Tag: Metabolomics

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  • Cellular ATP demand creates metabolically distinct subpopulations of mitochondria

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  • Temporal dynamics of the multi-omic response to endurance exercise training

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  • Department of Medicine, Stanford University, Stanford, CA, USA

    David Amar, David Jimenez-Morales, Malene E. Lindholm, Shruti Marwaha, Archana Natarajan Raja, Jimmy Zhen, Euan Ashley, Matthew T. Wheeler, Karen P. Dalton, Steven G. Hershman, Mihir Samdarshi & Christopher Teng

  • Department of Genetics, Stanford University, Stanford, CA, USA

    Nicole R. Gay, Bingqing Zhao, Jose J. Almagro Armenteros, Nasim Bararpour, Si Wu, Stephen B. Montgomery, Michael P. Snyder, Clarisa Chavez, Roxanne Chiu, Krista M. Hennig, Chia-Jui Hung, Christopher A. Jin & Navid Zebarjadi

  • Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

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  • Department of Internal Medicine, University of Iowa, Iowa City, IA, USA

    Dam Bae, Ana C. Lira, Sue C. Bodine, Michael Cicha, Luis Gustavo Oliveira De Sousa, Bailey E. Jackson, Kyle S. Kramer, Andrea G. Marshall & Collyn Z-T. Richards

  • Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

    Surendra Dasari

  • Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

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  • Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

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    Stephen B. Montgomery

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    Gary Cutter

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  • Department of Biomedical Sciences, University of Missouri, Columbia, MO, USA

    Frank W. Booth

  • Department of Medical Pharmacology and Physiology, University of Missouri, Columbia, MO, USA

    Frank W. Booth

  • Department of Nutrition and Exercise Physiology, University of Missouri, Columbia, MO, USA

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  • Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, USA

    Frank W. Booth

  • Department of Kinesiology and Health Education, University of Texas, Austin, TX, USA

    Roger Farrar

  • Department of Medicine, Division of Endocrinology and Diabetes, University of California, Los Angeles, CA, USA

    Andrea L. Hevener

  • Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA

    Benjamin G. Ke & Chongzhi Zang

  • Section on Clinical, Behavioral, and Outcomes Research, Joslin Diabetes Center, Boston, MA, USA

    Sarah J. Lessard

  • Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA

    Andrea G. Marshall

  • Department of Health Sciences, Stetson University, Deland, FL, USA

    Scott Powers

  • Department of Medicine, University of Missouri, Columbia, MO, USA

    R. Scott Rector

  • NextGen Precision Health, University of Missouri, Columbia, MO, USA

    R. Scott Rector

  • Cell Biology and Physiology, Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA

    John Thyfault

  • Center for Skeletal Muscle Research at Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA, USA

    Zhen Yan

  • Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA

    Zhen Yan

  • Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA, USA

    Zhen Yan

  • Department of Molecular Physiology and Biological Physics, University of Virginia School of Medicine, Charlottesville, VA, USA

    Zhen Yan

  • Fralin Biomedical Research Institute, Center for Exercise Medicine Research at Virginia Tech Carilion, Roanoke, VA, USA

    Zhen Yan

  • Department of Human Nutrition, Foods, and Exercise, College of Agriculture and Life Sciences, Virginia Tech, Blacksburg, VA, USA

    Zhen Yan

  • Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Ali Tugrul Balci & Maria Chikina

  • Petit Institute of Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA

    Samuel G. Moore

  • Department of Medicine, Emory University, Atlanta, GA, USA

    Karan Uppal

  • Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, USA

    Marcas Bamman & Anna Thalacker-Mercer

  • Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Bryan C. Bergman, Daniel H. Bessesen, Wendy M. Kohrt, Edward L. Melanson, Kerrie L. Moreau, Irene E. Schauer & Robert S. Schwartz

  • Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

    Thomas W. Buford

  • Human Performance Laboratory, Ball State University, Muncie, IN, USA

    Toby L. Chambers, Bridget Lester, Scott Trappe & Todd A. Trappe

  • Translational Research Institute, AdventHealth, Orlando, FL, USA

    Paul M. Coen, Bret H. Goodpaster & Lauren M. Sparks

  • Department of Pediatrics, University of California, Irvine, CA, USA

    Dan Cooper, Fadia Haddad & Shlomit Radom-Aizik

  • Pennington Biomedical Research Center, Baton Rouge, LA, USA

    Kishore Gadde, Melissa Harris, Neil M. Johannsen, Tuomo Rankinen & Eric Ravussin

  • College of Nursing, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Catherine M. Jankowski

  • Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Nicolas Musi

  • Population and Public Health, Pennington Biomedical Research Center, Baton Rouge, LA, USA

    Robert L. Newton Jr

  • Biochemistry and Structural Biology, Center for Metabolic Health, Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, San Antonio, TX, USA

    Blake B. Rasmussen

  • Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, San Antonio, TX, USA

    Elena Volpi

  • All authors reviewed and revised the manuscript. Detailed author contributions are provided in the Supplementary Information.

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  • Metabolic rewiring promotes anti-inflammatory effects of glucocorticoids

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  • How gut microbiome influences obesity onset differently in men and women

    How gut microbiome influences obesity onset differently in men and women

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    New research being presented at this year’s European Congress on Obesity (ECO) in Venice, Italy (12-15 May) identifies changes in the composition of gut bacteria that may play a key role in the onset and development of obesity, with differences in men and women, which might affect the metabolism of different nutrients and therefore the presence of bioactive molecules in the gut that influence the development of metabolic disease.

    The gut microbiota consists of a complex community of microorganisms (bacteria, viruses, fungi and protozoa) which inhabit the gastrointestinal tract. Disruption in this community (dysbiosis) significantly affects metabolic health and influences the risk of certain diseases, including obesity. However, it is still unclear which species represent a greater or lesser likelihood of developing obesity, as well as the impact of these species on our metabolic health.

    To find out more, researchers analyzed metagenomic and metabolomic data from a Spanish population to understand the mechanisms by which these microorganisms are involved in the development of obesity.

    They examined the fecal metabolome-;the diverse collection of metabolites (small molecules) found in the gut and shed in feces that are produced by gut bacteria as a byproduct of metabolizing food and make their way into the bloodstream impacting health.

    Overall, 361 adult volunteers (251 women/110 men, average (median age 44 years old) were included from the Spanish Obekit study-;a randomized trial examining the relationship between genetic variants and the response to a hypocaloric diet.

    All participants (65 normal-weight, 110 overweight, and 186 with obesity) were classified according to an obesity (OB) index-;LOW (BMI≤ 30 kg/m²; fat mass percentage ≤ 25% [women] or ≤ 32% [men]; waist circumference ≤88 cm [women] or ≤ 102 cm [men]) or HIGH (BMI > 30 kg/m²; fat mass >25% [women] or >32% [men]; waist circumference >88 cm [women] or >102 cm [men]) level of obesity.

    Researchers made sure that participants in the LOW and HIGH groups were matched for sex and age.

    Genetic microbiota profiling was done to identify the different types, composition, diversity, and relative abundance of bacteria present in stool samples of the participants.

    The analysis revealed that individuals with a HIGH OB index were characterised by significantly lower levels of Christensenella minuta-;a bacterium which has consistently been linked to leanness and health.

    In men, greater abundance of Parabacteroides helcogenes and Campylobacter canadensis species-;were strongly associated with higher BMI, fat mass, and waist circumference.

    Whereas in women, greater abundance of three species-;Prevotella micans, Prevotella brevis and Prevotella sacharolitica-;were highly predictive of higher BMI, fat mass and waist circumference, but not in men.

    In further untargeted metabolomics analyses, that looked at a broader range of metabolic compounds in the blood, researchers found variation in the abundance of certain metabolites-;especially higher levels of bioactive lipids-;phospholipids (implicated in the development of metabolic disease and critical modulators of insulin sensitivity) and sphingolipids (that play a role in the development of diabetes and the emergence of vascular complications)-;in participants with a HIGH OB index.

    Our findings reveal how an imbalance in distinct bacterial groups are likely to play an important role in the onset and development of obesity, with considerable differences between the sexes, which might affect the metabolism of different bioactive molecules present in the metabolome that influence the development of metabolic disease.”


    Dr Paula Aranaz, lead author from the Centre for Nutrition Research at the University of Navarra in Spain

    She adds, “Gut microbiome composition, specifically higher levels of the Christensenella minuta bacterium, appeared to protect against obesity. Whereas the species that influence the risk of developing obesity appear to be different between the sexes and interventions to help prevent an obesity-favourable microbiome may need to be different in men and women. Further research is needed to better understand when the switch to an obesity favorable gut microbiota may take place, and therefore the right timing for possible interventions.”

    She concludes, “We hope that this study demonstrates that using metagenomics in combination with metabolomics enables researchers to study the mechanisms involved in the development of metabolic diseases such as obesity with a high degree of confidence. This novel, broader approach could help to develop nutritional precision strategies for weight loss that modify the presence of specific bacteria strains, or the levels of bioactive molecules.”

    Despite the important findings, the authors note some limitations, including the small sample size (especially for men) and that the study was conducted in one area in Spain, and because climate, geography, diet, and culture are known to influence the gut microbiome, the findings might be not generalizable to other populations.

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  • Diabetes drug dulaglutide may reduce symptoms of depression

    Diabetes drug dulaglutide may reduce symptoms of depression

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    A recent Brain and Behavior study investigated the antidepressant effect of dulaglutide and the mechanism that underlies this effect.

    Study: Dulaglutide treatment reverses depression-like behavior and hippocampal metabolomic homeostasis in mice exposed to chronic mild stress. Image Credit: luchschenF/Shutterstock.com
    Study: Dulaglutide treatment reverses depression-like behavior and hippocampal metabolomic homeostasis in mice exposed to chronic mild stress. Image Credit: luchschenF/Shutterstock.com

    Background

    Depression is a chronic mood disorder that is associated with low mood, insomnia, weight loss, a state of unhappiness, aversion to activity, fatigue, and low self-esteem. According to the World Health Organization, depression has become one of the major health burdens across the world.

    This mental health condition is commonly treated with an antidepressant that takes around a month to alleviate the symptoms. However, several side effects are associated with the use of antidepressant drugs and could be toxic at high doses.

    A combination of psychological, genetic, and neurological factors contributes to the manifestations of depression. Even though the exact etiology of this mental health issue is not fully understood, research has shown chronic stress to be an inducer of depression. 

    The hippocampus is a region of the brain that is associated with depression and modifies functionally and morphologically in response to stress. Animal model studies have shown that a decrease in neuronal and glial size, reduction in synaptic markers, loss of dendrites, and increase in apoptosis in the hippocampus leads to depression.

    Many studies have uncovered the metabolic aspects of depression. For instance, diabetes and obesity are two common metabolic disorders that increase the risk of depression. Considering its high prevalence, novel therapies with high efficacy and fewer side effects are required to combat depression. The chronic mild stress (CMS) model has been recognized as a reliable rodent model to study depression. 

    Glucagon-like peptide-1 (GLP-1) and its receptor agonists are involved with anti-inflammatory effects and neuroprotective activities and can improve mental disorders, particularly depression and cognition. GLP-1 is a peptide hormone that stimulates the secretion of insulin and restricts the synthesis of glucagon in the pancreas in a glucose-dependent manner. Liraglutide is a GLP-1 analog that exhibited a positive effect in reducing anxiety and depression symptoms.

    Dulaglutide is a novel long-acting GLP-1 receptor agonist that improves cognitive dysfunction and neuronal damage in rats with vascular dementia. Although many studies highlighted the efficacy of dulaglutide in preventing depression-like behavior triggered by chronic social defeat stress (CSDS), the underlying mechanism of this effect is not clearly understood.

    About the study

    The current study used a metabolomics strategy to evaluate the effect of dulaglutide in a CMS model. Furthermore, the underlying mechanism of this effect was also assessed. Adult male ICR mice, which is a strain of albino mice, were selected for this study. All test mice were around seven weeks old.

    After one week of acclimatization, 60 mice were randomly assigned in four groups, namely, control (CON), the CMS and Vehicle group (CMS+Veh), the CMS and 0.3 mg/kg dulaglutide group (Low Dula), and the CMS and 0.6 mg/kg dulaglutide group (High Dula). Except for the CON group, all other groups were exposed to stressors.

    To establish the CMS model of depression, selected mice were exposed to two or three different stressors for 28 days continuously. For stress induction, mice were deprived of water and food for 12 hours, kept in wet bedding for 24 hours, kept in a tilted cage for 24 hours, pintail for 1 minute, and cold water treatment for five minutes. The body weight of each test mouse was measured weekly, and behavioral tests, such as the tail suspension test (TST), open field test (OFT), and forced swimming test (FST), were performed.

    Study findings

    The mice subjected to CMS for four weeks exhibited depressive- and anxiety-like symptoms. An LC-MS/MS metabolomics study was performed to understand the potential pathophysiological mechanisms and investigate the efficacy of drugs to alleviate depression-like symptoms.

    A distinct difference between the CON group, CMS+Veh group, and High Dula group was observed in accordance with the metabolic disorders induced by chronic stress, which was altered through dulaglutide treatment. Many potential biomarkers were identified that are associated with purine metabolism, arginine and proline metabolism, glycerophospholipid metabolism, glutamate metabolism, sphingolipid metabolism, and bile secretion.

    Lipid metabolism pathways could be potential targets through which dulaglutide alleviates depression. Lysophosphatidylcholine (LPC), phosphatidylethanolamine (PE), lysophosphatidylethanolamine (LPE), phosphatidylinositol (PI), sphingolipids, and phosphatidylcholine (PC), are involved with the therapeutic effect of dulaglutide in alleviating depression. Consistent with previous studies findings, this study highlighted the association between lipid metabolism and the antidepressant effect of dulaglutide.

    The current study indicated the downregulation of N-acetyl-L-aspartic acid (NAA) in the CMS model group. NAA, which is one of the most important metabolites of the vertebrate nervous system, was found in decreased levels in rats with chronic, unpredictable, mild stress. However, the current study indicated that dulaglutide therapy increased the levels of NAA through its upregulation in the hippocampus.

    In the CMS model group, an upregulation in L-glutamic acid and L-arginine was observed. Dulaglutide treatment caused a decrease in arginine and proline, thereby indirectly exhibiting a neuroprotective effect.

    Conclusions

    The current study highlighted the antidepressant effects of dulaglutide using the CMS depression model. Notably, the potential metabolisms that underlie the antidepressant effect of dulaglutide have been elucidated in this study. 

    Journal reference:

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  • Genome-wide characterization of circulating metabolic biomarkers

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    NMR metabolomics

    In this work, we expand our previous GWAS of 123 human metabolic traits in ~25,000 individuals4 to include additional cohorts and a more comprehensive panel of metabolic traits. Up to 233 serum or plasma metabolic traits were quantified in 33 cohorts (total sample size up to 136,016) using an updated quantification version of the same NMR metabolomics platform17 as in the previous study. The NMR metabolomics platform provides data of lipoprotein subclasses and their lipid concentrations and compositions, apoAI and apoB, cholesterol and triglyceride measures, albumin, various fatty acids and low-molecular-weight metabolites—for example, amino acids, glycolysis-related measures and ketone bodies. In this work, the metabolic traits were quantified in the following cohorts (described in detail in Supplementary Notes and Supplementary Table 1): Avon Longitudinal Study of Parents and Children (ALSPAC), China Kadoorie Biobank (CKB), Estonian Genome Center of University of Tartu Cohort (EGCUT), The Erasmus Rucphen Family study (ERF), European Genetic Database (EUGENDA), FINRISK 1997 (FR97), FINRISK 2007 (FR07, that is, DILGOM), The INTERVAL Bioresource (INTERVAL), CROATIA-Korcula Study (KORCULA), LifeLines-DEEP (LLD), Leiden Longevity Study (LLS), eight subcohorts from the London Life Sciences Prospective Population Study (LOLIPOP), The Metabolic Syndrome in Men study (METSIM), The Netherlands Epidemiology of Obesity Study (NEO), The Netherlands Study of Depression and Anxiety (NESDA), Northern Finland Birth Cohort 1966 (NFBC1966), NFBC1986, The Netherlands Twin Register (NTR), Oxford Biobank (OBB), Orkney Complex Disease Study (ORCADES), PROspective Study of Pravastatin in the Elderly at Risk (PROSPER), three subcohorts from the Rotterdam Study (RS), TwinsUK (TUK), and The Cardiovascular Risk in Young Finns Study (YFS). Most of the cohorts consisted of individuals of European ancestry (six Finnish and 21 non-Finnish), and six cohorts had individuals of Asian ancestry (one Han Chinese and five South Asian). All participants gave informed consent and all studies were approved by the ethical committees of the participating centres.

    Detailed description of the NMR method is given in the Supplementary Notes.

    Genome-wide association study

    A GWAS was performed for 233 metabolic traits (Supplementary Table 2) in each of 33 cohorts (Supplementary Table 1), leading to inclusion of up to 136,016 individuals with both NMR metabolic trait measurements and genome-wide SNP data available. Pregnant individuals or those using lipid-lowering medication were excluded from the study. SNPs were imputed using the Haplotype Reference Consortium release 1.1 or the 1000 Genomes Project phase 3 release, and GWAS was performed under the additive model separately in each cohort (details in Supplementary Table 3). Before analyses, the metabolic trait distributions were adjusted for age, sex, principal components and relevant study-specific covariates (see Supplementary Table 3), and inverse rank normal transformation of trait residuals was performed. The cohorts were combined in fixed-effect meta-analysis with METAL69, and the SNPs were filtered to those present in at least seven cohorts. The NMR metabolic traits are highly correlated and using the Bonferroni correction to account for multiple testing would result in an overconservative threshold for genome-wide significance. We therefore used the number of principal components (28) explaining >95% variation in the metabolic traits defined in the largest cohort, INTERVAL, to correct for multiple testing, and our genome-wide significance threshold was set to P < 1.8 × 10−9 (standard genome-wide significance level, P < 5 × 10−8, divided by 28). After the primary GWAS, fasting- and sample type-stratified analyses were performed for the 233 metabolic traits. In these analyses 26 of the cohorts were classified as fasted (n = 68,559), six cohorts were classified as non-fasted (n = 58,112), seventeen cohorts were classified as having serum samples (n = 90,223) and sixteen cohorts had plasma samples (n = 45,793; see Supplementary Table 1). To define associated loci across the metabolic traits, we defined a 500-kb window flanking each SNP meeting the significance threshold, pooled together these windows from all metabolic traits for each chromosome, and iteratively merged the windows. As this approach can lead to inclusion of multiple independent signals within these loci, we further defined potential independent signals that reside within the defined loci based on pairwise LD (r2 cut-off of 0.3, defined in INTERVAL and FINRISK97) of all the lead SNPs within each locus. Regional association plots were created in LocusZoom, v. 1.4. We assigned the associated lead SNPs to the most likely causal genes based on two criteria: (1) we prioritized genes with clear biological relevance to the associated metabolic traits; and (2) if no biologically plausible causal gene was detected and the lead SNP was a functional variant (missense, splice region or stop gained) or in high LD (r2 > 0.8 in INTERVAL) with such a variant, the gene with the functional variant was assigned as the most likely candidate gene. If criteria 1 and 2 were not fulfilled, the nearest gene was indicated as the candidate gene.

    Ancestry-specific analyses

    We conducted ancestry-stratified analyses within our primary discovery meta-analysis for South Asian (five cohorts, 11,340 participants), East Asian (one cohort, 4,435 participants), all European (27 cohorts, 120,241 participants), Finnish (six cohorts, 27,577 participants) and non-Finnish European (21 cohorts, 92,664 participants) participants. For these ancestry-specific analyses, we used the standard threshold for genome-wide significance (P < 5 × 10−8). To also compare to participants with African ancestry, we conducted an African-specific subgroup analysis using the UK Biobank dataset (March 2021 release). Using self-reported ethnicity information (Field 21000: Ethnicity background) from the baseline questionnaire, 1,405 participants with African ancestry were identified as having Caribbean (code 4001), African (code 4002), or any other Black background (code 4003). Variant QC was performed by excluding SNPs with minor allele frequency <1%, INFO score <0.4, and variants in complex LD regions. LD thinning was performed with r2 < 0.1, a window size of 1,000 and a step size of 80. Related individuals were identified and excluded using relatedness data provided by the UK Biobank (Field 22021: Genetic kinship to other participants). Outliers of the first 6 genetic principal components computed on the unrelated samples were removed from the analysis. NMR metabolic traits were adjusted for age, sex, fasting status and 10 genetic principal components, and trait residuals were inverse rank normal-transformed. Associations between SNPs and metabolic traits were tested using PLINK 2.0.

    Replication in publicly available data

    UK Biobank SNP–metabolic trait summary statistics were downloaded (https://gwas.mrcieu.ac.uk/datasets/?gwas_id__icontains=met-d) from the IEU Open GWAS Project70. These summary statistics were derived from the publicly available March 2021 release of the UK Biobank data in which the metabolic traits were measured with a similar NMR technology (newer version of the Nightingale Health platform) as in our study. The data were used to compare the association of our lead SNP–metabolic trait pairs within the 276 associated regions. Two thresholds were used to define an association in the UK Biobank data: the standard genome-wide significance level (P < 5 × 10−8) and the suggestive level of significance (p < 1 × 10−5).

    Heritability and variance explained

    We used GCTA-GREML71 v. 1.94 to estimate common variant heritability for each trait using an independent dataset, specifically the UK Biobank phase 1 NMR release. This research was conducted using the UKBB Resource under application number 7439. We randomly selected 10,000 unrelated UK Biobank participants of European ancestry with available NMR data and filtered imputed variants to minor allele frequency >0.005, missingness <0.1 and Hardy–Weinberg equilibrium P value <10−6. We removed technical variation from the traits using methods described previously72, and adjusted the traits for age, sex, lipid-lowering medication usage and the first 10 genetic principal components of ancestry. Traits were rank inverse normal-transformed prior to GREML analysis. Variance explained by the lead SNPs for each trait was estimated as described before73.

    Comparing to previous associations

    We performed an extensive comparison of our metabolic trait associations to previous GWASs of metabolic traits. Our comparisons were divided into three groups: (1) comparison to results of previously published large GWAS of circulating NMR traits4,5; (2) comparison with loci associated with clinical lipids (including those from the UK Biobank September 2019 version 3 release)21,25,26,27,74; and (3) comparison with an extensive list of associations from previous metabolite and metabolomic studies11,13,53,75,76,77,78,79,80,81,82,83,84,85,86,87. The comparisons were performed by indicating: (1) co-located known variants; (2) any known associations within a 500-kb flank of a lead SNP; or (3) known associations in LD (r2 > 0.3, defined in INTERVAL) with a lead SNP. Since we used the UK Biobank for replication, we did not compare the associations to those from studies that used UK Biobank NMR metabolomics as a single cohort without validation cohorts67,88.

    In addition to comparing to previous metabolic trait associations, we screened previous disease and trait associations (P value cut-off 5 × 10−8) of the lead SNPs using PhenoScanner, v244,45, and NHGRI-EBI GWAS Catalog46 (associations downloaded on 30 March 2023 using the gwasrapidd R package, v. 0.99.1489). In addition, we screened the FinnGen43 data freeze 7 summary statistics of 3,095 disease endpoints for overlapping associations (P value cut-off 5 × 10−8). Associations with gene expression and protein levels were screened using PhenoScanner, v244,45.

    Metabolic effects of lipoprotein loci

    To compare the metabolic effects of lipoprotein, lipid and apolipoprotein-associated variants, the effect estimates were visualized as colour-coded heat maps. To allow comparison of SNP effects, the estimates were scaled relative to the highest absolute value of the estimate for each SNP. In this analysis, we included lead SNPs at the 276 initially defined regions that were associated with any of the lipoprotein lipids or apolipoproteins at genome-wide significance and nominally associated (P < 0.05) with apoB. We used these criteria to restrict the analysis to SNPs associated with apoB, because apoB is known to be a causal part of lipoprotein metabolism for cardiovascular disease30,31,32. To exclude signals with similar effects across the metabolic traits due to the same causal gene, we included only a single SNP from the initially defined genomic regions that had multiple independent signals if the patterns of metabolic traits associations were similar (R > 0.5). In the heat maps each line represents a single SNP, each column corresponds to a single metabolic measure, and the scaled effect estimates for the SNP-metabolite associations are visualized with a colour range. Directions of effects are shown in relation to the allele associated with increased apoB. To group SNPs with similar effects together, dendrograms were constructed based on hierarchical clustering of the scaled SNP effects. Heat maps were constructed using the heatmap.2 function of the gplots v. 3.0.3 R package. Pearson correlations were assessed in R, v. 4.0.0.

    Intrahepatic cholestasis of pregnancy

    We assessed overlap of our metabolic trait associations with ICP using summary statistics from the FinnGen study43 data freeze 7 (O15_ICP; 1,460 cases, 172,286 controls). ICP cases were defined through hospital discharge registry, ICD10 code O26.6 and ICD9 codes 6467A and 6467X. Using the nearest genes at each associated locus, we performed gene ontology (GO) enrichment analysis to search for enriched biological process and molecular function GO terms90,91. We assessed colocalizations of association signals using the hypothesis prioritization for multi-trait colocalization (HyPrColoc) R library, v. 1.0, in which an efficient deterministic Bayesian algorithm is used to detect colocalization across vast numbers of traits simultaneously92. We searched for colocalization at single causal variants and shared regional associations. To visualize SNP effects across lipid and lipoprotein traits, heat maps were constructed using the heatmap.2 function of the gplots v. 3.0.3 R package. The following SNPs were included in the heat maps: GCKR-rs1260326, ABCB11-rs10184673, ABCB1-rs17209837, CYP7A1-rs9297994, SERPINA1-rs28929474 and HNF4A-rs1800961. Effects of the metabolic trait-associated SNPs were scaled relative to an odds ratio of 1.5 for ICP.

    Mendelian randomization

    Two-sample Mendelian randomization was performed using 20 NMR non-lipid metabolic traits (including amino acids (alanine, glutamine, glycine, histidine, isoleucine, leucine, valine, phenylalanine and tyrosine), ketone bodies (acetate, acetone and 3-hydroxybutyrate), and glycolysis/gluconeogenesis (glucose, lactate, pyruvate, glycerol and citrate), fluid balance (albumin and creatinine) or inflammation-related (glycoprotein acetylation) metabolic traits) as exposures and 460 Phecodes and 52 quantitative traits from the UK Biobank21 as outcomes. We defined two sets of instruments for the analyses that are referred to as full and strict instruments. As initial instruments we used the 334 lead variants (a single instrument SNP per each defined associated locus) associated with these traits (‘full instruments’). To avoid potential bias due to pleiotropy, we also selected a subset of 193 variants (‘strict instruments’) that had fewer than 5 associations across all 233 metabolic traits. Our threshold of 5 associations was based on empirical assessment of the distribution of per-variant trait associations. To investigate the sensitivity of the Mendelian randomization analyses to the choice of threshold, we also tested using fewer than 3 associations and fewer than 7 associations. We defined disease outcomes in UK Biobank using a curated list of major Phecodes available in the PheWAS R package93,94. To restrict our analysis to major disease outcomes, we discarded any sub-categories (that is, Phecodes with 4 or more characters) and removed outcomes with fewer than 100 events across up to 367,542 unrelated UK Biobank participants with European ancestry. The resulting 460 diseases were grouped into 15 broad domains: circulatory system, dermatologic, digestive, endocrine/metabolic, genitourinary, haematopoietic, infectious diseases, mental disorders, musculoskeletal, neoplasms, neurological, pregnancy complications, respiratory, sense organs and symptoms. We also analysed 52 quantitative traits available in UK Biobank, including blood pressure, lung function measures, blood cell traits and clinical chemistry biomarkers. In our replication analysis (acetone as the exposure and hypertension as the outcome), we used essential hypertension from the FinnGen study43 data freeze 7 as the outcome (hypertension essential, I9_HYPTENSESS; 70,651 cases, 223,663 controls). Cases were defined through hospital discharge registry, ICD10 code I10, ICD9 codes 4019X and 4039A, ICD8 codes 40199, 40299, 40399, 40499, 40209, 40100, 40291, 40191 and 40290.

    We performed univariable Mendelian randomization using the inverse variance-weighted method for each instrument95. We also performed sensitivity analyses using Mendelian randomization–Egger regression to account for unmeasured pleiotropy96 and weighted median regression to assess robustness to invalid genetic instruments97. Our primary analyses were based on fixed-effect models, but as sensitivity analyses we used random-effect models to account for between-variant heterogeneity, which we quantified using the I-squared statistic. The Mendelian randomization analyses were performed using the MendelianRandomization package v. 0.5.198 or the TwoSampleMR package v. 0.5.399. Single-SNP Mendelian randomization estimates were based on the Wald ratio. We considered the fixed-effects inverse variance-weighted method as the main Mendelian randomization model but report the results of all models in Supplementary Table 15. To account for multiple testing, associations with P < 4.88 × 10−6 were considered significant (Bonferroni correction to account for testing of 20 metabolic traits with 512 outcomes).

    FinnGen study

    In the present study, we used GWAS summary statistics of 3,095 disease endpoints from FinnGen data freeze 7. Full description of the FinnGen study43 and data analysis steps is provided in the Supplementary Notes. FinnGen contributors are listed in Supplementary Table 18.

    Statistics and reproducibility

    The meta-analyses were conducted independently by two investigators in two different centres (University of Oulu, Finland and University of Cambridge, UK), and the summary statistics were compared to verify consistency of results.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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  • Mediterranean diet and exercise reshape gut microbiome, aiding weight loss

    Mediterranean diet and exercise reshape gut microbiome, aiding weight loss

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    In a recent study published in The American Journal of Clinical Nutrition, researchers investigate the health benefits of the Mediterranean diet (MedDiet) and physical activity interventions on overweight and obese participants by measuring changes in fecal metabolomic- and gut microbiota.

    Study: Effect of 1-year lifestyle intervention with energy-reduced Mediterranean diet and physical activity promotion on the gut metabolome and microbiota: A randomized clinical trial. Image Credit: Valentyn Volkov / Shutterstock.comStudy: Effect of 1-year lifestyle intervention with energy-reduced Mediterranean diet and physical activity promotion on the gut metabolome and microbiota: A randomized clinical trial. Image Credit: Valentyn Volkov / Shutterstock.com

    Health benefits of the MedDiet

    Characterized by a high intake of healthy vegetables, legumes, fruits, whole cereals, and nuts, moderate intake of seafood, low intake of dairy products and processed meats, as well as olive oil comprising the primary fat source, the traditional MedDiet has been growing in global popularity.

    Previous studies have investigated the health benefits of the MedDiet, which include significant cardiovascular disease (CVD), obesity, neurological, and all-cause mortality risk reductions as compared to suboptimal dietary patterns like the Western diet.

    The high concentrations of dietary fiber and anti-inflammatory nutraceuticals in the MedDiet have also been associated with promoting and persisting beneficial gut microbiota. To date, the metabolomic consequences of these associations remain unknown.

    Exploring the blood metabolome provides important insights into how gut microbiota-derived metabolites correlate with cardiometabolic diseases. Through the use of plasma metabolomics and 16S sequencing, researchers can elucidate how diet, circulating metabolites, and gut microbiota impact cardiovascular health.

    Understanding the influence of dietary interventions on both gut microbial composition and metabolomic profiles can support clinical recommendations to follow the MedDiet and other healthy diets, particularly in high-risk patients. Furthermore, these data can provide foundational insights for future studies investigating the indirect effects of diet on other non-cardiovascular somatic systems.

    About the study

    In the present study, researchers used data from the PREvención con DIeta MEDiterránea (PREDIMED)-Plus randomized trial to investigate the effects of one year of intensive lifestyle intervention on fecal metabolites, gut microbiota, and cardiovascular risk factors, particularly in overweight and obese patients. The study comprised 400 individuals between the ages of 55 and 75 years from Alicante, Barcelona, Reus, and Valencia who were randomly divided between the intervention group (IG) and control group (CG).

    Data collection included dietary and lifestyle information obtained through the er-MedDiet questionnaire, a 17-item derivation of the 14-item Mediterranean Diet Adherence Screener (MEDAS) questionnaire. Blood and stool samples were also collected during baseline assessments and routine follow-up. Anthropometric measurements and demographic data were further obtained from medical and government records.

    The researchers encouraged all study participants to increase their usual physical activity levels to include at least 45 minutes daily of brisk walking or an equivalent activity. All study participants were also encouraged to perform specific exercises that increase their balance, strength, and flexibility to ultimately complete 150 minutes or more of moderate-to-vigorous physical activity each week.

    Changes in physical activity levels were quantified using questionnaires that assigned the metabolic equivalent of tasks (MET) min/week metrics to physical activity status and MET h/day for sedentary behaviors. The study intervention included lifestyle recommendations for physical activity and diet and in-person behavioral support from a licensed dietitian for the IG group. In contrast, CG was treated ad libitum with a regular MedDiet, which was the only intervention.

    Outcomes of interest were measured using liquid chromatography-tandem mass spectrometry (LC-MS) for metabolomics identification, characterization, and quantification and 16S amplicon sequencing for gut microbiome evaluations. Linear regression models and weighted gene co-expression network analysis (WGCNA) were used to identify between-group differences and metabolomic sub-networks, respectively.

    Study findings

    The present study highlights the combined health benefits of a dietitian-guided MedDiet alongside physical activity compared to an ad libitum MedDiet.

    Overweight and obese participants in the IG cohort exhibited a mean weight reduction of 4.2 kg and 4.4 cm lower waist circumference than their CG counterparts. The body mass index (BMI) and total energy intake estimates of the IG group were 1.5 kg/m2 and 113.9 kcal lower than controls, thus explaining the 0.1% observed reductions in glycated hemoglobin values as compared to controls.

    Fecal metabolomic analysis revealed a total of 532 fecal metabolites, four of which were significantly different between IG and CG following one year. These four metabolites included 4,7,10,13,16-docosapentaenoic acid (DPA) and adrenic acid, both of which decreased following the intervention, as well as oleic acid and 3-methyl-adipic acid (3-MAA), both of which increased following intervention. While preliminary analyses suggested an additional 56 metabolites of interest, these were non-significant following false discovery rate (FDR) corrections.

    Network analyses grouped the 532 identified metabolites into 16 subnetworks ranging in size from Grey60 to brown. The Black, Midnight Blue, Pink, and Salmon subnetworks significantly differed between IG and CG cohorts following one year of the study.

    The Black subnetwork comprised ceramides and spingosines, whereas the Midnight blue subnetwork consists of purines. The Pink subnetwork metabolites included fatty acids and carnitines, whereas the Salmon network comprised bile acids.

    Compared to the CG, the IG exhibited reduced levels of the Black, Midnight Blue, and Pink subnetworks. Comparatively, the IG exhibited increased levels of the Salmon subnetwork compared to the CG.  

    Gut microbial analysis determined that IG Shannon and Chao1 alpha diversity indices were significantly higher than CG indices by the end of the study, with the top two axes of the principal coordinate analysis (PCoA) explaining 36% of the observed differences. 

    The Eubacterium hallii group exhibited a significant reduction in population size in the IG compared to the CG. A reduced abundance of Dorea was also observed but to a lesser extent than in the Eubacterium hallii group.

    Conclusions

    The present lifestyle intervention-based clinical trial highlights the benefits of stringent dietary supervision and physical activity engagement for at-risk overweight and obese individuals, even when compared to equal-risk subjects consuming a similarly healthy diet. The energy-reduced MedDiet and physical activity intervention in the IG, when compared with an ad libitum MedDiet for the CG, significantly reduced weight metrics, including waist circumference and BMI.

    Even with similar healthy dietary patterns, the high intensity of the dietary intervention and weight-loss intervention components, such as caloric restriction and physical activity, could have significant benefits on CVD risk factors, potentially through modulation of the fecal microbiota and metabolome. Public health policies and interventions can be tailored to individual microbiome profiles, allowing for more precise and effective strategies for preventing and managing cardiometabolic diseases.”

    Journal reference:

    • García-Gavilán, J. F., Atzeni, A., Babio, N., et al. (2024). Effect of 1-year lifestyle intervention with energy-reduced Mediterranean diet and physical activity promotion on the gut metabolome and microbiota: A randomized clinical trial. The American Journal of Clinical Nutrition. doi:10.1016/j.ajcnut.2024.02.021

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  • Study suggests high levels of vitamin B3 breakdown products are linked to higher risk of mortality, heart attacks, and stroke

    Study suggests high levels of vitamin B3 breakdown products are linked to higher risk of mortality, heart attacks, and stroke

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    In a recent study published in Nature Medicine, researchers utilized an untargeted metabolomics technique to look for new compounds and pathways that may contribute to residual cardiovascular disease (CVD) risk.

    Study: A terminal metabolite of niacin promotes vascular inflammation and contributes to cardiovascular disease risk. Image Credit: Dragana Gordic/Shutterstock.com
    Study: A terminal metabolite of niacin promotes vascular inflammation and contributes to cardiovascular disease risk. Image Credit: Dragana Gordic/Shutterstock.com

    Background

    CVD is a worldwide health problem, with only a tiny proportion of the risk linked to known risk factors. Despite breakthroughs in therapeutics, the risk of CVD remains high, indicating the presence of other unidentified variables.

    Niacin, an essential vitamin in dietary staples, is critical in CVD. Treatment groups had mean LDL levels <50 mg/dl but significant cardiovascular event rates. Individuals with high inflammatory markers have an increased chance of developing CVD. However, dietary niacin intake has increased due to the increasing consumption of processed and fast food, raising concerns regarding the efficiency of therapeutic niacin in lowering CVD risk.

    About the study

    In the present study, researchers used untargeted mass spectrometry technology to identify circulating small molecules that predict incident CVD event risks without established risk factors.

    The researchers investigated clinical, genetic, and mechanistic links between the terminal breakdown products of excess niacin and the incidence of major adverse cardiac events (MACE). They conducted untargeted metabolomics analyses on fasting plasma from stable cardiac patients in a prospective discovery cohort and subjects with elective diagnostic cardiac examinations.

    The researchers postulated that the putative MACE-related analyte with m/z values of 153 Da may be a combination of two co-eluted structural isomers: the N1-methyl-2-pyridone-5-carboxamide (or 2PY) metabolite and the N1-methyl-4-pyridone-3-carboxamide (or 4PY) metabolite. They chemically synthesized both metabolite standards and conducted several chemical characterization tests.

    The team used stable-isotope-dilution liquid chromatography with tandem mass spectrometry (LC-MS/MS) to examine the relationship between structural isomer levels in circulation and new-onset major-type adverse cardiovascular event risk in two validation populations [United States (US) cohort of 2,331 individuals and the European cohort of 832 individuals]. They performed a sensitivity analysis on validation cohort data to account for confounding with known risk variables.

    The researchers used a genome-wide association study (GWAS) approach and meta-analyses to investigate the genetic determinants of circulating 2PY and 4PY levels. They combined the study results from the United States validation cohort with publicly available summary statistics for 2PY and 4PY levels from various multi-ancestry datasets. They reduced Acmsd expression in vivo by injecting mice with a liver-tropic adeno-associated virus (AAV) expressing either a short hairpin RNA (shRNA) targeting Acmsd or a scrambled control shRNA to directly test the notion that ACMSD influences 2PY and 4PY levels.

    The researchers also used Mendelian randomization (MR) analysis to determine if genetically higher 2PY and 4PY levels were causally associated with CVD outcomes. They conducted in vitro and in vivo functional studies to investigate whether 2PY or 4PY would induce VCAM-1 expression on endothelial cells. They used in vivo methods to investigate the immediate effects of 2PY or 4PY on arterial VCAM-1 expression and function.

    Results

    Niacin metabolites were associated with an increase in major adverse CVD events (MACEs). Chemical production of authentic 2PY and 4PY standards and additional chemical characterization tests demonstrated that the MACE-associated blood ‘analyte’ with m/z values of 153 Da was a combination of the co-eluting structural isomers 2PY and 4PY with the same elemental composition.

    In the US and European validation cohorts, serological 2PY and 4PY levels showed associations with increased three-year major-type adverse cardiovascular event risk [adjusted hazard ratios (HRs) for 2PY of 1.6 and 2.0, respectively; and for the 4PY metabolite: 1.9 and 2.0, respectively). Elevated 4PY levels were still strongly related to the incidence of major-type adverse cardiovascular event risk in both persons with relatively maintained and compromised renal function.

    A phenome-level association study of the rs10496731 genetic variant, strongly correlated with both metabolite levels, found a link to soluble-type vascular adhesion molecule 1 (sVCAM-1). A meta-analysis found a link between rs10496731 and sVCAM-1 in 106,000 individuals, including 53,075 women. The validation group (974 individuals, 333 females) showed a significant correlation between sVCAM-1 expression and the niacin metabolites.

    4PY metabolite (but not 2PY) administration in physiological amounts increased VCAM-1 expression and leukocyte adhesion to the vascular endothelial cells in murine animals. Both niacin metabolites were related to residual cardiovascular disease risk. The team also proposed an inflammation-dependent mechanism for the clinical connection between the 4PY metabolite and major adverse CVD events.

    The study findings showed that two terminal metabolites of niacin and NAD metabolism, 2PY and 4PY, are associated with CVD regardless of established risk factors. Both metabolites genetically link to vascular inflammation, with a gene variation strongly associated with circulating 2PY and 4PY levels and sVCAM-1 levels. Excess niacin, particularly 4PY, is linked to increased MACE risks and may contribute to residual cardiovascular disease risk via inflammatory pathways. Further research is required to improve understanding of these relationships.

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  • Ramadan fasting linked to favorable metabolic changes and reduced chronic disease risk

    Ramadan fasting linked to favorable metabolic changes and reduced chronic disease risk

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    In a recent study published in The American Journal of Clinical Nutrition, researchers carried out a metabolomics investigation to elucidate the impacts of Ramadan fasting on health and metabolism. Their study group comprised 72 participants who provided blood shortly before and after Ramadan fasting, based on which researchers generated metabolic scores. Study findings, obtained by comparing participants’ metabolic scores against those maintained by the UK Biobank, reveal that Ramadan fasting significantly reduced the risks of lung, colorectal, and breast cancers.

    Study: Metabolomics of Ramadan fasting and associated risk of chronic diseases. Image Credit: Odua Images / ShutterstockStudy: Metabolomics of Ramadan fasting and associated risk of chronic diseases. Image Credit: Odua Images / Shutterstock

    Can depriving your body of food make you healthier?

    Fasting, the intentional abstention from consuming food and sometimes liquids, is practiced for clinical, religious, political, and fitness reasons, the latter of which is rapidly growing in popularity. Reports reveal that globally, many health-conscious individuals are gravitating toward ‘time-restricted fasting,’ an approach that restricts daily eating to a predetermined period each day (usually six to eight hours). Popularized by the term’ intermittent fasting,’ this trend promises general health improvements, weight loss, and fitness benefits.

    Unfortunately, apart from observational evidence for weight loss, comprehensive metabolic and cohort-based studies into the other benefits of time-restricted fasting remain lacking. Ramadan, the Muslim month of fasting, reflection, prayer, and community, shares every trait of time-restricted fasting except its intent (Ramadan is religious fasting). This provides a ‘natural experiment’ to quantify the positive or negative impacts of time-restricted fasting.

    Two previous works have investigated the impacts of Ramadan fasting on health. However, these studies were small-scale (n = 11, 25) and used dated analytical tools focused on overweight and obese individuals not representative of the fitness-oriented. This presents the need for an updated study using the latest metabolomics techniques and a larger, more generalized sample cohort, the results of which will inform the billions of Muslims and health-minded people worldwide.

    About the study

    In the present study, researchers recorded the metabolomics alterations following Ramadan fasting. Their study cohort was the London Ramadan Study (LORANS), an observational cohort comprising 140 Muslims who observe the Ramadan fast. Study data collection included demographic data, medical records, and two blood samples provided a few days before and a few days following initiation of the fast. Additionally, blood pressure and body composition were recorded during routine blood collection.

    Study inclusion criteria comprised age (above 18 years), intended duration of fast (20 days or more), and completed data records. Pregnant women were excluded from the study. Following exclusions due to unmet criteria requirements, 72 participants were included for data analyses, all providing written informed consent to participate in the study.

    Blood samples were processed to separate and isolate the plasma, which in turn was subjected to high-throughput Nuclear Magnetic Resonance (NMR) spectroscopy using the Nightingale platform. The Nightingale platform was chosen due to its ability to identify and quantify 169 lipids and metabolites. It was also selected because the United Kingdom’s (UK) Biobank dataset includes Nightingale platform readings. The UK Biobank is a country-wide large-cohort prospective study comprising 500,000 English citizens representative of the nation.

    Linear mixed-effects models were used to compare NMR readings from blood samples provided before and after fasting, allowing a one-to-one comparison of the metabolite changes arising as outcomes of the fasting process. Additionally, UK Biobank Nightingale platform metabolite readings were used to compute metabolic risk scores for common chronic diseases, including cancers and cardiometabolic disorders. These values were then applied to NMR readings from this study to measure the relative change in chronic disease risk as a consequence of Ramadan (and, by extension, intermittent) fasting.

    Study findings

    Demographic analyses revealed that the mean age of the 72-strong study cohort was 45.7 years, 48.6% (n = 35) of whom were male. Body assessments during blood collection visits showed that, on average, participants lost 1.7 kg and 1.1% of their body fat in the two to three weeks between measurements. Nightingale platform analyses show that of the 169 measured metabolites, 14 were observed to change significantly when comparing blood collections.

    These included one inflammation marker, one amino acid, two glycolysis-related metabolites, two ketone bodies, two triglycerides, and six lipoprotein subclasses. The most significant differences before/after Ramadan were observed for lactate (β = -0.31, P <0.001), acetate (β= -0.22, P <0.001), tyrosine (β= – 0.10, P=0.019) (all inverse) and acetone (β= 0.10, P=0.019) (direct).

    For establishing the metabolic risk scores, baseline characteristics of 117,981 UK Biobank participants were used to establish seven scores, including diabetes (using 46 metabolites), coronary heart disease (16), hypertension (25), renal failure (12), lung cancer (nine), colorectal cancer (two), and breast cancer (one). Applying these scores to present study participants reveals that the relative risk of lung, colorectal, and breast cancers decreased by 9.6%, 2.4%, and 1.1%, respectively. In contrast, the other measured outcomes observed no changes in metabolic risk scores.

    Conclusions

    The present study uses Ramadan fasting as a natural experiment to investigate the effects of time-restricted fasting on people’s health and chronic illness risk. It used cutting-edge, high-throughput NRM spectroscopy via the Nightingale platform to compute chronic disease metabolite risk scores.

    When applied to the 72 included study participants, the metabolic risk scores highlight the beneficial role of Ramadan fasting in reducing the risk of certain cancers such as lung (-9.6%), colorectal (-2.4%), and breast (-1.1%), while having no measurable effects on cardiovascular disease risk.

    Ramadan fasting is associated with short-term favorable changes in the metabolic profile concerning the risk of some chronic diseases. These findings should be further investigated in future, larger studies of longer follow-up with clinical outcomes.

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