Browsing by Author "Ahola, Aila J."

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    Computational modelling of self-reported dietary carbohydrate intake on glucose concentrations in patients undergoing Roux-en-Y gastric bypass versus one-anastomosis gastric bypass
    (Informa Healthcare, 2021) Ashrafi, Reza A.; Ahola, Aila J.; Rosengård-Bärlund, Milla; Saarinen, Tuure; Heinonen, Sini; Juuti, Anne; Marttinen, Pekka; Pietiläinen, Kirsi H.; Department of Computer Science; Professorship Marttinen P.; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML); University of Helsinki
    Objectives: Our aim was to investigate in a real-life setting the use of machine learning for modelling the postprandial glucose concentrations in morbidly obese patients undergoing Roux-en-Y gastric bypass (RYGB) or one-anastomosis gastric bypass (OAGB). Methods: As part of the prospective randomized open-label trial (RYSA), data from obese (BMI ≥35 kg/m2) non-diabetic adult participants were included. Glucose concentrations, measured with FreeStyle Libre, were recorded over 14 preoperative and 14 postoperative days. During these periods, 3-day food intake was self-reported. A machine learning model was applied to estimate glycaemic responses to the reported carbohydrate intakes before and after the bariatric surgeries. Results: Altogether, 10 participants underwent RYGB and 7 participants OAGB surgeries. The glucose concentrations and carbohydrate intakes were reduced postoperatively in both groups. The relative time spent in hypoglycaemia increased regardless of the operation (RYGB, from 9.2 to 28.2%; OAGB, from 1.8 to 37.7%). Postoperatively, we observed an increase in the height of the fitted response curve and a reduction in its width, suggesting that the same amount of carbohydrates caused a larger increase in the postprandial glucose response and that the clearance of the meal-derived blood glucose was faster, with no clinically meaningful differences between the surgeries. Conclusions: A detailed analysis of the glycaemic responses using food diaries has previously been difficult because of the noisy meal data. The utilized machine learning model resolved this by modelling the uncertainty in meal times. Such an approach is likely also applicable in other applications involving dietary data. A marked reduction in overall glycaemia, increase in postprandial glucose response, and rapid glucose clearance from the circulation immediately after surgery are evident after both RYGB and OAGB. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.KEY MESSAGES The use of a novel machine learning model was applicable for combining patient-reported data and time-series data in this clinical study. Marked increase in postprandial glucose concentrations and rapid glucose clearance were observed after both Roux-en-Y gastric bypass and one-anastomosis gastric bypass surgeries. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.
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    New susceptibility Loci associated with kidney disease in type 1 diabetes
    (2012) Sandholm, Niina; Salem, Rany M.; McKnight, A.J.; Brennan, Eoin P.; Forsblom, Carol; Isakova, Tamara; McKay, Gareth J.; Williams, Winfred W.; Sadlier, Denise M.; Mäkinen, V.P.; Swan, E.J.; Palmer, Cameron D.; Boright, A.P.; Ahlqvist, Emma; Deshmukh, H.A.; Keller, B.J.; Huang, H.; Ahola, Aila J.; Fagerholm, Emma; Gordin, D.; Harjutsalo, Valma; He, B.; Heikkila, O.; Hietala, Kustaa; Kyto, J.; Lahermo, Päivi; Lehto, Markku; Lithovius, Raija; Österholm, Anne May; Parkkonen, Maija; Pitkäniemi, Janne; Rosengård-Bärlund, Milla; Saraheimo, Markku; Sarti, C.; Soderlund, J.; Soro-Paavonen, A.; Syreeni, Anna; Thorn, L.M.; Tikkanen, H.; Tolonen, Nina; Tryggvason, K.; Tuomilehto, Jaakko; Waden, J.; Gill, G.V.; Prior, S.; Guiducci, C.; Mirel, D.B.; Taylor, A.; Hosseini, S.M.; Parving, H.H.; Rossing, P.; Tarnow, L.; Ladenvall, C.; Alhenc-Gelas, F.; Lefebvre, P.; Rigalleau, V.; Roussel, R.; Tregouet, D.A.; Maestroni, A.; Maestroni, S.; Falhammar, H.; Gu, T.; Mollsten, A.; Cimponeriu, D.; Ioana, M.; Mota, M.; Mota, E.; Serafinceanu, C.; Stavarachi, M.; Hanson, R.L.; Nelson, R.G.; Kretzler, M.; Colhoun, H.M.; Panduru, N.M.; Gu, H.F.; Brismar, K.; Zerbini, G.; Hadjadj, S.; Marre, M.; Groop, L.; Lajer, M.; Bull, S.B.; Waggott, D.; Paterson, A.D.; Savage, D.A.; Bain, S.C.; Martin, F.; Hirschhorn, J.N.; Godson, C.; Florez, J.C.; Groop, P.H.; Maxwell, A.P.; He, Bin; BECS; Karolinska Institutet
    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.