Guide series, Part 2 of 5
HbA1c, the Three-Month Average That Is Not Average
HbA1c is treated as a simple summary of glucose. It is not. Two people with identical mean glucose can produce HbA1c values 10 mmol/mol apart. That gap is biology, not measurement error, and it changes what your TIR target should be.
What HbA1c actually measures
HbA1c is the proportion of haemoglobin (the oxygen-carrying protein in red blood cells) that has glucose chemically attached to it. Glucose binds to haemoglobin slowly and irreversibly across the lifespan of a red blood cell (around 120 days). The result is a weighted average of glucose exposure over roughly three months, with the most recent few weeks counting most.
This is why HbA1c is reported in millimoles per mole (mmol/mol) or as a percentage. It is a measure of how much haemoglobin has been glycated, not a direct measure of glucose. The translation from one to the other depends on biology, and that biology is not the same in everyone.
The unit conversion: 48 mmol/mol = 6.5%. 53 mmol/mol = 7.0%. 58 mmol/mol = 7.5%. The international consensus 70% TIR target corresponds approximately to an HbA1c of 53 mmol/mol (around 7.0%) for an average glycator on a Zone P device.
HbA1c is not always reliable. Certain conditions can distort HbA1c independently of glucose: iron deficiency anaemia (falsely raises it), haemoglobin variants such as HbS, HbC, or HbE (can raise or lower it depending on the assay), chronic kidney disease and haemolytic anaemias (falsely lower it by shortening red blood cell lifespan), vitamin B12 or folate deficiency (falsely raises it), and recent blood transfusion (disrupts the glycation window). These are analytical interferences, not the biological glycation variation (HGI) discussed later in this guide. If any of these conditions apply to you, discuss with your diabetes care team whether your HbA1c can be interpreted at face value before using the personalised target matrix in Part 4 (Lenters-Westra et al, 2025, Diabetic Medicine).
The DCCT backbone, why HbA1c is still indispensable
The Diabetes Control and Complications Trial (DCCT, 1993) randomised 1,441 people with Type 1 diabetes to intensive or conventional insulin therapy and followed them for 6.5 years. Intensive therapy reduced retinopathy by 76%, nephropathy by 50%, and neuropathy by 60%. Every 10% reduction in HbA1c was associated with around a 40% reduction in complication risk. The 30-year follow-up (EDIC, Braffett 2025) confirmed these benefits persist for decades.
This is Grade A evidence. It established HbA1c as the gold standard outcome measure in diabetes and has not been overturned. Any TIR framework still has to account for what HbA1c uniquely captures.
Lachin 2022 (DCCT/EDIC analysis): when HbA1c and estimated TIR were directly compared as predictors of retinopathy in the DCCT dataset, HbA1c was the stronger predictor. TIR added very little above HbA1c. This is the result that stops “TIR will replace HbA1c” being a clean argument.
Same glucose, different HbA1c
If HbA1c were a pure function of mean glucose, two people with the same average glucose would have the same HbA1c. They do not. The classic observation, established by Hempe, McCarter and colleagues across the early 2000s, is that two people with identical mean glucose can record HbA1c values that differ substantially, in some reported observations by up to around 10 mmol/mol.
McCarter (2004) used DCCT data to show that this between-person variation is real biology. The within-person biological variation (CV around 3.6%) actually exceeds analytical imprecision, confirming HGI is real biology, not measurement noise. The between-person variation is much larger again and reproducible across years. People are not noisy; they are different.
Two kinds of discordance. When HbA1c and CGM mean glucose disagree, the cause falls into one of three categories (Lenters-Westra et al, 2025): (1) analytical interference (haemoglobin variants, iron deficiency, renal disease; see the warning in the section above), (2) biological glycation variation (HGI, the subject of this guide), or (3) CGM wear and calibration issues. The clinical workflow is: rule out analytical causes first, then assess glycation phenotype, then check CGM coverage. This guide majors on category 2, but categories 1 and 3 must be excluded with your care team before the glycation interpretation applies.
This stable per-person tendency to glycate faster or slower than average has a name: the haemoglobin glycation index (HGI).
HGI = Measured HbA1c minus predicted HbA1c (where predicted HbA1c, also called GMI, is calculated from mean CGM glucose using the Bergenstal 2018 formula: GMI mmol/mol = 12.71 + 4.70587 x mean glucose mmol/L). A positive HGI means you glycate faster than the average; a negative HGI means slower.
Three glycator phenotypes
Across populations of people with Type 1 diabetes, HGI distributes across a wide range. The Pemberton-Chalew framework groups people into three working categories based on their mean HGI across multiple visits (mHGI, the average of HGI from at least 3 paired HbA1c and 90-day CGM windows over a minimum 9-month period):
Low glycator (mHGI < -3 mmol/mol)
Your HbA1c runs lower than your CGM mean glucose would predict. Same glucose, lower HbA1c. You may achieve good complication protection with a lower TIR target than the population consensus.
Average glycator (mHGI -3 to +3 mmol/mol)
Your HbA1c and glucose match the standard population relationship. The international 70% TIR target was modelled around your biology.
High glycator (mHGI > +3 mmol/mol)
Your HbA1c runs higher than your CGM mean glucose would predict. Same glucose, higher HbA1c. The standard 70% TIR target may not be enough; you may need a higher TIR to achieve the same complication protection as an average glycator.
Lachin (2007), using DCCT data and the correctly-specified mean-glucose-adjusted analysis, showed that high glycators had higher rates of retinopathy and nephropathy at the same mean blood glucose. (The same paper’s title-line claim that HGI was “not independent” came from a separate model that adjusted for HbA1c when testing HGI’s effect, a Table 2 fallacy because HGI is constructed as the residual of HbA1c minus GMI; Part 3 walks through this in detail.) The HGI signal has since been replicated in modern cohorts: Maran (2022), Bovee (2024), Shah (2024/2025), Hempe (2024), McCarter and Chalew (2024), Pemberton (2025). Eight of the nine studies cited here confirm; none refute. In Taleb’s framing, if you are a high glycator, the population average is not your probability; your individual risk accumulates along your own trajectory, not the ensemble’s.
The biological mechanism, why some people glycate faster
The mechanism is well characterised, largely through Hempe and colleagues at LSU Health Sciences Center.
Three biological features influence individual glycation rate:
- GLUT-1 transporter density on red blood cells. Red blood cells take up glucose passively via GLUT-1 transporters. The density of these transporters varies between individuals and is partly genetic. More transporters means more glucose entering the cell at a given blood glucose concentration, and more substrate available for glycation.
- The glucose oxidative pathway (GOP) and vitamin C recycling. Glucose inside the red blood cell can either enter the glycation reaction or be diverted into the pentose phosphate / GOP pathway. The GOP pathway also helps recycle vitamin C, which protects haemoglobin from glycation. Faster GOP flux means less glucose available for glycation; slower GOP flux means more.
- G6PDH enzyme activity. Glucose-6-phosphate dehydrogenase is the rate-limiting enzyme of the pentose phosphate pathway. Its activity influences how much glucose is diverted away from glycation. G6PDH activity has known genetic variants and varies between ethnic groups.
These are largely outside behavioural control. They are stable, trait-like properties of your red blood cell biology. This is why HGI is reproducible within an individual across years, and why it has a substantial heritable component.
The ethnicity effect, what GNL research shows
Pemberton, Uday, Krone, Fang and Chalew (2025, BMJ Open Diabetes Research and Care) studied 168 children and young people with Type 1 diabetes in Birmingham, UK, with paired laboratory HbA1c and 90-day CGM mean glucose data. After adjusting for mean blood glucose, CGM use, insulin delivery method, and socioeconomic deprivation, the Black ethnic group had HbA1c approximately 4 mmol/mol higher than White or South Asian peers.
This is not a measurement artefact and it is not explained by differences in technology access or care. On average across this Birmingham cohort, participants in the Black ethnic group showed faster glycation for the same glucose exposure than White or South Asian peers. This is a population-level mean difference with substantial within-group variation; it does not predict any individual’s glycation rate. McCarter and Chalew’s 2024 analysis using the DCCT cohort plus a separate non-Hispanic Black dataset reaches the same conclusion.
An intellectual honesty note on HbS trait. Sickle cell trait (HbS) is more prevalent in Black populations and can affect HbA1c assay results depending on the laboratory method used (some assays read falsely low in the presence of HbS). The Pemberton 2025 study adjusted for mean blood glucose, so the +4 mmol/mol finding reflects a glucose-independent biological difference in glycation. However, the possibility that assay-level interference from haemoglobin variants contributes to ethnic HbA1c differences in other cohorts using different laboratory methods should not be dismissed. This is an area where individual verification with your care team matters: if you carry a haemoglobin variant, your HbA1c may need interpreting through that lens as well as the glycation biology lens.
The treat-to-target trap: the same Pemberton 2025 cohort showed the Black group experienced significantly more time below 3.9 mmol/L and below 3.0 mmol/L. The likely explanation: clinical teams treat all patients to the same HbA1c target, which forces more aggressive insulin dosing in people whose biology produces higher HbA1c for the same glucose. The result is more hypoglycaemia, not better outcomes.
Treating everyone to the same HbA1c number ignores this. Recognising glycator status changes what good care looks like. The Via Negativa lens: remove the universal target before it creates harm. What you subtract (the one-size-fits-all assumption) matters more than what you add.
Why both HbA1c and TIR are needed, and neither alone is enough
HbA1c captures the biological consequence of glucose exposure for that individual: how much haemoglobin (and, by reasonable extension, other proteins relevant to complications) has actually been glycated. TIR captures the glucose exposure itself: how much time was spent in different ranges, and (with derivative metrics) how variable that exposure was.
If HGI were zero for everyone, TIR alone would be enough. It is not. Lachin’s 2022 finding that HbA1c outperforms estimated TIR as a predictor of retinopathy is exactly what you would expect when individual glycation biology contributes meaningfully to who develops complications. The implication is not “stop measuring TIR”; it is “interpret TIR through the lens of your glycator status”.
That interpretation is what Part 3 (calculate your status) and Part 4 (the personalised matrix) are about.
This guide is educational. The glycator categories are working clinical concepts derived from peer-reviewed evidence (DCCT, Lachin, McCarter, Hempe, Pemberton, Maran, Bovee, Shah) and an in-press / under-review framework (Pemberton-Chalew mHGI). They are not yet codified in mainstream clinical guidelines. HbA1c can also be distorted by analytical interferences (iron deficiency, haemoglobin variants, kidney disease, and others) that are outside the scope of this guide; discuss with your care team to rule these out before interpreting your HbA1c through the glycation lens. This guide covers the major patterns; the individual context can only come from a skilled clinician. It is not medical advice and cannot replace individual clinical guidance from your diabetes care team.
Evidence cited: DCCT 1993; Braffett 2025 (30-year EDIC); Lachin 2007, 2022 (DCCT secondary analyses); McCarter 2004; McCarter and Chalew 2024; Hempe and Hsia 2022; Hempe 2024 (GOP mechanism); Pemberton, Uday, Krone, Fang, Chalew 2025 (BMJ Open DRC); Maran 2022; Bovee 2024; Shah 2025; Bergenstal 2018 (GMI); Chalew, Pemberton et al. 2026 (ADA poster, mHGI framework); Lenters-Westra et al. 2025 (HbA1c-GMI discordance, analytical interference framework, Diabetic Medicine).