Digital lifestyle program cuts diabetes risk by 46% in prediabetics, study of 130k+ adults reveals

By Priyanjana Pramanik, MSc.Reviewed by Susha Cheriyedath, M.Sc.Apr 14 2025

Could a 45-minute weekly digital coach be the key to reversing diabetes? New study of 130k+ adults shows dramatic risk reduction and remission rates, without pills or extreme diets.

Study: Lifestyle Modification in Prediabetes and Diabetes: A Large Population Analysis. Image Credit: RSplaneta / Shutterstock

In a recent article published in the journal Nutrients, researchers in the United States assessed a large population of prediabetic, diabetic, and healthy individuals to test the effectiveness of a digital lifestyle modification program in reducing cardiovascular and diabetes risk and improving metabolic markers.

Their findings indicate that the lifestyle intervention significantly reduced 10-year diabetes risk among prediabetics by nearly 46% and increased the diabetes remission rate, highlighting the importance of lifestyle changes.

However, the study was not a randomized trial, and participation in the lifestyle intervention was voluntary, which may introduce selection bias.

Background

Diabetes mellitus is diagnosed based on elevated fasting glucose or HbA1c levels and is a significant risk factor for neuropathy, retinopathy, kidney disease, and atherosclerotic cardiovascular disease (ASCVD).

Prediabetes affects about one-third of middle-aged and older adults in the United States, and various factors such as inactivity, family history, and obesity increase the risk of progressing to diabetes.

Behavioral lifestyle interventions that target 7% weight loss and increased physical activity can halve diabetes risk, but traditional programs require frequent in-person sessions and are not widely adopted. Risk prediction models have been developed to identify high-risk individuals, using factors like glucose, body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and triglycerides.

The authors previously developed a 10-year diabetes risk model based on glycated serum albumin (not glycated serum protein), fasting glucose, adiponectin, and triglycerides, which achieved high predictive accuracy using prospective data from the Framingham Offspring Study. Given the importance of lifestyle changes in diabetes and ASCVD prevention, easily implementable interventions targeting high-risk individuals need to be tested.

About the study

The study evaluated 133,764 adults, categorizing them as diabetic (7.5%), prediabetic (36.2%), and healthy (56.3%), based on fasting glucose and HbA1c levels.

Participants underwent fasting blood tests measuring adiponectin, insulin, glucose, HbA1c, glycated serum protein, high-sensitivity C-reactive protein (hs-CRP), fibrinogen, myeloperoxidase, lipoprotein-associated phospholipase A2 (LpPLA2), direct low-density lipoprotein cholesterol (LDL-C), small dense LDL-C, and standard lipids, using automated and standardized assays.

After 6 to 12 months, follow-up blood sampling was conducted for slightly over 20% of prediabetics and 22% of diabetics. Among those with follow-up data, 12.2% of prediabetic and 9.7% of diabetic participants agreed to participate in a digital, voluntary, dietitian-guided lifestyle program focused on dietary and behavioral changes.

The program’s impact was assessed using a biochemical 10-year diabetes risk model previously developed by the authors using data from the Framingham Offspring Study. The model incorporated fasting glucose, glycated serum albumin, adiponectin, and triglyceride levels.

Changes in diabetes risk, metabolic markers, weight loss, and remission rates were analyzed to determine the program’s effectiveness compared to participants who did not engage in the intervention.

Findings

Diabetic and prediabetic groups had fewer women than the group of healthy subjects and were significantly older and heavier, with higher BMI and body weight.

Blood glucose control worsened across groups: HbA1c, fasting glucose, glycated serum protein, fasting insulin, and C-peptide levels were all significantly higher in prediabetic and diabetic men and women than in the population of healthy individuals.

Insulin resistance showed the most striking increase, greater by 75% and 260% in prediabetic and diabetic men, and 112% and 306% in women, respectively. Insulin production was notably lower only in diabetic subjects. Many diabetics showed both insulin resistance and reduced insulin production.

Insulin production and insulin sensitivity in healthy, prediabetic, and diabetic subjects. In this figure, we plotted data for the entire population of 133,764 subjects (56.3% healthy, 36.2% prediabetic, and 7.5% diabetic). Homeostasis assessment model assessment of insulin production, or HOMA_β, was calculated as equal to [360 × fasting insulin (µU/mL)]/[fasting plasma glucose (mg/dL) − 63] as previously described and plotted on the horizontal axis (22). The homeostasis model of insulin resistance, or HOMA_IR, was calculated as equal to [fasting insulin (µU/mL)] × [fasting plasma glucose (mg/dL)]/405 as previously described. We then plotted the reciprocal of this value multiplied by 100, or as [(1/HOMA_IR) × 100], for the same subjects as a measure of insulin sensitivity (HOMA_S). What can be clearly seen on the graph is that diabetic subjects not infrequently have HOMA_β of <60 (the 25th percentile value in healthy subjects), as well as decreased insulin sensitivity as compared to healthy and prediabetic subjects, with clear lines of demarcation between diabetic, prediabetic, and healthy subjects.

Inflammation markers were elevated, especially hs-CRP (by 90% for men and 200% for women in diabetics). Smaller changes were seen for adiponectin, fibrinogen, myeloperoxidase, and LpPLA2. The 10-year risk of diabetes was substantially higher in prediabetics (7% for men, 4.2% for women) compared to healthy subjects (0.6% for men, 0.3% for women).

Regarding lipids, only modest changes were seen for LDL-C and apolipoproteins. However, prediabetic and diabetic subjects had significantly higher fasting triglycerides and small dense LDL-C, and lower HDL-C levels, highlighting a distinctly more atherogenic lipid profile. For example, small dense LDL-C increased by up to 35% in diabetic women, while HDL-C decreased by 23%, and triglycerides rose by 70%.

Lifestyle modification in prediabetic individuals significantly reduced diabetes risk, triglycerides, LDL-C, and insulin resistance while increasing adiponectin levels compared to controls. The analysis found that prediabetic subjects experienced a 45.6% relative reduction in predicted diabetes risk, compared to only a 1.6% reduction in the control group.

Among diabetics, the lifestyle group achieved a 2.4-fold increase in remission rate (8.2% vs. 3.4%), along with greater weight loss and improvements in glycemic and inflammatory markers.

Conclusions

The study showed that prediabetic and diabetic individuals already display significant metabolic and inflammatory changes compared to healthy people. Insulin resistance, more than impaired insulin production, appears to drive early abnormalities.

However, in established diabetes, both insulin resistance and reduced insulin secretion are evident. Increased hs-CRP levels suggest an important role in inflammation, while changes in other markers were more modest.

Although lipid abnormalities were relatively mild for LDL-C and apolipoproteins, greater differences in triglycerides, HDL-C, and small dense LDL-C suggest an increased cardiovascular risk even before overt diabetes develops.

The results emphasize that metabolic deterioration starts well before clinical diabetes is diagnosed, underlining the importance of early identification and intervention in high-risk individuals.

While this digital lifestyle program was effective in improving markers of risk, the authors note that broader evidence suggests fully digital interventions may have more modest impacts than blended or face-to-face approaches. Meta-analyses cited by the authors show that combined in-person and digital interventions result in greater conversion to normoglycemia than digital-only programs.

Future studies should further explore how inflammation and lipid abnormalities contribute to diabetes progression and cardiovascular disease risk.