Helen C. Looker

Estimated Life Expectancy in a Scottish Cohort With Type 1 Diabetes, 2008-2010

IMPORTANCE Type 1 diabetes has historically been associated with a significant reduction in life expectancy. Major advances in treatment of type 1 diabetes have occurred in the past 3 decades. Contemporary estimates of the effect of type 1 diabetes on life expectancy are needed. OBJECTIVE To examine current life expectancy in people with and without type 1 diabetes in Scotland. We also examined whether any loss of life expectancy in patients with type 1 diabetes is confined to those who develop kidney disease. DESIGN, SETTING, AND PARTICIPANTS Prospective cohort of all individuals alive in Scotland with type 1 diabetes who were aged 20 years or older from 2008 through 2010 and were in a nationwide register (n=24,691 contributing 67,712 person-years and 1043 deaths). MAIN OUTCOMES AND MEASURES Differences in life expectancy between those with and those without type 1 diabetes and the percentage of the difference due to various causes. RESULTS Life expectancy at an attained age of 20 years was an additional 46.2 years among men with type 1 diabetes and 57.3 years among men without it, an estimated loss in life expectancy with diabetes of 11.1 years (95% CI, 10.1-12.1). Life expectancy from age 20 years was an additional 48.1 years among women with type 1 diabetes and 61.0 years among women without it, an estimated loss with diabetes of 12.9 years (95% CI, 11.7-14.1). Even among those with type 1 diabetes with an estimated glomerular filtration rate of 90 mL/min/1.73 m2 or higher, life expectancy was reduced (49.0 years in men, 53.1 years in women) giving an estimated loss from age 20 years of 8.3 years (95% CI, 6.5-10.1) for men and 7.9 years (95% CI, 5.5-10.3) for women. Overall, the largest percentage of the estimated loss in life expectancy was related to ischemic heart disease (36% in men, 31% in women) but death from diabetic coma or ketoacidosis was associated with the largest percentage of the estimated loss occurring before age 50 years (29.4% in men, 21.7% in women). CONCLUSIONS AND RELEVANCE Estimated life expectancy for patients with type 1 diabetes in Scotland based on data from 2008 through 2010 indicated an estimated loss of life expectancy at age 20 years of approximately 11 years for men and 13 years for women compared with the general population without type 1 diabetes.

Risk of cardiovascular disease and total mortality in adults with type 1 diabetes: Scottish registry linkage study

Helen Colhoun and colleagues report findings from a Scottish registry linkage study regarding contemporary risks for cardiovascular events and all-cause mortality among individuals diagnosed with type 1 diabetes.

Childhood Predictors of Young-Onset Type 2 Diabetes

OBJECTIVE—Optimal prevention of young-onset type 2 diabetes requires identification of the early-life modifiable risk factors. We aimed to do this using longitudinal data in 1,604 5- to 19-year-old initially nondiabetic American Indians. RESEARCH DESIGN AND METHODS—For type 2 diabetes prediction, we derived an optimally weighted, continuously distributed, standardized multivariate score (zMS) comprising commonly measured metabolic, anthropometric, and vascular traits (i.e., fasting and 2-h glucose, A1C, BMI, waist circumference, fasting insulin, HDL cholesterol, triglycerides, and blood pressures) and compared the predictive power for each feature against zMS. RESULTS—In separate Cox proportional hazard models, adjusted for age, sex, and ethnicity, zMS and each of its component risk factors were associated with incident type 2 diabetes. Stepwise proportional hazards models selected fasting glucose, 2-h glucose, HDL cholesterol, and BMI as independent diabetes predictors; individually, these were weaker predictors than zMS (P < 0.01). However, a parsimonious summary score combining only these variables had predictive power similar to that of zMS (P = 0.33). Although intrauterine diabetes exposure or parental history of young-onset diabetes increased a child’s absolute risk of developing diabetes, the magnitude of the diabetes-risk relationships for zMS and the parsimonious score were similar irrespective of familial risk factors. CONCLUSIONS—We have determined the relative value of the features of the metabolic syndrome in childhood for the prediction of subsequent type 2 diabetes. Our findings suggest that strategies targeting obesity, dysregulated glucose homeostasis, and low HDL cholesterol during childhood and adolescence may have the most success in preventing diabetes.

Genome-Wide Linkage Analyses to Identify Loci for Diabetic Retinopathy

Hyperglycemia and long duration of diabetes are widely recognized risk factors for diabetic retinopathy, but inherited susceptibility may also play a role because retinopathy aggregates in families. A genome-wide linkage analysis was conducted in 211 sibships in which ≥2 siblings had diabetes and retinal photographs were available from a longitudinal study. These sibships were a subset of 322 sibships who had participated in a previous linkage study of diabetes and related traits; they comprised 607 diabetic individuals in 725 sibpairs. Retinal photographs were graded for presence and severity of diabetic retinopathy according to a modification of the Airlie House classification system. The grade for the worse eye was adjusted for age, sex, and diabetes duration and analyzed as a quantitative trait. Heritability of diabetic retinopathy in this group was 18% (95% CI 2–36). A genome-wide linkage analysis using variance components modeling found evidence of linkage on chromosome 1p. Using single-point analysis, the peak logarithm of odds (LOD) was 3.1 for marker D1S3669 (34.2 cM), whereas with multipoint analysis the peak LOD was 2.58 at 35 cM. No other areas of suggestive linkage were found. We propose that an area on chromosome 1 may harbor a gene or genes conferring susceptibility to diabetic retinopathy.

Hospitalised hip fracture risk with rosiglitazone and pioglitazone use compared with other glucose-lowering drugs

Aims/hypothesisCurrent drug labels for thiazolidinediones (TZDs) warn of increased fractures, predominantly for distal fractures in women. We examined whether exposure to TZDs affects hip fracture in women and men and compared the risk to that found with other drugs used in diabetes.MethodsUsing a nationwide database of prescriptions, hospital admissions and deaths in those with type 2 diabetes in Scotland we calculated TZD exposure among 206,672 individuals. Discrete-time failure analysis was used to model the effect of cumulative drug exposure on hip fracture during 1999–2008.ResultsThere were 176 hip fractures among 37,479 exposed individuals. Hip fracture risk increased with cumulative exposure to TZD: OR per year of exposure 1.18 (95% CI 1.09, 1.28; p = 3 × 10−5), adjusted for age, sex and calendar month. Hip fracture increased with cumulative exposure in both men (OR 1.20; 95% CI 1.03, 1.41) and women (OR 1.18; 95% CI 1.07, 1.29) and risks were similar for pioglitazone (OR 1.18) and rosiglitazone (OR 1.16). The association was similar when adjusted for exposure to other drugs for diabetes and for other potential confounders. There was no association of hip fracture with cumulative exposure to sulfonylureas, metformin or insulin in this analysis. The 90-day mortality associated with hip fractures was similar in ever-users of TZD (15%) and in never-users (13%).Conclusions/interpretationHip fracture is a severe adverse effect with TZDs, affecting both sexes; labels should be changed to warn of this. The excess mortality is at least as much as expected from the reported association of pioglitazone with bladder cancer.

Interaction Between an 11βHSD1 Gene Variant and Birth Era Modifies the Risk of Hypertension in Pima Indians

11&bgr;-Hydroxysteroid dehydrogenase type 1 (11&bgr;HSD1) is a candidate gene for hypertension, diabetes, and obesity through altered glucocorticoid production. This study explored the association of 11&bgr;HSD1 gene variants with diabetes, hypertension, and obesity in a longitudinal population study of American Indians (N=918; exams=5508). In multivariate mixed models assuming an additive effect of genotype, a 5′ upstream variant (rs846910) was associated with blood pressure (diastolic blood pressure &bgr;=1.58 mm Hg per copy of the A allele, P=0.0008; systolic blood pressure &bgr;=2.28 mm Hg per copy of the A allele, P=0.004; mean arterial blood pressure &bgr;=1.83 mm Hg per copy of the A allele, P=0.0006) and hypertension (odds ratio=1.27 per copy of the A allele, P=0.02). However, birth date modified these associations (test for interaction: diastolic blood pressure P=0.16; systolic blood pressure P=0.007; mean arterial blood pressure P=0.01), such that the magnitude and direction of association between genotype and blood pressure changed with time. Finally, in models controlling for potential confounding by population stratification, we observed evidence of within-family effects for blood pressure (diastolic blood pressure &bgr;=1.77 mm Hg per copy of the A allele, P=0.004; systolic blood pressure &bgr;=2.04 mm Hg per copy of the A allele, P=0.07; mean arterial blood pressure &bgr;=1.85 mm Hg per copy of the A allele, P=0.01) and for hypertension (odds ratio=1.26 per copy of the A allele; P=0.08). No association was observed for obesity. Associations with diabetes were similar in magnitude as reported previously but were not statistically significant. These data demonstrate association between genetic variability at 11&bgr;HSD1 with hypertension, but these effects are modified by environmental factors.

Contemporary Risk of Hip Fracture in Type 1 and Type 2 Diabetes: A National Registry Study From Scotland

The purpose of this study was to compare contemporary risk of hip fracture in type 1 and type 2 diabetes with the nondiabetic population. Using a national diabetes database, we identified those with type 1 and type 2 diabetes who were aged 20 to 84 years and alive anytime from January 1, 2005 to December 31, 2007. All hospitalized events for hip fracture in 2005 to 2007 for diabetes patients were linked and compared with general population counts. Age‐ and calendar‐year‐adjusted incidence rate ratios were calculated by diabetes type and sex. One hundred five hip fractures occurred in 21,033 people (59,585 person‐years) with type 1 diabetes; 1421 in 180,841 people (462,120 person‐years) with type 2 diabetes; and 11,733 hip fractures over 10,980,599 person‐years in the nondiabetic population (3.66 million people). Those with type 1 diabetes had substantially elevated risks of hip fracture compared with the general population incidence risk ratio (IRR) of 3.28 (95% confidence interval [CI] 2.52–4.26) in men and 3.54 (CI 2.75–4.57) in women. The IRR was greater at younger ages, but absolute risk difference was greatest at older ages. In type 2 diabetes, there was no elevation in risk among men (IRR 0.97 [CI 0.92–1.02]) and the increase in risk in women was small (IRR 1.05 [CI 1.01–1.10]). There remains a substantial elevation relative risk of hip fracture in people with type 1 diabetes, but the relative risk is much lower than in earlier studies. In contrast, there is currently little elevation in overall hip fracture risk with type 2 diabetes, but this may mask elevations in risk in particular subgroups of type 2 diabetes patients with different body mass indexes, diabetes duration, or drug exposure.

Biomarkers of rapid chronic kidney disease progression in type 2 diabetes

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.

Elevated Plasma Levels of MMP-12 Are Associated With Atherosclerotic Burden and Symptomatic Cardiovascular Disease in Subjects With Type 2 Diabetes

Objective— Matrix metalloproteinases (MMPs) degrade extracellular matrix proteins and play important roles in development and tissue repair. They have also been shown to have both protective and pathogenic effects in atherosclerosis, and experimental studies have suggested that MMP-12 contributes to plaque growth and destabilization. The objective of this study was to investigate the associations between circulating MMPs, atherosclerosis burden, and incidence of cardiovascular disease with a particular focus on type 2 diabetes mellitus. Approach and Results— Plasma levels of MMP-1, -3, -7, -10, and -12 were analyzed by the Proximity Extension Assay technology in 1500 subjects participating in the SUMMIT (surrogate markers for micro- and macrovascular hard end points for innovative diabetes tools) study, 384 incident coronary cases, and 409 matched controls in the Malmö Diet and Cancer study and in 205 carotid endarterectomy patients. Plasma MMP-7 and -12 were higher in subjects with type 2 diabetes mellitus, increased with age and impaired renal function, and was independently associated with prevalent cardiovascular disease, atherosclerotic burden (as assessed by carotid intima-media thickness and ankle-brachial pressure index), arterial stiffness, and plaque inflammation. Baseline MMP-7 and -12 levels were increased in Malmö Diet and Cancer subjects who had a coronary event during follow-up. Conclusions— The plasma level of MMP-7 and -12 are elevated in type 2 diabetes mellitus, associated with more severe atherosclerosis and an increased incidence of coronary events. These observations provide clinical support to previous experimental studies, demonstrating a role for these MMPs in plaque development, and suggest that they are potential biomarkers of atherosclerosis burden and cardiovascular disease risk.

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes

Aims/hypothesisWe selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes.MethodsIn this nested case–control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC).ResultsSixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA1c.Conclusions/interpretationWe identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed.