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Metabolic syndrome including both elevated blood pressure and elevated fasting plasma glucose is associated with higher mortality risk: a prospective study

Diabetology & Metabolic Syndrome volume 17, Article number: 72 (2025) Cite this article

Abstract

Background

Metabolic syndrome (MetS) encompasses a collection of metabolic abnormalities. This study aims to determine which combination of MetS components has the highest mortality risk, and to investigate the causal relationships between MetS components and longevity.

Methods

Prospective analyses were conducted on 340,196 participants from the MJ cohort at baseline, and 121,936 participants had follow-up MetS information. We defined MetS according to the NCEP ATP III criteria. The study’s outcomes included mortality from cardiovascular disease (CVD), cancer, and all causes combined. We employed Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals. Multivariable Mendelian randomization (MVMR) was employed to infer causality using the genetic data of MetS components and longevity.

Results

Elevated blood pressure (BP) was the initial split for all-cause mortality, cancer mortality, and CVD mortality. Participants with MetS, especially those with elevated BP and elevated fasting plasma glucose (FPG), had higher mortality risks than those with other types of MetS. In the MJ cohort, participants with elevated BP and FPG (BG-type MetS) had a 44% (HR = 1.44, 95% CI = 1.37–1.51), 73% (HR = 1.73, 95% CI = 1.62–1.84), and 34% (HR = 1.34, 95% CI = 1.27–1.42) increased risk of all-cause mortality, cancer mortality, and CVD mortality, respectively, compared with non-BG-type MetS (12%, 24%, 5%). The highest mortality rate and mortality risk were observed in participants with BG-type MetS at baseline and follow-up (mortality rate/1000 person years = 9.73, 95% CI = 8.81–10.74; HR = 1.52, 95% CI = 1.35–1.72). SBP and FPG increases that were genetically proxied to a 1-standard deviation higher level decreased the probabilities of living to the 90th percentile age by 41% (OR = 0.59, 95% CI = 0.40–0.86) and 32% (OR = 0.68, 95% CI = 0.48–0.98) in MVMR, respectively.

Conclusions

Individuals with BG-type MetS are at a higher risk of death than those with other types of MetS. Therefore, these individuals should be targeted to improve MetS outcomes.

Introduction

Metabolic syndrome (MetS) is a group of cardiovascular risk factors characterized by insulin resistance [1, 2]. MetS encompasses various permutations of elevated blood pressure (BP), elevated fasting plasma glucose (FPG), atherogenic dyslipidemia characterized by elevated triglyceride (TG) levels, reduced high-density lipoprotein cholesterol (HDL-C) levels, and abdominal obesity [1]. These MetS components adversely affect several physiological systems, thereby increasing mortality risk [3,4,5]. Individuals with MetS experience a 50% increased risk of all-cause mortality [3], a 140% increased risk of cardiovascular disease (CVD) mortality [3], and a 60% increased risk of cancer mortality [6]. This syndrome negatively affects various body systems, highlighting the critical need for effective disease management strategies. According to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria [2], MetS is delineated by the presence of three or more of the five components. This yields 16 potential clusters of metabolic risk factors for a MetS diagnosis. Given the heterogeneity in the component combinations among individuals with MetS, these distinct clusters may harbor varying pathophysiological underpinnings [4].

Prior studies have primarily examined the relationships between baseline MetS status and the incidence of adverse long-term outcomes [7,8,9]. MetS components are modifiable and reversible based on dietary adjustments or medication regimens during the follow-up period [10]. He et al. [11] divided the dynamic MetS patterns into four categories as MetS-free, MetS-developed, MetS-recovery and MetS-stable. According to this study, the CVD risk was 59% greater for the MetS-recovery group and 78% higher in the MetS-developed group than in the MetS-free group. However, this study only divided participants into those with and without MetS and did not consider MetS subtypes. Viewing MetS as a singular entity may obscure crucial nuances in health assessment and mortality risk evaluation [12]. However, the prevailing research has predominantly examined MetS within the general population, neglecting to elucidate the mortality risks associated with specific component combinations [3, 12]. Moreover, relationships between the changes of MetS subtypes and the mortality risk have yet to be revealed.

Existing investigations on the association between MetS combinations and mortality have predominantly focused on all-cause mortality [13, 14], and empirical evidence concerning cause-specific mortality remains scarce. Particularly, this is evident regarding CVD and cancer-caused mortality, given the intimate ties between CVD, cancer, and MetS prognosis [2, 4,5,6]. Leveraging genetic alleles, which are randomly assorted during meiosis, are less susceptible to confounding biases and reverse causation risks from environmental factors. Mendelian Randomization (MR) analysis offers insights into genetic associations [15]. Multivariable Mendelian Randomization (MVMR) enables the simultaneous evaluation of individual yet interrelated exposures by combining genetic variants from all exposures into a single model. However, few studies have explored the causality between MetS components and longevity using MVMR.

To bridge this knowledge gap, we investigated the relationship between various combinations of MetS components, as determined by the NCEP ATP III criteria, and all-cause, cancer-caused, and CVD-caused mortality in a comprehensive Asian cohort study. We also investigated the relationship between dynamic status and subtypes of MetS and mortality risk. Moreover, we sought evidence on the causal links between MetS components and longevity using publicly available genetic data within the MR framework.

Methods

Study design and participants

The MJ Cohort is a sizeable prospective study based on a large database of the Taiwan MJ Group’s health screening program; comprehensive results about MJ cohort have been published elsewhere [16]. A series of medical tests were performed on the participants, including body measurements, functional evaluations, blood and urine analyses, physical examinations, and the completion of a self-administered survey with questions about demographics, medical history, and lifestyle. Participants from the MJ cohort between 1997 and 2011 were enrolled (n = 461,199). After excluding participants under 18 years of age (n = 14,513) and those with missing information regarding MetS components (n = 50,607) and other covariates (n = 55,883), a total of 340,196 participants were included. 121,936 participants with available information on MetS components within 1–3 years of enrollment were included in the analysis of the changes in MetS status and subtypes.

Definitions of MetS

Three of the following elements are required to be present for the NCEP ATP III modified criteria: (1) elevated BP (≥ 130/85 mmHg, and/or being treated with antihypertensive medication at baseline, as well as a history of hypertension); (2) elevated FPG (≥ 100 mg/dL); (3) elevated TG (≥ 150 mg/dL); (4) reduced HDL-C (< 40 mg/dL for men and < 50 mg/dL for women); and (5) elevated waist circumference (WC, ≥ 90 cm for men or ≥ 85 cm for women) [2]. All information on MetS components was obtained from physical examination results except for disease and medication history. All possible permutations were considered to determine the combinations of the five components with the highest risk of death. The MetS diagnoses at follow-up were based only on physical examination indicators.

Study outcomes

The study outcomes included all-cause mortality, cancer mortality, and CVD mortality. Deaths were obtained from the Taiwan Death Registry to 2011. Confirmed cancer deaths or CVD deaths were defined according to ICD-9 and ICD-10. cancer-caused deaths included ICD-9 codes 140 to 208, ICD-10 codes C00 to C97. CVD–related deaths included ICD-9 codes 390–459, ICD-10 codes I00-I99.

Covariates assessment

Potential confounding factors included sociodemographic variables such as age, gender, marital status, education levels, lifestyle factors including smoking status, alcohol drinking status, vegetable and fruit intake, and physical activity; and health conditions including body mass index (BMI). Baseline demographic and lifestyle information was collected using questionnaires. Detailed information of covariates was described in Additional text 1.

Statistical analysis

Using data from the MJ cohort, we calculated the time to an event from the enrollment date to the last follow-up or the date of death, whichever came first. Baseline characteristics were described by MetS status, and we used chi-square analysis to test the differences for categorical data and t-tests for continuous data. To determine the mortality rate, incident numbers per 1,000 person years of follow-up were used., and the confidence intervals were calculated according to a previous study [17]. Survival tree analysis, employing the rpart package in R, was conducted to explore potential interactions among MetS components without covariates in the model [18]. We imposed no restrictions on P-values to control tree growth, allowing the growth to continue until all 16 combinations were represented in terminal nodes. The adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality were computed using Cox regression models, In contrast, those for cancer- and CVD-caused mortality were determined using competing risk models (cause-specific models). Analyses were stratified by gender (male, female), age categories (< 30, 30–39, 40–49, 50–59, 60–69, ≥ 70 years), and BMI categories (< 18.5, 18.5–23.9, 24.0-27.9, ≥ 28 kg/m2), and adjusted for education (less than high school, high school or equivalent, college or higher), marital status (single, married, separated), smoking status (never, former, current), alcohol consumption status (never, former, current), physical activity (inactive, insufficient, active), and vegetable and fruit intake (< 4 portions, ≥ 4 portions). Two sensitivity analyses were conducted by removing incident events from the first 2 years of follow-up and, removing individuals with cancer or CVD at baseline. Additionally, we did the supplemental analysis to confirm the links between various forms of MetS and the risk of all-cause death in the United States (US) population using data from the National Health and Nutritional Examination Survey (NHANES). NHANES comprises a series of continuous cross-sectional surveys conducted on non-institutionalized US civilians, conducted biennially since 1999 to monitor the health and nutritional status of the US population. All NHANES protocols received approval from the National Center for Health Statistics ethics review board, and written informed consent was obtained from all participants. This study included participants from 10 NHANES cycles spanning 1999/2000 to 2017/2018.

Data sources and genetic instruments in MR analysis

We selected the results from large genome-wide association studies (GWAS) for BP, FPG, serum lipids, and WC to evaluate the causal link between MetS components and lifespan. These included ukb-b-20175 for systolic BP (SBP) based on 436,419 participants, ukb-b-7992 for diastolic BP (DBP) based on 436,424 participants, ebi-a-GCST90002232 for FPG based on 200,622 participants, ieu-a-61 for WC based on 232,101 participants, ieu-b-299 for HDL-C based on 187,167 participants, and ieu-b-302 for TG based on 177,861 participants. The results were shown as continuous variables, and the variation effects in the metabolic marker were measured in one standard deviation (SD) unit. These SDs were obtained from the corresponding GWAS datasets [19, 20]. Single nucleotide polymorphisms (SNPs) GWAS summary statistic data for longevity from the Longevity Genomics study served as the outcome. The detailed methods have been reported in previous studies [21, 22]. The longevity group included 11,262 participants, who lived to or beyond the 90th survival percentile age, and 25,483 controls, whose age at death or the last follow-up was at or below the age corresponding to the 60th survival percentile.

We first selected significant genome-wide SNPs (P < 5 × 10− 8) linked to each exposure contained in the outcome GWAS dataset for univariable MR analysis to evaluate the overall effects of each exposure. SNPs with significant associations (P < 5 × 10− 8) with longevity would be removed. These remained SNPs were clumped using a 10-Mb frame and pair-wise linkage disequilibrium (LD) r2 < 0.001, implemented using the TwoSampleMR program, in order to guarantee the independence of the genetic proxies. The F-statistic provides information about the strength of a genetic variant for the exposure of interest. An F of > 10 indicates that substantial weak instrument bias is unlikely. To identify independent SNPs and compute the adjusted estimates, MVMR was used to reduce the possibility of bias resulting from multiple exposures on the outcome. All significant genome-wide SNPs found in the lifespan GWAS dataset and linked to any exposure were combined for the MVMR analysis. Using a 10-Mb frame and pairwise LD r2 < 0.001, these SNPs were clumped according to the lowest p-value related to any of the exposures. Additional Table 1 presents the genetic instrument selection procedure.

Statistical analysis in MR analysis

Utilizing the instrumental variables (IVs) identified earlier, we conducted MR analyses for SBP, DBP, FPG, WC, TG, HDL-C, and longevity using the TwoSampleMR packages within the R software. MR analysis was conducted employing three distinct methods: inverse variance weighted (IVW), MR Egger, and weighted median. The IVW method is the most efficient and common MR method if all genetic variants are valid instruments, which is selected as the main method [23]. We further selected other methods to detect or adjust for pleiotropy, including the MR-Egger and weighted median. The MR-Egger method allows some or even all genetic variants to be invalid instruments but requires these genetic variants to satisfy the instrument strength independent of direct effect assumption. MR-Egger could detect the pleiotropy using the MR-Egger intercept test and correct the presence of pleiotropy. The weighted median could provide a consistent estimate of the causal effect when more than 50% of the weight is contributed by instrumental variables [23]. In our MR study, heterogeneity was evaluated by Cochran’s Q statistic (IVW) and Rucker’s Q statistic (MR Egger), where p < 0.05 indicated the heterogeneity was significant, thus the random-effects IVW method would be conducted [15]. The MR Egger intercept test was used to identify horizontal pleiotropy; a p > 0.05 resulted in the absence of horizontal pleiotropy [24]. We also used the summary effect estimates (CAUSE) methods to assess causal relationships, rather than genome-wide significant loci only, to correct for sample overlap and more effectively control for correlated and uncorrelated horizontal pleiotropy [25, 26]. To determine whether the causal relationship between the MetS components and longevity still exists after adjusting for these factors, we used the MVMR-IVW method for MR analysis. The false-discovery rate (FDR) approach was utilized to correct for multiple testing. Significant results were defined as those with Q value < 0.05. Findings with P < 0.05 but Q value > 0.05 were categorized as nominally significant. The odds ratio (OR) for longevity was calculated, with an OR greater than 1 signifying a positive association with increased longevity, whereas an OR less than 1 denoted a negative association.

Results

Baseline characteristics in observational study

During a mean follow-up of 8.5 years, 10,350 deaths were recorded (including 1,960 CVD-caused deaths and 4,304 cancer-caused deaths). The mean (SD) age of the cohort is 40.0 (13.6) years, and the mean BMI is 22.9 (3.6). Individuals with MetS were more frequently older, male, with less education, married, smoker, drinker, and with a higher level of physical activity, and less vegetables and fruit intake (Table 1). The most prevalent factor was elevated FPG, which was followed by elevated BP, reduced HDL-C, elevated TG, and greater WC (data not shown).

Table 1 Demographic characteristics for the cohort by metabolic syndrome status
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Different combinations of metabolic syndrome components and the risk of mortality

Additional Table 2 shows the baseline information of 16 combinations of MetS components. People with elevated TG, WC and reduced HDL-C constituted the youngest cohort, with a mean age of 41.3 years. Conversely, the groups characterized by ‘elevated BP + elevated FPG + greater WC’ and ‘elevated BP + elevated FPG + reduced HDL-C + greater WC’ represented the oldest cohorts, each with a mean age of 54.5 years. As shown in Fig. 1, among the all 16 combinations, 12, 10, and 8 combinations were significantly associated with higher risk of all-cause mortality, cancer-caused mortality, and CVD-caused mortality, respectively, and most of these significant combinations included both elevated BP and elevated FPG meanwhile. Among those who had three MetS components, individuals with elevated BP and FPG, and reduced HDL-C had the greatest risk of developing all-cause mortality, cancer mortality, and CVD mortality when compared with the reference group. Comparing with individuals with four MetS components with the reference group, those with elevated BP and FPG, greater WC and reduced HDL-C posed the greatest risk for all-cause mortality, cancer mortality, and CVD mortality, greater than those with all five components.

Fig. 1
figure 1

Hazard ratios for mortality according to the different combinations of metabolic syndrome components

Abbreviations: MetS, metabolic syndrome; CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval; HBP, elevated blood pressure; HFPG, elevated fasting plasma glucose; HTG, elevated blood triglycerides; LHDLC, low high-density lipoprotein cholesterol; HWC, greater waist circumference. Multivariate analysis was stratified by age groups, BMI groups, and gender, and adjusted for marriage, education, smoking status, drinking status, physical activity, and intake of vegetables and fruit

Full size image

As shown in the tree structure and HRs for all-cause mortality (Fig. 2), cancer mortality, and CVD mortality (Additional Figs. 1 and 2). The initial split on the survival tree was due to elevated BP, indicating that this component is the primary factor contributing to variation in all-cause mortality, cancer mortality, and CVD mortality risk in this population. For all-cause mortality analysis, the reference group for the analysis (node 1, elevated TG + reduced HDL-C + greater WC) was composed of individuals without the two most significant factors: elevated BP and elevated FPG. Node 15 (elevated BP + elevated FPG + reduced HDL-C + greater WC) has the highest risk of all-cause mortality (HR = 1.78, 95% CI = 1.41–2.26). For cancer mortality analysis, node 1 (elevated FPG + elevated TG + reduced HDL-C) was considered as the reference group, and people with elevated BP, elevated FPG, reduced HDL-C, and greater WC had the highest risk (HR = 1.66, 95% CI = 1.28–2.15), and this group also has the highest risk of CVD mortality when taken with the ‘elevated TG + reduced HDL-C + greater WC’ group as a reference.

Fig. 2
figure 2

Survival tree for all-cause mortality according to metabolic syndrome components

Abbreviations: HR, hazard ratio. HBP, elevated blood pressure; HFPG, elevated fasting plasma glucose; HTG, greater blood triglycerides; LHDLC, lowhigh-density lipoprotein cholesterol; HWC, elevated waist circumference

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Metabolic syndrome subtypes and the risk of mortality

We categorized participants with MetS into two groups: BG-type, encompassing both elevated BP and elevated FPG, and non-BG-type. The BG-type individuals tended to be older, female, no current smokers, with lower education levels, more vegetables and fruit intaking and higher physical activity levels (Table 2). The mortality rates and hazards for each MetS type are shown in Table 3. BG-type MetS group had the highest mortality rate of 10.67/1,000 person years for all-cause mortality, 3.61/1,000 person years for cancer-caused mortality and 2.52/1,000 person years for CVD-caused mortality. Compared with participants without MetS, participants with BG-type MetS were at 44% higher risk of all-cause mortality (HR = 1.44, 95% CI = 1.37–1.51), 73% higher risk of cancer mortality (HR = 1.73, 95% CI = 1.62–1.84), and 34% higher risk of CVD mortality (HR = 1.34, 95% CI = 1.27–1.42). This increased risk was notably higher than that of non-BG-type MetS (12%, 24%, and 5% respectively). Sensitivity analysis found consistent results with the original analyses (Additional Tables 3 and 4), and the findings were similar in the US population (Additional Table 5).

Table 2 Host characteristics according to metabolic syndrome subtypes
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Table 3 Hazard ratios for mortality according to the metabolic syndrome subtypes
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Table 4 shows the risk of all-cause mortality according to the changes in MetS status and subtypes. Sustained BG-type MetS was associated with the highest mortality rate (9.73, 95% CI = 8.81–10.74) and risk of all-cause mortality (HR = 1.52, 95% CI = 1.35–1.72) as compared with other groups. In populations without MetS or with non-BG-type MetS at baseline, there was an increased risk of all-cause mortality for those who developed BG-type MetS than for those with not BG-type MetS or without MetS.

Table 4 All-cause mortality risk according to changes in metabolic syndrome subtypes
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Mendelian randomization study

Following the SNP selection criteria, 235, 247, 64, 41, 55, and 86 SNPs remained for analyzing the genetic effects of SBP, DBP, FPG, WC, TG, and HDL-C on longevity respectively. The genetic instrument selection was presented in Additional Table 1. The F-statistics of the chosen SNPs, which range from 26.48 to 1650.63, and the overall F-statistics ranging from 71.05 to 157.14, show that poor instruments did not significantly affect our results. As shown in Additional Table 6, no evidence of horizontal pleiotropy was detected, however, the heterogeneity was significant for SBP, DBP, WC, TG, and HDL-C analysis, so the results of random-effects IVW analysis were kept, and revealed that SBP (P = 3.34E-12), DBP (P = 7.82E-09), TG (P = 0.009), and HDL-C (P = 0.006) had significant associations with longevity. FPG was significantly associated with longevity by IVW method only (P = 0.048), but not significantly when utilizing MR Egger and Weighted median, so the significant association could not be indicated. As shown in Additional Table 7, in MR analyses by the CAUSE methods we found evidence of a negative causal effect of SBP and DBP on longevity [-0.58 (-0.82—0.35), -0.58 (-0.83—-0.33), respectively].

To address potential bias from interactions among these factors on the outcome, MVMR based on the IVW method was also applied. We examined the SNP-related phenotypes included in the MVMR analysis to exclude the influence of confounders (e.g., gender). Referring to previous studies, no trait strongly associated with longevity was found (results not shown). Table 5 demonstrates that SBP was causally associated with longevity (SBP: OR = 0.59 per 1 SD higher, 95% CI = 0.40–0.86, P = 0.006, Q value = 0.038). FPG was nominally associated with longevity but not causally (OR = 0.68 per 1 SD higher, 95% CI = 0.48–0.98, P = 0.038, Q value = 0.113). The odds of living to reach the 90th age was lowered by 41% and 32%, respectively, for every 1-SD rise in SBP and FPG that was genetically proxied. Given the design of the MVMR analysis, we can infer a causal relationship between SBP and longevity, an associated relationship between FPG and longevity.

Table 5 Multivariable mendelian randomization between SBP, DBP, FPG, WC, TG, HDL-C and longevity
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Discussion

In this study, we found that MetS characterized by elevated BP and FPG at any period, was linked to a heightened mortality risk compared with other combinations, regardless of the presence of other MetS components. A causal relationship between BP and longevity was revealed, and an associated relationship between FPG and longevity was found.

Consistent with prior research [27,28,29,30], our study confirms that not all combinations of MetS components are equally associated with adverse outcomes, and that mortality risk varies among different MetS combination types [12, 14, 31,32,33,34]. Previous findings from our study indicated that hypertension and diabetes are more strongly linked to mortality risk than obesity and dyslipidemia [35]. Building upon these insights, we discovered that individuals with elevated BP and FPG, as MetS components, face a heightened risk of mortality [36]. Also, the prior studies have explored the link between MetS clusters and mortality in European and American populations, their conclusions have been inconclusive [13, 14, 34]. Guize et al. [14] found that combinations involving greater WC and higher FPG, with either higher BP or TG, were more strongly related to all-cause mortality risk than other combinations. To the best of our knowledge, this study is the first to investigate the associations between different clusters of MetS components and the risk of all-cause, cancer-related, and cardiovascular disease-related mortality in Asian populations. Our findings align with a study on 14,699 Americans [34], and a report on 82,717 Americans [13], indicating that individuals with MetS combinations featuring elevated BP are at a higher mortality risk. Notably, our survival tree analysis also highlights elevated BP as a significant risk factor, and elevated BP was evidenced as a harmful factor in reducing the probability of longevity according to MVMR analysis. In view of the fact that MetS may continue to be an important burden to the population, identifying high-risk clusters of MetS may be helpful to develop targeted intervention strategies and utilize limited resources effectively in order to reduce the burden of MetS-related mortality in the population.

A limited number of studies have addressed the impact of changes of MetS subtypes on mortality risk. We found that patients with BG-type MetS at either baseline or follow-up had significantly higher mortality risk. The heightened risk of mortality in patients with MetS who recovered to non-MetS from baseline MetS status suggests that the adverse impact of MetS, especially BG-type MetS, may not be fully reversible; however, the risk might be relatively lower with the revision of BG-type MetS. This is especially important when we consider the high prevalence of having elevated BP or elevated FPG as MetS components in the general population. It is essential to prioritize the existing components rather than the presence of MetS or the number of its components. From a therapeutic view, we highlight the significance of addressing MetS, especially in those with elevated BP and FPG, in order to reduce the burden of mortality, as BG-type MetS at any time can increase the risk of death.

The mechanisms underlying increased mortality risk associated with BG-type MetS are multifaceted. One potential explanation is the additive interaction between elevated BP and elevated FPG. Both high BP and high FPG are among the most detrimental factors contributing to mortality risk [37, 38]. They share common causes and risk factors and may concurrently induce physiological changes such as endothelial dysfunction [39, 40], increased inflammation [40], and renal dysfunction [41]. Consequently, elevated BP and FPG co-occurrence may synergistically increase the risk of adverse outcomes. The confluence of MetS components may interact with mortality risks through multiple mechanisms, warranting further elucidation in future research.

The primary strength of this study is its large sample size, sourced from well-established cohorts. Additionally, the integration of observational study methods with MR analysis bolsters the reliability of the results. Nevertheless, several limitations must be acknowledged. First, the reliance on self-reported data for most covariates introduces the potential for recall bias. Second, the study did not account for information on MetS during the follow-up period, highlighting the need for further longitudinal research to evaluate and investigate long-term effects. Further, although adjustments were made for various confounders at baseline, it is recommended that future epidemiological studies collect data on unmeasured variables, such as medication therapy and disabilities. These studies should also investigate the potential roles of confounding, interaction, and mediation within the causal pathway between MetS and mortality. Lastly, the MVMR analysis of this study revealed a significant causal association solely between SBP and longevity. More analytical methods, such as mediation analysis, can be employed to explore the relationship between MetS components and longevity.

Conclusions

The combination of a large observational study across two cohorts and MR analysis revealed that individuals with MetS, particularly those with both high BP and FPG, face a heightened risk of mortality. Policymakers and healthcare professionals should prioritize these individuals when designing and implementing health management programs.

Data availability

Original datasets from MJ cohort are available to researchers. Access to data can be requested from http://www.mjhrf.org/. All results presented in Mendelian randomization analysis were generated using publicly available data, which may be accessed through links provided in the respective publications.

Abbreviations

BP:

Blood Pressure

CI:

Confidence Interval

CVD:

Cardiovascular Disease

FDR:

False Discovery Rate

FPG:

Fasting Plasma Glucose

HDL-C:

High–density Lipoprotein Cholesterol

HR:

Hazard Ratio

MetS:

Metabolic Syndrome

MR:

Mendelian Randomization

MVMR:

Multivariable Mendelian Randomization

NCEP ATP III:

Adult Treatment Panel III guidelines of the National Cholesterol Education Program

OR:

Odds Ratio

SD:

Standard Deviation

TG:

Triglyceride

WC:

Waist Circumference

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Acknowledgements

Not applicable.

Funding

This study was supported by Zhejiang Key Laboratory of Intelligent Preventive Medicine,Healthy Zhejiang One Million People Cohort (K20230085), the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (2019R01007), Cancer Center, Zhejiang University and Key Research and Development Program of Zhejiang Province (2020C03002) to Xifeng Wu. The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.We appreciate the support and cooperation of all patients and their families who participated in the project.

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Author notes

  1. Shu Li and Chi Pang Wen contributed equally to this work.

Authors and Affiliations

  1. Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

    Shu Li, Huakang Tu, Sicong Wang, Xue Li, Andi Xu, Wenyuan Li & Xifeng Wu

  2. Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, Zhejiang, China

    Shu Li

  3. Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou, Zhejiang, China

    Shu Li

  4. National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China

    Chi Pang Wen & Xifeng Wu

  5. School of Medicine and Health Science, George Washington University, Washington, DC, USA

    Xifeng Wu

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  1. Shu Li
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Contributions

XW conceived and designed the study. SL and CPW analyzed and interpreted the data. SL drafted the manuscript. XW, CPW, HT, SW, WL, XL, and AX revised the draft for important intellectual content. XW supervised the entire project. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Wenyuan Li or Xifeng Wu.

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Ethics approval and consent to participate

Informed consent was obtained to authorize data processing and analysis. Ethical reviews were approved by the Institutional Review Boards at the Taiwan Health Research Institutes. Individually identifying data were removed and remained anonymous during the entire study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Li, S., Wen, C.P., Tu, H. et al. Metabolic syndrome including both elevated blood pressure and elevated fasting plasma glucose is associated with higher mortality risk: a prospective study. Diabetol Metab Syndr 17, 72 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01628-5

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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