OBJECTIVESThis study aimed to identify clinical features associated with premature mortality in a large contemporary cohort of adults with type 1 diabetes.RESEARCH DESIGN AND METHODSThe Finnish Diabetic Nephropathy (FinnDiane) study is a national multicenter prospective follow-up study of 4,201 adults with type 1 diabetes from 21 university and central hospitals, 33 district hospitals, and 26 primary health care centers across Finland.RESULTSDuring a median 7 years of follow-up, there were 291 deaths (7%), 3.6-fold (95% CI 3.2–4.0) more than that observed in the age- and sex-matched general population. Excess mortality was only observed in individuals with chronic kidney disease. Individuals with normoalbuminuria showed no excess mortality beyond the general population (standardized mortality ratio [SMR] 0.8, 95% CI 0.5–1.1), independent of the duration of diabetes. The presence of microalbuminuria, macroalbuminuria, and end-stage kidney disease was associated with 2.8, 9.2, and 18.3 times higher SMR, respectively. The increase in mortality across each stage of albuminuria was equivalent to the risk conferred by preexisting macrovascular disease. In addition, the glomerular filtration rate was independently associated with mortality, such that individuals with impaired kidney function, as well as those demonstrating hyperfiltration, had an increased risk of death.CONCLUSIONSAn independent graded association was observed between the presence and severity of kidney disease and mortality in a large contemporary cohort of individuals with type 1 diabetes. These findings highlight the clinical and public health importance of chronic kidney disease and its prevention in the management of type 1 diabetes.

Diabetic kidney disease, or diabetic nephropathy (DN), is a major complication of diabetes and the leading cause of end-stage renal disease (ESRD) that requires dialysis treatment or kidney transplantation. In addition to the decrease in the quality of life, DN accounts for a large proportion of the excess mortality associated with type 1 diabetes (T1D). Whereas the degree of glycemia plays a pivotal role in DN, a subset of individuals with poorly controlled T1D do not develop DN. Furthermore, strong familial aggregation supports genetic susceptibility to DN. However, the genes and the molecular mechanisms behind the disease remain poorly understood, and current therapeutic strategies rarely result in reversal of DN. In the GEnetics of Nephropathy: an International Effort (GENIE) consortium, we have undertaken a meta-analysis of genome-wide association studies (GWAS) of T1D DN comprising ∼2.4 million single nucleotide polymorphisms (SNPs) imputed in 6,691 individuals. After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5,873 individuals, combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene (P = 1.2×10−8) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, rs12437854 (P = 2.0×10−9). Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta (TGF-β1) pathway. The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene (rs7588550, P = 2.1×10−7), a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4. All these detected associations represent new signals in the pathogenesis of DN.

Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy and for the first time for the four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening and diagnosis, while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.

on behalf of the FinnDiane Study Group* OBJECTIVE-Poor glycemic control, elevated triglycerides, and albuminuria are associated with vascular complications in diabetes. However, few studies have investigated combined associations between metabolic markers, diabetic kidney disease, retinopathy, hypertension, obesity, and mortality. Here, the goal was to reveal previously undetected association patterns between clinical diagnoses and biochemistry in the FinnDiane dataset.RESEARCH DESIGN AND METHODS-At baseline, clinical records, serum, and 24-h urine samples of 2,173 men and 2,024 women with type 1 diabetes were collected. The data were analyzed by the self-organizing map, which is an unsupervised pattern recognition algorithm that produces a two-dimensional layout of the patients based on their multivariate biochemical profiles. At follow-up, the results were compared against allcause mortality during 6.5 years (295 deaths). RESULTS-The highest mortality was associated with advanced kidney disease. Other risk factors included 1) a profile of insulin resistance, abdominal obesity, high cholesterol, triglycerides, and low HDL 2 cholesterol, and 2) high adiponectin and high LDL cholesterol for older patients. The highest population-adjusted risk of death was 10.1-fold (95% CI 7.3-13.1) for men and 10.7-fold (7.9 -13.7) for women. Nonsignificant risk was observed for a profile with good glycemic control and high HDL 2 cholesterol and for a low cholesterol profile with a short diabetes duration.CONCLUSIONS-The self-organizing map analysis enabled detailed risk estimates, described the associations between known risk factors and complications, and uncovered statistical patterns difficult to detect by classical methods. The results also suggest that diabetes per se, without an adverse metabolic phenotype, does not contribute to increased mortality. Diabetes 57:2480-2487, 2008 P atients with type 1 diabetes are susceptible to severe microvascular complications such as proliferative retinopathy and chronic kidney disease, which are often accompanied by cardiovascular disease and premature death (1,2). Currently, the risk assessment and diagnostics rely on the urine albumin excretion, serum creatinine, and lipid profile (3,4). In many cases, however, the biochemical measurements are treated as independent factors without explicit attention to the overall metabolic imbalance behind the complications. Although the risk factors for cardiovascular disease and diabetes complications have been verified statistically in large clinical studies (5-7), the overall picture on the mutual relationships and their relevance for risk assessment remains fragmented.The metabolic syndrome (8) is one attempt to describe the co-occurrence of vascular complications and insulin resistance, but so far its applicability to type 1 diabetes and its exact definition remain controversial (9,10). Moreover, gradually developing conditions, such as cardiovascular disease, do not present a physiologically clear border between health and disease, so quanti...