Phenotypes of prediabetes and stratification of cardiometabolic risk

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Phenotypes of prediabetes and stratification of cardiometabolic risk Norbert Stefan, Andreas Fritsche, Fritz Schick, Hans-Ulrich Häring

Prediabetes is associated with increased risks of type 2 diabetes, cardiovascular disease, dementia, and cancer, and its prevalence is increasing worldwide. Lifestyle and pharmacological interventions in people with prediabetes can prevent the development of diabetes and possibly cardiovascular disease. However, prediabetes is a highly heterogeneous metabolic state, both with respect to its pathogenesis and prediction of disease. Improved understanding of these features and precise phenotyping of prediabetes could help to improve stratification of disease risk. In this Personal View, we focus on the extreme metabolic phenotypes of metabolically healthy obesity and metabolically unhealthy normal weight, insulin secretion failure, insulin resistance, visceral obesity, and nonalcoholic fatty liver disease. We present new analyses aimed at improving characterisation of phenotypes in lean, overweight, and obese people with prediabetes. We discuss evidence from lifestyle intervention studies to explore whether these phenotypes can also be used for individualised prediction and prevention of cardiometabolic diseases.

Introduction The prevalence of diabetes and prediabetes has been increasing worldwide, especially in the past 20 years. In the USA, more than 14% of adults are estimated to have diabetes (types 1 and 2) and more than 38% to have prediabetes.1,2 In China, more than 11% of adults are estimated to have diabetes and more than 50% to have prediabetes.3,4 Worryingly, type 2 diabetes is increasingly being diagnosed in young adults and adolescents, which raises the lifetime risk of diabetes-associated complications.5,6 Furthermore, in the Emerging Risk Factors Collaboration study, fasting glucose concentrations exceeding 5·6 mmol/L were associated with increased mortality,7 meaning that even individuals not classified as having diabetes can be at increased risk because of hyperglycaemia. Prediabetes is a metabolic condition characterised by hyperglycaemia, although at concentrations below that used to define a diagnosis of diabetes. According to the American Diabetes Association,8 a diagnosis of prediabetes is recommended when fasting plasma glucose concentrations are 5·6 mmol/L or higher but less than 7·0 mmol/L (termed impaired fasting glucose [IFG]), 2 h glucose concentrations during a 75 g oral glucose tolerance test (OGTT) are 7·8 mmol/L or higher but less than 11·1 mmol/L (termed impaired glucose tolerance [IGT]), or both. A diagnosis of prediabetes should also be made when plasma HbA1c concentration is 5·6% (39 mmol/mol) or higher but less than 6·5% (46 mmol/mol).8 Because prediabetes is a highly heterogeneous metabolic state, both with respect to its pathogenesis and prediction of disease, improved understanding of its pathophysiology could improve stratification of disease risk in patients. We hypothesise that these goals can be accomplished by the application of precise anthropometric and metabolic phenotyping strategies. In this Personal View, we integrate epidemiological knowledge about the heterogeneity in progression from prediabetes to diabetes with information about risk prediction derived from phenotyping studies, and present new information on

phenotypes of prediabetes. We suggest that this approach can translate into individualised risk prediction and prevention strategies for patients.

Disease risk associated with prediabetes A common characteristic of diseases that are associated with prediabetes is that impaired insulin secretion, insulin resistance, subclinical inflammation, disproportionate body fat distribution, or a combination of these factors, contribute substantially to the pathophysiology. An early sign of these conditions is raised glucose concentrations in blood. Since type 2 diabetes is diagnosed on the basis of increased blood glucose concentrations, it shares many of the pathophysiological characteristics associated with the prediabetic state.9–17 To predict the incidence of type 2 diabetes in people with prediabetes, several studies have been done to investigate the risk of type 2 diabetes associated with the presence of isolated IFG, isolated IGT, or IFG plus IGT. In a metaanalysis of prospective studies published between 1979 and 2004, the annual incidence of progression to diabetes was 6–9% in individuals with isolated IFG, 4–6% in those with isolated IGT, and 15–19% in those with IFG plus IGT.10,18 In the DPP study,19 the annual incidence of diabetes was 11% in people with IGT. Investigators of a population-based longitudinal study from Mexico City noted a similar relative risk (RR) of incident diabetes for IFG (3∙73) and IGT (4∙01),20 although the prevalence of IGT was five-times higher and, therefore, the populationattributable risk (the product of prevalence and RR) was also five times higher (29% vs 6%). In an analysis of data from the population-based Rotterdam Study, in individuals aged 45 years, the lifetime risk of progression from prediabetes to type 2 diabetes was 74% (when prediabetes was defined as fasting glucose concentrations >6∙0 mmol/L but <7∙0 mmol/L).21 The measurement of HbA1c has several advantages over that of fasting plasma glucose and glucose concentrations during an OGTT. It is more convenient because fasting is not required, the pre-analytical stability is superior, and

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Lancet Diabetes Endocrinol 2016 Published Online May 13, 2016 http://dx.doi.org/10.1016/ S2213-8587(16)00082-6 Department of Internal Medicine IV, (Prof N Stefan MD, Prof A Fritsche MD, Prof H-U Häring MD) and Section of Experimental Radiology (Prof F Schick MD), University Hospital Tübingen, Tübingen, Germany; Institute of Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Centre Munich at the University of Tübingen, Tübingen, Germany (Prof N Stefan, Prof A Fritsche, Prof R Schick, Prof H-U Häring); and German Centre for Diabetes Research (DZD), Tübingen, Germany (Prof N Stefan, Prof A Fritsche, Prof R Schick, Prof H-U Häring) Correspondence to: Prof Norbert Stefan, Department of Internal Medicine IV, University Hospital Tübingen, 72076 Tübingen, Germany [email protected]

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day-to-day variations during stress and illness are enumerated. However, these advantages are offset by poorer diagnostic sensitivity with measurement at the designated cutoff of 6∙5 mmol/L, higher costs, and imperfect correlation between HbA1c and the average glucose concentration in some individuals.8 Furthermore, in the Botnia Study,22 1 h glucose concentration greater than 8∙6 mmol/L during OGTT was the strongest predictor of future diabetes, whereas measurement of HbA1c had much less predictive power. Notably, the optimum predictive cutoff for HbA1c in that study was 5∙6%, which lies in the healthy range even for prediabetes (≥5∙6% [37⋅7 mmol/mol] but <6∙5% [46⋅0 mmol/mol]). Many studies have been done to explore the major pathophysiological mechanisms of isolated IFG and isolated IGT to investigate whether they have predictive value for type 2 diabetes and could be targets for pharmaceutical interventions. IFG seems to be driven mainly by impaired insulin secretion and progressive increase in hepatic glucose production, whereas IGT seems to be associated with low whole-body insulin sensitivity and secondary impairment of insulin secretion. These pathophysiological mechanisms do, however, overlap substantially and are both characterised by hyperglycaemia.9–17,23 Furthermore, the state of IFG plus IGT seems to differ more from isolated IGT than from isolated IFG.24 In the past 3 years, understanding of the different phenotypes of prediabetes and diabetes has greatly improved. In an analysis of the Whitehall II prospective cohort study, Faerch and colleagues25 showed that in patients diagnosed as having type 2 diabetes by measurement of 2 h glucose concentrations during a 75 g OGTT, insulin sensitivity substantially declined before the onset of diabetes, while β-cell function remained stable. By contrast, β-cell function was substantially reduced before the onset of diabetes in patients diagnosed early with the fasting plasma glucose test.25 Results from the Danish ADDITION-PRO study26 supported these pathophysiological patterns and showed that they were detectable early on in people with prediabetes. Notably, diagnosis of prediabetes or diabetes solely by measurement of HbA1c concentration did not capture this diversity in features.26 People with prediabetes have an increased risk of cardiovascular disease, which is predominantly explained by macrovascular atherosclerosis.6,9 Furthermore, prediabetes is associated with an increased risk of diabetic nephropathy and neuropathy.6,9 Prediabetes should, therefore, be viewed as an important risk factor for diseases that are associated with diabetes. However, hyperglycaemia varies widely in people with prediabetes, both in the fasting state and postprandially. Furthermore, large day-to-day variations are seen in fasting glucose concentrations, and especially 2 h glucose concentrations,27 which might lead to misclassification. Thus, an individual with mild fasting hyperglycaemia 2

might not have the same risks for major chronic diseases, such as cardiovascular disease, cancer, infectious diseases, and mental disorders, as an individual with severe hyperglycaemia, despite both having diagnoses of prediabetes. Data from several studies support a linear relation between fasting and postprandial glycaemia and these diseases in people with prediabetes.7,28 An important issue is the extent to which hyperglycaemia drives development of major chronic diseases in people with prediabetes. In the ORIGIN trial, lowering of glucose concentrations in blood with basal insulin in patients with prediabetes or newly diagnosed type 2 diabetes lessened the risk of microvascular outcomes in participants with baseline HbA1c concentrations of at least 6·4% (46⋅4 mmol/mol)29 and reduced progression of carotid intima–media thickness.30 The effects on cardiovascular outcomes and development of cancer, cognitive decline, and cognitive impairment, however, were neutral.31,32 Insulin resistance seems to be another important mechanism that drives the development of major chronic diseases in people with prediabetes.33 It is predictive of renal dysfunction even in the absence of hyperglycaemia.34 Furthermore, insulin resistance, which is closely associated with subclinical inflammation and dysfunctional lipid signalling,33,35–38 could explain why people with prediabetes have increased risk of dementia.39–43 Finally, insulin resistance, which is accompanied by hyperinsulinaemia and altered signalling of insulin-like growth factors 1 and 2,44,45 might explain the increased risk of cancer, particularly of colorectal, liver, pancreatic, breast, and endometrial cancers, in people with prediabetes.46,47 Thus, insulin resistance should be viewed as an important factor in the prediction, prevention, and treatment of prediabetes. The role of subclinical inflammation as an independent driver of major chronic diseases, and, therefore, a potential drug target, has been investigated extensively. Data from animal studies support a primary role for subclinical inflammation in the pathogenesis of diseases related to prediabetes and diabetes, but many of these findings have not been confirmed in human studies. Nevertheless, the proinflammatory cytokine interleukin 1β is among the promising candidate drug targets being assessed in human beings.48

Pathophysiological mechanisms of prediabetes Results from studies of the pathophysiology of type 2 diabetes suggest that taking individual pathophysiological mechanisms into account, specifically those related to insulin resistance, would be useful when addressing prevention and treatment.9–13 Prediabetes has been associated with a 40% reduction in whole-body insulin sensitivity, substantial decline in glucose sensitivity of β cells, and increased waist circumference and BMI when compared with normal glucose tolerance.9 Lowering of glucose concentrations in blood might also

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Lessons from diabetes prevention studies The insulin-sensitiser pioglitazone reduced the incidence of diabetes by 72% in 602 people with IGT (hazard ratio [HR] 0⋅28, 95% CI 0⋅16–0⋅49).50 By contrast, use of the insulin secretagogue nateglinide in 9306 people with IGT did not prevent progression to diabetes, and was possibly associated with an increased risk of progression compared with placebo (HR 1⋅07, 95% CI 1⋅00–1⋅15).51 This finding suggests that increasing the insulin secretory capacity of β cells is not always effective in the prevention of diabetes. On the basis of the hypothesis that glucose-induced and lipid-induced insulin hypersecretion is associated with β-cell stress,52 effective and safe implementation of such a pharmacological approach in clinical practice in the near future will be difficult. In people with IGT, lifestyle interventions focused on increased physical activity and eating a healthy diet significantly reduce the risk of progression to type 2 diabetes.19,53–57 This approach might also reduce the risk of cardiovascular death58 and is recommended for the prevention of cardiovascular disease.59 Thus, lifestyle interventions could be regarded as first-line strategies for the prevention of cardiometabolic diseases. However, even though lifestyle changes have reduced the risk of progression from prediabetes to diabetes in intervention studies, a notable proportion of study participants in the intervention groups still developed diabetes. In this respect, the numbers needed to treat to prevent one case of diabetes were 7⋅0 in 3 years in the US DPP study55 and 7⋅7 in 4 years in the Finnish Diabetes Prevention Study was.53 Investigation of phenotypic and genetic features showed that age and the initial diabetes risk, but not body fat mass, body fat distribution, or insulin sensitivity (estimated from fasting glucose and insulin concentrations), were associated with efficacy of the lifestyle intervention.60 In the follow-up to the US DPP, the DPP Outcome Study,61 the reversal of prediabetes and restoration of normal glucose regulation (NGR) during the intensive lifestyle intervention was strongly associated with a reduced incidence of diabetes. When the control, lifestyle intervention, and metformin groups were analysed together, lower fasting plasma glucose and 2 h blood glucose concentrations, younger age, and higher insulin secretion at baseline increased the chance of achieving NGR.62

Phenotypes of prediabetes Categorisation by metabolic traits Rather than working with continuous parameters, clinicians prefer to use categories of metabolic traits, or phenotypes, to stratify patients into high-risk or low-risk groups. We are particularly interested in the phenotypes related to insulin secretion failure, insulin resistance, visceral obesity, and non-alcoholic fatty liver disease (NAFLD). Results of studies in Pima Native American people showed that insulin secretion failure and insulin resistance predict the development of diabetes independently of simple anthropometric parameters and lifestyle factors.63 Findings of studies that focused on

4 Change in 2 h blood glucose concentration (mmol/L)

reduce the incidence of microvascular complications, although not necessarily macrovascular complications, of hyperglycaemia.9 Additionally, even part resolution of dysglycaemia could lessen the secretory burden of β cells.9 In a widely recognised concept, DeFronzo and colleagues13,49 advocate assessment of not only β-cell failure and insulin resistance in muscle and liver, but also antilipolytic insulin sensitivity, the incretin and the glucagon system, renal glucose reabsorption, and the CNS in the prevention of type 2 diabetes and related diseases in patients with prediabetes.

2 0 –2 –4 –6 –20

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–5 0 5 Change in body fat (%)

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Figure 1: Relation between changes in body fat and in 2 h glucose concentrations in blood during a lifestyle intervention Data from 120 people with prediabetes (impaired fasting glycaemia, impaired glucose tolerance, or both) at baseline who participated in a 9-month lifestyle intervention of increased physical activity and modified diet. Change in body fat was measured with bioimpedance analysis. Reproduced from reference 68, by permission of Springer.

High BMI

High waist circumference

Increased age Fasting and postprandial hyperglycaemia

Prediabetes Low-risk phenotype

High-risk phenotype

Insulin resistance and NAFLD

Lifestyle intervention

Restoration of NGR

Insulin secretion failure

Prediabetes ?

Diabetes

Figure 2: Determinants of regression from prediabetes to NGR during a lifestyle intervention Based on 120 individuals with prediabetes (impaired fasting glycaemia, impaired glucose tolerance, or both) at baseline who participated in a 9-month lifestyle intervention with increased physical activity and a modified diet. Insulin secretion failure or insulin resistance plus NAFLD comprised a high-risk phenotype associated with a low chance of restoration. NAFLD=non-alcoholic fatty liver disease. NGR=normal glucose regulation.

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trajectories of these and other parameters in the development of the disease supported this predictive role.25,64,65 Data from the Whitehall II study25 suggested that multiple phenotypes of prediabetes and diabetes exist that differ in progression of insulin secretion failure and insulin resistance before the onset of diabetes. These data support the use of these characteristics in the prediction of progression from prediabetes to type 2 diabetes. Furthermore, visceral obesity66 and NAFLD67 predicted the development of type 2 diabetes independently of established risk factors, including insulin resistance. In an analysis of data from the TULIP study,68 we investigated whether using these pathophysiology-based and other traits might enable the identification of a unique at-risk phenotype that is associated with no response to a 9-month structured lifestyle intervention aimed at restoring NGR. Changes in glycaemia varied

A 0 Adjusted change in bodyweight (%)

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See Online for appendix

Adjusted change in 2 h glucose concentration in blood (%)

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–30 –35 –40 High-risk phenotype (n=72)

Low-risk phenotype (n=48)

Figure 3: Changes in bodyweight and glucose tolerance in high-risk vs low-risk phenotypes during treatment with a lifestyle intervention Data are least square means and SDs. High-risk phenotype comprised low insulin secretion or low insulin sensitivity plus non-alcoholic fatty liver disease and the low-risk phenotype comprised the other combinations related to insulin secretion, insulin sensitivity, and non-alcoholic fatty liver disease. (A) Change in bodyweight, adjusted for age, sex, and baseline bodyweight. (B) Change in glucose concentration, adjusted for age, sex, and baseline glucose concentration. *p=0·49. †p=0·0009. Reproduced from reference 68, by permission of Springer.

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widely between study participants. Even among those who lost the greatest amount of body fat (upper quartile, mean value −6⋅9%), 2 h glucose concentrations did not improve in 27% (figure 1), and 40% did not revert to NGR. The predictive value of a range of variables at baseline were investigated: sex, age, BMI, waist circumference, liver fat content (¹H-proton magnetic resonance spectroscopy), insulin sensitivity (Matsuda index, estimated from the OGTT), 2 h glucose concentrations, fasting glucose concentrations, insulin secretion (insulinogenic index, estimated from the OGTT as insulin0−30/glucose0−30), and metabolically unhealthy NAFLD (insulin resistance × liver fat content).69 Insulin secretion and metabolically unhealthy NAFLD were strongly and independently associated with restoration of NGR (figure 2). Finally, participants classified as having a high-risk phenotype on the basis of insulin secretion failure or insulin resistance plus NAFLD, had smaller decreases in 2 h blood glucose concentrations than those with the low-risk phenotype (figure 3). 67% of individuals with the low-risk phenotype, compared with only 31% of those with the high-risk phenotype, reached NGR status.68 These findings suggest that patients with prediabetes could be stratified by phenotype at baseline, which might help to predict the effectiveness of a lifestyle intervention to achieve NGR. To explore whether and to what extent such high-risk phenotypes are enriched in prediabetes and whether they can be used to stratify the risk of cardiometabolic diseases, we analysed cross-sectional data from 1003 people in the Tübingen Family Study and the TULIP study,68–71 598 of whom had NGR and 405 of whom had prediabetes (based on 2 h glucose concentrations during a 75 g OGTT). The distribution of the sexes was similar in the two groups, but people with prediabetes were generally older and heavier; had lower insulin sensitivity, insulin secretion, and disposition index (product of insulin sensitivity and insulin secretion); and had higher visceral fat mass and liver fat content than those with NGR (appendix). We investigated whether various phenotypes could be independent determinants for the risk of prediabetes. To do so we first classifed all 1003 participants as having insulin resistance or insulin secretion failure when insulin sensitivity and disposition index, respectively, were lower than the median values. NAFLD was defined as liver fat content greater than 5⋅6% and visceral obesity as visceral fat mass greater than 4⋅6 kg in men and 2⋅0 kg in women (corresponding to the cutoffs for increased waist circumference [102 cm and 88 cm, respectively; appendix]). In a forward-stepwise logistic-regression analysis, insulin secretion failure, insulin resistance, NAFLD, and MRI-determined visceral obesity were independent determinants of prediabetes, whereas BMI category (normal weight, overweight, and obese) and visceral obesity based on waist circumference were not (appendix). Inclusion of concentrations of free fatty acids after fasting or at 2 h in the OGTT, for which

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Proportion of individuals (%)

An important question is whether the prevalence of these phenotypes differs between BMI categories and whether such differences might affect prevention and treatment strategies. Although insulin secretion failure, insulin resistance, NAFLD, and visceral adiposity seem to be superior to BMI as determinants of risk of prediabetes, their individual contributions might differ by lifestyle and genetic predisposition for obesity that are reflected in differences in BMI. The stratification of metabolic risk by BMI category has gained growing attention, especially the question of whether there is a phenotype of metabolically healthy obesity (MHO).72–78 Although there is no universally acknowledged definition for this phenotype, three classifications are widely used: having fewer than two criteria of the metabolic syndrome;72 having fewer than three of the metabolic risk criteria proposed by Wildman and colleagues73 (including hypertension, with raised fasting concentrations of triglycerides, glucose, and high-sensitivity C-reactive protein, increased homoeostasis model assessment of insulin resistance [HOMA-IR] value, and decreased concentrations of HDL cholesterol); and being insulin sensitive.70,74 In 2008, we showed that MHO is most strongly associated with reduced prevalence of NAFLD.70 This finding supports the hypothesis that NAFLD has an important role in the pathophysiology of cardiometabolic diseases.79–82 In a 15-year follow-up of 2011 people from the Italian Cremona Study,83 those who were obese without insulin resistance (HOMA-IR lower than 2⋅5) were not at greater risk of dying than non-obese insulinsensitive individuals (hazard ratio 0⋅99, 95% CI 0⋅46–2⋅11). In a meta-analysis of eight studies published in 2013, MHO (characterised by fewer than two criteria of the metabolic syndrome) was not associated with an increased risk of mortality, cardiovascular events, or both, compared with metabolically healthy normal weight (RR 1⋅19, 95% CI 0⋅98–1⋅38).84 Notably, however, when only the four studies with follow-up periods longer than 10 years were included in the meta-analysis, the RR increased to 1⋅24 (95% CI 1⋅02–1⋅55). Results from two further studies85,86 suggested that the MHO phenotype is a transient state in some individuals,

100 90 80 70 60 50 40 30 20 10 0

Normal glucose regulation Prediabetes

In su lin su re lin sis se cre tan ce tio nf ail ur e NA Vi sc F LD er al ob es ity In su In l in su re lin sis se cre tan ce tio nf ail ur e NA Vi sc F LD er al ob es ity In su In l i n su re lin sis se cre tan ce tio nf ail ur e NA Vi sc FL er D al ob es ity

Obesity and phenotypes of prediabetes

because change to at-risk phenotypes was seen in about a third of those assessed, most of whom were followed up for roughly 10 years. Furthermore, Bell and colleagues87 identified a link between MHO and future insulin resistance, which then led to cardiometabolic pathological changes. By contrast, Appleton and colleagues86 found no increased risk of diabetes, cardiovascular disease, or stroke in individuals with MHO at baseline who maintained the MHO phenotype throughout follow-up. In a systematic review and network meta-analysis,77 Lotta and colleagues provided convincing evidence that the use of binary definitions of metabolic health, based on criteria of the metabolic syndrome and fasting insulin concentrations, have little predictive relevance for type 2 diabetes. Sensitivity in obese individuals was satisfactory (0⋅81, 95% CI 0⋅76–0⋅86), but specificity was low (0⋅42, 0⋅35–0⋅49). The investigators suggested that more comprehensive approaches to the definition of metabolic health could improve predictive performance.77 Although the pathophysiology of the MHO phenotype (eg, consistently increased adiponectin concentrations and signalling, decreased proinflammatory and beneficial hepatokine signalling, and increased adipogenesis in subcutaneous adipose tissue) is partly understood,72,88–93 the characteristics of the other extreme metabolic state, the metabolically unhealthy normal weight phenotype (BMI 18⋅5 kg/m² up to 25⋅0 kg/m² and poor metabolic health) are less clear. Compared with metabolically healthy normal weight people, those with MHO had an RR of mortality, cardiovascular events, or both of only 1⋅19 (95% CI 0⋅98–1⋅38, 226 events in 5575 participants), the metabolically unhealthy normal weight phenotype was associated with an RR of 3⋅14 (2⋅36–3⋅93, 558 events

In

data were available from 950 individuals, did not affect these results. These findings support our results in smaller groups.68,70 Extended phenotyping with methods such as MRI and ¹H-proton magnetic resonance spectroscopy, therefore, seems to be superior to use of simple measurements of obesity and body fat distribution alone to assess metabolic risk. Notably, our studies were not population based. Additionally, because we mainly studied people at increased risk of developing type 2 diabetes, our findings might not be representative of the general population. Thus, we can recommend extended phenotyping only for people at increased risk of cardiometabolic diseases.

Normal weight (n=185) Distribution of glucose categories (%)

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IGT

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Overweight (n=363) 51%

25% 24%

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29%

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Figure 4: Proportion of individuals with insulin resistance, insulin secretion failure, NAFLD, and visceral obesity, and distributions of prediabetes glycaemic categories, by weight category Cross-sectional data from 1003 people with normal glucose regulation and prediabetes.68–71 Normal weight=BMI >18·5–<25·0 kg/m². Overweight=BMI 25·0–<30·0 kg/m². Obesity=BMI ≥30·0 kg/m². NAFLD=non-alcoholic fatty liver disease. IFG=impaired fasting glycaemia. IGT=impaired glucose tolerance.

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in 3578 participants).84 This finding is concerning because in clinical practice lean people are generally judged to be at lower risk of cardiometabolic diseases than overweight and obese people. In our analysis of 1003 people from the Tübingen Family Study and the TULIP study,68–71 normal weight individuals with prediabetes had a much higher prevalence of insulin secretion failure, insulin resistance, visceral obesity, and NAFLD than normal weight individuals with NGR. Insulin secretion failure and insulin resistance differed most between groups. Although a similar pattern was

Normal weight and prediabetes Insulin resistance (36%)

13% 4% None of the phenotypes

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2%

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Insulin secretion failure (84%) Overweight and prediabetes Insulin resistance (65%)

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None of the phenotypes 13%

2% 8%

Visceral obesity (92%)

3%

5%

53%

4% 3%

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Insulin secretion failure (81%)

Figure 5: Prevalence of insulin resistance, insulin secretion failure, NAFLD, and visceral obesity in prediabetes, by BMI category Cross-sectional data from 405 people with prediabetes.68–71 Normal weight=BMI >18·5–<25·0 kg/m². Overweight=BMI 25·0–<30·0 kg/m². Obesity=BMI ≥30·0 kg/m². NAFLD=non-alcoholic fatty liver disease.

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seen for overweight and obese people, NAFLD and visceral obesity became increasingly important in these states (figure 4). Notably, the prevalence of the glycaemic categories IFG, IGT, and IFG plus IGT did not differ substantially between BMI categories (figure 4). To what degree the phenotypes are exclusive to the three weight categories is of interest. Our analysis showed that in normal weight people with prediabetes, insulin secretion as a single phenotype was seen in 44% whereas insulin resistance as a single phenotype was present only in 4%. Combination phenotypes of insulin secretion failure with any of insulin resistance, NAFLD, or visceral obesity were seen in 42% of individuals. Only 6% had all four phenotypes and 13% had none (figure 5). In overweight people with prediabetes the proportion of single phenotypes was decreased substantially for insulin secretion failure, unchanged for insulin resistance, and increased for NAFLD and visceral obesity. A large increase was also seen in the proportion with all four phenotypes and a decrease in the proportion with no phenotypes (figure 5). Further changes in the same directions were seen in obese people, with the proportion reaching 53% for all four phenotypes and zero for no phenotypes (Figure 5). When the distributions were assessed in people with IFG, IGT, or IFG plus IGT, a little less than a third of individuals with IFG or IGT had all four phenotypes. Among those with IFG, somewhat more than among those with IGT had insulin resistance, NAFLD, or visceral obesity as single phenotypes, whereas in people with IGT, insulin secretion failure was the most prevalent single phenotype (figure 6). The differences in proportions of single phenotypes between glycaemic categories were smaller than those between BMI categories.

Normal weight people with prediabetes have a very high risk of hyperglycaemia, mainly because of impaired insulin secretion. The risk of insulin resistance without NAFLD is also raised (figures 4, 5), which suggests that insulin resistance in skeletal muscle is the main cause of impaired insulin action. The prevalence of IFG plus IGT, which is associated with a high risk of diabetes, is low in normal weight people (figure 4). Because many individuals convert directly from NGR to diabetes,9 those with normal weight who are at very high risk of diabetes might quickly pass through the state of prediabetes. In normal weight individuals who were caught in the transient phase of hyperglycaemia, except for low insulin secretion, the prevalence of the other three studied risk phenotypes is relatively low. Thus, a standard lifestyle intervention that aims mainly to reduce weight might be effective in normal weight people with prediabetes. In our lifestyle intervention study, the proportion of responders (ie, individuals who reverted to NGR) was highest among normal weight individuals (63%),

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compared with overweight (51%) or obese (36%) individuals, although in all three BMI categories the percentage of weight loss was similar (about 4%).68 The underlying mechanisms for this effect are unclear. The intervention might have been sufficient to reduce harmful effects from chronic hyperglycaemia and high blood lipids to an extent that allowed improvement of insulin sensitivity in skeletal muscle and reduction of metabolic stress on β cells. In overweight and obese individuals with prediabetes, in whom the other risk phenotypes are more prevalent, an intensified lifestyle intervention, or even a pharmacological intervention, might be more effective. People with prediabetes are at increased risk of diseases other than diabetes, particularly cardiovascular disease.6,9–11 Therefore, it would be important to know whether phenotypes can also help to stratify cardiovascular risk. We assessed data on carotid intima–media thickness measured in 300 people with NGR and 156 with prediabetes (appendix). In the absence of a standard cutoff, we took increased carotid intima–media thickness to be values in the highest quartile. Among the individuals with prediabetes, NAFLD was the strongest determinant of increased carotid intima–media thickness, followed by visceral obesity and IFG, IGT, or IFG plus IGT (appendix). Because glucose concentrations after OGTT, especially at 1 h, are suggested to be superior to fasting glucose values for the prediction of macrovascular and microvascular complications in people with diabetes,23 we included this factor in our statistical model. It remained significant as a determinant but had the weakest predictive power. Thus, as well as improving estimation of the risk of cardiovascular disease in people with prediabetes, precise phenotyping improves the classification of hyperglycaemia.

Conclusions On the basis of epidemiological and clinical data, and data from lifestyle intervention studies, we propose that after initial classification of the glucose categories IFG, IGT, or IFG plus IGT, and of insulin resistance and insulin secretion should be included in assessment of cardiometabolic risk in patients with raised glucose values in the non-diabetic range. We acknowledge that this approach increases effort and cost because insulin must be measured during a 75 g OGTT in blood samples taken from at least three different draws (eg, 0, 30, and 120 min). These measurements do, however, allow the calculation of the insulinogenic index and the Matsuda index. Calculations of insulin secretion and insulin sensitivity are being used in large consortia, such as the MetaAnalyses of Glucose and Insulin-related traits Consortium, which includes more than 15 000 people.94 Insulin resistance and insulin secretion have robust and well documented predictive effects for the incidence of type 2 diabetes, and insulin resistance is strongly associated with the incidence of microvascular and macrovascular

IFG Insulin resistance (68%) 3% NAFLD (45%)

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2%

None of the phenotypes

2% 17%

1%

29%

6%

8% 6%

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Visceral obesity (77%)

IGT Insulin resistance (62%) 9·2% NAFLD (48%)

<1% 3·4%

4% 13%

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27%

4% Visceral obesity (66%)

16%

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NAFLD (78%)

0%

8%

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7%

64% 6% 1%

5% Insulin secretion failure (96%)

Figure 6: Prevalence of insulin resistance, insulin secretion failure, NAFLD, and visceral obesity in people prediabetes, by glucose status Cross-sectional data from 405 people with prediabetes.68–71 IFG=impaired fasting glycaemia. IGT=impaired glucose tolerance. NAFLD=non-alcoholic fatty liver disease.

diseases, dementia, and some types of cancer. The costs of MRI techniques to measure NAFLD and visceral obesity precisely are high. These are, however, likely to fall because the number of machines available in the routine diagnostic setting has risen notably in the past 5 years, which has also increased the opportunity to use MRI for prediction purposes. In the meantime, NAFLD diagnosed from ultrasound images by an experienced physician is acceptable in a clinical setting.95 We suggest that phenotyping be done as part of prediction and prevention of cardiometabolic diseases in people with prediabetes, but we acknowledge that their efficacy must still be assessed in clinical outcome studies. To advance this work, we have started the Prediabetes Lifestyle Intervention Study (PLIS) in eight centres throughout Germany (NCT01947595), in which individuals with the high-risk phenotype are being randomly assigned to a standard or an intensified lifestyle intervention, and

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Search strategy and selection criteria

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We searched PubMed for work published in English up to and including Dec 31, 2015, using the terms “prediabetes”, “impaired fasting glucose”, “impaired glucose tolerance”, “impaired glucose regulation”, “insulin secretion”, “insulin resistance”, “obesity”, “visceral obesity”, “nonalcoholic fatty liver disease”, “lifestyle intervention”, “pharmacological treatment”, “cardiovascular disease”, “cardiovascular risk”, and “diabetic complications”, in combination with the terms “diabetes” and “prediabetes”. Whenever possible original research articles were selected in preference to review articles.

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those with the low-risk phenotype are being randomly assigned to a standard intervention or no intervention. In the future, it seems plausible that phenotypic stratification will be combined with information derived from omics and DNA analyses to permit the development of personalised preventive strategies and treatment of diseases. The application of precise phenotyping strategies in clinical trials will also help to improve understanding of the pathophysiology of cardiometabolic diseases.

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Contributors All authors contributed to the literature search, data analysis, and writing of the Personal View.

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Declaration of interests We declare no competing interests.

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Acknowledgments This work was supported by funding from the German Research Foundation (KFO 114 and STE 1096/1-3) and the German Federal Ministry of Education and Research to the German Centre of Diabetes Research.

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References 1 GBD 2013 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015; 386: 2287–323. 2 Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988–2012. JAMA 2015; 314: 1021–29. 3 Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract 2014; 103: 137–49. 4 Chan JC, Zhang Y, Ning G. Diabetes in China: a societal solution for a personal challenge. Lancet Diabetes Endocrinol 2014; 2: 969–79. 5 Huo X, Gao L, Guo L, et al. Risk of non-fatal cardiovascular diseases in early-onset versus late-onset type 2 diabetes in China: a cross-sectional study. Lancet Diabetes Endocrinol 2016; 4: 115–24. 6 Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol 2014; 2: 56–64. 7 Seshasai SR, Kaptoge S, Thompson A, et al, for the Emerging Risk Factors Collaboration. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 2011; 364: 829–41. 8 American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care 2016; 39 (suppl): S13–22. 9 Ferrannini E. Definition of intervention points in prediabetes. Lancet Diabetes Endocrinol 2014; 2: 667–75. 10 Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M. Prediabetes: a high-risk state for diabetes development. Lancet 2012; 379: 2279–90. 11 Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet 2014; 383: 1068–83.

23

24

25

26

27

28

29

30

Laakso M. Cardiovascular disease in type 2 diabetes from population to man to mechanisms: the Kelly West Award Lecture 2008. Diabetes Care 2010; 33: 442–49. 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. Stancáková A, Javorský M, Kuulasmaa T, Haffner SM, Kuusisto J, Laakso M. Changes in insulin sensitivity and insulin release in relation to glycemia and glucose tolerance in 6,414 Finnish men. Diabetes 2009; 58: 1212–21. Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 2008; 359: 2220–32. Laakso M, Zilinskaite J, Hansen T, et al, for the EUGENE2 Consortium. Insulin sensitivity, insulin release and glucagon-like peptide-1 levels in persons with impaired fasting glucose and/or impaired glucose tolerance in the EUGENE2 study. Diabetologia 2008; 51: 502–11. Weyer C, Bogardus C, Mott DM, Pratley RE. The natural history of insulin secretory dysfunction and insulin resistance in the pathogenesis of type 2 diabetes mellitus. J Clin Invest 1999; 104: 787–94. 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. Knowler WC, Fowler SE, Hamman RF, et al, for the Diabetes Prevention Program Research Group. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet 2009; 374: 1677–86. Ferrannini E, Massari M, Nannipieri M, et al. Plasma glucose levels as predictors of diabetes: the Mexico City diabetes study. Diabetologia 2009; 52: 818–24. Ligthart S, van Herpt TT, Leening MJ, et al. Lifetime risk of developing impaired glucose metabolism and eventual progression from prediabetes to type 2 diabetes: a prospective cohort study. Lancet Diabetes Endocrinol 2016; 4: 44–51. Abdul-Ghani MA, Lyssenko V, Tuomi T, DeFronzo RA, Groop L. Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia Study. Diabetes Care 2009; 32: 281–86. Faerch K, Hulmán A, Solomon TP. Heterogeneity of pre-diabetes and type 2 diabetes: implications for prediction, prevention and treatment responsiveness. Curr Diabetes Rev 2016; 12: 30–41. Faerch K, Vaag A, Holst JJ, Hansen T, Jørgensen T, Borch-Johnsen K. Natural history of insulin sensitivity and insulin secretion in the progression from normal glucose tolerance to impaired fasting glycemia and impaired glucose tolerance: the Inter99 study. Diabetes Care 2009; 32: 439–44. Faerch K, Witte DR, Tabák AG, et al. Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study. Lancet Diabetes Endocrinol 2013; 1: 43–51. Faerch K, Johansen NB, Witte DR, Lauritzen T, Jørgensen ME, Vistisen D. Relationship between insulin resistance and β-cell dysfunction in subphenotypes of prediabetes and type 2 diabetes. J Clin Endocrinol Metab 2015; 100: 707–16. Mooy JM, Grootenhuis PA, de Vries H, et al. Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia 1996; 39: 298–305. The Emerging Risk Factors Collaboration. Glycated hemoglobin measurement and prediction of cardiovascular disease. JAMA 2014; 311: 1225–33. Gilbert RE, Mann JF, Hanefeld M, et al, for the ORIGIN trial investigators. Basal insulin glargine and microvascular outcomes in dysglycaemic individuals: results of the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial. Diabetologia 2014; 57: 1325–31. Lonn EM, Bosch J, Diaz R, et al, for the GRACE and ORIGIN Investigators. Effect of insulin glargine and n-3FA on carotid intima-media thickness in people with dysglycemia at high risk for cardiovascular events: the glucose reduction and atherosclerosis continuing evaluation study (ORIGIN-GRACE). Diabetes Care 2013; 36: 2466–74.

www.thelancet.com/diabetes-endocrinology Published online May 13, 2016 http://dx.doi.org/10.1016/S2213-8587(16)00082-6

Personal View

31

32

33 34

35 36

37 38 39

40

41 42

43

44

45 46

47 48

49

50

51

52 53

54

55

Gerstein HC, Bosch J, Dagenais GR, et al, for the ORIGIN Trial Investigators. Basal insulin and cardiovascular and other outcomes in dysglycemia. N Engl J Med 2012; 367: 319–28. Cukierman-Yaffe T, Bosch J, Diaz R, et al, for the ORIGIN Investigators. Effects of basal insulin glargine and omega-3 fatty acid on cognitive decline and probable cognitive impairment in people with dysglycaemia: a substudy of the ORIGIN trial. Lancet Diabetes Endocrinol 2014; 2: 562–72. Saltiel AR, Kahn CR. Insulin signalling and the regulation of glucose and lipid metabolism. Nature 2001; 414: 799–806. Nerpin E, Risérus U, Ingelsson E, et al. Insulin sensitivity measured with euglycemic clamp is independently associated with glomerular filtration rate in a community-based cohort. Diabetes Care 2008; 31: 1550–55. Hotamisligil GS. Inflammation and metabolic disorders. Nature 2006; 444: 860–67. Osborn O, Olefsky JM. The cellular and signaling networks linking the immune system and metabolism in disease. Nat Med 2012; 18: 363–74. Rosen ED, Spiegelman BM. What we talk about when we talk about fat. Cell 2014; 156: 20–44. Shulman GI. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med 2014; 371: 2237–38. Tuligenga RH, Dugravot A, Tabák AG, et al. Midlife type 2 diabetes and poor glycaemic control as risk factors for cognitive decline in early old age: a post-hoc analysis of the Whitehall II cohort study. Lancet Diabetes Endocrinol 2014; 2: 228–35. Rawlings AM, Sharrett AR, Schneider AL, et al. Diabetes in midlife and cognitive change over 20 years: a cohort study. Ann Intern Med 2014; 161: 785–93. Crane PK, Walker R, Hubbard RA, et al. Glucose levels and risk of dementia. N Engl J Med 2013; 369: 540–48. Baker LD, Cross DJ, Minoshima S, Belongia D, Watson GS, Craft S. Insulin resistance and Alzheimer-like reductions in regional cerebral glucose metabolism for cognitively normal adults with prediabetes or early type 2 diabetes. Arch Neurol 2011; 68: 51–57. Xu W, Caracciolo B, Wang HX, et al. Accelerated progression from mild cognitive impairment to dementia in people with diabetes. Diabetes 2010; 59: 2928–35. LeRoith D, Yakar S. Mechanisms of disease: metabolic effects of growth hormone and insulin-like growth factor 1. Nat Clin Pract Endocrinol Metab 2007; 3: 302–10. Pollak M. The insulin and insulin-like growth factor receptor family in neoplasia: an update. Nat Rev Cancer 2012; 12: 159–69. Liao WC, Tu YK, Wu MS, Lin JT, Wang HP, Chien KL. Blood glucose concentration and risk of pancreatic cancer: systematic review and dose-response meta-analysis. BMJ 2015; 349: g7371. Huang Y, Cai X, Qiu M, et al. Prediabetes and the risk of cancer: a meta-analysis. Diabetologia 2014; 57: 2261–69. Herder C, Dalmas E, Böni-Schnetzler M, Donath MY. The IL-1 pathway in type 2 diabetes and cardiovascular complications. Trends Endocrinol Metab 2015; 26: 551–63. DeFronzo RA, Eldor R, Abdul-Ghani M. Pathophysiologic approach to therapy in patients with newly diagnosed type 2 diabetes. Diabetes Care 2013; 36 (suppl 2): S127–38. DeFronzo RA, Tripathy D, Schwenke DC, et al, for the ACT NOW Study. Pioglitazone for diabetes prevention in impaired glucose tolerance. N Engl J Med 2011; 364: 1104–15. Holman RR, Haffner SM, McMurray JJ, et al, for the NAVIGATOR Study Group. Effect of nateglinide on the incidence of diabetes and cardiovascular events. N Engl J Med 2010; 362: 1463–76. Eizirik DL, Cnop M. ER stress in pancreatic β cells: the thin red line between adaptation and failure. Sci Signal 2010; 3: pe7. Tuomilehto J, Lindström 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. Lindström 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: 1673–79. 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.

56

57

58

59

60

61

62

63

64

65

66

67

68

69 70

71

72

73

74

75

Pan XR, Li GW, Hu YH, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 1997; 20: 537–44. 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: 1783–89 Li G, Zhang P, Wang J, et al. Cardiovascular mortality, all-cause mortality, and diabetes incidence after lifestyle intervention for people with impaired glucose tolerance in the Da Qing Diabetes Prevention Study: a 23-year follow-up study. Lancet Diabetes Endocrinol 2014; 2: 474–80. Artinian NT, Fletcher GF, Mozaffarian D, et al, for the American Heart Association Prevention Committee of the Council on Cardiovascular Nursing. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation 2010; 122: 406–41. Lindström J, Peltonen M, Eriksson JG, et al. Determinants for the effectiveness of lifestyle intervention in the Finnish Diabetes Prevention Study. Diabetes Care 2008; 31: 857–62. Perreault L, Pan Q, Mather KJ, et al. Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study. Lancet 2012; 379: 2243–51. Perreault L, Kahn SE, Christophi CA, Knowler WC, Hamman RF, for the Diabetes Prevention Program Research Group. Regression from pre-diabetes to normal glucose regulation in the diabetes prevention program. Diabetes Care 2009; 32: 1583–88. Lillioja S, Mott DM, Spraul M, et al. Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus: prospective studies of Pima Indians. N Engl J Med 1993; 329: 1988–92. Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR. 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. Ohn JH, Kwak SH, Cho YM, et al. 10-year trajectory of β-cell function and insulin sensitivity in the development of type 2 diabetes: a community-based prospective cohort study. Lancet Diabetes Endocrinol 2016; 4: 27–34. Neeland IJ, Turer AT, Ayers CR, et al. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA 2012; 308: 1150–59. Zelber-Sagi S, Lotan R, Shibolet O, et al. Non-alcoholic fatty liver disease independently predicts prediabetes during a 7-year prospective follow-up. Liver Int 2013; 33: 1406–12. Stefan N, Staiger H, Wagner R, et al. A high-risk phenotype associates with reduced improvement in glycaemia during a lifestyle intervention in prediabetes. Diabetologia 2015; 58: 2877–84. Stefan N, Häring HU. The metabolically benign and malignant fatty liver. Diabetes 2011; 60: 2011–17. Stefan N, Kantartzis K, Machann J, et al. Identification and characterization of metabolically benign obesity in humans. Arch Intern Med 2008; 168: 1609–16. Stefan N, Ramsauer M, Jordan P, et al. Inhibition of 11β-HSD1 with RO5093151 for non-alcoholic fatty liver disease: a multicentre, randomised, double-blind, placebo-controlled trial. Lancet Diabetes Endocrinol 2014; 2: 406–16 Stefan N, Häring HU, Hu FB, Schulze MB. Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol 2013; 1: 152–62. Wildman RP, Muntner P, Reynolds K, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch Intern Med 2008; 168: 1617–24. McLaughlin T, Abbasi F, Lamendola C, Reaven G. Heterogeneity in the prevalence of risk factors for cardiovascular disease and type 2 diabetes mellitus in obese individuals: effect of differences in insulin sensitivity. Arch Intern Med 2007; 167: 642–48. Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET. Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab 2004; 89: 2569–75.

www.thelancet.com/diabetes-endocrinology Published online May 13, 2016 http://dx.doi.org/10.1016/S2213-8587(16)00082-6

9

Personal View

76

77

78

79 80

81

82

83

84

85

86

10

Samocha-Bonet D, Dixit VD, Kahn CR, et al. Metabolically healthy and unhealthy obese—the 2013 Stock Conference report. Obes Rev 2014; 15: 697–708. Lotta LA, Abbasi A, Sharp SJ, et al. Definitions of metabolic health and risk of future type 2 diabetes in BMI categories: a systematic review and network meta-analysis. Diabetes Care 2015; 38: 2177–87. Mathew H, Farr OM, Mantzoros CS. Metabolic health and weight: understanding metabolically unhealthy normal weight or metabolically healthy obese patients. Metabolism 2016; 65: 73–80. Stefan N, Kantartzis K, Häring HU. Causes and metabolic consequences of Fatty liver. Endocr Rev 2008; 29: 939–60. Targher G, Day CP, Bonora E. Risk of cardiovascular disease in patients with nonalcoholic fatty liver disease. N Engl J Med 2010; 363: 1341–50. Cusi K. Role of obesity and lipotoxicity in the development of nonalcoholic steatohepatitis: pathophysiology and clinical implications. Gastroenterology 2012; 142: 711–25.e6. Yki-Järvinen H. Non-alcoholic fatty liver disease as a cause and a consequence of metabolic syndrome. Lancet Diabetes Endocrinol 2014; 2: 901–10. Calori G, Lattuada G, Piemonti L, et al. Prevalence, metabolic features, and prognosis of metabolically healthy obese Italian individuals: the Cremona Study. Diabetes Care 2011; 34: 210–15. Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?: A systematic review and meta-analysis. Ann Intern Med 2013; 159: 758–69. Bell JA, Hamer M, Sabia S, Singh-Manoux A, Batty GD, Kivimaki M. The natural course of healthy obesity over 20 years. J Am Coll Cardiol 2015; 65: 101–02. Appleton SL, Seaborn CJ, Visvanathan R, et al, for the North West Adelaide Health Study Team. Diabetes and cardiovascular disease outcomes in the metabolically healthy obese phenotype: a cohort study. Diabetes Care 2013; 36: 2388–94.

87

88 89 90 91

92

93

94

95

Bell JA, Hamer M, Batty GD, Singh-Manoux A, Sabia S, Kivimäki M. Incidence of metabolic risk factors among healthy obese adults: 20-year follow-up. J Am Coll Cardiol 2015; 66: 871–73. Turer AT, Scherer PE. Adiponectin: mechanistic insights and clinical implications. Diabetologia 2012; 55: 2319–26. Blüher M. Are metabolically healthy obese individuals really healthy? Eur J Endocrinol 2014; 171: R209–19. Stefan N, Häring HU. The role of hepatokines in metabolism. Nat Rev Endocrinol 2013; 9: 144–52. Stefan N, Häring HU. Circulating fetuin-A and free fatty acids interact to predict insulin resistance in humans. Nat Med 2013; 19: 39495. Stefan N, Schick F, Häring HU. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med 2014; 371: 2236–37. Scott RA, Fall T, Pasko D, et al. Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity. Diabetes 2014; 63: 4378–87. Ingelsson E, Langenberg C, Hivert MF, et al. Detailed physiologic characterization reveals diverse mechanisms for novel genetic loci regulating glucose and insulin metabolism in humans. Diabetes 2010; 59: 1266–75. Schwenzer NF, Springer F, Schraml C, Stefan N, Machann J, Schick F. Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance. J Hepatol 2009; 51: 433–45.

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