May 2021 Issue
The Many Faces of Prediabetes
By Jill Weisenberger, MS, RDN, CDCES, CHWC, FAND
Vol. 23, No. 5, P. 38
Recent research shows prediabetes in one patient may present differently in another and therefore may require distinct approaches to treatment and management.
Almost all dietitians who work with clients have counseled those with diabetes or prediabetes. The number of individuals with elevated blood glucose levels is just that high.
The majority of the more than 34 million Americans with diabetes have type 2 diabetes, which is characterized by beta-cell dysfunction with decreased insulin production in the background of insulin resistance. Another 88 million adults have prediabetes, a chief risk factor for type 2 diabetes.1 Looked at another way, in a random crowd of 100 adults in the United States, more than 35 people have prediabetes or type 2 diabetes, and most of them don’t know they have the disorder.
Each Case Is Unique
Just as heterogeneity exists in the pathophysiology of type 2 diabetes, people with prediabetes present differently. Some people with prediabetes have impaired fasting glucose (IFG), some have impaired glucose tolerance (IGT), and others have both (see table below for information on these diagnostic criteria). There are many causes and manifestations of beta-cell dysfunction and insulin resistance. Thus, simply identifying those with IGT, IFG, or both doesn’t predict the trajectory of hyperglycemia, onset of type 2 diabetes, or severity of complications, including mortality. In fact, some individuals develop complications before blood glucose rises to the level of diabetes.
With the goal of identifying populations at high risk of morbidity and mortality, researchers from the Institute for Diabetes Research and Metabolic Diseases of Helmholtz Zentrum München at the University of Tübingen, Tübingen University Hospital, and the German Center for Diabetes Research used a panel of metabolic measurements to classify individuals at risk of developing type 2 diabetes into one of six subtypes or metabolic clusters. The study was published in the January 2021 issue of Nature Medicine.2
Using blood tests, genetic markers, and the results of imaging tests, researchers classified a subset of 899 participants in the Tübingen Family study and Tübingen Lifestyle Program. During this 25-year longitudinal study, they looked at glycemia, insulin sensitivity, insulin secretion, HDL cholesterol, liver fat content, and visceral fat. To account for genetic predisposition of type 2 diabetes, the researchers used a polygenic risk score.
As expected, those with the lowest long-term risk are lean and metabolically healthy. “They have low visceral fat volumes and very low liver fat content. Fitting to this, they have high insulin sensitivity, leading to low fasting insulin levels. HDL cholesterol is in the higher range, and blood pressure is low,” explains Robert Wagner, MD, an associate professor and consultant endocrinologist at the University Hospital Tübingen in Germany, and first author of the study. This subtype is called cluster 2.
Two other clusters also have low risks of morbidity and mortality. Both clusters 1 and 4 have largely normal glycemia, adequate insulin secretion, and adequate or good insulin sensitivity. Cluster 1 comprises individuals with overweight, and cluster 4 comprises people with obesity.
“Somewhat surprisingly, the highest mortality was seen for cluster 6, which is a group comprising of persons with ‘classic’ prediabetes—IFG, IGT, or both,” Wagner says. The subjects in cluster 6 have obesity, high renal sinus fat, and insulin resistance, but low genetic risk of type 2 diabetes. These participants partially compensated for low insulin sensitivity with high levels of insulin secretion, which led to high insulin levels in the blood. “This group didn’t show very rapid progression to diabetes, but persons in this cluster tended to hover in the intermediary state of prediabetes for years,” Wagner adds. Moreover, subjects in cluster 6 had a high risk of microalbuminuria and chronic kidney disease. Their cardiovascular risk wasn’t elevated, but their overall mortality was 40% higher than cluster 1, which is the most prevalent group. In this metabolic group, high insulin levels and renal fat—rather than blood glucose levels—may be the drivers of poor outcomes.
Cluster 5 is another group of high-risk individuals with obesity. Cluster 5 has the most pronounced fatty liver and is the most likely to have IGT (with or without IFG). Their risks of developing type 2 diabetes and vascular disease were the highest among the six clusters. They also have increased risks of kidney disease and mortality.
Though not as great as cluster 5, the incidence of type 2 diabetes is elevated in cluster 3. These individuals also have a high risk of CVD. They have overweight or obesity, an increased genetic risk of type 2 diabetes, and they’re characterized by beta-cell failure and low levels of insulin secretion. The researchers further validated their results through a comparison of their six subtypes to a dataset from an independent cohort of more than 10,000 subjects from the United Kingdom.
More Prediabetes Research
In another study, a separate group of German researchers examined 47 candidate blood biomarkers to predict type 2 diabetes.3 They found that a panel of six biomarkers significantly improved the prediction of type 2 diabetes among people at risk. The researchers noted that four of the relevant biomarkers are produced by adipose tissue, underscoring the interaction between adipose dysfunction, inflammation, and insulin resistance in the pathogenesis of type 2 diabetes. Those markers are adiponectin, insulinlike growth factor binding protein 2, soluble E-selectin, and decorin. The other two biomarkers in the panel are interleukin-1 receptor antagonist and HDL cholesterol.
While both studies offer emerging insights about the development and progression of type 2 diabetes, the subtype study looks to predict outcomes beyond the onset of diabetes. Wagner says examining the association of the other group’s biomarker panel with his team’s metabolic clusters is an area of research they may pursue someday to further define and understand the risks of morbidity and mortality.
Without additional research, the six proposed prototypes of different metabolic patterns can’t be applied precisely to individuals in a clinic for treatment, Wagner says. And according to Juliann Chavez, PhD, RD, LDN, an independent consultant in Knoxville, Tennessee, the measures used to identify clusters, such as renal fat and insulin sensitivity, frequently aren’t accessible to her patient population because of cost and availability.
However, many of the features identified in these classifications can be assessed and used as a guide in clinical practice, Wagner says. “The volume of visceral fat can be approximated with a physical examination or by measuring waist circumference. Liver fat content, which is very high in clusters 5 and 6, is assessable by point-of-care ultrasound. To identify insulinopenia, the characteristic of cluster 3, we have recently proposed the use of the C-peptide-to-glucose ratio as an easily assessable biomarker,” he says.
Dietitians and diabetes educators working in an endocrinology office are apt to have access to more tests and reports than those employed in other settings, says Joan Hill, RDN, CDCES, LDN, a staff member at Steward Health Care at a Steward Medical Practice in Foxboro, Massachusetts. Her office, an endocrinology practice, uses point-of-care ultrasound to determine organ fat and has the ability to assess beta-cell function. Most primary care offices don’t have these diagnostic tests. Aside from office setting, another potential barrier to access is insurance coverage. When there’s no third-party reimbursement for tests, few people are willing to pay out of pocket, she says.
Jiada Zhan, MS, RD, based in Cleveland Heights, Ohio, says that some readily available data may help classify individuals in some clusters. For example, he says that a patient with low HDL cholesterol levels and high triglyceride levels—which suggest insulin resistance—and who is diagnosed with fatty liver disease, is similar to an individual in cluster 5, who has a high risk of developing type 2 diabetes. For these patients, he recommends a Mediterranean-style diet or the DASH diet because eating patterns such as these, which are rich in unsaturated fatty acids and low in saturated fats, can help treat dyslipidemia and nonalcoholic fatty liver disease. However, Zhan says, the subjects in the study are of European descent, so the results may not translate to other populations.
And while people in cluster 4 have obesity, they didn’t have altered glycemic utilization. Thus, Hill suggests focusing on nutrient-dense diets. For the other groups with overweight or obesity, Hill sees a greater need for calorie and/or carbohydrate restriction.
Treatment for those in cluster 6 is problematic. Because their blood glucose levels may not progress to prediabetes or type 2 diabetes, people in this metabolic group may escape notice and early treatment. However, their high risk of renal disease and mortality warrant treatment even in the absence of glycemic progression. Therefore, if dietitians have reports of elevated visceral and renal fat, they should discuss risk of morbidity and mortality with these patients and offer counsel for diet and weight management. Even when laboratory data are limited, HDL cholesterol is a commonly available biomarker and is highly valuable when assessing risk in these patients. Using HDL cholesterol alone can help pinpoint patients with high insulin sensitivity and low risk of developing type 2 diabetes. “HDL is one of the best biomarkers of type 2 diabetes and the metabolic syndrome, with higher levels associated with higher insulin sensitivity, less metabolic syndrome, and less type 2 diabetes risk,” Wagner says. “In fact, clinicians should consider other potential causes of diabetes [of a different type other] than type 2 diabetes when seeing patients with high HDL levels,” Wagner adds.
What the Future May Hold
Wagner’s group plans to conduct prospective studies to validate its findings. Concurrently, he and his colleagues will study and apply interventions to the high-risk groups, with the goal of identifying the best treatment for the right people at the ideal time.
Currently, the American Diabetes Association’s best practices for delaying or preventing type 2 diabetes include nutrient-dense and heart-healthy diets, weight loss for those with overweight or obesity, at least 150 minutes of moderate-intensity exercise each week, and a reduction in sedentary behavior.
More personalization is the wave of the future for diabetes educators, Hill says. As more research becomes available, dietitians and diabetes educators will have more tools to determine an ideal meal planning approach for people at risk of developing type 2 diabetes or complications.
— Jill Weisenberger, MS, RDN, CDCES, CHWC, FAND, is the author of four books, including Prediabetes: A Complete Guide. She’s a freelance writer, course creator, and nutrition and diabetes consultant to the food industry.
1. Centers for Disease Control and Prevention. National diabetes statistics report 2020: estimates of diabetes and its burden in the United States. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Published 2020.
2. Wagner R, Heni M, Tabák AG, et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med. 2021;27(1):49-57.
3. Thorand B, Zierer A, Büyüközkan M, et al. A panel of six biomarkers significantly improves the prediction of type 2 diabetes in the MONICA/KORA study population [published online December 31, 2020]. J Clin Endocrinol Metab. doi: 10.1210/clinem/dgaa953.