Abstract
Background:
Previous studies indicated that glucosamine supplements may have a general anticancer effect. This study aimed to assess whether the potential effect differs across different types of cancers in a large prospective cohort study.
Methods:
All participants from the UK Biobank who were free of cancers and had complete information on glucosamine use at baseline were included and followed up from 2006 until 2021. Cox proportional hazards models were used to assess the associations between regular glucosamine use and different site-specific cancers. Subgroup analyses were performed to explore potential interactions. Several sensitivity analyses were conducted to assess the robustness of the main findings.
Results:
A total of 450,207 eligible participants (mean age: 56.2 years; females: 53.3%) were included, of whom 84,895 (18.9%) reported regular glucosamine use at baseline. During a median of 12.5 years follow-up, glucosamine use was significantly associated with an increased risk of overall cancer [HR, 1.04; 95% confidence interval (CI), 1.01–1.06], skin cancer (HR, 1.11; 95% CI, 1.07–1.15), and prostate cancer (HR, 1.07; 95% CI, 1.01–1.13), and with a reduced risk of lung cancer (HR, 0.88; 95% CI, 0.79–0.97) after adjusting for potential confounders. Statistical interaction was observed for gender, age, and education for the association of glucosamine use with overall cancer risk (all Pinteraction < 0.027). These results remained unchanged in the sensitivity analyses.
Conclusions:
Regular glucosamine use was associated with lower risk of lung cancer but higher risk of skin cancer, prostate cancer, and overall cancer.
Impact:
The roles of glucosamine use potentially differ in the development of different site-specific cancers.
Introduction
Cancer remains the leading cause of death and poses a huge threat to life expectancy worldwide, with an estimated 19.3 million newly diagnosed cancer cases and almost 10.0 million cancer-related deaths in 2020 (1). As inflammation plays an important role in carcinogenesis and cancer progression, anti-inflammatory drugs are considered a potential intervention for cancer prevention (2–4).
While commonly used for treating osteoarthritis and joint pain (3, 5–8), glucosamine has gained popularity, as a kind of nonvitamin and nonmineral dietary supplement with anti-inflammatory properties and low risk of adverse effects, in the United States and most European countries for its potential in the prevention of many inflammation-related diseases including cancer (9–11). However, no randomized controlled trials have evaluated the efficacy of glucosamine in preventing cancers. Current evidence supporting the anticancer benefit was mainly from population-based observational studies, which suggested the associations between glucosamine use and reduced risk of lung cancer and colorectal cancer (12–18). However, given huge heterogeneities in the carcinogenesis of different site-specific cancers, it is unclear whether this potential anticancer effect of glucosamine could apply to other cancer types, especially those that were less studied, such as prostate cancer and skin cancer. In addition, heterogeneities may also exist across previous studies conducted in different populations. Comprehensive investigations examining these associations for various site-specific cancers in the same population are urgently required to inform the proper use of glucosamine in the prevention of different types of cancers.
In this study, we examined the associations between glucosamine use and risk of overall cancer and 19 site-specific cancers of interest based on data from the large-scale nationwide prospective UK Biobank cohort study.
Materials and Methods
Participants and setting
The UK Biobank is a population-based prospective cohort study with over 500,000 participants aged 40 to 69 years enrolled between 2006 and 2010 in the United Kingdom. Detailed information on the UK Biobank has been described elsewhere (19). Baseline characteristics of all participants were recorded from self-reports, interviews, and physical measurements. All participants who had not been diagnosed with cancer before baseline enrollment were included in this study.
Exposure assessment
Participants were asked “do you regularly take any of the following” (data-field 6179) at baseline. Each participant could make multiple selections from a list of supplements including glucosamine, fish oil, selenium, iron, zinc, and calcium, or none of the above. Participants indicating regular use of glucosamine supplements were defined as glucosamine users, and otherwise as nonusers. Participants with missing data on this information were excluded from the study.
Outcome ascertainment
Data on incidence of cancers were obtained via linkage to national registries, in which cancer diagnoses were defined according to the International Classification of Disease, 10th Revision (ICD-10; data-field 40006; ref. 20). Outcomes of overall cancer and 19 site-specific cancers of interest (cervical, ovarian, endometrium, other female specific cancers, lung, brain, kidney, breast, prostate, other male specific cancers, malignant melanoma, esophagus, stomach, colorectal, pancreas, hepatobiliary, thyroid, skin, all other cancer) were identified from cancer diagnosis records using predefined ICD-10 codes (Supplementary Table S1). Participants were followed up from baseline enrollment until the onset of study outcomes, death, or the end of follow-up [defined as the date of the last occurrence of death in all included participants (November 10, 2021)], whichever came first.
Covariates ascertainment
In our analysis we adjusted for potential confounders, which were common known risk factors for cancers (21, 22). These factors included sociodemographic (gender, ethnicity, education, and Townsend Deprivation Index), lifestyle factors [smoking status, alcohol consumption, vegetable consumption, processed meat intake, fresh fruit intake, vitamin supplements, mineral supplements, body mass index (BMI), and physical activities], and medical conditions and services [osteoarthritis, rheumatoid arthritis, joint pain, multiple sclerosis, history of screening for bowel cancer or breast cancer, prostate-specific antigen test, aspirin, non-aspirin nonsteroidal anti-inflammatory drug (NSAIDs), hormone-replacement therapy, family history of cancer, and overall health rating]. Details of the measurements and definitions for all the covariates are in Supplementary Table S2.
Main analysis
Baseline characteristics of participants were summarized as mean and standard deviation (SD) for continuous variables and frequency and percentages for categorical variables according to the status of glucosamine use. Given the large sample size included in our study, we assessed the between-group differences in baseline characteristics using standardized mean difference (SMD), which is not as sensitive to sample size as traditional tests. An SMD > 0.1 indicated between-group imbalance of baseline characteristics (23).
Cox proportional hazards models were used to estimate the HRs and 95% confidence intervals (CI) for associations between glucosamine use and risk of overall cancer and different types of site-specific cancers. To adjust for potential effect of age, we used age as the time-scale and stratified by birth cohort (every 10-year interval; ref. 24). The proportional hazards assumption was tested using Schoenfeld residuals and we found no violation of the assumption in this study.
Four models were constructed. The basic model (model 1) adjusted for sociodemographic variables. Model 2 adjusted for sociodemographic variables and lifestyle factors. Model 3 adjusted for sociodemographic variables and medical conditions and services. The full model (model 4) adjusted for all the sociodemographic variables, lifestyle factors, and medical conditions and services. History of specific cancer screening was only adjusted for the corresponding cancer outcome. The main analysis was complete-case analysis. Baseline characteristics for all participants included in the study and those included in the main analysis were described to evaluate selection bias.
Secondary analysis
We conducted subgroup analyses using the fully-adjusted model (model 4) to assess potential interactions by the following factors at baseline: age group (<55/≥55 years old), gender, cancer screening history, smoking status, alcohol consumption, overall health rating, ethnicity, education, regular use of vitamin/mineral/other dietary supplements, regularly use of aspirin, regular use of non-aspirin NSAIDs, and disease history of osteoarthritis. The potential effect modifications were assessed by the statistical significance of the cross-product term of the stratifying covariate and glucosamine use in the full model.
Several sensitivity analyses were performed using the full model to test the robustness of our main results. First, to explore the impact of missing data, we conducted multiple imputation with chained equations (25), with five datasets imputed using cancer outcome, follow-up time, glucosamine use, and all potential confounder covariates as independent variables in the models (26). Second, to explore the influence of reverse causation, we excluded new incident cancer cases diagnosed within 2 years of follow-up. Third, we conducted a competing risk analysis treating death as a competing event for cancers. Fourth, we censored events of certain cancer types (i.e., prostate cancer, skin cancer, and malignant melanoma) for overall cancer incidence in male and female participants to explore whether these kinds of cancers contributed most to the increased risk of overall cancer associated with glucosamine use. Fifth, we adopted pack years, instead of smoking status, as a proxy of smoking to adjust for this confounder.
We used R V.3.6.2 (R Development Core Team) for all analyses and P < 0.05 (two-sided) was considered statistically significant.
Ethics statement
UK Biobank has approval from the North West Multi-centre Research Ethics Committee as a Research Tissue Bank approval (reference: 11/NW/0382). All participants provided written informed consent.
Data availability
This study was conducted using the UK biobank resource under Application No.80476. All data in this study are available from the UK biobank (www.ukbiobank.ac.uk) on reasonable request, subject to permission by the UK biobank.
Results
Baseline characteristics
A total of 450,207 eligible participants were included in the study, among whom 418,060 with complete data contributed to the main analysis (Fig. 1). Baseline characteristics for the 450,207 participants and 418,060 included in the main analysis were similar (Supplementary Table S3). Among 450,207 participants, 84,895 (18.9%) reported regular glucosamine use at baseline (Table 1). Compared with nonusers, glucosamine users were more likely to be older, women, and more exposed to non-aspirin NSAIDs, vitamin supplements, and mineral supplements. They also had larger proportions with a history of prostate-specific antigen (PSA) test and breast cancer screening. Among 450,207 participants, 68,579 (15.2%) were diagnosed with cancer during follow-up and tended to be older and receive cancer screenings (Supplementary Table S4).
Figure 1.
Flow chart of the UK Biobank participants included in the study. Abbreviation: N, number.
Table 1.
Baseline characteristics of all participants included in the study.
| Regular glucosamine use, number (%) | ||||
|---|---|---|---|---|
| Characteristics | Total number (%) | No | Yes | SMDa |
| Number of participants | 450,207 | 365,312 | 84,895 | |
| Mean (SDb) baseline age (years) | 56.2 (8.1) | 55.6 (8.2) | 58.8 (7.1) | 0.379 |
| Women | 240,097 (53.3) | 187,815 (51.4) | 52,282 (61.6) | 0.206 |
| TDIb, mean (SD) | −1.29 (3.09) | −1.18 (3.15) | −1.78 (2.80) | 0.201 |
| Mean (SD) body mass index (kg/m²) | 27.4 (4.8) | 27.5 (4.8) | 27.4 (4.7) | 0.019 |
| Education | ||||
| College degree or higher | 147,400 (32.7) | 119,146 (32.6) | 28,254 (33.3) | 0.023 |
| No college degree | 298,120 (66.2) | 242,235 (66.3) | 55,885 (65.8) | |
| Unknown | 4,687 (1.1) | 3,931 (1.1) | 756 (0.9) | |
| Ethnicity | ||||
| White | 407,183 (90.5) | 329,059 (90.1) | 78,124 (92) | 0.068 |
| Others | 41,451 (9.2) | 34,935 (9.6) | 6,516 (7.7) | |
| Unknown | 1,573 (0.3) | 1,318 (0.3) | 255 (0.3) | |
| Smoking status | ||||
| Current smokers | 47,549 (10.6) | 42,004 (11.5) | 5,545 (6.5) | 0.189 |
| Previous smokers (quit ≥10 years) | 74,378 (16.5) | 57,866 (15.8) | 16,512 (19.5) | |
| Previous smokers (quit <10 years) | 79,099 (17.6) | 63,454 (17.4) | 15,645 (18.4) | |
| Never | 247,530 (55) | 200,618 (54.9) | 46,912 (55.3) | |
| Unknown | 1,651 (0.3) | 1,370 (0.4) | 281 (0.3) | |
| Smoking (pack years), mean (SD) | 8.2 (15.7) | 7.7 (15.1) | 10.9 (18.7) | 0.093 |
| Alcohol consumption | ||||
| Daily | 91,437 (20.3) | 72,452 (19.8) | 18,985 (22.4) | 0.115 |
| Three or four times a week | 104,400 (23.2) | 83,234 (22.8) | 21,166 (24.9) | |
| Once or twice a week | 116,338 (25.8) | 94,947 (26) | 21,391 (25.1) | |
| One to three times a month | 50,151 (11.1) | 41,161 (11.3) | 8,990 (10.6) | |
| Special occasions only | 51,385 (11.4) | 42,414 (11.6) | 8,971 (10.6) | |
| Never | 36,102 (8.1) | 30,746 (8.4) | 5,356 (6.3) | |
| Unknown | 394 (0.1) | 358 (0.1) | 36 (0.1) | |
| METb, min/week | ||||
| <600 | 213,636 (47.5) | 178,036 (48.7) | 35,600 (41.9) | 0.137 |
| ≥600 | 236,571 (52.5) | 187,276 (51.3) | 49,295 (58.1) | |
| Vegetable consumption | ||||
| >10 | 18,341 (4.1) | 14,530 (4) | 3,811 (4.5) | 0.154 |
| 6–10 | 121,176 (26.9) | 95,219 (26.1) | 25,957 (30.6) | |
| 1–5 | 295,713 (65.7) | 242,057 (66.2) | 53,656 (63.2) | |
| never | 14,977 (3.3) | 13,506 (3.7) | 1,471 (1.7) | |
| Fruit consumption | ||||
| >10 | 733 (0.2) | 602 (0.2) | 131 (0.2) | 0.187 |
| 6–10 | 11,595 (2.6) | 8,934 (2.4) | 2,661 (3.1) | |
| 1–5 | 393,654 (87.4) | 316,426 (86.6) | 77,228 (91) | |
| never | 42,310 (9.4) | 37,616 (10.3) | 4,694 (5.5) | |
| Unknown | 1,915 (0.4) | 1,734 (0.5) | 181 (0.2) | |
| Processed meat intake | ||||
| Once or more daily | 3,779 (0.8) | 3,319 (0.9) | 460 (0.5) | 0.136 |
| 5–6 times a week | 14,399 (3.2) | 12,334 (3.4) | 2,065 (2.4) | |
| 2–4 times a week | 122,550 (27.2) | 102,007 (27.9) | 20,543 (24.2) | |
| Once a week | 130,850 (29.1) | 106,429 (29.1) | 24,421 (28.8) | |
| Less than once a week | 135,818 (30.2) | 107,014 (29.3) | 28,804 (33.9) | |
| Never | 41,767 (9.3) | 33,293 (9.1) | 8,474 (10) | |
| Unknown | 1,044 (0.2) | 916 (0.3) | 128 (0.2) | |
| Aspirin | ||||
| Yes | 61,686 (13.7) | 49,981 (13.7) | 11,705 (13.8) | 0.003 |
| No | 388,521 (86.3) | 315,331 (86.3) | 73,190 (86.2) | |
| Non-aspirin NSAIDsb | ||||
| Yes | 74,659 (16.6) | 52,779 (14.4) | 21,880 (25.8) | 0.285 |
| No | 375,548 (83.4) | 312,533 (85.6) | 63,015 (74.2) | |
| Hormone-replacement therapy in female | ||||
| Yes | 89,718 (37.4) | 63,887 (34.0) | 25,831 (49.4) | 0.316 |
| No | 149,511 (62.3) | 123,191 (65.6) | 26,230 (50.3) | |
| Unknown | 868 (0.4) | 737 (0.4) | 131 (0.3) | |
| Vitamin supplements | ||||
| Yes | 141,899 (31.5) | 94,833 (26) | 47,066 (55.4) | 0.631 |
| No | 306,735 (68.1) | 269,261 (73.7) | 37,474 (44.2) | |
| Unknown | 1,573 (0.4) | 1,218 (0.3) | 355 (0.4) | |
| Mineral supplements | ||||
| Yes | 165,771 (36.8) | 107,129 (29.3) | 58,642 (69.1) | 0.867 |
| No | 284,436 (63.2) | 258,183 (70.7) | 26,253 (30.9) | |
| Overall health rating | ||||
| Excellent | 75,636 (16.8) | 60,676 (16.6) | 14,960 (17.6) | 0.142 |
| Good | 260,897 (58) | 208,772 (57.1) | 52,125 (61.4) | |
| Fair | 92,432 (20.5) | 77,135 (21.1) | 15,297 (18) | |
| Poor | 19,284 (4.3) | 16,994 (4.7) | 2,290 (2.7) | |
| Unknown | 1,958 (0.4) | 1,735 (0.5) | 223 (0.3) | |
| Osteoarthritis | ||||
| Yes | 14,298 (3.2) | 9,886 (2.7) | 4,412 (5.2) | 0.128 |
| No | 435,909 (96.8) | 355,426 (97.3) | 80,483 (94.8) | |
| Rheumatoid arthritis | ||||
| Yes | 8,409 (1.9) | 6,628 (1.8) | 1,781 (2.1) | 0.020 |
| No | 441,798 (98.1) | 358,684 (98.2) | 83,114 (97.9) | |
| Joint pain | ||||
| Yes | 12,637 (2.8) | 9,649 (2.6) | 2,988 (3.5) | 0.051 |
| No | 437,570 (97.2) | 355,663 (97.4) | 81,907 (96.5) | |
| Multiple sclerosis | ||||
| Yes | 1,862 (0.4) | 1,572 (0.4) | 290 (0.3) | 0.014 |
| No | 448,345 (99.6) | 363,740 (99.6) | 84,605 (99.7) | |
| Family cancer history | ||||
| Yes | 155,429 (34.5) | 124,553 (34.1) | 30,876 (36.4) | 0.048 |
| No | 294,778 (65.5) | 240,759 (65.9) | 54,019 (63.6) | |
| Ever had bowel cancer screening | ||||
| Yes | 134,914 (30) | 104,061 (28.5) | 30,853 (36.3) | 0.169 |
| No | 307,902 (68.4) | 254,979 (69.8) | 52,923 (62.3) | |
| Unknown | 7,391 (1.6) | 6,272 (1.7) | 1,119 (1.3) | |
| Ever had PSAb test in male | ||||
| Yes | 57,444 (27.3) | 45,632 (25.7) | 11,812 (36.2) | 0.229 |
| No | 141,524 (67.4) | 122,272 (68.9) | 19,252 (59) | |
| Unknown | 11,142 (5.3) | 9,593 (5.4) | 1,549 (4.8) | |
| Ever had breast cancer screening in female | ||||
| Yes | 188,423 (78.5) | 141,579 (75.4) | 46,844 (89.6) | 0.381 |
| No | 51,290 (21.3) | 45,891 (24.4) | 5,399 (10.3) | |
| Unknown | 384 (0.2) | 345 (0.2) | 39 (0.1) | |
aSMD, standardized mean difference (shown as an absolute value). Participants with missing data were included as the “unknown” group when calculating SMD. An SMD >0.1 indicated a between-group imbalance of baseline characteristics.
bAbbreviations: SD, standard deviation; TDI, Townsend Deprivation Index; MET, Metabolic Equivalent Task; NSAIDs, nonsteroidal anti-inflammatory drugs; PSA, prostate-specific antigen.
Glucosamine use and risk of cancer
In complete-case analysis (Table 2), during a median follow-up of 12.4 years [interquartile range (IQR), 11.5–13.3 years] for 80,045 glucosamine users and 12.5 years (IQR, 11.6–13.3 years) for 338,015 nonusers, we identified 63,430 (15.2%) overall cancers. For gender-specific cancers, we identified 9,111 (4.8%) prostate cancers and 475 (3%) other site-specific cancers in males; 447 (0.2%) cervical cancers, 9,068 (4.0%) breast cancers, 843 (0.4%) ovarian cancers, 1,294 (0.6%) endometrium cancers, and 322 (0.1%) other site-specific cancers in females. Besides, we identified 3,136 (0.8%) lung cancers, 846 (0.2%) esophageal cancers, 573 (0.1%) stomach cancers, 5,113 (1.2%) colorectal cancers, 746 (0.2%) hepatobiliary cancers, 1,038 (0.2%) pancreas cancers, 3,467 (0.8%) malignant melanoma cases, 20,668 (4.9%) skin cancers, 1,256 (0.3%) kidney cancers, 359 (0.1%) thyroid cancers, 665 (0.2%) brain cancers, and 8,439 (2.0%) other site-specific cancers.
Table 2.
Association between regular glucosamine use and risk of cancers.
| Regular glucosamine use | Adjusted hazard ratio (95% confidence interval) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| No (N = 338,015) | Yes (N = 80,045) | Model 1a | Model 2b | Model 3c | Model 4d | |||||
| Site of cancer | Number of events (%) | Person-years | Incidence (per 1000 person-years) | Number of events (%) | Person years | Incidence (per 1000 person-years) | ||||
| Overall cancer | 49,596 (14.7) | 3,925,149 | 12.635 | 13,834 (17.3) | 918,147 | 15.067 | 1.03 (1.01–1.05)** | 1.03 (1.01–1.05)** | 1.04 (1.02–1.06)*** | 1.04 (1.01–1.06) *** |
| Prostate (male) | 7,264 (4.6) | 1,843,831 | 3.940 | 1847 (6.2) | 343,544 | 5.376 | 1.11 (1.05–1.17)*** | 1.09 (1.03–1.15)** | 1.08 (1.03–1.14)** | 1.07 (1.01–1.13)* |
| Other male specific | 401 (0.3) | 1,960,805 | 0.205 | 74 (0.2) | 368,863 | 0.201 | 0.90 (0.70–1.15) | 0.82 (0.63–1.06) | 0.93 (0.72–1.19) | 0.84 (0.64–1.09) |
| Cervical (female) | 373(0.2) | 2,242,193 | 0.166 | 74 (0.1) | 630,931 | 0.117 | 1.08 (0.84–1.39) | 1.18 (0.90–1.54) | 1.08 (0.84–1.40) | 1.18 (0.90–1.54) |
| Breast (female) | 6,937 (3.9) | 2,068,394 | 3.354 | 2131 (4.2) | 580,142 | 3.673 | 1.04 (0.99–1.09) | 1.04 (0.98–1.09) | 1.03 (0.98–1.08) | 1.03 (0.98–1.09) |
| Ovarian (female) | 648 (0.4) | 2,045,160 | 0.317 | 195 (0.4) | 574,998 | 0.339 | 0.94 (0.80–1.10) | 0.91 (0.77–1.09) | 0.94 (0.80–1.11) | 0.91 (0.77–1.09) |
| Endometrium (female) | 962 (0.5) | 2,244,076 | 0.429 | 332 (0.7) | 630,466 | 0.527 | 1.07 (0.95–1.22) | 1.01 (0.89–1.16) | 1.10 (0.97–1.25) | 1.03 (0.90–1.18) |
| Other female specific | 233 (0.1) | 2,244,369 | 0.104 | 89 (0.2) | 630,996 | 0.141 | 1.18 (0.92–1.52) | 1.18 (0.91–1.54) | 1.17 (0.91–1.50) | 1.17 (0.90–1.53) |
| Lung | 2,615 (0.8) | 4,202,895 | 0.622 | 521 (0.7) | 999,567 | 0.521 | 0.74 (0.67–0.81) *** | 0.85 (0.77–0.94) ** | 0.77 (0.70–0.85) *** | 0.88 (0.79–0.97)* |
| Esophagus | 700 (0.2) | 3,779,066 | 0.185 | 146 (0.2) | 898,356 | 0.163 | 0.81 (0.67–0.96)* | 0.85 (0.70–1.02) | 0.84 (0.70–1.00) | 0.87 (0.72–1.05) |
| Stomach | 471 (0.1) | 3,841,060 | 0.123 | 102 (0.1) | 913,081 | 0.112 | 0.85 (0.69–1.06) | 0.96 (0.76–1.21) | 0.89 (0.71–1.10) | 0.99 (0.78–1.25) |
| Colorectal | 4,072 (1.2) | 3,959,252 | 1.028 | 1041 (1.3) | 941,037 | 1.106 | 0.94 (0.87–1.00) | 0.95 (0.88–1.02) | 0.95 (0.89–1.02) | 0.96 (0.89–1.03) |
| Hepatobiliary | 600 (0.2) | 3,753,322 | 0.160 | 146 (0.2) | 892,010 | 0.164 | 0.90 (0.75–1.08) | 0.96 (0.79–1.17) | 0.95 (0.79–1.14) | 1.01 (0.83–1.23) |
| Pancreas | 790 (0.2) | 3,853,922 | 0.205 | 248 (0.3) | 915,380 | 0.271 | 1.11 (0.96–1.28) | 1.13 (0.97–1.32) | 1.11 (0.96–1.28) | 1.14 (0.97–1.33) |
| Malignant melanoma | 2,674 (0.8) | 4,024,980 | 0.664 | 793 (1.0) | 952,917 | 0.832 | 1.11 (1.03–1.21)** | 1.08 (0.99–1.17) | 1.10 (1.01–1.19)* | 1.07 (0.98–1.17) |
| Skin | 15,650 (4.6) | 4,112,501 | 3.805 | 5018 (6.3) | 969,892 | 5.174 | 1.13 (1.10–1.17)*** | 1.11 (1.07–1.15)*** | 1.14 (1.10–1.17)*** | 1.11 (1.07–1.15)*** |
| Kidney | 1,026 (0.3) | 3,801,720 | 0.270 | 230 (0.3) | 903,729 | 0.255 | 0.84 (0.73–0.97)* | 0.89 (0.76–1.03) | 0.87 (0.75–1.01) | 0.91 (0.78–1.07) |
| Thyroid | 297 (0.1) | 3,756,325 | 0.079 | 62 (0.1) | 889,399 | 0.070 | 0.73 (0.55–0.96)* | 0.78 (0.58–1.05) | 0.75 (0.57–0.99)* | 0.80 (0.60–1.08) |
| Brain | 537 (0.2) | 3,714,623 | 0.145 | 128 (0.2) | 882,834 | 0.145 | 0.93 (0.76–1.13) | 0.92 (0.75–1.13) | 0.92 (0.76–1.13) | 0.91 (0.74–1.12) |
| All other cancers | 6,734 (2.0) | 3,925,149 | 1.716 | 1705 (2.1) | 918,147 | 1.857 | 0.99 (0.93–1.03) | 0.99 (0.94–1.05) | 1.00 (0.94–1.05) | 1.01 (0.95–1.07) |
aAdjusted for sociodemographic variables (including gender, ethnicity, education and Townsend Deprivation Index).
bAdjusted for sociodemographic variables mentioned above and lifestyle factors (including smoking, alcohol consumption, vegetable consumption, processed meat intake, fresh fruit intake, vitamin supplements, mineral supplements, body mass index, and physical activities).
cAdjusted for sociodemographic variables mentioned above as well as medical conditions and services (including diagnosis of osteoarthritis/ rheumatoid arthritis/ joint pain/ multiple sclerosis, history of screening for bowel cancer/ breast cancer/ prostate-specific antigen test, use of aspirin/NSAIDs/Hormone-replacement therapy, family history of cancer, and overall health rating).
dAdjusted for all covariates mentioned above, including sociodemographic variables, lifestyle factors, and medical conditions and services.
*P ≤ 0.05.
**P ≤ 0.01.
***P ≤ 0.001.
The magnitudes of association generally decreased as more covariates were progressively adjusted in the model. In the full-adjusted model, glucosamine use was statistically significantly associated with increased risk for overall cancer (HR, 1.04; 95% CI, 1.01–1.06), prostate cancer (HR, 1.07; 95% CI, 1.01–1.13), and skin cancer (HR, 1.11; 95% CI, 1.07–1.15), but with reduced risk for lung cancer (HR, 0.88; 95% CI, 0.79–0.97). There seemed some evidence of an increased risk for malignant melanoma (HR, 1.07; 95% CI, 0.98–1.17) and a decreased risk for kidney (HR, 0.91; 95% CI, 0.78–1.07) and thyroid (HR, 0.80; 95% CI, 0.60–1.08) cancer although the estimates were not statistically significant.
Sensitivity analysis
For overall cancer, lung cancer, skin cancer, and prostate cancer, the statistically significant associations in the main analysis did not change appreciably in all predefined sensitivity analyses (Table 3). After censoring prostate cancer and malignant melanoma in male participants or malignant melanoma in female participants (Supplementary Table S5), the associations between glucosamine use and risk of overall cancer incidence did not change appreciably, but disappeared when censoring skin cancer in both groups.
Table 3.
Sensitivity analyses for the association between glucosamine use and risk of cancers.
| Adjusted hazard ratio (95% confidence interval)a | ||||
|---|---|---|---|---|
| Site of Cancer | Excluding incident cases within 2 years of enrollment (Number = 408,521) | Competing risk analysis (Number = 418,060) | Multiple imputation analysis (Number = 450,207) | Using pack-years adjusting smoking status (Number = 356,472) |
| Overall cancer | 1.03 (1.01–1.06)** | 1.03 (1.01–1.05)** | 1.04 (1.02–1.06)*** | 1.03 (1.01–1.06)** |
| Prostate (male) | 1.08 (1.02–1.15)* | 1.08 (1.02–1.14)** | 1.06 (1.00–1.12)* | 1.05 (1.01–1.10)* |
| Other male specific | 0.77 (0.57–1.04) | 0.84 (0.64–1.09) | 0.81 (0.63–1.05) | 0.79 (0.58–1.07) |
| Cervical (female) | 1.23 (0.90–1.68) | 1.03 (0.98–1.08) | 1.14 (0.87–1.49) | 1.25 (0.94–1.67) |
| Breast (female) | 1.03 (0.97–1.09) | 1.03 (0.98–1.08) | 1.04 (0.98–1.09) | 1.04 (0.98–1.10) |
| Ovarian (female) | 0.95 (0.78–1.15) | 0.91 (0.76–1.08) | 0.92 (0.78–1.10) | 0.92 (0.77–1.12)) |
| Endometrium (female) | 1.00 (0.86–1.16) | 1.04 (0.91–1.19) | 1.05 (0.92–1.19) | 1.03 (0.89–1.19)) |
| Other female specific | 1.06 (0.79–1.41) | 1.16 (0.89–1.50) | 1.15 (0.89–1.50) | 1.20 (0.90–1.59) |
| Lung | 0.89 (0.80–1.00)* | 0.85 (0.77–0.94)** | 0.89 (0.81–0.98)* | 0.87 (0.78–0.97)* |
| Esophagus | 0.81 (0.66–1.00) | 0.86 (0.71–1.04) | 0.86 (0.74–1.06) | 0.87 (0.71–1.08) |
| Stomach | 1.06 (0.83–1.36) | 0.97 (0.77–1.22) | 1.00 (0.80–1.24) | 0.94 (0.73–1.21) |
| Colorectal | 0.97 (0.90–1.05) | 0.96 (0.89–1.04) | 0.97 (0.91–1.05) | 0.98 (0.90–1.06) |
| Hepatobiliary | 1.00 (0.81–1.23) | 0.99 (0.82–1.21) | 1.03 (0.85–1.24) | 0.98 (0.79–1.22) |
| Pancreas | 1.12 (0.95–1.32) | 1.13 (0.97–1.33) | 1.17 (1.01–1.36)* | 1.18 (0.99–1.39) |
| Malignant melanoma | 1.06 (0.96–1.16) | 1.08 (0.99–1.17) | 1.07 (0.98–1.16) | 1.05 (0.96–1.16) |
| Skin | 1.11 (1.07–1.15)*** | 1.12 (1.08–1.16)*** | 1.12 (1.08–1.15)*** | 1.12 (1.08–1.16)*** |
| Kidney | 0.88 (0.75–1.05) | 0.92 (0.79–1.07) | 0.90 (0.78–1.04) | 0.90 (0.76–1.07) |
| Thyroid | 0.83 (0.60–1.16) | 0.89 (0.66–1.21) | 0.81 (0.61–1.07) | 0.80 (0.59–1.09) |
| Brain | 0.89 (0.71–1.12) | 0.93 (0.76–1.15) | 0.91 (0.74–1.11) | 0.92 (0.73–1.16) |
| All other cancer | 1.00 (0.94–1.07) | 1.00 (0.94–1.06) | 1.01 (0.96–1.07) | 1.01 (0.95–1.08) |
aAll sensitivity analyses were performed using the full model adjusted for sociodemographic variables (including gender, ethnicity, education and Townsend Deprivation Index), lifestyle factors (including smoking, alcohol consumption, vegetable consumption, processed meat intake, fresh fruit intake, vitamin supplements, mineral supplements, body mass index, and physical activities), and medical conditions and services (including diagnosis of osteoarthritis/rheumatoid arthritis / joint pain / multiple sclerosis, history of screening for bowel cancer / breast cancer / prostate-specific antigen test, use of aspirin/NSAIDs/hormone-replacement therapy, family history of cancer, and overall health rating).
*P ≤ 0.05.
**P ≤ 0.01.
***P ≤ 0.001.
Subgroup analysis
The positive association between glucosamine use and overall cancer risk was modified by gender (P < 0.0001), age (P = 0.016), and education (P = 0.027; Fig. 2). For site-specific cancers, age, smoking status, cancer screening experience, and aspirin use showed statistical interactions in certain cancer outcomes, with all P values for interaction less than 0.049 (Table 4). Details of results from subgroup analysis are shown in Supplementary Figs. S1 to S19.
Figure 2.
Subgroup analyses for the association between glucosamine use and risk of overall cancer incidence in participants of the UK Biobank. All HRs were adjusted for sociodemographic variables (including gender, ethnicity, education and Townsend Deprivation Index), lifestyle factors (including smoking, alcohol consumption, vegetable consumption, processed meat intake, fresh fruit intake, vitamin supplements, mineral supplements, body mass index, and physical activities), and medical conditions and services (including diagnosis of osteoarthritis / rheumatoid arthritis / joint pain / multiple sclerosis, use of aspirin/NASAIDS, family history of cancer, and overall health rating). Abbreviation: N, number.
Table 4.
Statistically significant between-group difference in the association between glucosamine use and risk of cancers.
| Stratifying covariates | Number of participants | Number of events | HR (95% CI)a | P interaction b | |
|---|---|---|---|---|---|
| Overall cancer | |||||
| Age | <55 | 168,493 | 13,880 | 1.05 (1.00–1.11) | 0.016 |
| ≥55 | 249,567 | 49,550 | 1.05 (1.02–1.07) | ||
| Gender | Male | 188,808 | 32,193 | 1.06 (1.03–1.10) | <0.0001 |
| Female | 229,252 | 31,237 | 1.04 (1.01–1.07) | ||
| Education | College degree or higher | 139,827 | 19,494 | 1.06 (1.02–1.10) | 0.027 |
| No college degree | 278,233 | 43,936 | 1.02 (1.00–1.05) | ||
| Lung cancer | |||||
| Smoking | Current | 42,886 | 1,238 | 0.69 (0.56–0.84) | 0.004 |
| Previous (quit <10 years) | 73,565 | 749 | 0.95 (0.78–1.15) | ||
| Previous (qui t≥10 years) | 69,109 | 679 | 0.85 (0.70–1.03) | ||
| Never | 232,500 | 470 | 1.17 (0.94–1.47) | ||
| Breast cancer | |||||
| Smoking | Current | 19,775 | 778 | 1.23 (1.02–1.50) | 0.026 |
| Previous (quit <10 years) | 39,708 | 1,685 | 0.96 (0.85–1.08) | ||
| Previous (quit ≥10 years) | 31,549 | 1,295 | 0.95 (0.83–1.09) | ||
| Never | 138,220 | 5,310 | 1.06 (0.99–1.13) | ||
| Ethnic group | White | 208,274 | 8,310 | 1.02 (0.96–1.08) | 0.011 |
| Others | 20,978 | 758 | 1.17 (0.98–1.40) | ||
| Cervical cancer | |||||
| Age | <55 | 93,651 | 323 | 1.44 (1.03–2.01) | 0.048 |
| ≥55 | 135,601 | 124 | 0.86 (0.55–1.36) | ||
| Aspirin use | yes | 21,730 | 28 | 2.92 (1.22–7.02) | 0.040 |
| no | 207,522 | 419 | 1.08 (0.81–1.44) | ||
| Colorectal cancer | |||||
| Ever had bowel cancer screening | yes | 125,016 | 1,846 | 1.01 (0.90–1.13) | 0.023 |
| no | 293,044 | 3,267 | 0.90 (0.82–0.99) | ||
| Osteoarthritis | yes | 12,908 | 192 | 1.64 (1.18–2.27) | 0.022 |
| no | 405,152 | 4,921 | 0.94 (0.87–1.02) | ||
| Esophageal cancer | |||||
| Aspirin use | yes | 55,451 | 187 | 1.19 (0.82–1.73) | 0.013 |
| no | 362,609 | 659 | 0.79 (0.63–0.99) | ||
| Osteoarthritis | yes | 12,908 | 18 | 4.00 (1.36–11.82) | 0.016 |
| no | 405,152 | 828 | 0.83 (0.68–1.01) | ||
| Malignant melanoma | |||||
| Vitamin/mineral supplements | yes | 196,453 | 1,719 | 1.15 (1.04–1.27) | 0.010 |
| no | 221,607 | 1,748 | 0.86 (0.71–1.04) | ||
| Other male cancer | |||||
| Age | <55 | 74,842 | 132 | 0.50 (0.23–1.10) | 0.049 |
| ≥55 | 113,966 | 343 | 0.88 (0.66–1.18) | ||
| Skin cancer | |||||
| Gender | male | 188,808 | 10,981 | 1.13 (1.07–1.19) | 0.009 |
| female | 229,252 | 9,687 | 1.11 (1.05–1.16) | ||
| Prostate cancer | |||||
| Ever had PSAc test | yes | 54,201 | 3,986 | 1.00 (0.92–1.09) | 0.007 |
| no | 134,607 | 5,125 | 1.14 (1.05–1.23) | ||
| Brain cancer | |||||
| Overall health rating | Excellent | 72,629 | 113 | 0.80 (0.49–1.31) | 0.021 |
| Fair | 83,321 | 126 | 1.22 (0.77–1.92) | ||
| Good | 245,649 | 398 | 0.94 (0.72–1.23) | ||
| Poor | 16,461 | 28 | Not estimatedd | ||
aAll sensitivity analyses were performed using the full model adjusted for sociodemographic variables (including gender, ethnicity, education and Townsend Deprivation Index), lifestyle factors (including smoking, alcohol consumption, vegetable consumption, processed meat intake, fresh fruit intake, vitamin supplements, mineral supplements, body mass index, and physical activities), and medical conditions and services (including diagnosis of osteoarthritis/rheumatoid arthritis/joint pain/multiple sclerosis, history of screening for bowel cancer/breast cancer/prostate-specific antigen test, use of aspirin/NSAIDs/hormone-replacement therapy, family history of cancer, and overall health rating).
bThe potential effect modifications were assessed by modeling the cross-product term of the stratifying covariate with glucosamine use in the full model. P < 0.05 for interaction was considered statistically significant.
cAbbreviation: PSA, prostate-specific antigen.
dNot estimated due to no events among the glucosamine users.
Discussion
To our best knowledge, this was the first population-based study to examine the risk of various site-specific cancers associated with glucosamine use in the same population. We discovered that regular glucosamine use was statistically significantly associated with a 4% higher risk of overall cancer, an 11% higher risk of skin cancer, and a 16% lower risk of lung cancer in all participants; and a 7% higher risk of prostate cancer in male participants. These associations were independent of sociodemographic factors, lifestyle factors, medical conditions and services. In addition, the positive association between glucosamine use and overall cancer risk was modified by gender, age, and education. The main conclusions did not change substantially in sensitivity analyses.
Our study suggested that regular glucosamine use may have different roles in the risk of site-specific cancers. Our study provided new evidence on the association of glucosamine use with an increased risk of skin cancer, which may be the main contributor to the increased risk of overall cancer. Although we found an increased risk of prostate cancer was associated with glucosamine supplement, the association was not statistically significant from the VITAL cohort (27). For lung cancer, previous studies also found reduced risk associated with glucosamine use (12, 13). These findings also agreed with the association of glucosamine use with the reduced risk of death from lung cancer (28). For colorectal cancer, previous findings have been inconsistent. Four of seven studies in a latest systematic review reported significantly reduced risk of colorectal cancer incidence associated with glucosamine use, but the other three studies did not find statistical significance as in our analysis (17). In addition, our study suggested possible associations for the increased risk of melanoma and lower risk of kidney and thyroid cancers, although not statistically significant, which should be further investigated. The association for kidney cancer was also in line with lower mortality from kidney cancer associated with glucosamine use (28). Given different risk of site-specific cancers associated with glucosamine use, any evidence on glucosamine use for cancer prevention (13, 17) should be treated with caution in the interim when clinical trials have yet to demonstrate the efficacy of glucosamine against these cancers.
Our study identified some novel statistical interactions for the association between glucosamine use and risk of cancers. The association for overall cancer is statistically stronger in man (HR, 1.06; 95% CI, 1.03–1.10) than in women (HR, 1.04; 95% CI, 1.01–1.07), which might be partially explained by the increased risk for prostate cancer only in male participants as well as the higher risk of skin cancer in male than in female participants. In addition, as indicated by censoring certain cancer types in the sensitivity analysis (Supplementary table S5), skin cancer seems to be the predominant cause for the elevated overall cancer risk in both genders. We also observed a stronger association of glucosamine use with lower risk of colorectal cancer (HR, 0.90; 95% CI, 0.82–0.99; Pinteraction = 0.023) in participants with no screening but no significant association in screened participants. This might be explained by detection bias where people taking glucosamine and participating in colorectal cancer screening tend to have colorectal cancer detected early. However, the case for prostate cancer was the opposite, with a higher risk of prostate cancer associated with glucosamine use (HR, 1.14; 95% CI, 1.05–1.23; Pinteraction = 0.007) among those without screening experience. Whether this opposite finding is more likely to indicate biological differences in carcinogenesis or just reflect confounding derived from health-related behaviors should be further investigated. We also observed glucosamine use was associated with increased risk of breast cancer (HR, 1.23; 95% CI, 1.02–1.50) and reduced risk of lung cancer (HR, 0.69; 95% CI, 0.56–0.84) in current smokers. Besides, glucosamine intake was associated with increased risk of esophageal cancer (HR, 4.00; 95% CI, 1.36–11.82) and colorectal cancer (HR, 1.64; 95% CI, 1.18–2.27) in those with a history of osteoarthritis. All these findings have not been reported in previous studies and need further confirmation. Discrepancies of these potential effect modifiers in different site-specific cancers may serve as another reflection of heterogeneity in the roles of glucosamine use in risk of these cancers.
Existing mechanism explorations for the roles of glucosamine in the development of cancers mainly focused on anticancer benefits while harmful effects were reported rarely. For anticancer benefits, glucosamine showed significant reduction of C-reactive protein concentration (6, 29, 30), and may also play a role in cell proliferation, apoptosis, angiogenesis, migration, and invasion (31). Besides, the antioxidant properties of glucosamine could help scavenge the superoxide and hydroxyl radicals and protect the macromolecules (32). Experimental study observed that glucosamine could mimic a low carbohydrate diet with reduced glycolysis and improve amino acid catabolism (33). However, some biological evidence has also been available to support the possibility of the increased risk we observed in prostate cancer. A recent experimental study reported that glucosamine supplementation could increase the concentration of insulin-like growth factor-I (34), which is considered as a risk factor for prostate cancer (35, 36). No previous studies have reported related evidence in terms of glucosamine supplements and increased risk of skin cancer. The increased risk may be a biological consequence of glucosamine use or could be independent of glucosamine but a convergent process due to health behavior factors, such as exposure to solar ultraviolet radiation (37).
This study provided new evidence on the potential heterogeneity in the roles of glucosamine use in the development of different site-specific cancers by examining their associations in the same population. We used data from a population-based prospective cohort with large sample size, long-term follow-up, and detailed information on covariates. Our study has several limitations. First, exposure measurement on glucosamine use was based on self-reports without detailed information on dosage, form, frequency, and duration. We only considered baseline exposure without account for the change in glucosamine use over time. We also recognized potential selection bias by including prevalent glucosamine users in the analysis (38). Because of the limited sample size and small number of outcome events among post-baseline users, however, it was not feasible to ascertain the influence of prevalent user bias in our study. Second, participants with cancer diagnoses of multiple sites were identified as incident cases in analyses for different cancer outcomes, and we could not discriminate those who suffered from tumor metastasis from those who actually developed two or more independent cancers. However, there were only 1,466 participants with diagnosis of more than two cancers in this study, which should not substantially affect our main findings. Third, although we have controlled for multiple covariates in the models, unmeasured residual confounding may still exist. Fourth, a low response rate in the UK Biobank may have compromised the representativeness of our study population since it has been noted that UK Biobank participants were more likely to be older, to be female, to live in less socioeconomically deprived areas and to have a healthier lifestyle than the UK population (39). However, a previous study showed that risk factor associations in the UK Biobank seemed comparable with those from other prospective studies with higher response rates. In addition, the association between glucosamine use and risk of lung cancer observed in the UK Biobank were consistent with that based on the VITAL cohort (12, 13). These consistent findings suggested the associations observed in our study would not be necessarily influenced by insufficient representativeness of the UK population (40). Fifth, we did not adjust statistical threshold for multiple hypothesis testing, and thus some of our findings at a statistical threshold of P < 0.05 may exaggerate the association, which should be interpreted with caution. Given the large sample size of the UK Biobank, some statistically significant associations or interactions may not be clinically relevant.
In conclusion, regular glucosamine use was associated with increased risk of overall cancer. For site-specific cancers, regular glucosamine use was associated with lower risk of lung cancer but higher risk of skin cancer and prostate cancer. These important findings suggest that glucosamine may not be consistently beneficial for the prevention of different site-specific cancers.
Supplementary Material
Supplementary Table S1 shows ICD codes used for identification of cancer diagnosis in the UK Biobank.
Supplementary Table S2 shows covariates used in the present study.
Supplementary Table S3 shows baseline characteristics of all study participants and those included in the main analysis.
Supplementary Table S4 shows baseline characteristics of all participants by cancer diagnosis.
Supplementary Table S5 shows sensitivity analyses for the association between glucosamine use and risk of overall cancer after censoring certain cancers by gender.
Supplementary Figure S1 shows subgroup analyses for the association between glucosamine use and incidence of prostate cancer in male participants.
Supplementary Figure S2 shows subgroup analyses for the association between glucosamine use and incidence of lung cancer.
Supplementary Figure S3 shows subgroup analyses for the association between glucosamine use and incidence of brain cancer.
Supplementary Figure S4 shows subgroup analyses for the association between glucosamine use and incidence of breast cancer in female participants.
Supplementary Figure S5 shows subgroup analyses for the association between glucosamine use and incidence of colorectal cancer.
Supplementary Figure S6 shows subgroup analyses for the association between glucosamine use and incidence of esophageal cancer.
Supplementary Figure S7 shows subgroup analyses for the association between glucosamine use and incidence of kidney cancer.
Supplementary Figure S8 shows subgroup analyses for the association between glucosamine use and incidence of melanoma cancer.
Supplementary Figure S9 shows subgroup analyses for the association between glucosamine use and incidence of ovary cancer in female participants.
Supplementary Figure S10 shows subgroup analyses for the association between glucosamine use and incidence of pancreas cancer.
Supplementary Figure S11 shows subgroup analyses for the association between glucosamine use and incidence of stomach cancer.
Supplementary Figure S12 shows subgroup analyses for the association between glucosamine use and incidence of thyroid cancer.
Supplementary Figure S13 shows subgroup analyses for the association between glucosamine use and incidence of cervical cancer in female participants.
Supplementary Figure S14 shows subgroup analyses for the association between glucosamine use and incidence of hepatobiliary cancer.
Supplementary Figure S15 shows subgroup analyses for the association between glucosamine use and incidence of other male cancer.
Supplementary Figure S16 shows subgroup analyses for the association between glucosamine use and incidence of other female cancer.
Supplementary Figure S17 shows subgroup analyses for the association between glucosamine use and incidence of endometrium cancer in female participants.
Supplementary Figure S18 shows subgroup analyses for the association between glucosamine use and incidence of skin cancer.
Supplementary Figure S19 shows subgroup analyses for the association between glucosamine use and incidence of all other cancer.
Acknowledgments
This work has been conducted using the UK Biobank resource under application number 80476, and we express our gratitude to the participants and those involved in building the resource. This work was supported by the Shenzhen Science and Technology Program (Grant No. KQTD20190929172835662, recipient: J-L. Tang), China Postdoctoral Science Foundation (Grant No. 2022M723289, recipient: F-X. Li), National Natural Science Foundation of China (Grant No. 72274193, recipient: Z-R. Yang), and SIAT Excellent Young Scientists Innovation Fund Program B (Grant No. E2G011, recipient: Z-R. Yang). The funders of this study had no role in study design, data collection, data analysis, data interpretation, and writing of the report.
The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.
Footnotes
Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).
Authors' Disclosures
F. Sha reports grants from Shenzhen Science and Technology Program during the conduct of the study. No disclosures were reported by the other authors.
Authors' Contributions
F.-X. Li: Conceptualization, data curation, software, formal analysis, funding acquisition, investigation, visualization, methodology, writing–original draft, writing–review and editing. H.-Y. Zhao: Investigation, methodology, writing–review and editing. T.-F. Lin: Methodology, writing–review and editing. Y.-W. Jiang: Methodology, writing–review and editing. D. Liu: Methodology, writing–review and editing. C. Wei: Data curation, software, validation. Z.-Y. Zhao: Data curation, software, validation. Z.-Y. Yang: Methodology, writing–review and editing. F. Sha: Supervision, methodology, project administration, writing–review and editing. Z.-R. Yang: Conceptualization, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. J.-L. Tang: Resources, supervision, funding acquisition, methodology, project administration, writing–review and editing.
References
- 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209–49. [DOI] [PubMed] [Google Scholar]
- 2. Coussens LM, Werb Z. Inflammation and cancer. Nature 2002;420:860–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Yu Z, Ju Y, Liu H. Antilung cancer effect of glucosamine by suppressing the phosphorylation of FOXO. Mol Med Rep 2017;16:3395–400. [DOI] [PubMed] [Google Scholar]
- 4. Wong RSY. Role of nonsteroidal anti-inflammatory drugs (NSAIDs) in cancer prevention and cancer promotion. Adv Pharmacol Sci 2019;2019:3418975. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Jordan KM, Arden NK, Doherty M, Bannwarth B, Bijlsma JW, Dieppe P, et al. EULAR Recommendations 2003: an evidence based approach to the management of knee osteoarthritis: report of a task force of the standing committee for international clinical studies including therapeutic trials (ESCISIT). Ann Rheum Dis 2003;62:1145–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Kantor ED, Lampe JW, Vaughan TL, Peters U, Rehm CD, White E. Association between use of specialty dietary supplements and C-reactive protein concentrations. Am J Epidemiol 2012;176:1002–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Mtewa AG, Annu A, Weisheit A, Tolo CU, Ogwang PE. Chapter 20 - Glucosamine and chondroitin in osteoarthritis treatment. In: Egbuna C, Mishra AP, Goyal MR, editors. Preparation of Phytopharmaceuticals for the Management of Disorders: Academic Press; 2021. p. 373–80. [Google Scholar]
- 8. Runhaar J, van Middelkoop M, Reijman M, Willemsen S, Oei EH, Vroegindeweij D, et al. Prevention of knee osteoarthritis in overweight females: the first preventive randomized controlled trial in osteoarthritis. Am J Med 2015;128:888–95. [DOI] [PubMed] [Google Scholar]
- 9. Ma H, Li X, Sun D, Zhou T, Ley SH, Gustat J, et al. Association of habitual glucosamine use with risk of cardiovascular disease: prospective study in UK Biobank. BMJ 2019;365:l1628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Ma H, Li X, Zhou T, Sun D, Liang Z, Li Y, et al. Glucosamine use, inflammation, and genetic susceptibility, and incidence of type 2 diabetes: a prospective study in UK biobank. Diabetes Care 2020;43:719–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Zhang XR, Zhang PD, Li ZH, Yang P, Wang XM, Liu HM, et al. Glucosamine use, smoking and risk of incident chronic obstructive pulmonary disease: a large prospective cohort study. Br J Nutr 2022;128:721–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Li G, Zhang X, Liu Y, Zhang J, Li L, Huang X, et al. Relationship between glucosamine use and the risk of lung cancer: data from a nationwide prospective cohort study. Eur Respir J 2022;59:2101399. [DOI] [PubMed] [Google Scholar]
- 13. Brasky TM, Lampe JW, Slatore CG, White E. Use of glucosamine and chondroitin and lung cancer risk in the vitamins and lifestyle (VITAL) cohort. Cancer Cause Control 2011;22:1333–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Ibanez-Sanz G, Diez-Villanueva A, Vilorio-Marques L, Gracia E, Aragones N, Olmedo-Requena R, et al. Possible role of chondroitin sulphate and glucosamine for primary prevention of colorectal cancer. Results from the MCC-Spain study. Sci Rep-Uk 2018;8:2040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Kantor ED, Zhang XH, Wu KN, Signorello LB, Chan AT, Fuchs CS, et al. Use of glucosamine and chondroitin supplements in relation to risk of colorectal cancer: Results from the nurses' health study and health professionals follow-up study. Int J Cancer 2016;139:1949–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Kantor ED, Lampe JW, Peters U, Shen DD, Vaughan TL, White E. Use of glucosamine and chondroitin supplements and risk of colorectal cancer. Cancer Cause Control 2013;24:1137–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Khan AA, Mannan V, Pervaiz MA, Akram A, Momin ES, Sanusi M, et al. The role of glucosamine and chondroitin sulfate in the prevention of colorectal cancer: a systematic review. Cureus 2022;14:e25401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Kantor ED, O'Connell K, Liang PS, Navarro SL, Giovannucci EL, Du M. Glucosamine use and risk of colorectal cancer: results from UK Biobank. Cancer Epidemiol Biomarkers Prev 2022;31:647–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12:e1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Larsson SC, Carter P, Kar S, Vithayathil M, Mason AM, Michaëlsson K, et al. Smoking, alcohol consumption, and cancer: a Mendelian randomisation study in UK Biobank and international genetic consortia participants. PLoS Med 2020;17:e1003178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Danaei G, Vander Hoorn S, Lopez AD, Murray CJ, Ezzati M. Comparative risk assessment collaborating. Causes of cancer in the world: comparative risk assessment of nine behavioural and environmental risk factors. Lancet 2005;366:1784–93. [DOI] [PubMed] [Google Scholar]
- 22. Sturmer T, Hasselbach P, Amelang M. Personality, lifestyle, and risk of cardiovascular disease and cancer: follow-up of population based cohort. BMJ 2006;332:1359. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Mamdani M, Sykora K, Li P, Normand SLT, Streiner DL, Austin PC, et al. Reader's guide to critical appraisal of cohort studies. 2. Assessing potential for confounding. BMJ-Brit Med J 2005;330:960–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Canchola Alison J, Stewart Susan L, Bernstein L. Cox Regression using different time-scales. Union City, CA: Lex Jansen. 2003. [Google Scholar]
- 25. Van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. Journal of Statistical Software 2011;45:1–67. [Google Scholar]
- 26. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med 2011;30:377–99. [DOI] [PubMed] [Google Scholar]
- 27. Brasky TM, Kristal AR, Navarro SL, Lampe JW, Peters U, Patterson RE, et al. Specialty supplements and prostate cancer risk in the vitamins and lifestyle (VITAL) cohort. Nutr Cancer 2011;63:573–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Zhou J, Wu ZY, Lin ZJ, Wang WC, Wan RJ, Liu T. Association between glucosamine use and cancer mortality: a large prospective cohort study. Front Nutr 2022;9:947818. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Singh N, Baby D, Rajguru JP, Patil PB, Thakkannavar SS, Pujari VB. Inflammation and cancer. Ann Afr Med 2019;18:121–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Kantor ED, Lampe JW, Navarro SL, Song X, Milne GL, White E. Associations between glucosamine and chondroitin supplement use and biomarkers of systemic inflammation. J Altern Complement Med 2014;20:479–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Zahedipour F, Dalirfardouei R, Karimi G, Jamialahmadi K. Molecular mechanisms of anticancer effects of glucosamine. Biomed Pharmacother 2017;95:1051–8. [DOI] [PubMed] [Google Scholar]
- 32. Filaire E, Dupuis C, Galvaing G, Aubreton S, Laurent H, Richard R, et al. Lung cancer: what are the links with oxidative stress, physical activity and nutrition. Lung Cancer 2013;82:383–9. [DOI] [PubMed] [Google Scholar]
- 33. Weimer S, Priebs J, Kuhlow D, Groth M, Priebe S, Mansfeld J, et al. D-Glucosamine supplementation extends life span of nematodes and of ageing mice. Nat Commun 2014;5:3563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Feng C, Yuan T, Wang S, Liu T, Tao S, Han D, et al. Glucosamine supplementation in premating drinking water improves within-litter birth weight uniformity of rats partly through modulating hormone metabolism and genes involved in implantation. Biomed Res Int 2020;2020:1630890. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 35. Travis RC, Appleby PN, Martin RM, Holly JMP, Albanes D, Black A, et al. A Meta-analysis of individual participant data reveals an association between circulating levels of IGF-I and prostate cancer risk. Cancer Res 2016;76:2288–300. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Perdana NR, Mochtar CA, Umbas R, Hamid AR. The risk factors of prostate cancer and its prevention: a literature review. Acta Med Indones 2016;48:228–38. [PubMed] [Google Scholar]
- 37. Gordon R. Skin cancer: an overview of epidemiology and risk factors. Semin Oncol Nurs 2013;29:160–9. [DOI] [PubMed] [Google Scholar]
- 38. Suissa K, Hudson M, Suissa S. Glucosamine and lower mortality and cancer incidence: selection bias in the observational studies. Pharmacoepidemiol Drug Saf 2022;31:1272–9. [DOI] [PubMed] [Google Scholar]
- 39. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol 2017;186:1026–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Batty GD, Gale CR, Kivimaki M, Deary IJ, Bell S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ 2020;368:m131. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Table S1 shows ICD codes used for identification of cancer diagnosis in the UK Biobank.
Supplementary Table S2 shows covariates used in the present study.
Supplementary Table S3 shows baseline characteristics of all study participants and those included in the main analysis.
Supplementary Table S4 shows baseline characteristics of all participants by cancer diagnosis.
Supplementary Table S5 shows sensitivity analyses for the association between glucosamine use and risk of overall cancer after censoring certain cancers by gender.
Supplementary Figure S1 shows subgroup analyses for the association between glucosamine use and incidence of prostate cancer in male participants.
Supplementary Figure S2 shows subgroup analyses for the association between glucosamine use and incidence of lung cancer.
Supplementary Figure S3 shows subgroup analyses for the association between glucosamine use and incidence of brain cancer.
Supplementary Figure S4 shows subgroup analyses for the association between glucosamine use and incidence of breast cancer in female participants.
Supplementary Figure S5 shows subgroup analyses for the association between glucosamine use and incidence of colorectal cancer.
Supplementary Figure S6 shows subgroup analyses for the association between glucosamine use and incidence of esophageal cancer.
Supplementary Figure S7 shows subgroup analyses for the association between glucosamine use and incidence of kidney cancer.
Supplementary Figure S8 shows subgroup analyses for the association between glucosamine use and incidence of melanoma cancer.
Supplementary Figure S9 shows subgroup analyses for the association between glucosamine use and incidence of ovary cancer in female participants.
Supplementary Figure S10 shows subgroup analyses for the association between glucosamine use and incidence of pancreas cancer.
Supplementary Figure S11 shows subgroup analyses for the association between glucosamine use and incidence of stomach cancer.
Supplementary Figure S12 shows subgroup analyses for the association between glucosamine use and incidence of thyroid cancer.
Supplementary Figure S13 shows subgroup analyses for the association between glucosamine use and incidence of cervical cancer in female participants.
Supplementary Figure S14 shows subgroup analyses for the association between glucosamine use and incidence of hepatobiliary cancer.
Supplementary Figure S15 shows subgroup analyses for the association between glucosamine use and incidence of other male cancer.
Supplementary Figure S16 shows subgroup analyses for the association between glucosamine use and incidence of other female cancer.
Supplementary Figure S17 shows subgroup analyses for the association between glucosamine use and incidence of endometrium cancer in female participants.
Supplementary Figure S18 shows subgroup analyses for the association between glucosamine use and incidence of skin cancer.
Supplementary Figure S19 shows subgroup analyses for the association between glucosamine use and incidence of all other cancer.
Data Availability Statement
This study was conducted using the UK biobank resource under Application No.80476. All data in this study are available from the UK biobank (www.ukbiobank.ac.uk) on reasonable request, subject to permission by the UK biobank.