Mäkinen Ville-Petteri, Forsblom Carol, Thorn Lena M, Wadén Johan, Gordin Daniel, Heikkilä Outi, Hietala Kustaa, Kyllönen Laura, Kytö Janne, Rosengård-Bärlund Milla, Saraheimo Markku, Tolonen Nina, Parkkonen Maija, Kaski Kimmo, Ala-Korpela Mika, Groop Per-Henrik
Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, Helsinki, Finland.
Diabetes. 2008 Sep;57(9):2480-7. doi: 10.2337/db08-0332. Epub 2008 Jun 10.
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.
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 all-cause mortality during 6.5 years (295 deaths).
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.
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.
血糖控制不佳、甘油三酯升高和蛋白尿与糖尿病血管并发症相关。然而，很少有研究调查代谢标志物、糖尿病肾病、视网膜病变、高血压、肥胖和死亡率之间的联合关联。在此，目标是揭示芬兰糖尿病数据集中临床诊断与生物化学之间先前未被发现的关联模式。
在基线时，收集了2173名1型糖尿病男性和2024名1型糖尿病女性的临床记录、血清和24小时尿液样本。数据通过自组织映射进行分析，自组织映射是一种无监督模式识别算法，可根据患者的多变量生化特征生成患者的二维布局。在随访期间，将结果与6.5年期间的全因死亡率（295例死亡）进行比较。
最高死亡率与晚期肾病相关。其他风险因素包括：1）胰岛素抵抗、腹型肥胖、高胆固醇、甘油三酯和低高密度脂蛋白（HDL）2胆固醇的特征，以及2）老年患者的高脂联素和高低密度脂蛋白胆固醇。男性经人群调整后的最高死亡风险为10.1倍（95%CI 7.3 - 13.1），女性为10.7倍（7.9 - 13.7）。血糖控制良好且高密度脂蛋白2胆固醇高的特征以及糖尿病病程短且胆固醇水平低的特征观察到无显著风险。
自组织映射分析能够进行详细的风险估计，描述已知风险因素与并发症之间的关联，并揭示经典方法难以检测到的统计模式。结果还表明，无不良代谢表型的糖尿病本身不会导致死亡率增加。
