Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

NMR metabolomic modeling of age and lifespan: A multicohort analysis

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

Associations between sarcopenia and circulating branched-chain amino acids: a cross-sectional study over 100,000 participants

BACKGROUND

Emerging evidence suggests that alterations in BCAA metabolism may contribute to the pathogenesis of sarcopenia. However, the relationship between branched-chain amino acids (BCAAs) and sarcopenia is incompletely understood, and existing literature presents conflicting results. In this study, we conducted a community-based study involving > 100,000 United Kingdom adults to comprehensively explore the association between BCAAs and sarcopenia, and assess the potential role of muscle mass in mediating the relationship between BCAAs and muscle strength.

METHODS

Multivariable linear regression analysis examined the relationship between circulating BCAAs and muscle mass/strength. Logistic regression analysis assessed the impact of circulating BCAAs and quartiles of BCAAs on sarcopenia risk. Subgroup analyses explored the variations in associations across age, and gender. Mediation analysis investigated the potential mediating effect of muscle mass on the BCAA-muscle strength relationship.

RESULTS

Among 108,017 participants (mean age: 56.40 ± 8.09 years; 46.23% men), positive associations were observed between total BCAA, isoleucine, leucine, valine, and muscle mass (beta, 0.56-2.53; p < 0.05) and between total BCAA, leucine, valine, and muscle strength (beta, 0.91-3.44; p < 0.05). Logistic regression analysis revealed that increased circulating valine was associated with a 47% reduced sarcopenia risk (odds ratio = 0.53; 95% confidence interval = 0.3-0.94; p = 0.029). Subgroup analyses demonstrated strong associations between circulating BCAAs and muscle mass/strength in men and individuals aged ≥ 60 years. Mediation analysis suggested that muscle mass completely mediated the relationship between total BCAA, and valine levels and muscle strength, partially mediated the relationship between leucine levels and muscle strength, obscuring the true effect of isoleucine on muscle strength.

CONCLUSION

This study suggested the potential benefits of BCAAs in preserving muscle mass/strength and highlighted muscle mass might be mediator of BCAA-muscle strength association. Our findings contribute new evidence for the clinical prevention and treatment of sarcopenia and related conditions involving muscle mass/strength loss.

A metabolic profile of xenon and metabolite associations with 6-month mortality after out-of-hospital cardiac arrest: A post-hoc study of the randomised Xe-Hypotheca trial

PURPOSE

Out-of-hospital cardiac arrest (OHCA) carries a relatively poor prognosis and requires multimodal prognostication to guide clinical decisions. Identification of previously unrecognized metabolic routes associated with patient outcome may contribute to future biomarker discovery. In OHCA, inhaled xenon elicits neuro- and cardioprotection. However, the metabolic effects remain unknown.

MATERIALS AND METHODS

In this post-hoc study of the randomised, 2-group, single-blind, phase 2 Xe-Hypotheca trial, 110 OHCA survivors were randomised 1:1 to receive targeted temperature management (TTM) at 33°C with or without inhaled xenon during 24 h. Blood samples for nuclear magnetic resonance spectroscopy metabolic profiling were drawn upon admission, at 24 and 72 h.

RESULTS

At 24 h, increased lactate, adjusted hazard-ratio 2.25, 95% CI [1.53; 3.30], p<0.001, and decreased branched-chain amino acids (BCAA) leucine 0.64 [0.5; 0.82], p = 0.007, and valine 0.37 [0.22; 0.63], p = 0.003, associated with 6-month mortality. At 72 h, increased lactate 2.77 [1.76; 4.36], p<0.001, and alanine 2.43 [1.56; 3.78], p = 0.001, and decreased small HDL cholesterol ester content (S-HDL-CE) 0.36 [0.19; 0.68], p = 0.021, associated with mortality. No difference was observed between xenon and control groups.

CONCLUSIONS

In OHCA patients receiving TTM with or without xenon, high lactate and alanine and decreased BCAAs and S-HDL-CE associated with increased mortality. It remains to be established whether current observations on BCAAs, and possibly alanine and lactate, could reflect neural damage via their roles in the metabolism of the neurotransmitter glutamate. Xenon did not significantly alter the measured metabolic profile, a potentially beneficial attribute in the context of compromised ICU patients.

TRIAL REGISTRATION

Trial Registry number: ClinicalTrials.gov Identifier: NCT00879892.

The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual's survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual's aging status.

Clinical metabolomics: Useful insights, perspectives and challenges

Metabolomics, a cutting-edge omics technique, is a rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and the discovery of novel metabolite signatures predictive of disease risk, aiding in earlier disease detection, prognosis and prediction of treatment response. The capacity of this omics approach to simultaneously quantify thousands of metabolites, i.e. small molecules less than 1500 Da in samples, positions it as a promising tool for research and clinical applications in personalized medicine. Clinical metabolomics studies have proven valuable in understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers predictive of disease risk. Liquid chromatography-mass spectrometry is the predominant analytical method used in metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, clinical chemistry data, imaging, health records, and other pertinent health-related data may yield significant advances beneficial for both public health initiatives, clinical applications and precision medicine, particularly in rare disorders and multimorbidity. This special issue has gathered original research articles in topics related to clinical metabolomics as well as research articles, reviews, perspectives and highlights in the broader field of translational and clinical metabolic research. Additional research is necessary to identify which metabolites consistently enhance clinical risk prediction across various populations and are causally linked to disease progression.

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases

BACKGROUND AND AIMS

A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheimer's disease (AD).

MATERIALS AND METHODS

The eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model.

RESULTS

The results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039-0.279), 0.113 (0.005-0.176), and 0.201 (-0.021-0.423), respectively; and IDI values of 0.098 (0.073-0.122), 0.070 (0.049-0.090), and 0.085 (0.068-0.101), respectively.

CONCLUSION

The performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.

Fish oil supplement use modifies the relationship between dietary oily fish intake and plasma n-3 PUFA levels: an analysis of the UK Biobank

Observational evidence linking dietary n-3 PUFA intake and health outcomes is limited by a lack of robust validation of dietary intake using blood n-3 PUFA levels and potential confounding by fish oil supplement (FOS) use. We investigated the relationship between oily fish intake, FOS use and plasma n-3 PUFA levels in 121 650 UK Biobank (UKBB) participants. Ordinal logistic regression models, adjusted for clinical and lifestyle factors, were used to quantify the contribution of dietary oily fish intake and FOS use to plasma n-3 PUFA levels (measured by NMR spectroscopy). Oily fish intake and FOS use were reported by 38 % and 31 % of participants, respectively. Increasing oily fish intake was associated with a higher likelihood of FOS use (P < 0·001). Oily fish intake ≥ twice a week was the strongest predictor of high total n-3 PUFA (OR 6·7 (95 % CI 6·3, 7·1)) and DHA levels (6·6 (6·3, 7·1). FOS use was an independent predictor of high plasma n-3 PUFA levels (2·0 (2·0, 2·1)) with a similar OR to that associated with eating oily fish < once a week (1·9 (1·8, 2·0)). FOS use was associated with plasma n-3 PUFA levels that were similar to individuals in the next highest oily fish intake category. In conclusion, FOS use is more common in frequent fish consumers and modifies the relationship between oily fish intake and plasma n-3 PUFA levels in UKBB participants. If unaccounted for, FOS use may confound the relationship between dietary n-3 PUFA intake, blood levels of n-3 PUFAs and health outcomes.

Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank

AIMS/HYPOTHESIS

The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers.

METHODS

From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts.

RESULTS

At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts.

CONCLUSIONS/INTERPRETATION

Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.

Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study

It is unclear how metabolomic age is associated with the risk of a wide range of chronic diseases. Our analysis included 110,692 participants (training: n = 27,673; testing: n = 27,673; validating: n = 55,346) aged 39-71 years at baseline (2006-2010) from the UK Biobank. Incident chronic diseases were identified using inpatient records, or death registers until January 2021. Predicted metabolomic age was trained and tested based on 168 metabolomics. Metabolomic age was linked to the risk of 50 diseases in the validation dataset. The median follow-up duration for individual diseases ranged from 11.2 years to 11.9 years. After controlling for false discovery rate, chronological age-adjusted age gap (CAAG) was significantly associated with the incidence of 25 out of 50 chronic diseases. After adjustment for full covariates, associations with 15 chronic diseases remained significant. Greater CAAG was associated with increased risk of eight cardiometabolic disorders (including cardiovascular diseases and diabetes), some cancers, alcohol use disorder, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease and age-related macular degeneration. The association between CAAG and risk of peripheral vascular disease, other cardiac diseases, fracture, cataract and thyroid disorder was stronger among individuals with unhealthy diet than in those with healthy diet. The association between CAAG and risk of some conditions was stronger in younger individuals, those with metabolic disorders or low education. Metabolomic age plays an important role in the development of multiple chronic diseases. Healthy diet and high education may mitigate the risk for some chronic diseases due to metabolomic age acceleration.

Association of ketone bodies with incident CKD and death: A UK Biobank study

AIMS

Although cellular and animal models have suggested a protective effect of ketone bodies (KBs), clinical data are still lacking to support these findings. This study aimed to investigate the association of KB levels with incident chronic kidney disease (CKD) and death.

METHODS

This was a prospective cohort study of 87,899 UK Biobank participants without baseline CKD who had plasma levels of β-hydroxybutyrate, acetoacetate, and acetone levels measured at the time of enrollment. The main predictor was plasma total KB, which was the sum of the aforementioned three KBs. The primary outcome was a composite of incident CKD, or all-cause mortality. Secondary outcomes included the individual components of the primary outcome.

RESULTS

During a median follow-up of 11.9 years, a total of 8,145 primary outcome events occurred (incidence rate 8.0/1,000 person-years). In the multivariable Cox model, a 1-standard deviation increase in log total KB was associated with a 7 % [adjusted hazard ratio (aHR), 1.07; 95 % confidence interval (CI), 1.05-1.10] higher risk of the primary outcome. When stratified into quartiles, the aHR (95 % CI) for Q4 versus Q1 was 1.18 (1.11-1.27). This association was consistent for incident CKD (aHR, 1.04; 95 % CI, 1.01-1.07), and all-cause mortality (aHR, 1.10; 95 % CI, 1.07-1.13). Compared with Q1, Q4 was associated with a 12 % (aHR 1.12; 95 % CI 1.02-1.24) and 26 % (aHR 1.26; 95 % CI 1.15-1.37) higher risk of incident CKD and all-cause mortality, respectively.

CONCLUSIONS

Higher KB levels were independently associated with higher risk of incident CKD and death.

Remnant Cholesterol, Not LDL Cholesterol, Explains Peripheral Artery Disease Risk Conferred by apoB: A Cohort Study

BACKGROUND

Elevated apoB-containing lipoproteins (=remnants+LDLs [low-density lipoproteins]) are a major risk factor for atherosclerotic cardiovascular disease, including peripheral artery disease (PAD) and myocardial infarction. We tested the hypothesis that remnants and LDL both explain part of the increased risk of PAD conferred by elevated apoB-containing lipoproteins. For comparison, we also studied the risk of chronic limb-threatening ischemia and myocardial infarction.

METHODS

apoB, remnant cholesterol, and LDL cholesterol were measured in 93 461 individuals without statin use at baseline from the Copenhagen General Population Study (2003-2015). During up to 15 years of follow-up, 1207 had PAD, 552 had chronic limb-threatening ischemia, and 2022 had myocardial infarction in the Danish National Patient Registry. Remnant and LDL cholesterol were calculated from a standard lipid profile. Remnant and LDL particle counts were additionally measured with nuclear magnetic resonance spectroscopy in 25 347 of the individuals. Results were replicated in 302 167 individuals without statin use from the UK Biobank (2004-2010).

RESULTS

In the Copenhagen General Population Study, multivariable adjusted hazard ratios for risk of PAD per 1 mmol/L (39 mg/dL) increment in remnant and LDL cholesterol were 1.9 (95% CI, 1.5-2.4) and 1.1 (95% CI, 1.0-1.2), respectively; corresponding results in the UK Biobank were 1.7 (95% CI, 1.4-2.1) and 0.9 (95% CI, 0.9-1.0), respectively. In the association from elevated apoB to increased risk of PAD, remnant and LDL cholesterol explained 73% (32%-100%) and 8% (0%-46%), respectively; corresponding results were 63% (30%-100%) and 0% (0%-33%) for risk of chronic limb-threatening ischemia and 41% (27%-55%) and 54% (38%-70%) for risk of myocardial infarction; results for remnant and LDL particle counts corroborated these findings.

CONCLUSIONS

PAD risk conferred by elevated apoB-containing lipoproteins was explained mainly by elevated remnants, while myocardial infarction risk was explained by both elevated remnants and LDL.

Influence of age and sex on longitudinal metabolic profiles and body weight trajectories in the UK Biobank

BACKGROUND

Accurate characterization of how age influences body weight and metabolism at different stages of life is important for understanding ageing processes. Here, we explore observational longitudinal associations between metabolic health and weight from the fifth to the seventh decade of life, using carefully adjusted statistical designs.

METHODS

Body measures and biochemical data from blood and urine (220 measures) across two visits were available from 10 104 UK Biobank participants. Participants were divided into stable (within ±4% per decade), weight loss and weight gain categories. Final subgroups were metabolically matched at baseline (48% women, follow-up 4.3 years, ages 41-70; n = 3368 per subgroup) and further stratified by the median age of 59.3 years and sex.

RESULTS

Pulse pressure, haemoglobin A1c and cystatin-C tracked ageing consistently (P < 0.0001). In women under 59, age-associated increases in citrate, pyruvate, alkaline phosphatase and calcium were observed along with adverse changes across lipoprotein measures, fatty acid species and liver enzymes (P < 0.0001). Principal component analysis revealed a qualitative sex difference in the temporal relationship between body weight and metabolism: weight loss was not associated with systemic metabolic improvement in women, whereas both age strata converged consistently towards beneficial (weight loss) or adverse (weight gain) phenotypes in men.

CONCLUSIONS

We report longitudinal ageing trends for 220 metabolic measures in absolute concentrations, many of which have not been described for older individuals before. Our results also revealed a fundamental dynamic sex divergence that we speculate is caused by menopause-driven metabolic deterioration in women.

Higher ratio of plasma omega-6/omega-3 fatty acids is associated with greater risk of all-cause, cancer, and cardiovascular mortality: A population-based cohort study in UK Biobank

Background

Circulating omega-3 and omega-6 polyunsaturated fatty acids (PUFAs) have been associated with various chronic diseases and mortality, but results are conflicting. Few studies examined the role of omega-6/omega-3 ratio in mortality.

Methods

We investigated plasma omega-3 and omega-6 PUFAs and their ratio in relation to all-cause and cause-specific mortality in a large prospective cohort, the UK Biobank. Of 85,425 participants who had complete information on circulating PUFAs, 6461 died during follow-up, including 2794 from cancer and 1668 from cardiovascular disease (CVD). Associations were estimated by multivariable Cox proportional hazards regression with adjustment for relevant risk factors.

Results

Risk for all three mortality outcomes increased as the ratio of omega-6/omega-3 PUFAs increased (all Ptrend <0.05). Comparing the highest to the lowest quintiles, individuals had 26% (95% CI, 15-38%) higher total mortality, 14% (95% CI, 0-31%) higher cancer mortality, and 31% (95% CI, 10-55%) higher CVD mortality. Moreover, omega-3 and omega-6 PUFAs in plasma were all inversely associated with all-cause, cancer, and CVD mortality, with omega-3 showing stronger effects.

Conclusions

Using a population-based cohort in UK Biobank, our study revealed a strong association between the ratio of circulating omega-6/omega-3 PUFAs and the risk of all-cause, cancer, and CVD mortality.

Funding

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institute of Health under the award number R35GM143060 (KY). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Adults prenatally exposed to the Dutch Famine exhibit a metabolic signature associated with a broad spectrum of common diseases

Background

Exposure to famine in the prenatal period is associated with an increased risk of metabolic disease, including obesity and type-2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profiling to provide a deeper insight into the metabolic changes associated with survival of prenatal famine exposure during the Dutch Famine at the end of World War II and explore their link to disease.

Methods

NMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine exposure with those of 154 common diseases.

Results

Prenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-0.3 SD, p < 3x10-3). The metabolic biomarker signature of prenatal famine exposure was positively correlated to that of incident type-2 diabetes (r = 0.77, p = 3x10-27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1x10-18). Remarkably, this association extended to 115 common diseases for which signatures were available (0.3 ≤ r ≤ 0.9, p < 3.2x10-4). Correlations among metabolic signatures of famine exposure and disease outcomes were attenuated when the famine signature was adjusted for body mass index.

Conclusions

Prenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvement of obesity.

Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank

The identification of metabolic biomarkers for aging-related diseases and mortality is of significant interest in the field of longevity. In this study, we investigated the associations between nuclear magnetic resonance (NMR) metabolomics biomarkers and aging-related diseases as well as mortality using the UK Biobank dataset. We analyzed NMR samples from approximately 110,000 participants and used multi-head machine learning classification models to predict the incidence of aging-related diseases. Cox regression models were then applied to assess the relevance of NMR biomarkers to the risk of death due to aging-related diseases. Additionally, we conducted survival analyses to evaluate the potential improvements of NMR in predicting survival and identify the biomarkers most strongly associated with negative health outcomes by dividing participants into health, disease, and death groups for all age groups. Our analysis revealed specific metabolomics profiles that were associated with the incidence of age-related diseases, and the most significant biomarker was intermediate density lipoprotein cholesteryl (IDL-CE). In addition, NMR biomarkers could provide additional contributions to relevant mortality risk prediction when combined with conventional risk factors, by improving the C-index from 0.813 to 0.833, with 17 NMR biomarkers significantly contributing to disease-related death, such as monounsaturated fatty acids (MUFA), linoleic acid (LA), glycoprotein acetyls (GlycA), and omega-3. Moreover, the value of free cholesterol in very large HDL particles (XL-HDL-FC) in the healthy control group demonstrated significantly higher values than the disease and death group across all age groups. This study highlights the potential of NMR metabolomics profiling as a valuable tool for identifying metabolic biomarkers associated with aging-related diseases and mortality risk, which could have practical implications for aging-related disease risk and mortality prediction.

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Serum metabolomics improves risk stratification for incident heart failure

AIMS

Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1H-NMR] spectroscopy) for incident HF.

METHODS AND RESULTS

Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP-HF]). During a median follow-up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP-HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004-0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200-0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463-27.825]). Models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI -0.004 to 0.027], continuous NRI: 0.097 [95% CI -0.025 to 0.217], relative IDI: 13.445% [95% CI -10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics.

CONCLUSION

Serum metabolomics improves incident HF risk prediction over PCP-HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost-effective, standardizable, and scalable single-domain alternative.

Comparison of the stability of glycoprotein acetyls and high sensitivity C-reactive protein as markers of chronic inflammation

It has been suggested that glycoprotein acetyls (GlycA) better reflects chronic inflammation than high sensitivity C-reactive protein (hsCRP), but paediatric/life-course data are sparse. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank, we compared short- (over weeks) and long-term (over years) correlations of GlycA and hsCRP, cross-sectional correlations between GlycA and hsCRP, and associations of pro-inflammatory risk factors with GlycA and hsCRP across the life-course. GlycA showed high short-term (weeks) stability at 15 years (r = 0.75; 95% CI = 0.56, 0.94), 18 years (r = 0.74; 0.64, 0.85), 24 years (r = 0.74; 0.51, 0.98) and 48 years (r = 0.82 0.76, 0.86) and this was comparable to the short-term stability of hsCRP at 24 years. GlycA stability was moderate over the long-term, for example between 15 and 18 years r = 0.52; 0.47, 0.56 and between 15 and 24 years r = 0.37; 0.31, 0.44. These were larger than equivalent correlations of hsCRP. GlycA and concurrently measured hsCRP were moderately correlated at all ages, for example at 15 years (r = 0.44; 0.40, 0.48) and at 18 years (r = 0.55; 0.51, 0.59). We found similar associations of known proinflammatory factors and inflammatory diseases with GlycA and hsCRP. For example, BMI was positively associated with GlycA (mean difference in GlycA per standard deviation change in BMI = 0.08; 95% CI = 0.07, 0.10) and hsCRP (0.10; 0.08, 0.11). This study showed that GlycA has greater long-term stability than hsCRP, however associations of proinflammatory factors with GlycA and hsCRP were broadly similar.

Serum lipoprotein subfractions are associated with the periodontal status: Results from the population-based cohort SHIP-TREND

AIM

To investigate the medium-term associations of serum protein subfractions derived from proton nuclear magnetic resonance (1 H-NMR) spectroscopy with periodontitis and tooth loss.

MATERIALS AND METHODS

A total of 3031 participants of the cohort Study of Health in Pomerania (SHIP-TREND) were included. In addition to conventional serum testing, serum lipoprotein contents and subfractions were analysed by 1 H-NMR spectroscopy. Confounder-adjusted associations of lipoprotein variables with periodontitis and the number of missing teeth variables were analysed using mixed-effects models with random intercepts for time across individuals, accounting for multiple testing.

RESULTS

While only spurious associations between lipoprotein levels from conventional blood tests were found-that is, triglycerides were associated with mean clinical attachment level (CAL) and low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C) ratio with the number of missing teeth - several associations emerged from serum lipoprotein subfractions derived from 1 H-NMR analysis. Specifically, elevated LDL triglycerides were associated with higher levels of mean probing depth (PD), mean CALs, and increased odds of having <20 teeth. HDL-4 cholesterol levels were inversely associated with mean PD. Systemic inflammation (C-reactive protein) might mediate the effects of LDL and HDL triglyceride contents on periodontitis severity.

CONCLUSIONS

Several associations between serum lipoprotein subfractions and periodontitis were observed. As the underlying biochemical mechanisms remain unclear, further research is needed.

The transition from genomics to phenomics in personalized population health

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

Dietary patterns, metabolomics and frailty in a large cohort of 120 000 participants

Objective: To examine the associations of dietary patterns with frailty and whether metabolic signatures (MSs) mediate these associations. Methods: We used UK Biobank data to examine (1) the associations of four dietary patterns (i.e., alternate Mediterranean diet [aMED], Recommended Food Score [RFS], Dietary Approaches to Stop Hypertension [DASH] and Mediterranean-DASH Intervention for Neurodegenerative Delay [MIND] diet) with frailty (measured by the frailty phenotype and the frailty index) using multivariable logistic regression (analytic sample 1: N = 124 261; mean age = 57.7 years), and (2) the mediating role of MSs (weighted sums of the metabolites selected from 168 plasma metabolites using the LASSO algorithm) in the above associations via mediation analysis (analytic sample 2: N = 26 270; mean age = 57.7 years). Results: Four dietary patterns were independently associated with frailty (all P < 0.001). For instance, compared to participants in the lowest tertile for RFS, those in the intermediate (odds ratio [OR]: 0.81; 95% confidence interval [CI]: 0.74, 0.89) and highest (OR: 0.62; 95% CI: 0.56, 0.68) tertiles had a lower risk of frailty. We found that 98, 68, 123 and 75 metabolites were associated with aMED, RFS, DASH and MIND, respectively, including 16 common metabolites (e.g., fatty acids, lipoproteins, acetate and glycoprotein acetyls). The MSs based on these metabolites partially mediated the association of the four dietary patterns with frailty, with the mediation proportion ranging from 26.52% to 45.83%. The results were robust when using another frailty measure, the frailty index. Conclusions: The four dietary patterns were associated with frailty, and these associations were partially mediated by MSs. Adherence to healthy dietary patterns may potentially reduce frailty development by modulating metabolites.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data

BACKGROUND

There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.

METHODS

We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.

RESULTS

The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.

CONCLUSIONS

Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits

We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.

Associations of Lifestyle Patterns with Glucose and Lipid Metabolism in Finnish Adults at Increased Risk of Type 2 Diabetes

SCOPE

Various lifestyle and sociodemographic factors have been associated with risk factors for type 2 diabetes (T2D). However, their combined associations with T2D risk factors have been studied much less.

MATERIALS AND RESULTS

This study investigates cross-sectional associations of lifestyle patterns with T2D risk factors among 2925 adults at increased risk participating in the Stop Diabetes study. Lifestyle patterns are determined using principal component analysis (PCA) with several lifestyle and sociodemographic factors. The associations of lifestyle patterns with measures of glucose and lipid metabolism and serum metabolites analyzed by nuclear magnetic resonance (NMR) spectroscopy are studied using linear regression analysis. "Healthy eating" pattern is associated with better glucose and insulin metabolism, more favorable lipoprotein and fatty acid profiles and lower serum concentrations of metabolites related to inflammation, insulin resistance, and T2D. "High socioeconomic status and low physical activity" pattern is associated with increased serum concentrations of branched-chain amino acids, as are "Meat and poultry" and "Sleeping hours" patterns. "Snacks" pattern is associated with lower serum concentrations of ketone bodies.

CONCLUSIONS

Our results show, in large scale primary care setting, that healthy eating is associated with better glucose and lipid metabolism and reveal novel associations of lifestyle patterns with metabolites related to glucose metabolism.

Maternal metabolic profiling across body mass index groups: An exploratory longitudinal study

INTRODUCTION

Increased BMI has been identified as a risk factor for most pregnancy complications, but the underlying metabolic factors mediating the detrimental effects of BMI are largely unknown. We aimed to compare metabolic profiles in overweight/obese women (body mass index [BMI] ≥ 25 kg/m2 ) and normal weight/underweight women (BMI < 25 kg/m2 ) across gestation. We also explored how gestational weight gain (GWG) affected maternal metabolic profiles.

MATERIAL AND METHODS

Exploratory nested case-control study based on a prospective longitudinal cohort of women who were healthy prior to pregnancy and gave birth at Oslo University Hospital from 2002 to 2008. The sample consisted of 48 women who were overweight/obese and 59 normal-weight/underweight women. Plasma samples from four time points in pregnancy (weeks 14-16, 22-24, 30-32 and 36-38) were analyzed by nuclear magnetic resonance spectroscopy and 91 metabolites were measured. Linear regression models were fitted for each of the metabolites at each time point.

RESULTS

Overweight or obese women had higher levels of lipids in very-low-density lipoprotein (VLDL), total triglycerides, triglycerides in VLDL, total fatty acids, monounsaturated fatty acids, saturated fatty acids, leucine, valine, and total branched-chain amino acids in pregnancy weeks 14-16 compared to underweight and normal-weight women. Docosahexaenoic acid and degree of unsaturation were significantly lower in overweight/obese women in pregnancy weeks 36-38. In addition, overweight or obese women had higher particle concentration of XXL-VLDL and glycoprotein acetyls (GlycA) at weeks 14-16 and 30-32. GWG did not seem to affect the metabolic profile, regardless of BMI group when BMI was treated as a dichotomous variable, ≥25 kg/m2 (yes/no).

CONCLUSIONS

Overweight or obese women had smaller pregnancy-related metabolic alterations than normal-weight/underweight women. There was a trend toward higher triglyceride and VLDL particle concentration in overweight/obese women. As this was a hypothesis-generating study, the similarities with late-onset pre-eclampsia warrant further investigation. The unfavorable development of fatty acid composition in overweight/obese women, with possible implication for the offspring, should also be studied further in the future.

Life course plasma metabolomic signatures of genetic liability to Alzheimer's disease

Mechanisms through which most known Alzheimer's disease (AD) loci operate to increase AD risk remain unclear. Although Apolipoprotein E (APOE) is known to regulate lipid homeostasis, the effects of broader AD genetic liability on non-lipid metabolites remain unknown, and the earliest ages at which metabolic perturbations occur and how these change over time are yet to be elucidated. We examined the effects of AD genetic liability on the plasma metabolome across the life course. Using a reverse Mendelian randomization framework in two population-based cohorts [Avon Longitudinal Study of Parents and Children (ALSPAC, n = 5648) and UK Biobank (n ≤ 118,466)], we estimated the effects of genetic liability to AD on 229 plasma metabolites, at seven different life stages, spanning 8 to 73 years. We also compared the specific effects of APOE ε4 and APOE ε2 carriage on metabolites. In ALSPAC, AD genetic liability demonstrated the strongest positive associations with cholesterol-related traits, with similar magnitudes of association observed across all age groups including in childhood. In UK Biobank, the effect of AD liability on several lipid traits decreased with age. Fatty acid metabolites demonstrated positive associations with AD liability in both cohorts, though with smaller magnitudes than lipid traits. Sensitivity analyses indicated that observed effects are largely driven by the strongest AD instrument, APOE, with many contrasting effects observed on lipids and fatty acids for both ε4 and ε2 carriage. Our findings indicate pronounced effects of the ε4 and ε2 genetic variants on both pro- and anti-atherogenic lipid traits and sphingomyelins, which begin in childhood and either persist into later life or appear to change dynamically.

Metabolites and depressive symptoms: Network- and longitudinal analyses from the Finnish Depression and Metabolic Syndrome in Adults (FDMSA) Study

BACKGROUND

Depression is associated with metabolic abnormalities linked to metabolic syndrome and tissue inflammation, but the interplay between metabolic markers and their association with subsequent depression is unknown. Therefore, we aimed to describe the network of metabolites and their prospective association with depressive symptoms.

METHODS

The Finnish Depression and Metabolic Syndrome in Adults (FDMSA) cohort, originally a prospective case-control study, comprised a group with Beck Depression Inventory (BDI)-I scores ≥10 at baseline, and controls (n = 319, BDI-I < 10); mean (sd) follow-up time: 7.4 (0.7) years. Serum metabolic biomarkers were determined by proton nuclear magnetic resonance (NMR), and depressive symptoms sum-score by using the BDI-I. We examined the prospective associations between metabolites at baseline and BDI score at follow-up utilizing multivariate linear regression, parsimonious predictions models and network analysis.

RESULTS

Some metabolites tended to be either negatively (e.g. histidine) or positively associated (e.g. glycoprotein acetylation, creatinine and triglycerides in very large high density lipoproteins [XL-HDL-TG]) with depressive symptoms. None of the associations were significant after correction for multiple testing. The network analysis suggested high correlation among the metabolites, but that none of the metabolites directly influenced subsequent depressive symptoms.

LIMITATIONS

Although the sample size may be considered satisfactory in a prospective context, we cannot exclude the possibility that our study was underpowered.

CONCLUSIONS

Our results suggest that the investigated metabolic biomarkers are not a driving force in the development of depressive symptoms. These findings should be confirmed in studies with larger samples and studies that account for the heterogeneity of depressive disorders.

Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

The impact of reproductive factors on the metabolic profile of females from menarche to menopause

We explore the relation between age at menarche, parity and age at natural menopause with 249 metabolic traits in over 65,000 UK Biobank women using multivariable regression, Mendelian randomization and negative control (parity only). Older age of menarche is related to a less atherogenic metabolic profile in multivariable regression and Mendelian randomization, which is largely attenuated when accounting for adult body mass index. In multivariable regression, higher parity relates to more particles and lipids in VLDL, which are not observed in male negative controls. In multivariable regression and Mendelian randomization, older age at natural menopause is related to lower concentrations of inflammation markers, but we observe inconsistent results for LDL-related traits due to chronological age-specific effects. For example, older age at menopause is related to lower LDL-cholesterol in younger women but slightly higher in older women. Our findings support a role of reproductive traits on later life metabolic profile and provide insights into identifying novel markers for the prevention of adverse cardiometabolic outcomes in women.

Validation in type 2 diabetes of a metabolomic signature of all-cause mortality

CONTEXT

Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed.

OBJECTIVE

To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models.

METHODS

This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics.

RESULTS

In type 2 diabetes, the 14-MS showed a significant (p < 0.0001) association with mortality, which was lower (p < 0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p = 0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p < 0.0001 for all measures).

CONCLUSIONS

The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers

BACKGROUND

Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges.

METHODS

We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking.

RESULTS

Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate.

CONCLUSIONS

Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.

Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study

People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36-75 of Round 4 (2003-2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987-1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.

Short-Term Metabolic Changes and Their Physiological Mediators in the Roux-en-Y Gastric Bypass Bariatric Surgery

BACKGROUND

The Roux-en-Y gastric bypass (RYGB) is a common bariatric surgery to treat obesity. Its metabolic consequences are favourable and long-term clinical corollaries beneficial. However, detailed assessments of various affected metabolic pathways and their mediating physiological factors are scarce.

METHODS

We performed a clinical study with 30 RYGB patients in preoperative and 6-month postoperative visits. NMR metabolomics was applied to profiling of systemic metabolism via 80 molecular traits, representing core cardiometabolic pathways. Glucose, glycated haemoglobin (HbA1c), insulin, and apolipoprotein B-48 were measured with standard assays. Logistic regression models of the surgery effect were used for each metabolic measure and assessed individually for multiple mediating physiological factors.

RESULTS

Changes in insulin concentrations reflected those of BMI with robust decreases due to the surgery. Six months after the surgery, triglycerides, remnant cholesterol, and apolipoprotein B-100 were decreased -24%, -18%, and -14%, respectively. Lactate and glycoprotein acetyls, a systemic inflammation biomarker, decreased -16% and -9%, respectively. The concentrations of branched-chain (BCAA; leucine, isoleucine, and valine) and aromatic (phenylalanine and tyrosine) amino acids decreased after the surgery between -17% for tyrosine and -23% for leucine. Except for the most prominent metabolic changes observed for the BCAAs, all changes were almost completely mediated by weight change and insulin. Glucose and type 2 diabetes had clearly weaker effects on the metabolic changes.

CONCLUSIONS

The comprehensive metabolic analyses indicate that weight loss and improved insulin sensitivity during the 6 months after the RYGB surgery are the key physiological outcomes mediating the short-term advantageous metabolic effects of RYGB. The clinical study was registered at ClinicalTrials.gov as NCT01330251.

Higher ratio of plasma omega-6/omega-3 fatty acids is associated with greater risk of all-cause, cancer, and cardiovascular mortality: a population-based cohort study in UK Biobank

Background

Circulating omega-3 and omega-6 polyunsaturated fatty acids (PUFAs) have been associated with various chronic diseases and mortality, but results are conflicting. Few studies examined the role of omega-6/omega-3 ratio in mortality.

Methods

We investigated plasma omega-3 and omega-6 PUFAs and their ratio in relation to all-cause and cause-specific mortality in a large prospective cohort, the UK Biobank. Of 85,425 participants who had complete information on circulating PUFAs, 6,461 died during follow-up, including 2,794 from cancer and 1,668 from cardiovascular disease (CVD). Associations were estimated by multivariable Cox proportional hazards regression with adjustment for relevant risk factors.

Results

Risk for all three mortality outcomes increased as the ratio of omega-6/omega-3 PUFAs increased (all Ptrend < 0.05). Comparing the highest to the lowest quintiles, individuals had 26% (95% CI, 15-38%) higher total mortality, 14% (95% CI, 0-31%) higher cancer mortality, and 31% (95% CI, 10-55%) higher CVD mortality. Moreover, omega-3 and omega-6 PUFAs in plasma were all inversely associated with all-cause, cancer, and CVD mortality, with omega-3 showing stronger effects.

Conclusions

Using a population-based cohort in UK Biobank, our study revealed a strong association between the ratio of circulating omega-6/omega-3 PUFAs and the risk of all-cause, cancer, and CVD mortality.

Phenotypic and metabolomic characteristics of mouse models of metabolic associated steatohepatitis

BACKGROUND

Metabolic associated steatohepatitis (MASH) is metabolic disease that may progress to cirrhosis and hepatocellular carcinoma. Mouse models of diet-induced MASH, which is characterized by the high levels of fats, sugars, and cholesterol in diets, are commonly used in research. However, mouse models accurately reflecting the progression of MASH in humans remain to be established. Studies have explored the potential use of serological metabolites as biomarkers of MASH severity in relation to human MASH.

METHODS

We performed a comparative analysis of three mouse models of diet-induced MASH in terms of phenotypic and metabolomic characteristics; MASH was induced using different diets: a high-fat diet; a Western diet; and a high-fat, high-cholesterol diet. Liver cirrhosis was diagnosed using standard clinical approaches (e.g., METAVIR score, hyaluronan level, and collagen deposition level). Mouse serum samples were subjected to nuclear magnetic resonance spectroscopy-based metabolomic profiling followed by bioinformatic analyses. Metabolomic analysis of a retrospective cohort of patients with hepatocellular carcinoma was performed; the corresponding cirrhosis scores were also evaluated.

RESULTS

Using clinically relevant quantitative diagnostic methods, the severity of MASH was evaluated. Regarding metabolomics, the number of lipoprotein metabolites increased with both diet and MASH progression. Notably, the levels of very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) significantly increased with fibrosis progression. During the development of diet-induced MASH in mice, the strongest upregulation of expression was noted for VLDL receptor. Metabolomic analysis of a retrospective cohort of patients with cirrhosis indicated lipoproteins (e.g., VLDL and LDL) as predominant biomarkers of cirrhosis.

CONCLUSIONS

Our findings provide insight into the pathophysiology and metabolomics of experimental MASH and its relevance to human MASH. The observed upregulation of lipoprotein expression reveals a feedforward mechanism for MASH development that may be targeted for the development of noninvasive diagnosis.

Association of tobacco smoke exposure with metabolic profile from childhood to early adulthood: the Special Turku Coronary Risk Factor Intervention Project

AIMS

To investigate the associations between passive tobacco smoke exposure and daily smoking with a comprehensive metabolic profile, measured repeatedly from childhood to adulthood.

METHODS AND RESULTS

Study cohort was derived from the Special Turku Coronary Risk Factor Intervention Project (STRIP). Smoking status was obtained by questionnaire, while serum cotinine concentrations were measured using gas chromatography. Metabolic measures were quantified by nuclear magnetic resonance metabolomics at 9 (n = 539), 11 (n = 536), 13 (n = 525), 15 (n = 488), 17 (n = 455), and 19 (n = 409) years. Association of passive tobacco smoke exposure with metabolic profile compared participants who reported less-than-weekly smoking and had serum cotinine concentration <1 ng/mL (no exposure) with those whose cotinine concentration was ≥10 ng/mL (passive tobacco smoke exposure). Associations of daily smoking with metabolic profile in adolescence were analysed by comparing participants reporting daily smoking with those reporting no tobacco use and having serum cotinine concentrations <1 ng/mL. Passive tobacco smoke exposure was directly associated with the serum ratio of monounsaturated fatty acids to total fatty acids [β = 0.34 standard deviation (SD), (0.17-0.51), P < 0.0001] and inversely associated with the serum ratios of polyunsaturated fatty acids. Exposure to passive tobacco smoke was directly associated with very-low-density lipoprotein particle size [β = 0.28 SD, (0.12-0.45), P = 0.001] and inversely associated with HDL particle size {β = -0.21 SD, [-0.34 to -0.07], P = 0.003}. Daily smokers exhibited a similar metabolic profile to those exposed to passive tobacco smoke. These results persisted after adjusting for body mass index, STRIP study group allocation, dietary target score, pubertal status, and parental socio-economic status.

CONCLUSION

Both passive and active tobacco smoke exposures during childhood and adolescence are detrimentally associated with circulating metabolic measures indicative of increased cardio-metabolic risk.

Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD

RATIONALE & OBJECTIVE

Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study.

STUDY DESIGN

Observational cohort study.

SETTING & PARTICIPANTS

A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy.

EXPOSURE

Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy.

OUTCOME

Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records.

ANALYTICAL APPROACH

Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals.

RESULTS

We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD.

LIMITATIONS

Potential underreporting of CKD cases because of case identification via electronic health records.

CONCLUSIONS

Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD.

PLAIN-LANGUAGE SUMMARY

The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.

Systemic Metabolic Signatures of Oral Diseases

Systemic metabolic signatures of oral diseases have been rarely investigated, and prospective studies do not exist. We analyzed whether signs of current or past infectious/inflammatory oral diseases are associated with circulating metabolites. Two study populations were included: the population-based Health-2000 (n = 6,229) and Parogene (n = 452), a cohort of patients with an indication to coronary angiography. Health-2000 participants (n = 4,116) provided follow-up serum samples 11 y after the baseline. Serum concentrations of 157 metabolites were determined with a nuclear magnetic resonance spectroscopy-based method. The associations between oral parameters and metabolite concentrations were analyzed using linear regression models adjusted for age, sex, number of teeth, smoking, presence of diabetes, and education (in Health-2000 only). The number of decayed teeth presented positive associations with low-density lipoprotein diameter and the concentrations of pyruvate and citrate. Negative associations were found between caries and the unsaturation degree of fatty acids (FA) and relative proportions of docosahexaenoic and omega-3 FAs. The number of root canal fillings was positively associated with very low-density lipoprotein parameters, such as diameter, cholesterol, triglycerides, and number of particles. Deepened periodontal pockets were positively associated with concentrations of cholesterol, triglycerides, pyruvate, leucine, valine, phenylalanine, and glycoprotein acetyls and negatively associated with high-density lipoprotein (HDL) diameter, FA unsaturation degree, and relative proportions of omega-6 and polyunsaturated FAs. Bleeding on probing (BOP) was associated with increased concentrations of triglycerides and glycoprotein acetyls, as well as decreased proportions of omega-3 and omega-6 FAs. Caries at baseline predicted alterations in apolipoprotein B-containing lipoproteins and HDL-related metabolites in the follow-up, and both caries and BOP were associated with changes in HDL-related metabolites and omega-3 FAs in the follow-up. Signs of current or past infectious/inflammatory oral diseases, especially periodontitis, were associated with metabolic profiles typical for inflammation. Oral diseases may represent a modifiable risk factor for systemic chronic inflammation and thus cardiometabolic disorders.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Background

Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival.

Methods

To investigate whether changes in circulating metabolites characterize the early stages of colorectal cancer (CRC) development, we examined the associations between a genetic risk score (GRS) associated with CRC liability (72 single-nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N = 6221). Linear regression models were applied to examine the associations between genetic liability to CRC and circulating metabolites measured in the same individuals at age 8 y, 16 y, 18 y, and 25 y.

Results

The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P < 0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N = 118,466, median age 58 y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk.

Conclusions

These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism and suggest that fatty acids may play an important role in CRC development.

Funding

This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/.

Weight-loss Independent Clinical and Metabolic Biomarkers Associated with Type 2 Diabetes Remission Post-bariatric/metabolic Surgery

PURPOSE

Remission of type 2 diabetes (T2D) can be achieved by many, but not all, people following bariatric/metabolic surgery. The mechanisms underlying T2D remission remain incompletely understood. This observational study aimed to identify novel weight-loss independent clinical, metabolic and genetic factors that associate with T2D remission using comprehensive phenotyping.

MATERIALS AND METHODS

Ten patients without T2D remission (non-remitters) were matched to 10 patients with T2D remission (remitters) for age, sex, type of surgery, body weight, BMI, post-operative weight loss, duration from surgery and duration of T2D. Detailed body composition assessed using magnetic resonance imaging, gut hormones, serum metabolomics, insulin sensitivity, and genetic risk scores for T2D and anthropometric traits were assessed.

RESULTS

Remitters had significantly greater β-cell function and circulating acyl ghrelin levels, but lower visceral adipose tissue (VAT): subcutaneous adipose tissue (SAT) ratio than non-remitters. Branched-chain amino acids (BCAAs) and VLDL particle size were the most discriminant metabolites between groups. A significant positive correlation between, VAT area, VAT:SAT ratio and circulating levels of BCAAs was observed, whereas a significant negative correlation between BCAAs and β-cell function was revealed.

CONCLUSION

We highlight a potentially novel relationship between VAT and BCAAs, which may play a role in glucoregulatory control. Improvement in β-cell function, and the role ghrelin plays in its recovery, is likely another key factor influencing T2D remission post-surgery. These findings suggest that adjunctive approaches that target VAT loss and restoration of BCAA metabolism might achieve higher rates of long-term T2D remission post-surgery.

Childhood socioeconomic position and sex-specific trajectories of metabolic traits across early life: prospective cohort study

BACKGROUND

Socioeconomic inequalities in cardiovascular disease risk begin early in life and are more pronounced in females than males later in life. Causal atherogenic traits explaining this are not well understood. We explored sex-specific associations between childhood socioeconomic position (SEP) and molecular measures of systemic metabolism across early life.

METHODS

Data were from the Avon Longitudinal Study of Parents and Children (ALSPAC), a population-based birth cohort in southwest England. Pregnant women with an expected delivery date between 1991 and 1992 were invited to participate. Maternal education was the primary indicator of SEP. Concentrations of 148 metabolic traits from targeted metabolomics (nuclear magnetic resonance spectroscopy) from research clinics at ages 7, 15, 18 and 25 years were analysed. The sex-specific slope index of inequality (SII) in trajectories of metabolic traits was estimated using multilevel models.

FINDINGS

Total number of participants included was 6537 (12,543 repeated measures). Lower maternal education was associated with more adverse levels of several atherogenic lipids and key metabolic traits among females at age 7 years, but not males. For instance, SII for very small very-low-density lipoprotein (VLDL) concentrations was 0.16SD (95% CI: 0.01, 0.30) among females and -0.02SD (95% CI: -0.16, 0.13) among males. Between 7 and 25 years, inequalities widened among females and emerged among males particularly for VLDL particle concentrations, apolipoprotein-B concentrations, and inflammatory glycoprotein acetyls. For instance, at 25 years, SII for very small VLDL concentrations was 0.36SD (95% CI: 0.20, 0.52) and 0.22SD (95% CI: 0.04, 0.40) among females and males respectively.

INTERPRETATION

Prevention of socioeconomic inequalities in cardiovascular disease risk requires a life course approach beginning at the earliest opportunity, especially among females.

FUNDING

The UK Medical Research Council and Wellcome (grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). KON is supported by a Health Research Board (HRB) of Ireland Investigator Led Award (ILP-PHR-2022-008). JB, GDS and KT work in a unit funded by the UK MRC (MC_UU_00011/1 and MC UU 00011/3) and the University of Bristol. OR is supported by a UKRI Future Leaders Fellowship (MR/S03532X/1). These funding sources had no role in the design and conduct of this study. This publication is the work of the authors and KON will serve as guarantor for the contents of this paper.

Metabolomic profiles, polygenic risk scores and risk of rheumatoid arthritis: a population-based cohort study in the UK Biobank

OBJECTIVE

To investigate the relationship between metabolomic profiles, genome-wide polygenic risk scores (PRSs) and risk of rheumatoid arthritis (RA).

METHODS

143 nuclear magnetic resonance-based plasma metabolic biomarkers were measured among 93 800 participants in the UK Biobank. The Cox regression model was used to assess the associations between these metabolic biomarkers and RA risk, and genetic correlation and Mendelian randomisation analyses were performed to reveal their causal relationships. Subsequently, a metabolic risk score (MRS) comprised of the weighted sum of 17 clinically validated metabolic markers was constructed. A PRS was derived by assigning weights to genetic variants that exhibited significant associations with RA at a genome-wide level.

RESULTS

A total of 620 incident RA cases were recorded during a median follow-up time of 8.2 years. We determined that 30 metabolic biomarkers were potentially associated with RA, while no further significant causal associations were found. Individuals in the top decile of MRS had an increased risk of RA (HR 3.52, 95% CI: 2.80 to 4.43) compared with those below the median of MRS. Further, significant gradient associations between MRS and RA risk were observed across genetic risk strata. Specifically, compared with the low genetic risk and favourable MRS group, the risk of incident RA in the high genetic risk and unfavourable MRS group has almost elevated by fivefold (HR 6.10, 95% CI: 4.06 to 9.14).

CONCLUSION

Our findings suggested the metabolic profiles comprising multiple metabolic biomarkers contribute to capturing an elevated risk of RA, and the integration of genome-wide PRSs further improved risk stratification.

Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Maternal glycemic status during pregnancy and mid-childhood plasma amino acid profiles: findings from a multi-ethnic Asian birth cohort

BACKGROUND

Increasing maternal glycaemia across the continuum during pregnancy may predispose offspring to subsequent cardiometabolic risk later in life. However, evidence of long-term impacts of maternal glycemic status on offspring amino acid (AA) profiles is scarce. We aimed to investigate the association between maternal antenatal glycaemia and offspring mid-childhood amino acid (AA) profiles, which are emerging cardiometabolic biomarkers.

METHODS

Data were drawn from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) study, a multi-ethnic Asian birth cohort. A subset of 422 mother-child dyads from the GUSTO study, who was followed from early pregnancy to mid-childhood, was included. Mothers underwent an oral glucose tolerance test (OGTT) at 26-28 weeks gestation, with fasting and 2-h plasma glucose concentrations measured and gestational diabetes mellitus (GDM) diagnosed per WHO 1999 guidelines. Offspring fasting plasma samples were collected at mean age 6.1 years, from which AA profiles of nine AAs, alanine, glutamine, glycine, histidine, isoleucine, leucine, valine, phenylalanine, and tyrosine were measured. Total branched-chain amino acids (BCAAs) were calculated as the sum of isoleucine, leucine, and valine concentrations. Multi-variable linear regression was used to estimate the association of maternal glycemic status and offspring mid-childhood AA profiles adjusting for maternal age, ethnicity, maternal education, parity, family history of diabetes, ppBMI, child sex, age and BMI z-scores.

RESULTS

Approximately 20% of mothers were diagnosed with GDM. Increasing maternal fasting glucose was significantly associated with higher offspring plasma valine and total BCAAs, whereas higher 2-h glucose was significantly associated with higher histidine, isoleucine, valine, and total BCAAs. Offspring born to mothers with GDM had higher valine (standardized mean difference 0.27 SD; 95% CI: 0.01, 0.52), leucine (0.28 SD; 0.02, 0.53), and total BCAAs (0.26 SD; 0.01, 0.52) than their counterparts. Inconsistent associations were found between maternal GDM and other amino acids among offspring during mid-childhood.

CONCLUSIONS

Increasing maternal fasting and post-OGTT glucose concentrations at 26-28 weeks gestation were significantly associated with mid-childhood individual and total BCAAs concentrations. The findings suggest that elevated maternal glycaemia throughout pregnancy, especially GDM, may have persistent programming effects on offspring AA metabolism which were strongly associated with adverse cardiometabolic profiles at mid-childhood.

Branched chain amino acids harbor distinct and often opposing effects on health and disease

BACKGROUND

The branched chain amino acids (BCAA) leucine, isoleucine, and valine are essential nutrients that have been associated with diabetes, cancers, and cardiovascular diseases. Observational studies suggest that BCAAs exert homogeneous phenotypic effects, but these findings are inconsistent with results from experimental human and animal studies.

METHODS

Hypothesizing that inconsistencies between observational and experimental BCAA studies reflect bias from shared lifestyle and genetic factors in observational studies, we used data from the UK Biobank and applied multivariable Mendelian randomization causal inference methods designed to address these biases.

RESULTS

In n = 97,469 participants of European ancestry (mean age = 56.7 years; 54.1% female), we estimate distinct and often opposing total causal effects for each BCAA. For example, of the 117 phenotypes with evidence of a statistically significant total causal effect for at least one BCAA, almost half (44%, n = 52) are associated with only one BCAA. These 52 associations include total causal effects of valine on diabetic eye disease [odds ratio = 1.51, 95% confidence interval (CI) = 1.31, 1.76], valine on albuminuria (odds ratio = 1.14, 95% CI = 1.08, 1.20), and isoleucine on angina (odds ratio = 1.17, 95% CI = 1.31, 1.76).

CONCLUSIONS

Our results suggest that the observational literature provides a flawed picture of BCAA phenotypic effects that is inconsistent with experimental studies and could mislead efforts developing novel therapeutics. More broadly, these findings motivate the development and application of causal inference approaches that enable 'omics studies conducted in observational settings to account for the biasing effects of shared genetic and lifestyle factors.

Feasibility and impact of ketogenic dietary interventions in polycystic kidney disease: KETO-ADPKD-a randomized controlled trial

Ketogenic dietary interventions (KDIs) are beneficial in animal models of autosomal-dominant polycystic kidney disease (ADPKD). KETO-ADPKD, an exploratory, randomized, controlled trial, is intended to provide clinical translation of these findings (NCT04680780). Sixty-six patients were randomized to a KDI arm (ketogenic diet [KD] or water fasting [WF]) or the control group. Both interventions induce significant ketogenesis on the basis of blood and breath acetone measurements. Ninety-five percent (KD) and 85% (WF) report the diet as feasible. KD leads to significant reductions in body fat and liver volume. Additionally, KD is associated with reduced kidney volume (not reaching statistical significance). Interestingly, the KD group exhibits improved kidney function at the end of treatment, while the control and WF groups show a progressive decline, as is typical in ADPKD. Safety-relevant events are largely mild, expected (initial flu-like symptoms associated with KD), and transient. Safety assessment is complemented by nuclear magnetic resonance (NMR) lipid profile analyses.

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Background

Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival.

Methods

To investigate whether changes in circulating metabolites characterise the early stages of colorectal cancer (CRC) development, we examined associations between a genetic risk score (GRS) associated with CRC liability (72 single nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N=6,221). Linear regression models were applied to examine associations between genetic liability to colorectal cancer and circulating metabolites measured in the same individuals at age 8, 16, 18 and 25 years.

Results

The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P<0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N=118,466, median age 58y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk.

Conclusions

These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism, and suggest that fatty acids may play an important role in CRC development.

Funding

This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/.