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. 2024 Nov 27;22(1):561.
doi: 10.1186/s12916-024-03779-0.

Even moderate liver fat accumulation below conventional fatty liver cutoffs is linked to multiple metabolomic alterations and gestational dysglycemia in Asian women of reproductive age

Priti Mishra  1 , Suresh Anand Sadananthan  1 , Jadegoud Yaligar  1 , Kok Hian Tan  2   3 , Yap Seng Chong  1   4 , Peter D Gluckman  1   5 , Keith M Godfrey  6   7 , Marielle V Fortier  1   8 , Johan G Eriksson  1   4   9   10 , Jerry Kok Yen Chan  2   11 , Shiao-Yng Chan  1   4 , Dennis Wang  1   12   13 , S Sendhil Velan  1 , Navin Michael  14
Affiliations

Even moderate liver fat accumulation below conventional fatty liver cutoffs is linked to multiple metabolomic alterations and gestational dysglycemia in Asian women of reproductive age

Priti Mishra et al. BMC Med. .

Abstract

Background: It is not clear if conventional liver fat cutoff of 5.56% weight which has been used for identifying fatty liver in western populations is also applicable for Asians. In Asian women of reproductive age, we evaluate the optimum metabolic syndrome (MetS)-linked liver fat cutoff, the specific metabolomic alterations apparent at this cutoff, as well as prospective associations of preconception liver fat levels with gestational dysglycemia.

Methods: Liver fat (measured by magnetic resonance spectroscopy), MetS, and nuclear magnetic resonance (NMR)-based plasma metabolomic profiles were assessed in 382 Asian women, who were planning to conceive. Ninety-eight women went on to become pregnant and received an oral glucose tolerance test at week 26 of gestation.

Results: The optimum liver fat cutoff for diagnosing MetS was 2.07%weight. Preconception liver fat was categorized into Low (liver fat < 2.07%), Moderate (2.07% ≤ liver fat < 5.56%), and High (liver fat ≥ 5.56%) groups. Individual MetS traits showed worsening trends, going from Low to Moderate to High groups. Multiple plasma metabolomic alterations, previously linked to incident type 2 diabetes (T2D), were already evident in the Moderate group (adjusted for ethnicity, age, parity, educational attainment, and BMI). Both a cross-sectional multi-metabolite score for incident T2D and mid-gestational glucose area under the curve showed increasing trends, going from Low to Moderate to High groups (p < 0.001 for both). Gestational diabetes incidence was 2-fold (p = 0.23) and 7-fold (p < 0.001) higher in the Moderate and High groups relative to the Low group.

Conclusions: In Asian women of reproductive age, moderate liver fat accumulation below the conventional fatty liver cutoff was not metabolically benign and was linked to gestational dysglycemia. The newly derived cutoff can aid in screening individuals before adverse metabolic phenotypes have consolidated, which provides a longer window for preventive strategies.

Keywords: Gestational diabetes; Magnetic resonance spectroscopy; Metabolic dysfunction-associated steatotic liver disease (MASLD); Metabolic syndrome; Metabolomics.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethical approval was obtained (No. 2014/692/D) from the SingHealth Centralized Institutional Review Board, and written informed consent was obtained from all women. The S-PRESTO study is registered at ClinicalTrials.gov (NCT 03531658). Consent for publication: Not applicable. Competing interests: 'DW is supported by the Wellcome Trust (17068/Z/19/Z). DW is additionally supported by the Academy of Medical Sciences Professorship (APR7_1002) and the Engineering and Physical Sciences Research Council (EP/V029045/1). KMG is supported by the UK Medical Research Council (MC_UU_12011/4), the National Institute for Health and Care Research (NIHR Senior Investigator (NF-SI-0515-10042) and NIHR Southampton Biomedical Research Centre (NIHR203319)) and Alzheimer’s Research UK (ARUK-PG2022A-008). K.M.G. received reimbursement for speaking at conferences sponsored by companies selling nutritional products. S.C. has received reimbursement from the Expert Group on Inositol in Basic and Clinical Research (EGOI; a not-for-profit academic organisation) and Nestlé Nutrition Institute for speaking at conferences. K.M.G., S.Y.C. and Y.S.C. are part of an academic consortium that has received research funding from Société Des Produits Nestlé S.A. and BenevolentAI Bio Ltd, and are co-inventors on patents filed on nutritional factors and metabolic risk outside the submitted work. All other authors declare that they have nothing to disclose.'

Figures

Fig. 1
Fig. 1
Box plots of metabolic-risk-based liver fat cutoff and liver enzymes across the different liver fat categories. a Receiver operator curve analysis for predicting MetS with continuous liver fat levels as the sole predictor, 95% CI indicated in brackets (n = 372). b Sensitivity of proposed vs conventional liver fat cutoffs. c Specificity of proposed vs conventional liver fat cutoffs. d ALT (U/L) (n = 373), e AST (U/L) (n = 373), f GGT (U/L) (n = 373). p for trend values represents trend of each liver enzyme with increasing liver fat
Fig. 2
Fig. 2
Flowchart of participant numbers (unclassified GDM cases were due to missing values of either 1-h plasma glucose, 2-h plasma glucose, or both)
Fig. 3
Fig. 3
Box plots of metabolic traits in MetS across the different liver fat categories: a waist circumference (cm) (n = 380), b triglyceride (mmoL/L) (n = 373), c fasting glucose (mmoL/L) (n = 380), d systolic blood pressure (mmHg) (n = 381), e diastolic blood pressure (mmHg) (n = 381), f HDL (mmoL/L) (n = 373)
Fig. 4
Fig. 4
Associations between the plasma lipoprotein and lipid levels and liver fat accumulation. Point estimates represent the beta coefficients for 1-SD change (95% CI) (n = 372) in the log10 transformed NMR-metabolite in the liver fat category, with respect to Low liver fat category (liver fat < 2.07%). Model adjusted for ethnicity, age, parity, education level, and BMI at pre-conception. Hollow/filled circles were/were not statistically significant with BH-adj p-values < 0.05, as determined by the Benjamini-Hochberg (BH) method
Fig. 5
Fig. 5
Associations between the liver fat accumulation and plasma lipoprotein composition and concentration. Point estimates represent the beta coefficients for 1-SD change (95% CI) (n = 372) in the log10 transformed NMR-metabolite in the liver fat category, with respect to Low liver fat category (liver fat < 2.07%). Model adjusted for ethnicity, age, parity, education level, and BMI at pre-conception. Hollow/filled circles were/were not statistically significant with BH-adj p-values < 0.05, as determined by the Benjamini-Hochberg (BH) method
Fig. 6
Fig. 6
Associations between the liver fat accumulation and plasma fatty acids and polar metabolites. Point estimates represent the beta coefficients for 1-SD change (95% CI) (fatty acids: n = 371, polar metabolites (except valine): n = 372, valine: n = 369) in the log10 transformed NMR-metabolite in the liver fat category, with respect to Low liver fat category (liver fat < 2.07%). Model adjusted for ethnicity, age, parity, education level, and BMI at pre-conception. Hollow/filled circles were/were not statistically significant with BH-adj p-values < 0.05, as determined by the Benjamini-Hochberg (BH) method
Fig. 7
Fig. 7
Associations between the liver fat accumulation and metabolic outcomes. a Box plot of multi-metabolite score for incident T2D across the different liver fat categories (n = 375). b Box plot of glucose AUC at mid-gestation across the different liver fat categories. c Percentage of GDM cases at mid-gestation across the different liver fat categories (n = 91). p for trend values represents trend of T2D score and Glucose AUC in the study with increasing liver fat
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