Cardiometabolic Proteins Associated with Metabolic Syndrome

Cardiometabolic profiles and proteomics associated with obesity phenotypes in a longitudinal cohort of young adults

To assess cardiometabolic profiles and proteomics to identify biomarkers associated with the metabolically healthy and unhealthy obesity. Young adults (N = 156) enrolled were classified as not having obesity, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) based on NCEP ATP-III criteria. Plasma proteomics at study entry were measured using Olink Cardiometabolic Explore panel. Linear regression was used to assess associations between proteomics and obesity groups as well as cardiometabolic traits of glucose, insulin, and lipid profiles at baseline and follow-up visits. Enriched biological pathways were further identified based on the significant proteomic features. Among the baseline 95 (61%) and 61 (39%) participants classified as not having obesity and having obesity (8 MHO and 53 MUHO), respectively. Eighty of the participants were followed-up with an average 4.6 years. Forty-one proteins were associated with obesity (FDR < 0.05), 29 of which had strong associations with insulin-related traits and lipid profiles (FDR < 0.05). Inflammation, immunomodulation, extracellular matrix remodeling and endoplasmic reticulum lumen functions were enriched by 40 proteins. In this study population, obesity and MHO were associated with insulin resistance and dysregulated lipid profiles. The underlying mechanism included elevated inflammation and deteriorated extracellular matrix remodeling function.

Comprehensive analyses of circulating cardiometabolic proteins and objective measures of fat mass

BACKGROUND

The underlying molecular pathways for the effect of excess fat mass on cardiometabolic diseases is not well understood. Since body mass index is a suboptimal measure of body fat content, we investigated the relationship of fat mass measured by dual-energy X-ray absorptiometry with circulating cardiometabolic proteins.

METHODS

We used data from a population-based cohort of 4950 Swedish women (55-85 years), divided into discovery and replication samples; 276 proteins were assessed with three Olink Proseek Multiplex panels. We used random forest to identify the most relevant biomarker candidates related to fat mass index (FMI), multivariable linear regression to further investigate the associations between FMI characteristics and circulating proteins adjusted for potential confounders, and principal component analysis (PCA) for the detection of common covariance patterns among the proteins.

RESULTS

Total FMI was associated with 66 proteins following adjustment for multiple testing in discovery and replication multivariable analyses. Five proteins not previously associated with body size were associated with either lower FMI (calsyntenin-2 (CLSTN2), kallikrein-10 (KLK10)), or higher FMI (scavenger receptor cysteine-rich domain-containing group B protein (SSC4D), trem-like transcript 2 protein (TLT-2), and interleukin-6 receptor subunit alpha (IL-6RA)). PCA provided an efficient summary of the main variation in FMI-related circulating proteins involved in glucose and lipid metabolism, appetite regulation, adipocyte differentiation, immune response and inflammation. Similar patterns were observed for regional fat mass measures.

CONCLUSIONS

This is the first large study showing associations between fat mass and circulating cardiometabolic proteins. Proteins not previously linked to body size are implicated in modulation of postsynaptic signals, inflammation, and carcinogenesis.

Inflammatory biomarkers after an exercise intervention in childhood acute lymphoblastic leukemia survivors

Cancer survivors show increased risk for non-communicable diseases and chronic low-grade inflammation characterizes the development of such diseases. We investigated inflammatory plasma protein profiles of survivors of childhood acute lymphoblastic leukemia (ALL) in comparison to healthy controls and after an intervention with a home-based exercise program. Survivors of childhood ALL aged 16-30 years (n = 21) with a median age at diagnosis 4.9 (1.6-12.9) years and a median time of 15.9 years from diagnosis, and sex- and age-matched healthy controls (n = 21) were studied. Stored plasma samples were analyzed with Olink's 92-protein-wide Inflammation panel in 21 ALL long-term survivors at baseline, after a previous 16-week home-based exercise intervention (n = 17) and in 21 age- and sex-matched controls at baseline. Protein expression levels were compared between the groups. Inflammatory protein levels did not differ between the survivors and controls at baseline. Significantly reduced levels after the intervention were found in 11 proteins related to either vascular inflammation, insulin resistance, or both: tumor necrosis factor superfamily member 14 (TNFSF14), oncostatin M (OSM), monocyte chemoattractant protein 1 (MCP-1), MCP-2, fibroblast growth factor 21 (FGF-21), chemokine (C-C motif) ligand 4 (CCL4), transforming growth factor alpha (TGF-α), tumor necrosis factor-related apoptosis-inducing ligand 10 (TRAIL), adenosine deaminase (ADA), chemokine (C-X-C motif) ligand 6 (CXCL6), and latency-associated peptide transforming growth factor beta 1 (LAP TGF-β1). The ALL survivors were not significantly more affected by inflammation than controls at baseline. The survivors' 16-week exercise intervention led to significant reduction in inflammatory protein levels. Physical exercise should be promoted for survivors of childhood cancer.

Long-term changes in plasma proteomic profiles in premenopausal and postmenopausal Black and White women: the Atherosclerosis Risk in Communities study

OBJECTIVE

The activity, localization, and turnover of proteins within cells and plasma may contribute to physiologic changes during menopause and may influence disease occurrence. We examined cross-sectional differences and long-term changes in plasma proteins between premenopausal and naturally postmenopausal women.

METHODS

We used data from 4,508 (19% Black) women enrolled in the Atherosclerosis Risk in Communities study. SOMAscan multiplexed aptamer technology was used to measure 4,697 plasma proteins. Linear regression models were used to compare differences in proteins at baseline (1993-1995) and 18-year change in proteins from baseline to 2011-2013.

RESULTS

At baseline, 472 women reported being premenopausal and 4,036 women reported being postmenopausal, with average ages of 52.3 and 61.4 years, respectively. A greater proportion of postmenopausal women had diabetes (15 vs 9%), used hypertension (38 vs 27%) and lipid-lowering medications (10 vs 3%), and had elevated total cholesterol and waist girth. In multivariable adjusted models, 38 proteins differed significantly between premenopausal and postmenopausal women at baseline, with 29 of the proteins also showing significantly different changes between groups over the 18-year follow-up as the premenopausal women also reached menopause. These proteins were associated with various molecular/cellular functions (cellular development, growth, proliferation and maintenance), physiological system development (skeletal and muscular system development, and cardiovascular system development and function), and diseases/disorders (hematological and metabolic diseases and developmental disorders).

CONCLUSIONS

We observed significantly different changes between premenopausal and postmenopausal women in several plasma proteins that reflect many biological processes. These processes may help to understand disease development during the postmenopausal period.

The metabolomic profile associated with clustering of cardiovascular risk factors—A multi-sample evaluation

Background

A clustering of cardiovascular risk factors is denoted the metabolic syndrome (MetS), but the mechanistic underpinnings of this clustering is not clear. Using large-scale metabolomics, we aimed to find a metabolic profile common for all five components of MetS.

Methods and findings

791 annotated non-xenobiotic metabolites were measured by ultra-performance liquid chromatography tandem mass spectrometry in five different population-based samples (Discovery samples: EpiHealth, n = 2342 and SCAPIS-Uppsala, n = 4985. Replication sample: SCAPIS-Malmö, n = 3978, Characterization samples: PIVUS, n = 604 and POEM, n = 501). MetS was defined by the NCEP/consensus criteria. Fifteen metabolites were related to all five components of MetS (blood pressure, waist circumference, glucose, HDL-cholesterol and triglycerides) at a false discovery rate of <0.05 with adjustments for BMI and several life-style factors. They represented different metabolic classes, such as amino acids, simple carbohydrates, androgenic steroids, corticosteroids, co-factors and vitamins, ceramides, carnitines, fatty acids, phospholipids and metabolonic lactone sulfate. All 15 metabolites were related to insulin sensitivity (Matsuda index) in POEM, but only Palmitoyl-oleoyl-GPE (16:0/18:1), a glycerophospholipid, was related to incident cardiovascular disease over 8.6 years follow-up in the EpiHealth sample following adjustment for cardiovascular risk factors (HR 1.32 for a SD change, 95%CI 1.07–1.63).

Conclusion

A complex metabolic profile was related to all cardiovascular risk factors included in MetS independently of BMI. This profile was also related to insulin sensitivity, which provide further support for the importance of insulin sensitivity as an important underlying mechanism in the clustering of cardiovascular risk factors.

ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.

The Roles and Associated Mechanisms of Adipokines in Development of Metabolic Syndrome

Metabolic syndrome is a cluster of metabolic indicators that increase the risk of diabetes and cardiovascular diseases. Visceral obesity and factors derived from altered adipose tissue, adipokines, play critical roles in the development of metabolic syndrome. Although the adipokines leptin and adiponectin improve insulin sensitivity, others contribute to the development of glucose intolerance, including visfatin, fetuin-A, resistin, and plasminogen activator inhibitor-1 (PAI-1). Leptin and adiponectin increase fatty acid oxidation, prevent foam cell formation, and improve lipid metabolism, while visfatin, fetuin-A, PAI-1, and resistin have pro-atherogenic properties. In this review, we briefly summarize the role of various adipokines in the development of metabolic syndrome, focusing on glucose homeostasis and lipid metabolism.

Proteins associated with incident metabolic syndrome in population-based cohorts

BACKGROUND

The metabolic syndrome (MetS) identifies persons with clustering of multiple cardiometabolic risk factors. The underlying pathology inducing this clustering is not fully known. We used a targeted proteomics assay to identify associations of circulating proteins with MetS and its components, cross-sectionally and longitudinally.

METHODS

We explored and validated associations of 86 cardiovascular proteins, assessed using a proximity extension assay, with the MetS in two independent cohorts; the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS, n = 996) and Uppsala Longitudinal Study of Adult Men (ULSAM, n = 785). The analyses were adjusted for smoking, exercise habits, education, and energy and alcohol intake.

RESULTS

Nine proteins were associated with all five components of the MetS in PIVUS using FDR < 0.05 in a cross-sectional analysis. Of those nine proteins, only Interleukin-1 receptor antagonist protein (IL-1RA) was associated with all five components of the MetS in ULSAM using p < 0.05. IL-1RA levels were associated with incident MetS (n = 109) in PIVUS during a 5-year follow-up (HR 1.76 for a 1 SD change (95% CI 1.38, 2.24), p = 4.3*10^-6). IL-1RA was however not causally related to MetS in a two-sample Mendelian randomization analysis using published data.

CONCLUSION

Circulating IL-1RA was related to all five components of the MetS in a cross-sectional analysis in two independent samples, as well as to incident MetS in a longitudinal analysis. However, Mendelian randomization analyses did not provide support for a causal role for IL-1RA in the development of MetS.

Metabolic syndrome and the plasma proteome: from association to causation

Background

The metabolic syndrome (MetS), defined by the simultaneous clustering of cardio-metabolic risk factors, is a significant worldwide public health burden with an estimated 25% prevalence worldwide. The pathogenesis of MetS is not entirely clear and the use of molecular level data could help uncover common pathogenic pathways behind the observed clustering.

Methods

Using a highly multiplexed aptamer-based affinity proteomics platform, we examined associations between plasma proteins and prevalent and incident MetS in the KORA cohort (n = 998) and replicated our results for prevalent MetS in the HUNT3 study (n = 923). We applied logistic regression models adjusted for age, sex, smoking status, and physical activity.

We used the bootstrap ranking algorithm of least absolute shrinkage and selection operator (LASSO) to select a predictive model from the incident MetS associated proteins and used area under the curve (AUC) to assess its performance. Finally, we investigated the causal effect of the replicated proteins on MetS using two-sample Mendelian randomization.

Results

Prevalent MetS was associated with 116 proteins, of which 53 replicated in HUNT. These included previously reported proteins like leptin, and new proteins like NTR domain-containing protein 2 and endoplasmic reticulum protein 29. Incident MetS was associated with 14 proteins in KORA, of which 13 overlap the prevalent MetS associated proteins with soluble advanced glycosylation end product-specific receptor (sRAGE) being unique to incident MetS. The LASSO selected an eight-protein predictive model with an (AUC = 0.75; 95% CI = 0.71–0.79) in KORA.

Mendelian randomization suggested causal effects of three proteins on MetS, namely apolipoprotein E2 (APOE2) (Wald-Ratio = − 0.12, Wald-p = 3.63e−13), apolipoprotein B (APOB) (Wald-Ratio = − 0.09, Wald-p = 2.54e−04) and proto-oncogene tyrosine-protein kinase receptor (RET) (Wald-Ratio = 0.10, Wald-p = 5.40e−04).

Conclusions

Our findings offer new insights into the plasma proteome underlying MetS and identify new protein associations. We reveal possible casual effects of APOE2, APOB and RET on MetS. Our results highlight protein candidates that could potentially serve as targets for prevention and therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01299-2.

Cardiovascular-related proteins and the abdominal visceral to subcutaneous adipose tissue ratio

BACKGROUND AND AIMS

An increased amount of visceral adipose tissues has been related to atherosclerosis and future cardiovascular events. The present study aims to investigate how the abdominal fat distribution links to plasma levels of cardiovascular-related proteins.

METHOD AND RESULTS

In the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (n = 326, all aged 50 years), abdominal visceral (VAT) and subcutaneous (SAT) adipose tissue volumes were quantified by MRI. Eighty-six cardiovascular-related proteins were measured by the proximity extension assay (PEA). Similar investigations were carried out in the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (n = 400, all aged 75 years). In the discovery dataset (POEM), 10 proteins were related to the VAT/SAT-ratio using false discovery rate <.05. Of those, Cathepsin D (CTSD), Interleukin-1 receptor antagonist protein (IL-1RA) and Growth hormone (GH) (inversely) were related to the VAT/SAT-ratio in the validation in PIVUS following adjustment for sex, BMI, smoking, education level and exercise habits (p < 0.05). In a secondary analysis, a meta-analysis of the two samples suggested that 15 proteins could be linked to the VAT/SAT-ratio following adjustment as above and Bonferroni-correction of the p-value.

CONCLUSION

Three cardiovascular-related proteins, cathepsin D, IL-1RA and growth hormone, were being associated with the distribution of abdominal adipose tissue using a discovery/validation approach. A meta-analysis of the two samples suggested that also a number of other cardiovascular-related proteins could be associated with an unfavorable abdominal fat distribution.

Genetic Determinants of Clustering of Cardiometabolic Risk Factors in U.K. Biobank

Objective: The metabolic syndrome (MetS) is a description of a clustering of cardiometabolic risk factors in the same individual. This study searched for genetic loci associated with all five prespecified components of MetS to find a common pathophysiological link for this risk factor clustering. Methods: Using data from 291,107 individuals in the U.K. biobank, a genome-wide association study (GWAS) was performed versus each of the five components of the syndrome as continuous variables (glucose, systolic blood pressure, triglycerides, waist circumference, and high-density lipoprotein-cholesterol). Results: Using false discovery rate <0.05, three loci were related to all five MetS components (rs7575523; nearest gene LINC0112, rs3936511; intron of C5orf67, and rs111970447; intron of GIP). Of those, C5orf67 seems the most interesting candidate for clustering of risk factors, since previous GWASs in other samples have identified this locus as being related to all five risk factors. Also, genetic loci being related to the different combinations of four or three MetS components were presented. Generally, each MetS component combination was related to a unique genetic profile, and the genetic overlap between these combinations was low. Conclusion: A genetic locus was discovered being related to each of the five MetS components, being a candidate for a common pathophysiological link for risk factor clustering. In addition, genetic loci being related to different combinations of four or three MetS components were presented, and the genetic overlap between those combinations of MetS was low.