International Journal of Diabetes Mellitus
Volume 2, Issue 1 , Pages 38-42, April 2010

Metabolic syndrome in type 1 diabetes

  • Sujoy Ghosh

      Affiliations

    • Diabetes Day Centre, The Ayr Hospital, Dalmellington Road, Ayr, Scotland KA6 6DX, United Kingdom
    • Corresponding Author InformationCorresponding author. Tel.: +44 1292 610555; fax: +44 1292 614538.
    • ,
  • Andrew Collier

      Affiliations

    • Diabetes Day Centre, The Ayr Hospital, Dalmellington Road, Ayr, Scotland KA6 6DX, United Kingdom
    • ,
  • Mario Hair

      Affiliations

    • Department of Physical Sciences, University of the West of Scotland, United Kingdom
    • ,
  • Iqbal Malik

      Affiliations

    • Diabetes Day Centre, The Ayr Hospital, Dalmellington Road, Ayr, Scotland KA6 6DX, United Kingdom
    • ,
  • Tarik Elhadd

      Affiliations

    • Diabetes Day Centre, The Ayr Hospital, Dalmellington Road, Ayr, Scotland KA6 6DX, United Kingdom

Received 28 July 2009; received in revised form 14 October 2009; accepted 24 October 2009. published online 30 November 2009.

Article Outline

Abstract 

Objectives

The aim of this study was to assess the prevalence and effects of the presence of metabolic syndrome in patients with type 1 diabetes.

Research design and methods

Retrospective analysis of data from a one year period of patients attending annual review clinic was undertaken. Body weight, height and blood pressure were measured along with assessment of micro-/macro-vascular complications. HbA1c, urea, cholesterol, triglyceride, urinary albumin: creatinine ratios were also measured. Patients were divided into those with and those without metabolic syndrome.

Results

Data from 365 type 1 diabetic patients was analysed. Hundred and twelve had metabolic syndrome. There was no difference according to gender or smoking. Type 1 diabetic patients with metabolic syndrome had longer duration of diabetes, were significantly older, heavier, had higher blood pressure, higher triglyceride and lower HDL cholesterol levels. There were significant increases in mean BMI, urea, serum creatinine, urinary albumin: creatinine ratio, cholesterol and triglyceride in the group with metabolic syndrome even after controlling for both age and duration of diabetes. Neuropathy and macro-vascular complications were commoner in patients with metabolic syndrome. Patients with metabolic syndrome were more likely to be on statins, ACE inhibitors and angiotensin receptor blockers and had a significantly higher mean insulin dosage requirement per kg.

Conclusions

This study highlights the importance of the presence of the metabolic syndrome in patients with type 1 diabetes. It shows that metabolic syndrome is associated with a higher incidence of diabetes-related complications, a need for higher insulin doses and a more aggressive multifactorial intervention.

Keywords: Type 1 diabetes, Metabolic syndrome, Hypertension, Dyslipidaemia, Obesity, Macro-vascular complications, Micro-vascular complications, Medication

 

Back to Article Outline

1. Background 

Metabolic syndrome, for which there are multiple definitions, consists of a clustering of cardiovascular risk factors. The most recent definition is the consensus document from the International Diabetes Federation (IDF) [1]. The World Health Organisation (WHO), National Cholesterol Education Program (NCEP) and the Adult Treatment Panel (ATP) III have each proposed different criteria for the diagnosis of the metabolic syndrome [2], [3], [4], [5].

The presence of metabolic syndrome is considered to be an important risk factor for cardiovascular disease and mortality in patients with type 2 diabetes and non-diabetic subjects [6], [7], [8], [9]. The phrase “double diabetes” has recently been coined for those patients who have type 1 diabetes and insulin resistance [10], [11]. Recent studies suggest that these patients appear to be at increased risk of developing macro- and micro-vascular complications [12], [13], [14], [15].

The aim of this study was to assess the prevalence and effects of metabolic syndrome in type 1 diabetic patients in a Scottish population. The study also reviewed the prevalence of macro- and micro-vascular complications, differences in metabolic parameters and the types of medications that these patients received. Statistical analysis was carried out to determine whether the prevalence of complications and the differences in the clinical and biochemical parameters were significantly different between those with and without metabolic syndrome.

Back to Article Outline

2. Research design and methods 

The study was undertaken at the Diabetes Day Centre, The Ayr Hospital, which is a District General Hospital in Scotland, UK. A comprehensive annual review is undertaken on all diabetic patients attending the clinic. Body weights of patients are recorded (in kg) using electronic scales (SECA, Birmingham, UK). Height was measured in meters using a (?) Harpenden Stadiometer. Blood pressure was recorded using a standard sphygmomanometer. Fundal photography was undertaken with pupils dilated (TopconTRC-NW6 Digital Retinal Camera (www.topcon.co.uk) fitted with a Nikon D70 (www.nikon.co.uk)) and formally assessed by trained graders.

Assessment for peripheral neuropathy was performed by testing using a 10g nylon monofilament, and ankle reflexes were checked. Peripheral vascular disease was considered to be present if pedal pulses were non-palpable or undetectable by a hand held Doppler or if there was history of claudication and/or rest pain. Ischaemic heart disease was documented as the presence of a history of angina, myocardial infarction or coronary revascularisation procedures. This was confirmed from history, clinical notes or investigations (such as ECG/ETT/Angiography). Similarly, history of stroke or Transient Ischaemic Attack was confirmed from history, clinical notes or investigations (such as neuroimaging).

Venous blood was taken for the measurement of HbA1c using HPLC (Menarini–Arkray HA 8140 haemoglobin A1c analyzer, Menarini diagnostics, Wokingham, UK; intra-assay CV 1.9%). Blood urea, serum creatinine, urinary albumin and creatinine, cholesterol and triglyceride were measured using Roche Modular Analyzer (Basel, Switzerland). Urine albumin concentration was measured by radioimmunoassay, CV<9%. Urinary creatinine concentration was measured by an end point Jaffe reaction, CV<6%.

Data was entered into the hospital-based SCI-DC database (Scottish Care Information – Diabetes Collaboration, www.DiabetesInScotland.org).We performed a retrospective analysis of our Diabetes Centre Database. For the purposes of this study, the computerised database was used to retrieve the data obtained for all patients attending the diabetes clinic for the year 2007. The database contains information on patient demography, diabetic complications, metabolic parameters and medications. Patients in whom the diagnosis of type 1 diabetes was uncertain and in whom data collection was incomplete were excluded from the study.

We used the WHO Criteria for the definition of the metabolic syndrome. The WHO criterion uses either Body Mass index (BMI) ⩾30kg/m2 to define obesity, or waist circumference for the diagnosis of metabolic syndrome [16]. We used BMI data for the diagnosis of obesity as this was the recording available from the database.

As all our patients were diabetic, patients were classified as having metabolic syndrome if two or more of the following criteria were present:

•Obesity as defined by BMI ⩾30kg/m2.

•Dyslipidaemia: triglyceride ⩾1.7mmol/l or HDL cholesterol <0.9 in males or <1.0 in females (mmol/l).

•Hypertension: BP ⩾140/90mm Hg or on treatment.

•Microalbuminuria: (albumin:creatinine ratio) male ⩾2.5; female ⩾3.5 (mg/mmol).

Using the above criterion the patients, were divided into those with and those without metabolic syndrome.

Back to Article Outline

3. Statistical analysis 

SPSS (Social Package for Statistical Sciences) version 15 was used to analyse the data obtained.

As both age and duration of diabetes are important extraneous variables, the analysis of the effect of metabolic syndrome on interval level variables was carried out using analysis of covariance (ANCOVA) with group membership as the fixed factor with age and duration as covariates.

For categorical variables, hierarchical logistic regression was carried out to assess the effect of metabolic syndrome. In the regression models, age and duration are added as independent variables in the first block and metabolic syndrome is added in the second block in order to determine the incremental improvement in fit when adding group membership after the effects of age and duration are taken into account.

Back to Article Outline

4. Results 

4.1. Comparison of baseline data (Table 1) 

Data from 365 type 1 diabetic patients was analysed. 9.3% patients were obese, 34.0% had dyslipidaemia, 27.4% had hypertension and 21.4% had microalbuminuria. Hundred and twelve patients fulfilled the criteria for metabolic syndrome. There was no difference according to gender or smoking status. Type 1 diabetic patients with metabolic syndrome were significantly older and had a significantly longer duration of diabetes. They were also heavier, had higher blood pressure, higher triglyceride and lower HDL cholesterol levels than their non-metabolic syndrome counterparts.

Table 1. Baseline and demographic data of the two groups of patients in terms of gender, age, duration of diagnosis, plus Body Mass index (BMI), triglycerides, High Density Lipoprotein (HDL) and hypertension.
With metabolic syndromeWithout metabolic syndromep Value
N112253
Mean age in years (SD)55.43 (20.35)38.62 (15.82)t(362)=8.54, p<0.01
Mean duration of diabetes in years (SD)21.58 (13.38)17.92 (12.58)t(356)=2.48, p<0.05
Males (%)56.356.1Χ2(1, N=365)=0.0005, p>0.05
Smoking status (%)42 (37.8%)92 (36.7%)Χ2(1, N=362)=0.05, p>0.05

Mean BMI (kg/m2) (SD)a26.74 (3.96)24.74 (3.23)F(1, 345)=21.22, p<0.01
Mean triglyceride (mmol/l) (SD)a2.36 (1.29)1.24 (0.67)F(1, 347)=99.91, p<0.01
Mean HDL (mmol/l) (SD)a1.45 (0.43)1.73 (0.46)F(1, 340)=23.87, p<0.01
Hypertension (%)72.3%7.5%Χ2(1)=97.91, p<0.01

aEstimated marginal means are evaluated at the mean values for covariates of length of diagnosis and age.

Analysis of covariance showed that there were significant increases in mean BMI and triglyceride in the group with metabolic syndrome after controlling for both age and duration.

4.2. Comparison of the biochemical parameters (Table 2) 

Those with metabolic syndrome had significantly higher mean levels of urea, serum creatinine, urinary albumin: creatinine ratio and total cholesterol levels after controlling for both age and duration of diabetes. Patients with metabolic syndrome had a higher mean LDL-cholesterol, but the difference was not significant.

Table 2. The biochemical parameters measured in the two groups.
With metabolic syndromeWithout metabolic syndromep Value
N112253
Mean urea (mmol/l) (SD)a7.54 (4.19)5.77 (2.01)F(1, 347)=27.06, p<0.01
Mean creatinine (μmol/l) (SD)a122.39 (61.69)96.55 (15.95)F(1, 345)=31.19, p<0.01
Mean cholesterol (mmol/l) (SD)a5.21 (1.09)4.71 (0.95)F(1, 347)=15.65, p<0.01
Mean LDL (mmol/l) (SD)a2.64 (0.92)2.43 (0.83)F(1, 327)=3.49, p=0.06
Mean albumin:creatinine (mg/mmol) (SD)a8.99 (9.46)2.01 (3.86)F(1, 305)=72.94, p<0.01

aEstimated marginal means are evaluated at the mean values for covariates of length of diagnosis and age.

4.3. Comparison of prevalence of complications (Table 3) 

Macro-vascular complications such as ischaemic heart disease/myocardial infarction (IHD) was more common in patients with metabolic syndrome as was neuropathy.

Table 3. Prevalence of complications in the two groups.
With metabolic syndromeWithout metabolic syndromep Value
N112253
Neuropathy (%)25 (22.3%)10 (4.0%)Χ2(1)=15.26, p<0.01
Retinopathy (%)12 (10.7%)25 (9.9%)Χ2(1)=0.15, p=0.70
IHD/MI (%)27 (24.1%)10 (4.0%)Χ2(1)=4.24, p<0.05
Stroke (%)6 (5.4%)5 (2.0%)Χ2(1)=0.06, p=0.81

Difference in the prevalence of retinopathy or stroke in patients with metabolic syndrome failed to reach levels of statistical significance.

4.4. Comparison of the use of medication in the two groups (Table 4) 

Patients with metabolic syndrome were more likely to be on statins, ACE inhibitors and angiotensin receptor blockers. Only two patients were on fibrates, and while both were in the metabolic syndrome group the numbers were too small.

Table 4. The comparative use of medication between the two groups.
With metabolic syndromeWithout metabolic syndromep Value
N112253
On statins (%)27 (24.1%)20 (7.9%)Χ2(1)=6.03, p<0.05
On fibrates (%)2 (1.8%)0 (0.0%)NS
On ACE I (%)45 40.2%)9 (3.6%)Χ2(1)=38.64, p<0.01
On ARB (%)7 (6.3%)4 (1.6%)Χ2(1)=3.89, p<0.05
Mean insulin dose per kg (SD)a0.86 (0.36)0.73 (0.35)F(1, 324)=9.01, p<0.01

aEstimated marginal means are evaluated at the mean values for covariates of length of diagnosis and age.

Finally, an analysis of covariance showed that patients with metabolic syndrome had a significantly higher mean insulin dose requirement per kg after controlling for age and duration.

Back to Article Outline

5. Conclusions 

In our cross-sectional study, metabolic syndrome was evident in ∼30% of type 1 diabetic patients, which is similar to that of the FinnDiane Study. The FinnDiane study, which used NCEP criteria, demonstrated that 38% of men and 40% of women with type 1 diabetes had metabolic syndrome [17], [18]. The DCCT Follow-up group and the Pittsburgh Epidemiology of Diabetes Complications study groups had a lower prevalence (between 8% and 22%). In the Pittsburgh Epidemiology of Diabetes Complications study groups, metabolic syndrome was most commonly found with the IDF criteria, and less so with AHA/NCEP criteria [19]. The different definitions used understandably give differing prevalence of metabolic syndrome in type 1 diabetes [20].

The metabolic syndrome generally predicts an adverse outcome and the risk of developing major micro- and macro-vascular complications, but the prevalence obviously varies according to the definition used. Microalbuminuria, which is a criterion in some definitions of the metabolic syndrome, seems to be the best marker of prediction for the development of micro- and macro-vascular complications [21]. Microalbuminuria appears to reflect both insulin resistance, which is central to both the concept of the metabolic syndrome, and vascular damage, as originally postulated by the Steno Group [22].

Cardiovascular disease is one of the leading causes of morbidity and mortality in Scotland, in part due to the increasing prevalence of obesity [23]. Obesity is a major public health concern, with more than 40% of the adult population in the industrialised world being overweight or clinically obese [24]. The reasons for the dramatic increase in obesity are complex, and include lifestyle changes plus demographic and political factors [25]. It seems likely that the prevalence of obesity in type 1 diabetes parallels the prevalence of obesity in the general population. With this increase, there is also an increased chance of clustering of cardiovascular risk factors including hypertension, dyslipidaemia, central obesity and low physical activity, all of which are associated with insulin resistance and contribute to the metabolic syndrome. Hence, it comes as no surprise that a significant proportion of our patients with type 1 diabetes fulfilled the criteria for the metabolic syndrome.

In our study, patients with metabolic syndrome were older, and had a longer duration of diabetes. As these type 1 patients would obviously have been younger and probably also leaner at diagnosis, our study probably under-estimates the effect of the risk factors that make up metabolic syndrome. Despite this under-estimation, our study clearly demonstrates a much higher prevalence of complications in this group of patients. Even after adjustment for age and duration of diabetes, these patients were more likely to have hypertension, dyslipidaemia and worse glycaemic control. In addition, the risk of developing macro-vascular complications such as ischaemic heart disease, myocardial infarction and peripheral vascular disease, along with micro-vascular complications such as nephropathy and neuropathy, were higher in the group with metabolic syndrome, despite receiving much greater therapeutic interventions.

Longitudinal studies, such as the Pittsburgh study and the DCCT Follow-up, clearly showed that patients with type 1 diabetes gain weight with time. It would seem likely that this weight gain would be associated with an increase in the prevalence of the metabolic syndrome [26], [27], [28], [29]. However, the DCCT Follow-up study showed that the intensively treated group fared better with diabetes-related outcomes despite the weight gain. It would appear from this study that the long-term benefits of good glycaemic control outweigh the deleterious effects of weight gain associated with the treatment of diabetes [30].

Even with relatively small numbers, our study demonstrates an increase in the prevalence of neuropathy and nephropathy in patients with metabolic syndrome confirming the findings of previous studies, including the Metascreen study [31]. Previous studies have demonstrated that the risk of stroke is higher in patients with type 1 diabetes with metabolic syndrome.[32] Our study demonstrated that in patients with the metabolic syndrome patients there was a threefold increase in the prevalence of stroke; however this did not reach levels of statistical significance.

The prevalence of retinopathy in our study was not increased in those with metabolic syndrome. Our results are similar to the findings of previous studies [33]. The reasons for this are not clearly understood.

Our patients with metabolic syndrome were on higher doses of insulin per kg of body weight, and yet achieved poorer glycaemic control, implying that even higher doses of insulin would have been required to achieve comparable glycaemic control. This is clearly in keeping with the concept that insulin resistance being a key feature of metabolic syndrome [34]. Other studies have also demonstrated that patients who are overweight/obese with metabolic syndrome have higher insulin requirements [35].

In type 2 diabetic patients, metformin provides cardiovascular protection reducing all-cause mortality by 36% and myocardial infarction by 39% [36]. The mechanism of action is not clearly understood, but it is suggested that metformin has significant effects on several markers of endothelial and thrombolytic function with reductions in vascular cell adhesion molecule, plasminogen activator inhibitor-1 and tissue plasminogen activator [37], [38]. In addition, other studies have demonstrated that the use of metformin can improve glycaemic control, and reduce total daily insulin dosage in overweight and adolescent type 1 diabetic patients [39]. Similarly there are small studies with the use of rosiglitazone in overweight subjects with type 1 diabetes which have demonstrated improved glycaemic control with lower insulin requirements [40].

Our study highlights the importance of the presence of the metabolic syndrome in patients with type 1 diabetes and clearly demonstrated that the metabolic syndrome is associated with a higher incidence of diabetes-related complications, a greater need for higher insulin doses and more aggressive treatment (multifactorial intervention). We would suggest that the presence of the metabolic syndrome should be used as a clinical marker of insulin resistance and that these patients should be encouraged to intensify lifestyle changes and should be considered for insulin sensitisers such as metformin. We would hope that this would lead to a reduction in total daily insulin requirements, better glycaemic control, better clinical outcomes and fewer diabetes-related complications.

Back to Article Outline

References 

  1. International Diabetes Federation. The IDF consensus worldwide definition of the metabolic syndrome; 2003. <http://www.idf.org/webdata/docs/IDF_Meta_def_final.pdf> [accessed 10.10.08].
  2. Executive Summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 2001;285:2486–97.
  3. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome: a new worldwide definition. Lancet. 2005;366:1778
  4. World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Geneva (Switzerland): Department of Noncommunicable Disease Surveillance; 1999.
  5. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung and Blood Institute Scientific Statement. Circulation. 2005;25(112):2735–2752
  6. Isomaa B, Almgren P, Tuomi T, Forse´n B, Lahti K, Nissen M, et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diab Care. 2001;24:683–689
  7. Lakka H-M, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;288:2709–2716
  8. Bruno G, Merletti F, Biggeri A, Bargero G, Ferrero S, Runzo C, et al. Metabolic syndrome as a predictor of all-cause and cardiovascular mortality in type 2 diabetes: the Casale Monferrato Study. Diab Care. 2004;27(11):2689–2694
  9. Bonora E, Targher G, Formentini G, Calcaterra F, Lombardi S, Marini F, et al. The metabolic syndrome is an independent predictor of cardiovascular disease in type 2 diabetic subjects: prospective data from the Verona Diabetes Complications Study. Diab Med. 2003;52–58
  10. Kilpatrick ES, Rigby AS, Atkin SL. Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: “double diabetes” in the Diabetes Control and Complications Trial. Diab Care. 2007;30(3):707–712
  11. Teupe B, Bergis K. Epidemiological evidence for “double diabetes”. Lancet. 1991;337:361–362
  12. Orchard TJ, Chang YF, Ferrell RE, Petro N, Ellis DE. Nephropathy in type 1 diabetes: a manifestation of insulin resistance and multiple genetic susceptibilities?. Kidney Int. 2002;62:963–970
  13. Orchard TJ, Olson JC, Erbey JR, Williams K, Forrest KYZ, Kinder LS, et al. Insulin resistance-related factors, but not glycemia, predict coronary artery disease in type 1 diabetes: 10-year follow-up data from the Pittsburgh Epidemiology of Diabetes Complications study. Diab Care. 2003;26:1374–1379
  14. Despres JP, Lamarche B, Mauriege P, Cantin B, Dagenais GR, Morrjani S, et al. Hyperinsulinaemia as an independent risk factor for ischaemic heart disease. N Engl J Med. 1996;334:952–957
  15. Bondonna RC. The syndrome of insulin resistance and its links to atherosclerosis. John Wiley and Sons; 2003;
  16. World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications: report of a WHO Consultation. Geneva: World Health Org., Department of Noncommunicable Disease Surveillance; 1999, p. 31–3.
  17. Thorn LM, Forsblom C, Fagerudd J, Thomas MC, Pettersson-Fernholm K, Saraheimo M, et al. Metabolic syndrome in type 1 diabetes. Association with diabetic nephropathy and glycemic control. Diab Care. 2005;28(8):2019–2024
  18. Saraheimo M, Teppo A-M, Forsblom C, Fagerudd J, Groop P-H FinnDiane Study Group. Diabetic nephropathy is associated with low-grade inflammation in type 1 diabetic patients. Diabetologia. 2003;46:1402–1407
  19. Pambianco G, Costacou T, Orchard TJ. The prediction of major outcomes of type 1 diabetes a 12-year prospective evaluation of three separate definitions of the metabolic syndrome and their components and estimated glucose disposal rate: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diab Care. 2007;30(5):1248–1254
  20. Saely CH, Koch L, Schmid F, Marte T, Aczel S, Langer P, et al. Treatment Panel III 2001 but not International Diabetes Federation 2005 criteria of the metabolic syndrome predict clinical cardiovascular events in subjects who underwent coronary angiography. Diab Care. 2006;29(4):901–907
  21. Yip J, Mattock MB, Morocutti A, Sethi M, Trevisan R, Viberti G. Insulin resistance in insulin-dependent diabetic patients with microalbuminuria. Lancet. 1993;342(8876):883–887
  22. Deckert T, Feldt-Rasmussen B, Borch-Johnsen K, Jensen T, Kofoed-Enevoldsen A. Albuminuria reflects widespread vascular damage: the Steno hypothesis. Diabetologia. 1989;32:219–226
  23. The Scottish Health Survey. In: Cardiovascular disease, vol. 1. Scotland: Information and Statistics; 2003. <http://www.scotland.gov.uk/Resource/Doc/76169/0019727.pdf> [accessed 10.10.08].
  24. Sobal J. Globalization and the epidemiology of obesity. Int J Epidemiol. 2001;30:1136–1137
  25. Sobal J. Social and cultural influences on obesity. In:  Bjorntorp P editors. International textbook of obesity. New York: John Wiley and Sons; 2001;p. 305–322
  26. Pambianco G, Costacou T, Orchard TJ. The prediction of major outcomes of type 1 diabetes: a 12-year prospective evaluation of three separate definitions of the metabolic syndrome and their components and estimated glucose disposal rate: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diab Care. 2007;30(5):1248–1254
  27. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977–986
  28. The Diabetes Control and Complications Trial Research Group. Effect of intensive diabetes management on macrovascular events and risk factors in the Diabetes Control and Complications Trial. Am J Cardiol. 1995;75(14):894–903
  29. Purnell JQ, Hokanson JE, Marcovina SM, Steffes MW, Cleary PA, Brunzell JD. Effect of excessive weight gain with intensive therapy of type 1 diabetes on lipid levels and blood pressure: results from the DCCT. Diabetes Control and Complications Trial. JAMA. 1998;280(2):140–146
  30. Nathan DM, Cleary PA, Backlund JY, Genuth SM, Lachin JM, Orchard TJ, et al. Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353(25):2643–2653
  31. Bonadonna RC, Cucinotta D, Fedele D, Riccardi G, Tiengo A. Metascreen Writing Committee. The metabolic syndrome is a risk indicator of microvascular and macrovascular complications in diabetes: results from Metascreen, a multicenter diabetes clinic-based survey. Diab Care. 2006;29(12):2701–2707
  32. Davis TM, Bruce DG, Davis WA. Prevalence and prognostic implications of the metabolic syndrome in community-based patients with type 1 diabetes: the Fremantle Diabetes Study. Diab Res Clin Pract. 2007;78(3):412–417
  33. McGill M, Molyneaux L, Twigg SM, Yue DK. The metabolic syndrome in type 1 diabetes: does it exist and does it matter?. J Diab Complicat. 2008;22(1):18–23
  34. Reaven GM. Banting Lecture 1988: role of insulin resistance in human disease. Diabetes. 1988;37:1595–1607
  35. Orchard TJ, Olson JC, Erbey JR, Williams K, Forrest KY-Z, Smithline Kinder L, et al. Insulin resistance-related factors, but not glycemia, predict coronary artery disease in type 1 diabetes. Diab Care. 2003;26:1374–1379
  36. UK Prospective Diabetes Study (UKPDS) Group . Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet. 1998;352:854–865
  37. Cusi K, De Fronzo RA. Metformin: a review of its metabolic effects. Diab Rev. 1998;6:132
  38. Janssen M, Rillaerts E, De Leeuw I. Effects of metformin on haemorheology, lipid parameters and insulin resistance in insulin-dependent diabetic patients (IDDM). Biomed Pharmacother. 1991;45(8):363–367
  39. Hamilton J, Cummings E, Zdravkovic V, Finegood D, Daneman D. Metformin as an adjunct therapy in adolescents with type 1 diabetes and insulin resistance: a randomized controlled trial. Diab Care. 2003;26(1):138–143
  40. Strowig SM, Raskin P. The effect of rosiglitazone on overweight subjects with type 1 diabetes. Diab Care. 2005;28(7):1562–1567

PII: S1877-5934(09)00057-5

doi:10.1016/j.ijdm.2009.10.005

International Journal of Diabetes Mellitus
Volume 2, Issue 1 , Pages 38-42, April 2010

Article Tools