Abstract
Decreased forkhead box O1 (FoxO1) activity induces hyperlipidemia and increased PPARγ, leading to hyperlipidemia in association with endoplasmic reticulum (ER) stress. In the liver, aging and comorbidities such as hyperlipidemia and diabetes significantly influence a wide variety of steatosis, but the underlying mechanisms are complex and remain elusive.To establish the modulatory role of FoxO1 and the functional consequences of its altered interaction with PPARγ in the present study, we utilized a cell culture system, aged rats and diabetic db/db mice.We found that, under ER stress, FoxO1 induces PPARγ-mediated lipid accumulation in aged rat livers. Our data showed that the FoxO1-induced hepatic lipid accumulation was negatively regulated by Akt signaling. PPARγ, a key lipogenesis transcription factor, was increased in aged liver, resulting in lipid accumulation via hepatic ER stress under hyperglycemic conditions. We further demonstrated that loss of FoxO1 causes a decline in PPARγ expression and reduces lipid accumulation. In addition, the interaction between FoxO1 and PPARγ was shown to induce hepatic steatosis in aging and db/db mice.We provide evidence that, in aged rats, FoxO1 interaction with PPARγ promotes hepatic steatosis, due to hyperglycemia-induced ER stress, which causes an impairment in Akt signaling, such in aging-related diabetes.
Highlights
IntroductionChanges in the length of life are usually analyzed by comparing average (mean) or median longevity
In studies of aging, changes in the length of life are usually analyzed by comparing average or median longevity
Results of the present study indicate that interventions which extend longevity in laboratory mice can alter the distribution and the variance of age at death
Summary
Changes in the length of life are usually analyzed by comparing average (mean) or median longevity. While values of the standard deviations or standard errors of the mean are routinely reported, the distribution of individual age at death is rarely analyzed or discussed. This contrasts with the recent interest in analyzing the distribution of biomarkers of aging using the statistical distance measure [1] to estimate the level of physiological dysregulation and to relate it to resilience and robustness during aging [2]. In a recent publication based on analysis of demographic data, Van Raalte and her colleagues reported that socioeconomic status influences the mean longevity and the variability of human life-span [3]. The practical implication of these findings is that for both the individuals and the health care systems it is more difficult to predict the age at death of the less privileged people than of the more privileged strata or for the entire population
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