Publications by authors named "Robert Koivula"

Article Synopsis
  • Researchers are studying type 2 diabetes, which happens when there is too much sugar in the blood, to see how certain substances in the body, called metabolites, are connected to it.
  • They looked at 3,000 blood samples and analyzed 911 metabolites to find out how these substances relate to blood sugar levels.
  • They discovered several metabolites that are different in people with normal blood sugar, those with prediabetes, and those with type 2 diabetes, mainly focusing on specific amino acids and fats.
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Context: The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood.

Objective: We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D.

Methods: We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D.

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Article Synopsis
  • Precision medicine can enhance the prediction of cardiovascular disease (CVD) risk in individuals with Type 2 diabetes (T2D), based on a systematic review of various studies.
  • Out of 9380 studies, 416 met criteria, focusing on biomarkers, genetic markers, and risk score/models to find new prognostic factors.
  • Only 13 biomarkers improved prediction, with NT-proBNP showing the strongest evidence, while other markers like troponin-T and triglyceride-glucose also showed moderate promise, highlighting a need for further research in this area.
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Article Synopsis
  • Precision medicine is an evolving approach in healthcare that aims to enhance decision-making and health outcomes, particularly in managing diabetes, which poses serious health risks for millions globally.
  • The second international consensus report on precision diabetes medicine reviews current findings on prevention, diagnosis, treatment, and prognosis across different forms of diabetes, highlighting the potential for translating research into clinical practice.
  • The report also identifies knowledge gaps and sets out key milestones for better clinical implementation, emphasizing the need for standards addressing cost-effectiveness, health equity, and accessibility in treatment options.
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Article Synopsis
  • The study examines genetic regulation of mRNA, proteins, and metabolites in blood samples from over 3,000 people, revealing that many genetic variants influence multiple molecular traits.* -
  • It finds that there's a strong genetic connection between gene expression and protein levels (66.6%), and shows broad connections across various tissues, highlighting the shared genetic basis for different traits.* -
  • By creating networks of known genetic variants, the research indicates that these variants are more frequently linked to gene expression rather than other molecular traits, helping to clarify the mechanisms behind genetic associations.*
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Article Synopsis
  • The study reviews the potential of precision medicine to enhance cardiovascular disease risk prediction in people with type 2 diabetes by analyzing long-term research studies.
  • Out of 9380 studies, only 416 met the criteria for inclusion, with a focus on biomarkers, genetic markers, and risk models; 13 showed significant improvement in prediction.
  • The most effective predictors identified were NT-proBNP, troponin-T, TyG index, and Genetic Risk Score for Coronary Heart Disease, although the overall findings indicate a need for more rigorous research to establish practical clinical applications.
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Aims/hypothesis: The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes.

Methods: Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA, C-peptide and HDL-cholesterol.

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The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium.

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Background: Type III hyperlipidaemia (T3HL) is characterised by equimolar increases in plasma triglycerides (TG) and cholesterol in <10% of APOE22 carriers conveying high cardiovascular disease (CVD) risk. We investigate the role of a weighted triglyceride-raising polygenic score (TG.PS) precipitating T3HL.

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Triglyceride (TG)-lowering LPL variants in combination with genetic LDL-C-lowering variants are associated with reduced risk of coronary artery disease (CAD). Genetic variation in the APOA5 gene encoding apolipoprotein A-V also strongly affects TG levels, but the potential clinical impact and underlying mechanisms are yet to be resolved. Here, we aimed to study the effects of APOA5 genetic variation on CAD risk and plasma lipoproteins through factorial genetic association analyses.

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The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables.

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Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants ( = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA was also measured.

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Background: Meal-induced metabolic changes trigger an acute inflammatory response, contributing to chronic inflammation and associated diseases.

Objectives: We aimed to characterize variability in postprandial inflammatory responses using traditional (IL-6) and novel [glycoprotein acetylation (GlycA)] biomarkers of inflammation and dissect their biological determinants with a focus on postprandial glycemia and lipemia.

Methods: Postprandial (0-6 h) glucose, triglyceride (TG), IL-6, and GlycA responses were measured at multiple intervals after sequential mixed-nutrient meals (0 h and 4 h) in 1002 healthy adults aged 18-65 y from the PREDICT (Personalised REsponses to DIetary Composition Trial) 1 study, a single-arm dietary intervention study.

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Background And Aims: Mendelian randomization studies have shown that triglyceride (TG)- lowering lipoprotein lipase (LPL) alleles and low-density lipoprotein-cholesterol (LDL-C)-lowering alleles have independent beneficial associations on cardiovascular disease (CVD) risk. We aimed to provide further insight into this observation by applying Mendelian randomization analyses of genetically-influenced TG and LDL-C levels on plasma metabolomic profiles.

Methods: We quantified over 100 lipoprotein metabolomic measures in the Netherlands Epidemiology of Obesity (NEO) study (N = 4838) and Oxford Biobank (OBB) (N = 6999) by nuclear magnetic resonance (NMR) spectroscopy.

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Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).

Research Design And Methods: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.

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Article Synopsis
  • The study examines transcriptomic signatures related to type 2 diabetes (T2D) in blood to better understand metabolic dysfunction and the inflammatory role in insulin resistance.
  • Researchers analyzed gene co-expression in blood samples from individuals with and without T2D, identifying 55 co-expression modules that show links to insulin action and glucose tolerance.
  • The findings suggest significant associations between certain gene modules, especially those related to immune cells, and clinical traits of T2D, aiming to provide a comprehensive insight into blood's molecular regulation and its relevance to diabetes.
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Aim: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.

Methods: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study.

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Context: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity.

Objective: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies.

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Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.

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Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.

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Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle.

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Aims/hypothesis: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up).

Methods: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes).

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Objective: Gastrointestinal adverse effects occur in 20-30% of patients with metformin-treated type 2 diabetes, leading to premature discontinuation in 5-10% of the cases. Gastrointestinal intolerance may reflect localized high concentrations of metformin in the gut. We hypothesized that reduced transport of metformin via the plasma membrane monoamine transporter (PMAT) and organic cation transporter 1 (OCT1) could increase the risk of severe gastrointestinal adverse effects.

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