Phenotypic Age and Neutrophil-to-Lymphocyte Ratio mediate the association between the triglyceride glucose-body mass index and the cardiovascular mortality risk in hypertensive patients

Phenotypic Age and Neutrophil-to-Lymphocyte Ratio mediate the association between the triglyceride glucose-body mass index and the cardiovascular mortality risk in hypertensive patients | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Phenotypic Age and Neutrophil-to-Lymphocyte Ratio mediate the association between the triglyceride glucose-body mass index and the cardiovascular mortality risk in hypertensive patients View ORCID Profile Yu Pan , Xinxin Liu , Siming Pan , Shuang Yin , Xu Zhao , kai Zhang , Yinong Jiang , Yan Liu doi: https://doi.org/10.1101/2025.07.20.25331891 Yu Pan 1 Department of Cardiology, the First Affiliated Hospital of Dalian Medical University , Dalian, Liaoning, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yu Pan Xinxin Liu 2 Department of Cardiology, the First People’s Hospital of Jinzhou District , Dalian, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Siming Pan 3 Department of Intensive Care, Dalian Public Health Clinical Center , Dalian, Liaoning, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shuang Yin 1 Department of Cardiology, the First Affiliated Hospital of Dalian Medical University , Dalian, Liaoning, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xu Zhao 1 Department of Cardiology, the First Affiliated Hospital of Dalian Medical University , Dalian, Liaoning, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site kai Zhang 1 Department of Cardiology, the First Affiliated Hospital of Dalian Medical University , Dalian, Liaoning, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yinong Jiang 1 Department of Cardiology, the First Affiliated Hospital of Dalian Medical University , Dalian, Liaoning, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yan Liu 1 Department of Cardiology, the First Affiliated Hospital of Dalian Medical University , Dalian, Liaoning, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: liuyanjulie{at}outlook.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background The triglyceride glucose-body mass index (TyG-BMI) has emerged as a novel marker for insulin resistance and metabolic syndrome. However, its relationship with prognosis in hypertensive patients remains uncertain. This study investigates the association between TyG-BMI and all-cause and cardiovascular mortality in hypertensive patients. Additionally, it explores the mediating roles of the neutrophil-to-lymphocyte ratio (NLR) and Phenotypic Age (PhenoAge) in these associations. Methods Data from 5164 hypertensive adults participating in the National Health and Nutritional Examination Surveys (NHANES) from 1999 to 2018 were analyzed. Mortality details were obtained from the National Death Index (NDI). Restricted cubic spline (RCS) was used to assess potential nonlinear associations, and weighted Cox proportional hazards models assessed the independent association of TyG-BMI with mortality risk. Time-dependent ROC curve analysis evaluated the predictive ability of TyG-BMI for survival. Structural equation modeling (SEM) explored indirect effects of NLR and PhenoAge on mortality associations. Results Over a median follow-up of 8.33 years, there were 1005 deaths, including 398 from cardiovascular causes. Participants were stratified by TyG-BMI classes using K-means clustering. Higher TyG-BMI levels were associated with increased risks of all-cause (HR 1.28, 95% CI 1.02–1.60, P = 0.03) and cardiovascular mortality (HR 1.62, 95% CI 1.06–2.46, P = 0.025). Time-dependent ROC analysis showed increasing TyG-BMI predictive accuracy for both all-cause and cardiovascular mortality over 10-, 15-, and 20-years, respectively. RCS analysis revealed a U-shaped relationship between TyG-BMI and mortality risk, with an inflection point at TyG-BMI = 239; deviations below or above this threshold significantly impacted mortality risk. SEM identified NLR and PhenoAge as mediators in high-risk hypertensive individuals (TyG-BMI >239). Conclusion TyG-BMI predicts all-cause and cardiovascular mortality in hypertensive patients, mediated by NLR and PhenoAge. Incorporating TyG-BMI, NLR, and PhenoAge into clinical practice may help improve risk stratification and guide targeted interventions for high-risk individuals. Download figure Open in new tab Introduction Hypertension represents a significant global public health challenge 1 – 3 , affecting an estimated 1.3 billion adults worldwide, with prevalence expected to rise in the forthcoming years 4 . This condition markedly increases the risk of cardiovascular diseases (CVDs), including coronary heart disease, stroke, and heart failure, and is also strongly linked to all-cause mortality 5 – 7 . The increasing prevalence of hypertension and its poor prognosis are driven by factors such as an aging population and unhealthy lifestyle factors, including high salt and sugar diets and physical inactivity 8 , 9 . Recently, the triglyceride-glucose (TyG) index in conjunction with body mass index (BMI) has emerged as a novel marker, referred to as the TyG-BMI, which closely correlates with insulin resistance (IR) and metabolic syndrome 10 , 11 . These metabolic disturbances are critical factors associated with hypertension and the elevated risk of cardiovascular events 12 . Despite evidence suggesting that TyG-BMI predicts adverse health outcomes 13 , 14 , its relationship with mortality in hypertensive individuals remains underexplored. Moreover, systemic inflammation and biological aging are emerging areas of interest in hypertension research. The neutrophil-to-lymphocyte ratio (NLR) is an established marker of inflammation associated with negative outcomes in hypertensive patients 15 , 16 . Concurrently, Phenotypic Age (PhenoAge), which assesses biological age through clinical biomarkers, offers potentially superior predictive power over chronological age 17 . While PhenoAge has been linked to health outcomes in those with IR 18 , its mediating role alongside NLR in TyG-BMI-related mortality has yet to be thoroughly investigated. This study aims to bridge the knowledge gap by leveraging data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. We will investigate the relationship between TyG-BMI and both all-cause and cardiovascular mortality, focusing on hypertensive individuals. Additionally, we will explore the potential mediating roles of NLR and PhenoAge using structural equation modeling (SEM). By examining these interactions, we strive to deepen our understanding of the complex interplay among metabolic dysregulation, inflammation, biological aging, and mortality risk. Our findings aim to offer novel insights and strategies for enhancing long-term survival outcomes in patients with hypertension. Methods Data source The information utilized for this research was obtained from the NHANES database from 1999 to 2018, which is managed by the National Center for Health Statistics (NCHS) under the Centers for Disease Control and Prevention (CDC). NHANES represents a nationwide survey designed to collect data regarding the health and nutritional status of civilian residents in the United States. Informed consent was obtained from all participants prior to their involvement in the survey, and the NHANES dataset ensures that identifiable patient information is not included. Study population Blood pressure measurements were conducted by qualified healthcare professionals using a mercury sphygmomanometer at mobile examination centers (MEC). Participants were seated during the measurements, which were primarily taken from the right arm, unless specific circumstances dictated otherwise. After a five-minute resting period, blood pressure readings were obtained consecutively three times, and the average of these readings was used for subsequent analysis. Hypertension was defined as a systolic blood pressure of 140 mmHg or higher, a diastolic blood pressure of 90 mmHg or higher, a self-reported diagnosis of hypertension by a physician, or the use of antihypertensive medication 19 . Participants who lacked complete survival data, blood cell counts, TyG-BMI index, other covariables, and fasting sample weights were excluded from the study. Additionally, individuals with a confirmed diagnosis of cancer or malignant tumors, as well as those with a history of dialysis, were also excluded. Ultimately, the analysis included a total of 5164 participants ( Fig. 1 ). Download figure Open in new tab Fig. 1 Flowchart of the study population. TyG-BMI: Triglyceride glucose-body mass index; CAD: coronary arterial disease Assessment of triglyceride glucose with body mass index The calculation formula for TyG index is ln [TG (mg/dl) × FPG (mg/dl)/2], and the TyG-BMI index is calculated as TyG×BMI. 10 . Ascertainment of mortality and follow-up Mortality status was determined by linking NHANES data with records from the National Death Index (NDI), accessible at https://www.cdc.gov/nchs/data-linkage/mortality-public.htm . Participants were classified as either deceased or living based on the information obtained from the NDI. Participants who were not matched with any death records were considered to be alive through the follow-up period. The follow-up duration was calculated from the date of the NHANES examination until either the date of death or December 31, 2019 (updated in 2022), depending on which event occurred first. The underlying causes of death were classified according to the International Classification of Diseases, Tenth Revision (ICD-10). The main outcomes of this study included mortality from all causes and specifically from cardiovascular diseases. Cardiovascular mortality was categorized by the NCHS as deaths associated with heart diseases, identified by ICD-10 codes (I00-I09, I11, I13, I20-I51, I60-I69) 20 . Assessment of inflammation Indicators The formulas for calculating the NLR, Platelet-to-Lymphocyte Ratio (PLR), Lymphocyte-to-Monocyte Ratio (LMR),Systemic Inflammation Index (SII), and Systemic Inflammation Response Index (SIRI) are as follows: NLR = Neutrophil count / Lymphocyte count, PLR = Platelet count / Lymphocyte count, LMR = Lymphocyte count / Monocyte count, SII = Platelet count × Neutrophil count / Lymphocyte count, and SIRI = Neutrophil count ×Monocyte count / Lymphocyte count 21 . Evaluation of Biological Aging Indicators Indicators of biological aging were evaluated through the Levine method for PhenoAge and the Klemera-Doubal technique for BioAge, both of which utilize clinical laboratory blood chemistry analyses. The BioAge R package was utilized to compute both algorithms 22 . PhenoAge is established through a multivariate analysis approach that combines chronological age with eight specific biomarkers: Albumin, Alkaline phosphatase, Creatinine, Glycated hemoglobin, White blood cell count, Lymphocyte percentage, Mean cell volume, and Red cell distribution width. These biomarkers, together with age, yield a ‘mortality score,’ which is later transformed into a biological age value 23 . In contrast, BioAge is derived using the Klemera-Doubal method, a regression-oriented approach that determines biological age by reducing the discrepancy between the predicted and chronological age within a reference group. This model encompasses nine biomarkers: Forced expiratory volume in 1 second, Systolic blood pressure, Albumin, Alkaline phosphatase, Blood urea nitrogen, Creatinine, C-reactive protein, Glycated hemoglobin, and Total cholesterol 24 , 25 . Within the BioAge R package, an output will not be generated if more than two of the biomarkers are absent, signifying that while the model can accommodate some missing data, the majority of biomarkers are crucial for precise calculations 26 . Ascertainment of covariates Data pertaining to various demographic and health-related variables were collected, including age, gender, ethnicity, educational level, the ratio of family income to poverty (PIR), BMI, diabetes mellitus, smoking habits, alcohol consumption status, history of cardiovascular disease (CVD), HbA1c, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), total cholesterol (TC), TG, serum creatinine (Scr), serum uric acid (UA), and estimated glomerular filtration rate (eGFR). Race was categorized into Mexican American, Non-Hispanic Black, Non-Hispanic White, and other groups. Education levels were classified as ‘Below high school’ and ‘High School or above’ 27 . Smoking status was divided into three categories: never smokers, former smokers, and current smokers 28 . BMI was calculated as weight in kilograms divided by the square of height in meters. According to the diagnostic criteria for diabetes established by the American Diabetes Association, diabetes was defined by the use of insulin or oral hypoglycemic medication, FBG≥126 mg/dL, or an HbA1c level≥6.5% 29 . CVD is defined as a composite of Congestive Heart Failure (CHF), Coronary Artery Disease (CAD), and stroke 30 .The CVD section includes inquiries such as, ‘Has a doctor or other health professional ever informed you that you/he/she had congestive heart failure, coronary heart disease, angina, heart attack, stroke, etc.?’ These questions, labeled as MCQ160B-F in the household questionnaires administered during home interviews, were utilized to identify participants with a history of CVD if they responded ‘yes’ to any of these inquiries. The eGFR was calculated as the following formula: eGFR (mL/min/1.73 m²) = 186 × Scr(-1.154) × Age(-0.203) × 0.742 (if female) 31 . Statistical analyses The NHANES survey utilizes a complex, multistage, probability sampling design to obtain a nationally representative sample of the non-institutionalized U.S. population. To account for this sampling design and ensure valid statistical inferences, it is essential to apply appropriate sampling weights in the analysis. We utilized the weight of the fasting sample from 1999 to 2018 (WTSAF4YR/ WTSAF2YR) as the new weight. The specific calculation method is as follows: for the years 1999-2002, the new weight is calculated as WTSA4YR × (4 / 20); for the years 2003 and later, the new weight is calculated as WTSAF2YR × (2 / 20). Data are presented as mean ± standard deviation or proportions. We employed either a weighted χ 2 test for categorical variables or a weighted linear regression model for continuous variables to assess differences among the various TyG-BMI classes. We employed the K-means clustering algorithm based on Euclidean distance to categorize patients according to their TyG-BMI values. K-means was selected for its computational efficiency and ease of visualization. This method partitions the data into K clusters by minimizing the within-cluster sum of squares. The procedure involves applying the elbow rule to identify the optimal number of clusters, randomly selecting initial cluster centers, assigning each patient to the nearest centroid, and sequentially updating the centroids. This iterative process continues until the within-cluster sum of squares is minimized. Through cluster analysis, we classified TyG-BMI into three categories: the first category (low level) has a TyG-BMI value of 223 ± 33.8; the second category (moderate level) has a value of 281 ± 43.8; and the third category (high level) has a value of 360 ± 55.1, as illustrated in Figure 2 . Download figure Open in new tab Fig. 2 The results of the K-means clustering. ( A) Three clusters were found using the K-means method with Euclidean distance: the x- and y-axes are principal components of the change in the TyG-BMI; (B) Data visualization for the classes of the change in the TyG-BMI: Class 1 (n=2317) exhibited a TyG-BMI value of 223±33.8; Class 2 (n=1869) showed a TyG-BMI value of 281±43.8; Class 3 (n=978) had a TyG-BMI value of 360±55.1; ( C) The distribution of TyG-BMI classes by years. We employed restricted cubic splines (RCS) for evaluation. Following the criteria set by the Akaike Information Criterion (AIC), we selected four knots. The shape of the curve derived from the RCS determines the position of the inflection point. Cox proportional hazards models, weighted by survey data, were utilized to evaluate the independent relationship between TyG-BMI and both all-cause and cardiovascular mortality among patients with hypertension. The ‘timeROC’ package was utilized to assess the accuracy of TyG-BMI in predicting survival outcomes at different time points 15 , 32 . Stratified and interaction analyses were conducted based on variables such as age (<60 years vs. ≥60 years), sex (male vs. female), race (Mexican American, non-Hispanic Black, non-Hispanic White, and other), and eGFR (< 90 vs. ≥90 mL/min/1.73 m²). In this study, we employed SEM to investigate the impact of a TyG-BMI value greater than 239 on all-cause mortality and cardiovascular mortality. Analyses were conducted using the lavaan package in R. Our hypothesis posits that TyG-BMI influences all-cause mortality or cardiovascular mortality in hypertensive patients through two mediating variables: NLR and PhenoAge. Simultaneously, we adjusted for multiple potential confounders in the model, including age, gender, race, smoking status, alcohol consumption, marital status, education level, and PIR. Specific methods for SEM can be referenced in the supplementary materials. Statistical analyses were performed using R version 4.4.2 and R studio version 2024.9.1.394. A two-sided p-value of less than 0.05 was considered statistically significant. Results Baseline characteristics of study participants This study included a total of 5,164 participants who were categorized into three groups based on TyG-BMI levels using cluster analysis. The baseline characteristics and intergroup differences are presented in Table 1 . Participants in Class 3, which represented the group with the highest TyG-BMI levels, had the youngest average age and the highest proportion of female patients, Paradoxically, aging-related indicators, such as phenoAge and bioAge, were the lowest in this group, despite the presence of elevated cardiovascular and inflammatory risks factors. This group also had the lowest proportion of individuals who had never smoked or consumed alcohol. Additionally, class 3 demonstrated the highest eGFR values, indicating better renal function. However, obesity-related indicators, such as BMI and abdominal circumference, exhibited the highest values in Class 3, along with elevated levels of inflammatory markers, such as SII and SIRI. Regarding cardiovascular diseases, the incidence of stroke was the highest. View this table: View inline View popup Table 1 Baseline characteristics of study participants Associations of TyG-BMI with all-cause and cardiovascular mortality in patients with hypertension The median duration of follow-up was 8.33 years, with an interquartile range of 4.58 to 12.42 years. Over 5,164 person-years of follow-up, there were 1,005 deaths from all causes, of which 398 were linked to CVD. We developed three models to assess the independent impact of TyG-BMI classes on mortality. The hazard ratios (HRs) and 95% confidence intervals (CIs) for the three classifications are detailed in Table 2 . In the completely adjusted model (Model 3), we categorized TyG-BMI according to the findings from the cluster analysis. In comparison to individuals in Class 1 (low TyG-BMI), those classified in Class 3 (high TyG-BMI) showed a significantly higher risk of all-cause mortality, with an HR of 1.28 (95% CI, 1.02–1.60). Furthermore, participants in both Class 2 (moderate TyG-BMI) and Class 3 demonstrated a significantly increased risk of cardiovascular mortality, with HRs of 1.36 (95% CI, 1.04–1.78) and 1.62 (95% CI, 1.06–2.46), respectively. View this table: View inline View popup Download powerpoint Table 2 The relationships between different TyG-BMI classes and mortality risk Subgroup analyses were conducted to further explore the association between TyG-BMI and both all-cause and cardiovascular mortality risks across various demographic and clinical variables, including age, gender, race, and eGFR. The results indicated that the effect of TyG-BMI on all-cause and cardiovascular mortality was consistent across all subgroups, with no significant interactions effects observed ( P for interaction > 0.05) (Supplementary Table 1). Nonlinear association of TyG-BMI with all-cause and cardiovascular mortality in patients with hypertension To explore the potential nonlinearity between TyG-BMI and mortality, we utilized a Cox proportional hazards regression model featuring restricted cubic splines. The smooth curve fitting, fully adjusted for age, sex, race, marital, education level, the ratio of family income to poverty, smoking status, alcohol consumption, Diabetes Mellitus, eGFR, SBP, and DBP, indicated a nonlinear U-shaped relationship between TyG-BMI and both all-cause and cardiovascular mortality ( Fig. 3 ). Furthermore, we assessed the link between TyG-BMI and mortality through both a Cox proportional hazards model and a piecewise two-part Cox proportional hazards model. As indicated in Table 3 , the inflection point for TyG-BMI was identified at a value of 239. For TyG-BMI values below this threshold, a one standard deviation increase in TyG-BMI was significantly correlated with a lower risk of all-cause mortality (HR = 0.53; 95% CI, 0.38-0.72; P < 0.001) and a similarly reduced risk of cardiovascular mortality (HR = 0.28; 95% CI, 0.32-0.80; P = 0.004). Conversely, for TyG-BMI values surpassing 239, a one standard deviation increase was significantly associated with a heightened risk of all-cause mortality (HR = 1.22; 95% CI, 1.07-1.38; P = 0.003) and cardiovascular mortality (HR = 1.24; 95% CI, 1.04-1.48; P = 0.018). Download figure Open in new tab Fig. 3 The association of TyG-BMI with mortality among hypertension visualized by restricted cubic spline. (A) All-cause mortality; (B) Cardiovascular mortality. Hazard ratios were adjusted for age, sex, race, marital, education level, the ratio of family income to poverty, smoking status, alcohol consumption and Diabetes Mellitus, eGFR, SBP and DBP. View this table: View inline View popup Download powerpoint Table 3 A piecewise two-part Cox proportional hazards model The predictive ability of TyG-BMI for all-cause and cardiovascular mortality in patients with hypertension Time-dependent ROC curve analysis revealed that AUC for the TyG-BMI was 0.81, 0.85, and 0.89 for 10-year, 15-year, and 20-year all-cause mortalities, respectively ( Fig. 4A ). Similarly, the AUC for TyG-BMI was 0.83, 0.87, and 0.91 for 10-year, 15-year, and 20-year cardiovascular mortalities, respectively ( Fig. 4B ). These results demonstrate the strong predictive ability of TyG-BMI for both all-cause and cardiovascular mortality over time. Download figure Open in new tab Fig. 4 Time-dependent ROC curves of the NLR for predicting all-cause mortality. (A) All-cause mortality; (B) Cardiovascular mortality. The ROC curves were adjusted for age, sex, race, marital, education level, the ratio of family income to poverty, smoking status, alcohol consumption and Diabetes Mellitus, eGFR, SBP and DBP. Mediation analysis In the SEM analysis focused on individuals with a TyG-BMI greater than 239 (n=3326), we observed significant indirect effects of TyG-BMI on all-cause mortality, mediated through PhenoAge and NLR. Specifically, the indirect effect of TyG-BMI on all-cause mortality through NLR was 0.002 ( P =0.034), while the indirect effect through PhenoAge was 0.006 ( P =0.002). The total effect of TyG-BMI on all-cause mortality was estimated to be 0.013 ( P <0.001). Model fit indices demonstrated good performance, with CFI=0.995, TLI=0.963, and RMSEA=0.061. The indirect effect of TyG-BMI on cardiovascular mortality through NLR alone was not significant (0.001, P =0.194), whereas the indirect effect of TyG-BMI on cardiovascular mortality through PhenoAge alone was significant, estimated at 0.004 ( P =0.003). The total effect of TyG-BMI on cardiovascular mortality was estimated to be 0.008 ( P =0.001). Model fit indices demonstrated strong performance in this context (CFI=0.997, TLI=0.977, RMSEA=0.047), and the effect values of each mediation path are illustrated in Fig. 5 (A, B). Download figure Open in new tab Fig. 5 Sequential Chain Mediation Analysis Using Structural Equation Modeling. (A) All-cause mortality; (B) Cardiovascular mortality. stdTyG-BMI: Standardized Triglyceride-Glucose Body Mass Index. We combined the method of principal component analysis (PCA) to help adjust for confounding factors such as age, gender, race, smoking status, alcohol consumption, marital status, education level, and PIR, while also helping to reduce model complexity and dimensionality. Discussion In this analysis of 5,164 hypertensive patients based on the NHANES database, we identified a significant association between TyG-BMI and both all-cause and cardiovascular mortality, with TyG-BMI demonstrating robust, time-dependent predictive power. Specifically, hypertensive individuals with a TyG-BMI greater than 239 exhibited a notably higher mortality risk. Additionally, our findings revealed that NLR and PhenoAge mediated the relationship between TyG-BMI and mortality risk, further elucidating the mechanisms underlying this association. Obesity is widely recognized as one of the most prominent risk factors for the development of hypertension 33 .The interplay between obesity and IR exacerbates the pathophysiology of hypertension, exerting a synergistic effect on blood pressure regulation 34 . The TyG index, derived from triglycerides and glucose levels, has emerged as a readily accessible indicator of insulin sensitivity and a recognized risk marker for IR 35 , 36 . On the other hand, BMI, calculated from height and weight, is commonly utilized to assess the risk of obesity and metabolic diseases 37 , 38 . Recent studies have indicated that the TyG-BMI index, which incorporates TyG index and BMI, is superior to the TyG index or BMI alone in predicting IR. Comparative analyses of the TyG-BMI index with other related indicators, such as the TyG index combined with waist circumference (TyG-WC) or the waist-to-height ratio (TyG-WHtR), have indicated that the TyG-BMI index outperforms these other markers in predicting IR 10 , 39 . Moreover, TyG-BMI index has been linked to several adverse health outcomes, including ischemic stroke 14 , 40 , cardiovascular mortality risk in diabetic patients 41 , and the incidence and prognosis of cardiovascular disease 41 , 42 . Previous study has also suggested an association between TyG-BMI index and mortality risk in patients with hypertension or coronary heart disease 43 . The current study extends these findings by demonstrating that TyG-BMI index is a significant predictor of both all-cause and cardiovascular mortality in patients with hypertension. Importantly, our study is the first to illustrate that the predictive accuracy of TyG-BMI improves over time. This time-dependent association likely reflects the cumulative effects of metabolic abnormalities and IR, which progressively worsen over the course of hypertension. The results from time-dependent ROC curve underscore the importance of recognizing these cumulative metabolic abnormalities, as they have a profound impact on the predictive model’s performance. The NLR is a straightforward and reliable indicator of systemic inflammatory status 44 . Elevated NLR levels are often observed with increasing obesity grades, reflecting a heightened inflammatory response, particularly in individuals with chronic low-grade inflammation 45 . Inflammation is widely recognized as a critical factor in the onset and progression of atherosclerosis. Previous studies have indicated that hypertensive patients are particularly prone to low-grade chronic inflammation, which accelerates arteriosclerosis and increases the risk of cardiovascular events 46 , 47 . This study demonstrated that NLR significantly mediated the relationship between TyG-BMI and both all-cause and cardiovascular mortality. This suggested that metabolic abnormalities not only elevated mortality risk directly, but also amplified it through the promotion of systemic inflammatory responses. Elevated NLR levels in individuals with high TyG-BMI further reinforced the strong link between metabolic syndrome and systemic inflammation. These results align with the findings of Xuexue Zhang et al. (2024), who reported a positive association between NLR and cardiovascular risk in hypertensive patients 15 . Notably, our study advances this understanding by clarifying the mediating role of NLR in the pathway between metabolic indicators, such as TyG-BMI, and mortality risk. The mediating effect of NLR highlights the critical role of inflammation in exacerbating the adverse outcomes associated with metabolic syndrome and hypertension. Targeting systemic inflammation through pharmacological or lifestyle interventions may thus represent an effective approach to reducing cardiovascular and all-cause mortality in hypertensive patients with high TyG-BMI. PhenoAge, a measure of a “biological age”, is calculated using a series of biomarkers that provide a more accurate reflection of an individual’s health status and mortality risk compared to chronological age. An increased PhenoAge typically indicates accelerated biological aging and a heightened risk of diseases 25 . Previous research has demonstrated that hypertension contributes to accelerated biological aging, potentially influencing PhenoAge through mechanisms such as oxidative stress, inflammation, and metabolic disorders 48 . While the association between biological aging and adverse cardiovascular outcomes is well-documented, the underlying mechanisms remain unclear 49 . Our findings indicate that PhenoAge serves as a mediator in the relationship between TyG-BMI and mortality, suggesting that metabolic disorders increase the mortality risk not only via traditional cardiovascular risk factors but also by hastening the biological aging process. This highlights a novel pathway by which metabolic disorders, including insulin resistance and obesity, may contribute to increased mortality, independent of established cardiovascular risks. Moreover, the NLR not only independently increase mortality risk, but also promotes cellular senescence, exacerbating the biological aging process 50 . During the aging process, a phenomenon known as the ‘Senescence-Associated Secretory Phenotype (SASP)’ occurs, wherein senescent cells release a substantial array of inflammatory factors, including chemokines, proteases, and other substances 51 . The release of these SASP factors aggravates both local and systemic inflammatory responses, further promoting the senescence of neighboring cells and perpetuating a vicious cycle 52 . In the context of hypertension and elevated TyG-BMI, this chronic inflammatory environment, marked by elevated NLR, accelerates systemic aging and impairs local tissue. This bidirectional feedback loop between inflammation and biological aging significantly elevates mortality risk in patients with hypertension, particularly in those with a higher TyG-BMI index. The U-shaped relationship observed in the RCS curve indicates that both high and low TyG-BMI values are associated with increased mortality risk. For individuals with a low TyG-BMI, several potential factors may contribute to this elevated risk: 1. Malnutrition: A low BMI can be indicative of insufficient nutritional intake, which may weaken immune function and increase vulnerability to infections and other diseases 53 ; 2. Metabolic disorders: An excessively low TyG index may suggest that the body has been in a prolonged low energy state, adversely affecting insulin sensitivity and metabolic balance; 3. Underlying diseases: Several chronic conditions can lead to reductions in body weight and lipid levels, indicating that low TyG-BMI may be a marker for these underlying health issues; 4. Insufficient physiological reserves: Lower body weight often correlates with reduced physiological reserves, limiting the body’s ability to cope with acute health events, such as trauma or surgery. The primary strengths of this study lie in its status as the first systematic investigation of the association between the TyG-BMI index and mortality in patients with hypertension, along with a comprehensive analytical approach utilizing advanced statistical methods such as RCS, weighted Cox proportional hazards models, and SEM. SEM is a robust multivariate analysis tool capable of simultaneously addressing multiple path relationships and accounting for various confounding factors, thereby providing more comprehensive insights into the complex relationships between TyG-BMI, inflammation, biological aging and mortality. Our study contributes to the existing literature by uncovering the intricate mediating effects within these associations, suggesting novel potential biomarkers, such as NLR and phenoAge, for early intervention and risk stratification in patients with hypertension. Ultimately, understanding these mechanisms may guide the development of more targeted treatment strategies aimed at reducing mortality risk throughout the course of hypertension. Additionally, time-dependent ROC curve analysis demonstrated the robust predictive ability of TyG-BMI for all-cause and cardiovascular mortality at different time points, underscoring its potential utility in clinical risk stratification. Limitations While we possess follow-up data characteristic of cohort studies, the NHANES dataset lacks detailed information regarding the grade of hypertension, which may constrain the findings. Additionally, the study population is drawn solely from the United States, which may restrict the generalizability of the results to other populations. Another limitation is that the SEM-based sequential chained mediation analysis focused only on individuals with a TyG-BMI exceeding 239, which does not adequately explain the adverse impact of excessively low TyG-BMI values on outcomes. In this context, although significant efforts were made to adjust for confounding factors, further external validation is necessary to confirm these findings and enhance their applicability. Conclusion This study highlights the mediating roles of NLR and PhenoAge in the relationship between TyG-BMI and mortality, shedding light on the complex mechanisms by which metabolic syndrome contributes to increased mortality risk in patients with hypertension. These findings not only reinforce the intricate interplay among metabolism, inflammation, and biological aging, but also offers new insights and potential biomarkers for future clinical intervention strategies. Data Availability The data used for these analyses are all publicly available at online ( https://www.cdc.gov/nchs/nhanes/index.htm ). Author Contributions YL and YJ designed the study. YP conducted the primary research and performed data analysis and wrote the manuscript. XL and SP assisted with data analysis, drafted the results part and helped the manuscript revisions. SY assisted with data collection and interpretation. XZ assisted with data analysis. KZ contributed to the literature review and manuscript revisions. All authors had full access to all the data in the study. All authors reviewed and approved the final manuscript and had final responsibility for the decision to submit for publication. Funding The authors reported there is no funding associated with the work. Data sharing The data used for these analyses are all publicly available at online ( https://www.cdc.gov/nchs/nhanes/index.htm ). Ethics approval and consent to participate The study was approved by the National Center for Health Statistics NCHS Research Ethics Review Board. All participants signed the informed consent before participating in this survey. Acknowledgements We highly appreciate the work by participants in the NHANES project. References 1. ↵ Zhang Y , Yin L , Mills K , Chen J , He J , Palacios A , et al. Cost-effectiveness of a Multicomponent Intervention for Hypertension Control in Low-Income Settings in Argentina . JAMA Netw Open 2021 ; 4 : e2122559 . doi: 10.1001/jamanetworkopen.2021.22559 OpenUrl CrossRef 2. ↵ Kearney PM , Whelton M , Reynolds K , Muntner P , Whelton PK , He J . Global burden of hypertension: analysis of worldwide data . Lancet 2005 ; 365 : 217 – 223 . doi: 10.1016/s0140-6736(05)17741-1 OpenUrl CrossRef PubMed Web of Science 3. ↵ Lawes CM , Vander Hoorn S , Rodgers A . Global burden of blood-pressure-related disease, 2001 . Lancet 2008 ; 371 : 1513 – 1518 . doi: 10.1016/s0140-6736(08)60655-8 OpenUrl CrossRef PubMed Web of Science 4. ↵ Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants . Lancet 2021 ; 398 : 957 – 980 . doi: 10.1016/s0140-6736(21)01330-1 OpenUrl CrossRef PubMed 5. ↵ Phillips RA , Arnold RM , Peterson LE . Hypertension Guidelines: The Threads That Bind Them . J Am Coll Cardiol 2018 ; 72 : 1246 – 1251 . doi: 10.1016/j.jacc.2018.07.014 OpenUrl FREE Full Text 6. Cheung AK , Rahman M , Reboussin DM , Craven TE , Greene T , Kimmel PL , et al. Effects of Intensive BP Control in CKD . J Am Soc Nephrol 2017 ; 28 : 2812 – 2823 . doi: 10.1681/asn.2017020148 OpenUrl Abstract / FREE Full Text 7. ↵ Boehme AK , Esenwa C , Elkind MS . Stroke Risk Factors, Genetics, and Prevention . Circ Res 2017 ; 120 : 472 - 495 . doi: 10.1161/circresaha.116.308398 OpenUrl Abstract / FREE Full Text 8. ↵ Wu S , Xu Y , Zheng R , Lu J , Li M , Chen L , et al. Hypertension Defined by 2017 ACC/AHA Guideline, Ideal Cardiovascular Health Metrics, and Risk of Cardiovascular Disease: A Nationwide Prospective Cohort Study . Lancet Reg Health West Pac 2022 ; 20 : 100350 . doi: 10.1016/j.lanwpc.2021.100350 OpenUrl CrossRef 9. ↵ Mills KT , Bundy JD , Kelly TN , Reed JE , Kearney PM , Reynolds K , et al. Global Disparities of Hypertension Prevalence and Control: A Systematic Analysis of Population-Based Studies From 90 Countries . Circulation 2016 ; 134 : 441 – 450 . doi: 10.1161/circulationaha.115.018912 OpenUrl Abstract / FREE Full Text 10. ↵ Er LK , Wu S , Chou HH , Hsu LA , Teng MS , Sun YC , et al. Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate Marker for Insulin Resistance in Nondiabetic Individuals . PLoS One 2016 ; 11 : e0149731 . doi: 10.1371/journal.pone.0149731 OpenUrl CrossRef PubMed 11. ↵ Wang R , Dai L , Zhong Y , Xie G . Usefulness of the triglyceride glucose-body mass index in evaluating nonalcoholic fatty liver disease: insights from a general population . Lipids Health Dis 2021 ; 20 : 77 . doi: 10.1186/s12944-021-01506-9 OpenUrl CrossRef 12. ↵ Dou J , Guo C , Wang Y , Peng Z , Wu R , Li Q , et al. Association between triglyceride glucose-body mass and one-year all-cause mortality of patients with heart failure: a retrospective study utilizing the MIMIC-IV database . Cardiovasc Diabetol 2023 ; 22 : 309 . doi: 10.1186/s12933-023-02047-4 OpenUrl CrossRef 13. ↵ Li F , Wang Y , Shi B , Sun S , Wang S , Pang S , et al. Association between the cumulative average triglyceride glucose-body mass index and cardiovascular disease incidence among the middle-aged and older population: a prospective nationwide cohort study in China . Cardiovasc Diabetol 2024 ; 23 : 16 . doi: 10.1186/s12933-023-02114-w OpenUrl CrossRef PubMed 14. ↵ Huo RR , Zhai L , Liao Q , You XM . Changes in the triglyceride glucose-body mass index estimate the risk of stroke in middle-aged and older Chinese adults: a nationwide prospective cohort study . Cardiovasc Diabetol 2023 ; 22 : 254 . doi: 10.1186/s12933-023-01983-5 OpenUrl CrossRef 15. ↵ Zhang X , Wei R , Wang X , Zhang W , Li M , Ni T , et al. The neutrophil-to-lymphocyte ratio is associated with all-cause and cardiovascular mortality among individuals with hypertension . Cardiovasc Diabetol 2024 ; 23 : 117 . doi: 10.1186/s12933-024-02191-5 OpenUrl CrossRef 16. ↵ Song M , Graubard BI , Rabkin CS , Engels EA . Neutrophil-to-lymphocyte ratio and mortality in the United States general population . Sci Rep 2021 ; 11 : 464 . doi: 10.1038/s41598-020-79431-7 OpenUrl CrossRef PubMed 17. ↵ Kuo CL , Pilling LC , Atkins JL , Masoli JAH , Delgado J , Tignanelli C , et al. Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants . J Gerontol A Biol Sci Med Sci 2021 ; 76 : e133 – e141 . doi: 10.1093/gerona/glab060 OpenUrl CrossRef 18. ↵ Levine ME , Lu AT , Quach A , Chen BH , Assimes TL , Bandinelli S , et al. An epigenetic biomarker of aging for lifespan and healthspan . Aging (Albany NY ) 2018 ; 10 : 573 – 591 . doi: 10.18632/aging.101414 OpenUrl CrossRef PubMed 19. ↵ Tan L , Liu Y , Liu J , Zhang G , Liu Z , Shi R . Association between insulin resistance and uncontrolled hypertension and arterial stiffness among US adults: a population-based study . Cardiovasc Diabetol 2023 ; 22 : 311 . doi: 10.1186/s12933-023-02038-5 OpenUrl CrossRef 20. ↵ Yang F , Wang M , Chen Y , Wu J , Li Y . Association of cardio-renal biomarkers and mortality in the U.S.: a prospective cohort study . Cardiovasc Diabetol 2023 ; 22 : 265 . doi: 10.1186/s12933-023-01986-2 OpenUrl CrossRef 21. ↵ Li L , Wang Y , He X , Li Z , Lu M , Gong T , et al. Hematological Prognostic Scoring System Can Predict Overall Survival and Can Indicate Response to Immunotherapy in Patients With Osteosarcoma . Front Immunol 2022 ; 13 : 879560 . doi: 10.3389/fimmu.2022.879560 OpenUrl CrossRef 22. ↵ Kwon D , Belsky DW . A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge . GeroScience 2021 ; 43 : 2795 – 2808 . doi: 10.1007/s11357-021-00480-5 OpenUrl CrossRef PubMed 23. ↵ Li J , Vitiello MV , Gooneratne NS . Sleep in Normal Aging . Sleep Med Clin 2018 ; 13 : 1 – 11 . doi: 10.1016/j.jsmc.2017.09.001 OpenUrl CrossRef PubMed 24. ↵ Klemera P , Doubal S . A new approach to the concept and computation of biological age . Mech Ageing Dev 2006 ; 127 : 240 – 248 . doi: 10.1016/j.mad.2005.10.004 OpenUrl CrossRef PubMed Web of Science 25. ↵ Levine ME . Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci 2013 ; 68 : 667 – 674 . doi: 10.1093/gerona/gls233 OpenUrl CrossRef PubMed Web of Science 26. ↵ Li X , Wang J , Zhang M , Li X , Fan Y , Zhou X , et al. Biological aging mediates the associations of metabolic score for insulin resistance with all-cause and cardiovascular disease mortality among US adults: A nationwide cohort study . Diabetes Obes Metab 2024 . doi: 10.1111/dom.15694 OpenUrl CrossRef 27. ↵ Ballou S , Katon J , Singh P , Rangan V , Lee HN , McMahon C , et al. Chronic Diarrhea and Constipation Are More Common in Depressed Individuals . Clin Gastroenterol Hepatol 2019 ; 17 : 2696 – 2703 . doi: 10.1016/j.cgh.2019.03.046 OpenUrl CrossRef PubMed 28. ↵ Knudsen MD , Berstad P , Hjartåker A , Gulichsen EH , Hoff G , de Lange T , et al. Lifestyle predictors for non-participation and outcome in the second round of faecal immunochemical test in colorectal cancer screening . Br J Cancer 2017 ; 117 : 461 – 469 . doi: 10.1038/bjc.2017.189 OpenUrl CrossRef PubMed 29. ↵ Passiak BS , Liu D , Kresge HA , Cambronero FE , Pechman KR , Osborn KE , et al. Perivascular spaces contribute to cognition beyond other small vessel disease markers . Neurology 2019 ; 92 : e1309 – e1321 . doi: 10.1212/wnl.0000000000007124 OpenUrl CrossRef PubMed 30. ↵ Silvestre OM , Nadruz W , Jr . ., Querejeta Roca G , Claggett B , Solomon SD , Mirabelli MC , et al. Declining Lung Function and Cardiovascular Risk: The ARIC Study . J Am Coll Cardiol 2018 ; 72 : 1109 – 1122 . doi: 10.1016/j.jacc.2018.06.049 OpenUrl FREE Full Text 31. ↵ Zhao Q , Zhang TY , Cheng YJ , Ma Y , Xu YK , Yang JQ , et al. Impacts of triglyceride-glucose index on prognosis of patients with type 2 diabetes mellitus and non-ST-segment elevation acute coronary syndrome: results from an observational cohort study in China . Cardiovasc Diabetol 2020 ; 19 : 108 . doi: 10.1186/s12933-020-01086-5 OpenUrl CrossRef PubMed 32. ↵ Kamarudin AN , Cox T , Kolamunnage-Dona R . Time-dependent ROC curve analysis in medical research: current methods and applications . BMC Med Res Methodol 2017 ; 17 : 53 . doi: 10.1186/s12874-017-0332-6 OpenUrl CrossRef PubMed 33. ↵ Tutor AW , Lavie CJ , Kachur S , Milani RV , Ventura HO . Updates on obesity and the obesity paradox in cardiovascular diseases . Prog Cardiovasc Dis 2023 ; 78 : 2 – 10 . doi: 10.1016/j.pcad.2022.11.013 OpenUrl CrossRef PubMed 34. ↵ Zhang T , Zhang H , Li S , Li Y , Liu Y , Fernandez C , et al. Impact of Adiposity on Incident Hypertension Is Modified by Insulin Resistance in Adults: Longitudinal Observation From the Bogalusa Heart Study . Hypertension 2016 ; 67 : 56 – 62 . doi: 10.1161/hypertensionaha.115.06509 OpenUrl CrossRef 35. ↵ Mohd Nor NS , Lee S , Bacha F , Tfayli H , Arslanian S . Triglyceride glucose index as a surrogate measure of insulin sensitivity in obese adolescents with normoglycemia, prediabetes, and type 2 diabetes mellitus: comparison with the hyperinsulinemic-euglycemic clamp . Pediatr Diabetes 2016 ; 17 : 458 – 465 . doi: 10.1111/pedi.12303 OpenUrl CrossRef PubMed 36. ↵ Guerrero-Romero F , Simental-Mendía LE , González-Ortiz M , Martínez-Abundis E , Ramos-Zavala MG , Hernández-González SO , et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp . J Clin Endocrinol Metab 2010 ; 95 : 3347 – 3351 . doi: 10.1210/jc.2010-0288 OpenUrl CrossRef PubMed 37. ↵ Ding J , Chen X , Bao K , Yang J , Liu N , Huang W , et al. Assessing different anthropometric indices and their optimal cutoffs for prediction of type 2 diabetes and impaired fasting glucose in Asians: The Jinchang Cohort Study . J Diabetes 2020 ; 12 : 372 – 384 . doi: 10.1111/1753-0407.13000 OpenUrl CrossRef PubMed 38. ↵ Liu Q , Pan J , Berzuini C , Rutter MK , Guo H . Integrative analysis of Mendelian randomization and Bayesian colocalization highlights four genes with putative BMI-mediated causal pathways to diabetes . Sci Rep 2020 ; 10 : 7476 . doi: 10.1038/s41598-020-64493-4 OpenUrl CrossRef PubMed 39. ↵ Bala C , Gheorghe-Fronea O , Pop D , Pop C , Caloian B , Comsa H , et al. The Association Between Six Surrogate Insulin Resistance Indexes and Hypertension: A Population-Based Study . Metab Syndr Relat Disord 2019 ; 17 : 328 – 333 . doi: 10.1089/met.2018.0122 OpenUrl CrossRef PubMed 40. ↵ Du Z , Xing L , Lin M , Sun Y . Estimate of prevalent ischemic stroke from triglyceride glucose-body mass index in the general population . BMC Cardiovasc Disord 2020 ; 20 : 483 . doi: 10.1186/s12872-020-01768-8 OpenUrl CrossRef 41. ↵ Liu C , Liang D . The association between the triglyceride-glucose index and the risk of cardiovascular disease in US population agedlJ≤lJ65 years with prediabetes or diabetes: a population-based study . Cardiovasc Diabetol 2024 ; 23 : 168 . doi: 10.1186/s12933-024-02261-8 OpenUrl CrossRef 42. ↵ Dang K , Wang X , Hu J , Zhang Y , Cheng L , Qi X , et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003-2018 . Cardiovasc Diabetol 2024 ; 23 : 8 . doi: 10.1186/s12933-023-02115-9 OpenUrl CrossRef 43. ↵ Hou XZ , Lv YF , Li YS , Wu Q , Lv QY , Yang YT , et al. Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study . Cardiovasc Diabetol 2024 ; 23 : 86 . doi: 10.1186/s12933-024-02173-7 OpenUrl CrossRef PubMed 44. ↵ Viers BR , Thompson RH , Lohse CM , Cheville JC , Leibovich BC , Boorjian SA , et al. Pre-treatment neutrophil-to-lymphocyte ratio predicts tumor pathology in newly diagnosed renal tumors . World J Urol 2016 ; 34 : 1693 – 1699 . doi: 10.1007/s00345-016-1821-7 OpenUrl CrossRef PubMed 45. ↵ Vahit D , Akboga MK , Samet Y , Hüseyin E . Assessment of monocyte to high density lipoprotein cholesterol ratio and lymphocyte-to-monocyte ratio in patients with metabolic syndrome . Biomark Med 2017 ; 11 : 535 – 540 . doi: 10.2217/bmm-2016-0380 OpenUrl CrossRef PubMed 46. ↵ Song J , Frieler RA , Vigil TM , Ma J , Brombacher F , Goonewardena SN , et al. Inactivation of Interleukin-4 Receptor α Signaling in Myeloid Cells Protects Mice From Angiotensin II/High Salt-Induced Cardiovascular Dysfunction Through Suppression of Fibrotic Remodeling . J Am Heart Assoc 2021 ; 10 : e017329 . doi: 10.1161/jaha.120.017329 OpenUrl CrossRef PubMed 47. ↵ Brown JM , Zhou W , Weber B , Divakaran S , Barrett L , Bibbo CF , et al. Low coronary flow relative to myocardial mass predicts heart failure in symptomatic hypertensive patients with no obstructive coronary artery disease . Eur Heart J 2022 ; 43 : 3323 – 3331 . doi: 10.1093/eurheartj/ehab610 OpenUrl CrossRef PubMed 48. ↵ Chen Z , Zuurmond MG , van der Schaft N , Nano J , Wijnhoven HAH , Ikram MA , et al. Plant versus animal based diets and insulin resistance, prediabetes and type 2 diabetes: the Rotterdam Study . Eur J Epidemiol 2018 ; 33 : 883 – 893 . doi: 10.1007/s10654-018-0414-8 OpenUrl CrossRef PubMed 49. ↵ Hammadah M , Al Mheid I , Wilmot K , Ramadan R , Abdelhadi N , Alkhoder A , et al. Telomere Shortening, Regenerative Capacity, and Cardiovascular Outcomes . Circ Res 2017 ; 120 : 1130 - 1138 . doi: 10.1161/circresaha.116.309421 OpenUrl Abstract / FREE Full Text 50. ↵ Zhang L , Zhao J , Mu X , McGowan SJ , Angelini L , O’Kelly RD , et al. Novel small molecule inhibition of IKK/NF-κB activation reduces markers of senescence and improves healthspan in mouse models of aging . Aging Cell 2021 ; 20 : e13486 . doi: 10.1111/acel.13486 OpenUrl CrossRef PubMed 51. ↵ He S , Sharpless NE. Senescence in Health and Disease . Cell 2017 ; 169 : 1000 - 1011 . doi: 10.1016/j.cell.2017.05.015 OpenUrl CrossRef PubMed 52. ↵ Guo G , Watterson S , Zhang SD , Bjourson A , McGilligan V , Peace A , et al. The role of senescence in the pathogenesis of atrial fibrillation: A target process for health improvement and drug development . Ageing Res Rev 2021 ; 69 : 101363 . doi: 10.1016/j.arr.2021.101363 OpenUrl CrossRef PubMed 53. ↵ Shangguan X , Xiong J , Shi S , Liao Y , Chen L , Deng J , et al. Impact of the Malnutrition on Mortality in Patients With Osteoporosis: A Cohort Study From NHANES 2005-2010 . Front Nutr 2022 ; 9 : 868166 . doi: 10.3389/fnut.2022.868166 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted July 21, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Phenotypic Age and Neutrophil-to-Lymphocyte Ratio mediate the association between the triglyceride glucose-body mass index and the cardiovascular mortality risk in hypertensive patients Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Phenotypic Age and Neutrophil-to-Lymphocyte Ratio mediate the association between the triglyceride glucose-body mass index and the cardiovascular mortality risk in hypertensive patients Yu Pan , Xinxin Liu , Siming Pan , Shuang Yin , Xu Zhao , kai Zhang , Yinong Jiang , Yan Liu medRxiv 2025.07.20.25331891; doi: https://doi.org/10.1101/2025.07.20.25331891 Share This Article: Copy Citation Tools Phenotypic Age and Neutrophil-to-Lymphocyte Ratio mediate the association between the triglyceride glucose-body mass index and the cardiovascular mortality risk in hypertensive patients Yu Pan , Xinxin Liu , Siming Pan , Shuang Yin , Xu Zhao , kai Zhang , Yinong Jiang , Yan Liu medRxiv 2025.07.20.25331891; doi: https://doi.org/10.1101/2025.07.20.25331891 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (569) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1511) Epidemiology (15230) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6610) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6607) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3337) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (664) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5448) Public and Global Health (9238) Radiology and Imaging (2202) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a01c2255694f3fe2',t:'MTc3OTc5MTczNw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();