Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample

Impact of smoking cessation on non-alcoholic fatty liver disease prevalence: a systematic review and meta-analysis

OBJECTIVES

The negative effects of smoking on numerous cardiovascular and metabolic diseases have been widely acknowledged. However, the potential effect of smoking cessation is relatively unelucidated. The objective of this study is to explore whether the prevalence of non-alcoholic fatty liver disease (NAFLD) in former smokers differs from the prevalence in current smokers.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Four databases, that is, PubMed, Web of Science, Journal@Ovid and Scopus were searched from inception to 31 January 2023.

ELIGIBILITY CRITERIA

Population-based cross-sectional studies, including the baseline data of cohort studies with identified NAFLD diagnostic methods, and smoking status (current smoker or former smoker) of participants were included.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently extracted the data including cigarette smoking status, country/region of studies, NAFLD diagnostic methods, sex, the average age and body mass index (BMI) of NAFLD participants and assessed the risk of bias with Agency for Healthcare Research and Quality (AHRQ) methodology checklist. Risk ratio (RR) of NAFLD prevalence in former smokers was pooled using the random-effects model.

RESULTS

28 studies involving 4 465 862 participants were included. Compared with current smokers, the RR of overall NAFLD prevalence in former smokers was 1.13 (95% CI: 1.08 to 1.19, prediction interval: 0.92-1.39). This result persisted after adjustment for diagnostic methods, country/region, sex, age and BMI. Sensitivity analysis and risk of bias assessment indicated a stable conclusion.

CONCLUSIONS

NAFLD prevalence in former smokers was at least not lower than that in current smokers and was partially related to increased BMI after smoking cessation, indicating that smoking cessation was possibly not a protective factor against NAFLD. Although the meta-analysis based on cross-sectional studies cannot conclude the causal relationships between smoking cessation and NAFLD onset, the potential onset of NAFLD associated with smoking cessation should be highlighted.

PROSPERO REGISTRATION NUMBER

CRD42023394944.

Identification of Dietary Supplements Associated with Blood Metabolites in the Hispanic Community Health Study/Study of Latinos Cohort Study

BACKGROUND

Metabolomics approaches have been widely used to define the consumption of foods but have less often been used to study exposure to dietary supplements.

OBJECTIVES

This study aimed to identify dietary supplements associated with metabolite levels and to examine whether these metabolites predicted incident diabetes risk.

METHODS

We studied 3972 participants from a prospective cohort study of 18-74-y-old Hispanic/Latino adults. At a baseline examination, we ascertained use of dietary supplements using recall methods and concurrently, a serum metabolomic panel. After adjustment for potential confounders, we identified dietary supplements associated with metabolites. We then examined the association of these metabolites with incident diabetes at the 6-y study examination.

RESULTS

We observed a total of 110 dietary supplement-metabolite associations that met the criteria for statistical significance adjusted for age, sex, field center, Hispanic/Latino background, body mass index, diet, smoking, physical activity, and number of medications (adjusted P < 0.05). This included 13 metabolites uniquely associated with only one dietary supplement ingredient. Vitamin C had the most associated metabolites (n = 15), including positive associations with oxalate, tartronate, threonate, and isocitrate, which were each in turn protective for the risk of incident diabetes. Vitamin C was also associated with higher N-acetylvaline level, which was an unfavorable diabetes risk factor. Other findings related to branched chain amino acid related compounds including α-hydroxyisovalerate and 2-hydroxy-3-methylvalerate, which were inversely associated with thiamine or riboflavin intake and also predicted higher diabetes risk. Vitamin B12 had an inverse association with γ-glutamylvaline, levels of which were positively associated with the risk of diabetes.

CONCLUSIONS

Our data point to potential metabolite changes associated with vitamin C and B vitamins, which may have favorable metabolic effects. Knowledge of blood metabolites that can be modified by dietary supplement intake may aid understanding the health effects of dietary supplements and identify potential biological mediators.

Application of metabolomics in the diagnosis of non-alcoholic fatty liver disease and the treatment of traditional Chinese medicine

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease around the world, and it often coexists with insulin resistance-related diseases including obesity, diabetes, hyperlipidemia, and hypertension, which seriously threatens human health. Better prevention and treatment strategies are required to improve the impact of NAFLD. Although needle biopsy is an effective tool for diagnosing NAFLD, this method is invasive and difficult to perform. Therefore, it is very important to develop more efficient approaches for the early diagnosis of NAFLD. Traditional Chinese medicine (TCM) can play a certain role in improving symptoms and protecting target organs, and its mechanism of action needs to be further studied. Metabolomics, the study of all metabolites that is thought to be most closely associated with the patients’ characters, can provide useful clinically biomarkers that can be applied to NAFLD and may open up new methods for diagnosis. Metabolomics technology is consistent with the overall concept of TCM, and it can also be used as a potential mechanism to explain the effects of TCM by measuring biomarkers by metabolomics. Based on PubMed/MEDLINE and other databases, this paper retrieved relevant literature NAFLD and TCM intervention in NAFLD using metabolomics technology in the past 5 years were searched, and the specific metabolites associated with the development of NAFLD and the potential mechanism of Chinese medicine on improving symptoms were summarized.

Amino Acid-Related Metabolic Signature in Obese Children and Adolescents

The growing interest in metabolomics has spread to the search for suitable predictive biomarkers for complications related to the emerging issue of pediatric obesity and its related cardiovascular risk and metabolic alteration. Indeed, several studies have investigated the association between metabolic disorders and amino acids, in particular branched-chain amino acids (BCAAs). We have performed a revision of the literature to assess the role of BCAAs in children and adolescents' metabolism, focusing on the molecular pathways involved. We searched on Pubmed/Medline, including articles published until February 2022. The results have shown that plasmatic levels of BCAAs are impaired already in obese children and adolescents. The relationship between BCAAs, obesity and the related metabolic disorders is explained on one side by the activation of the mTORC1 complex-that may promote insulin resistance-and on the other, by the accumulation of toxic metabolites, which may lead to mitochondrial dysfunction, stress kinase activation and damage of pancreatic cells. These compounds may help in the precocious identification of many complications of pediatric obesity. However, further studies are still needed to better assess if BCAAs may be used to screen these conditions and if any other metabolomic compound may be useful to achieve this goal.

Adiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study

BACKGROUND

Globally, the burden of obesity and associated nonalcoholic fatty liver disease (NAFLD) are rising, but little is known about the role that circulating metabolomic biomarkers play in mediating their association.

OBJECTIVES

We aimed to examine the observational and genetic associations of adiposity with metabolomic biomarkers and the observational associations of metabolomic biomarkers with incident NAFLD.

METHODS

A case-subcohort study within the prospective China Kadoorie Biobank included 176 NAFLD cases and 180 subcohort individuals and measured 1208 metabolites in stored baseline plasma using a Metabolon assay. In the subcohort the observational and genetic associations of BMI with biomarkers were assessed using linear regression, with adjustment for multiple testing. Cox regression was used to estimate adjusted HRs for NAFLD associated with biomarkers.

RESULTS

In observational analyses, BMI (kg/m2; mean: 23.9 in the subcohort) was associated with 199 metabolites at a 5% false discovery rate. The effects of genetically elevated BMI with specific metabolites were directionally consistent with the observational associations. Overall, 35 metabolites were associated with NAFLD risk, of which 15 were also associated with BMI, including glutamate (HR per 1-SD higher metabolite: 1.95; 95% CI: 1.48, 2.56), cysteine-glutathione disulfide (0.44; 0.31, 0.62), diaclyglycerol (C32:1) (1.71; 1.24, 2.35), behenoyl dihydrosphingomyelin (C40:0) (1.92; 1.42, 2.59), butyrylcarnitine (C4) (1.91; 1.38, 2.35), 2-hydroxybehenate (1.81; 1.34, 2.45), and 4-cholesten-3-one (1.79; 1.27, 2.54). The discriminatory performance of known risk factors was increased when 28 metabolites were also considered simultaneously in the model (weighted C-statistic: 0.84 to 0.90; P  < 0.001).

CONCLUSIONS

Among relatively lean Chinese adults, a range of metabolomic biomarkers are associated with NAFLD risk and these biomarkers may lie on the pathway between adiposity and NAFLD.

Metabolomics and lipidomics in NAFLD: biomarkers and non-invasive diagnostic tests

Nonalcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide and is often associated with aspects of metabolic syndrome. Despite its prevalence and the importance of early diagnosis, there is a lack of robustly validated biomarkers for diagnosis, prognosis and monitoring of disease progression in response to a given treatment. In this Review, we provide an overview of the contribution of metabolomics and lipidomics in clinical studies to identify biomarkers associated with NAFLD and nonalcoholic steatohepatitis (NASH). In addition, we highlight the key metabolic pathways in NAFLD and NASH that have been identified by metabolomics and lipidomics approaches and could potentially be used as biomarkers for non-invasive diagnostic tests. Overall, the studies demonstrated alterations in amino acid metabolism and several aspects of lipid metabolism including circulating fatty acids, triglycerides, phospholipids and bile acids. Although we report several studies that identified potential biomarkers, few have been validated.

A branched-chain amino acid-based metabolic score can predict liver fat in children and adolescents with severe obesity

BACKGROUND

Eighty percent of adolescents with severe obesity suffer from non-alcoholic fatty liver disease (NAFLD). Non-invasive prediction models have been tested in adults, however, they performed poorly in paediatric populations.

OBJECTIVE

This study aimed to investigate novel biomarkers for NAFLD and to develop a score that predicts liver fat in youth with severe obesity.

METHODS

From a population with a BMI >97th percentile aged 9-19 years (n = 68), clinically thoroughly characterized including MRI-derived proton density fat fraction (MRI-PDFF), amino acids and acylcarnitines were measured by HPLC-MS.

RESULTS

In children with NAFLD, higher levels of plasma branched-chain amino acids (BCAA) were determined. BCAAs correlated with MRI-PDFF (R = 0.46, p < .01). We identified a linear regression model adjusted for age, sex and pubertal stage consisting of BCAAs, ALT, GGT, ferritin and insulin that predicted MRI-PDFF (R = 0.75, p < .01). ROC analysis of this model revealed AUCs of 0.85, 0.85 and 0.92 for the detection of any, moderate and severe steatosis, respectively, thus markedly outperforming previously published scores.

CONCLUSION

BCAAs could be an important link between obesity and other metabolic pathways. A BCAA-based metabolic score can predict steatosis grade in high-risk children and adolescents and may provide a feasible alternative to sophisticated methods like MRI or biopsy in the future.

Metabolic perturbations prior to hepatocellular carcinoma diagnosis: Findings from a prospective observational cohort study

Hepatocellular carcinoma (HCC) development entails changes in liver metabolism. Current knowledge on metabolic perturbations in HCC is derived mostly from case-control designs, with sparse information from prospective cohorts. Our objective was to apply comprehensive metabolite profiling to detect metabolites whose serum concentrations are associated with HCC development, using biological samples from within the prospective European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (>520 000 participants), where we identified 129 HCC cases matched 1:1 to controls. We conducted high-resolution untargeted liquid chromatography-mass spectrometry-based metabolomics on serum samples collected at recruitment prior to cancer diagnosis. Multivariable conditional logistic regression was applied controlling for dietary habits, alcohol consumption, smoking, body size, hepatitis infection and liver dysfunction. Corrections for multiple comparisons were applied. Of 9206 molecular features detected, 220 discriminated HCC cases from controls. Detailed feature annotation revealed 92 metabolites associated with HCC risk, of which 14 were unambiguously identified using pure reference standards. Positive HCC-risk associations were observed for N1-acetylspermidine, isatin, p-hydroxyphenyllactic acid, tyrosine, sphingosine, l,l-cyclo(leucylprolyl), glycochenodeoxycholic acid, glycocholic acid and 7-methylguanine. Inverse risk associations were observed for retinol, dehydroepiandrosterone sulfate, glycerophosphocholine, γ-carboxyethyl hydroxychroman and creatine. Discernible differences for these metabolites were observed between cases and controls up to 10 years prior to diagnosis. Our observations highlight the diversity of metabolic perturbations involved in HCC development and replicate previous observations (metabolism of bile acids, amino acids and phospholipids) made in Asian and Scandinavian populations. These findings emphasize the role of metabolic pathways associated with steroid metabolism and immunity and specific dietary and environmental exposures in HCC development.

The Plasma Metabolomic Profile is Differently Associated with Liver Fat, Visceral Adipose Tissue, and Pancreatic Fat

CONTEXT

Metabolic differences between ectopic fat depots may provide novel insights to obesity-related diseases.

OBJECTIVE

To investigate the plasma metabolomic profiles in relation to visceral adipose tissue (VAT) volume and liver and pancreas fat percentages.

DESIGN

Cross-sectional.

SETTING

Multicenter at academic research laboratories.

PATIENTS

Magnetic resonance imaging (MRI) was used to assess VAT volume, the percentage of fat in the liver and pancreas (proton density fat fraction [PDFF]) at baseline in 310 individuals with a body mass index ≥ 25 kg/m2 and with serum triglycerides ≥ 1.7 mmol/l and/or type 2 diabetes screened for inclusion in the 2 effect of omega-3 carboxylic acid on liver fat content studies.

INTERVENTION

None.

MAIN OUTCOME MEASURE

Metabolomic profiling with mass spectroscopy enabled the determination of 1063 plasma metabolites.

RESULTS

Thirty metabolites were associated with VAT volume, 31 with liver PDFF, and 2 with pancreas PDFF when adjusting for age, sex, total body fat mass, and fasting glucose. Liver PDFF and VAT shared 4 metabolites, while the 2 metabolites related to pancreas PDFF were unique. The top metabolites associated with liver PDFF were palmitoyl-palmitoleoyl-GPC (16:0/16:1), dihydrosphingomyelin (d18:0/22:0), and betaine. The addition of these metabolites to the Liver Fat Score improved C-statistics significantly (from 0.776 to 0.861, P = 0.0004), regarding discrimination of liver steatosis.

CONCLUSION

Liver PDFF and VAT adipose tissue shared several metabolic associations, while those were not shared with pancreatic PDFF, indicating partly distinct metabolic profiles associated with different ectopic fat depots. The addition of 3 metabolites to the Liver Fat Score improved the prediction of liver steatosis.

Insights into genetic variants associated with NASH-fibrosis from metabolite profiling

Several genetic discoveries robustly implicate five single-nucleotide variants in the progression of non-alcoholic fatty liver disease to non-alcoholic steatohepatitis and fibrosis (NASH-fibrosis), including a recently identified variant in MTARC1. To better understand these variants as potential therapeutic targets, we aimed to characterize their impact on metabolism using comprehensive metabolomics data from two population-based studies. A total of 9135 participants from the Fenland study and 9902 participants from the EPIC-Norfolk cohort were included in the study. We identified individuals with risk alleles associated with NASH-fibrosis: rs738409C>G in PNPLA3, rs58542926C>T in TM6SF2, rs641738C>T near MBOAT7, rs72613567TA>T in HSD17B13 and rs2642438A>G in MTARC1. Circulating levels of 1449 metabolites were measured using targeted and untargeted metabolomics. Associations between NASH-fibrosis variants and metabolites were assessed using linear regression. The specificity of variant-metabolite associations were compared to metabolite associations with ultrasound-defined steatosis, gene variants linked to liver fat (in GCKR, PPP1R3B and LYPLAL1) and gene variants linked to cirrhosis (in HFE and SERPINA1). Each NASH-fibrosis variant demonstrated a specific metabolite profile with little overlap (8/97 metabolites) comprising diverse aspects of lipid metabolism. Risk alleles in PNPLA3 and HSD17B13 were both associated with higher 3-methylglutarylcarnitine and three variants were associated with lower lysophosphatidylcholine C14:0. The risk allele in MTARC1 was associated with higher levels of sphingomyelins. There was no overlap with metabolites that associated with HFE or SERPINA1 variants. Our results suggest a link between the NASH-protective variant in MTARC1 to the metabolism of sphingomyelins and identify distinct molecular patterns associated with each of the NASH-fibrosis variants under investigation.

Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake

Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

BACKGROUND

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

METHODS AND FINDINGS

We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one.

CONCLUSIONS

In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03814915.

Sugar-Induced Obesity and Insulin Resistance Are Uncoupled from Shortened Survival in Drosophila

High-sugar diets cause thirst, obesity, and metabolic dysregulation, leading to diseases including type 2 diabetes and shortened lifespan. However, the impact of obesity and water imbalance on health and survival is complex and difficult to disentangle. Here, we show that high sugar induces dehydration in adult Drosophila, and water supplementation fully rescues their lifespan. Conversely, the metabolic defects are water-independent, showing uncoupling between sugar-induced obesity and insulin resistance with reduced survival in vivo. High-sugar diets promote accumulation of uric acid, an end-product of purine catabolism, and the formation of renal stones, a process aggravated by dehydration and physiological acidification. Importantly, regulating uric acid production impacts on lifespan in a water-dependent manner. Furthermore, metabolomics analysis in a human cohort reveals that dietary sugar intake strongly predicts circulating purine levels. Our model explains the pathophysiology of high-sugar diets independently of obesity and insulin resistance and highlights purine metabolism as a pro-longevity target.

Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition

Obesity and type 2 diabetes (T2D) are metabolic disorders that are linked to microbiome alterations. However, their co-occurrence poses challenges in disentangling microbial features unique to each condition. We analyzed gut microbiomes of lean non-diabetic (n = 633), obese non-diabetic (n = 494), and obese individuals with T2D (n = 153) from German population and metabolic disease cohorts. Microbial taxonomic and functional profiles were analyzed along with medical histories, serum metabolomics, biometrics, and dietary data. Obesity was associated with alterations in microbiome composition, individual taxa, and functions with notable changes in Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes, as well as in serum metabolites that correlated with gut microbial patterns. However, microbiome associations were modest for T2D, with nominal increases in Escherichia/Shigella. Medications, including antihypertensives and antidiabetics, along with dietary supplements including iron, were significantly associated with microbiome variation. These results differentiate microbial components of these interrelated metabolic diseases and identify dietary and medication exposures to consider in future studies.

Metabolomics reveals plausible interactive effects between dairy product consumption and metabolic syndrome in humans

BACKGROUND & AIMS

Metabolic syndrome (MetS) induces major disturbances in plasma metabolome, reflecting abnormalities of several metabolic pathways. Recent evidences have demonstrated that the consumption of dairy products may protect from MetS, but the mechanisms remains unknown. The present study aimed at identify how the consumption of different types of dairy products could modify the changes in plasma metabolome during MetS.

METHODS

In this observational study, we analyzed how the consumption of dairy products could modify the perturbations in the plasma metabolome induced by MetS in a sample of 298 participants (61 with MetS) from the French MONA LISA survey. Metabolomic profiling was analyzed with UPLC-MS/MS.

RESULTS

Subjects with MetS exhibited major changes in plasma metabolome. Significant differences in plasma levels of branched chain amino acids, gamma-glutamyl amino acids, and metabolites from arginine and proline metabolism were observed between healthy control and Mets subjects. Plasma levels of many lipid species were increased with MetS (mono- and diacylglycerols, eicosanoids, lysophospholipids and lysoplasmalogens), with corresponding decreases in short chain fatty acids and plasmalogens. The consumption of dairy products, notably with a low fat content (milk and fresh dairy products), altered metabolite profiles in plasma from MetS subjects. Specifically, increasing consumption of dairy products promoted accumulation of plasma C15:0 fatty acid and was inversely associated to some circulating lysophospholipids, sphingolipids, gamma-glutamyl amino acids, leukotriene B4 and lysoplasmalogens.

CONCLUSIONS

the consumption of low fat dairy products could mitigate some of the variations induced by MetS.

Metabolomics signature associated with circulating serum selenoprotein P levels

PURPOSE

Selenoprotein P (SELENOP) has been previously related to various metabolic traits with partially conflicting results. The identification of SELENOP-associated metabolites, using an untargeted metabolomics approach, may provide novel biological insights relevant to disentangle the role of SELENOP in human health.

METHODS

In this cross-sectional study, 572 serum metabolites were identified by comparing the obtained LC-MS/MS spectra with spectra stored in Metabolon's spectra library. Serum SELENOP levels were measured in 832 men and women using an ELISA kit.

RESULTS

Circulating SELENOP levels were associated with 24 out of 572 metabolites after accounting for the number of independent dimensions in the metabolomics data, including inverse associations with alanine, glutamate, leucine, isoleucine and valine, an unknown compound X-12063, urate and the peptides gamma-glutamyl-leucine, and N-acetylcarnosine. Positive associations were observed between SELENOP and several lipid compounds. Of the identified metabolites, each standard deviation increase in the branched-chain amino acids (isoleucine, leucine, valine), alanine and gamma-glutamyl-leucine was related to higher odds of having T2DM [OR (95% CI): 1.96 (1.41-2.73); 1.62 (1.15-2.28); 1.94 (1.45-2.60), 1.57 (1.17-2.11), and 1.52 (1.13-2.05), respectively].

CONCLUSIONS

Higher serum SELENOP levels were associated with an overall healthy metabolomics profile, which may provide further insights into potential mechanisms of SELENOP-associated metabolic disorders.