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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

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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

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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: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.

Project description:ImportanceThe associations of estimated cardiorespiratory fitness (eCRF) during midlife with subclinical atherosclerosis, arterial stiffness, incident cardiometabolic disease, and mortality are not well understood.ObjectiveTo examine associations of midlife eCRF with subclinical atherosclerosis, arterial stiffness, incident cardiometabolic disease, and mortality.Design, setting, and participantsThis cohort study included 2962 participants in the Framingham Study Second Generation (conducted between 1979 and 2001). Data were analyzed from January 2020 to June 2020.ExposureseCRF was calculated using sex-specific algorithms (including age, body mass index, waist circumference, physical activity, resting heart rate, and smoking) and was categorized as: (1) tertiles of standardized eCRF at examination cycle 7 (1998 to 2001); (2) tertiles of standardized average eCRF between examination cycles 2 and 7 (1979 to 2001); and (3) eCRF trajectories between examination cycles 2 and 7, with the lowest tertile or trajectory (ie, low eCRF) as referent group.Main outcomes and measuresSubclinical atherosclerosis (carotid intima-media thickness [CIMT], coronary artery calcium [CAC] score); arterial stiffness (carotid-femoral pulse wave velocity [-1000/CFPWV]); incident hypertension, diabetes, chronic kidney disease (CKD), cardiovascular disease (CVD), and mortality after examination cycle 7.ResultsA total of 2962 participants were included in this cohort study (mean [SD] age, 61.5 [9.2] years; 1562 [52.7%] women). The number of events or participants at risk after examination cycle 7 (at a mean follow-up of 15 years) was 728 of 1506 for hypertension, 214 of 2268 for diabetes, 439 of 2343 for CKD, 500 of 2608 for CVD, and 770 of 2962 for mortality. Compared with the low eCRF reference value, high single examination eCRF was associated with lower CFPWV (β [SE], -11.13 [1.33] ms/m) and CIMT (β [SE], -0.12 [0.05] mm), and lower risk of hypertension (hazard ratio [HR], 0.63; 95% CI, 0.46-0.85), diabetes (HR, 0.38; 95% CI, 0.23-0.62), and CVD (HR, 0.71; 95% CI, 0.53-0.95), although it was not associated with CKD or mortality. Similarly, compared with the low eCRF reference, high eCRF trajectories and mean eCRF were associated with lower CFPWV (β [SE], -11.85 [1.89] ms/m and -10.36 [1.54] ms/m), CIMT (β [SE], -0.19 [0.06] mm and -0.15 [0.05] mm), CAC scores (β [SE], -0.67 [0.25] AU and -0.63 [0.20] AU), and lower risk of hypertension (HR, 0.54; 95% CI, 0.34-0.87 and HR, 0.48; 95% CI, 0.34-0.68), diabetes (HR, 0.27; 95% CI, 0.15-0.48 and HR, 0.31; 95% CI, 0.18-0.54), CKD (HR, 0.63; 95% CI, 0.40-0.97 and HR, 0.64; 95% CI, 0.44-0.94), and CVD (HR, 0.46; 95% CI, 0.31-0.68 and HR, 0.43; 95% CI, 0.30-0.60). Compared with the reference value, a high eCRF trajectory was associated with lower risk of mortality (HR, 0.69; 95% CI, 0.50-0.95).Conclusions and relevanceIn this cohort study, higher midlife eCRF was associated with lower burdens of subclinical atherosclerosis and vascular stiffness, and with a lower risk of hypertension, diabetes, chronic kidney disease, cardiovascular disease, and mortality. These findings suggest that midlife eCRF may serve as a prognostic marker for subclinical atherosclerosis, arterial stiffness, cardiometabolic health, and mortality in later life.

Project description:BackgroundThe prevalence of cardiometabolic multimorbidity (CMM), which significantly increases the risk of mortality, is increasing globally. However, the role of healthy lifestyle in the secondary prevention of CMM is unclear.MethodsIn total, 290,795 participants with CMM, which was defined as coexistence of at least two of hypertension (HTN), diabetes mellitus (DM), coronary heart disease (CHD), and stroke (ST), and those without these four diseases at baseline were derived from UK Biobank. The associations between specific CMM patterns and mortality, and that between healthy lifestyle (including physical activity, smoking, alcohol consumption, and vegetable and fruit consumption) and mortality in patients with specific CMM patterns were calculated using the flexible parametric Royston-Parmar proportion-hazard model. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated.ResultsDuring a median 12.3-year follow up period, 15,537 (5.3%) deaths occurred. Compared with participants without cardiometabolic diseases, the HRs for all-cause mortality were 1.54 [95% confidence interval (CI): 1.30, 1.82] in participants with HTN + DM, 1.84 (95% CI: 1.59, 2.12) in those with HTN + CHD, 1.89 (95% CI: 1.46, 2.45) in those with HTN + ST, and 2.89 (95% CI: 2.28, 3.67) in those with HTN + DM + CHD. At the age of 45 years, non-current smoking was associated with an increase in life expectancy by 3.72, 6.95, 6.75, and 4.86 years for participants with HTN + DM, HTN + CHD, HTN + ST, and HTN + DM + CHD, respectively. A corresponding increase by 2.03, 1.95, 2.99, and 1.88 years, respectively, was observed in participants with regular physical activity. Non-/moderate alcohol consumption and adequate fruit/vegetable consumption were not significantly associated with life expectancy in patients with specific CMM patterns.ConclusionCardiometabolic multimorbidity was associated with an increased risk of mortality. Regular physical activity and non-current smoking can increase life expectancy in patients with specific CMM patterns.

Project description:BACKGROUND:Multimorbidity and frailty are relevant conditions among older adult population. There is growing evidence about their association with poor health outcomes like disability, worst quality of life, and death. Nonetheless, the independent associations of both conditions have been studied, and few evidence exists about an interaction between them. Our aims were to assess the association of frailty and multimorbidity with the disability, quality of life and all-cause mortality as well as to analyze a potential interaction between these conditions. METHODS:Analytical samples included 1410 respondents for disability and quality of life, and 1792 for mortality. We performed a longitudinal analysis with older Mexican adults aged 50, using data collected from the WHO's Study on global AGEing and Adult Health Waves 1 and 2. Disability was measured using the World Health Organization Disability Assessment Schedule (WHODAS 2.0), and quality of life using the WHOQOL (WHO Quality of Life) instrument. All-cause mortality was determined by reviewing death certificates. Associations of frailty and multimorbidity with disability, quality of life and mortality were estimated using linear regression and Cox proportional hazards models. RESULTS:Multimorbidity assessed through three patterns (cardiopulmonary, vascular-metabolic, and mental-musculoskeletal) was associated with the three outcomes in this study. Cardiopulmonary and mental-musculoskeletal patterns increased the WHODAS mean score (??=?5.05; p <?0.01 and ??=?5.10; p <?0.01, respectively) and decreased WHOQOL score (??=?-?1.81; p <?0.01 and ??=?-?2.99; p <?0.01, respectively). Vascular-metabolic was associated with mortality (HR?=?1.47; p =?0.04), disability (??=?3.27; p <?0.01) and quality of life (??=?-?1.30; p =?0.02). Frailty was associated with mortality (pre-frail: HR?=?1.48; p?=?0.02 and frail: HR?=?1.68; p =?0.03), disability (pre-frail: ??=?5.02; p <?0.01; frail: ??=?13.29; p <?0.01) and quality of life (pre-frail: ??=?-?2.23; p <?0.01; frail: ??=?-?4.38; p <?0.01). Interaction terms of frailty and multimorbidity were not statistically significant. CONCLUSIONS:Multimorbidity and frailty are important predictors of poor health outcomes. These results highlight the importance of carrying out health promotion and prevention actions as well as specific interventions aimed at older adults who suffer from multimorbidity and frailty, in such a way that deleterious effects on health can be avoided.

Project description:Objective: While cardiometabolic abnormalities are associated with elevated risk of morbidity, they may not occur in all individuals with obesity. Less is known about associations with mortality, especially cancer mortality. This study examined associations between cardiometabolic-weight categories and mortality from cardiovascular disease (CVD), cancer, and all causes.Methods: Cox proportional hazards regressions of time to all-cause, CVD, and cancer mortalities were used to examine associations with cardiometabolic-weight status, in the Multiethnic Cohort (n=157,865). Cardiometabolic-weight status categories were: Metabolically Healthy Normal Weight, Metabolically Healthy Obese, Metabolically Healthy Overweight, Metabolically Unhealthy Normal Weight, Metabolically Unhealthy Obese, and Metabolically Unhealthy Overweight.Results: Higher mortality, especially for all-cause and CVD, was found for all metabolically unhealthy groups no matter the weight classification when compared to the Metabolically Healthy Normal Weight category across sex-ethnic groups. For all-cause mortality, a reduction in mortality was seen for males in the Metabolically Healthy Overweight category (HR: 0.88, 95% CI: 0.84, 0.93), especially for African American, Native Hawaiian, and Latino males. Mortality was elevated in the Metabolically Healthy Obese category for all-cause and CVD mortality in both sexes (HRrange: 1.08-1.93). Few associations were seen with cancer mortality.Conclusions: Past examinations of cardiometabolic-weight status and mortality have been hampered by a lack of diversity. In a racially/ethnically diverse population, metabolically unhealthy groups exhibited a substantially higher risk of death from all causes and CVD than metabolically healthy groups. A reduction in all-cause mortality was seen for some males classified as Metabolically Healthy Overweight; however, being classified as Metabolically Healthy Obese elevated mortality risk for males and females compared to Metabolically Healthy Normal Weight. Future research is needed to examine how sex-ethnic differences in body fat distribution and changes in weight over time influence associations between cardiometabolic-weight status and mortality.

Dataset Information

Association of Cardiometabolic Multimorbidity With Mortality.

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Association of Cardiometabolic Multimorbidity With Mortality.

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