Jasmina Kravic
Lund University
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Jasmina Kravic.
Nature Genetics | 2014
Jason Flannick; Gudmar Thorleifsson; Nicola L. Beer; Suzanne B.R. Jacobs; Niels Grarup; Noël P. Burtt; Anubha Mahajan; Christian Fuchsberger; Gil Atzmon; Rafn Benediktsson; John Blangero; Bowden Dw; Ivan Brandslund; Julia Brosnan; Frank Burslem; John Chambers; Yoon Shin Cho; Cramer Christensen; Desiree Douglas; Ravindranath Duggirala; Zachary Dymek; Yossi Farjoun; Timothy Fennell; Pierre Fontanillas; Tom Forsén; Stacey Gabriel; Benjamin Glaser; Daniel F. Gudbjartsson; Craig L. Hanis; Torben Hansen
Loss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets, but none have yet been described for type 2 diabetes (T2D). Through sequencing or genotyping of ∼150,000 individuals across 5 ancestry groups, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8) and harbors a common variant (p.Trp325Arg) associated with T2D risk and glucose and proinsulin levels. Collectively, carriers of protein-truncating variants had 65% reduced T2D risk (P = 1.7 × 10−6), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34Serfs*50) demonstrated reduced glucose levels (−0.17 s.d., P = 4.6 × 10−4). The two most common protein-truncating variants (p.Arg138* and p.Lys34Serfs*50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested that reduced zinc transport increases T2D risk, and phenotypic heterogeneity was observed in mouse Slc30a8 knockouts. In contrast, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, suggesting ZnT8 inhibition as a therapeutic strategy in T2D prevention.
Genetic Epidemiology | 2011
Lin T. Guey; Jasmina Kravic; Olle Melander; Noël P. Burtt; Jason M. Laramie; Valeriya Lyssenko; Anna Maria Jönsson; Eero Lindholm; Tiinamaija Tuomi; Bo Isomaa; Peter Nilsson; Peter Almgren; Sekar Kathiresan; Leif Groop; Albert B. Seymour; David Altshuler; Benjamin F. Voight
Next‐generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost‐effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two‐stage design. Two‐stage designs include a broad‐based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow‐up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false‐negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well‐cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling. Genet. Epidemiol. 35: 236‐246, 2011. © 2011 Wiley‐Liss, Inc.
Diabetes | 2013
Anna Maria Jönsson; Claes Ladenvall; Tarunveer Singh Ahluwalia; Jasmina Kravic; Ulrika Krus; Jalal Taneera; Bo Isomaa; Tiinamaija Tuomi; Erik Renström; Leif Groop; Valeriya Lyssenko
Although meta-analyses of genome-wide association studies have identified >60 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes and/or glycemic traits, there is little information on whether these variants also affect α-cell function. The aim of the current study was to evaluate the effects of glycemia-associated genetic loci on islet function in vivo and in vitro. We studied 43 SNPs in 4,654 normoglycemic participants from the Finnish population-based Prevalence, Prediction, and Prevention of Diabetes-Botnia (PPP-Botnia) Study. Islet function was assessed, in vivo, by measuring insulin and glucagon concentrations during oral glucose tolerance test, and, in vitro, by measuring glucose-stimulated insulin and glucagon secretion from human pancreatic islets. Carriers of risk variants in BCL11A, HHEX, ZBED3, HNF1A, IGF1, and NOTCH2 showed elevated whereas those in CRY2, IGF2BP2, TSPAN8, and KCNJ11 showed decreased fasting and/or 2-h glucagon concentrations in vivo. Variants in BCL11A, TSPAN8, and NOTCH2 affected glucagon secretion both in vivo and in vitro. The MTNR1B variant was a clear outlier in the relationship analysis between insulin secretion and action, as well as between insulin, glucose, and glucagon. Many of the genetic variants shown to be associated with type 2 diabetes or glycemic traits also exert pleiotropic in vivo and in vitro effects on islet function.
artificial intelligence in medicine in europe | 2015
Francesco Sambo; Andrea Facchinetti; Liisa Hakaste; Jasmina Kravic; Barbara Di Camillo; Giuseppe Fico; Jaakko Tuomilehto; Leif Groop; Rafael Gabriel; Tuomi Tiinamaija; Claudio Cobelli
We propose a novel Bayesian network tool to model the probabilistic relations between a set of type 2 diabetes risk factors. The tool can be used for probabilistic reasoning and for imputation of missing values among risk factors.
international conference of the ieee engineering in medicine and biology society | 2015
Francesco Sambo; Barbara Di Camillo; Alberto Franzin; Andrea Facchinetti; Liisa Hakaste; Jasmina Kravic; Giuseppe Fico; Jaakko Tuomilehto; Leif Groop; Rafael Gabriel; Tiinamaija Tuomi; Claudio Cobelli
In order to better understand the relations between different risk factors in the predisposition to type 2 diabetes, we present a Bayesian Network analysis of a large dataset, composed of three European population studies. Our results show, together with a key role of metabolic syndrome and of glucose after 2 hours of an Oral Glucose Tolerance Test, the importance of education, measured as the number of years of study, in the predisposition to type 2 diabetes.
European Journal of Endocrinology | 2018
Barbara Di Camillo; Liisa Hakaste; Francesco Sambo; Rafael Gabriel; Jasmina Kravic; Bo Isomaa; Jaakko Tuomilehto; Margarita Alonso; Enrico Longato; Andrea Facchinetti; Leif Groop; Claudio Cobelli; Tiinamaija Tuomi
OBJECTIVE Type 2 diabetes arises from the interaction of physiological and lifestyle risk factors. Our objective was to develop a model for predicting the risk of T2D, which could use various amounts of background information. RESEARCH DESIGN AND METHODS We trained a survival analysis model on 8483 people from three large Finnish and Spanish data sets, to predict the time until incident T2D. All studies included anthropometric data, fasting laboratory values, an oral glucose tolerance test (OGTT) and information on co-morbidities and lifestyle habits. The variables were grouped into three sets reflecting different degrees of information availability. Scenario 1 included background and anthropometric information; Scenario 2 added routine laboratory tests; Scenario 3 also added results from an OGTT. Predictive performance of these models was compared with FINDRISC and Framingham risk scores. RESULTS The three models predicted T2D risk with an average integrated area under the ROC curve equal to 0.83, 0.87 and 0.90, respectively, compared with 0.80 and 0.75 obtained using the FINDRISC and Framingham risk scores. The results were validated on two independent cohorts. Glucose values and particularly 2-h glucose during OGTT (2h-PG) had highest predictive value. Smoking, marital and professional status, waist circumference, blood pressure, age and gender were also predictive. CONCLUSIONS Our models provide an estimation of patients risk over time and outweigh FINDRISC and Framingham traditional scores for prediction of T2D risk. Of note, the models developed in Scenarios 1 and 2, only exploited variables easily available at general patient visits.
Diabetes Care | 2018
Angela C. Shore; Helen M. Colhoun; Andrea Natali; Carlo Palombo; Faisel Khan; Gerd Östling; Kunihiko Aizawa; Cecilia Kennbäck; Francesco Casanova; Margaretha Persson; Kim Gooding; Phillip E. Gates; Helen C. Looker; Fiona Dove; J. J. F. Belch; Silvia Pinnola; Elena Venturi; Michaela Kozakova; Isabel Gonçalves; Jasmina Kravic; Harry Björkbacka; Jan Nilsson
OBJECTIVE Cardiovascular disease (CVD) risk prediction represents an increasing clinical challenge in the treatment of diabetes. We used a panel of vascular imaging, functional assessments, and biomarkers reflecting different disease mechanisms to identify clinically useful markers of risk for cardiovascular (CV) events in subjects with type 2 diabetes (T2D) with or without manifest CVD. RESEARCH DESIGN AND METHODS The study cohort consisted of 936 subjects with T2D recruited at four European centers. Carotid intima-media thickness and plaque area, ankle-brachial pressure index, arterial stiffness, endothelial function, and circulating biomarkers were analyzed at baseline, and CV events were monitored during a 3-year follow-up period. RESULTS The CV event rate in subjects with T2D was higher in those with (n = 440) than in those without (n = 496) manifest CVD at baseline (5.53 vs. 2.15/100 life-years, P < 0.0001). New CV events in subjects with T2D with manifest CVD were associated with higher baseline levels of inflammatory biomarkers (interleukin 6, chemokine ligand 3, pentraxin 3, and hs-CRP) and endothelial mitogens (hepatocyte growth factor and vascular endothelial growth factor A), whereas CV events in subjects with T2D without manifest CVD were associated with more severe baseline atherosclerosis (median carotid plaque area 30.4 mm2 [16.1–92.2] vs. 19.5 mm2 [9.5–40.5], P = 0.01). Conventional risk factors, as well as measurements of arterial stiffness and endothelial reactivity, were not associated with CV events. CONCLUSIONS Our observations demonstrate that markers of inflammation and endothelial stress reflect CV risk in subjects with T2D with manifest CVD, whereas the risk for CV events in subjects with T2D without manifest CVD is primarily related to the severity of atherosclerosis.
Diabetologia | 2010
Tarun Ahluwalia; Claes Ladenvall; Anna Maria Jönsson; Jasmina Kravic; Esa Laurila; B Isomaa; Tiinamaija Tuomi; Leif Groop; Valeriya Lyssenko
Background and aims: The association between type 2 diabetes and different forms of cognitive impairment is well established. The mechanism behind the association is however still unrevealed. We ha ...
American Journal of Human Genetics | 2014
Sophie R. Wang; Vineeta Agarwala; Jason Flannick; Charleston W. K. Chiang; David Altshuler; Alisa Manning; Christopher Hartl; Pierre Fontanillas; Todd Green; Eric Banks; Mark A. DePristo; Ryan Poplin; Khalid Shakir; Timothy Fennell; Jacquelyn Murphy; Noël P. Burtt; Stacey Gabriel; Christian Fuchsberger; Hyun Min Kang; Xueling Sim; Clement Ma; Adam E. Locke; Thomas W. Blackwell; Anne U. Jackson; Tanya M. Teslovich; Heather M. Stringham; Peter S. Chines; Phoenix Kwan; Jeroen R. Huyghe; Adrian Tan
Diabetologia | 2017
Gopal Peddinti; Jeff Cobb; Loic Yengo; Philippe Froguel; Jasmina Kravic; Beverley Balkau; Tiinamaija Tuomi; Tero Aittokallio; Leif Groop