Search Results (7)

Background: The growing challenge of multimorbidity for healthcare systems worldwide demands a dual prevention framework, targeting both primary and secondary prevention. Multimorbidity–multibehaviours can provide such a theoretical and clinical framework to explore new aetiological evidence for multimorbidity risk. While the role of single health risk behaviours, such as smoking, nutrition, alcohol, and physical activity (SNAP), in chronic disease prevention is well-documented, their synergistic effect on multimorbidity has received relatively little attention. Methods: Using retrospective observational data from electronic health records of 21,079 patients from a convenience sample of three general practices in Staffordshire, UK (2015–2018), we examined the association between SNAP-multibehaviours and multimorbidity risk, defined as follows: MM2+ (≥2 morbidities), MM3+ (≥3 morbidities), and complex multimorbidity (accumulated morbidities affecting ≥3 body systems). Multiple logistic regression models, stratified by sex and adjusted for age and area, were applied to analyse the associations between both combined and accumulative SNAP-health risk behaviours (HRBs) and all multimorbidity operational definitions. Results: A dose–response association was observed, indicating increased multimorbidity risk with greater accumulation of SNAP-HRBs. Additionally, sex-specific patterns were identified, which varied according to the operational definitions of multimorbidity. These findings underscored both the clinical significance of the identified outcomes for promoting tailored multimorbidity guidelines and the need for further sex-sensitive research. Conclusion: These findings support the importance of transcending traditional silos in healthcare and public health research by integrating preventive and curative medicines under a multimorbidity–multibehaviour framework. Embracing the complexity of coexisting morbidities and health risk behaviours, healthcare systems can move beyond disease-specific and behaviour-specific paradigms. This approach has the potential to enhance clinical outcomes and to address the complex needs of individuals with multimorbidity in real-world healthcare settings. Full article

Background: A conceptual shift in healthcare emphasises multimorbidity and multibehaviours as interconnected phenomena, highlighting dose–response associations and sex-specific differences. Data-driven approaches have been suggested for overcoming methodological challenges, of multimorbidity research. By using exploratory factor analysis, this study aimed to identify sex specific lifestyle associative multimorbidity patterns, providing valuable evidence to primary care providers and informing future multimorbidity guidelines. Methods: A retrospective observational study examined the electronic health records of three general practices in the UK between 2015 and 2018. The participants were aged 18+ with lifestyle multimorbidity, having engaged with multiple health risk behaviours. Stratified exploratory factor analysis with oblique rotation was used to identify sex specific lifestyle associative multimorbidity patterns. Results: The study included N = 7560 patients, with females comprising 53.9%. Eight independent lifestyle associative multimorbidity patterns were identified and distributed as follows. For females, three patterns emerged: cardiometabolic–neurovascular spectrum disorders (42.97% variance), respiratory conditions (8.08%), and sensory impairment (5.63%), with 25.4% assigned to these patterns. For males, five patterns were revealed: cardiometabolic–vascular spectrum disorders (34.10%), genitourinary (9.19%), respiratory–vision (8.20%), ocular (5.70%), and neurovascular–gastro–renal syndrome (4.54%), with 43%. Conclusions: We revealed eight different sex-specific lifestyle-associated patterns, implying the need for tailored clinical approaches. The application of exploratory factor analysis yielded clinically valuable and scientifically rigorous multimorbidity patterns. Clinically, the findings advocate for a paradigm shift towards person-centred care, integrating multimorbidity and SNAP multibehaviours to enhance the complexity of inquiry and treatment of high-risk populations. Full article

Juvenile polyposis syndrome (JPS) is a rare autosomal dominant disorder characterized by multiple juvenile polyps in the gastrointestinal tract, often associated with mutations in genes such as Smad4 and BMPR1A. This study explores the impact of Smad4 knock-out on the development of intestinal polyps using collaborative cross (CC) mice, a genetically diverse model. Our results reveal a significant increase in intestinal polyps in Smad4 knock-out mice across the entire population, emphasizing the broad influence of Smad4 on polyposis. Sex-specific analyses demonstrate higher polyp counts in knock-out males and females compared to their WT counterparts, with distinct correlation patterns. Line-specific effects highlight the nuanced response to Smad4 knock-out, underscoring the importance of genetic variability. Multimorbidity heat maps offer insights into complex relationships between polyp counts, locations, and sizes. Heritability analysis reveals a significant genetic basis for polyp counts and sizes, while machine learning models, including k-nearest neighbors and linear regression, identify key predictors, enhancing our understanding of juvenile polyposis genetics. Overall, this study provides new information on understanding the intricate genetic interplay in the context of Smad4 knock-out, offering valuable insights that could inform the identification of potential therapeutic targets for juvenile polyposis and related diseases. Full article

Multimorbidity, the coexistence of multiple health conditions in an individual, has emerged as one of the greatest challenges facing health services, and this crisis is partly driven by the aging population. Aging is associated with increased aortic stiffness (AoStiff), which in turn is linked with several morbidities frequently affecting and having disastrous consequences for the elderly. These include hypertension, ischemic heart disease, heart failure, atrial fibrillation, chronic kidney disease, anemia, ischemic stroke, and dementia. Two or more of these disorders (multimorbidity) often coexist in the same elderly patient and the specific multimorbidity pattern depends on several factors including sex, ethnicity, common morbidity routes, morbidity interactions, and genomics. Regular exercise, salt restriction, statins in patients at high atherosclerotic risk, and stringent blood pressure control are interventions that delay progression of AoStiff and most likely decrease multimorbidity in the elderly. Full article

(1) Introduction: Cardiovascular disease is associated with high mortality, especially in older people. This study aimed to characterize the evolution of combined multimorbidity and polypharmacy patterns in older people with different cardiovascular disease profiles. (2) Material and methods: This longitudinal study drew data from the Information System for Research in Primary Care in people aged 65 to 99 years with profiles of cardiovascular multimorbidity. Combined patterns of multimorbidity and polypharmacy were analysed using fuzzy c-means clustering techniques and hidden Markov models. The prevalence, observed/expected ratio, and exclusivity of chronic diseases and/or groups of these with the corresponding medication were described. (3) Results: The study included 114,516 people, mostly men (59.6%) with a mean age of 78.8 years and a high prevalence of polypharmacy (83.5%). The following patterns were identified: Mental, behavioural, digestive and cerebrovascular; Neuropathy, autoimmune and musculoskeletal; Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological; Non-specific; Multisystemic; Respiratory, cardiovascular, behavioural and genitourinary; Diabetes and ischemic cardiopathy; and Cardiac. The prevalence of overrepresented health problems and drugs remained stable over the years, although by study end, cohort survivors had more polypharmacy and multimorbidity. Most people followed the same pattern over time; the most frequent transitions were from Non-specific to Mental, behavioural, digestive and cerebrovascular and from Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological to Non-specific. (4) Conclusions: Eight combined multimorbidity and polypharmacy patterns, differentiated by sex, remained stable over follow-up. Understanding the behaviour of different diseases and drugs can help design individualised interventions in populations with clinical complexity. Full article

Identifying the population at risk of COVID-19 infection severity is a priority for clinicians and health systems. Most studies to date have only focused on the effect of specific disorders on infection severity, without considering that patients usually present multiple chronic diseases and that these conditions tend to group together in the form of multimorbidity patterns. In this large-scale epidemiological study, including primary and hospital care information of 166,242 patients with confirmed COVID-19 infection from the Spanish region of Andalusia, we applied network analysis to identify multimorbidity profiles and analyze their impact on the risk of hospitalization and mortality. Our results showed that multimorbidity was a risk factor for COVID-19 severity and that this risk increased with the morbidity burden. Individuals with advanced cardio-metabolic profiles frequently presented the highest infection severity risk in both sexes. The pattern with the highest severity associated in men was present in almost 28.7% of those aged ≥ 80 years and included associations between cardiovascular, respiratory, and metabolic diseases; age-adjusted odds ratio (OR) 95% confidence interval (1.71 (1.44–2.02)). In women, similar patterns were also associated the most with infection severity, in 7% of 65–79-year-olds (1.44 (1.34–1.54)) and in 29% of ≥80-year-olds (1.35 (1.18–1.53)). Patients with mental health patterns also showed one of the highest risks of COVID-19 severity, especially in women. These findings strongly recommend the implementation of personalized approaches to patients with multimorbidity and SARS-CoV-2 infection, especially in the population with high morbidity burden. Full article

The pressing problem of multimorbidity and polypharmacy is aggravated by the lack of specific care models for this population. We aimed to investigate the evolution of multimorbidity and polypharmacy patterns in a given population over a 4-year period (2011–2015). A cross-sectional, observational study among the EpiChron Cohort, including anonymized demographic, clinical and drug dispensation information of all users of the public health system ≥65 years in Aragon (Spain), was performed. An exploratory factor analysis, stratified by age and sex, using an open cohort was carried out based on the tetra-choric correlations among chronic diseases and dispensed drugs during 2011 and compared with 2015. Seven baseline patterns were identified during 2011 named as: mental health, respiratory, allergic, mechanical pain, cardiometabolic, osteometabolic, and allergic/derma. Of the epidemiological patterns identified in 2015, six were already present in 2011 but a new allergic/derma one appeared. Patterns identified in 2011 were more complex in terms of both disease and drugs. Results confirmed the existing association between age and clinical complexity. The systematic associations between diseases and drugs remain similar regarding their clinical nature over time, helping in early identification of potential interactions in multimorbid patients with a high risk of negative health outcomes due to polypharmacy. Full article