Targeting REV-ERBα/BNIP3 axis attenuates pulmonary arterial hypertension by repressing mitophagy in mice

Nature Communications · 2026-04-02

Pulmonary arterial hypertension (PAH) is a life-threatening metabolic disorder. Nuclear receptors REV-ERBα and REV-ERBβ are established regulators of circadian rhythm and metabolic homeostasis, however their roles in PAH remain unclear. Using Rev-erbα+/-, VSMC-specific Rev-erbα-/-, and Rev-erbβ-/- mice (only male mice were used in the study), along with pharmacological activation and AAV-mediated overexpression, we found that Rev-erbα deficiency, particularly in vascular smooth muscle cells (VSMCs), exacerbates Su5416+hypoxia (SuHx)-induced PAH, whereas REV-ERBα activation or overexpression alleviates disease. In contrast, Rev-erbβ loss does not affect PAH. Notably, late-stage administration of REV-ERBα agonist significantly improves established PAH. Mechanistically, REV-ERBα directly represses Bnip3 transcription, thereby inhibiting BNIP3-driven mitophagy and improving mitochondrial function in hypoxic pulmonary artery smooth muscle cells (PASMCs). Bnip3 knockdown phenocopies REV-ERBα activation, while Bnip3 overexpression abrogates REV-ERBα’s anti-proliferative effects and accelerates PAH. Collectively, REV-ERBα protects against PAH by inhibiting BNIP3-driven mitophagy and preserving mitochondrial homeostasis in PASMCs. Targeting the REV-ERBα/BNIP3 axis holds promise as a circadian-based therapeutic strategy for PAH. Pulmonary arterial hypertension (PAH) is a fatal disease involving disrupted metabolic and mitochondrial regulation. Here the authors show that REV-ERBα protects against PAH by repressing BNIP3-driven mitophagy in pulmonary vascular smooth muscle cells, revealing a potential circadian-based therapeutic target.

Circadian rhythms and microbiota: molecular crosstalk and its implications for health and disease

Biology Direct · 2026-03-07

Circadian rhythms, evolutionarily conserved 24-hour oscillations, exert precise regulatory control over microbial communities across host niches including the gastrointestinal tract, oral cavity, urinary bladder, and skin. This bidirectional interplay is critical to host physiology: host circadian clocks shape the composition and functional rhythms of resident microbiota, while microbiota-derived signals reciprocally modulate circadian entrainment and tissue-specific rhythmicity. Circadian disruption from shift work, irregular feeding, light pollution, or sleep deprivation trigger microbial dysbiosis and circadian misalignment, contributing to metabolic diseases, gastrointestinal disorders, neuropsychiatric conditions, cardiovascular diseases, and dermatological or reproductive disorders. Mechanistically, this crosstalk is mediated by rhythmic hormonal secretion, microbial metabolites, epigenetic regulation, and immune signaling. Therapeutic strategies such as time-restricted feeding, probiotics, melatonin, and polyphenol-rich diets show promise in restoring temporal homeostasis. This review synthesizes current evidence on circadian-microbiota interplay, elucidates its roles in physiology and disease, and highlights translational opportunities for chrono-microbiome-based interventions to optimize host health.

Abstract 04: Proteomics insights into heterogeneous associations between adiposity measures and all-cause mortality

Circulation · 2026-03-24

Background: Heterogeneity of associations between adiposity traits and all-cause mortality is well-reported, but its modifiable contributors are poorly characterized. Proteomics systematically characterizes physiology and pathophysiology of health. No study has comprehensively investigated effect-measure modifications of proteomics on the mortality risk associated with adiposity traits. Methods: Using the UK Biobank data, we evaluated effect-measure modifications of proteomics on all-cause mortality risk associated with nine adiposity traits (i.e., BMI, waist circumference, waist-to-hip ratio, hip circumference, whole body fat mass, arm fat mass, leg fat mass, trunk fat mass, and VAT) using Cox regressions. Restricted cubic splines were implemented to model effect-measure modifications for proteins on the associations between adiposity and all-cause mortality. Through integrating adiposity, proteins, and interaction terms, we developed ObesRISK for predicting all-cause mortality risk using gradient boosting machine. Discrimination was assessed using Harrell C-statistics. We further estimated associations of ObesRISK with multi-organ traits using multivariable linear models. Results: This study analyzed proteomics data from 46,233 participants. During a median follow-up of 14.6 years, 4621 deaths were documented. After applying Bonferroni correction, ADIPOQ ( P int =4.1×10 -6 ), LEP ( P int =7.8×10 -10 ), and RTN4R ( P int =3.9×10 -7 ) modified the associations between BMI and all-cause mortality. We further identified 16 proteins that modified the all-cause mortality risk associated with the other eight adiposity traits. Pathway analyses mapped these proteins to inflammation, hormones (estradiol and glucocorticoid), and lipid metabolism. The mortality risk of adiposity varied depending on different levels of the identified proteins (all Bonferroni-corrected P int <0.05). Empowered by protein-adiposity interactions, ObesRISK outperformed adiposity and routine biochemical profiling in predicting all-cause mortality (ΔC-index=0.036, 95%CI: 0.027-0.042). Furthermore, ObesRISK was associated with multi-organ dysfunctions including the brain, cardiac, and metabolic systems. Conclusion: This study advances the understanding of heterogeneities in mortality risk associated with adiposity traits by utilizing a proteome-wide interaction modelling framework, thereby providing a proteomics approach that may improve precision risk classification and risk prediction of all-cause mortality.

Abstract P1006: Trajectories of Cardiometabolic Diseases and Cancer: Transition Patterns, Multiomics Signatures, and Prediction Model

Circulation · 2025-03-11

Objective Multimorbidity is a growing but understudied global challenge in an aging world. Cardiometabolic disease (CMD) and cancer are the 2 most common chronic diseases, yet their transition patterns remain unclear, and reliable tools for early prediction of disease trajectories across the lifespan are scarce. Methods: This study was conducted on 429,909 UK Biobank participants without baseline CMD and cancer. CMD included type 2 diabetes, coronary disease, heart failure, and stroke. A multistate analysis was used to investigate transition patterns and identify multiomics signatures for transitions from baseline to single morbidity and to multimorbidity. Machine learning prediction models for disease trajectories were constructed using genomics, metabolomics, and proteomics data, and predictive performance was assessed. Results: During a median follow-up of 15 years, 105,855 participants developed morbidity and 15,044 developed multimorbidity of CMD and cancer. Participants with multimorbidity had a 9%-30% higher mortality risk and 2.4-5.3 years of shorter survival time than those healthy or with single morbidity. Development of CMD before cancer presented a poorer prognosis than the reverse order. Distinct and shared multiomics signatures underlying disease trajectories were identified. Top 10 multiomics signatures presented a dose-response characteristic from health to single morbidity and to multimorbidity of CMD and cancer. Proteomics scores showed superior prediction performance than metabolomics and genomics scores. For 10-year outcome prediction, proteomics scores showed varying performance across disease trajectories, with area under receiver-operating characteristic curves ranging from 0.64 for health-to-only cancer trajectory to 0.90 for health-to-CMD-to-death trajectory, significantly better than the base model and traditional clinical model. Conclusions This study revealed disease trajectories of CMD and cancer with varying prognostic implications that were predicted with differing accuracy through multiomics approaches.

Association of blood pressure levels in the nonhypertensive range with mortality among people without traditional risk factors

Journal of Hypertension · 2025-12-17

BACKGROUND: While emerging evidence suggests guideline-defined nonhypertensive blood pressure (BP) may encompass heterogeneous risk, the relationship between BP variations within nonhypertensive ranges and mortality risk remains inadequately characterized among individuals without traditional cardiovascular risk factors. This study investigated whether nonhypertensive range of SBP, DBP, and pulse pressure (PP) are associated with long-term mortality in a healthy population. METHODS: This study included 80 730 UK Biobank participants without traditional cardiovascular risk factors and with nonhypertensive BP (SBP <140 mmHg, DBP <90 mmHg, and PP <60 mmHg). Participants were followed up for all-cause, cardiovascular, and noncardiovascular mortality. Associations were assessed using multivariable Cox proportional hazards models with restricted cubic splines. RESULTS: Over a median follow-up of 13.7 years, 2553 deaths occurred. SBP and PP showed significant nonlinear associations with all-cause mortality ( P -overall <0.01), while DBP showed a linear inverse association ( P -overall = 0.049). Compared to the third quintile, the lowest PP quintile (<40 mmHg) was associated with 26% higher mortality risk (hazard ratio 1.26, 95% confidence interval [95% CI] 1.10-1.44), and the highest quintile (53-60 mmHg) with 14% higher risk (hazard ratio 1.14, 95% CI 1.01-1.28). The lowest SBP quintile (<114 mmHg) was associated with 16% higher risk (hazard ratio 1.16, 95% CI 1.02-1.32) compared to the third quintile (120-126 mmHg). CONCLUSION: Even within nonhypertensive ranges, the lowest and highest quintiles of PP level, as well as low-normal SBP and DBP levels, were associated with increased mortality risk in a healthy population.

Biological age acceleration and interaction with genetic predisposition in the risk of type 2 diabetes and coronary artery disease

GeroScience · 2025-04-29 · 4 citations

Abstract P3029: Phenotypic and Genetic Correlations of Imaging Traits in the Abdomen-Heart-Brain Axis: Insights into Multimorbidity of Cardiometabolic Diseases and Depression

Circulation · 2025-03-11

Objective: The development of multimorbidity of cardiometabolic diseases (CMDs) and depression likely involves pathophysiological processes across multiple organs. This study aimed to investigate imaging traits in the abdomen-heart-brain axis linked to the CMDs-depression multimorbidity and assess their phenotypic and genetic connections using magnetic resonance imaging (MRI) data. Methods: This study analyzed multi-organ MRI data including 7 abdominal, 82 cardiac, and 676 brain traits from 31,839 UK Biobank participants. CMDs included coronary artery disease, stroke, heart failure, type 2 diabetes, hypertension and hyperlipidemia. First, multivariable logistic regressions estimated associations of imaging traits with the CMDs-depression multimorbidity. Second, multivariable linear regressions quantified pairwise correlations among significant cross-organ traits. Third, transcriptome-wide association study (TWAS) identified shared genes of traits significantly correlated with each other across all 3 regions (i.e., triads). Fourth, LASSO regressions constructed genomics scores using single nucleotide polymorphisms from the shared genes. Fifth, the prediction performance of imaging traits and genomics scores for the CMDs-depression multimorbidity was evaluated by AUCs. FDR correction was used to adjust for multiple testing. Results: Seven abdominal, 27 cardiac, and 340 brain traits were significantly associated with the CMDs-depression multimorbidity. Among 11,749 pairs of significant cross-organ imaging traits, 6385 abdomen-heart-brain triads were identified, with liver volume as the most connected node. The directions of associations among imaging traits in these cross-organ triads aligned with directions of their biological functions, suggesting multiple organs acting as a coordinated system. TWAS revealed TNFSF12 from whole blood as the shared gene underlying the significant liver-heart-brain connection associated with the CMDs-depression multimorbidity. The integration of imaging traits with genomics score (AUC = 0.84) improved prediction performance for the CMDs-depression multimorbidity, outperforming demographic and lifestyle models (AUC = 0.78) (P = 0.014). Conclusions: This study identified highly correlated cross-organ imaging traits that were associated with the CMDs-depression multimorbidity. A shared gene was found for a liver-heart-brain triad. Imaging traits and genomic score significantly enhanced prediction performance on top of traditional factors.

A review on the mechanisms and applications of bacteria in responding, adsorbing and transporting heavy metals

Archives of Microbiology · 2025-10-06 · 2 citations

Abstract 4359776: Adverse Childhood Experiences, Genetic Predisposition, and Lifestyle Factors: Roles in Cardiometabolic-Depression Multimorbidity Trajectories

Circulation · 2025-11-03

Background: Adverse Childhood Experiences (ACEs) have profound effects on physical and mental health across the lifespan. However, how ACEs influence the co-occurrence and progression patterns between cardiometabolic diseases (CMDs) and depression remains poorly understood. This study aimed to investigate ACE-related disease progression patterns and evaluate the effect modification of genetic predisposition and lifestyle factors. Method: This study was conducted on a prospective cohort study of 203,449 UK Biobank participants who were free of CMDs and depression at baseline and had complete ACE data. ACEs, including emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect, were assessed, with cumulative ACE scores (range 0-5) categorized as low (0), intermediate (1-2), or high (≥3) exposure. Outcomes included incident CMDs (a composite of type 2 diabetes, coronary artery disease, stroke, and heart failure), depression, their multimorbidity, and mortality. Disease trajectories and transitions were analyzed using multistate models. Both additive and multiplicative interactions of genetic predisposition and healthy lifestyle with ACE exposure were investigated to assess their modification effects. Result: During a median follow-up of 14.8 years, 5859 individuals developed CMD only, 6523 developed depression only, and 1507 developed multimorbidity of CMD and depression. ACE exposure showed dose-response associations with risk of CMD-depression multimorbidity (HRs: 1.07-3.89), with stronger associations observed for depression-related trajectories (HRs: 1.19-3.89) than CMD-related trajectories (HRs: 1.07-3.61). The probability of progressing from depression to multimorbidity (12.8-15.5%) was significantly higher than that from CMD to multimorbidity (4.5-7.2%) across three ACE groups. Emotional abuse showed the strongest associations with depression-related trajectories. High genetic predisposition amplified ACE-associated risks (up to 8.51-fold for the depression-CMD transition), while healthy lifestyle attenuated 30.4-93.2% of the ACE-associated risks. Conclusion: This study underscores the dose-response effect of ACEs on CMD-depression multimorbidity, revealing transition-specific vulnerability to early-life adversity. The findings that genetic predisposition amplifies, while healthy lifestyle attenuates ACE-associated risks, suggest opportunities for targeted intervention strategies.

Epigenetic Signatures of Type 2 Diabetes Remission and Substantial Weight Loss: The Role of DNA Methylation in Response to Low-Calorie Diet Intervention

Journal of the American Nutrition Association · 2025-06-05

OBJECTIVES: Low-calorie diet (LCD) interventions can lead to remission of type 2 diabetes (T2D), but the underlying mechanisms are not well understood. As a diet-sensitive regulator of gene expression, DNA methylation may reveal pathways underlying remission. However, whether individuals with different responses to LCD-induced T2D remission and weight loss exhibit distinct DNA methylation patterns remains unclear. METHODS: A 3-month intensive weight loss intervention (815-835 kcal/d) and a following 3-month weight loss maintenance phase were conducted among participants with T2D. DNA methylation was measured using the Infinium Methylation EPIC BeadChip (935K) at 4 timepoints. Differentially methylated cytosine-phosphate-guanine (CpG) sites and regions were analyzed to identify changes between those who achieved T2D remission and weight loss >12 kg and those who did not. Predictive models based on DNA methylation profiles at baseline and week 1 were developed to forecast individual responses to LCD. RESULTS: . Pathway analysis mapped these genes to glucose and lipid metabolism pathways. Methylation-inferred scores showed a greater improvement in predicting T2D remission and weight loss status following the LCD intervention than the base models (area under curve ranges: 0.86-0.88 vs. 0.73-0.80). CONCLUSIONS: Variation in DNA methylation profiles across individuals with differing responses to the LCD, highlighting the epigenetic roles in the effects of the LCD on weight loss and T2D remission. Baseline DNA methylation status may serve as a predictor of T2D remission in response to LCD intervention. CLINICAL TRIAL REGISTRY NUMBER AND WEBSITE: This trial was registered at the https://clinicaltrials.gov/study/NCT05472272?cond=NCT05472272&rank=1 as NCT05472272.