When to Test
The U.S. Preventive Services Task Force recommends screening adults aged 35 to 70 who are overweight or obese and repeating tests at least every three years — or annually for high-risk individuals. Risk factors include a family history of diabetes, hypertension, dyslipidemia, a sedentary lifestyle, gestational diabetes, and polycystic ovary syndrome.


Core Diagnostic Tests

HbA1c (Glycated Hemoglobin)

Reflects the average glucose level over the past three months.

• Diabetes: ≥ 6.5 % (48 mmol/mol)
• Prediabetes: 5.7 – 6.4 %
• Normal: < 5.7 %

Advantages: convenient, no fasting required.
Limitations: may be inaccurate in anemia, hemoglobinopathies, pregnancy, or chronic kidney disease.

Fasting Plasma Glucose (FPG)

• Diabetes: ≥ 126 mg/dL (7.0 mmol/L)
• Prediabetes: 100 – 125 mg/dL (5.6 – 6.9 mmol/L)
• Normal: < 100 mg/dL

Oral Glucose Tolerance Test (OGTT, 75 g)

Measured two hours after glucose intake.

• Diabetes: ≥ 200 mg/dL (11.1 mmol/L)
• Prediabetes: 140 – 199 mg/dL (7.8 – 11.0 mmol/L)

The OGTT is the most sensitive method for early glucose intolerance.


Random Plasma Glucose
A reading ≥ 200 mg/dL (11.1 mmol/L) in the presence of classic symptoms confirms diabetes.


Clinical Interpretation

A diagnosis should be confirmed by two abnormal results, either from the same test on different days or from two distinct methods (for example, HbA1c + FPG). Borderline findings warrant retesting after lifestyle intervention within 6–12 months.

Common errors include overreliance on HbA1c in unsuitable cases, failure to fast before testing, and neglecting clinical symptoms when results appear normal.


Why Aima Diagnostics Provides a More Accurate Assessment

Modern diagnostics are evolving beyond single-parameter evaluation. Aima Diagnostics integrates artificial intelligence with clinical validation to offer a multidimensional, precise interpretation of diabetes-related tests.


Comprehensive Metabolic Profiling
The platform examines not only glucose and HbA1c but also lipid, hepatic, inflammatory, and hormonal markers, identifying early metabolic imbalances associated with insulin resistance.


AI Models Trained on Large-Scale Medical Data
Algorithms developed by Aima are trained on millions of validated laboratory cases. They detect hidden correlations across biomarkers, enhancing diagnostic precision and minimizing false interpretations.


Contextual and Personalized Reports
Each report is individualized — considering age, sex, health background, and data trends — providing an evidence-based risk assessment rather than a simple “normal/abnormal” result.


Clinically Validated Accuracy
Every interpretive model is reviewed by medical experts from partner laboratories and clinics, ensuring compliance with GDPR and ISO 15189 standards. The hybrid AI + expert review approach guarantees reliability without replacing physician judgment.


Early Detection of Complications
Aima’s algorithms also evaluate early biochemical signals of vascular, renal, and hepatic stress, helping clinicians prevent complications before they manifest.


In Practice
Patients can securely upload their test results to the Aima Diagnostics platform.
Within minutes, they receive a structured, reader-friendly report highlighting abnormal findings, interpreting risk patterns, and offering actionable next steps.
Partner clinics and laboratories can integrate these reports into existing LIS/EMR systems, improving workflow and consistency in diagnostic interpretation.


Summary
Accurate interpretation of diabetes-related tests requires context, pattern recognition, and longitudinal analysis.
Aima Diagnostics combines medical expertise with artificial intelligence to deliver faster, deeper, and more precise insights — empowering both clinicians and patients to detect diabetes earlier and manage it more effectively.


References

• American Diabetes Association (ADA), Standards of Care in Diabetes — 2025
• U.S. Preventive Services Task Force, Screening for Prediabetes and Type 2 Diabetes (2021)
• National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
• Frontiers in Medical Engineering (2024) — AI Accuracy in Laboratory Diagnostics
• Stanford Medicine News (2025) — AI and Clinical Decision Support in Laboratory Medicine