SECTM1 Regulates Monocyte Levels and Is Associated With Incident Coronary Heart Disease

Visual Abstract

Highlights

• •Proteomic profiling in large epidemiologic cohorts identified SECTM1 as a novel protein associated with incident CHD.
• •A variant associated with plasma levels of SECTM1 is associated with circulating monocyte percentage of WBC in large-scale genetic databases.
• •In vivo functional studies helped characterize SECTM1 as a novel regulator of circulating monocytes.
• •Further studies are needed to assess whether targeting SECTM1 can help prevent development of atherosclerosis.

Summary

Proteomic profiling may provide insights into new biomarkers and pathways in coronary heart disease (CHD). We profiled ∼1,300 proteins in 1,967 Black individuals in the Jackson Heart Study and found Secreted and Transmembrane Protein 1 (SECTM1), a monocyte chemoattractant, to be our top novel finding associated with incident CHD. We validated our findings in the Cardiovascular Health Study. The top variant (rs116473040) associated with SECTM1 was associated with circulating monocyte percentage of white blood cells in a genomic database. In vivo studies demonstrated that recombinant SECTM1a increased the proportion of proatherogenic Ly6Chi monocytes, suggesting a pathway by which SECTM1 may contribute to CHD.

Despite significant advancements in our understanding of risk factors,1, 2, 3 biological mechanisms and preventative therapies, coronary heart disease (CHD) remains the leading cause of morbidity and mortality worldwide.⁴ Although specific disease mechanisms are well described, the residual burden of CHD likely reflects biological pathways yet to be discovered. Although genome-wide association studies (GWAS) have identified hundreds of loci linked to CHD,5, 6, 7 mapping these genetic loci to disease pathways has been challenging because of our still incomplete knowledge underlying the operative gene at a disease-associated locus and the paucity of follow-up functional studies in model systems probing potential mechanisms. To address both genetic and environmental influences in CHD pathogenesis, there has been an increasing focus on the study of proteins, which are downstream of genetic loci and the main effectors of biological processes.⁸ Emerging proteomic technologies have enabled systemic characterization of the plasma proteome to identify new biomarkers and pathways across a spectrum of cardiometabolic diseases.9, 10, 11, 12, 13, 14 These platforms often employ aptamer or antibody-based assays, measuring thousands of plasma proteins across diverse pathways including inflammation, metabolism, and others.

Proteomic techniques have informed novel markers of acute myocardial infarction (MI)¹¹ and long-term cardiovascular morbidity and mortality in individuals with CHD.^15,16 Ganz et al¹⁵ developed a proteomic risk score that improved risk stratification for major adverse cardiovascular events beyond the Framingham Secondary Event Risk Score for individuals with stable CHD. However, atherosclerosis formation begins well before clinical disease,^17,18 and proteomic studies in individuals with existing CHD may miss antecedent biological processes critical in the pathogenesis of atherosclerosis. To help bridge this gap, we performed proteomic profiling using the aptamer-based SOMALogic 1.3K platform in individuals without baseline CHD in the JHS (Jackson Heart Study), to identify proteins associated with incident disease, and validated our findings in the CHS (Cardiovascular Health Study). We highlight secreted and transmembrane protein 1 (SECTM1), a poorly characterized inflammatory protein, as the top novel association with incident CHD. We integrated genetic and functional in vivo studies to study SECTM1’s potential role in the pathogenesis of leukocyte biology with implications for disease pathogenesis.

Methods

The JHS is a community-based longitudinal cohort study begun in 2000 of 5,306 self-identified Black individuals from the Jackson, Mississippi, metropolitan tricounty (Hinds, Rankin, and Madison) area, the design of which is previously described; standard clinical definitions of comorbidities were used.¹⁹ Baseline characteristics were assessed at visit 1 between 2000 and 2004. Routine laboratory measurements were made at the time of first examination using standard venipuncture and laboratory techniques. Estimated glomerular filtration rate was estimated using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. Prevalent coronary heart disease (CHD) at visit 1 was determined as a composite of patient reported angina, patient reported MI, and/or evidence of previous MI on electrocardiography. Incident CHD in JHS was defined as probable or definite MI, coronary revascularization, and fatal CHD. Annual follow-up was performed through telephone interviews and patient reported CHD events were validated by review of hospital discharges by trained medical personnel.²⁰ Follow-up for incident CHD was through 2016. The JHS was approved by the Institutional Review Boards of Jackson State University, Tougaloo College, and the University of Mississippi Medical Center in Jackson, Mississippi. All study participants provided written informed consent. The current analysis was approved by the Institutional Review Board of Beth Israel Deaconess Medical Center.

The CHS is a comprehensive, population-based, longitudinal research project focused on coronary heart disease and stroke in adults aged 65 years and older.²¹ The study aims to uncover factors influencing the development and progression of coronary heart disease and stroke. CHS investigates the role of traditional cardiovascular risk factors in older adults and seeks to identify new risk factors specific to this age group. Incident CHD in CHS was defined as a composite of probable or definite angina/MI, definite angioplasty/coronary artery bypass grafting, or CHD death. Study participants were seen in the clinic annually between enrollment and 1998-1999, and were contacted by telephone at 6-month intervals through June 2023 to collect information about hospitalizations and cardiovascular events, which were adjudicated by committee through 2015.

Proteomic profiling

Proteomic measurements were performed using SOMAscan, a single-stranded DNA aptamer-based proteomics platform. All assays were performed using SOMAscan reagents according to the manufacturer’s detailed protocol.²² Blood samples were collected at visit 1 in ethylenediaminetetraacetic acid tubes. Plasma was isolated and maintained in −70 °C freezers. Samples were run in 3 separate batches, and each batch is divided into several plates. Proteomic profiling was performed in subset of individuals from JHS. Individuals with prevalent CHD were excluded from the analyses.

Statistical analyses

Baseline demographics are presented as either continuous data as mean ± SD or median with 25th-75th percentiles (Q1-Q3). Data were assessed for normal distribution by visual inspection using histograms. Proteins were log normalized and scaled with mean of 0 and SD of 1. We examined the association with proteins and incident CHD in Cox proportional hazard regression models adjusting for age, sex, batch, body mass index, systolic blood pressure, hypertension status, estimated glomerular filtration rate (CKD-EPI), diabetes status, smoking history, total cholesterol, and high-density lipoprotein. Individuals were censored at loss to follow-up or death. Missing observations were dropped from the analyses. We used a Benjamini Hochberg false discovery rate (FDR, Q-value) significance level of <5%. Cox proportional hazards assumptions were tested using the Schoenfeld residual tests. Cox model results are presented using HRs with 95% CIs. Continuous variables were tested using Pearson’s correlation coefficient (rho). Analyses were conducted using R software version 4.3.2.²³

Genetic studies

GWAS methods were described previously.²⁴ All cohorts' values were residualized for age, sex, batch, and ancestry principal components. Residuals were inverse-normalized. Associations between these values and genetic variants were tested using linear mixed-effects models, adjusted for age, sex, genetic relationship matrix, and principal components. Variants with a minor allele count <5 were excluded. The association between these values and genetic variants was tested using linear mixed-effects models adjusted for age, sex, the genetic relationship matrix, and principal components 1 to 10 using the fastGWA model implemented in the GCTA software package.²⁵ Variants were clumped and pruned around the 1 MB region. Estimates between variants and proteins were represented by regression coefficients (β). The top cis-protein quantitative trait loci (cis-pQTL) for SECTM1 was queried against PhenoScanner V2²⁶ to assess relationships with clinical phenotypes. The single-nucleotide variation (SNV) (formerly SNP)–phenotype associations were analyzed using the PhenoScanner tool in R, specifying genome build "38," a P value threshold of 0.05, the "pQTL" catalog, and proxies set to “None.”

In vivo SECTM1a studies

The rSECTM1a is a recombinant murine SECTM1a-FC/IgG2A protein construct obtained from R&D systems. The 8-week-old male C57BL/6J mice (the Jackson Laboratory; stock number 000664) were injected with intraperitoneally with either rSECTM1a (250 μg/kg; n = 6) or phosphate buffered saline (PBS) (n = 6) for 5 days, and plasma was collected 6 hours after injection on day 5. Red blood cells were lysed twice using 1× red blood cell lysis buffer (Biolegend). Spleens were passed through a 40-μm nylon mesh (BD Falcon) and washed with 1× PBS and centrifuged; then red blood cells were lysed to yield a single-cell suspension. For bone marrow, femurs were collected, the ends of the bone cut off, and the bone marrow contents flushed out with PBS using a 25-gauge needle. The sample was pipetted to break up cell clumps, washed with 1× PBS and centrifuged, then red blood cells were lysed to yield a single-cell suspension. Single-cell suspensions were stained in at 4 °C in fluorescence-activated cell sorting buffer (0.5% bovine serum albumin and 2 mmol/L ethylenediaminetetraacetic acid in PBS) with an antibody cocktail at a concentration of 1:700 unless otherwise specified. Live cells were identified using Live/Dead Zombie Aqua (Biolegend) at a concentration of 1:1,000 and stained at 4 °C.

Flow cytometry

The HEMAVET850 Multispecies Hematology Analyzer was utilized for complete blood count (CBC) analysis. For our blood flow cytometry analysis, we followed a standard gating strategy. Initially, a density plot of forward scatter (FSC) and side scatter was used to exclude debris and nonrelevant events, followed by a density plot of FSC height and FSC area to eliminate doublets. Subsequently, a double density plot of CD45 and viability dye helped identify viable immune cells, which were then classified into neutrophils, Ly6C-hi/low monocytes, eosinophils, B cells, or T cells using classical sequential gating strategies. The IQR method was used to detect and remove outliers. Welch’s t-tests were used to compare values between rSECTM1a vs PBS-treated mice. P value <0.05 was used for statistical significance.

Results

Among 1,967 JHS participants (1,213 women [61.7%]; mean age 55 ± 11 years) who were included in the analysis, 233 individuals developed CHD with a median follow-up of 13.5 years (Q1-Q3: 10.4-14.6 years) (Table 1). In general, individuals who developed incident CHD were older (mean age, 63 ± 11 years vs 54 ± 13 years), with lower kidney function (mean estimated glomerular filtration rate, 84 ± 22 mL/min/1.73 m² vs 96 ± 17 mL/min/1.73 m²) and had a higher prevalence of diabetes (105% ± 45% individuals vs 334% ± 19% individuals). The median time of follow-up for individuals who developed incident CHD event was 5.1 years (Q1-Q3: 3.0-8.9 years). Patient demographics for individuals who underwent proteomic profiling and included in the incident CHD analyses were similar compared with the rest of the JHS cohort and are detailed in Supplemental Table 1.

Table 1.

Baseline Characteristics of Study Cohort From the Jackson Heart Study

	Incident CHD		Overall (n = 1,967)
	No (n = 1,734)a	Yes (n = 233)
Age, y	54 ± 13	63 ± 11	55 ± 13
Male	654 (38)	100 (43)	754 (38)
Current smoker	211 (12)	37 (16)	248 (13)
BMI, kg/m2	32 ± 7	31 ± 6	32 ± 7
Systolic blood pressure, mm Hg	126 ± 18	133 ± 17	127 ± 18
Diabetes	334 (19)	105 (45)	439 (22)
eGFR (CKD-EPI), mL/min/1.73 m2	96 ± 21	84 ± 26	94 ± 22
LDL, mg/dL	127 ± 37	130 ± 38	127 ± 37
Hypertension	1,016 (59)	191 (82)	207 (61)
Statin medication	149 (11)	51 (24)	200 (13)
HDL, mg/dL	52 ± 14	51 ± 15	52 ± 14
Total cholesterol, mg/dL	200 ± 41	205 ± 44	200 ± 41
Follow-up time, y	13.7 (12.9-14.7)	5.1 (3.0-8.9)	13.5 (10.4-14.6)

Values are mean ± SD, n (%), or median (Q1-Q3).

BMI = body mass index; eGFR = estimated glomerular filtration rate; HDL = high-density lipoprotein, LDL = low-density lipoprotein.

^aIndividuals lost to follow-up or who died of non–coronary heart disease (CHD) causes were censored. Analysis included 1,967 individuals with proteomics and without baseline CHD.

SECTM1 is the top novel association with incident CHD

In age-, sex-, and batch-adjusted analyses (Model 1), we identified 91 proteins associated with incident CHD (FDR <5%) (Supplemental Table 2). The median correlation between proteins in this model was 0.09 (Q1-Q3: 0.06-0.12), suggesting significant orthogonality of CHD associated proteins. In multivariable adjusted analyses (Model 2), we identified 16 proteins associated with incident CHD (FDR <5%) (Figure 1, Supplemental Table 3). This analysis confirmed established CVD biomarkers²⁷ (eg, N-terminal probrain natriuretic peptide; HR: 1.56 per SD increment; 95% CI: 1.4-1.8; FDR Q value = 0.003) and growth differentiated factor-15 (GDF-15) (HR: 1.40; 95% CI: 1.18-1.65; FDR Q value = 0.016). The strongest novel association with CHD was SECTM1, a putative monocyte chemoattractant and proinflammatory protein (SECTM1; HR: 1.35; 95% CI: 1.2-1.5; FDR Q value = 0.004; top vs bottom quartile HR: 2.21; 95% CI 1.54-3.18) (Supplemental Figure 1). Relationships between SECTM1 and clinical traits are detailed in Supplemental Table 4. We also identified CHD associations with proteins involved in glucose homeostasis and energy balance including glucagon (HR: 1.38; 95% CI: 1.17-1.66; FDR Q value = 0.022) and Peptide YY (PYY) (HR: 1.30; 95% CI: 1.1-1.51; FDR Q value = 0.048) (Figure 1).

Figure 1.

Proteomic Associations With Incident Coronary Heart Disease in the Jackson Heart Study

Baseline plasma protein levels in the Jackson Heart Study were tested for their associations with incident coronary heart disease adjusted for age, sex, body mass index, hypertension, total cholesterol, high-density lipoprotein, diabetes, estimated glomerular filtration rate, smoking status, and statin use (false discovery rate [FDR] <5%).

Although SECTM1 has been implicated in inflammatory and immune pathways in model systems, it is an understudied protein in humans and has not been previously examined in the context of CHD. We next tested for replication in 2,549 individuals in the CHS cohort, a group of predominantly White (84%), older individuals (mean age 74.2 years vs 55.1 years in JHS) (Supplemental Table 5). The median time of follow-up in CHS was 11.4 years (Q1-Q3: 6.4-18.0 years), with 689 participants developing CHD. SECTM1 was associated with incident CHD in an age- and sex-adjusted analyses (model 1 HR: 1.15; 95% CI: 1.08-1.23; P < 0.001. SECTM1 remained associated with incident CHD in a multivariable analysis (model 2 HR: 1.09; 95% CI: 1.00-1.18; P = 0.037). External validation results in CHS for proteins associated with incident CHD in JHS are included in Supplemental Table 6.

SECTM1 is associated with circulating monocytes in humans

To test whether SECTM1, a putative proinflammatory molecule and monocyte chemoattractant, was associated circulating monocytes in humans, we analyzed 1,703 JHS participants with both proteomics and CBC data. We found that circulating SECTM1 levels were correlated with monocyte percentage of white blood cells (WBCs) (rho = 0.17; P = 0.002). Next, we tested whether SECTM1 might play a causal role in leukocyte biology in humans. We performed a GWAS analysis for SECTM1 in JHS and then used the sentinel SNV to test for associations with clinical phenotypes in large-scale human data sets. After further refining our genetic signal for SECTM1 by clumping (merging overlapping SNV associations) and pruning SNVs (removing variants in high linkage disequilibrium >0.8) we identified a cis-variant (variants within 1 MB upstream or downstream of transcription start site) in an enhancer region of the SECTM1 gene, as the sentinel SNV (rs116473040; beta coefficient −0.39; P < 0.001 for SECTM1 levels). This cis-pQTL for SECTM1 was validated in the HERITAGE Family Study which had undergone proteomic profiling using a similar platform.²⁴ We leveraged this sentinel SNV and tested its association with clinical phenotypes using PhenoScanner version 2.0. Consistent with our hypothesis that SECTM1 may modify immune biology in humans, the top clinical phenotype associated with genetically predicted increase in SECTM1 levels using our cis-pQTL was increased “monocyte percentage of white blood cells” (beta coefficient: 0.039; P < 0.001) (FDR <5%) (Figure 2). To evaluate a potential causal relationship between SECTM1 and monocyte percentage of WBCs, we utilized summary statistics for a GWAS performed for CBC data in ∼430,000 participants from the Million Veterans Program²⁸ and performed a Mendelian randomization analysis using the Wald ratio method. Leveraging the sentinel SNV for SECTM1, we found that genetically predicted increases in circulating SECTM1 levels were associated with higher circulating monocyte percentage of WBCs (beta coefficient 0.05; P = 0.001). Further, a nominal association was observed between genetically predicted increase in SECTM1 and “ischemic heart disease” in MVP (Beta coefficient = 0.021; P = 0.042).

Figure 2.

Association of the Sentinel Single-Nucleotide Variation for Circulating SECTM1 in the Jackson Heart Study Across Clinical Phenotypes in Large-Scale Population Studies

rs116473040 was tested against clinical phenotypes in PhenoScanner V2 and was significantly associated with monocyte percentage of white blood cells and granulocyte percentage of myeloid white cells (false discovery rate <5%).

Administration of SECTM1a increases the circulating monocyte percentage of WBCs in mice

To experimentally validate our genetic findings, we tested whether intraperitoneal (IP) administration of recombinant SECTM1a (rSECTM1a), the mouse homologue of human SECTM1, increased the monocyte percentage of circulating WBCs in mice. We first performed an enzyme-linked immunosorbent assay to assess the relationship between IP administered rSECTM1a and SECTM1a blood levels and demonstrated a linear dose-response relationship (Supplemental Figure 2). Based on this dose-response curve, we selected a dose of 250 μg/kg for our experiments to achieve a ∼5-fold increase in circulating SECTM1a over baseline levels. This dose aligned with recent studies using rSECTM1a in mice.²⁹ Following 5 days of daily administration of SECTM1a vs PBS control to C57BL/6 mice (n = 6 for each), the rSECTM1a-treated animals had a significantly increased monocyte percentage of WBCs as compared with control-treated animals (5.3% vs 3.4%; P = 0.011) (Figure 3). Mean percentages of neutrophils, lymphocytes, basophils, and eosinophils were not significantly different between rSECTM1a treated vs control mice.

Figure 3.

Effect of rSECTM1a on Monocyte Percentage of White Blood Cells in Mice

Intraperitoneal recombinant SECTM1a protein (rSECTM1a) (250 μg/kg) was administered to c57BL6 (n = 6) vs phosphate buffered saline (n = 6). t-tests were used to compare values between rSECTM1a vs phosphate buffered saline–treated mice. rSECTM1-treated mice had a greater monocyte (MO) percentage of white blood cells (5.3% vs 3.4%; P = 0.011).

rSECTM1a increases the proinflammatory Ly6chi monocyte percentage of WBCs in blood

Toward refining our understanding of potential mechanisms underlying SECTM1’s association with monocytes and CHD, we performed flow cytometry analyses of leukocytes in plasma, spleen, and bone marrow. SECTM1a-treated mice had increased Ly6Chi monocyte percentage in the peripheral blood vs control-treated mice (Figure 4, Supplemental Table 7) and a significantly increased Ly6Chi monocyte percentage of WBC in the spleen. Interestingly, we observed similar patterns for neutrophil percentage of WBCs (Supplemental Table 8). We did not observe any effects of rSECTM1a administration on bone marrow WBC subsets. We repeated our experiment in the peripheral blood of mice using IgG2A as the control instead of PBS. After 5 days of IP injections, we observed a significant increase in circulating Ly6Chi monocyte percentage of WBCs with rSECTM1a treatment compared with IgG2A. These findings demonstrate the increase in circulating Ly6Chi monocyte percentage is attributable to rSECTM1a rather than IgG2A component of the chimera protein (Supplemental Figure 3).

Figure 4.

Effect of rSECTM1a on Ly6cHi Monocyte Percentage of White Blood Cells in Mice

C57BL/6 mice were administered intraperitoneal recombinant SECTM1a protein (rSECTM1a) (250 μg/kg; n = 6) vs phosphate buffered solution (PBS) (n = 6). Flow cytometry was performed to identify leukocyte subsets. t-tests were used to compare values between rSECTM1a vs PBS-treated mice. rSECTM1a increased circulating percentage of Ly6Chi monocytes and neutrophils. ∗P < 0.05, ∗∗P < 0.01.

Discussion

Our study establishes SECTM1 as a novel marker of incident CHD. We found SECTM1, a proinflammatory monocyte chemoattractant, to be associated with circulating monocyte percentage of WBCs in JHS. Through genetic analyses and in vivo functional studies, we further demonstrated that SECTM1 may regulate proatherogenic monocyte levels, raising the possibility of a mechanistic role in CHD development. These findings underscore the value of integrating plasma proteomic profiling with genetic and functional studies to uncover potentially novel pathways involved in CHD pathogenesis.

Although SECTM1 emerged as the top novel protein associated with incident CHD, its function has been incompletely characterized. Soluble SECTM1 is a putative ligand of CD7 and potentially acts as a monocyte chemoattractant in a CD7-dependent manner.³⁰ More recently, SECTM1 has been suggested to play a role in the innate immune response during sepsis.²⁹ In a polymicrobial sepsis mouse model, bone marrow-derived macrophages from SECTM1a (mouse homologue of human SECTM1) knockout mice showed reduced bactericidal activity. In vitro treatment with rSECTM1a improved macrophage phagocytosis and in vivo rSECTM1a treatment improved survival of septic SECTM1a knockout mice. These findings suggest that SECTM1 may play a role in regulating macrophages in inflammatory responses and immune pathways. However, these studies in small animal models have yet to characterize the effect of SECMT1a deficiency on other leukocyte subsets nor the effect of SECTM1a on leukocyte counts in normal mice. Additionally, SECTM1’s role in human disease, including atherosclerosis development, has yet to be explored.

We were interested to note that plasma levels of SECTM1 were associated circulating monocyte percentage of WBCs’ in JHS. To further examine SECTM1’s potential association with leukocyte biology, we leveraged a variant in a regulatory region near the SECTM1 transcription start site to explore its association with clinical phenotypes. We found genetically predicted increases in circulating SECTM1 to be associated with increased monocyte percentage of WBCs in 2 large population-based studies. We functionally validated our genetic findings with in vivo animal studies, demonstrating that the administration of rSECTM1a increases the circulating monocyte percentage of WBCs in mice. Monocytes are critical drivers of atherosclerosis and increased monocyte counts are associated with increased risk of myocardial infarction.^31,32

Prior knockout studies of MCP-1/CCL2 and its receptor (CCR2) confirm a causal role for this pathway in atherosclerotic lesion formation in hyperlipidemic mice. Given the strong expression of CCL2 in early lesions (as well as diminished monocyte accumulation in the lesions in both genetic knockouts), it is believed that the ligand-receptor pair acts in the arrest of monocytes on the activated endothelium and the subsequent extravasation of monocytes into the lesion.^33,34 By contrast, our human genetic analyses and in vivo studies describe a role for SECTM1 in augmenting circulating monocyte levels, particularly the Lys6C subset that ultimately accumulates in atherosclerotic lesions. Others have also described a role for SECTM1 as a chemoattractant for monocytes in cell-based assays. Our new findings, along with this prior work, strongly motivate future investigations including immunohistochemical analyses to test whether SECTM1 is present in atherosclerotic lesions and SECTM1 knockout studies in atherosclerosis-prone mice, examining various leukocyte reservoirs as well as the lesions themselves.

Monocyte subsets have specific roles in inflammation and atherosclerosis. Given the heterogeneity of monocyte populations and to refine potential mechanisms of SECTM1’s role in the development of CHD, we performed additional in vivo flow cytometry assays and found rSECTM1a administration increased the circulating Ly6cHi monocyte percentage of WBCs. Broadly, the Ly6Chi or “classical” monocytes are proinflammatory and are recruited to sites of infection or vascular injury while Ly6Clow monocytes play an important role in the resolution of inflammatory processes.³⁵ Proinflammatory Ly6Chi monocytes are recruited to atherosclerotic lesions in a CCR2-dependent manner and are the predominant source of plaque macrophages.³⁶ Thus, our flow cytometry studies suggest SECTM1 may promote an atherogenic immune response by selectively increasing proinflammatory monocytes in the blood. Taken together, the alignment of proteomic, genetic in vivo functional studies supports the role of SECTM1 in monocyte biology. Further studies are needed to assess the potential causal role of SECTM1 for atherosclerosis.

Although our primary focus was on the effect of rSECTM1a on monocyte subsets in the blood caused by our human proteomic and genetic observations, we also investigated its effects in other lymphoid tissues that may contribute to circulating monocyte levels. Notably, we found a similar increase of Ly6Chi monocyte percentage in the spleen, the site of highest tissue expression of SECTM1.³⁷ However, the exact mechanism of SECTM1’s effect on circulating monocytes—whether through recruitment from the spleen, marginalization from the vessel wall, bone marrow production, or other pathways—warrants further study. Finally, in addition to increases in Ly6cHi monocyte percentage of WBCs with rSECTM1a administration in mice, we also observed increases in the percentage neutrophils in the circulation. Therefore, future studies must explore a potential functional role for SECTM1 on neutrophils as well as in inflammatory pathologies.

Systematic protein profiling identified several secreted hormones that participate in metabolic pathways associated with increased risk of CHD. Dysregulated metabolic pathways in energy balance, glucose homeostasis, and insulin sensitivity contribute to the pathogenesis of atherosclerosis.³⁸ FSTL3 is an inhibitor of activin and myosin, growth factors in the TGF-β protein family, which promote inflammation by inducing adipose tissue dysfunction and cytokine release.³⁹ PYY, primarily produced in the gut, helps regulate appetite and energy balance⁴⁰ while improving insulin sensitivity.⁴¹ Recently, GLP1 agonists have been remarkably effective for weight loss in obesity and impart reduced risk of CVD events in this population,²² although the precise mechanisms for their CVD benefit have yet to be elucidated. We also found increasing levels of glucagon, a closely related hormone to GLP-1 and cleaved from the same peptide protein, associated with incident CHD. Glucagon is critical in regulating glucose homeostasis, lipolysis, and insulin sensitivity.⁴² Although these secreted hormones play roles in the pathogenesis of cardiometabolic diseases such as obesity and diabetes, they remained strongly associated with CHD in JHS despite rigorous covariate adjustment. Further mechanistic studies are also needed to elucidate their potential on-target effects on atherosclerosis formation.

Study limitations

The effect size of the SECTM1-CHD association in CHS was attenuated compared with JHS after adjustment for risk factors. However, we validated the SECTM1-CHD association across 2 cohorts with distinct patient demographics, which suggests generalizability across diverse populations. We administered rSECTM1a via IP injections to increase circulating levels of SECTM1. Although we demonstrated a strong linear relationship between short-term administration of SECTM1a and its concentration in the blood of mice, future studies should assess longer-term SECTM1a treatments durations as well as the effects of localized SECTM1a delivery to specific sites such as blood vessel walls. Although our human genetic and in vitro studies support a potential causal association between SECTM1 and circulating monocyte percentage of WBCs, further studies are needed to establish a mechanistic link between SECTM1 and development of atherosclerosis.

Conclusions

Our study leveraged proteomic profiling, genetics, and in vivo functional studies to identify new potential mechanisms in the development of CHD, specifically highlighting SECTM1 as a novel CHD-associated protein, which may regulate circulating levels of proatherogenic monocytes. These findings could ultimately inform the development of potential therapeutic targets for inflammatory pathologies.

Perspectives.

COMPETENCY IN MEDICAL KNOWLEDGE: Despite advances in identifying seminal risk factors, CHD remains the leading cause of morbidity and mortality worldwide. Inflammation contributes to the residual burden of CHD, although the specific inflammatory mediators and pathways in humans remain incompletely understood. We leveraged plasma proteomics to identify novel pathways to the development of CHD and identified a poorly characterized proinflammatory protein, SECTM1, associated with incident CHD in 2 population-based cohorts. We integrated genetic and in vivo functional studies and demonstrated that SECTM1 increases circulating proatherogenic monocytes, providing a potential mechanistic link between SECTM1 and CHD pathogenesis. These findings highlight SECTM1’s potential role in mediating inflammation and CHD risk.

TRANSLATIONAL OUTLOOK: SECTM1’s role in regulating monocyte levels, a critical driver of atherosclerosis, represents a potential therapeutic target in in CHD. Further research is needed to determine the effect of SECTM1-targeted therapies in CHD and other inflammatory pathologies.

Funding Support and Author Disclosures

The Jackson Heart Study is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities. The views expressed in this paper are those of the authors and do not necessarily represent the views of the NHLBI, the National Institutes of Health, or the U.S. Department of Health and Human Services. The Cardiovascular Health Study was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and 75N92021D00006, and grants U01HL080295 and U01HL130114 from the NHLBI, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by R01AG023629 from the National Institute on Aging. A full list of principal Cardiovascular Health Study investigators and institutions can be found at CHS-NHLBI.org. Dr Tahir is supported by the National Institutes of Health K08 HL161445-01A1. Dr Gerszten is supported by the following National Institutes of Health grants: HHSN268201600034I, HL133870. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.