Abstract
Objective
Physiologically, pregnancy-associated plasma protein-A (PAPP-A) serves to liberate bound IGF1 by enzymatic cleavage of IGF-binding proteins (IGFBPs), IGFBP4 in particular. Clinically, PAPP-A has been linked to cardiovascular disease (CVD). Stanniocalcin-2 (STC2) is a natural inhibitor of PAPP-A enzymatic activity, but its association with CVD is unsettled. Therefore, we examined associations between the STC2–PAPP-A–IGFBP4–IGF1 axis and all-cause mortality and CVD in patients with type 2 diabetes (T2D).
Design
We followed 1284 participants with T2D from the ADDITION trial for 5 years.
Methods
Circulating concentrations of STC2, PAPP-A, total and intact IGFBP4 and IGF1 and -2 were measured at inclusion. End-points were all-cause mortality and a composite CVD event: death from CVD, myocardial infarction, stroke, revascularisation or amputation. Survival analysis was performed by Cox proportional hazards model.
Results
During follow-up, 179 subjects presented with an event. After multivariable adjustment, higher levels of STC2, PAPP-A, as well as intact and total IGFBP4, were associated with all-cause mortality; STC2: hazard ratio (HR) = 1.84 (1.09–3.12) (95% CI); P = 0.023, PAPP-A: HR = 2.81 (1.98–3.98); P < 0.001, intact IGFBP4: HR = 1.43 (1.11–1.85); P = 0.006 and total IGFBP4: HR = 3.06 (1.91–4.91); P < 0.001. Higher PAPP-A levels were also associated with CVD events: HR = 1.74 (1.16–2.62); P = 0.008, whereas lower IGF1 levels were associated with all-cause mortality: HR = 0.51 (0.34–0.76); P = 0.001.
Conclusions
This study supports that PAPP-A promotes CVD and increases mortality. However, STC2 is also associated with mortality. Given that STC2 inhibits the enzymatic effects of PAPP-A, we speculate that STC2 either serves to counteract harmful PAPP-A actions or possesses effects independently of the PAPP-A–IGF1 axis.
Significance statement
PAPP-A has pro-atherosclerotic effects and exerts these most likely through IGF1. IGF1 is regulated by the STC2–PAPP-A–IGFBP4–IGF1 axis, where STC2, an irreversible inhibitor of PAPP-A, has been shown to reduce the development of atherosclerotic lesions in mice. We examined the association of this axis to mortality and CVD in T2D. We demonstrated an association between PAPP-A and CVD. All components of the STC2–PAPP-A–IGFBP4–IGF1 axis were associated with mortality and it is novel that STC2 was associated with mortality in T2D. Our study supports that inhibition of PAPP-A may be a new approach to reducing mortality and CVD. Whether modification of STC2 could serve as potential intervention warrants further investigation.
Introduction
Cardiovascular disease (CVD) is one of the leading causes of death (1), and consequently, many efforts have been invested in identifying modifiable pathogenic targets that by intervention can reduce the risk of CVD. One such target is the enzyme pregnancy-associated plasma protein-A (PAPP-A). The first report linking PAPP-A to CVD demonstrated a higher presence of PAPP-A in unstable plaques as compared to stable plaques (2). Subsequent epidemiological studies have supported the role of PAPP-A in CVD by identifying associations between increased circulating PAPP-A concentrations or indices of an increased PAPP-A activity and an elevated risk of CVD-related morbidity and mortality (3, 4, 5).
PAPP-A promotes CVD by stimulating the development of atherosclerosis. This has been thoroughly demonstrated in preclinical studies in mice (6, 7), where PAPP-A silencing by genetic knockout prevents the development of atherosclerosis (8). Noteworthy, and of potential clinical interest, preclinical studies have demonstrated that the pro-atherosclerotic properties of PAPP-A can be reduced by treatment with PAPP-A-inhibiting monoclonal antibodies (9) as well as by stanniocalcin-2 (STC2), a naturally occurring glycoprotein that covalently interacts with PAPP-A and thereby irreversibly blocks its enzymatic activity (10). However, clinically, the role of STC2 is less settled than that of PAPP-A.
The mechanism by which PAPP-A influences CVD is yet to be fully clarified but is most likely related to its ability to increase insulin-like growth factor-1 (IGF1) action. PAPP-A activates IGF1 through proteolytic cleavage of IGF-binding proteins (IGFBP) -2, -4 and -5, which provides IGFBP fragments with low ligand affinity. By this mechanism, PAPP-A leads to liberation of IGF1. PAPP-A appears to be the only physiological protease degrading IGFBP4 (11), whereas IGFBP2 and IGFBP5 are also cleaved by other proteases;for example, the PAPP-A homologue PAPP-A2 (12, 13). Therefore, levels of intact vs fragmented IGFBP4 have often been used as a proxy for the enzymatic effects of PAPP-A in vivo, and indeed, such studies have confirmed that an elevated PAPP-A enzymatic activity promotes CVD (4, 14). As STC2 irreversibly binds and inactivates PAPP-A, it is likely that PAPP-A and STC2 act in concert to regulate IGF1 action (15). However, whereas PAPP-A is believed to serve solely as a regulator of IGF1 action, STC2 may have other physiological effects (16, 17).
The presence of type 2 diabetes (T2D) increases the risk of CVD (18, 19), and to our knowledge, no studies have examined mortality and CVD and their association with PAPP-A and STC2 in T2D. Therefore, we aimed to examine the STC2–PAPP-A–IGFBP4–IGF1 axis and its association with CVD and mortality in a cohort of patients with T2D followed for 5 years.
Methods
Study population and design
This cohort analysis included patients with T2D from the Danish arm of the Anglo-Danish–Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care (ADDITION-Denmark) (20), a pragmatic randomised controlled trial comparing intensive multifactorial cardiometabolic risk management to routine care in general practice. The trial was initiated in 2001 and consisted of patients between 40 and 69 years with newly diagnosed T2D identified by screening in general practice throughout the period 2001–2006. The presence of diabetes was diagnosed according to 1999 WHO criteria (21). Details of the original study have been described elsewhere (22). As blood samples drawn at the trial baseline were unavailable, baseline of the present study corresponded to the 5-year follow-up of the original study. This was the end of the pre-specified trial follow-up period, after which the participants were further followed observationally. In the present analysis, the participants from both intervention arms were analysed jointly as a cohort as former studies have not been able to detect significant differences in regards to cardiovascular events and mortality between interventions groups (23).
Follow-up and outcome parameters
We examined the following outcomes: all-cause mortality and a composite CVD event consisting of a first event of either CVD mortality, non-fatal myocardial infarction, non-fatal stroke, revascularisation or amputation within the study period. CVD mortality included death due to acute myocardial infarction, heart failure, cardiac arrhythmia, stroke or death related to a cardiovascular procedure. Revascularisation included invasive coronary as well as peripheral revascularisation procedures. This clinical information was collected from death certificates, post-mortem reports, medical records, hospital discharge summaries, electrocardiographs, laboratory results, etc. and sent to two members of the expert committee for independent adjudication according to an agreed protocol. Outcomes were recorded on standard case report forms. Committee members met to reach consensus over discrepancies. After the first CVD event, the participant was censored. Inclusion date was the date the blood sample was drawn for the 5-year follow-up of the original study. For each participant, we calculated the time to all-cause mortality or the composite CVD event as appropriate, using the inclusion date until the event or the date the study ended (31 December 2014). All participants gave their informed consent. The study was performed in compliance with the Helsinki Declaration and was approved by The Central Denmark Region Committees on Health Research Ethics. Besides event information, baseline characteristics included age, sex, BMI, waist circumference, systolic and diastolic blood pressure, as well as baseline biochemical variables including haemoglobin A1c (HbA1c), creatinine, cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Furthermore, self-reported data included information on medication, alcohol and smoking habits, as well as information on known or former diseases, including former myocardial infarction or stroke, a diagnosis of angina or arrhythmia and former coronary intervention or surgery. We created a CVD comorbidity variable that combined the self-reported information relevant to CVD (former myocardial infarction, stroke or coronary intervention/surgery or a diagnosis of angina). A positive score in the CVD comorbidity variable indicated that the participant had experienced at least one of the above.
Laboratory measurements
Concentrations of STC2, PAPP-A, intact IGFBP4, total IGFBP4 (including intact and fragmented IGFBP4), as well as IGF1 and IGF2 were measured in blood samples, which were drawn at the 5-year follow-up examination of the Danish arm of the international ADDITION trial. All samples were stored at −80°C till the day of analysis. None of the participants were administered heparin in connection with blood sampling. Commercial enzyme-linked immunosorbent assays from AnshLabs (Webster, Texas, USA) were used to determine serum levels of STC2 (Cat# AL-143), PAPP-A (Cat# AL-101) and IGF2 (Cat# AL-131) and EDTA plasma levels of intact and total IGFBP4 (Cat# AL-128 and Cat# AL-126), as described by the manufacturer. Serum levels of IGF1 were measured by an IDS-iSYS Multidiscipline Automated Analyser (Immunodiagnostic Systems, Bolden Colliery, UK, Cat# IS-3900), as previously described (24). Basal biochemical values that included HbA1c, creatinine, cholesterol, LDL-C, HDL-C and triglycerides were measured using in-hospital routine methods.
Statistics
All measurements and descriptive data were tested for normality. IGF variables (STC2, PAPP-A, IGF1, IGF-2, intact and total IGFBP4) were log2-transformed prior to statistical analysis. Other non-normally distributed variables were transformed by the natural logarithm. All events of all-cause mortality and the composite CVD outcome were combined for comparison of baseline characteristics. Categorical variables were compared using Pearson’s chi-squared test, and continuous variables were compared using a two-sample t-test for normally distributed variables and the Wilcoxon rank-sum test for non-normally distributed variables. Basic characteristics are presented as mean ± s.d. (parametric data), median (25th and 75th percentiles) (non-parametric data) or sums and percentages (categorical variables), as appropriate. Displayed data are based on non-missing data. Correlations were examined with a Bonferroni-adjusted significance level using the Spearman correlation coefficient (r).
Survival analyses were performed using Cox proportional hazards model. For the composite CVD event, non-CVD death was considered a competing event. Multiple imputation methods were applied to handle missing covariable data. No systematic differences between the non-missing and missing data were observed. Consequently, data were assumed to be missing at random. The CVD comorbidity variable had the highest proportion of missing at 11.7%. Survival analyses were performed with and without the CVD comorbidity variable and this did not significantly affect the final results. Imputations were not performed on IGF variables or outcome variables. We performed univariable and multivariable analyses and included potential confounders in two models. Model 1 included traditional CVD risk factors (age, sex, smoking and waist circumference) and the trial randomisation. Model 2 consisted of model 1 variables and furthermore included the CVD comorbidity variable, cholesterol, creatinine and HbA1c levels as well as the average diastolic blood pressure at baseline. STC2, PAPP-A, IGF1 and -2, intact and total IGFBP4 were added in the log2-transformed version, and results are reported as hazard ratios (HR) and 95% CI. One-unit increase on the log2-scale corresponds to a doubling in the non-transformed version. Assumptions of proportionality were evaluated by log-minus-log plots and assessment of Schoenfeld residuals. A two-tailed P-value of P < 0.05 was considered statistically significant. All statistics were performed by using Stata version 15 (StataCorp, College Station, TX, USA).
Results
The original trial included 1533 T2D patients, whereof 1281 participated in the 5-year follow-up, constituting the present study population. Median follow-up time was 5.20 (5.10; 5.29) years, and the median diabetes duration at baseline was 6.1 (5.2, 6.8) years. A total of 179 participants presented with an event, either all-cause mortality or their first CVD event in the study period. Hereof, we registered 114 deaths caused by CVD (31.6%), cancer (45.6 %) and other causes (22.8%). Baseline characteristics are presented in Table 1. In general, participants with events were older and the proportion of men was larger. Diastolic blood pressure was lower in participants with events (80.6 ± 11.8 mmHg)vs non-events (83.7 ± 10.4 mmHg). Basic biochemical measurements were similar between participants with and without events, although creatinine was higher (80 (67, 93)vs 73 (63, 84) µmol/L) and HDL-C was marginally lower among subjects experiencing an event. In unadjusted analyses, concentrations of STC2, PAPP-A, intact IGFBP4 and total IGFBP4 were significantly higher in participants with events, whereas IGF1 and IGF2 concentrations were significantly lower (Table 2).
Baseline characteristics.
| All participants (n = 1281) | No events (n = 1102) | Events (n = 179) | P-value | |
|---|---|---|---|---|
| Age (years) | 66.3 (61.2, 71.2) | 65.8 (60.6, 70.6) | 69.2 (63.3, 72.9) | <0.001 |
| Gender (female) | 543 (42.4%) | 492 (44.6%) | 51 (28.5%) | <0.001 |
| BMI (kg/m2) | 30.6 ± 5.5 | 30.7 ± 5.6 | 30.3 ± 5.4 | 0.329 |
| Waist circumference (cm) | 104.1 ± 13.6 | 103.8 ± 13.6 | 106.1 ± 13.9 | 0.045 |
| Systolic blood pressure (mmHg) | 134 (123, 146) | 134 (123, 146) | 137 (122, 145) | 0.621 |
| Diastolic blood pressure (mmHg) | 83.3 ± 10.6 | 83.7 ± 10.4 | 80.6 ± 11.8 | <0.001 |
| Baseline biochemical values | ||||
| HbA1c % | 6.3 (6.0, 6.8) | 6.3 (6.0, 6.8) | 6.4 (5.9, 6.8) | 0.801 |
| Creatinine (µmol/L) | 74 (63, 85) | 73 (63, 84) | 80 (67, 93) | <0.001 |
| Cholesterol (mmol/L) | 4.2 (3.7, 4.9) | 4.2 (3.7, 4.9) | 4.2 (3.6, 4.9) | 0.139 |
| LDL-C (mmol/L) | 2.1 (1.6. 2.6) | 2.1 (1.6, 2.6) | 2.0 (1.6, 2.5) | 0.389 |
| HDL-C (mmol/L) | 1.3 (1.1, 1.6) | 1.3 (1.1, 1.6) | 1.2 (1.0, 1.5) | 0.029 |
| Triglycerides (mmol/L) | 1.5 (1.0, 2.1) | 1.5 (1.0, 2.1) | 1.4 (1.0, 2.3) | 0.489 |
| Comorbidity (self-reported) | ||||
| Previous myocardial infarctiona | 81 (7.2%) | 60 (6.1%) | 21 (13.8%) | <0.001 |
| Previous strokea | 66 (5.8 %) | 44 (4.5%) | 22 (14.5%) | <0.001 |
| Previous or current angina pectorisa | 109 (9.6%) | 79 (8.1%) | 30 (19.6%) | <0.001 |
| Previous coronary intervention/surgerya | 95 (8.2%) | 64 (6.4%) | 31 (20.4%) | <0.001 |
| Combined CVD comorbidity | 219 (19.4%) | 162 (16.5%) | 57 (37.5%) | <0.001 |
| Previous or current arrhythmia | 190 (16.8%) | 156 (15.9%) | 34 (22.7%) | 0.040 |
| Smoking (former or current) | 799 (69.2%) | 666 (66.7%) | 133 (85.8%) | <0.001 |
| Smoking (never) | 355 (30.8%) | 343 (33.3%) | 22 (14.2%) | <0.001 |
| (Former) | 515 (44.6%) | 421 (42.1%) | 94 (60.6%) | |
| (Current) | 284 (24.6%) | 245 (24.5%) | 39 (25.2%) | |
| Units of alcohol per week (avg.) | 5 (1, 13) | 5 (1, 13) | 5 (0, 16) | 0.660 |
| Medication (self-reported) | ||||
| Anti-diabetic medication | 695 (55.5%) | 591 (54.9%) | 104 (59.1%) | 0.297 |
| Anti-hypertensive medication | 1019 (81.3%) | 862 (80.0%) | 157 (89.2%) | 0.004 |
| Lipid-lowering medication | 951 (75.9%) | 818 (76.0%) | 133 (75.6%) | 0.912 |
Continuous data are displayed as mean ± s.d. or median (25th and 75th percentile). Categorical data are presented as sums and percentages. All data are based on non-missing data.
aIncluded in the combined CVD comorbidity variable.
HbA1c, haemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein.
IGF variables at baseline.
| All participants (n = 1281) | No events (n = 1102) | Events (n = 179) | P-value | |
|---|---|---|---|---|
| STC2 (µg/L) | 29.6 (24.8, 35.5) | 29.1 (24.5, 35.1) | 31.7 (26.7, 37.4) | <0.001 |
| PAPP-A (µg/L) | 0.96 (0.78, 1.18) | 0.95 (0.78, 1.15) | 1.09 (0.86, 1.38) | <0.001 |
| IGF1 (µg/L) | 122 (99, 147) | 123 (100, 148) | 118 (98, 144) | 0.019 |
| IGF2 (µg/L) | 533 (422, 659) | 535 (425, 660) | 515 (409, 652) | 0.023 |
| Intact IGFBP4 (µg/L) | 104.7 (72.9, 137.5) | 103.4 (72.4, 137.2) | 113.2 (77.4, 144.2) | 0.010 |
| Total IGFBP4 (µg/L) | 156.9 (135.8, 185.7) | 155.1 (134.4, 182.2) | 171.2 (146.0, 201.7) | <0.001 |
Data are displayed as median (25th and 75th percentile). All data are based on non-missing data.
Correlations within the STC2–PAPP-A–IGFBP4–IGF1 axis and comparison to baseline characteristics and biochemical values
STC2 correlated positively with intact IGFBP4 (r = 0.16, P < 0.001) as well as total IGFBP4 (r = 0.24, P < 0.001) (Supplementary Table 1, see section on supplementary materials given at the end of this article). IGF1 and -2 were positively correlated (r = 0.26, P < 0.001) and both were negatively correlated with PAPP-A (IGF1: r = −0.11, P = 0.014; IGF2: r = −0.12, P = 0.001) and intact IGFBP4 (IGF1: r = −0.29, P < 0.001; IGF2: r = −0.18, P < 0.001).
STC2, PAPP-A, IGF1 and total IGFBP4 all correlated positively with creatinine (STC2: r = 0.25, P < 0.001; PAPP-A: r = 0.15,P < 0.001; IGF1: r = 0.14, P < 0.001; total IGFBP4: r = 0.36, P < 0.001). STC2 correlated positively with both waist circumference (r = 0.20, P < 0.001) and BMI (r = 0.19, P < 0.001).
Survival analyses
Kaplan–Meier failure curves for all-cause mortality for STC2, PAPP-A, total IGFBP4 and IGF1 are depicted in Figure 1. Variables were divided into two groups based on the median value. In the Cox regression analyses, PAPP-A, STC2, as well as intact IGFBP4 and total IGFBP4, all associated with a higher risk of all-cause mortality in univariable as well as multivariable analyses (Table 3). Thus, in the fully adjusted model, a doubling in the concentration of PAPP-A was associated with a higher risk of all-cause mortality with an HR of 2.81 (1.98–3.98, P < 0.001). Similarly, STC2 was associated with all-cause mortality with an HR of 1.84 (1.09–3.12, P = 0.023). The HRs for the association of all-cause mortality with intact IGFBP4 and total IGFBP4 were 1.43 (1.11–1.85, P = 0.006) and 3.06 (1.91–4.91, P < 0.001), respectively. In contrast, for IGF1, a doubling of the concentration in the fully adjusted model was associated with a lower risk of all-cause mortality with an HR of 0.51 (0.34–0.76, P = 0.001). A similar tendency was observed for IGF2, but the fully adjusted model did not return statistically significant results. In contrast to the other variables, the association of IGF1 with mortality appears to be U-shaped (25). Therefore, we included a sub-analysis, focusing on IGF1 quintiles vs mortality. When using the middle quintile as reference, neither univariable nor multivariable analyses returned statistically significant findings (data not shown). However, when the lowest quintile served as reference, a statistical difference was obtained in comparison with the highest quintile in all models, with an HR of 0.41 (0.21–0.79, P = 0.008) for the fully adjusted model. Thus, the latter approach confirmed the inverse linear relationship between serum IGF1 concentrations and mortality in our cohort.
Kaplan–Meier failure curves for all-cause mortality. IGF variables are divided by the median value. (A) STC2, (B) PAPP-A, (C) IGF1, (D) total IGFBP4.
Citation: Endocrine Connections 12, 3; 10.1530/EC-22-0451
Hazard ratios for all-cause mortality and the composite CVD event.
| Model | All-cause mortality (n = 114) | Composite CVD event (n = 105) | ||
|---|---|---|---|---|
| HR (95% CI) | P-value | HR (95% CI) | P-value | |
| Log2-STC2 (n = 1281) | ||||
| Univariable | 2.52 (1.53–4.12) | <0.001 | 2.44 (1.55–3.82) | <0.001 |
| Model 1 | 2.18 (1.33–3.59) | 0.002 | 2.01 (1.26–3.21) | 0.003 |
| Model 2 | 1.84 (1.09–3.12) | 0.023 | 1.60 (0.97–2.63) | 0.064 |
| Log2-PAPP-A (n = 1281) | ||||
| Univariable | 2.83 (2.09–3.85) | <0.001 | 1.96 (1.38–2.79) | <0.001 |
| Model 1 | 2.75 (1.95–3.88) | <0.001 | 1.83 (1.25–2.68) | 0.002 |
| Model 2 | 2.81 (1.98–3.98) | <0.001 | 1.74 (1.16–2.62) | 0.008 |
| Log2-IGF1 (n = 1281) | ||||
| Univariable | 0.52 (0.35–0.77) | 0.001 | 0.79 (0.50–1.26) | 0.329 |
| Model 1 | 0.53 (0.36–0.79) | 0.002 | 0.80 (0.50–1.30) | 0.371 |
| Model 2 | 0.51 (0.34–0.76) | 0.001 | 0.77 (0.48–1.24) | 0.287 |
| Log2-IGF2 (n = 1276) | ||||
| Univariable | 0.63 (0.45–0.87) | 0.005 | 0.80 (0.57–1.12) | 0.191 |
| Model 1 | 0.68 (0.47–0.98) | 0.041 | 0.95 (0.63–1.41) | 0.781 |
| Model 2 | 0.69 (0.47–1.01) | 0.056 | 0.88 (0.59–1.33) | 0.547 |
| Log2-intact IGFBP4 (n = 1278) | ||||
| Univariable | 1.44 (1.13–1.84) | 0.003 | 1.21 (0.94–1.55) | 0.140 |
| Model 1 | 1.44 (1.11–1.86) | 0.006 | 1.13 (0.87–1.48) | 0.355 |
| Model 2 | 1.43 (1.11–1.85) | 0.006 | 1.10 (0.85–1.43) | 0.460 |
| Log2-total IGFBP4 (n = 1278) | ||||
| Univariable | 3.39 (2.42–4.75) | <0.001 | 2.06 (1.44–2.94) | <0.001 |
| Model 1 | 2.89 (2.02–4.14) | <0.001 | 1.79 (1.19–2.68) | 0.005 |
| Model 2 | 3.06 (1.91–4.91) | <0.001 | 1.31 (0.85–2.01) | 0.223 |
Data are displayed as HR and 95% CI. Model 1 included age, gender, waist circumference, randomisation, former or current smoking. Model 2 included model 1 plus the CVD comorbidity variable, diastolic blood pressure, creatinine, cholesterol and HbA1c. A one-unit increase in a specific protein (e.g. STC2) on the log2-scale corresponds to a duplication in the level of that protein.
For the composite CVD event, PAPP-A behaved similarly to all-cause mortality, as higher levels were associated with an event, resulting in an HR of 1.74 (1.16–2.62, P = 0.008) in the fully adjusted model. Although STC2 and total IGFBP4 were significantly associated with a higher risk of the composite CVD event in the univariable and partially adjusted models, results became statistically insignificant after full adjustment (STC2: HR = 1.60 (0.97–2.63), P = 0.064 and total IGFBP4: HR = 1.31 (0.85–2.01), P = 0.223). There was no association between the composite CVD event and IGF1 or IGF2.
Cancer mortality accounted for more than 45% of all deaths, and thus, survival analyses were repeated using cancer death as the outcome variable. Total IGFBP4 was significantly associated with the event in univariable and partially adjusted model, whereas associations were lacking for the other proteins (data not shown). The associations with cancer mortality were not stronger than those found with all-cause mortality for any of our proteins of interest.
Discussion
This study is to the best of our knowledge the first to examine associations of the entire STC2–PAPP-A–IGFBP4–IGF1 axis with mortality and CVD in patients with T2D. Our data identified circulating concentrations of PAPP-A, STC2, as well as total and intact IGFBP4 as predictors of all-cause mortality. In addition, high levels of PAPP-A are associated with the composite CVD event. Conversely, low circulating levels of IGF1 are associated with an increased risk of all-cause mortality.
In the present study, PAPP-A is associated with all-cause mortality as well as the composite CVD event. These associations appear to be robust as they have been demonstrated in both acute (26, 27, 28) and chronic clinical settings. Regarding the latter, an elevated PAPP-A concentration in patients with stable angina is associated with increased severity of coronary artery disease (29), as well as increased risk for mortality (30). In patients with heart failure, Funayama et al. found PAPP-A to be predictive of future CVD events (31), whereas a study by Nilsson et al. examining patients with stable coronary heart disease found PAPP-A to be associated with all-cause mortality but not the CVD outcome (32). Thus, despite some disagreeing results, we believe it is reasonable to conclude that an elevated serum concentration of PAPP-A is linked to an unfavourable outcome in various patient cohorts suffering from CVD.
The present study adds patients with uncomplicated T2D to the group of diseases where an elevated concentration of PAPP-A is unfavourable. Hence, in our cohort of patients treated in general practice, an elevated baseline concentration of PAPP-A was associated with increased mortality and risk for CVD. This finding agrees with another study examining more severely affected T2D patients on haemodialysis, which demonstrated that PAPP-A is associated with all-cause mortality as well as other cardiovascular events (33).
The majority of epidemiological PAPP-A studies have used the serum concentration of the enzyme as an indicator of its endogenous activity, and obviously, this is a limitation as the current assays measure enzymatically active PAPP-A as well as inactive PAPP-A; for example, PAPP-A complexed to STC2. However, this limitation has been acknowledged, and therefore, newer studies of PAPP-A have included either measurements of the two IGFBP4 fragments that are generated when PAPP-A cleaves IGFBP4 (34), or measurements of intact IGFBP4 and total (intact + fragments) IGFBP4 (35). Fortunately, these studies have confirmed the association between PAPP-A and CVD. In a study of patients with type 1 diabetes, Hjortebjerg et al. could not establish any significant associations for PAPP-A, whereas significant associations were established between cardiovascular mortality and the IGFBP4 fragments emerging after the specific cleavage of IGFBP4 by PAPP-A (4). Thus, that study proposed that estimations of PAPP-A activity provided an even better predictor of adverse CVD outcomes than the PAPP-A concentration itself. In the present study, we measured intact IGFBP4 as well as total IGFBP4 (i.e. intact and fragments), and we demonstrated that both variables were associated with all-cause mortality. Experimental studies have shown that without IGFBP4 serving as an IGF donor, PAPP-A has no effects or conversely, IGFBP4 may be considered to serve as an IGF1 donor for PAPP-A (11). Hence, we interpret the elevated levels of intact IGFBP4 in those dying as an elevated substrate availability for PAPP-A, and correspondingly, an elevated level of total IGFBP4 (intact and fragmented IGFBP4) as an indicator of an increased PAPP-A mediated IGFBP4 proteolysis.
STC2, the most recently discovered member of the IGF system, is less studied in relation to CVD. Cediel et al. examined the STC2–PAPP-A–IGFBP4–IGF1 axis in patients with ST-elevation myocardial infarction (35). As STC2 is an irreversible inhibitor of PAPP-A, one would expect that an elevated STC2 level is beneficial as this can reduce the harmful effects of PAPP-A on CVD and mortality. However, Cediel et al. (35) observed that elevated levels of STC2 were associated with all-cause mortality, and we came to the same conclusion.
We can think of various explanations: hence, an elevated STC2 level could reflect a compensatory mechanism that serves to counterbalance an increased PAPP-A activity and consequently, the harmful effects of PAPP-A. The obtained positive correlation between STC2 and PAPP-A favours this point of view. Alternatively, STC2 possesses cardiovascular effects that are independent of PAPP-A and the IGF system. In this context, we are unaware of PAPP-A possessing IGF1-independent effects, whereas it appears more likely that STC2 possesses IGF1-unrelated effects. Indeed, preclinical studies suggest that STC2, but not PAPP-A, is related to glucose metabolism (36, 37), and we have clinical studies supporting this. We recently examined the effect of gastric surgery on STC2 and PAPP-A (17). At 1 year follow-up after gastric surgery, STC2 had decreased and its reduction correlated with improvements in HbA1c, fasting insulin and fasting glucose, whereas PAPP-A levels remained unchanged.
In the present study, STC2 correlated positively with BMI and waist circumference, although we could not establish a correlation between STC2 and HbA1c. Thus, STC2 appears to be more than just an endogenous regulator of PAPP-A and could theoretically influence CVD by mechanisms unrelated to PAPP-A and IGF1. However, more data are needed to confirm the idea that the physiological role of STC2 extends beyond the boundaries of the IGF system.
STC2 appears to be involved in various cancers. STC2 associates with cellular immortalization, is overexpressed in several tumors and associates with disease severity and prognosis. Various studies have indicated that the expression level of STC2 in tumors may be used as a prognostic marker, but its value appears to depend on cancer subtype (38, 39). In this study, STC2 was significantly associated with all-cause mortality in the fully adjusted analysis but not with CVD. Thus, since most deaths were due to malignancy, the association between STC2 and mortality could potentially be driven by its association with cancer disease. However, survival analyses using cancer mortality as outcome did not reveal an association with STC2 levels at baseline, maybe because the number of events was too low. Nevertheless, we believe there is a need for more studies examining the role of STC2 in cancer. Finally, STC2 has been ascribed pivotal functions in calcium and phosphate regulation, cytoprotection, cell development and angiogenesis (16). Thus, STC2 possesses numerous cellular and molecular functions that may influence human health and disease.
In the present study, lower levels of IGF1 were associated with all-cause mortality. This association has been observed in some studies (40), but not all (41). Others have reported that high levels of IGF1 were associated with an increased risk of all-cause mortality (42, 43). It has, therefore, been suggested that a U-shaped relationship exists between circulating IGF1 levels and mortality (44, 45), and this concept has been confirmed in larger meta-analyses (25, 46). Consequently, we divided IGF1 concentrations into quintiles but found no indications of a U-shaped association with all-cause mortality. On the contrary, the relationship was linear: the higher the level, the lower the risk. In studies of cause-specific mortality, the U-shape seems stronger for CVD mortality than cancer mortality. Xie et al. found a nonlinear but not a definite U-shape association between IGF1 levels and cancer mortality, especially for the highest IGF1 levels (45). As most deaths in the present study were due to cancer, this may explain our observation. Interestingly, this finding is in agreement with recent data from UK. In 2021, Pearson-Stuttard et al. demonstrated a reduction in vascular death and a transition from vascular diseases to cancers as the leading contributor to diabetes-related death (47). The authors explained their findings with an increased focus on prevention of CVD as well as improved survival in those with the disease.
In this study we examined circulating levels of the STC2–PAPP-A–IGFBP4–IGF1 axis, and this may not reflect local tissue concentrations, interactions and effects. PAPP-A is known for its ability to promote local IGF1 action independently of the circulating levels of IGF1 (36). This could explain how elevated PAPP-A levels but reduced IGF1 levels were both associated with an increased mortality.
The present study is accompanied by limitations. First, some covariables were self-reported and therefore entail some degree of uncertainty. However, we performed survival analyses also without the CVD comorbidity score, which was the self-reported covariable with the largest missing rate. And results did not significantly differ, so we do not believe that this issue is of substantial significance. Secondly, the study might have favoured from collection of sequential blood sampling over time as earlier studies of IGF1 have demonstrated that multiple longitudinal measurements may provide a better understanding of risk associations (48) and this may also include STC2, PAPP-A and IGFBP4. Thirdly, analyses of the composite CVD event resulted in fewer significant results as compared to all-cause mortality. This may partly be due to a slightly lower number of events in this group, thus resulting in reduced analytical power.
Conclusion
This study demonstrates an association between PAPP-A and CVD in patients with T2D. In addition, we could demonstrate that all components of the STC2–PAPP-A–IGFBP4–IGF1 axis are associated with all-cause mortality. Thus, our study supports the concept that inhibition of PAPP-A may serve as a means to enhance health and life span by reducing mortality and CVD events (36). To which extent modification of STC2 also may be of value has to await our understanding of the physiological role that STC2 may play in regard to mortality.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/EC-22-0451.
Declaration of interest
The authors have no conflicts of interest.
Funding
This work was supported by the Independent Research Fund Denmark (grant number 6110-00382B), the Novo Nordisk Foundation (grant number NNF150C0017630), the A.P. Møller and Chastine Mc-Kinney Møller Foundation and the Department of Clinical Medicine, Health, Aarhus University.
Acknowledgements
The authors thankfully acknowledge Susanne Sørensen at Medical/Steno Aarhus Research Laboratory for technical assistance. The authors thankfully acknowledge AnshLabs (TX, US) for partly supporting the study with immunoassay kits.
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