(2024) 22:325
Huang et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03546-1

BMC Medicine

RESEARCH

Open AccessDiscrepancies between general and central
obesity in arterial stiffness: observational
studies and Mendelian randomization study
Wuqing Huang1†, Zhaojing Gan2†, Ziting Gao1†, Qiaofen Lin1†, Xiaojiang Li2, Wenhui Xie2, Zesen Gao1,
Zhixian Zhou1, Ziyi Qiu1, Weihong Qiu1, Shanshan Du1, Liangwan Chen3,4*†, Huashan Hong2*† andWeimin Ye1,5,6*†

Abstract
Background Obesity has been linked to arterial stiffness, while no consensus was reached on the association. We
aimed to clarify the association of general and central obesity with arterial stiffness by combining observational studies and Mendelian randomization (MR) study.
Methods Two cross-sectional studies were performed in UK Biobank and Fuqing Cohort, respectively. Two-sample
MR study was conducted using summary data of GWASs from GIANT consortium and UK Biobank. General obesityand central obesity were measured using body mass index (BMI) and waist circumference (WC), respectively. Arterial
stiffness was measured by arterial stiffness index (ASI) in UK Biobank or branchial-ankle pulse wave velocity (baPWV)in Fuqing Cohort.
Results Two observational studies found a consistent positive association of BMI and WC with arterial stiffness
when adjusting for age, sex, education, smoking, alcohol drinking, physical activity, and LDL cholesterol. However,when additionally adjusting for metabolic traits (i.e., systolic blood pressure, diastolic blood pressure, blood glucose,
triglycerides, high-density lipoprotein cholesterol, and WC or BMI), the association with BMI changed to be inverse.
As compared to the lowest quintile group, the adjusted ORs across groups of second to fifth quintile were 0.93,
0.90, 0.83, and 0.72 in UK Biobank and 0.88, 0.65, 0.63, and 0.50 in Fuqing Cohort. In contrast, the positive relationship with WC remained stable with the adjusted ORs of 1.23, 1.46, 1.60, and 1.56 in UK Biobank and 1.35, 1.44, 1.77,
and 1.64 in Fuqing Cohort. MR analyses provided supportive evidence of the negative association with BMI (OR = 0.97,
95%CI = 0.94–1.00) and the positive association with WC (OR = 1.14, 95%CI = 1.08–1.20).

†

Wuqing Huang, Zhaojing Gan, Ziting Gao and Qiaofen Lin Share the firstauthorship.

†

Liangwan Chen, Huashan Hong and Weimin Ye Share the senior authorship.

*Correspondence:
Liangwan Chenchenliangwan@fjmu.edu.cn
Huashan Hong
15959159898@163.com
Weimin Ye
ywm@fjmu.edu.cn
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
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Huang et al. BMC Medicine(2024) 22:325

Page 2 of 11

Conclusions Observational and genetic analyses provide concordant results that central obesity is independently
related to arterial stiffness, while the role of general obesity depends on metabolic status.
Keywords Obesity, Arterial stiffness, Observational study, Mendelian randomization, Epidemiology

Background
Arterial stiffness is a sensitive predictor of various
vascular pathologies, including vascular aging, atherosclerosis, calcification, inflammation, and so on,which has been demonstrated to be strongly related tothe incidence and mortality of cardiovascular diseases[1–3]. As a non-invasive marker, it has the potential
to be used in early screening and intervention of cardiovascular diseases. Several indices have been used toassess the stiffness of arteries, such as carotid-femoral
pulse wave velocity (cfPWV), branchial-ankle PWV(baPWV), and arterial stiffness index (ASI) [4, 5]. The
cfPWV is the most commonly used and both baPWV
and ASI have been reported to be highly correlatedwith cfPWV [6–8].
Obesity has mounted to an epidemic level and has
been reported to increase the risk of multiple chronic
diseases, such as coronary heart disease, stroke, andsome types of cancer. Body mass index (BMI) is themost widely used index for general obesity assessment, but incapable of accurately evaluating theregional adiposity [9]. Central adiposity can be better measured by waist circumference (WC), which
always coexists with other metabolic disorders, suchas hypertension, diabetes, or dyslipidemia, leading
to metabolic syndrome [10]. Some studies found discrepancies between general and central obesity in therole of cardiovascular diseases, for example, central
obesity was more closely associated with major cardiac events than general obesity [11, 12]. A numberof studies have explored the role of obesity in arterial stiffness, some of which investigated the separate
effects of general and abdominal obesity on arterial
stiffness, while no consensus has been reached on thecorrelation [12–29]. For example, previous studies
have reported a positive, negative, or non-significant
association between BMI and arterial stiffness, so as
for WC, and most of which had relatively small sample size and rarely further explored the underlying
reasons [12–29].
Thus, in this study, we aimed to clarify the association of general obesity measured by BMI and central
obesity measured by WC with arterial stiffness in two
cross-sectional studies from both European and Asian
populations, and further examine the causality via
two-sample Mendelian randomization (MR) approach.

Methods
Cross‑sectional studies
Study population

We conducted two cross-sectional studies among participants enrolled in baseline survey of UK Biobank Projectand Fuqing Cohort Project, respectively. UK Biobank is alarge-scale population-based study recruiting more than
500,000 residents in 22 assessment centers across theUK, in which the baseline visit was completed between
March 2006 and July 2010 [30]. The baseline survey inboth projects included questionnaires, physical measurements, and biological sample collection. Fuqing Cohort isan ongoing project in Fuqing city of China, the baseline
survey of which was initiated in 2019 [31].
A total of 169,741 and 7593 participants have completed both baseline assessment and arterial stiffness
measurements in UK Biobank and Fuqing Cohort (until
December 2022), respectively. The exclusion criteria
included: (1) participants with missing data of measurements for BMI, WC, or other metabolic traits (bloodpressure, blood glucose, HDL cholesterol, and triglycerides); (2) participants with outliers of BMI, WC, or ASI/
baPWV, in which mean ± 5 standard deviations were used
to establish the upper and lower boundaries of outlier. As
shown in the flowchart (Fig. 1), after exclusion, a total of
141,402 and 7486 participants were finally included in
two cross-sectional studies, respectively.
Assessments of exposures

General obesity and central obesity were measured by
BMI and WC, respectively. Standing height was measured using a Seca 202 telescopic height-measuring rod
in UK Biobank and using a Tanita BC-601 bioimpedance
analyzer in Fuqing Cohort. Weight was measured usinga Tanita BC-418 MA body composition analyzer in UK
Biobank and a Tanita BC-601 bioimpedance analyzer
in Fuqing Cohort. BMI was calculated as the formula ofweight/height2 (kg/m2). WC was measured with a nonstretchable sprung tape at the halfway between the lowest rib and the top of iliac crest.
Assessment of outcome

Arterial stiffness was the outcome of this study, assessedby a photoplethysmograph transducer (PulseTrace PCA
2TM, CareFusion, USA) in UK Biobank or baPWV
with an automatic waveform analyzer (BP-203 RPE III,

Huang et al. BMC Medicine(2024) 22:325

Page 3 of 11

Fig. 1 Study flowchart

OMRON Health Medical (China) Co, Ltd) in Fuqing
Cohort [32]. In brief, in UK Biobank, the participant wasseated, and removed restrictive clothing from the upperarm; clipping the PulseTrace infrared sensor onto theindex finger of dominant hand and measurement takenover 10 to 15 s. The ASI was assessed as the height of theparticipants divided by the time between the first (systolic) and second (diastolic) wave peaks [33]. In Fuqing
Cohort, the measurement was undertaken by trained
investigators. Participants wore light clothes and wereasked to lie in a supine position; both arms and ankles
were wrapped with cuffs, connected to a plethysmographic sensor and oscillometric pressure sensor, anda heartbeat monitor was set on the left edge of the sternum and electrocardiogram electrodes were set on bothwrists. The average values of baPWV on the left and rightsides were used for analysis. ASI > 13 m/s or BaPWV ≥ 18
m/s is considered indicative of arterial stiffness [34, 35].
Assessment of covariates

In both projects, each participant was interviewed
using a structured questionnaire to collect data ofsocio-demographic information, lifestyle factors, history of disease, and medications. Age was recorded atbaseline assessment. Education background was classified as college/university degree, A levels/AS levels/Olevels/GCSEs/equivalent, CSEs/equivalent/NVQ/equivalent, other professional qualifications/unknown in UK
Biobank; or four categories, corresponding to ≥ 10, 7–9,
1–6, and 0 years of schooling in Fuqing Cohort. Alcohol
drinking status was categorized as never, former, and current drinker. Smoking was also categorized as never, former, and current smoker. Physical activity was assessedusing the International Physical Activity Questionnaire,recorded as summed metabolic equivalent (MET) minutes per week for all activity and grouped by quartiles [3,36]. Blood pressure was consecutively measured twice byusing blood pressure measuring device (Omron HEM7015IT in UK Biobank, Omron U30 in Fuqing Cohort),
and the average blood pressure of two readings was usedin the analysis. Blood samples collected at baseline wereused for serum lipids and blood glucose measurement.
Serum lipids and blood glucose were measured using a
Beckman Coulter clinical chemistry analyzer (AU5800)
in UK Biobank or a Toshiba automatic biochemical analyzer (TBA-120FR) in Fuqing Cohort [34, 37]. The alteredmetabolic traits (i.e., increased blood pressure, increased
blood glucose, increased triglycerides, decreased HDL

Huang et al. BMC Medicine(2024) 22:325

cholesterol, and general obesity by BMI/central obesity
by WC) were defined according to the International Diabetes Federation definition [38].
Two‑sample Mendelian randomization study
Data source

In two-sample MR study, we obtained single nucleotide
polymorphisms (SNPs) strongly associated with bodysizes from the GIANT consortium with sample sizes of
681,275 for BMI and 245,746 for WC, respectively [39,40]. The study outcome was arterial stiffness, measured
by ASI, and the GWAS summary data was obtained fromUK Biobank comprising 127,121 participants [41]. These
GWASs were performed on rank based inverse normaltransformed (INT) residuals, so in which the estimate
was equivalent to per 1-SD of inverse rank normal transformed continuous traits increase in per additional effectallele. All GWAS studies used in this study were conducted in population of European ancestry. Instrumental
variants of metabolic traits were obtained from Global
Lipids Genetics Consortium (GLGC) for HDL cholesterol and triglycerides, Meta-Analyses of Glucose andInsulin-related Traits Consortium (MAGIC) for blood
glucose, and International Consortium of Blood Pressure(ICBP) for blood pressure, respectively. Details of thedata resources are shown in Additional file 1: Table S1.
Genetic instruments selection

In the univariable MR analysis, SNPs strongly associated with exposure at the genomic significance level(P < 5*10−8) were selected as candidate instrumental
variables. The SNP with smallest P value retained in thepresence of linkage disequilibrium, which was assessedby r2 > 0.001 (clumping window size = 10,000 kb) withgenomic data from 1000 Genomes Project as the reference. Available proxies were used as substitutes if SNPs
were unavailable in the GWAS dataset of outcome,while SNPs without available proxies and palindromic SNPs were excluded. In multivariable MR analysis,
SNPs from the univariable MR analysis and available in
GWASs of both exposures were used after linkage disequilibrium clumping by r2 > 0.001 (clumping window
size = 10,000 kb) for building instruments.
Statistical analyses
Cross‑sectional studies

Baseline characteristics are presented as frequency andpercentage for categorical variables and as mean andstandard deviation for continuous variables, and χ2 test
or t-test was used to test the difference in baseline characteristics between participants with and without arterial stiffness. Logistic regression was used to explore theassociation between BMI or WC and arterial stiffness, inPage 4 of 11which both BMI and WC were modeled as quintiles andthe lowest quintile used as reference. Two multivariable
models were built, in which model 1 included age, sex,
education, smoking, alcohol drinking, physical activity,and low-density lipoprotein (LDL) cholesterol; model 2
additionally adjusted for traits included in the metabolic
syndrome (i.e., systolic blood pressure, diastolic bloodpressure, blood glucose, triglycerides, high-density lipoprotein (HDL) cholesterol, and WC or BMI). Test fortrend was conducted by modeling quintiles as continuous
variables. A series of sensitivity analyses were conducted.
First, logistic regression with restricted cubic spline wasapplied to explore the non-linear relationship between
BMI or WC and arterial stiffness by including the exposure as a continuous variable. Second, BMI or WC quintile-specific estimated marginal means were reported andcompared by using analysis of covariance. Third, multivariable models were built by adding each metabolic trait
one by one to adjust for potential confounding variables.
Fourth, stratified analysis by the number of altered metabolic traits (< 3 vs ≥ 3) was performed to evaluate how theassociation between BMI/WC and arterial stiffness differed between the different number of altered metabolic
traits. And the product term of exposure and number ofaltered metabolic traits was generated for assessing theinteraction effect between BMI or WC and the cumulative burden of altered metabolic traits in arterial stiffness.
Fifth, we explored the relation by other anthropometricindices, including waist-to-hip ratio or waist-to-heightratio.
Two‑sample Mendelian randomization study

Inverse variance weighting Mendelian randomization
method (IVW-MR) was used as the main method to generate the estimate, in which random effect model wasused in the presence of heterogeneity. The other four
methods, including MR-Egger, weighted median method(WME), weighted mode, and simple mode, were further
applied to assess the robustness of the results. The R2(proportion of the variance explained by the SNP) andthe F-statistics were calculated to assess the strengthof each instrument [42]. The heterogeneity or horizontal pleiotropy of instrumental variables was tested by
Cochrane Q test or MR-Egger regression intercept term,
respectively. Leave-one-out analysis was performed toassess the sensitivity of each IV. Besides, multivariable
MR analyses including exposure and each metabolic trait
were further performed to explore the role of metabolic
traits in the relationship between exposure and outcome.
All analyses were performed in R 4.1.2 or SAS 9.4
and P value of < 0.05 was considered as of statistical significance. Bonferroni correction approach was also
applied to take multiple testing into account. ThereHuang et al. BMC Medicine(2024) 22:325

were two cross-sectional studies and two outcomes, we
thus adjusted the thresholds of the significance level
as P < 0.0125 (calculated as 0.05/(2*2)) in observational
studies, and P < 0.025 (calculated as 0.05/2) in MR study
with two outcomes.

Results
Cross‑sectional association between general or central
obesity and arterial stiffness

As shown in Additional file 1: Table S2, a total of 141,402
and 7486 participants were included in cross-sectional
studies from UK Biobank and Fuqing Cohort, in which
16,020 (11.33%) and 1427 (19.06%) participants experienced elevated arterial stiffness, respectively. Significant
difference was observed in most characteristics between
groups, except for alcohol drinking in UK Biobank or sex,
alcohol drinking, and physical activity in Fuqing Cohort.
Individuals with arterial stiffness were older and had ahigher mean value of BMI, WC, blood pressure, fasting
glucose, triglycerides, and LDL cholesterol; a lower meanvalue of HDL cholesterol; and a greater proportion of low
education level in both projects.
Figure 2 and Additional file 1: Table S3 show the association between general obesity measured by BMI andarterial stiffness. There was evidence for a consistent

Page 5 of 11

positive association between BMI and arterial stiffness
when adjusting for confounders in both studies. As compared to the lowest quintile group, groups with quintiles
2–5 were significantly associated with elevated arterial
stiffness with the adjusted ORs in model 1 of 1.17, 1.34,
1.40, and 1.39 (P-trend < 0.001) in UK Biobank and 1.51,
1.50, 1.77, and 1.92 (P-trend < 0.001) in Fuqing Cohort.
However, after additionally adjusting for metabolic traits(i.e., WC, blood pressure, blood glucose, triglycerides,and HDL cholesterol), the observed associations changed
to inverse associations with the adjusted ORs in model
2 of 0.93, 0.90, 0.83, and 0.72 (P-trend < 0.001) in UK
Biobank and 0.88, 0.65, 0.63, and 0.50 (P-trend < 0.001)
in Fuqing Cohort. After Bonferroni correction, theseobserved associations kept significant. Similar results
were observed when using restricted cubic spline regression in both studies (Additional file 2: Fig. S1). Analysis
of covariance also reported concordant evidence in bothstudies that means of ASI or baPWV increased withincreasing BMI quintiles in model 1 but declined withincreasing BMI quintiles in model 2 (Additional file 2:
Fig. S2). Additional file 1: Table S4 shows that waist circumference and blood pressure played a more importantrole in the association between BMI and arterial stiffness.
Stratified analysis after adjustment of all confounders

Fig. 2 Association between body mass index and arterial stiffness. A Model 1 in UK Biobank Project; B Model 2 in UK Biobank. C Model 1 in Fuqing
Cohort; D Model 2 in Fuqing Cohort; *Model 1 was adjusted for age, sex, education, smoking, alcohol drinking, physical activity, and LDL cholesterol.
Model 2 was adjusted for age, sex, education, smoking, alcohol drinking, physical activity, LDL cholesterol, blood pressure, blood glucose,
triglycerides, HDL cholesterol, and waist circumferenceHuang et al. BMC Medicine(2024) 22:325

found a negative association in the population withless than three altered metabolic traits, which was notobserved among those with ≥ 3 altered metabolic traits(Additional file 1: Table S5). And significant interaction
was observed between BMI and number of altered metabolic traits in arterial stiffness (P-interaction < 0.001 in
both studies).
Figure 3 and Additional file 1: Table S3 show the association between central obesity measured by WC andarterial stiffness. When adjusting for confounders inmodel 1, there was a positive relation between WC
and arterial stiffness. As compared to the lowest quintile group, groups with quintiles 2–5 were significantly
related to elevated arterial stiffness with the adjusted
ORs of 1.30, 1.57, 1.76, and 1.70 (P-trend < 0.001) in UK
Biobank and 1.61, 1.87, 2.45, and 2.45 (P-trend < 0.001) in
Fuqing Cohort. In contrast to BMI, the observed positive
relationship remained stable after additionally adjusting for metabolic traits in model 2 with the adjusted
ORs of 1.23, 1.46, 1.60, and 1.56 (P-trend < 0.001) in UK
Biobank and 1.35, 1.44, 1.77, and 1.64 (P-trend = 0.009)
in Fuqing Cohort. After Bonferroni correction, theseobserved associations kept significant. Restricted cubic
spline regression and analysis of covariance also generated consistent results in terms of the positive relation

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in two models across both studies (Additional file 2: Figs.
S3 and 4). Additional file 1: Table S4 shows that bloodpressure played a more pronounced role in the association between WC and arterial stiffness. Stratified analysis
after adjustment of all confounders found similar associations of WC with arterial stiffness between groups
with < 3 and ≥ 3 altered metabolic traits in UK Biobank,while a stronger but insignificant association in the groupwith ≥ 3 altered metabolic traits in Fuqing Cohort (Additional file 1: Table S5). And significant interaction wasobserved between WC and number of altered metabolic
traits in arterial stiffness (P-interaction < 0.001 in UK
Biobank, P-interaction = 0.019 in Fuqing Cohort). Additional file 1: Table S6 shows similar positive associations
when measuring central obesity by waist-to-hip ratio or
waist-to-height ratio in both projects.
Mendelian randomization between general or central
obesity and arterial stiffness

As shown in Additional file 1: Tables S7 and S8, F-statistics for all genetic instrument variants were over 10,
indicating minimized weak instrument bias. Figure 4
and Additional file 1: Table S9 present the results from
univariable MR analyses of BMI and WC with arterial
stiffness. A borderline, negative causal association wasFig. 3 Association between waist circumference and arterial stiffness. A Model 1 in UK Biobank Project; B Model 2 in UK Biobank. C Model 1
in Fuqing Cohort; D Model 2 in Fuqing Cohort; *Model 1 was adjusted for age, sex, education, smoking, alcohol drinking, physical activity, and LDLcholesterol. Model 2 was adjusted for age, sex, education, smoking, alcohol drinking, physical activity, LDL cholesterol, blood pressure, blood
glucose, triglycerides, HDL cholesterol, and body mass index

Huang et al. BMC Medicine(2024) 22:325

Page 7 of 11

Fig. 4 Univariable Mendelian randomization analysis of body mass index and waist circumference with arterial stiffness

observed between BMI and arterial stiffness in IVW-MR
analysis (OR = 0.97, 95%CI = 0.94–1.00), which was consistent with the results from the other four MR methods. In Additional file 1: Table S10, sensitivity analysis
by using another GWAS for BMI without sample overlap
with the outcome generated similar results. Positive relationship between WC and arterial stiffness was concordantly observed across five MR methods, with statisticalsignificance in IVW-MR (OR = 1.14, 95%CI = 1.08–1.20),
MR-Egger, and weighted median approaches. After
Bonferroni correction, the association of BMI with AS
tended to be insignificant while the relation of WC with
AS remained significant. Cochrane Q test indicated thepresence of heterogeneity, while leave-one-out analysis
showed that the direction of observed associations kept
stable after leaving each IV (Additional file 2: Figs. S5
and 6). There was no evidence of horizontal pleiotropy.
Multivariable MR study showed that the observed association between BMI and arterial stiffness attenuated tonull when adjusting for blood pressure, blood glucose,or triglycerides, while the association between WC andarterial stiffness always remained significant after adjustment for any metabolic trait (Fig. 5 and Additional file 1:
Table S11).

Discussion
In the present study, results from observational studies inboth European and Asian populations demonstrated that
central obesity measured by WC was independently associated with elevated arterial stiffness, and the causality ofwhich was supported via MR analysis. However, the roleof general obesity measured by BMI was dependent on
metabolic status, in which the positive relationship attenuated to null when adjusting for blood pressure or WC,
even changed to a negative association after adjustment
for all other metabolic phenotypes. Besides, MR analysis
also suggested a negative association between BMI andarterial stiffness. These findings provided observationaland genetic evidence that central obesity is an independent predictor of arterial stiffness, but the impact of general obesity is dependent on metabolic status, indicatingthat appropriate overweight free of abdominal visceraladiposity and metabolic disorders (i.e., metabolically
healthy obesity) might benefit for arterial stiffness.
With the development of economic level, the prevalence of obesity is continuously growing. Although obesity has been recognized as a risk factor of numerous
diseases, emerging data suggested that body fat distribution might play a more pronounced role in the risk ofchronic diseases [43]. BMI is the most handy and common metric to measure general obesity. Several indiceshave been used to assess central obesity, such as WC,
WHR, and WHtR, in which WC is the most commonlyused [10]. A number of studies have investigated theassociation of obesity with arterial stiffness but generated mixed results. For example, cross-sectional studies
in NHANES of 5309 healthy participants and in 1442
Chinese obese and overweight adults found a positive
association of obesity with arterial stiffness regardlessof being measured by BMI, WC, or other related indicesHuang et al. BMC Medicine(2024) 22:325

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Fig. 5 Multivariable Mendelian randomization analysis of body mass index and waist circumference with arterial stiffness[16, 28]. However, some observational studies found discrepancies between general and abdominal obesity inrelation to arterial stiffness. Three cross-sectional studies
in European or Japanese populations with sample sizes of
305, 1031, and 2789, respectively, found that central obesity measured by WC or WHR was significantly related
to arterial stiffness, while no evidence for the association
between BMI and arterial stiffness [19, 23, 25]. Anotherthree cross-sectional studies including 1662 individuals
in Brazil, 697 participants in Korea, and 794 older adults
in China found a negative association between BMI andarterial stiffness [27, 29, 44]. Some studies suggested thatthe role of obesity in arterial stiffness might be affectedby metabolic status. For example, a cohort study consisted of 1190 Chinese participants reported that theassociation of increased BMI and arterial stiffness was
predominantly mediated through blood pressure, andanother cross-sectional study also indicated that metabolically unhealthy individuals defined as individuals
having two or more of the metabolic syndrome components suffered increased risk of arterial stiffness and BMI
was positively associated with arterial stiffness only inmetabolically healthy individuals, in which WC was notincluded in multivariable model [15, 26]. Regarding central obesity, the Brazilian study found a positive association between WC and arterial stiffness but depending on
blood pressure, while the study in Korea reported a negative relation between central obesity and arterial stiffness

in subjects with metabolic syndrome [20, 27]. These
inconsistent results may be related to relatively small
sample size and difference in covariates included in multivariable models.
In this study, we thus performed two large-scalecross-sectional studies from European and Asian populations to clarify the association, which yielded concordant results regarding the discrepancies between generaland central obesity in arterial stiffness. A positive association of both general and central obesity was observedwith arterial stiffness if not including metabolic phenotypes as covariates. However, the observed association
with BMI changed to be inverse after additional adjustment for metabolic phenotypes, while the association
between central obesity and arterial stiffness kept stable
when considering metabolic phenotypes. MR analyses
provided additional supportive genetic evidence of thenegative association with BMI and positive association
with WC. These findings suggest that general obesity
may not be an independent risk factor of arterial stiffness
but affect the occurrence of arterial stiffness via related
metabolic sequelae. Besides, central obesity has previously been demonstrated to be more closely related tometabolic impairment than general obesity [45]. The roleof central obesity in developing metabolic syndrome has
been described as early as in 1991, which is now a criterion for metabolic syndrome diagnosis [21, 45]. All thesepieces of evidence indicate that obesity-related metabolic

Huang et al. BMC Medicine(2024) 22:325

impairments played a predominated role in the arterial stiffness than obesity itself, which may provide someclues for the phenomenon of “obesity paradox” in cardiovascular outcomes.
There were several strengths in this study. First,
the observational study and MR study have specificstrengths and limitations, enabling to complement
each other to some extent. Second, two large-scaleobservational studies from European and Asian populations were conducted that can provide more statistical power to detect associations than previous studies
and validation in different populations. Third, several
MR methods were applied to examine the causality forthe observed association and generated similar results,
contributing to ensuring the robustness of results.
Fourth, the extensive data collected in UK Biobank andFuqing Cohort allowed us for comprehensive adjustments of potential confounders and explored the roleof metabolic phenotypes in the associations. Nevertheless, several limitations should be noted. First, the
ASI and baPWV were used to measure the arterial
stiffness in UK Biobank and Fuqing Cohort, respectively, instead of the cfPWV, which is regarded as thegold standard. However, the validity and accuracy of
ASI and baPWV have been demonstrated in previous
studies as compared to cfPWV. Besides, analyses using
ASI and baPWV yielded concordant results, supporting
the validity of both indices in turn and suggesting thereliability of the results. Future large-scale population
study using cfPWV as measurement is needed to confirm these findings. Second, the possibility of residualbias cannot be completely ruled out, such as measurement error for WC. However, similar results from bothobservational studies, and again similar results whenusing waist-to-hip ratio and waist-to-height ratio as
alternative exposure variables, suggested the robustness of the findings and possibly small influence ofbias due to measurement error for WC. Third, there
were over 50% overlapping sample between GWASs
from BMI and ASI, which may lead to around 5% relative bias [46]. However, we have performed sensitivity analysis by using another GWAS data source for
BMI which has no sample overlap with the outcome.
The similar results suggest the robustness of the study
findings. Fourth, GWASs used in this MR study wereobtained from European populations, and further testing is needed in other ethnic populations.

Conclusions
Observational and genetic analyses provide concordantresults that central obesity is an independent risk factor of arterial stiffness, while the role of general obesity
depends on metabolic status.

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Abbreviations
ASI	Arterial stiffness index
baPWV	Branchial-ankle pulse wave velocity
BMI	Body mass index
cfPWV	Carotid-femoral pulse wave velocity
CI	Confidence interval
LDL	Low-density lipoprotein
HDL	High-density lipoprotein
WC	Waist circumference

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03546-1.
Additional file 1: Table S1. Information of GWAS summary data. Table S2.
Characteristics of the study population between groups with and without
arterial stiffness in cross-sectional studies. Table S3. Association of bodymass index or waist circumference with arterial stiffness. Table S4. Association of body mass index or waist circumference with arterial stiffness inmodels additionally adjusted for each metabolic trait. Table S5. Stratification and interaction analysis of body mass index or waist circumferencewith arterial stiffness by number of altered metabolic traits. Table S6.
Association of waist-to-hip ratio or waist-to-height with arterial stiffness.
Table S7. Genetic variants for body mass index. Table S8. Genetic variants
for waist circumference. Table S9. Mendelian randomization of body massindex or waist circumference with arterial stiffness. Table S10. Sensitivity analysis for mendelian randomization of body mass index or waist
circumference with arterial stiffness. Table S11. Multivariable mendelianrandomization of body mass index or waist circumference with arterial
stiffness.
Additional file 2: Fig. S1. Association between body mass index and arterial
stiffness via restricted cubic spline regression. Fig. S2. Body mass index
quintile-specific estimated marginal means of brachial-ankle pulse wave
velocity (Ba-PWV) or arterial stiffness index (ASI) via analysis of covariance.
Fig. S3. Association between waist circumference and arterial stiffness via
restricted cubic spline regression. Fig. S4. Waist circumference quintilespecific estimated marginal means of brachial-ankle pulse wave velocity(Ba-PWV) or arterial stiffness index (ASI) via analysis of covariance. Fig. S5.
MR leave-one-out sensitivity analysis for body mass index on arterial stiffness. Fig. S6. MR leave-one-out sensitivity analysis for waist circumference
on arterial stiffness.
Acknowledgements
We thank the participants and investigators who contributed to the Fuqing
cohort, UK Biobank, GIANT Consortium, Global Lipids Genetics Consortium,
ICBP consortium, MAGIC consortium.
Authors’ contributions
All authors were responsible for the study concept and design. WH, WY, HH,
and LC obtained funding. ZG1, QL, XL, WX, ZG3, ZZ, ZQ and SD attended field
investigation. WH, ZG1, SD and WQ participated in the pre-processing of thedatasets. WH and ZG2 did the statistical analysis and drafted the manuscript.
WH, WY, HH, LC and ZG2 critically reviewed and improved the drafts of themanuscript. All authors read and approved the final manuscript.
Funding
This work was supported by Major Scientific Research Program for
Young and Middle-aged Health Professionals of Fujian Province, China(2023ZQNZD01020044), National Natural Science Foundation of China(82304213), and Start-up Fund for high-level talents of Fujian Medical
University (XRCZX2021026) to Dr. Wuqing Huang; Fujian Province central
guide local science and technology development fund project (2023L3009)
to Dr. Huashan Hong; Government of Fuqing city [grant number: 2019B003],
Department of Science and Technology of Fujian [grant number: 2019L3006,
2019Y9021], and Start-up Fund for high-level talents of Fujian Medical
University (XRCZX2017035, XRCZX2020034, XRCZX2023030) to Dr. Weimin Ye;
National Natural Science Foundation of China (82241209), Major Science andTechnology Project of Fujian Provincial Health Commission (2022ZD01004),

Huang et al. BMC Medicine(2024) 22:325

and Fujian Provincial Special Reserve Talents Fund (2021–25) to Dr. Liangwan
Chen; and Start-up Fund for high-level talents of Fujian Medical University(XCRZX2023005) to Dr. Shanshan Du. The funder had no role in study design,
data collection and interpretation, or the decision to submit the work for
publication.
Availability of data and materials
Genetic summary statistics and analytic code will be made available upon
reasonable request to the corresponding author (ywm@fjmu.edu.cn).

Declarations
Ethics approval and consent to participate
UK Biobank study has been approved by the North West Multi-Centre
Research Ethics Committee (REC reference: 21/NW/0157). Fuqing Cohort study
has been approved by the Ethics Review Committee of Fujian Medical University (approval number [2017–07], [2020–58], [2021–109], and [2022–85]). All
participants in UK Biobank and Fuqing Cohort have signed informed consent.
Mendelian randomization study is an analysis of publicly available summary
data that does not require ethical approval.
Consent for publication
Not applicable.
Competing interests
The authors declare no conflict of interest.
Author details
1
Department of Epidemiology and Health Statistics, School of Public Health,Fujian Medical University, University Town, No 1, Xue Yuan Road, Fujian 350108,
China. 2 Department of Geriatrics, Fujian Key Laboratory of Vascular Aging,
Fujian Institute of Geriatrics, Union Hospital, Fujian Medical University,,
Fujian 350001, China. 3 Department of Cardiovascular Surgery, Union Hospital,
Fujian Medical University, No. 29 Xinquan Road, Fuzhou, Fujian 350001, China.
4
Key Laboratory of Cardio‑Thoracic Surgery, Fujian Medical University, Fuzhou,
Fujian, China. 5 Key Laboratory of Ministry of Education for Gastrointestinal
Cancer, Fujian Medical University, Fuzhou, China. 6 Department of Medical
Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Received: 11 March 2024 Accepted: 29 July 2024

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