Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Background

Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.

Methods

A cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to train and interpret the model. Receiver-operating characteristic curves were used to analyze the predictive value.

Results

We found the significant difference in body composition parameters between the metabolic healthy normal weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy normal weight (MUNW) and metabolic unhealthy obesity (MUO) individuals, especially among the MHNW, MUNW and MUO phenotype. MHNW phenotype had significantly lower whole fat mass (FM), trunk FM and trunk free fat mass (FFM), and had significantly lower visceral fat areas compared to MUNW and MUO phenotype, respectively. The bioimpedance phase angle, waist-hip ratio (WHR) and free fat mass index (FFMI) were found to be remarkably lower in MHNW than in MUNW and MUO groups, and lower in MHO than in MUO group. For predictive analysis, the LightGBM-based model identified 32 status-predicting features for MUNW with MHNW group as the reference, MUO with MHO as the reference and MUO with MHNW as the reference, achieved high discriminative power, with area under the curve (AUC) values of 0.842 [0.658, 1.000] for MUNW vs. MHNW, 0.746 [0.599, 0.893] for MUO vs. MHO and 0.968 [0.968, 1.000] for MUO and MHNW, respectively. A 2-variable model was developed for more practical clinical applications. WHR > 0.92 and FFMI > 18.5 kg/m2 predict the increased risk of MU.

Conclusion

Body composition measurement and validation of this model could be a valuable approach for the early management and prevention of MU, whether in obese or normal population.

Profiling of N6-methyladenosine methylation in porcine longissimus dorsi muscle and unravelling the hub gene ADIPOQ promotes adipogenesis in an m6A-YTHDF1-dependent manner

BACKGROUND

Intramuscular fat (IMF) content is a critical indicator of pork quality, and abnormal IMF is also relevant to human disease as well as aging. Although N6-methyladenosine (m⁶A) RNA modification was recently found to regulate adipogenesis in porcine intramuscular fat, however, the underlying molecular mechanisms was still unclear.

RESULTS

In this work, we collected 20 longissimus dorsi muscle samples with high (average 3.95%) or low IMF content (average 1.22%) from a unique heterogenous swine population for m⁶A sequencing (m⁶A-seq). We discovered 70 genes show both differential RNA expression and m⁶A modification from high and low IMF group, including ADIPOQ and SFRP1, two hub genes inferred through gene co-expression analysis. Particularly, we observed ADIPOQ, which contains three m⁶A modification sites within 3' untranslated and protein coding region, could promote porcine intramuscular preadipocyte differentiation in an m⁶A-dependent manner. Furthermore, we found the YT521‑B homology domain family protein 1 (YTHDF1) could target and promote ADIPOQ mRNA translation.

CONCLUSIONS

Our study provided a comprehensive profiling of m⁶A methylation in porcine longissimus dorsi muscle and characterized the involvement of m⁶A epigenetic modification in the regulation of ADIPOQ mRNA on IMF deposition through an m⁶A-YTHDF1-dependent manner.

Association of ADIPOQ Single-Nucleotide Polymorphisms with the Two Clinical Phenotypes Type 2 Diabetes Mellitus and Metabolic Syndrome in a Kinh Vietnamese Population

PURPOSE

Genetic factors play an important role in the development of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS). However, few genetic association studies related to these disorders have been performed with Vietnamese subjects. In this study, the potential associations of ADIPOQ single nucleotide polymorphisms (SNPs) with T2DM and MetS in a Kinh Vietnamese population were investigated.

PATIENTS AND METHODS

A study with 768 subjects was conducted to examine the associations of four ADIPOQ SNPs (rs266729, rs1501299, rs3774261, and rs822393) primarily with T2DM and secondarily with MetS. The TaqMan SNP genotyping assay was used to determine genotypes from subjects' DNA samples.

RESULTS

After statistical adjustment for age, sex, and body mass index, the ADIPOQ SNP rs266729 was found to be associated with increased risk of T2DM under multiple inheritance models: codominant (OR = 2.30, 95% CI = 1.16-4.58), recessive (OR = 2.17, 95% CI = 1.11-4.26), and log-additive (OR = 1.32, 95% CI = 1.02-1.70). However, rs1501299, rs3774261, and rs822393 were not associated with risk for T2DM. Additionally, rs266729, rs3774261, and rs822393 were statistically associated with MetS, while rs1501299 was not. Haplotype analysis showed a strong linkage disequilibrium between the SNP pairs rs266729/rs822393 and rs1501299/rs3774261, and the haplotype rs266729(G)/rs822393(T) was not statistically associated with MetS.

CONCLUSION

The results show that rs266729 is a lead candidate SNP associated with increased risk of developing T2DM and MetS in a Kinh Vietnamese population, while rs3774261 is associated with MetS only. Further functional characterization is needed to uncover the mechanism underlying the potential genotype-phenotype associations.

A genetic analysis identifies haplotype at adiponectin locus: Association with the metabolic health and obesity phenotypes

BACKGROUND

Obesity and metabolic syndrome frequently co-exist and define obese individuals into different obesity phenotypes, such as metabolically healthy obese (MHO), metabolically unhealthy obese (MUO) and metabolically unhealthy normal weight (MUNW). Growing evidence suggests that genetic predisposition and environmental factors can explain the heterogeneity among these phenotypes.

METHODS

We conducted a case-control study including 130 MHO, 251 MUNW, 208 MUO and 336 health controls by genotyping 2 SNPs (rs2241766, rs1501299) in ADIPOQ to investigate possible associations between SNPs in the ADIPOQ gene with susceptibility to three obese phenotypes respectively in Chinese Han population. Unconditional logistic regressions were used to detect the association between ADIPOQ SNPs and MHO/MUNW/MUO risks.

RESULTS

Variant G allele of rs2241766 was associated with a reduced odds of MUO (additive model: Adjusted OR = 0.55; 95% CI = 0.40-0.75; P < 0.001) and no evidence of any significant association between rs2241766 and MHO phenotype (additive model: Adjusted OR = 0.84; 95% CI = 0.61-1.16; P = 0.306) or MUNW phenotype (additive model: Adjusted OR = 0.95; 95% CI = 0.73-1.24; P = 0.720) was found. Minor allele T of rs1501299 were significantly associated with decreased risk of MHO (Adjusted OR = 0.53; 95% CI = 0.37-0.76; P < 0.001) and MUNW (Adjusted OR = 0.63; 95% CI = 0.48-0.83; P = 0.001) in additive genetic model after correction for multiple testing.

CONCLUSIONS

The variant G allele of rs2241766 was negatively associated with risk of MUO and variant T allele of rs1501299 exhibited reduced odds for MHO and MUNW. Beyond that, future studies are warranted to validate and extend our findings.

Effect of Metabolic Health and Obesity Phenotype on Risk of Diabetes Mellitus: A Population-Based Longitudinal Study

BACKGROUND

Epidemiologic evidence on body mass index (BMI)-metabolic status phenotypes and diabetes risk remains controversial, especially for metabolically healthy obesity (MHO). We aimed to examine the effect of metabolic health and obesity phenotype on diabetes risk in the Chinese population.

METHODS

A population-based cohort study was carried out. The baseline survey was conducted in 2017, with two follow-up visits in 2018 and 2020. Diabetes was defined based on the criteria of the World Health Organization. Robust generalized estimating equation models with a binary distribution using a log link and exchange structure were applied for the pooled analysis sample.

RESULTS

A total sample of 9623 observations was pooled for the longitudinal data analysis. The average follow-up time was 1.64 years per person and the overall incidence density of diabetes was 6.94% person-years. Decreased diabetes risk was found in metabolically healthy overweight phenotype (RR = 0.65; 95% CI = 0.47-0.90) and no significant associations were detected for the MHO individuals (RR = 0.99; 95% CI = 0.63-1.53) compared with those of metabolically healthy normal weight, in contrast to metabolically unhealthy normal weight (MU-NW) (RR = 1.81; 95% CI = 1.28-2.55), metabolically unhealthy overweight (MU-OW) (RR = 2.02; 95% CI = 1.57-2.61) and metabolically unhealthy obesity (MUO) (RR = 2.48; 95% CI = 1.89-3.26) phenotypes. Significant associations between BMI-metabolic status phenotypes and diabetes were found in both males and females.

CONCLUSION

The MUO phenotype needs to be accorded much more importance. MU-NW and MU-OW are also important component for targeted prevention. Our findings can be targeted for optimizing preventive strategies to mitigate the obviously increased prevalence of diabetes.