Dataset Information

Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study.

ABSTRACT:

Background

Multimorbidity 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 findings

Data 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.

Conclusions

The 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.

SUBMITTER: Singh-Manoux A

PROVIDER: S-EPMC5962054 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Publications

Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study.

Singh-Manoux Archana A Fayosse Aurore A Sabia Séverine S Tabak Adam A Shipley Martin M Dugravot Aline A Kivimäki Mika M

PLoS medicine 20180521 5

<h4>Background</h4>Multimorbidity 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.<h4>Methods and findings< ...[more]

PMID: 29782486

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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:BackgroundNumerous patients around the globe are dying from coronavirus disease 2019 (COVID-19). While age is a known risk factor, risk analysis in the young generation is lacking. The present study aimed to evaluate the clinical features and mortality risk factors in younger patients (≤ 50 years) with a critical case of COVID-19 in comparison with those among older patients (> 50 years) in Korea.MethodsWe analyzed the data of adult patients only in critical condition (requiring high flow nasal cannula oxygen therapy or higher respiratory support) hospitalized with PCR-confirmed COVID-19 at 11 hospitals in Korea from July 1, 2021 to November 30, 2021 when the delta variant was a dominant strain. Patients' electronic medical records were reviewed to identify clinical characteristics.ResultsDuring the study period, 448 patients were enrolled. One hundred and forty-two were aged 50 years or younger (the younger group), while 306 were above 50 years of age (the older group). The most common pre-existing conditions in the younger group were diabetes mellitus and hypertension, and 69.7% of the patients had a body mass index (BMI) > 25 kg/m². Of 142 younger patients, 31 of 142 patients (21.8%, 19 women) did not have these pre-existing conditions. The overall case fatality rate among severity cases was 21.0%, and it differed according to age: 5.6% (n = 8/142) in the younger group, 28.1% in the older group, and 38% in the ≥ 65 years group. Age (odds ratio [OR], 7.902; 95% confidence interval [CI], 2.754-18.181), mechanical ventilation therapy (OR, 17.233; 95% CI, 8.439-35.192), highest creatinine > 1.5 mg/dL (OR, 17.631; 95% CI, 8.321-37.357), and combined blood stream infection (OR, 7.092; 95% CI, 1.061-18.181) were identified as independent predictors of mortality in total patients. Similar patterns were observed in age-specific analyses, but most results were statistically insignificant in multivariate analysis due to the low number of deaths in the younger group. The full vaccination rate was very low among study population (13.6%), and only three patients were fully vaccinated, with none of the patients who died having been fully vaccinated in the younger group. Seven of eight patients who died had a pre-existing condition or were obese (BMI > 25 kg/m²), and the one remaining patient died from a secondary infection.ConclusionAbout 22% of the patients in the young critical group did not have an underlying disease or obesity, but the rate of obesity (BMI > 25 kg/m²) was high, with a fatality rate of 5.6%. The full vaccination rate was extremely low compared to the general population of the same age group, showing that non-vaccination has a grave impact on the progression of COVID-19 to a critical condition. The findings of this study highlight the need for measures to prevent critical progression of COVID-19, such as vaccinations and targeting young adults especially having risk factors.

Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has caused the death of thousands of patients worldwide. Although age is known to be a risk factor for morbidity and mortality in COVID-19 patients, critical illness or death is occurring even in the younger age group as the epidemic spreads. In early 2022, omicron became the dominant variant of the COVID-19 virus in South Korea, and the epidemic proceeded on a large scale. Accordingly, this study aimed to determine whether young adults (aged ≤ 50 years) with critical COVID-19 infection during the omicron period had different characteristics from older patients and to determine the risk factors for mortality in this specific age group.MethodsWe evaluated 213 critical adult patients (high flow nasal cannula or higher respiratory support) hospitalized for polymerase chain reaction-confirmed COVID-19 in nine hospitals in South Korea between February 1, 2022 and April 30, 2022. Demographic characteristics, including body mass index (BMI) and vaccination status; underlying diseases; clinical features and laboratory findings; clinical course; treatment received; and outcomes were collected from electronic medical records (EMRs) and analyzed according to age and mortality.ResultsOverall, 71 critically ill patients aged ≤ 50 years were enrolled, and 142 critically ill patients aged over 50 years were selected through 1:2 matching based on the date of diagnosis. The most frequent underlying diseases among those aged ≤ 50 years were diabetes and hypertension, and all 14 patients who died had either a BMI ≥ 25 kg/m² or an underlying disease. The total case fatality rate among severe patients (S-CFR) was 31.0%, and the S-CFR differed according to age and was higher than that during the delta period. The S-CFR was 19.7% for those aged ≤ 50 years, 36.6% for those aged > 50 years, and 38.1% for those aged ≥ 65 years. In multivariate analysis, age (odds ratio [OR], 1.084; 95% confidence interval [CI], 1.043-1.127), initial low-density lipoprotein > 600 IU/L (OR, 4.782; 95% CI, 1.584-14.434), initial C-reactive protein > 8 mg/dL (OR, 2.940; 95% CI, 1.042-8.293), highest aspartate aminotransferase > 200 IU/L (OR, 12.931; 95% CI, 1.691-98.908), and mechanical ventilation implementation (OR, 3.671; 95% CI, 1.294-10.420) were significant independent predictors of mortality in critical COVID-19 patients during the omicron wave. A similar pattern was shown when analyzing the data by age group, but most had no statistical significance owing to the small number of deaths in the young critical group. Although the vaccination completion rate of all the patients (31.0%) was higher than that in the delta wave period (13.6%), it was still lower than that of the general population. Further, only 15 (21.1%) critically ill patients aged ≤ 50 years were fully vaccinated. Overall, the severity of hospitalized critical patients was significantly higher than that in the delta period, indicating that it was difficult to find common risk factors in the two periods only with a simple comparison.ConclusionOverall, the S-CFR of critically ill COVID-19 patients in the omicron period was higher than that in the delta period, especially in those aged ≤ 50 years. All of the patients who died had an underlying disease or obesity. In the same population, the vaccination rate was very low compared to that in the delta wave, indicating that non-vaccination significantly affected the progression to critical illness. Notably, there was a lack of prescription for Paxlovid for these patients although they satisfied the prescription criteria. Early diagnosis and active initial treatment was necessary, along with the proven methods of vaccination and personal hygiene. Further studies are needed to determine how each variant affects critically ill patients.

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Project description:Diverging trends of longer lives and increased inequalities in age-at-death invite to updated research on late-life mortality. Earlier studies have identified health behavior, childhood, psychosocial, and material conditions as key determinants of life expectancy, but the role of psychological factors remains a topic of debate. The current study is framed in a life course developmental perspective and assesses the mediating role of secondary control strategies (subjective age) and primary control capacity (perceived control) to socioeconomic (wealth and education) inequality in mortality after age 67. Data are derived from the second wave of the Norwegian Life Course, Ageing and Generation study (N=1,432 and age 67-85). All in all, 366 deaths were observed over a mean follow-up of 9.6years. Perceived control was measured by the Pearlin and Schooler Mastery Scale. SA was measured with proportional discrepancy scores in felt age and ideal age. Stepwise Cox proportional hazards regression analyses were conducted to analyze the relative contribution of SES (education and accumulated wealth), felt age, and ideal age discrepancies and perceived control on 11-year mortality. Findings show that lower levels of wealth and perceived control independently predict increased elderly mortality over an 11-year period. Feeling younger and smaller ideal age discrepancies are positively associated with perceived control, but do not account for additional variability in longevity among older adults. Findings add to the interdisciplinary field of socioeconomic inequalities in elderly mortality and underline the specific importance of structural conditions (wealth) and the continued importance of (perceived) primary control capacity for longevity also after age 67. Future research may assess in more detail how wealth and perceived control complement each other in contributing to healthy aging and longevity, for example, by longitudinal research including the role of significant life events in the second half of life in different welfare state contexts.

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.

Dataset Information

Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study.

Background

Methods and findings

Conclusions

Publications

Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study.

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