Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Cardiometabolic and immune response to exercise training in patients with metabolic syndrome: retrospective analysis of two randomized clinical trials

Background

Metabolic syndrome (MetS) is defined by the presence of central obesity plus ≥two metabolic/cardiovascular risk factors (RF), with inflammation being a major disease-driving mechanism. Structured endurance exercise training (ET) may positively affect these traits, as well as cardiorespiratory fitness (V̇O2peak).

Aims

We explore individual ET-mediated improvements of MetS-associated RF in relation to improvements in V̇O2peak and inflammatory profile.

Methods

MetS patients from two randomized controlled trials, ExMET (n = 24) and OptimEx (n = 34), had performed 4- or 3-months supervised ET programs according to the respective trial protocol. V̇O2peak, MetS-defining RFs (both RCTs), broad blood leukocyte profile, cytokines and plasma proteins (ExMET only) were assessed at baseline and follow-up. Intra-individual changes in RFs were analysed for both trials separately using non-parametric approaches. Associations between changes in each RF over the exercise period (n-fold of baseline values) were correlated using a non-parametrical approach (Spearman). RF clustering was explored by uniform manifold approximation and projection (UMAP) and changes in RF depending on other RF or exercise parameters were explored by recursive partitioning.

Results

Four months of ET reduced circulating leukocyte counts (63.5% of baseline, P = 8.0e-6), especially effector subtypes. ET response of MetS-associated RFs differed depending on patients' individual RF constellation, but was not associated with individual change in V̇O2peak. Blood pressure lowering depended on cumulative exercise duration (ExMET: ≥102 min per week; OptimEx-MetS: ≥38 min per session) and baseline triglyceride levels (ExMET: <150 mg/dl; OptimEx-MetS: <174.8 mg/dl). Neuropilin-1 plasma levels were inversely associated with fasting plasma triglycerides (R: -0.4, P = 0.004) and changes of both parameters during the ET phase were inversely correlated (R: -0.7, P = 0.0001).

Conclusions

ET significantly lowered effector leukocyte blood counts. The improvement of MetS-associated cardiovascular RFs depended on individual basal RF profile and exercise duration but was not associated with exercise-mediated increase in V̇O2peak. Neuropilin-1 may be linked to exercise-mediated triglyceride lowering.

Association of Remote Patient Monitoring with Mortality and Healthcare Utilization in Hypertensive Patients: a Medicare Claims-Based Study

BACKGROUND

Hypertension management is complex in older adults. Recent advances in remote patient monitoring (RPM) have warranted evaluation of RPM use and patient outcomes.

OBJECTIVE

To study associations of RPM use with mortality and healthcare utilization measures of hospitalizations, emergency department (ED) utilization, and outpatient visits.

DESIGN

A retrospective cohort study.

PATIENTS

Medicare beneficiaries aged ≥65 years with an outpatient hypertension diagnosis between July 2018 and September 2020. The first date of RPM use with a corresponding hypertension diagnosis was recorded (index date). RPM non-users were documented from those with an outpatient hypertension diagnosis; a random visit was selected as the index date. Six months prior continuous enrollment was required.

MAIN MEASURES

Outcomes studied within 180 days of index date included (i) all-cause mortality, (ii) any hospitalization, (iii) cardiovascular-related hospitalization, (iv) non-cardiovascular-related hospitalization, (v) any ED, (vi) cardiovascular-related ED, (vii) non-cardiovascular-related ED, (viii) any outpatient, (ix) cardiovascular-related outpatient, and (x) non-cardiovascular-related outpatient. Patient demographics and clinical variables were collected from baseline and index date. Propensity score matching (1:4) and Cox regression were performed. Hazard ratios (HR) and 95% confidence intervals (CI) are reported.

KEY RESULTS

The matched sample had 16,339 and 63,333 users and non-users, respectively. Cumulative incidences of mortality outcome were 2.9% (RPM) and 4.3% (non-RPM), with a HR (95% CI) of 0.66 (0.60-0.74). RPM users had lower hazards of any [0.78 (0.75-0.82)], cardiovascular-related [0.79 (0.73-0.87)], and non-cardiovascular-related [0.79 (0.75-0.83)] hospitalizations. No significant association was observed between RPM use and the three ED measures. RPM users had higher hazards of any [1.10 (1.08-1.11)] and cardiovascular-related outpatient visits [2.17 (2.13-2.19)], while a slightly lower hazard of non-cardiovascular-related outpatient visits [0.94 (0.93-0.96)].

CONCLUSIONS

RPM use was associated with substantial reductions in hazards of mortality and hospitalization outcomes with an increase in cardiovascular-related outpatient visits.

Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.

Role of Epigenetics and Metabolomics in Predicting Endothelial Dysfunction in Type 2 Diabetes

Type 2 diabetes (T2D) is a worldwide health problem and cardiovascular disease (CVD) is a leading cause of morbidity and mortality in T2D patients, making the prevention of CVD onset a major priority. It is therefore crucial to optimize diagnosis and treatment to reduce this burden. Endothelial dysfunction is one of the most important prognostic factors for CVD progression, thus novel approaches to identify the early phase of endothelial dysfunction may lead to specific preventive measures to reduce the occurrence of CVD. Nowadays, multiomics approaches have provided unprecedented opportunities to stratify T2D patients into endotypes, improve therapeutic treatment and outcome and amend the survival prediction. Among omics strategies, epigenetics and metabolomics are gaining increasing interest. Recently, a dynamic correlation between metabolic pathways and gene expression through chromatin remodeling, such as DNA methylation, has emerged, indicating new perspectives on the regulatory networks impacting cellular processes. Thus, a better understanding of epigenetic-metabolite relationships can provide insight into the physiological processes altered early in the endothelium that ultimately head to disease development. Here, recent studies on epigenetics and metabolomics related to CVD prevention potentially useful to identify disease biomarkers, as well as new therapies hopefully targeting the early phase of endothelial dysfunction are highlighted.

AGR2-induced cholesterol synthesis drives lovastatin resistance that is overcome by combination therapy with allicin

Anterior gradient 2 (AGR2), a protein disulfide isomerase (PDI), is a multifunctional protein under physiological and pathological conditions. In this study we investigated the roles of AGR2 in regulating cholesterol biogenesis, lipid-lowering efficiency of lovastatin as well as in protection against hypercholesterolemia/statin-induced liver injury. We showed that AGR2 knockout significantly decreased hepatic and serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) in mice with whole-body or hepatocyte-specific Agr2-null mutant, compared with the levels in their wild-type littermates fed a normal chow diet (NCD) or high-fat diet (HFD). In contrast, mice with AGR2 overexpression (Agr2/Tg) exhibited an increased cholesterol level. Mechanistic studies revealed that AGR2 affected cholesterol biogenesis via activation of AKT/sterol regulatory element-binding protein-2 (SREBP2), to some extent, in a PDI motif-dependent manner. Moreover, elevated AGR2 led to a significant decrease in the lipid-lowering efficacy of lovastatin (10 mg· kg-1· d-1, ip, for 2 weeks) in mice with hypercholesterolemia (hyperCho), which was validated by results obtained from clinical samples in statin-treated patients. We showed that lovastatin had limited effect on AGR2 expression, but AGR2 was inducible in Agr2/Tg mice fed a HFD. Further investigations demonstrated that drug-induced liver toxicity and inflammatory reactions were alleviated in hypercholesterolemic Agr2/Tg mice, suggesting the dual functions of AGR2 in lipid management and hyperCho/statin-induced liver injury. Importantly, the AGR2-reduced lipid-lowering efficacy of lovastatin was attenuated, at least partially, by co-administration of a sulfhydryl-reactive compound allicin (20 mg· kg-1· d-1, ip, for 2 weeks). These results demonstrate a novel role of AGR2 in cholesterol metabolism, drug resistance and liver protection, suggesting AGR2 as a potential predictor for selection of lipid-lowering drugs in clinic.

Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine

INTRODUCTION

Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high- -risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state.

OBJECTIVE

We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy.

CONCLUSION

The interactome or protein-protein interactions (PPIs) is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network- oriented biomarkers and drugs in the management of DM.

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood-brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug-gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.