To date, there is no scientific consensus on whether insomnia symptoms increase mortality risk. We investigated longitudinal associations between time-varying insomnia symptoms (difficulty initiating sleep, difficulty maintaining sleep, early-morning awakening, and non-restorative sleep) and all-cause mortality among middle-aged and older adults during 14 years of follow-up. Data were obtained from 2004 through 2018 survey waves of the Health and Retirement Study in the United States for a population-representative sample of 15,511 respondents who were ≥50 years old in 2004. Respondents were interviewed biennially and followed through the end of the 2018 survey wave for the outcome. Marginal structural discrete-time survival analyses were employed to account for time-varying confounding and selection bias. Of the 15,511 cohort respondents (mean [±SD] age at baseline, 63.7 [±10.2] years; 56.0% females), 5,878 (31.9%) died during follow-up. At baseline (2004), 41.6% reported experiencing at least one insomnia symptom. Respondents who experienced one (HR=1.11; 95% CI: 1.03–1.20), two (HR=1.12; 95% CI: 1.01–1.23), three (HR=1.15; 95% CI: 1.05–1.27), or four (HR=1.32; 95% CI: 1.12–1.56) insomnia symptoms had on average a higher hazard of all-cause mortality, compared to those who were symptom-free. For each insomnia symptom, respondents who experienced difficulty initiating sleep (HR=1.12; 95% CI: 1.02–1.22), early-morning awakening (HR=1.09; 95% CI: 1.01–1.18), and nonrestorative sleep (HR=1.17; 95% CI: 1.09–1.26), had a higher hazard of all-cause mortality compared to those not experiencing the symptom. The findings demonstrate significant associations between insomnia symptoms and all-cause mortality, both on a cumulative scale and independently, except for difficulty maintaining sleep. Further research should investigate the underlying mechanisms linking insomnia symptoms and mortality.
Cardiovascular disease is an important driver of the increased mortality associated with chronic kidney disease (CKD). Higher left ventricular mass (LVM) predicts increased risk of adverse cardiovascular outcomes and total mortality, but previous reviews have shown no clear association between intervention-induced LVM change and all-cause or cardiovascular mortality in CKD.
The primary objective of this meta-analysis was to investigate whether treatment-induced reductions in LVM over periods ≥12 months were associated with all-cause mortality in patients with CKD. Cardiovascular mortality was investigated as a secondary outcome. Measures of association in the form of relative risks (RRs) with associated variability and precision (95% confidence intervals [CIs]) were extracted directly from each study, when reported, or were calculated based on the published data, if possible, and pooled RR estimates were determined.
The meta-analysis included 42 trials with duration ≥12 months: 6 of erythropoietin stimulating agents treating to higher vs. lower hemoglobin targets, 10 of renin-angiotensin-aldosterone system inhibitors vs. placebo or another blood pressure lowering agent, 14 of modified hemodialysis regimens, and 12 of other types of interventions. All-cause mortality was reported in 121/2584 (4.86%) subjects in intervention groups and 168/2606 (6.45%) subjects in control groups. The pooled RR estimate of the 27 trials ≥12 months with ≥1 event in ≥1 group was 0.72 (95% CI 0.57 to 0.90, p = 0.005), with little heterogeneity across studies. Directionalities of the associations in intervention subgroups were the same. Sensitivity analyses of ≥6 months (34 trials), ≥9 months (29 trials), and >12 months (10 trials), and including studies with no events in either group, demonstrated similar risk reductions to the primary analysis. The point estimate for cardiovascular mortality was similar to all-cause mortality, but not statistically significant: RR 0.67, 95% CI 0.39 to 1.16.
These results suggest that LVM regression may be a useful surrogate marker for benefits of interventions intended to reduce mortality risk in patients with CKD.
Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply.
We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios.
The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population.
Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.
Background:Serum calciprotein particle maturation time (T50), a measure of vascular calcification propensity, is associated with cardiovascular morbidity and mortality. We aimed to identify genetic loci associated with serum T50 and study their association with cardiovascular disease and mortality.Methods:We performed a genome-wide association study of serum T50 in 2,739 individuals of European descent participating in the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study, followed by a two-sample Mendelian randomization (MR) study to examine causal effects of T50 on cardiovascular outcomes. Finally, we examined associations between T50 loci and cardiovascular outcomes in 8,566 community-dwelling participants in the Rotterdam study.Results:We identified three independent genome-wide significant single nucleotide polymorphism (SNPs) in the AHSG gene encoding fetuin-A: rs4917 (p = 1.72 × 10−101), rs2077119 (p = 3.34 × 10−18), and rs9870756 (p = 3.10 × 10−8), together explaining 18.3% of variation in serum T50. MR did not demonstrate a causal effect of T50 on cardiovascular outcomes in the general population. Patient-level analyses revealed that the minor allele of rs9870756, which explained 9.1% of variation in T50, was associated with a primary composite endpoint of all-cause mortality or cardiovascular disease [odds ratio (95% CI) 1.14 (1.01–1.28)] and all-cause mortality alone [1.14 (1.00–1.31)]. The other variants were not associated with clinical outcomes. In patients with type 2 diabetes or chronic kidney disease, the association between rs9870756 and the primary composite endpoint was stronger [OR 1.40 (1.06–1.84), relative excess risk due to interaction 0.54 (0.01–1.08)].Conclusions:We identified three SNPs in the AHSG gene that explained 18.3% of variability in serum T50 levels. Only one SNP was associated with cardiovascular outcomes, particularly in individuals with type 2 diabetes or chronic kidney disease.
Background. Chronological age (CA) is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. This study was aimed at evaluating the association between PhenoAge and long-term outcome of patients with multivessel coronary artery disease (CAD). Methods. A total of 609 multivessel CAD patients who received PCI attempt and with follow-up were enrolled. The clinical outcome was all-cause mortality on follow-up. PhenoAge was calculated using an equation constructed from CA and 9 clinical biomarkers. Cox proportional hazards regression models and receiver operating characteristic (ROC) curves were performed to evaluate the association between PhenoAge and mortality. Results. Overall, patients with more diseases had older PhenoAge and phenotypic age acceleration (PhenoAgeAccel). After a median follow-up of 33.5 months, those with positive PhenoAgeAccel had a significantly higher incidence of all-cause mortality (
). After adjusting for CA, Cox proportional hazards models showed that both PhenoAge and PhenoAgeAccel were significantly associated with all-cause mortality. Even after further adjusting for confounding factors, each 10-year increase in PhenoAge was also associated with a 51% increased mortality risk. ROC curves revealed that PhenoAge, with an area under the curve of 0.705, significantly outperformed CA, the individual clinical chemistry measure, and other risk factors. When reexamining the ROC curves using various combinations of variables, we found that PhenoAge provides additional predictive power to all models. Conclusions. In conclusion, PhenoAge was strongly associated with all-cause mortality even after adjusting for CA. Our findings suggest that PhenoAge measure may be complementary in predicting mortality risk for patients with multivessel CAD.
Background: The prognostic value of elevated lipoprotein(a) [Lp(a)] in coronary artery disease (CAD) patients is inconsistent in previous studies, and whether such value changes at different low-density-lipoprotein cholesterol (LDL-C) levels is unclear.Methods and Findings: CAD patients treated with statin therapy from January 2007 to December 2018 in the Guangdong Provincial People's Hospital (NCT04407936) were consecutively enrolled. Individuals were categorized according to the baseline LDL-C at cut-off of 70 and 100 mg/dL. The primary outcome was 5-year all-cause death. Multivariate Cox proportional models and penalized spline analyses were used to evaluate the association between Lp(a) and all-cause mortality. Among 30,908 patients, the mean age was 63.1 ± 10.7 years, and 76.7% were men. A total of 2,383 (7.7%) patients died at 5-year follow-up. Compared with Lp(a) <50 mg/dL, Lp(a) ≥ 50 mg/dL predicted higher all-cause mortality (multivariable adjusted HR = 1.19, 95% CI 1.07–1.31) in the total cohort. However, when analyzed within each LDL-C category, there was no significant association between Lp(a) ≥ 50 mg/dL and higher all-cause mortality unless the baseline LDL-C was ≥ 100 mg/dL (HR = 1.19, 95% CI 1.04–1.36). The results from penalized spline analyses were robust.Conclusions: In statin-treated CAD patients, elevated Lp(a) was associated with increased risks of all-cause death, and such an association was modified by the baseline LDL-C levels. Patients with Lp(a) ≥ 50 mg/dL had higher long-term risks of all-cause death compared with those with Lp(a) <50 mg/dL only when their baseline LDL-C was ≥ 100 mg/dL.
Background: The effectiveness of glucose-lowering drugs (GLDs) is unknown among patients with dementia. Objective: To analyze all-cause mortality among users of six GLDs in dementia and dementia-free subjects, respectively. Methods: This was a longitudinal open-cohort registry-based study using data from the Swedish Dementia Registry, Total Population Register, and four supplemental registers providing data on dementia status, drug usage, confounders, and mortality. The cohort comprised 132,402 subjects with diabetes at baseline, of which 11,401 (8.6%) had dementia and 121,001 (91.4%) were dementia-free. Subsequently, comparable dementia – dementia-free pairs were sampled. Then, as-treated and intention-to-treat exposures to metformin, insulin, sulfonylurea, dipeptidyl-peptidase-4 inhibitors, glucagon-like peptide-1 analogues (GLP-1a), and sodium-glucose cotransporter-2 inhibitors (SGLT-2i) were analyzed in the parallel dementia and dementia-free cohorts. Confounding was addressed using inverse-probability weighting and propensity-score matching, and flexible parametric survival models were used to produce hazard ratios (HR) and 95% confidence intervals (CI) of the association between GLDs and all-cause mortality. Results: In the as-treated models, increased mortality was observed among insulin users with dementia (HR 1.34 [95%CI 1.24–1.45]) as well as in dementia-free subjects (1.54 [1.10–1.55]). Conversely, sulfonylurea was associated with higher mortality only in dementia subjects (1.19 [1.01–1.42]). GLP-1a (0.44 [0.25–0.78]) and SGLT-2i users with dementia (0.43 [0.23–0.80]) experienced lower mortality compared to non-users. Conclusion: Insulin and sulfonylurea carried higher mortality risk among dementia patients, while GLP-1a and SGLT-2i were associated with lower risk. GLD-associated mortality varied between dementia and comparable dementia-free subjects. Further studies are needed to optimize GLD use in dementia patients.
Background: Serum adiponectin level predicts cardiovascular (CV) outcomes and progression of coronary artery calcification (CAC) in the general population, although the association has not been validated in patients with chronic kidney disease (CKD). In this study, we investigated the association of high serum adiponectin level with the risk of adverse CV outcomes and progression of CAC in patients with pre-dialysis CKD.Methods: A total of 1,127 patients with pre-dialysis CKD from a nationwide prospective cohort of patients with pre-dialysis CKD in Korea were divided into the tertile by serum adiponectin level at the baseline. CV outcome of interest was fatal and non-fatal CV events and all-cause mortality. Progression of CAC was defined as coronary artery calcium score (CACS) change more than 200 during a 4-year follow-up.Results: Cox regression analysis revealed that high serum adiponectin is associated with increased risk of fatal and non-fatal CV events (adjusted hazard ratio 2.799, 95% CI 1.348–5.811). In contrast, high serum adiponectin level was not significantly associated with all-cause mortality (adjusted hazard ratio 0.655, 95% CI 0.203–2.113). Binary logistic regression analysis revealed that high serum adiponectin level is also associated with increased risk of progression of CAC (adjusted odds ratio [OR] 2.078, 95% CI 1.014–4.260). Subgroup analyses demonstrated that the association of high serum adiponectin with increased risk of fatal and non-fatal CV events is not modified by age, gender, history of diabetes, estimated glomerular filtration rate (eGFR), or spot urine albumin-to-creatinine ratio (ACR).Conclusions: High serum adiponectin level is associated with adverse CV outcomes and progression of CAC in patients with pre-dialysis CKD.