Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field

Development and Validation of a Prediction Model for Future Estimated Glomerular Filtration Rate in People With Type 2 Diabetes and Chronic Kidney Disease

Importance: Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking. Objective: To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts. Design, Setting, and Participants: This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of ≥30 mL/min/1.73 m2) were included. Data were analyzed between June 30, 2021, and January 31, 2023. Main Outcomes and Measures: Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A1c[mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated. Results: Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R2ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5. Conclusions and Relevance: In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting kidney function decline up to 5 years after baseline. The results and prediction model are publicly available in an accompanying web-based application, which may open the way for improved prediction of individual eGFR trajectories and disease progression.</p

Genetics of serum urate concentrations and gout in a high risk population, patients with chronic kidney disease

We evaluated genetics of hyperuricemia and gout, their interaction with kidney function and medication intake in chronic kidney disease (CKD) patients. Genome-wide association studies (GWAS) of urate and gout were performed in 4941 CKD patients in the German Chronic Kidney Disease (GCKD) study. Effect estimates of 26 known urate-associated population-based single nucleotide polymorphisms (SNPs) were examined. Interactions of urate-associated variants with urate-altering medications and clinical characteristics of gout were evaluated. Genome-wide significant associations with serum urate and gout were identified for known loci at SLC2A9 and ABCG2, but not for novel loci. Effects of the 26 known SNPs were of similar magnitude in CKD patients compared to population based individuals, except for SNPs at ABCG2 that showed greater effects in CKD. Gene-medication interactions were not significant when accounting for multiple testing. Associations with gout in specific joints were significant for SLC2A9 rs12498742 in wrists and midfoot joints. Known genetic variants in SLC2A9 and ABCG2 were associated with urate and gout in a CKD cohort, with effect sizes for ABCG2 significantly greater in CKD compared to the general population. CKD patients are at high risk of gout due to reduced kidney function, diuretics intake and genetic predisposition, making treatment to target challenging

Genetics of osteopontin in patients with chronic kidney disease: The German chronic kidney disease study

Osteopontin (OPN), encoded by SPP1, is a phosphorylated glycoprotein predominantly synthesized in kidney tissue. Increased OPN mRNA and protein expression correlates with proteinuria, reduced creatinine clearance, and kidney fibrosis in animal models of kidney disease. But its genetic underpinnings are incompletely understood. We therefore conducted a genome-wide association study (GWAS) of OPN in a European chronic kidney disease (CKD) population. Using data from participants of the German Chronic Kidney Disease (GCKD) study (N = 4,897), a GWAS (minor allele frequency [MAF]>= 1%) and aggregated variant testing (AVT, MAFAuthor summaryOsteopontin (OPN) is involved in many (patho)physiological processes of the human body. Among others, it is known to be associated with adverse kidney outcomes. Since its genetic underpinnings are incompletely understood, we conducted a genome-wide association study of OPN in a European chronic kidney disease (CKD) population (N = 4,897). Of the three detected signals, two could be replicated within a population-based study of Finns. One locus is located upstream of SPP1 which encodes the OPN protein and is related to OPN production. This gene was also disclosed by an analysis of rare variants, all presumably effecting the gene product. Another locus maps into KLKB1 encoding prekallikrein (PK) that after processing to kallikrein (KAL) is implicated in blood pressure control and inflammation among others. Overall, our results highlight the multi-functional role of OPN and its possible pathological role in CKD. Further studies are needed to elucidate the complex role of OPN in humans.</p

Glycaemic control and antidiabetic therapy in patients with diabetes mellitus and chronic kidney disease - cross-sectional data from the German Chronic Kidney Disease (GCKD) cohort

Background: Diabetes mellitus (DM) is the leading cause of end-stage renal disease. Little is known about practice patterns of anti-diabetic therapy in the presence of chronic kidney disease (CKD) and correlates with glycaemic control. We therefore aimed to analyze current antidiabetic treatment and correlates of metabolic control in a large contemporary prospective cohort of patients with diabetes and CKD. Methods: The German Chronic Kidney Disease (GCKD) study enrolled 5217 patients aged 18-74 years with an estimated glomerular filtration rate (eGFR) between 30-60 mL/min/1.73 m(2) or proteinuria >0.5 g/d. The use of diet prescription, oral anti-diabetic medication, and insulin was assessed at baseline. HbA1c, measured centrally, was the main outcome measure. Results: At baseline, DM was present in 1842 patients (35 %) and the median HbA1C was 7.0 % (25th-75th percentile: 6.8-7.9 %), equalling 53 mmol/mol (51, 63);24.2 % of patients received dietary treatment only, 25.5 % oral antidiabetic drugs but not insulin, 8.4 % oral antidiabetic drugs with insulin, and 41.8 % insulin alone. Metformin was used by 18.8 %. Factors associated with an HbA1C level >7.0 % (53 mmol/mol) were higher BMI (OR = 1.04 per increase of 1 kg/m(2), 95 % CI 1.02-1.06), hemoglobin (OR = 1.11 per increase of 1 g/dL, 95 % CI 1.04-1.18), treatment with insulin alone (OR = 5.63, 95 % CI 4.26-7.45) or in combination with oral antidiabetic agents (OR = 4.23, 95 % CI 2.77-6.46) but not monotherapy with metformin, DPP-4 inhibitors, or glinides. Conclusions: Within the GCKD cohort of patients with CKD stage 3 or overt proteinuria, antidiabetic treatment patterns were highly variable with a remarkably high proportion of more than 50 % receiving insulin-based therapies. Metabolic control was overall satisfactory, but insulin use was associated with higher HbA1C levels

Land Inequality and Rice Security in Northern Laos

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Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine.

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments

GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer

Correction: Volume12, Issue1 Article Number7354 DOI10.1038/s41467-021-27675-w PublishedDEC 16 2021Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. To better understand the genetic contribution to TSH levels, we conduct a GWAS meta-analysis at 22.4 million genetic markers in up to 119,715 individuals and identify 74 genome-wide significant loci for TSH, of which 28 are previously unreported. Functional experiments show that the thyroglobulin protein-altering variants P118L and G67S impact thyroglobulin secretion. Phenome-wide association analysis in the UK Biobank demonstrates the pleiotropic effects of TSH-associated variants and a polygenic score for higher TSH levels is associated with a reduced risk of thyroid cancer in the UK Biobank and three other independent studies. Two-sample Mendelian randomization using TSH index variants as instrumental variables suggests a protective effect of higher TSH levels (indicating lower thyroid function) on risk of thyroid cancer and goiter. Our findings highlight the pleiotropic effects of TSH-associated variants on thyroid function and growth of malignant and benign thyroid tumors. Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. Here, the authors conduct a GWAS and suggest protective effect of higher TSH on risk of thyroid cancer and goitre.Peer reviewe

Author Correction:GWAS of thyroid stimulating hormone highlights the pleiotropic effects and inverse association with thyroid cancer

The original version of this article contained an error in the results, in the second paragraph of the subsection entitled “Fine-mapping for potentially causal variants among TSH loci”, in which effect sizes for two variants were incorrectly reported

Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases