Multimorbidity Clusters Research Articles

Is an antioxidant-rich or a pro-inflammatory diet during pregnancy associated with allergic and respiratory multimorbidity in children from the ELFE birth cohort?

The antioxidant capacity and the inflammatory potential of diet during pregnancy may represent a prevention opportunity for allergic and respiratory diseases. We aimed to investigate the associations between the antioxidant and inflammatory potential of maternal diet in the last three months of pregnancy with allergic and respiratory diseases in children. Analyses were performed on 9679 mother-child pairs from the ELFE birth cohort. The dietary total antioxidant capacity (DTAC), without coffee, was estimated with the Trolox equivalent antioxidant capacity (TEAC), the total radical trapping antioxidant parameter (TRAP), and the ferric reducing-antioxidant power (FRAP). The inflammatory potential of the maternal diet was assessed by the energy-adjusted Dietary Inflammatory Index (E-DII). Allergic and respiratory diseases in children up to 5.5 years were considered jointly through five allergic and respiratory multimorbidity clusters ("asymptomatic" - reference, "early wheeze without asthma", "asthma only", "allergies without asthma", "multi-allergic"). Multinomial logistic regressions were performed and adjusted for main confounders. A diet with a higher antioxidant potential was associated with a lower risk of belonging to the "early wheeze without asthma" cluster (aOR(95%CI) = 0.95 (0.90;0.99) per standard deviation (SD) of TEAC score). A higher E-DII was associated with a higher risk of belonging to the "asthma only" cluster (aOR(95% CI)= 1.09 (1.00;1.19) per SD). No association was found with the "allergies without asthma" or "multi-allergic" clusters. An antioxidant-rich diet during pregnancy was associated with better respiratory health while a pro-inflammatory diet was associated with poorer respiratory health in children up to 5.5 years, though the associations were weak.

Associations between general practice and outpatient care continuity in people with multimorbidity

Abstract Background Relational continuity in primary and secondary care is linked to better health outcomes for patients, but it is unclear whether metrics of continuity in each setting are associated. Our study examined the association between relational continuity in general practice (GP) and continuity of hospital outpatient specialties in people with clusters of multimorbidity. Methods We used GP records from the Clinical Practice Research Datalink, linked to Hospital Episode Statistics outpatient records. Included were patients ≥18 years, with two or more of 212 Long-Term Conditions (LTCs), registered throughout 2019 and with at least three GP and three outpatient appointments. The Continuity of Care Index (COCI) was calculated separately for visits to the same i) GP and ii) hospital specialty, where 0 represents no continuity and 1 represents all visits to the same GP or specialty. We assessed the association between the indices using univariable and multivariable fractional logit regression. We calculated expected COCI scores for each LTC and assessed variation in the COCI between LTCs assigned to one of 15 clusters of co-occurring diseases derived previously. A sensitivity analysis repeated analysis using the Sequential Continuity (SECON) score, which captures longitudinal continuity. Results Of 1,135,903 patients, 56.2% were ≥60 years. The median (IQR) COCI in GP was 0.22 (0.11-0.40) and in outpatients was 0.33 (0.20-0.52). Although statistically significant (p < 0.001) the relationship between the two measures was clinically insignificant in both univariable and multivariable models. No strong relationships were found between COCI scores and LTC clusters. Similar patterns were found using the SECON score. Conclusions We found no strong association between continuity of care in GP and outpatient settings, and no clear patterns with LTC clusters. This suggests that lack of continuity in secondary care is not compensated for by increased continuity of care in primary care. Key messages • We found no meaningful association between measures of continuity of care in general practice and hospital outpatient settings. • Our findings suggest that lack of continuity of outpatient care is not compensated for by increased continuity in primary care.

Resilience among older adults with multimorbidity using the Connor-Davidson scale in the Canadian Longitudinal Study on Aging: health behaviour, socio-economic, and social support predictors

ObjectiveMultimorbidity is recognized as a serious health condition faced by a majority of older adults. Research investigating adaptive responses to multimorbidity, termed multimorbidity resilience, has been growing. This paper examines protective and risk factors, with a focus on health behaviours, socio-economic resources, and social support using an established measure of resilience (Connor-Davidson Resilience Scale) among older adults, focusing on older persons with two or more concurrent chronic conditions.MethodsUsing Baseline (2011–2015), Follow-up One (2015–2018), and Follow-up Two (2018–2021) data from the Comprehensive Cohort of the Canadian Longitudinal Study on Aging, we tested hypotheses using 13,064 participants aged 65 years and older, who completed all waves and reported two or more of 27 chronic conditions, for the full sample of multimorbid individuals and three multimorbidity clusters: Cardiovascular/Metabolic, Musculoskeletal, and Mental Health. Associations between protective and risk factors and resilience were examined using linear regression to model the Connor-Davidson resilience scale, adjusting for illness context and social determinants of health.ResultsAmong all multimorbid individuals, the strongest associations with resilience were found for higher self-rated health, greater sleep satisfaction, better appetite, higher household income, more relatives and friends, being overweight (compared to normal weight), fewer housing problems, and fewer skipped meals. Weaker associations were found for non-smokers, less alcohol consumption, less pain, sedentary behaviour, being non-married (compared to married), and among Canadian born (compared to foreign). The analyses for the three multimorbidity clusters were largely replicated for the three multimorbidity clusters, but with some nuances depending on the cluster.DiscussionThis research provides confirmatory evidence for several protective and risk factors affecting the ability to cope and recover from multimorbidity adversity among older adults. There are consistent patterns for the multimorbidity disease clusters, but some distinct relationships arise that are worthy of attention. The implications of the findings for modifiable health behaviours and socio-economic factors are discussed for their public health and clinical relevance.

The association of a low-inflammatory diet with the trajectory of multimorbidity: a large community-based longitudinal study

BackgroundA proinflammatory diet has been associated with a risk of individual chronic diseases, however, evidence on the association between inflammatory dietary patterns and the trajectory of chronic disease multimorbidity is sparse. ObjectivesWe aimed to investigate the associations of a low-inflammatory diet with the multimorbidity trajectory. MethodsWithin the UK Biobank, 102,424 chronic disease-free participants (mean age 54.7 ± 7.9 y, 54.8% female) were followed up to detect multimorbidity trajectory (annual change in the number of 59 chronic diseases). Baseline inflammatory diet index (IDI) and empirical dietary inflammatory pattern (EDIP) were separately calculated from the weighted sum of 32 posteriori-derived (15 anti-inflammatory) and 18 prior-defined (9 anti-inflammatory) food groups, and tertiled as low-, moderate-, and high-inflammatory diet. Data were analyzed using linear mixed effects model, Cox model, and Laplace regression with adjustment for potential confounders. ResultsDuring the follow-up (median 10.23 y), 15,672 and 35,801 participants developed 1 and 2+ chronic conditions, respectively. Adherence to a low-inflammatory diet was associated with decreased multimorbidity risk (hazard ratio [HRIDI] = 0.84, 95% confidence interval [CI]: 0.81, 0.86; HREDIP = 0.91, 95% CI: 0.89, 0.94) and a slower multimorbidity accumulation (βIDI = −0.033, 95% CI: −0.036, −0.029; βEDIP = −0.006, 95% CI: −0.010, −0.003) compared with a high-inflammatory diet, especially in participants aged > 60 y (βIDI = −0.051, 95% CI: −0.059, −0.042; βEDIP = −0.020, 95% CI: −0.029, −0.012; both P-interactions < 0.05). The 50th percentile difference (95% CI) of chronic disease-free survival time was prolonged by 0.81 (0.64, 0.97) and 0.49 (0.34, 0.64) y for participants with a low IDI and EDIP, respectively. Higher IDI and EDIP were associated with the development of 4 and 3 multimorbidity clusters (especially for cardiometabolic diseases), respectively. ConclusionsA low-inflammatory diet is associated with a lower risk and slower accumulation of multimorbidity (especially in participants aged > 60 y). A low-inflammatory diet may prolong chronic disease-free survival time.

Enhancing clinical service design for multimorbidity management: A comprehensive approach to joined-up care for diabetes, chronic kidney disease, and heart failure.

Multimorbidity is becoming the norm rather than the exception, especially among the ageing population and people with lower socio-economic status. In addition to the rising healthcare cost, multimorbidity poses considerable difficulty in the delivery of adequate holistic care for affected patients. This review presents a discussion of the current barriers to delivering holistic care to people with multimorbidity and proposes a model of clinical care for people living with cardiovascular-kidney-metabolic (CKM) syndrome as an exemplar of a multimorbidity cluster. Single organ/disease services may not be able to provide optimum care to people with multimorbidity due to the potential complex interactions between multiple disease symptoms and management. In addition, people with multimorbidity may be required to attend multiple appointments in different healthcare centres. This may negatively impact access to services due to time and financial burden. Other barriers include co-ordinating communication between healthcare professionals and reduced continuity of care. Optimising CKM health requires patient-centred care led by an interdisciplinary care team who ideally should possess CKM competencies utilising a shared care protocol to coordinate evidence-based care and use of telehealth to empower patients. Stakeholders and policymakers need to adapt new policy models to establish and enhance CKM care models by allocating funds and implementing frameworks for educational reforms. A CKM service has the potential to increase the uptake of cardiac and renal protective medications as well as optimising metabolic care, increase capacity in both primary and secondary care, improve quality of life and clinical outcomes, reduce patient inconvenience, and importantly allow rapid translation of advances in cardiorenal metabolic diseases into clinical practice.

Multimorbidity clusters potentially superior to individual diseases for stratifying fracture risk in older people: a nationwide cohort study.

Comorbidities are common in fracture patients, but the interaction between fracture and comorbidities remains unclear. This study aimed to define specific multimorbidity clusters in older adults and quantify the association between the multimorbidity clusters and fracture risk. This nationwide cohort study includes 1.7 million adults in Denmark aged ≥50years who were followed from 2001 through 2014 for an incident low-trauma fracture. Chronic diseases and fractures were identified from the Danish National Hospital Discharge Register. Latent class analysis and Cox's regression were conducted to define the clusters and quantify fracture risk, respectively. The study included 793815 men (age: 64 ± 10) and 873524 women (65.5 ± 11), with a third having ≥1 chronic disease. The pre-existent chronic diseases grouped individuals into low-multimorbidity (80.3% in men, 83.6% in women), cardiovascular (12.5%, 10.6%), malignant (4.1%, 3.8%), diabetic (2.4%, 2.0%) and hepatic clusters (0.7%, men only). These clusters distinguished individuals with advanced, complex, or late-stage disease from those having earlier-stage disease. During a median follow-up of 14years (IQR: 6.5, 14), 95372 men and 212498 women sustained an incident fracture. The presence of multimorbidity was associated with a significantly greater risk of fracture, independent of age and sex. Importantly, the multimorbidity clusters had the highest discriminative performance in assessing fracture risk, whereas the strength of their association with fracture risk equalled or exceeded that of both the individual chronic diseases most prevalent in each cluster and of counts-based comorbidity indices. Future fracture prevention strategies should take comorbidities into account. Multimorbidity clusters may provide greater insight into fracture risk than individual diseases or counts-based comorbidity indices.

Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use. Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n=1,825,289), UK Biobank (n=502,363), and the UK Household Longitudinal Study (UKHLS) (n=49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL). Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the "Pain+" cluster in the age-group 18-36 years (mortality IRR=4.47, hospitalisation IRR=1.84; GP use IRR=2.87) and the "Hypertension, Diabetes & Heart disease" cluster in the age-group 37-54 years (mortality IRR=4.52, hospitalisation IRR=1.53, GP use IRR=2.36). In UK Biobank, the "Cancer, Thyroid disease & Rheumatoid arthritis" cluster in the age group 37-54 years had the largest association with mortality (IRR=2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18-36 years. Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs. This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).

A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging

ObjectivesTo examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters. Study Design and SettingUsing cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45–85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians. ResultsResults differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from −0.02 to 0.24, indicating little similarity. ConclusionThese analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.

Maternal diet quality with child allergic and respiratory multimorbidity in the Elfe birth cohort

Evidence linking maternal diet during pregnancy to allergic or respiratory diseases in children remains sparse, and outcomes were mainly studied separately. We aim to investigate these associations by considering clusters of allergic and respiratory multimorbidity among 9679 mother–child pairs from the Elfe birth cohort. Maternal diet quality was evaluated using a food-based score (Diet Quality score), a nutrient-based score (PANDiet score) and food group intakes. Adjusted multinomial logistic regressions on allergic and respiratory multimorbidity clusters up to 5.5 years were performed. Child allergic and respiratory diseases were described through five clusters: “asymptomatic” (43%, reference), “early wheeze without asthma” (34%), “asthma only” (7%), “allergies without asthma” (7%), “multi-allergic” (9%). A higher PANDiet score and an increased legume consumption were associated with a reduced risk of belonging to the “early wheeze without asthma” cluster. A U-shaped relationship was observed between maternal fish consumption and the “allergies without asthma” cluster. To conclude, adequate nutrient intake during pregnancy was weakly associated with a lower risk of “early wheeze without asthma” in children. No association was found with food groups, considered jointly or separately, except for legumes and fish, suggesting that maternal adherence to nutritional guidelines might be beneficial for allergic and respiratory diseases prevention.

Prevalence of hepatitis B and C infections among HIV-positive men who have sex with men: A systematic review and meta-analysis.

Human immunodeficiency virus (HIV) infection is highly prevalent and often coexists with other infectious diseases, especially Hepatitis B virus (HBV) and Hepatitis C virus (HCV). Men who have sex with men (MSM) represent a vulnerable population in terms of HIV infection. We aimed to determine the prevalence of HCV, HBV among HIV-infected MSM. This systematic review and meta-analysis searched PubMed, Cochrane, Scopus, Web of Science, and ProQuest up-to 2023/04/22. All studies reporting the prevalence of HBV or HCV infection in MSM PLHIV were included. Meta-analysis used random effect model for synthesis and I 2 along with prediction interval for heterogeneity. Subgroup analysis based on continent and meta-regression for study size, average age and year of publication were used to explore heterogeneity. Modified Newcastle-Ottawa Scale was used to evaluate the quality of studies according to the protocol (PROSPERO: CRD42023428764). Fifty-sixof 5948 studies are included. In 53 studies with 3,07,589 participants, a pooled prevalence of 7% (95% confidence interval [CI]: 5-10) was found for HCV among MSM PLHIV, while a 9% (95% CI: 4-18) prevalence was found for HBV infection from five studies which included 5641 MSM PLHIV. Asia reported the lowest pooled prevalence at 5.84% (95% CI: 2.98-11.13) for HCV while Europe reported the highest pooled prevalence at 7.76% (95% CI: 4.35-13.45). Baujat plot and influence diagnostic identified contributors to influence and between-study heterogeneity. Sensitivity analyses omitting these studies result in considerably more precise estimates. Another sensitivity analysis as leave-one-out meta-analysis did not change any pooled estimate significantly. There is a significant burden of HCV and HBV among MSM PLHIV worldwide, with varying prevalence rates. Future studies should focus on these multimorbidity clusters and investigate factors influencing disease burden, long-term outcomes, optimal testing strategies, and tailored interventions.

Divergent age-associated and metabolism-associated gut microbiome signatures modulate cardiovascular disease risk.

Insight into associations between the gut microbiome with metabolism and aging is crucial for tailoring interventions to promote healthy longevity. In a discovery cohort of 10,207 individuals aged 40-93 years, we used 21 metabolic parameters to classify individuals into five clusters, termed metabolic multimorbidity clusters (MCs), that represent different metabolic subphenotypes. Compared to the cluster classified as metabolically healthy (MC1), clusters classified as 'obesity-related mixed' (MC4) and 'hyperglycemia' (MC5) exhibited an increased 11.1-year cardiovascular disease (CVD) risk by 75% (multivariable-adjusted hazard ratio (HR): 1.75, 95% confidence interval (CI): 1.43-2.14) and by 117% (2.17, 1.72-2.74), respectively. These associations were replicated in a second cohort of 9,061 individuals with a 10.0-year follow-up. Based on analysis of 4,491 shotgun fecal metagenomes from the discovery cohort, we found that gut microbial composition was associated with both MCs and age. Next, using 55 age-specific microbial species to capture biological age, we developed a gut microbial age (MA) metric, which was validated in four external cohorts comprising 4,425 metagenomic samples. Among individuals aged 60 years or older, the increased CVD risk associated with MC4 or MC5, as compared to MC1, MC2 or MC3, was exacerbated in individuals with high MA but diminished in individuals with low MA, independent of age, sex and other lifestyle and dietary factors. This pattern, in which younger MA appears to counteract the CVD risk attributable to metabolic dysfunction, implies a modulating role of MA in cardiovascular health for metabolically unhealthy older people.

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

BackgroundMultimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years.MethodsA retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group.Results3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories.ConclusionsIn a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning.Trial registration numberNCT05717309.

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters

BackgroundMajor depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters. MethodsWe analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression. ResultsAt SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction. LimitationsCTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders. ConclusionsThe first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

Musculoskeletal Diseases as the Most Prevalent Component of Multimorbidity: A Population-Based Study.

Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a "component disorder" of multimorbidity. Our objective was to assess the prevalence of multimorbidity and to identify the most common clusters of diagnosis within multimorbidity states, with the primary hypothesis that the most common clusters of multimorbidity are MSDs. Methods: The current study employed data from a population-based 2019 European Health Interview Survey (EHIS). Multimorbidity was defined as a ≥2 diagnosis from the list of 17 chronic non-communicable diseases, and to define clusters, the statistical method of hierarchical cluster analysis (HCA) was performed. Results: Out of 13,178 respondents, multimorbidity was present among 4398 (33.4%). The HCA method yielded six multimorbidity clusters representing the most common diagnoses. The primary multimorbidity cluster, which was prevalent among both genders, age groups, incomes per capita, and statistical regions, consisted of three diagnoses: (1) lower spine deformity or other chronic back problem (back pain), (2) cervical deformity or other chronic problem with the cervical spine, and (3) osteoarthritis. Conclusions: Given the influence of musculoskeletal disorders on multimorbidity, it is imperative to implement appropriate measures to assist patients in relieving the physical discomfort and pain they endure. Public health information, programs, and campaigns should be utilized to promote a healthy lifestyle. Policymakers should prioritize the prevention of MSDs by encouraging increased physical activity and a healthy diet, as well as focusing on improving functional abilities.

Characteristics of infant formula consumed in the first months of life and allergy in the EDEN mother-child cohort.

The evidence regarding the association between infant formula (IF) composition and the prevention of allergy and respiratory diseases remains sparse and inconclusive. This study aimed to evaluate whether some IF characteristics were associated with the risk of allergy or respiratory diseases in childhood. Among1243 formula-fed children from the EDEN mother-child cohort, IF characteristics concerning long-chain polyunsaturated fatty acids (LCPUFAs) enrichment, prebiotic/probiotic enrichment, and hydrolysis of proteins were identified from the ingredients list. Eczema, wheezing, food allergy, asthma, and allergic rhinitis up to age 8 years were prospectively collected and summarized into four allergic and respiratory multimorbidity clusters. Associations between 4-month IF characteristics and risk of allergy or respiratory diseases were tested using logistic regressions adjusted on main confounders. The consumption of LCPUFA-enriched formula was not linked to allergic and respiratory multimorbidity clusters, but to a lower risk of any allergy, eczema, and wheezing. Probiotic-enriched formula consumption was associated with a lower risk of belonging to the 'Allergy without asthma' cluster (odds ratio [OR][95% confidence interval,CI] = 0.63 [0.40-0.99]), and consumption of a formula enriched in Bifidobacterium lactis was associated with a lower risk of any allergy (OR[95% CI] = 0.59 [0.41-0.85]). Partially hydrolysed formula (pHF) consumption was associated with a higher risk of belonging to the 'Allergy without asthma' cluster (OR[95%CI] = 2.73 [1.65-4.51]). This study confirms the positive association between pHFconsumption and the risk of allergy found in previous observational studies and suggests that consumption of LCPUFA-enriched or probiotic-enriched formula was associated with a lower risk of allergy.

#559 Validation of the Kidney Failure Risk Equation (KFRE) in individuals with chronic kidney disease and multimorbidity

Abstract Background and Aims Increasing numbers of individuals with chronic kidney disease (CKD) experience multiple long-term conditions (multimorbidity). Clinical guidance recommends the use of validated risk prediction equations, such as the kidney failure risk equation (KFRE), to guide referral to specialist kidney care services. However, multimorbidity may impact the accuracy of creatinine-based eGFR and bias the prognostic utility of KFRE. Equations with alternative filtration markers, such as Cystatin C eGFR (eGFRcys) or the combined creatinine and Cystatin C eGFR (eGFRcrcys), may improve KFRE's risk stratification and discrimination of future kidney failure risks. This study aimed to validate the four-variable KFRE in individuals with CKD, with and without multimorbidity in a research-based cohort (UK Biobank) and a population-based cohort (Stockholm Creatinine Measurements project (SCREAM)). The study also assessed the performance of KFRE when using eGFRcr, eGFRcys and eGFRcrcys. Method Individuals from both cohorts were included if they had CKD, defined as eGFR&amp;lt;60 mL/min/1.73 m2 by any of the three eGFR equations (eGFRcr, eGFRcys and eGFRcrcys), and had available proteinuria measurement at time of testing or within the previous 12 months. Multimorbidity was defined as the presence of two or more long-term conditions in addition to CKD and was grouped into multimorbidity clusters based on previous research. The outcome was kidney failure, defined as the need for long-term dialysis or kidney transplantation. KFRE performance at 2- and 5-years was assessed using the area under the receiver operating characteristic curve and c-index for discrimination and calibration curves for calibration. Results We included 24 489 individuals from UK Biobank and 42 902 individuals from SCREAM (mean age 62.8 (SD 5.6) and 70.1 (SD14.1), 54% and 66% female, respectively). In UK biobank, 14 998 individuals had multimorbidity, of which 5 375 had cardiometabolic multimorbidity. In SCREAM, 30 147 had multimorbidity and 17 854 cardiometabolic multimorbidity. Overall, there were 252 and 312 kidney failure events and 1 108 and 1 471 death events in UK Biobank and 918 and 1 098 kidney failure events and 8 785 and 10 152 death events in SCREAM within 2- and 5-years respectively. Model performance was consistent across both cohorts. Discrimination power of KFRE was good in individuals with and without multimorbidity and across the 2-year and 5-year models (Table 1). Calibration plots revealed over-estimation of risk at 2-years in both cohorts and under-estimation of risk at 5-years in patients with multimorbidity in UK Biobank. There was no improvement in model performance when using eGFRcys or eGFRcrcys compared to eGFRCr. There was a higher cumulative incidence of both kidney failure and death in the multimorbidity group, with a prominent increase in risk of death over time compared to the no multimorbidity group. This was most evident in the SCREAM cohort (Fig. 1). Conclusion KFRE adequately predicts kidney failure in individuals with multimorbidity and either creatinine or cystatin C can be used. Given the high rates of mortality amongst people with multimorbidity, exploration of models that account for the competing risk of death is warranted.

Multimorbidity disease clusters are associated with venous thromboembolism: an extended cross-sectional national study

Multimorbidity, i.e., two or more non-communicable diseases (NCDs), is an escalating challenge for society. Venous thromboembolism (VTE) is a common cardiovascular disease and it is unknown which multimorbidity clusters associates with VTE. Our aim was to examine the association between different common disease clusters of multimorbidity and VTE. The study is an extended (1997–2015) cross-sectional Swedish study using the National Patient Register and the Multigeneration Register. A total of 2,694,442 Swedish-born individuals were included in the study. Multimorbidity was defined by 45 NCDs. A principal component analysis (PCA) identified multimorbidity disease clusters. Odds ratios (OR) for VTE were calculated for the different multimorbidity disease clusters. There were 16% (n = 440,742) of multimorbid individuals in the study population. Forty-four of the individual 45 NCDs were associated with VTE. The PCA analysis identified nine multimorbidity disease clusters, F1-F9. Seven of these multimorbidity clusters were associated with VTE. The adjusted OR for VTE in the multimorbid patients was for the first three clusters: F1 (cardiometabolic diseases) 3.44 (95%CI 3.24–3.65), F2 (mental disorders) 2.25 (95%CI 2.14–2.37) and F3 (digestive system diseases) 4.35 (95%CI 3.63–5.22). There was an association between multimorbidity severity and OR for VTE. For instance, the occurrence of at least five diseases was in F1 and F2 associated with ORs for VTE: 8.17 (95%CI 6.32–10.55) and 6.31 (95%CI 4.34–9.17), respectively. In this nationwide study we have shown a strong association between VTE and different multimorbidity disease clusters that might be useful for VTE prediction.Graphical abstract

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.

Abstract P263: Cardiometabolic Multimorbidity Among Rural Low-Wage Workers Participating in a Worksite-Based Health Assessment

Introduction: Multimorbidity or the co-occurrence of two or more chronic conditions, is common among adults in the US and cardiometabolic multimorbidity is becoming progressively more common. Non-medical factors, such as lower educational attainment and poverty can increase a person’s risk of having cardiometabolic multimorbidity. The burden of cardiometabolic multimorbidity and its association with self-perceived health among a sample of rural, low-wage workers has not been established. Methods: Between December 2020 and August 2023, we partnered with 34 business to conduct worksite-based health screening clinics. Participants completed questionnaires, including the EQ-5D, and a physical assessment. Disease screenings included diabetes, obesity, and hypertension. Results: A total of 1128 individuals (45.1% Black, 56.7% male) were screened and included in this report. On a scale from 0 (poor health) to 100 (best health) the mean self-reported health was 78.4. On-site lab results identified 104 (9.2%) participants with an indicator of diabetes (HbA1c≥6.5), 575 participants were obese (51.0%), and 450 had a systolic blood pressure ≥ 140 (39.9%). Cardiometabolic multimorbidity was found in over a quarter of participants (28.3%, see figure). Self-perceived health differed among multimorbidity clusters. Participants with the lowest self-perceived health included those who screened positive for all three conditions (diabetes, obesity, and hypertension) (68.9), followed by those who screened positive for both diabetes and obesity (72.4), and those with only obesity (76.3). Participants without any positive screens had the highest health score (81.5). Conclusion: Of the 1128 individuals screened, over a quarter of participants screened positive for cardiometabolic multimorbidity. An additional 40.3% of the sample screened positive for one of the chronic conditions. Self-perceived health was associated with cardiometabolic multimorbidity; obesity appears to be an important factor in this relationship.

Sex Differences in the Role of Multimorbidity on Poststroke Disability: The Taiwan Stroke Registry.

Multimorbidity is common in patients who experience stroke. Less is known about the effect of specific multimorbidity patterns on long-term disability in patients with stroke. Furthermore, given the increased poststroke disability frequently seen in female vs male patients, it is unknown whether multimorbidity has a similar association with disability in both sexes. We assessed whether specific multimorbidity clusters were associated with greater long-term poststroke disability burden overall and by sex. In the Taiwan Stroke Registry, an ongoing nationwide prospective registry, patients with first-ever ischemic stroke were enrolled; this analysis is restricted to those individuals surviving to at least 6 months poststroke. Using a hierarchical clustering approach, clusters of prestroke multimorbidity were generated based on 16 risk factors; the algorithm identified 5 distinct clusters. The association between clusters and 12-month poststroke disability, defined using the modified Rankin Scale (mRS), was determined using logistic regression models, with additional models stratified by sex. The longitudinal association between multimorbidity and functional status change was assessed using mixed-effects models. Nine-thousand eight hundred eighteen patients with first-ever ischemic stroke were included. The cluster with no risk factors was the reference, "healthier" risk group (N = 1,373). Patients with a cluster profile of diabetes, peripheral artery disease (PAD), and chronic kidney disease (CKD) (N = 1882) had significantly greater disability (mRS ≥ 3) at 1 month (OR [95% CI] = 1.36 [1.13-1.63]), 3 months (OR [95% CI] = 1.27 [1.04-1.55]), and 6 months (OR [95% CI] = 1.30 [1.06-1.59]) but not at 12 months (OR [95% CI] = 1.16 [0.95-1.42]) than patients with a healthier risk factor profile. In the sex-stratified analysis, the associations with this risk cluster remained consistent in male patients (OR [95% CI] = 1.42 [1.06-1.89]) at 12 months, who also had a higher comorbidity burden, but not in female patients (OR [95% CI] = 0.95 [0.71-1.26]), who had higher proportions of severe strokes and severe disability (p-interaction = 0.04). Taiwanese patients with multimorbidity, specifically the concurrent presence of diabetes, PAD, and CKD, had higher odds of a worse functional outcome in the first 6 months poststroke. Clusters of multimorbidity may be less informative for long-term disability in female patients. Further studies should evaluate other mechanisms for worse disability in female patients poststroke.