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SUMMIT sums up to date 49 published captivating discoveries, two book chapters and other manuscripts in preparations
  1. Zhou K, L Donnelly, et al. (2014). Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis. Lancet Diabetes Endocrinol 2(6): 481-7.


  2. Sandholm N, C Forsblom, et al. (2014). Genome-wide association study of urinary albumin excretion rate in patients with type 1 diabetes. Diabetologia 57(6): 1143-53.


  3. Sambo F, A Malovini, et al. (2014). Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification. Diabetologia 57(8): 1611-22.


  4. Sambo F, B Di Camillo, et al. (2014). Compression and fast retrieval of SNP data. Bioinformatics 30(21): 3078-85.


  5. Pagliaccia F, A Habib, et al. (2014). Stability of urinary thromboxane A2 metabolites and adaptation of the extraction method to small urine volume. Clin Lab 60(1): 105-11.


  6. Nilsson T, S Segstedt, et al. (2014). Automatic measurements of diameter, distension and intima media thickness of the aorta in premature rabbit pups using B-mode images. Ultrasound Med Biol 40(2): 371-7.


  7. McLeod O, A Silveira, et al. (2014). Plasma autoantibodies against apolipoprotein B-100 peptide 210 in subclinical atherosclerosis. Atherosclerosis 232(1): 242-8.


  8. Di Camillo B, F Sambo, et al. (2014). ABACUS: an entropy-based cumulative bivariate statistic robust to rare variants and different direction of genotype effect. Bioinformatics 30(3): 384-91.


  9. Zetterqvist AV, LM Berglund, et al. (2013). Inhibition of nuclear factor of activated T-cells (NFAT) suppresses accelerated atherosclerosis in diabetic mice. PLoS One 8(6): e65020.


  10. Yaghootkar H, C Lamina, et al. (2013). Mendelian randomization studies do not support a causal role for reduced circulating adiponectin levels in insulin resistance and type 2 diabetes. Diabetes 62(10): 3589-98.


  11. Xie W, AR Wood, et al. (2013). Genetic variants associated with glycine metabolism and their role in insulin sensitivity and type 2 diabetes. Diabetes 62(6): 2141-50.


  12. Sandholm N, AJ McKnight, et al. (2013). Chromosome 2q31.1 associates with ESRD in women with type 1 diabetes. J Am Soc Nephrol 24(10): 1537-43.


  13. Rocca B, A Dragani, et al. (2013). Identifying determinants of variability to tailor aspirin therapy. Expert Rev Cardiovasc Ther 11(3): 365-79.


  14. Ricci S, R Matera, et al. (2013). BAPES  implementation on DSP for fast spectral Doppler estimation. IEEE International Symposium, Prague.


  15. Ricci S, M Cinthio, et al. (2013). Accuracy and reproducibility of a novel dynamic volume flow measurement method. Ultrasound Med Biol 39(10): 1903-14.


  16. Ramalli A, L Bassi, et al. (2013). An integrated system for the evaluation of flow mediated dilation. IEEE International Conference on Accoustics, Speech and Signal


  17. Patrono C, B Rocca, et al. (2013). Platelet activation and inhibition in polycythemia vera and essential thrombocythemia. Blood 121(10): 1701-11.


  18. Patrono C, (2013). “Aspirin.” In: Platelets, 3rd Edition, Chapter 53. Ed. A.D. Michelson


  19. Patrono C (2013). Low-dose aspirin in primary prevention: cardioprotection, chemoprevention, both, or neither? Eur Heart J 34(44): 3403-11.


  20. Nilsson T, Å Rydén Ahlgren, et al. (2013). A fast 2D tissue motion estimator based on the phase of the intensity enables visualization of the propagation of the longitudinal movement in the carotid artery wall. Joint UFFD, EFTF and PFM Symposium.


  21. Lenge M, A Ramalli, et al. (2013). Frequency-domain high frame-rate 2D vector flow imaging. IEEE International Symposium.


  22. Lamerz J, A Friedlein, et al. (2013). Determination of free desmosine in human plasma and its application in two experimental medicine studies. Anal Biochem 436(2): 127-36.


  23. Kinnunen K, SE Heinonen, et al. (2013). LDLR-/-ApoB100/100 mice with insulin-like growth factor II overexpression reveal a novel form of retinopathy with photoreceptor atrophy and altered morphology of the retina. Mol Vis 19: 1723-33.


  24. Heinonen SE, AM Kivela, et al. (2013). The effects of VEGF-A on atherosclerosis, lipoprotein profile, and lipoprotein lipase in hyperlipidaemic mouse models. Cardiovasc Res 99(4): 716-23.


  25. He B, AM Osterholm, et al. (2013). A remote cis-acting variant at 3q links glomerular NCK1 to diabetic nephropathy. PLoS One 8(2): e56414.


  26. Fall T, S Hagg, et al. (2013). The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis. PLoS Med 10(6): e1001474.


  27. Deleskog A, O Piksasova, et al. (2013). Serum 25-hydroxyvitamin D concentration in subclinical carotid atherosclerosis. Arterioscler Thromb Vasc Biol 33(11): 2633-8.


  28. Davì G, F Santilli, et al. (2013). Platelet activation in Diabetes Mellitus. In: Platelets. Edit. A.D. Michelson.


  29. Bénardeau A, P Verry, et al. (2013). Effects of the dual PPAR-alpha/gamma agonist aleglitazar on glycaemic control and organ protection in the Zucker diabetic fatty rat. Diabetes Obes Metab 15(2): 164-74.


  30. Williams WW, RM Salem, et al. (2012). Association testing of previously reported variants in a large case-control meta-analysis of diabetic nephropathy. Diabetes 61(8): 2187-94.


  31. Segstedt S, T Nilsson, et al. (2012). Arterial diameter change measurements in premature rabbit pups using B-Mode images. IEEE Int Ultrasonics Symposium Proceedings: 922-24.


  32. Sandholm N, RM Salem, et al. (2012). New susceptibility loci associated with kidney disease in type 1 diabetes. PLoS Genet 8(9): e1002921.


  33. Sambo F, E Trifoglio, et al. (2012). Bag of Naive Bayes: biomarker selection and classification from genome-wide SNP data. BMC Bioinformatics 13 Suppl 14: S2.


  34. Russu A, A Malovini, et al. (2012). Stochastic model search with binary outcomes for genome-wide association studies. J Am Med Inform Assoc 19(e1): e13-20.


  35. Rocca B, F Santilli, et al. (2012). The recovery of platelet cyclooxygenase activity explains interindividual variability in responsiveness to low-dose aspirin in patients with and without diabetes. J Thromb Haemost 10(7): 1220-30.


  36. Ricci S, M Cinthio, et al. (2012). Volume flow assessment through simultaneous B-mode and Multigate Doppler. IEEE Int Ultrasonics Symposium Proceedings.


  37. Nilsson T, S Segstedt, et al. (2012). A robust and fast method for arterial lumen diameter and intima-media thickness measurements. IEEE International Ultrasonics Symposium.


  38. Malovini A, N Barbarini, et al. (2012). Hierarchical Naive Bayes for genetic association studies. BMC Bioinformatics 13 Suppl 14: S6.


  39. Fagerholm E, E Ahlqvist, et al. (2012). SNP in the genome-wide association study hotspot on chromosome 9p21 confers susceptibility to diabetic nephropathy in type 1 diabetes. Diabetologia 55(9): 2386-93.


  40. Engelbertsen D, F To, et al. (2012). Increased inflammation in atherosclerotic lesions of diabetic Akita-LDLr(-)/(-) mice compared to nondiabetic LDLr(-)/(-) mice. Exp Diabetes Res 2012: 176162.


  41. Di Camillo B, T Sanavia, et al. (2012). Effect of size and heterogeneity of samples on biomarker discovery: synthetic and real data assessment. PLoS One 7(3): e32200.


  42. Boni E, A Cellai, et al. (2012). A high performance board for acquisition of 64-channel ultrasound RF data. IEEE International Ultrasonics Symposium


  43. Koivisto VA, L Groop, et al. (2011). The Innovative Medicines Initiative – a public private partnership to promote European diabetes research. Diabetologia 54(5).


  44. Tortoli P, C Palombo, et al. (2011). Simultaneous ultrasound assessment of brachial artery shear stimulus and flow-mediated dilation during reactive hyperemia. Ultrasound Med Biol 37(10): 1561-70.


  45. Silvola JM, A Saraste, et al. (2011). Effects of age, diet, and type 2 diabetes on the development and FDG uptake of atherosclerotic plaques. JACC Cardiovasc Imaging 4(12): 1294-301.


  46. Heinonen SE, M Merentie, et al. (2011). Left ventricular dysfunction with reduced functional cardiac reserve in diabetic and non-diabetic LDL-receptor deficient apolipoprotein B100-only mice. Cardiovasc Diabetol 10: 59.


  47. Agakov FV, P McKeigue, et al. (2011). Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions. J Syst. Cib Inf.


  48. Boni E, L Bassi, et al. (2011). A reconfigurable and programmable FPGA-based system for nonstandard ultrasound methods. IEEE Transaction on Ultrasonics, Ferroelectrics and Frequency Control 59: 1378-85.


  49. Barbarini N, A Tiengo, et al. (2011). Prediction of peptide reactivity with human IVIg through a knowledge-based approach. PLoS One 6(8): e23616.


  50. Ahluwalia TS, E Lindholm, et al. (2011). Common variants in CNDP1 and CNDP2, and risk of nephropathy in type 2 diabetes. Diabetologia 54(9): 2295-302.


  51. Agakov FV, P McKeigue, et al. (2010). Sparse Instrumental Variables (SPIV) for Genome-Wide Studies. Adv Neur Inf Proc Sys 23 (ed. J. Lafferty et al)


To DISCOVER, DEVELOP and QUALIFY potential MARKERS that empower:

  • the identification of patients at high risk of diabetes complications

  • the monitoring of the complications' progression and patients‘ response to therapy 

To use the discovered markers as SURROGATE ENDPOINTS in clinical trials.

Thereby, SHORTEN the long lasting CLINICAL TRIALS to bring about EARLIER availability of NEW THERAPY to diabetic patients.

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