Association of Klotho and interleukin 6 gene polymorphisms with aging in Han Chinese population

Involvement of Single Nucleotide Variants in the Klotho Gene Among Obesity Individuals with and without Type 2 Diabetes Mellitus in the Saudi Population

Purpose

Aging is characterized by the gradual physiological changes and alterations that accumulate over time in the human body. The combination of obesity and ageing can lead to an increased risk of serious health issues or death. Single nucleotide variants (SNVs) in the Klotho gene were commonly studied, including that in type 2 diabetes mellitus (T2DM).

Aim

The aim of this study is to examine the possible effect of SNVs in Klotho on the obese population in Saudi Arabia using middle-aged participants with and without T2DM.

Methods

This study consists of 100 controls and 100 obesity patients, in which 50 had T2DM and the remaining 50 were obese without T2DM. Genotyping was performed with PCR, and Sanger sequencing analysis was used to validate the molecular association.

Results

In this study, rs1207568 (p = 0.001-0.003) and rs9527025 (p = 0.001-0.00004) SNVs were associated with obesity cases. However, none of the genotypes or allele frequencies showed a positive association with the rs564481 SNV (p = 0.344-0.881). The multiple linear regression model showed that waist and hip were associated (p = 0.01-0.02). ANOVA analysis showed age (p = 0.04), hip (p = 0.002), SBP, and TC (p = 0.02) were associated. Finally, SNV (rs1207568 and rs95270250) and obesity (p < 0.001) associations were confirmed through gene multifactor dimensionality reduction analysis with gene-gene interaction, dendrogram, and graphical depletion method.

Conclusion

This study concludes that rs1207568 and rs9527025 SNVs are associated with obesity in the Saudi population. Additional genetical statistics showed significant association between dependent and independent variables. SNVs in Klotho play a role in the Saudi population's susceptibility to obesity.

Insulin resistance, an unmasked culprit in depressive disorders: Promises for interventions

Depressive disorders constitute a set of debilitating diseases with psychological, societal, economic and humanitarian consequences for millions of people worldwide. Scientists are beginning to understand the reciprocal communication between the brain and the rest of the body in the etiology of these diseases. In particular, scientists have noted a connection between depressive disorders, which are primarily seen as brain-based, and, insulin resistance (IR), a modifiable metabolic inflammatory state that is typically seen as peripheral. We highlight evidence showing how treating IR, with drugs or behavioral interventions, may ameliorate or possibly prevent, depressive disorders and their long-term consequences at various stages of the life course. This article is part of the Special Issue entitled 'Metabolic Impairment as Risk Factors for Neurodegenerative Disorders.'

Common methods of biological age estimation

At present, no single indicator could be used as a golden index to estimate aging process. The biological age (BA), which combines several important biomarkers with mathematical modeling, has been proposed for >50 years as an aging estimation method to replace chronological age (CA). The common methods used for BA estimation include the multiple linear regression (MLR), the principal component analysis (PCA), the Hochschild's method, and the Klemera and Doubal's method (KDM). The fundamental differences in these four methods are the roles of CA and the selection criteria of aging biomarkers. In MLR and PCA, CA is treated as the selection criterion and an independent index. The Hochschild's method and KDM share a similar concept, making CA an independent variable. Previous studies have either simply constructed the BA model by one or compared the four methods together. However, reviews have yet to illustrate and compare the four methods systematically. Since the BA model is a potential estimation of aging for clinical use, such as predicting onset and prognosis of diseases, improving the elderly's living qualities, and realizing successful aging, here we summarize previous BA studies, illustrate the basic statistical steps, and thoroughly discuss the comparisons among the four common BA estimation methods.