Is The A1c Accurate for Every Prediabetic?
From the Reddit forum r/Prediabetes, mi_Swimmer writes:
“My most recent A1c of 5.6%, just below the pre-diabetic cutoff, rose from 5.4 about 6 months. As a healthy (BMI 21.5, BF 22%), active (HIIT 4 days a week), mid-20’s female, this really surprised me. I’ve been eating better. I decided to invest in a CGM (Dexcom G7). My average CGM blood glucose over the two weeks was 98 mg/dL. How can I resolve this high A1c with normal CGM results?”
This is a frustrating and not uncommon situation to find ones A1c stubbornly in the prediabetic range. mi_Swimmer is not overweight, and regularly exercises. Why doesn’t her CGM (Continuous Glucose Meter) data match her A1c?
The CGM, which only recently became available to non-diabetics without prescription, has upset the proverbial apple cart, but serendipitously revealed rotten apples underneath.
For years, I could not fathom why high A1c’s mar the health record of seemingly healthy patients who don’t qualify for prediabetes by other tests. The A1c attempts to assign a number to the average glucose over time. Because the CGM directly averages glucose readings over time, the CGM holds primacy and believability over the A1c. To uncover the foundations and faults of the A1c test, let’s start with its birth.
The orphan A1c was adopted into diabetic testing, tethered to the CGM.
In 1957, Dr. Rhinesmith’s lab discovered a substance stubbornly glued to one of the hemoglobin chains. By 1975, researchers had determined this was a sugar chain and within the following decade, the A1c’s realization as a diabetes test began to unfold. The A1c, it was hoped, could serve as a holy grail replacement test for diabetes. The old stalwart, FBS (Fasting Blood Sugar) requires fasting and is ridiculously sensitive to common colds and insomnia, whereas the cumbersome GTT (Glucose Tolerance Test) consumes hours of lab time.
In 2008, A1c-Derived Average Glucose (ADAG) Study Group linked A1c to the CGM with a “mathematical relationship between A1c and average glucose (AG) levels,” one which we use everyday. This equation allowed the A1c to become ascendant and replace the other two tests. A single number, easy to interpret, could gauge the condition of those with an established diagnosis of Type 2 diabetes. It was easy to see if a diabetic was improving or backsliding with treatment.
The seeds of the present A1c problem begins with the push to extend the A1c for diagnosing and monitoring prediabetes. There are three red flag underlying issues in retrospect.
In the ADAG study, the researchers took great pains to minimize real world variations in A1c. For example, they mitigated against machine analyzer variation. There are four test methods for A1c, and dozens of machines, each with their own little quirks. Some are more sensitive to hemoglobin variants for example, but the same sample will render results which can vary significantly. The researchers attempted to remove these variables, employing four select, highly consistent and precise instruments which met standards set by the National Glycohemoglobin Standardization Program, (NGSP). We will return to this later when we talk about real world testing. By taking the average of the four A1c analyzer results the test variation was tamed.
They went so far as to shut down an entire study arm in Asia when it was discovered that the samples were not being stored correctly. By doing this, the researchers markedly improved the A1c test variability, but distancing it from mirroring real world patient testing results.
Second, ADAG sought to vigorously reduce A1c naturally-occurring variability by screening eligible participants. To enter the study, non-diabetic participants were initially screened for A1c variation and eliminated if they demonstrated a single percentage point in changing A1c over the preceding six months. Similarly, entrants were eliminated if they were pregnant, trying to get pregnant, or more broadly, “any conditions or treatments” which might cause a deviation in glucose levels. Nearly a quarter of the enrolled participants did not make the first stage of the cut. It’s not hard to see that in the real world, a substantial fraction would not have had their A1c numbers considered valid for the subsequent comparison to CGM in this benchmarking study.
By today’s standards, the technology of the ADAG CGM’s would be considered inaccurate and crude. Participants had to double check their CGM’s with frequent fingersticks. Today’s CGM’s are deemed to be of such high quality, that the FDA no longer recommends fingerstick glucose comparisons.
The ADAG Study Group should be commended for rigorously winnowing down variation in their subjects, selecting precise analyzers and averaging out variations. Let’s see what the study found, particularly in regard for non-diabetics.
In the scattergraph from their study, a “best-fit” line can be drawn. From this line, they calculate that an A1c of 5.0% corresponds to an average glucose of 97 mg/dL and A1c of 6.0% corresponds to an average glucose of 126 mg/dL, for example. (Table 2 from the original study below)
Look closely, not a tight fit. Plotted vertically, above and below any point along this line, plenty of averaged glucoses levels correspond to any particular A1c value. In fact, researchers state that “90% of the estimates fell within the 15% range of the regression line.”
Take as an example in their table, an A1c of 5.0% corresponds to an average glucose input potentially ranging from 76 to 120 mg/dL. (CI = Confidence Interval) In other words, if you had an A1c of 5.0% your average glucose value could have been as low as 76 mg/dL or as high as 120 mg/dL. An A1c of 6.0% corresponds to a confidence interval of average glucose levels ranging between 100 (normal) and 152 mg/dL, (diabetic)! This is a much wider range than most are aware of.
Good Enough for Diabetes Work, But Not Prediabetes
The A1c has enough precision (accuracy) for monitoring diabetic care and demonstrated value from studies of diabetic complications. If a diabetic’s A1c is 9.0%, it doesn’t really matter for treatment if the real value is 8.7 or 9.3. Only recently have researchers asked the question, whether the correlation of A1c and average glucose holds at lower levels. This is a different question than the preceding section of how wide is the band of average glucose corresponding to an A1c value.
A 2023 paper coming out of Dr. Bergenstal’s lab attempted to replicate the ADAG study, for those without diabetes. (HbA1c <5.7% and negative islet antibodies to preclude those with autoimmune diabetes) Instead of 12 weeks of CGM data, their ten-day CGM recording more closely parallels someone who is like our Redditor. Take a look at the scattergraph from their study, ignoring the red line, which I drew.
Most will be hard-pressed to show any relationship between A1c and mean (average) glucose. If there is a relationship between the A1c and average glucose in the prediabetic range, then you should see a general relationship between the line which I superimposed from the 2008 ADAG study.
Here is a more personal way of looking at data. Extending a horizontal line near the top of the graph from the A1c of 5.5% (between 5.4 and 5.6%) to dots farthest left to the farthest right, you can see a CGM reporting anywhere from 91 to 117 mg/dL would have had the same 5.5% A1c result. In other words, someone who has a 5.5% A1c could have an average glucose spanning 26 mg/dL, from normal to prediabetic. The ADA defines the Prediabetes eAG (Estimated Average Glucose) average to be greater than 114 mg/dL and less than 140 mg/dL.)
Without prematurely breaching the subject of analyzer precision, to be discussed later, there seems to be a disconnect between the base assumptions for the A1c. Long understood are A1c’s problems in diseases such as iron deficiency, liver and kidney failure, sickle cell and hemoglobin variations.
This random dot scatter fractures the bedrock assumptions of the A1c for prediabetic diagnosis and monitoring. It reflects the untestable individual natural differences in healthy individuals.
One is glycation (the rate sugars attach to hemoglobin). Assumed to be universally constant, and starting from the same baseline, this presumption does not seem to be justified. Secondly, red cell lifespan is known to differ significantly between individuals. A 5-day difference between the assumed 90-day average survival time and another person’s 95 day red cell survival time will lead to higher A1c.
A different 2024 Harvard study concluded, “Nonglycemic effects of HbA1c led to a mean absolute error for average glucose calculated from HbA1c of 26 mg/dL. Mismatches between CGM and HbA1c >40 mg/dL occurred more than 5% of the time.” The authors conclude that, “Discrepancies may thus be larger than the difference between common thresholds for the diagnosis of prediabetes and diabetes.”
There’s icing on the cake. According to several Chinese research labs, red cells of diabetics have significantly shorter survival times. Prediabetics have significantly longer survival times, so the A1c overestimates the average glucose — higher than it should.
This brings into serious question the 2010 step the ADA took to expand the A1c for setting a diagnostic threshold for diagnosing diabetes and monitoring those who are in the prediabetic zone.
Controversy over the Cutoff
In 2008, an international expert committee composed of representatives from the American Diabetes Association, the European Association for the Study of Diabetes, and the International Diabetes Federation convened to set an A1c value of 6.0% cutoff for identifying people in need of “demonstrably effective interventions.” This is not to say that people who are below 6.0% could not be at risk, but it was gray-zone, difficult to establish with evidence.
The resulting consensus paper also cast disdain on the term “Prediabetes” for this reason. The full hyphenated word, “Pre-diabetes,” implies that all in this group are on the road to diabetes. In fact, numerous studies have shown that claim to be overreaching. “Pre” also might wrongly imply that falling into this category carries with it the present but lesser dangers of diabetes. Many conditions which do not involve insulin resistance can raise glucose levels. Despite this academic consensus in 2010, the ADA, lowered the cutoff for the diagnosis of prediabetes to 5.7% A1c. While the European Association for the Study of Diabetes subsequently publicly adopted the ADA standard in 2019, the other two international diabetes organizations have yet to follow suit.
The ADA’s ostensible reason is well-meaning, to ring the alarm bell and wake up those who would not otherwise pay attention to their poor eating habits or their growing waistlines or lack of regular exercise. A flagged lab value could spur change in the complacent patient, but terrify and ensnare others, possibly unnecessarily.
The CDC, a US government organization, adopted the ADA standard the following year. Keep in mind, the ADA is a private organization which receives corporate funding, and in the years following, endorsed metformin and other drugs for treating prediabetes. Corporations such as Post Cereals have made donations to the ADA, according to Marion Nestle in her book, What to Eat, allowing them to prominently display the ADA logo on their packaging. Post is but one of many companies seeking to promote their product as diabetes preventive.
Different lab analyzers have significant bias in the Prediabetic zone
A1c has sufficient ability to monitor diabetes, but lacks sufficient precision and discriminatory power to discriminate normal from prediabetes, or gauge incremental change in the prediabetic zone. Most prediabetics attempting to compare consecutive A1c between labs do not realize that near impossibility. Different labs using different analyzers often yield differing results. Labs will routinely retire equipment, often without informing their ordering physicians. I was therefore pleasantly surprised to see Quest Lab add this comment to their A1c results.
“This test was performed on the Roche cobas c503 platform. Effective 11/6/23, a change in test platforms from the Abbott Architect to the Roche cobas c503 may have shifted HbA1c results compared to historical results. Based on laboratory validation testing conducted at Quest, the Roche platform relative to the Abbott platform had an average increase in HbA1c value of < or = 0.3%. This difference is within accepted variability established by the National Glycohemoglobin Standardization Program. Note that not all individuals will have had a shift in their results and direct comparisons between historical and current results for testing conducted on different platforms is not recommended.”
To recap, the results since the analyzer changeover were on average +0.3 compared with before. For example, an A1c on the outgoing Abbott with a 5.5% result, on the new Roche, would be on average, 5.8%. They go on to say that this is an accepted norm, well within the NGSP parameters, but don’t try to simply add 0.3, since not every sample behaves uniformly.
Three times a year, five samples are sent out to labs all over the US from the College of American Pathologists (CAP) to see whether they meet the benchmarks set out by the NGSP. According to the “Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus,” https://doi.org/10.2337/dci23-0048. “The goals for imprecision for HbA1c measurement interlaboratory is a CV <2.5% …. and ideally no measurable bias.” (CV is a measure of variability)
Bias is the consistent difference between Abbott and Roche. Here’s a typical situation, in the spring 2024 challenge event, the Abbott Architect gave an average A1c result of 5.17% compared to the Roche Cobas 500 series’ 5.36% or a difference of 0.2%. This closely mirrors Quest’s lab comment of +0.3% bias caused by their changeover. A gap of 0.3 between the highest and lowest is typical between A1c manufacturers from 5.0% to 6.0%. Let’s continue by talking about variability or CV.
Different lab analyzers have precision and repeatability issues in the Prediabetic zone
While the A1c test has sufficient accuracy in the diabetic range, for reasons which are both analyzer-based and biologic in origin, the test lacks the precision (accuracy) and reproducibility (inconsistent) in the prediabetic range evidence by routine CAP proficiency testing results. Proficiency testing is an ongoing program where challenge samples are sent as unknown to labs.
Recall, the NGSP’s “goals for imprecision for HbA1c measurement interlaboratory is a CV <2.5%.” When all sample CV’s are averaged between labs, they get close, ~3%. However, focusing in on the samples in the prediabetic range, A1c<6%, the failure rate markedly increases such that on average, 33% of analyzers by manufacturer fail, (top left quadrant) compared with 23% (bottom right quadrant) when benchmarked against the <2.5% NGSP standard.
This problem in analytics is termed “linearity.” Any test has upper and lower regions outside of a “sweet spot” where it begins to lose accuracy or precision. The graph data was compiled from more than three years worth of national blinded challenge proficiency testing conducted by the College of American Pathologists and demonstrates a large fraction of A1c analyzers have waning linearity-related precision below 6.0%. (top left quadrant of the two intersecting lines) This is exactly the A1c zone for prediabetics, who scratch their heads wondering why their A1c doesn’t seem to reflect their hard work.
To Summarize:
- Many prediabetics are mistakenly believe there is a close correspondence and consistent concordance between average glucose levels and their A1c. There is a relationship between THEIR average glucose and a particular analyzer’s A1c, but:
A) There is a wide population band of average glucose levels that correspond to any single A1c value. (mi_Swimmer falls into this category.)
B) Only (extended) CGM data can relate an individual’s average glucose to their A1c. (At least 14 day’s worth of data.) A longer period will more closely approximate the true 90-day average of an A1c.
2. There is demonstrated, very poor correspondence between CGM average glucose levels and A1c in the prediabetic range. Emerging evidence supports that non-diabetics have longer surviving red cells than diabetics.
3. In the prediabetic range of A1c testing, more than a third of commercial analyzers fail to achieve the precision goals set forth by the NGSP.
A) Labs change analyzers without notice and with regularity. Analyzers are selected usually based upon factors other than A1c precision.
B) Providers are frequently unaware that analyzers have been replaced. The subsequent changes in A1c values can have a positive or negative bias, but individually can be unpredictable from preceding values, especially in the prediabetic ranges. (a distinct possibility for mi_Swimmer).
C) A1c tests are more variable when testing below 6.0%.
4) Bottom line: Provided the same analyzer has been used, the A1c CAN reflect significant changes in glucose metabolism. When in doubt, call the lab to inquire about equipment changes since last testing. Not everyone with a prediabetic A1c has an elevated average glucose. Consider getting a CGM.
“Estimating glycemic control from HbA1c alone is in essence applying a population average to an individual, which can be misleading.” — Dr. Roy Beck
About the author: William Shang, MD, no longer works for a living, but enjoys writing about prediabetes and has two books, The FIRST Program, and The Thin Prediabetic, (both available on Amazon).
He offers affordable online educational prediabetic counselling based upon individual needs and questions, accessible at https://sites.google.com/view/prediabetes-coach/home.
References:
Beck, Roy W et al. “The Fallacy of Average: How Using HbA1c Alone to Assess Glycemic Control Can Be Misleading.” Diabetes Care vol. 40,8 (2017): 994–999. doi:10.2337/dc17–0636
Bergenstal RM, et al. Racial Differences in the Relationship of Glucose Concentrations and Hemoglobin A1c Levels. Ann Intern Med. 2017 Jul 18;167(2):95–102. doi: 10.7326/M16–2596.
Cosentino, Francesco et al. “2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD.” European heart journal vol. 41,2 (2020): 255–323. doi:10.1093/eurheartj/ehz486
John, W G. “Glycated haemoglobin analysis.” Annals of clinical biochemistry vol. 34 ( Pt 1) (1997): 17–31. doi:10.1177/000456329703400105
Nathan, David M et al. “Translating the A1C assay into estimated average glucose values.” Diabetes Care vol. 31,8 (2008): 1473–8. doi:10.2337/dc08–0545
Shah, Viral N et al. “Discordance Between Glucose Management Indicator and Glycated Hemoglobin in People Without Diabetes.” Diabetes Technology & Therapeutics Vol. 25,5 (2023): 324–328. doi:10.1089/dia.2022.0544
Tozzo V, et al. Estimating Glycemia From HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy. Diabetes Care 23 February 2024; 47 (3): 460–466. https://doi.org/10.2337/dc23-1177
Wang, Junmei et al. “The influence of shorter red blood cell lifespan on the rate of HbA1c target achieved in type 2 diabetes patients with a HbA1c detection value lower than 7.” Journal of diabetes vol. 15,1 (2023): 7–14. doi:10.1111/1753–0407.13345
