Abstract
Aims
To examine the relationships of abdominal obesity indices [visceral adiposity index (VAI), lipid accumulation product (LAP), and waist circumference (WC)] with all-cause and cardiovascular disease (CVD) mortality in varying glucose metabolism statuses populations.
Methods
This study utilized NHANES data (1999–2018), including 17,718 participants, and linked mortality documents from the National Death Index through December 31, 2019. Participants were categorized by glucose metabolism status into diabetes mellitus (DM), prediabetes (Pre-DM), or normal glucose regulation (NGR), and separately categorized as quartiles based on abdominal obesity indices. Cox proportional hazards regression models assessed the associations between glucose metabolism status, abdominal obesity indices, and all-cause and CVD mortality. The joint effect of glucose metabolism status and abdominal obesity was also analyzed. Restricted cubic splines (RCS) were applied to investigate potential non-linear relationships. The predictive performance of the models was evaluated using C-statistics to assess improvements with the inclusion of LAP or WC across different glucose metabolism statuses.
Results
Across a median follow-up period of 9.4 years, 2,685 deaths (846 from CVD) were documented. Compared to NGR, Pre-DM and DM were associated with increased all-cause mortality [adjusted HR = 1.249 (1.132, 1.377) and 1.628 (1.429, 1.853)] and CVD mortality [HR = 1.321 (1.105, 1.579) and 1.665 (1.322, 2.097)]. As well as WC, were significant predictors of all-cause [HR = 1.527 (1.255, 1.859)] and CVD mortality [HR = 1.576 (1.104, 2.249)], while LAP was only associated with all-cause mortality [HR = 1.199 (1.029, 1.395)]. Combined analysis revealed that individuals with Pre-DM or DM in the highest quartiles of WC had 1.567-fold and 2.493-fold elevated risks of all-cause mortality, in contrast with individuals having NGR in the lowest quartiles. The inclusion of WC into the baseline models enhanced the C-statistics for assessing all-cause mortality in Pre-DM groups (p < 0.001).
Conclusions
WC and LAP are important predictors of mortality, especially in those with impaired glucose metabolism. Integrating WC into predictive models enhances risk assessment for all-cause mortality in Pre-DM groups. These findings emphasize the importance of comprehensive risk assessments that incorporate both abdominal obesity and glucose metabolic health.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-24113-0.
Keywords: Abdominal obesity indices, Pre-diabetes, Diabetes, All-cause and cardiovascular mortality, NHANES
Introduction
Diabetes mellitus (DM) represents a growing global health burden, associated with markedly increased risks of cardiovascular disease (CVD) and premature mortality [1, 2]. Individuals with DM have a two- to four-fold higher likelihood of CVD and all-cause mortality compared to those without diabetes [2]. Pre-diabetes (Pre-DM), an intermediate metabolic state of impaired glucose regulation, also confers elevated cardiometabolic risk and a high likelihood of progression to overt DM [3, 4]. Without timely intervention, approximately 10% of individuals with Pre-DM develop DM annually [3]. Identifying individuals at risk across the glycemic spectrum is therefore critical for the prevention of CVD-related mortality.
Abdominal obesity is a key driver of insulin resistance and cardiometabolic complications [5, 6] yet conventional measures such as body mass index (BMI) fail to adequately capture central fat distribution, which is more strongly associated with adverse outcomes [7]. Although radiological imaging techniques may effectively quantify abdominal adiposity, they are unsuitable for epidemiological studies on account of their time-consuming nature, costly expense, and radiation exposure [8]. To address this, several accessible indices, including waist circumference (WC), visceral adiposity index (VAI), and lipid accumulation product (LAP), have been proposed to estimate abdominal fat based on metabolic and anthropometric parameters. These indices predict CVD risk in general and diabetic populations [9–11]. However, few studies have evaluated the prognostic significance of these indices in individuals with Pre-DM and their combined utility with glucose metabolism status in predicting all-cause and CVD mortality. Understanding these associations is essential for identifying high-risk individuals and improving risk stratification in populations with varying degrees of glucose metabolism impairment.
To address this knowledge gap, we examine the associations of three abdominal obesity indices (VAI, LAP, and WC) in combination with glucose metabolism statuses on all-cause and CVD mortality. This analysis was conducted by employing data from a sample that represents U.S. adults on a national scale and is involved in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018.
Methods
Study design and population
The present cohort study utilizes data obtained from 10 successive cycles of the NHANES conducted between 1999 and 2018, and links these records to mortality data from the National Death Index (NDI) up to December 31, 2019. Figure S1 illustrates the flowchart of this study. The initial study sample consisted of participants aged 20 years and older. Participants were excluded if they were pregnant (n = 1,547) or lacked sufficient information to calculate abdominal obesity indices—VAI, LAP, or WC (n = 31,557). Further exclusions were made for individuals with missing data on glucose metabolism status (n = 85), incomplete follow-up information (n = 34), or missing key covariates, including demographic and physiological information (e.g., education, poverty income ratio, smoking status, alcohol consumption, hypertension, lipid-lowering therapy), and low-density lipoprotein cholesterol (LDL-C) (n = 4,140). DM was determined through a self-reported medical diagnosis by a physician, current use of diabetes medications (e.g., oral hypoglycemic agents or insulin), fasting plasma glucose (FPG) levels of at least 7.0 mmol/L (at least 126 mg/dL), two-hour plasma glucose levels during an oral glucose tolerance test (OGTT) of at least 11.1 mmol/L (at least 200 mg/dL), or glycated hemoglobin A1c (HbA1c) of at least 6.5% [12]. Pre-DM was determined as having impaired fasting glucose (FPG: 6.1–6.9 mmol/L [110–125 mg/dL]), impaired glucose tolerance (OGTT: 7.8–11.0 mmol/L [140–199 mg/dL]), or HbA1c between 5.7 and 6.5% [3]. Participants not meeting any of these criteria were considered to have normal glucose regulation (NGR). After applying these exclusion criteria, the final study sample included 17,718 participants with complete data on abdominal obesity indices and glucose metabolism status.
Clinical and laboratory evaluations
Data on age, sex, ethnicity (categorized as White, Black, Mexican American or Hispanic, or other), education level (less than high school, high school or equivalent, and college or above), and poverty income ratio (PIR < 1, ≥1) were collected using standardized interview questionnaires. Information on smoking and drinking status was obtained through standardized questionnaires. Smoking status was defined through the answers given by participants to two questions: “Have you smoked at least 100 cigarettes in your entire life?” and “Are you smoking cigarettes at present?”. Participants were then categorized into never-smokers (no more than 100 cigarettes in their lifetime), former smokers (at least 100 cigarettes but now quit), and current smokers (over 100 cigarettes and smoking at present) [13]. Drinking behavior was divided into never (never consuming alcohol), former (used to drink and now quit), and current drinking. Hypertension was diagnosed based on systolic blood pressure (SBP) reaching or exceeding 140 mmHg, diastolic blood pressure (DBP) reaching or exceeding 90 mmHg [14] or based on self-reported diagnosis of hypertension and current antihypertensive medications usage. Lipid-lowering therapy was based on self-reported use of prescription medications for cholesterol management, including statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors. Participants were categorized into two groups: “Yes” (current use of lipid-lowering medication), “No or other” (non-use of such medications or unspecified or non-standard lipid-modifying agents, e.g., herbal supplements). Baseline levels of triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), fasting glucose, and glycated hemoglobin (HbA1c) were measured in the fasting state according to standardized NHANES laboratory protocols [15–17].
Exposure measurement
Waist circumference (WC, in cm) was measured once at baseline by trained NHANES staff following a standardized protocol, at the midpoint between the iliac crest and the lower rib margin during minimal respiration, as described in the NHANES Anthropometry Procedures Manual. VAI and LAP were calculated using the following validated sex-specific formulas [11]:
Male:
 |
Female:
 |
All subjects were categorized into quartiles based on their levels of VAI, LAP, and WC, respectively.
Outcome ascertainment
Mortality records, including the principal cause of death, were ascertained by connecting the NHANES data to the NDI database, with follow-up information available through December 31, 2019. The causes of death were distinguished based on the International Statistical Classification of Diseases, 10th Revision (ICD-10). CVD mortality was determined as deaths resulting from heart diseases (codes I00–I09, I11, I13, I20–I51) and stroke (codes I60–I69).
Statistical analysis
All statistical analysis were conducted in accordance with Centers for Disease Control guidelines [18]. Normally distributed continuous variables are expressed as mean (standard deviation, SD), and non-normally distributed continuous variables are represented as median (interquartile range). Categorical variables were reported as counts (percentages). Group differences were assessed employing one-way analysis of variance (ANOVA) (for normally distributed continuous variables), Kruskal-Wallis test (for non-normally distributed continuous variables), and chi-square (χ²) test (for categorical variables), as appropriate. Multiple comparisons were adjusted using the Bonferroni correction method. Kaplan-Meier analysis, combined with the log-rank test, was employed to contrast survival rates among different groups. The present study employed univariate and multivariate Cox proportional hazard modeling to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause and CVD mortality. The Multivariate Cox regression model was adjusted taking into account age, sex, education status, ethnicity, smoking, drinking, hypertension, BMI category, TC, lipid-lowering therapy, LDL-C, glucose, HbA1c, and with or without glucose metabolism status (NGR, Pre-DM, DM). Stratified analyses were conducted in age (< 50, ≥ 50 years), sex (male, female), ethnicity (White, Black, Mexican American or Hispanic, other), education (less than high school, high school or equivalent, college or above), poverty income ratio (< 1, ≥ 1), smoking (never, former, now), drinking (never, former, now), hypertension (no, yes) and BMI category (< 25, 25–30, ≥ 30 kg/m2) groups and further examining the p for the interaction of LAP and WC with stratified variables.
The restricted cubic spline (RCS) model was used to visualize the dose-response correlation between the concentration of LAP and WC and all-cause and CVD mortality [19]. C-statistic (Harrell’s concordance statistic) was applied to assess the predictive effect of LAP and WC for all-cause and CVD mortality in different glucose metabolism subgroups. The differences in the C-statistics between the original model and the original model plus LAP or WC were compared using the Likelihood ratio test. All statistical analyses were conducted utilizing R software (version 4.1.3). The conducted tests were two-sided, and the significance cut-off was established as p < 0.05. The code used to generate the results that support the conclusions of this study is available in the Supplementary Materials.
Results
Baseline characteristics
In total, 17,718 subjects aged 20 years and older were finally eligible for analysis from the NHANES 1999–2018 in the current study, of whom 8914 (50.3%) were male, and the mean age was 50 ± 17.8 years. The participants’ baseline demographic, lifestyle, and clinical characteristics were summarized in Table 1 in accordance with their glucose metabolism status: NGR, pre-DM, and DM. Specifically, participants in the DM group tended to be older, with a significantly higher proportion of individuals aged 50 and above (80.7%) in contrast with the NGR (31.5%) and Pre-DM (57.3%) groups. They also exhibited lower educational attainment and PIR levels, higher proportions of former and current smokers, former drinkers, a higher prevalence of hypertension, and higher proportions of lipid-lowering therapy. Individuals with DM also exhibited significantly elevated levels of BMI, TG, fasting glucose, and HbA1c, as well as lower levels of TC, LDL-C, and HDL-C. Furthermore, the median values of the abdominal obesity indices—VAI, LAP, and WC—were considerably elevated in the DM group, indicating greater abdominal adiposity (Table 1).
Table 1.
Baseline demographic, lifestyle, and medical characteristics of patients with different glucose metabolism status, NHANES 1999–2018
| Variables | Overall | NGR | Pre-DM | DM | p |
|---|---|---|---|---|---|
| N = 17,718 | N = 7809 | N = 6689 | N = 3220 | ||
| Age (years) | 50.0 (17.8) | 42.6 (16.9) | 53.0 (16.9) | 61.3 (13.7) | <0.001 |
| <0.001 | |||||
| < 50 | 8825 (49.8%) | 5350 (68.5%) | 2853 (42.7%) | 622 (19.3%) | |
| ≥ 50 | 8893 (50.2%) | 2459 (31.5%) | 3836 (57.3%) | 2598 (80.7%) | |
| Sex, n (%) | <0.001 | ||||
| Female | 8804 (49.7%) | 4334 (55.5%) | 2942 (44.0%) | 1528 (47.5%) | |
| Male | 8914 (50.3%) | 3475 (44.5%) | 3747 (56.0%) | 1692 (52.5%) | |
| Ethnicity, n (%) | <0.001 | ||||
| White | 8387 (47.3%) | 3943 (50.5%) | 3133 (46.8%) | 1311 (40.7%) | |
| Black | 3421 (19.3%) | 1467 (18.8%) | 1222 (18.3%) | 732 (22.7%) | |
| Mexican American or Hispanic | 4435 (25.0%) | 1785 (22.9%) | 1725 (25.8%) | 925 (28.7%) | |
| Other | 1475 (8.4%) | 614 (7.8%) | 609 (9.1%) | 252 (7.9%) | |
| Education, n (%) | <0.001 | ||||
| Less than high school | 4470 (25.2%) | 1626 (20.8%) | 1751 (26.2%) | 1093 (33.9%) | |
| High school or equivalent | 4098 (23.1%) | 1719 (22.0%) | 1589 (23.8%) | 790 (24.5%) | |
| College or above | 9150 (51.6%) | 4464 (57.2%) | 3349 (50.1%) | 1337 (41.5%) | |
| Smoking, n (%) | <0.001 | ||||
| Never | 9418 (53.2%) | 4413 (56.5%) | 3420 (51.1%) | 1585 (49.2%) | |
| Former | 4588 (25.9%) | 1617 (20.7%) | 1877 (28.1%) | 1094 (34.0%) | |
| Now | 3712 (21.0%) | 1779 (22.8%) | 1392 (20.8%) | 541 (16.8%) | |
| Drinking, n (%) | <0.001 | ||||
| Never | 2360 (13.3%) | 952 (12.2%) | 855 (12.8%) | 553 (17.2%) | |
| Former | 3060 (17.3%) | 1051 (13.5%) | 1156 (17.3%) | 853 (26.5%) | |
| Now | 12,298 (69.4%) | 5806 (74.4%) | 4678 (69.9%) | 1814 (56.3%) | |
| Hypertension, n (%) | <0.001 | ||||
| No | 10,219 (57.7%) | 5710 (73.1%) | 3566 (53.3%) | 943 (29.3%) | |
| Yes | 7499 (42.3%) | 2099 (26.9%) | 3123 (46.7%) | 2277 (70.7%) | |
| Poverty income ratio, n (%) | 0.001 | ||||
| < 1 | 3353 (18.9%) | 1407 (18.0%) | 1266 (18.9%) | 680 (21.1%) | |
| ≥ 1 | 14,365 (81.1%) | 6402 (82.0%) | 5423 (81.1%) | 2540 (78.9%) | |
| BMI, (kg/m2) | 27.8 [24.2, 32.1] | 26.1 [23.0, 30.0] | 28.5 [25.0, 32.8] | 30.6 [26.8, 35.7] | <0.001 |
| BMI categories, n (%) | <0.001 | ||||
| <25 | 5365 (30.3%) | 3220 (41.2%) | 1656 (24.8%) | 489 (15.2%) | |
| 25–30 | 6036 (34.1%) | 2639 (33.8%) | 2413 (36.1%) | 984 (30.6%) | |
| ≥30 | 6317 (35.7%) | 1950 (25.0%) | 2620 (39.2%) | 1747 (54.3%) | |
| Lipid-lowering therapy, n (%) | <0.001 | ||||
| No or other | 14,477 (81.7%) | 7256 (92.9%) | 5460 (81.6%) | 1761 (54.7%) | |
| Yes | 3241 (18.3%) | 553 (7.08%) | 1229 (18.4%) | 1459 (45.3%) | |
| TC, (mg/dL) | 191 [166, 219] | 189 [164, 215] | 197 [171, 224] | 184 [158, 214] | <0.001 |
| TG, (mg/dL) | 105 [74.0, 153] | 92.0 [65.0, 133] | 112 [79.0, 159] | 129 [92.0, 185] | <0.001 |
| LDL-C, (mg/dL) | 113 [91.0, 138] | 112 [90.0, 135] | 119 [96.0, 142] | 105 [81.0, 132] | <0.001 |
| HDL-C, (mg/dL) | 51.0 [42.0, 62.0] | 54.0 [45.0, 65.0] | 50.0 [42.0, 61.0] | 47.0 [40.0, 57.0] | <0.001 |
| Glucose, (mg/dL) | 99.6 [92.4, 109] | 92.7 [88.0, 96.4] | 105 [101, 110] | 132 [114, 164] | <0.001 |
| HbA1c (%) | 5.50 [5.20, 5.80] | 5.30 [5.10, 5.40] | 5.60 [5.30, 5.80] | 6.60 [6.00, 7.60] | <0.001 |
| VAI | 1.47 [0.90, 2.44] | 1.22 [0.77, 1.99] | 1.58 [0.97, 2.55] | 2.02 [1.24, 3.14] | <0.001 |
| LAP | 43.1 [24.0, 73.0] | 31.5 [17.4, 53.9] | 48.1 [28.8, 77.3] | 66.6 [41.7, 101] | <0.001 |
| WC (cm) | 97.4 [87.4, 108] | 91.5 [82.4, 102] | 99.6 [91.0, 110] | 107 [97.0, 118] | <0.001 |
Values are mean (SD)/median [IQR] for continuous variables or numbers (%) for categorical variables. NHANES: National Health and Nutrition Examination Survey; NGR: normal glucose regulation; Pre-DM: pre-diabetes mellitus; DM: diabetes mellitus; BMI: body mass index; TC: total cholesterol; TG: triglycerides; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; HbA1c: glycosylated hemoglobin A1c; VAI: visceral adiposity index; LAP: lipid accumulation product; WC: waist circumference; SD: standard deviation
Group differences were assessed using one-way analysis of variance (ANOVA) (for normally distributed continuous variables), Kruskal-Wallis test (for non-normally distributed continuous variables), and χ² test (for categorical variables)
p < 0.05 suggests significant differences
Meanwhile, the study population was classified into four subgroups according to the quartile concentrations of the three abdominal obesity indices (Table S1, S2, and S3). Participants in the higher quartile of VAI, compared to those in the lower quartiles, were more likely to be older, female, white, have hypertension, Pre-DM, or DM, and were less likely to be never smokers. They also tended to have a high proportion of lower educational attainment and lower PIR, along with elevated levels of BMI, TC, TG, glucose, HbA1c, LAP, and WC, as well as lower levels of HDL-C (Table S1, all p for trend < 0.001). Additionally, in contrast with those in the lower quartiles, individuals in the higher quartiles of LAP and WC tended to be older, white, had higher proportions of former smokers and former drinkers, and were more likely to with lipid-lowering therapy, hypertension, and DM. They also presented elevated levels of BMI, TC, TG, glucose, and HbA1c, along with reduced levels of HDL-C (Table S2, S3, all p for trend < 0.001). Participants who experienced all-cause mortality were more likely to have pre-DM or DM and exhibited elevated levels of VAI, LAP, and WC compared to those who remained alive (Table S4, all p < 0.001).
Glucose metabolism status, abdominal obesity indices, all-cause and CVD mortality
Over a median follow-up duration of 9.4 years, a cumulative total of 2,685 deaths were documented, with 846 deaths attributable to CVD. The all-cause mortality rates in the NGR, pre-DM, and DM groups were 9.93%, 15.73%, and 26.63%, respectively, while the corresponding CVD mortality rates were 2.78%, 5.02%, and 9.12%. Kaplan-Meier analysis revealed a significant decline in survival rates for both all-cause and CVD mortality across glucose metabolism groups, from NGR to pre-DM and DM. (Figure S2A and S2B, all p < 0.001). Similarly, participants in the higher quartiles of VAI, LAP, and WC had lower event-free survival rates for both all-cause and CVD mortality, in contrast with those in the lower quartiles. (Figure S2C-S2H, all p < 0.001).
Univariate Cox regression models demonstrated that participants with pre-DM or DM had a considerably elevated risk of all-cause and CVD mortality as opposed to individuals with NGR. These relationships stayed consistent notwithstanding adjusting for multiple potential confounders, indicating the independent effect of glucose metabolism status on mortality risk (Table 2, all p < 0.01). In addition, participants in the highest quartiles of WC demonstrated considerably heightened risks of both all-cause and CVD mortality when contrasted with individuals in the lowest quartile after multivariable adjustment. These associations remained robust when WC was analyzed as continuous variables, further confirming their predictive value (Table 2, all p < 0.05). The LAP was correlated only with all-cause mortality and showed no association with cardiovascular mortality when analyzed as a continuous variable (Table 2). However, after multivariable adjustment, no significant differences were observed in the associations between VAI quartiles and mortality outcomes (Table 2, all p > 0.05). RCS analysis revealed linear relationships between LAP and WC with the risk of all-cause mortality, and between WC and the risk of CVD mortality in the study population (Figure S3-S4, all p for non-linear > 0.05).
Table 2.
Associations of abdominal obesity indices and glucose metabolism status with all-cause and CVD mortality in patients from the NHANES 1999–2018 cohort study
| Subjects | Events | Unadjusted model | Adjusted model | |||
|---|---|---|---|---|---|---|
| HR (95%CI) | p | HR (95%CI) | p | |||
| All-cause mortality | ||||||
| Glucose metabolism status | ||||||
| NGR | 7809 | 775 | Ref | Ref | ||
| Pre-DM | 6689 | 1053 | 2.092 (1.906, 2.297) | < 0.001 | 1.249 (1.132, 1.377) | < 0.001 |
| DM | 3220 | 857 | 4.153 (3.764, 4.582) | < 0.001 | 1.628 (1.429, 1.853) | < 0.001 |
| WC | ||||||
| per-SD increase | 17,718 | 2685 | 1.241 (1.196, 1.287) | < 0.001 | 1.195 (1.113, 1.283) | < 0.001 |
| Quartiles | ||||||
| Q1 | 4442 | 481 | Ref | Ref | ||
| Q2 | 4439 | 646 | 1.370 (1.217, 1.541) | < 0.001 | 1.083 (0.949, 1.235) | 0.254 |
| Q3 | 4422 | 767 | 1.703 (1.520, 1.909) | < 0.001 | 1.294 (1.100, 1.523) | 0.002 |
| Q4 | 4415 | 791 | 1.909 (1.704, 2.138) | < 0.001 | 1.527 (1.255, 1.859) | < 0.001 |
| LAP | ||||||
| per-SD increase | 17,718 | 2685 | 1.191 (1.153, 1.231) | < 0.001 | 1.062 (1.014, 1.112) | 0.011 |
| Quartiles | ||||||
| Q1 | 4430 | 409 | Ref | Ref | ||
| Q2 | 4429 | 648 | 1.584 (1.400, 1.793) | < 0.001 | 1.098 (0.963, 1.251) | 0.162 |
| Q3 | 4429 | 771 | 1.833 (1.625, 2.066) | < 0.001 | 1.143 (0.996, 1.312) | 0.058 |
| Q4 | 4430 | 857 | 1.998 (1.776, 2.247) | < 0.001 | 1.199 (1.029, 1.395) | 0.020 |
| VAI | ||||||
| per-SD increase | 17,718 | 2685 | 1.135 (1.098, 1.173) | < 0.001 | 1.022 (0.984, 1.061) | 0.270 |
| Quartiles | ||||||
| Q1 | 4430 | 471 | Ref | Ref | ||
| Q2 | 4429 | 628 | 1.207 (1.071, 1.360) | 0.002 | 0.975 (0.864, 1.101) | 0.686 |
| Q3 | 4429 | 747 | 1.358 (1.210, 1.524) | < 0.001 | 1.008 (0.893, 1.138) | 0.893 |
| Q4 | 4430 | 839 | 1.508 (1.347, 1.688) | < 0.001 | 1.014 (0.895, 1.154) | 0.830 |
| CVD mortality | ||||||
| Glucose metabolism status | ||||||
| NGR | 7812 | 217 | Ref | Ref | ||
| Pre-DM | 6693 | 336 | 2.405 (2.026, 2.855) | < 0.001 | 1.321 (1.105, 1.579) | 0.002 |
| DM | 3222 | 293 | 5.129 (4.295, 6.127) | < 0.001 | 1.665 (1.322, 2.097) | < 0.001 |
| WC | ||||||
| per-SD increase | 17,718 | 846 | 1.328 (1.245, 1.415) | < 0.001 | 1.168 (1.030, 1.324) | 0.012 |
| Quartiles | ||||||
| Q1 | 4442 | 124 | Ref | |||
| Q2 | 4439 | 199 | 1.638 (1.309, 2.050) | < 0.001 | 1.150 (0.897, 1.475) | 0.265 |
| Q3 | 4422 | 254 | 2.192 (1.769, 2.718) | < 0.001 | 1.373 (1.017, 1.853) | 0.038 |
| Q4 | 4415 | 269 | 2.524 (2.040, 3.123) | < 0.001 | 1.576 (1.104, 2.249) | 0.012 |
| LAP | ||||||
| per-SD increase | 17,718 | 846 | 1.220 (1.153, 1.291) | < 0.001 | 1.025 (0.944, 1.112) | 0.423 |
| Quartiles | ||||||
| Q1 | 4430 | 105 | Ref | Ref | ||
| Q2 | 4429 | 220 | 2.097 (1.662, 2.646) | < 0.001 | 1.310 (1.026, 1.673) | 0.022 |
| Q3 | 4429 | 242 | 2.243 (1.784, 2.820) | < 0.001 | 1.211 (0.934, 1.572) | 0.101 |
| Q4 | 4430 | 279 | 2.535 (2.025, 3.172) | < 0.001 | 1.283 (0.967, 1.701) | 0.050 |
| VAI | ||||||
| per-SD increase | 17,718 | 846 | 1.126 (1.061, 1.195) | < 0.001 | 0.992 (0.926, 1.064) | 0.898 |
| Quartiles | ||||||
| Q1 | 4430 | 135 | Ref | Ref | ||
| Q2 | 4429 | 211 | 1.414 (1.139, 1.755) | 0.001 | 1.101 (0.884, 1.372) | 0.341 |
| Q3 | 4429 | 245 | 1.552 (1.258, 1.915) | < 0.001 | 1.085 (0.871, 1.353) | 0.454 |
| Q4 | 4430 | 255 | 1.597 (1.296, 1.968) | < 0.001 | 0.994 (0.790, 1.251) | 0.920 |
Model adjusted for age (< 50, ≥ 50 years), gender (male, female), ethnicity (White, Black, Mexican American or Hispanic, Other), education (less than high school, high school or equivalent, college or above), smoking (never, former, now), drinking (never, former, now), hypertension (no, yes), body mass index (< 25, 25–30, ≥ 30), lipid-lowering therapy, total cholesterol, low-density lipoprotein cholesterol, glucose and glycosylated hemoglobin A1c in multiple Cox regression analysis. NHANES: National Health and Nutrition Examination Survey; CVD: cardiovascular disease; SD: standard deviation; HR: hazard ratio; CI confidence interval; VAI: visceral adiposity index; LAP: lipid accumulation product; WC: waist circumference; NGR: normal glucose regulation; Pre-DM: pre-diabetes mellitus; DM: diabetes mellitus
p < 0.05 suggests significant differences
Stratified analyses revealed significant interactions of HR per 1-SD increase in LAP and WC with age, ethnicity, and hypertension for all-cause mortality risk (Fig. 1, all p for interaction < 0.05). Additionally, WC exhibited significant interactions with age and hypertension for CVD mortality risk (Figure S5, p for interaction < 0.05). Specifically, the relationships of LAP and WC with all-cause mortality were stronger in participants under the age of 50 compared with those of 50 years of age and above. Furthermore, Black participants exhibited a stronger association compared to White participants, and participants with hypertension had an elevated risk compared with individuals without hypertension (Fig. 1). Additionally, no interactions of the two abdominal obesity indices with all-cause mortality were observed across other subgroups (Fig. 1, all p for interaction > 0.05). These findings suggest that the associations between abdominal obesity indices and mortality may vary significantly across different demographic and clinical subgroups.
Fig. 1.
Associations of the hazard ratios for LAP (per-SD increase) and WC (per-SD increase) with all-cause mortality in different subgroups. Data were adjusted for age (< 50, ≥ 50 years), gender (male, female), ethnicity (White, Black, Mexican American or Hispanic, Other), education (less than high school, high school or equivalent, college or above), smoking (never, former, now), drinking (never, former, now), hypertension (no, yes), body mass index (< 25, 25–30, ≥ 30), lipid-lowering therapy, total cholesterol, low-density lipoprotein cholesterol, glucose and glycosylated hemoglobin A1c in multiple Cox regression analysis. CVD: cardiovascular disease; SD: standard deviation; HR: hazard ratio; CI confidence interval; LAP: lipid accumulation product; WC: waist circumference; NGR: normal glucose regulation; Pre-DM: pre-diabetes mellitus; DM: diabetes mellitus; BMI: body mass index. p < 0.05 suggests significant differences
Association of combined LAP, WC, and metabolic status with mortality risk
Given that only LAP and WC were related to elevated risks of all-cause mortality, we further investigated the predictive value of LAP and WC in combination with different glucose metabolism statuses for these outcomes. Kaplan-Meier analysis, stratifying participants by both glucose metabolism status and LAP or WC quartiles, demonstrated that individuals in the higher quartiles of LAP or WC exhibited significantly increased risks of all-cause and CVD mortality across all glucose metabolism statuses compared to the reference group (Fig. 2, p < 0.001). Furthermore, in fully adjusted Cox regression models, the HRs for all-cause mortality across the quartiles of LAP or WC, stratified by glucose metabolism status, are presented in Table 3. Notably, in comparison with the reference group, participants with pre-DM or DM who were in the highest LAP quartile exhibited a 1.397- and 1.936-fold elevated risk of all-cause mortality, respectively (Table 3, all p < 0.01). Furthermore, individuals in the highest quartile of WC had a 1.567- and 2.493-fold increased risk of all-cause mortality, and a 1.921- and 2.827-fold increased risk of CVD mortality in the pre-DM and DM groups, respectively (Table S5, all p < 0.01).
Fig. 2.
Kaplan-Meier analysis of all-cause (A and B) and CVD (C and D) mortality according to the status of the combination of LAP or WC quartiles and glucose metabolism. CVD: cardiovascular disease; LAP: lipid accumulation product; WC: waist circumference; NGR: normal glucose regulation; Pre-DM: pre-diabetes mellitus; DM: diabetes mellitus. p < 0.05 suggests significant differences
Table 3.
LAP and WC levels in relation to all-cause mortality in patients with different glucose metabolism status
| LAP | All-cause mortality | WC | All-cause mortality | ||
|---|---|---|---|---|---|
| HR(95%CI) | p | HR(95%CI) | p | ||
| NGR | NGR | ||||
| LAP Q1 | Ref | WC Q1 | Ref | ||
| LAP Q2 | 1.357 (1.093, 1.684) | 0.006 | WC Q2 | 1.307 (1.070, 1.597) | 0.009 |
| LAP Q3 | 2.053 (1.525, 2.764) | < 0.001 | WC Q3 | 2.256 (1.712, 2.972) | < 0.001 |
| LAP Q4 | 1.138 (0.934, 1.386) | 0.199 | WC Q4 | 1.085 (0.893, 1.318) | 0.414 |
| Pre-DM | Pre-DM | ||||
| LAP Q1 | 1.423 (1.171, 1.731) | < 0.001 | WC Q1 | 1.406 (1.163, 1.700) | < 0.001 |
| LAP Q2 | 1.858 (1.473, 2.342) | < 0.001 | WC Q2 | 1.842 (1.470, 2.308) | < 0.001 |
| LAP Q3 | 1.321 (1.077, 1.620) | 0.008 | WC Q3 | 1.587 (1.275, 1.975) | < 0.001 |
| LAP Q4 | 1.397 (1.145, 1.704) | 0.001 | WC Q4 | 1.567 (1.270, 1.933) | < 0.001 |
| DM | DM | ||||
| LAP Q1 | 1.790 (1.436, 2.232) | < 0.001 | WC Q1 | 1.863 (1.481, 2.343) | < 0.001 |
| LAP Q2 | 1.104 (0.867, 1.404) | 0.420 | WC Q2 | 1.356 (1.031, 1.781) | 0.029 |
| LAP Q3 | 1.533 (1.248, 1.883) | < 0.001 | WC Q3 | 1.943 (1.534, 2.461) | < 0.001 |
| LAP Q4 | 1.936 (1.558, 2.406) | < 0.001 | WC Q4 | 2.493 (1.944, 3.197) | < 0.001 |
Data were adjusted for age (< 50, ≥ 50 years), gender (male, female), ethnicity (White, Black, Mexican American or Hispanic, Other), education (less than high school, high school or equivalent, college or above), smoking (never, former, now), drinking (never, former, now), hypertension (no, yes), body mass index (< 25, 25–30, ≥ 30), lipid-lowering therapy, total cholesterol, low-density lipoprotein cholesterol, glucose and glycosylated hemoglobin A1c in multiple Cox regression analysis. CVD: cardiovascular disease; NGR: normal glucose regulation; Pre-DM: pre-diabetes mellitus; DM: diabetes mellitus; HR: hazard ratio; CI confidence interval; LAP: lipid accumulation product; WC: waist circumference
p < 0.05 suggests significant differences
Risk prediction of LAP and WC for mortality in different glucose metabolism groups
The C-statistic was employed to evaluate the ability of the two abdominal obesity indices (LAP and WC) to predict mortality throughout various glucose metabolism statuses. In the Cox regression models, adding WC to the baseline models considerably enhanced the C-statistic for predicting all-cause mortality in participants with NGR and Pre-DM (Table S6, p < 0.05). However, adding LAP to the baseline models did not significantly enhance the prediction of all-cause mortality across different glucose metabolism statuses (Table S6, all p > 0.05). In addition, the inclusion of WC did not lead to a significant improvement in the C-statistic for predicting CVD mortality (Table S7, p > 0.05).
Discussion
In this large, nationwide, typical prospective cohort study centered on U.S. adults, we noted that individuals with Pre-DM or DM had notably higher levels of VAI, LAP, and WC in contrast with those with NGR (Table 1). Cox regression analyses further revealed that both glucose metabolism status and WC were significant predictors of all-cause and CVD mortality, while LAP was correlated only with all-cause mortality (Table 2). Further combined analysis revealed that individuals with Pre-DM or DM in the highest quartiles of WC had considerably elevated risks of all-cause and CVD mortality in contrast with those with NGR in the lowest quartiles, even after adjusting for potential confounders (Table 3). Additionally, incorporating WC into the predictive models enhanced the predictive power for all-cause mortality, especially in the Pre-DM groups (Table S6). However, LAP did not markedly enhance the prediction of all-cause mortality (Table S6). These findings underscore the importance of incorporating abdominal obesity indices such as WC into risk assessment tools, especially for identifying individuals at higher risk of mortality among those with impaired glucose metabolism.
Increasing evidence suggested that abdominal obesity, characterized by excessive accumulation of intraabdominal adipose tissue, was more closely associated with CVD than overall obesity [20, 21]. In our study, elevated WC and LAP were notably associated with all-cause mortality, which is in line with previous studies [11, 22, 23]. Subgroup analysis suggested a notable interaction between age and both WC and LAP regarding the risk of mortality. Among individuals aged < 50 years, WC and LAP were more strongly correlated with all-cause mortality compared to those aged ≥ 50 years. This may be explained by the presence of multiple comorbidities and chronic conditions, such as CVD, hypertension, and diabetes, diminishing the independent effect of abdominal obesity on mortality risk [24]. Additionally, our findings indicated a significant interaction between ethnicity, hypertension, and abdominal obesity indices (LAP, WC) on all-cause mortality. Black individuals faced an increased risk of all-cause mortality in contrast with White individuals [25, 26]. These disparities may be influenced by racial/ethnic discrimination, socioeconomic status, lifestyle differences, and unequal access to healthcare [26–28]. In addition, populations with African ancestry exhibit higher frequencies of genetic risk variants, such as APOL1 (linked to insulin resistance and cardiometabolic risk) [29, 30] and adiposity-related polymorphisms in FTO and MC4R (affecting visceral fat distribution and metabolic dysfunction) [31–33] which may contribute to accelerated progression of DM, cardiovascular complications, and other chronic conditions. The interaction between hypertension and abdominal obesity is likely driven by shared pathophysiological mechanisms, such as systemic inflammation, endothelial dysfunction, and insulin resistance, which are exacerbated in individuals with both conditions [34, 35]. Furthermore, consistent with previous research, our study demonstrated that women with higher WC exhibited an association with increased CVD mortality compared to men [36]. The average age of participants in our study was 50 years. For women, postmenopausal estrogen loss contributes to abdominal adiposity and reduces cardioprotective effects, including impaired endothelial function, increased systemic inflammation, and dyslipidemia. Notably, HDL-C levels decline significantly after menopause, diminishing reverse cholesterol transport and antioxidant capacity [37, 38]. These mechanisms may synergistically elevate CVD risk in postmenopausal women with prediabetes, independent of fat distribution [39].
However, no significant relation between VAI and all-cause or CVD mortality was observed after adjusting for potential confounders, despite individuals with Pre-DM and DM having higher VAI levels. A possible reason for this outcome may be that VAI is an indicator of visceral fat mass, while WC and LAP are used to evaluate the accumulation of abdominal fat tissue without discrimination between visceral and subcutaneous fat [40]. Additionally, several studies have indicated a J-shaped relationship between VAI levels and all-cause mortality, which may explain the inconsistent findings [41, 42]. Our results call for subsequent studies to validate them.
The predictive value of LAP and WC has been evaluated in various subgroups and populations [43, 44]. However, no prior study has specifically explored the relationship between elevated LAP or WC and mortality in individuals with impaired glucose metabolism. Our study revealed that both Pre-DM and DM were considerably related to increased risks of all-cause mortality, even after adjusting for confounding variables (Table 2). These findings underscore the importance of early intervention in individuals with impaired glucose regulation. Notably, pre-DM is a modifiable risk factor, and intensive lifestyle management can dramatically reduce the incidence of DM, CVD and death in pre-DM [45, 46]. In addition to assessing WC and LAP as independent predictors, our study examined the combined impact of abdominal obesity indices and glucose metabolism status on mortality risk. An important finding of the study was that individuals with Pre-DM or DM, especially those within the highest quartiles of LAP or WC, exhibited a considerably elevated risk of all-cause mortality relative to individuals with NGR in the lowest quartiles (Table 3). Moreover, incorporating WC into the predictive models enhanced the predictive accuracy for all-cause mortality in both the NGR and Pre-DM groups. However, the addition of WC did not enhance the predictive power for CVD mortality, which potentially be attributed to the limited sample size in this subgroup. These results highlight the critical need for early clinical intervention targeting abdominal obesity in individuals with impaired glucose metabolism.
The present study possesses a number of strengths. To begin with, it represents a prospective cohort study based on a relatively large national sample, indicating that our results may be generalized to the general population. Second, it is the first study to assess the joint association of glucose metabolic status, specifically pre-DM, and abdominal obesity indices between all-cause and CVD mortality. Several limitations should be considered. First, in spite of the substantial number of samples and lengthy follow-up duration in this prospective cohort study, causal inference cannot be established because of the observational study design. Second, The NHANES data did not have the ability to discriminate diverse forms of diabetes. Nevertheless, the present study may be more applicable to individuals with Type 2 DM as individuals aged < 20 years were excluded. Third, there is only a single record of diabetes status and abdominal obesity indices in NHANES data, potential bias from measurement error should be taken into account, and it may not represent the average levels during the extended follow-up span. Nonetheless, the single assessment of diabetes status and abdominal obesity indices has been described in numerous extensive cohort studies [11, 47]. Moreover, our study relied on baseline glycemic status due to NHANES data constraints, potentially underestimating the impact of transitioning from Pre-DM to DM. Future research with longitudinal glycemic measurements is needed to confirm these findings. Finally, we incorporated as many potential confounders as possible in the analysis and conducted subgroup and sensitivity analyses to ensure the robustness of our findings. However, the potential effect of residual confounders on our analysis cannot be eliminated.
In conclusion, the present study indicated that individuals with pre-DM and DM who had high LAP or WC tended to exhibit a worse prognosis. Furthermore, high levels of LAP or WC were an effective predictor for all-cause and CVD mortality in individuals with impaired glucose metabolism, indicating the clinical importance of early intervention for abdominal obesity in populations with impaired glucose metabolism.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors acknowledge the participants and staff of the National Health and Nutrition Examination Survey 1999-2018 for their important contribution.
Author contributions
XL C and J P conceived the research and the paper. J P and LL D completed data collection and analyses. XL C, AH L, and QL M interpreted the results. XL C and J P wrote the paper and QL M revised the manuscript. All authors approved the final version. All authors read and approved the final manuscript.
Funding
The National Natural Science Foundation of China (No. 82300677) and the Hunan Provincial Natural Science Foundation of China (No. 2023JJ40978) partly supported this study.
Data availability
The data for this study were obtained from the public database of the National Center for Health Statistics. https:// www.cdc.gov/nchs/nhanes/.
Declarations
Ethics approval and consent to participate
Exempt approval was obtained from the Committee on Human Research at the University of California, San Francisco, and informed consent was obtained from all participants involved in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xiaoli Chen and Aihua Li have contributed equally to this work.
Contributor Information
Jia Peng, Email: 22022183@csu.edu.cn.
Qilin Ma, Email: mqilin2013@csu.edu.cn.
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Supplementary Materials
Data Availability Statement
The data for this study were obtained from the public database of the National Center for Health Statistics. https:// www.cdc.gov/nchs/nhanes/.