Dataset Information

Effects of potential risk factors on the development of cardiometabolic multimorbidity and mortality among the elders in China.

ABSTRACT:

Objectives

To examine the impact of demographic, socioeconomic, and behavioral factors on the development of cardiometabolic multimorbidity and mortality in Chinese elders.

Methods

Data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2002-2018 was used in the study. Cardiometabolic multimorbidity was defined as the presence of two or more cardiometabolic disorders, such as hypertension, diabetes, cardiovascular disease (CVD), heart disease, or stroke. Cox regression model and multi-state Markov model were developed to evaluate the association of the study factors with the progression of cardiometabolic conditions and mortality. The outcomes included three states (first cardiometabolic disease, cardiometabolic multimorbidity, and all-cause mortality) and five possible transitions among the three states.

Results

Of the 13,933 eligible individuals, 7,917 (56.8%) were female, and 9,540 (68.50%) were over 80 years old. 2,766 (19.9%) participants had their first cardiometabolic disease, 975 (7.0%) participants suffered from cardiometabolic multimorbidity, and 9,365 (67.2%) participants died. The progression to cardiometabolic multimorbidity was positively associated with being female (HR = 1.42; 95%CI, 1.10 - 1.85), living in the city (HR = 1.41; 95%CI, 1.04 - 1.93), overweight (HR = 1.43; 95%CI, 1.08 - 1.90), and obesity (HR = 1.75; 95% CI, 1.03 - 2.98). A higher risk for the first cardiometabolic disease was associated with being female (HR = 1.26; 95% CI, 1.15 - 1.39), higher socioeconomic status (SES, HR = 1.17; 95%CI, 1.07 - 1.28), lack of regular physical activity (HR = 1.13; 95%CI, 1.04 - 1.23), smoking (HR = 1.20; 95%CI, 1.08 - 1.33), ≤ 5 h sleep time (HR = 1.15; 95%CI, 1.02 - 1.30), overweight (HR = 1.48; 95% CI, 1.32 - 1.66), and obesity (HR = 1.34; 95%CI, 1.06 - 1.69). It also should be noted that not in marriage, lower SES and unhealthy behavioral patterns were risk factors for mortality.

Conclusion

This study emphasized the importance of lifestyle and SES in tackling the development of cardiometabolic conditions among Chinese elders and provided a reference for policy-makers to develop a tailored stage-specific intervention strategy.

SUBMITTER: Zhang H

PROVIDER: S-EPMC9502033 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Effects of potential risk factors on the development of cardiometabolic multimorbidity and mortality among the elders in China.

Zhang Huihui H Duan Xinyu X Rong Peixi P Dang Yusong Y Yan Mingxin M Zhao Yaling Y Chen Fangyao F Zhou Jing J Chen Yulong Y Wang Duolao D Pei Leilei L

Frontiers in cardiovascular medicine 20220909

<h4>Objectives</h4>To examine the impact of demographic, socioeconomic, and behavioral factors on the development of cardiometabolic multimorbidity and mortality in Chinese elders.<h4>Methods</h4>Data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2002-2018 was used in the study. Cardiometabolic multimorbidity was defined as the presence of two or more cardiometabolic disorders, such as hypertension, diabetes, cardiovascular disease (CVD), heart disease, or stroke. Cox regression ...[more]

PMID: 36158847

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Project description:ImportanceThe prevalence of cardiometabolic multimorbidity is increasing.ObjectiveTo estimate reductions in life expectancy associated with cardiometabolic multimorbidity.Design, setting, and participantsAge- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates.ExposuresA history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI).Main outcomes and measuresAll-cause mortality and estimated reductions in life expectancy.ResultsIn participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy.Conclusions and relevanceMortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.

Project description:BackgroundMultimorbidity is increasingly common and is associated with adverse health outcomes, highlighting the need to broaden the single-disease framework that dominates medical research. We examined the role of midlife clinical characteristics, socioeconomic position, and behavioural factors in the development of cardiometabolic multimorbidity (at least 2 of diabetes, coronary heart disease, and stroke), along with how these factors modify risk of mortality.Methods and findingsData on 8,270 men and women were drawn from the Whitehall II cohort study, with mean follow-up of 23.7 years (1985 to 2017). Three sets of risk factors were assessed at age 50 years, each on a 5-point scale: clinical profile (hypertension, hypercholesterolemia, overweight/obesity, family history of cardiometabolic disease), occupational position, and behavioural factors (smoking, alcohol consumption, diet, physical activity). The outcomes examined were cardiometabolic disease (diabetes, coronary heart disease, stroke), cardiometabolic multimorbidity, and mortality. We used multi-state models to examine the role of risk factors in 5 components of the cardiometabolic disease trajectory: from healthy state to first cardiometabolic disease, from first cardiometabolic disease to cardiometabolic multimorbidity, from healthy state to death, from first cardiometabolic disease to death, and from cardiometabolic multimorbidity to death. A total of 2,501 participants developed 1 of the 3 cardiometabolic diseases, 511 developed cardiometabolic multimorbidity, and 1,406 died. When behavioural and clinical risk factors were considered individually, only smoking was associated with all five transitions. In a model containing all 3 risk factor scales, midlife clinical profile was the strongest predictor of first cardiometabolic disease (hazard ratio for the least versus most favourable profile: 3.74; 95% CI: 3.14-4.45) among disease-free participants. Among participants with 1 cardiometabolic disease, adverse midlife socioeconomic (1.54; 95% CI: 1.10-2.15) and behavioural factors (2.00; 95% CI: 1.40-2.85), but not clinical characteristics, were associated with progression to cardiometabolic multimorbidity. Only midlife behavioural factors predicted mortality among participants with cardiometabolic disease (2.12; 95% CI: 1.41-3.18) or cardiometabolic multimorbidity (3.47; 95% CI: 1.81-6.66). A limitation is that the study was not large enough to estimate transitions between each disease and subsequent outcomes and between all possible pairs of diseases.ConclusionsThe importance of specific midlife factors in disease progression, from disease-free state to single disease, multimorbidity, and death, varies depending on the disease stage. While clinical risk factors at age 50 determine the risk of incident cardiometabolic disease in a disease-free population, midlife socioeconomic and behavioural factors are stronger predictors of progression to multimorbidity and mortality in people with cardiometabolic disease.

Project description:BackgroundAlthough lifestyle factors have been studied in relation to individual non-communicable diseases (NCDs), their association with development of a subsequent NCD, defined as multimorbidity, has been scarcely investigated. The aim of this study was to investigate associations between five lifestyle factors and incident multimorbidity of cancer and cardiometabolic diseases.MethodsIn this prospective cohort study, 291,778 participants (64% women) from seven European countries, mostly aged 43 to 58 years and free of cancer, cardiovascular disease (CVD), and type 2 diabetes (T2D) at recruitment, were included. Incident multimorbidity of cancer and cardiometabolic diseases was defined as developing subsequently two diseases including first cancer at any site, CVD, and T2D in an individual. Multi-state modelling based on Cox regression was used to compute hazard ratios (HR) and 95% confidence intervals (95% CI) of developing cancer, CVD, or T2D, and subsequent transitions to multimorbidity, in relation to body mass index (BMI), smoking status, alcohol intake, physical activity, adherence to the Mediterranean diet, and their combination as a healthy lifestyle index (HLI) score. Cumulative incidence functions (CIFs) were estimated to compute 10-year absolute risks for transitions from healthy to cancer at any site, CVD (both fatal and non-fatal), or T2D, and to subsequent multimorbidity after each of the three NCDs.ResultsDuring a median follow-up of 11 years, 1910 men and 1334 women developed multimorbidity of cancer and cardiometabolic diseases. A higher HLI, reflecting healthy lifestyles, was strongly inversely associated with multimorbidity, with hazard ratios per 3-unit increment of 0.75 (95% CI, 0.71 to 0.81), 0.84 (0.79 to 0.90), and 0.82 (0.77 to 0.88) after cancer, CVD, and T2D, respectively. After T2D, the 10-year absolute risks of multimorbidity were 40% and 25% for men and women, respectively, with unhealthy lifestyle, and 30% and 18% for men and women with healthy lifestyles.ConclusionPre-diagnostic healthy lifestyle behaviours were strongly inversely associated with the risk of cancer and cardiometabolic diseases, and with the prognosis of these diseases by reducing risk of multimorbidity.

Project description:BackgroundCardiometabolic disorders may worsen Covid-19 outcomes. We investigated features and Covid-19 outcomes for patients with or without diabetes, and with or without cardiometabolic multimorbidity.MethodsWe collected and compared data retrospectively from patients hospitalized for Covid-19 with and without diabetes, and with and without cardiometabolic multimorbidity (defined as ≥ two of three risk factors of diabetes, hypertension or dyslipidaemia). Multivariate logistic regression was used to assess the risk of the primary composite outcome (any of mechanical ventilation, admission to an intensive care unit [ICU] or death) in patients with diabetes and in those with cardiometabolic multimorbidity, adjusting for confounders.ResultsOf 354 patients enrolled, those with diabetes (n = 81), compared with those without diabetes (n = 273), had characteristics associated with the primary composite outcome that included older age, higher prevalence of hypertension and chronic obstructive pulmonary disease (COPD), higher levels of inflammatory markers and a lower PaO2/FIO2 ratio. The risk of the primary composite outcome in the 277 patients who completed the study as of May 15th, 2020, was higher in those with diabetes (Adjusted Odds Ratio (adjOR) 2.04, 95%CI 1.12-3.73, p = 0.020), hypertension (adjOR 2.31, 95%CI: 1.37-3.92, p = 0.002) and COPD (adjOR 2.67, 95%CI 1.23-5.80, p = 0.013). Patients with cardiometabolic multimorbidity were at higher risk compared to patients with no cardiometabolic conditions (adjOR 3.19 95%CI 1.61-6.34, p = 0.001). The risk for patients with a single cardiometabolic risk factor did not differ with that for patients with no cardiometabolic risk factors (adjOR 1.66, 0.90-3.06, adjp = 0.10).ConclusionsPatients with diabetes hospitalized for Covid-19 present with high-risk features. They are at increased risk of adverse outcomes, likely because diabetes clusters with other cardiometabolic conditions.

Project description:BackgroundIndividual cardiometabolic disorders and genetic factors are associated with an increased dementia risk; however, the relationship between dementia and cardiometabolic multimorbidity is unclear. We investigated whether cardiometabolic multimorbidity increases the risk of dementia, regardless of genetic risk, and examined for associated brain structural changes.MethodsWe examined health and genetic data from 203 038 UK Biobank participants of European ancestry, aged 60 years or older without dementia at baseline assessment (2006-10) and followed up until March 31, 2021, in England and Scotland and Feb 28, 2018, in Wales, as well as brain structural data in a nested imaging subsample of 12 236 participants. A cardiometabolic multimorbidity index comprising stroke, diabetes, and myocardial infarction (one point for each), and a polygenic risk score for dementia (with low, intermediate, and high risk groups) were calculated for each participant. The main outcome measures were incident all-cause dementia and brain structural metrics.FindingsThe dementia risk associated with high cardiometabolic multimorbidity was three times greater than that associated with high genetic risk (hazard ratio [HR] 5·55, 95% CI 3·39-9·08, p<0·0001, and 1·68, 1·53-1·84, p<0·0001, respectively). Participants with both a high genetic risk and a cardiometabolic multimorbidity index of two or greater had an increased risk of developing dementia (HR 5·74, 95% CI 4·26-7·74, p<0·0001), compared with those with a low genetic risk and no cardiometabolic conditions. Crucially, we found no interaction between cardiometabolic multimorbidity and polygenic risk (p=0·18). Cardiometabolic multimorbidity was independently associated with more extensive, widespread brain structural changes including lower hippocampal volume (F2, 12 110 = 10·70; p<0·0001) and total grey matter volume (F2, 12 236 = 55·65; p<0·0001).InterpretationCardiometabolic multimorbidity was independently associated with the risk of dementia and extensive brain imaging differences to a greater extent than was genetic risk. Targeting cardiometabolic multimorbidity might help to reduce the risk of dementia, regardless of genetic risk.FundingWellcome Trust, Alzheimer's Research UK, Alan Turing Institute/Engineering and Physical Sciences Research Council, the National Institute for Health Research Applied Research Collaboration South West Peninsula, National Health and Medical Research Council, JP Moulton Foundation, and National Institute on Aging/National Institutes of Health.

Dataset Information

Effects of potential risk factors on the development of cardiometabolic multimorbidity and mortality among the elders in China.

Objectives

Methods

Results

Conclusion

Publications

Effects of potential risk factors on the development of cardiometabolic multimorbidity and mortality among the elders in China.

Similar Datasets

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