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The Role of Biomarkers Derived from Adipocyte Inflammation in Metabolic Health and End-stage Renal Disease
Adipose inflammation,Obesity,Metabolically unhealthy,Adipokine,Leucine-rich repeat alpha-2 glycoprotein 1 (LRG1),Secretory leukocyte peptidase inhibitor (SLPI),Neutrophil-gelatinase associated lipocalin (NGAL, LCN2),End-stage renal disease,
|Publication Year :||2020|
Metabolic health is closely linked with risk of cardiovascular disease, diabetes mellitus (DM), metabolic syndrome (MS) and many types of cancers. The morbidity and mortality associated with obesity pose a significant burden on public health. Body fat excess is characterized by adipocyte inflammation and an altered adipokine profile. Adipose hypertrophy, increased infiltration of macrophages and switch to M1 phenotype, and secretion of proinflammatory cytokines contribute to development of insulin resistance and long-term cardiometabolic complications. Adipokines released by adipocytes and macrophages in the stromal vascular fraction of adipose tissue can affect adipose tissue per se as well as distant effect organs. Obesity is characterized by increase in IL-6, TNF-α, IL-1, resistin and a reduction of adiponectin. Obesity is most commonly defined by excess body mass index (BMI) and assessment of metabolic health will also include measurement of body composition, along with parameters of lipid and glucose metabolism. Investigation of adipokines in different metabolic traits may provide diagnostic and interventional clues and adoption of systems approach may provide better overview of adipose inflammation in dealing with a complex, multi-system alteration triggered by obesity.
Mouse 3T3-L1 preadipocyte is a well-established model for studying adipogenesis. 3T3-L1 preadipocyte was induced to differentiated adipocytes, and was treated with IL-1β 40 mg/ml for 24 hours to induce insulin resistance and inflammation in vitro. The cultured adipocytes with and without IL-1β treatment were submitted for Affymetrix GeneChip Mouse Genome 430 2.0 microarray analysis. There were 42 genes upregulated by more than 6-fold and adiponectin expression was reduced by 50% in the IL-1β treated adipocytes. Validation with real-time qPCR was performed. STRING database was employed to created protein-protein interaction network and enriched pathways were reviewed to provide insights of the molecular functions of our candidate genes. Three candidate genes, leucine-rich alpha-2 glycoprotein 1 (LRG1), secretory leukocyte peptidase inhibitor (SLPI), and neutrophil gelatinase-associated lipocalin (NGAL) encoded by LCN2 were not a part of well-recognized inflammatory and chemotactic pathways. They were selected as potential novel biomarkers and further investigation was performed to explore their association with metabolic health and inflammation.
One clinical cohort enrolled 175 women aged 37 to 67 years who did not have established cardiovascular disease. Body composition was assessed by dual energy X-ray absorptiometry (DXA) and biochemical assay and adipocyte biomarkers were measured. We found out that obesity and central obesity were characterized by a general increase in body fat, while in MS and insulin resistance, fat gain only occurred in arms and central regions. NGAL was positively correlated with the size of total and central fat depots, as well as obesity and central obesity phenotypes. NGAL was also associated with dysregulated glucose metabolism, and the presence of DM and insulin resistance.
Among 140 women with BMI < 27 kg/m2, 50 of them had 2 or more components of metabolic risk criteria and were categorized as metabolically obese non-obese (MONO) phenotype. The components of metabolic risk criteria included MS criteria except central obesity and inclusion of insulin resistance and subclinical inflammation. Serum ferritin was used as a surrogate marker of inflammation. MONO women had a comparable degree of central adiposity to those with BM ≥ 27 kg/m2. One of the features unique to MONO was the lack of expansion of fat and lean mass in leg and gynoid region. SLPI was found to be an independent risk factor for metabolically unhealthy status in non-obese women after adjusting for age and android-gynoid fat mass ratio.
End-stage renal disease (ESRD) is characterized by chronic inflammation. The second cohort enrolled 169 ESRD patients treated with hemodialysis, including 77 diabetics (45.3%). BMI was available in 134 patients at baseline and 16.4% of them were obese. Comorbid conditions including coronary artery disease (CAD), heart failure, ischemic stroke, peripheral artery disease (PAD), liver cirrhosis, and malignancy were recorded at baseline. The patients were followed from April, 2016 until death or study end in October, 2019
At baseline, higher LRG1 was associated with increased frequency of PAD and ischemic stroke. The correlation of LRG1 with PAD remained significant after multivariate adjustment. The biomarkers were not correlated with obesity. We further investigate if adipocyte-derived biomarkers were associated with patient survival. The mean follow-up duration was 1142.65 ± 305.52 days. Thirty-four patients (20.1%) expired, 14 patients died of cardiovascular events, including 4 out-of-hospital cardiac arrests (OHCA). Lower baseline SLPI and NGAL levels were associated with better overall survival and inverse relationship was observed for LRG1. Mortality was further divided into cardiovascular and non- cardiovascular causes. Cox-regression analysis showed higher LRG1, lower SLPI and lower NGAL were associated with poor non-cardiac survival, identical to the trend of all-cause mortality. After adjusting for age, albumin, hemoglobin, and key comorbidities, baseline lower SLPI and NGAL levels were predictive better all-cause mortality.
By transcriptomic analysis of cultured adipocyte treated with IL-1β, we identified three markers associated with adipose inflammation. In the cohort of otherwise healthy women, circulating NGAL levels were proportional to total and central adiposity as well abnormal glucose metabolism. SLPI was associated with another aspect of metabolic health; it was linked with metabolic unhealthy state and subclinical inflammation rather than fatness or insulin resistance. In the second cohort of hemodialysis patients, there was no association between biomarkers and BMI or lipid profiles. LRG1 was correlated with inflammatory markers, CRP and IL-6, as well as increased prevalence of PAD, but this was not translated to cardiovascular mortality. Lower SLPI and NGAL were associated with better overall survival, which may be explained by reduced non-cardiac mortality risk. In this study, we demonstrated the effectiveness of transcriptome profiling by microarray in combination with network analysis. Three biomarkers, NGAL, SLPI, LRG1, reflecting different aspects of metabolic health were identified and they may be employed in further disease phenotyping or risk stratification in metabolic disorders.
|Appears in Collections:||基因體與系統生物學學位學程|
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