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. 2026 Feb 20;105(8):e47705. doi: 10.1097/MD.0000000000047705

Causal relationship between serum creatinine levels and bone mineral density: A Mendelian randomization study

Zhengtao Ban a, Minghua Shi b,*, Defei Gong a, Jihu Wei a, Jin Yi a, Rui Liu a, Ruzhuan Liu a
PMCID: PMC12928921  PMID: 41731764

Abstract

To study serum creatinine (Scr) levels and bone mineral density (BMD), we used Mendelian randomization (MR) to investigate the causal relationship between them. The genetic association data of Scr levels and total body BMD across age groups were retrieved from the genome-wide association studies, and the causal relationship between Scr levels and BMD was analyzed using the inverse-variance weighting method, MR-Egger regression method, weighted median estimation method, simple mode, and weighted mode. The results of the inverse-variance weighted method indicated that the creatinine level might be a protective factor for the 45 to 60 age group (P = .005, odds ratio = 1.176, 95% confidence interval = 1.049–1.318). All error lines in the sensitivity analysis graph of the retention method were on the right side of 0, suggesting that the results were highly stable and that the MR results were stable, and no association was found in other age groups. Scr levels exhibited a causal relationship with BMD. As a standard proxy indicator for skeletal muscle mass, Scr levels are correlated with BMD. Reduced Scr levels may indicate sarcopenia, which adversely affects skeletal health. Consequently, monitoring Scr could enable the early identification of individuals at risk of low BMD.

Keywords: bone mineral density, causality, genetics, Mendelian randomization, serum creatinine levels

1. Introduction

Osteoporosis (OP) is a systemic metabolic bone disorder characterized by decreased bone mass, weakened bone structure, and increased susceptibility to fractures, and is prone to brittle fractures.[1] The most prominent clinical manifestation of OP is fragility fracture, which causes complications that seriously affect the life, health, and safety of OP patients. The prevalence of OP is 10.3% among people aged ≥50 years. Each year, more people die due to osteoporotic fractures than from the most common cancers, with an almost 30% 1-year mortality rate for brittle hip fractures.[2] With the growth of the aging population, OP has emerged as one of the most common diseases threatening the health of the elderly and has drawn extensive attention from scholars worldwide.

Creatinine, the dehydration product of the creatine metabolic cycle, is almost entirely excreted via glomerular filtration. Consequently, its levels serve as indicators of total muscle mass and renal function.[3] Advancing age and reduced physical activity are associated with decreases in muscle creatine, muscle mass, bone mineral density (BMD), and strength, culminating in sarcopenia. Sarcopenia is linked to decreased bone mass and bone strength and may be a contributing factor to the increased risk of falls and fractures commonly observed in the elderly. Concomitant creatine supplementation and resistance training have been shown to increase body weight, fatigue resistance, muscle strength, and BMD in older adults. As the metabolic end product of creatine, serum creatinine (Scr) levels also reflect total muscle mass, although this relationship is modulated by factors, such as physical activity, training history, and genetics.[4,5] Beyond its close association with sarcopenia, Scr level is intrinsically linked to renal function. Creatinine clearance, calculated using age, weight, and Scr values, is a key metric for assessing renal function and quantifying kidney disease.[6] The relationship between creatinine and OP differs significantly between healthy individuals and those with renal disease. Elevated Scr is a risk factor for OP in patients with chronic kidney disease,[7] whereas lower creatinine levels have been associated with OP in healthy individuals without apparent renal impairment.[8] Given that BMD reflects the severity of OP, investigating the causal relationship between Scr and BMD is of considerable importance for OP prevention and management. This study aimed to employ two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between Scr levels and the risk of OP (characterized by BMD), utilizing genetic variants as instrumental variables.

2. Materials and methods

2.1. Study design

In this study, Scr levels were employed as an exposure factor, and significantly associated single-nucleotide polymorphisms (SNPs) were selected from the exposure dataset as instrumental variables, with BMD serving as the outcome variable. A two-sample MR analysis approach was used to explore the causal association between type 2 diabetes and BMD across all ages. The Cochran Q test and the MR-Egger method were adopted to evaluate heterogeneity and horizontal pleiotropy, thereby ensuring the robustness of the analysis results. Finally, a sensitivity analysis was conducted to verify the reliability of the research findings.[9] Three assumptions were fulfilled in this study: there exists a strong correlation between the instrumental variables and Scr levels; there were no confounding factors in the association between Scr levels and BMD, that is, no genetic polymorphism; and the instrumental variables had no direct effect on the outcome and could only be influenced by Scr levels.[10]

2.2. Data sources

We systematically searched the genome-wide association studies (GWAS) database (https://gwas.mrcieu.ac.uk/)[11] using the terms “creatinine” and “BMD” and selected the Scr level– related SNP dataset (BBJ-a-61) as the exposed variable. Bone density-related single-nucleotide polymorphism dataset (ebi-a-GCST005345, ebi-a-GCST005344, ebi-a-GCST005346, ebi-a-GCST005350, ebi-a-GCST005349, ebi-a-GCST005348) was used as the outcome variable. Dataset sample information is shown in Table 1.

Table 1.

Raw data for serum creatinine levels and BMD studies from the GWAS database.

Trait GWAS ID Sample size Number of SNPs Population Yr
Creatinine bbj-a-61 142,097 6,108,953 East Asian 2019
Total body BMD (age 0–15 yr) ebi-a-GCST005345 11,807 9,351,693 Mixed 2018
Total body BMD (age 15–30 yr) ebi-a-GCST005344 4180 8,509,502 Mixed 2018
Total body BMD (age 30–45 yr) ebi-a-GCST005346 10,062 9,656,698 Mixed 2018
Total body BMD (age 45–60 yr) ebi-a-GCST005350 18,805 10,304,110 Mixed 2018
Total body BMD (age over 60 yr) ebi-a-GCST005349 22,504 11,932,096 Mixed 2018
Total body BMD without age group ebi-a-GCST005348 56,284 16,162,733 European 2018
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BMD = bone mineral density, GWAS = genome-wide association studies, SNPs = single-nucleotide polymorphisms.

2.3. Analysis strategy

We met the condition: by selecting highly relevant SNPs from the exposed GWAS data and designed specific parameters to screen out highly relevant SNPs from the exposed GWAS summary data and set them to P-value < 5E–08.[12] MR requires that instrumental variables are highly correlated with exposure, with F > 10 as a strong correlation criterion (F > 10 means no weak instrumental variable bias, and the F-value is calculated to ensure that there is no weak instrumental variable bias).[13] To meet the condition, we extracted the relevant SNPs from the Scr level GWAS summary data, set the linkage disequilibrium coefficient R2 to 0.001, and set the linkage disequilibrium region width to 10,000 kb to ensure that each single-nucleotide polymorphism is independent and to exclude the influence of genetic polymorphisms on the results.[14] Second, the secondary phenotype of each single-nucleotide polymorphism was searched using LDlink (https://ldlink.nih.gov/), and SNPs for confounding factors associated with exposure and outcome were removed. The exposure data and outcome data were combined, and the SNPs that MR-PRESSO detects bias needs to be eliminated if P < .05.[15]

2.4. MR analysis

Five Mendelian randomized analysis methods were used to analyze the data, including inverse-variance weighted (IVW). The MR-Egger regression method (MR-Egger), weighted median estimation method,[16] simple mode, and weighted mode were used for MR analysis, and all statistical data were calculated using R4.4.0 two-sample MR, and P < .05, which was set as a statistically significant difference or correlation. IVW used the Wald ratio method to calculate each corresponding exposure outcome effect and then performed weighted linear regression. When IVs met the 3 assumed conditions, the IVW method usually had higher accuracy, so IVW is often used as the preferred method.[17] MR-Egger, weighted median, simple mode, and weighted mode were used as supplementary analytical methods. If it is assumed that all the included SNPs can be used as valid instrumental variables, the IVW method can provide accurate estimates.[18]

2.5. Horizontal pleiotropy, heterogeneity, and sensitivity tests

The MR-PRESSO function in R4.4.0 was used to detect the bias of SNP, and the heterogeneity analysis was conducted using the MR-heterogeneity function, and the result was visualized. P > .05, indicating that there was no heterogeneity of instrumental variables.[19] The MR-pleiotropy test function conducts a horizontal pleiotropy test and visualizes results. P > .05, indicating that the instrumental variable has no horizontal pleiotropy, while the presence of horizontal pleiotropy indicates that the selected instrumental variable may be affected by ways other than exposure factors, thus ensuring the accuracy of the MR analysis.[20] The mr_leaveoneout_plot function was used for the sensitivity analysis, and the results were visualized. If the remaining SNP results after removing any SNP are all on the right side of the invalid line, it indicates that regardless of which SNP is removed, the results will not be affected so as to verify the stability of MR analysis results.[21,22]

3. Result

3.1. Instrumental variables

After the clumped function was used to remove linkage imbalance, 66 SNPs remained at the Scr levels. In this study, the F values corresponding to SNPs were all >10 and there was no weak instrumental variable bias; therefore, the results were reliable, as shown in Table 2.

Table 2.

Creatinine SNP data sheet.

Number SNP Number SNP Number SNP Number SNP
1 rs848302 19 rs3812036 37 rs10459012 55 rs78709322
2 rs10917375 20 rs9263692 38 rs7123489 56 rs12935539
3 rs2990246 21 rs17198238 39 rs2511162 57 rs7212715
4 rs34720381 22 rs9272117 40 rs3782787 58 rs9895661
5 rs4665987 23 rs881858 41 rs4399402 59 rs740755
6 rs11123169 24 rs2894816 42 rs1275609 60 rs16942751
7 rs7596689 25 rs4715491 43 rs10744892 61 rs2337106
8 rs16856823 26 rs6907843 44 rs2040571 62 rs549752
9 rs715 27 rs241812 45 rs2106696 63 rs7247977
10 rs35925637 28 rs2781656 46 rs11525583 64 rs6026578
11 rs307558 29 rs316020 47 rs67332916 65 rs75530000
12 rs73134740 30 rs1533988 48 rs478141 66 rs17001974
13 rs4690095 31 rs898696 49 rs12899321
14 rs4859682 32 rs1705694 50 rs62005955
15 rs10857147 33 rs75834729 51 rs7177266
16 rs6851943 34 rs7475348 52 rs79170539
17 rs7714709 35 rs10840341 53 rs10794486
18 rs11742501 36 rs963837 54 rs77924615
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SNPs = single-nucleotide polymorphisms.

3.2. Results of MR analysis

Table 3 presents the analysis results of MR IVW, MR-Egger, weighted median estimation method, simple mode, and weighted mode. Through the analysis of Scr levels and BMD data of different ages and BMD without age group, it was found that the results of the IVW method showed that Scr levels may be a risk factor for BMD in the age group of 45 to 60 years (P = .005, odds ratio (OR) = 1.176, 95% confidence interval (CI) = 1.049–1.318) and without age group (P = .031, OR = 1.095, 95% CI = 1.008–1.19), and the results were statistically significant. Scatterplots of Scr levels and BMD at all ages and without age are shown in Figure 1. A forest map of SNPs is shown in Figure 2. The OR value forest diagram is shown in Figure 3.

Table 3.

Results of Mendelian randomization, horizontal pleiotropy, and heterogeneity analysis.

Trait Method β SE OR 95% CI P Horizontal pleiotropy test P-value Heterogeneity test P-value
Total body BMD (age 0–15 yr) IVW 0.095 0.061 1.1 0.974–1.241 .121 .6328 .2266
MR-Egger 0.007 0.194 1.007 0.688–1.474 .971
WME 0.181 0.086 1.199 1.012–1.419 .035
Simple mode 0.190 0.173 1.21 0.861–1.70 .275
Weighted mode 0.179 0.121 1.197 0.944–1.518 .142
Total body BMD (age 15–30 yr) IVW 0.057 0.103 1.058 0.864–1.297 .58 .4650 .8554
MR-Egger −0.168 0.323 0.845 0.448–1.593 .604
WME 0.174 0.151 1.191 0.885–1.601 .246
Simple mode 0.068 0.311 1.071 0.581–1.973 .825
Weighted mode 0.165 0.253 1.179 0.717–1.939 .517
Total body BMD (age 30–45 yr) IVW 0.053 0.075 1.055 0.909–1.224 .478 .0180 .1443
MR-Egger −0.472 0.228 0.623 0.398–0.975 .042
WME −0.094 0.103 0.91 0.742–1.115 .363
Simple mode −0.062 0.199 0.939 0.635–1.388 .753
Weighted mode −0.177 0.159 0.837 0.612–1.145 .27
Total body BMD (age 45–60 yr) IVW 0.162 0.058 1.176 1.049–1.318 .005 .1283 .0236
MR-Egger −0.098 0.179 0.905 0.637–1.286 .583
WME 0.069 0.078 1.072 0.919–1.249 .372
Simple mode 0.0006 0.153 1.0006 0.741–1.35 .996
Weighted mode −0.094 0.105 0.909 0.739–1.117 .37
Total body BMD (age over 60 yr) IVW 0.067 0.057 1.069 0.955–1.197 .24 .0383 .0023
MR-Egger −0.282 0.174 0.753 0.535–1.061 .11
WME 0.019 0.074 1.019 0.881–1.178 .795
Simple mode 0.228 0.179 1.256 0.883–1.785 .208
Weighted mode −0.154 0.118 0.857 0.679–1.08 .196
Total body BMD without age group IVW 0.091 0.042 1.095 1.008–1.19 .031 .0132 2.3207
MR-Egger −0.214 0.126 0.806 0.629–1.033 .094
WME 0.033 0.046 1.034 0.943–1.132 .472
Simple mode 0.052 0.118 1.054 0.835–1.33 .658
Weighted mode −0.085 0.078 0.917 0.786–1.071 .28
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BMD = bone mineral density, CI = confidence interval, IVW = inverse-variance weighted, MR = Mendelian randomization, OR = odds ratio, SE = standard error, WME = weighted median estimation method.

Figure 1.

Figure 1.

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Mendelian randomization analysis scatter plot. BMD = bone mineral density, MR = Mendelian randomization, SNP = single-nucleotide polymorphism.

Figure 2.

Figure 2.

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Forest plot of Mendelian randomization analysis results. BMD = bone mineral density, MR = Mendelian randomization.

Figure 3.

Figure 3.

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OR value forest map. BMD = bone mineral density, MR = Mendelian randomization, OR = odds ratio.

3.3. Results of horizontal pleiotropy, heterogeneity, and sensitivity analysis

Horizontal pleiotropy analysis showed that there was no horizontal pleiotropy in total body BMD (0–15 years old), total body BMD (15–30 years old), and total body BMD (45–60 years old), while there was horizontal pleiotropy in total body BMD (30–45 years old), total body BMD (over 60 years old), and total body BMD without age. The presence of horizontal pleiotropy suggests that the selected instrumental variable may influence the outcome variable by means other than exposure. Heterogeneity test analysis indicated that there was no heterogeneity in BMD (0–15 years old), BMD (15–30 years old), BMD (30–45 years old), and BMD without age, but there was heterogeneity in BMD (45–60 years old) and BMD (over 60 years old) (Table 3). The funnel plot is shown in Figure 4. The association between Scr levels and BMD was not dominated by a SNP, and the sensitivity analysis showed that no SNPs had a significant impact on the causal association estimate in the one-by-one exclusion method, as shown in Figure 5.

Figure 4.

Figure 4.

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Mendelian randomized funnel plot. BMD = bone mineral density, MR = Mendelian randomization.

Figure 5.

Figure 5.

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Leave-one method for sensitivity analysis. BMD = bone mineral density, MR = Mendelian randomization.

4. Discussion

With the accelerating global aging of the population, OP has emerged as a major chronic disease posing a serious threat to human health. BMD serves as a key clinical indicator for assessing OP severity. Creatinine, a compound generated during muscular energy metabolism, is produced by phosphocreatine degradation. It is released from the muscle tissue via an irreversible nonenzymatic dehydration reaction, enters the bloodstream, and is subsequently excreted by the kidneys, making it a crucial biomarker of renal filtration function. Scr levels are dependent on muscle mass and significantly correlated with age. Skeletal muscle mass and strength begin to decline linearly around the age of 40 years, with potential losses reaching up to 50% by age 80 years.[23] In healthy individuals, Scr levels reflect the physical activity status and skeletal muscle mass, both of which are vital for maintaining skeletal health. Consequently, Scr levels can serve as a marker for assessing individual bone health status. Under normal physiological conditions, Scr levels in generally healthy individuals exhibit relative stability, with fluctuations within 24 hours typically not exceeding 10% to 15% of the mean value. Scr levels are closely correlated with the total body muscle mass. Studies indicate that among individuals with normal renal function engaged in moderate physical labor, Scr levels show a positive correlation with BMD.[24,25] Previous research suggests that low Scr levels may indicate sarcopenia (muscle loss), which can subsequently lead to OP. Furthermore, individuals with sarcopenia are at an increased risk of falls and fractures, which significantly affects the health and well-being of the patients.[26] However, the utility of Scr alone as a reliable biomarker for assessing nutritional status or muscle mass is limited because of its susceptibility to various confounding factors.[27] The creatinine/cystatin C ratio is a simple, cost-effective, and valuable screening tool for sarcopenia. As cystatin C is influenced by the glomerular filtration rate, higher Scr levels within the normal range (assuming normal cystatin C in healthy individuals) generally correlate with greater muscle mass.[28] Low Scr levels are associated with reduced BMD. This association may arise because low Scr levels correlate with biomarkers of cellular senescence and oxidative stress, which can contribute to BMD deterioration.[29] Additionally, patients with higher normal Scr levels tended to have higher serum insulin levels than those with lower Scr levels did. Since insulin stimulates osteoblast differentiation and possesses osteogenic properties, the group with lower Scr levels may experience diminished osteoanabolic effects owing to increased insulin clearance.[8]

While BMD is the established metric for evaluating OP severity, no MR study has investigated its causal relationship with Scr levels. This study utilized publicly available GWAS databases to conduct MR analysis to explore the causal link between Scr levels and BMD. Our analysis detected horizontal pleiotropy for the effect of creatinine on total body BMD in the 30 to 45 years age group, the >60 years age group, and the unstratified total body BMD sample. This indicates that the selected instrumental variables may influence the outcome via pathways other than exposure, potentially compromising the accuracy of the MR estimates for these 3 outcomes. However, a significant causal relationship was identified between creatinine and total body BMD, specifically in the 45 to 60 years age group (P = .005, OR = 1.176, 95% CI = 1.049–1.318). Supporting this finding, the scatter plot using the IVW method showed an upward slope, the SNP forest plot indicated the overall effect estimate line to the right of zero (suggesting a positive association), and the leave-one-out sensitivity analysis demonstrated that all error bars remained to the right of zero, confirming the robustness of the result. This indicates that Scr levels act as protective factors against OP in this age group.

This study employed rigorous methods to minimize confounding, select SNPs strongly associated with the exposure variables, and utilize large-scale GWAS datasets, thereby providing robust evidence for the associations observed. Nevertheless, this study has some limitations. Primarily, data heterogeneity was observed for some age groups, likely stemming from the combined analysis of datasets comprising East Asian, admixed, and European populations, potentially introducing a bias. However, given that the majority of the datasets were based on admixed populations, we maintain that the overall findings are reliable.

In summary, this MR study provides evidence supporting a causal relationship between Scr level and BMD. Scr levels are influenced not only by muscle mass but also by renal function. In healthy individuals with normal renal function, variations in Scr levels primarily reflect differences in muscle mass, rather than impaired kidney function. Therefore, when interpreting Scr levels as a marker of muscle or bone health, renal function must be carefully considered to avoid confounding effects. Future research should incorporate direct measurements of renal function (e.g., estimated glomerular filtration rate) along with Scr levels to better elucidate these relationships. Furthermore, enhancing our understanding of the relationship between Scr levels is crucial. Scr level can serve as a surrogate marker for skeletal muscle mass and is associated with BMD. Declining Scr levels may indicate sarcopenia, which negatively impacts bone health and could facilitate the early identification of individuals at risk of low BMD. This association provides a potential basis for early detection and intervention of OP.

Author contributions

Conceptualization: Zhengtao Ban, Rui Liu.

Formal analysis: Zhengtao Ban, Minghua Shi, Defei Gong, Jihu Wei, Jin Yi.

Funding acquisition: Ruzhuan Liu.

Writing – original draft: Zhengtao Ban, Minghua Shi.

Writing – review & editing: Zhengtao Ban.

Abbreviations:

BMD
bone mineral density
CI
confidence interval
GWAS
genome-wide association studies
IVW
inverse-variance weighted
MR
Mendelian randomization
OP
osteoporosis
OR
odds ratio
Scr
serum creatinine
SNPs
single-nucleotide polymorphisms

2024 Guangxi Famous Chinese Medicine Inheritance Studio Construction project – Liu Ruzhuan Guangxi Famous Chinese Medicine Studio (GZY2024018); self-funded research project of Administration of Traditional Chinese Medicine of Guangxi Zhuang Autonomous Region (GXZYZ20210480); self-funded research project of Administration of Traditional Chinese Medicine of Guangxi Zhuang Autonomous Region (GXZYZ20210480); Guangxi University of Traditional Chinese Medicine Youth Project (2022QN025).

The authors have no conflicts of interest to disclose.

This Mendelian randomization study constitutes a secondary analysis that exclusively utilized publicly available genome‐wide association study (GWAS) summary statistics. No individual‐level data were used. The GWAS summary data for all exposures and outcomes were sourced from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/).

The respective Institutional Review Boards obtained the written informed consent from all participants.

How to cite this article: Ban Z, Shi M, Gong D, Wei J, Yi J, Liu R, Liu R. Causal relationship between serum creatinine levels and bone mineral density: A Mendelian randomization study. Medicine 2026;105:8(e47705).

Contributor Information

Zhengtao Ban, Email: 308046318@qq.com.

Defei Gong, Email: 147508208@qq.com.

Jihu Wei, Email: weijihu1988@126.com.

Jin Yi, Email: 314084119@qq.com.

Rui Liu, Email: liuruzhuan2@126.com.

Ruzhuan Liu, Email: liuruzhuan2@126.com.

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