Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

The effect of liver enzymes on body composition: a Mendelian randomization study

Jun Xi Liu, Shiu Lun Au Yeung, Man Ki Kwok, June Yue Yan Leung, Lai Ling Hui, Gabriel Matthew Leung, C. Mary Schooling
doi: https://doi.org/10.1101/732685
Jun Xi Liu
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shiu Lun Au Yeung
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Man Ki Kwok
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
June Yue Yan Leung
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lai Ling Hui
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
2Department of Paediatrics, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gabriel Matthew Leung
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
C. Mary Schooling
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
3City University of New York Graduate School of Public Health and Health Policy, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: cms1@hku.hk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Background Higher alanine transaminase (ALT) is positively associated with diabetes but inversely associated with body mass index (BMI) in Mendelian randomization (MR) studies, suggesting liver function may affect body composition. To clarify, we assessed the association of liver function with muscle and fat mass observationally with two-sample MR as a validation.

Methods In the population-representative “Children of 1997” birth cohort, we used multivariable linear regression to assess the adjusted associations of ALT and alkaline phosphatase (ALP) (IU/L) at ~17.5 years with muscle mass (kg) and body fat percentage (%). Genetic variants predicting ALT, ALP and gamma glutamyltransferase (GGT) (100% change in concentration) were applied to fat-free and fat mass (kg) in the UK Biobank (n=~331,000) to obtain unconfounded estimates using MR.

Results Observationally, ALT was positively associated with muscle mass (0.11, 95% confidence interval (CI) 0.10 to 0.12) and fat percentage (0.15, 95% CI 0.13 to 0.17). ALP was inversely associated with muscle mass (−0.03, 95% CI −0.04 to −0.02) and fat percentage (−0.02, 95% CI −0.03 to −0.01). Using MR, ALT was inversely associated with fat-free mass (−0.41, 95% CI −0.64 to −0.19) and fat mass (−0.58, 95% CI −0.85 to −0.30). ALP was not clearly associated with body composition. GGT was positively associated with fat-free (0.30, 95% CI 0.01 to 0.06) and fat mass (0.41, 95% CI 0.10 to 0.71).

Conclusion ALT reducing fat-free mass provides a possible pathway for the positive association of ALT with diabetes, and suggests a potential target of intervention.

Introduction

Observationally, poorer liver function, particularly nonalcoholic fatty liver disease (NAFLD), is associated with a higher risk of type 2 diabetes mellitus.1 Mendelian randomization (MR) studies have clarified that higher alanine aminotransferase (ALT) rather than other aspects of liver function, could be the relevant factor causing diabetes.2–4 However, modifiable targets on the pathway from poor liver function to diabetes are unclear and worthy of exploration. Recently, an unpublished MR study found ALT inversely associated with body mass index (BMI), indicating higher ALT might reduce BMI (bioRxiv. doi:10.1101/404319). This finding appears to contradict observational studies which show adiposity associated with poor liver function.5 Besides, using BMI as a proxy measure for adiposity might not be correct, because it cannot distinguish muscle mass from fat mass.6 Nevertheless, ALT reducing the muscle mass component of BMI would be consistent with ALT increasing the risk of diabetes, given low muscle mass is a potential cause of diabetes.7,8 Observationally, liver function is associated with muscle mass, although these studies are not always consistent.9,10 These inconsistencies could be due to confounding by lifestyle, health status, and socioeconomic position (SEP), or to selection bias in studies conducted in patients.

To clarify the roles of liver enzymes, indicating liver function, in body composition in the absence of experimental evidence, we conducted two analyses with different assumptions and study designs. Observationally, we examined the associations of ALT and alkaline phosphatase (ALP) with commonly used measures of muscle mass, i.e., muscle mass and grip strength,11 and fat percentage in young people in a setting with little socioeconomic patterning of obesity, so as to reduce confounding by poor health and SEP, i.e., in Hong Kong’s “Children of 1997” birth cohort.12 Given the difference in body composition by sex, we also examined whether the associations differed by sex because such differences are likely interpretable even when other associations are confounded.13 To validate the impact of liver enzymes on body composition, taking advantage of the random allocation of genetic endowment to avoid confounding,14 we also used an MR design to assess the effects of genetically predicted ALT, ALP and gamma glutamyltransferase (GGT)15 on body composition (fat-free mass, grip strength and fat mass) from the UK Biobank.16

Materials and Methods

Observational study – Children of 1997

The “Children of 1997” birth cohort is a population-representative Chinese birth cohort (n=8,327) which included 88% of all births in Hong Kong from 1 April 1997 to 31 May 1997.17 The study was initially established to examine the effects of second-hand smoke exposure and breastfeeding on health services utilization to 18 months. Participants were recruited at the first postnatal visit to any of the 49 Maternal and Child Health Centers in Hong Kong, which parents of all newborns are strongly encouraged to attend to obtain free preventive care and vaccinations for their child/children up to 5 years of age. Information including parental characteristics (maternal age, paternal age, parental smoking, and parental education) and infant characteristics (birth weight, gestational age, and sex) was obtained from a self-administered questionnaire in Chinese at recruitment and subsequent routine visits. Parental occupation, type of housing and income were also recorded.

At the Biobank clinical follow-up at age ~17·5 years, as a compromise between cost and comprehensiveness, liver enzymes were assessed from ALT and ALP, a marker of hepatocyte integrity and a marker of cholestasis.18 These were analyzed using the Roche Cobas C8000 System, a discrete photometric chemistry analyzer, with International Federation of Clinical Chemistry standardized method with pyridoxal phosphate and substrates of L-alanine and 2-oxoglutarate for ALT, and an optimized substrate concentration and 2-amino-2-methyl-1-propanol as buffer plus the cations magnesium and zinc for ALP. These analyses were conducted at an accredited laboratory serving a teaching hospital in Hong Kong. Body composition indices including muscle mass and fat percentage were measured using bioimpedance analysis (BIA) by a Tanita segmental body composition monitor (Tanita BC-545, Tanita Co., Tokyo, Japan). Grip strength was measured by the Takei T.K.K.5401 GRIP D handgrip dynamometer (Takei Scientific Instruments Co. Ltd, Tokyo, Japan).

Exposures - liver enzymes

Liver function at ~17·5 years was assessed from plasma ALT (IU/L) and ALP (IU/L).

Outcomes – Body composition

Muscle was assessed from muscle mass (kg) and dominant hand grip strength (kg). Fat mass was assessed from body fat percentage.

Mendelian randomization study

Exposure - genetic predictors of liver enzymes

Single nucleotide polymorphisms (SNPs) predicting plasma log transformed ALT, ALP and GGT at genome-wide significance (p-value<5 × 10−8) adjusted for age and sex were obtained from the largest available genome-wide association study (GWAS) of plasma levels of liver enzymes comprising 61,089 adults (~86% European, mean age 52.8 years, 50.6% women).15,19 For SNPs in linkage disequilibrium (R2>0.01), we retained SNPs with the lowest p-value using the “Clumping” function of MR-Base (TwoSampleMR) R package, based on the 1000 Genomes catalog.20 Whether any of the selected SNPs were associated with potential confounders was assessed from their Bonferroni corrected associations with height, alcohol use (intake frequency and intake versus 10 years previously), smoking (current smoking and past smoking), education, financial situation, physical activity (moderate and vigorous physical activity), and age of puberty (menarche and voice breaking) in the UK Biobank.16 (ALT, 10 traits × 4 SNPs, p-value<1×10−3; ALP, 10 traits × 14 SNPs, p-value<3×10−4; GGT, 10 traits × 26 SNPs, p-value<1×10−4). Additionally, we assessed the pleiotropic effects (related to body compositions directly rather than through liver enzymes) of the selected SNPs from comprehensive curated genotype to phenotype cross-references, i.e., Ensembl (http://www.ensembl.org/index.html) and the GWAS Catalog (https://www.ebi.ac.uk/gwas/). Lastly, we considered SNPs in the ABO and GCKR genes as potentially pleiotropic SNPs because these genes have many different effects that could possibly affect body composition directly rather than via liver enzymes.

Outcome - genetic associations with body composition

Genetic associations with fat-free mass (kg), grip strength (kg) (left and right hand), and fat mass (kg) were obtained from UK Biobank (~331,000 people of genetically verified white British ancestry) where the associations were obtained from multivariable linear regression adjusted for the first 20 principal components, sex, age, age-squared, the sex and age interaction and the sex and age-squared interaction.16

Statistical analyses

Observational analyses

In the “Children of 1997” birth cohort, baseline characteristics were compared between cohort participants who were included and excluded using chi-squared tests and Cohen effect sizes which indicate the magnitude of differences between groups independent of sample size. Cohen effect sizes are usually categorized as 0.20 for small, 0.50 for medium and 0.80 for large, but when considering categorical variables they are categorized as 0.10 for small, 0.30 for medium and 0.50 for large.21 The associations of body composition with potential confounders were assessed using independent t-tests or analysis of variance (ANOVA). We assessed the associations of liver enzymes with body composition indices using multivariable linear regression, adjusted for household income, highest parental education, type of housing, highest parental occupation, second-hand and maternal smoking, height and sex. For a small proportion of the observations, ALT was lower than 10 IU/L (n=254) and was fixed at 5 IU/L. We also assessed whether associations differed by sex from the significance of interactions adjusted for the other potential confounding interactions by sex.

Mendelian randomization analyses

We assessed the strength of the genetic instruments based on the F-statistic, where a higher F-statistic indicates a lower risk of weak instrument bias.22 All SNPs were aligned according to the effect allele frequency for both the exposure and outcome.

We obtained the effects of liver enzymes on body composition indices based on meta-analysis of SNP-specific Wald estimates (SNP-outcome association divided by SNP-exposure association) using inverse variance weighting (IVW) with multiplicative random effects for 4+ SNPs, which assumes balanced pleiotropy, and zero average pleiotropic effect of variants, and with fixed effects for 3 SNPs or fewer. Heterogeneity was assessed using the I2 statistic where a high I2 may indicate the presence of invalid SNPs.23 Power calculations were performed using the approximation that the sample size for Mendelian randomization equates to that of the same regression analysis with the sample size divided by the r2 for genetic variant on exposure.24 Differences by sex were also assessed.

Sensitivity analyses

First, we repeated the analyses excluding potentially pleiotropic SNPs and those associated with confounders in the UK Biobank. Second, we used a weighted median (WM) which may generate correct estimates as long as >50% of weight is contributed by valid SNPs.25 Third, we used MR-Egger which generates correct estimates even when all the SNPs are invalid instruments as long as the instrument strength independent of direct effect (InSIDE) assumption, that the pleiotropic effects of genetic variants are independent of the instrument strength, is satisfied.23 A non-null intercept from MR-Egger indicates potentially directional pleiotropy and an invalid IVW estimate.25 Finally, as an additional check on the validity of the MR estimates, we used Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), which precisely detects and corrects for pleiotropic outliers assuming >50% of the instruments are valid, balanced pleiotropy and the InSIDE assumption are satisfied. Ideally, it gives a causal estimate with less bias and better precision than IVW and MR-Egger additionally assuming ≤10% of horizontal pleiotropic variants.26,27

All statistical analyses were conducted using R version 3·4·2 (R Foundation for Statistical Computing, Vienna, Austria). The R packages MendelianRandomization28 and MRPRESSO27 were used to generate the estimates.

Results

Children of 1997

Of 8,327 initially recruited, 6,850 are contactable and living in Hong Kong, of whom 3,460 (51%) took part in the Biobank clinical follow-up. Of these 3,460, 3,455 had measures of muscle mass, grip strength or fat percentage, as shown in Figure 1. The mean and standard deviation (SD) of muscle mass, grip strength and fat percentage were 42.6kg (SD 8.8kg), 25.8kg (SD 8.3kg) and 21.7% (SD 8.8%). Boys had higher muscle mass and grip strength but lower fat percentage than girls, but body composition had little association with SEP (Table 1). There were some differences between participants included and excluded from the study, such as sex, second-hand and maternal smoking exposure, and SEP, but the magnitude of these differences was small (Cohen effect size <0.15) (Supplemental Table 1).

View this table:
  • View inline
  • View popup
  • Download powerpoint
Supplemental Table 1.

Baseline characteristics of the participants who were included (n=3455) and excluded (n=4872) in the analyses of the Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016

Figure 1.
  • Download figure
  • Open in new tab
Figure 1.

Flowchart of the Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 1.

Baseline characteristics muscle mass and fat percentage among participants in Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016

The associations of liver enzymes with muscle mass and fat percentage differed by sex (Table 2). ALT was more strongly positively associated with muscle mass and fat percentage in boys. ALT was not clearly associated with grip strength. ALP was inversely associated with muscle mass, fat percentage and grip strength in boys, whereas, ALP was unclearly associated with muscle mass but positively associated with fat percentage and grip strength in girls.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 2

Adjusted associations of liver function ALT and ALP with muscle mass, grip strength and fat percentage at ~17.5 years in the Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China

Mendelian randomization

Genetic instruments for liver enzymes

Altogether, 4 SNPs independently predicting ALT, 14 SNPs independently predicting ALP and 26 SNPs independently predicting GGT at genome-wide significance were obtained.15 Palindromic SNPs were all aligned according to effect allele frequency (Supplemental Table 2). The F statistic and variance explained (r2) were 15 and 0.001 for ALT, 158 and 0.035 for ALP, and 45 and 0.019 for GGT. As such the MR study had 80% power with 5% alpha to detect a difference of 0.15, 0.03 and 0.04 in fat-free mass and fat mass effect size for ALT, ALP, and GGT respectively.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Supplemental Table 2.

Characteristics of palindromic single nucleotide polymorphisms (SNPs) in the exposure and outcome genome-wide association studies.

One SNP, rs2954021 (TRIB1), predicting ALT was associated with potential confounders. Seven SNPs, rs174601 (C11orf10, FADS1, FADS2), rs2236653 (ST3GAL4), rs281377 (FUT2), rs2954021 (TRIB1), rs579459 (ABO), rs6984305 (PPP1R3B) and rs7923609 (JMJD1C, NRBF2) predicting ALP were associated with potential confounders. Eight SNPs, rs10908458 (DPM3, EFNA1, PKLR), rs12145922 (CCBL2, PKN2), rs1260326 (GCKR), rs1497406 (RSG1, EPHA2), rs17145750 (MLXIPL), rs516246 (FUT2), rs7310409 (HNF1A, C12orf27) and rs754466 (DLG5), predicting GGT were associated with potential confounders in UK Biobank at Bonferroni corrected significance (Supplemental Table 3).

View this table:
  • View inline
  • View popup
Supplemental Table 3.

Single nucleotide polymorphisms (SNPs) with potential pleiotropic effects other than via the specific liver enzyme from Ensembl and from GWAS Catalog and potential confounders from UK Biobank

Among the 4 SNPs predicting ALT, rs2954021 (TRIB1) predicts both ALT and ALP. Among the 14 SNPs predicting ALP, rs281377 (FUT2) is highly associated with resting metabolic rate, rs579459 is located in the ABO gene whose impact is extensive but unclear. Among the 26 SNPs predicting GGT, rs12968116 (ATP8B1) is associated with body height, rs1260326 (GCKR) and rs516246 (FUT2) are associated with Crohn’s disease which might be associated with body composition (Supplemental Table 3).

Mendelian randomization estimates

Table 3 shows similar inverse estimates of genetically predicted ALT with fat-free mass and fat mass from all methods and by sex, however, the confidence intervals included the null value. ALT was not clearly associated with grip strength. Nevertheless, using MR-PRESSO ALT was inversely associated with fat-free mass and fat mass.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 3:

Estimates of the effect of genetically instrumented (ALT (per 100% change in concentration) on fat-free mass, fat mass, and grip strength (left and right) using Mendelian randomization with different methodological approaches with and without potentially pleiotropic SNPs and potentially confounded SNPs

Table 4 shows genetically predicted ALP was not clearly associated with fat-free mass, fat mass, or grip strength using any method or by sex.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 4:

Estimates of the effect of genetically instrumented ALP (per 100% change in concentration) on fat-free mass, fat mass, and grip strength (left and right) using Mendelian randomization with different methodological approaches with and without potentially pleiotropic SNPs and potentially confounded SNPs

Table 5 shows genetically predicted GGT was not clearly associated with fat-free mass, fat mass or grip strength, but after excluding potential pleiotropy the corrected MR-PRESSO estimates suggested a positive association with fat-free mass and fat mass, particularly in women. GGT was not clearly associated with grip strength, although the WM estimate gave positive associations in women.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 5:

Estimates of the effect of genetically instrumented GGT (per 100% change in concentration) on fat-free mass, fat mass, and grip strength (left and right) using Mendelian randomization with different methodological approaches with and without potentially pleiotropic SNPs and potentially confounded SNPs

Discussion

Using two different complimentary designs with different strengths and weaknesses, we examined the impact of liver enzymes on body composition. Although there were discrepancies between the observational and MR estimates, some associations of ALT and GGT with body composition were found.

These two study designs have contrasting limitations. Observational studies are open to residual confounding, possibly by diet, lifestyle, and physical activity, although smoking is rare and alcohol consumption is low in Hong Kong.29–31 Disentangling correlated factors is also difficult in an observational study. Inevitably, follow-up was incomplete (51%), but participants with and without body composition indices were similar, making selection bias unlikely. We also identified some sex differences which are less open to confounding. Inaccessibility, cost, and exposure to low-dose radiation precluded the use of dual-energy X–ray absorptiometry The reliability of BIA measurements particularly of body fat could vary for many reasons32 but unlikely with liver function, so any bias was likely towards the null. The discrepancy between the observational and MR estimates might be due to reverse causality in the cross-sectional setting, and other limitations of observational studies. Differences by race/ethnicity are also possible. Lack of relevant data in Chinese precludes examining this possibility. However, we would normally expect causal factors to act consistently unless we know of reasons why the relevance of the specific operating mechanism varies by race/ethnicity.33 MR assumes the genetic instruments strongly predict the exposure, are not confounded, and are only linked with the outcome by affecting the exposure. The F statistics were all >10 suggesting weak instrument bias is unlikely. We repeated the analyses excluding SNPs potentially associated with confounders. We conducted several sensitivity analyses to assess potential pleiotropy statistically, such as MR-Egger and MR-PRESSO, but found no evidence of directional pleiotropy. The MR estimates were relatively small, which might not be clinically significant, but could be relevant at the population level and may provide etiological insights.34 The MR analyses were mainly restricted to people of European ancestry. Given the distribution of body composition varies by ethnicity, it is possible that the drivers of body composition also vary by ethnicity. However, more parsimoniously, it is likely that the drivers of body composition are similar across populations but their relevance varies. Specifically, ALT is higher in Chinese than in Westerners35–37 which might be relevant to the lower fat-free mass in Chinese than in Westerners,38 although ethnic variation in both ALT and fat-free mass could just be due to chance. The use of summary statistics in the MR study, means we could not comprehensively assess the differences by age, sex or by baseline levels of liver enzymes; but we assessed the differences by sex observationally. Replicating the MR study in a Chinese population would be very helpful. Liver enzymes might not completely or only represent liver function, for example ALT may be transitorily affected by physical exertion, but liver enzymes are widely used as a surrogate of liver function.18 Here, SNPs associated with vigorous physical activity were excluded. Fat-free mass and muscle mass are not identical. Fat-free mass also includes organs, skin, bones and body water, but does not vary as much as muscle mass. Finally, some overlap of participants between the GWAS used is inevitable, however, any effect on the estimates is likely to be small.

These observations are similar to previous observational studies.9,39,40 However, only some of the previous observations, i.e., higher ALT associated with lower fat-free mass41 and higher GGT associated with adiposity42,43 were confirmed using MR. Consistent with observational studies I also found some differences by sex.39,40,43

The association of higher ALT, a measure of hepatocyte integrity, with lower fat-free mass, possibly differing by sex, may be due to growth hormone (GH)/insulin-like growth factor 1 (IGF-1) or sex hormones which are associated with chronic liver diseases and muscle mass.44–47 Studies using IGF-1 gene knock out animal models suggest IGF-1 is associated with hyperinsulinaemia and muscle insulin insensitivity,48–50 although whether GH/IGF-1 also specifically affects ALT and muscle mass overall or differentially by sex is unknown. Schooling et al. have previously suggested that lower levels of androgens might cause higher risk of diabetes via lower muscle mass46 and poor liver function may reduce androgens,47 consistent with the sex differences observed. Additionally, it is also consistent with statins usage which is associated with lower testosterone,51 elevated aminotransferase levels,52 and higher diabetes risk.53 Etiologically, these findings are consistent with the evolutionary public health, i.e., growth and reproduction trading-off against longevity, which may inform the identification of interventions. Reasons for an inverse association of ALT with fat mass are unclear but are consistent with a previous MR study (bioRxiv. doi:10.1101/404319) showing ALT negatively associated with BMI using the same genetic variants predicting ALT applied to the 2018 GIANT and UK Biobank meta-analysis.

Conclusion

Higher ALT, representing hepatocyte integrity, might reduce fat-free mass and fat mass with differences by sex; whilst higher GGT, as a marker of cholestasis, might increase fat-free mass and fat mass. As such, our study provides some indications that lower fat-free mass may mediate the positive effect of ALT on diabetes risk, which requires confirmation in other studies.

Funding

This work is a substudy of the “Children of 1997” birth cohort which was initially supported by the Health Care and Promotion Fund, Health and Welfare Bureau, Government of the Hong Kong SAR [HCPF grant 216106] and reestablished in 2005 with support from the Health and Health Services Research Fund, Government of the Hong Kong SAR, [HHSRF grant 03040771]; the Research Fund for the Control of Infectious Diseases in Hong Kong, the Government of Hong Kong SAR [RFCID grant 04050172]; the University Research Committee Strategic Research Theme (SRT) of Public Health, the University of Hong Kong. The Biobank clinical follow-up was partly supported by the WYNG Foundation.

Data availability

The data that support the findings of this study are available on request from the “Children of 1997” data access committee: aprmay97{at}hku.hk. The data are not publicly available due to the participants could be identifiable from such extensive data which would comprise participant privacy. The datasets analyzed during the current MR study are publicly available summary data. These datasets were derived from the public domain resources: a publicly available GWAS study https://www.nature.com/articles/ng.970 and the UK Biobank GWAS http://www.nealelab.is/blog/2017/9/11/details-and-considerations-of-the-uk-biobank-gwas.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (the University of Hong Kong, Hospital Authority Hong Kong West Cluster Joint Institutional Review Board) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval from an Institutional Review Board is not required for the MR study since it only uses publicly available summary data. This article does not contain any studies with animals performed by any of the authors

Informed consent

Informed written consent was obtained from the parents/guardians or participant if 18 years or older before participation in the Clinical Follow-up.

Declaration of interests

The authors declare that they have no conflict of interest.

Author contributions

JX Liu conducted the literature review, the analysis and drafted the manuscript. SL Au Yeung and CM Schooling conceptualized ideas, designed and directed the analytic strategy and supervised the study from conception to completion, with assistance from GM Leung. MK Kwok, JYY Leung, and LL Hui had full access to all the data in the study and took responsibility for the integrity of the data. All the authors contributed to the interpretation of the data, critically revising the paper and approval of the final version.

Acknowledgments

The authors thank colleagues at the Student Health Service and Family Health Service of the Department of Health for their assistance and collaboration. They also thank late Dr. Connie O for coordinating the project and all the fieldwork for the initial study in 1997-1998.

References

  1. ↵
    Hazlehurst, J. M., Woods, C., Marjot, T., Cobbold, J. F. & Tomlinson, J. W. Non-alcoholic fatty liver disease and diabetes. Metabolism: clinical and experimental 65, 1096–1108, doi:10.1016/j.metabol.2016.01.001 (2016).
    OpenUrlCrossRef
  2. ↵
    Liu, J., Au Yeung, S. L., Lin, S. L., Leung, G. M. & Schooling, C. M. Liver Enzymes and Risk of Ischemic Heart Disease and Type 2 Diabetes Mellitus: A Mendelian Randomization Study. Scientific reports 6, 38813, doi:10.1038/srep38813 (2016).
    OpenUrlCrossRef
  3. Nano, J. et al. Gamma-glutamyltransferase levels, prediabetes and type 2 diabetes: a Mendelian randomization study. International journal of epidemiology 46, 1400–1409, doi:10.1093/ije/dyx006 (2017).
    OpenUrlCrossRef
  4. ↵
    De Silva, N. M. G. et al. Liver Function and Risk of Type 2 Diabetes: Bidirectional Mendelian Randomization Study. Diabetes, db181048, doi:10.2337/db18-1048 (2019).
    OpenUrlAbstract/FREE Full Text
  5. ↵
    Fall, T. et al. Age- and sex-specific causal effects of adiposity on cardiovascular risk factors. Diabetes 64, 1841–1852, doi:10.2337/db14-0988 (2015).
    OpenUrlAbstract/FREE Full Text
  6. ↵
    Nevill, A. M., Stewart, A. D., Olds, T. & Holder, R. Relationship between adiposity and body size reveals limitations of BMI. American journal of physical anthropology 129, 151–156, doi:10.1002/ajpa.20262 (2006).
    OpenUrlCrossRefPubMedWeb of Science
  7. ↵
    Hou, W. W., Tse, M. A., Lam, T. H., Leung, G. M. & Schooling, C. M. Adolescent testosterone, muscle mass and glucose metabolism: evidence from the ‘Children of 1997’ birth cohort in Hong Kong. Diabetic medicine: a journal of the British Diabetic Association 32, 505–512, doi:10.1111/dme.12602 (2015).
    OpenUrlCrossRef
  8. ↵
    Yeung, C. H. C., Au Yeung, S. L., Fong, S. S. M. & Schooling, C. M. Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study. Diabetologia, doi:10.1007/s00125-019-4826-0 (2019).
    OpenUrlCrossRef
  9. ↵
    Lee, Y. H. et al. Sarcopenia is associated with significant liver fibrosis independently of obesity and insulin resistance in nonalcoholic fatty liver disease: Nationwide surveys (KNHANES 2008-2011). Hepatology (Baltimore, Md.) 63, 776–786, doi:10.1002/hep.28376 (2016).
    OpenUrlCrossRefPubMed
  10. ↵
    Hong, H. C. et al. Relationship between sarcopenia and nonalcoholic fatty liver disease: the Korean Sarcopenic Obesity Study. Hepatology (Baltimore, Md.) 59, 1772–1778, doi:10.1002/hep.26716 (2014).
    OpenUrlCrossRefPubMed
  11. ↵
    Bohannon, R. W. Muscle strength: clinical and prognostic value of hand-grip dynamometry. Current opinion in clinical nutrition and metabolic care 18, 465–470, doi:10.1097/mco.0000000000000202 (2015).
    OpenUrlCrossRefPubMed
  12. ↵
    Schooling, C. M., Yau, C., Cowling, B. J., Lam, T. H. & Leung, G. M. Socio-economic disparities of childhood Body Mass Index in a newly developed population: evidence from Hong Kong’s ‘Children of 1997’ birth cohort. Archives of disease in childhood 95, 437–443, doi:10.1136/adc.2009.168542 (2010).
    OpenUrlAbstract/FREE Full Text
  13. ↵
    VanderWeele, T. J. Explanation in causal inference: methods for mediation. (Oxford University Press, 2015).
  14. ↵
    Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in medicine 27, 1133–1163, doi:10.1002/sim.3034 (2008).
    OpenUrlCrossRefPubMed
  15. ↵
    Chambers, J. C. et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nature genetics 43, 1131–1138, doi:10.1038/ng.970 (2011).
    OpenUrlCrossRefPubMed
  16. ↵
    Howrigan, D. DETAILS AND CONSIDERATIONS OF THE UK BIOBANK GWAS, <http://www.nealelab.is/blog/2017/9/11/details-and-considerations-of-the-uk-biobank-gwas> (September 20, 2017).
  17. ↵
    Schooling, C. M., Hui, L. L., Ho, L. M., Lam, T.-H. & Leung, G. M. Cohort Profile: ‘Children of 1997’: a Hong Kong Chinese birth cohort. International journal of epidemiology 41, 611–620, doi:10.1093/ije/dyq243 (2012).
    OpenUrlCrossRefPubMedWeb of Science
  18. ↵
    Giannini, E. G., Testa, R. & Savarino, V. Liver enzyme alteration: a guide for clinicians. CMAJ: Canadian Medical Association journal = journal de I’Association medicale canadienne 172, 367–379, doi:10.1503/cmaj.1040752 (2005).
    OpenUrlAbstract/FREE Full Text
  19. ↵
    Kahali, B., Halligan, B. & Speliotes, E. K. Insights from Genome-Wide Association Analyses of Nonalcoholic Fatty Liver Disease. Seminars in liver disease 35, 375–391, doi:10.1055/s-0035-1567870 (2015).
    OpenUrlCrossRef
  20. ↵
    Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408, doi:10.7554/eLife.34408 (2018).
    OpenUrlCrossRefPubMed
  21. ↵
    Cohen, J. Statistical power analysis for the behavioral sciences. (Academic Press, 1977).
  22. ↵
    Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genetic Epidemiology 40, 597–608, doi:10.1002/gepi.21998 (2016).
    OpenUrlCrossRefPubMed
  23. ↵
    Burgess, S., Bowden, J., Fall, T., Ingelsson, E., & Thompson, S. G. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology (Cambridge, Mass.) (2016).
  24. ↵
    Freeman, G., Cowling, B. J. & Schooling, C. M. Power and sample size calculations for Mendelian randomization studies using one genetic instrument. Int J Epidemiol 42, 1157–1163, doi:10.1093/ije/dyt110 (2013).
    OpenUrlCrossRefPubMedWeb of Science
  25. ↵
    Bowden, J., Smith, G. D., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic Epidemiology 40, 304–314, doi:10.1002/gepi.21965 (2016).
    OpenUrlCrossRefPubMed
  26. ↵
    Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International journal of epidemiology 44, 512–525, doi:10.1093/ije/dyv080 (2015).
    OpenUrlCrossRefPubMed
  27. ↵
    Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature genetics 50, 693–698, doi:10.1038/s41588-018-0099-7 (2018).
    OpenUrlCrossRefPubMed
  28. ↵
    Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. International journal of epidemiology 46, 1734–1739, doi:10.1093/ije/dyx034 (2017).
    OpenUrlCrossRefPubMed
  29. ↵
    Statistics on Youth Health-related Behaviour, <https://www.chp.gov.hk/en/statistics/data/10/757/5522.html> (
  30. Au, W. M. et al. Alcohol Drinking and Pro-drinking Practices in Parents of Hong Kong Adolescents. Alcohol and Alcoholism 49, 668–674, doi:10.1093/alcalc/agu063 (2014).
    OpenUrlCrossRefPubMed
  31. ↵
    HKSAR, H. K. C. a. S. D. o. (ed Hong Kong: Census and Statistics Department of HKSAR) (2018).
  32. ↵
    Dehghan, M. & Merchant, A. T. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutrition journal 7, 26–26, doi:10.1186/1475-2891-7-26 (2008).
    OpenUrlCrossRefPubMed
  33. ↵
    Lopez, P., Subramanian, S. V. & Schooling, C. M. Effect measure modification conceptualized using selection diagrams as medication by mechanisms of varying population-level relevance. J Clin Epidemiol, doi:10.1016/j.jclinepi.2019.05.005 (2019).
    OpenUrlCrossRef
  34. ↵
    Burgess, S., Butterworth, A., Malarstig, A. & Thompson, S. G. Use of Mendelian randomisation to assess potential benefit of clinical intervention. BMJ: British Medical Journal 345, e7325, doi:10.1136/bmj.e7325 (2012).
    OpenUrlFREE Full Text
  35. ↵
    Mu, R. et al. First Definition of Reference Intervals of Liver Function Tests in China: A Large-Population-Based Multi-Center Study about Healthy Adults. PloS one 8, e72916, doi:10.1371/journal.pone.0072916 (2013).
    OpenUrlCrossRef
  36. Li, Y., Mussa, A. E., Tang, A., Xiang, Z. & Mo, X. Establishing reference intervals for ALT, AST, UR, Cr, and UA in apparently healthy Chinese adolescents. Clinical biochemistry 53, 72–76, doi:https://doi.org/10.1016/j.clinbiochem.2018.01.019 (2018).
    OpenUrl
  37. ↵
    Zhang, G.-m. et al. Reference intervals for total bilirubin, ALT, AST and creatinine in healthy Chinese elderly. Med Sci Monit 20, 1778–1782, doi:10.12659/MSM.892148 (2014).
    OpenUrlCrossRefPubMed
  38. ↵
    Lear, S. A., Kohli, S., Bondy, G. P., Tchernof, A. & Sniderman, A. D. Ethnic Variation in Fat and Lean Body Mass and the Association with Insulin Resistance. The Journal of Clinical Endocrinology & Metabolism 94, 4696–4702, doi:10.1210/jc.2009-1030 (2009).
    OpenUrlCrossRefPubMedWeb of Science
  39. ↵
    Ruhl, C. E. & Everhart, J. E. Trunk fat is associated with increased serum levels of alanine aminotransferase in the United States. Gastroenterology 138, 1346-1356, 1356.e1341–1343, doi:10.1053/j.gastro.2009.12.053 (2010).
    OpenUrlCrossRef
  40. ↵
    Booth, M. L. et al. The population prevalence of adverse concentrations and associations with adiposity of liver tests among Australian adolescents. Journal of paediatrics and child health 44, 686–691, doi:10.1111/j.1440-1754.2008.01407.x (2008).
    OpenUrlCrossRefPubMed
  41. ↵
    Hong, H. C. et al. Relationship between sarcopenia and nonalcoholic fatty liver disease: The Korean Sarcopenic Obesity Study. Hepatology (Baltimore, Md.) 59, 1772–1778, doi:10.1002/hep.26716 (2014).
    OpenUrlCrossRefPubMed
  42. ↵
    Elshorbagy, A. K., Refsum, H., Smith, A. D. & Graham, I. M. The association of plasma cysteine and gamma-glutamyltransferase with BMI and obesity. Obesity (Silver Spring, Md.) 17, 1435–1440, doi:10.1038/oby.2008.671 (2009).
    OpenUrlCrossRef
  43. ↵
    Stranges, S. et al. Body fat distribution, relative weight, and liver enzyme levels: a populationbased study. Hepatology (Baltimore, Md.) 39, 754–763, doi:10.1002/hep.20149 (2004).
    OpenUrlCrossRefPubMed
  44. ↵
    Guichelaar, M. M. & Charlton, M. R. Decreased muscle mass in nonalcoholic fatty liver disease: new evidence of a link between growth hormone and fatty liver disease? Hepatology (Baltimore, Md.) 59, 1668–1670, doi:10.1002/hep.27058 (2014).
    OpenUrlCrossRefPubMed
  45. Cabrera, D. et al. Diet-Induced Nonalcoholic Fatty Liver Disease Is Associated with Sarcopenia and Decreased Serum Insulin-Like Growth Factor-1. Digestive diseases and sciences 61, 3190–3198, doi:10.1007/s10620-016-4285-0 (2016).
    OpenUrlCrossRefPubMed
  46. ↵
    Schooling, C. M., Au Yeung, S. L. & Leung, G. M. Why do statins reduce cardiovascular disease more than other lipid modulating therapies? European journal of clinical investigation 44, 1135–1140, doi:10.1111/eci.12342 (2014).
    OpenUrlCrossRefPubMed
  47. ↵
    Jaruvongvanich, V., Sanguankeo, A., Riangwiwat, T. & Upala, S. Testosterone, Sex Hormone-Binding Globulin and Nonalcoholic Fatty Liver Disease: a Systematic Review and Meta-Analysis. Annals of hepatology 16, 382–394, doi:10.5604/16652681.1235481 (2017).
    OpenUrlCrossRef
  48. ↵
    Sjogren, K. et al. Liver-derived IGF-I is of importance for normal carbohydrate and lipid metabolism. Diabetes 50, 1539–1545 (2001).
    OpenUrlAbstract/FREE Full Text
  49. Yakar, S. et al. Liver-specific igf-1 gene deletion leads to muscle insulin insensitivity. Diabetes 50, 1110–1118 (2001).
    OpenUrlAbstract/FREE Full Text
  50. ↵
    Sandhu, M. S. Insulin-like growth factor-I and risk of type 2 diabetes and coronary heart disease: molecular epidemiology. Endocrine developments 9, 44–54, doi:10.1159/000085755 (2005).
    OpenUrlCrossRef
  51. ↵
    Schooling, C. M., Au Yeung, S. L., Freeman, G. & Cowling, B. J. The effect of statins on testosterone in men and women, a systematic review and meta-analysis of randomized controlled trials. BMC medicine 11, 57, doi:10.1186/1741-7015-11-57 (2013).
    OpenUrlCrossRefPubMed
  52. ↵
    Jose, J. Statins and its hepatic effects: Newer data, implications, and changing recommendations. Journal of pharmacy & bioallied sciences 8, 23–28, doi:10.4103/0975-7406.171699 (2016).
    OpenUrlCrossRef
  53. ↵
    Crandall, J. P. et al. Statin use and risk of developing diabetes: results from the Diabetes Prevention Program. BMJ Open Diabetes Research && Care 5, e000438, doi:10.1136/bmjdrc-2017-000438 (2017).
    OpenUrlAbstract/FREE Full Text
Back to top
PreviousNext
Posted August 14, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
The effect of liver enzymes on body composition: a Mendelian randomization study
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
The effect of liver enzymes on body composition: a Mendelian randomization study
Jun Xi Liu, Shiu Lun Au Yeung, Man Ki Kwok, June Yue Yan Leung, Lai Ling Hui, Gabriel Matthew Leung, C. Mary Schooling
bioRxiv 732685; doi: https://doi.org/10.1101/732685
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
The effect of liver enzymes on body composition: a Mendelian randomization study
Jun Xi Liu, Shiu Lun Au Yeung, Man Ki Kwok, June Yue Yan Leung, Lai Ling Hui, Gabriel Matthew Leung, C. Mary Schooling
bioRxiv 732685; doi: https://doi.org/10.1101/732685

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genetics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4688)
  • Biochemistry (10379)
  • Bioengineering (7695)
  • Bioinformatics (26373)
  • Biophysics (13547)
  • Cancer Biology (10724)
  • Cell Biology (15460)
  • Clinical Trials (138)
  • Developmental Biology (8509)
  • Ecology (12843)
  • Epidemiology (2067)
  • Evolutionary Biology (16887)
  • Genetics (11416)
  • Genomics (15493)
  • Immunology (10638)
  • Microbiology (25257)
  • Molecular Biology (10241)
  • Neuroscience (54595)
  • Paleontology (402)
  • Pathology (1671)
  • Pharmacology and Toxicology (2899)
  • Physiology (4355)
  • Plant Biology (9263)
  • Scientific Communication and Education (1588)
  • Synthetic Biology (2561)
  • Systems Biology (6789)
  • Zoology (1471)