Gender-specific implications of the Waist-to-Weight Index in predicting prediabetes prevalence

doi:10.21203/rs.3.rs-4274690/v1

Purpose

To explore how WWI correlates with prediabetes prevalence.

Methods

analyzing data from 13,688 individuals with prediabetes and 14,753 non-diabetic individuals, multifactorial logistic regression models were used to assess the relationship between WWI and the incidence of prediabetes, considering variables such as age, gender, BMI, race, and various health markers, and compared with BMI, WC (Waist Circumference), and WHt (Waist-to-Height Ratio).

Results

The study found a direct and significant association between an increase in WWI and an increased prevalence of prediabetes, especially in the highest WWI quartile (Q4). Gender differences were also significant in the prevalence of prediabetes, and the evaluation effect of WWI was on par with WC and greater than that of BMI and WHt.

Conclusion

WWI has been proven to be a key indicator for assessing the prevalence of prediabetes, emphasizing the need for gender-specific approaches in health strategies.

Waist-to-Weight Index (WWI)

Prediabetes

NHANES

Obesity metrics

Multifactorial logistic regression

In recent years, type 2 diabetes (T2D) has burgeoned into a global health crisis, presenting substantial challenges to public health systems worldwide. The International Diabetes Federation (IDF) reported a staggering 537 million adults living with diabetes in 2021, representing a significant fraction of the global adult demographic. This amount is on an upward trajectory, projected to reach 643 million by 2030 and 783 million by 2045. A disproportionate burden of this condition is borne by low- and middle-income countries, where over three-quarters of these individuals reside, marking diabetes as an escalating health concern in these areas. Furthermore, diabetes was attributed to approximately 6.7 million deaths in 2021, illustrating the disease’s profound impact on global mortality rates, with one life claimed every five seconds [1]. These alarming statistics highlight the critical need for enhanced preventive and therapeutic strategies on a global scale to curb the diabetes epidemic’s societal and economic repercussions.

The progression from prediabetes to diabetes represents a pivotal health concern, with estimates suggesting that 5%–10% of individuals with prediabetes advance to diabetes annually, however, a similar proportion may revert to normoglycemia [2]. This underscores the significance of prompt detection and lifestyle interventions. A longitudinal study observed a diabetes development rate of 22.85% over 13 years among the prediabetic cohort, pinpointing prevalence factors such as smoking, body mass index (BMI), and alcohol intake [3]. Additionally, gender-specific lifestyle variables, including physical activity in males and waist circumference in females, have been identified as crucial determinants in the prediabetes-to-diabetes continuum [4]. Although BMI is widely used as an obesity indicator, its inability to distinguish between visceral and subcutaneous fat restricts its utility. Visceral fat accumulation, more so than subcutaneous fat, is intimately linked with metabolic complications like insulin resistance, hypertension, and dyslipidemia, thereby exerting a greater threat to health. The “obesity paradox”—the observed discordance or inverse relationship between BMI and mortality across various populations—calls into doubt BMI’s efficacy as a health assessment tool. Other metrics, such as waist circumference (WC) and waist-to-height ratio (WHtR), have advantages over BMI, but they, too, are mired in controversies regarding their predictive accuracy for obesity-related illnesses [5–8].

In light of these considerations, the Waist-to-Weight Index (WWI) emerges as a novel obesity indicator, conceived to more precisely gauge central obesity and its health implications. Comparative studies suggest that WWI surpasses traditional metrics like BMI, WC, and WHtR in predicting cardiovascular disease mortality and diabetes prevalence [9–11]. Nevertheless, the association between WWI and prediabetes remains unexplored, a critical gap given the prevalence of prediabetes over diabetes. Identifying prediabetic individuals through early screening is essential for preventing the transition to diabetes.

This study leverages NHANES data spanning 2009 to 2020 to investigate the potential link between WWI and prediabetes. We posit that elevated WWI levels are indicative of an increased prediabetes prevalence and aim to assess WWI’s predictive power against conventional obesity indices, such as BMI and WC, in forecasting prediabetes, thereby evaluating WWI’s utility in prediabetes prevalence assessment and prediction.

The data used in this research were obtained from the National Health and Nutrition Examination Survey (NHANES) database, spanning the years 2009 to 2020. NHANES is a survey that is periodically conducted and selects a representative sample of the U.S. population using a complex multi-stage probability sampling method. The aim is to assess the health and nutrition status of adults and children in the United States. This research project received approval from the Ethics Review Committee of the National Center for Health Statistics (NCHS), and all participants signed written informed consent forms. More detailed information can be found on the CDC’s official website.

We utilized the most recent data from the National Health and Nutrition Examination Survey (NHANES, 2009–2020) to conduct a cross-sectional study on individuals aged 18 and above (n = 40,045). The three diagnosable conditions for prediabetes—fasting blood glucose, glycated hemoglobin, and no prior notification of a prediabetes condition in the questionnaire—were excluded based on the lack of data proving their absence. Additionally, patients lacking WWI data were excluded. Ultimately, a total of 13,688 participants in a prediabetic state were examined. The specific exclusion criteria are shown in Fig. 1.

For the definition of exposure and outcome variables

The Waist-to-Weight Index (WWI), calculated as the waist circumference (WC in centimeters) divided by the square root of weight (in kilograms), served as our primary variable with prediabetes as the outcome. According to the 2023 Guidelines of the American Diabetes Association (ADA), prediabetes in this study was defined as meeting any one of the following three conditions: being notified by a doctor of a prediabetes condition, having a fasting blood glucose (FBG) level between 100 and 125 mg/dL, or a glycated hemoglobin (HbA1c) level between 5.7% and 6.4%. Ultimately, we selected 13,688 individuals with prediabetes for analysis alongside 14,753 individuals without prediabetes or diabetes. Data for this study were provided by the public database, and participants’ written informed consent was collected by NHANES. In this analysis, the combined occurrence of prediabetes and diabetes was considered the final event.

Potential covariates

Covariates in this study included continuous variables (age, Body Mass Index [BMI, kg/m²], Systolic Blood Pressure [SBP, mmHg], Diastolic Blood Pressure [DBP, mmHg], Triglycerides [TGs, mg/dL], Total Cholesterol [TC, mg/dL], Low-Density Lipoprotein Cholesterol [LDL-C], High-Density Lipoprotein Cholesterol [HDL-C], and TGs) and categorical variables (gender, race, and hypertension status). Interviews collected demographic data on age, gender (male or female), poverty income ratio, and race (Non-Hispanic White, Non-Hispanic Black, Mexican American, other Hispanic, or others). Physical measurement indices included height, weight, and blood pressure (BP). Height and weight were measured without shoes in lightweight clothing using a medical scale, and BMI was calculated as weight divided by the square of the height. Blood pressure was measured using a mercury sphygmomanometer according to the then-current American Heart Association recommended standard blood pressure measurement protocol. Three blood pressure readings were obtained consecutively from the same arm. This study defined SBP and DBP as the average value of the three blood pressure measurements. Fasting venous blood was drawn from each subject for TC and TG measurement.

Statistical analysis

This study conducted a statistical analysis using R version 4.2.0 (R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). The level of statistical significance was set at P < 0.05. Baseline characteristics were expressed as means and standard deviations (SD) or medians (interquartile range) for continuous variables, and proportions for categorical variables. Quartiles of the WWI group were compared using Student’s t-test, Mann-Whitney U test, and chi-square test for categorical variables. In the multivariate logistic regression model, covariates known as prediabetes or suspected prevalence factors for diabetes, or WWI, changed the estimates by more than 10%. The relationship between WWI and the overall population as well as the groups of male and female with prediabetes and diabetes was analyzed by using multifactor logistic regression analysis. Model 1 represented unadjusted data. In Model 2, data were adjusted for age, gender (only for the overall population), and race. In Model 3, results were adjusted for age, gender (applicable only to the overall population), race, SBP, DBP, TG, TC, and the poverty income ratio.

The results of the logistic regression analysis were presented as odds ratios (ORs) and 95% confidence intervals (CIs). Additionally, the effect response between WWI and prediabetes and diabetes was evaluated using a generalized additive model and fitted curves. Stratified analysis and interaction tests were added to the regression model to assess the interaction between WWI and different genders on prediabetes and diabetes.

In this study, in order to more precisely explore the relationship between WWI and prediabetes, we excluded the population already suffering from diabetes, analyzing a total of 30,850 participants, including both prediabetic and non-prediabetic/diabetic groups. The average age was 45.98 ± 18.23 years, with women comprising 51.93% of the total sample. Notably, analysis of the prediabetic group revealed that out of 13,598 participants categorized as prediabetic, 50.93% were male and 49.07% were female, highlighting the prevalence of prediabetes in our study population and a relatively balanced gender distribution in this condition. The WWI quartiles ranged from Q1 (8.04 to 10.36) to Q4 (11.53 to 14.79), with a mean WWI of 10.95 and a standard deviation of 0.86. Table 1 shows the demographic and clinical characteristics of participants across baseline WWI quartiles. Significant statistical differences were observed in race, BMI, hypertension status, LDL, HDL-C, waist circumference, and TGs across WWI quartiles (all P < 0.05). Compared to the lowest WWI group, the highest WWI group (Q4) had significantly higher BMI, hypertension prevalence, LDL, HDL-C, waist circumference, and TGs (all <0.05). The Q4 group mainly comprised middle-aged and older individuals, non-Hispanic Whites, women, those with a lower poverty income ratio, and hypertensive individuals, suggesting a potentially higher prevalence of diabetes and cardiovascular health issues in these populations.

Table 1 Demographic and clinical characteristics across Waist-to-Weight index quartiles

Variables^a		WWI quartiles				P value
Variables^a	Mean+SD	Q1(8.04 to 10.36)	Q2(10.36 to 10.95)	Q3(10.95 to 11.53)	Q4(11.53 to14.79）	P value
Participants		7713	7712	7711	7714
Males, N (%)	14,829 (48.07%)	4746 (61.53%)	4033 (52.30%)	3615 (46.88%)	2435 (31.57%)	<0.001
Females, N (%)	16,021 (51.93%)	2967 (38.47%)	3679 (47.70%)	4096 (53.12%)	5279 (68.43%)	<0.001
Age, year	45.98 ± 18.23	33.78 ± 14.17	43.63 ± 15.92	50.02 ± 17.01	56.49 ± 17.47	<0.001
BMI, kg/m²	26.07 ± 8.05	24.37 ± 4.61	27.61 ± 5.52	29.85 ± 6.22	32.95 ± 7.60	<0.001
Race
Non-Hispanic White, N(%)	4277 (13.86%)	633 (8.21%)	1008 (13.07%)	1276 (16.55%)	1360 (17.63%)	<0.001
Non-Hispanic Black, N(%)	3146 (10.20%)	586 (7.60%)	791 (10.26%)	917 (11.89%)	852 (11.04%)	<0.001
Mexican American, N (%)	12,003 (38.91%)	2808 (36.41%)	2907 (37.69%)	2902 (37.63%)	3386 (43.89%)	<0.001
Other Hispanic, N (%)	6849 (22.20%)	2346 (30.42%)	1684 (21.84%)	1515 (19.65%)	1304 (16.90%)	<0.001
Other races, N (%)	4575 (14.83%)	1340 (17.37%)	1322 (17.14%)	1101 (14.28%)	812 (10.53%)	<0.001
Poverty income ratio	2.50 ± 1.64	2.57 ± 1.70	2.66 ± 1.67	2.54 ± 1.64	2.23 ± 1.53	<0.001
TGMG	1.25 ± 0.98	87.24 ± 66.97	110.56 ± 93.12	120.07 ± 96.34	127.91 ± 80.98	<0.001
Glucose metabolism state
None prediabetes	17,252 (55.92%)	5750 (74.55%)	4618 (59.88%)	3866 (50.14%)	3018 (39.12%)	<0.001
Prediabetes	13,598 (44.08%)	1963 (25.45%)	3094 (40.12%)	3845 (49.86%)	4696 (60.88%)	<0.001
Hypertension
No hypertension	12,405 (68.71%)	3839 (82.05%)	3261 (70.57%)	2867 (63.85%)	2438 (57.18%)	<0.001
Stage 1 hypertension	3207 (17.76%)	597 (12.76%)	827 (17.90%)	901 (20.07%)	882 (20.68%)	<0.001
Stage 2 hypertension	2442 (13.53%)	243 (5.19%)	533 (11.53%)	722 (16.08%)	944 (22.14%)	<0.001
FASTING.GLU	99.19 ± 10.11	95.21 ± 8.73	98.84 ± 9.70	100.48 ± 10.03	102.46 ± 10.52	<0.001
HBLA	5.46 ± 0.40	5.29 ± 0.35	5.41 ± 0.37	5.52 ± 0.39	5.62 ± 0.39	<0.001
WEIGHT	63.54 ± 32.61	72.47 ± 16.60	79.00 ± 19.56	82.95 ± 21.16	86.89 ± 24.26	<0.001
WAIST	88.26 ± 22.72	83.36 ± 10.31	94.13 ± 11.74	101.49 ± 12.89	111.28 ± 15.77	<0.001
HDLDBMG	1.40 ± 0.42	57.00 ± 16.14	54.22 ± 16.81	52.56 ± 16.02	52.07 ± 15.04	<0.001
LDLMG	2.91 ± 0.90	103.00 ± 32.10	114.65 ± 34.26	116.29 ± 36.13	116.09 ± 34.72	<0.001

In the three multivariate regression models, a significant positive correlation was observed between WWI and prediabetes (OR = 1.96, 95% CI: 1.91–2.02, P < 0.0001), indicating that for every unit increase in WWI score, the effect value increased by 1.96 units. The authors further transformed WWI from a continuous variable to a categorical variable (quartiles) for sensitivity analysis. Compared to the lowest quartile of WWI, the effect value increased with the rise of WWI. The highest WWI quartile showed an average AAC effect value of 1.58 units higher than the lowest quartile (OR = 2.58, 95% CI: 2.17–3.06, ***P < 0.001) (Table 2).

Table 2 Association of WWI quartiles with prediabetes prevalence: Adjusted and non-adjusted models

WWI	Events(%)	Prediabetes OR(95%CI)
WWI	Events(%)	Non-adjusted	Adjust I	Adjust II
WWI		1.96 (1.91, 2.02) ^***
Q1 (8.04 to 10.36)	7711 (25.00%)	1	1	1
Q2 (10.36 to 10.95)	7712 (25.00%)	1.96 (1.83, 2.10)^***	1.54 (1.43, 1.65)^***	1.54 (1.34, 1.77)^***
Q3 (10.95 to 11.53)	7714 (25.00%)	2.91 (2.72, 3.12)^***	1.94 (1.80, 2.09)^***	1.75 (1.50, 2.04)^***
Q4 (11.53 to 14.79）	7711 (25.00%)	4.56 (4.26, 4.88)^***	2.77 (2.55, 3.00)^***	2.58 (2.17, 3.06)^***
P for trend		^***	^***	^***

^*P < 0.05

^**P < 0.01

^***P < 0.001

Non-adjusted model adjust for: None

Adjust I Model adjusts for: GENDER; AGE; RACE

Adjust II Model adjusts for: HDLDBMG; TGMG; LDLMG; GENDER; RACE; AGE; HYPERTENSION

In reference to the Q1 group, after adjusting for multiple variables including age, gender, BMI, race, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TGs), total cholesterol (TC), hypertension status, and the poverty income ratio, the prevalence of prediabetes gradually increased with higher WWI quartiles. The OR values were 1.16 (95% CI: 0.97–1.39), 1.46 (95% CI: 1.20–1.78), and 1.95 (), respectively. All P-values were less than 0.0001, indicating a linear positive correlation between WWI and the development of prediabetes and diabetes. This analysis result was validated by multivariate regression analysis, showing a trend consistent with the non-linear fitting curve.

Table 3 Gender-specific analysis of WWI impact on prediabetes prevalence

WWI	Gender	Prediabetes OR(95%CI)
WWI	Gender	Non-adjusted	Model I	Model II
Q1 (8.04 to 10.36)	Male	Ref.	Ref.	Ref.
Q2 (10.36 to 10.95)	Male	2.14 (1.96, 2.33)^***	1.54 (1.41, 1.69)^***	1.29 (1.07, 1.55)^***
Q3 (10.95 to 11.53)	Male	3.13 (2.86, 3.43)^***	1.81 (1.64, 2.00)^***	1.55 (1.26, 1.92)^***
Q4 (11.53 to 14.79）	Male	4.70 (4.24, 5.22)^***	2.23 (1.99, 2.51)^***	2.00 (1.52, 2.65)^***
Q1 (8.04 to 10.36)	Female	0.58 (0.52, 0.65)^***	0.57 (0.51, 0.64)^***	0.42 (0.33, 0.52)^***
Q2 (10.36 to 10.95)	Female	1.18 (1.07, 1.29)^**	0.91 (0.82, 1.00)^**	0.80 (0.66, 0.97)^*
Q3 (10.95 to 11.53)	Female	1.91 (1.75, 2.08)^***	1.24 (1.12, 1.36)^***	0.85 (0.71, 1.03)
Q4 (11.53 to14.79)	Female	3.41 (3.14, 3.71)^***	1.86 (1.69, 2.04)^***	1.37 (1.13, 1.66)^**
P interaction		0.0008	<0.0001	0.0189

^*P < 0.05

^**P < 0.01

^***P < 0.001

Results in table: β (95% CI) P value/OR (95% CI) P value

Outcome variable: PREDIABETES

prevalence factor: WWI

Effect modifier: GENDER

Model I was adjusted for AGE, RACE

Model II was adjusted for AGE, RACE, PIR, HDLDBMG, TGMG, LDLMG, HYPERTENSION

Table 3 shows the relationship between WWI and prediabetes and diabetes among men and women, revealing gender differences. As WWI increased in the three models, the prevalence of prediabetes and diabetes was more significant in men than in women (P < 0.0001). Similarly, apart from WWI quartiles classified by gender, the influence of WWI as a categorical variable on prediabetes was analyzed.

For men, compared to the reference group (Q1), an increase in WWI quartiles was significantly associated with an increased prevalence of prediabetes. The highest WWI group (Q4) had an effect value of 1.23 units higher than the first group (Q4: OR = 2.23, 95% CI: 1.99–2.51, P = 0.0189). Compared to men, women in the first quartile (Q1) showed a lower prevalence of prediabetes (OR = 0.42, 95% CI: 0.33–0.52), and this trend was not evident in higher WWI quartiles. Especially in the Q4 group, the prevalence ratio for women was 1.37 (95% CI: 1.13, 1.66), not as significantly increased as in men. Interaction analysis showed that in the original model, the interaction P-value between gender and WWI was 0.0008, and in the Model I adjusted for age, race, personal income ratio (PIR), blood lipid levels, and hypertension, the interaction P-value was even more significant at <0.0001. This result indicates that gender plays a key role in the association between WWI quartiles and prediabetes prevalence rate.

Indicator comparison

Fig. 2 ompares four indicators used to predict prediabetes: WWI, BMI, waist circumference, and WHtR, using Receiver Operating Characteristic (ROC) curves. The areas under these curves were relatively close, indicating that each indicator had a similar ability to predict prediabetes. WWI and WHtR had an AUC of 0.66, waist circumference had an AUC of 0.65, and BMI had a slightly lower AUC of 0.63. In the ROC curve results, WWI did not show a slightly better predictive ability compared to BMI and WC and was close to WHtR in predictive ability.

This is a cross-sectional study based on data from 30,850 participants in the National Health and Nutrition Examination Survey from 2009 to 2020, where the authors observed a positive correlation between WWI and prediabetes. Further analysis found that the prevalence in men was higher than in women. This is the first study to assess the gender difference in WWI and its relationship with prediabetes. The results of this study suggest that managing WWI can alleviate the occurrence of prediabetes.

To our knowledge, previous studies have assessed the relationship between various body indices and diabetes. In assessing the prevalence of prediabetes, waist circumference (WC), Waist-to-Height Ratio (WHtR), and Body Mass Index (BMI) are commonly used parameters. Given the high proportion of prediabetes to diabetes conversion, these body measurement indices are particularly important. However, in recent years, the newly proposed indicator—the ratio of waist circumference to the square root of weight (WWI)—has begun to show its potential in predicting the prevalence of diabetes. In the Chinese adult population, waist circumference and WHtR have a stronger correlation with the prevalence of diabetes than BMI, suggesting that central obesity may play a more critical role in predicting the prevalence of diabetes [12]. Meanwhile, a meta-analysis by Vazquez et al. [13] found that BMI, WC, and Waist-to-Hip Ratio (WHR) had similar predictive power for diabetes onset. This suggests that while these indicators are important diabetes prevalence factors, there is no clear superiority among them.

For WWI, a study by Yu et al. [14] in rural Chinese adults found that as WWI increased, so did the prevalence of newly diagnosed T2D. This finding highlights the potential application value of WWI as a novel indicator in diabetes prevalence assessment. Although a study by Sun et al. [15] in the Japanese population indicated that WWI did not show superior predictive power compared to traditional indicators, this does not diminish its potential as a promising alternative or complementary tool for further evaluation in different populations.

In summary, although WC, WHtR, and BMI are still commonly used indicators in clinical practice to assess the prevalence of diabetes, WWI as an emerging indicator is still worth attention in future research. Future studies should explore the correlation of these indicators in different races, age groups, and gender populations, as well as their utility in predicting the prevalence of diabetes and prediabetes [16]. This will provide important information for public health policy-making and individualized medical intervention strategies. Considering the sensitivity and specificity of waist circumference and Waist-to-Height Ratio in predicting the prevalence of diabetes, and their prevalence prediction ability is not entirely the same as BMI, it is recommended to use these indices as assessment tools in public health interventions, especially in diabetes prevalence screening and management of target populations [17,18]. The use of these measurement indices can support the formulation of more precise prevention strategies, thereby reducing the prevalence of prediabetes developing into diabetes.

Considering the high prevalence of prediabetes transforming into diabetes, this study is the first to use WWI grouping to examine the association between this transitional state and WWI. This study has several advantages. Firstly, it is based on data from NHANES, a national population-based sampling data obtained using standard protocols. All analyses were conducted considering the appropriate NHANES sampling weights, making the study sample more representative. The authors also adjusted for confounding covariates to ensure that the current results are more reliable. However, the limitations of this study cannot be ignored. Due to the cross-sectional study design, the authors were unable to establish a clear cause-and-effect relationship. Additionally, the gender subgroup analysis showed that the prevalence estimates for women did not significantly exceed those for men, indicating in this particular population, this is inconsistent with previous literature reports on gender and diabetes prevalence, which might be due to biological differences, hormonal level changes, or other unconsidered confounding factors. Therefore, further studies need to consider gender differences and other potential influencing factors. Lastly, although some potential covariates have been adjusted, the authors cannot completely rule out the influence of other possible confounding factors, such as the use of medications.

The mechanism of WWI in prediabetes and diabetes is not yet clear, but the following biological mechanisms can be explained: individuals with higher WWI show reduced insulin sensitivity, possibly originating from inflammation and cytokine level changes caused by visceral fat accumulation, particularly free fatty acid-induced pancreatic β-cell dysfunction [19]. As visceral fat increases, its metabolic activity and hormonal secretion changes may further enhance insulin resistance and metabolic disorders, collectively exacerbating the burden on β-cells, and leading to glucose homeostasis imbalance [20]. WWI is a succinct indicator of the relationship between central obesity and diabetes development, serving as a practical tool for the early identification of individuals’ prevalence rate, potentially significant for diabetes prevention and management.

Funding

This work was supported by Project Supported by the Fundamental Research Funds for the China Institute of Sport Science 2330.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author contributions

Yang Zhang: Formal Analysis, Investigation, Methodology, Writing—original draft. Qiangman Wei: Investigation, Methodology, Validation, Writing—original draft. Qianzhi Chen: Conceptualization, Funding acquisition, Project administration, Supervision, Writing—review and editing.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

D.J. Magliano, E.J. Boyko, IDF Diabetes Atlas 2021 (10th ed.). International Diabetes Federation, Brussels (2021).
A.G. Tabák, C. Herder, W. Rathmann, E.J. Brunner, M. Kivimäki, Prediabetes: a high- prevalence state for diabetes development. Lancet 379, 2279–2290 (2012). https://doi.org/10.1016/S0140-6736(12)60283-9
F. Santoro, A. Mallardi, A. Leopizzi, E. Vitale, E. Rawish, T. Stiermaier, et al. Current knowledge and future challenges in takotsubo syndrome: part 2-treatment and prognosis. J Clin. Med. 10, 468 (2021). https://doi.org/10.3390/jcm10030468
X. Song, P. Jousilahti, C.D.A. Stehouwer, S. Söderberg, A. Onat, T. Laatikainen, et al. Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur. J. Clin. Nutr. 67, 1298–1302 (2013). https://doi.org/10.1038/ejcn.2013.203
A. Sijtsma, G. Bocca, C. L'abée, E.T. Liem, P.J.J. Sauer, E. Corpeleijn, Waist-to-height ratio, waist circumference and BMI as indicators of percentage fat mass and cardiometabolic prevalence factors in children aged 3-7 years. Clin. Nutr. 33, 311–315 (2014). https://doi.org/10.1016/j.clnu.2013.05.010
M. Ashwell, P. Gunn, S. Gibson, Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic prevalence factors: systematic review and meta-analysis. Obes. Rev. 13, 275–286 (2012). https://doi.org/10.1111/j.1467-789X.2011.00952.x
L.M. Browning, S.D. Hsieh, M. Ashwell, A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr. Res. Rev.23, 247–269 (2010). https://doi.org/10.1017/S0954422410000144
S.D. Hsieh, H. Yoshinaga, T. Muto, Waist-to-height ratio, a simple and practical index for assessing central fat distribution and metabolic prevalence in Japanese men and women. Int. J. Obes. Relat. Metab. Disord. 27, 610–616 (2003). https://doi.org/10.1038/sj.ijo.0802259
Q. Zhu, F. Shen, T. Ye, Q. Zhou, H. Deng, X. Gu, Waist-to-height ratio is an appropriate index for identifying cardiometabolic prevalence in Chinese individuals with normal body mass index and waist circumference. J. Diabetes. 6, 527–534 (2014). https://doi.org/10.1111/1753-0407.12157
H. Hubert, C.B. Guinhouya, L. Allard, A. Durocher, Comparison of the diagnostic quality of body mass index, waist circumference and waist-to-height ratio in screening skinfold-determined obesity among children. J. Sci. Med. Sport 12, 449–451 (2009). https://doi.org/10.1016/j.jsams.2008.05.002
C.J. Lavie, A. De Schutter, P. Parto, et al. Obesity and Prevalence of Cardiovascular Diseases and Prognosis-The Obesity Paradox Updated. Prog Cardiovasc Dis 58, 537–547. https://doi.org/10.1016/j.pcad.2016.01.008X. Hou, S. Chen, G. Hu, et al. Stronger associations of waist circumference and waist-to-height ratio with diabetes than BMI in Chinese adults. Diabetes Res Clin Pract 147, 9–18. https://doi.org/10.1016/j.diabres.2018.07.029
G. Vazquez, S. Duval, D.R. Jacobs, Jr., K. Silventoinen, Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis. Epidemiol. Rev. 29, 115–128 (2007). https://doi.org/10.1093/epirev/mxm008
K. He, W. Zhang, X. Hu, et al. Stronger Associations of Body Mass Index and Waist Circumference with Diabetes than Waist-Height Ratio and Triglyceride Glucose Index in the Middle-Aged and Elderly Population: A Retrospective Cohort Study. J Diabetes Res 2022, 9982390. https://doi.org/10.1155/2022/9982390
S. Li, Y.Wang, Y.Ying, et al. Independent and Joint Associations of BMI and Waist Circumference With the Onset of Type 2 Diabetes Mellitus in Chinese Adults: Prospective Data Linkage Study. JMIR Public Health and Surveillance 9, e39459. https://doi.org/10.2196/39459
D. Zheng, S. Zhao, D. Luo, F. Lu, Z. Ruan, X. Dong, et al. Association between the weight-adjusted waist index and the odds of type 2 diabetes mellitus in United States adults: a cross-sectional study. Front Endocrinol. 14, 1325454 (2024). https://doi.org/10.3389/fendo.2023.1325454
N.A. ElSayed, G. Aleppo, V.R. Aroda, R.R. Bannuru, F.M. Brown, D. Bruemmer, et al. 2. classification and diagnosis of diabetes: standards of care in diabetes-2023. Diabetes Care 46, S19–S40 (2023). https://doi.org/10.2337/dc23-S002
Z. Xing, Z. Peng, X. Wang, et al. Waist circumference is associated with major adverse cardiovascular events in male but not female patients with type-2 diabetes mellitus. Cardiovasc Diabetol 19, 1–8. https://doi.org/10.1186/s12933-020-01007-6
K.E. Battle, J.K. Baird, The global burden of Plasmodium vivax malaria is obscure and insidious. PLoS Med. 18, e1003799 (2021). https://doi.org/10.1371/journal.pmed.1003799
L. Ji, J. Liu, Z. Xu, Z. Wei, R. Zhang, S. Malkani, et al. Efficacy and safety of ertugliflozin added to metformin: a pooled population from Asia with type 2 diabetes and overweight or obesity. Diabetes Ther. 14, 319–334 (2023). https://doi.org/10.1007/s13300-022-01345-6

No competing interests reported.

Gender-specific implications of the Waist-to-Weight Index in predicting prediabetes prevalence

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Abstract

Purpose

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Introduction

Study design and population

Results

Discussion

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Additional Declarations

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Version 1