Elsevier

Obesity Research & Clinical Practice

Volume 8, Issue 2, March–April 2014, Pages e178-e182
Obesity Research & Clinical Practice

Short report
Obesity is associated with retinopathy and macrovascular disease in type 1 diabetes

https://doi.org/10.1016/j.orcp.2013.03.007Get rights and content

Summary

Excessive body weight is increasingly seen in type 1 diabetes but its impact is debated. To address this uncertainty, we aimed to determine the association between excess body weight and the macro- and microvascular complications of type 1 diabetes. We identified 501 adults with type 1 diabetes attending an Australian hospital clinic and extracted their clinical and biochemical data from our patient management database. In both men and women, obesity (BMI > 30 kg/m2) was the predominant risk factor for retinopathy and cardiovascular disease despite similar HbA1c and increased use of cardioprotective drugs compared to non-obese patients. Obesity was associated with albuminuria in women, but not renal impairment or neuropathy in either sex. We conclude that obesity in type 1 diabetes may promote retinopathy and macrovascular disease. Future trials to determine the effect of weight loss on type 1 diabetes in obese people are needed.

Introduction

People with type 1 diabetes are at increased risk of excessive body weight. Excess fat mass may have predisposed to type 1 diabetes [1], [2], [3], [4], and intensive insulin therapy to prevent vascular complications can promote weight gain [5], [6]. Several groups have found increased body mass index (BMI) or metabolic syndrome in type 1 diabetes to be associated with albuminuria, retinopathy, neuropathy and macrovascular disease [7], [8], [9]. However, studies of the relationship between body mass index (BMI) and both micro- and macrovascular outcomes of type 1 diabetes have not been described. To better define the potential impact of overweight and obesity in type 1 diabetes, we performed a cross-sectional study to determine the relationship between BMI and vascular outcomes using ‘real life’ data from a single clinical center.

Section snippets

Methods

Data was captured prospectively from treating clinicians and through linkage with Biogrid Victoria. We extracted data corresponding to the most recent clinic visit of 1223 patients with type 1 diabetes who attended between 1998 and 2011. We excluded 662 patients who did not have height data and, to avoid the potentially confounding effect of decreasing BMI in the elderly [10], another 60 patients older than 65 years.

Retinopathy was defined as prior retinal photocoagulation and/or visual acuity

Results

We identified 221 female and 280 male patients whose last clinic attendance was between 1998 and 2011 (Table 1). More than half of each gender was overweight or obese. Mean ± sd age was 30.9 ± 11.5 and 31.9 ± 11.8 years in women and men respectively. Age and disease duration increased progressively across the BMI groups, but this trend was only statistically significant for disease duration in men.

The incidence of retinopathy increased with increasing BMI in both sexes. In women, the incidence of

Conclusions

This is the first analysis of the association between obesity and both the micro- and macrovascular complications of type 1 diabetes. Consistent with prior reports [6], [8] we find a substantial rate (∼15%) of obesity in adults with type 1 diabetes. This is associated with increased risk of retinopathy and macrovascular disease that is not obviously explained by differences in glycemic control, blood pressure or use of cardioprotective drugs. These findings suggest a role for excess body mass

Conflict of interest

None of the authors declares a conflict of interest.

Author contributions

SAP collated data and prepared the manuscript, AG performed statistical analyses, SF and PGC curated the clinical database and revised the manuscript, JMW designed the study, collated data and prepared the manuscript. JMW is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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