Progression Of Multimorbidity Research Articles

Associations between socioeconomic status and dynamic development of physical,psychological and cognitive degenerative multimorbidity among middle aged and older adults in China

Objective: To analyze the dynamic development of physical, psychological, and cognitive degenerative multimorbidity among middle-aged and older Chinese adults (≥45 years old) while estimating the longitudinal association between socioeconomic status (SES) and the progression of multimorbidity. Methods: Based on data from the China Health and Retirement Longitudinal Study (2011-2020), the Sankey diagram was used to show the dynamic development of physical, psychological, and cognitive degenerative multimorbidity from 2011 to 2020. SES was constructed based on the level of education and total household wealth. Logistic regression was used to estimate OR and 95%CI to evaluate the association between SES and the progression of multimorbidity. Results: Of the 5 393 participants included, 4 484 (83.14%) of them developed new diseases, and the prevalence of physical, psychological, and cognitive degenerative multimorbidity increased from 38.04% to 74.23%. Compared to those with no reported disorders at baseline, participants with psychological disorder (for newly developed physical-cognitive multimorbidity: OR=4.59,95%CI: 2.89-7.29), cognitive disorder (for newly developed physical-psychological multimorbidity: OR=2.24,95%CI: 1.40-3.60), or their multimorbidity at baseline were more likely to progress to physical, psychological, and cognitive degenerative multimorbidity. After adjusting covariates, individuals with low SES were more likely to develop physical diseases (OR=1.45, 95%CI: 1.11-1.89), cognitive disorder (OR=1.84, 95%CI: 1.16-2.91), physical-psychological multimorbidity (OR=1.87, 95%CI: 1.37-2.56), physical-cognitive multimorbidity (OR=3.58, 95%CI: 2.54-5.06), psychological-cognitive multimorbidity (OR=5.66, 95%CI: 3.04-10.55), and physical-psychological-cognitive multimorbidity (OR=3.21, 95%CI: 2.06-5.01) in comparison to those with high SES. There is a dose-response relationship between SES and the multimorbidity progression (all trend P<0.001). Conclusions: The prevalence of physical, psychological, and cognitive degenerative multimorbidity increased significantly among middle-aged and older Chinese adults. Lower SES was associated with multiple patterns of physical, psychological, and cognitive disorders progression.

Social Deprivation and Multimorbidity Among Community-Based Health Center Patients in the United States.

Multimorbidity - having 2 or more chronic diseases - is a national public health concern that entails burdensome and costly care for patients, their families, and public health programs. Adults residing in socially deprived areas often have limited access to social and material resources. They also experience a greater multimorbidity burden. We conducted a retrospective cohort analysis of electronic health record (EHR) data from 678 community-based health centers (CHCs) in 27 states from the Accelerating Data Value Across a National Community Health Center (ADVANCE) Network, a clinical research network, from 2012-2019. We used mixed-effects Poisson regression to examine the relationship of area-level social deprivation (eg, educational attainment, household income, unemployment) to chronic disease accumulation among a sample of patients aged 45 years or older (N = 816,921) residing across 9,362 zip code tabulation areas and receiving care in safety-net health organizations. We observed high rates of chronic disease among this national sample. Prevalence of multimorbidity varied considerably by geographic location, both within and between states. People in more socially deprived areas with Social Deprivation Index (SDI) scores in quartiles 2, 3, and 4 had greater initial chronic disease counts - 17.1%, 17.7%, and 18.0%, respectively - but a slower rate of accumulation compared with people in the least-deprived quartile. Our findings were consistent for models of the composite SDI and those evaluating disaggregated measures of area-level educational attainment, household income, and unemployment. Social factors play an important role in the development and progression of multimorbidity, which suggests that an assessment and understanding of area-level social deprivation is necessary for developing public health strategies to address multimorbidity.

The extent and burden of high multimorbidity on older adults in the US: a descriptive analysis of Medicare beneficiaries

BackgroundThe impact of multimorbidity (≥ 2 chronic diseases) on the well-being of older adults is substantial but variable. The burden of multimorbidity varies by the number and kinds of conditions, and timing of onset. The impact varies by age, race, ethnicity, socioeconomic status, and health indicators. Large scale longitudinal surveys linked to medical claims provide unique opportunities to characterize this variability.MethodsWe analyzed Medicare-linked Health and Retirement Study data for respondents 65 and older with 3 or more years of fee-for-service coverage (n = 17,199; 2000–2016). We applied standardized claims algorithms for operationalizing 21 chronic diseases. We compared multimorbidity levels, demographics, and outcomes at baseline and over time and escalation to high multimorbidity levels (≥ 5 conditions).ResultsAt baseline, 51.2% had no multimorbidity, 36.5% had multimorbidity, and 12.4% had high multimorbidity. Loss of function, cognitive decline, and higher healthcare utilization were up to ten times more prevalent in the high multimorbidity group. Greater rates of high multimorbidity were seen among non-Hispanic Black and Hispanic groups, those with lower wealth, younger birth cohorts, and adults with obesity. Rates of transition to high multimorbidity varied greatly and was highest among Hispanic and respondents with lower education.ConclusionsThe development and progression of multimorbidity in old age is influenced by many factors. Higher levels of multimorbidity are associated with sociodemographic characteristics, suggesting possible mitigation strategies.

A Cohort Tree-based Representation and a Multimorbidity Progression Across Pregnancies in Scotland

Pregnancy-related research using routinely collected data is increasing. Electronic Health Records (EHR) are a robust resource because they can provide information on a large population of pregnant women covering prolonged periods at a reasonable cost. Moreover, information is collected prospectively and is thus free from recall bias. However, these data sources also present challenges. Information is gathered for clinical purposes and often recorded in different datasets (prescriptions, hospital admission, demography, birth records, etc.). Therefore, a significant data management effort must be made to convert all the information into structured and meaningful datasets in which identifying the timing of conception, outcomes of interest, multiple pregnancies, and interpregnancy periods can easily be done. In this study, we linked Scottish Maternity Records between 2007 and 2021 (1,575,153 pregnancies, 493,629 women) to demographics data, datasets with International Classification of Diseases (ICD) diagnostic codes for all inpatient stays and day cases in hospitals, and Prescribing Information System (PIS) for data on all medications from primary care. After combining these datasets using R codes, we developed a simple tree-based algorithm that visually represents each woman at each pregnancy and the related health conditions. Presenting data in this way helps identify relevant clinical questions and the temporal relation of different pregnancies. As an example, we identified the progression of multimorbidity through pregnancies and characterised how multimorbidity changes from one pregnancy to another, considering the woman's health conditions and social class.

Mapping multimorbidity progression among 190 diseases

BackgroundCurrent clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them.MethodsIn this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations.ResultsWe show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes.ConclusionOur findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Association of Depression With the Progression of Multimorbidity in Older Adults: A Population-Based Cohort Study

The relationship between depression and the risk of multimorbidity progression has rarely been studied in older adults. This study was aimed to determine whether depression is associated with progression in the severity and complexity of multimorbidity, considering the influence of depression's severity and subtype. As a part of the Korean Longitudinal Study on Cognitive Aging and Dementia, this population-based cohort study followed a random sample of community-dwelling Koreans aged 60 and older for 8 years at 2-year intervals starting in 2010. Participants included those who completed mood and multimorbidity assessments and did not exhibit complex multimorbidity at the study's outset. Depression was assessed using the Geriatric Depression Scale, while multimorbidity was evaluated using the Cumulative Illness Rating Scale. The study quantified multimorbidity complexity by counting affected body systems and measured multimorbidity severity by averaging scores across 14 body systems. The 2,486 participants (age = 69.1 ± 6.5 years, 57.6% women) were followed for 5.9 ± 2.4 years. Linear mixed models revealed that participants with depression had a faster increase in multimorbidity complexity score (β = .065, SE = 0.019, p = 0.001) than those without depression, but a comparable increase in multimorbidity severity score (β = .001, SE = .009, p = 0.870) to those without depression. Cox proportional hazard models revealed that depression was associated with the risk of developing highly complex multimorbidity affecting five or more body systems, particularly in severe or anhedonic depression. Depression was associated with the worsening of multimorbidity in Korean older adults, particularly when severe or anhedonic. Early screening and management of depression may help to reduce the burden of multimorbidity in older adults.

SYNTHESIS AND APPLICATION OF QUANTITATIVE METHODS FOR MODELING CHANGE IN MULTIMORBIDITY AND RELATED HEALTH DOMAINS

Abstract The longitudinal examination of multimorbidity (≥ 2 chronic conditions) and related health outcomes (e.g., cognition, disability, mortality) has the potential to identify disparities and intervention points in the age-related progression of chronic disease. This symposium will organize discussion of methodological and applied research projects that review and demonstrate the use of longitudinal methods to address critical questions in the study of multimorbidity and related health domains. The lead presentation “Longitudinal Methods for Assessing Progression of Multimorbidity and Related Health Outcomes” will review the use and limitations of several longitudinal methods that can be used to study the progression of multimorbidity and linked health outcomes. Following presentations will demonstrate the application of specific longitudinal methods to key research questions in the study of multimorbidity. “Emerging Cohort Disparities in Multimorbidity in U.S. Adults Entering Early Older Adulthood” presents evidence of cohort disparities in multimorbidity trajectories using mixed-effects regression to estimate age, period, and cohort effects, “Personalized and Typical Concurrent Risk: Joint Models of Recurrent Outcomes and Mortality by Race/Ethnicity” applies joint models of self-rated health, disability, and mortality to examine disparities among minoritized groups, and “Sequence Analysis of Cardiometabolic Multimorbidity and Association With Subsequent Dementia” identifies common patterning of cardiometabolic conditions over a 5-year period and their relationship with prospective dementia classification. This symposium will encourage attendees to apply the presented methods to their own multimorbidity research questions and demonstrate how these methods are being applied to address key gaps in our understating of multimorbidity progression in older adulthood. This is a Measurement, Statistics, and Research Design Interest Group Sponsored Symposium.

Transcriptomic analysis of mRNAs in human whole blood identified age-specific changes in healthy individuals.

Older age is one of the most important shared risk factors for multiple chronic diseases, increasing the medical burden to contemporary societies. Current research focuses on identifying aging biomarkers to predict aging trajectories and developing interventions aimed at preventing and delaying the progression of multimorbidity with aging. Here, a transcriptomic changes analysis of whole blood genes with age was conducted. The age-related whole blood gene-expression profiling datasets were downloaded from the Gene Expression Omnibus (GEO) database. We screened the differentially expressed genes (DEGs) between healthy young and old individuals and performed functional enrichment analysis. Cytoscape with Cytohubba and MCODE was used to perform an interaction network of DEGs and identify hub genes. In addition, ROC curves and correlation analysis were used to evaluate the accuracy of hub genes. In total, we identified 29 DEGs between young and old samples that were enriched mainly in immunoglobulin binding and complex, humoral immune response, and immune response-activating signaling pathways. In combination with the PPI network and topological analysis, 4 hub genes (IGLL5, Jchain, POU2AF1, and Bach2) were identified. Pearson analysis showed that the expression changes of these hub genes were highly correlated with age. Among them, 3 hub genes (IGLL5, POU2AF1, and Bach2) were identified with good accuracy (AUC score > 0.7), indicating that these genes were the best indicators of age. Together, our results provided potential biomarkers IGLL5, POU2AF1, and Bach2 to identify individuals at high early risk of age-related disease to be targeted for early interventions and contribute to understanding the molecular mechanisms in the progression of aging.

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.

Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results. Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.

Early-Life Factors and Multimorbidity Risk Later in Older Age: Evidence Based on CHARLS

Introduction: Early-life factors were reported to exert influence on the health condition of individuals in the long-term. However, limited research explored the connection between early-life factors and multimorbidity in later years. Methods: We utilized the data from the China Health and Retirement Longitudinal Study to assess this possible association in the present cross-sectional study. Multimorbidity was determined based on 14 common chronic diseases included in the study. Logistic regression was employed to examine the link between early-life factors and subsequent multimorbidity. Results: Out of 7,578 participants who met the inclusion criteria for analysis, 3,765 (49.68%) were females. The mean age was 68.25 ± 6.70 years. Participants who rated their health during childhood as average (odds ratio [OR] 0.78, 95% confidence interval [CI] 0.63–0.96) or better [OR 0.72, 95% CI 0.57–0.91] were significantly less likely to experience multimorbidity in older life. By contrast, experiencing violence from two of the family members was significantly associated with future multimorbidity (OR [95% CI], 1.29 [1.04–1.60]). A superior family financial situation was also negatively associated with multimorbidity, with average (OR [95% CI], 0.72 [0.63–0.83]) and better off than average (OR [95% CI], 0.76 [0.62–0.93]). Discussion: Individuals with poor health status, inferior family socioeconomic status, or experienced violence from family members in childhood were more likely to suffer from multimorbidity in later life. Enhanced social monitoring of potentially adverse conditions in youngsters and targeted interventions could help mitigate the progression of multimorbidity in later life.

Exploring the link between Multimorbidity and direct healthcare costs in Ireland: A cross-sectional study

Background Multimorbidity has emerged as a major challenge facing health services globally, which will place a substantial burden on health systems going forward. This paper seeks to estimate the association between multimorbidity and direct healthcare costs among older people in Ireland from a healthcare system perspective. Methods Cross-sectional analysis of data on 8,447 community-dwelling adults aged 50 and over collected between 2009 and 2011 as part of the Irish Longitudinal Study on Ageing. Multivariable generalised linear model regression, employing a log-link and Poisson family distribution, is used to assess the association between self-reported multimorbidity status and direct healthcare costs. Results For the full sample, 21.20% reported having no chronic conditions, 27.39% had one chronic condition, and 51.40% had multimorbidity. After controlling for a range of socio-demographic and health status variables, we found that relative to those reporting no chronic conditions, one chronic condition was associated with additional average annual costs of €513 (95% CIs: 245, 781), increasing to €1277 (95% CIs: 942, 1612) for those with 6 or more chronic conditions. Relative to those reporting 2 chronic conditions, 4 chronic conditions were associated with additional costs of €411 (95% CIs: 106, 716), 5 chronic conditions with €591 (95% CIs: 214, 969), and 6 or more chronic conditions with additional average costs of €1006 (95% CIs: 641, 1371). Conclusion This study finds positive and significant associations between the number of chronic conditions and direct healthcare costs and further highlights the potential economic benefits from preventing the onset and progression of multimorbidity

The association between depression during perimenopause and progression of chronic conditions and multimorbidity: results from a Chinese prospective cohort.

The association between perimenopausal depression and many chronic conditions among women has been well-established. However, the role of depression during perimenopause in the progression of multiple chronic conditions (multimorbidity) remains poorly understood. A total of 1,216 community-dwelling women in their perimenopause period between 2010 and 2016 were enrolled in our analysis, and followed up for the progression of multimorbidity. Depression, as well as its severity, was evaluated by the Center for Epidemiologic Studies Depression 10-item scale (CES-D-10). Progression of multimorbidity was defined as the first report of two or more chronic conditions for participants without multimorbidity or the new report of one or more conditions for those with multimorbidity. Univariable and multivariable Cox proportional hazards model and the restricted cubic spline regression model were performed to assess the prospective association between perimenopausal depression and the progression of multimorbidity. A total of 480 (39.5%) women reported depression during perimenopause, and 529 (43.5%) women progressed to multimorbidity. After adjusting for socio-demographic and lifestyle factors, perimenopausal depression was independently associated with the progression of multimorbidity (hazard ratio [HR]: 1.34; 95% confidence interval [CI]: 1.13 to 1.60). Moreover, the severity of depression was positively and linearly associated with the progression of multimorbidity (P < 0.05). Our finding reveals a prospective association between perimenopausal depression and the progression of multimorbidity, indicating interventions targeting perimenopausal depression may reduce the burden of chronic diseases and multimorbidity in women's post-menopausal life.

Bidirectional Association Between Multimorbidity and Frailty and the Role of Depression in Older Europeans.

Although previous studies have reported an association between multimorbidity and frailty, its direction and mechanism remain unclear. This study aimed to investigate the direction of this association, as well as the role of depression among older Europeans. We used a cross-lagged panel design to evaluate the temporal relationship between multimorbidity and frailty and the role of depression. Multimorbidity status was assessed by the self-reporting of 14 chronic diseases. Frailty was assessed based on the frailty phenotype. The European-Depression Scale (EURO-D) was used to assess depression. There was a bidirectional relationship between frailty and multimorbidity. More severe multimorbidity predicted greater frailty (β = 0.159; p < .001) and vice versa (β = 0.107; p < .001). All paths from multimorbidity to frailty were stronger than the paths from frailty to multimorbidity (b1-a1: β = 0.051; p < .001). Likewise, early multimorbidity change was a significant predictive factor for late frailty change (β = 0.064; p < .001) and vice versa (β = 0.048; p < .001). Depression in Wave 5 (T5) mediated the association between frailty in Wave 4 (T4) and multimorbidity in Wave 6 (T6; indirect effect: β = 0.004; bootstrap 95% confidence interval: 0.003, 0.006). A positive, bidirectional association was observed between multimorbidity and frailty. Depression may be a potential cause of an increased risk of multimorbidity later in life in frail older adults. Early monitoring of frailty and depression may slow the progression of multimorbidity, thereby interrupting the vicious cycle.

Multimorbidity and its Importance in Future Health Care in Germany: a Secondary Data Analysis Based on 67 Million Health Insurance Policy Holders

Multimorbidity is a particular challenge for health care systems. In Germany, epidemiological findings are primarily sample-based. The estimated prevalence among adults in Germany is high, but there are methodological problems, such as a lack of a uniform definition. Statutory health insurance data from the information system for health care data ("Informationssystem Versorgungsdaten") of the former German Institute for Medical Documentation and Information were analyzed. A total of 67.3 million people with statutory health insurance in 2014 were included. Multimorbidity was defined by the presence of at least three chronic diseases from a list of 46 diseases per ICD-10. A total of 21,157,937 individuals, or 31.4% of the total cohort, were defined as multimorbid. In men, progression of multimorbidity occurred at the age of about 40 years, whereas the increase was seen at the age of about 35 years in women. The different disease burden varied in different age and sex groups. A better understanding of complex disease interactions in relation to age and sex is needed. Interdisciplinary approaches with specific care concepts for multimorbidity, adapted to the chronic care model, should be explored in order to achieve an ideal care situation in Germany.

Sleep Behaviors and Progression of Multimorbidity in Middle-Aged and Older Adults: AProspective Cohort Study From China.

Sleep behavior (eg, sleep duration, sleep quality, and nap) is closely related to many chronic conditions. However, less is known about its association with multiple chronic conditions (multimorbidity), particularly evidence from cohort studies. Data were collected from a cohort of 8937 individuals aged 45 and older from the China Health and Retirement Longitudinal Study (2011-2018). Sleep duration, sleep quality, and nap duration were collected in 2011 and 2013. Progression of multimorbidity was defined as the first report of 2 or more chronic conditions for participants without multimorbidity or the new report of 1 or more conditions for those with multimorbidity. Cox regression models were performed to calculate the hazard ratios and 95% confidence intervals (CIs) of the associations between sleep behaviors and the progression of multimorbidity. Short sleep duration and poor sleep quality were associated with the progression of multimorbidity independently and jointly, especially in those less than 65years and females. The U-shaped dose-response relationships were observed between nighttime and total sleep duration and the progression of multimorbidity. Persistent short and unsteadily changed sleep behaviors increased the risk of multimorbidity progression. Individuals sleeping ≤5h/night with 5-7 restless days/week had 1.53 times higher risk of multimorbidity progression (95% CI: 1.37-1.71), compared to those sleeping 7-8h/night with <1 restless day/week. Short sleep duration and poor sleep quality were independently and jointly associated with a higher risk of multimorbidity progression in a mid-to-older population. Optimal sleep duration and sleep quality should be emphasized in multimorbidity prevention and control.

Synergistic interactions of obesity with sex, education, and smoking and accumulation of multi-morbidity (MM) across the lifespan.

Obesity is a potentially modifiable risk factor that has been consistently associated with the development and progression of multi-morbidity (MM). However, obesity may be more problematic for some persons compared to others because of interactions with other risk factors. Therefore, we studied the effect of interactions between patient characteristics and overweight and obesity on the rate of accumulation of MM. We studied 4 cohorts of persons ages 20-, 40-, 60-, and 80-years residing in Olmsted County, Minnesota between 2005 and 2014 using the Rochester Epidemiology Project (REP) medical records-linkage system. Body mass index, sex, race, ethnicity, education, and smoking status were extracted from REP indices. The rate of accumulation of MM was calculated as the number of new chronic conditions accumulated per 10 person years through 2017. Poisson rate regression models were used to identify associations between characteristics and rate of MM accumulation. Additive interactions were summarized using relative excess risk due to interaction, attributable proportion of disease, and the synergy index. Greater than additive synergistic associations were observed between female sex and obesity in the 20- and 40-year cohorts, between low education and obesity in the 20-year cohort (both sexes), and between smoking and obesity in the 40-year cohort (both sexes). Interventions targeted at women, persons with lower education, and smokers who also have obesity may result in the greatest reduction in the rate of MM accumulation. However, interventions may need to focus on persons prior to mid-life to have the greatest effect.

Solid Fuel Use and the Progression of Multimorbidity in Middle-Aged Chinese Participants: A Prospective Cohort Study.

Objectives: This study aimed to examine the association of solid fuel use for cooking and heating with the progression of multimorbidity. Methods: A total of 5,437 participants from the China Health and Retirement Longitudinal Study were included. Multivariate logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of the independent and joint effects of solid fuel use for cooking and heating with the progression of multimorbidity. Results: The proportion of participants reporting solid fuel use for both cooking and heating was 59.0% at baseline. Solid fuel use for both cooking and heating was associated with the progression of multimorbidity (adjusted OR: 1.42, 95% CI: 1.19-1.70), compared with clean fuel use for both. Conclusion: Solid fuel use for cooking and heating play an important role in the progression of multimorbidity. Therefore, solid fuel reduction should be considered in developing multimorbidity control and prevention programmes.

Associations of Inflammatory, Metabolic, Malnutrition, and Frailty Indexes with Multimorbidity Incidence and Progression, and Mortality Impact: Singapore Longitudinal Aging Study

Introduction: The detection of systemic risk factors aids in the formulation of strategies to prevent multimorbidity and its associated mortality impact. We aimed to determine the associations of inflammatory, metabolic, malnutrition, and frailty indexes with multimorbidity onset and progression and their predictions of multimorbidity-associated mortality risk. Methods: A prospective cohort study (Singapore Longitudinal Aging Study [SLAS]) of 5,089 community-dwelling older adults aged ≥55 years in two waves of recruitment (SLAS-1: March 2005-September 2007, SLAS-2: January 2013-August 2018). Baseline variables included inflammatory (neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR)) and metabolic profiles (atherogenic index of plasma (AIP), triglyceride-glucose index of insulin resistance (TyG)), physical frailty, and nutritional risk (Mini Nutritional Assessment-Short Form (MNA-SF), Nutritional Screening Initiative (NSI), Elderly Nutritional Indicators for Geriatric Malnutrition Assessment (ENIGMA)). At follow-up, 3–5 years after the baseline interview, incident multimorbidity (≥2 chronic diseases) was determined among multimorbidity-free participants (N = 1,657) and worsening multimorbidity (increase of ≥2 chronic diseases) among participants with baseline multimorbidity (N = 1,207). Mortality in all participants and those with multimorbidity (N = 2,291) was determined up to 31 December, 2016. Odds ratio (OR), hazard ratio (HR), and 95% confidence intervals (95% CI) were estimated in multivariate logistic and Cox regression models, in base model adjustments for age, sex, ethnicity, housing type, smoking, and a number of comorbidities, and further stepwise selection adjustment for other systemic risk indexes. Results: At baseline, NLR, LMR, AIP, TyG, physical frailty, ENIGMA, NSI, and MNA-SF were significantly associated with prevalent multimorbidity (p < 0.001). Among multimorbidity-free participants, LMR, TyG, and ENIGMA were significantly associated with incident multimorbidity in both the base model and further stepwise selection models: LMR (OR = 0.87, 95% CI: 0.81–0.94), TyG (OR = 1.36, 95% CI: 1.06–1.75), and ENIGMA (OR = 1.15, 95% CI: 1.02–1.30). Among participants with baseline multimorbidity, NLR, LMR, and TyG significantly predicted worsened multimorbidity at follow-up in base model analysis, and LMR (OR = 0.72, 95% CI: 0.60–0.86) and TyG (OR = 1.96, 95% CI: 1.24–3.09) remained as independent predictors in further stepwise selection models. Among participants with prevalent multimorbidity, NLR, TyG, frailty, MNA, and ENIGMA were significantly associated with mortality risk with base model adjustments and further stepwise selection models: NLR (HR = 1.20, 95% CI: 1.10–1.32), TyG (HR = 1.27, 95% CI: 1.04–1.54), frailty (HR = 1.22, 95% CI: 1.10–1.36), ENIGMA (HR = 1.13, 95% CI: 1.05–1.22), MNA (HR = 0.91, 95% CI: 0.85–0.97). A combined systemic risk index shows increasing quartiles, adjusted for age, sex, housing, and smoking status, significantly predicting mortality risk. Discussion/Conclusion: The onset and progression of multimorbidity and its mortality impact are driven by systemic factors, including inflammation, metabolic dysfunction (insulin resistance), malnutrition, and frailty. The measurement of these systemic factors using simple, inexpensive clinical and blood chemistry tools can help in strategies to prevent and reduce its mortality impact.

Multimorbidity and cognitive decline related functional limitations in middle-aged and older Chinese

BackgroundOur study aimed to investigate the association between multimorbidity and the cognitive decline related functional limitations. MethodsData were obtained from the 2011–2018 China Health and Retirement Longitudinal Study, and generalized estimating equation models were used for estimation. A Chinese multimorbidity-weighted index (CMWI) was used to quantify the cumulative disease burden of multimorbidity. A margin plot was used to show the probability of functional limitations by global cognitive function and CMWI. Then, subgroup analysis was performed by demographic factors, the severity of functionally impaired ADL/IADL, and the trajectories of cognitive function. ResultsMultimorbidity was demonstrated to be associated cognitive decline related functional limitations over 8 years, with the coefficient of the interaction of cognitive function −0.001 (−0.001, −0.001). This moderating effect was only significant in the relationship between cognitive function and ≥2-item impaired ADL/IADL (coefficient = −0.008, 95% CI: 0.009, −0.007) but was not significant in the relationship between cognitive function and 1-item impaired ADL/IADL. The accelerated role of multimorbidity associated with more severe function limitations than the relatively mild limitations attributed to cognitive decline, and the role of multimorbidity was stronger in individuals with continuously high function of cognitive trajectories and gradually declining cognitive function than in those with low level of cognitive trajectories. ConclusionsEffectively managing multimorbidity is important for preventing cognitive decline. Preventing the onset and progression of multimorbidity may be one potential strategy for early prevention and intervention to reverse or postpone cognitive decline and its further dementia risk.

DISCRIMINATION TRAJECTORIES, RESILIENCE CHARACTERISTICS, AND MULTIMORBIDITY AMONG MIDDLE-AGED AND OLDER BLACKS

Abstract Discrimination is a chronic psychosocial stressor that can accelerate aging. While strong empirical evidence demonstrating linkages between discrimination and adverse health outcomes, the role of discrimination in multimorbidity burden has received less attention. Prior research on discrimination and health largely uses a single, static, cross-sectional measure to predict health at a second time point, that may mask significant heterogeneity in the dynamic nature of repeated exposures to discrimination. Characterizing longitudinal patterns of discrimination may be a better predictor of health risk and provide insight on resilience processes that influence aging-related outcomes such as multimorbidity. However, this relationship is not well understood. We investigate the association between discrimination trajectories, resilience characteristics, and multimorbidity burden among a sample of middle-aged and older black adults. Specifying discrimination trajectories and resilience characteristics that differentially predict multimorbidity burden may inform the design of culturally-relevant interventions to delay the development and progression of multimorbidity.