Comparative analysis of obesity-related cardiometabolic and renal biomarkers in human plasma and serum

Abstract

The search for biomarkers associated with obesity-related diseases is ongoing, but it is not clear whether plasma and serum can be used interchangeably in this process. Here we used high-throughput screening to analyze 358 proteins and 76 lipids, selected because of their relevance to obesity-associated diseases, in plasma and serum from age- and sex-matched lean and obese humans. Most of the proteins/lipids had similar concentrations in plasma and serum, but a subset showed significant differences. Notably, a key marker of cardiovascular disease PAI-1 showed a difference in concentration between the obese and lean groups only in plasma. Furthermore, some biomarkers showed poor correlations between plasma and serum, including PCSK9, an important regulator of cholesterol homeostasis. Collectively, our results show that the choice of biofluid may impact study outcome when screening for obesity-related biomarkers and we identify several markers where this will be the case.

Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study

“…In line with our results, both proteins are reduced with increasing obesity 24 and an US‐American cohort study using single biomarkers measured with radioimmunoassays showed that higher total per cent fat and higher visceral fat were associated with lower IGFBP1 and IGFBP2 25 . In a Swedish PEA‐based‐proteomics study, IGFBP1 was inversely associated with BMI‐defined obesity 15 and by using aptamer methodology, IGFBP1 was inversely associated with fat mass (kg) 16 . Concerning muscle mass, IGFBP1 was inversely associated with DXA‐measured low relative muscle mass in a Swedish cohort of elderly women 26 and IGFBP2 was inversely associated with DXA‐measured total muscle mass in an US‐American study 27 .…”

“…FABP4, ADM, PRSS27, and PON3 were only associated with the continuous and categorical parameters of BFMI. In a previous PEA‐proteomics study, ADM and FABP4 were positively associated with BMI‐defined obesity 15 . An increase in FABP4 was also associated with an increase in BMI and WHR over 10 years 14 .…”

“…However, a few previous studies investigated related body composition parameters. Six studies investigated proteomics measured with PEA technology by Olink® using the CVDII panel with BMI‐defined obesity, 12 inflammation panel with BMI and WC, 13 CVDI panel with changes in BMI and WHR, 14 inflammation, cardiometabolic, CVDII, and CVDIII panels with BMI‐defined obesity, 15 immuno‐oncology panel with BMI, 19 and a large‐scale mapping of genetics of the proteome investigated causal relationships of CVDI panel with BMI, body fat (%), and WHR 18 . No previous study investigated PEA‐measured proteomics and muscle mass.…”

“…As a response, multiplex measurements including a high number of proteins, that is, proteomics, have been established over the last years. Several recently published cross‐sectional and longitudinal studies used different proteomics measurements to investigate various body composition parameters including body mass index (BMI), waist circumference (WC), waist‐to‐hip ratio (WHR), body fat mass (kg), and body fat (%), 12–19 but only one study investigated a muscle mass parameter, lean body mass (kg) 17 . Studies using high‐throughput proteomics to assess associations with muscle and fat mass parameters in combination are lacking.…”

Rapid Decline of Serum Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) in Non-Cirrhotic Patients with Chronic Hepatitis C Infection Receiving Direct-Acting Antiviral Therapy

“…Bridge et al showed that HCV genotype 3 patients had lower and HCV genotype 1 patients had higher plasma PCSK9 levels compared to uninfected controls [ 24 ]. It must be noted that plasma and serum PCSK9 levels do not correlate, and it is debatable whether studies using different biofluids can be compared [ 34 ]. In HIV-infected patients, coinfection with HCV increased plasma PCSK9 levels [ 35 ].…”