Healthy Ageing and Biomarkers Cohort Study (HABCS): a cohort profile

Lv, Yuebin; Chen, Mao; Yin, Zhaoxue; Li, Furong; Wu, Xianbo; Shi, Xiaoming

doi:10.1136/bmjopen-2018-026513

Cited by 35 publications

(24 citation statements)

References 27 publications

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“…statins). Studies of the relationship between low-density lipoprotein cholesterol and healthy aging have led to conflicting conclusions, with some studies reporting a negative association ( Barzilai et al, 2001 ; Postmus et al, 2015 ) and other studies reporting a positive association ( Lv et al, 2015 ; Lv et al, 2019 ; Rantanen et al, 2015 ). One possible explanation for this discrepancy is survival bias, if individuals with higher low-density lipoprotein cholesterol disproportionately experience mortality prior to the onset of hypertension.…”

Section: Discussionmentioning

confidence: 99%

Time-to-event modeling of hypertension reveals the nonexistence of true controls

Shriner

Bentley

Zhou

et al. 2020

eLife

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Given a lifetime risk of ~90% by the ninth decade of life, it is unknown if there are true controls for hypertension in epidemiological and genetic studies. Here, we compared Bayesian logistic and time-to-event approaches to modeling hypertension. The median age at hypertension was approximately a decade earlier in African Americans than in European Americans or Mexican Americans. The probability of being free of hypertension at 85 years of age in African Americans was less than half that in European Americans or Mexican Americans. In all groups, baseline hazard rates increased until nearly 60 years of age and then decreased but did not reach zero. Taken together, modeling of the baseline hazard function of hypertension suggests that there are no true controls and that controls in logistic regression are cases with a late age of onset.

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Section: Discussionmentioning

confidence: 99%

Time-to-event modeling of hypertension reveals the nonexistence of true controls

Shriner

Bentley

Zhou

et al. 2020

eLife

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“…In this study, we derived data from the HABCS. The HABCS was conducted in eight longevity areas from CLHLS [16]. The HABCS covers similar domains that CLHLS has investigated and shared the sampling strategy as CLHLS.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and Prediction of All-Cause Mortality among Chinese Older Adults

Sun

Zhu

et al. 2021

Preprint

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Background and aimMortality risk stratification was vital for targeted intervention. This study aimed at building the prediction model of all-cause mortality among Chinese dwelling elderly with different methods including regression models and machine learning models and to compare the performance of machine learning models with regression model on predicting mortality. Additionally, this study also aimed at ranking the predictors of mortality within different models and comparing the predictive value of different groups of predictors using the model with best performance.MethodI used data from the sub-study of Chinese Longitudinal Healthy Longevity Survey (CLHLS) - Healthy Ageing and Biomarkers Cohort Study (HABCS). The baseline survey of HABCS was conducted in 2008 and covered similar domains that CLHLS has investigated and shared the sampling strategy. The follow-up of HABCS was conducted every 2-3 years till 2018.The analysis sample included 2,448 participants from HABCS. I used totally 117 predictors to build the prediction model for survival using the HABCS cohort, including 61 questionnaire, 41 biomarker and 15 genetics predictors. Four models were built (XG-Boost, random survival forest [RSF], Cox regression with all variables and Cox-backward). We used C-index and integrated Brier score (Brier score for the two years’ mortality prediction model) to evaluate the performance of those models.ResultsThe XG-Boost model and RSF model shows slightly better predictive performance than Cox models and Cox-backward models based on the C-index and integrated Brier score in predicting surviving. Age. Activity of daily living and Mini-Mental State Examination score were identified as the top 3 predictors in the XG-Boost and RSF models. Biomarker and questionnaire predictors have a similar predictive value, while genetic predictors have no addictive predictive value when combined with questionnaire or biomarker predictors.ConclusionIn this work, it is shown that machine learning techniques can be a useful tool for both prediction and its performance sightly outperformed the regression model in predicting survival.

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“…Due to the high death rate in older adults, new participants were recruited in the follow-up surveys to maintain a stable sample size for the dynamic cohort. Details of this survey design have been described elsewhere (30). A total of 5074 older adults from the 2008 survey and the recruits from the follow-up surveys (2012-2014 waves) from the HABCS were included in this study.…”

Section: Study Populationmentioning

confidence: 99%

“…Central obesity was defined as 85 cm or larger of WC in men and 80 cm or larger of WC in women. These covariates have been demonstrated as important correlations with blood UA level (36), or cognition (37), or both (30).…”

Section: Covariatesmentioning

confidence: 99%

High blood uric acid is associated with reduced risks of mild cognitive impairment among older adults in China: a 9-year prospective cohort study

Chen

Yin

et al. 2021

Preprint

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Background: It remains unsolved that whether blood uric acid (UA) is a neuroprotective or neurotoxic agent. This study aimed to evaluate the longitudinal association of blood UA with mild cognitive impairment (MCI) among older adults in China. Methods: A total of 3103 older adults (aged 65+ years) free of MCI at baseline were included from the Healthy Aging and Biomarkers Cohort Study (HABCS). Blood UA level was determined by the uricase colorimetry assay and analyzed as categorical (by quartile) variables. Global cognition was assessed using the Mini-Mental State Examination four times between 2008 and 2017, with a score below 24 being considered as MCI. Cox proportional hazards models were used to examine the associations. Results: During a 9-year follow-up, 486 (15.7%) participants developed MCI. After adjustment for all covariates, higher UA had a dose-response association with a lower risk of MCI (all P for trend< 0.05). Participants in the highest UA quartile group had a reduced risk (hazard ratio [HR], 0.73; 95% [CI]: 0.55-0.96) of MCI, compared with those in the lowest quartile group. The associations were still robust even when considering death as a competing risk. Subgroup analyses revealed that these associations were statistically significant in younger older adults (65-79 years) and those without hyperuricemia. Conclusions: High blood UA level is associated with reduced risks of MCI among Chinese older adults, highlighting the potential of managing UA in daily life for maintaining late-life cognition.

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Healthy Ageing and Biomarkers Cohort Study (HABCS): a cohort profile

Cited by 35 publications

References 27 publications

Time-to-event modeling of hypertension reveals the nonexistence of true controls

Time-to-event modeling of hypertension reveals the nonexistence of true controls

Machine Learning and Prediction of All-Cause Mortality among Chinese Older Adults

High blood uric acid is associated with reduced risks of mild cognitive impairment among older adults in China: a 9-year prospective cohort study

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