Fasting Insulin Fasting Glucose
Healthy Individual Normal Normal
Insulin Resistance High Normal or High
Not enough insulin produced by beta cells (ex: diabetes) Low High
Hypoglycemia due to excess insulin Normal or High Low

 

For optimal health and disease prevention, fasting insulin between 3-8 uIU/mL is ideal. (29)

 

Hemoglobin A1c (HbA1c)

Hemoglobin A1c measures the percentage of glycated (or sugar-coated) hemoglobin on red blood cells over 90 days. Several conditions influence HbA1c, including anemia (iron deficiency and B12), sickle cell disease, thalassemia, liver disease, and recent severe bleeding.

According to a study in the International Journal of Endocrinology, anything that lengthens or shortens red blood cell survival can lead to A1c alterations. (12) Anemia, specifically, can lead to increased RBC survival and falsely elevated HbA1c levels. (13, 14) Bleeding and hemolysis can reduce RBC survival time and falsely lower HbA1c values.

The most useful application of HbA1c is to monitor diabetes over time. HbA1c between 4.50-5.50% is ideal for optimal health and disease prevention.

HbA1c % Finding
<5.6% Normal HbA1c
5.7-6.5% Prediabetes or Impaired Glucose
>6.5% Diabetes

For optimal health and disease prevention, considering red blood cell health is normal and an average of 90 days, an HbA1c between 4.50-5.50% is ideal. (29)

 

HOMA-IR

Homeostatic Model Assessment – Insulin Resistance (HOMA-IR) is an equation used to assess or quantify insulin resistance. Compared to the euglycemic clamp, this mathematical approach is more efficient.

HOMA = Fasting glucose (mg/dL) x Fasting insulin (uU/mL)

405 mg/dL

While this is a convenient option, there’s still debate on how to interpret the score and if it should be the same or standardized across ethnicities. (15)

HOMA-IR Interpretation
<1.0 Insulin sensitive (ideal)
>1.9 Early insulin resistance
>2.9 Severe insulin resistance

 

An oral glucose tolerance test assesses a person’s ability to tolerate a 75-gram glucose load. (16) A typical 2-hour OGTT looks like this:

Step 1: Check fasting glucose.

Step 2: Drink a 75-gram glucose beverage.

Step 3: Check blood glucose after 2 hours.

Step 4: Assess glucose at the start of the OGTT and the 2-hour mark.

Fasting Glucose Results Interpretation
<100 mg/dL Normal Fasting Glucose
100-125 mg/dL Impaired Fasting Glucose
>126 mg/dL Hyperglycemia

 

After 2-Hours Glucose Results Interpretation
<140 mg/dL Normal Glucose Tolerance
140-199 mg/dL Pre-Diabetes
>200 mg/dL Hyperglycemia

 

Other metrics to consider:

  • C-Peptide: low C-peptide indicates the pancreas is producing little to no insulin. High C-peptide means the pancreas is making a lot of insulin, which often occurs in people with insulin resistance and type II diabetes. (17)
  • Uric Acid: studies have found a connection between high uric acid and impaired fasting glucose and insulin resistance. (18)
  • Triglycerides: high triglycerides are associated with insulin resistance and impaired beta-cell function. (19)
  • Lipopolysaccharide (LPS): an increase in LPS has been shown in people with insulin resistance. (20)
  • Breath Biomarkers: emerging research shows that 10 breath metabolites, including limonene, undecane, and 2,7-dimethyl-undecane, positively correlate with fasting glucose, fasting insulin, and HOMA-IR score. (21)

 

What is a continuous glucose monitor (CGM), and how does it work?

A continuous glucose monitor (CGM) is a small wearable device that measures glucose concentration over 24 hours. While sleeping, eating, exercising, working, or traveling – it provides a constant flow of glucose information – no finger prick required.

A CGM works through a tiny filament inserted into the arm or abdomen. It reads the glucose concentration of the interstitial fluid of subcutaneous tissue and sends the glucose information to an output device, usually a smartphone or other handheld device. CGMs are crucial for people with type I diabetes because the device can identify and send alarms in cases of critical values. (22)

Common CGM brands include Freestyle Libre 14 Day System, Dexcom G6, and Medtronic Guardian Connect.

CGMs are prescription-only, but a handful of health companies, like Levels, NutriSense, and Supersapians, are making it easier for people to access the devices. But because CGMs are becoming more popular, there’s broader discussion (and debate) about the usefulness of the devices in people without diabetes.

Some believe CGMs should be reserved only for people with diabetes. Others view CGMs as a disease-prevention tool and a way for people to understand the impact of diet and lifestyle better.

 

Benefits of CGMs in people without diabetes

According to Holzer, et al, CMGs have multiple possible applications:

  • Early insulin resistance screening
  • Improved nutritional behaviors
  • Enhancing physical activity
  • Stress regulation
  • Optimizing athletic performance

 

Early Insulin Resistance Screening:

A 2013 study used CGM data to review the relationship between HbA1c and 24-hour mean glucose in 747 Chinese subjects. HbA1c and 24-hour mean glucose significantly increased in those with pre-diabetes, confirming that CGM data can help identify insulin-resistant patterns that can lead to elevated HbA1c. (23)

Furthermore, a 2015 study assessed the CGM data of 98 obese adolescents. The CGM data identified pre-diabetes participants via elevated mean glucose values, higher glucose area under the curve (AUC), and higher and prolonged peak glucose values >140 mg/dL. (24)

A 2012 study compared glycemic variability (or intraday glucose swings) in 162 subjects with either impaired glucose regulation (n=53), type II diabetes (n=56), and normal glucose tolerance (n=53). The participants each wore a CGM for three consecutive days. They found that people with impaired glucose regulation and type II diabetes have larger intraday glucose fluctuation, higher post-prandial glucose responses, and higher overnight values than normal glucose tolerance. (25)

Because 80% of people with pre-diabetes are unaware of it, early screening and detection are crucial for disease prevention. CGMs can display glucose dynamics throughout the day and help identify insulin-resistant glucose trends, which a snap-shot finger prick reading cannot. Seeing the data in real-time can also help motivate healthy behavior change.

 

Improved Nutritional Behaviors:

According to Holzer et al., “wearable technology such as fitness trackers in lifestyle interventions have shown that they can be beneficial tools in improving body mass index (BMI) and waist circumference in overweight or obese individuals.” (26)

Real-time glucose values via CGM technology can also be an effective strategy to inspire diet changes, increase exercise and physical activity, and improve stress and sleep patterns. This is already demonstrated in adults and children with type I diabetes, where results show that CGMs can help improve HbA1c and reduce the frequency of hypoglycemia. (26).

Furthermore, a 2008 study found that 3-days of CGM tracking every month for 12-weeks produced a decrease in calorie intake, increased activity, improved body weight, and a 1% decrease in HbA1c in people with type II diabetes. (26)

A 2016 pilot study found that CGMs can help foster self-monitoring and exercise adherence in people with pre-diabetes and type II diabetes. (26). Regular exercise is vital in people with insulin resistance because sedentary behavior is linked to impaired glucose metabolism and decreased glucose tolerance. (27)

 

Stress:

Chronically heightened stress hormones, like cortisol and adrenaline, can increase the risk of insulin resistance. (28) CGMs can show real-time glucose changes in response to stressful situations, potentially motivating people to manage stressors and stimulants throughout the day better.

Optimizing athletic performance:

As we know, diet optimization is essential for athletic performance. CGMs can help athletes identify carbohydrate timing and threshold pre-sport for optimal performance and glucose homeostasis. Furthermore, the CGM data can reveal “bonking” or low glucose during activity. Athletes can use this information to adjust their pre-sport meal and intra-sport fuel timing.

Should you use a CGM in practice?

Suppose a client presents with blood sugar-related symptoms like frequent fatigue, increased hunger and thirst, reactive hypoglycemia, weight challenges, disrupted sleep, and elevated HgA1c. In that case, a CGM might be a valuable tool.

As functional nutrition practitioners, we understand the importance of taking a whole-person, root-cause approach to wellness. CGMs are simply another tool in our functional nutrition toolbox.

Conclusion

Insulin resistance is a major health crisis and financial burden in the United States. It’s important to take a comprehensive look at different biometrics to assess an individual’s insulin resistance risk. CGMs can also be a valuable tool for detecting abnormal glucose control. Rather than capturing just one data point, as in fasting blood glucose, CGMs can capture glucose changes in response to many different influencers. This is particularly useful in people who have a family history of diabetes or for those with other risk factors, as described above.

Glucose, insulin, and metabolic health are discussed widely throughout the IFNA curriculum. To learn more, sign up today to become an IFNCP and take your nutrition knowledge to the next level!

Tori Eaton, RDN, LDN, IFNCP

 

References

  1. Centers for Disease Control and Prevention. National Diabetes Statistics Report website. Updated Jan 18, 2022. Accessed June 5, 2022. https://www.cdc.gov/diabetes/data/statistics-report/index.html.
  2. Dimitriadis G, Mitrou P, Lambadiari V, Maratou E, Raptis SA. Insulin effects in muscle and adipose tissue. Diabetes Res Clin Pract. 2011;93. doi: 10.1016/S0168-8227(11)70014-6. PMID: 21864752.
  3. Freeman A, Pennings N. Insulin resistance. StatPearls. 2021. PMID: 29939616
  4. Swarup S, Goyal A, Grigorova Y, et al. Metabolic Syndrome. StatPearls. 2022. PMID: 29083742
  5. Gill A, Kukreja S, Malhotra N, Chhabra N. Correlation of the serum insulin and the serum uric Acid levels with the glycated haemoglobin levels in the patients of type 2 diabetes mellitus. J Clin Diagn Res. 2013;7(7):1295-1297. doi:10.7860/JCDR/2013/6017.3121
  6. Ormazabal, V, Nair, S, Elfeky, O. et al. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 122 (2018). https://doi.org/10.1186/s12933-018-0762-4
  7. Abdul-Ghani M, DeFronzo R. Pathogenesis of insulin resistance in skeletal muscle.  Biomed Res. Int. 2010,  https://doi.org/10.1155/2010/476279
  8. Zhou MS, Wang A, Yu H. Link between insulin resistance and hypertension: What is the evidence from evolutionary biology?. Diabetol Metab Syndr. 2014;6(1):12. doi:10.1186/1758-5996-6-12
  9. Rojas J, Chávez M, Olivar L, et al. Polycystic ovary syndrome, insulin resistance, and obesity: navigating the pathophysiologic labyrinth. Int J Reprod Med. 2014;2014:719050. doi:10.1155/2014/719050
  10. Brum, M, Dantas-Filho F, et al. Night shift work, short sleep, and obesity. Diabetol Metab Syndr. 2020;13. https://doi.org/10.1186/s13098-020-0524-9
  11. Nichols GA, Hillier TA, Brown JB. Normal fasting plasma glucose and risk of type 2 diabetes diagnosis. Am J Med. 2008;121(6):519-24. doi: 10.1016/j.amjmed.2008.02.026. PMID: 18501234.
  12. Nadelson J, Satapathy S, Nair S. Glycated hemoglobin levels in patients with decompensated cirrhosis. Int J Endocrinol. 2016;2016:8390210. doi:10.1155/2016/8390210
  13. Son JI, Rhee SY, Woo JT, et al. Hemoglobin a1c may be an inadequate diagnostic tool for diabetes mellitus in anemic subjects. Diabetes Metab J. 2013;37(5):343-348. doi:10.4093/dmj.2013.37.5.343
  14. Kim C, McKeever-Bullard K, Herman W, et al.; Association between iron deficiency and A1C levels among adults without diabetes in the national health and nutrition examination survey. Diabetes Care. 2010; 33 (4): 780–785. https://doi.org/10.2337/dc09-0836
  15. Qu HQ, Li Q, Rentfro AR, et al. The definition of insulin resistance using HOMA-IR for Americans of Mexican descent using machine learning. PLoS ONE. 2011;6(6). doi:10.1371/journal.pone.0021041
  16. Eyth E, Basit H, Smith CJ. Glucose tolerance test. StatPearls. 2022. PMID: 30422510
  17. UCSF Health. C-peptide test. Feb 22, 2018. Accessed June 5, 2022. https://www.ucsfhealth.org/medical-tests/insulin-c-peptide-test
  18. Yu P, Huang L, Wang Z, et al. The Association of serum uric acid with beta-cell function and insulin resistance in nondiabetic individuals: a cross-sectional study. Diabetes Metab Syndr Obes. 2021;14:2673-2682. https://doi.org/10.2147/DMSO.S312489
  19. Ma M, Liu, H, Yu, J. et al. Triglyceride is independently correlated with insulin resistance and islet beta cell function: a study in population with different glucose and lipid metabolism states. Lipids Health Dis. 2020; 12. https://doi.org/10.1186/s12944-020-01303-w
  20. Liang H, Hussey S, Sanchez-Avila A, et al. Effect of lipopolysaccharide on inflammation and insulin action in human muscle. PLoS ONE. 2013;8(5). doi:10.1371/journal.pone.0063983
  21. Khan, M, Cuda S, Karere G, et al. Breath biomarkers of insulin resistance in pre-diabetic Hispanic adolescents with obesity. Sci Rep. 2022; 12(339). https://doi.org/10.1038/s41598-021-04072-3
  22. Holzer R, Bloch W, Brinkmann C. Continuous glucose monitoring in healthy adults—possible applications in health care, wellness, and sports. Sensors. 2022; 22(2030). https:// doi.org/10.3390/s22052030
  23. Zhou J, Li H, Ran X, et al. Reference values for continuous glucose monitoring in chinese subjects. Diabetes Care. 2009;32:1188–1193.
  24. Chan CL, Pyle L, Newnes L, et al. Continuous glucose monitoring and its relationship to hemoglobin A1c and oral glucose tolerance testing in obese and prediabetic youth. J Clin Endocrinol Metab. 2015;100(3):902-910. doi:10.1210/jc.2014-3612
  25. Soliman A, DeSanctis V, Yassin M, Elalaily R, Eldarsy NE. Continuous glucose monitoring system and new era of early diagnosis of diabetes in high risk groups. Indian J Endocrinol Metab. 2014;18(3):274-282. doi:10.4103/2230-8210.131130
  26. Ehrhardt N, Zaghal E. Behavior modification in prediabetes and diabetes: potential use of real-tme continuous glucose monitoring. SAGE Open Med. 2019;13(2):271-275.https://doi.org/10.1177/1932296818790994
  27. Sparks J, Kishman E, Sarzynski M, et al. Glycemic variability: importance, relationship with physical activity, and the influence of exercise. SMHS. 2021;3(4):183-193. https://doi.org/10.1016/j.smhs.2021.09.004
  28. Yan Y, Xiao, HB, Wang SS, et al. Investigation of the relationship between chronic stress and insulin resistance in a Chinese population. J Epidemiol. 2016;26(7):355-360 doi:10.2188/jea.JE20150183
  29. Weatherby D, Ferguson S. Blood Chemistry Analysis From a Functional Perspective. Bear Mountain Publishing; 2004.

 

 

 

 

Have you ever done an Oral Glucose Insulin Tolerance Test?

 

I have not, but work with many who have.

 

Yes, we used to do them more frequently in our clinic

 

Article looks great Tori!  I did try to review again – Sheila will want to take a look and may have additional comments

 

Excellent!

Can you add a statement after these about some other metrics that are being used in research/on the horizon :

https://www.nature.com/articles/s41598-021-04072-3

I think we will see more and more breath biomarkers used in the future

 

https://www.sciencedirect.com/science/article/pii/S2405844021002449

 

Breath test study is really cool!