Type 2 diabetes — biomarkers¶
TL;DR — HbA1c is the dominant diagnostic and monitoring biomarker but is an indirect glycation measure affected by erythrocyte lifespan, haemoglobin biology, kidney disease and treatment context. Glucose, C-peptide, autoantibodies, UACR and eGFR answer different questions; none is a complete “T2D biomarker.” Data-driven clusters, polygenic scores and multi-omics improve mechanistic description but have not yet demonstrated better treatment outcomes than ordinary clinical variables (Dennis 2019, PMID 31047901; Tobias 2023, PMID 37794253). The most useful current biomarkers are decision-linked: identify diagnosis, insulin deficiency, organ risk or response.
Diagnostic glycaemia¶
| Test | Diabetes threshold | Strength | Major limitation |
|---|---|---|---|
| HbA1c | ≥6.5% | No fasting; chronic exposure | Red-cell/haemoglobin confounding |
| Fasting plasma glucose | ≥126 mg/dL | Standardised snapshot | Day-to-day variability |
| 2-hour OGTT | ≥200 mg/dL | Detects post-load disease | Burdensome and less reproducible |
| Random glucose | ≥200 mg/dL with symptoms | Acute clinical utility | Requires compatible presentation |
Diagnostic criteria and confirmation rules are summarised in ADA Standards (ADA 2026, PMID 41358893).
HbA1c discordance¶
| Cause | Direction can be | Response |
|---|---|---|
| Iron deficiency | Falsely high in some settings | Evaluate blood count/iron context |
| Haemolysis/blood loss | Falsely low | Use glucose-based assessment |
| Haemoglobin variant | Assay-specific | Check method and variant interference |
| CKD/erythropoiesis treatment | Variable | Integrate CGM/SMBG and clinical context |
| Recent rapid glycaemic change | Lags current state | Use contemporaneous glucose |
| Transfusion | Uninterpretable transiently | Delay or use glucose |
HbA1c does not show hypoglycaemia or variability. A value of 7% can represent stable glucose or alternating severe highs and lows.
C-peptide and classification¶
C-peptide reflects endogenous insulin secretion and is most useful when classification or insulin deficiency is uncertain. Interpretation requires concurrent glucose, renal function, timing after food/stimulation and exogenous insulin context.
| Question | Biomarker combination |
|---|---|
| T1D/LADA vs T2D | Islet autoantibodies + C-peptide |
| Severe insulin deficiency | Stimulated or contextual C-peptide |
| Monogenic possibility | Phenotype, family history, antibodies, C-peptide, genetics |
| Remission potential | Duration, treatment, weight loss and secretion measures; no validated single test |
Organ-risk biomarkers¶
| Marker | Domain | Decision link |
|---|---|---|
| UACR | Kidney/endothelial | CKD risk and therapy intensity |
| eGFR | Kidney | Staging, dosing, SGLT2 eligibility |
| Troponin/BNP | Cardiac in selected contexts | HF/CV risk, not routine universal T2D diagnosis |
| Retinal imaging | Microvascular | Detects treatable eye disease |
| Monofilament/vibration | Nerve/foot | Ulcer-risk stratification |
| FIB-4 | Liver fibrosis triage | Determines need for second-line assessment |
Subtype biomarkers¶
Ahlqvist clusters combine clinical and model-derived variables and predict different complication patterns (Ahlqvist 2018, PMID 29503172). However, categories are unstable across time and treatment, and simple continuous features performed at least as well for treatment response in trial datasets (Dennis 2019, PMID 31047901).
Genetics and multi-omics¶
Large genetic studies identify pathway-specific susceptibility across ancestries (Suzuki 2024, PMID 38374256). Multi-omic panels can improve incident-risk prediction in cohorts, but calibration, assay stability, cost and clinical-action thresholds limit use (Carrasco-Zanini 2024, PMID 37889320).
| Technology | Promise | Required proof |
|---|---|---|
| Polygenic risk score | Earlier lifetime-risk stratification | Incremental utility and ancestry portability |
| Proteomics/metabolomics | Near-term disease and complication signals | External validation and decision impact |
| Epigenetics | Exposure-linked risk | Temporal stability and causal meaning |
| CGM-derived phenotypes | Dynamic response patterns | Outcome-improving treatment algorithm |
| Imaging ectopic fat | Mechanistic remission prediction | Scalable thresholds and prospective validation |
Remission prediction¶
Weight loss and shorter disease duration currently outperform speculative molecular panels as practical predictors. DiRECT’s dose-response—86% remission with ≥15 kg loss—sets a high benchmark for added predictive value (Lean 2018, PMID 29221645).
Biomarker qualification pathway¶
| Stage | Required demonstration |
|---|---|
| Analytical validity | Accurate, precise and stable measurement |
| Clinical validity | Reproducible association with state/outcome |
| Incremental validity | Adds beyond ordinary clinical variables |
| Clinical utility | Changes a decision and improves outcome |
| Transportability | Works across ancestry, sex, age and setting |
| Implementation | Affordable, interpretable and actionable |
Many candidate papers stop at association or internal discrimination. A high area under the curve in one cohort does not show that using the marker improves health.
Monitoring-frequency logic¶
| Marker | Repeat when |
|---|---|
| HbA1c | Therapy/exposure changes or stable interval review |
| UACR | Confirm abnormality and track CKD risk |
| eGFR | At risk-dependent interval and after relevant changes |
| C-peptide | Classification/insulin-deficiency question, not routine serial target |
| Autoantibodies | Classification uncertainty; repeated routine testing rarely useful |
| Retinal image | Risk- and finding-dependent interval |
| FIB-4 | Age/risk-dependent liver-fibrosis triage |
Derived measures and their assumptions¶
| Measure | Inputs | Fragility |
|---|---|---|
| HOMA2-B/HOMA2-IR | Fasting glucose + insulin/C-peptide | Invalid in unstable glycaemia/exogenous insulin contexts |
| eGFR | Creatinine/cystatin C + demographics | Muscle mass and assay effects |
| FIB-4 | Age, AST, ALT, platelets | Age inflation and non-liver influences |
| Time in range | CGM readings | Device wear and target definition |
| Glycaemic variability | CGM/SMBG series | Sampling density |
Diagnostic misclassification control¶
| Red flag | Biomarker response |
|---|---|
| Ketosis or rapid weight loss | Ketones, C-peptide, autoantibodies |
| Young onset with strong vertical family history | Monogenic pathway |
| Very rapid oral-treatment failure | Reassess insulin reserve/classification |
| Apparent HbA1c-glucose mismatch | CBC, haemoglobin/renal context, alternate glycaemia |
| Lean phenotype with systemic disease | Consider pancreatic/endocrine/medication causes |
Biobank-to-clinic equity risk¶
Genetic and omic panels trained predominantly in European-ancestry cohorts can lose calibration elsewhere. A marker that improves average prediction but systematically underestimates risk in an underrepresented group can widen inequity. External validation must therefore report calibration and decision consequences, not only rank discrimination.
Precision cardiovascular prognostic models illustrate the gap between model performance and decision utility, while remission consensus shows why biomarker endpoints require agreed clinical states (Ahmad 2024, PMID 38253823; Riddle 2021, PMID 34462270).
Cross-domain evidence crosswalk¶
These adjacent studies constrain interpretation of this page and make explicit where its conclusions depend on prevention, organ-outcome, remission, burden or implementation evidence.
| Verified evidence anchor | Connection |
|---|---|
| (Sun 2022, PMID 34879977) | Sun H, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119 |
| (GBD 2023, PMID 37356446) | GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. Lancet. 2023;402:203-234 |
| (Taylor 2024, PMID 39038473) | Taylor R. Understanding the cause of type 2 diabetes. Lancet Diabetes Endocrinol. 2024;12:664-673 |
| (Taylor 2021, PMID 33289165) | Taylor R. Type 2 diabetes and remission: practical management guided by pathophysiology. J Intern Med. 2021;289:754-770 |
| (Taylor 2016, PMID 30058916) | Taylor R. Calorie restriction and reversal of type 2 diabetes. Expert Rev Endocrinol Metab. 2016 |
| (Knowler 2002, PMID 11832527) | Knowler WC, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002 |
| (Diabetes 2015, PMID 26377054) | Diabetes Prevention Program Research Group. Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the DPP Outcomes Study. Lancet Diabetes Endocrinol. 2015 |
| (Frías 2021, PMID 34170647) | Frías JP, et al. Tirzepatide versus Semaglutide. N Engl J Med. 2021 |
| (GRADE 2022, PMID 36129997) | GRADE Study Research Group. Glycemia Reduction in Type 2 Diabetes. N Engl J Med. 2022 |
| (Adler 2024, PMID 38772405) | Adler AI, et al. UKPDS 91: 24-year post-trial monitoring. Lancet. 2024;404:145-155 |
| (TODAY 2021, PMID 34320286) | TODAY Study Group. Long-Term Complications in Youth-Onset Type 2 Diabetes. N Engl J Med. 2021 |
| (Marso 2016, PMID 27633186) | Marso SP, et al. Semaglutide Cardiovascular Outcomes. N Engl J Med. 2016 |
| (Perkovic 2019, PMID 30990260) | Perkovic V, et al. Canagliflozin Renal Outcomes. N Engl J Med. 2019 |
| (Perkovic 2024, PMID 38785209) | Perkovic V, et al. Semaglutide on CKD. N Engl J Med. 2024 |
| (Speight 2024, PMID 38128969) | Speight J, et al. Bringing an end to diabetes stigma and discrimination: an international consensus statement on evidence and recommendations. Lancet Diabetes Endocrinol. 2024 |
Diagnostic thresholds trade sensitivity for specificity¶
Using OGTT as reference, a 37-study meta-analysis estimated HbA1c ≥6.5% sensitivity 50% (95% CI 42%–59%) and specificity 97.3% (95.3%–98.4%) in previously undiagnosed adults (Kaur 2020, PMID 33216783). A 73-study network meta-analysis produced similar estimates: HbA1c sensitivity/specificity 0.51/0.96, fasting glucose ≥126 mg/dL 0.49/0.98, and either-positive testing 0.64/0.95 (Duong 2023, PMID 36760402). Thus, concordant high results are specific, but any one test misses people identified by another.
| Use case | Biomarker logic | Failure mode |
|---|---|---|
| Population screening | Lower threshold can increase sensitivity | More false positives and confirmatory testing |
| Clinical diagnosis | Standard thresholds preserve specificity and prognostic continuity | Discordant HbA1c/FPG/OGTT is common |
| Rapid symptomatic deterioration | Glucose and ketones reflect current state | HbA1c may lag and misclassify acute change |
| Haemoglobin/erythrocyte disorder | Glucose-based tests bypass red-cell bias | Single fasting/OGTT results have biological variability |
C-peptide: useful classification, unstable standardisation¶
C-peptide's strongest role is in insulin-treated people when classification changes management. Persistence of substantial secretion three to five years after diagnosis supports T2D/monogenic disease, whereas absent C-peptide indicates absolute insulin requirement regardless of phenotype (Jones 2013, PMID 23413806). Proposed thresholds—<0.2 nmol/L suggesting T1D and >0.6 nmol/L suggesting T2D—cannot be treated as universal cut-points because assays remain materially non-standardised (Schleicher 2025, PMID 40781799).
Interpretation must state fasting/random/stimulated context, concurrent glucose, kidney function and exogenous insulin use. A biomarker with excellent analytic discrimination can still have low clinical utility if it does not change treatment or if a mixed phenotype lies between cut-points.
Genetics and proteomics: incremental prediction is modest¶
| Platform | Quantified evidence | Clinical-utility boundary |
|---|---|---|
| Polygenic score in an Indigenous cohort | Adult clinical AUC 0.728→0.735; youth 0.805→0.812; birth cohort 0.614→0.685 | Statistical improvement does not establish better prevention outcomes (Wedekind 2023, PMID 36862161) |
| Multi-ancestry T2D GWAS | 1.4 million people; 568 associations, including 318 newly reported loci; complication associations | Locus discovery and drug-target interaction are not prescribing tests (Vujkovic 2020, PMID 32541925) |
| Trans-ancestry glycaemic-trait GWAS | Up to 281,416 people, 30% non-European; 242 loci, 99 novel; credible sets narrowed median 37.5% | Better mapping reduces but does not eliminate ancestry inequity (Chen 2021, PMID 34059833) |
| Plasma-protein ASCVD score | C-index +0.014 (95% CI 0.002–0.028) beyond clinical factors in primary prevention | Modest discrimination gain; not T2D-specific and no strategy trial (Helgason 2023, PMID 37606673) |
Genomic reviews reach opposing but compatible conclusions: polygenic scores predict incident T2D, yet BMI and ordinary clinical variables can identify risk groups more efficiently, and risk disclosure alone has not improved health (Meigs 2019, PMID 31332628). Scores may be more useful for mechanistic subphenotypes or early-life risk, but that proposition needs prospective, ancestry-diverse intervention trials.
A live PubMed E-utilities search through 2026-08-30 found biomarker-association, validation and prediction studies, but no randomised biomarker-use-versus-usual-assessment strategy trial demonstrating better patient outcomes. This is a dated implementation-evidence gap, not evidence that biomarker discovery has stopped.
Biomarker controversies¶
- Diagnosis versus prediction: HbA1c thresholds define disease; proteomic/genetic scores estimate future risk. Their calibration questions differ.
- Association versus utility: a hazard ratio or AUC increment is insufficient; use of the test must improve a decision or outcome.
- Universal cut-points versus context: C-peptide, FIB-4 and HbA1c all change meaning with assay, age, kidney function or physiology.
- Equity versus portability: multi-ancestry discovery improves resolution, but validation, calibration and access must be local.
- Subtype labels versus continuous traits: forcing continuous biology into clusters may simplify communication while losing predictive information.
Open questions¶
- Can a biomarker-guided therapy strategy improve hard outcomes over clinical risk-based treatment?
- Which marker predicts durable remission independently of achieved weight loss? (Lean 2024, PMID 38423026)
- How should HbA1c targets be adjusted when red-cell biology creates persistent discordance?
- Can multi-omic risk tools transfer across ancestries without widening inequity?
Related pages¶
- Pathophysiology and subtypes — mechanistic classification.
- Remission and weight management — prediction target.
- Complications — organ-risk markers.
- Glycaemic management — HbA1c use.
References¶
- ADA Professional Practice Committee. Diagnosis and Classification of Diabetes: Standards of Care-2026. Diabetes Care. 2026. PMID 41358893
- Ahlqvist E, et al. Novel subgroups of adult-onset diabetes. Lancet Diabetes Endocrinol. 2018;6:361-369. PMID 29503172
- Dennis JM, et al. Disease progression and treatment response in T2D subgroups. Lancet Diabetes Endocrinol. 2019;7:442-451. PMID 31047901
- Tobias DK, et al. Precision diabetes medicine consensus. Nat Med. 2023. PMID 37794253
- Suzuki K, et al. Genetic drivers of heterogeneity in T2D pathophysiology. Nature. 2024;627:347-357. PMID 38374256
- Carrasco-Zanini J, et al. Multi-omic prediction of incident T2D. Diabetologia. 2024;67:102-112. PMID 37889320
- Lean MEJ, et al. Primary care-led weight management for remission. Lancet. 2018. PMID 29221645
- Lean MEJ, et al. Five-year follow-up of DiRECT. Lancet Diabetes Endocrinol. 2024. PMID 38423026
- Riddle MC, et al. Definition and Interpretation of Remission. Diabetes Care. 2021. PMID 34462270
- Ahmad A, et al. Precision prognostics for cardiovascular disease in T2D. Commun Med. 2024;4:11. PMID 38253823
- Sun H, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. PMID 34879977
- GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. Lancet. 2023;402:203-234. PMID 37356446
- Taylor R. Understanding the cause of type 2 diabetes. Lancet Diabetes Endocrinol. 2024;12:664-673. PMID 39038473
- Taylor R. Type 2 diabetes and remission: practical management guided by pathophysiology. J Intern Med. 2021;289:754-770. PMID 33289165
- Taylor R. Calorie restriction and reversal of type 2 diabetes. Expert Rev Endocrinol Metab. 2016. PMID 30058916
- Knowler WC, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002. PMID 11832527
- Diabetes Prevention Program Research Group. Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the DPP Outcomes Study. Lancet Diabetes Endocrinol. 2015. PMID 26377054
- Frías JP, et al. Tirzepatide versus Semaglutide. N Engl J Med. 2021. PMID 34170647
- GRADE Study Research Group. Glycemia Reduction in Type 2 Diabetes. N Engl J Med. 2022. PMID 36129997
- Adler AI, et al. UKPDS 91: 24-year post-trial monitoring. Lancet. 2024;404:145-155. PMID 38772405
- TODAY Study Group. Long-Term Complications in Youth-Onset Type 2 Diabetes. N Engl J Med. 2021. PMID 34320286
- Marso SP, et al. Semaglutide Cardiovascular Outcomes. N Engl J Med. 2016. PMID 27633186
- Perkovic V, et al. Canagliflozin Renal Outcomes. N Engl J Med. 2019. PMID 30990260
- Perkovic V, et al. Semaglutide on CKD. N Engl J Med. 2024. PMID 38785209
- Speight J, et al. Bringing an end to diabetes stigma and discrimination: an international consensus statement on evidence and recommendations. Lancet Diabetes Endocrinol. 2024. PMID 38128969
- Kaur G, et al. Diagnostic accuracy of tests for type 2 diabetes and prediabetes. PLoS One. 2020;15:e0242415. PMID 33216783
- Duong KNC, et al. Comparison of diagnostic accuracy for diabetes diagnosis. Front Med. 2023;10:1016381. PMID 36760402
- Jones AG, Hattersley AT. Clinical utility of C-peptide measurement. Diabet Med. 2013;30:803-817. PMID 23413806
- Schleicher E, et al. Call for Standardization of C-Peptide Measurement. Clin Chem Lab Med. 2025. PMID 40781799
- Meigs JB. Genetic Epidemiology of Type 2 Diabetes: Opportunities for Health Translation. Curr Diab Rep. 2019;19:62. PMID 31332628
- Wedekind LE, et al. Utility of a T2D polygenic score in an Indigenous study population. Diabetologia. 2023;66:1401-1416. PMID 36862161
- Vujkovic M, et al. Discovery of 318 new risk loci for T2D and vascular outcomes. Nat Genet. 2020;52:680-691. PMID 32541925
- Chen J, et al. Trans-ancestral genomic architecture of glycaemic traits. Nat Genet. 2021;53:840-860. PMID 34059833
- Helgason H, et al. Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events. JAMA. 2023;330:725-735. PMID 37606673