PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Lancet. Author manuscript; available in PMC 2014 January 14.
Published in final edited form as:
PMCID: PMC3891203
NIHMSID: NIHMS535847

Prediabetes: A high-risk state for developing diabetes

Adam G. Tabák, MD, PhD,1,2 Christian Herder, PhD,3 Wolfgang Rathmann, MSPH,4 Eric J. Brunner, PhD, FFPH,1 and Mika Kivimäki, PhD, Professor1

Summary

Prediabetes (or “intermediate hyperglycaemia”), based on glycaemic parameters above normal but below diabetes thresholds is a high risk state for diabetes with an annualized conversion rate of 5%–10%; with similar proportion converting back to normoglycaemia. The prevalence of prediabetes is increasing worldwide and it is projected that >470 million people will have prediabetes in 2030. Prediabetes is associated with the simultaneous presence of insulin resistance and β-cell dysfunction, abnormalities that start before glucose changes are detectable. Observational evidence shows associations of prediabetes with early forms of nephropathy, chronic kidney disease, small fibre neuropathy, diabetic retinopathy, and increased risk of macrovascular disease. Multifactorial risk scores could optimize the estimation of diabetes risk using non-invasive parameters and blood-based metabolic traits in addition to glycaemic values. For prediabetic individuals, lifestyle modification is the cornerstone of diabetes prevention with evidence of a 40%–70% relative risk reduction. Accumulating data also suggests potential benefits from pharmacotherapy.

Introduction

Prediabetes, typically defined as blood glucose levels above normal but below diabetes thresholds, is a risk state that defines a high chance of developing diabetes. Diagnostic criteria for prediabetes have changed over time and currently vary depending on the institution (table 1).

Table 1
Diagnostic criteria for prediabetes

According to the World Health Organization (WHO), high risk for developing diabetes relates to two distinct states, impaired fasting glucose (IFG) defined as fasting plasma glucose (FPG) of 6.1–6.9 mmol/L (in the absence of impaired glucose tolerance – IGT) and IGT defined as postload plasma glucose of 7.8–11.0 mmol/L based on 2-h oral glucose tolerance test (OGTT) or a combination of both.1 The American Diabetes Association (ADA), although applying the same thresholds for IGT, uses a lower cut-off value for IFG (FPG 5.6–6.9 mmol/L) and has additionally introduced haemoglobin A1c levels of 5.7–6.4% as a new category of high diabetes risk.2

The term prediabetes itself has been critised on the basis that (1) many people with prediabetes do not progress to diabetes, (2) the term may imply that no intervention is necessary as no disease is present, and (3) diabetes risk does not necessarily differ between people with prediabetes and those with a combination of other diabetes risk factors. Indeed, the WHO used the term ‘Intermediate Hyperglycaemia’ and an International Expert Committee convened by the ADA the ‘High Risk State of Developing Diabetes’ rather than ‘prediabetes’.1,3 For brevity, we use the term prediabetes in this seminar to refer to IFG, IGT and high risk based on A1c.

The reproducibility of prediabetes (~50%) is lower than that for diabetes (>70%)4 and the alternative definitions based on IFG, IGT and A1c define overlapping prediabetic groups with single or combined abnormalities. Isolated IFG and isolated IGT may define persons with different pathophysiological abnormalities and their combination marks a more advanced disturbance of glycaemic homeostasis.5 In Caucasians, for example, the overlap between IFG and IGT can be as low as 25%.5

Individual risk factors for diabetes (eg history of gestational diabetes, first degree relative with diabetes) or a combination of risk factors (eg metabolic syndrome) can also be used to define populations at-risk for diabetes but their predictive value is poorer than that of prediabetes. In addition, risk scores for incident diabetes based on a combination of non-invasive or blood-based risk factors are under development to identify individuals at high risk of developing diabetes.6 The aim of this seminar is to provide an updated review of the evidence of vascular complications and the underlying pathophysiology of prediabetes and to discuss the clinical implications.

Epidemiology and time trends

Glycaemic levels are rapidly rising in developed and developing countries.7 According to pooled data from 2.7 million adults participating in health surveys and epidemiological studies, age-standardised mean fasting plasma glucose (FPG) was 5.5 mmol/L in men and 5.4 mmol/L in women in 2008, a rise of 0.1 mmol/L since 1980. Oceania had the highest mean FPG of any region (6.1 mmol/L for men and women), but mean FPG was also high in some other regions (South and Central Asia, Latin America, the Carribean, North Africa, and the Middle East).7

Increases in glycaemia have resulted in a rise in prediabetes prevalence, although in some populations IGT has not risen despite increasing diabetes incidence, probably because increases in obesity influence FPG more than 2-h glucose and because of improved detection of diabetes.8 The population-based U.S. National Health and Nutrition Examination Survey (NHANES) suggests that 35% of U.S. adults over 20 years of age and 50% of those over 65 had prediabetes in 2005–2008 based on fasting glucose or A1c levels.9 Applying these percentages to the entire U.S. population in 2010 yields an estimated 79 million adults with prediabetes.9 The prevalences of IFG and IGT vary between ethnic groups and both conditions are more common in older people.10 In addition, IFG is more prevalent among men than women, although the reasons for this remain poorly understood.10

Figure 1 shows worldwide projections of IGT prevalence for the next twenty years according to the International Diabetes Federation.11 The number of adults with IGT is expected to increase globally, reaching 472 million by 2030. The greatest absolute rises are expected in South-East Asia and the Western Pacific Region.11

Figure 1
The number of people with IGT (in millions) by region among adults aged 20–79 years for the years 2010 and 203011

Progression from prediabetes to diabetes

Around 5–10% of people with prediabetes become diabetic annually although conversion rate varies by population characteristics and the definition of prediabetes.12,13 In a meta-analysis of prospective studies published up to 2004, annualised incidence rates of diabetes for isolated IGT (4–6%) and isolated IFG (6–9%) were lower than those for IFG and IGT combined (15–19%).14 In more recent major studies, progression estimates have been similar: the annualised incidence was 11% in the Diabetes Prevention Program (DPP) Outcomes Study,15 6% among participants with IFG in the US Multi-Ethnic Study of Atherosclerosis (MESA),16 9% among participants with IFG and 7% among those with an A1c 5.7–6.4% in a Japanese population-based study.17 Studies suggest that the risk of diabetes development on the basis of FPG and 2-h postload glucose is broadly similar to that posed by A1c.14,18

According to an ADA expert panel, up to 70% of individuals with prediabetes will eventually develop diabetes. In a Chinese diabetes prevention trial, the 20-year cumulative incidence of diabetes was even higher (>90%) among controls with an IGT defined with repeated OGTTs.19 For comparison, women with gestational diabetes have been suggested to have a 20%–60% risk of developing diabetes 5 to 10 years after pregnancy.2022 This large heterogeneity in the estimates is probably due to the variation in the criteria used to define gestational diabetes and type 2 diabetes in these studies. In a recent meta-analysis of 20 studies, 13% of mothers with gestational diabetes developed diabetes after pregnancy compared to 1% of mothers without gestational diabetes.23

Reversion to normoglycaemia

Several trials have demonstrated reductions in the risk of developing diabetes among prediabetic individuals after lifestyle and drug-based interventions.15,2428 Prediabetes may also convert back to normoglycaemia. In a population-based observational study of the natural history of diabetes in England, 55%–80% of the participants with IFG at baseline had normal fasting glucose at 10-year follow-up.12 Other studies have reported lower conversion rates29 (19% in controls in the DPP Outcomes study).15

Risk prediction

As with prediabetic status, diabetes risk models provide a method for identifying individuals at risk of developing diabetes based on parameters available to the general practitioner. There is no single universally accepted diabetes prediction model and given that ethnicity is strongly related to diabetes risk, recalibration of prediction algorithms may be necessary when they are applied to different populations.30 Table 2 presents a selection of current diabetes risk models used in the US, Europe, and Australia. These models include a broadly similar combination of risk factors but they weigh these components differently.

Table 2
Examples of externally validated diabetes risk models

In clinical practice, a two-stage process could be efficient: diabetes prediction models with non-invasive parameters such as age, sex, BMI, blood pressure, diabetes family history and lifestyle information allow a first assessment of diabetes risk with little effort and costs. Laboratory measures, in particular glucose values, can improve the performance of non-invasive models. Thus, for patients with an increased risk at the first stage, models including routinely collected blood measures can be applied for a more precise risk estimation.

A categorisation of persons as either ‘normal’ or ‘prediabetic’ (IFG, IGT) neglects the fact that diabetes risk significantly increases for FPG values within the normal range.31 Thus, in diabetes risk prediction, glycaemic measures (fasting and 2-h glucose, A1c) may perform better if treated as continuous traits rather than categorical variables.32,33 Furthermore, there is some evidence to suggest that incorporating postload glucose into a model that already includes FPG improves prediction. The Framingham Study and the KORA Study demonstrated the utility of simple clinical and laboratory measurements to derive diabetes prediction models suitable for general practices.32,33 The derivation of both models indicated that some information on metabolic traits (eg, glucose, uric acid, lipids) beyond personal diabetes risk factors is important to adequately determine the future risk of type 2 diabetes. Most recent attempts to improve diabetes prediction using measurements from genetics and transcriptomics have not shown significant improvements in predictive performance but it is unknown whether serial measurements may decrease variations in non-genetic biomarkers allowing for a more precise estimation of their levels.3436

The pathophysiology of prediabetes

In healthy people blood glucose is strictly regulated. Fasting glucose is maintained between 3.9 and 5.6 mmol/L37 and the post-meal increases rarely exceed 3 mmol/L.38 During the development of type 2 diabetes, the homeostasis of fasting and postload glucose becomes abnormal.39

As evidenced by studies with repeat measures of glucose levels, insulin sensitivity and insulin secretion, the development of diabetes from NGT is a continuous process.35,36,40,41 Recently we described trajectories of fasting and postload glucose in addition to trajectories of HOMA insulin sensitivity and insulin secretion (β-cell function) preceding the development of type 2 diabetes in the British Whitehall II study (figure 2).36 In people who developed diabetes, increased glucose values were observed already at the beginning of the follow-up, 13 years before diagnosis, although glucose values seemed to be tightly regulated within the normal range until 2–6 years before diagnosis when an abrupt increase was found. This pattern of glycaemic changes was confirmed by others.35,40,41

Figure 2
Fasting and 2-hour postload glucose, Homeostasis model assessment insulin sensitivity (HOMA2-%S) and HOMA β-cell function (HOMA2-%B) trajectories before the diagnosis of diabetes mellitus in the British Whitehall II study

Figure 2 shows that insulin sensitivity was reduced already 13 years before the onset of diabetes, with a steeper decline observed 5 years before diagnosis. Insulin secretion was elevated throughout the 13-year observation period and showed a marked increase 4-3 years before diagnosis, followed by a steep decrease until diagnosis.36 This is in line with the notion that insulin resistance starts years before diabetes development and that decreased beta-cell function is already present in the prediabetic stage.37,42

Multistage model of diabetes development

Weir coined a multistage model of diabetes development43 that corresponds to the above-described findings. The first stage of diabetes development is a long compensatory period when insulin resistance is present and accompanied by increased rates of insulin secretion44 and an increased β-cell mass.39

The second stage is the stable adaptation period when β-cells are no longer fully compensating for increased insulin resistance; thus fasting and/or postload glucose values are not completely maintained. This period is likely to start when fasting and postload glucose levels are within the normal range36,39,43 and is usually accompanied by a decrease in acute insulin secretion that is present at FPG levels around 5.6 mmol/L.39 Much of the first and second stages therefore occur before the prediabetic phase is achieved.

During the unstable early decompensation period, the third stage of diabetes development, the β-cells become unable to compensate for a given insulin resistance and consequently glucose levels start to increase rapidly39,43 as it was seen in Whitehall II and other longitudinal studies.36,41 This period probably extends from prediabetes to manifest diabetes.

The subsequent two stages of diabetes development (stable decompensation and severe decompensation) relate to manifest diabetes and thus are beyond the scope of this review.43

Glucose dysregulation, insulin resistance and β-cell dysfunction

Fasting plasma glucose values are determined by endogenous glucose production (EGP) that is mostly dependent on the liver. The product of EGP and fasting insulin is used as a marker of hepatic insulin resistance and it shows a relatively strong relationship with fasting glycaemia.38,39,45

During absorption of a glucose-containing meal, changes in glucose levels are determined by intestinal absorption, suppression of EGP and by total body glucose uptake.38,39 EGP is markedly suppressed in people with NGT after glucose ingestion while this suppression is less pronounced in prediabetes and diabetes.38,39 In type 2 diabetes, total body glucose disposal is also decreased and 85–90% of this impairment is related to muscle insulin resistance.46

If insulin secretion were able to compensate for insulin resistance perfectly, no observable changes in glucose levels would occur. This means that, by definition, β-cell dysfunction is already present in the prediabetic phase. β-cell function however cannot be characterised solely on the basis of insulin secretion without the consideration of the underlying insulin resistance. The β-cell responds to a given increase in glucose level with a given rise in insulin secretion that is conditioned on whole body insulin sensitivity. According to this concept, the relation between insulin secretion and insulin sensitivity is hyperbolic; the ratio of incremental insulin over incremental glucose divided by insulin resistance is described by a constant, the disposition index.39,47 The disposition index therefore is a measure of insulin secretion after accounting for the underlying degree of insulin resistance that is higher for healthy people and lower for prediabetic and diabetic individuals.

Studies using different measures of β-cell function have reported severely abnormal (up to 80% decreased) insulin secretion in prediabetic people.37,42,48 This observation is supported by autopsy studies reporting a 50% decrease in β-cell volume among those with glucose values within the IFG range.49

Specific differences between subjects with isolated IFG and isolated IGT

Subjects with isolated IFG and isolated IGT differ in their fasting and 2-h postload glucose values and by the shape of the glucose concentration curves during the OGTT.

Both IFG and IGT subjects present with insulin resistance, however the site of insulin resistance is different: High hepatic insulin resistance is a typical finding in IFG with almost normal values in skeletal muscle.38,39,45 In IGT, the main site of insulin resistance is the muscle with only modest changes in liver insulin sensitivity.37,38 This notion is reflected by the finding that total body glucose disposal gradually worsens from NGT to IFG to IGT and type 2 diabetes.48

β-cell dysfunction is present both in people with isolated IFG and isolated IGT. IFG people have severely impaired early insulin response during OGTT but their insulin secretion improves during the second phase of the OGTT. In contrast, IGT people present with impaired early and late phase insulin secretion.38,39,50

These findings suggest distinct pathophysiological mechanisms of isolated IFG and isolated IGT although the clinical relevance of these observations requires further clarification.

Nephropathy and kidney disease in prediabetes

People with prediabetes may have concomitant damage to end organs, such as eyes, kidneys, blood vessels and the heart that are traditionally considered to be complications of diabetes. In the following, we briefly review evidence on complications that are particularly relevant to prediabetes: (1) nephropathies and chronic kidney disease, (2) neuropathies, (3) diabetic retinopathy, and (4) macrovascular diseases.

There is evidence to link prediabetes to increased risk of early forms of nephropathy and chronic kidney disease (CKD), defined based on methods such as urinary albumin excretion rate (AER) and estimated glomerular filtration rate (eGFR).5155 The NHANES 1999–2006 showed increasing frequency of albuminuria in tandem with the glycaemia range when classified from normoglycaemia through IFG, undiagnosed diabetes and diagnosed diabetes for both microalbuminuria, which may also relate to hypertension and is therefore an imprecise marker of diabetes-related early nephropathy (6%, 10%, 29% and 29%, respectively) and macroalbuminuria (0.6%, 1.1%, 3.3% and 7.7%).54 Other data on increased albuminuria and glomerular filtration rates, an early marker of a kidney involvement in hyperglycaemia, also support the concept that some nephropathic changes may be present already in the prediabetic stage before the onset of diabetes.51,53,5658 In contrast, evidence of a cross-sectional association between prediabetes and eGFR, a late marker of CKD, is mixed, including both studies with positive54 and null findings.55,57 Longitudinal studies suggest that prediabetes is a risk factor for subsequent CKD but it is unclear whether this prospective association is attributable to the effects of prediabetes itself, increased incidence of diabetes, or common causes contributing to both hyperglycaemia and kidney pathology.59,60

Neuropathies in prediabetes

Neuropathies can be further divided into subcategories; the strongest supportive evidence relates to autonomic neuropathy, although the method of detection seems to be critical. Prediabetes has been found to be associated with decreased heart rate variability61 (HRV – a marker of parasympathetic function),6265, decreased postural changes in heart rate,62 increased prevalence of erectile dysfunction among men,66 and a worse profile in tests of sympathetic and parasympathetic function.67 No consistent evidence is available to suggest that prediabetes is associated with measurements of orthostatic blood pressure changes,63 which is a late marker of diabetic neuropathy,61 or with decreased expiratory-inspiratory ratio or change in heart rate during breathing.63

Studies in prediabetes and sensorimotor neuropathy 6870 suggest that IGT and early diabetic neuropathy may involve small demyelinated fibres.61 Distal intraepidermal nerve fibre density, quantitative sudomotor testing, total sweat volume and arm-to-foot sweat responses, deep tendon reflexes, and temperature sensation are sensitive markers of sensorimotor neuropathy71,72 whereas tests, such as the Michigan Neuropathy Screening Instrument; calibrated tuning fork; classical nerve conduction tests; vibration and temperature perception threshold may not capture neuropathy among prediabetic people.

Finally, evidence is accumulating on increased prevalence of idiopathic polyneuropathy (eg, idiopathic sensory/painful neuropathy,7378 and sensory/small fibre only neuropathy)73,75,78 among prediabetic individuals with IGT being more strongly related to painful than non-painful neuropathy.73,75,78

Diabetic retinopathy

Prediabetes status may be associated with an increased risk of diabetic retinopathy although the findings vary depending on the method of detection.51,7983 In a study of >5000 Pima Indians, retinopathy ascertained by direct ophthalmoscopy was associated with prediabetic status.51 Measures of retinal vascular changes, such as lower arteriole-to-venule ratio, increased retinal arteriole or venular calibre, have also been shown to be related to prediabetes or increased risk of diabetes, but the evidence is not entirely consistent.8183

Macrovascular disease

Prediabetes is linked with increased risks of major manifestations of vascular disease, but it remains unclear whether the elevated disease risks depend on development of clinical diabetes.84,85 Cross-sectional studies argue in favour of vascular risk effects of mild or moderate hyperglycaemia as there is an excess prevalence of coronary disease in those with fasting or postload hyperglycaemia below the diabetic level.86,87 Compared with coronary disease, there is less certainty with respect to cerebrovascular disease and aortic aneurysm.87 Diabetes is a known risk factor for ischaemic and haemorrhagic stroke, but it remains to be established whether risk increases before development of diabetes. 84

The dose-response effect of the degree of fasting hyperglycaemia for vascular mortality may be weaker than is the case for postload glucose. The DECODE pooling study of European cohorts found IGT to be associated with increased risk of coronary death and total cardiovascular death, independent of the level of FPG, while the converse was not the case.88 Whether it is the basal or the challenged blood glucose level that is more important for atherogenesis, average glucose levels, indexed by HbA1c concentration, predict incident coronary disease at least as well as fasting and postload glucose, though there are comparatively few prospective studies of HbA1c.89

The epidemiological relation between prediabetes and macrovascular disease may be confounded by clustering of vascular risk factors within individuals. Blood glucose in the prediabetic range is modestly correlated with many risk factors, including general and central obesity, blood pressure, triglyceride and lipoprotein levels.84 In consequence the strength of the glycaemia effect in itself depends on the extent to which related vascular risk factors are taken into account. A recent individual-level evidence from prospective studies suggests that fasting hyperglycaemia (figure 3), postload glucose, and A1c are all robust predictors of vascular mortality86,88,89 and, according to multivariable adjusted analyses, these associations are independent of vascular risk factors, such as obesity, blood pressure, triglyceride and lipoproteins.84,85,87

Figure 3
Hazard ratios for vascular death according to baseline levels of fasting glucose

Treatment

The reasons for treating prediabetes include prevention of progression to diabetes, mitigation of some of the potential consequences of progression to diabetes as well as prevention of the potential consequences of prediabetes itself. Majority of the studies in this field of research have focused on diabetes incidence among prediabetic individuals and support the concept that lifestyle change should be the cornerstone for diabetes prevention. Evidence is also accumulating to demonstrate potential benefits from pharmacotherapy.

Lifestyle intervention

The primary aim of lifestyle interventions is to prevent or delay the development of type 2 diabetes and its complications13,45 by targeting obesity and physical inactivity, the two most important modifiable risk factors of diabetes development.3,25 The Finnish Diabetes Prevention Study and, the largest to date, the US DPP with a 3-year follow-up found a 58% risk reduction after interventions aimed at weight loss, dietary change and increase in physical activity.25,28 In the first trial, the benefits were dependent on the number of goals achieved by the participant (weight reduction >5%, fat intake< 30%, saturated fat intake < 10%, fiber intake >15g/1000 kcal, exercise > 4h/week),28 while in the DPP the most important determinant of risk reduction was weight loss (each 1-kg decrease reduced the risk by 16%).90 The beneficial effect of lifestyle interventions has also been confirmed among Asian populations.26,91 Successful lifestyle interventions seem to improve insulin sensitivity and β-cell function.92,93

Pharmacological intervention based on antidiabetic drugs

(1) Biguanides

Metformin, used for decades to treat diabetes, has beneficial effects on BMI and lipid levels and has been proven to be safe based on trial evidence showing that there were no serious adverse effects (only minor gastrointestinal side effects were detected).94 Metformin reduces fasting glucose mainly through its effect on hepatic glucose output.95 According to trial evidence among people with IGT, metformin lowers the risk of type 2 diabetes by 45%.96 Its effect was similar to lifestyle intervention in the Indian DPP-1 study,26 while in the US DPP it was less effective than lifestyle.25 The beneficial effect of metformin was greater in prediabetic persons with a higher baseline BMI and higher FPG compared to their leaner counterparts with lower FPG levels.25 Gastrointestinal side effects of the drug were mostly mild to moderate, so the intervention seemed to be safe.25,26

(2) Thiazolidinediones

The glitazones, such as troglitazone, rosiglitazone and pioglitazone, act through the PPAR-γ receptor by increasing hepatic and peripheral insulin sensitivity and preserving insulin secretion.45,95 Rosiglitazone was effective in a 3-year randomised trial that showed a 60% reduction in incident diabetes risk but this drug was also associated with significant weight increase (~2 kg compared to placebo) and increased risk of heart failure (0.1% vs. 0.5% in controls).24,97 Pioglitazone showed effectiveness in the ACT NOW Study on obese people with IGT. The risk of diabetes decreased by >70% and the drug was associated with improved diastolic blood pressure, improved HDL-cholesterol and a reduced rate of carotid intima-media thickening. However, weight gain was greater with pioglitazone (~3 kg compared to placebo) and edema was more frequently reported (13% vs. 6%).29 There is also some suggestion of a possible link between pioglitazone and bladder cancer and therefore individuals with a history of bladder cancer or unexplained hematuria should probably not receive this drug.98,99 In the Indian DPP-2 study no difference in the rate of diabetes development was found between lifestyle intervention alone and lifestyle intervention plus pioglitazone during a 3-year trial.100

Two thiazolidinedione drugs were withdrawn from the European market, troglitazone for probable serious hepatotoxicity, and rosiglitazone due to possible increases in cardiovascular risk.45,95 In the recently published CANOE trial, low doses of rosiglitazone (2 mg b.i.d.) in combination with metformin were tested against placebo to examine whether the lower doses would cause less side effects. The risk of incident diabetes was reduced by 66% in the active treatment group with no significant difference in weight gain as compared to controls. However more people complained of diarrhoea in the active treatment group (16% vs. 6%).101

(3) α-glucosidase inhibitors

α-glucosidase inhibitors reduce the rate of polysaccharide digestion from the proximal small intestine. They primarily lower postprandial glucose without causing hypoglycaemia. Since their effect on A1c is smaller than that of other oral antidiabetic agents, they are seldom used in the treatment of type 2 diabetes.95 However, two large trials support the effectiveness of these drugs in the prevention of diabetes and importantly one of them also shows evidence of decreased CVD and hypertension risk among treated IGT patients.102,103 In the STOP-NIDDM trial, a 25% relative risk reduction for diabetes was found among people with IGT who were randomised to either acarbose 100 mg t.i.d. or identical placebo during 3.3 years of follow-up,103,104 but almost one third of the acarbose group could not complete the trial because of gastrointestinal side effects, such as flatulence and diarrhoea.103 A recent study investigating voglibose, another α-glucosidase inhibitor, found a 40% reduction in incident diabetes risk over 48 weeks of follow-up among high-risk Japanese individuals with IGT. Although the gastrointestinal side effects were similar as reported in previous trials, more people completed that study.102

(4) The GLP-1 analogues

Exenatide and liraglutide were both found to produce sustained weight loss among obese subjects and were associated with increased reversion from prediabetes to normoglycaemia over 1–2 years of follow-up. The most frequent side effects were nausea and vomiting in these studies.105107

(5) Insulin secretagouges

A multicentre multinational study investigated the effect of nateglinide (a short-acting insulin secretagogue) in over 9000 persons with IGT and found no effect on the rate of diabetes or the cardiovascular outcomes during 6.5 years of follow-up.108

Pharmacological interventions based on non-antidiabetic drugs

(1) Anti-obesity drugs

The anti-obesity drug orlistat is a gastrointestinal lipase inhibitor. In a post hoc analysis of obese people, this drug was associated with greater weight loss (6.7 vs. 3.8 kg) compared to placebo with a significantly reduced conversion from IGT to diabetes (7.6% vs. 3.0%) in a 1.5-year follow-up.109 This finding is consistent with the 4-year XENDOS trial that reported a 37% reduction in relative risk of diabetes among obese people treated with orlistat although in that study only 52% completed treatment compared with 34% of placebo recipients. An explanatory analysis suggested that the preventive effect was mainly confined to subjects with IGT.27

There is at least one randomised trial with 6-month duration in people with prediabetes and hypertriglyceridemia that found higher rates of regression to normoglycaemia among fenofibrate (>50%) treated subjects compared to placebo (30%). Lipotoxicity is thought to be an important factor in the development of diabetes that makes these findings attractive.110

(2) Renin-angiotensin-aldosteron system blockers

Secondary analyses of hypertension trials have suggested lower incidences of diabetes among people with high cardiovascular risk who receive ACE (angiotensin converting enzyme) inhibitors, or ARBs (angiotensin receptor blockers).111 However, these findings may be biased as the comparator active treatment groups had different proportions of other antihypertensive treatments that are known to increase the risk of diabetes (β-blockers, thiazide diuretics).112,113 Furthermore, in the DREAM trial, there was no significant association between ramipril, another renin-angiotensin-aldosteron system blocker, and new-onset diabetes.113 In light of current evidence, the effect of these drugs is much smaller than that of the antidiabetic drugs and they are not recommended for the treatment of prediabetes.

Other treatments that reduce diabetes risk

In morbidly obese people, bariatric surgery was associated with a sustained weight loss, a substantial reduction in 2- and 10-year incidence of type 2 diabetes114 and among individuals with blood glucose above 4.5 mmol/L reduced risk of cardiovascular disease.115 Corresponding benefits have not been reported for other weight loss interventions.

Long-term effects of lifestyle and antidiabetic drug interventions

Several trials support a long-term reduction in diabetes risk or a delay in the onset of the disease as a result of lifestyle and drug-based interventions.15,19,116118 In the 20-year follow-up of the DaQing Diabetes Prevention Study, for example, those receiving a lifestyle intervention had a 43% reduced risk of diabetes, translating to a mean 3.6 year delay in the development of diabetes.119

The Diabetes Prevention Program Outcomes Study found that reversion from prediabetes to normoglycaemia during the randomised phase of the study, even if transient, was associated with a 56% reduced risk of future diabetes independent of whether the reversion occurred spontaneously or during lifestyle or metformin therapy during the 5.7 year follow-up. Those who remained prediabetic despite intensive lifestyle treatment had an even higher risk of developing diabetes than those on metformin or placebo treatment.120

In the 20-year follow-up of the DaQing Study, the lifestyle intervention was also associated with an almost 50% reduction in the relative risk of incident severe retinopathy, while the rates of other microvascular complications, such as nephropathy and neuropathy, were similar as in controls.119

The evidence of intervention effects on macrovascular complications is not consistent. In a recent meta-analysis of trials among prediabetic people, lifestyle and drug-based interventions had no significant effect on the risk of all-cause mortality or cardiovascular death during the mean follow-up of 3.8 years, except for a borderline significant reduction in stroke risk.116 All-cause mortality was lower in the diet and exercise intervention group compared to the control group during a 12-year follow-up in the Malmö Preventive Project, but in this study participation in intervention was not randomised.121

Clinical and public health implications

The concept of prediabetes (also known as Intermediate Hyperglycaemia or High Risk for Diabetes) identifies a heterogeneous patient population that is characterized by the simultaneous presence of insulin resistance and β-cell dysfunction. Multifactorial diabetes risk scores are promising tools to further improve identification of individuals at high risk of developing diabetes although it is not yet known whether use of risk scores would help in the prevention of diabetes above the classical definition of prediabetes.

Prediabetes is not only related to an increased risk of diabetes and its complications but there is also accumulating evidence to suggest damage on kidney and nerves already at the prediabetic stage. Identification and treatment of prediabetic individuals is therefore crucial. Recent evidence suggest that preventing progression of prediabetes to diabetes is possible although evidence of reduced cardiovascular disease risk is limited. Based on randomized trials that show the effectiveness of lifestyle intervention and several antidiabetic drugs in the prevention of diabetes, lifestyle intervention aimed at >7% of weight reduction and 150 minutes/week of moderate intensity physical activity is recommended for all subjects with prediabetes. Based on the decades long safety information on metformin, this drug could also be used in people that are unable to comply with lifestyle advice. For the other potential drugs, further long-term studies are required on safety and on vascular outcomes before lifelong treatment could be safely recommended.

Economic considerations are important for policy makers, public health agencies, insurers, health care providers and consumers, but currently there are little data assessing different prediabetes screening and treatment strategies in terms of cost-effectiveness and health benefits. The fact that diabetes is projected to be within the 5 leading causes of death in high-income countries by 2030 and within the 10 leading causes of death globally highlights the public health importance of reducing diabetes risk at the population level. Strategies targeting interventions at the entire population in order to shift the distribution of key diabetes risk factors, such as adiposity and physical inactivity, are important. However, our seminar has shown that these need to be complemented with diabetes prevention strategies based on interventions specifically aimed at prediabetic and other high-risk individuals.

Search strategy

We searched Pubmed (from inception to January 2012). In the epidemiology section, we used the search terms “incidence” or “prevalence”, in the complications section “nephropathy” or “albuminuria” or “microalbuminuria” or “chronic kidney disease” or “neuropathy” or “autonomic” or “heart rate variability” or “orthostatic” or “idiopathic neuropathy” or “erectile dysfunction” or “Valsalva”, in the pathophysiology section “pathophysiology” or “clamp” or “intravenous glucose tolerance test” or “insulin secretion” or “insulin sensitivity”, in the treatment section “diabetes prevention” or “lifestyle intervention” or “metformin” or “troglitazone” or “rosiglitazone” or “pioglitazone” or “acarbose” or “voglibose” or “exenatide” or “liraglutide” or “nateglinide” or “ramipril” or “valsartan” or “orlistat” or “bariatric surgery” or “fibrate” in combination with the terms “prediabetes”, “impaired glucose tolerance” or “impaired fasting glucose”. We primarily selected publications in the past 5 years, but did not exclude commonly referenced and highly regarded older publications. We also searched the reference lists of articles identified by this search strategy and selected those we judged relevant. Review articles and book chapters are cited to provide readers with more details and more references than this Seminar has room for. Our reference list was modified on the basis of comments from peer reviewers and was limited to 120 references.

Acknowledgments

MK is supported by the UK Medical Research Council, the US National Institutes of Health (R01HL036310; R01AG034454) and the Academy of Finland.

Abbreviations

A1c
hemoglobin A1c
ADA
American Diabetes Association
ACE inhibitors
angiotensin converting enzyme inhibitors
AER
albumin excretion rate
ARB
angiotensin receptor blockers
ARIC
Atherosclerosis Risk in Communities study
AusDiab
Australian, Diabetes, Obesity, and Lifestyle Study
CKD
Chronic kidney disease
CVD
cardiovascular disease
DPP
Diabetes Prevention Programme
eGFR
estimated glomerular filtration rate
EGP
endogenous glucose production
FPG
fasting plasma glucose
HRV
heart rate variability
IFG
impaired fasting glucose
HOMA
homeostatic model assessment
IGT
impaired glucose tolerance
IENFD
intraepidermal nerve fibre density
MESA
Multi-Ethnic Study of Atherosclerosis
NGT
normal glucose tolerance
NHANES
National Health and Nutrition Examination Survey
OGTT
oral glucose tolerance test
WHO
World Health Organization

Footnotes

Contributors

All authors contributed to the literature search and to writing parts of this Seminar. AGT and MK wrote the first draft of the paper and all authors contributed to the writing of the final version.

Conflict of Interest Statement

We declare that we have no conflict of interest.

References

1. World Health Organization, International DF. Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation. Geneva: World Health Organization; 2006.
2. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2011;34 (Suppl 1):S62–S69. [PMC free article] [PubMed]
3. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32:1327–34. [PMC free article] [PubMed]
4. Balion CM, Raina PS, Gerstein HC, et al. Reproducibility of impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) classification: a systematic review. Clin Chem Lab Med. 2007;45:1180–5. [PubMed]
5. Age- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts. Diabetes Care. 2003;26:61–9. [PubMed]
6. Buijsse B, Simmons RK, Griffin SJ, et al. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011;33:46–62. [PMC free article] [PubMed]
7. Danaei G, Finucane MM, Lu Y, et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2. 7 million participants. Lancet. 2011;378:31–40. [PubMed]
8. Katikireddi SV, Morling JR, Bhopal R. Is there a divergence in time trends in the prevalence of impaired glucose tolerance and diabetes? A systematic review in South Asian populations. Int J Epidemiol. 2011;40:1542–53. [PubMed]
9. Centers for Disease Control and Prevention. National Diabetes Fact Sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2011.
10. Cowie CC, Rust KF, Ford ES, et al. Full accounting of diabetes and pre-diabetes in the U.S. population in 1988–1994 and 2005–2006. Diabetes Care. 2009;32:287–94. [PMC free article] [PubMed]
11. International Diabetes Federation. IDF Diabetes Atlas. 5. Brussels: International Diabetes Federation; 2011.
12. Forouhi NG, Luan J, Hennings S, et al. Incidence of Type 2 diabetes in England and its association with baseline impaired fasting glucose: the Ely study 1990–2000. Diabet Med. 2007;24:200–7. [PubMed]
13. Nathan DM, Davidson MB, DeFronzo RA, et al. Impaired fasting glucose and impaired glucose tolerance: implications for care. Diabetes Care. 2007;30:753–9. [PubMed]
14. Gerstein HC, Santaguida P, Raina P, et al. Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies. Diabetes Res Clin Pract. 2007;78:305–12. [PubMed]
15. Knowler WC, Fowler SE, Hamman RF, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009;374:1677–86. [PMC free article] [PubMed]
16. Yeboah J, Bertoni AG, Herrington DM, et al. Impaired fasting glucose and the risk of incident diabetes mellitus and cardiovascular events in an adult population: MESA (Multi-Ethnic Study of Atherosclerosis) J Am Coll Cardiol. 2011;58:140–6. [PMC free article] [PubMed]
17. Heianza Y, Hara S, Arase Y, et al. HbA1c 5.7–6. 4% and impaired fasting plasma glucose for diagnosis of prediabetes and risk of progression to diabetes in Japan (TOPICS 3): a longitudinal cohort study. Lancet. 2011;378:147–55. [PubMed]
18. Zhang X, Gregg EW, Williamson DF, et al. A1C level and future risk of diabetes: a systematic review. Diabetes Care. 2010;33:1665–73. [PMC free article] [PubMed]
19. Li G, Zhang P, Wang J, et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008;371:1783–9. [PubMed]
20. Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care. 2002;25:1862–8. [PubMed]
21. Feig DS, Zinman B, Wang X, et al. Risk of development of diabetes mellitus after diagnosis of gestational diabetes. CMAJ. 2008;179:229–34. [PMC free article] [PubMed]
22. Lauenborg J, Hansen T, Jensen DM, et al. Increasing incidence of diabetes after gestational diabetes: a long-term follow-up in a Danish population. Diabetes Care. 2004;27:1194–9. [PubMed]
23. Bellamy L, Casas JP, Hingorani AD, et al. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009;373:1773–9. [PubMed]
24. Gerstein HC, Yusuf S, Bosch J, et al. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial. Lancet. 2006;368:1096–105. [PubMed]
25. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393–403. [PMC free article] [PubMed]
26. Ramachandran A, Snehalatha C, Mary S, et al. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1) Diabetologia. 2006;49:289–97. [PubMed]
27. Torgerson JS, Hauptman J, Boldrin MN, et al. XENical in the prevention of diabetes in obese subjects (XENDOS) study: a randomized study of orlistat as an adjunct to lifestyle changes for the prevention of type 2 diabetes in obese patients. Diabetes Care. 2004;27:155–61. [PubMed]
28. Tuomilehto J, Lindstrom J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344:1343–50. [PubMed]
29. DeFronzo RA, Tripathy D, Schwenke DC, et al. Pioglitazone for diabetes prevention in impaired glucose tolerance. N Engl J Med. 2011;364:1104–15. [PubMed]
30. Noble D, Mathur R, Dent T, et al. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163. [PMC free article] [PubMed]
31. Tirosh A, Shai I, Tekes-Manova D, et al. Normal fasting plasma glucose levels and type 2 diabetes in young men. N Engl J Med. 2005;353:1454–62. [PubMed]
32. Rathmann W, Kowall B, Heier M, et al. Prediction models for incident type 2 diabetes mellitusin the older population: KORA S4/F4 cohort study. Diabet Med. 2010;27:1116–23. [PubMed]
33. Wilson PW, Meigs JB, Sullivan L, et al. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007;167:1068–74. [PubMed]
34. Carstensen M, Herder C, Kivimaki M, et al. Accelerated increase in serum interleukin-1 receptor antagonist starts 6 years before diagnosis of type 2 diabetes: Whitehall II prospective cohort study. Diabetes. 2010;59:1222–7. [PMC free article] [PubMed]
35. Sattar N, McConnachie A, Ford I, et al. Serial metabolic measurements and conversion to type 2 diabetes in the west of Scotland coronary prevention study: specific elevations in alanine aminotransferase and triglycerides suggest hepatic fat accumulation as a potential contributing factor. Diabetes. 2007;56:984–91. [PubMed]
36. Tabak AG, Jokela M, Akbaraly TN, et al. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet. 2009;373:2215–21. [PMC free article] [PubMed]
37. Abdul-Ghani MA, Tripathy D, DeFronzo RA. Contributions of beta-cell dysfunction and insulin resistance to the pathogenesis of impaired glucose tolerance and impaired fasting glucose. Diabetes Care. 2006;29:1130–9. [PubMed]
38. Ferrannini E, Gastaldelli A, Iozzo P. Pathophysiology of prediabetes. Med Clin North Am. 2011;95:327, viii. [PubMed]
39. DeFronzo RA. Banting Lecture. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes. 2009;58:773–95. [PMC free article] [PubMed]
40. Ferrannini E, Nannipieri M, Williams K, et al. Mode of onset of type 2 diabetes from normal or impaired glucose tolerance. Diabetes. 2004;53:160–5. [PubMed]
41. Mason CC, Hanson RL, Knowler WC. Progression to type 2 diabetes characterized by moderate then rapid glucose increases. Diabetes. 2007;56:2054–61. [PubMed]
42. Gastaldelli A, Ferrannini E, Miyazaki Y, et al. Beta-cell dysfunction and glucose intolerance: results from the San Antonio metabolism (SAM) study. Diabetologia. 2004;47:31–9. [PubMed]
43. Weir GC, Bonner-Weir S. Five stages of evolving beta-cell dysfunction during progression to diabetes. Diabetes. 2004;53 (Suppl 3):S16–S21. [PubMed]
44. Polonsky KS, Given BD, Hirsch L, et al. Quantitative study of insulin secretion and clearance in normal and obese subjects. J Clin Invest. 1988;81:435–41. [PMC free article] [PubMed]
45. DeFronzo RA, Abdul-Ghani MA. Preservation of beta-cell function: the key to diabetes prevention. J Clin Endocrinol Metab. 2011;96:2354–66. [PubMed]
46. Pendergrass M, Bertoldo A, Bonadonna R, et al. Muscle glucose transport and phosphorylation in type 2 diabetic, obese nondiabetic, and genetically predisposed individuals. Am J Physiol Endocrinol Metab. 2007;292:E92–100. [PubMed]
47. Kahn SE. The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of Type 2 diabetes. Diabetologia. 2003;46:3–19. [PubMed]
48. Ferrannini E, Balkau B, Coppack SW, et al. Insulin resistance, insulin response, and obesity as indicators of metabolic risk. J Clin Endocrinol Metab. 2007;92:2885–92. [PubMed]
49. Butler AE, Janson J, Bonner-Weir S, et al. Beta-cell deficit and increased beta-cell apoptosis in humans with type 2 diabetes. Diabetes. 2003;52:102–10. [PubMed]
50. Kanat M, Mari A, Norton L, et al. Distinct beta-Cell Defects in Impaired Fasting Glucose and Impaired Glucose Tolerance. Diabetes. 2012;61:447–53. [PMC free article] [PubMed]
51. Gabir MM, Hanson RL, Dabelea D, et al. Plasma glucose and prediction of microvascular disease and mortality: evaluation of 1997 American Diabetes Association and 1999 World Health Organization criteria for diagnosis of diabetes. Diabetes Care. 2000;23:1113–8. [PubMed]
52. Hoehner CM, Greenlund KJ, Rith-Najarian S, et al. Association of the insulin resistance syndrome and microalbuminuria among nondiabetic native Americans. The Inter-Tribal Heart Project. J Am Soc Nephrol. 2002;13:1626–34. [PubMed]
53. Metcalf PA, Baker JR, Scragg RK, et al. Microalbuminuria in a middle-aged workforce. Effect of hyperglycemia and ethnicity. Diabetes Care. 1993;16:1485–93. [PubMed]
54. Plantinga LC, Crews DC, Coresh J, et al. Prevalence of chronic kidney disease in US adults with undiagnosed diabetes or prediabetes. Clin J Am Soc Nephrol. 2010;5:673–82. [PubMed]
55. Xu M, Li XY, Wang JG, et al. Retinol-binding protein 4 is associated with impaired glucose regulation and microalbuminuria in a Chinese population. Diabetologia. 2009;52:1511–9. [PubMed]
56. Fujita H, Narita T, Ito S. Abnormality in urinary protein excretion in Japanese men with impaired glucose tolerance. Diabetes Care. 1999;22:823–6. [PubMed]
57. Hermans MM, Henry R, Dekker JM, et al. Estimated glomerular filtration rate and urinary albumin excretion are independently associated with greater arterial stiffness: the Hoorn Study. J Am Soc Nephrol. 2007;18:1942–52. [PubMed]
58. Melsom T, Mathisen UD, Ingebretsen OC, et al. Impaired fasting glucose is associated with renal hyperfiltration in the general population. Diabetes Care. 2011;34:1546–51. [PMC free article] [PubMed]
59. Fox CS, Larson MG, Leip EP, et al. Glycemic status and development of kidney disease: the Framingham Heart Study. Diabetes Care. 2005;28:2436–40. [PubMed]
60. Thomas G, Sehgal AR, Kashyap SR, et al. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol. 2011;6:2364–73. [PubMed]
61. Tesfaye S, Boulton AJ, Dyck PJ, et al. Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care. 2010;33:2285–93. [PMC free article] [PubMed]
62. Wu JS, Yang YC, Lin TS, et al. Epidemiological evidence of altered cardiac autonomic function in subjects with impaired glucose tolerance but not isolated impaired fasting glucose. J Clin Endocrinol Metab. 2007;92:3885–9. [PubMed]
63. Gerritsen J, Dekker JM, TenVoorde BJ, et al. Glucose tolerance and other determinants of cardiovascular autonomic function: the Hoorn Study. Diabetologia. 2000;43:561–70. [PubMed]
64. Singh JP, Larson MG, O’Donnell CJ, et al. Association of hyperglycemia with reduced heart rate variability (The Framingham Heart Study) Am J Cardiol. 2000;86:309–12. [PubMed]
65. Schroeder EB, Chambless LE, Liao D, et al. Diabetes, glucose, insulin, and heart rate variability: the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Care. 2005;28:668–74. [PubMed]
66. Grover SA, Lowensteyn I, Kaouache M, et al. The prevalence of erectile dysfunction in the primary care setting: importance of risk factors for diabetes and vascular disease. Arch Intern Med. 2006;166:213–9. [PubMed]
67. Putz Z, Tabak AG, Toth N, et al. Noninvasive evaluation of neural impairment in subjects with impaired glucose tolerance. Diabetes Care. 2009;32:181–3. [PMC free article] [PubMed]
68. Ziegler D, Rathmann W, Dickhaus T, et al. Prevalence of polyneuropathy in pre-diabetes and diabetes is associated with abdominal obesity and macroangiopathy: the MONICA/KORA Augsburg Surveys S2 and S3. Diabetes Care. 2008;31:464–9. [PubMed]
69. Ylitalo KR, Sowers M, Heeringa S. Peripheral vascular disease and peripheral neuropathy in individuals with cardiometabolic clustering and obesity: National Health and Nutrition Examination Survey 2001–2004. Diabetes Care. 2011;34:1642–7. [PMC free article] [PubMed]
70. Bruce SG, Young TK. Prevalence and risk factors for neuropathy in a Canadian First Nation community. Diabetes Care. 2008;31:1837–41. [PMC free article] [PubMed]
71. Smith AG, Russell J, Feldman EL, et al. Lifestyle intervention for pre-diabetic neuropathy. Diabetes Care. 2006;29:1294–9. [PubMed]
72. Grandinetti A, Chow DC, Sletten DM, et al. Impaired glucose tolerance is associated with postganglionic sudomotor impairment. Clin Auton Res. 2007;17:231–3. [PubMed]
73. Hoffman-Snyder C, Smith BE, Ross MA, et al. Value of the oral glucose tolerance test in the evaluation of chronic idiopathic axonal polyneuropathy. Arch Neurol. 2006;63:1075–9. [PubMed]
74. Smith AG, Singleton JR. The diagnostic yield of a standardized approach to idiopathic sensory-predominant neuropathy. Arch Intern Med. 2004;164:1021–5. [PubMed]
75. Singleton JR, Smith AG, Bromberg MB. Increased prevalence of impaired glucose tolerance in patients with painful sensory neuropathy. Diabetes Care. 2001;24:1448–53. [PubMed]
76. Nebuchennykh M, Loseth S, Jorde R, et al. Idiopathic polyneuropathy and impaired glucose metabolism in a Norwegian patient series. Eur J Neurol. 2008;15:810–6. [PubMed]
77. Smith AG, Ramachandran P, Tripp S, et al. Epidermal nerve innervation in impaired glucose tolerance and diabetes-associated neuropathy. Neurology. 2001;57:1701–4. [PubMed]
78. Sumner CJ, Sheth S, Griffin JW, et al. The spectrum of neuropathy in diabetes and impaired glucose tolerance. Neurology. 2003;60:108–11. [PubMed]
79. Algvere P, Efendic S, Luft R, et al. Retinal microangiopathy and pigment epithelial lesions in subjects with normal, borderline, and decreased oral glucose tolerance. Br J Ophthalmol. 1985;69:416–9. [PMC free article] [PubMed]
80. Tapp RJ, Tikellis G, Wong TY, et al. Longitudinal association of glucose metabolism with retinopathy: results from the Australian Diabetes Obesity and Lifestyle (AusDiab) study. Diabetes Care. 2008;31:1349–54. [PMC free article] [PubMed]
81. Nguyen TT, Wang JJ, Wong TY. Retinal vascular changes in pre-diabetes and prehypertension: new findings and their research and clinical implications. Diabetes Care. 2007;30:2708–15. [PubMed]
82. Wong TY, Klein R, Sharrett AR, et al. Retinal arteriolar narrowing and risk of diabetes mellitus in middle-aged persons. JAMA. 2002;287:2528–33. [PubMed]
83. Nguyen TT, Wang JJ, Islam FM, et al. Retinal arteriolar narrowing predicts incidence of diabetes: the Australian Diabetes, Obesity and Lifestyle (AusDiab) Study. Diabetes. 2008;57:536–9. [PubMed]
84. Sarwar N, Gao P, Seshasai SR, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375:2215–22. [PMC free article] [PubMed]
85. Seshasai SR, Kaptoge S, Thompson A, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med. 2011;364:829–41. [PMC free article] [PubMed]
86. Barr EL, Zimmet PZ, Welborn TA, et al. Risk of cardiovascular and all-cause mortality in individuals with diabetes mellitus, impaired fasting glucose, and impaired glucose tolerance: the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab) Circulation. 2007;116:151–7. [PubMed]
87. Brunner EJ, Shipley MJ, Witte DR, et al. Relation between blood glucose and coronary mortality over 33 years in the Whitehall Study. Diabetes Care. 2006;29:26–31. [PubMed]
88. Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med. 2001;161:397–405. [PubMed]
89. Sarwar N, Aspelund T, Eiriksdottir G, et al. Markers of dysglycaemia and risk of coronary heart disease in people without diabetes: Reykjavik prospective study and systematic review. PLoS Med. 2010;7:e1000278. [PMC free article] [PubMed]
90. Hamman RF, Wing RR, Edelstein SL, et al. Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care. 2006;29:2102–7. [PMC free article] [PubMed]
91. Saito T, Watanabe M, Nishida J, et al. Lifestyle modification and prevention of type 2 diabetes in overweight Japanese with impaired fasting glucose levels: a randomized controlled trial. Arch Intern Med. 2011;171:1352–60. [PubMed]
92. Snehalatha C, Mary S, Selvam S, et al. Changes in insulin secretion and insulin sensitivity in relation to the glycemic outcomes in subjects with impaired glucose tolerance in the Indian Diabetes Prevention Programme-1 (IDPP-1) Diabetes Care. 2009;32:1796–801. [PMC free article] [PubMed]
93. Kitabchi AE, Temprosa M, Knowler WC, et al. Role of insulin secretion and sensitivity in the evolution of type 2 diabetes in the diabetes prevention program: effects of lifestyle intervention and metformin. Diabetes. 2005;54:2404–14. [PMC free article] [PubMed]
94. Salpeter SR, Buckley NS, Kahn JA, et al. Meta-analysis: metformin treatment in persons at risk for diabetes mellitus. Am J Med. 2008;121:149–57. [PubMed]
95. Nathan DM, Buse JB, Davidson MB, et al. Medical management of hyperglycaemia in type 2 diabetes mellitus: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia. 2009;52:17–30. [PubMed]
96. Lilly M, Godwin M. Treating prediabetes with metformin: systematic review and meta-analysis. Can Fam Physician. 2009;55:363–9. [PMC free article] [PubMed]
97. Dagenais GR, Gerstein HC, Holman R, et al. Effects of ramipril and rosiglitazone on cardiovascular and renal outcomes in people with impaired glucose tolerance or impaired fasting glucose: results of the Diabetes REduction Assessment with ramipril and rosiglitazone Medication (DREAM) trial. Diabetes Care. 2008;31:1007–14. [PubMed]
98. Piccinni C, Motola D, Marchesini G, et al. Assessing the association of pioglitazone use and bladder cancer through drug adverse event reporting. Diabetes Care. 2011;34:1369–71. [PMC free article] [PubMed]
99. Tseng CH. Pioglitazone and Bladder Cancer: A population-based study of Taiwanese. Diabetes Care. 2012;35:278–80. [PMC free article] [PubMed]
100. Ramachandran A, Snehalatha C, Mary S, et al. Pioglitazone does not enhance the effectiveness of lifestyle modification in preventing conversion of impaired glucose tolerance to diabetes in Asian Indians: results of the Indian Diabetes Prevention Programme-2 (IDPP-2) Diabetologia. 2009;52:1019–26. [PubMed]
101. Zinman B, Harris SB, Neuman J, et al. Low-dose combination therapy with rosiglitazone and metformin to prevent type 2 diabetes mellitus (CANOE trial): a double-blind randomised controlled study. Lancet. 2010;376:103–11. [PubMed]
102. Kawamori R, Tajima N, Iwamoto Y, et al. Voglibose for prevention of type 2 diabetes mellitus: a randomised, double-blind trial in Japanese individuals with impaired glucose tolerance. Lancet. 2009;373:1607–14. [PubMed]
103. Chiasson JL, Josse RG, Gomis R, et al. Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet. 2002;359:2072–7. [PubMed]
104. Chiasson JL, Josse RG, Gomis R, et al. Acarbose treatment and the risk of cardiovascular disease and hypertension in patients with impaired glucose tolerance: the STOP-NIDDM trial. JAMA. 2003;290:486–94. [PubMed]
105. Astrup A, Carraro R, Finer N, et al. Safety, tolerability and sustained weight loss over 2 years with the once-daily human GLP-1 analog, liraglutide. Int J Obes (Lond) 2011 [PMC free article] [PubMed]
106. Rosenstock J, Klaff LJ, Schwartz S, et al. Effects of exenatide and lifestyle modification on body weight and glucose tolerance in obese subjects with and without pre-diabetes. Diabetes Care. 2010;33:1173–5. [PMC free article] [PubMed]
107. Astrup A, Rossner S, Van GL, et al. Effects of liraglutide in the treatment of obesity: a randomised, double-blind, placebo-controlled study. Lancet. 2009;374:1606–16. [PubMed]
108. Holman RR, Haffner SM, McMurray JJ, et al. Effect of nateglinide on the incidence of diabetes and cardiovascular events. N Engl J Med. 2010;362:1463–76. [PubMed]
109. Heymsfield SB, Segal KR, Hauptman J, et al. Effects of weight loss with orlistat on glucose tolerance and progression to type 2 diabetes in obese adults. Arch Intern Med. 2000;160:1321–6. [PubMed]
110. Wan Q, Wang F, Wang F, et al. Regression to normoglycaemia by fenofibrate in pre-diabetic subjects complicated with hypertriglyceridaemia: a prospective randomized controlled trial. Diabet Med. 2010;27:1312–7. [PubMed]
111. Al-Mallah M, Khawaja O, Sinno M, et al. Do angiotensin converting enzyme inhibitors or angiotensin receptor blockers prevent diabetes mellitus? A meta-analysis. Cardiol J. 2010;17:448–56. [PubMed]
112. McMurray JJ, Holman RR, Haffner SM, et al. Effect of valsartan on the incidence of diabetes and cardiovascular events. N Engl J Med. 2010;362:1477–90. [PubMed]
113. Bosch J, Yusuf S, Gerstein HC, et al. Effect of ramipril on the incidence of diabetes. N Engl J Med. 2006;355:1551–62. [PubMed]
114. Sjostrom L, Lindroos AK, Peltonen M, et al. Lifestyle, diabetes, and cardiovascular risk factors 10 years after bariatric surgery. N Engl J Med. 2004;351:2683–93. [PubMed]
115. Sjostrom L, Peltonen M, Jacobson P, et al. Bariatric surgery and long-term cardiovascular events. JAMA. 2012;307:56–65. [PubMed]
116. Hopper I, Billah B, Skiba M, et al. Prevention of diabetes and reduction in major cardiovascular events in studies of subjects with prediabetes: meta-analysis of randomised controlled clinical trials. Eur J Cardiovasc Prev Rehabil. 2011;18:813–23. [PubMed]
117. Lindstrom J, Ilanne-Parikka P, Peltonen M, et al. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet. 2006;368:1673–9. [PubMed]
118. Gerstein HC, Mohan V, Avezum A, et al. Long-term effect of rosiglitazone and/or ramipril on the incidence of diabetes. Diabetologia. 2011;54:487–95. [PubMed]
119. Gong Q, Gregg EW, Wang J, et al. Long-term effects of a randomised trial of a 6-year lifestyle intervention in impaired glucose tolerance on diabetes-related microvascular complications: the China Da Qing Diabetes Prevention Outcome Study. Diabetologia. 2011;54:300–7. [PubMed]
120. Perreault L, Pan Q, Mather KJ, et al. Regression from Pre-diabetes to Normal Glucose Regulation is Associated with Long-term Reduction in Diabetes Risk: Results from the Diabetes Prevention Program Outcomes Study. Lancet. 2012 [PMC free article] [PubMed]
121. Eriksson KF, Lindgarde F. No excess 12-year mortality in men with impaired glucose tolerance who participated in the Malmo Preventive Trial with diet and exercise. Diabetologia. 1998;41:1010–6. [PubMed]