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Prediabetes

Original Editor - Nupur Smit Shah

Top Contributors - Nupur Smit Shah, Angeliki Chorti, Vidya Acharya and Alexandra Stead  

Introduction

Prediabetes is a condition where blood glucose levels are elevated, but below the level of diabetes.[1] Based on WHO impaired glucose tolerance (2 h glucose 140 to 199mg/dL) and impaired fasting glucose (110 to 125 mg/dL) are commonly used to define prediabetes. Impaired glucose tolerance is defined by insulin resistance in muscle and reduced glucose uptake and impaired fasting glucose is defined as insulin resistance in liver and excess hepatic glucose production. [2] People with both of these are at risk of developing diabetes.

Non Modifiable Risk Factors

  • Age
  • Family history of diabetes [3]

Modifiable Risk Factors

  • Abdominal obesity
  • Dyslipidaemia, smoking and alcohol (men specific)
  • Hypertension and poor diet quality (women specific)
  • It is necessary to take gender differences into account to provide personalized care of prediabetes at the primary level.[4]

Diagnostic Criteria

  • Since prediabetes refers to the state of intermediate hyperglycaemia, it uses two specific parameters, one is impaired fasting glucose which is defined as fasting plasma glucose 6.1 to 6.9mmol/L and the other is impaired glucose tolerance which is defined as 2 h plasma glucose of 7.8-11.0 mmol/L after ingestion of 75g of glucose orally or a combination of two based on a 2 h oral glucose tolerance test. [2]
  • According to the American Diabetes association, the cut off value for IGT is the same as the WHO, but has lower cut off value for IFG(100 -125 g/dl) and has HbA1C criteria of 5.7% to 6.4% for prediabetes.[5]

Other Diagnostic Tools

  1. Diabetes Risk Score : A simple non-invasive tool for identifying Type 2 DM. Four simple parameters are noted from the known modifiable (physical inactivity and waist circumference) and non modifiable risk factors (age and family history of diabetes). The scoring system of 0 to 100 is used. The scores of all the four parameters are added, if the score is 60 and above the risk of having diabetes is very high, between 30 to 50 is moderate risk and less than 30 is low risk.[3]
  2. Physical Activity Level: This is calculated to know whether the lifestyle is sedentary or active. The mean Physical Activity Level(PAL) is calculated using the following formula: Time spent on each activity of the day (minutes)* energy cost of each activity(kcal)/1440.
    • Sedentary / light activity lifestyle :PAL value of 1.49 to 1.69.
    • Active /moderate activity lifestyle :PAL value of 1.70 to 1.99
    • Vigorous or vigorously active lifestyle: PAL value of 2.00 to 2.40.[3]

Management

Nutrition, physical activity and behavioural therapy is generally recommended for management of Type 2 DM, especially in overweight and obese individuals.[6] There are programmes like National Diabetes Prevention which can be recommended.

Lifestyle modifications

  • It mainly includes calorie restriction, increased physical activity (>150 minutes/week), self monitoring and motivational support has proven to reduce the incidence of diabetes.[7]
  • Regular exercise and weight loss reduces the risk of diabetes in the long run.[8]
  • It should include high frequency counselling (>16 sessions/6 months) and behavioural strategies to achieve the energy deficit of 500 to 750kcal/day for overweight individuals.[6]

Dietary Changes

  • ADA suggests to increase the intake of fibre, whole grains ,fruits and vegetables while reducing the intake of saturated and trans fat.[9]
  • Nutritional planning has to be based on individual needs and preferences and the main focus should be on energy deficit.[6]
  • While making the diet plan, systemic, cultural, structural and socioeconomic factors should be taken into consideration.
  • Low carbohydrate diet is found to be effective in reducing the HbA1C in three months compared to low fat diet.[10]
  • When the aim is to loose the weight by 7%,fat less than 25% of total energy and physical activity of more than 150 minutes per week has proven to reduce the incidence of type2 diabetes mellitus in the patients with diabetes.[11]
  • Monounsaturated fatty acid enriched diet was less proinflammatory and found to reduce hepatic fat. It also improved insulin sensitivity and showed positive changes in reducing body weight, blood glucose levels and plasma triglycerides.[11]
  • In conclusion, a balanced diet along with good amount of fruits and vegetables along with exercises is effective in reducing diabetes progression.[11]

Stress Management

  • Mindfulness based stress reduction is proved to have positive effects on the patients with pre diabetes.[12]
  • Muscle relaxation techniques, learning social communication skills such as empathy, learning strategies to reduce irrational thoughts and learning coping mechanisms to deal with stressful situations has an positive impact on HbA1C levels.[13]

The Use of AI in Type 2 DM Prevention

DM prevention programme suggestions lately include the use of innovative approaches such the use of artificial intelligence (AI) for risk assessment, lifestyle improvement and early intervention. Preventive intervention, which is about DM [14] but also its complications once established, [15] is enabled through wearable technology that allows continuous and real-time glycemic monitoring and insulin management. [16] Mathioudakis et al. [14] compared the use of an AI technology led lifestyle intervention based on the Diabetes Prevention Program (DPP) in 368 participants with prediabetes and overweight or obesity to human led lifestyle interventions. [14] Their findings supported the use of AI-led prevention programmes since these demonstrated similar outcomes to a human-led prevention programmes (i.e. weight reduction, physical activity, and HbA1c). [14] Contreras and Vehi [15] reviewed the use of AI in the management of DM and its associated complications and noted an acceleration of research activity in the area of AI-powered tools for prediction and prevention of complications associated with diabetes. [15] However, AI's wider clinical application to DM prevention is yet to be proven because of cost, limited accessibility and interpretability of complex data, problems with device interoperability, and ethical limitations. [16][17]

Resources

Global DM prevention initiatives by the World Health Organisation (WHO)

References

  1. ↑ Rooney MR, Fang M, Ogurtsova K, Ozkan B, Echouffo-Tcheugui JB, Boyko EJ, Magliano DJ, Selvin E. Global Prevalence of Prediabetes. Diabetes Care. 2023 Jul 1;46(7):1388-94.
  2. ↑ 2.0 2.1 Rao SS, Disraeli P, McGregor T. Impaired glucose tolerance and impaired fasting glucose. Am Fam Physician. 2004 Apr 15;69(8):1961-8.
  3. ↑ 3.0 3.1 3.2 Kaur H, Singla N, Jain R. Role of nutrition counseling and lifestyle modification in managing prediabetes. Food and Nutrition Bulletin. 2021 Dec;42(4):584-96.
  4. ↑ Siddiqui S, Zainal H, Harun SN, Ghadzi SM, Ghafoor S. Gender differences in the modifiable risk factors associated with the presence of prediabetes: A systematic review. Diabetes Metab Syndr. 2020 Sep 1;14(5):1243-52.
  5. ↑ Bansal N. Prediabetes diagnosis and treatment: A review. World J Diabetes. 2015 Mar 15;6(2):296-303.
  6. ↑ 6.0 6.1 6.2 American Diabetes Association Professional Practice Committee. 8. Obesity and Weight Management for the Prevention and Treatment of Type 2 Diabetes: Standards of Care in Diabetes-2025. Diabetes Care. 2025 Jan 1;48(1 Suppl 1):S167-S180.
  7. ↑ Echouffo-Tcheugui JB, Perreault L, Ji L, Dagogo-Jack S. Diagnosis and management of prediabetes: a review. JAMA. 2023 Apr 11;329(14):1206-16.
  8. ↑ Dansinger ML, Gleason JA, Maddalena J, Asztalos BF, Diffenderfer MR. Lifestyle Modification in Prediabetes and Diabetes: A Large Population Analysis. Nutrients. 2025 Apr 11;17(8):1333.
  9. ↑ Thipsawat S. Dietary consumption on glycemic control among prediabetes: A review of the literature. SAGE Open Nurs. 2023 Dec;9:23779608231218189.
  10. ↑ Munawaroh EF, Herawati DM, Megawati G, Wijayakesuma A, Apriani L, Sofiatin Y, Dani D. Effectiveness of personalized nutrition on management diabetes mellitus type 2 and prediabetes in adults: A systematic review. Diabetes Metab Syndr Obes. 2025 Aug 9;18:2783-96.
  11. ↑ 11.0 11.1 11.2 Yau JW, Thor SM, Ramadas A. Nutritional strategies in prediabetes: A scoping review of recent evidence. Nutrients. 2020 Sep 29;12(10):2990.
  12. ↑ Ellis DA, Carcone AI, Slatcher R, Naar‐King S, Hains A, Graham A, Sibinga E. Efficacy of mindfulness‐based stress reduction in emerging adults with poorly controlled, type 1 diabetes: a pilot randomized controlled trial. Pediatric Diabetes. 2019 Mar;20(2):226-34.
  13. ↑ Zamani-Alavijeh F, Araban M, Koohestani HR, Karimy M. The effectiveness of stress management training on blood glucose control in patients with type 2 diabetes. Diabetol Metab Syndr. 2018 May 8;10:39.
  14. ↑ 14.0 14.1 14.2 14.3 Mathioudakis N, Lalani B, Abusamaan MS, Alderfer M, Alver D, Dobs A, Kane B, McGready J, Riekert K, Ringham B, Shehadeh A, Vandi F, Wanigatunga AA, Zade D, Maruthur NM; AI-DPP Study Group. An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial. JAMA. 2025 Dec 16;334(23):2079-2089.
  15. ↑ 15.0 15.1 15.2 Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res. 2018 May 30;20(5):e10775.
  16. ↑ 16.0 16.1 Fraser R, Walker R, Campbell J, Ekwunife O, Egede L. Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes. npj Digital Medicine. 2025; 8:687.
  17. ↑ Kohli M, Pandey P, Jakhmola V, Saha S, Chaudhary M, Ansori ANM, Negi A. Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care. J Diabetes Metab Disord. 2025 May 30;24(1):132.
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