Molecular bases of comorbidities: present and future perspectives

Temporomandibular disorders and mental health: shared etiologies and treatment approaches

The biopsychosocial model suggests that temporomandibular disorders (TMDs) often coexist with mental health disorders, particularly depression and anxiety, affecting a significant portion of the global population. The interplay between TMDs and mental health disorders contributes to a complex comorbidity, perpetuating a cycle of mutual influence and reinforcement. This review investigates the neurobiological mechanisms and epidemiological evidence supporting the shared etiology of TMDs and mental health disorders, exploring potential shared vulnerabilities and bidirectional causal relationships. Shared vulnerabilities between TMDs and mental health disorders may stem from genetic and epigenetic predispositions, psychosocial factors, and behavioral aspects. Inflammatory cytokines, neurotransmitters, neurotrophins, and neuropeptides play pivotal roles in both peripheral and central sensitization as well as neuroinflammation. Brain imaging studies suggest that TMDs and mental health disorders exhibit overlapping brain regions indicative of reward processing deficits and anomalies within the triple network model. Future research efforts are crucial for developing a comprehensive understanding of the underlying mechanisms and confirming the reciprocal causal effects between TMDs and mental health disorders. This review provides valuable insights for oral healthcare professionals, stressing the importance of optimizing treatment strategies for individuals dealing with concurrent TMDs and mental health issues through a personalized, holistic, and multidisciplinary approach.

Entangled Illnesses: Embodied Experiences of Managing Multimorbidity

Multimorbidity, meaning multiple long-term conditions impacting a person's health, has become a rising societal and public health issue. The article contributes to the sociological study of chronic illness and multimorbidity by analysing how the blurriness of illnesses and entanglement of symptoms in multimorbidity is experienced and negotiated by people with coexisting chronic conditions. Drawing on qualitative interviews with people who live with endometriosis, fibromyalgia or hormonal migraine in Finland, we show how people with multiple chronic conditions distinguish between evolving symptoms based on past embodied experiences to make decisions about how to best manage their health. We argue that coexisting illnesses become entangled in ambiguous and open-ended ways, which, if left unaddressed, complicates treatment. Our analysis of illness experiences is aligned with the growing body of literature that argues that the single-disease model underlying healthcare systems fails to address the needs of patients living with multiple chronic conditions. Our emphasis on evolving entanglements between illnesses and the blurriness of conditions makes visible crucial discrepancies between lived illness and existing biomedical models and healthcare structures.

Sex-specific transcriptome similarity networks elucidate comorbidity relationships

Humans present sex-driven biological differences. Consequently, the prevalence of analyzing specific diseases and comorbidities differs between the sexes, directly impacting patients' management and treatment. Despite its relevance and the growing evidence of said differences across numerous diseases (with 4,370 PubMed results published within the past year), knowledge at the comorbidity level remains limited. In fact, to date, no study has attempted to identify the biological processes altered differently in women and men, promoting differences in comorbidities. To shed light on this problem, we analyze expression data for more than 100 diseases from public repositories, analyzing each sex independently. We calculate similarities between differential expression profiles by disease pairs and find that 13-16% of transcriptomically similar disease pairs are sex-specific. By comparing these results with epidemiological evidence, we recapitulate 53-60% of known comorbidities distinctly described for men and women, finding sex-specific transcriptomic similarities between sex-specific comorbid diseases. The analysis of shared underlying pathways shows that diseases can co-occur in men and women by altering alternative biological processes. Finally, we identify different drugs differentially associated with comorbid diseases depending on patients' sex, highlighting the need to consider this relevant variable in the administration of drugs due to their possible influence on comorbidities.

An atlas of the shared genetic architecture between atopic and gastrointestinal diseases

Comorbidity among atopic diseases (ADs) and gastrointestinal diseases (GIDs) has been repeatedly demonstrated by epidemiological studies, whereas the shared genetic liability remains largely unknown. Here we establish an atlas of the shared genetic architecture between 10 ADs or related traits and 11 GIDs, comprehensively investigating the comorbidity-associated genomic regions, cell types, genes and genetically predicted causality. Although distinct genetic correlations between AD-GID are observed, including 14 genome-wide and 28 regional correlations, genetic factors of Crohn's disease (CD), ulcerative colitis (UC), celiac disease and asthma subtypes are converged on CD4+ T cells consistently across relevant tissues. Fourteen genes are associated with comorbidities, with three genes are known treatment targets, showing probabilities for drug repurposing. Lower expressions of WDR18 and GPX4 in PBMC CD4+ T cells predict decreased risk of CD and asthma, which could be novel drug targets. MR unveils certain ADs led to higher risk of GIDs or vice versa. Taken together, here we show distinct genetic correlations between AD-GID pairs, but the correlated genomic loci converge on the dysregulation of CD4+ T cells. Inhibiting WDR18 and GPX4 expressions might be candidate therapeutic strategies for CD and asthma. Estimated causality indicates potential guidance for preventing comorbidity.

Emerging and reemerging infectious diseases: global trends and new strategies for their prevention and control

To adequately prepare for potential hazards caused by emerging and reemerging infectious diseases, the WHO has issued a list of high-priority pathogens that are likely to cause future outbreaks and for which research and development (R&D) efforts are dedicated, known as paramount R&D blueprints. Within R&D efforts, the goal is to obtain effective prophylactic and therapeutic approaches, which depends on a comprehensive knowledge of the etiology, epidemiology, and pathogenesis of these diseases. In this process, the accessibility of animal models is a priority bottleneck because it plays a key role in bridging the gap between in-depth understanding and control efforts for infectious diseases. Here, we reviewed preclinical animal models for high priority disease in terms of their ability to simulate human infections, including both natural susceptibility models, artificially engineered models, and surrogate models. In addition, we have thoroughly reviewed the current landscape of vaccines, antibodies, and small molecule drugs, particularly hopeful candidates in the advanced stages of these infectious diseases. More importantly, focusing on global trends and novel technologies, several aspects of the prevention and control of infectious disease were discussed in detail, including but not limited to gaps in currently available animal models and medical responses, better immune correlates of protection established in animal models and humans, further understanding of disease mechanisms, and the role of artificial intelligence in guiding or supplementing the development of animal models, vaccines, and drugs. Overall, this review described pioneering approaches and sophisticated techniques involved in the study of the epidemiology, pathogenesis, prevention, and clinical theatment of WHO high-priority pathogens and proposed potential directions. Technological advances in these aspects would consolidate the line of defense, thus ensuring a timely response to WHO high priority pathogens.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

Current insights into transcriptional role(s) for the nutraceutical Withania somnifera in inflammation and aging

The health-beneficial effects of nutraceuticals in various diseases have received enhanced attention in recent years. Aging is a continuous process wherein physiological activity of an individual declines over time and is characterized by various indefinite hallmarks which contribute toward aging-related comorbidities in an individual which include many neurodegenerative diseases, cardiac problems, diabetes, bone-degeneration, and cancer. Cellular senescence is a homeostatic biological process that has an important function in driving aging. Currently, a growing body of evidence substantiates the connection between epigenetic modifications and the aging process, along with aging-related diseases. These modifications are now being recognized as promising targets for emerging therapeutic interventions. Considering that almost all the biological processes are modulated by RNAs, numerous RNA-binding proteins have been found to be linked to aging and age-related complexities. Currently, studies have shed light on the ability of the nutraceutical Withania somnifera (Ashwagandha) to influence RNA expression, stability, and processing, offering insights into its mechanisms of action. By targeting RNA-related pathways, Withania somnifera may exhibit promising effects in ameliorating age-associated molecular changes, which include modifications in gene expression and signaling networks. This review summarizes the potential role of Withania somnifera as a nutraceutical in modulating RNA-level changes associated with aging, encompassing both in vitro and in vivo studies. Taken together, the putative role(s) of Withania in modulation of key RNAs will provide insights into understanding the aging process and facilitate the development of various preventive and therapeutic strategies employing nutraceuticals for healthy aging.

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM.

METHODS

Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA).

RESULTS

A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers.

CONCLUSIONS

Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.