Limited data exist on the performance of high‐throughput proteomics profiling in epidemiological settings, including the impact of specimen collection and within‐person variability over time. Thus, ...the Olink (972 proteins) and SOMAscan7Kv4.1 (7322 proteoforms of 6596 proteins) assays were utilized to measure protein concentrations in archived plasma samples from the Nurses’ Health Studies and Health Professionals Follow‐Up Study. Spearman's correlation coefficients (r) and intraclass correlation coefficients (ICCs) were used to assess agreement between (1) 42 triplicate samples processed immediately, 24‐h or 48‐h after blood collection from 14 participants; and (2) 80 plasma samples from 40 participants collected 1‐year apart. When comparing samples processed immediately, 24‐h, and 48‐h later, 55% of assays had an ICC/r ≥ 0.75 and 87% had an ICC/r ≥ 0.40 in Olink compared to 44% with an ICC/r ≥ 0.75 and 72% with an ICC/r ≥ 0.40 in SOMAscan7K. For both platforms, >90% of the assays were stable (ICC/r ≥ 0.40) in samples collected 1‐year apart. Among 817 proteins measured with both platforms, Spearman's correlations were high (r > 0.75) for 14.7% and poor (r < 0.40) for 44.8% of proteins. High‐throughput proteomics profiling demonstrated reproducibility in archived plasma samples and stability after delayed processing in epidemiological studies, yet correlations between proteins measured with the Olink and SOMAscan7K platforms were highly variable.
The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of ...the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.
Objective
The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity‐related metabolomic signatures (MESs) and evaluated their ...relationships with incident T2D.
Methods
In a nested case‐control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk.
Results
A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00‐9.06) for BMI MES and 9.60 (4.22‐21.88) for WC MES (both p‐trend < 0.001). The MES‐T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11‐9.98) for BMI MES and 10.38 (6.36‐16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001).
Conclusions
Multiple obesity‐related metabolites and MESs strongly associated with T2D in Chinese adults were identified.
G protein‐coupled receptors (GPCRs) comprise the largest group of membrane receptors in eukaryotic genomes and collectively they regulate nearly all cellular processes. Despite the widely recognized ...importance of this class of proteins, many GPCRs remain understudied. G protein‐coupled receptor 27 (Gpr27) is an orphan GPCR that displays high conservation during vertebrate evolution. Although, GPR27 is known to be expressed in tissues that regulate metabolism including the pancreas, skeletal muscle, and adipose tissue, its functions are poorly characterized. Therefore, to investigate the potential roles of Gpr27 in energy metabolism, we generated a whole body gpr27 knockout zebrafish line. Loss of gpr27 potentiated the elevation in glucose levels induced by pharmacological or nutritional perturbations. We next leveraged a mass spectrometry metabolite profiling platform to identify other potential metabolic functions of Gpr27. Notably, genetic deletion of gpr27 elevated medium‐chain acylcarnitines, in particular C6‐hexanoylcarnitine, C8‐octanoylcarnitine, C9‐nonanoylcarnitine, and C10‐decanoylcarnitine, lipid species known to be associated with insulin resistance in humans. Concordantly, gpr27 deletion in zebrafish abrogated insulin‐dependent Akt phosphorylation and glucose utilization. Finally, loss of gpr27 increased the expression of key enzymes in carnitine shuttle complex, in particular the homolog to the brain‐specific isoform of CPT1C which functions as a hypothalamic energy senor. In summary, our findings shed light on the biochemical functions of Gpr27 by illuminating its role in lipid metabolism, insulin signaling, and glucose homeostasis.
As there is significant heterogeneity in the weight loss response to pharmacotherapy, one of the most important clinical questions in obesity medicine is how to predict an individual's response to ...pharmacotherapy. The present study examines patterns of weight loss among overweight and obese women who demonstrated early robust response to twice daily exenatide treatment compared to those treated with hypocaloric diet and matched placebo injections.
We randomized 182 women (BMI 25-48 kg/m2) to treatment with exenatide alone or matched placebo injections plus hypocaloric diet. In both treatment groups, women who demonstrated ≥ 5% weight loss at 12 weeks were characterized as high responders and those who lost ≥10% of body weight were classified as super responders. Our primary outcome was long-term change in body weight among early high responders to either treatment. An exploratory metabolomic analysis was also performed.
We observed individual variability in weight loss with both exenatide and hypocaloric diet plus placebo injections. There was a trend toward a higher percentage of subjects who achieved ≥ 5% weight loss with exenatide compared to diet (56% of those treated with exenatide, 76% of those treated with diet, p = 0.05) but no significant difference in those who achieved ≥ 10% weight loss (23% of individuals treated with exenatide and 36% of those treated with diet, p = 0.55). In both treatment groups, higher weight loss at 3 months of treatment predicted super responder status (diet p=0.0098, exenatide p=0.0080). Both treatment groups also demonstrated similar peak weight loss during the study period. We observed lower cysteine concentrations in the exenatide responder group (0.81
0.48 p < 0.0001) and a trend toward higher levels of serotonin, aminoisobutyric acid, anandamide, and sarcosine in the exenatide super responder group.
In a population of early high responders, longer term weight loss with exenatide treatment is similar to that achieved with a hypocaloric diet.
www.clinicaltrialsgov, identifier NCT01590433.
Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification ...where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.
The authors developed a method to estimate CIs for genetic correlations. The method is particularly useful when applied on a large panel of omics assays. The authors demonstrate its application to study genetic correlations between tens of thousands of protein pairs.
Cardiovascular events, ranging from arrhythmias to decompensated heart failure, are common during and after cancer therapy. Cardiovascular complications can be life-threatening, and from the ...oncologist's perspective, could limit the use of first-line cancer therapeutics. Moreover, an aging population increases the risk for comorbidities and medical complexity among patients who undergo cancer therapy. Many have established cardiovascular diagnoses or risk factors before starting these therapies. Therefore, it is essential to understand the molecular mechanisms that drive cardiovascular events in patients with cancer and to identify new therapeutic targets that may prevent and treat these 2 diseases. This review will discuss the metabolic interaction between cancer and the heart and will highlight current strategies of targeting metabolic pathways for cancer treatment. Finally, this review highlights opportunities and challenges in advancing our understanding of myocardial metabolism in the context of cancer and cancer treatment.
Proteomics has been used to study type 2 diabetes, but the majority of available data are from White participants. Here, we extend prior work by analyzing a large cohort of self-identified African ...Americans in the Jackson Heart Study (n = 1,313). We found 325 proteins associated with incident diabetes after adjusting for age, sex, and sample batch (false discovery rate q < 0.05) measured using a single-stranded DNA aptamer affinity-based method on fasting plasma samples. A subset was independent of established markers of diabetes development pathways, such as adiposity, glycemia, and/or insulin resistance, suggesting potential novel biological processes associated with disease development. Thirty-six associations remained significant after additional adjustments for BMI, fasting plasma glucose, cholesterol levels, hypertension, statin use, and renal function. Twelve associations, including the top associations of complement factor H, formimidoyltransferase cyclodeaminase, serine/threonine-protein kinase 17B, and high-mobility group protein B1, were replicated in a meta-analysis of two self-identified White cohorts-the Framingham Heart Study and the Malmö Diet and Cancer Study-supporting the generalizability of these biomarkers. A selection of these diabetes-associated proteins also improved risk prediction. Thus, we uncovered both novel and broadly generalizable associations by studying a diverse population, providing a more complete understanding of the diabetes-associated proteome.
Throughout a vertebrate organism's lifespan, skeletal muscle mass and function progressively decline. This age-related condition is termed sarcopenia. In humans, sarcopenia is associated with risk of ...falling, cardiovascular disease, and all-cause mortality. As the world population ages, projected to reach 2 billion older adults worldwide in 2050, the economic burden on the healthcare system is also projected to increase considerably. Currently, there are no pharmacological treatments for sarcopenia, and given the long-term nature of aging studies, high-throughput chemical screens are impractical in mammalian models. Zebrafish is a promising, up-and-coming vertebrate model in the field of sarcopenia that could fill this gap. Here, we developed a surface electrical impedance myography (sEIM) platform to assess skeletal muscle health, quantitatively and noninvasively, in adult zebrafish (young, aged, and genetic mutant animals). In aged zebrafish (~85% lifespan) as compared to young zebrafish (~20% lifespan), sEIM parameters (2 kHz phase angle, 2 kHz reactance, and 2 kHz resistance) robustly detected muscle atrophy (
< 0.000001, q = 0.000002;
= 0.000004, q = 0.000006;
= 0.000867, q = 0.000683, respectively). Moreover, these same measurements exhibited strong correlations with an established morphometric parameter of muscle atrophy (myofiber cross-sectional area), as determined by histological-based morphometric analysis (r = 0.831,
= 2 × 10
; r = 0.6959,
= 2 × 10
; and r = 0.7220;
= 4 × 10
, respectively). Finally, the genetic deletion of
, an orphan G-protein coupled receptor (GPCR), exacerbated the atrophy of skeletal muscle in aged animals, as evidenced by both sEIM and histology. In conclusion, the data here show that surface EIM techniques can effectively discriminate between healthy young and sarcopenic aged muscle as well as the advanced atrophied muscle in the
KO animals. Moreover, these studies show how EIM values correlate with cell size across the animals, making it potentially possible to utilize sEIM as a "virtual biopsy" in zebrafish to noninvasively assess myofiber atrophy, a valuable measure for muscle and gerontology research.
N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential ...cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genomic associations of four plasma N-acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2351 individuals from the Jackson Heart Study. We find that plasma levels of specific N-acyl amino acids are associated with cardiometabolic disease endpoints independent of free amino acid plasma levels and in patterns according to the amino acid head group. By integrating whole genome sequencing data with N-acyl amino acid levels, we identify that the genetic determinants of N-acyl amino acid levels also cluster according to the amino acid head group. Furthermore, we identify the CYP4F2 locus as a genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels in human plasma. In experimental studies, we demonstrate that CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids. These studies provide a structural framework for understanding the regulation and disease associations of N-acyl amino acids in humans and identify that the diversity of this lipid signaling family can be significantly expanded through CYP4F-mediated ω-hydroxylation.
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