Every mammalian cell contains a molecular clock built from interlocking transcription-translation feedback loops. Core activators such as BMAL1 and CLOCK drive the rhythmic expression of thousands of genes, which in turn coordinate time-of-day-dependent processes including glucose handling, lipid oxidation, protein quality control and immune surveillance. When these clocks falter, the consequences are well documented: inflammation, impaired glucose homeostasis and distorted lipid metabolism, all of which are hallmark features of cancer cachexia. Previous work has shown that tumours can remotely rewire circadian programs in the liver, and the Florida group had already demonstrated that pancreatic cancer disrupts circadian gene expression in a limb muscle, the tibialis anterior. What remained unknown was whether the cardiorespiratory muscles, whose wasting is thought to be a key driver of cachexia-related morbidity and mortality, suffer the same fate.

To find out, the researchers used an orthotopic model of pancreatic ductal adenocarcinoma in which aggressive KPC tumour cells are injected directly into the pancreas of mice. Twelve days after inoculation, a time point corresponding precisely to the onset of cachexia, they collected diaphragm and heart tissue every four hours across a full twenty-four-hour cycle in constant darkness, ensuring that any rhythms they detected reflected endogenous clocks rather than external light cues. They then performed bulk RNA sequencing on each sample and applied sophisticated statistical models that treat gene expression as a continuous sinusoidal function over the day, allowing them to identify rhythmically expressed genes and to detect changes in amplitude, phase and basal expression. The design was powered at over ninety-seven percent to detect circadian rhythmicity, lending confidence to the results.

The findings were striking. In healthy mice, the diaphragm showed robust rhythmicity in 308 genes, but in tumour-bearing mice sixty-four percent of those genes lost their daily oscillations entirely. At the same time, 718 genes that were never rhythmic in healthy diaphragm gained rhythmicity in the cancer state, a wholesale remodelling of the tissue’s temporal architecture. Among the genes losing their rhythm were Nrf1, which guards mitochondrial homeostasis; Pik3c3, a key mediator of autophagy; and Fgf1, a positive modulator of glucose disposal. Genes that gained rhythmicity included inflammatory players such as Tlr7, Il18 and Il18r1, alongside troubling time-of-day-dependent losses in Glut4, the major insulin-regulated glucose transporter, and in Tfeb and Psme4, master regulators of the autophagy-lysosome system and proteasomal quality control.

The heart told a partially different story. Of 1274 genes rhythmic in healthy hearts, fifty-six percent lost their oscillations in tumour-bearing animals, while 246 genes gained rhythmicity. Crucially, core clock gene rhythmicity was largely preserved in the heart, with only Per1 showing elevated basal expression, whereas the diaphragm exhibited significant repression of core clock components including Bmal1 itself, the gene whose deletion in muscle is known to cause metabolic reprogramming, weakness and atrophy. Only about ten percent of the rhythmic genes disrupted in the cancerous hearts overlapped with those altered by cardiomyocyte-specific Bmal1 deletion, suggesting that factors extrinsic to the cardiac clock, such as systemic inflammatory or autonomic signals, are driving much of the disruption.

The temporal coordination of biological processes also collapsed in a tissue-specific manner. In healthy diaphragms, peak expression times of rhythmic genes are spread evenly across the day, reflecting a smooth distribution of daily functions. In cachectic diaphragms, that even distribution vanished, with peak expression re-clustering around the transitions between rest and activity. Genes governing lipid metabolism and amino acid sensing, which normally peak during rest and activity respectively, no longer fired at their proper times. Instead, inflammatory genes and components of ubiquitin-dependent protein breakdown dominated those windows, implying that the muscle’s daily schedule had been hijacked to prioritize immune activation and protein catabolism over metabolic maintenance.

In the heart, the overall distribution of peak expression remained even, but the content of the schedule changed. Genes that regulate heart rate, including Hcn4 and Pde4d, normally peak during the active phase to support the diurnal rise in heart rate and cardiac output driven by sympathetic tone. In tumour-bearing mice, this temporal upregulation was lost, and inflammatory genes such as Il33 and Edn1 peaked during the active phase instead. Phase-stratified analysis revealed that genes involved in adrenergic signalling and contractility, including the beta-1 adrenergic receptor Adrb1, the SERCA2 calcium pump encoded by Atp2a2, and the fatty acid oxidation regulator Ppara, were downregulated in a time-of-day-dependent fashion, while platelet activation genes rose specifically during the active phase, echoing processes implicated in cancer-associated thromboinflammation.

Perhaps the most clinically provocative discovery came from a time-course analysis spanning the entire cachexia trajectory. Drawing on a published RNA-sequencing dataset from diaphragms collected before cachexia, during mild-to-moderate disease and at severe endpoint, the team found that Bmal1 and Clock were significantly repressed by day eight after tumour inoculation, days before any measurable loss of body or muscle mass, and remained suppressed thereafter. The secondary-loop genes Rora and Nr1d1, which encode RORα and REV-ERBα, were also repressed early. Both factors have clock-independent roles in muscle: REV-ERBα promotes mitochondrial biogenesis, represses atrophy genes and is essential for muscle mass maintenance, while RORα regulates lipid homeostasis and protects against pathological fat accumulation. Their early suppression, combined with the fact that both normally restrain NF-κB inflammatory signalling, suggests that clock disruption may precede and actively contribute to diaphragm wasting rather than merely accompanying it.

Why the diaphragm is hit hardest remains an open question, but the authors point to its anatomical proximity to the pancreas, which may expose it to a concentrated bath of tumour-derived factors and a heightened inflammatory microenvironment. Previous work from the same group documented pronounced immune cell infiltration in the diaphragm before cachexia onset, and NF-κB signalling is known to suppress core clock gene expression and interfere with CLOCK:BMAL1 activity. The researchers caution that their study has limitations, including the absence of phenotypic measurements on the same animals used for circadian sequencing and uncertainty about whether the milder cardiac disruption reflects a distinct pathology or simply an earlier stage of a shared trajectory.

The therapeutic implications are considerable. Because the transcriptional damage in these muscles is time-of-day dependent, the findings support testing chronotherapy-based interventions timed to protect muscle mass and function, and they reinforce growing evidence that restoring circadian machinery can halt wasting. Recent work has shown that re-establishing hepatic circadian function in tumour-bearing mice through REV-ERBα overexpression is sufficient to spare both limb muscles and the heart from atrophy, pointing to circadian programs as master regulators of tissue cross-talk during cancer. If similar strategies can be developed for the diaphragm and heart, they could offer a fundamentally new way to defend the muscles that pancreatic cancer patients can least afford to lose.

Subject of Research: Circadian clock disruption in cardiorespiratory muscles during pancreatic cancer cachexia

Article Title: Pancreatic Cancer Disrupts Circadian Patterns of Gene Expression in Cardiorespiratory Muscles

Article References: Ducharme, J. B., Schonk, M. M., Gutierrez‐Monreal, M. A., Ferreira, L. F., Esser, K. A., Judge, A. R., & Judge, S. M. (2026). Pancreatic Cancer Disrupts Circadian Patterns of Gene Expression in Cardiorespiratory Muscles. Journal of Cachexia, Sarcopenia and Muscle, 17(5), Article e70383. https://doi.org/10.1002/jcsm.70383

Image Credits: AI Generated

DOI: 10.1002/jcsm.70383

Keywords: pancreatic cancer, cachexia, circadian clock, diaphragm, heart, gene expression, Bmal1, inflammation, muscle wasting, RNA sequencing, metabolism, chronotherapy

The research team, led by Hsiu-Hua Shih and Tsai-Wei Huang of Taipei Medical University, searched five major biomedical databases—PubMed, MEDLINE, CINAHL, Embase, and the Cochrane Library—through March 2026. Their sweep identified 28 studies encompassing 3,232 participants and spanning nine categories of cancer. To be eligible, studies had to include adults with physician-confirmed malignancy and HRV measured either by electrocardiography or by photoplethysmography, the optical sensing technology embedded in most modern smartwatches and fitness bands. The breadth of the evidence base matters, because HRV research in oncology has long been fragmented across individual small trials, leaving clinicians without a clear quantitative picture of how large the autonomic differences between cancer patients and healthy controls actually are.

A key methodological decision shaped the entire analysis. Time-domain HRV indices such as SDNN, the standard deviation of normal-to-normal heartbeat intervals, scale directly with the length of the recording: a 24-hour ambulatory measurement yields systematically larger values than a five-minute resting snapshot. Pooling studies with different recording durations would therefore conflate measurement artifacts with genuine biology. The researchers restricted their primary quantitative pooling—designated Part A of the review—to short-term resting recordings of five to ten minutes obtained with age-matched non-cancer controls. Only five studies met these strict criteria. Long-term ambulatory recordings of twelve to thirty hours were reported separately, and the remaining studies were synthesized narratively or examined for longitudinal change over time. The team used the DerSimonian–Laird random-effects model and graded the certainty of all findings with the GRADE framework, registering the protocol in advance as PROSPERO CRD42026130103.

The pooled results were unambiguous in direction. Cancer patients showed lower SDNN than controls by a mean difference of 14.05 milliseconds, with a 95 percent confidence interval spanning 9.90 to 18.20 milliseconds. RMSSD, the root mean square of successive heartbeat-interval differences and a sensitive marker of vagal parasympathetic activity, was lower by 16.76 milliseconds. pNN50, the percentage of consecutive heartbeat intervals differing by more than 50 milliseconds, was reduced by 6.05 percentage points. All three differences were statistically significant at p less than 0.001, and heterogeneity across studies was modest, with I-squared values between 22.1 and 33.7 percent—remarkably low for a meta-analysis of clinical studies. Leave-one-out sensitivity analyses, in which each study is removed in turn to test whether any single dataset drives the result, confirmed the robustness of the pooled estimates.

Not every HRV metric told the same story. High-frequency power, a frequency-domain index that also reflects parasympathetic modulation, showed no statistically significant difference between cancer patients and controls, with a p-value of 0.118. The divergence between suppressed time-domain indices and an unchanged high-frequency component is scientifically intriguing, and it underscores a broader point the authors stress: HRV is not a single number but a family of indices, each capturing different aspects of autonomic regulation and each vulnerable to different technical and physiological confounders. Time-domain measures computed over short recordings are particularly sensitive to overall beat-to-beat variability, while spectral measures depend heavily on breathing patterns, recording conditions, and the mathematical assumptions of the underlying algorithms.

Beyond the between-group comparison, the review examined how HRV relates to one of the most common and debilitating symptoms in oncology: cancer-related fatigue. Within individual cancer cohorts, studies consistently reported inverse correlations between HRV and fatigue severity—lower variability, greater exhaustion. This pattern aligns with a physiological substrate proposed by earlier work, including studies of breast cancer survivors showing that low HRV tracks with persistent fatigue long after treatment ends. If reduced vagal tone is genuinely linked to the fatigue experienced by millions of cancer survivors, HRV could eventually help clinicians identify patients at risk and monitor responses to interventions such as exercise programs, which prior meta-analytic work has suggested may partially reverse autonomic dysfunction in this population.

The longitudinal strand of the review adds a dynamic dimension. Studies that followed patients over time documented HRV trajectories across the cancer journey, from preoperative measurements through surgery, chemotherapy, and radiation, into survivorship. Perioperative vagal withdrawal, the cardiotoxic effects of certain chemotherapeutic agents, and the physiological burden of the malignancy itself can each leave distinct imprints on autonomic regulation. Some studies even explored whether HRV patterns differ by tumor location or disease progression, and whether nonlinear HRV measures—fractal and entropy-based indices known to predict cardiovascular mortality in other populations—might carry prognostic information in patients with brain metastases, gastric cancer, or colorectal cancer. These exploratory findings remain preliminary, but they sketch a research agenda in which autonomic monitoring could complement existing clinical assessment.

Yet the authors are emphatic about what the pooled estimate cannot do. Because the aggregate combines mechanistically distinct sources of HRV suppression—the malignancy itself, treatment-related cardiotoxicity, perioperative vagal withdrawal, and long-term survivorship remodeling—the pooled figure cannot attribute the observed suppression to any single cause. Moreover, GRADE assessment rated all pooled outcomes as Very Low certainty, the weakest tier on the five-level scale, reflecting limitations in study design, risk of bias, and imprecision. On this basis, the team explicitly declines to recommend HRV as an established clinical biomarker for cancer care. The distinction between a consistent research signal and a validated clinical tool is one that the field has sometimes blurred, and this review draws the line carefully.

The path forward, according to the authors, requires studies stratified by treatment phase, standardized measurement protocols, and the incorporation of nonlinear HRV indices alongside conventional time-domain and frequency-domain metrics. Wearable devices, which have already been used to classify cancer-related fatigue from HRV signals in recent observational studies, could make large-scale, longitudinal autonomic monitoring feasible outside the clinic. If future research confirms that HRV suppression tracks with symptom burden, treatment toxicity, or survival, the humble millisecond-to-millisecond wobble of the heartbeat could evolve from a research curiosity into a practical, non-invasive vital sign for oncology. For now, the message of this meta-analysis is one of cautious confirmation: the autonomic nervous system is measurably altered in cancer, the alteration is real and reproducible in short-term recordings, and understanding what it means for patients is the next great challenge.

Subject of Research: Heart rate variability suppression in cancer patients versus non-cancer controls: a systematic review and meta-analysis

Article Title: Heart rate variability suppression in cancer patients versus non-cancer controls: a systematic review and meta-analysis

Article References: Heart rate variability suppression in cancer patients versus non-cancer controls: a systematic review and meta-analysis. (n.d.). https://doi.org/10.1007/s00520-026-11229-1

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11229-1

Keywords: Heart, rate, variability, suppression, cancer, patients, versus, non-cancer, controls, systematic, review, meta-analysis

Emerging cardiac magnetic resonance imaging (MRI) studies have revolutionized diagnostic accuracy and epidemiological assessment of the disorder, presenting a prevalence estimate that approaches 1 in 220 individuals. This figure is striking, capturing the subtle burden of both clinically overt and early, subclinical disease stages that may have gone undetected by traditional modalities. Intriguingly, the predisposition toward DCM is not shared equally among sexes; men exhibit a prevalence nearly double that observed in women, suggesting sex-specific biological or possibly behavioral factors modulating disease susceptibility or expression.

At the heart of non-ischaemic dilated cardiomyopathy lies a complex genetic architecture, increasingly illuminated by advances in genetic technologies over the past three decades. These innovations have empowered clinicians and researchers to identify pathogenic variants in approximately 30 to 40 percent of affected patients, reshaping our understanding of DCM from a purely acquired entity toward one with substantial heritable underpinnings. The spectrum uncovered includes monogenic variants traditionally thought to underwrite disease causality, as well as subtler polygenic contributions that interact to shape disease onset, progression, and phenotypic diversity.

Central to modern conceptualizations of DCM is the appreciation that it defies simplistic classification as a monogenic disorder. Instead, it emerges as a complex interplay where monogenic mutations, polygenic risk factors, and environmental exposures converge. These gene-environment interactions crucially modulate disease penetrance—the likelihood that a person carrying a genetic variant will manifest the disease—and impact clinical phenotype variability, such as severity of symptoms and age of onset. Understanding these multifactorial dynamics is pivotal for devising predictive models and personalized therapeutic strategies.

The epidemiological landscape of non-ischaemic DCM continues to evolve with ongoing research efforts. Large-scale population studies and multicenter registries are invaluable in capturing disease heterogeneity across diverse demographics. Moreover, they underscore the necessity of incorporating genetic screening into routine clinical workflows, especially for individuals with unexplained cardiomyopathy or a family history suggestive of inherited cardiac disorders. Such proactive approaches promise early detection and timely intervention, potentially mitigating progression to end-stage heart failure.

Genetic counseling emerges as a cornerstone of managing DCM in clinical practice. As genetic testing becomes more accessible, interpreting variant pathogenicity demands expertise and robust databases enriched by collaborative international efforts. Patients and their families benefit from comprehensive risk assessment, enabling informed decisions about surveillance, lifestyle modifications, and therapeutic options. This patient-centered model embodies the ethos of precision medicine—a tailored approach respecting individual genetic and environmental contexts.

Despite considerable progress, translational challenges persist. The complexity and heterogeneity of genetic contributions to DCM mean that many pathogenic mechanisms remain elusive. Research into modifier genes, epigenomic factors, and molecular pathways promises to illuminate these gaps. Furthermore, ongoing work in functional genomics and experimental modeling seeks to validate candidate variants and unravel pathophysiologic cascades, steering the field toward novel targets for pharmacologic intervention.

Public health implications of this heightened understanding are profound. Recognizing the true prevalence of non-ischaemic DCM mandates refinement of screening recommendations, particularly for populations at elevated risk. Strategies could include targeted imaging, genetic testing, and longitudinal monitoring, optimizing resource allocation and enhancing patient outcomes. Enhanced awareness among primary care providers and cardiologists is essential, facilitating timely referral and multidisciplinary collaboration.

Looking to the future, innovative technologies such as machine learning and artificial intelligence hold promise in synthesizing complex clinical and genomic datasets. These tools may aid in risk stratification, prediction of disease trajectory, and response to therapies, ushering in a new era of data-driven cardiology. Furthermore, advances in gene editing and regenerative medicine provide a tantalizing prospect of disease modification or cure, although such approaches remain in early investigative stages.

In conclusion, non-ischaemic dilated cardiomyopathy exemplifies a multifaceted cardiovascular disease where genetic insights intersect with clinical practice to forge new paradigms. Capturing the nuance of its genetic and epidemiological dimensions invites a holistic strategy encompassing early detection, precision therapeutics, and public health initiatives. As research efforts intensify and technology advances, the prospect of mitigating the burden of this condition grows ever more tangible, heralding hope for affected patients worldwide.

This comprehensive understanding of DCM’s complex genetic landscape and epidemiology not only challenges past assumptions but also stimulates transformative approaches in heart failure management. It calls for sustained investment in research infrastructures and interdisciplinary collaboration to unlock further biological secrets. Ultimately, such endeavors align with the broader mission of cardiovascular medicine—to enhance quality of life and longevity through scientific innovation and compassionate care.

The challenge lies not merely in identifying susceptibility but in translating these findings into actionable clinical solutions. The integration of genomic data within electronic health records, combined with robust patient registries, may facilitate continuous learning healthcare systems. These systems adapt to emerging evidence, refine clinical guidelines, and personalize intervention strategies, thus embodying the future of DCM management.

As awareness grows, patient advocacy and education assume vital roles. Empowering individuals with knowledge about genetic risks and available resources fosters engagement and adherence to monitoring protocols. It also helps destigmatize genetic conditions, promoting equity in healthcare access and encouraging participation in research initiatives that drive discovery.

Moreover, understanding sex-specific differences in DCM prevalence and manifestation invites further inquiry into hormonal influences, gene regulation, and lifestyle factors. Elucidating such nuances promises refined therapeutic approaches that account for gender-based biological variability, enhancing treatment efficacy and safety.

In sum, the evolving narrative of non-ischaemic dilated cardiomyopathy underscores the convergence of technology, genetics, and clinical medicine. It demands a paradigm shift—from reactive treatment of overt heart failure toward proactive identification and modulation of disease trajectories. Through multidisciplinary efforts and continuous innovation, the medical community moves closer to alleviating the global impact of this challenging cardiomyopathy.

Subject of Research: Epidemiology and genetic basis of non-ischaemic dilated cardiomyopathy.

Article Title: Epidemiology of non-ischaemic dilated cardiomyopathy.

Article References:
Ramos-López, N., Domínguez, F., Ochoa, J.P. et al. Epidemiology of non-ischaemic dilated cardiomyopathy. Nat Rev Cardiol (2026). https://doi.org/10.1038/s41569-026-01300-z

Image Credits: AI Generated