A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Melanoxetin: A Hydroxylated Flavonoid Attenuates Oxidative Stress and Modulates Insulin Resistance and Glycation Pathways in an Animal Model of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (DM) continues to escalate, necessitating innovative therapeutic approaches that target distinct pathways and address DM complications. Flavonoids have been shown to possess several pharmacological activities that are important for DM. This study aimed to evaluate the in vivo effects of the flavonoid melanoxetin using Goto-Kakizaki rats. Over a period of 14 days, melanoxetin was administered subcutaneously to investigate its antioxidant, anti-inflammatory, and antidiabetic properties. The results show that melanoxetin reduced insulin resistance in adipose tissue by targeting protein tyrosine phosphatase 1B. Additionally, melanoxetin counteracted oxidative stress by reducing nitrotyrosine levels and modulating superoxide dismutase 1 and hemeoxygenase in adipose tissue and decreasing methylglyoxal-derived hydroimidazolone (MG-H1), a key advanced glycation end product (AGE) implicated in DM-related complications. Moreover, the glyoxalase 1 expression decreased in both the liver and the heart, correlating with reduced AGE levels, particularly MG-H1 in the heart. Melanoxetin also demonstrated anti-inflammatory effects by reducing serum prostaglandin E2 levels, and increasing the antioxidant status of the aorta wall through enhanced acetylcholine-dependent relaxation in the presence of ascorbic acid. These findings provide valuable insights into melanoxetin's therapeutic potential in targeting multiple pathways involved in type 2 DM, particularly in mitigating oxidative stress and glycation.

Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale genome-wide association studies summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index, and nine quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the nine traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each of the nine traits at baseline as well as the change of trait values during a 3- to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curve values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 3-6 years, respectively.

Precision medicine for cardiometabolic disease: a framework for clinical translation

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

Precision medicine of obesity as an integral part of type 2 diabetes management - past, present, and future

Obesity is a complex and heterogeneous condition that leads to various metabolic complications, including type 2 diabetes. Unfortunately, for some, treatment options to date for obesity are insufficient, with many people not reaching sustained weight loss or having improvements in metabolic health. In this Review, we discuss advances in the genetics of obesity from the past decade-with emphasis on developments from the past 5 years-with a focus on metabolic consequences, and their potential implications for precision management of the disease. We also provide an overview of the potential role of genetics in guiding weight loss strategies. Finally, we propose a vision for the future of precision obesity management that includes developing an obesity-centred multidisease management algorithm that targets both obesity and its comorbidities. However, further collaborative efforts and research are necessary to fully realise its potential and improve metabolic health outcomes.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Precision nutrition for targeting pathophysiology of cardiometabolic phenotypes

Obesity is a heterogenous disease accompanied by a broad spectrum of cardiometabolic risk profiles. Traditional paradigms for dietary weight management do not address biological heterogeneity between individuals and have catastrophically failed to combat the global pandemic of obesity-related diseases. Nutritional strategies that extend beyond basic weight management to instead target patient-specific pathophysiology are warranted. In this narrative review, we provide an overview of the tissue-level pathophysiological processes that drive patient heterogeneity to shape distinct cardiometabolic phenotypes in obesity. Specifically, we discuss how divergent physiology and postprandial phenotypes can reveal key metabolic defects within adipose, liver, or skeletal muscle, as well as the integrative involvement of the gut microbiome and the innate immune system. Finally, we highlight potential precision nutritional approaches to target these pathways and discuss recent translational evidence concerning the efficacy of such tailored dietary interventions for different obesity phenotypes, to optimise cardiometabolic benefits.

Relevance of body composition in phenotyping the obesities

Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic alterations such as type 2 diabetes, hypertension, and dyslipidemia. Body mass index (BMI) remains the most frequently used tool for classifying patients with obesity, but it does not accurately reflect body adiposity. In this document we review classical and new classification systems for phenotyping the obesities. Greater accuracy of and accessibility to body composition techniques at the same time as increased knowledge and use of cardiometabolic risk factors is leading to a more refined phenotyping of patients with obesity. It is time to incorporate these advances into routine clinical practice to better diagnose overweight and obesity, and to optimize the treatment of patients living with obesity.

The DECON pilot project investigates predictive markers for successful bariatric surgery

Obesity is a chronic, multifactorial disease which is linked to a number of adverse endocrinological and metabolic conditions. Currently, bariatric surgery is one of the most effective treatments for individuals diagnosed with severe obesity. However, the current indications for bariatric surgery are based on inadequate metrics (i.e., BMI) which do not account for the complexity of the disease, nor the heterogeneity among the patient population. Moreover, there is a lack of understanding with respect to the biological underpinnings that influence successful and sustained weight loss post-bariatric surgery. Studies have implicated age and pre-surgery body weight as two factors that are associated with favorable patient outcomes. Still, there is an urgent medical need to identify other potential factors that could improve the specificity of candidate selection and better inform the treatment plan of patients with obesity. In this report, we present and describe the cohort of the DECON pilot project, a multicenter study which aims to identify predictive biomarkers of successful weight loss after bariatric surgery.

Irisin: A Possible Marker of Adipose Tissue Dysfunction in Obesity

Adipose tissue (AT) secretes pro- and anti-inflammatory cytokines involved in AT homeostasis, including tumor necrosis factor-α (TNFα) and irisin. The functionality of AT is based on a regulated equilibrium between adipogenesis and extracellular matrix (ECM) remodeling. We investigated the contributions of adipose progenitors (ASCs) and adipocytes (AMCs) to TNFα-induced ECM remodeling and a possible implication of irisin in AT impairment in obesity. ASCs and AMCs were exposed to TNFα treatment and nuclear factor-kappa (NF-kB) pathway was investigated: Tissue Inhibitor of Metalloproteinase (TIMP-1), Twist Family Transcription Factor 1 (TWIST-1), and peroxisome proliferator-activated receptor-γ (PPARγ) expression levels were analyzed. The proteolytic activity of matrix metalloproteinases (MMPs) -2 and -9 was analyzed by zymography, and the irisin protein content was measured by ELISA. In inflamed AMCs, a TIMP-1/TWIST-1 imbalance leads to a drop in PPARγ. Adipogenesis and lipid storage ability impairment come with local tissue remodeling due to MMP-9 overactivation. In vitro and ex vivo measurements confirm positive correlations among inflammation, adipose secreting irisin levels, and circulating irisin levels in patients with visceral obesity. Our findings identify the NF-kB downstream effectors as molecular initiators of AT dysfunction and suggest irisin as a possible AT damage and obesity predictive factor.

Consumption of Common Bean Suppresses the Obesogenic Increase in Adipose Depot Mass: Impact of Dose and Biological Sex

Obesity prevention is stated as a simple objective in the public health guidelines of most countries: avoid adult weight gain. However, the success of the global population in accomplishing this goal is limited as reflected in the persisting pandemic of overweight and obesity. While many intervention strategies have been proposed, most are directed at mitigating the consequences of obesity. Efforts intended to prevent unintentional weight gain and associated adiposity are termed anti-obesogenic. Herein, evidence is presented that a neglected category of foods, pulses, i.e., grain legumes, have anti-obesogenic activity. Using a preclinical mouse model of obesity, a dose-response study design in animals of both biological sexes, and cooked, freeze-dried, and milled common bean as a representative pulse, data are presented showing that the rate of body weight gain is slowed, and fat accumulation is suppressed when 70% of the dietary protein is provided from common bean. These anti-obesogenic effects are reduced at lower amounts of common bean (17.5% or 35%). The anti-obesogenic responsiveness is greater in female than in male mice. RNA sequence analysis indicates that the sex-related differences extend to gene expression patterns, particularly those related to immune regulation within adipose tissue. In addition, our findings indicate the potential value of a precision nutrition approach for human intervention studies that identify "pulse anti-obesogenic responders". A precision approach may reduce the concentration of pulses required in the diet for benefits, but candidate biomarkers of responsivity to pulse consumption remain to be determined.

Spore Oil-Functionalized Selenium Nanoparticles Protect Pancreatic Beta Cells from Palmitic Acid-Induced Apoptosis via Inhibition of Oxidative Stress-Mediated Apoptotic Pathways

Oxidative stress damage of pancreatic β-cells is a key link in the pathogenesis of type 2 diabetes mellitus. A long-term increase of free fatty acids induces the increase of reactive oxygen species (ROS) in β-cells, leading to apoptosis and dysfunction of β-cells. Ganoderma lucidum spore oil (GLSO) is a functional food complex with strong antioxidant activity, but its solubility and stability are poor. In the present study, GLSO-functionalized selenium nanoparticles (GLSO@SeNPs) with high stability and uniform particle size were synthesized by a high-pressure homogeneous emulsification method. The aim of this study was to investigate the protective effects of GLSO@SeNPs on INS-1E rat insulinoma β-cells against palmitic-acid (PA)-induced cell death, as well as the underlying mechanisms. Our results showed that GLSO@SeNPs had good stability and biocompatibility, and they significantly inhibited the PA-induced apoptosis of INS-1E pancreatic cells by regulating the activity of related antioxidant enzymes, including thioredoxin reductase (TrxR), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px). Western blot analysis showed that GLSO@SeNPs reversed the PA-induced changes in MAPK pathway protein expression levels. Thus, the present findings provided a new theoretical basis for utilizing GLSO@SeNPs as a treatment for type 2 diabetes.