An open science study of ageing in companion dogs.

The Dog Aging Project is a long-term longitudinal study of ageing in tens of thousands of companion dogs. The domestic dog is among the most variable mammal species in terms of morphology, behaviour, risk of age-related disease and life expectancy. Given that dogs share the human environment and have a sophisticated healthcare system but are much shorter-lived than people, they offer a unique opportunity to identify the genetic, environmental and lifestyle factors associated with healthy lifespan. To take advantage of this opportunity, the Dog Aging Project will collect extensive survey data, environmental information, electronic veterinary medical records, genome-wide sequence information, clinicopathology and molecular phenotypes derived from blood cells, plasma and faecal samples. Here, we describe the specific goals and design of the Dog Aging Project and discuss the potential for this open-data, community science study to greatly enhance understanding of ageing in a genetically variable, socially relevant species living in a complex environment.

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes.

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.

A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity.

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank (N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74-2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.

The forecasted prevalence of comorbidities and multimorbidity in people with HIV in the United States through the year 2030: A modeling study.

BACKGROUND: Estimating the medical complexity of people aging with HIV can inform clinical programs and policy to meet future healthcare needs. The objective of our study was to forecast the prevalence of comorbidities and multimorbidity among people with HIV (PWH) using antiretroviral therapy (ART) in the United States (US) through 2030. METHODS AND FINDINGS: Using the PEARL model-an agent-based simulation of PWH who have initiated ART in the US-the prevalence of anxiety, depression, stage ≥3 chronic kidney disease (CKD), dyslipidemia, diabetes, hypertension, cancer, end-stage liver disease (ESLD), myocardial infarction (MI), and multimorbidity (≥2 mental or physical comorbidities, other than HIV) were forecasted through 2030. Simulations were informed by the US CDC HIV surveillance data of new HIV diagnosis and the longitudinal North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) data on risk of comorbidities from 2009 to 2017. The simulated population represented 15 subgroups of PWH including Hispanic, non-Hispanic White (White), and non-Hispanic Black/African American (Black/AA) men who have sex with men (MSM), men and women with history of injection drug use and heterosexual men and women. Simulations were replicated for 200 runs and forecasted outcomes are presented as median values (95% uncertainty ranges are presented in the Supporting information). In 2020, PEARL forecasted a median population of 670,000 individuals receiving ART in the US, of whom 9% men and 4% women with history of injection drug use, 60% MSM, 8% heterosexual men, and 19% heterosexual women. Additionally, 44% were Black/AA, 32% White, and 23% Hispanic. Along with a gradual rise in population size of PWH receiving ART-reaching 908,000 individuals by 2030-PEARL forecasted a surge in prevalence of most comorbidities to 2030. Depression and/or anxiety was high and increased from 60% in 2020 to 64% in 2030. Hypertension decreased while dyslipidemia, diabetes, CKD, and MI increased. There was little change in prevalence of cancer and ESLD. The forecasted multimorbidity among PWH receiving ART increased from 63% in 2020 to 70% in 2030. There was heterogeneity in trends across subgroups. Among Black women with history of injection drug use in 2030 (oldest demographic subgroup with median age of 66 year), dyslipidemia, CKD, hypertension, diabetes, anxiety, and depression were most prevalent, with 92% experiencing multimorbidity. Among Black MSM in 2030 (youngest demographic subgroup with median age of 42 year), depression and CKD were highly prevalent, with 57% experiencing multimorbidity. These results are limited by the assumption that trends in new HIV diagnoses, mortality, and comorbidity risk observed in 2009 to 2017 will persist through 2030; influences occurring outside this period are not accounted for in the forecasts. CONCLUSIONS: The PEARL forecasts suggest a continued rise in comorbidity and multimorbidity prevalence to 2030, marked by heterogeneities across race/ethnicity, gender, and HIV acquisition risk subgroups. HIV clinicians must stay current on the ever-changing comorbidities-specific guidelines to provide guideline-recommended care. HIV clinical directors should ensure linkages to subspecialty care within the clinic or by referral. HIV policy decision-makers must allocate resources and support extended clinical capacity to meet the healthcare needs of people aging with HIV.

Characterizing multimorbidity in ALIVE: comparing single and ensemble clustering methods.

Multimorbidity, defined as having 2 or more chronic conditions, is a growing public health concern, but research in this area is complicated by the fact that multimorbidity is a highly heterogenous outcome. Individuals in a sample may have a differing number and varied combinations of conditions. Clustering methods, such as unsupervised machine learning algorithms, may allow us to tease out the unique multimorbidity phenotypes. However, many clustering methods exist, and choosing which to use is challenging because we do not know the true underlying clusters. Here, we demonstrate the use of 3 individual algorithms (partition around medoids, hierarchical clustering, and probabilistic clustering) and a clustering ensemble approach (which pools different clustering approaches) to identify multimorbidity clusters in the AIDS Linked to the Intravenous Experience cohort study. We show how the clusters can be compared based on cluster quality, interpretability, and predictive ability. In practice, it is critical to compare the clustering results from multiple algorithms and to choose the approach that performs best in the domain(s) that aligns with plans to use the clusters in future analyses.

Long-term multimorbidity trajectories in older adults: The role of cancer, demographics, and health behaviors.

BACKGROUND: Multimorbidity is associated with premature mortality and excess health care costs. The burden of multimorbidity is highest among patients with cancer, yet trends and determinants of multimorbidity over time are poorly understood. METHODS: Via Medicare claims linked to Cancer Prevention Study II data, group-based trajectory modeling was used to compare National Cancer Institute comorbidity index score trends for cancer survivors and older adults without a cancer history. Among cancer survivors, multinomial logistic regression analyses evaluated associations between demographics, health behaviors, and comorbidity trajectories. RESULTS: In 82,754 participants (mean age, 71.6 years [SD, 5.1 years]; 56.9% female), cancer survivors (n = 11,265) were more likely than older adults without a cancer history to experience the riskiest comorbidity trajectories: (1) steady, high comorbidity scores (remain high; odds ratio [OR], 1.36; 95% CI, 1.29-1.45), and (2) high scores that increased over time (start high and increase; OR, 1.51; 95% CI, 1.38-1.65). Cancer survivors who were physically active postdiagnosis were less likely to fall into these two trajectories (OR, 0.73; 95% CI, 0.64-0.84, remain high; OR, 0.42; 95% CI, 0.33-0.53, start high and increase) compared to inactive survivors. Cancer survivors with obesity were more likely to have a trajectory that started high and increased (OR, 2.83; 95% CI, 2.32-3.45 vs. normal weight), although being physically active offset some obesity-related risk. Cancer survivors who smoked postdiagnosis were also six times more likely to have trajectories that started high and increased (OR, 6.86; 95% CI, 4.41-10.66 vs. never smokers). CONCLUSIONS: Older cancer survivors are more likely to have multiple comorbidities accumulated at a faster pace than older adults without a history of cancer. Weight management, physical activity, and smoking avoidance postdiagnosis may attenuate that trend.

Factors associated with multimorbidity in England: an analysis of the English Longitudinal Study of Ageing.

BACKGROUND: Multimorbidity, defined as the presence of two or more long-term conditions, is a growing public health challenge, especially in terms of prevention and accumulation of long-term conditions among particular population cohorts. To date, efforts to understand multimorbidity has focused mainly on specific disease combinations, with little known about the sociodemographic factors associated with it. The study aimed to assess the factors associated with multimorbidity in England. METHODS: A cross-sectional study was conducted using the English Longitudinal Study of Ageing (ELSA), a dataset of people aged 50 years and older. The study identified ten long-term conditions from waves 2 to 9. Wave 2 to 9 were conducted between June 2004 to July 2005, May 2006 to August 2007, May 2008 to July 2009, June 2010 to July 2011, May 2012 to June 2013, June 2014 to May 2015, May 2016 to June 2017 and June 2018 to July 2019, respectively. The study included people with two or more long-term conditions. We identified the number of long-term conditions and multimorbidity, and we examined their association with age, gender, ethnicity, marital status, employment status, education, weekly contact with relative, and feeling lonely, sad or depressed using multinomial logistic regression. FINDINGS: Of 16 731 people recruited from wave 2 to wave 9, we identified 10 026 people with multimorbidity aged 50 years and older. The majority had two conditions (39%) and were female (55%), aged 50-69 years (32%), of white ethnicity (96%), married (69%) and unemployed (65·3%). The adjusted odds ratio (aOR) of having more than two long-term conditions increased with age, after adjusting for sex and ethnicity (≥5 conditions: aOR 12·89, 95% CI 2·23-3·76). Being female was associated with an increased risk of having more than two long-term conditions (≥5 conditions: aOR 1·21, 1·04-1·42). Similarly, being separated, divorced, or widowed were associated with having more than two long-term conditions (≥5 conditions: aOR 1·45, 1·21-1·74). Not owning a home was independently associated with more than two long-term conditions (≥5 conditions: aOR 1·59, 1·35-1·88). INTERPRETATION: The current analysis used only ten long-term conditions that were available in the ELSA data, so a different association might have arisen if other conditions had been considered. Our findings provide insights into which particular groups of the multimorbid population could be the target of preventive public health strategies and wider clinical and social care interventions in England to reduce the burden of multimorbidity. FUNDING: National Institute for Health and Care Research (NIHR).

Co-designing care for multimorbidity: a systematic review.

BACKGROUND: The co-design of health care enables patient-centredness by partnering patients, clinicians and other stakeholders together to create services. METHODS: We conducted a systematic review of co-designed health interventions for people living with multimorbidity and assessed (a) their effectiveness in improving health outcomes, (b) the co-design approaches used and (c) barriers and facilitators to the co-design process with people living with multimorbidity. We searched MEDLINE, EMBASE, CINAHL, Scopus and PsycINFO between 2000 and March 2022. Included experimental studies were quality assessed using the Cochrane risk of bias tool (ROB-2 and ROBINS-I). RESULTS: We screened 14,376 reports, with 13 reports meeting the eligibility criteria. Two reported health and well-being outcomes: one randomised clinical trial (n = 134) and one controlled cohort (n = 1933). Outcome measures included quality of life, self-efficacy, well-being, anxiety, depression, functional status, healthcare utilisation and mortality. Outcomes favouring the co-design interventions compared to control were minimal, with only 4 of 17 outcomes considered beneficial. Co-design approaches included needs assessment/ideation (12 of 13), prototype (11 of 13), pilot testing (5 of 13) (i.e. focus on usability) and health and well-being evaluations (2 of 13). Common challenges to the co-design process include poor stakeholder interest, passive participation, power imbalances and a lack of representativeness in the design group. Enablers include flexibility in approach, smaller group work, advocating for stakeholders' views and commitment to the process or decisions made. CONCLUSIONS: In this systematic review of co-design health interventions, we found that few projects assessed health and well-being outcomes, and the observed health and well-being benefits were minimal. The intensity and variability in the co-design approaches were substantial, and challenges were evident. Co-design aided the design of novel services and interventions for those with multimorbidity, improving their relevance, usability and acceptability. However, the clinical benefits of co-designed interventions for those with multimorbidity are unclear.

Sleep alterations as a function of 88 health indicators.

BACKGROUND: Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between in sleep macrostructure, quantitative sleep EEG, and health. METHODS: We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N = 3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors, and the use of 48 medications. RESULTS: Of sleep macrostructure variables, increased REM latency and reduced REM duration were the most common findings across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG power spectral density (PSD) alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85-95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases were associated with cardiovascular problems and associated medications, in line with a recently proposed hypothesis of immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects. CONCLUSIONS: Sleep alterations are present in various health conditions.

The impact of adverse childhood experiences on multimorbidity: a systematic review and meta-analysis.

BACKGROUND: Adverse childhood experiences (ACEs) have been implicated in the aetiology of a range of health outcomes, including multimorbidity. In this systematic review and meta-analysis, we aimed to identify, synthesise, and quantify the current evidence linking ACEs and multimorbidity. METHODS: We searched seven databases from inception to 20 July 2023: APA PsycNET, CINAHL Plus, Cochrane CENTRAL, Embase, MEDLINE, Scopus, and Web of Science. We selected studies investigating adverse events occurring during childhood (< 18 years) and an assessment of multimorbidity in adulthood (≥ 18 years). Studies that only assessed adverse events in adulthood or health outcomes in children were excluded. Risk of bias was assessed using the ROBINS-E tool. Meta-analysis of prevalence and dose-response meta-analysis methods were used for quantitative data synthesis. This review was pre-registered with PROSPERO (CRD42023389528). RESULTS: From 15,586 records, 25 studies were eligible for inclusion (total participants = 372,162). The prevalence of exposure to ≥ 1 ACEs was 48.1% (95% CI 33.4 to 63.1%). The prevalence of multimorbidity was 34.5% (95% CI 23.4 to 47.5%). Eight studies provided sufficient data for dose-response meta-analysis (total participants = 197,981). There was a significant dose-dependent relationship between ACE exposure and multimorbidity (p < 0.001), with every additional ACE exposure contributing to a 12.9% (95% CI 7.9 to 17.9%) increase in the odds for multimorbidity. However, there was heterogeneity among the included studies (I2 = 76.9%, Cochran Q = 102, p < 0.001). CONCLUSIONS: This is the first systematic review and meta-analysis to synthesise the literature on ACEs and multimorbidity, showing a dose-dependent relationship across a large number of participants. It consolidates and enhances an extensive body of literature that shows an association between ACEs and individual long-term health conditions, risky health behaviours, and other poor health outcomes.

Trajectories of health conditions and their associations with the risk of cognitive impairment among older adults: insights from a national prospective cohort study.

BACKGROUND: The associations between trajectories of different health conditions and cognitive impairment among older adults were unknown. Our cohort study aimed to investigate the impact of various trajectories, including sleep disturbances, depressive symptoms, functional limitations, and multimorbidity, on the subsequent risk of cognitive impairment. METHODS: We conducted a prospective cohort study by using eight waves of national data from the Health and Retirement Study (HRS 2002-2018), involving 4319 adults aged 60 years or older in the USA. Sleep disturbances and depressive symptoms were measured using the Jenkins Sleep Scale and the Centers for Epidemiologic Research Depression (CES-D) scale, respectively. Functional limitations were assessed using activities of daily living (ADLs) and instrumental activities of daily living (IADLs), respectively. Multimorbidity status was assessed by self-reporting physician-diagnosed diseases. We identified 8-year trajectories at four examinations from 2002 to 2010 using latent class trajectory modeling. We screened participants for cognitive impairment using the 27-point HRS cognitive scale from 2010 to 2018 across four subsequent waves. We calculated hazard ratios (HR) using Cox proportional hazard models. RESULTS: During 25,914 person-years, 1230 participants developed cognitive impairment. In the fully adjusted model 3, the trajectories of sleep disturbances and ADLs limitations were not associated with the risk of cognitive impairment. Compared to the low trajectory, we found that the increasing trajectory of depressive symptoms (HR = 1.39; 95% CI = 1.17-1.65), the increasing trajectory of IADLs limitations (HR = 1.88; 95% CI = 1.43-2.46), and the high trajectory of multimorbidity status (HR = 1.48; 95% CI = 1.16-1.88) all posed an elevated risk of cognitive impairment. The increasing trajectory of IADLs limitations was associated with a higher risk of cognitive impairment among older adults living in urban areas (HR = 2.30; 95% CI = 1.65-3.21) and those who smoked (HR = 2.77; 95% CI = 1.91-4.02) (all P for interaction < 0.05). CONCLUSIONS: The results suggest that tracking trajectories of depressive symptoms, instrumental functioning limitations, and multimorbidity status may be a potential and feasible screening method for identifying older adults at risk of cognitive impairment.

Exploring the Impact of Age, Frailty, and Multimorbidity on the Effect of ICU Interventions: A Systematic Review of Randomized Controlled Trials.

OBJECTIVES: To date, age, frailty, and multimorbidity have been used primarily to inform prognosis in older adults. It remains uncertain, however, whether these patient factors may also predict response to critical care interventions or treatment outcomes. DATA SOURCES: We conducted a systematic search of top general medicine and critical care journals for randomized controlled trials (RCTs) examining critical care interventions published between January 1, 2011, and December 31, 2021. STUDY SELECTION: We included RCTs of critical care interventions that examined any one of three subgroups-age, frailty, or multimorbidity. We excluded cluster RCTs, studies that did not report interventions in an ICU, and studies that did not report data examining subgroups of age, frailty, or multimorbidity. DATA EXTRACTION: We collected study characteristics (single vs. multicountry enrollment, single vs. multicenter enrollment, funding, sample size, intervention, comparator, primary outcome and secondary outcomes, length of follow-up), study population (inclusion and exclusion criteria, average age in intervention and comparator groups), and subgroup data. We used the Instrument for assessing the Credibility of Effect Modification Analyses instrument to evaluate the credibility of subgroup findings. DATA SYNTHESIS: Of 2037 unique citations, we included 48 RCTs comprising 50,779 total participants. Seven (14.6%) RCTs found evidence of statistically significant effect modification based on age, whereas none of the multimorbidity or frailty subgroups found evidence of statistically significant subgroup effect. Subgroup credibility ranged from very low to moderate. CONCLUSIONS: Most critical care RCTs do not examine for subgroup effects by frailty or multimorbidity. Although age is more commonly considered, the cut-point is variable, and relative effect modification is rare. Although interventional effects are likely similar across age groups, shared decision-making based on individual patient preferences must remain a priority. RCTs focused specifically on critically ill older adults or those living with frailty and/or multimorbidity are crucial to further address this research question.

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity.

Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease-disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease-disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients' multiple chronic conditions, their risk stratification and personalization of treatment strategies.

Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

MOTIVATION: Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. RESULTS: Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. AVAILABILITY AND IMPLEMENTATION: An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/.

Interactions between patterns of multimorbidity and functional status among hospitalized older patients: a novel approach using cluster analysis and association rule mining.

BACKGROUND: Multimorbidity (MM) is generally defined as the presence of 2 or more chronic diseases in the same patient and seems to be frequently associated with frailty and poor quality of life. However, the complex interplay between MM and functional status in hospitalized older patients has not been fully elucidated so far. Here, we implemented a 2-step approach, combining cluster analysis and association rule mining to explore how patterns of MM and disease associations change as a function of disability. METHODS: This retrospective cohort study included 3366 hospitalized older patients discharged from acute care units of Ancona and Cosenza sites of Italian National Institute on Aging (INRCA-IRCCS) between 2011 and 2017. Cluster analysis and association rule mining (ARM) were used to explore patterns of MM and disease associations in the whole population and after stratifying by dependency in activities of daily living (ADL) at discharge. Sensitivity analyses in men and women were conducted to test for robustness of study findings. RESULTS: Out of 3366 included patients, 78% were multimorbid. According to functional status, 22.2% of patients had no disability in ADL (functionally independent group), 22.7% had 1 ADL dependency (mildly dependent group), and 57.4% 2 or more ADL impaired (moderately-severely dependent group). Two main MM clusters were identified in the whole general population and in single ADL groups. ARM revealed interesting within-cluster disease associations, characterized by high lift and confidence. Specifically, in the functionally independent group, the most significant ones involved atrial fibrillation (AF)-anemia and chronic kidney disease (CKD) (lift = 2.32), followed by coronary artery disease (CAD)-AF and heart failure (HF) (lift = 2.29); in patients with moderate-severe ADL disability, the most significant ARM involved CAD-HF and AF (lift = 1.97), thyroid dysfunction and AF (lift = 1.75), cerebrovascular disease (CVD)-CAD and AF (lift = 1.55), and hypertension-anemia and CKD (lift = 1.43). CONCLUSIONS: Hospitalized older patients have high rates of MM and functional impairment. Combining cluster analysis to ARM may assist physicians in discovering unexpected disease associations in patients with different ADL status. This could be relevant in the view of individuating personalized diagnostic and therapeutic approaches, according to the modern principles of precision medicine.

Genome-wide association study of cardiometabolic multimorbidity in the UK Biobank.

Considering the high prevalence and poor prognosis of cardiometabolic multimorbidity (CMM), identifying causal factors and actively implementing preventive measures is crucial. However, Mendelian randomization (MR), a key method for identifying the causal factors of CMM, requires knowledge of the effects of SNPs on CMM, which remain unknown. We first analyzed the genetic overlap of single cardiometabolic diseases (CMDs) using the latest genome-wide association study (GWAS) for evidential support and comparison. We observed strong positive genetic correlations and shared loci among all CMDs. Further, GWAS and post-GWAS analyses of CMM were performed in 407 949 European ancestry individuals from the UK Biobank. Eleven loci and 12 lead SNPs were identified. By comparison, we found these SNPs were a subset of SNPs associated with CMDs, including both shared and non-shared SNPs. Then, the polygenic risk score model predicted the risk of CMM (C-index = 0.62) and we identified candidate genes related to lipid metabolism and immune function. Finally, as an example, two-sample MR analysis based on the GWAS revealed potential causal effects of total cholesterol, serum urate, body mass index, and smoking on CMM. These results provide a basis for future MR research and inspire future studies on the mechanism and prevention of CMM.

Evidence-Based Therapist-Supported Digital Mental Health Intervention for Patients Experiencing Medical Multimorbidity: A Retrospective Cohort Intent-to-Treat Study.

OBJECTIVE: Multimorbidity or the co-occurrence of multiple health conditions is increasing globally and is associated with significant psychological complications. It is unclear whether digital mental health (DMH) interventions for patients experiencing multimorbidity are effective, particularly given that this patient population faces more treatment resistance. The goal of the current study was to examine the impact of smartphone-delivered DMH interventions for patients presenting with elevated internalizing symptoms that have reported multiple lifetime medical conditions. METHODS: This preregistered (see https://osf.io/vh2et/ ) retrospective cohort intent-to-treat study with 2819 patients enrolled in a therapist-supported DMH intervention examined the associations between medical multimorbidity (MMB) and mental health outcomes. RESULTS: Results indicated that more MMB was significantly associated with greater presenting mental health symptom severity. MMB did not have a deleterious influence on depressive symptom trajectories across treatment, although having one medical condition was associated with a steeper decrease in anxiety symptoms compared to patients with no medical conditions. Finally, MMB was not associated with time to dropout, but was associated with higher dropout and was differentially associated with fewer beneficial treatment outcomes, although this is likely attributable to higher presenting symptom severity, rather than lesser symptom reductions during treatment. CONCLUSIONS: Overall, the Meru Health Program was associated with large effect size decreases in depressive and anxiety symptoms regardless of the number of MMB. Future DMH treatments and research might investigate tailored barrier reduction and extended treatment lengths for patients experiencing MMB to allow for greater treatment dose to reduce symptoms below clinical outcome thresholds.

Lifestyle factors and their relative contributions to longitudinal progression of cardio-renal-metabolic multimorbidity: a prospective cohort study.

BACKGROUND: The role of lifestyle factors and their relative contributions to the development and mortality of cardio-renal-metabolic multimorbidity (CRMM) remains unclear. METHODS: A study was conducted with 357,554 UK Biobank participants. CRMM was defined as the coexistence of two or three cardio-renal-metabolic diseases (CRMDs), including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD). The prospective study examined the associations of individual and combined lifestyle scores (diet, alcohol consumption, smoking, physical activity, sedentary behavior, sleep duration and social connection) with longitudinal progression from healthy to first cardio-renal-metabolic disease (FCRMD), then to CRMM, and ultimately to death, using a multistate model. Subsequently, quantile G-computation was employed to assess the relative contribution of each lifestyle factor. RESULTS: During a median follow-up of 13.62 years, lifestyle played crucial role in all transitions from healthy to FCRMD, then to CRMM, and ultimately to death. The hazard ratios (95% CIs) per score increase were 0.91 (0.90, 0.91) and 0.90 (0.89, 0.91) for healthy to FCRMD, and for FCRMD to CRMM, and 0.84 (0.83, 0.86), 0.87 (0.86, 0.89), and 0.90 (0.88, 0.93) for mortality risk from healthy, FCRMD, and CRMM, respectively. Among the seven factors, smoking status contributed to high proportions for the whole disease progression, accounting for 19.88-38.10%. High-risk diet contributed the largest proportion to the risk of transition from FCRMD to CRMM, with 22.53%. Less-frequent social connection contributed the largest proportion to the risk of transition from FCRMD to death, with 28.81%. When we further consider the disease-specific transitions, we find that lifestyle scores had slightly stronger associations with development to T2D than to CVD or CKD. CONCLUSIONS: Our study indicates that a healthy lifestyle may have a protective effect throughout the longitudinal progression of CRMM, informing more effective management and treatment. Smoking status, diet, and social connection played pivotal roles in specific disease transitions.

Combining the Hospital Frailty Risk Score With the Charlson and Elixhauser Multimorbidity Indices to Identify Older Patients at Risk of Poor Outcomes in Acute Care.

OBJECTIVE: The Hospital Frailty Risk Score (HFRS) can be applied to medico-administrative datasets to determine the risks of 30-day mortality and long length of stay (LOS) in hospitalized older patients. The objective of this study was to compare the HFRS with Charlson and Elixhauser comorbidity indices, used separately or combined. DESIGN: A retrospective analysis of the French medical information database. The HFRS, Charlson index, and Elixhauser index were calculated for each patient based on the index stay and hospitalizations over the preceding 2 years. Different constructions of the HFRS were considered based on overlapping diagnostic codes with either Charlson or Elixhauser indices. We used mixed logistic regression models to investigate the association between outcomes, different constructions of HFRS, and associations with comorbidity indices. SETTING: 743 hospitals in France. PARTICIPANTS: All patients aged 75 years or older hospitalized as an emergency in 2017 (n=1,042,234).Main outcome measures: 30-day inpatient mortality and LOS >10 days. RESULTS: The HFRS, Charlson, and Elixhauser indices were comparably associated with an increased risk of 30-day inpatient mortality and long LOS. The combined model with the highest c-statistic was obtained when associating the HFRS with standard adjustment and Charlson for 30-day inpatient mortality (adjusted c-statistics: HFRS=0.654; HFRS + Charlson = 0.676) and with Elixhauser for long LOS (adjusted c-statistics: HFRS= 0.672; HFRS + Elixhauser =0.698). CONCLUSIONS: Combining comorbidity indices and HFRS may improve discrimination for predicting long LOS in hospitalized older people, but adds little to Charlson's 30-day inpatient mortality risk.