Application of adaptive neuro-fuzzy inference systems (ANFIS) to delineate estradiol, glutathione and homocysteine interactions

Plasma homocysteine levels and handgrip strength in postmenopausal women

OBJECTIVE

This study evaluated handgrip strength (HGS), circulating homocysteine levels and related factors in postmenopausal women.

METHODS

This study is a sub-analysis of a prospective cohort of 303 postmenopausal women aged 62.7 ± 6.9 years who had HGS measures with a digital dynamometer as the primary outcome, and plasma homocysteine and creatinine levels and glomerular filtration rate (GFR) measures as the secondary outcomes.

RESULTS

The average HGS was 22.5 ± 4.0 kg, 29.4% of women had dynapenia (HGS < 20 kg), adiposity was 40.3 ± 5.4% and 9.57% of women had hyperhomocysteinemia (homocysteine >15 μmol/l). There were no differences between tertiles of homocysteine and HGS (p = 0.641). Plasma homocysteine levels were unrelated to HGS (r = -0.06) and correlated with age (r = 0.17), GFR (r = -0.28) and creatinine (r = 0.23). Hyperhomocysteinemia was not associated with HGS (odds ratio [OR] = 0.98 [95% confidence interval (CI): 0.89; 1.08]) or dynapenia (OR = 1.10 [95% CI: 0.45; 2.47]). The risk of presenting low HGS were not significantly associated with homocysteine (OR = -0.08 [95% CI: -0.21; 0.06]) and were associated with age (OR = -0.23 [95% CI: -0.29; -0.17]), adiposity (OR = -6.52 [95% CI: -9.53; -3.50]) and creatinine (OR = 6.22 [95% CI: 2.48; 9.97]).

CONCLUSIONS

HGS and dynapenia were unrelated to hyperhomocysteinemia. Age, GFR and creatinine were significantly associated with plasma homocysteine levels.

Homocysteine level is positively and independently associated with serum creatinine and urea nitrogen levels in old male patients with hypertension

A cross-sectional study to show whether and how serum fasting homocysteine levels are associated with renal function changes in patients with hypertension. Homocysteine levels were associated with serum creatinine and blood urea nitrogen (BUN) levels with coefficients of 2.04 and 0.07, respectively, only in males and independent of confounders. In addition, low density lipoprotein cholesterol (LDL-C) levels were positively and left ventricular ejection fraction (LVEF) was negatively associated with serum creatinine level in males; age was positively associated with serum creatinine levels in females. Age was a common risk factor positively associated with BUN levels in both sexes, while total cholesterol (TC) levels and glycemic control were independent risk factors that were positively associated with BUN levels only in males. LDL-C levels and LVEF were negatively associated with BUN levels in females. Body mass index (BMI) was positively associated and hemoglobin A1c (HbA1c) levels, high density lipoprotein cholesterol (HDL-C) levels and the presence of stroke were negatively associated with serum uric acid levels in male patients. In contrast, only LVEF was positively associated with uric acid levels in females. In conclusion, homocysteine level is an independent risk factor associated with serum creatinine and BUN levels in male patients with hypertension.

A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in southern Chinese men

BACKGROUND

Phenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort.

RESULT

A total of 1999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson's test, Jaccard's index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P < 10^- 4) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalogue, 31 SNPs were found to be associated with several diseases (P < 10^- 8). Using ALDH2 as an example to preliminarily explore its biological function, we also confirmed that the rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3 T3-L1 preadipocytes.

CONCLUSION

All these findings indicated a network of shared genetics and 29 biochemical indices, which will help fully understand the genetics participating in biochemical metabolism.