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Meta-Analysis
. 2018 Oct 29;10(10):CD012661.
doi: 10.1002/14651858.CD012661.pub2.

Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia

Bernd Richter  1 , Bianca Hemmingsen, Maria-Inti Metzendorf, Yemisi Takwoingi
Affiliations
Meta-Analysis

Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia

Bernd Richter et al. Cochrane Database Syst Rev. .

Abstract

Background: Intermediate hyperglycaemia (IH) is characterised by one or more measurements of elevated blood glucose concentrations, such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated glycosylated haemoglobin A1c (HbA1c). These levels are higher than normal but below the diagnostic threshold for type 2 diabetes mellitus (T2DM). The reduced threshold of 5.6 mmol/L (100 mg/dL) fasting plasma glucose (FPG) for defining IFG, introduced by the American Diabetes Association (ADA) in 2003, substantially increased the prevalence of IFG. Likewise, the lowering of the HbA1c threshold from 6.0% to 5.7% by the ADA in 2010 could potentially have significant medical, public health and socioeconomic impacts.

Objectives: To assess the overall prognosis of people with IH for developing T2DM, regression from IH to normoglycaemia and the difference in T2DM incidence in people with IH versus people with normoglycaemia.

Search methods: We searched MEDLINE, Embase, ClincialTrials.gov and the International Clinical Trials Registry Platform (ICTRP) Search Portal up to December 2016 and updated the MEDLINE search in February 2018. We used several complementary search methods in addition to a Boolean search based on analytical text mining.

Selection criteria: We included prospective cohort studies investigating the development of T2DM in people with IH. We used standard definitions of IH as described by the ADA or World Health Organization (WHO). We excluded intervention trials and studies on cohorts with additional comorbidities at baseline, studies with missing data on the transition from IH to T2DM, and studies where T2DM incidence was evaluated by documents or self-report only.

Data collection and analysis: One review author extracted study characteristics, and a second author checked the extracted data. We used a tailored version of the Quality In Prognosis Studies (QUIPS) tool for assessing risk of bias. We pooled incidence and incidence rate ratios (IRR) using a random-effects model to account for between-study heterogeneity. To meta-analyse incidence data, we used a method for pooling proportions. For hazard ratios (HR) and odds ratios (OR) of IH versus normoglycaemia, reported with 95% confidence intervals (CI), we obtained standard errors from these CIs and performed random-effects meta-analyses using the generic inverse-variance method. We used multivariable HRs and the model with the greatest number of covariates. We evaluated the certainty of the evidence with an adapted version of the GRADE framework.

Main results: We included 103 prospective cohort studies. The studies mainly defined IH by IFG5.6 (FPG mmol/L 5.6 to 6.9 mmol/L or 100 mg/dL to 125 mg/dL), IFG6.1 (FPG 6.1 mmol/L to 6.9 mmol/L or 110 mg/dL to 125 mg/dL), IGT (plasma glucose 7.8 mmol/L to 11.1 mmol/L or 140 mg/dL to 199 mg/dL two hours after a 75 g glucose load on the oral glucose tolerance test, combined IFG and IGT (IFG/IGT), and elevated HbA1c (HbA1c5.7: HbA1c 5.7% to 6.4% or 39 mmol/mol to 46 mmol/mol; HbA1c6.0: HbA1c 6.0% to 6.4% or 42 mmol/mol to 46 mmol/mol). The follow-up period ranged from 1 to 24 years. Ninety-three studies evaluated the overall prognosis of people with IH measured by cumulative T2DM incidence, and 52 studies evaluated glycaemic status as a prognostic factor for T2DM by comparing a cohort with IH to a cohort with normoglycaemia. Participants were of Australian, European or North American origin in 41 studies; Latin American in 7; Asian or Middle Eastern in 50; and Islanders or American Indians in 5. Six studies included children and/or adolescents.Cumulative incidence of T2DM associated with IFG5.6, IFG6.1, IGT and the combination of IFG/IGT increased with length of follow-up. Cumulative incidence was highest with IFG/IGT, followed by IGT, IFG6.1 and IFG5.6. Limited data showed a higher T2DM incidence associated with HbA1c6.0 compared to HbA1c5.7. We rated the evidence for overall prognosis as of moderate certainty because of imprecision (wide CIs in most studies). In the 47 studies reporting restitution of normoglycaemia, regression ranged from 33% to 59% within one to five years follow-up, and from 17% to 42% for 6 to 11 years of follow-up (moderate-certainty evidence).Studies evaluating the prognostic effect of IH versus normoglycaemia reported different effect measures (HRs, IRRs and ORs). Overall, the effect measures all indicated an elevated risk of T2DM at 1 to 24 years of follow-up. Taking into account the long-term follow-up of cohort studies, estimation of HRs for time-dependent events like T2DM incidence appeared most reliable. The pooled HR and the number of studies and participants for different IH definitions as compared to normoglycaemia were: IFG5.6: HR 4.32 (95% CI 2.61 to 7.12), 8 studies, 9017 participants; IFG6.1: HR 5.47 (95% CI 3.50 to 8.54), 9 studies, 2818 participants; IGT: HR 3.61 (95% CI 2.31 to 5.64), 5 studies, 4010 participants; IFG and IGT: HR 6.90 (95% CI 4.15 to 11.45), 5 studies, 1038 participants; HbA1c5.7: HR 5.55 (95% CI 2.77 to 11.12), 4 studies, 5223 participants; HbA1c6.0: HR 10.10 (95% CI 3.59 to 28.43), 6 studies, 4532 participants. In subgroup analyses, there was no clear pattern of differences between geographic regions. We downgraded the evidence for the prognostic effect of IH versus normoglycaemia to low-certainty evidence due to study limitations because many studies did not adequately adjust for confounders. Imprecision and inconsistency required further downgrading due to wide 95% CIs and wide 95% prediction intervals (sometimes ranging from negative to positive prognostic factor to outcome associations), respectively.This evidence is up to date as of 26 February 2018.

Authors' conclusions: Overall prognosis of people with IH worsened over time. T2DM cumulative incidence generally increased over the course of follow-up but varied with IH definition. Regression from IH to normoglycaemia decreased over time but was observed even after 11 years of follow-up. The risk of developing T2DM when comparing IH with normoglycaemia at baseline varied by IH definition. Taking into consideration the uncertainty of the available evidence, as well as the fluctuating stages of normoglycaemia, IH and T2DM, which may transition from one stage to another in both directions even after years of follow-up, practitioners should be careful about the potential implications of any active intervention for people 'diagnosed' with IH.

PubMed Disclaimer

Conflict of interest statement

BR: the World Health Organization (WHO) funded this review.

MIM: none known.

BH: none known.

YT: none known.

Figures

1
1
Study flow diagram
2
2
Risk of bias graph for studies of overall prognosis of people with intermediate hyperglycaemia for developing type 2 diabetes: review authors' judgements about each risk of bias item presented as percentages across all included studies
3
3
'Risk of bias' summary for studies of overall prognosis in people with intermediate hyperglycaemia for developing type 2 diabetes: review authors' judgements about each risk of bias item for each included study (part 1). The summary was split into part 1 (Figure 3) and part 2 (Figure 4) for better legibility
4
4
Risk of bias summary for studies of overall prognosis of people with intermediate hyperglycaemia for developing type 2 diabetes: review authors' judgements about each risk of bias item for each included study (part 2)
5
5
Risk of bias graph for studies of intermediate hyperglycaemia versus normoglycaemia as a prognostic factor for developing type 2 diabetes: review authors' judgements about each risk of bias item presented as percentages across all included studies
6
6
Risk of bias summary for studies of intermediate hyperglycaemia versus normoglycaemia as a prognostic factor for developing type 2 diabetes: review authors' judgements about each risk of bias item for each included study
7
7
Impaired fasting glucose 5.6 mmol/L (IFG5.6) threshold: association with cumulative type 2 diabetes mellitus (T2DM) incidence over 2–5 years
 *Isolated IFG5.6
 CI: confidence interval; M: men; n/N: events/number of participants; W: women
8
8
Impaired fasting glucose 5.6 mmol/L (IFG5.6) threshold: association with cumulative type 2 diabetes mellitus (T2DM) incidence over 6–12 years
 *Isolated IFG5.6 
 **'Africa': African Surinamese cohort, 'Asia': Asian Surinamese cohort, 'Australia/Europe/North America': 'ethnic Dutch' cohort.
 CI: confidence interval; M: men; n/N: events/number of participants; W: women
9
9
Impaired fasting glucose 6.1 mmol/L (IFG6.1) threshold: association with cumulative type 2 diabetes mellitus (T2DM) incidence over 2–5 years
 *Isolated IFG6.1
 CI: confidence interval; M: men; n/N: events/number of participants; W: women
10
10
Impaired fasting glucose 6.1 mmol/L (IFG6.1) threshold: association with cumulative type 2 diabetes mellitus (T2DM) incidence over 6–15 years
 *Isolated IFG6.1
 CI: confidence interval; n/N: events/number of participants
11
11
Impaired glucose tolerance (IGT): association with cumulative type 2 diabetes mellitus (T2DM) incidence over 1–5 years
 *Isolated IGT
 CI: confidence interval; n/N: events/number of participants
12
12
Impaired glucose tolerance (IGT): association with cumulative type 2 diabetes mellitus (T2DM) incidence over 6–20 years *Isolated IGT
 CI: confidence interval; M: men; n/N: events/number of participants; W: women
13
13
Combined impaired glucose tolerance (IGT) and impaired fasting glucose (IFG): association with cumulative type 2 diabetes mellitus (T2DM) incidence over 1–12 years
 CI: confidence interval; M: men; n/N: events/number of participants; W: women
14
14
Elevated glycosylated haemoglobin A1c (HbA1c) 5.7% threshold: association with cumulative type 2 diabetes mellitus (T2DM) incidence over 4–10 years
 CI: confidence interval; n/N: events/number of participants
15
15
Elevated glycosylated haemoglobin A1c (HbA1c) 6.0% threshold: association with cumulative type 2 diabetes mellitus (T2DM) incidence over 3–15 years
 CI: confidence interval; n/N: events/number of participants
16
16
Cumulative type 2 diabetes mellitus (T2DM) incidence in children/adolescents over 1–10 years
 CI: confidence interval; HbA1c 5.7: glycosylated haemoglobin A1c 5.7% threshold; (i‐)IGT: (isolated) impaired glucose tolerance; n/N: events/number of participants; NO: non‐overweight; OV: overweight
17
17
Regression from intermediate hyperglycaemia to normoglycaemia in adults over 1–5 years
 CI: confidence interval; HbA1c5.7: glycosylated haemoglobin A1c 5.7%; i‐IFG5.6/6.1: (isolated) impaired fasting glucose 5.6/6.1 mmol/L threshold;IGT: impaired glucose tolerance; n/N: events/number of participants
18
18
Regression from intermediate hyperglycaemia to normoglycaemia in adults over 6–11 years
 CI: confidence interval; i‐IFG5.6/6.1: (isolated) impaired fasting glucose 5.6/6.1 mmol/L threshold; i‐IGT: (isolated) impaired glucose tolerance; n/N: events/number of participants
19
19
Regression from intermediate hyperglycaemia to normoglycaemia in children/adolescents over 1–4 years
 CI: confidence interval; IGT: impaired glucose tolerance; n/N: events/number of participants
20
20
IFG: impaired fasting glucose; IRR: incidence rate ratio; n: number of cases; T: person‐time in years
21
21
IFG: impaired fasting glucose; IRR: incidence rate ratio; n: number of cases; T: person‐time in years
22
22
IGT: impaired glucose tolerance; IRR: incidence rate ratio; n: number of cases; T: person‐time in years
23
23
IFG: impaired fasting glucose; IGT: impaired glucose tolerance; IRR: incidence rate ratio; n: number of cases; T: person‐time in years
24
24
IFG: impaired fasting glucose; HbA1c: glycosylated haemoglobin A1c; IRR: incidence rate ratio; n: number of cases; T: person‐time in years
25
25
Overall prognosis of people with intermediate hyperglycaemia (cumulative type 2 diabetes incidence and regression to normoglycaemia) associated with measures of intermediate hyperglycaemia
 HbA1c5.7/HbA1c6.0: glycosylated haemoglobin A1c 5.7%/6.0% threshold; IFG5.6/6.1: impaired fasting glucose 5.6/6.1 mmol/L threshold; IGT: impaired glucose tolerance
26
26
Intermediate hyperglycaemia versus normoglycaemia as a prognostic factor for developing type 2 diabetes (associated with different measures and relative risks of intermediate hyperglycaemia)
 HbA1c5.7/HbA1c6.0: glycosylated haemoglobin A1c 5.7%/6.0% threshold; IFG5.6/6.1: impaired fasting glucose 5.6/6.1 mmol/L threshold; IGT: impaired glucose tolerance; IRR: incidence rate ratio; OR: odds ratio; HR: hazard ratio
1.1
1.1. Analysis
Comparison 1 Hazard ratio as the effect measure for the development of T2DM, Outcome 1 T2DM incidence (IFG5.6).
1.2
1.2. Analysis
Comparison 1 Hazard ratio as the effect measure for the development of T2DM, Outcome 2 T2DM incidence (IFG6.1).
1.3
1.3. Analysis
Comparison 1 Hazard ratio as the effect measure for the development of T2DM, Outcome 3 T2DM incidence (IGT).
1.4
1.4. Analysis
Comparison 1 Hazard ratio as the effect measure for the development of T2DM, Outcome 4 T2DM incidence (IFG + IGT).
1.5
1.5. Analysis
Comparison 1 Hazard ratio as the effect measure for the development of T2DM, Outcome 5 T2DM incidence (HbA1c5.7).
1.6
1.6. Analysis
Comparison 1 Hazard ratio as the effect measure for the development of T2DM, Outcome 6 T2DM incidence (HbA1c6.0).
1.7
1.7. Analysis
Comparison 1 Hazard ratio as the effect measure for the development of T2DM, Outcome 7 T2DM incidence (HbA1c + IFG).
2.1
2.1. Analysis
Comparison 2 Odds ratio as the effect measure for the development of T2DM, Outcome 1 T2DM incidence (IFG5.6).
2.2
2.2. Analysis
Comparison 2 Odds ratio as the effect measure for the development of T2DM, Outcome 2 T2DM incidence (IFG6.1).
2.3
2.3. Analysis
Comparison 2 Odds ratio as the effect measure for the development of T2DM, Outcome 3 T2DM incidence (IGT).
2.4
2.4. Analysis
Comparison 2 Odds ratio as the effect measure for the development of T2DM, Outcome 4 T2DM incidence (IFG + IGT).
2.5
2.5. Analysis
Comparison 2 Odds ratio as the effect measure for the development of T2DM, Outcome 5 T2DM incidence (HbA1c5.7).
2.6
2.6. Analysis
Comparison 2 Odds ratio as the effect measure for the development of T2DM, Outcome 6 T2DM incidence (HbA1c6.0).
2.7
2.7. Analysis
Comparison 2 Odds ratio as the effect measure for the development of T2DM, Outcome 7 T2DM incidence (HbA1c5.7 + IFG5.6).
See this image and copyright information in PMC

Update of

  • doi: 10.1002/14651858.CD012661

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Ferrannini 2009 {published data only}
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Filippatos 2016 {published data only}
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Forouhi 2007 {published data only}
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Garcia 2016 {published data only}
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Gautier 2010 {published data only}
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Gomez‐Arbelaez 2015 {published data only}
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Guerrero‐Romero 2006 {published data only}
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Han 2017 {published data only}
    1. Choi SH, Kim TH, Lim S, Park KS, Jang HC, Cho NH. Hemoglobin A1c as a diagnostic tool for diabetes screening and new‐onset diabetes prediction: a 6‐year community‐based prospective study. Diabetes Care 2011;34(4):944‐9. [PUBMED: 21335372] - PMC - PubMed
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Hanley 2005 {published data only}
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Heianza 2012 {published data only}
    1. Heianza Y, Arase Y, Fujihara K, Hsieh SD, Saito K, Tsuji H, et al. Longitudinal trajectories of HbA1c and fasting plasma glucose levels during the development of type 2 diabetes: the Toranomon hospital health management center study 7 (TOPICS 7). Diabetes Care 2012;35(5):1050‐2. [PUBMED: 22456865] - PMC - PubMed
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Inoue 1996 {published data only}
    1. Inoue I, Takahashi K, Katayama S, Harada Y, Negishi K, Ishii J, et al. A higher proinsulin response to glucose loading predicts deteriorating fasting plasma glucose and worsening to diabetes in subjects with impaired glucose tolerance. Diabetoc Medicine 1996;13(4):330‐6. [PUBMED: 9162608] - PubMed
Janghorbani 2015 {published data only}
    1. Amini M, Janghorbani M. Comparison of metabolic syndrome with glucose measurement for prediction of type 2 diabetes: the Isfahan diabetes prevention study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2009;3(2):84‐9.
    1. Haghighatdoost F, Amini M, Feizi A, Iraj B. Are body mass index and waist circumference significant predictors of diabetes and prediabetes risk: results from a population based cohort study. World Journal of Diabetes 2017;8(7):365‐73. [PUBMED: 28751960] - PMC - PubMed
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Jaruratanasirikul 2016 {published data only}
    1. Jaruratanasirikul S, Thammaratchuchai S, Puwanant M, Mo‐Suwan L, Sriplung H. Progression from impaired glucose tolerance to type 2 diabetes in obese children and adolescents: a 3‐6‐year cohort study in southern Thailand. Journal of Pediatric Endocrinology & Metabolism 2016;29(11):1267‐75. [PUBMED: 27740930] - PubMed
Jeong 2010 {published data only}
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Jiamjarasrangsi 2008a {published data only}
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Kim 2005 {published data only}
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Kim 2008 {published data only}
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Kim 2014 {published data only}
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Kim 2016a {published data only}
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Kleber 2010 {published data only}
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Kleber 2011 {published data only}
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Li 2003 {published data only}
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Ligthart 2016 {published data only}
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Lipska 2013 {published data only}
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Liu 2008 {published data only}
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Stengard 1992 {published data only}
    1. Keys A, Aravanis C, Blackburn HW, Buchem FS, Buzina R, Djordjevic BD, et al. Epidemiological studies related to coronary heart disease: characteristics of men aged 40‐59 in seven countries. Acta Medica Scandinavica. Supplementum 1966;460:1‐392. [MEDLINE: ] - PubMed
    1. Nissinen A, Kivela SL, Pekkanen J, Tuomilehto J, Kostiainen E, Piippo H, et al. Levels of some biological risk indicators among elderly men in Finland. Age and Ageing 1986;15(4):203‐11. [PUBMED: 3751746] - PubMed
    1. Stengård JH, Pekkanen J, Tuomilehto J, Kivinen P, Kaarsalo E, Tamminen M, et al. Changes in glucose tolerance among elderly Finnish men during a five‐year follow‐up: the Finnish cohorts of the seven countries study. Diabete & Metabolisme 1992;19(1 Pt 2):121‐9. [PUBMED: 8314414] - PubMed
Toshihiro 2008 {published data only}
    1. Toshihiro M, Saito K, Takikawa S, Takebe N, Onoda T, Satoh J. Psychosocial factors are independent risk factors for the development of type 2 diabetes in Japanese workers with impaired fasting glucose and/or impaired glucose tolerance. Diabetic Medicine 2008;25(10):1211‐7. [PUBMED: 19046200] - PMC - PubMed
Vaccaro 1999 {published data only}
    1. Vaccaro O, Ruffa G, Imperatore G, Iovino V, Rivellese AA, Riccardi G. Risk of diabetes in the new diagnostic category of impaired fasting glucose: a prospective analysis. Diabetes Care 1999;22(9):1490‐3. [PUBMED: 10480514] - PubMed
Valdes 2008 {published data only}
    1. Valdes S, Botas P, Delgado E, Alvarez F, Cadorniga FD. Population‐based incidence of type 2 diabetes in northern Spain: the Asturias study. Diabetes Care 2007;30(9):2258‐63. [PUBMED: 17536076] - PubMed
    1. Valdes S, Botas P, Delgado E, Alvarez F, Diaz‐Cadorniga F. HbA(1c) in the prediction of type 2 diabetes compared with fasting and 2‐h post‐challenge plasma glucose: the Asturias study (1998‐2005). Diabetes & Metabolism 2011;37(1):27‐32. [PUBMED: 20934897] - PubMed
    1. Valdés S, Botas P, Delgado E, Álvarez F, Cadórniga FD. Does the new American Diabetes Association definition for impaired fasting glucose improve its ability to predict type 2 diabetes mellitus in Spanish persons? The Asturias study. Metabolism 2008;57(3):399‐403. [PUBMED: 18249214] - PubMed
Vijayakumar 2017 {published data only}
    1. Vijayakumar P, Nelson R G, Hanson R L, Knowler W C, Sinha M. HbA1c and the prediction of type 2 diabetes in children and adults. Diabetes Care 2017;40(1):16‐21. [PUBMED: 27810987] - PMC - PubMed
Viswanathan 2007 {published data only}
    1. Viswanathan V, Clementina M, Nair BM, Satyavani K. Risk of future diabetes is as high with abnormal intermediate post‐glucose response as with impaired glucose tolerance. Journal of the Association of Physicians of India 2007;55:833‐7. [PUBMED: 18405128] - PubMed
Wang 2007 {published data only}
    1. Wang JJ, Li HB, Kinnunen L, Hu G, Jarvinen TM, Miettinen ME, et al. How well does the metabolic syndrome defined by five definitions predict incident diabetes and incident coronary heart disease in a Chinese population?. Atherosclerosis 2007;192(1):161‐8. [PUBMED: 16720024] - PubMed
    1. Wang JJ, Qiao Q, Miettinen ME, Lappalainen J, Hu G, Tuomilehto J. The metabolic syndrome defined by factor analysis and incident type 2 diabetes in a Chinese population with high postprandial glucose. Diabetes Care 2004;27(10):2429‐37. [PUBMED: 15451912] - PubMed
    1. Wang JJ, Yuan SY, Zhu LX, Fu HJ, Li HB, Hu G, et al. Effects of impaired fasting glucose and impaired glucose tolerance on predicting incident type 2 diabetes in a Chinese population with high post‐prandial glucose. Diabetes Research and Clinical Practice 2004;66(2):183‐91. [PUBMED: 15533586] - PubMed
Wang 2011 {published data only}
    1. Howard BV, Lee ET, Cowan LD, Devereux RB, Galloway JM, Go OT, et al. Rising tide of cardiovascular disease in American Indians. The strong heart study. Circulation 1999;99(18):2389‐95. - PubMed
    1. Lee ET, Howard BV, Go O, Savage PJ, Fabsitz RR, Robbins DC, et al. Prevalence of undiagnosed diabetes in three American Indian populations. A comparison of the 1997 American Diabetes Association diagnostic criteria and the 1985 World Health Organization diagnostic criteria: the strong heart study. Diabetes Care 2000;23(2):181‐6. - PubMed
    1. Lee ET, Howard BV, Savage PJ, Cowan LD, Fabsitz RR, Oopik AJ, et al. Diabetes and impaired glucose tolerance in three American Indian populations aged 45‐74 years. The strong heart study. Diabetes Care 1995;18(5):599‐610. - PubMed
    1. Lee ET, Welty TK, Cowan LD, Wang W, Rhoades DA, Devereux R, et al. Incidence of diabetes in American Indians of three geographic areas: the strong heart study. Diabetes Care 2002;25(1):49‐54. [PUBMED: 11772900] - PubMed
    1. Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA, Oopik AJ, et al. The strong heart study. A study of cardiovascular disease in American Indians: design and methods. American Journal of Epidemiology 1990;132(6):1141‐55. [PUBMED: 2260546] - PubMed
Warren 2017 {published data only}
    1. Leong A, Daya N, Porneala B, Devlin JJ, Shiffman D, McPhaul MJ, et al. Prediction of type 2 diabetes by hemoglobin A1c in two community‐based cohorts. Diabetes Care 2018;41(1):60‐8. [PUBMED: 29074816] - PMC - PubMed
    1. Schmidt MI, Duncan BB, Bang H, Pankow JS, Ballantyne CM, Golden SH, et al. Identifying individuals at high risk for diabetes ‐ the atherosclerosis risk in communities study. Diabetes Care 2005;28(8):2013‐8. [PUBMED: 16043747] - PubMed
    1. Selvin E, Rawlings AM, Grams M, Klein R, Sharrett AR, Steffes M, et al. Fructosamine and glycated albumin for risk stratification and prediction of incident diabetes and microvascular complications: a prospective cohort analysis of the atherosclerosis risk in communities (ARIC) study. Lancet Diabetes & Endocrinology 2014;2(4):279‐88. [PUBMED: 24703046] - PMC - PubMed
    1. Selvin E, Steffes M W, Gregg E, Brancati F L, Coresh J. Performance of A1C for the classification and prediction of diabetes. Diabetes Care 2011;34(1):84‐9. [PUBMED: 20855549] - PMC - PubMed
    1. Selvin E, Steffes MW, Zhu H, Matsushita K, Wagenknecht L, Pankow J, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. New England Journal of Medicine 2010;362(9):800‐11. [PUBMED: 20200384] - PMC - PubMed
Wat 2001 {published data only}
    1. Janus ED. Epidemiology of cardiovascular risk factors in Hong Kong. Clinical and Experimental Pharmacology & Physiology 1997;24(12):987‐8. [PUBMED: 9406673] - PubMed
    1. Janus ED, Watt NM, Lam KS, Cockram CS, Siu ST, Liu LJ, et al. The prevalence of diabetes, association with cardiovascular risk factors and implications of diagnostic criteria (ADA 1997 and WHO 1998) in a 1996 community‐based population study in Hong Kong Chinese. Diabetic Medicine 2000;17(10):741‐5. [PUBMED: 11110508] - PubMed
    1. Tan KC, Wat NM, Tam SC, Janus ED, Lam TH, Lam KS. C‐reactive protein predicts the deterioration of glycemia in Chinese subjects with impaired glucose tolerance. Diabetes Care 2003;26(8):2323‐8. - PubMed
    1. Wat NM, Lam TH, Janus ED, Lam KS. Central obesity predicts the worsening of glycemia in southern Chinese. International Journal of Obesity and Related Metabolic Disorders 2001;25(12):1789‐93. [PUBMED: 11781759] - PubMed
Weiss 2005 {published data only}
    1. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. New England Journal of Medicine 2004;350(23):2362‐74. [PUBMED: 15175438] - PubMed
    1. Weiss R, Taksali SE, Tamborlane WV, Burgert TS, Savoye M, Caprio S. Predictors of changes in glucose tolerance status in obese youth. Diabetes Care 2005;28(4):902‐9. [PUBMED: 15793193] - PubMed
Wheelock 2016 {published data only}
    1. Wheelock KM, Sinha M, Knowler WC, Nelson RG, Fufaa GD, Hanson RL. Metabolic risk factors and type 2 diabetes incidence in American Indian children. Journal of Clinical Endocrinology & Metabolism 2016;101(4):1437‐44. [PUBMED: 26913636] - PMC - PubMed
Wong 2003 {published data only}
    1. Tan CE, Emmanuel SC, Tan BY, Jacob E. Prevalence of diabetes and ethnic differences in cardiovascular risk factors. The 1992 Singapore national health survey. Diabetes Care 1999;22(2):241‐7. [PUBMED: 10333940] - PubMed
    1. Wong MS, Gu K, Heng D, Chew SK, Chew LS, Tai ES. The Singapore impaired glucose tolerance follow‐up study: does the ticking clock go backward as well as forward?. Diabetes Care 2003;26(11):3024‐30. [PUBMED: 14578234] - PubMed
Yeboah 2011 {published data only}
    1. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi‐ethnic study of atherosclerosis: objectives and design. American Journal of Epidemiology 2002;156(9):871‐81. [PUBMED: 12397006] - PubMed
    1. Yeboah J, Bertoni A G, Herrington DM, Post WS, Burke GL. Impaired fasting glucose and the risk of incident diabetes mellitus and cardiovascular events in an adult population: MESA (multi‐ethnic study of atherosclerosis). Journal of the American College of Cardiology 2011;58(2):140‐6. [PUBMED: 21718910] - PMC - PubMed
Zethelius 2004 {published data only}
    1. Byberg L, McKeigue PM, Zethelius B, Lithell HO. Birth weight and the insulin resistance syndrome: association of low birth weight with truncal obesity and raised plasminogen activator inhibitor‐1 but not with abdominal obesity or plasma lipid disturbances. Diabetologia 2000;43(1):54‐60. [PUBMED: 10663216] - PubMed
    1. Hedstrand H. A study of middle‐aged men with particular reference to risk factors for cardiovascular disease. Upsala Journal of Medical Sciences 1975;Suppl 19:1‐61. [PUBMED: 1216390] - PubMed
    1. Zethelius B, Hales CN, Lithell HO, Berne C. Insulin resistance, impaired early insulin response, and insulin propeptides as predictors of the development of type 2 diabetes: a population‐based, 7‐year follow‐up study in 70‐year‐old men. Diabetes Care 2004;27(6):1433‐8. [PUBMED: 15161800] - PubMed

References to studies excluded from this review

Abdul‐Ghani 2011 {published data only}
    1. Abdul‐Ghani MA, Abdul‐Ghani T, Muller G, Bergmann A, Fischer S, Bornstein S, et al. Role of glycated hemoglobin in the prediction of future risk of T2DM. Journal of Clinical Endocrinology and Metabolism 2011;96(8):2596‐600. - PubMed
Alvarsson 2009 {published data only}
    1. Alvarsson M, Hilding A, Ostenson CG. Factors determining normalization of glucose intolerance in middle‐aged Swedish men and women: a 8‐10‐year follow‐up. Diabetic Medicine 2009;26(4):345‐53. - PubMed
    1. Andersson CM, Bjaras GE, Ostenson CG. A stage model for assessing a community‐based diabetes prevention program in Sweden. Health Promotion International 2002;17(4):317‐27. - PubMed
    1. Eriksson AK, Ekbom A, Granath F, Hilding A, Efendic S, Ostenson CG. Psychological distress and risk of pre‐diabetes and type 2 diabetes in a prospective study of Swedish middle‐aged men and women. Diabetic Medicine 2008;25(7):834‐42. - PubMed
    1. Eriksson K F, Lindgärde F. Poor physical fitness, and impaired early insulin response but late hyperinsulinaemia, as predictors of NIDDM in middle‐aged Swedish men. Diabetologia 1996;39(5):573‐9. - PubMed
Alyass 2015 {published data only}
    1. Alyass A, Almgren P, Akerlund M, Dushoff J, Isomaa B, Nilsson P, et al. Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts. Diabetologia 2015;58(1):87‐97. - PubMed
Amoah 2002 {published data only}
    1. Amoah AG. Undiagnosed diabetes and impaired glucose regulation in adult Ghanaians using the ADA and WHO diagnostic criteria. Acta Diabetologica 2002;39(1):7‐13. - PubMed
Andreou 2017 {published data only}
    1. Andreou E, Papandreou D, Hajigeorgiou P, Kyriakou K, Avraam T, Chappa G, et al. Type 2 diabetes and its correlates in a first nationwide study among Cypriot adults. Primary Care Diabetes 2017;11(2):112‐8. - PubMed
Bancks 2015 {published data only}
    1. Bancks MP, Odegaard AO, Koh WP, Yuan JM, Gross MD, Pereira MA. Glycated hemoglobin and incident type 2 diabetes in Singaporean Chinese adults: the Singapore Chinese health study. PLOS ONE 2015;10(3):e0119884. - PMC - PubMed
Birmingham Diabetes Survey Working Party 1976 {published data only}
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Bjornholt 2000 {published data only}
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Bodicoat 2017 {published data only}
    1. Bodicoat DH, Khunti K, Srinivasan BT, Mostafa S, Gray LJ, Davies MJ, et al. Incident type 2 diabetes and the effect of early regression to normoglycaemia in a population with impaired glucose regulation. Diabetic Medicine 2017;34(3):396‐404. [PUBMED: 26871995] - PubMed
Boned 2016 {published data only}
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Boucher 2015 {published data only}
    1. Boucher AB, Adesanya EA, Owei I, Gilles AK, Ebenibo S, Wan J, et al. Dietary habits and leisure‐time physical activity in relation to adiposity, dyslipidemia, and incident dysglycemia in the pathobiology of prediabetes in a biracial cohort study. Metabolism 2015;64(9):1060‐7. - PMC - PubMed
Brantsma 2005 {published data only}
    1. Brantsma AH, Bakker SJL, Hillege HL, Zeeuw D, Jong PE, Gansevoort RT. Urinary albumin excretion and its relation with C‐reactive protein and the metabolic syndrome in the prediction of type 2 diabetes. Diabetes Care 2005;28(10):2525‐30. - PubMed
Brateanu 2017 {published data only}
    1. Brateanu A, Barwacz T, Kou L, Wang S, Misra‐Hebert AD, Hu B, et al. Determining the optimal screening interval for type 2 diabetes mellitus using a risk prediction model. PLOS ONE 2017;12(11):e0187695. - PMC - PubMed
Braun 1996 {published data only}
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Burchfiel 1995 {published data only}
    1. Burchfiel CM, Curb JD, Rodriguez BL, Yano K, Hwang LJ, Fong KO, et al. Incidence and predictors of diabetes in Japanese‐American men. The Honolulu heart program. Annals of Epidemiology 1995;5(1):33‐43. - PubMed
Chamukuttan 2016 {published data only}
    1. Chamukuttan S, Ram J, Nanditha A, Shetty AS, Sevick MA, Bergman M, et al. Baseline level of 30‐min plasma glucose is an independent predictor of incident diabetes among Asian Indians: analysis of two diabetes prevention programmes. Diabetes‐Metabolism Research and Reviews 2016;32(7):762‐7. - PubMed
Chang 2017 {published data only}
    1. Chang CH, Yeh YC, Shih SR, Lin JW, Chuang LM, Caffrey JL, et al. Association between thyroid dysfunction and dysglycaemia: a prospective cohort study. Diabetic Medicine 2017;34(11):1584‐90. - PubMed
Chen 1995 {published data only}
    1. Chen KW, Boyko EJ, Bergstrom RW, Leonetti DL, Newell‐Morris L, Wahl PW, et al. Earlier appearance of impaired insulin secretion than of visceral adiposity in the pathogenesis of NIDDM: 5‐year follow‐up of initially nondiabetic Japanese‐American men. Diabetes Care 1995;18(6):747‐53. - PubMed
Cheng 2011 {published data only}
    1. Cheng P, Neugaard B, Foulis P, Conlin PR. Hemoglobin A1c as a predictor of incident diabetes. Diabetes Care 2011;34(3):610‐5. - PMC - PubMed
Cheung 2007 {published data only}
    1. Cheung BM, Wat NM, Man YB, Tam S, Thomas GN, Leung GM, et al. Development of diabetes in Chinese with the metabolic syndrome: a 6‐year prospective study. Diabetes Care 2007;30(6):1430‐6. - PubMed
Choi 2002 {published data only}
    1. Choi KM, Lee J, Kim DR, Kim SK, Shin DH, Kim NH, et al. Comparison of ADA and WHO criteria for the diagnosis of diabetes in elderly Koreans. Diabetic Medicine 2002;19(10):853‐7. - PubMed
Cicero 2005 {published data only}
    1. Cicero AF, Derosa G, Rosticci M, D'Addato S, Agnoletti D, Borghi C, et al. Long‐term predictors of impaired fasting glucose and type 2 diabetes in subjects with family history of type 2 diabetes: a 12‐years follow‐up of the Brisighella heart study historical cohort. Diabetes Research and Clinical Practice 2014;104(1):183‐8. - PubMed
    1. Cicero AF, Dormi A, Nascetti S, Panourgia MP, Grandi E, D'Addato S, et al. Relative role of major risk factors for type 2 diabetes development in the historical cohort of the Brisighella heart study: an 8‐year follow‐up. Diabetic Medicine 2005;22(9):1263‐6. - PubMed
Cosson 2011 {published data only}
    1. Cosson E, Nguyen MT, Hamo‐Tchatchouang E, Banu I, Chiheb S, Charnaux N, et al. What would be the outcome if the American Diabetes Association recommendations of 2010 had been followed in our practice in 1998‐2006?. Diabetic Medicine 2011;28(5):567‐74. - PubMed
Costa 2005 {published data only}
    1. Costa B, Vizcaino J, Pinol J, Martin F, Cabre J J, Basora J, et al. The RECORD project. continuous blood glucose monitoring among high risk subjects for developing diabetes in Spanish primary health care. Atencion Primaria 2005;35(2):99‐104. - PMC - PubMed
Cree‐Green 2013 {published data only}
    1. Cree‐Green M, Triolo TM, Nadeau KJ. Etiology of insulin resistance in youth with type 2 diabetes. Current Diabetes Reports 2013;13(1):81‐8. - PMC - PubMed
Cropano 2017 {published data only}
    1. Cropano C, Santoro N, Groop L, Dalla Man C, Cobelli C, Galderisi A, et al. The rs7903146 variant in the TCF7L2 gene increases the risk of prediabetes/type 2 diabetes in obese adolescents by impairing beta‐cell function and hepatic insulin sensitivity. Diabetes Care 2017;40(8):1082‐9. - PMC - PubMed
Dagogo‐Jack 2011 {published data only}
    1. Dagogo‐Jack S, Edeoga C, Ebenibo S, Chapp‐Jumbo E. Pathobiology of prediabetes in a biracial cohort (POP‐ABC) study: baseline characteristics of enrolled subjects. Journal of Clinical Endocrinology and Metabolism 2013;98(1):120‐8. - PMC - PubMed
    1. Dagogo‐Jack S, Edeoga C, Ebenibo S, Nyenwe E, Wan J. Lack of racial disparity in incident prediabetes and glycemic progression among black and white offspring of parents with type 2 diabetes: the pathobiology of prediabetes in a biracial cohort (POP‐ABC) study. Journal of Clinical Endocrinology and Metabolism 2014;99(6):E1078‐87. - PMC - PubMed
    1. Dagogo‐Jack S, Edeoga C, Nyenwe E, Chapp‐Jumbo E, Wan J. Pathobiology of prediabetes in a biracial cohort (POP‐ABC): design and methods. Ethnicity & Disease 2011;21(1):33‐9. - PMC - PubMed
    1. Edeoga C, Owei I, Siwakoti K, Umekwe N, Ceesay F, Wan J, et al. Relationships between blood pressure and blood glucose among offspring of parents with type 2 diabetes: prediction of incident dysglycemia in a biracial cohort. Journal of Diabetes and Its Complications 2017;31(11):1580‐6. - PubMed
    1. Owei I, Umekwe N, Wan J, Dagogo‐Jack S. Plasma lipid levels predict dysglycemia in a biracial cohort of nondiabetic subjects: potential mechanisms. Experimental Biology and Medicine 2016;241(17):1961‐7. - PMC - PubMed
Daniel 1999 {published data only}
    1. Daniel M, Rowley KG, McDermott R, Mylvaganam A, O'Dea K. Diabetes incidence in an Australian aboriginal population. An 8‐year follow‐up study. Diabetes Care 1999;22(12):1993‐8. - PubMed
Decode 2003 {published data only}
    1. Decode Study Group European Diabetes Epidemiology Group. Is the current definition for diabetes relevant to mortality risk from all causes and cardiovascular and noncardiovascular diseases?. Diabetes Care 2003;26(3):688‐96. - PubMed
Deedwania 2013 {published data only}
    1. Deedwania P, Patel K, Fonarow GC, Desai RV, Zhang Y, Feller MA, et al. Prediabetes is not an independent risk factor for incident heart failure, other cardiovascular events or mortality in older adults: findings from a population‐based cohort study. International Journal of Cardiology 2013;168(4):3616‐22. - PMC - PubMed
DeFina 2012 {published data only}
    1. DeFina LF, Vega GL, Leonard D, Grundy SM. Fasting glucose, obesity, and metabolic syndrome as predictors of type 2 diabetes: the Cooper center longitudinal study. Journal of Investigative Medicine 2012;60(8):1164‐8. - PubMed
DeJesus 2016 {published data only}
    1. DeJesus RS, Breitkopf CR, Rutten LJ, Jacobson DJ, Wilson PM, Sauver JS. Incidence rate of prediabetes progression to diabetes: modeling an optimum target group for intervention. Population Health Management 2016;30:30. - PubMed
Deschenes 2016 {published data only}
    1. Deschenes SS, Burns RJ, Graham E, Schmitz N. Prediabetes, depressive and anxiety symptoms, and risk of type 2 diabetes: a community‐based cohort study. Journal of Psychosomatic Research 2016;89:85‐90. - PubMed
Dinneen 1998 {published data only}
    1. Dinneen SF, Maldonado D, Leibson CL, Klee GG, Li H, Melton LJ, et al. Effects of changing diagnostic criteria on the risk of developing diabetes. Diabetes Care 1998;21(9):1408‐13. - PubMed
Doi 2007 {published data only}
    1. Doi Y, Kubo M, Yonemoto K, Ninomiya T, Iwase M, Tanizaki Y, et al. Liver enzymes as a predictor for incident diabetes in a Japanese population: the Hisayama study. Obesity 2007;15(7):1841‐50. - PubMed
    1. Mukai N, Doi Y, Ninomiya T, Hata J, Hirakawa Y, Fukuhara M, et al. Cut‐off values of fasting and post‐load plasma glucose and HbA1c for predicting type 2 diabetes in community‐dwelling Japanese subjects: the Hisayama study. Diabetic Medicine 2012;29(1):99‐106. - PubMed
Du 2016 {published data only}
    1. Du TT, Yuan G, Zhou XR, Sun XX. Sex differences in the effect of HbA1c‐defined diabetes on a wide range of cardiovascular disease risk factors. Annals of Medicine 2016;48(1‐2):34‐41. - PMC - PubMed
Edelman 2004 {published data only}
    1. Edelman D, Olsen MK, Dudley TK, Harris AC, Oddone EZ. Utility of hemoglobin A1c in predicting diabetes risk. Journal of General Internal Medicine 2004;19(12):1175‐80. - PMC - PubMed
Edelstein 1997 {published data only}
    1. Edelstein SL, Knowler WC, Bain RP, Andres R, Barrett‐Connor EL, Dowse GK, et al. Predictors of progression from impaired glucose tolerance to NIDDM: an analysis of six prospective studies. Diabetes 1997;46(4):701‐10. - PMC - PubMed
Engberg 2010 {published data only}
    1. Engberg S, Glumer C, Witte D R, Jorgensen T, Borch‐Johnsen K. Differential relationship between physical activity and progression to diabetes by glucose tolerance status: the Inter99 Study. Diabetologia 2010;53(1):70‐8. - PubMed
    1. Engberg S, Vistisen D, Lau C, Glumer C, Jorgensen T, Pedersen O, et al. Progression to impaired glucose regulation and diabetes in the population‐based Inter99 study. Diabetes Care 2009;32(4):606‐11. - PMC - PubMed
    1. Glumer C, Jorgensen T, Borch‐Johnsen K. Prevalences of diabetes and impaired glucose regulation in a Danish population: the Inter99 study. Diabetes Care 2003;26(8):2335‐40. - PubMed
    1. Jorgensen T, Borch‐Johnsen K, Thomsen TF, Ibsen H, Glumer C, Pisinger C. A randomized non‐pharmacological intervention study for prevention of ischaemic heart disease: baseline results Inter99. Europan Journal of Cardiovascular Prevention and Rehabilitation 2003;10(5):377‐86. - PubMed
    1. Soulimane S, Simon D, Shaw JE, Zimmet PZ, Vol S, Vistisen D, et al. Comparing incident diabetes as defined by fasting plasma glucose or by HbA(1c). The AusDiab, Inter99 and DESIR studies. Diabetic Medicine 2011;28(11):1311‐8. - PubMed
Eskesen 2013 {published data only}
    1. Eskesen K, Jensen MT, Galatius S, Vestergaard H, Hildebrandt P, Marott JL, et al. Glycated haemoglobin and the risk of cardiovascular disease, diabetes and all‐cause mortality in the Copenhagen city heart study. Journal of Internal Medicine 2013;273(1):94‐101. - PubMed
Feizi 2017 {published data only}
    1. Feizi A, Meamar R, Eslamian M, Amini M, Nasri M, Iraj B. Area under the curve during OGTT in first‐degree relatives of diabetic patients as an efficient indicator of future risk of type 2 diabetes and prediabetes. Clinical Endocrinology 2017;87(6):696‐705. - PubMed
Feskens 1989 {published data only}
    1. Feskens EJ, Kromhout D. Cardiovascular risk factors and the 25‐year incidence of diabetes mellitus in middle‐aged men. The Zutphen study. American Journal of Epidemiology 1989;130(6):1101‐8. - PubMed
Festa 2003 {published data only}
    1. Festa A. Inflammation in the prediabetic state is related to increased insulin resistance rather than decreased insulin secretion. Circulation 2003;108(15):1822‐30. - PubMed
Folsom 2000 {published data only}
    1. Folsom AR, Kushi LH, Hong CP. Physical activity and incident diabetes mellitus in postmenopausal women. American Journal of Public Health 2000;90(1):134‐8. - PMC - PubMed
Gil‐Montalban 2015 {published data only}
    1. Gil‐Montalban E, Martin‐Rios MD, Ortiz‐Marron H, Zorrilla‐Torras B, Martinez‐Cortes M, Esteban‐Vasallo MD, et al. Incidence of type 2 diabetes and associated factors in the adult population of the community of Madrid. PREDIMERC cohort. Revista Clinica Espanola 2015;215(9):495‐502. - PubMed
Giraldez‐Garcia 2015 {published data only}
    1. Giraldez‐Garcia C, Sangros FJ, Diaz‐Redondo A, Franch‐Nadal J, Serrano R, Diez J, et al. Cardiometabolic risk profiles in patients with impaired fasting glucose and/or hemoglobin A1c 5.7% to 6.4%: evidence for a gradient according to diagnostic criteria: the PREDAPS study. Medicine 2015;94(44):e1935. - PMC - PubMed
Glauber 2018 {published data only}
    1. Glauber H, Vollmer WM, Nichols GA. A simple model for predicting two‐year risk of diabetes development in individuals with prediabetes. Permanente Journal 2018;22:17‐050. [DOI: 10.7812/TPP/17-050] - DOI - PMC - PubMed
Gonzalez‐Villalpando 2014 {published data only}
    1. Gonzalez‐Villalpando C, Davila‐Cervantes CA, Zamora‐Macorra M, Trejo‐Valdivia B, Gonzalez‐Villalpando ME. Risk factors associated to diabetes in Mexican population and phenotype of the individuals who will convert to diabetes. Salud Publica de Mexico 2014;56(4):317‐22. - PubMed
Gopinath 2013 {published data only}
    1. Gopinath B, Rochtchina E, Flood VM, Mitchell P. Diet quality is prospectively associated with incident impaired fasting glucose in older adults. Diabetic Medicine 2013;30(5):557‐62. - PubMed
Gu 2015 {published data only}
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Gupta 2011 {published data only}
    1. Gupta AK, Prieto‐Merino D, Dahlof B, Sever PS, Poulter NR. Metabolic syndrome, impaired fasting glucose and obesity, as predictors of incident diabetes in 14 120 hypertensive patients of ASCOT‐BPLA: comparison of their relative predictability using a novel approach. Diabetic Medicine 2011;28(8):941‐7. - PubMed
Hackett 2014 {published data only}
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References to studies awaiting assessment

Li 2001 {published data only}
    1. Li G, Wang J, Chen C. [Model of development of diabetes mellitus in adult Chinese]. Zhonghua Yi Xue Za Zhi [National Medical Journal of China] 2001;81(15):914‐7. - PubMed
Misnikova 2011 {published data only}
    1. Misnikova IV, Dreval AV, Barsukov IA, Dzebisashvili TG. Risk of diabetes and cardiovascular events in persons with early glucose metabolism impairments. Diabetologia 2011;54(Suppl 1):S119.
NCT00816608 {published data only}
    1. NCT00816608. The effect of maximum body weight in lifetime on the development of type 2 diabetes (MAXWEL) [Study of MAXimum Weight in Lifetime on glucose homeostasis (MAXWEL)]. clinicaltrials.gov/show/NCT00816608 (first received 1 January 2009).

References to ongoing studies

NCT00786890 {unpublished data only}
    1. NCT00786890. A survey to evaluate the cardiovascular risk status of subjects with pre‐diabetes in Hong Kong (JADE‐HK2). clinicaltrials.gov/show/NCT00786890 (accessed 1 November 2017).
NCT02838693 {unpublished data only}
    1. NCT02838693. Assessing progression to type‐2 diabetes (APT‐2D): a prospective cohort study expanded from BRITE‐SPOT (bio‐bank and registry for stratIfication and targeted interventions in the spectrum of type 2 diabetes) (APT‐2D). clinicaltrials.gov/show/NCT02838693 (accessed 1 November 2017).
NCT02958579 {unpublished data only}
    1. NCT02958579. A population based study on metabolic syndrome complications, and mortality (MetSCoM). clinicaltrials.gov/show/NCT02958579 (accessed 1 November 2017).
Vilanova 2017 {published data only}
    1. Vilanova MB, Falguera M, Marsal JR, Rubinat E, Alcubierre N, Castelblanco E, et al. Prevalence, clinical features and risk assessment of pre‐diabetes in Spain: the prospective Mollerussa cohort study. BMJ Open 2017;7(6):e015158. - PMC - PubMed

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