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Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes.
Bradfield, JP, Kember, RL, Ulrich, A, Balkiyarova, Z, Alyass, A, Aris, IM, Bell, JA, Broadaway, KA, Chen, Z, Chai, JF, et al
Genome biology. 2024;(1):22
Abstract
BACKGROUND Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. RESULTS Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation. CONCLUSION We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single "optimal" pubertal growth pattern.
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Effect of Calorie-Unrestricted Low-Carbohydrate, High-Fat Diet Versus High-Carbohydrate, Low-Fat Diet on Type 2 Diabetes and Nonalcoholic Fatty Liver Disease : A Randomized Controlled Trial.
Hansen, CD, Gram-Kampmann, EM, Hansen, JK, Hugger, MB, Madsen, BS, Jensen, JM, Olesen, S, Torp, N, Rasmussen, DN, Kjærgaard, M, et al
Annals of internal medicine. 2023;(1):10-21
Abstract
BACKGROUND It remains unclear if a low-carbohydrate, high-fat (LCHF) diet is a possible treatment strategy for type 2 diabetes mellitus (T2DM), and the effect on nonalcoholic fatty liver disease (NAFLD) has not been investigated. OBJECTIVE To investigate the effect of a calorie-unrestricted LCHF diet, with no intention of weight loss, on T2DM and NAFLD compared with a high-carbohydrate, low-fat (HCLF) diet. DESIGN 6-month randomized controlled trial with a 3-month follow-up. (ClinicalTrials.gov: NCT03068078). SETTING Odense University Hospital in Denmark from November 2016 until June 2020. PARTICIPANTS 165 participants with T2DM. INTERVENTION Two calorie-unrestricted diets: LCHF diet with 50 to 60 energy percent (E%) fat, less than 20E% carbohydrates, and 25E% to 30E% proteins and HCLF diet with 50E% to 60E% carbohydrates, 20E% to 30E% fats, and 20E% to 25E% proteins. MEASUREMENTS Glycemic control, serum lipid levels, metabolic markers, and liver biopsies to assess NAFLD. RESULTS The mean age was 56 years (SD, 10), and 58% were women. Compared with the HCLF diet, participants on the LCHF diet had greater improvements in hemoglobin A1c (mean difference in change, -6.1 mmol/mol [95% CI, -9.2 to -3.0 mmol/mol] or -0.59% [CI, -0.87% to -0.30%]) and lost more weight (mean difference in change, -3.8 kg [CI, -6.2 to -1.4 kg]). Both groups had higher high-density lipoprotein cholesterol and lower triglycerides at 6 months. Changes in low-density lipoprotein cholesterol were less favorable in the LCHF diet group than in the HCLF diet group (mean difference in change, 0.37 mmol/L [CI, 0.17 to 0.58 mmol/L] or 14.3 mg/dL [CI, 6.6 to 22.4 mg/dL]). No statistically significant between-group changes were detected in the assessment of NAFLD. Changes were not sustained at the 9-month follow-up. LIMITATION Open-label trial, self-reported adherence, unintended weight loss, and lack of adjustment for multiple comparisons. CONCLUSION Persons with T2DM on a 6-month, calorie-unrestricted, LCHF diet had greater clinically meaningful improvements in glycemic control and weight compared with those on an HCLF diet, but the changes were not sustained 3 months after intervention. PRIMARY FUNDING SOURCE Novo Nordisk Foundation.
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European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation.
Budu-Aggrey, A, Kilanowski, A, Sobczyk, MK, , , Shringarpure, SS, Mitchell, R, Reis, K, Reigo, A, , , Mägi, R, et al
Nature communications. 2023;(1):6172
Abstract
Atopic dermatitis (AD) is a common inflammatory skin condition and prior genome-wide association studies (GWAS) have identified 71 associated loci. In the current study we conducted the largest AD GWAS to date (discovery N = 1,086,394, replication N = 3,604,027), combining previously reported cohorts with additional available data. We identified 81 loci (29 novel) in the European-only analysis (which all replicated in a separate European analysis) and 10 additional loci in the multi-ancestry analysis (3 novel). Eight variants from the multi-ancestry analysis replicated in at least one of the populations tested (European, Latino or African), while two may be specific to individuals of Japanese ancestry. AD loci showed enrichment for DNAse I hypersensitivity and eQTL associations in blood. At each locus we prioritised candidate genes by integrating multi-omic data. The implicated genes are predominantly in immune pathways of relevance to atopic inflammation and some offer drug repurposing opportunities.
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Author Correction: Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.
Allesøe, RL, Lundgaard, AT, Hernández Medina, R, Aguayo-Orozco, A, Johansen, J, Nissen, JN, Brorsson, C, Mazzoni, G, Niu, L, Biel, JH, et al
Nature biotechnology. 2023;(7):1026
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Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine.
Tobias, DK, Merino, J, Ahmad, A, Aiken, C, Benham, JL, Bodhini, D, Clark, AL, Colclough, K, Corcoy, R, Cromer, SJ, et al
Nature medicine. 2023;(10):2438-2457
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Abstract
Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
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Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease.
Young, WJ, Haessler, J, Benjamins, JW, Repetto, L, Yao, J, Isaacs, A, Harper, AR, Ramirez, J, Garnier, S, van Duijvenboden, S, et al
Nature communications. 2023;(1):1411
Abstract
The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.
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Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits.
Brown, AA, Fernandez-Tajes, JJ, Hong, MG, Brorsson, CA, Koivula, RW, Davtian, D, Dupuis, T, Sartori, A, Michalettou, TD, Forgie, IM, et al
Nature communications. 2023;(1):5062
Abstract
We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.
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Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.
Allesøe, RL, Lundgaard, AT, Hernández Medina, R, Aguayo-Orozco, A, Johansen, J, Nissen, JN, Brorsson, C, Mazzoni, G, Niu, L, Biel, JH, et al
Nature biotechnology. 2023;(3):399-408
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Abstract
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. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide.
Andrikopoulos, P, Aron-Wisnewsky, J, Chakaroun, R, Myridakis, A, Forslund, SK, Nielsen, T, Adriouch, S, Holmes, B, Chilloux, J, Vieira-Silva, S, et al
Nature communications. 2023;(1):5843
Abstract
The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk.
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Microbiome and metabolome features of the cardiometabolic disease spectrum.
Fromentin, S, Forslund, SK, Chechi, K, Aron-Wisnewsky, J, Chakaroun, R, Nielsen, T, Tremaroli, V, Ji, B, Prifti, E, Myridakis, A, et al
Nature medicine. 2022;(2):303-314
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Abstract
Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.