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• Research Paper Volume 11, Issue 15 pp 5518-5534

  Frailty in middle age is associated with frailty status and race-specific changes to the transcriptome

  Relevance score: 9.583165

  Calais S. Prince, Nicole Noren Hooten, Nicolle A. Mode, Yongqing Zhang, Ngozi Ejiogu, Kevin G. Becker, Alan B. Zonderman, Michele K. Evans

  Keywords: frailty, race, gene expression, sequencing, health disparities, middle age

  Published in Aging on August 8, 2019

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  Frailty is an aging-associated syndrome resulting from diminished capacity to respond to stressors and is a significant risk factor for disability and mortality. Although frailty is usually studied in old age, it is present in mid-life. Given the increases in mortality statistics among middle-aged Americans, understanding molecular drivers of frailty in a younger, diverse cohort may facilitate identifying pathways for early intervention. We analyzed frailty-associated, genome-wide transcriptional changes in middle-aged blacks and whites. Next generation RNA sequencing was completed using total RNA from peripheral blood mononuclear cells (n = 16). We analyzed differential gene expression patterns and completed a parametric analysis of gene set enrichment (PAGE). Differential gene expression was validated using RT-qPCR (n = 52). We identified 5,082 genes differentially expressed with frailty. Frailty altered gene expression patterns and biological pathways differently in blacks and whites, including pathways related to inflammation and immunity. The validation study showed a significant two-way interaction between frailty, race, and expression of the cytokine IL1B and the transcription factor EGR1. The glucose transporter, SLC2A6, the neutrophil receptor, FCGR3B, and the accessory protein, C17orf56, were decreased with frailty. These results suggest that there may be demographic dependent, divergent biological pathways underlying frailty in middle-aged adults.

  

  Workflow and schematic representation of RNA sequencing and RT-qPCR analysis. Overview of study design for RNA sequencing (n=16) and RT-qPCR in validation cohort (n=52) (A), and RNA sequencing analysis for alignment and identification of frailty-associated genes (B).

  
  
  

  

  Global gene expression changes by frailty and race. Total RNA was isolated from PBMCs from non-frail and frail blacks and whites (n=16). Differential gene expression was assessed using RNA sequencing. The graph shows log2 fold change of genes significantly altered with frailty (5,082 genes) (A). A list of all these genes can be found in Supplementary Table 1. Venn diagrams of the total number of significant differentially expressed genes in blacks and whites with frailty (B). Significantly increased (C; up arrow) and decreased (D; down arrow) genes in blacks and whites with frailty.

  
  
  

  

  Frailty-associated biological pathways in blacks. Differentially expressed genes between non-frail and frail blacks were imputed into Parametric Analysis of Gene Set Enrichment (PAGE) analysis. Significantly changed gene sets associated with biological processes, organized by Z-score, are shown here. GO terms associated with inflammation and immune response are highlighted in red.

  
  
  

  

  Frailty-associated biological pathways in whites. Differentially expressed genes between non-frail and frail whites were imputed into Parametric Analysis of Gene Set Enrichment (PAGE) analysis. Significantly changed gene sets, associated with biological processes, organized by Z-score, are shown here. GO terms associated with inflammation and immune response are highlighted in red.

  
  
  

  

  Interaction network analysis of frailty-associated differential gene expression. The interaction network for significant, differentially expressed protein coding genes with frailty in blacks (A) and whites (B) is shown. Network nodes represent proteins and lines (edges) represent protein-protein interactions. The solid lines represent direct interactions between proteins, dashed lines represent indirect interactions between proteins, grey lines represent putative protein interactions. The line colors represent the molecular action type: green (activation), dark blue (binding), black (reaction), red (inhibition), purple (catalysis), yellow (transcriptional regulation). The action effect is represented by the shape at the end of the line: arrow head (positive), perpendicular line (negative), dot (unspecified). The prominent functional clusters in blacks and whites are indicated.

  
  
  

  

  Frailty and race-associated changes in gene expression in the validation cohort. Total RNA was isolated from PBMCs from non-frail and frail blacks and whites in the validation cohort (Table 1B; n=52). Gene expression was analyzed using RT-qPCR with gene specific primers (Refer to Supplementary Table 6). The scatter plots show the relative expression (log2 transformed) in non-frail vs frail (A) and blacks (B) vs whites (W) in this same cohort (B). The open bars represent the mean and error bars show standard error of the mean. Significance was determined using linear regression models on the log2 transformed values.

  
  
  

  

  Frailty-associated changes in gene expression with race. Total RNA was isolated from PBMCs from non-frail and frail blacks and whites in the validation cohort (n=52). Gene expression was analyzed using RT-qPCR with gene specific primers. The scatter plots show the relative expression (log2 transformed) in non-frail and frail blacks (B) and whites (W). The open bars represent the mean and error bars show standard error of the mean. There is a significant two-way interaction between frailty status and race for IL1B (p=0.041) and EGR1 (p=0.019). Significance was determined using linear regression models on the log2 transformed values.

  
  
  

• Research Paper Volume 9, Issue 1 pp 173-186

  Impaired fasting blood glucose is associated to cognitive impairment and cerebral atrophy in middle-aged non-human primates

  Relevance score: 7.9564905

  Fathia Djelti, Marc Dhenain, Jérémy Terrien, Jean-Luc Picq, Isabelle Hardy, Delphine Champeval, Martine Perret, Esther Schenker, Jacques Epelbaum, Fabienne Aujard

  Keywords: blood glucose, middle age, spatial memory performance, hippocampal/septal atrophy, spontaneous models, Microcebus murinus

  Published in Aging on December 28, 2016

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  Age-associated cognitive impairment is a major health and social issue because of increasing aged population. Cognitive decline is not homogeneous in humans and the determinants leading to differences between subjects are not fully understood. In middle-aged healthy humans, fasting blood glucose levels in the upper normal range are associated with memory impairment and cerebral atrophy. Due to a close evolutional similarity to Man, non-human primates may be useful to investigate the relationships between glucose homeostasis, cognitive deficits and structural brain alterations. In the grey mouse lemur, Microcebus murinus, spatial memory deficits have been associated with age and cerebral atrophy but the origin of these alterations have not been clearly identified. Herein, we showed that, on 28 female grey mouse lemurs (age range 2.4-6.1 years-old), age correlated with impaired fasting blood glucose (r_s=0.37) but not with impaired glucose tolerance or insulin resistance. In middle-aged animals (4.1-6.1 years-old), fasting blood glucose was inversely and closely linked with spatial memory performance (r_s=0.56) and hippocampus (r_s=-0.62) or septum (r_s=-0.55) volumes. These findings corroborate observations in humans and further support the grey mouse lemur as a natural model to unravel mechanisms which link impaired glucose homeostasis, brain atrophy and cognitive processes.

  

  Relationship between glucose homeostasis parameters and age. Glucose metabolism parameters were evaluated in young (grey symbols, range: 2.4 to 3.5 years old) and middle-aged (black symbols, range: 4.1 to 6.1 years old) mouse lemurs. (A) Spearman correlation of fasting blood glucose levels and age. (B) Spearman correlation of fasting blood insulin levels and age. (C) Oral glucose tolerance test (OGTT): Blood glucose was measured 0, 30, 60 and 120 min after oral administration of 1.75g glucose/g of body mass (Two-way ANOVA: age p<0.01, time p<0.001, time x age p=0.11). (D) Spearman correlation of area under curve of OGTT and age.

  
  
  

  

  Relationship between spatial memory performance and age. Spatial memory performance as reflected by the number of errors was determined with Barnes maze test and was measured in young (grey symbols, range: 2.4 to 3.5 years old) and middle-aged (black symbols, range: 4.1 to 6.1 years old) mouse lemurs. Spearman correlation of error number and age. The horizontal dotted line illustrates the threshold differentiating good and poor performers.

  
  
  

  

  Relationship between fasting blood glucose and spatial memory performance. (A) Spearman correlation between fasting blood glucose levels and number of errors in all lemurs (young in grey symbols and middle-aged in black symbols). (B) Spearman correlation between fasting blood glucose levels and number of errors in middle-aged lemurs only. Horizontal and vertical dotted lines illustrate the threshold differentiating good and poor performers in spatial memory test or high and normal blood glucose levels, respectively.

  
  
  

  

  Relationship between fasting blood glucose, hippocampus/septum volume, and spatial memory performance in middle-aged lemurs. (A-B) MRI coronal sections of hippocampus (right hippocampus outlined by dotted line) in a non atrophied middle-aged (A) and an atrophied middle-aged (B) mouse lemurs. (C) Spearman correlation between number of errors and hippocampus volume. (D) Spearman correlation between fasting blood glucose and hippocampus volume. (E-F) MRI coronal sections of septum (outlined by dotted line) in a non atrophied middle-aged (E) and an atrophied middle-aged (F) animals. (G) Spearman correlation between number of errors and septum volume. (H) Spearman correlation between fasting blood glucose and septum volume.

  
  
  

  

  Principal Component Analysis on middle-aged animals. (A) Variable factor map. Fasting blood glucose was considered as the qualitative variable on age, body mass (BM), number of errors (errors), volumes of hippocampus (Hipp Vol), septum (Sept Vol) and caudate nucleus (Caud Vol), insulin (Ins) and glucose tolerance index (OGTT-AUC) (B) Individual dispersion of PCA: x-axis: principal component 1 (PC1: 34.3%), y axis: principal component 2 (PC2: 17.8%). A threshold of 4 mmol/L defined normal blood glucose (fasting glucose less than 4 mmol/L, blue) and high blood glucose (fasting glucose more than 4 mmol/L, red).