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Cardiac intermediary metabolism in heart failure: substrate use, signalling roles and therapeutic targets.
The number of patients with heart failure is expected to rise sharply owing to ageing populations, poor dietary habits, unhealthy lifestyles and improved survival rates from conditions such as hypertension and myocardial infarction. Heart failure is classified into two main types: heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). These forms fundamentally differ, especially in how metabolism is regulated, but they also have shared features such as mitochondrial dysfunction. HFrEF is typically driven by neuroendocrine activation and mechanical strain, which demands a higher ATP production to sustain cardiac contraction. However, the primary energy source in a healthy heart (fatty acid β-oxidation) is often suppressed in HFrEF. Although glucose uptake increases in HFrEF, mitochondrial dysfunction disrupts glucose oxidation, and glycolysis and ketone oxidation only partially compensate for this imbalance. Conversely, HFpEF, particularly in individuals with metabolic diseases, such as obesity or type 2 diabetes mellitus, results from both mechanical and metabolic overload. Elevated glucose and lipid levels overwhelm normal metabolic pathways, leading to an accumulation of harmful metabolic byproducts that impair mitochondrial and cellular function. In this Review, we explore how disruptions in cardiac metabolism are not only markers of heart failure but also key drivers of disease progression. We also examine how metabolic intermediates influence signalling pathways that modify proteins and regulate gene expression in the heart. The growing recognition of the role of metabolic alterations in heart failure has led to groundbreaking treatments that target these metabolic disruptions, offering new hope for these patients.
AAV microdystrophin gene replacement therapy for Duchenne muscular dystrophy: progress and prospects.
Duchenne muscular dystrophy (DMD) is caused by pathogenic sequence variants occurring in the DMD gene which lead to the loss of the dystrophin protein, a molecular 'shock absorber' that protects muscle from contraction-induced injury. The large size of the dystrophin open reading frame precludes delivery of the full-length protein using a single adeno-associated virus (AAV) vector, which led to the development of internally-deleted dystrophin minigenes encoding partially-functional dystrophin. Indeed, five such microdystrophin therapies have been assessed in various clinical programmes. In 2023, Elevidys (Sarepta Therapeutics) received accelerated approval based on levels of dystrophin as a surrogate biomarker. In 2024, it received full approval despite unclear efficacy (i.e. not meeting primary or secondary outcomes in a phase 3 trial). Additionally, in 2025, two DMD individuals treated with Elevidys died after acute liver failure. A separate microdystrophin therapy, PF-06939926 (Pfizer) was discontinued for both efficacy and safety reasons (including the deaths of two clinical trial participants). Solid Biosciences, Genethon, REGENXBIO, and Insmed continue to develop microdystrophin therapies differing in transgene structure, promoter sequences, and AAV serotype. Here we describe recent progress in AAV-microdystrophin therapeutics development, and discuss the challenges facing such approaches, including pre-existing anti-capsid immunity, anti-transgene immunity, the unknown functionality of microdystrophin transgenes, transduction of muscle stem cells, and long-term transgene persistence.
Changes in sensorimotor network dynamics in resting-state recordings in Parkinson's disease.
Non-invasive recordings of magnetoencephalography have been used for developing biomarkers for neural changes associated with Parkinson's disease that can be measured across the entire course of the disease. These studies, however, have yielded inconsistent findings. Here, we investigated whether analysing motor cortical activity within the context of large-scale brain network activity provides a more sensitive marker of changes in Parkinson's disease using magnetoencephalography. We extracted motor cortical beta power and beta bursts from resting-state magnetoencephalography scans of patients with Parkinson's disease (N = 28) and well-matched healthy controls (N = 36). To situate beta bursts in their brain network contexts, we used a time-delay-embedded hidden Markov model to extract brain network activity and investigated co-occurrence patterns between brain networks and beta bursts. Parkinson's disease was associated with decreased beta power in motor cortical power spectra, but no significant differences in motor cortical beta-burst dynamics occurred when using a conventional beta-burst analysis. Dynamics of a large-scale sensorimotor network extracted with the time-delay-embedded hidden Markov model approach revealed significant decreases in the occurrence of this network with Parkinson's disease. By comparing conventional burst and time-delay-embedded hidden Markov model state occurrences, we observed that motor beta bursts occurred during both sensorimotor and non-sensorimotor network activations. When using the large-scale network information provided by the time-delay-embedded hidden Markov model to focus on bursts that were active during sensorimotor network activations, significant decreases in burst dynamics could be observed in patients with Parkinson's disease. In conclusion, our findings suggest that decreased motor cortical beta power in Parkinson's disease is prominently associated with changes in sensorimotor network dynamics using magnetoencephalography. Thus, investigating large-scale networks or considering the large-scale network context of motor cortical activations may be crucial for identifying alterations in the sensorimotor network that are prevalent in Parkinson's disease and might help resolve contradicting findings in the literature.
Alcohol Use and Risk of Dementia in Diverse Populations: Evidence from Cohort, Case-control and Mendelian randomization Approaches
Objectives To investigate the relationship between alcohol consumption and dementia. Design Prospective cohort and case-control analyses combined with linear and nonlinear Mendelian randomization. Setting Two large-scale population-based cohorts: the US Million Veteran Program and UK Biobank. Genetic analyses used summary statistics from genome-wide association studies (GWAS). Participants 559,559 adults aged 56–72 years at baseline were included in observational analyses (mean follow-up: 4 years in the US cohort; 12 years in the UK cohort). Genetic analyses used summary data from multiple large GWAS consortia (2.4 million participants). Main outcome measures Incident all-cause dementia, determined through health record linkage, and genetic proxies. Results During follow-up, 14,540 participants developed dementia and 48,034 died. Observational phenotype-only analyses revealed U-shaped associations between alcohol and dementia risk: higher risk was observed among non-drinkers, heavy drinkers (>40 drinks per week; hazard ratio [HR]=1.41, 95% confidence interval[CI] 1.15-1.74), and those with alcohol use disorder (AUD) (HR=1.51[CI 1.42-1.60]) compared with light drinkers. In contrast, Mendelian randomization genetic analysis identified a monotonic increase in dementia risk with greater alcohol consumption. A one standard deviation increase in log-transformed drinks per week was associated with a 15% dementia increase (IVW OR=1.15[1.03-1.27]). A two-fold increase in AUD prevalence was associated with a 16% increase in dementia risk (inverse-variance weighted [IVW] OR=1.16[1.03-1.30]). Alcohol intake increased dementia, but individuals who developed dementia also experienced a decline in alcohol intake over time, suggesting reverse causation—where early cognitive decline leads to reduced alcohol consumption— underlies the supposed protective alcohol effects in observational studies. Conclusions These findings provide evidence for a relationship between all types of alcohol use and increased dementia risk. While correlational observational data suggested a protective effect of light drinking, this could be in part attributable to reduced drinking seen in early dementia; genetic analyses did not support this, suggesting that any level of alcohol consumption may contribute to dementia risk. Public health strategies that reduce the prevalence of alcohol use disorder could potentially lower the incidence of dementia by up to 16%.
Will Human-Animal Chimeras Cause Moral Confusion? Exploring Public Attitudes.
Recent medical research involving human-monkey chimeras, human brain organoids in rats, and the transplantation of a gene-edited pig heart and gene-edited pig kidneys in living human beings have intensified the debate about whether we should create human-animal chimeras for biomedical purposes and, if so, how we should treat them. Influential views in the debate frequently appeal to assumptions regarding how people will react to such chimeras. It has, for example, been argued that the most important objection against creating such chimeras is that this will result in inexorable moral confusion about species boundaries and will, as a result, threaten the social order. But is this indeed the case? We conducted three empirical studies to examine laypeople's views on the creation and treatment of various types of human-animal chimeras. Our studies indicate that laypeople find typical cases of xenotransplantation (i.e., the transplantation of an animal organ into a human patient) morally unproblematic. They assign the same moral status to humans with animal organs as to non-chimeric humans. By contrast, they sometimes (but not always) assign slightly higher moral status to animals with human organs than to non-chimeric animals. Overall, however, there is little indication of chimera technology blurring the line between humans and animals, and thus of the technology causing moral confusion.
A cross-species analysis of neuroanatomical covariance sex differences in humans and mice.
BACKGROUND: Structural covariance within the brain is thought to reflect inter-regional sharing of developmental influences. This hypothesis has proved difficult to test but can be informatively probed by the study of sex differences. Here, we use neuroimaging in humans and mice to study sex-differences in anatomical covariance- asking (1) are there sex differences in structural covariance and (2) do regions that share the same developmental influences, as exhibited by shared sex differences in volume, also show shared sex differences in volume covariance. This study design illuminates both the biology of sex-differences and theoretical models for anatomical covariance- benefitting from tests of inter-species convergence. METHODS: Brain volume correlations for males and females across 255 regions in mice (n = 423) and 378 regions in humans (n = 436) were calculated using volumetric measures obtained from structural MRI. Mean correlations for each sex were compared within species to determine whether covariance sex differences exist. Specific covariances with strong sex differences in each species were identified via permutation tests for statistical significance. Brain maps of regional average structural covariance sex-bias were generated for mice and humans. Regional average structural covariance sex-bias and volumetric sex-bias were correlated to identify whether these features align in their direction of sex-bias. RESULTS: We find that volumetric structural covariance is stronger in adult females than males for both wild-type mice and healthy human subjects: 98% of comparisons with statistically significant covariance sex differences in mice are female-biased, while 76% of such comparisons are female-biased in humans (q
Lysosomal free sialic acid storage disorder iPSC-derived neural cells display altered glycosphingolipid metabolism.
Lysosomal free sialic acid storage disorder (FSASD) is a rare neurodegenerative disease caused by biallelic mutations in SLC17A5, encoding the lysosomal sialic acid exporter, SLC17A5 (sialin). While the involvement of oligodendroglia in FSASD pathogenesis is established, the roles of other neural cell types remain elusive. In this study, we utilized radial glial cells (iRGCs), immature and mature astrocytes (iIAs and iMAs, respectively), and cortical neurons (iCNs) differentiated from induced pluripotent stem cells (iPSCs) derived from two individuals with FSASD, alongside two independent healthy donors for comparison. We employed a multifaceted profiling approach, including the assessment of cellular glycosphingolipids (GSLs), transcriptomics focused on GSL metabolism genes, and 4-methylumbelliferone-based lysosomal enzyme activity measurements. Our findings revealed significant elevations in free sialic acid levels across all FSASD cell types, indicating that iPSCs and derived iRGCs, iIAs, iMAs and iCNs may be used to model FSASD in vitro. We observed significant alterations in the abundance of specific GSL species, predominantly in mature astrocytes, with fewer changes in other cell types. Transcriptomic analyses uncovered differential expression of genes involved in GSL catabolism, including those encoding glycohydrolases. Enzyme assays corroborated the transcriptomic findings, showing heightened glycohydrolase activities, particularly in mature astrocytes. Collectively, these data may help refine our understanding of neural cell phenotypes and potential contributors to selective vulnerability in FSASD.
Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease.
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. METHOD: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. RESULTS: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. CONCLUSIONS: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.
The relationship between isolated hypertension with brain volumes in UK Biobank.
BACKGROUND: Hypertension is a well-established risk factor for cognitive impairment, brain atrophy, and dementia. However, the relationship of other types of hypertensions, such as isolated hypertension on brain health and its comparison to systolic-diastolic hypertension (where systolic and diastolic measures are high), is still relatively unknown. Due to its increased prevalence, it is important to investigate the impact of isolated hypertension to help understand its potential impact on cognitive decline and future dementia risk. In this study, we compared a variety of global brain measures between participants with isolated hypertension to those with normal blood pressure (BP) or systolic-diastolic hypertension using the largest cohort of healthy individuals. METHODS: Using the UK Biobank cohort, we carried out a cross-sectional study using 29,775 participants (mean age 63 years, 53% female) with BP measurements and brain magnetic resonance imaging (MRI) data. We used linear regression models adjusted for multiple confounders to compare a variety of global, subcortical, and white matter brain measures. We compared participants with either isolated systolic or diastolic hypertension with normotensives and then with participants with systolic-diastolic hypertension. RESULTS: The results showed that participants with isolated systolic or diastolic hypertension taking BP medications had smaller gray matter but larger white matter microstructures and macrostructures compared to normotensives. Isolated systolic hypertensives had larger total gray matter and smaller white matter traits when comparing these regions with participants with systolic-diastolic hypertension. CONCLUSIONS: These results provide support to investigate possible preventative strategies that target isolated hypertension as well as systolic-diastolic hypertension to maintain brain health and/or reduce dementia risk earlier in life particularly in white matter regions.
Learning Variability Network Exchange (LEVANTE): A Global Framework for Measuring Children's Learning Variability Through Collaborative Data Sharing.
Despite the ubiquity of variation in child development within individuals, across groups, and across tasks, timescales, and contexts, dominant methods in developmental science and education research still favor group averages, short snapshots of time, and single environments. The Learning Variability Network Exchange (LEVANTE) is a framework designed to enable coordinated data collection by research teams worldwide, with the goal of measuring variability in children's learning and development. The LEVANTE measure set aims to capture variability in learning outcomes (literacy and numeracy) as well as in core cognitive and social constructs. LEVANTE will yield a large, open access longitudinal dataset for long-term research use, both creating a multidisciplinary research network and facilitating the science of learning variability.
Disorders of Language and Literacy Across Learning Contexts
A child struggling in school causes concern. Difficulties with school tasks can leave children frustrated and parents and teachers wondering why they are not learning. Indeed, school underachievement is one of the most common reasons for referral to a visiting specialist in school or to a child guidance clinic. Underachievement may arise for many different reasons. In this chapter we will focus on difficulties of language and literacy, noting that many children with such difficulties also struggle with maths. The rationale for our focus is that language and literacy are the foundation for learning across school systems — here we present a global perspective on the critical issues of how to identify, assess, prevent, and remediate such difficulties, the symptoms of which may differ according to the language of schooling and the learning context. We will also consider underachievement when it is secondary to another condition focusing on a small selection of co-occurring (comorbid) disorders. Sections on assessment and intervention focus on the current evidence base. We close the chapter with a discussion of priorities that low-income communities can set for the educational management of children with learning difficulties.
Risk stratification of childhood infection using host markers of immune and endothelial activation in Asia (Spot Sepsis): a multi-country, prospective, cohort study.
BACKGROUND: Prognostic tools for febrile illnesses are urgently required in resource-constrained community contexts. Circulating immune and endothelial activation markers stratify risk in common childhood infections. We aimed to assess their use in children with febrile illness presenting from rural communities across Asia. METHODS: Spot Sepsis was a prospective cohort study across seven hospitals in Bangladesh, Cambodia, Indonesia, Laos, and Viet Nam that serve as a first point of contact with the formal health-care system for rural populations. Children were eligible if aged 1-59 months and presenting with a community-acquired acute febrile illness that had lasted no more than 14 days. Clinical parameters were recorded and biomarker concentrations measured at presentation. The primary outcome measure was severe febrile illness (death or receipt of organ support) within 2 days of enrolment. Weighted area under the receiver operating characteristic curves (AUC) were used to compare prognostic accuracy of endothelial activation markers (ANG-1, ANG-2, and soluble FLT-1), immune activation markers (CHI3L1, CRP, IP-10, IL-1ra, IL-6, IL-8, IL-10, PCT, soluble TNF-R1, soluble TREM1 [sTREM1], and soluble uPAR), WHO danger signs, the Liverpool quick Sequential Organ Failure Assessment (LqSOFA) score, and the systemic inflammatory response syndrome (SIRS) score. Prognostic accuracy of combining WHO danger signs and the best performing biomarker was analysed in a weighted logistic regression model. Weighted measures of classification were used to compare prognostic accuracies of WHO danger signs and the best performing biomarker and to determine the number of children needed to test (NNT) to identify one additional child who would progress to severe febrile illness. The study was prospectively registered on ClinicalTrials.gov, NCT04285021. FINDINGS: 3423 participants were recruited between March 5, 2020, and Nov 4, 2022, 18 (0·5%) of whom were lost to follow-up. 133 (3·9%) of 3405 participants developed severe febrile illness (22 deaths, 111 received organ support; weighted prevalence 0·34% [95% CI 0·28-0·41]). sTREM1 showed the highest prognostic accuracy to identify patients who would progress to severe febrile illness (AUC 0·86 [95% CI 0·82-0·90]), outperforming WHO danger signs (0·75 [0·71-0·80]; p<0·0001), LqSOFA (0·74 [0·69-0·78]; p<0·0001), and SIRS (0·63 [0·58-0·68]; p<0·0001). Combining WHO danger signs with sTREM1 (0·88 [95% CI 0·85-0·91]) did not improve accuracy in identifying progression to severe febrile illness over sTREM1 alone (p=0·24). Sensitivity for identifying progression to severe febrile illness was greater for sTREM1 (0·80 [95% CI 0·73-0·85]) than for WHO danger signs (0·72 [0·66-0·79]; NNT=3000), whereas specificities were comparable (0·81 [0·78-0·83] for sTREM1 vs 0·79 [0·76-0·82] for WHO danger signs). Discrimination of immune and endothelial activation markers was best for children who progressed to meet the outcome more than 48 h after enrolment (sTREM1: AUC 0·94 [95% CI 0·89-0·98]). INTERPRETATION: sTREM1 showed the best prognostic accuracy to discriminate children who would progress to severe febrile illness. In resource-constrained community settings, an sTREM1-based triage strategy might enhance early recognition of risk of poor outcomes in children presenting with febrile illness. FUNDING: Médecins Sans Frontières, Spain, and Wellcome. TRANSLATIONS: For the Arabic and French translations of the abstract see Supplementary Materials section.
Sensitivity to betrayal and new intimate relationship building in survivors of intimate partner violence.
OBJECTIVES: There is evidence that prior experience of intimate partner violence (IPV) can lead to high levels of sensitivity to betrayal, shame and self-criticism and interfere with initiation, development and maintenance of future intimate relationships. We measured these variables in women survivors of IPV, evaluating whether they are associated with the quality of current relationships. DESIGN: A cross-sectional, between-groups design was used, comparing women survivors of IPV divided into those satisfied with current intimate relationships, those dissatisfied and IPV survivors not in such a relationship. Women without a history of IPV were included as a benchmark group. METHOD: Four groups: IPV single (N = 34), IPV dissatisfied (N = 25), IPV satisfied (N = 32) and those who had not experienced IPV (N = 42) were compared for betrayal sensitivity, followed by a secondary comparison of shame and self-criticism. Online questionnaires were completed by participants recruited through social media and screened for IPV and relationship status. RESULTS: All IPV groups had significantly higher scores for betrayal sensitivity than the non-clinical group, with IPV satisfied having significantly lower scores than other IPV groups in two subscales: betrayal causing life change and lack of trust due to betrayal. CONCLUSIONS: Betrayal sensitivity is prominent in survivors of IPV, with evidence of a specific link between survivors' relationship satisfaction/status and their lack of trust and ideas of being permanently changed. Those appraisals may make it more challenging to build and maintain satisfactory relationships, or positive relationships may help survivors change their appraisals about betrayal, leading to a lack of trust and life-altering changes.
Peripheral neuronal sensitization and neurovascular remodelling in osteoarthritis pain.
Pain is the primary complaint in individuals with osteoarthritis (OA) and changes as the disease progresses. Anatomical changes in several joint structures potentially contribute to pain, including the increased innervation of the periosteum, synovium and subchondral bone, and the pathological innervation of articular cartilage, which is aneural under physiological conditions. Research has focused on molecules that sensitize afferent neurons, such as neuropeptides, neurotrophins, pro-inflammatory cytokines and ion channels. The neurotrophin nerve growth factor (NGF) is the best validated target in OA pain, with proven analgesic effects in preclinical and clinical studies, although the development of NGF-targeted therapeutics has been hampered by serious side effects. One relatively neglected area of research is the contribution to OA pain of the molecular pathways that mediate remodelling of nerves in disease. Remodelling requires coordination between the nerve and the associated vasculature, along with signals that are received from the surrounding parenchyma. Key cell guidance molecules, including angiogenic factors, ephrins, semaphorins and SLIT proteins are involved in nerve growth during development, and their expression is increased in osteoarthritic joints.