Sage Journals: Discover world-class research

Abstract

Objective

Observational studies on glucosamine supplementation and type 2 diabetes risk have shown inconsistent results, necessitating the use of Mendelian randomization to clarify the true causal relationship.

Methods

The glucosamine supplementation–related genome-wide association study dataset was obtained from the MRC Integrative Epidemiology Unit consortium, whereas type 2 diabetes–related genome-wide association study datasets were obtained from the FinnGen consortium (discovery) and Xue et al.’s meta-analysis (validation). Two-sample Mendelian randomization analyses were performed separately in the discovery and validation datasets, followed by meta-analysis and multivariable Mendelian randomization analyses to verify the robustness of the results of two-sample Mendelian randomization. The estimation of the causal relationship was conducted through the inverse variance weighted method.

Results

Glucosamine supplementation exhibited a significant protective effect against type 2 diabetes, as identified by two-sample Mendelian randomization analysis in the FinnGen consortium (odds ratio: 0.13, 95% confidence interval: 0.02–0.89) and validated in Xue et al.’s meta-analysis (odds ratio: 0.06, 95%; confidence interval: 0.01–0.29). A combined meta-analysis (odds ratio: 0.08, 95%; confidence interval: 0.02–0.27) of the results of two-sample Mendelian randomization confirmed the robustness of these findings. Additionally, multivariable Mendelian randomization analysis (odds ratio: 0.12, 95%; confidence interval: 0.02–0.94), after adjusting for confounding factors, supported the results of two-sample Mendelian randomization. No evidence of heterogeneity or pleiotropy was observed.

Conclusion

Overall, our results revealed that genetically predicted glucosamine supplementation was inversely associated with the risk of type 2 diabetes, highlighting the potential importance of glucosamine supplementation in preventing type 2 diabetes.

Keywords

Type 2 diabetes glucosamine Mendelian randomization causal relationship genome-wide association study

Introduction

As the global population ages, the incidence of type 2 diabetes (T2D) is rising, with the prevalence rate approaching 10% worldwide, imposing a substantial burden on medical systems and the society.^1,2 T2D, the most common type of diabetes, accounts for 90%–95% of all diabetes cases.^3,4 T2D is primarily caused by insulin resistance, which refers to the decreased metabolic responsiveness and sensitivity of cells to insulin.^5–7 Consequently, alleviating insulin resistance is crucial in preventing the onset of T2D. Previous studies have indicated an association between glucosamine supplementation and insulin resistance^8–10; however, there is insufficient evidence regarding the causal relationship between glucosamine supplementation and T2D.

Glucosamine, a common over-the-counter dietary supplement, is widely recommended for the management of osteoarthritis and joint pain.^11,12 However, existing evidence linking glucosamine supplementation to T2D remains limited and controversial. According to Pham et al., at doses used for oral treatment of osteoarthritis in humans, glucosamine exacerbated insulin resistance among individuals with T2D.¹³ In contrast, Simon et al. reported that glucosamine had no impact on insulin sensitivity in healthy participants, those with diabetes, or those with impaired glucose tolerance at any oral dose level.¹⁴ Furthermore, a recent prospective study revealed that glucosamine use was associated with a lower risk of developing T2D.¹⁵

Given the widespread use of glucosamine as a dietary supplement and the prevalence of T2D within the population, it is crucial to clarify the true causal relationship between them. Mendelian randomization (MR) is an emerging method that utilizes genetic variants, usually single nucleotide polymorphisms (SNPs), as instrumental variables (IVs) to investigate a causal relationship. Genetic variants are randomly allocated at conception, analogous to the random assignment of treatment in randomized controlled trials, thereby reducing the influence of confounding factors.¹⁶ Furthermore, as genetic variants are not affected by disease status, MR can avoid the interference of reverse causality.¹⁷ Therefore, we conducted an MR study to evaluate the causal relationship between glucosamine supplementation and T2D by analyzing the summary-level genome-wide association study (GWAS) datasets of glucosamine supplementation and T2D.

Materials and methods

Study design

The overall study design is illustrated in Figure 1. We conducted two-sample MR (TSMR) to assess the causal relationship between glucosamine supplementation and T2D in two independent population-scale T2D GWAS datasets. For valid causal inference, the MR study relied on three main assumptions about IVs: (a) relevance assumption (the IVs should be strongly associated with the exposure); (b) independence assumption (the IVs should not be associated with any confounding factors of the exposure–outcome relationship); and (c) exclusion restriction assumption (the IVs should affect the risk of the outcome only through exposure, not via alternative pathways). We utilized the T2D GWAS dataset from the FinnGen consortium, featuring a large sample size, as the discovery dataset, and the T2D GWAS dataset of Xue et al.’s meta-analysis as the validation dataset. Subsequently, meta-analysis was conducted on the TSMR estimates from the discovery and validation phases under a fixed-effects model. Furthermore, multivariable MR (MVMR) was performed using the summary-level GWAS datasets in the validation phase to test the independence of the causal relationship between glucosamine supplementation and T2D after accounting for potential confounding factors. The study was reported based on recommendations by the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) as well as Reports and Guidelines for Mendelian Randomization Analysis (Supplement Tables S1 and S2).^18,19

Figure 1.

The MR study design. MR: Mendelian randomization; TSMR: two-sample Mendelian randomization; MVMR: multivariable Mendelian randomization; MRC-IEU: MRC Integrative Epidemiology Unit; SNPs: single nucleotide polymorphisms.

Data sources

The summary-level GWAS dataset of glucosamine supplementation (ukb-b-11535) was obtained from the MRC Integrative Epidemiology Unit (MRC-IEU) consortium, which consisted of 89,339 cases and 372,045 controls. The summary-level GWAS dataset of T2D utilized in the discovery phase originated from the FinnGen consortium, which comprised 32,469 cases and 183,185 controls (finn-b-E4_DM2), whereas that utilized in the validation phase originated from Xue et al.’s meta-analysis, comprising 61,714 cases and 1178 controls (ebi-a-GCST006867).²⁰

Other GWAS datasets utilized in our study included high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol, and triglycerides with 187,167, 173,082, 187,365, and 177,861 participants, respectively, from the Global Lipids Genetics Consortium (GLGC; HDL-C: ieu-a-299, LDL-C: ieu-a-300, total cholesterol: ieu-a-301, triglycerides: ieu-a-302)²¹; coronary artery disease with 141,217 participants (42,096 cases and 361 controls) from Nikpay et al.’s meta-analysis (ebi-a-GCST003116)²²; body mass index with 681,275 participants from the Genetic Investigation of Anthropometric Traits (GIANT) consortium (ieu-b-40)²³; smoking initiation with 607,291 participants (311,629 cases and 321,173 controls) from the GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) consortium (ieu-b-4877)²⁴; and alcohol consumption with 112,117 participants from the UK Biobank consortium (ieu-a-1283).²⁵

All abovementioned GWAS datasets were of European ancestry or mainly comprised data from European ancestry, and they were available from the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/). Our study exclusively utilized publicly available summary-level GWAS datasets, thereby obviating the need for ethical approval and informed consent.

IVs selection

SNPs significantly associated (P < 5 × 10⁻⁸) with glucosamine supplementation were selected as IVs from the relevant GWAS dataset. First, we filtered the IVs for linkage disequilibrium (the linkage disequilibrium correlation coefficient [r²] > 0.001) and discarded variants located within a 10,000 kb distance from other IVs exhibiting a stronger association. If r² = 1, it denotes that the two loci are in complete linkage disequilibrium, indicating a nonrandom association between them. Conversely, if r² = 0, it suggests that the two loci are in complete linkage disequilibrium and coinherited. Subsequently, we excluded IVs that were significantly associated (P < 5 × 10⁻⁸) with T2D. F statistics were calculated for each single nucleotide polymorphism (SNP) to assess the strength of the IVs. An F-statistic less than 10 indicated evidence of weak instrument bias,²⁶ which compromised the estimation of causal effects.^27,28 Therefore, SNPs with an F-statistic below 10 were excluded. Additionally, we employed Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) (P < 0.05) to detect outliers.²⁹

MR analysis

The investigation of the causal relationship was conducted through the inverse variance weighted (IVW) method under a fixed-effects model,^30–32 supplemented with four MR analyses, including MR Egger, weighted median, weighted mode, and simple mode. After TSMR analyses, sensitivity analyses were conducted to assess the validity and robustness of the results, incorporating heterogeneity test (Cochran’s Q test), pleiotropy test (MR Egger intercept test), and leave-one-out analysis.

The IVW method was employed under a fixed-effects model if no significant heterogeneity was detected (P of Cochran’s Q test ≥ 0.05); otherwise, a random-effects model was utilized.^33,34 A significantly nonzero intercept in the MR Egger method implies the existence of pleiotropic effects (P of MR Egger intercept < 0.05).³⁵ Leave-one-out analysis, which recalibrates the results by removing SNPs one by one, was performed to identify outliers with significant influence on the causal effect. In addition, we used the Steiger directionality test to investigate the direction of the causal relationship between glucosamine supplementation and T2D. Moreover, the meta-analysis of IVW results was employed to combine the TSMR estimates from the discovery and validation phases (I² < 50%: fixed-effects model; I² ≥ 50%: random-effects model).

We considered the results as significant when they fulfilled the following criteria: (a) the directions of the estimates obtained by all five MR methods (IVW, MR Egger, weighted median, weighted mode, and simple mode) were consistent; (b) the P-value for the IVW method was less than 0.05; and (c) there was no significant pleiotropy or heterogeneity detected.

Although a range of statistical methods have been employed in the sensitivity analyses, we performed confounding analyses via LDlink (https://ldlink.nih.gov/) to explore the traits significantly related to the selected SNPs. To assess the robustness of our results, MVMR analyses were performed with adjustments for potential confounding factors identified by confounding analyses.

The R packages ‘ieugwasr’ (version 1.0.1, available at https://mrcieu.github.io/ieugwasr/) and ‘TwosampleMR’ (version 0.6.6, available at https://github.com/MRCIEU/TwoSampleMR) were used for univariate and multivariate MR analyses. The R package ‘MR-PRESSO’ (version 1.0, available at https://github.com/rondolab/MR-PRESSO) was used for sensitivity analysis. The R package ‘Meta’ (version 6.5-0, available at https://cran.r-project.org/web/packages/meta/index.html) was utilized for meta-analysis. Additionally, the R packages ‘grid’ (a base package included in the R distribution) and ‘forestploter’ (version 1.1.0, available at https://cran.r-project.org/web/packages/forestploter/index.html) were used for plotting. All these R packages were executed in R version 4.2.3 software. The threshold of significance was P = 0.05.

Results

Identification and validation of the causal effect of glucosamine supplementation on T2D

Based on the criteria for selecting IVs, five and four independent SNPs were ultimately selected as IVs for glucosamine supplementation in the discovery and validation phases, respectively (Supplement Table S3). The F-statistic for each SNP was greater than 10, indicating no evidence of weak instrument bias.

As shown in Figure 2, TSMR analyses identified a significant causal relationship between genetically predicted glucosamine supplementation and a reduced risk of T2D in the discovery phase using the FinnGen consortium (odds ratio (OR): 0.13, 95% confidence interval (CI): 0.02–0.89, P = 0.0380; IVW), which was subsequently validated in the validation phase using Xue et al.’s meta-analysis (OR: 0.06, 95% CI: 0.01–0.29, P = 0.0005; IVW).

Figure 2.

TSMR analyses of the causal relationship between glucosamine supplementation and T2D in the discovery and validation phases. TSMR: two-sample Mendelian randomization; T2D: type 2 diabetes.

Sensitivity analysis in the discovery and validation phases revealed no evidence of heterogeneity or pleiotropy, and the directionality of the causal relationship between glucosamine supplementation and T2D was further confirmed by the Steiger directionality test, as shown in Table 1. Moreover, the leave-one-out analysis indicated that the causal relationship was effective and robust (Figure 3).

Table 1.

Results of heterogeneity, horizontal pleiotropy, and direction tests in the TSMR analyses.

Phase	Heterogeneity		Horizontal pleiotropy			Directionality
Phase	Cochran’s Q	P	MR Egger intercept	SE	P	P of the Steiger directionality test
Discovery	2.329	0.676	−0.060	0.070	0.453	7.936e-06
Validation	3.435	0.329	−0.060	0.053	0.375	0.042

MR: Mendelian randomization; SE: standard error; TSMR: two-sample Mendelian randomization.

Figure 3.

Results of “leave-one-out” sensitivity analysis in the discovery and validation phases. (a) The FinnGen consortium and (b) Xue et al.’s meta-analysis.

Meta-analysis combining the results of TSMR in the discovery and validation phases

To ensure the statistical power and reliability of our findings, we conducted a meta-analysis based on the IVW results obtained from the discovery and validation phases, employing a fixed-effects model (Figure 4). The meta-analysis provided credible evidence that glucosamine supplementation was associated with a reduced risk of T2D (OR: 0.08, 95% CI: 0.02–0.27, P < 0.0001).

Figure 4.

Meta-analysis under a fixed-effects model to verify the robustness of the causal relationship between glucosamine supplementation and T2D. T2D: type 2 diabetes.

Confounding and MVMR analyses during the validation phase

After summarizing and analyzing the relevant information about the selected SNPs via LDlink (Supplement Table S4), we identified several potential confounding factors, mainly including HDL-C, LDL-C, total cholesterol, triglycerides, coronary artery disease, body mass index, smoking initiation, and alcohol consumption.

To further assess the causal relationship between glucosamine supplementation and T2D, we performed MVMR analyses of the T2D GWAS datasets obtained from Xue et al.’s meta-analysis (Figure 5). In MVMR analyses, the causal effect of glucosamine supplementation on T2D remained consistent with the TSMR results after adjusting for LDL-C (OR: 0.03, 95% CI: 0.00–0.62, P = 0.0245) or body mass index (OR: 0.15, 95% CI: 0.03–0.84, P = 0.0312). Notably, after adjusting for all confounding factors, except for total cholesterol, the causal relationship between glucosamine supplementation and T2D remained significant (OR: 0.12, 95% CI: 0.02–0.94, P = 0.0439).

Figure 5.

MVMR analyses of the effects of glucosamine supplementation on T2D (validation phase). MVMR: multivariable Mendelian randomization; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol.

Discussion

To the best of our knowledge, this is the first MR study to investigate the causal relationship between glucosamine supplementation and T2D. Herein, we provided robust evidence regarding a potentially causal relationship between glucosamine supplementation and a reduced risk of T2D. Furthermore, we confirmed the consistency of the causal effect of glucosamine supplementation on T2D with TSMR results, after adjusting for HDL-C, LDL-C, triglycerides, coronary artery disease, body mass index, smoking initiation, and alcohol consumption in MVMR analyses.

Previous studies have suggested an association between glucosamine supplementation and susceptibility to T2D.^10,36 Glucosamine exerted a slight glucose-lowering effect in participants who had undergone a 3-year intervention in a long-term controlled trial that involved 212 participants with arthritis.³⁷ Ma et al.¹⁵ and Anderson et al.³⁸ reported that habitual glucosamine use was associated with a lower risk of T2D, which is consistent with our results. Previous studies have demonstrated that glucosamine possesses anti-inflammatory properties.^39,40 Moreover, glucosamine has the potential to modulate inflammation by regulating the activation of the nuclear factor kappa B (NF-κB) pathway. Specifically, it may attenuate the process of NF-κB nuclear translocation,⁴¹ thereby reducing the expression of various proinflammatory mediators, including cytokines (e.g. interleukin-6, interleukin-1β, and tumor necrosis factor-alpha), chemokines, and cellular adhesion molecules.^42,43 This, in turn, leads to a significant reduction in the levels of C-reactive protein, a marker of systemic inflammation.^44,45 By mitigating these detrimental inflammatory responses, glucosamine may help alleviate systemic inflammation and improve insulin resistance. In addition, glucosamine may influence glucose metabolism through its effects on the hexosamine biosynthesis pathway, which has been implicated in insulin signaling and glucose homeostasis.⁴⁶

Several animal studies have shown inconsistent findings. Baron et al.⁸ and Patti et al.¹⁰ revealed that high-dose intravenous administration of glucosamine induces insulin resistance in animals. The contradictory results may be partly due to the considerably higher dosages of glucosamine used in animal studies compared with humans’ oral dosage as well as the significantly higher blood concentration achieved through intravenous administration than through oral administration. Furthermore, a recent animal study demonstrated that the oral administration of glucosamine in rats fed with a high-fat diet attenuated insulin resistance.⁴⁷

Herein, we revealed a significant causal relationship between glucosamine supplementation and a reduced risk of T2D, which remained significant after accounting for all confounding factors, except for total cholesterol (HDL-C, LDL-C, triglycerides, coronary artery disease, body mass index, smoking initiation, and alcohol consumption). Our findings suggested that glucosamine supplementation is a vital measure for preventing T2D, offering a new perspective on T2D prevention.

There are certain limitations in our study. First, the GWAS datasets used in this study were primarily or entirely of European ancestry. Although this may reduce population stratification bias, the findings may not be generalizable to other populations. Second, although our evidence supports a causal relationship between glucosamine supplementation and T2D, the mechanisms underlying how glucosamine supplementation reduces the risk of T2D as well as the clinical benefits of such supplementation in humans remain unclear and require further investigation. Finally, it is necessary to determine the most suitable amounts of glucosamine supplementation for the prevention of T2D in future research.

Conclusion

This study revealed that genetically predicted glucosamine supplementation is inversely associated with the risk of T2D, highlighting the potential importance of glucosamine supplementation in preventing T2D.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605251334460 - Supplemental material for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis

Supplemental material, sj-pdf-1-imr-10.1177_03000605251334460 for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis by Shuai Zhou, Peiwen Zhou, Tianshi Yang, Junzhuo Si, Wenyan An and Yanfang Jiang in Journal of International Medical Research

Supplemental Material

sj-pdf-2-imr-10.1177_03000605251334460 - Supplemental material for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis

Supplemental material, sj-pdf-2-imr-10.1177_03000605251334460 for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis by Shuai Zhou, Peiwen Zhou, Tianshi Yang, Junzhuo Si, Wenyan An and Yanfang Jiang in Journal of International Medical Research

Supplemental Material

sj-pdf-3-imr-10.1177_03000605251334460 - Supplemental material for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis

Supplemental material, sj-pdf-3-imr-10.1177_03000605251334460 for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis by Shuai Zhou, Peiwen Zhou, Tianshi Yang, Junzhuo Si, Wenyan An and Yanfang Jiang in Journal of International Medical Research

Supplemental Material

sj-pdf-4-imr-10.1177_03000605251334460 - Supplemental material for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis

Supplemental material, sj-pdf-4-imr-10.1177_03000605251334460 for Glucosamine supplementation contributes to reducing the risk of type 2 diabetes: Evidence from Mendelian randomization combined with a meta-analysis by Shuai Zhou, Peiwen Zhou, Tianshi Yang, Junzhuo Si, Wenyan An and Yanfang Jiang in Journal of International Medical Research

Footnotes

Acknowledgments

The authors would like to thank the IEU OpenGWAS project. All GWAS datasets used in this study were available from the IEU OpenGWAS project (). We thank the individual patients who provided the samples and made the data available; the study would not have been possible without them. We would also like to thank all investigators who provided these data to support this study.

Authors’ contributions

Shuai Zhou collected the data and wrote the manuscript. Shuai Zhou, Peiwen Zhou, and Wenyan An performed MR analyses and analyzed the data. Junzhuo Si, Tianshi Yang, and Yanfang Jiang conceived the study, supervised the research, and edited the manuscript. All authors contributed to the article and approved the submitted version.

Data availability statement

All data used in this study are available in the public repository (IEU OpenGWAS project: ). The detailed statistical code is included in the supplementary material.

Declaration of conflicting interest

The authors declare that there are no conflicts of interest.

Funding

This work was supported by Jilin Province Science and Technology Development (grant number YDZJ202501ZYTS066) and Heilongjiang Medical Award Foundation (grant number 3D4230020428).

We extend our sincere thanks to these organizations for their financial support and faith in our research.

Supplementary material

Supplemental material for this article is available online.

ORCID iD

Shuai Zhou

References

Mordi

Lumbers

Palmer

CNA

; HERMES Consortiumet al. Type 2 diabetes, metabolic traits, and risk of heart failure: a Mendelian randomization study. Diabetes Care 2021; 44: 1699–1705.

Sun

Saeedi

Karuranga

, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022; 183: 109119.

Sha

Song

Huang

, et al. Inhibitory effect and potential mechanism of Lactobacillus plantarum YE4 against dipeptidyl peptidase-4. Foods 2021; 11: 80.

Zheng

Ley

FB.

Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018; 14: 88–98.

Qin

, et al. Elevated TyG index predicts incidence of contrast-induced nephropathy: a retrospective cohort study in NSTE-ACS patients implanted with DESs. Front Endocrinol (Lausanne) 2022; 13: 817176.

Urakaze

Kobashi

Satou

, et al. The beneficial effects of astaxanthin on glucose metabolism and modified low-density lipoprotein in healthy volunteers and subjects with prediabetes. Nutrients 2021; 13: 4381.

Perumalsamy

Ahmad

WAW

Huri

HZ.

Retinol-binding protein-4-A predictor of insulin resistance and the severity of coronary artery disease in type 2 diabetes patients with coronary artery disease. Biology (Basel) 2021; 10: 858.

Baron

Zhu

, et al. Glucosamine induces insulin resistance in vivo by affecting GLUT 4 translocation in skeletal muscle. Implications for glucose toxicity. J Clin Invest 1995; 96: 2792–2801.

Hawkins

Barzilai

Liu

, et al. Role of the glucosamine pathway in fat-induced insulin resistance. J Clin Invest 1997; 99: 2173–2182.

10.

Patti

Virkamaki

Landaker

, et al. Activation of the hexosamine pathway by glucosamine in vivo induces insulin resistance of early postreceptor insulin signaling events in skeletal muscle. Diabetes 1999; 48: 1562–1571.

11.

Clegg

Reda

Harris

, et al. Glucosamine, chondroitin sulfate, and the two in combination for painful knee osteoarthritis. N Engl J Med 2006; 354: 795–808.

12.

Hou

Fang

, et al. The role of type 2 diabetes in the association between habitual glucosamine use and dementia: a prospective cohort study. Alzheimers Res Ther 2022; 14: 184.

13.

Pham

Cornea

Blick

, et al. Oral glucosamine in doses used to treat osteoarthritis worsens insulin resistance. Am J Med Sci 2007; 333: 333–339.

14.

Simon

Marks

Leeds

, et al. A comprehensive review of oral glucosamine use and effects on glucose metabolism in normal and diabetic individuals. Diabetes Metab Res Rev 2011; 27: 14–27.

15.

Zhou

, et al. Glucosamine use, inflammation, and genetic susceptibility, and incidence of type 2 diabetes: a prospective study in UK Biobank. Diabetes Care 2020; 43: 719–725.

16.

Larsson

Butterworth

Burgess

Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J 2023; 44: 4913–4924.

17.

Salem

Shoemaker

Bastarache

, et al. Association of thyroid function genetic predictors with atrial fibrillation: a phenome-wide association study and inverse-variance weighted average meta-analysis. JAMA Cardiol 2019; 4: 136–143.

18.

Au Yeung

Gill

Standardizing the reporting of Mendelian randomization studies. BMC Med 2023; 21: 187.

19.

Lor

GCY

Risch

Fung

, et al. Reporting and guidelines for mendelian randomization analysis: a systematic review of oncological studies. Cancer Epidemiol 2019; 62: 101577.

20.

Xue

Zhu

; eQTLGen Consortiumet al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 2018; 9: 2941.

21.

Willer

Schmidt

Sengupta

; Global Lipids Genetics Consortiumet al. Discovery and refinement of loci associated with lipid levels. Nat Genet 2013; 45: 1274–1283.

22.

Nikpay

Goel

Won

, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet 2015; 47: 1121–1130.

23.

Yengo

Sidorenko

Kemper

; GIANT Consortiumet al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum Mol Genet 2018; 27: 3641–3649.

24.

Liu

Jiang

Wedow

; HUNT All-In Psychiatryet al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 2019; 51: 237–244.

25.

Clarke

Adams

Davies

, et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117). Mol Psychiatry 2017; 22: 1376–1384.

26.

Weng

Zhang

, et al. Association between uric acid and risk of venous thromboembolism in East Asian populations: a cohort and Mendelian randomization study. Lancet Reg Health West Pac 2023; 39: 100848.

27.

Huang

, et al. Mendelian randomization study of inflammatory bowel disease and bone mineral density. BMC Med 2020; 18: 312.

28.

Sun

Liu

, et al. Genetically predicted serum albumin and risk of colorectal cancer: a bidirectional Mendelian randomization study. Clin Epidemiol 2022; 14: 771–778.

29.

Verbanck

Chen

Neale

, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018; 50: 693–698.

30.

Cohen

Mitchell

Gill

, et al. Arterial stiffness and diabetes risk in Framingham Heart Study and UK Biobank. Circ Res 2022; 131: 545–554.

31.

Mariam

Miller-Atkins

Pantalone

, et al. A type 2 diabetes subtype responsive to ACCORD intensive glycemia treatment. Diabetes Care 2021; 44: 1410–1418.

32.

Soremekun

Karhunen

, et al. Lipid traits and type 2 diabetes risk in African ancestry individuals: a Mendelian randomization study. EBioMedicine 2022; 78: 103953.

33.

Zhang

Zhao

Man

, et al. Investigating causal associations of diet-derived circulating antioxidants with the risk of digestive system cancers: a Mendelian randomization study. Nutrients 2022; 14: 3237.

34.

Bao

, et al. Alleviating insomnia should decrease the risk of irritable bowel syndrome: evidence from Mendelian randomization. Front Pharmacol 2022; 13: 900788.

35.

Choi

Lee

Spiller

, et al. Causal associations between serum bilirubin levels and decreased stroke risk: a two-sample Mendelian randomization study. Arterioscler Thromb Vasc Biol 2020; 40: 437–445.

36.

Monauni

Zenti

Cretti

, et al. Effects of glucosamine infusion on insulin secretion and insulin action in humans. Diabetes 2000; 49: 926–935.

37.

Reginster

Deroisy

Rovati

, et al. Long-term effects of glucosamine sulphate on osteoarthritis progression: a randomised, placebo-controlled clinical trial. Lancet 2001; 357: 251–256.

38.

Anderson

Nicolosi

Borzelleca

JF.

Glucosamine effects in humans: a review of effects on glucose metabolism, side effects, safety considerations and efficacy. Food Chem Toxicol 2005; 43: 187–201.

39.

Yomogida

Hua

Sakamoto

, et al. Glucosamine suppresses interleukin-8 production and ICAM-1 expression by TNF-alpha-stimulated human colonic epithelial HT-29 cells. Int J Mol Med 2008; 22: 205–211.

40.

Azuma

Osaki

Wakuda

, et al. Suppressive effects of N-acetyl-D-glucosamine on rheumatoid arthritis mouse models. Inflammation 2012; 35: 1462–1465.

41.

Largo

Alvarez-Soria

Diez-Ortego

, et al. Glucosamine inhibits IL-1beta-induced NFkappaB activation in human osteoarthritic chondrocytes. Osteoarthritis Cartilage 2003; 11: 290–298.

42.

Leu

Chen

Guh

JH.

Extract from Plectranthus amboinicus inhibit maturation and release of interleukin 1beta through inhibition of NF-kappaB nuclear translocation and NLRP3 inflammasome activation. Front Pharmacol 2019; 10: 573.

43.

Jin

Yan

Wang

, et al. Hyperoside exerts anti-inflammatory and anti-arthritic effects in LPS-stimulated human fibroblast-like synoviocytes in vitro and in mice with collagen-induced arthritis. Acta Pharmacol Sin 2016; 37: 674–686.

44.

Largo

Martinez-Calatrava

Sanchez-Pernaute

, et al. Effect of a high dose of glucosamine on systemic and tissue inflammation in an experimental model of atherosclerosis aggravated by chronic arthritis. Am J Physiol Heart Circ Physiol 2009; 297: H268–H276.

45.

Liu

Zhang

, et al. Associations of habitual glucosamine supplementation with incident gout: a large population based cohort study. Biol Sex Differ 2022; 13: 52.

46.

Paneque

Fortus

Zheng

, et al. The hexosamine biosynthesis pathway: regulation and function. Genes (Basel) 2023; 14: 933.

47.

Barrientos

Perez

Vazquez

Ameliorative effects of oral glucosamine on insulin resistance and pancreatic tissue damage in experimental Wistar rats on a high-fat diet. Comp Med 2021; 71: 215–221.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.39 MB

0.02 MB

0.13 MB

0.30 MB