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Kim H, Bedsaul-Fryer JR, Schulze KJ, Sincerbeaux G, Baker S, Rebholz CM, Wu LSF, Gogain J, Cuddeback L, Yager JD, De Luca LM, Siddiqua TJ, West KP. An Early Gestation Plasma Inflammasome in Rural Bangladeshi Women. Biomolecules 2024; 14:736. [PMID: 39062451 PMCID: PMC11274825 DOI: 10.3390/biom14070736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/28/2024] Open
Abstract
Circulating α1-acid glycoprotein (AGP) and C-reactive protein (CRP) are commonly measured to assess inflammation, but these biomarkers fail to reveal the complex molecular biology of inflammation. We mined the maternal plasma proteome to detect proteins that covary with AGP and CRP. In 435 gravida predominantly in <12-week gestation, we correlated the relative quantification of plasma proteins assessed via a multiplexed aptamer assay (SOMAScan®) with AGP and CRP, quantified by immunoassay. We defined a plasma inflammasome as protein correlates meeting a false discovery rate <0.05. We examined potential pathways using principal component analysis. A total of 147 and 879 of 6431 detected plasma proteins correlated with AGP and CRP, respectively, of which 61 overlapped with both biomarkers. Positive correlates included serum amyloid, complement, interferon-induced, and immunoregulatory proteins. Negative correlates were micronutrient and lipid transporters and pregnancy-related anabolic proteins. The principal components (PCs) of AGP were dominated by negatively correlated anabolic proteins associated with gestational homeostasis, angiogenesis, and neurogenesis. The PCs of CRP were more diverse in function, reflecting cell surface and adhesion, embryogenic, and intracellular and extra-hepatic tissue leakage proteins. The plasma proteome of AGP or CRP reveals wide proteomic variation associated with early gestational inflammation, suggesting mechanisms and pathways that merit future research.
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Affiliation(s)
- Hyunju Kim
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA 98105, USA
| | - Jacquelyn R. Bedsaul-Fryer
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD 20850, USA
- Department of International Health (Human Nutrition), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Kerry J. Schulze
- Department of International Health (Human Nutrition), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Gwen Sincerbeaux
- Department of Nutritional Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Sarah Baker
- Department of International Health (Human Nutrition), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Lee SF Wu
- Department of International Health (Human Nutrition), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | | | - James D. Yager
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Luigi M. De Luca
- Department of International Health (Human Nutrition), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | - Keith P. West
- Department of International Health (Human Nutrition), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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Kim H, Lichtenstein AH, Ganz P, Miller ER, Coresh J, Appel LJ, Rebholz CM. Associations of circulating proteins with lipoprotein profiles: proteomic analyses from the OmniHeart randomized trial and the Atherosclerosis Risk in Communities (ARIC) Study. Clin Proteomics 2023; 20:27. [PMID: 37400771 PMCID: PMC10316599 DOI: 10.1186/s12014-023-09416-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 06/19/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Within healthy dietary patterns, manipulation of the proportion of macronutrient can reduce CVD risk. However, the biological pathways underlying healthy diet-disease associations are poorly understood. Using an untargeted, large-scale proteomic profiling, we aimed to (1) identify proteins mediating the association between healthy dietary patterns varying in the proportion of macronutrient and lipoproteins, and (2) validate the associations between diet-related proteins and lipoproteins in the Atherosclerosis Risk in Communities (ARIC) Study. METHODS In 140 adults from the OmniHeart trial, a randomized, cross-over, controlled feeding study with 3 intervention periods (carbohydrate-rich; protein-rich; unsaturated fat-rich dietary patterns), 4,958 proteins were quantified at the end of each diet intervention period using an aptamer assay (SomaLogic). We assessed differences in log2-transformed proteins in 3 between-diet comparisons using paired t-tests, examined the associations between diet-related proteins and lipoproteins using linear regression, and identified proteins mediating these associations using a causal mediation analysis. Levels of diet-related proteins and lipoprotein associations were validated in the ARIC study (n = 11,201) using multivariable linear regression models, adjusting for important confounders. RESULTS Three between-diet comparisons identified 497 significantly different proteins (protein-rich vs. carbohydrate-rich = 18; unsaturated fat-rich vs. carbohydrate-rich = 335; protein-rich vs. unsaturated fat-rich dietary patterns = 398). Of these, 9 proteins [apolipoprotein M, afamin, collagen alpha-3(VI) chain, chitinase-3-like protein 1, inhibin beta A chain, palmitoleoyl-protein carboxylesterase NOTUM, cathelicidin antimicrobial peptide, guanylate-binding protein 2, COP9 signalosome complex subunit 7b] were positively associated with lipoproteins [high-density lipoprotein (HDL)-cholesterol (C) = 2; triglyceride = 5; non-HDL-C = 3; total cholesterol to HDL-C ratio = 1]. Another protein, sodium-coupled monocarboxylate transporter 1, was inversely associated with HDL-C and positively associated with total cholesterol to HDL-C ratio. The proportion of the association between diet and lipoproteins mediated by these 10 proteins ranged from 21 to 98%. All of the associations between diet-related proteins and lipoproteins were significant in the ARIC study, except for afamin. CONCLUSIONS We identified proteins that mediate the association between healthy dietary patterns varying in macronutrients and lipoproteins in a randomized feeding study and an observational study. TRIAL REGISTRATION NCT00051350 at clinicaltrials.gov.
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Affiliation(s)
- Hyunju Kim
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 East Monument Street, Suite 2-500, Baltimore, MD 21287 USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD USA
| | - Alice H. Lichtenstein
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA USA
| | - Peter Ganz
- Department of Medicine, University of California San Francisco, San Francisco, CA USA
| | - Edgar R. Miller
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 East Monument Street, Suite 2-500, Baltimore, MD 21287 USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 East Monument Street, Suite 2-500, Baltimore, MD 21287 USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Lawrence J. Appel
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 East Monument Street, Suite 2-500, Baltimore, MD 21287 USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 East Monument Street, Suite 2-500, Baltimore, MD 21287 USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
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Kim H, Lichtenstein AH, Ganz P, Du S, Tang O, Yu B, Chatterjee N, Appel LJ, Coresh J, Rebholz CM. Identification of Protein Biomarkers of the Dietary Approaches to Stop Hypertension Diet in Randomized Feeding Studies and Validation in an Observational Study. J Am Heart Assoc 2023; 12:e028821. [PMID: 36974735 PMCID: PMC10122905 DOI: 10.1161/jaha.122.028821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/15/2023] [Indexed: 03/29/2023]
Abstract
Background The Dietary Approaches to Stop Hypertension (DASH) diet is recommended for cardiovascular disease prevention. We aimed to identify protein biomarkers of the DASH diet using data from 2 randomized feeding studies and validate them in an observational study, the ARIC (Atherosclerosis Risk in Communities) study. Methods and Results Large-scale proteomic profiling was conducted in serum specimens (SomaLogic) collected at the end of 8-week and 4-week DASH diet interventions in multicenter, randomized controlled feeding studies of the DASH trial (N=215) and the DASH-Sodium trial (N=396), respectively. Multivariable linear regression models were used to compare the relative abundance of 7241 proteins between the DASH and control diet interventions. Estimates from the 2 trials were meta-analyzed using fixed-effects models. We validated significant proteins in the ARIC study (N=10 490) using the DASH diet score. At a false discovery rate <0.05, there were 71 proteins that were different between the DASH diet and control diet in the DASH and DASH-Sodium trials. Nineteen proteins were validated in the ARIC study. The 19 proteins collectively improved the prediction of the DASH diet intervention in the feeding studies (range of difference in C statistics, 0.267-0.313; P<0.001 for both tests) and the DASH diet score in the ARIC study (difference in C statistics, 0.017; P<0.001) beyond participant characteristics. Conclusions We identified 19 proteins robustly associated with the DASH diet in 3 studies, which may serve as biomarkers of the DASH diet. These results suggest potential pathways that are impacted by consumption of the DASH diet. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03403166, NCT00000608.
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Affiliation(s)
- Hyunju Kim
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology, and Clinical ResearchJohns Hopkins UniversityBaltimoreMD
| | | | - Peter Ganz
- Cardiovascular Division, Zuckerberg San Francisco General HospitalUniversity of California, San FranciscoSan FranciscoCA
| | - Shutong Du
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology, and Clinical ResearchJohns Hopkins UniversityBaltimoreMD
| | - Olive Tang
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology, and Clinical ResearchJohns Hopkins UniversityBaltimoreMD
| | - Bing Yu
- Department of Epidemiology, Human Genetics & Environmental SciencesUniversity of Texas Health Sciences Center at Houston School of Public HealthHoustonTX
| | - Nilanjan Chatterjee
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMD
| | - Lawrence J. Appel
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology, and Clinical ResearchJohns Hopkins UniversityBaltimoreMD
- Division of Nephrology, Department of MedicineJohns Hopkins School of MedicineBaltimoreMD
| | - Josef Coresh
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology, and Clinical ResearchJohns Hopkins UniversityBaltimoreMD
- Division of Nephrology, Department of MedicineJohns Hopkins School of MedicineBaltimoreMD
| | - Casey M. Rebholz
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology, and Clinical ResearchJohns Hopkins UniversityBaltimoreMD
- Division of Nephrology, Department of MedicineJohns Hopkins School of MedicineBaltimoreMD
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