
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

70648
10.1038/s41598-024-70648-4
Article
Association between the weight-adjusted-waist index and circadian syndrome in findings from a nationwide study
Zeng Weiwei 827881200@qq.com

https://ror.org/030e09f60 grid.412683.a 0000 0004 1758 0400 Department of Hepatology, The First Affiliated Hospital of Fujian Medical University, No. 20, Chazhong Road, Fuzhou, 350005 Fujian China
6 9 2024
6 9 2024
2024
14 208839 5 2024
20 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Weight-adjusted-waist index (WWI) is an emerging parameter for evaluating obesity. We sought to ascertain the link between WWI and circadian syndrome (CircS). The study population consisted of 8275 eligible subjects who were included in the ultimate analysis from the NHANES 2011–2018. By using multivariable regression models, the association of WWI and CircS was analyzed. In subgroup analysis, we explored the relationship in different groups and tested the stability of the intergroup connection using interaction testing. To investigate whether WWI and CircS had a potential non-linear relationship, smooth curve fittings, and threshold effects tests were also constructed. In a multivariate linear regression model, WWI is significantly positively related to CircS (OR = 1.77, 95% CI 1.50–2.08). Through subgroup analysis and interaction testing, the stability of this positive association was also validated. It was further found that there was an inverted U-shaped association, with a turning point of 11.84, between WWI and CircS. Our findings supported a strong association between WWI values and CircS. Central obesity management is pivotal for preventing or alleviating CircS.

Keywords

Weight-adjusted-waist index
Circadian syndrome
Circadian rhythm
Obesity
NHANES
Subject terms

Medical research
Risk factors
Diseases
Metabolic disorders
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Circadian rhythm is an intrinsic regulatory mechanism that adapts to the external 24-h day/night cycles and regulates homeostasis by participating in the metabolic pathways of multiple organs in the body1. The retinohypothalamic tract carries light signals to the suprachiasmatic nucleus (SCN), a master clock that regulates the peripheral clock through nerves and hormones and harmonizes the entire body with the external environment2,3. Circadian syndrome (CircS) is considered when ≥ 4 risk factors are met simultaneously: diabetes mellitus, hypertension, dyslipidemia, waist circumference, sleep deprivation, and depressive symptoms4,5. The contemporary social environment presents challenges to the biological clock including night shifts, artificial lighting, and social jetlag6,7.

Obesity contributes to a train of disease burdens and poses a significant threat to a man’s health8. WWI is a recently emerged indicator for evaluating central obesity, which takes into account the ratio between an individual’s waist circumference and weight, providing a more accurate reflection of fat distribution around the waist and adverse metabolic conditions9.

Obesity is reportedly tied to circadian rhythmicity. Obese people are more inclined to have delayed bedtimes and shorter sleep periods than healthy individuals10,11. Additionally, sleep deficiency and social jetlag can lead to overweight and metabolic syndrome12–14. A series of surveys have explored the relationship between WWI and such metabolism-related risk factors as diabetes and hypertension15,16, but there has been no description of the WWI–CircS relationship. Here, we utilized a large sample of the National Health and Nutrition Examination Survey (NHANES) to study the connection.

Methods

Study population

The NHANES combines interviews with physical examinations, helping in providing vital data on various aspects of the general health status. The study samples were drawn from the NHANES 2011–2018, with an initial selection of 39,156 participants. After removing individuals with incomplete data on WWI (n = 6407) and circadian syndrome diagnosis items (n = 23,962), as well as pregnant individuals (n = 75) and those under 20 years old (n = 437), 8275 eligible subjects were enrolled in the final analysis (Fig. 1).Fig. 1 Flow chart of participants selection.

Ethics statement

The NHANES program is approved by the Research Ethics Review Board of the National Center for Health Statistics, and all survey participants provide written informed consent. All procedures were conducted in accordance with the Declaration of Helsinki. The detailed methodology, ethics and data of NHANES can be accessed at https://www.cdc.gov/nchs/nhanes/index.htm.

Calculation of weight-adjusted-waist index

The WWI is a novel measure used to assess an individual’s body fat distribution and overall obesity level. A raised WWI score reflected an increased level of obesity. To calculate WWI, an individual’s waist circumference (cm) is divided by the 1/2 power of weight (kg)17. Relevant physical measurements were recorded in the Mobile Examination Center by qualified healthcare personnel. In the study, we regarded WWI as an exposure factor. WWI was not only handled as a continuous variable, but also stratified in tertiles based on the values.

Diagnosis of the circadian syndrome

For the diagnosis of CircS, at least four of the following items need to be met4,5: (1) male waist circumference ≥ 102 cm or female waist circumference ≥ 88 cm. (2) Triglycerides above 150 mg/dL or taking antihyperlipidemic drugs. (3) Decreased high-density lipoprotein-cholesterol (< 40 mg/dL in males or < 50 mg/dL in females). (4) Fasting glucose > 100 mg/dL or taking diabetic medication to reduce blood sugar. (5) Raised blood pressure (≥ 130/85 mmHg) or taking a prescription for hypertension. (6) Sleep hours ≤ 6 h/day. (7) A status of depression (having a Patient Health Questionnaire score ≥ 10).

Covariate collection

In this study, covariates included gender, age, race, educational background, marriage, ratio of family income to poverty (PIR), smoking habits, alcohol intake, body mass index (BMI), serum creatinine levels, total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, physical activity, cancer, hypertension, and diabetes. The level of alcohol consumption is categorized into three levels18: mild, moderate, and heavy. Consuming ≥ 3 drinks daily for females or ≥ 4 drinks daily for males was considered heavy. Consuming ≥ 2 drinks daily for females and ≥ 3 drinks daily for males was deemed moderate. Other alcohol consumption was regarded as mild. Smoking habits were grouped as never, former, and now smoking19. People who smoked < 100 cigarettes in lives were characterized as never smokers; subjects who smoked ≥ 100 cigarettes but do not smoke anymore are termed former smokers; current smokers smoked ≥ 100 cigarettes and occasionally or continue to smoke.

Statistical analysis

When presenting groupings characteristics, proportions are indicated for categorical variables, while mean ± standard deviations (SD) for continuous variables. Participants groupings were compared based on WWI tertiles using the weighted one-way ANOVA or the weighted chi-square test. Three different statistical models were employed for determining the relationship between WWI and CircS through the application of multivariable regression analysis. Firstly, no covariates were put to use in Model 1, while Model 2 was adjusted for gender, age, and race. In terms of Model 3, additional modifications were implemented to covariables including education background, marriage, PIR, smoking habits, alcohol intake, BMI, serum creatinine, total cholesterol, HDL-C, triglycerides, physical activity, cancer, hypertension and diabetes. In subgroup analysis, we adopted multivariable logistic regression calculations for seeking out the WWI–CircS relationship in different groups stratified by gender, age, hypertension, diabetes, and cancer. Interaction tests were conducted to detect the stability of that connection within subgroups. To investigate whether WWI and CircS had a non-linear relationship, smooth curve fittings, and the two-piecewise model were also constructed. Data collation and analysis were carried out with R package (3.4.3) and Empowerstats (2.0). P < 0.05 was considered statistically significant.

Ethical approval

The NHANES program is approved by the Research Ethics Review Board of the National Center for Health Statistics, and all survey participants provide written informed consent.

Results

Baseline characteristics

Of the 8275 adults participating in the research, the average age was 50.04 ± 17.46 years and 50.13% were male. CircS prevalence was 28.17% of overall participants, increasing with higher WWI tertiles. The ranges of WWI from tertile1 to tertile3 were 8.38–10.71, 10.71–11.45, and 11.45–14.20. The mean of WWI was 11.08 ± 0.85. Higher WWI tertiles were associated with higher proportions of older adults, women, and people who had fewer activities. Meanwhile, participants with higher WWI had a trend toward higher triglyceride, fasting blood sugar, and total cholesterol levels and lower HDL-C levels. They had a higher likelihood of diabetes, hypertension, or cancer than the lowest WWI tertile (Table 1).Table 1 Baseline characteristics of participants based on weight-adjusted-waist index (WWI).

Characteristics	Weight-adjusted-waist index	
Tertile 1 (n = 2758)	Tertile 2 (n = 2758)	Tertile 3 (n = 2759)	P value	
Age (years)	39.11 ± 14.16	50.31 ± 15.68	57.43 ± 15.60	< 0.001	
Gender (%)	< 0.001	
 Male	60.39	51.79	36.26		
 Female	39.61	48.21	63.74		
Race/ethnicity (%)	< 0.001	
 Mexican American	6.64	9.63	9.41		
 Other Hispanic	6.24	6.51	5.55		
 Non-Hispanic White	66.18	66.05	70.11		
 Non-Hispanic Black	12.12	8.95	8.09		
 Other race	8.83	8.86	6.84		
Education level (%)	< 0.001	
 Less than high school	10.13	14.56	18.65		
 High school	20.61	22.80	25.01		
 More than high school	69.26	62.64	56.33		
Marital status (%)	< 0.001	
 Married/living with partner	61.67	68.75	61.32		
 Others	38.33	31.25	38.68		
 PIR	3.10 ± 1.66	3.08 ± 1.65	2.74 ± 1.59	< 0.001	
Alcohol consumption level (%)	< 0.001	
 Mild	40.02	38.52	34.95		
 Moderate	19.66	18.33	14.66		
 Heavy	24.80	18.77	13.98		
 Unknown	15.52	24.37	36.41		
Smoked status	< 0.001	
 Never	60.46	52.60	50.95		
 Former	19.31	28.97	31.68		
 Now	20.23	18.43	17.37		
Vigorous recreational activities (%)	< 0.001	
 Yes	42.81	21.87	9.87		
 No	57.19	78.13	90.13		
Moderate recreational activities (%)	< 0.001	
 Yes	53.23	47.54	37.89		
 No	46.77	52.46	62.11		
Hypertension (%)	< 0.001	
 Yes	21.21	41.49	59.85		
 No	78.79	58.51	40.15		
Diabetes (%)	< 0.001	
 Yes	4.71	12.71	31.40		
 No	95.29	87.29	68.60		
Cancer (%)	< 0.001	
 Yes	6.28	10.20	15.21		
 No	93.72	89.80	84.79		
Triglycerides (mg/dL)	110.02 ± 100.77	136.28 ± 114.60	151.05 ± 142.47	< 0.001	
HDL-C (mg/dL)	56.79 ± 16.58	53.45 ± 16.81	52.18 ± 16.20	< 0.001	
Total cholesterol (mg/dL)	185.97 ± 38.30	194.51 ± 41.12	192.55 ± 43.32	< 0.001	
Fasting plasma Glucose (mg/dL)	99.53 ± 21.81	107.51 ± 29.34	118.75 ± 39.55	< 0.001	
Creatinine (mg/dL)	0.89 ± 0.26	0.87 ± 0.39	0.86 ± 0.33	0.009	
BMI (kg/m2)	25.68 ± 4.96	29.19 ± 5.76	33.66 ± 7.64	< 0.001	
Waist circumference (cm)	88.60 ± 11.43	100.56 ± 12.28	113.84 ± 15.85	< 0.001	
Weight (kg)	76.74 ± 17.84	83.82 ± 20.37	91.60 ± 24.55	< 0.001	
Circadian syndrome (%)	7.65	25.28	49.47	< 0.001	
PIR, ratio of family income to poverty; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index.

The WWI–CircS association

In three different models, multivariable logistic regression analysis revealed that WWI is significantly positively related to CircS (all P < 0.01). For example, this positive correlation remains stable after complete adjustment (OR = 1.77, 95% CI 1.50–2.08), suggesting that for every unit increment in WWI, there was a 77% rise in the likelihood of CircS. When WWI was transformed to tertiles, we similarly learnt a robust positive correlation between the two variables (all P < 0.01). In model 3, the risk of CircS in the Tertile 3 group (OR = 3.00, 95% CI 2.23–4.05) was considerably increased by three times than the Tertile 1 (Table 2).Table 2 Association between weight-adjusted-waist index and circadian syndrome.

WWI	OR (95% CI), P value	
Model 1 (Crude model)	Model 2 (Partially adjusted model)	Model 3 (Fully adjusted model)	
Continuous	3.66 (3.40, 3.95) < 0.001	3.33 (3.06, 3.62) < 0.001	1.77 (1.50, 2.08) < 0.001	
Categories	
 Tertile 1	Reference	Reference	Reference	
 Tertile 2	4.37 (3.70, 5.15)	3.59 (3.03, 4.27)	2.03 (1.55, 2.66)	
< 0.001	< 0.001	< 0.001	
 Tertile 3	12.87 (10.97, 15.11)	9.47 (7.94, 11.28)	3.00 (2.23, 4.05)	
< 0.001	< 0.001	< 0.001	
P for trend	< 0.001	< 0.001	< 0.001	
Model 1: no covariates were adjusted. Model 2: adjusted for gender, age, and race. Model 3: adjusted for gender, age, race, education level, marital status, ratio of family income to poverty (PIR), smoking status, alcohol consumption level, body mass index (BMI), serum creatinine, total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, vigorous activity, moderate activity, cancer, hypertension and diabetes.

WWI, weight-adjusted-waist index.

Through the construction of curve fittings as well as examination of threshold effects, we detected a non-linear correlation concerning these two variables. Meanwhile, the turning point was 11.84 (P < 0.001) (Fig. 2, Table 3). When stratified by gender, the positive trend persisted among women until WWI was 11.83 (P < 0.001) (Fig. 3, Table 3). For males, a turning point was calculated as 11.86 (P < 0.01). There was an obvious positive link between WWI and CircS when it was less than 11.86 (OR = 1.99, 95% CI 1.47–2.69). While the two had no statistically significant link when the WWI exceeded 11.86 (OR = 0.56, 95% CI 0.25–1.23) (Table 3).Fig. 2 The nonlinear relationship between WWI and CircS.

Table 3 Threshold effect analysis of WWI on circadian syndrome using a two-piecewise linear regression model.

	Adjusted OR/(95% CI) P value	
WWI	
 Turning point	11.84	
  < 11.84	2.47 (2.00, 3.06) < 0.001	
  > 11.84	0.64 (0.42, 0.97) 0.034	
 Log likelihood ratio	< 0.001	
Male	
 Turning point	11.86	
  < 11.86	1.99 (1.47, 2.69) < 0.001	
  > 11.86	0.56 (0.25, 1.23) 0.150	
 Log likelihood ratio	0.006	
Female	
 Turning point	11.83	
  < 11.83	2.69 (1.95, 3.71) < 0.001	
  > 11.83	0.68 (0.41, 1.14) 0.147	
 Log likelihood ratio	< 0.001	
WWI, weight-adjusted-waist.

Gender, age, race, education level, marital status, ratio of family income to poverty (PIR), smoking status, alcohol consumption level, body mass index (BMI), serum creatinine, total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, vigorous activity, moderate activity, cancer, hypertension and diabetes were adjusted.

Fig. 3 The nonlinear relationship between WWI and CircS stratified by gender.

Subgroup analysis

To investigate if such a connection was consistent across different population groupings, subgroup analyses were carried out. Figure 4 indicated that subgroups based on age, gender, hypertension, diabetes, and cancer showed a strong linkage between WWI and CircS (all P < 0.05). According to the results of the interaction tests, there was no statistical difference in the WWI–CircS connection within different subgroups (all P for interaction > 0.05), which suggested that these factors did not significantly affect the positive connection (Fig. 4).Fig. 4 Subgroup analysis for the association between WWI and CircS.

Discussion

Through representative population statistics, we found that higher WWI was associated with a higher incidence of CircS and validated the stability of such a positive association in different subgroups. It was further found that there was an inverted U-shaped association, with a turning point of 11.84, between WWI and CircS. Accordingly, WWI was considered an independent risk element for CircS when the index fell below 11.84. The results highlighted the importance of obesity management or intervention to mitigate CircS by assessing WWI.

As traditionally measured indices, BMI and waist circumference (WC) cannot distinguish between muscle mass and fat mass20. A study from Asia showed that WWI was positively correlated with all measures of fat mass, while WWI was negatively correlated with measures of muscle mass21. Moreover, WWI may more effectively depict the connection between obesity and metabolic disease and reveal central obesity regardless of weight15,22. WWI is widely acknowledged, adaptable to a variety of racial and demographic groups, and potentially more stable, particularly in cross-race or multi-center investigations9. Here, we measured the degree of “real obesity” using WWI. A recent study found that WWI performed better than BMI for diabetic kidney disease prediction23. Qin et al.24 also reported that WWI had a stronger link with albuminuria than other obesity indicators like BMI. Compared to some composite metrics such as a body shape index (ABSI) and waist-to-hip ratio (WHR), WWI is more convenient and easy to calculate. This evidence demonstrated the accessibility, reliability, and promising of WWI as an emerging indicator.

The connections between WWI and diabetes, hypertension, depression, and trouble sleeping have been studied, but they have always been examined independently. In a study that ultimately included 31,001 participants, researchers found that WWI was favourably associated with the incidence of type 2 diabetes15. WWI also played a significant role in predicting cardiometabolic risk, with some positive correlations with left ventricular hypertrophy, hypertension, and heart failure16,25,26. The definition of CircS takes into account these factors comprehensively. However, the WWI–CircS association has not been described. Unlike traditional lifestyles, modern living patterns are changing markedly with the increase in irregular schedules, social pressures, and shift work. CircS may be more in line with the current background, providing a more rational pathophysiological structure and clinical perspective for preventing and intervening in various metabolic diseases, rather than just a single disease such as cardiovascular disease6. Since WWI eliminates the impact of body weight on WC, it offers a more accurate representation of the risk of abdominal obesity in individuals with lower body weights17,27. Nour Makarem et al.28 found that a stronger and more regular circadian rhythm pattern was associated with lower odds of central obesity. Similar findings were discovered in children. A research by Rodríguez-Martín et al.29 that evaluated 391 children’s circadian health using the global circadian health score revealed that children with abdominal obesity had a significantly higher risk of poor circadian health than controls.

Our research also found that WWI and CircS have a link when it was less than 11.86 but not exceeded 11.86 in males. In addition, the turning point was 11.83 in females. We speculated that there may be several reasons for this: first, an elevated WWI indicates an increase in abdominal fat. An increase in adipocyte hypertrophy or the creation of new adipocytes from precursor differentiation through the process of adipogenesis can cause the expansion of adipose depots. Importantly, the detrimental metabolic effects of obesity can be countered by the expansion of adipose30. Further studies have shown that small adipocytes have a particularly significant role in preventing the metabolic decline linked to obesity31, and have demonstrated that small adipocytes are associated with a lower risk of getting diabetes30. Second, depression is a component of CircS, while the relationship between central obesity and depression could be inverse. For instance, Luo et al.32 discovered that in middle-aged and older men, the hazard ratio (HR) of depression falls as waist circumference rise. Compared to males without central obesity, males with central obesity had a lower risk of depressive symptoms. This may partly contribute to the non-linear relationship of the WWI–CircS link. Third, sex hormones and lipid metabolism are intimately related. Changes in the levels of sex hormones may be involved in the relationship between central obesity and dysrhythmia33.

The circadian system, consisting of a series of biological clock genes at the molecular level, regulates critical physiological processes regarding hormone release, glucose homeostasis, and lipid metabolism1. Numerous evidence support that circadian dysrhythmia has emerged as a compelling role in the complex web of obesity development. As a typical example of circadian rhythm disorder, shift work has been extensively investigated, and the inferences consistently highlighted a strong link between this type of work schedule and a higher likelihood of obesity, particularly abdominal obesity34,35. Shift and night workers exhibited lower levels of HDL-C and greater levels of triglycerides than daytime workers36,37. On the other hand, the circadian clock genes of obese individuals also exhibited abnormal rhythmic expression, which may be influenced by lipid metabolism disorders38. Dysfunction of circadian rhythms, like clock gene expression rhythms or locomotor activity patterns, has also been confirmed in the obesity model10,39.

Some of the relevant mechanisms are worth noting. The suprachiasmatic nucleus (SCN) in the hypothalamus acts as a primary regulator of the body’s internal clock. Obesity affects physiological processes of the hypothalamus, such as eating habits and stress responses. These physiological alterations exacerbate metabolic disorders and raise the risk of cardiovascular disease in obese subjects40. Afferent nerves transmit signals about lipid metabolism in visceral tissue to the central nervous system (CNS), which then modulates feeding behavior and sympathetic nerves41,42. Keizo Kaneko et al. found that obesity disrupted CNS biological clock expression, and PPARα may be involved in this process43. Aside from that, a high-fat diet not only interfered with the usual daily rhythms of plasma insulin, adiponectin, and leptin44 but also contributed to delay the activation of adiponectin signal pathway components in peripheral tissues and inhibited the recruitment of CLOCK: BMAL145,46. Backed up by mounting clues, obesity could be a sign of secretion irregularities of insulin and leptin47,48, which can reset the intrinsic timekeeping44. Third, melatonin, as a time messenger, maintains the synchronization of the peripheral tissues with the master biological clock49. Although melatonin follows a rhythm pattern of higher nighttime levels and lower daytime levels, it was not observed to have an obvious circadian rhythm in obese patients38. Moreover, melatonin supplementation protects against lipid metabolism misalignment by altering the component and rhythm of the gut flora50. These confirm the function of melatonin in the obesity-circadian system interaction.

This study has both strengths and limitations. In terms of advantages, firstly, an extensive and representative sample was utilized for the study, which ensures its generalizability and reliability. In addition, we used subgroup analyses and adjusted multifactor regression models to ensure the stability of the results. Lastly, CircS was a novel concept integrated with biological rhythms, including severe circadian-related comorbidities that were often overlooked. It was the first time to explore the relationship of CircS and WWI. As for limitations, we cannot make accurate causal inferences about the relationship between WWI and CircS from this cross-sectional study. Secondly, as a component of the CircS diagnostic criteria, the evaluation of the sleep duration depended on individual self-reports, which may have biased information.

Conclusion

Our findings supported a strong association between WWI values and CircS. Central obesity management or intervention is pivotal for preventing and alleviating CircS. In the future, the interplay between obesity, metabolism, and circadian rhythm entails further exploration.

Abbreviations

WWI Weight-adjusted-waist index

WC Waist circumference

BMI Body mass index

CircS Circadian syndrome

NHANES National Health and Nutrition Examination Survey

SCN Suprachiasmatic nucleus

HDL-C High-density lipoprotein cholesterol

PIR Ratio of family income to poverty

PPARα Peroxisome proliferator-activated receptor α

CLOCK Circadian locomotor output cycles kaput

BMAL1 Brain and muscle arnt-like protein 1

CNS Central nervous system

HR Hazard ratio

OR Odds ratio

CI Confidence interval

Acknowledgements

The author express gratitude to each participant and staff member involved in this study. The author also thank Dr. Hanxin Xue and Dr. Yaling Liu.

Author contributions

W.Z. conducted data collection, analysis, and wrote the manuscript. W.Z. designed the study and reviewed the manuscript.

Data availability

The datasets are publicly available on the website: www.cdc.gov/nchs/nhanes/.

Competing interests

The author declares no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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