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Background Metformin has been used for the treatment of type 2 diabetes for over 60 years; however, its mechanism of pharmacological action is not fully clear. Different hypotheses exist regarding metformin distribution and redistribution mechanisms between plasma and erythrocytes/red blood cells (RBCs). Objective We aimed to test the hypothesis that the metformin distribution between plasma and RBC occurs via concentration difference-driven passive transport and estimated transport rate coefficient values based on metformin concentration time series in plasma and RBCs from in vivo studies. Methods An ordinary differential equation (ODE) system with two compartments was used to describe diffusion-based passive transport between plasma and RBCs. Metformin concentration time series in plasma and RBCs of 35 individuals were used for metformin transport parametrization. Plasma concentration has been approximated by biexponential decline. Results A single passive transport coefficient, k = 0.044 ± 0.014 (h–1), can be applied, describing the uptake and release transport rate versus the linear equation v = k × (Mpl − MRBC), where Mpl is the metformin concentration in plasma and MRBC is the metformin concentration in RBCs. Conclusions Our research suggests that passive transport can explain metformin distribution dynamics between plasma and RBCs because transport speed is proportional to the metformin concentration difference and independent of the transport direction. Concentration difference-driven passive transport can explain the mechanism of faster metformin distribution to RBCs the first few hours after administration, and faster release and domination of the redistribution transport rate after metformin concentration in plasma becomes smaller than in RBCs. Supplementary Information The online version contains supplementary material available at 10.1007/s40262-021-01058-2.

Introduction: The Minimum Information About BIobank data Sharing (MIABIS) was initiated in 2012. MIABIS aims to create a common biobank terminology to facilitate data sharing in biobanks and sample collections. The MIABIS Core terminology consists of three components describing biobanks, sample collections, and studies, in which information on samples and sample donors is provided at aggregated form. However, there is also a need to describe samples and sample donors at an individual level to allow more elaborate queries on available biobank samples and data. Therefore the MIABIS terminology has now been extended with components describing samples and sample donors at an individual level.Materials and Methods: The components were defined according to specific scope and use cases by a large group of experts, and through several cycles of reviews, according to the new MIABIS governance model of BBMRI-ERIC (Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium). The guiding principles applied in developing these components included the following terms: model should consider only samples of human origin, model should be applicable to all types of samples and all sample donors, and model should describe the current status of samples stored in a given biobank.Results: A minimal set of standard attributes for defining samples and sample donors is presented here. We added an "event" component to describe attributes that are not directly describing samples or sample donors but are tightly related to them. To better utilize the generic data model, we suggest a procedure by which interoperability can be promoted, using specific MIABIS profiles.Discussion: The MIABIS sample and donor component extensions and the new generic data model complement the existing MIABIS Core 2.0 components, and substantially increase the potential usability of this terminology for better describing biobank samples and sample donors. They also support the use of individual level data about samples and sample donors to obtain accurate and detailed biobank availability queries.

Due to the severe impact of COVID-19 on public health, rollout of the vaccines must be large-scale. Current solutions are not intended to promote an active collaboration between communities and public health researchers. We aimed to develop a digital platform for communication between scientists and the general population, and to use it for an exploratory study on factors associated with vaccination readiness. The digital platform was developed in Latvia and was equipped with dynamic consent management. During a period of six weeks 467 participants were enrolled in the population-based cross-sectional exploratory study using this platform. We assessed demographics, COVID-19-related behavioral and personal factors, and reasons for vaccination. Logistic regression models adjusted for the level of education, anxiety, factors affecting the motivation to vaccinate, and risk of infection/severe disease were built to investigate their association with vaccination readiness. In the fully adjusted multiple logistic regression model, factors associated with vaccination readiness were anxiety (odds ratio, OR = 3.09 [95% confidence interval 1.88; 5.09]), feelings of social responsibility (OR = 1.61 [1.16; 2.22]), and trust in pharmaceutical companies (OR = 1.53 [1.03; 2.27]). The assessment of a large number of participants in a six-week period show the potential of a digital platform to create a data-driven dialogue on vaccination readiness.

Aims: To investigate the association between depressive symptomatology and health markers in type 1 diabetes. Methods: Four countries from the InterDiane Consortium had adopted the Finnish Diabetic Nephropathy Study protocol, including the Beck Depression Inventory (BDI). Associations between depression symptomatology, diabetes complications (diabetic nephropathy, proliferative retinopathy, major adverse cardiovascular events [MACE]) and vascular risk factors (metabolic syndrome, body mass index, glycaemic control) were investigated. Results: In a sample of 1046 participants (Croatia n = 99; Finland n = 314; Latvia n = 315; Lithuania n = 318), 13.4% displayed symptoms of depression (BDI score 16) with no statistically significant difference in the prevalence of depression among the cohorts. The highest rates of diabetic nephropathy (37.1%) and proliferative retinopathy (36.3%) were observed in Lithuania. The rates of MACE and metabolic syndrome were highest in Finland. In joint analyses, individuals exhibiting depression symptomatology had higher HbA(1c) (79 vs. 72 mmol/mol, p

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans’ known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T1/2 and muscle, kidney, and red blood cells that have longer T1/2. The highest maximal concentrations (Cmax) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Melanocortin 4 receptor (MC4R) is an important regulator of food intake and number of studies report genetic variations influencing the risk of obesity. Here we explored the role of common genetic variation from MC4R locus comparing with SNPs from gene FTO locus, as well as the frequency and functionality of rare MC4R mutations in cohort of 380 severely obese individuals (BMI39 kg/m(2)) and 380 lean subjects from the Genome Database of Latvian Population (LGDB). We found correlation for two SNPs--rs11642015 and rs62048402 in the fat mass and obesity-associated protein (FTO) with obesity but no association was detected for rs17782313 located in the MC4R locus in these severely obese individuals. We sequenced the whole gene MC4R coding region in all study subjects and found five previously known heterozygous non-synonymous substitutions V103I, I121T, S127L, V166I and I251L. Expression in mammalian cells showed that the S127L, V166I and double V103I/S127L mutant receptors had significantly decreased quantity at the cell surface compared to the wild type MC4R. We carried out detailed functional analysis of V166I that demonstrated that, despite low abundance in plasma membrane, the V166I variant has lower EC50 value upon αMSH activation than the wild type receptor, while the level of AGRP inhibition was decreased, implying that V166I cause hyperactive satiety signalling. Overall, this study suggest that S127L may be the most frequent functional MC4R mutation leading to the severe obesity in general population and provides new insight into the functionality of population based variants of the MC4R.

Variations in the FTO gene and near the TMEM18 gene are risk factors for common form of obesity, but have also been linked with type 2 diabetes (T2D). Our aim was to investigate the contribution of these variants to risk of T2D in a population in Latvia. Four single nucleotide polymorphisms (SNP) in the first and fourth intronic regions of FTO and one close to TMEM18 were genotyped in 987 patients with T2D and 1080 controls selected from the Latvian Genome Data Base (LGDB). We confirmed association of SNPs in the first intron (rs11642015, rs62048402 and rs9939609) of FTO and rs7561317 representing the TMEM18 locus with T2D. Association between SNP in FTO and T2D remained significant after correction for body mass index (BMI). The rs57103849 located in the fourth intron of FTO and rs7561317 in TMEM18 showed BMI independent association with younger age at diagnosis of T2D. Our results add to the evidence that BMI related variants in and near FTO and TMEM18 may increase the risk for T2D not only through secondary effects of obesity. The influence of variants in the fourth intron of the FTO gene on development of T2D may be mediated by mechanisms other than those manifested by SNPs in the first intron of the same gene.

Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear(1). Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (P = 6.6 x 10(-14)) greater metformin-induced reduction in hemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 was the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550 mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.

Objective(s) High variability in clinical response to metformin is often observed in type 2 diabetes (T2D) patients, and it highlights the need for identification of genetic components affecting the efficiency of metformin therapy. Aim of this observational study is to evaluate the role of tagSNPs (tagging single nucleotide polymorphisms) from genomic regions coding for six metformin transporter genes with respect to the short-term efficiency. Design 102 tagSNPs in 6 genes coding for metformin transporters were genotyped in the group of 102 T2D patients treated with metformin for 3 months. Methods Most significant hits were analyzed in the group of 131 T2D patients from Slovakia. Pharmacokinetic study in 25 healthy nondiabetic volunteers was conducted to investigate the effects of identified polymorphisms. Results In the discovery group of 102 patients, minor alleles of rs3119309, rs7757336 and rs2481030 were significantly nominally associated with metformin inefficiency (P = 1.9 × 10−6 to 8.1 × 10−6). Effects of rs2481030 and rs7757336 did not replicate in the group of 131 T2DM patients from Slovakia alone, whereas rs7757336 was significantly associated with a reduced metformin response in combined group. In pharmacokinetic study, group of individuals harboring risk alleles of rs7757336 and rs2481030 displayed significantly reduced AUC∞ of metformin in plasma. Conclusions For the first time, we have identified an association between the lack of metformin response and SNPs rs3119309 and rs7757336 located in the 5′ flanking region of the genes coding for Organic cation transporter 2 and rs2481030 located in the 5′ flanking region of Organic cation transporter 3 that was supported by the results of a pharmacokinetic study on 25 healthy volunteers.

Background: The Genome Database of the Latvian Population (LGDB) is a national biobank that collects, maintains, and processes health information, data, and biospecimens collected from representatives of the Latvian population. These specimens serve as a foundation for epidemiological research and prophylactic and therapeutic purposes. Methods: Participant recruitment and biomaterial and data processing were performed according to specifically designed standard protocols, taking into consideration international quality requirements. Legal and ethical aspects, including broad informed consent and personal data protection, were applied according to legal norms of the Republic of Latvia. Results: Since its start in 2006, the LGDB is comprised of biosamples and associated phenotypic and clinical information from over 31,504 participants, constituting approximately 1.5% of the Latvian population. The LGDB represents a mixed-design biobank and includes participants from the general population as well as disease-based cohorts. The standard set of biosamples stored in the LGDB consists of DNA, plasma, serum, and white blood cells; in some cohorts, these samples are complemented by cancer biopsies and microbiome and urine samples. The LGDB acts as a core structure for the Latvian Biomedical Research and Study Centre (BMC), representing the national node of Latvia in Biobanking and BioMolecular resources Research Infrastructure – European Research Infrastructure Consortium (BBMRI-ERIC). Conclusions: The development of the LGDB has enabled resources for biomedical research and promoted genetic testing in Latvia. Further challenges of the LGDB are the enrichment and harmonization of collected biosamples and data, the follow-up of selected participant groups, and continued networking and participation in collaboration projects.

Background Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally. Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02–3.64) and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87–3.34), rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71–3.01), rs6032 (F5, OR = 2.12; 95% CI = 1.63–2.77), rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69–3.01) and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66–2.87) and ophthalmic complications. By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy. Conclusions Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions. Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.

Background Metformin is a widely prescribed antihyperglycemic agent that has been also associated with multiple therapeutic effects in various diseases, including several types of malignancies. There is growing evidence regarding the contribution of the epigenetic mechanisms in reaching metformin’s therapeutic goals; however, the effect of metformin on human cells in vivo is not comprehensively studied. The aim of our study was to examine metformin-induced alterations of DNA methylation profiles in white blood cells of healthy volunteers, employing a longitudinal study design. Results Twelve healthy metformin-naïve individuals where enrolled in the study. Genome-wide DNA methylation pattern was estimated at baseline, 10 h and 7 days after the start of metformin administration. The whole-genome DNA methylation analysis in total revealed 125 differentially methylated CpGs, of which 11 CpGs and their associated genes with the most consistent changes in the DNA methylation profile were selected: POFUT2, CAMKK1, EML3, KIAA1614, UPF1, MUC4, LOC727982, SIX3, ADAM8, SNORD12B, VPS8, and several differentially methylated regions as novel potential epigenetic targets of metformin. The main functions of the majority of top-ranked differentially methylated loci and their representative cell signaling pathways were linked to the well-known metformin therapy targets: regulatory processes of energy homeostasis, inflammatory responses, tumorigenesis, and neurodegenerative diseases. Conclusions Here we demonstrate for the first time the immediate effect of short-term metformin administration at therapeutic doses on epigenetic regulation in human white blood cells. These findings suggest the DNA methylation process as one of the mechanisms involved in the action of metformin, thereby revealing novel targets and directions of the molecular mechanisms underlying the various beneficial effects of metformin. Trial registration EU Clinical Trials Register, 2016-001092-74. Registered 23 March 2017, https://www.clinicaltrialsregister.eu/ctr-search/trial/2016-001092-74/LV. Electronic supplementary material The online version of this article (10.1186/s13148-018-0593-x) contains supplementary material, which is available to authorized users.

BackgroundThe study was conducted to investigate the effects of metformin treatment on the human gut microbiome's taxonomic and functional profile in the Latvian population, and to evaluate the correlation of these changes with therapeutic efficacy and tolerance.MethodsIn this longitudinal observational study, stool samples for shotgun metagenomic sequencing-based analysis were collected in two cohorts. The first cohort included 35 healthy nondiabetic individuals (metformin dose 2x850mg/day) at three time-points during metformin administration. The second cohort was composed of 50 newly-diagnosed type 2 diabetes patients (metformin dose-determined by an endocrinologist) at two concordant times. Patients were defined as Responders if their HbA1c levels during three months of metformin therapy had decreased by ≥12.6 mmol/mol (1%), while in Non-responders HbA1c were decreased by ResultsMetformin reduced the alpha diversity of microbiota in healthy controls (p = 0.02) but not in T2D patients. At the species level, reduction in the abundance of Clostridium bartlettii and Barnesiella intestinihominis, as well as an increase in the abundance of Parabacteroides distasonis and Oscillibacter unclassified overlapped between both study groups. A large number of group-specific changes in taxonomic and functional profiles was observed. We identified an increased abundance of Prevotella copri (FDR = 0.01) in the Non-Responders subgroup, and enrichment of Enterococcus faecium, Lactococcus lactis, Odoribacter, and Dialister at baseline in the Responders group. Various taxonomic units were associated with the observed incidence of side effects in both cohorts.ConclusionsMetformin effects are different in T2D patients and healthy individuals. Therapy induced changes in the composition of gut microbiome revealed possible mediators of observed short-term therapeutic effects. The baseline composition of the gut microbiome may influence metformin therapy efficacy and tolerance in T2D patients and could be used as a powerful prediction tool.