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  • Matthews, David R.; Paldanius, Päivi M.; Stumvoll, Michael; Han, Jackie; Bader, Giovanni; Chiang, YannTong; Proot, Pieter; Del Prato, Stefano (2019)
    Aims To ensure the integrity of the planned analyses and maximize the clinical utility of the VERIFY study results by describing the detailed concepts behind its statistical analysis plan (SAP) before completion of data collection and study database lock. The SAP will be adhered to for the final primary data analysis of the VERIFY trial. Materials and Methods Vildagliptin efficacy in combination with metformin for early treatment of T2DM (VERIFY) is an ongoing, multicentre, randomized controlled trial aiming to demonstrate the clinical benefits of glycaemic durability and glucose control achieved with an early combination therapy in newly-diagnosed type 2 diabetes (T2DM) patients. Results The SAP was initially designed at the study protocol conception phase and later modified, as reported here, in collaboration between the steering committee members, statisticians, and the VERIFY study leadership team. All authors were blinded to treatment allocation. An independent statistician has additionally retrieved and presented unblinded data to the independent data safety monitoring committee. An overview of the trial design with a focus on describing the fine-tuning of the analysis plan for the primary efficacy endpoint, risk of initial treatment failure, and secondary, exploratory and pre-specified subgroup analyses is provided here. Conclusion According to optimal trial practice, the details of the statistical analysis and data-handling plan prior to locking the database are reported here. The SAP accords with high-quality standards of internal validity to minimize analysis bias and will enhance the utility of the reported results for improved outcomes in the management of T2DM.
  • Nowak, Christoph; Salihovic, Samira; Ganna, Andrea; Brandmaier, Stefan; Tukiainen, Taru; Broeckling, Corey D.; Magnusson, Patrik K.; Prenni, Jessica E.; Wang-Sattler, Rui; Peters, Annette; Strauch, Konstantin; Meitinger, Thomas; Giedraitis, Vilmantas; Arnlov, Johan; Berne, Christian; Gieger, Christian; Ripatti, Samuli; Lind, Lars; Pedersen, Nancy L.; Sundstrom, Johan; Ingelsson, Erik; Fall, Tove (2016)
    Insulin resistance (IR) and impaired insulin secretion contribute to type 2 diabetes and cardiovascular disease. Both are associated with changes in the circulating metabolome, but causal directions have been difficult to disentangle. We combined untargeted plasma metabolomics by liquid chromatography/mass spectrometry in three non-diabetic cohorts with Mendelian Randomization (MR) analysis to obtain new insights into early metabolic alterations in IR and impaired insulin secretion. In up to 910 elderly men we found associations of 52 metabolites with hyperinsulinemic-euglycemic clamp-measured IR and/or beta-cell responsiveness (disposition index) during an oral glucose tolerance test. These implicated bile acid, glycerophospholipid and caffeine metabolism for IR and fatty acid biosynthesis for impaired insulin secretion. In MR analysis in two separate cohorts (n = 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (n = 7,824 / 8,961 / 8,330), we discovered and replicated causal effects of IR on lower levels of palmitoleic acid and oleic acid. A trend for a causal effect of IR on higher levels of tyrosine reached significance only in meta-analysis. In one of the largest studies combining "gold standard" measures for insulin responsiveness with non-targeted metabolomics, we found distinct metabolic profiles related to IR or impaired insulin secretion. We speculate that the causal effects on monounsaturated fatty acid levels could explain parts of the raised cardiovascular disease risk in IR that is independent of diabetes development.
  • Kettunen, Jarno L. T.; Tuomi, Tiinamaija (2020)
    The last decade has revealed hundreds of genetic variants associated with type 2 diabetes, many especially with insulin secretion. However, the evidence for their single or combined effect on beta-cell function relies mostly on genetic association of the variants or genetic risk scores with simple traits, and few have been functionally fully characterized even in cell or animal models. Translating the measured traits into human physiology is not straightforward: none of the various indices for beta-cell function or insulin sensitivity recapitulates the dynamic interplay between glucose sensing, endogenous glucose production, insulin production and secretion, insulin clearance, insulin resistance-to name just a few factors. Because insulin sensitivity is a major determinant of physiological need of insulin, insulin secretion should be evaluated in parallel with insulin sensitivity. On the other hand, multiple physiological or pathogenic processes can either mask or unmask subtle defects in beta-cell function. Even in monogenic diabetes, a clearly pathogenic genetic variant can result in different phenotypic characteristics-or no phenotype at all. In this review, we evaluate the methods available for studying beta-cell function in humans, critically examine the evidence linking some identified variants to a specific beta-cell phenotype, and highlight areas requiring further study. (C) 2020 The Authors. Published by Elsevier Ltd.
  • Rembeck, Karolina; Maglio, Cristina; Lagging, Martin; Christensen, Peer Brehm; Färkkilä, Martti Antero; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Morch, Kristine; Norkrans, Gunnar; Hellstrand, Kristoffer; Lindh, Magnus; Pirazzi, Carlo; Burza, Maria Antonella; Romeo, Stefano; Westin, Johan; NORDynamIC Grp (2012)
  • Prasad, Rashmi B.; Asplund, Olof; Shukla, Sharvari R.; Wagh, Rucha; Kunte, Pooja; Bhat, Dattatrey; Parekh, Malay; Shah, Meet; Phatak, Sanat; Karajamaki, Annemari; Datta, Anupam; Kakati, Sanjeeb; Tuomi, Tiinamaija; Saboo, Banshi; Ahlqvist, Emma; Groop, Leif; Yajnik, Chittaranjan S. (2022)
    Aim/hypothesis Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes. Methods We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205). Results Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy. Conclusions /interpretation Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.
  • Isokuortti, Elina; Zhou, You; Peltonen, Markku; Bugianesi, Elisabetta; Clement, Karine; Bonnefont-Rousselot, Dominique; Lacorte, Jean-Marc; Gastaldelli, Amalia; Schuppan, Detlef; Schattenberg, Joern M.; Hakkarainen, Antti; Lundbom, Nina; Jousilahti, Pekka; Mannisto, Satu; Keinanen-Kiukaanniemi, Sirkka; Saltevo, Juha; Anstee, Quentin M.; Yki-Jarvinen, Hannele (2017)
    Aims/hypothesis Recent European guidelines for nonalcoholic fatty liver disease (NAFLD) call for reference values for HOMA-IR. In this study, we aimed to determine: (1) the upper limit of normal HOMA-IR in two population-based cohorts; (2) the HOMA-IR corresponding to NAFLD; (3) the effect of sex and PNPLA3 genotype at rs738409 on HOMA-IR; and (4) inter-laboratory variations in HOMA-IR. Methods We identified healthy individuals in two population-based cohorts (FINRISK 2007 [n = 5024] and the Programme for Prevention of Type 2 Diabetes in Finland [FIN-D2D; n = 2849]) to define the upper 95th percentile of HOMA-IR. Non-obese individuals with normal fasting glucose levels, no excessive alcohol use, no known diseases and no use of any drugs were considered healthy. The optimal HOMA-IR cut-off for NAFLD (liver fat >= 5.56%, based on the Dallas Heart Study) was determined in 368 non-diabetic individuals (35% with NAFLD), whose liver fat was measured using proton magnetic resonance spectroscopy (1H-MRS). Samples from ten individuals were simultaneously analysed for HOMA-IR in seven European laboratories. Results The upper 95th percentiles of HOMA-IR were 1.9 and 2.0 in healthy individuals in the FINRISK (n = 1167) and FIN-D2D (n = 459) cohorts. Sex or PNPLA3 genotype did not influence these values. The optimal HOMA-IR cutoff for NAFLD was 1.9 (sensitivity 87%, specificity 79%). A HOMA-IR of 2.0 corresponded to normal liver fat ( Conclusions/interpretation The upper limit of HOMA-IR in population-based cohorts closely corresponds to that of normal liver fat. Standardisation of insulin assays would be the first step towards definition of normal values for HOMA-IR.