Changes in Proteomic Profiles are Related to Changes in BMI and Fat Distribution During 10 Years of Aging

Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression

The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Dual Role of Caspase 8 in Adipocyte Apoptosis and Metabolic Inflammation

Caspases are cysteine-aspartic proteases that were initially discovered to play a role in apoptosis. However, caspase 8, in particular, also has additional nonapoptotic roles, such as in inflammation. Adipocyte cell death and inflammation are hypothesized to be initiating pathogenic factors in type 2 diabetes. Here, we examined the pleiotropic role of caspase 8 in adipocytes and obesity-associated insulin resistance. Caspase 8 expression was increased in adipocytes from mice and humans with obesity and insulin resistance. Treatment of 3T3-L1 adipocytes with caspase 8 inhibitor Z-IETD-FMK decreased both death receptor-mediated signaling and targets of nuclear factor κ-light-chain-enhancer of activated B (NF-κB) signaling. We generated novel adipose tissue and adipocyte-specific caspase 8 knockout mice (aP2Casp8-/- and adipoqCasp8-/-). Both males and females had improved glucose tolerance in the setting of high-fat diet (HFD) feeding. Knockout mice also gained less weight on HFD, with decreased adiposity, adipocyte size, and hepatic steatosis. These mice had decreased adipose tissue inflammation and decreased activation of canonical and noncanonical NF-κB signaling. Furthermore, they demonstrated increased energy expenditure, core body temperature, and UCP1 expression. Adipocyte-specific activation of Ikbkb or housing mice at thermoneutrality attenuated improvements in glucose tolerance. These data demonstrate an important role for caspase 8 in mediating adipocyte cell death and inflammation to regulate glucose and energy homeostasis.

ARTICLE HIGHLIGHTS

Caspase 8 is increased in adipocytes from mice and humans with obesity and insulin resistance. Knockdown of caspase 8 in adipocytes protects mice from glucose intolerance and weight gain on a high-fat diet. Knockdown of caspase 8 decreases Fas signaling, as well as canonical and noncanonical nuclear factor κ-light-chain-enhancer of activated B (NF-κB) signaling in adipose tissue. Improved glucose tolerance occurs via reduced activation of NF-κB signaling and via induction of UCP1 in adipocytes.

Evaluation of circulating plasma proteins in breast cancer using Mendelian randomisation

Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.

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.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Proteomic Profiles of Body Mass Index and Waist-to-Hip Ratio and Their Role in Incidence of Diabetes

CONTEXT

It is unclear to what extent the plasma proteome of abdominal fat distribution differs from that of body mass index, and whether the differences have clinical implications.

OBJECTIVE

To evaluate the difference between the plasma proteomic profiles of body mass index (BMI) and waist-to-hip ratio (WHR), and then examine the identified BMI- or WHR-specific proteins in relation to incidence of diabetes.

METHODS

Data were obtained from the Malmö Diet and Cancer-Cardiovascular Cohort study in the general community. Participants (n = 4203) with no previous diabetes (aged 57.2 ± 6.0 years, 37.8% men) were included. Plasma proteins (n = 136) were measured by the Proseek proximity extension method. BMI- and WHR-specific proteins were identified at baseline using a 2-step iterative resampling approach to optimize internal replicability followed by β coefficient comparisons. The identified proteins were considered internally replicated and were then studied in relation to incident diabetes by Cox proportional hazards regression analysis. The main outcome measure was incident diabetes over a mean follow-up of 20.3 ± 5.9 years.

RESULTS

After excluding 21 overlapping proteins and proteins that did not show significantly different associations with BMI vs WHR, 10 internally replicated proteins were found to be specific to BMI, and 22 were found to be specific to WHR (false discovery rate-adjusted P < .05). Of the WHR-specific proteins, 18 remained associated with diabetes risk after multivariate adjustments, whereas none of the BMI-specific proteins showed associations with diabetes risk.

CONCLUSION

Abdominal fat distribution was associated with some unique characteristics of the plasma proteome that potentially could be related to its additional risk of diabetes beyond general obesity.

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

BACKGROUND

The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High-throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers.

METHODS

Data derived from the prospective population-based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow-up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55-74 years in the cross-sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical impedance analysis at baseline and follow-up. At baseline, 233 plasma proteins were measured using proximity extension assay. We implemented boosting with stability selection to enable false positives-controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100× bootstrapping by cross-validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors.

RESULTS

In the cross-sectional analysis, we identified kallikrein-6, C-C motif chemokine 28 (CCL28), and tissue factor pathway inhibitor as previously unknown biomarkers for muscle mass [association with low ASMM: odds ratio (OR) per 1-SD increase in log2 normalized protein expression values (95% confidence interval (CI)): 1.63 (1.37-1.95), 1.31 (1.14-1.51), 1.24 (1.06-1.45), respectively] and serine protease 27 for fat mass [association with high BFMI: OR (95% CI): 0.73 (0.61-0.86)]. CCL28 and metalloproteinase inhibitor 4 (TIMP4) constituted new biomarkers for the combination of low muscle and high fat mass [association with low ASMM combined with high BFMI: OR (95% CI): 1.32 (1.08-1.61), 1.28 (1.03-1.59), respectively]. Including protein biomarkers selected in ≥90% of group lasso bootstrap iterations on top of classical risk factors improved the performance of models predicting low ASMM, high BFMI, and their combination [delta AUC (95% CI): 0.16 (0.13-0.20), 0.22 (0.18-0.25), 0.12 (0.08-0.17), respectively]. In the longitudinal analysis, N-terminal prohormone brain natriuretic peptide (NT-proBNP) was the only protein selected for loss in ASMM and loss in ASMM combined with gain in BFMI over 14 years [OR (95% CI): 1.40 (1.10-1.77), 1.60 (1.15-2.24), respectively].

CONCLUSIONS

Proteomic profiling revealed CCL28 and TIMP4 as new biomarkers of low muscle mass combined with high fat mass and NT-proBNP as a key biomarker of loss in muscle mass combined with gain in fat mass. Proteomics enable us to accelerate biomarker discoveries in muscle research.

Inflammation-related plasma protein levels and association with adiposity measurements in young adults

Obesity-related inflammation is associated with cardiovascular, metabolic, and pulmonary diseases. The aim of this study was to demonstrate associations between adiposity measurements and levels of inflammation-related plasma proteins in a population of young adults. Subjects from a population-based birth cohort with a mean age of 22.5 years were included in the study population (n = 2074). Protein levels were analyzed using the Olink Proseek Multiplex Inflammation panel. Percentage body fat (%BF) and visceral fat rating (VFR) measurements were collected using Tanita MC 780 body composition monitor. Linear regression of standardized values was used to investigate associations. Potential effect modifications by sex and BMI category were assessed. Of 71 investigated proteins, 54 were significantly associated with all adiposity measurements [%BF, body mass index (BMI), VFR and waist circumference]. Among proteins associated with %BF, seven showed a larger or unique association in overweight/obese subjects and three showed a significant effect modification by sex. Fourteen proteins more strongly associated with VFR in females compared to males. Adipose-associated systemic inflammation was observed in this young adult population. Sex and adiposity localization influenced some of the associations. Our results highlight specific proteins as suitable biomarkers related to adiposity.

In-Depth Analysis of the Hidradenitis Suppurativa Serum Proteome Identifies Distinct Inflammatory Subtypes

Hidradenitis suppurativa is a chronic inflammatory dermatosis with presentations ranging from painful nodules and abscesses to draining tunnels. Using an unbiased proteomics approach, we assessed cardiovascular-, cardiometabolic-, and inflammation-related biomarkers in the serum of patients with moderate-to-severe hidradenitis suppurativa. The serum of patients with hidradenitis suppurativa clustered separately from that of healthy controls and had an upregulation of neutrophil-related markers (Cathepsin D, IL-17A, CXCL1). Patients with histologically diagnosed dermal tunnels had higher serum lipocalin-2 levels compared with those without tunnels. Consistent with this, patients with tunnels had a more neutrophilic-rich serum signature, marked by Cathepsin D, IL-17A, and IL-17D alterations. There was a significant serum‒skin correlation between proteins in the serum and the corresponding mRNA expression in skin biopsies, with healthy-appearing perilesional skin demonstrating a significant correlation with neutrophil-related proteins in the serum. CSF3 mRNA levels in lesional skin significantly correlated with neutrophil-related proteins in the serum, suggesting that CFS3 in the skin may be a driver of neutrophilic inflammation. Clinical significantly correlated with the levels of lipocalin-2 and IL-17A in the serum. Using an unbiased, large-scale proteomic approach, we demonstrate that hidradenitis suppurativa is a systemic neutrophilic dermatosis, with a specific molecular signature associated with the presence of dermal tunnels.

Abnormalities of Serum Fatty Acids in Children With Henoch-Schönlein Purpura by GC-MS Analysis

Purpose: The objectives of this work were to test the levels of serum medium- and long- chain fatty acids (MLCFAs) in children and to discover their possible relationship with Henoch-Schönlein Purpura (HSP), also known as Immunoglobulin A vasculitis. Methods: A total of 57 children with HSP (HSP group) and 28 healthy children (CON group) were recruited for this study. Serum specimens were collected to detect the compositions and contents of MLCFAs by gas chromatography with mass spectrometry (GC-MS) analysis. Results: The contents of all detected 37 MLCFAs in the HSP group were higher than the healthy group. Thirty-one species of MLCFAs were discovered to have a significant difference (p < 0.05) in two groups. Comparing to healthy controls, there were 31, 31, 18 fatty acids showed a statistical difference in the untreated group, regular treated group, and withdrawal group of HSP, respectively. The trend of fatty acids in the three HSP groups was similar to the healthy controls, as well as the untreated group and regular treated group changed more obviously than the withdrawal group. Almitate (C16:0) and 18 carbon atoms (C18) of fatty acids were abundant in all three HSP groups, divided according to the treatment of glucocorticoid. Some fatty acids were found having considerable differences (p < 0.05) in three groups. Monounsaturated fatty acids (MUFAs), including elaidate (C18:1T), cis-11,14,17-eicosatrienoic acid ester (C20:1), and cis-15-tetracosenoate (C24:1), were distinctly higher in HSP children with renal damage. Conclusion: Our study revealed that the abnormalities in MLCFA may be associated with the development of HSP. Another interesting finding was that fatty acids contents were changing during the glucocorticoid treatment. Meanwhile, long-chain MUFAs may have an impact on renal damage in HSP patients. Further studies need to be carried out in order to explore the specific mechanism of fatty acids in the course of HSP.

Effects of Age on Inflammatory Profiles and Nutrition/Energy Metabolism in Domestic Cats

BACKGROUND

Animals tend to increase in body weight and body condition score (BCS) with aging. Serum diagnostic markers related to energy metabolism may show changes even in healthy cats with aging.

MATERIALS AND METHODS

Seventy domestic cats were recruited for this study. Based upon the modified AAFP-AAHA Feline Life Stage Guidelines, animals were divided into six groups: Junior (7 months-2 years), Prime (3 -6 years), Mature (7-10 years), Senior (11-14 years), Geriatric-obese (15 years ≤) and Geriatric-thin (15 years ≤). Their body condition scores (BCS) ranged from 3/9 to 9/9. Changes in metabolites, inflammatory markers, hormone concentrations and enzyme activities related to energy metabolism were investigated in serum of 70 domestic cats of various ages.

RESULTS

Serum glucose (GLU) concentrations in the Mature, Senior, and Geriatric-obese groups were significantly higher than those in the Junior group. Serum amyloid A (SAA) concentrations in the Geriatric-thin group were significantly increased compared with the Junior group. SAA concentrations in the Geriatric-obese group tended to increase although there were no statistically significant differences. In the Mature, Senior, Geriatric-obese and Geriatric-thin groups, malate dehydrogenase/lactate dehydrogenase (M/L) ratio, an energy metabolic indicator, tended to decrease compared with the Junior group. In the Senior group, triglyceride (TG) concentrations were significantly increased compared with the Junior group. In the Geriatric-obese and Geriatric-thin groups, blood urea nitrogen (BUN) concentrations were significantly increased compared with the Junior group. In the Geriatric-obese group, albumin (ALB) concentrations were decreased compared with the Junior group.

CONCLUSION

Aged domestic cats tend to increase in body weight and BCS. In addition, serum GLU, TG, SAA, and BUN concentrations increased and serum ALB concentrations and M/L ratio decreased. These diagnostic markers may be useful to detect small changes related to energy metabolism with aging that may cause obesity with light inflammation in healthy cats.