Dataset Information

Association of Cardiometabolic Multimorbidity With Mortality.

ABSTRACT: IMPORTANCE:The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE:To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS:Age- 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. EXPOSURES:A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES:All-cause mortality and estimated reductions in life expectancy. RESULTS:In 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 RELEVANCE:Mortality 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.

SUBMITTER: Emerging Risk Factors Collaboration

PROVIDER: S-EPMC4664176 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association of Cardiometabolic Multimorbidity With Mortality.

Di Angelantonio Emanuele E Kaptoge Stephen S Wormser David D Willeit Peter P Butterworth Adam S AS Bansal Narinder N O'Keeffe Linda M LM Gao Pei P Wood Angela M AM Burgess Stephen S Freitag Daniel F DF Pennells Lisa L Peters Sanne A SA Hart Carole L CL Håheim Lise Lund LL Gillum Richard F RF Nordestgaard Børge G BG Psaty Bruce M BM Yeap Bu B BB Knuiman Matthew W MW Nietert Paul J PJ Kauhanen Jussi J Salonen Jukka T JT Kuller Lewis H LH Simons Leon A LA van der Schouw Yvonne T YT Barrett-Connor Elizabeth E Selmer Randi R Crespo Carlos J CJ Rodriguez Beatriz B Verschuren W M Monique WM Salomaa Veikko V Svärdsudd Kurt K van der Harst Pim P Björkelund Cecilia C Wilhelmsen Lars L Wallace Robert B RB Brenner Hermann H Amouyel Philippe P Barr Elizabeth L M EL Iso Hiroyasu H Onat Altan A Trevisan Maurizio M D'Agostino Ralph B RB Cooper Cyrus C Kavousi Maryam M Welin Lennart L Roussel Ronan R Hu Frank B FB Sato Shinichi S Davidson Karina W KW Howard Barbara V BV Leening Maarten J G MJ Leening Maarten M Rosengren Annika A Dörr Marcus M Deeg Dorly J H DJ Kiechl Stefan S Stehouwer Coen D A CD Nissinen Aulikki A Giampaoli Simona S Donfrancesco Chiara C Kromhout Daan D Price Jackie F JF Peters Annette A Meade Tom W TW Casiglia Edoardo E Lawlor Debbie A DA Gallacher John J Nagel Dorothea D Franco Oscar H OH Assmann Gerd G Dagenais Gilles R GR Jukema J Wouter JW Sundström Johan J Woodward Mark M Brunner Eric J EJ Khaw Kay-Tee KT Wareham Nicholas J NJ Whitsel Eric A EA Njølstad Inger I Hedblad Bo B Wassertheil-Smoller Sylvia S Engström Gunnar G Rosamond Wayne D WD Selvin Elizabeth E Sattar Naveed N Thompson Simon G SG Danesh John J

JAMA 20150701 1

<h4>Importance</h4>The prevalence of cardiometabolic multimorbidity is increasing.<h4>Objective</h4>To estimate reductions in life expectancy associated with cardiometabolic multimorbidity.<h4>Design, setting, and participants</h4>Age- 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; ...[more]

PMID: 26151266

Similar Datasets

Project description:BackgroundCardiometabolic multimorbidity (CMM) and obesity represent two major health problems. The relationship between adiposity indices and CMM, however, remains understudied. This study aimed to investigate the associations of body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), a body shape index (ABSI), body roundness index (BRI), and conicity index (CI) with CMM among Chinese adults.MethodsData of 101,973 participants were collected from a population-based screening project in Southern China. CMM was defined as having two or more of the following diseases: coronary heart disease, stroke, hypertension, and diabetes. The relationship between the six adiposity indices and CMM was investigated by multivariate logistic regression and restricted cubic splines. Receiver operator characteristic curve, C-statistic and net reclassification index were used to estimate the discriminative and incremental values of adiposity indices on CMM.ResultsLogistic regression models showed the six adiposity indices were all significantly associated with the odds of CMM with non-linear relationships. For per SD increment, WC (Odds ratio [OR]: 1.66; 95% confidence interval (CI): 1.62-1.70) and WHtR (OR, 1.61; 95% CI, 1.58-1.65) were more significantly associated with a higher prevalence of CMM than BMI (OR, 1.55; 95% CI, 1.52-1.58) (all P < 0.05). In addition, WC, WHtR, and BRI displayed significantly better performance in detecting CMM compared with BMI (all P < 0.05). Their respective area under the curve (AUC) values were 0.675 (95% CI: 0.670-0.680), 0.679 (95% CI: 0.675-0.684), and 0.679 (95% CI: 0.675-0.684), while BMI yielded an AUC of 0.637 (95% CI: 0.632-0.643). These findings hold true across all subgroups based on sex and age. When Adding WC, WHtR, or BRI to a base model, they all provided larger incremental values for the discrimination of CMM compared with BMI (all P < 0.05).ConclusionsAdiposity indices were closely associated with the odds of CMM, with WC and WHtR demonstrating stronger associations than BMI. WC, WHtR, and BRI were superior to BMI in discriminative ability for CMM. Avoidance of obesity (especially abdominal obesity) may be the preferred primary prevention strategy for CMM while controlling for other major CMM risk factors.

Project description:ImportanceLimited knowledge about interactions among health disorders impedes optimal patient care. Because comorbidities are common among patients 50 years and older with fractures, these fractures provide a useful setting for studying interactions among disorders.ObjectiveTo define multimorbidity clusters at the time of fracture and quantify the interaction between multimorbidity and fracture in association with postfracture excess mortality.Design, setting, and participantsThis nationwide cohort study included 307 870 adults in Denmark born on or before January 1, 1951, who had an incident low-trauma fracture between January 1, 2001, and December 31, 2014, and were followed up through December 31, 2016. Data were analyzed from February 1 to March 31, 2022.Main outcomes and measuresFracture and 32 predefined chronic diseases recorded within 5 years before the index fracture were identified from the Danish National Hospital Discharge Register. Death was ascertained from the Danish Register on Causes of Death. Latent class analysis was conducted to identify multimorbidity clusters. Relative survival analysis was used to quantify excess mortality associated with the combination of multimorbidity and fractures at specific sites.ResultsAmong the 307 870 participants identified with incident fractures, 95 372 were men (31.0%; mean [SD] age at fracture, 72.3 [11.2] years) and 212 498 were women (69.0%; mean [SD] age at fracture, 74.9 [11.2] years). During a median of 6.5 (IQR, 3.0-11.0) years of follow-up, 41 017 men (43.0%) and 81 727 women (38.5%) died. Almost half of patients with fractures (42.9%) had at least 2 comorbidities. Comorbidities at fracture were categorized as low-multimorbidity (60.5% in men and 66.5% in women), cardiovascular (23.7% in men and 23.5% in women), diabetic (5.6% in men and 5.0% in women), malignant (5.1% in men and 5.0% in women), and mixed hepatic and/or inflammatory (5.1% in men only) clusters. These clusters distinguished individuals with advanced, complex, or late-stage disease from those with earlier-stage disease. Multimorbidity and proximal or lower leg fractures were associated with increased mortality risk, with the highest excess mortality found in patients with hip fracture in the malignant cluster (1-year excess mortality: 40.8% [95% CI: 38.1%-43.6%]). The combination of multimorbidity and fracture compounded the association with mortality, conferring much greater risk than either alone.Conclusions and relevanceConcomitant illnesses were common and clustered into distinct multimorbidity clusters that were associated with excess postfracture mortality. The compound contribution of multimorbidity to postfracture excess mortality highlights the need for more comprehensive approaches in these high-risk patients. The analytical approach applied to fracture could also be used to examine other sentinel health events.

Project description:BackgroundCardiometabolic multimorbidity (CMM) has emerged as a prominent public health concern. Hypertensive patients are prone to develop comorbidities. Moreover, the accumulation of visceral adipose tissue is the main cause for the development of cardiometabolic diseases. The cardiometabolic index (CMI), lipid accumulation product (LAP), visceral adiposity index (VAI), and Chinese visceral adiposity index (CVAI) not only assess adipose tissue mass but also reflect adipose tissue dysfunction. So far, no study has been reported to evaluate the association of CMI, LAP, VAI, and CVAI with CMM risk in hypertensive patients. Therefore, this study aimed to assess the association between these adiposity indicators and the risk of CMM among Chinese hypertensive patients.MethodsIn this cross-sectional study, a total of 229,287 hypertensive patients aged 35 years and older were included from the National Basic Public Health Service Project. All participants underwent a face-to-face questionnaire survey, physical examination, and the collection of fasting venous blood samples. Multivariable logistic regression models were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Receiver operating characteristic curve was utilized to evaluate the identification ability for CMM.ResultsAfter adjusting for confounders, each 1-standard deviation increase in CMI, LAP, VAI, and CVAI was associated with a 14%, 8%, 12%, and 54% increased risk of CMM, respectively. When comparing the highest quartile of these indicators with the lowest quartile, individuals in the highest quartile of CMM, LAP, VAI, and CVAI had a 1.39-fold (95% CI 1.30, 1.48), 1.28-fold (95% CI 1.19, 1.37), 1.37-fold (95% CI 1.29, 1.46), and 2.56-fold (95% CI 2.34, 2.79) increased risk of CMM after adjusting for potential confounders. Notably, a nonlinear association was observed for CMI, LAP, and VAI with the risk of CMM (all P nonlinearity < 0.001). CVAI exhibited the highest area under the receiver operating characteristic curve (AUC) among all the included adiposity indices in this analysis.ConclusionThis study indicated the significant positive association of CMI, LAP, VAI, and CVAI with the risk of CMM in hypertensive patients. Among these indicators, CVAI demonstrated the most robust performance in predicting CMM risk and may serve as a valuable tool for identifying CMM risk in Chinese hypertensive patients.

Project description:AimsCoronavirus disease 2019 (COVID-19) can lead to multiorgan damage. MicroRNAs (miRNAs) in blood reflect cell activation and tissue injury. We aimed to determine the association of circulating miRNAs with COVID-19 severity and 28 day intensive care unit (ICU) mortality.Methods and resultsWe performed RNA-Seq in plasma of healthy controls (n = 11), non-severe (n = 18), and severe (n = 18) COVID-19 patients and selected 14 miRNAs according to cell- and tissue origin for measurement by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in a separate cohort of mild (n = 6), moderate (n = 39), and severe (n = 16) patients. Candidates were then measured by RT-qPCR in longitudinal samples of ICU COVID-19 patients (n = 240 samples from n = 65 patients). A total of 60 miRNAs, including platelet-, endothelial-, hepatocyte-, and cardiomyocyte-derived miRNAs, were differentially expressed depending on severity, with increased miR-133a and reduced miR-122 also being associated with 28 day mortality. We leveraged mass spectrometry-based proteomics data for corresponding protein trajectories. Myocyte-derived (myomiR) miR-133a was inversely associated with neutrophil counts and positively with proteins related to neutrophil degranulation, such as myeloperoxidase. In contrast, levels of hepatocyte-derived miR-122 correlated to liver parameters and to liver-derived positive (inverse association) and negative acute phase proteins (positive association). Finally, we compared miRNAs to established markers of COVID-19 severity and outcome, i.e. SARS-CoV-2 RNAemia, age, BMI, D-dimer, and troponin. Whilst RNAemia, age and troponin were better predictors of mortality, miR-133a and miR-122 showed superior classification performance for severity. In binary and triplet combinations, miRNAs improved classification performance of established markers for severity and mortality.ConclusionCirculating miRNAs of different tissue origin, including several known cardiometabolic biomarkers, rise with COVID-19 severity. MyomiR miR-133a and liver-derived miR-122 also relate to 28 day mortality. MiR-133a reflects inflammation-induced myocyte damage, whilst miR-122 reflects the hepatic acute phase response.

Project description:ObjectivesTo examine the impact of demographic, socioeconomic, and behavioral factors on the development of cardiometabolic multimorbidity and mortality in Chinese elders.MethodsData 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.ResultsOf 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.ConclusionThis 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.

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:OBJECTIVES:The evolution of multimorbidity describes the continuum from a healthy status to the development of a single disease and further progression to multimorbidity with additional diseases. We investigated the evolution of cardiometabolic multimorbidity and risk for mortality in a Chinese population. DESIGN:Longitudinal cohort study using data from the CHinese Electronic health Records Research in Yinzhou (CHERRY) study, with 5.43 million person-years follow-up (median 5.16 years). PARTICIPANTS:Data for 1 038 704 adults (total 22 750 deaths) were analysed. EXPOSURE:Cardiometabolic multimorbidity was defined as ever being diagnosed with two or more of three diseases: hypertension, diabetes and cardiovascular disease (CVD). PRIMARY AND SECONDARY OUTCOME MEASURES:Age-adjusted and sex-adjusted HRs were calculated for all-cause mortality. RESULTS:The cardiometabolic disease status of 105 209 (10.1%) individuals changed during the follow-up. The prevalence of cardiometabolic multimorbidity increased from 2.41% (95% CI: 2.38% to 2.44%) to 5.94% (95% CI: 5.90% to 5.99%). Baseline multimorbidity status showed the HR (95% CI) was 1.37 (1.33 to 1.42) in those with one disease, 1.71 (1.64 to 1.79) in those with two diseases and 2.22 (2.00 to 2.46) in those with three diseases. The highest HRs were observed for CVD only (3.31, 95% CI: 3.05 to 3.59) or diabetes and CVD (3.12, 95% CI: 2.37 to 4.11). Those with hypertension only had the lowest HR (1.26, 95% CI: 1.22 to 1.30). Longitudinal data showed the HRs (95% CI) in patients with one, two and three diseases were 1.36 (1.32 to 1.41), 2.03 (1.96 to 2.10) and 2.16 (2.05 to 2.29), respectively. CONCLUSIONS:The prevalence of cardiometabolic multimorbidity in a general Chinese population increased more than doubled over 5 years, indicating rapid evolution of cardiometabolic multimorbidity. A history of CVD dominates the risk for mortality. A complementary strategy for primary and secondary prevention of cardiometabolic diseases is needed in China.

Dataset Information

Association of Cardiometabolic Multimorbidity With Mortality.

Publications

Association of Cardiometabolic Multimorbidity With Mortality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets