Changes in late-life systolic blood pressure and all-cause mortality among oldest-old people in China: the chinese longitudinal healthy longevity survey

Modifiable pathways for longevity: A Mendelian randomization analysis

BACKGROUND

A variety of factors, including diet and lifestyle, obesity, physiology, metabolism, hormone levels, psychology, and inflammation, have been associated with longevity. The specific influences of these factors, however, are poorly understood. Here, possible causal relationships between putative modifiable risk factors and longevity are investigated.

METHODS

A random effects model was used to investigate the association between 25 putative risk factors and longevity. The study population comprised 11,262 long-lived subjects (≥90 years old, including 3484 individuals ≥99 years old) and 25,483 controls (≤60 years old), all of European ancestry. The data were obtained from the UK Biobank database. Genetic variations were used as instruments in two-sample Mendelian randomization to reduce bias. The odds ratios for genetically predicted SD unit increases were calculated for each putative risk factor. Egger regression was used to determine possible violations of the Mendelian randomization model.

RESULTS

Thirteen potential risk factors showed significant associations with longevity (≥90th) after correction for multiple testing. These included smoking initiation (OR:1.606; CI: 1.112-2.319) and educational attainment (OR:2.538, CI: 1.685-3.823) in the diet and lifestyle category, systolic and diastolic blood pressure (OR per SD increase: 0.518; CI: 0.438-0.614 for SBP and 0.620; CI 0.514-0.748 for DBP) and venous thromboembolism (OR:0.002; CI: 0.000-0.047) in the physiology category, obesity (OR: 0.874; CI: 0.796-0.960), BMI (OR per 1-SD increase: 0.691; CI: 0.628-0.760), and body size at age 10 (OR per 1-SD increase:0.728; CI: 0.595-0.890) in the obesity category, type 2 diabetes (T2D) (OR:0.854; CI: 0.816-0.894), LDL cholesterol (OR per 1-SD increase: 0.743; CI: 0.668-0.826), HDL cholesterol (OR per 1-SD increase: 1.243; CI: 1.112-1.390), total cholesterol (TC) (OR per 1-SD increase: 0.786; CI: 0.702-0.881), and triglycerides (TG) (OR per 1-SD increase: 0.865; CI: 0.749-0.998) in the metabolism category. Both longevity (≥90th) and super-longevity (≥99th), smoking initiation, body size at age 10, BMI, obesity, DBP, SBP, T2D, HDL, LDL, and TC were consistently associated with outcomes. The examination of underlying pathways found that BMI indirectly affected longevity through three pathways, namely, SBP, plasma lipids (HDL/TC/LDL), and T2D (p < 0.05).

CONCLUSION

BMI was found to significantly affect longevity through SBP, plasma lipid (HDL/TC/LDL), and T2D. Future strategies should focus on modifying BMI to improve health and longevity.

Development, validation, and visualization of a web-based nomogram to predict 5-year mortality risk in older adults with hypertension

Background

Hypertension-related mortality has been increasing in older adults, resulting in serious burden to society and individual. However, how to identify older adults with hypertension at high-risk mortality remains a great challenge. The purpose of this study is to develop and validate the prediction nomogram for 5-year all-cause mortality in older adults with hypertension.

Methods

Data were extracted from National Health and Nutrition Examination Survey (NHANES). We recruited 2691 participants aged 65 years and over with hypertension in the NHANES 1999-2006 cycles (training cohort) and 1737 participants in the NHANES 2007-2010 cycles (validation cohort). The cohorts were selected to provide at least 5 years follow-up for evaluating all-cause mortality by linking National Death Index through December 31, 2015. We developed a web-based dynamic nomogram for predicting 5-year risk of all-cause mortality based on a logistic regression model in training cohort. We conducted internal validation by 1000 bootstrapping resamples and external validation in validation cohort. The discrimination and calibration of nomogram were evaluated using concordance index (C-index) and calibration curves.

Results

The final model included eleven independent predictors: age, sex, diabetes, cardiovascular disease, body mass index, smoking, lipid-lowering drugs, systolic blood pressure, hemoglobin, albumin, and blood urea nitrogen. The C-index of model in training and validation cohort were 0.759 (bootstrap-corrected C-index 0.750) and 0.740, respectively. The calibration curves also indicated that the model had satisfactory consistence in two cohorts. A web-based nomogram was established (https://hrzhang1993.shinyapps.io/dynnomapp).

Conclusions

The novel developed nomogram is a useful tool to accurately predict 5-year all-cause mortality in older adults with hypertension, and can provide valuable information to make individualized intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03087-3.