author: Enzo Bonora; Jaakko TuomilehtoCorresponding authors: Enzo Bonora, <email@example.com>, and Jaakko Tuomilehto, <firstname.lastname@example.com>.
date: 2011-05
references:
title: The Pros and Cons of Diagnosing Diabetes With A1C

An International Expert Committee was convened in 2008 by the American Diabetes Association (ADA), the European Association for the Study of Diabetes, and the International Diabetes Federation to consider the means for diagnosing diabetes in nonpregnant individuals, with particular focus on the possibility to indicate A1C as an alternative if not a better tool (1). After reviewing the available literature and a thorough discussion on the advantages and the limits of previous diagnostic strategies (essentially based on fasting glucose assessment) and the considered alternative approach (based on A1C measurement), a consensus was reached that the latter (i.e., A1C) should be included among diagnostic tools for diabetes and, with the exception of a number of clinical conditions, should even be preferred in diabetes diagnosis in nonpregnant adults.

The main conclusion of the International Expert Committee was implemented in the most recent clinical recommendations issued by the ADA. However, in these guidelines, A1C is indicated as a diagnostic tool alternative but not superior to blood glucose, leaving to the health care professional the decision about what test to use in an individual.

The World Health Organization is currently examining the proposal made by the International Expert Committee and is carefully addressing the controversial issues still remaining, most of which have been the subject of letters to the editor and articles recently published in the literature. Nevertheless, the use of A1C for diagnosing diabetes is rapidly becoming a reality in many Western countries.

In the text that follows, one of us (E.B.) will present the main points supporting A1C (pros) and the other (J.T.) will illustrate the main counterpoints challenging A1C (cons) as the primary tool for diabetes diagnosis. The text has been prepared in full coordination and the final conclusions represent the opinion of both authors. Tables 1 and 2 summarize the pros and cons.

# PROs

## A1C captures chronic hyperglycemia better than two assessments of fasting or 2-h oral glucose tolerance test plasma glucose

Diabetes has been diagnosed for decades with fasting plasma glucose (FPG) assessment or, much less frequently, with an oral glucose tolerance test (OGTT). Hyperglycemia as the biochemical hallmark of diabetes is unquestionable. However, fasting and 2-h OGTT gauge just a moment of a single day. In addition, the two assessments required to confirm diagnosis might be fallacious in describing a chronic and complex clinical condition. In this respect, there is no doubt that a biochemical or clinical parameter describing the extent of a biological phenomenon over a long period provides a more robust indicator of glycemia than a parameter describing it in the short term or in a given moment only. Accordingly, there are some good examples in medicine: urinary albumin excretion rate provides more reliable information on the presence and the degree of microalbuminuria than spot urinary albumin-to-creatinine ratio; serum IGF-I is definitely more efficacious than serum growth hormone when monitoring patients with acromegaly, etc.

Labeling a person with a diagnosis of diabetes has several psychological and legal implications and requires a robust and reliable approach. The measurement of A1C equals the assessment of hundreds (virtually thousands) of fasting glucose levels and also captures postprandial glucose peaks; therefore, it is a more robust and reliable measurement than FPG and/or 2-h OGTT plasma glucose. This is particularly valid when FPG oscillates above and below the cut point of 126 mg/dL or 2-h plasma glucose (PG) oscillates above and below the cut point of 200 mg/dL. Of note, the 2-h PG had poor reproducibility. From a clinical standpoint, having an FPG of 120 or 130 mg/dL or having a 2-h PG of 185 or 215 is virtually the same, but from the patient's perspective (perception of having a disease, psychological well-being, health insurance, recognition of particular benefits, or imposition of certain limitations, etc.), it makes a substantial difference. Therefore, a diagnostic tool gauging chronic rather than spot hyperglycemia is certainly preferable.

## A1C is better associated with chronic complications than FPG

Different from National Diabetes Data Group criteria, which were essentially based on distribution of glucose levels within the general population, the 1997 ADA criteria (and the subsequently recommended World Health Organization criteria) established diabetic glycemic levels by means of their association with retinopathy, the most exclusive and specific diabetes complication. Various observational studies documented that an increased prevalence of nonproliferative diabetic retinopathy can be observed with fasting glucose levels around 7.0 mmol/L (126 mg/dL) and 2-h PG around 11.1 mmol/L (200 mg/dL). Interestingly, the same studies documented that retinopathy increased with A1C levels around 6.5% (2–4). These results were confirmed in a more recent study including almost 30,000 subjects recruited in several countries. Such study clearly showed that prevalent retinopathy started to increase in the A1C category of 6.5–7.0% (5). Therefore, a cut point of A1C for diagnosing diabetes with an approach similar to the one used with FPG and 2-h PG is available (and indeed already was available in older studies).

It is well known that cardiovascular disease (CVD) is the most frequent chronic complication of diabetes, with incidence rates 5- to 10-fold higher than with microvascular disease. For this reason, the association of A1C with CVD can be considered a major issue when discussing the potential use of A1C for diagnosing diabetes. In this regard, it is worth mentioning that, in the general population, FPG is a poor marker of future CVD events, whereas 2-h OGTT and A1C are good predictors (6,7).

## Fasting is not needed for A1C assessment and no acute perturbations (e.g., stress, diet, exercise) affect A1C

Plasma glucose levels are not stable but rather vary throughout the day, mainly in postprandial periods. Although it is believed that fasting glucose levels are reproducible across days, a number of acute perturbations of glucose homeostasis have been described. Acute stress can increase endogenous glucose production substantially and impair glucose utilization. People who are worried about blood sampling or experience a stressful situation in the hours preceding blood sampling can have an increase in fasting glucose concentration. On the contrary, exercise can decrease glucose levels, and an evening or early-morning session of physical exercise can affect the level of fasting glycemia. Moreover, most individuals do not pay attention to the request or are not asked to consume a diet with at least 200 g carbohydrate in the days before testing glucose. Some individuals do not abstain from food in the 8 h before testing, thus arriving to the laboratory in the postabsorptive rather than fasting condition. In addition, smoking or taking certain medications can adversely affect fasting glucose. The lack of appropriate preparation for glucose testing makes FPG less reliable for diabetes diagnosis, with results sometimes falsely elevated and sometimes apparently normal. On the contrary, A1C is not influenced by acute perturbations or insufficient fasting. Indeed, A1C can be measured anytime, irrespective of fasting or feeding.

## A1C has a greater pre-analytical stability than plasma glucose

Even when preparation to glucose testing is optimal, plasma glucose values may still be misleading because of pre-analytical instability. In fact, tubes for blood collection do not always contain antiglycolytic substances, and even when they do, significant glucose consumption occurs in blood cells in the first 1–2 h after sampling because glycolysis is inhibited in its more distal steps by NaF or other preservatives. As long as the sample is not processed and plasma and blood cells are separated by centrifugation, a significant glucose loss is observed. In this regard, it must be emphasized that, quite often, blood samples reach the laboratory and are processed hours after withdrawal. Consistently, glucose concentration decreases 5–7% (on average ∼0.5 mmol/L) per hour and even more rapidly in cases of high ambient temperature (8,9). In such cases, glucose levels can show results lower than they are and diabetes diagnosis can be missed. It has been estimated that pre-analytical variability of FPG is 5–10%. On the contrary, pre-analytical variability of A1C is negligible. As for analytical variability, it is superimposable for glucose and A1C, being ∼2%.

## Standardization of A1C assay is not inferior to standardization of glucose assay

One of the main concerns surrounding A1C and raising perplexities on its use for diabetes diagnosis is the poor standardization of the assay. Quite surprisingly, the same concerns and perplexities do not extend to A1C use for diabetes monitoring despite the understanding that only when A1C is aligned to the Diabetes Control and Complications Trial (DCCT)/UK Prospective Diabetes Study (UKPDS) standard should the recommended target be pursued (in general \<7%). A great effort was made in the U.S. and other countries to make reproducible A1C across laboratories with an effective standardization program. Such a program has been recently completed and is being implemented worldwide to provide more reliable information to physicians who monitor diabetic patients (10). The standardization is expected to minimize laboratory biases and is a prerequisite to use A1C not only for monitoring but also for diagnosing diabetes.

Although it is generally believed that glucose assay is highly reproducible across laboratories, this is not true. A recent survey conducted in 6,000 U.S. laboratories clearly documented a significant bias in glucose assessment in as many as 41% of them, yielding a misclassification of glucose tolerance in 12% of subjects (11). Therefore, the argument that A1C cannot be used for diabetes diagnosis because of poor standardization is no longer tenable.

## Biological variability of A1C is lower than that for FPG

When the same subjects have two assessments of the available glucose-related parameters, the correlation is stronger among the individual A1C measurements than among the FPG or 2-h PG measurements. The coefficients of variation of A1C, FPG, and 2-h PG are 3.6, 5.7, and 16.6%, respectively (12). This reflects of course both biological and analytical variability. However, although the latter was similar for A1C and FPG (∼2%), biological variability of A1C was severalfold lower than that of FPG (\<1 vs. ∼4%) (13). This finding confirms that the two required assessments of FPG to diagnose diabetes can provide quite unreliable information, whereas A1C, especially if measured twice as recommended, provides more robust clinical information.

## Individual susceptibility to glycation might be an additional benefit of A1C assessment

It is a common clinical finding that many subjects have an A1C value lower or higher than expected when examining their daily glycemic profiles. Using the DCCT database, McCarter et al. (14) calculated the hemoglobin glycation index (HGI) as the difference between observed and predicted A1C level and identified categories of patients with low, moderate, or high HGI. Most interestingly, they found that subjects with high HGI had a greater risk of developing retinopathy and nephropathy, even when they had good glucose control, and that subjects with lower HGI had a low incidence of microangiopathy despite high mean blood glucose levels. This finding demonstrates that A1C assessment might provide not only information on chronic hyperglycemia but also a measure of whole-body susceptibility of protein glycation and, therefore, risks of diabetes complications that are more strictly related to this pathogenic mechanism.

## Using the same biomarker for diagnosing and monitoring diabetes might be an advantage

A1C is used to monitor diabetes and to establish the degree of metabolic control. Deviation from individualized A1C targets prompts physicians to modify treatment strategies with lifestyle intervention and/or drug titration or changes. The use of A1C for diagnosing diabetes has the advantage that, in subjects with A1C ≥6.5% (i.e., diabetes), baseline A1C is already measured and deviation from target is immediately available (no A1C measurement as a second step after FPG assessment). In subjects with A1C of 6.00–6.49% (i.e., high risk of diabetes), an effective prevention strategy can be immediately undertaken with the awareness that a single A1C is definitely more reliable than a single FPG to stratify the risk of the disease. Yet, in subjects with A1C of 5.50–5.99% plus other diabetes risk factors (e.g., central obesity, atherogenic dyslipidemia, hypertension, and/or metabolic syndrome), counseling can be immediately offered because diabetes risk is substantial, and single A1C assessment is definitely more reliable than single FPG to capture chronically high-normal glucose levels.

Pertinent to this issue is the firm belief that the implementation of the standardization of A1C assay would proceed more rapidly worldwide if A1C were to also be used for diagnosing diabetes. A1C assessment is crucial for diabetes monitoring, and establishing the individual A1C target definitely requires that the parameter is International Federation of Clinical Chemistry (IFCC) standardized and DCCT aligned. In fact, the A1C target and the deviation from it in the single patient remain totally uncertain when the laboratory provides A1C data that are not aligned to standard.

## Cost of the assay: savings or no savings?

One of the major concerns raised by critics of the use of A1C for diagnosing diabetes is the higher cost of the assay when compared with FPG. There is no doubt that from an analytical point of view (cost of reagents and equipment), FPG is cheaper than A1C. However, other considerations about cost should be made. FPG assessment requires overnight fasting, whereas A1C can be assessed any time. This means that a person could go or could be driven by a relative/friend to the laboratory, even during lunch or in the late afternoon, avoiding loss of work hours. It is also possible to collect blood for A1C assessment in the evening and hand it to the laboratory in the following days. Yet, in subjects with FPG ≥7 mmol/L (≥126 mg/dL), A1C assessment would be needed the next few days as a second step in a newly diagnosed diabetes workup. On the contrary, when A1C assessment yields a value ≥6.5%, the second step required to initiate diabetes monitoring after diagnosis would be completed, with a substantial savings of both analytical and nonanalytical costs. On the other hand, when using FPG to screen for diabetes and finding a value in the range of 5.6–6.9 mmol/L (100–125 mg/dL; impaired fasting glucose), an OGTT is frequently prescribed (mainly in Europe and less frequently in the U.S.) to establish glucose tolerance. This test requires hours in the laboratory, with additional analytical and nonanalytical costs. In such cases, which represent a sizable portion of the general population, A1C rather than FPG would provide an immediate diabetes diagnosis or a valuable risk stratification (15) without supplementary testing.

## Impact of changing the diagnostic laboratory parameter on epidemiology of diabetes

A further critique to the program of moving from FPG to A1C for diabetes diagnosis comes from people who state that epidemiology of the disease is based on FPG and that the scenario would change if A1C were used instead of FPG. A recent report based on the U.S. population (16) showed that the use of A1C rather than FPG would not significantly change diabetes prevalence and that the categorization would not change in as many as 97.7% of subjects. Moreover, this study showed that half of the subjects with FPG ≥7 mmol/L (≥126 mg/dL) had an A1C value in the 6.00–6.49% range, thus deserving strict monitoring and an intervention. In this regard, however, it should be emphasized that any comparison of A1C with FPG (or 2-h OGTT PG) is equivocal because a true gold standard is not available. FPG, which in classic studies relating glucose parameters (including A1C) to retinopathy was measured just one time and with less than optimal pre-analytical and analytical procedures, cannot be taken as the gold standard. Therefore, any study examining sensitivity and specificity of A1C for diagnosing diabetes suffers from these limitations and is questionable. At present, the gold standard is probably the combination of FPG, 2-h PG, and A1C assessments with optimal pre-analytical, analytical, and standardized procedures and confirmatory testing for all parameters. This is not feasible on a large-scale basis and cannot be recommended. A1C seems to be a reasonable approach for all reasons discussed above (summarized in Table 1).

Reasons to prefer A1C compared with plasma glucose determination for diagnosing diabetes

|  |
|----|
| Chronic hyperglycemia is captured by A1C but not by FPG (even when repeated twice). |
| Microangiopathic complications (retinopathy) are associated with A1C as strongly as with FPG. |
| A1C is better related to cardiovascular disease than FPG. |
| Fasting is not needed for A1C assessment. |
| No acute perturbations (e.g., stress, diet, exercise, smoking) affect A1C. |
| A1C has a greater pre-analytical stability than blood glucose. |
| A1C has an analytical variability not inferior to blood glucose. |
| Standardization of A1C assay is not inferior to blood glucose assay. |
| Biological variability of A1C is lower than FPG and 2-h OGTT PG. |
| Individual susceptibility to protein glycation might be caught by A1C. |
| A1C can be used concomitantly for diagnosing and initiating diabetes monitoring. |
| Diabetes assessment with A1C assay is not necessarily greater than with glucose assessment. |

# CONs

## Diabetes is clinically defined by high blood glucose and not by glycation of proteins

The introduction of A1C as the diagnostic tool for diabetes, in particular, if this parameter is considered the primary tool, will lead to a major change in the pathophysiological paradigm that defines the syndrome called "diabetes." So far, diabetes has been defined as "a clinical condition of elevated glucose concentration in blood". High A1C represents high glycation of proteins in the body, which is a substantially different biochemical abnormality, although it is certainly secondary to high blood glucose. In medicine, it is important to pay attention to primary phenomena before emphasizing the secondary ones. Moreover, high A1C is only observed subsequently to an increase in blood glucose, but there are few data on how long the delay is. Regardless of the length of this delay (weeks, months), diagnosis of diabetes using A1C would occur later than with blood glucose assessment. In many cases, such a delay might have negative clinical consequences.

## A1C is a poor marker of important pathophysiological abnormalities featuring diabetes

OGTT and 2-h post-glucose levels do reflect the pathophysiology behind diabetes better than any other glycemic parameter, since they provide information on what occurs in the postprandial state, when glucose levels are at the highest levels during the day and when the health of the pancreatic β-cell is essential. On the contrary, fasting glucose is the least informative among glycemic parameters, since in most subjects, it corresponds to the lowest glucose level during the day and it reflects the long nocturnal period when there is no intake of food and no particular stress for β-cells. However, humans spend most of their time in postprandial or postabsorptive states that are deranged in diabetes. A1C is a poor indicator of what occurs in the postprandial state. A1C captures only chronic hyperglycemia, but it will miss acute hyperglycemia. Normal blood glucose levels 2 h after glucose load indicates a good β-cell capacity, whereas high 2-h OGTT glucose levels document an impairment of β-cell function (17). This means that only 2-h OGTT PG can provide reliable information on the key pathophysiological defect of diabetes, also providing advice regarding the correct therapy to overcome it. This can be compared with ambulatory blood pressure monitoring (ABPM), where the main features predicting cardiovascular events are not only the long-term average blood pressure but the daily variation in blood pressure (especially the lack of a physiological nocturnal dip). Thus, ABPM is clinically useful in finding out blood pressure patterns, not estimating the long-term average. Recently, the Insulin Resistance Atherosclerosis Study (IRAS) showed that A1C is a weaker correlate of insulin resistance and insulin secretion in studies of metabolism compared with FPG and 2-h PG (18).

## A1C has a poor sensitivity in diabetes diagnosis and would change the epidemiology of diabetes

Diabetes diagnosis based on A1C misses a large proportion of asymptomatic early cases of diabetes that can only be identified by the OGTT. According to a recent Chinese study, A1C sensitivity is inferior compared with fasting blood glucose at the population level (19). Also, people with impaired glucose tolerance (IGT), in whom the efficacy of diabetes prevention has been unequivocally proven (20), cannot be detected by A1C.

Epidemiological studies carried out in the general population showed that A1C and plasma glucose (FPG and/or 2-h OGTT) identify partially different groups of diabetic subjects (21). A1C ≥6.5% identifies \~30–40% of previously undiagnosed patients with diabetes (16). A larger percentage is detected by FPG (\~50%) and 2-h PG (\~90%). These findings are based on several recent studies, including the 2003–2006 NHANES (30% of diabetic individuals detected by A1C ≥6.5%, 46% by FPG ≥126 mg/dL, and 90% by 2-h PG ≥200 mg/dL) (18) and the IRAS (32, 45, and 87%, respectively) (18). In Qingdao, China, the ≥6.5% A1C cut point detects 30% of individuals with diabetes according to 2003 ADA criteria (19). In Chennai, India, however, A1C ≥6.5% detects 78% of individuals with newly diagnosed diabetes according to these criteria (22). In the IRAS, A1C of 5.7–6.4% predicted type 2 diabetes better with increasing BMI, and there were significant ethnic differences in the performance of A1C of 5.7–6.4% to detect diabetes (18). The ethnic differences in A1C compared with glucose measurements were also well demonstrated in the Diabetes Prevention Program population (23) and in a recent multiethnic database by Christensen et al. (24) that showed that there are no systematic interpretations as to why a shift to an A1C-based diagnosis for diabetes has substantially different consequences for diabetes prevalence across ethnic groups and populations.

## 2-h Glucose level and IGT are stronger predictors of CVD than A1C

Because high glucose is toxic and causes many types of tissue damage, any indicator of hyperglycemia is predictive of diabetes complications. In the general population, FPG is a poor marker of mortality and future CVD events, whereas 2-h PG and A1C are better predictors (8,10,25–27). When analyzed jointly, only 2-h PG remains a statistically significant predictor of mortality and CVD (28,29). The findings regarding associations of FPG, 2-h PG, and A1C with retinopathy from the Pima Indians in the ADA 1997 report describing diagnostic thresholds of each glycemic parameter were derived by univariate analyses, and the multivariate analysis aiming at identifying the best glycemic parameters for diagnosis has never been reported. One of the main issues is that people with IGT have \~40% increased mortality compared with normoglycemic people, and these individuals cannot be identified by measuring FPG or A1C. In addition, lifestyle intervention has been shown to prevent the progression from IGT to diabetes and also reduce their mortality risk to the level observed among normoglycemic people (30,31). Such prevention trial evidence does not exist for A1C or FPG, and this evidence should not be forgotten when deciding the approaches to identify intermediate hyperglycemia. Moreover, these results indicate that early intervention is effective in reducing mortality in people with IGT, and therefore, we should attempt to make the diagnosis of hyperglycemia as early as possible.

## Fasting is not essential to identify perturbation in glucose metabolism

Measuring blood glucose in the fasting state in nondiabetic individuals is probably the least efficient way to identify early signs of perturbations in glucose metabolism. Because excessive postprandial glucose excursions are marking the first signs of abnormal glucose regulation and they also seem to best predict cardiovascular outcome, fasting is not really the central issue. It is likely that fasting has been overemphasized in diagnosing type 2 diabetes. We may pay attention to approaches used in the diagnosis of high blood pressure that also vary markedly during the day, but despite this variation, we are able to identify individuals with hypertension, even though measurements are not restricted to certain hours of the day but are done at any time.

## Standardization of A1C assay is very poor and standardization of glucose assay is easier to implement

Inaccuracies in measurement and poor standardization of A1C assays are still a common problem, even in Western countries. Although a less than perfect standardization also exists for plasma glucose, this assay might be more easily aligned to a standard than A1C. Such programs now exist in the U.S., Japan, and Sweden, but there is still a long way to a global standardization of the A1C assays. Actually, all glycemic assessments require confirmation to make the diagnosis of diabetes correctly, mainly to avoid errors in sample handling and laboratory procedures.

## A1C assay is unreliable and cannot be used in many subjects

Abnormal hemoglobin traits are not uncommon in many regions of the world, and they significantly interfere with A1C assay (32), leading to spurious results. Also, there are several clinical conditions that influence erythrocyte turnover (e.g., malaria, chronic anemia, major blood loss, hemolysis, uremia, pregnancy, smoking, and various infections) that are responsible for misleading A1C data. Still, we are aware of ethnic differences in the relation between blood glucose and A1C levels (33) as well as an effect of aging. If different cut points regarding all these conditions need to be considered, A1C cannot be easily used to diagnose diabetes.

## Within-day biological variability of plasma glucose might unveil disturbance of glucose metabolism

Biological variability in plasma glucose reflects our daily patterns of diet, physical and mental activity, sleep, etc., and also depends on possible pathophysiological processes that may underlie type 2 diabetes. By definition, postprandial, and also 2-h PG, vary more than FPG. In this regard, A1C, which does not have any substantial biological variability, provides little information on pathophysiological processes leading to type 2 diabetes. The variability in A1C is entirely due to other phenomena, not pathophysiological disturbances.

## Individual susceptibility to glycation of hemoglobin is not relevant to diabetes diagnosis

The HGI was calculated in patients with type 1 diabetes from the DCCT (17). This parameter is not relevant to the diagnosis of diabetes in the general population, in which 99% of subjects have A1C levels definitely lower than patients with type 1 diabetes. Subjects with high HGI had a greater risk of developing retinopathy and nephropathy, even when they had good glucose control (i.e., FPG was not very high), whereas subjects with lower HGI had a very low incidence of microangiopathy despite high mean blood glucose levels. This finding indicates that postprandial glucose excursions must have been very high in the former and very low in the latter. A1C reflects high mean exposure to glucose but not glucose fluctuations during the day. Unfortunately, in this analysis with HGI, postprandial glucose excursions and daily glucose variability were not taken into account.

## Using the same biomarker for diagnosing and monitoring diabetes might not have positive effects only

This approach may be useful, but it also may lead to problems in two ways. First, people who have diabetes (based on their glucose values) will remain undiagnosed and untreated, since they are considered "nondiabetic" according to their A1C. Also, if the intermediate level of A1C (6.00–6.49 or 5.70–6.49%) was used to predict diabetes, it performed less well than impaired fasting glucose and/or IGT (18). Whereas the 6.5% A1C threshold misses a large percentage of previously undiagnosed diabetes, its clinical consequences remain unknown. It is important to recognize this problem. One obvious consequence is that with a less sensitive test, individuals who fall below the threshold are not considered in cardiovascular management algorithms as high-risk individuals and are probably treated less effectively for other risk factors.

Second, a large proportion of newly diagnosed diabetic patients based on current glucose criteria have A1C \<6.5%. In the Finnish Diabetes Prevention Study, the sensitivity of A1C ≥6.5% to diagnose diabetes was only 39%, i.e., 61% of newly diagnosed case subjects had A1C \<6.5% (34). If this same threshold were to be used for treatment, these patients would not be accepted to be treated, even though their glucose levels were twice the glucose threshold for diabetes. This would also mean that in 61% of high-risk people who were regularly monitored for diabetes, the actual diagnosis would have been delayed—for how long, we do not know, since diabetic people were referred to antidiabetic therapy based on their high glucose values.

## Cost of the assay: glucose is unquestionably cheaper than A1C

Whichever way we calculate the assay costs, A1C assay is more expensive than glucose assay, and it will thus remain so despite the speculative claim that the cost of A1C assay will become less expensive when used more extensively. In addition, many individuals at high risk of diabetes would need other laboratory tests that require fasting (e.g., lipid profile, hepatic profile, etc.), and therefore adding a glucose determination to the panel is not really a major issue. Also, the vast majority of laboratories in primary care collect samples in the morning, and they do not operate after "working hours." This makes the claim that A1C can be measured "any time of the day" rather theoretical.

In a large part of the world, A1C is not available, and its cost is so high that it is meaningless to even discuss whether it should be given a priority over simple and inexpensive glucose measurements. This step would divide the world into two categories: developed societies in which diabetes diagnosis is made with A1C and less developed societies (between and within countries) in which diabetes diagnosis is made with plasma glucose: such a division should be avoided. It would add to the inequities in health and health care.

Reasons not to prefer A1C compared with plasma glucose determination for diagnosing diabetes

|  |
|----|
| Diabetes is clinically defined by high blood glucose and not by glycation of proteins. |
| A1C is a poor marker of important pathophysiological abnormalities featuring diabetes. |
| A1C has a poor sensitivity in diabetes diagnosis and would change the epidemiology of diabetes. |
| 2-h glucose level and IGT are stronger predictors of CVD than A1C. |
| Fasting is not essential to identify perturbation in glucose metabolism. |
| Standardization of A1C assay is poor, even in Western countries, and standardization of glucose assay would be easier to implement. |
| In many subjects, A1C assay is unreliable and cannot be used. |
| A1C has significant differences in various ethnic groups, which are poorly understood and characterized. |
| Within-days biological variability of plasma glucose might unveil disturbance of glucose metabolism. |
| Individual susceptibility to glycation of hemoglobin is not relevant to diabetes diagnosis. |
| Using the same biomarker for diagnosing and monitoring diabetes might have negative effects. |
| Cost of the assay: glucose is unquestionably cheaper than A1C, and A1C assay is not available on a large scale in most of the countries. |
| A1C levels vary not only according to glycemia, but also to erythrocyte turnover rates (e.g., hemoglobinopathies, malaria, anemia, blood loss) as well as other factors. |
| Correlation between A1C and FPG is \~0.85%, which means that as many as 30% of the variation in FPG is not explained by A1C and vice versa. |
| Nothing is known about changes in A1C during the development of diabetes. |
| A1C levels of 6.0–6.5% do not predict diabetes as effectively as FPG and 2-h PG (OGTT). |
| Sensitivity of A1C to detect diabetes defined by the OGTT is \<50%; thus, the majority of diabetic individuals will remain undiagnosed if A1C is used. |
| The levels of A1C predicting future retinopathy, nephropathy, etc., in the population is not well established (\<6.5%?). |
| No diabetes prevention trials have selected their populations based on A1C. |
| Using A1C will delay the diagnosis of diabetes in \~60% of incident cases. |

# CONCLUSIONS

There is no doubt that hyperglycemia is the biochemical hallmark of diabetes and is a prerequisite for diagnosis. In this respect, moving from blood glucose to A1C might sound like a sort of heresy. There is also no doubt that all epidemiological data based on blood glucose assessment might be considered less important if the disease were mainly diagnosed with A1C. This might create confusion, disappointment, anxiety, and concern in all who lived a glucocentric existence. Partly rewarding would be the fact that, in several clinical conditions, A1C could not be used and blood glucose assessment would remain the standard diagnostic procedure. In all other conditions (most subjects), A1C could become the reference method, provided that its assay be aligned to international standards. Also mandatory is that the cost of assay declines and becomes affordable in less developed societies. Longitudinal studies should also reassure us about the relative benignity of clinical conditions in which A1C is below the diagnostic threshold of 6.5% but FPG and/or 2-h OGTT PG are above the thresholds of 7 or 11 mmol/L, respectively. This is currently one of the most relevant worries related to potentially missing diagnosis.

Glucose assessment is familiar and cheaper, but A1C seems to provide several advantages, especially in a scenario in which OGTT is rarely used and never repeated as a confirmatory testing. Perhaps accepting a double diagnostic approach in which both blood glucose and A1C do coexist as diagnostic tools is reasonable. In the meantime, epidemiological and clinical studies will hopefully provide further data to better understand whether the current recommendations to replace FPG with A1C are well founded.

We agree that the research and debate on the pros and cons of using A1C versus glucose assay as a diagnostic tool for diabetes should continue in a constructive manner until a larger and truly evidence-based consensus is reached.

## Acknowledgments

No potential conflicts of interest relevant to this article were reported.

# References


abstract: # Background
  .
  The frequency of outpatient diagnostic errors is challenging to determine due to varying error definitions and the need to review data across multiple providers and care settings over time. We estimated the frequency of diagnostic errors in the US adult population by synthesising data from three previous studies of clinic-based populations that used conceptually similar definitions of diagnostic error.
  .
  # Methods
  .
  Data sources included two previous studies that used electronic triggers, or algorithms, to detect unusual patterns of return visits after an initial primary care visit or lack of follow-up of abnormal clinical findings related to colorectal cancer, both suggestive of diagnostic errors. A third study examined consecutive cases of lung cancer. In all three studies, diagnostic errors were confirmed through chart review and defined as missed opportunities to make a timely or correct diagnosis based on available evidence. We extrapolated the frequency of diagnostic error obtained from our studies to the US adult population, using the primary care study to estimate rates of diagnostic error for acute conditions (and exacerbations of existing conditions) and the two cancer studies to conservatively estimate rates of missed diagnosis of colorectal and lung cancer (as proxies for other serious chronic conditions).
  .
  # Results
  .
  Combining estimates from the three studies yielded a rate of outpatient diagnostic errors of 5.08%, or approximately 12 million US adults every year. Based upon previous work, we estimate that about half of these errors could potentially be harmful.
  .
  # Conclusions
  .
  Our population-based estimate suggests that diagnostic errors affect at least 1 in 20 US adults. This foundational evidence should encourage policymakers, healthcare organisations and researchers to start measuring and reducing diagnostic errors.
author: Hardeep Singh; Ashley N D Meyer; Eric J ThomasCorrespondence to Dr Hardeep Singh, VA Medical Center (152), 2002 Holcombe Blvd, VAMC 152, Houston, TX 77030, USA; <firstname.lastname@example.com>
date: 2014-09
institute: 1Houston Veterans Affairs Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and the Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA; 2Division of General Medicine, Department of Medicine, University of Texasat Houston, Memorial Hermann Center for Healthcare Quality and Safety, University of Texas Medical School at Houston, Houston, Texas, USA
references:
title: The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations

# Background

Diagnostic errors pose an important threat to healthcare quality and safety in outpatient settings.1 2 The frequency of outpatient diagnostic errors is unknown,3 due in part to varying definitions across studies and to the challenge of detecting these errors, which typically emerge across multiple episodes of care over time. A recent review by Graber discussed the use of several methods to study diagnostic errors, including autopsies, case reviews, surveys, incident reporting, standardised patients, second reviews and malpractice claims.3 However, most of these methods have limited use in determining the frequency of diagnostic error in outpatient medical practice.

Population-based estimates of diagnostic error could help prioritise ongoing patient safety efforts. We aimed to estimate the frequency of outpatient diagnostic errors in the US adult population by synthesising data from three previous studies of clinic-based populations that used conceptually similar definitions of diagnostic error.4–6

# Methods

Data sources included two previous studies that used electronic triggers, or algorithms, to detect unusual patterns of return visits (primary care study) or lack of follow-up of abnormal clinical findings for colorectal cancer (CRC) (colon cancer study), both suggestive of diagnostic errors.4 6 A third study examined consecutive cases of lung cancer in two institutions (lung cancer study).5 In all three studies, diagnostic errors were confirmed through chart review and defined as missed opportunities to make a timely or correct diagnosis based on available evidence. The criteria for diagnostic errors were comparable across the three studies and excluded atypical presentations and appropriate decisions to watch and wait.

The triggers in the primary care study6 were based on the occurrence of unexpected return visits and were therefore selective for misdiagnosis of acute conditions and exacerbations of chronic conditions. Trigger 1 was a primary care index visit followed by unplanned hospitalisation within 14 days; trigger 2 was a primary care index visit followed by one or more unscheduled visit(s) within 14 days. Control visits did not meet either criterion. We considered for inclusion all primary care visits (n=212 165 visits and 81 483 unique patients) in two large health systems between 1 October 2006 and 30 September 2007. For the record review, we reviewed all trigger 1 records from one of the sites (n=220 records) and randomly chose the rest from the second site, yielding a reviewed sample of 674 records out of the total of 1086 visits selected. Maintaining a record sampling ratio from each health system that was consistent with trigger 1, we reviewed 669 of 14 777 visits selected by trigger 2 and 614 of 193 810 control non-triggered visits. Errors in triggered and non-triggered (control) cases were judged to have occurred when adequate data to suggest the final, correct diagnosis existed at an earlier primary care visit, or if documented abnormal findings at the visit should have prompted additional evaluation that would have revealed the correct diagnosis. Determination of errors was made on retrospective review by two independent raters. In extrapolating error rates to all primary care patients at our study sites, we assumed that rates of error in randomly selected samples of triggered and non-triggered cases were applicable to the entire cohort of triggered and non-triggered records in each clinic population.

Although we presumed that the types of errors detected in the primary care study were likely to account for the majority of outpatient diagnostic errors, we also aimed to estimate the rate of errors related to initial diagnosis of less common but serious chronic conditions. Unlike misdiagnosis of acute conditions, these errors may not become apparent for weeks or months. As a proxy for such errors in outpatient care, we obtained frequency estimates for missed and delayed diagnosis of CRC and lung cancer, represented in the two cancer studies,4 5 respectively. These conditions were selected due to their relative frequency in several types of prior observational studies related to diagnostic errors.5 7–12 In both of our studies, diagnostic errors were characterised by lack of timely follow-up on specific 'red flags' or 'alarm' features5 10 13 of cancer in patients who were eventually diagnosed with either cancer. For instance, for CRC this was operationally defined as lack of follow-up 60 days after documentation of hematochezia (bright red blood per rectum), positive faecal occult blood test and iron deficiency anaemia. For lung cancer, we defined lack of follow-up as failure to initiate further investigation or an appropriate action within 7 days of a documented 'red flag' such as an abnormal chest X-ray. None of these data overlapped with the primary care study. We assumed that failure to follow-up on these 'red flags' accounted for all diagnostic errors associated with these types of cancer.

We extrapolated the frequency of diagnostic error obtained from our studies to the US adult population,14 using the primary care study as a proxy to estimate rates of diagnostic error for acute conditions (and exacerbations of existing conditions) and the two cancer studies to conservatively estimate rates of missed diagnosis of CRC and lung cancer (as proxies for other serious chronic conditions). Because of the heterogeneity of the conditions we included, we assumed that they represented the spectrum of diseases at risk for outpatient diagnostic errors.

# Results

In the primary care study, samples of triggered visits and control visits were reviewed to determine the percentages of visits that contained errors. These percentages were extrapolated to the larger triggered and non-triggered samples. For trigger 1, 141 errors were found in 674 visits reviewed, yielding an error rate of 20.9%. Extrapolating to all 1086 trigger 1 visits yielded an estimate of 227.2 errors. For trigger 2, 36 errors were found in 669 visits reviewed, yielding an error rate of 5.4%. Extrapolating to all 14 777 trigger 2 visits yielded an estimate of 795.2 errors. Finally, for the control visits, 13 errors were found in 614 visits reviewed, yielding an error rate of 2.1%. Extrapolating to all 193 810 control visits yielded an estimate of 4103.5 errors. Thus, we estimated that 5126 errors would have occurred across the three groups. We then divided this figure by the number of unique primary care patients in the initial cohort (81 483) and arrived at an estimated error rate of 6.29%. Because approximately 80.5% of US adults seek outpatient care annually,15 the same rate when applied to all US adults gives an estimate of 5.06% (see figure 1).

The colon cancer study estimated errors in CRC diagnosis in 26 out of 291 773 patients seen in outpatient care (0.009%). Extrapolating to the US adult population that seeks outpatient care, we estimated 0.007% of all adults would have errors related to CRC per year. In the lung cancer study, we found 127 errors in 587 cases of lung cancer, yielding a 21.64% error rate. Extrapolating to the 80.5% of the US adult population that seeks outpatient care and the 0.072% of the population that becomes newly diagnosed with lung cancer each year,16 we estimate 0.013% of all adults would have errors related to lung cancer per year.

Combining estimates from the three studies yields a rate of outpatient diagnostic errors of 5.08%, or approximately 12 million US adults every year. Based on our previous work,17 we estimated that about one-half of errors would have the potential to lead to severe harm.

# Discussion

Although diagnostic errors are difficult to define and measure, there is mounting evidence of their importance and frequency across several types of conditions.3 7 11 18 Applying aggregate diagnostic error rates from three previous studies conducted by our group, we estimate that approximately 12 million adults in the USA could experience outpatient diagnostic errors each year. Our estimates are generally consistent with data from the general public about diagnostic errors. A recent telephone survey of a random probability sample of over 2000 US adults suggested that more than one in 10 respondents reported a 'diagnostic mishap'.19 Similarly, a survey of 726 paediatricians revealed that more than half (54%) reported making a diagnostic error at least once or twice per month.20 Thus, other recent data corroborate our finding that diagnostic errors are common.

Previous estimations of the frequency of diagnostic error largely relied on expert opinion. For example, Elstein estimated errors occur in about 10–15% of diagnoses.21 As noted by Graber, many methods to study diagnostic errors have limited use in calculating the frequency of the problem. For example, autopsy rates have declined,22 the number of self-reports remains low and malpractice claims data are prone to bias and are non-representative.11 20 23–25 On the other hand, population-based studies have relied on medical record reviews, which are generally a gold standard for determining diagnostic error.26 Our electronic trigger approach facilitated selective record reviews, thus making the error determination process more efficient than either consecutive or random record reviews. Our estimate, based on a large sample size, is likely to be the most robust one thus far to address the frequency of diagnostic error in routine outpatient practice.

Although it is unknown how many patients will be harmed from diagnostic errors, our previous work17 suggests that about one-half of diagnostic errors have the potential to lead to severe harm. While this is only an estimate and does not imply all those affected will actually have harm, this risk potentially translates to about 6 million outpatients per year. Additionally, while the contribution of the two cancers to the overall estimation was small, we believed it was important to include them to show their relative contribution. This is because delayed cancer diagnosis is believed to be one of the most harmful and costly types of diagnostic error in the outpatient setting and its significance has become apparent not only in malpractice claims but also in retrospective studies of consecutive cancer cases, surveys and studies of failures to follow-up abnormal test results.5 7–12 In many of these studies, lung cancer and CRC are the most common cancers for which diagnosis is delayed.

Our estimates are limited by our assumptions of generalisability. To keep the definition of diagnostic error uniform across studies, we only used data from our own work. Other studies have used different methods and definitions of diagnostic errors and we believed that this would make our extrapolations weaker. Similar to other methods to study medical errors, our trigger was not sensitive enough to capture all types of diagnostic errors in outpatient settings. Our triggers did not adequately capture errors in diagnosis of uncommon diseases, some of which might evolve over a prolonged period of time in the outpatient setting. Our studies also might not have adequately addressed other types of common chronic conditions that could be vulnerable to diagnostic error.27 For example, a recent study from the UK suggests that opportunities to diagnose chronic obstructive pulmonary disease at an earlier stage are being missed in the majority of cases.28 While it is tempting to do so, we did not extrapolate our chronic condition proxy estimates to all types of cancer or other types of chronic diseases because it would not be possible to do this accurately. Lastly, chart reviews inherently would miss errors due to documentation-related issues and this false negative rate is largely unknown. All of these limitations thus suggest that we underestimated the frequency of diagnostic errors and frequency could be well over 5%.

In conclusion, we estimate the frequency of diagnostic error to be at least 5% in US outpatient adults, a number that entails a substantial patient safety risk. This population-based estimate should provide a foundation for policymakers, healthcare organisations and researchers to strengthen efforts to measure and reduce diagnostic errors.

We thank Annie Bradford, PhD for assistance with medical editing.

# References


author: Bob Weinhold
date: 2006-03
title: Epigenetics: The Science of Change

For nearly a century after the term "epigenetics" first surfaced on the printed page, researchers, physicians, and others poked around in the dark crevices of the gene, trying to untangle the clues that suggested gene function could be altered by more than just changes in sequence. Today, a wide variety of illnesses, behaviors, and other health indicators already have some level of evidence linking them with epigenetic mechanisms, including cancers of almost all types, cognitive dysfunction, and respiratory, cardiovascular, reproductive, autoimmune, and neurobehavioral illnesses. Known or suspected drivers behind epigenetic processes include many agents, including heavy metals, pesticides, diesel exhaust, tobacco smoke, polycyclic aromatic hydrocarbons, hormones, radioactivity, viruses, bacteria, and basic nutrients.

In the past five years, and especially in the past year or two, several groundbreaking studies have focused fresh attention on epigenetics. Interest has been enhanced as it has become clear that understanding epigenetics and epigenomics—the genomewide distribution of epigenetic changes—will be essential in work related to many other topics requiring a thorough understanding of all aspects of genetics, such as stem cells, cloning, aging, synthetic biology, species conservation, evolution, and agriculture.

# Multiple Mechanisms

The word "epigenetic" literally means "in addition to changes in genetic sequence." The term has evolved to include any process that alters gene activity without changing the DNA sequence, and leads to modifications that can be transmitted to daughter cells (although experiments show that some epigenetic changes can be reversed). There likely will continue to be debate over exactly what the term means and what it covers.

Many types of epigenetic processes have been identified—they include methylation, acetylation, phosphorylation, ubiquitylation, and sumolyation. Other epigenetic mechanisms and considerations are likely to surface as work proceeds. Epigenetic processes are natural and essential to many organism functions, but if they occur improperly, there can be major adverse health and behavioral effects.

Perhaps the best known epigenetic process, in part because it has been easiest to study with existing technology, is DNA methylation. This is the addition or removal of a methyl group (CH~3~), predominantly where cytosine bases occur consecutively. DNA methylation was first confirmed to occur in human cancer in 1983, and has since been observed in many other illnesses and health conditions.

Another significant epigenetic process is chromatin modification. Chromatin is the complex of proteins (histones) and DNA that is tightly bundled to fit into the nucleus. The complex can be modified by substances such as acetyl groups (the process called acetylation), enzymes, and some forms of RNA such as microRNAs and small interfering RNAs. This modification alters chromatin structure to influence gene expression. In general, tightly folded chromatin tends to be shut down, or not expressed, while more open chromatin is functional, or expressed.

One effect of such processes is imprinting. In genetics, imprinting describes the condition where one of the two alleles of a typical gene pair is silenced by an epigenetic process such as methylation or acetylation. This becomes a problem if the expressed allele is damaged or contains a variant that increases the organism's vulnerability to microbes, toxic agents, or other harmful substances. Imprinting was first identified in 1910 in corn, and first confirmed in mammals in 1991.

Researchers have identified about 80 human genes that can be imprinted, although that number is subject to debate since the strength of the evidence varies. That approximate number isn't likely to rise much in years to come, writes a team including Ian Morison, a senior research fellow in the Cancer Genetics Laboratory at New Zealand's University of Otago, in the August 2005 *Trends in Genetics*. Others in the field disagree. Randy Jirtle, a professor of radiation oncology at Duke University Medical Center, and his colleagues estimated in the June 2005 issue of *Genome Research* that there could be about 600 imprinted genes in mice; in an October 2005 interview Jirtle said he's anticipating a similar tally for humans, even though the known imprintable genes of mice and people have an overlap of only about 35%.

# Links to Disease

Among all the epigenetics research conducted so far, the most extensively studied disease is cancer, and the evidence linking epigenetic processes with cancer is becoming "extremely compelling," says Peter Jones, director of the University of Southern California's Norris Comprehensive Cancer Center. Halfway around the world, Toshikazu Ushijima is of the same mind. The chief of the Carcinogenesis Division of Japan's National Cancer Center Research Institute says epigenetic mechanisms are one of the five most important considerations in the cancer field, and they account for one-third to one-half of known genetic alterations.

Many other health issues have drawn attention. Epigenetic immune system effects occur, and can be reversed, according to research published in the November–December 2005 issue of the *Journal of Proteome Research* by Nilamadhab Mishra, an assistant professor of rheumatology at the Wake Forest University School of Medicine, and his colleagues. The team says it's the first to establish a specific link between aberrant histone modification and mechanisms underlying lupus-like symptoms in mice, and they confirmed that a drug in the research stage, trichostatin A, could reverse the modifications. The drug appears to reset the aberrant histone modification by correcting hypoacetylation at two histone sites.

Lupus has also been a focus of Bruce Richardson, chief of the Rheumatology Section at the Ann Arbor Veterans Affairs Medical Center and a professor at the University of Michigan Medical School. In studies published in the May–August 2004 issue of *International Reviews of Immunology* and the October 2003 issue of *Clinical Immunology*, he noted that pharmaceuticals such as the heart drug pro-cainamide and the antihypertensive agent hydralazine cause lupus in some people, and demonstrated that lupus-like disease in mice exposed to these drugs is linked with DNA methylation alterations and interruption of signaling pathways similar to those in people.

# Substantial Changes

Most epigenetic modification, by whatever mechanism, is believed to be erased with each new generation, during gameto-genesis and after fertilization. However, one of the more startling reports published in 2005 challenges this belief and suggests that epigenetic changes may endure in at least four subsequent generations of organisms.

Michael Skinner, a professor of molecular biosciences and director of the Center for Reproductive Biology at Washington State University, and his team described in the 3 June 2005 issue of *Science* how they briefly exposed pregnant rats to individual relatively high levels of the insecticide methoxychlor and the fungicide vinclozolin, and documented effects such as decreased sperm production and increased male infertility in the male pups. Digging for more information, they found altered DNA methylation of two genes. As they continued the experiment, they discovered the adverse effects lasted in about 90% of the males in all four subsequent generations they followed, with no additional pesticide exposures.

The findings are not known to have been reproduced. If they are reproducible, however, it could "provide a new paradigm for disease etiology and basic mechanisms in toxicology and evolution not previously appreciated," says Skinner. He and his colleagues are conducting follow-up studies, assessing many other genes and looking at other effects such as breast and skin tumors, kidney degeneration, and blood defects.

Other studies have found that epigenetic effects occur not just in the womb, but over the full course of a human life span. Manel Esteller, director of the Cancer Epigenetics Laboratory at the Spanish National Cancer Center in Madrid, and his colleagues evaluated 40 pairs of identical twins, ranging in age from 3 to 74, and found a striking trend, described in the 26 July 2005 issue of *Proceedings of the National Academy of Sciences*. Younger twin pairs and those who shared similar lifestyles and spent more years together had very similar DNA methylation and histone acetylation patterns. But older twins, especially those who had different lifestyles and had spent fewer years of their lives together, had much different patterns in many different tissues, such as lymphocytes, epithelial mouth cells, intra-abdominal fat, and selected muscles.

As one example, the researchers found four times as many differentially expressed genes between a pair of 50-year-old twins compared to 3-year-old twins, and the 50-year-old twin with more DNA hypomethylation and histone hyperacetylation (the epigenetic changes usually associated with transcriptional activity) had the higher number of overexpressed genes. The degree of epigenetic change therefore was directly linked with the degree of change in genetic function.

Sometimes the effects of epigenetic mechanisms show up in living color. Changes in the pigmentation of mouse pup fur, ranging from yellow to brown, were directly tied to supplementation of the pregnant mother's diet with vitamin B~12~, folic acid, choline, and betaine, according to studies by Jirtle and Robert Waterland published in August 2003 (issue 15) in *Molecular and Cellular Biology*. The color changes were directly linked to alterations in DNA methylation. In a study forthcoming in the April 2006 issue of *EHP*, Jirtle and his colleagues also induced these alterations through maternal ingestion of genistein, the major phytoestrogen in soy, at doses comparable to those a human might receive from a high-soy diet. The methylation changes furthermore appeared to protect the mouse offspring against obesity in adulthood, although there are hints that genistein may also cause health problems, via additive or synergistic effects on DNA methylation, when it interacts with other substances such as folic acid.

# Other Drivers of Change

Substances aren't the only sources of epigenetic changes. The licking, grooming, and nursing methods that mother rats use with their pups can affect the long-term behavior of their offspring, and those results can be tied to changes in DNA methylation and histone acetylation at a glucocorticoid receptor gene promoter in the pup's hippocampus. This finding was published in the August 2004 issue of *Nature Neuroscience* by Moshe Szyf, a professor in McGill University's Department of Pharmacology and Therapeutics, and his colleagues. In the same study, the researchers found that the effects weren't written in stone; giving the drug trichostatin A to older pups could help reverse the effects of poor maternal care received when they were younger. In the 6 June 2003 *Journal of Biological Chemistry* and the 23 November 2005 *Journal of Neuroscience*, Szyf and many of the same colleagues also demonstrated that giving the amino acid l-methionine to older pups could negate the benefits of high-quality maternal care received when they were younger.

Along with behavior, mental health may be affected by epigenetic changes, says Arturas Petronis, head of the Krembil Family Epigenetics Laboratory at the Centre for Addiction and Mental Health in Toronto. His lab is among the first in the world, and still one of only a few, to study links between epigenetics and psychiatry. He and his colleagues are conducting large-scale studies investigating links between schizophrenia and aberrant methylation, and he says understanding epigenetic mechanisms is one of the highest priorities in human disease biology research. "We really need some radical revision of key principles of the traditional genetic research program," he says. "Epigenetics brings a new perspective on the old problem and new analytical tools that will help to test the epigenetic theory." He suggests that more emphasis is needed on studying non-Mendelian processes in diseases such as schizophrenia, asthma, multiple sclerosis, and diabetes.

The past decade has also been productive in developing strong links between aberrant DNA methylation and aging, says Jean-Pierre Issa, a professor of medicine at The University of Texas M.D. Anderson Cancer Center. He presented information on aging and epigenetic effects at a November 2005 conference titled "Environmental Epigenomics, Imprinting, and Disease Susceptibility," held in Durham, North Carolina, and sponsored in part by the NIEHS. Some of the strongest, decade-old evidence shows progressive increases in DNA methylation in aging colon tissues, and more recent evidence links hypermethylation with atherosclerosis. Altered, age-related methylation has also been found in tissues in the stomach, esophagus, liver, kidney, and bladder, as well as the tissue types studied by Esteller. Much of Issa's current work focuses on the links between epigenetic processes, aging, the environment, and cancer, and possible ways to therapeutically reverse methylation linked with cancer.

# Current and Future Quandaries

The accumulated evidence indicates that many genes, diseases, and environmental substances are part of the epigenetics picture. However, the evidence is still far too thin to form a basis for any overarching theories about which substances and which target genes are most likely to mediate adverse effects of the environment on diseases, says Melanie Ehrlich, a biochemistry professor at the Tulane University School of Medicine and Tulane Cancer Center who has been conducting research on the topic for more than two decades.

That sense of uncertainty generally leaves epigenetics out of the regulatory picture. "It's \[too early\] to actually use it at the moment," says Julian Preston, acting associate director for health at the EPA's National Health and Environmental Effects Research Laboratory. But Preston says the agency already relies more on its improving understanding of mechanistic processes, including epigenetics, and there is a clear effort within the EPA to expand genomics efforts both within the agency and with others with whom the agency works.

At the FDA, scientists are investigating many drugs that function through epigenetic mechanisms (although as spokes-woman Christine Parker notes, the agency bases its approvals on results of clinical trials, not consideration of the mechanism by which a drug works). One such drug, azacitidine, has been approved for use in the United States to treat myelodysplastic syndrome, a blood disease that can progress to leukemia. The drug turns on genes that had been shut off by methylation. The drug's epigenetic function doesn't make it a "miracle drug," however. Trials indicate it benefits only 15% of those who take it, and a high percentage of people suffer serious side effects, including nausea (71%), anemia (70%), vomiting (54%), and fever (52%).

Ehrlich points out that azacitidine also has effects at the molecular level—such as inhibiting DNA replication and apoptosis—that may be part of its therapeutic benefits. The drug's mixed results might also be explained in part by a study published in the October 2004 issue of *Cancer Cell* by Andrew Feinberg, director of the Johns Hopkins University Center for Epigenetics in Common Human Disease, and his colleagues. They found that each of two tested drugs, trichostatin A and 5-aza-2′-deoxycytidine (which is related to azacitidine), can turn on hundreds of genes while also turning off hundreds of others. If that finding holds in other studies, it suggests one key reason why it is so difficult to create a drug that doesn't cause unintended side effects.

# Public and Private

Despite the potentially huge role that epigenetics may play in human disease, investment in this area of study remains tiny compared to that devoted to traditional genetics work. Several efforts to change that are under way.

In Europe, the Human Epigenome Project was officially launched in 2003 by the Wellcome Trust Sanger Institute, Epigenomics AG, and the Centre National de Génotypage. The group's focus is on DNA methylation research tied to chromosomes 6, 13, 20, and 22. They may be joined soon by organizations in Germany and India, where scientists plan to work on chromosomes 21 and X, respectively, says Sanger senior investigator Stephan Beck.

But comprehensively studying all the epigenetic and epigenomic factors related to a multitude of diseases and health conditions will take much more work. "A \[comprehensive\] Human Epigenome Project is a lot more complicated than a Human Genome Project," Jones says. "There's only one genome, \[but\] an epigenome varies in each and every tissue." The Human Genome Project was a worldwide effort that took more than a decade and billions of dollars to complete.

Jones and Robert Martienssen addressed some of the complexities of a comprehensive, worldwide Human Epigenome Project in the 15 December 2005 issue of *Cancer Research*. Reporting on a June 2005 workshop convened by the American Association for Cancer Research, they concluded that, despite all the looming difficulties, such a project is essential, and the technology is sufficiently advanced to begin.

"I think it's going to happen a lot sooner than I thought just a year or so ago," Jirtle says. A group of researchers has already started the footwork to launch a U.S. complement to the European Human Epigenome Project effort \[see box, p. A165\].

Other efforts are gaining ground. Another European group, the Epigenome Network of Excellence, took off in June 2004. This information exchange network includes members in the public and private sectors spread throughout ten Western European countries. Their objectives are to coordinate research, provide mentors, and encourage dialogue via their website. And in Asia, a conference held 7–10 November 2005 in Tokyo, "Genome-Wide Epigenetics 2005," was dedicated in large part to facilitating a coordinated epigenomics research effort in Japan and possibly all of Asia, says Ushijima, one of the conference's organizers.

In the United States, the National Cancer Institute and the National Human Genome Research Institute formally kicked off a major effort 13 December 2005 that will include epigenomic work. The pilot project of The Cancer Genome Atlas, funded by \$50 million each from the two institutes, is designed to lay the groundwork for comprehensive study of genomic factors related to human cancer. The initial three-year effort is expected to focus on just two or three of the more than 200 cancers known to exist, but if it's successful in developing methods and technologies, the number of cancers evaluated could then expand. If a high number of cancer genes are eventually scrutinized, the effort would be the equivalent of thousands of Human Genome Projects.

To help push the boundaries further, the NIEHS and the National Cancer Institute are in the midst of awarding grants totaling \$3.75 million to study a wide range of epigenetic topics, such as identification of high-risk populations, dietary influences on cancer, and detailed study of numerous specific mechanisms linking environmental agents with epigenetic mechanisms and resulting disease. The dozen or so recipients are expected to launch their projects by fall 2006.

The NIEHS has also begun to integrate epigenomics projects into its research portfolio over the past five to six years. "It's an emerging area that's very important," says Frederick Tyson, a program administrator in the NIEHS Division of Extramural Research and Training. And epigenetics is likely to be one of the half dozen or so most important considerations as NIEHS proceeds with its Environmental Genome Project, according to institute director David Schwartz.

The DNA Methylation Society, a professional group, has been growing slowly but steadily over the past decade, says founder and current vice president Ehrlich. As part of its efforts, the society launched a journal, *Epigenetics*, in January 2006 with the goal of covering a full spectrum of epigenetic considerations—medical, nutritional, psychological, behavioral—in any organism. Such groups are a valuable rallying point for this field, Jirtle says. He himself slowly worked his way into epigenetics from an initial cancer focus, and his segue is typical of many. "If you study epigenetics, you don't have a home; we come from all different fields," he says.

Interest in the private sector is also picking up. For instance, Epigenomics AG, with offices in Berlin and Seattle, is working on early detection and diagnosis of cancer and endometriosis (for which there is limited evidence of an epigenetic component), as well as development of products to predict effectiveness of drugs to treat these diseases. Founded in 1998, and now with about 150 employees, the company is focusing on DNA methylation mechanisms, and is working with companies such as Abbott Laboratories, Johnson & Johnson, Philip Morris, Roche Diagnostics, Pfizer, and AstraZeneca. CEO Oliver Schacht says the surging interest in this field is typified by the difference between the 2004 American Association for Cancer Research conference, which had half a dozen or so talks or posters on epigenetics, and the 2005 event, which had about 200.

# Tool Time

If epigenetic work is to continue breaking new ground, many observers say technology will need to continue advancing. Jones and Martienssen note in their paper that there must be additional improvements in high-throughput technologies, analytical techniques, computational capability, mechanistic studies, and bioinformatic strategies. They also say there is a need for basics such as standardized reagents and a consistent supply of antibodies for testing.

Preston agrees with many of these ideas, and says there is also a need to develop a comprehensive tally of all proteins in the cell and to get better protein modification information. He says universities are recognizing the demand for the talents needed to solve epigenomics problems, and are increasing their efforts to cover these topics in various ways, especially at the graduate school level.

Other groups are doing their part by creating tools to further the field. All the imprinted genes identified so far are tracked in complementary efforts by Morison's and Jirtle's groups and the Mammalian Genetics Unit of the U.K. Medical Research Council. The European managers of the DNA Methylation Database have assembled a compendium of known DNA methylations that, although not comprehensive, still provides a useful tool for researchers investigating the roughly 22,000 human genes.

Kunio Shiota, a professor of cellular biochemistry at the University of Tokyo and one of the co-organizers of the November 2005 Tokyo conference, says epigenetic advances will rely in part on a range of processes that are slowly becoming familiar to more researchers—massively parallel signature sequencing (MPSS), chromatin immunoprecipitation microarray analysis (ChIP-chip), DNA adenine methyltransferase identification (Dam-ID), protein binding microarrays (PBM), DNA immunoprecipitation microarray analysis (DIP-chip), and more. Someday, he says, these terms could become fully as familiar as MRI and EKG.

The rapidly growing acceptance of epigenetics, a century after it first surfaced, is a huge step forward, in Jirtle's opinion. "We've done virtually nothing so far," he says. "I'm biased, but the tip of the iceberg is genomics and single-nucleotide polymorphisms. The bottom of the iceberg is epigenetics."

# Resources

**Professional Organizations and Projects**

-   DNA Methylation Society (international) <http://www.dnamethsoc.com/main.htm>

-   Epigenome Network of Excellence (Europe) <http://www.epigenome-noe.net>

-   Human Epigenome Project (Europe) <http://www.epigenome.org>

**Journal**

Epigenetics

<http://www.landesbioscience.com/journals/epigenetics/>

**DNA Methylation Database**

<http://www.methdb.de/front.html>

**Imprinted Gene Databases**

-   <http://igc.otago.ac.nz/home.html>

-   <http://www.geneimprint.com/databases/?c=clist>

-   <http://www.mgu.har.mrc.ac.uk/research/imprinting/>

# U.S. Human Epigenome Project

In December 2005 a group of 40 international scientists publicly proposed a U.S. Human Epigenome Project to complement a European project of the same name launched in 2003. Group member Andrew Feinberg, a geneticist at the Johns Hopkins University School of Medicine, says, "We're hoping to see how this idea takes hold. There is this ocean of information that is largely unexplored."

The goal of the U.S. project will be to comprehensively map methylation and histone modifications —the two main classes of epigenetic modifications—in a diverse set of normal tissues. These epigenomes would then serve as a reference for comparison with diseased tissues, revealing epigenetic causes of disease. Project organizers are now compiling a detailed proposal, with budget estimates and a timeline.

Although both the U.S. and European projects ultimately aim to map all genes, the U.S. effort will look at different tissue and cell types than the European effort, and will also look at model organisms like yeast and the fly. The two groups are already working closely together in planning their projects to avoid redundancies, and this cooperation will likely continue.

Understanding cancer would be one long-term goal for the U.S. project, but epigenetics—changes in gene expression heritable from cell to daughter cell without changes in DNA sequence—transcends any one disease. "It has profound implications in aging, neurological disorders, and child development," says Peter Jones, another group member and director of the Norris Comprehensive Cancer Center at the University of Southern California. Jones and his colleagues argue that the importance of epigenetics in human disease, together with the maturing of technologies for mapping epigenetic changes, make a human epigenome project both critical and feasible.

Epigenetics, says cancer biologist Jean-Pierre Issa of The University of Texas M.D. Anderson Cancer Center, could prove more important than genetics for understanding environmental causes of disease. "Cancer, atherosclerosis, Alzheimer's disease \[are all\] acquired diseases where the environment very likely plays an important role," he points out. "And there's much more potential for the epigenome to be affected … than the genome itself. It's just more fluid and more easy to be the culprit." **– Ken Garber**