A study on the correlation between liver cirrhosis and osteoporosis based on the East Asian populations

doi:10.3877/cma.j.issn.2095-8757.2024.01.008

Abstract

Abstract:

Objective

To explore the causal relationship between liver cirrhosis and osteoporosis.

Methods

The aggregate data of genome-wide association study (GWAS) were analyzed. Taking liver cirrhosis as exposure factor, single nucleotide polymorphism (SNP) as instrumental variable, and osteoporosis as outcome variable. MR-Egger method, weighted median method, inverse variance weighted (IVW) method, simple model method and weighted model method were used for Mendelian randomized analysis, and the sensitivity analysis was carried out by heterogeneity test, multiplicity test and retention test.

Results

A total of 7 SNP was included as tool variables. The results of IVW analysis showed that there was a causal relationship between liver cirrhosis and osteoporosis, and liver cirrhosis increased the incidence of osteoporosis (β=0.090, OR=1.095, 95%CI: 1.025-1.169, P < 0.01); weighted median analysis also showed that liver cirrhosis increased the risk of osteoporosis (β=0.094, OR=1.099, 95%CI:1.012-1.194, P < 0.05). Other methods also suggest that liver cirrhosis can increase the risk of osteoporosis, and the causal effects of the five methods are in the same direction. The results of heterogeneity test showed that there was no heterogeneity among all SNPs. MR-Egger regression showed that there was no horizontal polymorphism of genes. The results of leaving one method showed that the results were robust.

Conclusion

There may be a causal relationship between liver cirrhosis and osteoporosis, and liver cirrhosis may increase the incidence of osteoporosis.

Key words: Osteoporosis, Cirrhosis, Genome-wide association studies, East Asian populations

Wenjia Di, Aiyuan Niu. A study on the correlation between liver cirrhosis and osteoporosis based on the East Asian populations[J]. Chinese Journal of Geriatrics Research(Electronic Edition), 2024, 11(01): 40-44.

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References 40

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SNP	位置	染色体	效应等位基因	非效应等位基因	效应等位基因频率	β值	SE	P值
rs3129943	32338695	6	G	A	0.364108	0.220089	0.031664	<0.01
rs9264396	31229307	6	T	C	0.756677	0.208642	0.037789	<0.01
rs2276448	31323020	6	C	T	0.420261	0.175936	0.031053	<0.01
rs9275171	32653972	6	G	A	0.519631	0.20744	0.031505	<0.01
rs3135402	33024654	6	C	A	0.430088	-0.1859	0.030711	<0.01
rs187095086	32368942	6	A	G	0.187562	0.272629	0.042747	<0.01
rs12484700	44327273	22	G	A	0.45445	0.219008	0.030744	<0.01

SNP	位置	染色体	效应等位基因	非效应等位基因	效应等位基因频率	β值	SE	P值
rs3129943	32338695	6	G	A	0.364108	0.220089	0.031664	<0.01
rs9264396	31229307	6	T	C	0.756677	0.208642	0.037789	<0.01
rs2276448	31323020	6	C	T	0.420261	0.175936	0.031053	<0.01
rs9275171	32653972	6	G	A	0.519631	0.20744	0.031505	<0.01
rs3135402	33024654	6	C	A	0.430088	-0.1859	0.030711	<0.01
rs187095086	32368942	6	A	G	0.187562	0.272629	0.042747	<0.01
rs12484700	44327273	22	G	A	0.45445	0.219008	0.030744	<0.01

分析方法	OR	95%CI	P值	β值
MR-Egger法	1.195	0.695～2.056	>0.05	0.178
加权中位数法	1.099	1.012～1.194	<0.05	0.094
逆方差加权法	1.095	1.025～1.169	<0.01	0.090
简单模型法	1.093	0.966～1.236	>0.05	0.089
加权模型法	1.095	0.970～1.238	>0.05	0.091

分析方法	OR	95%CI	P值	β值
MR-Egger法	1.195	0.695～2.056	>0.05	0.178
加权中位数法	1.099	1.012～1.194	<0.05	0.094
逆方差加权法	1.095	1.025～1.169	<0.01	0.090
简单模型法	1.093	0.966～1.236	>0.05	0.089
加权模型法	1.095	0.970～1.238	>0.05	0.091

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