Mendelian randomization to evaluate the relationship between frailty index and the risk of osteoporosis, falls and fractures

doi:10.21203/rs.3.rs-3769316/v1

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Mendelian randomization to evaluate the relationship between frailty index and the risk of osteoporosis, falls and fractures

https://doi.org/10.21203/rs.3.rs-3769316/v1

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Background

Observational research suggested a suggestive relationship between frailty index (FI) and osteoporosis, falls and fractures.

Aims

This study aimed to explore genetic causality and identified that higher genetically predicted FI positively correlated with increased risks of osteoporosis, falls, and fractures.

Methods

Genome-wide association study summary data were used to identify genetic variants strongly related to FI. GWAS data of Heel Bone mineral density, osteoporosis, fall risk and fracture at different site was considered as outcome. Inverse variance-weighted (IVW) analysis was mainly applied; MR-Egger and the weighted median were also used to obtain robust estimates. A series of sensitivity analyses were used to detect pleiotropy or heterogeneity. Then, we adjustment of Body Mass Index (BMI) and type 2 diabetes (T2DM) using multivariable Mendelian randomization (MVMR).

Results

In the main analysis, a genetically predicted higher Frailty index was associated with a higher risk of Osteoporosis and fall. What’s more, frailty index significantly contributed to an increased risk of Heel Body Mass Density (BMD). In MVMR, after correcting for type 2 diabetes, the causal relationship between FI and Heel BMD has turned into negative regulation. Results of comprehensive sensitivity analysis were consistent with the main causality estimate. Horizontal pleiotropy and heterogeneity were absent in our MR study.

Discussion

The results provide evidence that offering avenues for interventions alleviates the burden of FI associated with these health concerns.

Conclusions

Our results suggest a potentially causal relationship between FI and osteoporosis, falls, and fractures.

Mendelian randomization

Osteoporosis

fall risk

fracture

frailty index

Frailty, the age-linked condition characterized by diminished physiological resilience across various organ systems, heightens vulnerability to adverse consequences such as hospitalization and death[1, 2]. Frailty encompasses clinical, physical, psychological, and cognitive aspects, along with social capacity, all of which can be effectively measured using a Frailty Index (FI)[3]. Despite numerous study reports frailty is closely related to the deterioration of the musculoskeletal system, affecting bone mineral density (BMD), balance, mobility, falls and ultimately osteoporosis and fractures, the connection between frailty and alterations in body pathological condition remains inadequately understood[4–6]. Osteoporosis, characterized by weakened bones, not only increases the likelihood of fractures but is closely tied to the broader concept of frailty. This prevalent skeletal disorder can significantly restrict the ability to perform daily tasks, given the heightened risk of bone fractures, thereby diminishing overall quality of life[7, 8]. Therefore, understanding how frailty influences osteoporosis becomes crucial, as it not only impacts fall risk but also exacerbates the potential for fractures. In this exploration, we delve into the interconnected realms of frailty, osteoporosis, and their collective influence on the risks associated with falls and fractures.

Numerous epidemiological studies have found associations between frailty and osteoporosis. For example, a Hertfordshire Cohort Study of 405 participants found that early identification of osteoporosis not only allows implementation of treatment strategies but also presents an opportunity to mitigate frailty risk[9]. A prospective cohort study of 3,149 (138 had diabetes) participants showed frailty increases the risk of fragility fracture and enhances the effect of diabetes on fragility fractures[10]. In addition, a multicentre osteoporosis study of 9,423 Canadian found that irrespective of age and bone mineral density, the frailty Index was associated with hip, vertebral, and all-type clinical fractures[11]. Another prospective study of 235 community-dwelling women demonstrated that frailty status as defined by the Vulnerable Elders Survey (VES-13) predicts a decrease in BMD after 1 year[12]. Furthermore, a community cohort 10-year longitudinal study, including 1,044 older women, confirmed that the frailty index was highly predictive of mortality and frailty was associated with BMD and osteoporosis[13].

Mendelian randomization (MR) is a cutting-edge epidemiological analytical approach employing genetic variables, particularly single nucleotide polymorphisms (SNPs), as instrumental variables (IVs). This method aims to elucidate causality and evaluate the association between risk factors and disease outcomes[14]. The discovery of thousands of genetic variations associated with diverse complex diseases through Genome-wide association studies (GWASs) has propelled the extensive adoption of Mendelian randomization (MR) to a more advanced and prevalent stage[15].

Building upon the aforementioned knowledge, we applied a bidirectional Mendelian randomization (MR) analysis, leveraging recent comprehensive Genome-wide association studies (GWASs), to investigate the causal links among frailty, osteoporosis, falls and fracture risk.

Study Design

Mendelian Randomization (MR) analysis employs genetic variants as instrumental variables to assess causal relationships. Three critical assumptions underlie MR analysis:

1) the chosen genetic variants, used as instrumental variables, exhibit strong and consistent associations with the exposure of interest. 2) the selected genetic variants are not associated with any potential confounding factors. 3)the genetic variants influence the risk of the outcome solely through the specified risk factor, excluding alternative pathways.

In this study, we utilized publicly available summary-level data derived from extensive genome-wide association studies and consortia to make a causality inference between Frailty index and osteoporosis, fall risk and fracture risk.

Data Sources

The summary-level data for the Frailty index were derived from a comprehensive GWAS meta-analysis involving European participants from both the UK Biobank (with a sample size of 164,610) and TwinGene (with a sample size of 10,616)[16]. The Frailty index was established by cumulatively incorporating deficits, as previously detailed[17]. The summary-level data of Osteoporosis[18], Heel bone mineral density[19], fall risk and fall in the last year, and fracture in different sites were from UK Biobank (http://www.nealelab.is/uk-biobank, accessed on 2 October 2023). A detailed information of these GWAS data is shown in the Table 1.

Table 1

Causality analysis of the associations between Frailty index and the risk of bone related diseases traits.
Exposure	Outcome		Nsnp	Inverse variance weighted		Weighted median		MR Egger
Exposure	Outcome		Nsnp	OR (95%CI)	P value	OR (95%CI)	P value	OR (95%CI)	P value
Frailty index	Osteoporosis		10	1.011(1.002,1.019)	0.013	1.011(1.001,1.022)	0.037	1.001(0.914,1.097)	0.977
	Heel bone mineral density		10	1.107(1.045,1.173)	4.23×10^− 4	1.087(1.005,1.176)	0.038	1.049(0.793,1.386)	0.748
	Fracture	Fracture	15	1.129(0.940,1.356)	0.195	1.067(0.846,1.346)	0.583	1.071(0.460,2.496)	0.876
		Fracture-leg	14	1.006(1.002,1.011)	4.23×10^− 3	1.007(1.001,1.013)	0.030	1.016(0.997,1.036)	0.118
		Fracture-ankle	15	1.007(1.000,1.013)	0.038	1.007(0.998,1.015)	0.142	0.981(0.956,1.007)	0.178
		Fracture-wrist	15	1.002(0.995,1.009)	0.595	1.004(0.995,1.013)	0.396	0.980(0.950,1.011)	0.217
		Fracture-arm	15	1.003(0.998,1.008)	0.299	1.002(0.996,1.009)	0.514	0.993(0.971,1.015)	0.531
		Fracture-spine	11	0.999(0.995,1.003)	0.583	0.998(0.994,1.002)	0.308	1.001(0.985,1.017)	0.938
	fall	fall in the last year	11	1.084(1.050,1.120)	8.49×10^− 7	1.095(1.048,1.145)	6.15×10^− 5	1.072(0.941,1.221)	0.322
	fall	fall risk	12	1.272(1.129,1.432)	7.13×10^− 5	1.258(1.065,1.485)	6.77×10^− 3	1.255(0.766,2.054)	0.388
MR, Mendelian Randomization; IVW, inverse variance-weighted; OR: odds ratio; CI, confidence interval.

All of these SNPs achieved genome-wide association significance (P < 5×10^− 8) after accounting for age, gender, and 10 principal components. Moreover, they exhibited minimal linkage disequilibrium with each other (r2 < 0.001 within a 10,000 kb window) according to the European 1000 genomes panel[20]. For SNPs that were absent in the outcome GWAS dataset, we identified suitable proxies with an R2 value exceeding 0.8. In cases where no appropriate proxy could be found, those SNPs were excluded. Among the selected instrumental SNPs, we excluded those palindromic SNPs with middle allele frequency ranging from 0.01 to 0.30 (A palindromic SNP is defined as a SNP with either A/T or G/C alleles). We subsequently employed MR Presso and MR radial regression (MR PRESSO)[21] served as the more straightforward methods to detect and remove outliers and influential data points. The phenotypic variance was explained by R2. To attain the first assumption of MR design, SNPs with F statistics [F = R2(n − k − 1)/k (1 − R2)] higher than 10 were considered valid IVs.

Statistical analyses

The Frailty index trait was paired with each respective outcome disease to estimate causality. Initially, the inverse variance-weighted (IVW) method was employed to assess potential causal relationships[22]. The weighted median (WM) method[23] and the MR-Egger method[24] were then applied for sensitivity analyses. The causal effect estimates indicate the change in the risk of the outcome trait for every one-unit increase in the standard deviation (SD) of the Frailty index, presented as odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs) in the natural scale.

Potential heterogeneity was quantified and tested by Cochran’s Q test[25], and directional pleiotropy was estimated by the MR-Egger regression test[24]. We then conducted a leave-one-out analysis to identify potential outliers that might independently influence the observed causal relationship. To mitigate the impact of confounding factors, we conducted a search on Phenoscanner (http://www.phenoscanner.medschl.cam.ac.uk/) to determine if the selected SNPs were associated with other confounding risk factors. Following the exclusion of SNPs related to confounders, we reassessed whether the causal relationship remained statistically significant. These statistical analyses were primarily performed using the two-sample MR package (version 0.5.6) within the R software (version 4.1.2). All reported p-values were two-tailed, and statistical significance was defined as p < 0.05.

MVMR

For MVMR analyses, we constructed instruments using SNPs in each of the GWASs meeting our single-variable MR selection criteria. We combined the SNPs from the confounders relevant European GWASs from ieu GWAS database: BMI (ukb-b-19953, N = 461,460); Type 2 diabetes (ebi-a-GCST90018926, N = 38,841) plus Frailty index instrument. We removed duplicate and correlated SNPs (within 10 000 kilobase pairs; R2 ≥ 0.001), then extracted the SNPs effects and corresponding standard errors from the exposures and outcome GWASs. We used the MVMR extension of the IVW MR method to correct for confounders.

All the remaining SNPs, along with their associated data including beta (β), standard error (SE), effect allele/other allele, and p-value, were utilized in the subsequent causal analysis and are comprehensively documented in Supplementary File 1 (Tables S1–S10). Among these, the effect size (β) for each SNP was calculated by modeling its association with the trait through a regression model. For quantitative traits (each Frailty index), the regression coefficients are presented per one-unit increase in the standard deviation, while for qualitative outcome traits, they are presented per one-unit increase in log odds, enabling comparability across different traits. It's worth noting that the F-statistics for all the SNPs exceeded 10, indicating a robust correlation between the instrumental variables and exposure traits.

The results of the preliminary analyses of the effects of genetically predicted Frailty index on the risk of different bone-related traits are listed in Table 2. Among them, a genetically predicted higher Frailty index was associated with a higher risk of Osteoporosis (OR: 1.011, 95% CI: 1.002 to 1.019, p = 0.013), higher risk of fall, including fall in the last year (OR: 1.084, 95% CI: 1.050 to 1.120, p = 8.49 × 10^− 7) and fall risk (OR: 1.272, 95% CI: 1.129 to 1.432, p = 7.13 × 10^− 5) and leg fracture (OR: 1.006, 95% CI: 1.002 to 1.011, p = 4.23 × 10^− 3). What’s more, frailty index significantly contributed to an increased risk of Heel BMD (OR: 1.107, 95% CI: 1.045 to 1.173, p = 4.23×10^− 4). Figure 1 shows a scatter plot of the causal estimates of the effects of genetically predicted Frailty index on the risk of different bone-related traits. No causal relationship could be supported based on our estimates between Frailty index and other

Table 2

Sensitivity analysis of the associations between Frailty index and the risk of bone related diseases traits.
Exposure	Outcome		Nsnp	Cochran’s Q test		MR-Egger regression		IVW*
Exposure	Outcome		Nsnp	Q	p value	Egger intercept	p value	OR (95%CI)	P value
Frailty index	Osteoporosis		10	3.702	0.930	9.38×10^− 4	0.849	1.011(1.002,1.021)	0.019
	Heel bone mineral density		10	7.983	0.536	1.22×10^− 3	0.707	1.12(1.044,1.201)	1.47×10^− 3
	Fracture-leg		14	14.598	0.333	2.29×10^− 4	0.314	1.005(1.000,1.010)	0.046
	fall	fall in the last year	11	5.327	0.868	2.61×10^− 4	0.865	1.069(1.025,1.114)	1.77×10^− 3
	fall	fall risk	12	7.145	0.787	3.11×10^− 4	0.957	1.182(1.018,1.374)	0.028
*Causality inference after removing the SNPs associated with confounding factors detected on Phenoscanner. MR, Mendelian Randomization; IVW, inverse variance-weighted; OR: odds ratio; CI, confidence interval.

fracture sites except leg.

The estimates obtained through the weighted median method were in agreement with the aforementioned results. Additionally, we performed MR-Egger regression analysis to evaluate directional pleiotropy. The MR-Egger regression intercepts provided no indication of directional pleiotropy for any of the causal estimates (p > 0.05) (Table 2). Cochran's Q test indicated the absence of significant heterogeneity. The leave-one-out analysis revealed that the core causal effects remained consistent irrespective of the SNP that was excluded, underscoring the robustness of our MR analysis (Supplemental Fig. 1).

In MVMR, assessing the genetic liabilities for Frailty index retained a robust relationship with the risk of osteoporosis (IVW OR, 1.017; 95% CI, 1.011 to 1.023; p = 1.00×10^− 7), leg fracture (IVW OR, 1.004; 95% CI, 1.000-1.008; P = 0.041), fall in the last year (IVW OR, 1.125; 95% CI, 1.089–1.161; P = 8.66×10^− 7) and fall risk (IVW OR, 1.490; 95% CI, 1.321 to 1.679; p = 7.31×10^− 11) (Table 3). Interestingly, after correcting for type 2 diabetes (IVW OR, 1.032; 95% CI, 1.014 to 1.049; p = 2.71×10^− 4), although Frailty index retained a robust causal relationship with the risk of Heel bone mineral density, the causal relationship has turned into negative regulation (IVW OR, 0.881; 95% CI, 0.790 to 0.983; p = 0.023), indicating the exclusion of type 2 diabetes interference, and there is a causal association between the Frailty Index and the decline in BMD.

Table 3

Multivariable Mendelian Randomization analysis of the associations between Frailty index and the risk of bone related diseases traits.
Outcome		Factors
		Frailty index		Type 2 diabetes		BMI
		OR (95%CI)	P value	OR (95%CI)	P value	OR (95%CI)	P value
Osteoporosis		1.017(1.011,1.023)	1.00×10^− 7	0.998(0.997,0.999)	3.71×10^− 7	1.000(0.997,1.002)	0.785
Heel bone mineral density		0.881(0.79,0.983)	0.023	1.032(1.014,1.049)	2.71×10^− 4	1.026(0.983,1.071)	0.244
Fracture-leg		1.004(1.000,1.008)	0.041	1.000(0.999,1.001)	0.837	0.999(0.998,1.001)	0.217
fall	fall in the last year	1.125(1.089,1.161)	8.66×10^− 13	0.997(0.992,1.002)	0.191	1.037(1.024,1.05)	2.61×10^− 8
fall	fall risk	1.490(1.321,1.679)	7.31×10^− 11	0.985(0.967,1.003)	0.103	1.129(1.077,1.184)	4.80×10^− 7

Our comprehensive MR study supported a clear causal relationship between frailty index and osteoporosis and fall risk, with frailty increasing the susceptibility of individuals to fractures (Fig. 2). Understanding this relationship is crucial for developing effective preventive and management strategies to reduce the impact of fractures, particularly leg fractures, in frail individuals.

Frailty is a clinical syndrome characterized by alterations in multiple physiological systems, leading to a decline in functional reserve and heightened susceptibility to external stressors [26, 27]. Currently, there is a scarcity of longitudinal studies exploring the relationship between frailty and musculoskeletal diseases [11, 28]. In the English Longitudinal Study of Ageing, which included participants aged 50 years and older, lower body strength was found to be linked to a higher incidence of frailty (Hazard Ratio 1.07, 95% CI 1.06–1.08) [28]. Conversely, in a study involving 235 community-dwelling women aged 70 years and older, frailty status was a predictor of reduced bone mineral density after one year [12]. In an observational study of 1035 older adults with human immunodeficiency virus aged 40 years and older, baseline frailty was associated with the development of bone diseases [29]. Collectively, these studies imply an association between musculoskeletal health and frailty.

Previous study showed a significant decrease in bone mass density in frail and pre-frail subjects compared to robust ones, frail patients displayed lower calcium and phosphate levels, it may suggest a role of malnutrition in the development of bone loss[30]. Meanwhile, frailty is a significant risk factor for fall and fractures risk[31, 32]. Frailty encompasses many physical changes related to fall propensity such as impaired balance, sarcopenia, and musculoskeletal dysfunction[6]. The frailty phenotype is often related to muscle loss and has significant negative effects on muscle mass, strength, and physical performance which may affect physical abilities, gait speed, endurance, and balance in the older adults, and all of those have been linked to falls[33]. Furthermore, among the older adults, aging and frailty reinforce each other in a vicious cycle that makes frailty more prone to falls[33].

Reduced protein and energy intake are predictors of both osteoporosis and frailty, with weight loss serving as a diagnostic criterion for frailty in the older persons. Diminished food consumption, especially protein, hastens the transition to frailty and increases the risks of osteoporosis, falls, and fractures [34–36]. A case-control design in reduced-obese postmenopausal women indicated that BMD alterations coincide with weight loss[37]. Serum albumin levels can serve as a useful indicator for assessing nutritional status in the context of frailty, as low serum albumin levels are often associated with an increased risk of frailty in older individuals. And the correlation between BMD and serum albumin levels, highlights the impact of an individual's nutritional status on their bone density[38, 39]. These associations elucidate why individuals with hip fractures typically exhibit lower body weight compared to individuals of the same age who do not experience fractures[40]. Nevertheless, ascertaining whether the increased fracture risk is attributed to a reduced adipose tissue layer with limited impact energy absorption capabilities or to low body weight that mirrors diminished bone density, quality, and nutritional well-being remains challenging.

Impaired cognition has been connected to an increased risk of falls and fall-related fractures[41] due to an altered gait or a slower walking speed [42]. A plausible hypothesis is that reduced motor performance may signal a lower level of physical fitness, thereby increasing the risk of dementia [43]. These findings reinforce the concept that the decline in functional abilities, such as walking and balance, occurs before cognitive deterioration, and this decline is also a predictor of falls[44].

Frailty is characterized by systemic deficiencies, including endocrine dysregulation and elevated pro-inflammatory cytokines, which overlap with mechanisms involved in osteoporosis. Managing factors related to frailty may substantially reduce the risk of hip fractures by preventing falls, disability, and decreased BMD, while also facilitating osteoporosis diagnosis and treatment. Low testosterone is frequently recognized as a significant contributor to frailty in aging men[45]. With age, there is a decline in insulin-like growth factor-I (IGF-I) and growth hormone (GH) levels, which may serve as potential risk factors for the development of frailty, particularly in the context of osteoporosis[46]. GH may also exert a direct modulatory influence on vitamin D receptors [47]. The interplay of vitamin D status and physical inactivity is pivotal in determining the risk of osteoporosis and falls[48, 49]. Currently, the potential connections between GH, IGF-I, and bone tissue, while intriguing, lack conclusive evidence regarding the benefits of these growth factors on bone tissue. Further mechanistic studies are required.

Mendelian Randomization (MR) serves as a valuable approach for mitigating various biases encountered in observational studies. Nevertheless, it is crucial to acknowledge the potential for residual pleiotropy to introduce bias into our estimates. Fortunately, we have taken measures to minimize this risk by conducting multiple sensitivity analyses and employing the MVMR method. These approaches come with distinct assumptions and sources of bias, yet they consistently yield similar results. This convergence of evidence bolsters our confidence that our estimates remain largely unaffected by horizontal pleiotropy. Furthermore, our application of the MR-Egger regression test did not reveal any clear evidence of directional pleiotropy, providing additional support for the robustness of our findings.

Additionally, it's important to note that we could not account for potential sample overlap between the GWAS datasets used for exposure and outcome assessments. Nevertheless, we employed robust instrumental variables to estimate the association between the risk factors and the outcomes. As a result, any potential sample overlap is unlikely to significantly distort our findings. However, we do recommend with caution when interpreting our results.

Furthermore, the majority of the study participants belonged to European ancestry. Consequently, it would be inappropriate to directly extrapolate our findings to other ethnicity. To enhance the applicability of our results across diverse populations, it is imperative to undertake additional initiatives aimed at obtaining non-European samples. These efforts should strive to address disparities in data accessibility and mitigate potential variations in statistical power and instrument strength among different ancestral backgrounds.

Another limitation associated with the utilization of summary-level data in MR is the inability to perform stratified analyses based on covariates of interest, such as age and gender. These stratified analyses could have provided insights into potential interactions between the risk factors, although it's worth noting that prior observational studies have typically not revealed significant interactions involving these variables. However, it is imperative to evaluate whether variations may arise when our results are applied to distinct, stratified populations independently.

To the best of our knowledge, this study represents the inaugural attempt to investigate the causal relationship between FI and osteoporosis, falls, and fractures. Given that frailty status deteriorates with aging, everyone becomes frailer, and risk factors for osteoporosis, falls, and fractures will accumulate, thus, it is highly possible that controlling frailty may play an important role in preventing these clinical implications. Therefore, it appears highly probable that managing frailty could play a pivotal role in averting these clinical consequences. This correlation holds significant implications for clinical decision-making, prompting the initiation of interventions with immediate impacts, primarily aimed at preventing falls that may lead to fractures. Such interventions encompass adjustments to the home environment, medication reconciliation, the use of walking aids, and programs for enhancing balance and muscle strength [50]. In individuals experiencing frailty, proactive interventions to mitigate risks can be implemented at an earlier stage, with a focus on enhancing the quality of life and incorporating strategies to reduce physical symptoms into daily routines. These measures are essential components in the preventive approach against osteoporosis, falls, and fractures.

In conclusion, we present initial genetic evidence indicating that the Frailty index is associated with decreased BMD and an increased risk of osteoporosis, falls, and leg fractures. These findings point to significant clinical implications, potentially aiding in the identification of patients that would benefit from targeted prevention strategies aimed at reducing the incidence of falls and fractures. However, it's important to acknowledge that further investigations are warranted to unravel the intricate causal relationships between the Frailty index and bone-related diseases. Our results also suggest the potential for future studies to explore the utility of changes in the Frailty index as a tool for assessing the risk of osteoporosis and fractures.

Competing Interests: All authors report no conflict of interest.

Funding: No specific funding was received for this work.

Author contributions: Conceptualization: L.Z., J.Z. and Y.L. Writing—original draft: L.Z. and Y.Z. Writing—review and editing: All authors.

Data availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the materials cited herein.

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No competing interests reported.

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Mendelian randomization to evaluate the relationship between frailty index and the risk of osteoporosis, falls and fractures

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