Dataset Information

Association of habitual glucosamine use with risk of cardiovascular disease: prospective study in UK Biobank.

ABSTRACT: OBJECTIVE:To prospectively assess the association of habitual glucosamine use with risk of cardiovascular disease (CVD) events. DESIGN:Prospective cohort study. SETTING:UK Biobank. PARTICIPANTS:466?039 participants without CVD at baseline who completed a questionnaire on supplement use, which included glucosamine. These participants were enrolled from 2006 to 2010 and were followed up to 2016. MAIN OUTCOME MEASURES:Incident CVD events, including CVD death, coronary heart disease, and stroke. RESULTS:During a median follow-up of seven years, there were 10?204 incident CVD events, 3060 CVD deaths, 5745 coronary heart disease events, and 3263 stroke events. After adjustment for age, sex, body mass index, race, lifestyle factors, dietary intakes, drug use, and other supplement use, glucosamine use was associated with a significantly lower risk of total CVD events (hazard ratio 0.85, 95% confidence interval 0.80 to 0.90), CVD death (0.78, 0.70 to 0.87), coronary heart disease (0.82, 0.76 to 0.88), and stroke (0.91, 0.83 to 1.00). CONCLUSION:Habitual use of glucosamine supplement to relieve osteoarthritis pain might also be related to lower risks of CVD events.

SUBMITTER: Ma H

PROVIDER: S-EPMC6515311 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association of habitual glucosamine use with risk of cardiovascular disease: prospective study in UK Biobank.

Ma Hao H Li Xiang X Sun Dianjianyi D Zhou Tao T Ley Sylvia H SH Gustat Jeanette J Heianza Yoriko Y Qi Lu L

BMJ (Clinical research ed.) 20190514

<h4>Objective</h4>To prospectively assess the association of habitual glucosamine use with risk of cardiovascular disease (CVD) events.<h4>Design</h4>Prospective cohort study.<h4>Setting</h4>UK Biobank.<h4>Participants</h4>466 039 participants without CVD at baseline who completed a questionnaire on supplement use, which included glucosamine. These participants were enrolled from 2006 to 2010 and were followed up to 2016.<h4>Main outcome measures</h4>Incident CVD events, including CVD death, cor ...[more]

PMID: 31088786

Similar Datasets

Project description:BackgroundIdentifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions.Methods and findingsUsing data on 423,604 participants without CVD at baseline in UK Biobank, we developed a ML-based model for predicting CVD risk based on 473 available variables. Our ML-based model was derived using AutoPrognosis, an algorithmic tool that automatically selects and tunes ensembles of ML modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms). We compared our model with a well-established risk prediction algorithm based on conventional CVD risk factors (Framingham score), a Cox proportional hazards (PH) model based on familiar risk factors (i.e, age, gender, smoking status, systolic blood pressure, history of diabetes, reception of treatments for hypertension and body mass index), and a Cox PH model based on all of the 473 available variables. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Overall, our AutoPrognosis model improved risk prediction (AUC-ROC: 0.774, 95% CI: 0.768-0.780) compared to Framingham score (AUC-ROC: 0.724, 95% CI: 0.720-0.728, p < 0.001), Cox PH model with conventional risk factors (AUC-ROC: 0.734, 95% CI: 0.729-0.739, p < 0.001), and Cox PH model with all UK Biobank variables (AUC-ROC: 0.758, 95% CI: 0.753-0.763, p < 0.001). Out of 4,801 CVD cases recorded within 5 years of baseline, AutoPrognosis was able to correctly predict 368 more cases compared to the Framingham score. Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals' usual walking pace and their self-reported overall health rating. Furthermore, our model improved risk prediction in potentially relevant sub-populations, such as in individuals with history of diabetes. We also highlight the relative benefits accrued from including more information into a predictive model (information gain) as compared to the benefits of using more complex models (modeling gain).ConclusionsOur AutoPrognosis model improves the accuracy of CVD risk prediction in the UK Biobank population. This approach performs well in traditionally poorly served patient subgroups. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. We found that the "information gain" achieved by considering more risk factors in the predictive model was significantly higher than the "modeling gain" achieved by adopting complex predictive models.

Project description:BackgroundInternational dietary guidelines aim to reduce risks of all-cause mortality, cardiovascular disease (CVD), and fatal CVD often associated with poor dietary habits. However, most studies have examined associations with individual nutrients, foods, or dietary patterns, as opposed to quantifying the pooled health effects of adherence to international dietary recommendations. We investigated associations between total adherence to the World Health Organization (WHO) dietary recommendations for saturated fats, free sugars, fibre, and fruits and vegetables and all-cause mortality and fatal and non-fatal CVD.MethodsWe included participants from the UK Biobank cohort recruited in 2006-2010, which provided at least two valid 24-h dietary assessments. We defined adherence to dietary recommendations as ≤ 10% saturated fats, ≤ 10% free sugars, ≥ 25 g/day fibre, and ≥ 5 servings of fruits and vegetables/day. Multivariable Cox-proportional hazards models were used to investigate prospective associations with all-cause mortality and fatal and non-fatal CVD. In cross-sectional analyses, multivariable linear regression was used to examine associations with cardiometabolic risk factors.ResultsAmong 115,051 participants (39-72 years), only 29.7%, 38.5%, 22.3%, and 9.5% met 0, 1, 2, or 3-4 recommendations, respectively. There was a lower risk of all-cause mortality among participants meeting more dietary recommendations (Ptrend < 0.001), with a significantly lower risk among participants meeting 2: HR 0.91 (95% confidence interval [CI] 0.85-0.97) and 3-4: HR 0.79 (95% CI 0.71-0.88) recommendations. There was no trend with CVD risk, but a significantly lower risk of fatal CVD with 3-4 recommendations: HR 0.78 (95% CI 0.61-0.98). Meeting more recommendations resulted in significant cross-sectional trends (Ptrend < 0.001) towards lower body fat, waist circumference, LDL cholesterol, apolipoprotein B, triglycerides, alkaline phosphatase, gamma glutammyltransferase, and hs-CRP, but higher glucose and aspartate aminotransferase.ConclusionsMeeting dietary recommendations is associated with additive reductions in premature mortality. Motivating and supporting people to adhere to dietary guidelines may help extend years of healthy life expectancy.

Project description:OBJECTIVE:To investigate the association of macronutrient intake with all cause mortality and cardiovascular disease (CVD), and the implications for dietary advice. DESIGN:Prospective population based study. SETTING:UK Biobank. PARTICIPANTS:195?658 of the 502?536 participants in UK Biobank completed at least one dietary questionnaire and were included in the analyses. Diet was assessed using Oxford WebQ, a web based 24 hour recall questionnaire, and nutrient intakes were estimated using standard methodology. Cox proportional models with penalised cubic splines were used to study non-linear associations. MAIN OUTCOME MEASURES:All cause mortality and incidence of CVD. RESULTS:4780 (2.4%) participants died over a mean 10.6 (range 9.4-13.9) years of follow-up, and 948 (0.5%) and 9776 (5.0%) experienced fatal and non-fatal CVD events, respectively, over a mean 9.7 (range 8.5-13.0) years of follow-up. Non-linear associations were found for many macronutrients. Carbohydrate intake showed a non-linear association with mortality; no association at 20-50% of total energy intake but a positive association at 50-70% of energy intake (3.14 v 2.75 per 1000 person years, average hazard ratio 1.14, 95% confidence interval 1.03 to 1.28 (60-70% v 50% of energy)). A similar pattern was observed for sugar but not for starch or fibre. A higher intake of monounsaturated fat (2.94 v 3.50 per 1000 person years, average hazard ratio 0.58, 0.51 to 0.66 (20-25% v 5% of energy)) and lower intake of polyunsaturated fat (2.66 v 3.04 per 1000 person years, 0.78, 0.75 to 0.81 (5-7% v 12% of energy)) and saturated fat (2.66 v 3.59 per 1000 person years, 0.67, 0.62 to 0.73 (5-10% v 20% of energy)) were associated with a lower risk of mortality. A dietary risk matrix was developed to illustrate how dietary advice can be given based on current intake. CONCLUSION:Many associations between macronutrient intake and health outcomes are non-linear. Thus dietary advice could be tailored to current intake. Dietary guidelines on macronutrients (eg, carbohydrate) should also take account of differential associations of its components (eg, sugar and starch).

Dataset Information

Association of habitual glucosamine use with risk of cardiovascular disease: prospective study in UK Biobank.

Publications

Association of habitual glucosamine use with risk of cardiovascular disease: prospective study in UK Biobank.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure