Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Biomarkers of aging: from molecules and surrogates to physiology and function

Many countries face an unprecedented challenge in aging demographics. This has led to an exponential growth in research on aging, which, coupled to a massive financial influx of funding in the private and public sectors, has resulted in seminal insights into the underpinnings of this biological process. However, critical validation in humans has been hampered by the limited translatability of results obtained in model organisms, additionally confined by the need for extremely time-consuming clinical studies in the ostensible absence of robust biomarkers that would allow monitoring in shorter time frames. In the future, molecular parameters might hold great promise in this regard. In contrast, biomarkers centered on function, resilience, and frailty are available at the present time, with proven predictive value for morbidity and mortality. In this review, the current knowledge of molecular and physiological aspects of human aging, potential antiaging strategies, and the basis, evidence, and potential application of physiological biomarkers in human aging are discussed.

Interpretable machine learning leverages proteomics to improve cardiovascular disease risk prediction and biomarker identification

BACKGROUND

Cardiovascular diseases (CVDs) rank amongst the leading causes of long-term disability and mortality. Predicting CVD risk and identifying associated genes are crucial for prevention, early intervention, and drug discovery. The recent availability of UK Biobank Proteomics data enables investigation of blood proteins and their association with a variety of diseases. We sought to predict 10 year CVD risk using this data modality and known CVD risk factors.

METHODS

We focused on the UK Biobank participants that were included in the UK Biobank Pharma Proteomics Project. After applying exclusions, 50,057 participants were included, aged 40-69 years at recruitment. We employed the Explainable Boosting Machine (EBM), an interpretable machine learning model, to predict the 10 year risk of primary coronary artery disease, ischemic stroke or myocardial infarction. The model had access to 2978 features (2923 proteins and 55 risk factors). Model performance was evaluated using 10-fold cross-validation.

RESULTS

The EBM model using proteomics outperforms equation-based risk scores such as PREVENT, with a receiver operating characteristic curve (AUROC) of 0.767 and an area under the precision-recall curve (AUPRC) of 0.241; adding clinical features improves these figures to 0.785 and 0.284, respectively. Our models demonstrate consistent performance across sexes and ethnicities and provide insights into individualized disease risk predictions and underlying disease biology.

CONCLUSIONS

In conclusion, we present a more accurate and explanatory framework for proteomics data analysis, supporting future approaches that prioritize individualized disease risk prediction, and identification of target genes for drug development.

Metabolic characteristics of benign and malignant pulmonary nodules and establishment of invasive lung adenocarcinoma model by high-resolution mass spectrometry

BACKGROUND

Increasing pulmonary nodule presentations in lung adenocarcinoma patients reveal diagnostic limitations of CT-based invasiveness assessment. The critical unmet need lies in developing non-invasive biomarkers differentiating invasive adenocarcinoma from premalignant lesions and benign nodules, while characterizing metabolic trajectory from health to metastatic disease.

METHODS

Untargeted metabolomics analyzed plasma samples from 102 subjects stratified into four cohorts: confirmed adenocarcinoma (n = 35), benign nodules (n = 22), precursor lesions (n = 24), and healthy controls (n = 21). Multivariate analysis identified discriminative metabolites for constructing an infiltration prediction model.

RESULTS

Three diagnostic groups exhibited distinct metabolic profiles. Hexaethylene glycol, tetraethylene glycol, and Met-Thr showed stage-dependent concentration gradients. Progressive malignancy correlated with elevated levels of 41 metabolites. An eight-metabolite panel achieved AUC 0.933 (0.873-0.994) in distinguishing precursors from early malignancies, sustained through internal validation (AUC 0.934, 0.905-0.966).

CONCLUSIONS

Met-Thr depletion inversely correlates with malignancy progression, while eight-metabolite signatures demonstrate diagnostic potential for preoperative infiltration assessment in nodular adenocarcinoma.

Anti-HBV activity of (R)-gentiandiol, a metabolite of Swertiamarin, in transgenic mice: Insights from non-targeted serum metabolomics

Swertiamarin, a predominant component in many traditional Chinese swertia herbs, shows significant anti-HBV activity clinically. (R)-gentiandiol and (S)-gentiandiol are the metabolites of swertiamarin in vivo. In this study, HBsAg, HBeAg and HBV-DNA were determined in liver tissue of HBV-transgenic C57BL/6NCrl mice to analyze anti-HBV activities of swertiamarin, (R)-gentiandiol and (S)-gentiandiol. It was found that HBsAg, HBeAg and HBV-DNA levels were significantly reduced in a dose-dependent manner when (R)-gentiandiol was administered at 1.5, 3 and 6 mg/kg. However, (S)-gentiandiol showed no anti-HBV activity at all. In addition, we also performed untargeted metabolomics to discover biomarkers and metabolic pathways of swertiamarin and (R)-gentiandiol in HBV-transgenic C57BL/6NCrl mice. A total of 15 candidate biomarkers were obtained. Meanwhile, the metabolic disorders including 8 metabolic pathways, such as taurine and hypotaurine metabolism were explored. Taurine and hypotaurine metabolism was the primary pathway for (R)-gentiandiol to regulate HBV-transgenic C57BL/6NCrl mice. It is the first time to clarify real active anti-HBV metabolites of swertiamarin, which can offer more insights into anti-HBV activities of swertia herbs, and bring novel ideas for new drug development in anti-HBV herbs.

Genome-wide association study for circulating metabolic traits in 619,372 individuals

Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis-MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.

Genetic causes of obesity: mapping a path forward

Over the past 30 years, significant progress has been made in understanding the genetic causes of obesity. In the coming years, catalogs that map each genetic variant to its genomic function are expected to accelerate variant-to-function (V2F) translation. Given that obesity is a heterogeneous disease, research will have to move beyond body mass index (BMI). Gene discovery efforts for more refined adiposity traits are poised to reveal additional genetic loci, pointing to new biological mechanisms. Obesity genetics research is reaching unprecedented heights and, along with a renewed interest in the development of weight-loss medication, it holds the potential to identify new drug targets. Polygenic scores (PGSs) that predict obesity risk are expected to further improve and will be particularly valuable early in life for timely prevention.

Pathway level metabolomics analysis identifies carbon metabolism as a key factor of incident hypertension in the Estonian Biobank

The purpose of this study was to find metabolic changes associated with incident hypertension in the volunteer-based Estonian Biobank. We used a subcohort of the Estonian Biobank where metabolite levels had been measured by mass-spectrometry (LC-MS, Metabolon platform). We divided annotated metabolites of 989 individuals into KEGG pathways, followed by principal component analysis of metabolites in each pathway, resulting in a dataset of 91 pathway components. Next, we defined incident hypertension cases and controls based on electronic health records, resulting in a dataset of 101 incident hypertension cases and 450 controls. We used Cox proportional hazards models and replicated the results in a separate cohort of the Estonian Biobank, assayed with LC-MS dataset of the Broad platform and including 582 individuals. Our results show that body mass index and a component of the carbon metabolism KEGG pathway are associated with incident hypertension in both discovery and replication cohorts. We demonstrate that a high-dimensional dataset can be meaningfully reduced into informative pathway components that can subsequently be analysed in an interpretable way, and replicated in a metabolomics dataset from a different platform.