Derivation and validation of 10-year all-cause and cardiovascular disease mortality prediction model for middle-aged and elderly community-dwelling adults in Taiwan

Hypertension as a Novel Link for Shared Heritability in Age at Menarche and Cardiometabolic Traits

CONTEXT

Extremely early age at menarche, also called precocious puberty, has been associated with various cardiometabolic traits, but their shared heritability remains unclear.

OBJECTIVES

This work aimed to identify new shared genetic variants and their pathways for age at menarche and cardiometabolic traits and to investigate the influence of central precocious puberty on childhood cardiometabolic traits.

METHODS

Using the conjunction false discovery rate method, this study analyzed genome-wide association study data from the menarche-cardiometabolic traits among 59 655 females of Taiwanese ancestry and systemically investigated pleiotropy between age at menarche and cardiometabolic traits. To support the novel hypertension link, we used the Taiwan Puberty Longitudinal Study (TPLS) to investigate the influence of precocious puberty on childhood cardiometabolic traits.

RESULTS

We discovered 27 novel loci, with an overlap between age at menarche and cardiometabolic traits, including body fat and blood pressure. Among the novel genes discovered, SEC16B, CSK, CYP1A1, FTO, and USB1 are within a protein interaction network with known cardiometabolic genes, including traits for obesity and hypertension. These loci were confirmed through demonstration of significant changes in the methylation or expression levels of neighboring genes. Moreover, the TPLS provided evidence regarding a 2-fold higher risk of early-onset hypertension that occurred in girls with central precocious puberty.

CONCLUSION

Our study highlights the usefulness of cross-trait analyses for identifying shared etiology between age at menarche and cardiometabolic traits, especially early-onset hypertension. The menarche-related loci may contribute to early-onset hypertension through endocrinological pathways.

Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China

Background

Considering that the previously developed mortality prediction models have limited applications to the Chinese population, a questionnaire-based prediction model is of great importance for its accuracy and convenience in clinical practice.

Methods

Two national cohort, namely, the China Health and Nutrition Survey (8,355 individual older than 18) and the China Health and Retirement Longitudinal Study (12,711 individuals older than 45) were used for model development and validation. One hundred and fifty-nine variables were compiled to generate predictions. The Cox regression model and six machine learning (ML) models were used to predict all-cause mortality. Finally, a simple questionnaire-based ML prediction model was developed using the best algorithm and validated.

Results

In the internal validation set, all the ML models performed better than the traditional Cox model in predicting 6-year mortality and the random survival forest (RSF) model performed best. The questionnaire-based ML model, which only included 20 variables, achieved a C-index of 0.86 (95%CI: 0.80-0.92). On external validation, the simple questionnaire-based model achieved a C-index of 0.82 (95%CI: 0.77-0.87), 0.77 (95%CI: 0.75-0.79), and 0.79 (95%CI: 0.77-0.81), respectively, in predicting 2-, 9-, and 11-year mortality.

Conclusions

In this prospective population-based study, a model based on the RSF analysis performed best among all models. Furthermore, there was no significant difference between the prediction performance of the questionnaire-based ML model, which only included 20 variables, and that of the model with all variables (including laboratory variables). The simple questionnaire-based ML prediction model, which needs to be further explored, is of great importance for its accuracy and suitability to the Chinese general population.

Development and validation of a model to categorize cardiovascular cause of death using health administrative data

Study objective

Develop and evaluate a model that uses health administrative data to categorize cardiovascular (CV) cause of death (COD).

Design

Population-based cohort.

Setting

Ontario, Canada.

Participants

Decedents ≥ 40 years with known COD between 2008 and 2015 in the CANHEART cohort, split into derivation (2008 to 2012; n = 363,778) and validation (2013 to 2015; n = 239,672) cohorts.

Main outcome measures

Model performance. COD was categorized as CV or non-CV with ICD-10 codes as the gold standard. We developed a logistic regression model that uses routinely collected healthcare administrative to categorize CV versus non-CV COD. We assessed model discrimination and calibration in the validation cohort.

Results

The strongest predictors for CV COD were history of stroke, history of myocardial infarction, history of heart failure, and CV hospitalization one month before death. In the validation cohort, the c-statistic was 0.80, the sensitivity 0.75 (95 % CI 0.74 to 0.75) and the specificity 0.71 (95 % CI 0.70 to 0.71). In the primary prevention validation sub-cohort, the c-statistic was 0.81, the sensitivity 0.71 (95 % CI 0.70 to 0.71) and the specificity 0.75 (95 % CI 0.75 to 0.75) while in the secondary prevention sub-cohort the c-statistic was 0.74, the sensitivity 0.81 (95 % CI 0.81 to 0.82) and the specificity 0.54 (95 % CI 0.53 to 0.54).

Conclusion

Modelling approaches using health administrative data show potential in categorizing CV COD, though further work is necessary before this approach is employed in clinical studies.

Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis

Background

There is an increasing prevalence of cardiovascular disease (CVD) in China, which represents the leading cause of mortality. Precise CVD risk identification is the fundamental prevention component. This study sought to systematically review the CVD risk prediction models derived and/or validated in the Chinese population to promote primary CVD prevention.

Methods

Reports were included if they derived or validated one or more CVD risk prediction models in the Chinese population. PubMed, Embase, CINAHL, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), VIP database, etc., were searched. The risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed in R using the package metamisc.

Results

From 55,183 records, 22 studies were included. Twelve studies derived 18 CVD risk prediction models, of which seven models were derived based on a multicentre cohort including more than two provinces of mainland China, and one was a model developed based on a New Zealand cohort including Chinese individuals. The number of predictors ranged from 6 to 22. The definitions of predicted outcomes showed considerable heterogeneity. Fourteen articles described 29 validations of 8 models. The Framingham model and pooled cohort equations (PCEs) are the most frequently validated foreign tools. Discrimination was acceptable and similar for men and women among models (0.60–0.83). The calibration estimates changed substantially from one population to another. Prediction for atherosclerotic cardiovascular disease Risk in China (China-PAR) showed good calibration [observed/expected events ratio = 0.99, 95% PI (0.57,1.70)] and female sex [1.10, 95% PI (0.23,5.16)].

Conclusions

Several models have been developed or validated in the Chinese population. The usefulness of most of the models remains unclear due to incomplete external validation and head-to-head comparison. Future research should focus on externally validating or tailoring these models to local settings.

Trail registration

This systematic review was registered at PROSPERO (International Prospective Register of Systematic Reviews, CRD42021277453).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13995-z.

The relationship between urinary albumin to creatinine ratio and all-cause mortality in the elderly population in the Chinese community: a 10-year follow-up study

BACKGROUND

In patients with diabetes and hypertension, proteinuria is independently associated with all-cause death. However, in the general population, urinary albumin to creatinine ratio (UACR) is less used to predict all-cause mortality. When the urinary albumin to creatinine ratio is within the normal range (UACR< 30 mg/g), the clinical relevance of an increased urinary albumin excretion rate is still debated. We studied the relationship between UACR and all-cause mortality in community populations, and compared UACR groups within the normal range.

METHODS

The participants were the inhabitants from the Wanshoulu community in Beijing, China. The average age is 71.48 years, and the proportion of women is 60.1%. A total of 2148 people completed random urine samples to determine the urinary albumin to creatinine ratio (UACR). The subjects were divided into three groups according to UACR: Group 1 (UACR< 10 mg/g), Group 2 (10 mg/g < UACR< 30 mg/g), Group 3 (UACR> 30 mg/g). We used Kaplan-Meier survival analysis and Cox regression model to verify the relationship between UACR and all-cause mortality.

RESULTS

At an average follow-up of 9.87 years (718,407.3 years), the total mortality rate were 183.4/1000. In the Cox proportional hazards model, after adjusting for possible confounders, those with normal high-value UACR (group 2) showed a higher all-cause mortality than those with normal low-value UACR (group 1) [hazard ratio (HR) 1.289, 95% confidence interval (CI) 1.002 ~ 1.659 for all-cause mortality]. Those with proteinuria (group 3) showed a higher all-cause mortality than those with normal low-value UACR (group 1) [hazard ratio (HR) 1.394, 95% confidence interval (CI) 1.020 ~ 1.905 for all-cause mortality].

CONCLUSION

Urinary albumin to creatinine ratio is an important risk factor for all-cause death in community population. Even if it is within the normal range (UACR< 30 mg/g), it occurs in people with high normal value (10 mg/g < UACR< 30 mg/g), the risk of all-cause death will also increase.

Adding Estimated Cardiorespiratory Fitness to the Framingham Risk Score and Mortality Risk in a Korean Population-Based Cohort Study

BACKGROUND

The added value of non-exercise-based estimation of cardiorespiratory fitness (eCRF) to cardiovascular disease (CVD) risk factors for mortality risk has not been examined in Korean populations.

METHODS

This population-based prospective cohort study examined the relationship of the 10-year Framingham risk score (FRS) for CVD risk and eCRF with all-cause and CVD mortality in a representative sample of Korean adults aged 30 years and older. Data regarding a total of 38,350 participants (16,505 men/21,845 women) were obtained from the 2007-2015 Korea National Health and Nutrition Examination Survey (KNHANES). All-cause and CVD mortality were the main outcomes. The 10-year FRS point sum and eCRF level were the main exposures.

RESULTS

All-cause and CVD mortality was positively correlated with the 10-year FRS point summation and inversely correlated with eCRF level in this study population. The protective of high eCRF against all-cause and CVD mortality was more prominent in the middle and high FRS category than in the low FRS category. Notably, the FRS plus eCRF model has better predictor power for estimating mortality risk compared to the FRS only model.

CONCLUSIONS

The current findings indicate that eCRF can be used as an alternative to objectively measured CRF for mortality risk prediction.