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The role of metabolomic profiling on different multimorbidity patterns remains unknown. This study aims to assess the prospective relationship between plasma concentrations of amino acids and three different multimorbidity patterns: musculoskeletal and mental multimorbidity, cardiometabolic multimorbidity, and cardiopulmonary multimorbidity. The study comprised a total of 1488 older adults from the Seniors-ENRICA 2 Spanish cohort. Plasma concentrations of alanine, glutamine, glycine, histidine, branched-chain amino acids, and aromatic amino acids were measured. Generalized estimating equation models were used to assess the prospective association between amino acids and multimorbidity patterns. Higher plasma concentrations of branched-chain amino acids as leucine [Odds Ratio (OR) per 1-SD increment = 1.05, 95% confidence interval (CI) = 1.01–1.08)], isoleucine (OR = 1.05, 95% CI = 1.01–1.08), and valine (OR = 1.04, 95% CI = 1.01–1.08) were related to cardiometabolic multimorbidity, while lower concentrations of glycine (OR = 0.95, 95% CI = 0.91–0.99) and tyrosine (OR = 0.96, 95% CI = 0.93–0.98) were also associated with the same multimorbidity pattern. On the other hand, higher plasma concentrations of glutamine (OR = 1.18, 95% CI = 1.03–1.34) were related to cardiopulmonary multimorbidity. In conclusion, branched-chain amino acids may serve as risk markers of cardiometabolic multimorbidity in older adults. Plasma concentrations of other amino acid species such as glycine, tyrosine, and glutamine could also help to identify cardiometabolic and cardiopulmonary multimorbidity patterns.

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.

[This corrects the article DOI: 10.3389/fimmu.2026.1810241.].

Background This study aimed to evaluate the association of three multimorbidity indicators with mortality risk among senior adults, and compare their predictive performance on mortality risk. Methods This prospective cohort study was conducted using data from the Yuexiu Ageing and Health Cohort, a dynamic cohort established since January 2016. Annual health examination is conducted for the senior adults aged ≥ 65 years in the jurisdiction. Following exclusions for insufficient follow-up (< 6 months) and missing data, 162,958 participants were included. Mortality data up to 31 December 2023 were obtained from the National Death Registry of China. Information on eleven system diseases was extracted; three multimorbidity indicators (condition count, multimorbidity patterns, and multimorbidity index) were created. Hazard ratio (HR) with 95% confidence intervals (CI) was calculated using Cox proportional hazard model after adjustment for confounders. The C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the performance of multimorbidity indicators. Results Fifteen thousand five hundred twenty-five deaths were identified during a median of 4.79 years of follow-up. Compared with participants with no multimorbidity, those with multimorbidity had a 1.56-fold risk of all-cause mortality. Every one condition count increment was associated with a 17% increased risk of all-cause mortality. Three multimorbidity patterns labeled as Patterns I, II, and III were extracted and were significantly associated with the increased mortality risk, with HR being 1.97, 1.41, and 1.44 for Patterns I, II, and III respectively. Every 1-unit increment of multimorbidity index was associated with an 18% increased mortality risk. The multimorbidity index demonstrated a slightly better discriminative ability compared to the condition count (IDI: 0.003, NRI: 0.0046) and multimorbidity pattern (IDI: 0.007, NRI: 0.0055). Conclusions Three multimorbidity indicators were all associated with the increased mortality risk in community-dwelling older Chinese. The multimorbidity index is considered an optimal indicator for predicting mortality risk in community-dwelling older adults. The condition count is also suggested due to its simplicity and superior predictive performance.

Whether a healthy lifestyle impacts longevity in the presence of multimorbidity is unclear. We investigated the associations between healthy lifestyle and life expectancy in people with and without multimorbidity. A total of 480,940 middle-aged adults (median age of 58 years [range 38-73], 46% male, 95% white) were analysed in the UK Biobank; this longitudinal study collected data between 2006 and 2010, and participants were followed up until 2016. We extracted 36 chronic conditions and defined multimorbidity as 2 or more conditions. Four lifestyle factors, based on national guidelines, were used: leisure-time physical activity, smoking, diet, and alcohol consumption. A combined weighted score was developed and grouped participants into 4 categories: very unhealthy, unhealthy, healthy, and very healthy. Survival models were applied to predict life expectancy, adjusting for ethnicity, working status, deprivation, body mass index, and sedentary time. A total of 93,746 (19.5%) participants had multimorbidity. During a mean follow-up of 7 (range 2-9) years, 11,006 deaths occurred. At 45 years, in men with multimorbidity an unhealthy score was associated with a gain of 1.5 (95% confidence interval [CI] -0.3 to 3.3; P = 0.102) additional life years compared to very unhealthy score, though the association was not significant, whilst a healthy score was significantly associated with a gain of 4.5 (3.3 to 5.7; P < 0.001) life years and a very healthy score with 6.3 (5.0 to 7.7; P < 0.001) years. Corresponding estimates in women were 3.5 (95% CI 0.7 to 6.3; P = 0.016), 6.4 (4.8 to 7.9; P < 0.001), and 7.6 (6.0 to 9.2; P < 0.001) years. Results were consistent in those without multimorbidity and in several sensitivity analyses. For individual lifestyle factors, no current smoking was associated with the largest survival benefit. The main limitations were that we could not explore the consistency of our results using a more restrictive definition of multimorbidity including only cardiometabolic conditions, and participants were not representative of the UK as a whole. In this analysis of data from the UK Biobank, we found that regardless of the presence of multimorbidity, engaging in a healthier lifestyle was associated with up to 6.3 years longer life for men and 7.6 years for women; however, not all lifestyle risk factors equally correlated with life expectancy, with smoking being significantly worse than others.

Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. In this study, we used Taiwan's National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society.

Background: With the aging of populations worldwide, the extension of people’s working lives has become a crucial policy issue. The aim of this study is to assess the impact of complex multimorbidity (CMM) as a predictor of working status among retirement-aged adults in Japan. Methods: Using a nationwide longitudinal cohort study of people aged over 65 who were free of documented disability at baseline, we matched individuals with respect to their propensity to develop CMM. The primary outcome of the study was working status after the six-year follow-up. Results: Among 5613 older adults (mean age: 74.2 years) included in the study, 726 had CMM and 2211 were still working at the end of the follow-up. In propensity-matched analyses, the employment rate was 6.4% higher in the CMM-free group at the end of the six-year follow-up compared to the CMM group (725 pairs; 29.5% vs. 35.9%; p = 0.012). Logistic regression analysis showed that CMM prevented older people from continuing to work beyond retirement age and was a more important factor than socioeconomic factors (income or educational attainment) or psychological factors (depressive symptoms or purpose in life). Conclusions: Our study found that CMM has an adverse impact on the employment rate of older adults in Japan. This finding suggests that providing appropriate support to CMM patients may extend their working lives.

Autistic people experience challenges in healthcare, including disparities in health outcomes and multimorbidity patterns distinct from the general population. This study investigated the efficacy of existing multimorbidity indices in predicting COVID- 19 mortality among autistic adults and proposes a bespoke index, the ASD-MI, tailored to their specific health profile. Using data from the CVD-COVID-UK/COVID-IMPACT Consortium, encompassing England’s entire population, we identified 1,027 autistic adults hospitalized for COVID- 19, among whom 62 died due to the virus. Predictors were selected using logistic regression with fivefold cross-validation, comparing AUCs amongst multimorbidity indices. Diabetes, coronary heart disease, and thyroid disorders were selected as predictors for the ASD-MI, outperforming the Quan Index, a general population-based measure, with an AUC of 0.872 versus 0.828, respectively. Notably, the ASD-MI exhibited better model fit (pseudo-R2 0.25) compared to the Quan Index (pseudo-R2 0.20). These findings underscore the need for tailored indices in predicting mortality risks among autistic individuals. However, caution is warranted in interpreting results, given the limited understanding of morbidity burden in this population. Further research is needed to refine autism-specific indices and elucidate the complex interplay between long-term conditions and mortality risk, informing targeted interventions to address health disparities in autistic adults. This study highlights the importance of developing healthcare tools tailored to the unique needs of neurodivergent populations to improve health outcomes and reduce disparities.

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.