Differences In Life Expectancy Research Articles

#4706 THERAPEUTIC AND PROGNOSTIC IMPACT OF KIDNEY BIOPSY FINDINGS IN PATIENTS AGED 80 YEARS AND OLDER: A MULTICENTER RETROSPECTIVE COHORT STUDY (KB-OLD)

Abstract Background and Aims The great phenotypic heterogeneity in the presentation of kidney diseases makes the diagnostic strategy all the more complex in the elderly. While kidney biopsy should play a pivotal role, its non-negligible risk of bleeding complications (2-5% in the general population) justifies a certain reluctance to its application in the elderly. To date, however, there is little data on the benefit-risk ratio of kidney biopsy in the elderly. Most of these studies have focused on a relatively young population (60-70 years old) and on a limited number of patients. Concerning patients aged 80 years and older, very few studies have described the real therapeutic influence of kidney biopsy findings with regard to the rate of complications. Moreover, none of these studies have evaluated the long-term prognostic impact of therapeutic changes induced by the results of the kidney biopsy. The main objectives of this study are to describe the indications, diagnoses, complication rate, therapeutic influence and the prognostic impact of a change in therapeutic management after native kidney biopsies performed in patients aged 80 years and older. Method The KB-Old study (Kidney Biopsy for Old) is a retrospective multicenter cohort that consecutively included all patients aged 80 years and older who underwent percutaneous native kidney biopsy in 17 centers in the northern region of France, between 2010 and 2020. Clinical, biological and anatomopathological data as well as post-biopsy follow-up (therapeutic strategy, occurrence of complications) were collected from medical records. All pathology examinations were analyzed centrally by a team of experienced nephropathologists. Events of death or kidney failure were identified by specific registry cross-linking. To analyze the prognostic impact of therapeutic management following kidney biopsy (either initiation of a specific treatment or simple nephroprotection) on the risk of kidney failure and death, we performed Cox models weighted by propensity score (Inverse Probability of Treatment Weighting -IPTW-) in the population potentially eligible for treatment (exclusion of diseases without specific treatment). The areas under Kaplan Meier curves were calculated up to 6 years of follow-up (Restricted Mean Survival Time - RMST) and compared according to the initiation of a specific treatment after the kidney biopsy. This study was approved by Institutional Review Board (#AUG-20-707). Results Overall, the cohort included 453 patients (54% men, median age 83 years), half of whom underwent biopsy in the context of acute kidney injury (median serum creatinine level 3.0 mg/dl). The main diagnoses were nephroangiosclerosis (15%), renal involvement of hematological malignancies (13%), acute tubulointerstitial nephritis (12%) and vasculitis (10%). The complication rate was approximately 10%, with only 2.8% of serious complications requiring therapeutic intervention (mostly transfusion). The kidney biopsy identified a disease potentially accessible to a specific treatment in 73% of cases. After exclusion of patients with ineligible diseases, a specific treatment was initiated in about one out of two cases (163/332, 49%). After weighting on propensity score, the two treatment groups were globally balanced. A beneficial effect of treatment on dialysis-free survival was observed (HR = 0.51 [0.28-0.92], p = 0.02), without any major influence on mortality (Figure 1). Over a 6-year follow-up period, there was a gain in dialysis-free survival in the treated group estimated by the delta RMST at +10.46 months [3.39 - 17.54] compared with the untreated group (p = 0.004), with no difference in overall life expectancy. Conclusion To the best of our knowledge, this is the largest multicenter cohort of patients aged 80 years and over who have undergone kidney biopsy. It seems to confirm the interest and safety of this examination for specific indications, and a potentially important benefit on the prognosis when it leads to an adapted therapeutic management.

Changes in contributions of age- and cause-specific mortality to the widening life expectancy gap between North and South Korea, 1990–2019: An analysis of the Global Burden of Disease Study 2019

BackgroundLife expectancy gaps between North and South Korea have increased but contributions to these gaps remain poorly understood. Using data from the Global Burden of Disease Study (GBD) 2019, we examined how much death from specific diseases contributed to these gaps in different age groups over three decades. MethodsData for death numbers and population by sex and 5-year age groups in both North and South Korea from 1990 to 2019 were extracted from the GBD 2019 to calculate life expectancy. Joinpoint regression analysis was conducted to investigate changes in life expectancy in North and South Korea. We used decomposition analysis to partition differences in life expectancy within and between the two Koreas into changes in age- and cause-specific death contributions. ResultsLife expectancy increased in two Koreas from 1990 to 2019, but North Korea experienced a marked decline in life expectancy during the mid-1990s. The life expectancy gaps between the two Koreas were greatest in 1999, with a difference of 13.3 years for males and 14.9 years for females. The main contributors to these gaps were higher under-5 mortality from nutritional deficiencies for males (4.62 years) and females (4.57 years) in North Korea, accounting for about 30% of the total gap in life expectancy. After 1999, the life expectancy gaps reduced but persisted with differences of about ten years by 2019. Notably, chronic diseases contributed to about 8 out of 10 years of life expectancy gap between the two Koreas in 2019. Differential cardiovascular disease mortality in the older groups was the main contributor to the life expectancy gap. ConclusionsThe contributors to this gap have shifted from nutritional deficiencies in children younger than five years to cardiovascular disease among elderly people. Efforts for strengthening social and healthcare systems are needed to curb this large gap.

Difference in the lifeexpectancy between countries: Mortality, health, lifestyle habits, national conditions, and related socioeconomic status (SES)

Our world has made remarkable progress since the year 2000 in increasing life expectancy. But this progress has been fragile and uneven across countries. The objective of the study was to estimate the factors that affect inequality in life expectancy, particularly between developed and developing countries. There is some evidence that the inequality gap has increased in different countries. It was observed that some variables have a significant correlation with life expectancy, while some of them take on obvious region characteristic. Proposed explanations for correlated differentials in life expectancy will be explored. The paper discusses logistic regression (LR) and the t-test as methods that can be used to determine what the independent variable would be, obtain the significant factors related to life expectancy and investigate the coefficient for all countries in the data set. The scatter diagrams and tables were constructed in the period 2000-2015 by linking life expectancy on some variables that cross 133 countries. Mortality, health, lifestyle habits, national conditions, and related socioeconomic status (SES) can all influence the distribution of life expectancy around the world. The result shows that health outcomes could benefit from a number of government measures, such as reducing adult mortality, productive use of resources, or promoting education. While Percentage spending, Infant deaths, and Alcohol are associated with life expectancy in developing countries more than in developed countries. These findings are intended to contribute to the growing body of literature that is needed to inform policies or strategies for a fairer and healthier world for all.

The cost of demographic losses due to premature death of the population from cerebrovascular diseases

Background. Losses in the labor and demographic potential of society concerning mortality due to cardiovascular pathology are significant, accounting for more than a third of all deaths of the population. The study of the socio-economic damage caused by the main nosological groups coronary heart disease and cerebrovascular diseases, is an urgent task.
 Aim. To determine the cost of demographic losses due to premature mortality from cerebrovascular diseases in the Irkutsk region.
 Material and methods. The subject of the study was deaths from cerebrovascular diseases in the population of the Irkutsk region for the period 20152019. A continuous method of statistical observation was used. The assessment of the value of the persons average life was carried out by collecting statistical information from special accounting forms. Demographic losses were determined by calculating the indicator of years of potential life lost (unlived years). Economic losses were determined by calculating the cost of years lost as a result of premature death from cerebrovascular disease.
 Results. As a result of the study, it was found that the cost of one year of life for men was higher than for women. The differences were due to the high difference in the average life expectancy of the male and female population. Demographic losses due to premature mortality from cerebrovascular diseases were increasing in dynamics for the male population, while decreasing for the female population. The age range of maximum losses from mortality for the male population was in the age group of 6064 years, for the female population 7074 years. The dynamics of economic losses for the region in 2015 amounted to 45.9 billion rubles, in 2019 48.7 billion rubles. The cost of one year of average life was significantly affected by the extremely high gap in the average life expectancy of the male and female population of the region.
 Conclusion. The indicator of demographic losses in the Irkutsk region due to the unlived years of the population associated with mortality from cerebrovascular diseases was 12,026.9 person-years in 2015, and 11,129.3 person-years in 2019.

Reducing mitochondrial ribosomal gene expression does not alter metabolic health or lifespan in mice

Maintaining mitochondrial function is critical to an improved healthspan and lifespan. Introducing mild stress by inhibiting mitochondrial translation invokes the mitochondrial unfolded protein response (UPRmt) and increases lifespan in several animal models. Notably, lower mitochondrial ribosomal protein (MRP) expression also correlates with increased lifespan in a reference population of mice. In this study, we tested whether partially reducing the gene expression of a critical MRP, Mrpl54, reduced mitochondrial DNA-encoded protein content, induced the UPRmt, and affected lifespan or metabolic health using germline heterozygous Mrpl54 mice. Despite reduced Mrpl54 expression in multiple organs and a reduction in mitochondrial-encoded protein expression in myoblasts, we identified few significant differences between male or female Mrpl54+/− and wild type mice in initial body composition, respiratory parameters, energy intake and expenditure, or ambulatory motion. We also observed no differences in glucose or insulin tolerance, treadmill endurance, cold tolerance, heart rate, or blood pressure. There were no differences in median life expectancy or maximum lifespan. Overall, we demonstrate that genetic manipulation of Mrpl54 expression reduces mitochondrial-encoded protein content but is not sufficient to improve healthspan in otherwise healthy and unstressed mice.

Change trend and gender differences in disability-free life expectancy among older adults in China, 2010-2020.

The goal of the present study was to investigate gender differences in disability-free life expectancy (DFLE) and DFLE/LE ratio among older adults in China; portray changing trend from 2010 to 2020; and discuss the implications for public policies. Mortality data and disability rate data were derived from the Sixth China Population Census in 2010 and the Seventh China Population Census in 2020. The study assessed disability status of older adults based on self-assessment health in the above censuses. Life table and Sullivan method were used to estimate LE, DFLE, and DFLE/LE ratio by gender. DFLE increased from 19.33 to 21.78 years for 60-year-old males, while from 21.94 to 24.80 years for 60-year-old females, from 2010 to 2020, respectively. DFLE/LE ratio was 96.40% for 60-year-old males and 94.86% for 60-year-old females in 2010, while DFLE/LE ratio was 96.63% for 60-year-old males and 95.44% for 60-year-old females in 2020, respectively. In terms of gender differences in DFLE/LE ratio, men aged 60 are 1.19 percentage points higher than women at the same age; men aged 70 are 1.71 percentage points higher than women; men aged 80 are 2.87 percentage points higher than women. From 2010 to 2020, the DFLE of China's male and female older adults increased simultaneously with the increase of LE, and the DFLE/LE ratio also increased. However, the DFLE/LE ratio of female older adults is lower than that of male at the same age, and this gender difference is narrowing over the decade but has not yet been eliminated, especially the health disadvantage of female older adults among the oldest old age group (age 80 and above) is more prominent.

Snapshots of ID Week 2022

Snapshots of ID Week 2022

Life expectancy following a cardiovascular event in individuals with and without type 2 diabetes: A UK multi-ethnic population-based observational study

Background and aimsWe aimed to evaluate the life expectancy following the first cardiovascular disease (CVD) event by type 2 diabetes (T2D) status and ethnicity. Methods and resultsWe used the Clinical Practice Research Datalink database in England (UK), linked to the Hospital Episode Statistics information, to identify individuals with and without T2D who survived a first CVD event between 1st Jan 2007 and 31st Dec 2017; subsequent death events were extracted from the Office for National Statistics database. Ethnicity was categorised as White, South Asian (SA), Black, or other. Flexible parametric survival models were used to estimate survival and predict life expectancy. 59,939 individuals with first CVD event were included: 7596 (12.7%) with T2D (60.9% men; mean age at event: 69.7 years [63.2 years in SA, 65.9 in Black, 70.2 in White]) and 52,343 without T2D (56.7% men; 65.9 years [54.7 in Black, 58.2 in SA, 66.3 in White]). Accounting for potential confounders (sex, deprivation, lipid-lowering medication, current smoking, and pre-existing hypertension), comparing individuals with vs without T2D the mortality rate was 53% higher in White (hazard ratio [HR]: 1.53 [95% CI: 1.44, 1.62]), corresponding to a potential loss of 3.87 (3.30, 4.44) life years at the age of 50 years in individuals with T2D. No evidence of a difference in life expectancy was observed in individuals of SA (HR: 0.82 [0.52, 1.29]; −1.36 [-4.58, 1.86] life years), Black (HR: 1.26 [0.59, 2.70]; 1.21 [-2.99, 5.41] life years); and other (HR: 1.64 [0.80, 3.39]; 3.89 [-2.28, 9.99] life years) ethnic group. ConclusionFollowing a CVD event, T2D is associated with a different prognosis and life years lost among ethnic groups.

Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data.

London has outperformed smaller towns and rural areas in terms of life expectancy increase. Our aim was to investigate life expectancy change at very-small-area level, and its relationship with house prices and their change. We performed a hyper-resolution spatiotemporal analysis from 2002 to 2019 for 4835 London Lower-layer Super Output Areas (LSOAs). We used population and death counts in a Bayesian hierarchical model to estimate age- and sex-specific death rates for each LSOA, converted to life expectancy at birth using life table methods. We used data from the Land Registry via the real estate website Rightmove (www.rightmove.co.uk), with information on property size, type and land tenure in a hierarchical model to estimate house prices at LSOA level. We used linear regressions to summarise how much life expectancy changed in relation to the combination of house prices in 2002 and their change from 2002 to 2019. We calculated the correlation between change in price and change in sociodemographic characteristics of the resident population of LSOAs and population turnover. In 134 (2.8%) of London's LSOAs for women and 32 (0.7%) for men, life expectancy may have declined from 2002 to 2019, with a posterior probability of a decline >80% in 41 (0.8%, women) and 14 (0.3%, men) LSOAs. The life expectancy increase in other LSOAs ranged from <2 years in 537 (11.1%) LSOAs for women and 214 (4.4%) for men to >10 years in 220 (4.6%) for women and 211 (4.4%) for men. The 2.5th-97.5th-percentile life expectancy difference across LSOAs increased from 11.1 (10.7-11.5) years in 2002 to 19.1 (18.4-19.7) years for women in 2019, and from 11.6 (11.3-12.0) years to 17.2 (16.7-17.8) years for men. In the 20% (men) and 30% (women) of LSOAs where house prices had been lowest in 2002, mainly in east and outer west London, life expectancy increased only in proportion to the rise in house prices. In contrast, in the 30% (men) and 60% (women) most expensive LSOAs in 2002, life expectancy increased solely independently of price change. Except for the 20% of LSOAs that had been most expensive in 2002, LSOAs with larger house price increases experienced larger growth in their population, especially among people of working ages (30-69 years), had a larger share of households who had not lived there in 2002, and improved their rankings in education, poverty and employment. Large gains in area life expectancy in London occurred either where house prices were already high, or in areas where house prices grew the most. In the latter group, the increases in life expectancy may be driven, in part, by changing population demographics. Wellcome Trust; UKRI (MRC); Imperial College London; National Institutes of Health Research.

Cause-Specific Mortality and Racial Differentials in Life Expectancy, Chicago 2018-2019.

In Chicago in 2018, the average life expectancy (ALE) for NH Blacks was 71.5years, 9.1 fewer years than for NH Whites (80.6years). Inasmuch as some causes of death are increasingly recognized products of structural racism, in urban areas, such causes may have potential for reducing racial inequities through public health intervention. Our purpose is to allocate racial inequities in ALE in Chicago to differentials in cause-specific mortality. Using multiple decrement processes and decomposition analysis, we examine cause-specific mortality in Chicago to determine the causes of death that contribute to the gap in life expectancy between NH Blacks and NH Whites. Among females, the racial difference in ALE was 8.21years; for males, it was 10.53years. We find that cancer and heart disease mortality account for 3.03years or 36% of the racial gap in average life expectancy among females. Differences in homicide and heart disease mortality rates comprised over 45% of the disparity among males. Strategies for improving inequities in life expectancy should account for differences between males and females in cause-specific mortality rates. In urban areas with high levels of segregation, reducing inequities in ALE may be possible by dramatically reducing mortality rates from some causes. This paper illustrates the state of inequities in ALE between NH Blacks and NH Whites in Chicago for the period just prior to the onset of the COVID-19 pandemic, using a well-established method of decomposing mortality differentials for sub-populations.

Comparison of Bayesian spatio-temporal models for small-area life expectancy: A simulation study.

The purpose of this study was to assess the precision, uncertainty, and normality of small-area life expectancy estimates calculated using Bayesian spatio-temporal models. We hypothesized six scenarios in which all 247 districts of Korea had the same year-specific female population of 500, 1,000, 2,000, 5,000, 10,000, and 25,000 individuals during the study period (2013-2017). We generated 1,000 hypothetical datasets for each scenario and calculated district-year life expectancies. The precision and uncertainty of life expectancy estimates were compared between the two Bayesian spatio-temporal models and the traditional method and Bayesian spatial models. We examined the normality of the life expectancy distributions generated by each method and investigated an optimal cut-off value for the comparisons. The Bayesian spatio-temporal models produced precise life expectancy estimates. However, the 95% uncertainty interval contained the true value with a probability of less than 95%. The Bayesian spatio-temporal models violated the normality assumption in scenarios with small population sizes. Therefore, life expectancy comparisons should be conducted using a cut-off value that minimizes false-positive and false-negative rates. We proposed 0.8 as a cut-off value to determine the statistical significance of the difference in life expectancy.

Association between Life's Essential 8 score and risk of premature mortality in people with and without type 2 diabetes: A prospective cohort study.

To evaluate the association of cardiovascular health (CVH), measured by Life's Essential 8 score, with the risk of premature mortality and to determine the patterns of CVH-related differences in life expectancy among people with and without type 2 diabetes (T2D). This prospective study included 309,789 participants (age 56.6±8.1years; 46% men) enroled in the UK Biobank. The Life's Essential 8 composite measure consists of four health behaviours (diet, physical activity, nicotine exposure, and sleep) and four health factors (BMI, non-HDL cholesterol, blood glucose, and blood pressure), and the maximum CVH score was 100 points. CVH was categorised into low, moderate, and high groups. Premature death was defined as death before the age of 75. Cox proportional hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated, and life expectancy was estimated. During a median follow-up of 12.7years, 13,683 cases of premature death were documented. Compared to participants with low CVH, the multivariable HRs (95% CIs) of premature death were 0.59 (0.56-0.62) and 0.42 (0.39-0.45) for the moderate and high CVH groups, respectively. This association was stronger in participants with T2D compared with those without T2D. At the age of 50years, compared to low CVH groups, high CVH was associated with a gain of 9.79 (9.70-9.87) and 5.58 (5.48-5.67) additional life years for men with and without T2D, respectively. The corresponding life gain for women with and without T2D was 24.21 (24.13-24.27) and 10.18 (10.10-10.27), respectively. Maintaining an ideal Life's Essential 8 score may provide more benefits for people with T2D than for those without T2D, including a lower risk of premature death and an increased lifespan.

Cause-specific mortality in Spain during the pandemic: educational differences and its impact on life expectancy.

Life expectancy in Spain fell by 1 year between 2018/19 and 2020. Yet, little is known on the impact on cause-of-death (COD) dynamics and educational inequalities therein. We use individual-level death counts data by age, sex, education and multiple causes of death (MCOD) and the corresponding population exposures from national registers in Spain. Deaths were examined both as underlying cause of death (UCOD) and as contributory cause. We estimated life expectancies and lifespan inequalities by subpopulation groups and decomposed life expectancy differences between 2018/19 and 2020 by age groups and COD to assess the impact of COVID-19 (as MCOD) and major UCOD. COVID-19 contributed to a decline in male and female life expectancy in Spain between 2018/19 and 2020 (respectively, -1.7 and -1.4 years). Conversely, cancer, respiratory and circulatory system diseases and ill-defined causes as UCOD contributed to life expectancy increases. Life expectancy declines equalled -1.4 years among the low-educated in both sexes (population 30+), -1.0 and -0.7 years among middle-educated and -1.1 and -0.9 years among high-educated men and women. Without COVID-19, educational inequalities in life expectancy would have remained at similar levels, whereas lifespan variation would have been lower (-22% for women and -8% for men). Life expectancy declines in Spain in 2020 were mainly driven by COVID-19, with possible substitution effects, especially for respiratory system diseases (fewer deaths compared to 2018/19 when coded as UCOD but more as contributing cause). We therefore advocate analysing MCOD when studying changing COD patterns during the pandemic.

Trends in mortality patterns in two countries with different welfare models: comparisons between CUBA and Denmark 1955-2020.

Cuba and Denmark represent states with different welfare models that have reached the same level of life expectancy. The purpose was to investigate and compare mortality changes in the two countries. Systematically collected information on population numbers and deaths for the entire Cuban and Danish populations was the basis of life table data used to quantify differences in the change in age-at-death distributions since 1955, age-specific contributions to differences in life expectancy, lifespan variation, and other changes in mortality patterns in Cuba and Denmark. Life expectancy in Cuba and Denmark converged until 2000, when the increase in life expectancy for Cuba slowed down. Since 1955, infant mortality has fallen in both countries but mostly in Cuba. Both populations experienced compression of mortality as lifespan variation decreased markedly, primarily due to postponement of early deaths. Given the different starting point in the mid-1900s and living conditions for Cubans and Danes, health status achieved among Cubans is striking. A rapidly ageing population is challenging both countries, but Cuban health and welfare are further burdened by a deteriorating economy in recent decades.

Acute Kidney Injury after Bone Marrow Transplantation in Patients with Lymphomas and Leukemias

Introduction: Hematologic malignancies, including lymphomas and leukemias, may be treated with autologous or allogeneic bone marrow transplantation. However, these approaches can increase the risk of infection, sepsis, graft-versus-host disease, and nephrotoxicity, possibly resulting in acute kidney injury (AKI). Objective: To evaluate AKI in patients with lymphomas or leukemia submitted to bone marrow transplantation (BMT). Method: Retrospective, observational cohort study of cases from a database of 256 patients (53.9% males) hospitalized for BMT between 2012 and 2014 at a cancer hospital in Sao Paulo, Brazil. Of these, 79 were selected randomly for analysis. Demographic data, length of hospitalization, and associated morbidities were recorded. AKI was identified according to Kidney Diseases Improving Global Outcomes (KDIGO) criteria. Results: The most frequent diagnoses for the 79 cases were non-Hodgkin’s lymphoma (30.4%), acute myeloid leukemia (26.6%), and Hodgkin’s lymphoma (24.1%). The probability of 100 days-survival after BMT was 81%, and three years after BMT was 61%. In-hospital mortality was significantly higher among patients who presented AKI during hospitalization (p&lt;0.001). However, there was no difference in overall life expectancy (p=0.770). Conclusion: A significant prevalence of AKI was found in patients with leukemia or lymphoma while they were hospitalized for BMT, resulting in significantly increased rates of in-hospital mortality. The presence of AKI during hospitalization was not associated with a subsequent reduction in life expectancy.

Gender equality related to gender differences in life expectancy across the globe gender equality and life expectancy.

Life expectancy (LE) depends on the wider determinants of health, many of which have gendered effects worldwide. Therefore, this study aimed to investigate whether gender equality was associated with LE for women and men and the gender gap in LE across the globe. Gender equality in 156 countries was estimated using a modified global gender gap index (mGGGI), based on the index developed by the World Economic Forum between 2010 and 2021. Linear regression was used to investigate the association between the mGGGI and its economic, political, and education subindices and the gender gap in LE and women and men's LE. Overall, the mGGGI increased from 58% in 2010 to 62% in 2021. Globally, changes in the mGGGI and its economic and political subindexes were not associated with changes in the gender gap in LE or with LE for women and men between 2010 and 2020. Improvements in gender equality in education were associated with a longer LE for women and men and widening of the gender gap in LE. In 2021, each 10% increase in the mGGGI was associated with a 4.3-month increase in women's LE and a 3.5-month increase in men's LE, and thus with an 8-month wider gender gap. However, the direction and magnitude of these associations varied between regions. Each 10% increase in the mGGGI was associated with a 6-month narrower gender gap in high-income countries, and a 13- and 16-month wider gender gap in South and Southeast Asia and Oceania, and in Sub-Saharan Africa, respectively. Globally, greater gender equality is associated with longer LE for both women and men and a widening of the gender gap in LE. The variation in this association across world regions suggests that gender equality may change as countries progress towards socioeconomic development and gender equality.

Influence of poor sleep on cardiovascular disease-free life expectancy: a multi-resource-based population cohort study

BackgroundThe complexity of sleep hinders the formulation of sleep guidelines. Recent studies suggest that different unhealthy sleep characteristics jointly increase the risks for cardiovascular disease (CVD). This study aimed to estimate the differences in CVD-free life expectancy between people with different sleep profiles.MethodsWe included 308,683 middle-aged adults from the UK Biobank among whom 140,181 had primary care data linkage. We used an established composite sleep score comprising self-reported chronotype, duration, insomnia complaints, snoring, and daytime sleepiness to derive three sleep categories: poor, intermediate, and healthy. We also identified three clinical sleep disorders captured by primary care and inpatient records within 2 years before enrollment in the cohort: insomnia, sleep-related breathing disorders, and other sleep disorders. We estimated sex-specific CVD-free life expectancy with three-state Markov models conditioning on survival at age 40 across different sleep profiles and clinical disorders.ResultsWe observed a gradual loss in CVD-free life expectancy toward poor sleep such as, compared with healthy sleepers, poor sleepers lost 1.80 [95% CI 0.96–2.75] and 2.31 [1.46–3.29] CVD-free years in females and males, respectively, while intermediate sleepers lost 0.48 [0.41–0.55] and 0.55 [0.49–0.61] years. Among men, those with clinical insomnia or sleep-related breathing disorders lost CVD-free life by 3.84 [0.61–8.59] or 6.73 [5.31–8.48] years, respectively. Among women, sleep-related breathing disorders or other sleep disorders were associated with 7.32 [5.33–10.34] or 1.43 [0.20–3.29] years lost, respectively.ConclusionsBoth self-reported and doctor-diagnosed poor sleep are negatively associated with CVD-free life, especially pronounced in participants with sleep-related breathing disorders.

Abstract 36: Life’s Essential 8 and Life Expectancy Among Adults in the United States

Background: Cardiovascular disease (CVD) may be the main reason for stagnant growth in life expectancy in the US since 2010. The American Heart Association (AHA) recently released updated algorithm for evaluating cardiovascular health (CVH)--life’s essential 8 (LE8) score. Hypothesis: We aimed to quantify the associations of CVH levels, estimated by the LE8 score, with life expectancy in a nationally representative sample of US adults. Methods: We included 23003 adults who participated in the National Health and Nutrition Examination Survey (NHANES) 2005-2018 and whose mortality was identified through linkage to the National Death Index through December 31, 2019. Life table method was used to estimate life expectancy by levels of the CVH. The cause-specific decomposition of the life expectancy differences was evaluated by the Arriaga’s method. Results: During a median of 7.8 years of follow-up, 1359 total deaths occurred (772 in men and 587 in women). The estimated life expectancy at age 50 was 25.5 years (95% CI, 23.8-27.2), 31.2 years (95% CI, 30.4-31.9) and 33.1 years (95% CI, 29.8-36.2) in men with poor, intermediate and ideal CVH, respectively. The corresponding estimated life expectancy at age 50 in women was 29.5 years (95% CI, 27.8-31.1), 34.2 years (95% CI, 33.5-34.9) and 38.4 years (95% CI, 35.4-41.0), respectively. Equivalently, men and women with ideal CVH had an average 7.5 (95% CI, 3.5-11.5) and 8.9 (95% CI, 5.2-12.5) more years of life expectancy at age 50, respectively, compared with their counterparts with poor CVH. In men, on average, 41.8 % of the gained life expectancy at age 50 from adhering to ideal CVH was attributable to reduced CVD death. The corresponding percentage was 44.1 % in women. Similarly significant associations of CVH with life expectancy were observed in Non-Hispanic Whites and Non-Hispanic Blacks but not in Mexican Americans. Conclusions: Adhering to an ideal CVH, defined as the LE8 score, is related to a considerably increased life expectancy in the US adults.

Association of Cardiovascular Health With Life Expectancy Free of Cardiovascular Disease, Diabetes, Cancer, and Dementia in UK Adults

The average life expectancy has increased substantially in the past few decades in most industrialized countries; however, not all of the increased life expectancy is being spent in optimal health, especially among individuals with low socioeconomic status. To quantify the associations between levels of cardiovascular health (CVH), estimated by the American Heart Association's Life's Essential 8 (LE8) metrics, with life expectancy free of major chronic disease, including cardiovascular disease (CVD), diabetes, cancer, and dementia, in UK adults. This cohort study included 135 199 adults in the UK Biobank study who were initially free of major chronic disease and had complete data on LE8 metrics. Data analyses were performed in August 2022. Cardiovascular health levels, as estimated by LE8 score. The LE8 score, which consists of 8 components: (1) diet, (2) physical activity, (3) tobacco/nicotine exposure, (4) sleep, (5) body mass index, (6) non-high-density lipoprotein cholesterol, (7) blood glucose, and (8) blood pressure. The CVH level was evaluated at baseline and categorized into low (LE8 score <50), moderate (LE8 score ≥50 but <80), and high (LE8 score ≥80) levels. The primary outcome was the life expectancy free of 4 major chronic diseases (CVD, diabetes, cancer, and dementia). Of the 135 199 adults (44.7% men; mean [SD] age, 55.4 [7.9] years) included in the study, a total of 4712, 48 955, and 6748 men had low, moderate, and high CVH levels, respectively, and the corresponding numbers for women were 3661, 52 192, and 18 931. At age 50 years, the estimated disease-free years were 21.5 (95% CI, 21.0-22.0), 25.5 (95% CI, 25.4-25.6), and 28.4 (95% CI, 27.8-29.0) for men with low, moderate, and high CVH levels, respectively; the corresponding estimated disease-free years at age 50 years for women were 24.2 (95% CI, 23.5-24.8), 30.5 (95% CI, 30.4-30.6), and 33.6 (95% CI, 33.1-34.0). Equivalently, men with moderate or high CVH levels lived on average 4.0 (95% CI, 3.4-4.5) or 6.9 (95% CI, 6.1-7.7) longer years free of chronic disease, respectively, at age 50 years, compared with men with low CVH levels. The corresponding longer years lived free of disease for women were 6.3 (95% CI, 5.6-7.0) or 9.4 (95% CI, 8.5-10.2). For participants with high CVH level, there was not a statistically significant difference in disease-free life expectancy between participants with low and other socioeconomic status. In this cohort study, a high level of CVH, evaluated using the LE8 metrics, was associated with longer life expectancy free of major chronic diseases and may contribute to narrowing socioeconomic health inequalities in both men and women.

Could Country-Level Factors Explain Sex Differences in Dementia Incidence and Prevalence? A Systematic Review and Meta-Analysis.

Despite rising interest in sex differences in dementia, it is unclear whether sex differences in dementia incidence and prevalence are apparent globally. We examine sex differences in incidence and prevalence of Any dementia, Alzheimer's disease (AD), and vascular dementia (VaD), and evaluate whether country-level indicators of gender inequality account for differences. Systematic review with meta-analysis was used to obtain estimates of incidence and prevalence of Any dementia, AD, and VaD using random effects meta-analysis, and population-based studies with clinical or validated dementia measures. Meta-regression was used to evaluate how country-specific factors of life expectancy, education, and gender differences in development, unemployment, and inequality indices influenced estimates. We identified 205 eligible studies from 8,731 articles, representing 998,187 participants across 43 countries. There were no sex differences in the incidence of Any dementia, AD, or VaD, except in the 90+ age group (women higher). When examined by 5-year age bands, the only sex difference in prevalence of Any dementia was in the 85+ group and there was no sex difference in VaD. AD was more prevalent in women at most ages. Globally, the overall prevalence of dementia in adults 65 + was higher for women (80.22/1000, 95% CI 62.83-97.61) than men (54.86/1000, 95% CI 43.55-66.17). Meta-regression revealed that sex differences in Any dementia prevalence were associated with gender differences in life expectancy and in education. Globally, there are no sex differences in age-specific dementia incidence, but prevalence of AD is higher in women. Country-level factors like life expectancy and gender differences in education may explain variability in sex differences.