Participants attended one of 22 assessment centers across the UK where they completed a touch screen questionnaire. We used the baseline touch screen questionnaire to assess several potential confounders: age, sex, race, household income, smoking status, and alcohol intake we calculated ethanol intake by multiplying the quantity of each type of drink—red wine, white wine, beer or cider, fortified wine, or spirits—by its standard drink size and reference alcohol content ; self reported diabetes and high cholesterol level; drugs to treat high cholesterol, high blood pressure, and diabetes; aspirin and other non-steroidal anti-inflammatory drug use; and dietary intakes red meat, vegetables, fruit, fish, and cereals.
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We calculated the healthy diet score by using the following factors: red meat intake up to three times each week median ; vegetable intake at least four tablespoons each day median ; fruit intake at least three pieces each day median ; fish intake at least four times each week median ; cereal intake at least five bowls each week median ; and urinary sodium concentration up to We gave 1 point for each favorable diet factor, and the total diet score ranged from 0 to 6.
The ion selective electrode method AU analyzer, Beckman Coulter was used to measure sodium levels in stored urine samples. Details on quality control and sample preparation have been published previously. Hypertension was defined as a self reported history of hypertension, systolic blood pressure of mm Hg or higher, diastolic blood pressure of 90 mm Hg or higher, or taking antihypertensive drugs. Arthritis was defined by ICD international classification of diseases, 10th revision codes MM Detailed information about genotyping and imputation in the UK Biobank has been previously published.
Each SNP was multiplied by a weighted risk estimate natural logarithm of the odds ratio for CHD or stroke obtained from the previous genome wide association study. We then added up these products. The CHD genetic risk score ranged from 3. Higher scores indicate a higher genetic predisposition to CHD or stroke. Secondary outcomes were individual CHD events fatal and non-fatal and individual stroke events fatal and non-fatal; ischemic and hemorrhagic stroke.
Information on CVD events and timing of events was collected through certified death records until 16 February and cumulative medical records of hospital diagnoses. Additional information was collected through two repeated surveys the first visit was completed between 12 December and 7 June ; the second visit between 30 April and 10 August ICD codes were used in death records, whereas ICD and ICD-9 international classification of diseases, ninth revision codes were used in medical records.
The proportional hazards assumption was tested using Schoenfeld residuals. We coded missing data as a missing indicator category for categorical variables such as smoking status, and with mean values for continuous variables. We evaluated potential effect modification by modeling the cross product term of the stratifying variable with glucosamine use.
We conducted several sensitivity analyses. First, because participants who took glucosamine also tended to take other supplements more often than participants who did not take glucosamine, we did a sensitivity analysis by excluding participants who used any other supplements. Second, to minimize the influence of reverse causation, we performed a sensitivity analysis by excluding participants who developed CVD events within two years of follow-up. Third, to control the influence of genetic predisposition to CHD or stroke, we adjusted for CHD or stroke genetic risk score. We conducted all statistical analyses by using SAS version 9.
All statistical tests were two sided, and we considered a P value less than 0. No patients were involved in setting the research question or the outcome measures, nor were they involved in the design and implementation of the study. No plans exist to disseminate the results to study participants. Table 1 shows baseline characteristics of the study participants according to the use of glucosamine.
Overall, Compared with non-users, glucosamine users were older, more likely to be women, not current smokers, more physically active, had a healthy diet, had a lower alcohol intake, and had a higher prevalence of hypertension, arthritis, and high cholesterol, but a lower prevalence of diabetes. Glucosamine users also tended to take more aspirin, non-aspirin non-steroidal anti-inflammatory drugs, vitamins, minerals, and other dietary supplements than non-users.
Baseline characteristics of UK Biobank participants by glucosamine use.
Greg Murr – CMBR Study No. 33
Values are numbers percentages unless stated otherwise. Table 2 shows the associations between glucosamine use and incident CVD events. In the multivariable adjusted analyses, the hazard ratios associated with glucosamine use were 0. We analyzed the relations between glucosamine use and subtypes of CHD and stroke. For stroke, glucosamine use was associated with a marginally significantly lower risk of non-fatal stroke 0.
There was no significant inverse association between glucosamine use and risk of ischemic stroke 0. We conducted stratified analyses according to potential CVD risk factors. The associations between glucosamine use and these CVD outcomes were stronger among current smokers than among former or never smokers. The associations between glucosamine use and CVD outcomes were not modified by other risk factors, including age, sex, body mass index, physical activity, healthy diet, diabetes, hypertension, high cholesterol, arthritis, and aspirin and non-aspirin non-steroidal anti-inflammatory drug use figs 1 and 2.
Association of glucosamine supplement use and risk of cardiovascular disease event and cardiovascular disease death stratified by potential risk factors. Association of glucosamine supplement use and risk of coronary heart disease and stroke stratified by potential risk factors. In our sensitivity analyses, the associations between glucosamine use and CVD outcomes did not change appreciably: first, when we excluded participants who used any other supplements supplementary table 3 ; second, when we excluded participants who developed CVD events within two years of follow-up supplementary table 4 ; and third, after additional adjustment for genetic predisposition to CHD or stroke CHD or stroke genetic risk score; supplementary table 5.
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Such associations were independent of traditional risk factors, including sex, age, income, body mass index, physical activity, healthy diet, alcohol intake, smoking status, diabetes, hypertension, high cholesterol, arthritis, drug use, and other supplement use. In addition, we found that the associations between glucosamine use and CVD outcomes were statistically significantly modified by smoking status. Our findings are in line with several previous studies that show inverse associations of glucosamine use with CVD risk and mortality.
Our lack of statistically significant associations between glucosamine use and subtypes of stroke is probably because of small numbers of participants in the subtype groups. Several potential mechanisms could explain the observed protective relation between glucosamine use and CVD diseases.
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In the National Health and Nutrition Examination Survey NHANES study, regular use of glucosamine was associated with a statistically significant reduction in C reactive protein concentrations, which is a marker for systemic inflammation. We found consistent interactions between glucosamine use and smoking on CVD outcomes. Inverse associations of glucosamine use with CVD outcomes were stronger in current smokers than in former smokers or never smokers. We could not rule out the possibility that these results were due to chance.
However, smokers have higher levels of inflammation and a higher risk of CVD compared with non-smokers. Given the important role smoking has in the development of CVD, further studies are needed to evaluate the effect of glucosamine in CVD prevention, particularly among current smokers. Our study has several major strengths, including the large sample size and the wealth of information on lifestyle, diet, and other covariates, which enabled us to conduct comprehensive sensitivity analyses and subgroup analyses.
We acknowledge that our study also has potential limitations. First, the UK Biobank did not record detailed information on glucosamine use, such as the dosage and the duration of use. Therefore, further studies are needed to investigate such associations.
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