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This study aimed to examine and compare the associations between different multimorbidity measures and mortality among older Chinese adults. Using the Chinese Longitudinal Healthy Longevity Survey 2002–2018, data on fourteen chronic conditions from 13,144 participants aged ≥65 years were collected. Multimorbidity measures included condition counts, multimorbidity patterns (examined by exploratory factor analysis), and multimorbidity trajectories (examined by a group-based trajectory model). Mortality risk associated with different multimorbidity measures was each analyzed using Cox regression. C-statistic, the Integrated Discrimination Improvement (IDI), and the Net Reclassification Index (NRI) were used to compare the performance of different multimorbidity measures. Participants with multimorbidity, regardless of measurements, had a higher risk of death compared with people without multimorbidity. Compared with the mortality prediction model using age and sex, C-statistics showed added discrimination (over 0.77, all P < .05) for models with multimorbidity measures. Multimorbidity trajectory showed integrated discrimination and net reclassification improvement for mortality prediction compared to condition count (IDI = 0.042, NRI = 0.033) and multimorbidity pattern (IDI = 0.041, NRI = 0.069). Adding multimorbidity measures significantly improved the performance of a mortality prediction model using age and sex as predictors. Trajectory-based measures of multimorbidity performed better than count- and pattern-based measures for mortality prediction.

Hip fractures significantly affect patients’ health and quality of life. Despite therapeutic treatments, many patients continue to experience poor prognoses that include recurrent fractures and mortality, especially the older ones. Therefore, understanding important factors associated with post-fracture prognoses is critical. This study focuses on patients’ multimorbidity trajectories and examines how the trajectory’s time span, number of coexisting chronic diseases, and sequential disease patterns relate to distinct prognostic outcomes. From the National Health Insurance Research Database in Taiwan, we obtain a sample of 128,822 patients who suffered an initial hip fracture between 1996 and 2011. We use this sample to analyze the relationships between multimorbidity trajectories and prognostic outcomes after an initial hip fracture. The results reveal that a patient’s multimorbidity trajectory’s time span and number of chronic diseases significantly associate with his or her post-fracture prognosis. In addition, essential sequential patterns of chronic diseases relate to post-fracture prognoses too. We then leverage the discovered relationships to develop a cross-attention neural network method for estimating patients’ post-fracture prognoses and demonstrate its predictive utilities relative to several prevalent machine leaning methods. This study underscores the importance of leveraging the time span, number of chronic diseases, and sequential disease patterns in patients’ multimorbidity trajectory profile to estimate their prognoses within three years of an initial hip fracture, which can support physicians’ clinical decisions and patient management.

Multimorbidity-having two or more coexisting chronic conditions-is highly prevalent, costly, and disabling to older adults. Questions remain regarding chronic diseases accumulation over time and whether this differs by racial and ethnic background. Answering this knowledge gap, this study identifies differences in rates of chronic disease accumulation and multimorbidity development among non-Hispanic white, non-Hispanic black, and Hispanic study participants starting in middle-age and followed up to 16 years. We analyzed data from the Health and Retirement Study (HRS), a biennial, ongoing, publicly-available, longitudinal nationally-representative study of middle-aged and older adults in the United States. We assessed the change in chronic disease burden among 8,872 non-Hispanic black, non-Hispanic white, and Hispanic participants who were 51-55 years of age at their first interview any time during the study period (1998-2014) and all subsequent follow-up observations until 2014. Multimorbidity was defined as having two or more of seven somatic chronic diseases: arthritis, cancer, heart disease (myocardial infarction, coronary heart disease, angina, congestive heart failure, or other heart problems), diabetes, hypertension, lung disease, and stroke. We used negative binomial generalized estimating equation models to assess the trajectories of multimorbidity burden over time for non-Hispanic black, non-Hispanic white, and Hispanic participants. In covariate-adjusted models non-Hispanic black respondents had initial chronic disease counts that were 28% higher than non-Hispanic white respondents (IRR 1.279, 95% CI 1.201, 1.361), while Hispanic respondents had initial chronic disease counts that were 15% lower than non-Hispanic white respondents (IRR 0.852, 95% CI 0.775, 0.938). Non-Hispanic black respondents had rates of chronic disease accumulation that were 1.1% slower than non-Hispanic whites (IRR 0.989, 95% CI 0.981, 0.998) and Hispanic respondents had rates of chronic disease accumulation that were 1.5% faster than non-Hispanic white respondents (IRR 1.015, 95% CI 1.002, 1.028). Using marginal effects commands, this translates to predicted values of chronic disease for white respondents who begin the study period with 0.98 chronic diseases and end with 2.8 chronic diseases; black respondents who begin the study period with 1.3 chronic diseases and end with 3.3 chronic diseases; and Hispanic respondents who begin the study period with 0.84 chronic diseases and end with 2.7 chronic diseases. Middle-aged non-Hispanic black adults start at a higher level of chronic disease burden and develop multimorbidity at an earlier age, on average, than their non-Hispanic white counterparts. Hispanics, on the other hand, accumulate chronic disease at a faster rate relative to non-Hispanic white adults. Our findings have important implications for improving primary and secondary chronic disease prevention efforts among non-Hispanic black and Hispanic Americans to stave off greater multimorbidity-related health impacts.

Background Menopausal women undergo substantial physiological changes that can impact their overall health. Objectives We examined relationships between circadian rest-activity rhythms (CRARs) and multimorbidity progression in this population. Methods We used UK Biobank data, involving 10,138 participants, who were initially free of chronic conditions. We primarily focused on the relative amplitude (RA) of CRARs, tracking incident first chronic conditions (FCC), multimorbidity, and all-cause mortality. Multimorbidity was indicated by the presence of any 2/35 chronic conditions during the follow-up period. We used a multi-state model to assess the RA impact on the multimorbidity progression trajectory, encompassing transition from health to an FCC, to consequent multimorbidity, and ultimately to mortality, in parallel with sensitivity analyses to ensure results stability and reliability. Results During a mean 8.13-year follow-up period, we identified 855 incident multimorbidity cases and recorded 88 deaths. In a multi-state model, a lower RA was associated with an increased risk of transition from health to FCC onset [hazard ratio (HR): 1.18, 95% confidence interval (CI): 1.07–1.31] and also from an FCC to multimorbidity development (HR: 1.34, 95% CI: 1.12–1.61), even after adjusting for several confounding factors. Conclusions Among menopausal women, circadian rhythm disturbance increased the risk of transitioning from health to a single chronic condition, as well as transitioning from a single chronic condition to multimorbidity.

Background Despite established associations between insulin resistance (IR)-related indices and cardiometabolic diseases (CMDs), most studies are limited to single CMD outcomes. The study aimed to examine the influence of IR-related indices on the incidence, predictive value, and progression trajectory of cardiometabolic multimorbidity (CMM), as well as potential biological mechanisms. Methods This prospective study included 374,274 individuals from the UK Biobank who were free of CMDs at baseline. CMM was defined as the presence of two or more CMDs, including type 2 diabetes (T2D), coronary heart disease (CHD), and stroke. Five indices were developed to assess IR levels: triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist-height ratio (TyG-WHtR), and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio. Cox proportional hazards and multi-state models were utilized to examine the associations between IR-related indices and CMM incidence and transition, respectively, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). The predictive utility of these indices was assessed using the net reclassification index (NRI) and integrated discrimination improvement index (IDI). Mediation analyses were conducted to quantify the potential mediating roles of biomarkers. Results During a mean follow-up period of 13.7 years, 5048 (1.3%) individuals developed CMM. Elevated baseline IR-related indices were associated with higher risks of incident CMM. The HRs (95% CIs) for each 1-standard deviation increase were as follows: 1.30 (1.26–1.34) for the TyG index, 1.42 (1.39–1.46) for the TyG-BMI, 1.54 (1.49–1.59) for the TyG-WC, 1.52 (1.48–1.57) for the TyG-WHtR, and 1.19 (1.17–1.21) for the TG/HDL-C ratio. Besides, TyG-WHtR and TyG-WC exhibited significantly higher NRI and IDI, indicating superior predictive performance for CMM risk. These indices played critical yet distinct roles in the progression of CMM. For transitions from being free of CMDs to single CMDs, these indices had the strongest impact on T2D (all P  < 0.001). Participants initially diagnosed with CHD were more likely to progress to CMM when exposed to higher IR-related indices (all P  < 0.001). The effect sizes for TyG-WC and TyG-WHtR were greater than those of other indices across all transitions. Mediation analyses revealed that biomarkers associated with liver function, renal function, and inflammation collectively mediated approximately one-third of the associations of the TyG-WHtR and TyG-WC indices with incident CMM. Conclusions Our findings highlight the critical role of IR-related indices, particularly TyG-WHtR and TyG-WC, in the incidence, progression, and prevention of CMM. The mediation effects of biomarkers indicate the potential for targeted interventions to reduce CMM risk in high-IR individuals.

Background Patients with multimorbidity are frequent users of healthcare, but fragmented care may lead to suboptimal treatment. Yet, this has never been examined across healthcare sectors on a national scale. We aimed to quantify care fragmentation using various measures and to analyze the associations with patient outcomes. Methods We conducted a register-based nationwide cohort study with 4.7 million Danish adult citizens. All healthcare contacts to primary care and hospitals during 2018 were recorded. Clinical fragmentation indicators included number of healthcare contacts, involved providers, provider transitions, and hospital trajectories. Formal fragmentation indices assessed care concentration, dispersion, and contact sequence. The patient outcomes were potentially inappropriate medication and all-cause mortality adjusted for demographics, socioeconomic factors, and morbidity level. Results The number of involved healthcare providers, provider transitions, and hospital trajectories rose with increasing morbidity levels. Patients with 3 versus 6 conditions had a mean of 4.0 versus 6.9 involved providers and 6.6 versus 13.7 provider transitions. The proportion of contacts to the patient’s own general practice remained stable across morbidity levels. High levels of care fragmentation were associated with higher rates of potentially inappropriate medication and increased mortality on all fragmentation measures after adjusting for demographic characteristics, socioeconomic factors, and morbidity. The strongest associations with potentially inappropriate medication and mortality were found for ≥ 20 contacts versus none (incidence rate ratio 2.83, 95% CI 2.77–2.90) and ≥ 20 hospital trajectories versus none (hazard ratio 10.8, 95% CI 9.48–12.4), respectively. Having less than 25% of contacts with your usual provider was associated with an incidence rate ratio of potentially inappropriate medication of 1.49 (95% CI 1.40–1.58) and a mortality hazard ratio of 2.59 (95% CI 2.36–2.84) compared with full continuity. For the associations between fragmentation measures and patient outcomes, there were no clear interactions with number of conditions. Conclusions Several clinical indicators of care fragmentation were associated with morbidity level. Care fragmentation was associated with higher rates of potentially inappropriate medication and increased mortality even when adjusting for the most important confounders. Frequent contact to the usual provider, fewer transitions, and better coordination were associated with better patient outcomes regardless of morbidity level.

Background There is limited evidence on the long-term associations between cardiometabolic multimorbidity (CMM) and multiple domains of functioning, and the potential role of social activity in these associations. We aimed to examine the associations between CMM and joint trajectories of physical disability, depressive symptom, and cognitive function, and whether social activity modifies these trajectories. Methods This multicohort study used pooled data of four prospective cohorts of adults from China, the UK, the USA, and Europe. Participants with complete information on cardiometabolic diseases (CMDs, including heart diseases, stroke, and diabetes) and social activity at baseline were included. Physical disability, depressive symptom, and cognitive function were measured one to five times. Longitudinal modelling was used to describe post-baseline trajectories of the three domains, stratified by CMM status and social activity. Results Among 73,778 participants (age 63.4 ± 9.1 years, 56.0% female), 20.0% had single CMD and 4.3% had CMM at baseline. Participants with single CMD or CMM had persistently worse physical disability, depressive symptom, and cognitive function compared to those without CMD. CMM was associated with faster worsening of physical disability ( β linear change*CMM  = − 0.017 [95% confidence interval = − 0.030 to − 0.004]) and cognitive function (− 0.035 [− 0.050 to − 0.019]), particularly among those with social inactivity. Among 40,883 participants (age 62.9 ± 8.9 years, 55.6% female) in joint trajectory analysis, four trajectories were identified: ‘favourable trajectories of physical disability, depressive symptom, and cognitive function’ (48.1%); ‘worsening cognitive function’ (32.6%); ‘worsening depressive symptom and cognitive function’ (14.6%); and ‘rapidly-worsening physical disability and worsening depressive symptom and cognitive function’ (4.7%). CMD or CMM were associated with all the three worsening joint trajectories. The highest odds were observed for concurrent worsening across all three functional domains in participants with CMM (odds ratio 3.43 [2.80–4.20]), with more unfavourable trajectories in those with social inactivity (7.26 [5.49–9.59], P for additive interaction < 0.001). Conclusions CMM was associated with worse levels, faster progression, and concurrent deterioration in physical disability, depressive symptom, and cognitive function, with the poorest trajectories among those who were socially inactive. This underscores the importance of implementing prevention strategies that integrate physical, psychological, cognitive, and social activities.

Background Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. Methods Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. Results In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2–0.3%), medium (0.3–1%) and high in-hospital mortality (2–11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50–59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. Conclusions Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.

Background Cardiometabolic multimorbidity (CM) has been found to be associated with higher mortality and functional limitations. However, few studies have investigated the longitudinal association between CM and disability in the Chinese population and whether these associations vary by smoking status. Methods The study included 16,754 participants from four waves (2011, 2013, 2015, and 2018) of China Health and Retirement Longitudinal Study (CHARLS) (mean age: 59, female: 51%). CM was assesed at baseline and defined as having two or more of diabetes, stroke, or heart disease. Disability was repeatedly measured by summing the number of impaired activities of daily living (ADL) and instrumental activities of daily living (IADL) during the 7-year follow-up. Linear mixed-effects model was used to determine the association of CM and trajectories of disability and to assess the modification effect of smoking status in these associations. Results Participants with CM at baseline had a faster progression of disability compared to those without CM (CM: β = 0.13, 95% CI: 0.05 to 0.21). Current smokers with CM developed disability faster than their counterparts (P interaction for smoking =0.011). In addition, there was a significant association between CM and the annual change of disability in current smokers (β = 0.34, 95% CI: 0.17 to 0.50) while no such association was observed in current non-smokers (β = 0.08, 95% CI: -0.02 to 0.17). Conclusion CM was associated with more a rapid disability progression. Notably, being current smokers may amplify the adverse effects of CM on disability progression.

Background Insulin resistance and central obesity are major risk factors for cardiometabolic diseases. The triglyceride-glucose index (TyG) and lipid accumulation product (LAP) are markers that independently predict cardiometabolic risk. However, their combined long-term trajectories and impact on cardiometabolic multimorbidity (CMM) development remain unclear. Methods This cohort study utilized data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, which tracked 3467 participants at baseline. Dual-trajectory of TyG and LAP were identified using a group-based dual-trajectory model. Cox proportional hazards models were employed to assess the relationships between dual-trajectory groups and primary cardiometabolic outcomes, including first cardiometabolic disease (FCMD), CMM (two or more conditions such as type 2 diabetes, coronary heart disease, or stroke), and all-cause mortality. Multi-state models were performed to assess the associations of dual-trajectory with CMM development. Results The study included 3467 participants with a mean age of 25.08 years (SD = 3.59). Of these, 43.4% ( n  = 1505) were male, and 53.2% ( n  = 1561) were White. Three distinct dual-trajectory groups were identified: low-increasing (61.5%), high-amplitude fluctuation (7.6%), and high-increasing (30.9%). After multivariate adjustment, compared with the low-increasing group, the high-amplitude fluctuation group exhibited significantly higher risks for FCMD (hazard ratio [HR] 1.38, 95% confidence interval [CI]: 1.08–1.77), CMM (HR 2.63, 95% CI 1.21–5.71), and all-cause mortality (HR 2.16, 95% CI 1.30–3.56), as well as elevated risks for transitions from baseline to FCMD (HR 1.41, 95% CI 1.17–1.63), FCMD to CMM (HR 2.07, 95% CI 1.53–3.96), CMM to death (HR 2.87, 95% CI 1.19–7.62). The high-increasing group showed similar results. Conclusions Elevated and fluctuating trajectories of TyG and LAP from early adulthood are associated with increased risks of CMM development in midlife. Graphical abstract