Obesity and Smoking are causal factors for meniscal injury: A mendelian randomization study

doi:10.21203/rs.3.rs-4374889/v1

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Obesity and Smoking are causal factors for meniscal injury: A mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4374889/v1

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Background

Previous observational studies have indicated a potential link between obesity, smoking, and meniscal injury. However, it is important to note that these studies are vulnerable to reverse causation and residual confounding. Therefore, the purpose of this study was to examine the causal estimates regarding the impact of these risk factors on meniscal injury.

Methods

In this study, single nucleotide polymorphisms associated with obesity and smoking were extracted as instrumental variables from the Gene-Wide Association Study database (GWAS). Data on genetic variants of meniscal injuries were obtained from the Finnish database. Heterogeneity of the data was assessed using IVW, MR-Egger and Cochran's Q statistics. Potential causality was assessed using inverse variance weighting, Mendelian randomisation Egger, and weighted median methods.

Results

Our study showed that obesity and smoking were causal factors for meniscal injuries. (Waist circumference: IVW: OR = 1.59; 95%CI = 1.41–1.80; P<0.001. Hip circumference: IVW: OR = 1.37; 95%CI = 1.23–1.53; P<0.001. BMI: IVW: OR = 1.53; 95%CI = 1.39–1.68; P<0.001. Smoking initiation: IVW: OR = 1.17; 95%CI = 1.00-1.37; P = 0.04. Current smoking: IVW: OR = 2.35; 95%CI = 1.18–4.66; P = 0.01. Past smoking: IVW: OR = 0.75; 95%CI = 0.62–0.90; P<0.01).

Conclusion

Our results enriched findings from previous epidemiology studies and provided evidence from MR that obesity and smoking have a clear causal effect on meniscal injuries.

mendelian randomization

obesity

smoking

meniscal injury

causal association

The meniscal is a fibrocartilaginous structure that plays a crucial role in maintaining the normal function of knee joint. More specifically, it mainly undertakes load transmission, joint lubrication, distributing stress, stabilizing and motion coordination of knee [1, 2]. Meniscal injury (MI) is a prevalent form of knee injuries that can occur at any age [3]. Most MI types are often accompanied by knee pain, articular cartilage degeneration, and knee instability, which in turn raises the likelihood of developing into knee osteoarthritis [4]. Previous research indicated that MIs were observed in approximately 154 cases per 100,000 individuals [5].

Epidemiological studies have identified obesity and smoking as modifiable risk factors for MI [6]. It’s been proved that the forces exerted on the knee are approximately 2–3 times the bodyweight during walking on level ground and 4–5 times during squatting. Besides, even higher forces are transmitted through the knee when one is going upstairs or downstairs [7]. The meniscus acts as a cushion to these forces, distributing them over a larger surface area and limiting articular surface wear; however, an increase in body weight exponentially increases the load on the meniscus and predisposes it to injury [8]. Moreover, Ruzbarsky reported that substances like nicotine found in tobacco could impair the blood supply of meniscus which would impede the natural healing process [8]. However, a study involving 243 cases of meniscal tears indicated that smoking does not seem to elevate the risk of MI [9]. To date, current evidences supporting the correlations between MI and those risk factors remain inconsistent and inconclusive. It is crucial to note that reverse causation, misclassification, unobserved confounders, and other biases in observational epidemiological studies can significantly impede the ability to draw causal inferences from these associations. Therefore, it is essential todetermine the causal relationship between potentially modifiable risk factors and meniscal injuries. This research has significant practical implications for understanding the causes of the disease and informing strategies for its prevention and management in public health.

Mendelian randomization (MR) was performed to detect association between risk factors and disease outcomes by using genetic variants as instrumental variables (IVs), abiding by the law of independent assortment, i.e. genetic variants are randomly assigned at conception [10–12]. As genotypes precede the diseases process and are largely independent of postnatal lifestyle or environmental factors, this method can minimize confusion and avoid the bias of reverse causation [13, 14]. Furthermore, the published genome-wide association study (GWAS) summary datasets has made MR a time- and cost-saving way to investigate causal effect of a risk factor on an outcome, it could be seenthat growing popularity in assessing and screening [15].

Therefore, we performed a MR study to evaluate the possible causal associations of obesity (waist circumference, hip circumference, and BMI), smoking (Smoking initiation, Current smoking, Past smoking) with risk of MI.

Study overview

This study used MR analysis to determine whether obesity and smoking were causally related to MI. The MR analysis procedure has to meet 3 assumptions (Fig. 1). First, the genetic variants used as instrumental variables (IVs) should be strongly associated with exposure. Second, the genetic variants should not be associated with confounders. Third, the genetic variants should influence the outcome through the risk factor rather than other pathways. During MR analysis the STROBEMR statement was strictly followed [16]. The experimental workflow is illustrated in Fig. 2.

As this study was based on an existing public database (GWAS), no additional ethical approval or consent was required.

Data sources

The obesity and smoking GWAS data are from the MRC IEU Open GWAS database (https://gwas.mrcieu.ac.uk/). Meniscal Injury GWAS data is from FinnGen database (https://www.finngen.fi/en). MI GWAS dataset consists of 13568 cases and 147221 controls from European populations. We extracted data on SNPs and the following three obesity-linked anthropometric traits from the GWAS database: body mass index, waist circumference, hip circumference. The smoking dataset consists of 31506099 individuals included smoking initiation, current smoking, and past smoking. The detail of studies and datasets was presented in Table 1.

Table 1

Details of Studies and Datasets Used in the Study
Exposure/Outcome	Sample Size	Number of SNPs	Web Source	Year	Population
Waist circumference	462166	9851867	https://gwas.mrcieu.ac.uk/	2018	European
Hip circumference	336601	10894596	https://gwas.mrcieu.ac.uk/	2017	European
Body mass index	461460	9851867	https://gwas.mrcieu.ac.uk/	2018	European
Smoking initiation	607291	11802365	https://gwas.mrcieu.ac.uk/	2019	European
Current smoking	462434	9851867	https://gwas.mrcieu.ac.uk/	2018	European
Past smoking	424960	9851867	https://gwas.mrcieu.ac.uk/	2018	European
Meniscus injury	160789	16380200	https://www.finngen.fi/	2021	European

selection of instrumental variables

From the GWAS summary data of obesity and smoking, we conducted a series of quality control steps to select eligible instrumental SNPs. Firstly, we extracted single nucleotide polymorphisms (SNPs) associated with exposure at a genome-wide significance level (p<5×10^− 8). Secondly, To ensure the accuracy of our findings, we also checked that these SNPs for the exposure are not in linkage disequilibrium (LD) (r²<0.001, kb = 10000). This step is crucial as instrumental SNPs in strong LD can introduce bias in the results. Thirdly, we obtained data for above selected SNPs from the outcome trait (MI) GWAS summary. By default, if a specific requested SNP was not present in the outcome GWAS, we searched for a proxy SNP in LD to substitute the requested target SNP. Fourthly, we addressed the issue of ambiguous SNPs with nonconcordant alleles and palindromic SNPs with an ambiguous strand. We either corrected the alleles or excluded these ambiguous and palindromic SNPs from our selected instrument SNPs, ensuring that the effect of a SNP on the exposure and the effect of the same SNP on the outcome corresponded to the same allele.

According to the assumptions of the MR analysis, the selected instrumental SNPs should be strongly associated with exposure. To assess the presence of weak instrumental variable bias, we calculated the F statistic (F = R²(n − k − 1)/k (1 − R²); R², variance of exposure explained by selected instrumental variables, and we got the value of R² in MR Steiger directionality test; n, sample size; and k, number of instrumental variables) [17]. If the F statistic>10, it indicates a strong correlation between the instrumental variables (IVs) and exposure [18]. This ensures that the results of the MR analysis are not influenced by weak-tool bias. Finally, we used these rigorously selected SNPs as instrumental variables for our MR analysis (Table 2).

Table 2

Mendelian randomisation analysis of the causal relationship between exposure and outcome
Exposure	MR method	Number of SNPs	OR	95%CI	Association P-value	Cochran’s Q Statistic	Heterogeneity P-value	MR-Egger intercept (P-value)
Waist circumference
	IVW	342	1.59	1.41–1.80	7.95e-14	441.9	1.8e-04
	MR-Egger	342	1.91	1.35–2.69	2.55e-04	440.5	2.0e-04	0.003(0.26)
	WM	342	1.60	1.32–1.93	1.45e-06
Hip circumference
	IVW	272	1.37	1.23–1.53	1.63e-08	411.67	7.25e-08
	MR-Egger	272	1.40	1.03–1.92	3.45e-02	411.63	5.87e-08	0.001(0.88)
	WM	272	1.41	1.23–1.64	4.47e-06
Body mass index
	IVW	417	1.53	1.39–1.68	1.47e-18	524.7	2.27e-04
	MR-Egger	417	1.34	1.04–1.72	2.57e-02	523.1	2.39e-04	0.003(0.26)
	WM	417	1.45	1.23–1.70	1.98e-06
Smoking intiation
	IVW	83	1.17	1.00-1.37	0.04	123.3	3.60e-03
	MR-Egger	83	2.20	1.02–4.77	0.04	119.3	2.18e-03	-0.017(0.11)
	WM	83	1.25	1.03–1.51	0.02
Current smoking
	IVW	33	2.35	1.18–4.66	0.01	37.2	0.21
	MR-Egger	33	1.45	0.10-21.08	0.78	37.1	0.24	0.004(0.72)
	WM	33	2.93	1.15–7.45	0.02
Past smoking
	IVW	93	0.75	0.60–0.90	2.31e-03	124.76	0.01
	MR-Egger	93	0.81	0.37–1.81	0.61	124.70	0.01
	WM	93	0.73	0.57–0.94	0.01			0.008(0.83)

MR analysis

The inverse variance weighted (IVW) method was selected as the main MR analytical method to estimate the causal effect [19]. The causal effect of exposure on MI was considered indicative if the effect estimate was significant in the IVW method and no contradictory results were found in other methods [20]. Several sensitivity analyses, including the weighted median [21], MR-Egger [22], and MR-PRESSO [23] were conducted to examine the consistency of associations and detect and correct for horizontal pleiotropy. The IVW method examines the causal link by performing a meta-analysis of each Wald ratio for the included SNPs. It is important to note that the IVW analysis is predicated on the assumption that all of the contained SNPs are genuine variables [24]. Unlike the IVW method, the MR-Egger regression can work even if all of the SNPs are invalid. However, MR-Egger may not be accurate, especially when the correlation coefficient between SNPs and the exposure is similar or when there are only a small number of genetic instruments [25, 26]. The weighted median model provides consistent estimates as long as at least 50% of the weight in the analysis comes from valid IVs [21]. The estimation of the causal relationship between obesity, smoking and MI was expressed as odds ratio(OR) with a 95% confidence interval(CI). In addition, a significance level of P < 0.05 was used to determine statistical significance.

Various methods were introduced in this study for pleiotropy and heterogeneity analysis. Firstly, the heterogeneity of IVs was assessed via Cochran’s Q test. If significant heterogeneity was detected in some exposures, the random effect model was used to estimate the MR effect size; otherwise, the fixed-effects IVW method was considered the main result. Secondly, potential horizontal pleiotropy of IVs was evaluated by the MR-Egger regression intercept. If the p-value was less than 0.05, the MR analysis might obey the hypothesis that genetic exposure influenced the outcome directly. Thirdly, leave-one-out sensitivity test examined whether a single SNP caused the results. Finally, funnel plots and scatter plots were evaluated as a visual inspection of symmetry and the effect estimates. All tests were performed using the “TwoSampleMR” packages in the R software (version 4.3.1).

Characteristics of instrumental variables

Genetic variants were screened according the criteria of screening (P < 5×10^− 8, r² < 0.001, kb = 10000, F>10). Eventually, we screened 342, 242, and 417 single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for waist circumference, hip circumference, and BMI. Furthermore, 83, 33, and 93 SNPs as IVs for smoking initiation, current smoking, and past smoking, respectively. None of these IVs had an F-statistic below the threshold of 10, indicating that there was low evidence of weak instrument bias in this study.

MR analyses

Table 2 presents the MR estimates of various methods used to assess the causal effect of obesity and smoking on MI.

Genetically predicted higher waist circumference, hip circumference, and BMI were associated with an increased risk of MI. The combined ORs of MI were 1.59 (95% CI:1.41–1.80; p<0.01), 1.37 (95% CI: 1.23–1.53; p<0.01) and 1.53 (95% CI: 1.39–1.68; p<0.01) for waist circumference, hip circumference and BMI, respectively. The association calculated by different methods (the weighted median, MR-Egger regression,) was directionally consistent.

As for smoking behaviors, genetic predisposition to smoking initiation (OR = 1.17; 95%CI = 1.00-1.37; P = 0.04), Current smoking:(OR = 2.35; 95%CI = 1.18–4.66; P = 0.01) were both associated with MI. Interesting, past smoking was not found to be associated with MI (OR = 0.75, 95%CI = 0.62–0.90; P<0.01). The result suggest that stop smoking does not continue to increase the risk of MI (Fig. 3).

Heterogeneity and pleiotropy analysis

In the heterogeneity test, except for current smoking, the P values of Q statistics for inverse variance-weighted and MR-Egger analyses suggested the existence of heterogeneity, so random-effects regression model was used (Table 2). In the pleiotropy test, the MR-Egger regression intercepts were close to zero, indicating no evidence of pleiotropy (Table 2).

In this study, we used MR to systematically explore genetically causal effects of obesity and smoking on the risk of MI. Our study showed that obesity (waist circumference, hip circumference, BMI), smoking initiation, and Current smoking were a causal factor for MI: waist circumference: (OR = 1. 59, 95% CI: 1.41–1.80, P < 0.001), hip circumference: (OR = 1.37, 95% CI: 1.23–1.53, P < 0.001), BMI: (OR = 1.53, 95% CI: 1.39–1.68, P < 0.001), Smoking initiation: (OR = 1.17; 95%CI = 1.00-1.37; P = 0.04), Current smoking: (OR = 2.35; 95%CI = 1.18–4.66; P = 0.01). However, we did not observe evidence supporting that genetic causal effects of past smoking was associated with MI (OR = 0.75; 95%CI = 0.62–0.90; P<0.01).

The meniscal plays a crucial role in the knee joint and its injury can significantly impact knee function and reduce the quality of life for patients. epidemiological study has identified obesity and smoking as independent risk factors for MI [6]. In a retrospective analysis, it was found that patients with a BMI ≥ 30 had a 22% higher likelihood of experiencing a MI compared to those with a BMI < 30 [27]. Furthermore, patients with a BMI ≥ 30 had a 4-fold increase in the risk of MI. [28]. Similar findings have been reported in other studies, which have observed a five-fold greater risk of meniscus injury in overweight patients compared to those with a BMI < 25 [29–31]. The increase in BMI is associated with higher stress and torque during knee rotation, as well as inadequate nutrient supply to the meniscus in obese individuals due to meniscal compression. This combination leads to a higher risk of meniscal injury. Furthermore, Smoking is a pressing issue in modern medicine due to its significant impact on public health. It is well-known that tobacco exposure can harm every organ in the body, causing immediate damage [32–34]. Smoking has a multifactorial impact on the soft tissue microenvironment and healing. It temporarily reduces tissue perfusion and oxygenation, attenuates reparative cell functions, decreases the synthesis and deposition of collagen, and impairs the inflammation and proliferation phase of the healing process [35–38]. According to research, smoking populations often experience impaired tissue vascularity and blood flow, which can impact the recovery following MI [38–40]. A previous study involving 140 patients with MI found that smoking was a risk factor to both the occurrence and healing of such injuries [41]. In a study by Blackwell et al., smokers were found to have a 3.8 times higher risk of meniscal repair failure compared to non-smokers [42]. Additionally, studies have shown that smoking significantly affects the anatomy of the meniscal, by comparing the incidence of MI between smokers and non-smokers, with a significantly higher incidence in the smoking group [43, 44]

Observational studies are prone to reverse causation bias and confounding factors, which restrict their ability to provide causal estimates of the effect of exposures and outcomes, thereby reducing their ability to inform prevention and treatment strategies against the disease [45]. Unlike observational studies, MR uses exceptional genetic variants that are assumed to satisfy the IVs hypothesis to investigate the question of causality in epidemiological studies, which minimizes the possibility of inherent bias [46]. Moreover, MR analysis is cost-effective and feasible when compared to randomized controlled trials [47].

To ensure the reliability of our results, we screened SNPs with gene-wide association (P < 5x10^− 8) and removed any linkage disequilibrium (r² < 0.001, kb = 10,000). Additionally, we accounted for horizontal pleiotropy, which refers to the possibility that SNPs may affect outcomes through other pathways rather than exposure. The consistency across three MR analysis methods, namely IVW, MR-Egger, and MM provides robust evidence to support our conclusions. Additionally, to address potential bias and account for ethnic differences, we exclusively utilized GWAS data from European populations for both the exposure and outcome variables.

Despite the validity and stability of our MR results, there are several limitations of the current study. Firstly, it should be noted that the GWAS data we used are exclusively from European populations. Therefore, caution must be exercised when extrapolating our findings to other populations and ethnic groups. Secondly, the selection of SNPs from different large-sample GWAS data introduces the possibility of sampling overlap between the exposure and outcome variables, which may potentially lead to biased results. Lastly, it is crucial to acknowledge that MI is influenced by a combination of genetic factors, environmental factors, lifestyles, and epigenetic modifications. Thus, our findings only provide a partial explanation for the causal effect of obesity and smoking on MI.

In conclusion, we enriched findings from previous epidemiology studies and provided evidence from MR that obesity and smoking were independent causal factors for MI. These findings have important implications for guiding individuals in adopting scientific health management practices, such as reducing body fat percentage and quitting smoking, in order to mitigate the risk of MI.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Publicly available datasets were analyzed in this study. Obesity and complications of prosthesis GWAS data are from the MRC IEU Open GWAS database (https://gwas.mrcieu.ac.uk/).

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported by Doctoral Science Foundation of Affiliated Hospital of Guangdong Medical University (BJ201803).

Authors' contributions

B.H. conceived the presented idea, B.H. and H.X. performed the manuscript writing, Q.D. and Z. W. was involved in acquisition and processing of data, Z.H. was involved in interpretation of data, T.X. and O.Y. prepared figures, J.C. prepared tables. All authors contributed to the article and approved the submitted version.

Acknowledgements

We thank the IEU Open GWAS consortium, the FinnGen team and other researchers and participants for providing publicly available data for this analysis.

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No competing interests reported.

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Obesity and Smoking are causal factors for meniscal injury: A mendelian randomization study

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Abstract

Background

Methods

Results

Conclusion

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Introduction

Materials and methods

Study overview

Data sources

selection of instrumental variables

MR analysis

Results

Characteristics of instrumental variables

MR analyses

Heterogeneity and pleiotropy analysis

Discussion

Declarations

References

Additional Declarations

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