Florian Kronenberg

Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts

Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797–22,562 persons, aged 18–104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 × 10−8), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 × 10−11; LDL, P = 2.6 × 10−10), TMEM57 (TC, P = 5.4 × 10−10), CTCF-PRMT8 region (HDL, P = 8.3 × 10−16), DNAH11 (LDL, P = 6.1 × 10−9), FADS3-FADS2 (TC, P = 1.5 × 10−10; LDL, P = 4.4 × 10−13) and MADD-FOLH1 region (HDL, P = 6 × 10−11). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors.

Human metabolic individuality in biomedical and pharmaceutical research

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10–60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn’s disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Fibroblast Growth Factor 23 (FGF23) Predicts Progression of Chronic Kidney Disease: The Mild to Moderate Kidney Disease (MMKD) Study

It has not been firmly established whether disturbed calcium-phosphate metabolism affects progression of chronic kidney disease (CKD) in humans. In this cohort study of 227 nondiabetic patients with CKD, we assessed fibroblast growth factor 23 (FGF23) plasma concentrations in addition to other variables involved in calcium-phosphate metabolism, and we followed 177 of the patients prospectively for a median of 53 months to assess progression of renal disease. In the baseline cohort, we found a significant inverse correlation between glomerular filtration rate and both c-terminal and intact FGF23 levels (both P < 0.001). The 65 patients who experienced a doubling of serum creatinine and/or terminal renal failure were significantly older, had a significantly lower glomerular filtration rate at baseline, and significantly higher levels of intact parathormone, c-terminal and intact FGF23, and serum phosphate (all P < 0.001). Cox regression analysis revealed that both c-terminal and intact FGF23 independently predict progression of CKD after adjustment for age, gender, GFR, proteinuria, and serum levels of calcium, phosphate, and parathyroid hormone. The mean follow-up time to a progression end point was 46.9 (95% CI 40.2 to 53.6) months versus 72.5 (95% CI 67.7 to 77.3) months for patients with c-terminal FGF23 levels above or below the optimal cut-off level of 104 rU/mL (derived by receiver operator curve analysis), respectively. In conclusion, FGF23 is a novel independent predictor of progression of renal disease in patients with nondiabetic CKD. Its pathophysiological significance remains to be elucidated.

Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10−16 to 10−21). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.

A genome-wide perspective of genetic variation in human metabolism

Serum metabolite concentrations provide a direct readout of biological processes in the human body, and they are associated with disorders such as cardiovascular and metabolic diseases. We present a genome-wide association study (GWAS) of 163 metabolic traits measured in human blood from 1,809 participants from the KORA population, with replication in 422 participants of the TwinsUK cohort. For eight out of nine replicated loci (FADS1, ELOVL2, ACADS, ACADM, ACADL, SPTLC3, ETFDH and SLC16A9), the genetic variant is located in or near genes encoding enzymes or solute carriers whose functions match the associating metabolic traits. In our study, the use of metabolite concentration ratios as proxies for enzymatic reaction rates reduced the variance and yielded robust statistical associations with P values ranging from 3 × 10−24 to 6.5 × 10−179. These loci explained 5.6%–36.3% of the observed variance in metabolite concentrations. For several loci, associations with clinically relevant parameters have been reported previously.

Predictive Performance of Renal Function Equations for Patients with Chronic Kidney Disease and Normal Serum Creatinine Levels

Accurate renal function measurements are important for the diagnosis and treatment of kidney disease, proper medication dosing, interpretation of possible uremic symptoms, and decision-making regarding when to initiate renal replacement therapy. Because the use of highly accurate filtration markers to measure renal function has traditionally been limited by cumbersome and costly techniques and the involvement of radioactivity (among other factors), renal function is typically estimated by using specially derived prediction equations. These formulae usually use serum creatinine levels, i.e., a marker of filtration that is insensitive to mild/moderate decreases in GFR. Although attempts have been made to validate certain renal function prediction equations among patients with chronic kidney disease (CKD) with abnormal serum creatinine levels, this is the first study to specifically evaluate the predictive performance of these equations for patients with CKD and serum creatinine levels in the normal range. The results of eight prediction equations for 109 patients with CKD and serum creatinine levels of < or =1.5 mg/dl were compared with standard iohexol GFR values. The most accurate results were obtained with the Cockroft-Gault and Bjornsson equations. The most precise formulae were the Modification of Diet in Renal Disease Study equations, although they were highly biased. Even the most accurate results exhibited levels of error that made them suboptimal for clinical treatment of these patients. These results suggest that measurement of GFR with endogenous or exogenous filtration markers might be the most prudent strategy for the assessment of renal function in the CKD population with normal serum creatinine levels. Further studies are needed to confirm the generalizability of these findings for this patient subgroup.

Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts

We studied here the independent associations of estimated glomerular filtration rate (eGFR) and albuminuria with mortality and end-stage renal disease (ESRD) in individuals with chronic kidney disease (CKD). We performed a collaborative meta-analysis of 13 studies totaling 21,688 patients selected for CKD of diverse etiology. After adjustment for potential confounders and albuminuria, we found that a 15 ml/min per 1.73 m² lower eGFR below a threshold of 45 ml/min per 1.73 m² was significantly associated with mortality and ESRD (pooled hazard ratios (HRs) of 1.47 and 6.24, respectively). There was significant heterogeneity between studies for both HR estimates. After adjustment for risk factors and eGFR, an eightfold higher albumin- or protein-to-creatinine ratio was significantly associated with mortality (pooled HR 1.40) without evidence of significant heterogeneity and with ESRD (pooled HR 3.04), with significant heterogeneity between HR estimates. Lower eGFR and more severe albuminuria independently predict mortality and ESRD among individuals selected for CKD, with the associations stronger for ESRD than for mortality. Thus, these relationships are consistent with CKD stage classifications based on eGFR and suggest that albuminuria provides additional prognostic information among individuals with CKD.

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting.

Background Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study. Methodology/Principal Findings 40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid). Conclusions/Significance Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.

Telomere Length and Risk of Incident Cancer and Cancer Mortality

CONTEXT Telomeres are essential to preserve the integrity of the genome. Critically short telomeres lead to replicative cell senescence and chromosomal instability and may thereby increase cancer risk. OBJECTIVE To determine the association between baseline telomere length and incident cancer and cancer mortality. DESIGN, SETTING, AND PARTICIPANTS Leukocyte telomere length was measured by quantitative polymerase chain reaction in 787 participants free of cancer at baseline in 1995 from the prospective, population-based Bruneck Study in Italy. MAIN OUTCOME MEASURES Incident cancer and cancer mortality over a follow-up period of 10 years (1995-2005 with a follow-up rate of 100%). RESULTS A total of 92 of 787 participants (11.7%) developed cancer (incidence rate, 13.3 per 1000 person-years). Short telomere length at baseline was associated with incident cancer independently of standard cancer risk factors (multivariable hazard ratio [HR] per 1-SD decrease in log(e)-transformed telomere length, 1.60; 95% confidence interval [CI], 1.30-1.98; P < .001). Compared with participants in the longest telomere length group, the multivariable HR for incident cancer was 2.15 (95% CI, 1.12-4.14) in the middle length group and 3.11 (95% CI, 1.65-5.84) in the shortest length group (P < .001). Incidence rates were 5.1 (95% CI, 2.9-8.7) per 1000 person-years in the longest telomere length group, 14.2 (95% CI, 10.0-20.1) per 1000 person-years in the middle length group, and 22.5 (95% CI, 16.9-29.9) per 1000 person-years in the shortest length group. The association equally applied to men and women and emerged as robust under a variety of circumstances. Furthermore, short telomere length was associated with cancer mortality (multivariable HR per 1-SD decrease in log(e)-transformed telomere length, 2.13; 95% CI, 1.58-2.86; P < .001) and individual cancer subtypes with a high fatality rate. CONCLUSION In this study population, there was a statistically significant inverse relationship between telomere length and both cancer incidence and mortality.

SLC2A9 influences uric acid concentrations with pronounced sex-specific effects

Serum uric acid concentrations are correlated with gout and clinical entities such as cardiovascular disease and diabetes. In the genome-wide association study KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K (n = 1,644), the most significant SNPs associated with uric acid concentrations mapped within introns 4 and 6 of SLC2A9, a gene encoding a putative hexose transporter (effects: −0.23 to −0.36 mg/dl per copy of the minor allele). We replicated these findings in three independent samples from Germany (KORA S4 and SHIP (Study of Health in Pomerania)) and Austria (SAPHIR; Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk), with P values ranging from 1.2 × 10−8 to 1.0 × 10−32. Analysis of whole blood RNA expression profiles from a KORA F3 500K subgroup (n = 117) showed a significant association between the SLC2A9 isoform 2 and urate concentrations. The SLC2A9 genotypes also showed significant association with self-reported gout. The proportion of the variance of serum uric acid concentrations explained by genotypes was about 1.2% in men and 6% in women, and the percentage accounted for by expression levels was 3.5% in men and 15% in women.