Association between weight-adjusted-waist index and depression: a cross-sectional study

in Endocrine Connections
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Sun Fei Wuxi Medical College of Jiangnan University, Wuxi, China

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Min Liu Wuxi Maternity and Child Health Care Hospital, Wuxi, China

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Hu Shanshan Wuxi Maternity and Child Health Care Hospital, Wuxi, China

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Ruijie Xie Department of Microsurgery, University of South China, Hengyang, China

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Wu Danni Wuxi Medical College of Jiangnan University, Wuxi, China

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Zhou Ningying Wuxi Medical College of Jiangnan University, Wuxi, China

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Correspondence should be addressed to M Liu: 9862023312@jiangnan.edu.cn
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Background

Depression has become a multifaceted global health issue, with complex connections to obesity. Weight-adjusted-waist index (WWI) can effectively evaluate central obesity, but the relationship between WWI and depression has not been well studied. The study aims to investigate the potential correlation between these two health parameters.

Methods

According to the data from National Health and Nutrition Examination Survey, this cross-sectional study used multiple regression analysis, subgroup analysis, and smooth curve fitting to explore the relationship between WWI and depression. The assessment ability of WWI was evaluated and compared to other obesity indicators using the receiver operating characteristic (ROC) curve.

Results

This study analyzed 38,154 participants. Higher WWI is associated with higher depression scores (β = 0.41; 95% CI, 0.36–0.47). After adjusting for various confounding factors, the positive correlation between WWI and depression remained significant (P for trend < 0.0001). Nonlinear positive correlation was detected with a breakpoint of 11.14. ROC analysis shows that compared to other obesity indicators (ROCWWI = 0.593; ROCBMI = 0.584; and ROCWC = 0.581), the correlation between WWI and depression has better discrimination and accuracy. DII mediated 4.93%, SII mediated 5.08%, and sedentary mediated 0.35% of the total association between WWI and depression.

Conclusion

WWI levels were related to an increased likelihood of depression and showed a stronger relationship than BMI and waist circumference. Our findings indicated that WWI may serve as a simple anthropometric index to evaluate depression.

Abstract

Background

Depression has become a multifaceted global health issue, with complex connections to obesity. Weight-adjusted-waist index (WWI) can effectively evaluate central obesity, but the relationship between WWI and depression has not been well studied. The study aims to investigate the potential correlation between these two health parameters.

Methods

According to the data from National Health and Nutrition Examination Survey, this cross-sectional study used multiple regression analysis, subgroup analysis, and smooth curve fitting to explore the relationship between WWI and depression. The assessment ability of WWI was evaluated and compared to other obesity indicators using the receiver operating characteristic (ROC) curve.

Results

This study analyzed 38,154 participants. Higher WWI is associated with higher depression scores (β = 0.41; 95% CI, 0.36–0.47). After adjusting for various confounding factors, the positive correlation between WWI and depression remained significant (P for trend < 0.0001). Nonlinear positive correlation was detected with a breakpoint of 11.14. ROC analysis shows that compared to other obesity indicators (ROCWWI = 0.593; ROCBMI = 0.584; and ROCWC = 0.581), the correlation between WWI and depression has better discrimination and accuracy. DII mediated 4.93%, SII mediated 5.08%, and sedentary mediated 0.35% of the total association between WWI and depression.

Conclusion

WWI levels were related to an increased likelihood of depression and showed a stronger relationship than BMI and waist circumference. Our findings indicated that WWI may serve as a simple anthropometric index to evaluate depression.

Introduction

Depression is a collective term for a series of mental problems characterized by a loss of interest and enjoyment in daily life, low mood, and symptoms affecting emotional, cognitive, physical, and behavioral aspects (1). Depression is one of the most common mental disorders, with approximately 5% of people worldwide diagnosed with depression at specific times (2). The COVID-19 pandemic is estimated to have triggered a 25% increase in the prevalence of anxiety and depression worldwide (3). Depression is an important cause of disability and has a significant impact on quality of life and body function, posing a serious threat to public health (4). Depression is also a major factor in the global burden of diseases, leading to the occurrence or prolongation of treatment time for many diseases due to differences in duration and severity, ultimately resulting in high social costs and medical burdens (5).

The overall obesity prevalence rate among American adults has reached 39.5% and is still increasing (6). The association between depression and metabolic syndrome indicates a particularly close relationship between depression and obesity (7). Most studies believe that obesity is positively correlated with depression, particularly in women, but a few studies have found that the incidence of depression actually decreases with increasing weight (8, 9). Central obesity is the type of obesity that has the greatest impact on health, and the results of its relationship with depression are not yet unified (10). Obesity promotes an increased risk of cardiovascular and psychiatric diseases, which may depend on abdominal fat and its function (11). Currently, most studies use body mass index (BMI) and waist circumference (WC) to assess overall and abdominal fat content (12). However, simple obesity parameters such as BMI and WC still have their limitations, as they cannot distinguish the weight of fat and muscle, and cannot accurately reflect the overall fat content and abdominal fat distribution (13, 14).

Park et al. (15) first proposed a new obesity index called the weight-adjusted-waist index (WWI). WWI is an anthropometric measure of central obesity, defined as WC divided by the square root of body weight. It can reflect the composition of fat and muscle mass, even in different BMI categories (16). Previous studies have shown that WWI is positively correlated with hypertension, diabetes, and even all-cause cardiovascular mortality, which is a better predictor than BMI and WC (17, 18, 19). Although WWI can serve as an indicator of central obesity, the relationship between WWI and depression remains to be studied.

Therefore, we aimed to investigate the association between WWI and depression among the US population using the National Health and Nutrition Examination Survey 1999–2020 (NHANES).

Materials and methods

Survey description

This study strictly followed relevant ethical guidelines such as the Helsinki Declaration. We are using publicly available NHANES data, a national study conducted by the National Center for Health Statistics (NCHS) to evaluate the nutritional and health status of the United States (https://www.cdc.gov/nchs/nhanes/irba98.htm). The data were collected with the informed consent of participants and formally approved by the Ethics Review Committee (IRB/EC) of the Centers for Disease Control and Prevention (CDC). The Research Ethics Review Committee of NCHS approved all NHANES research protocols. Participants provided demographic, dietary, examination, laboratory, and questionnaire data. The data collection is orchestrated using a multilevel, complex sampling methodology, further elucidated on the official NHANES website (https://www.cdc.gov/nchs/index.htm). This study followed the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies.

Study population

The NHANES datasets were utilized for this investigation from 1999 to 2020. To ensure the validity and robustness of the data analysis results, we removed all missing data from WWI and depression, and only retained valid data. Initially, a total of 107,614 participants were recruited. After excluding binary classification data in depression (n = 31,125), those missing data on depression (n = 37,027), and those missing data on WWI (n = 1308), our final analysis included 38,154 eligible participants. For other covariates, missing values of continuous covariates were filled in with the mean, while missing values of categorical covariates were handled by categorizing them as ‘other’ (Fig. 1).

Figure 1
Figure 1

Flowchart of the sample selection from NHANES 1999–2020. A total of 107,614 participants were enrolled at first. After excluding those with depression as a binary variable (n = 31,125), missing data on depression (n = 37,027), and missing data on WWI (n = 1308), 38,154 eligible participants were included in our final analysis.

Citation: Endocrine Connections 13, 6; 10.1530/EC-23-0450

Assessment of weight-adjusted-waist index

WWI samples of NHANES participants were obtained from examination data. The WWI calculation was based on the participant's WC divided by the square root of their weight (15). The unit of waist circumference is centimeters, and the unit of weight is kilograms. The WWI score is positively correlated with central obesity (20). In our study, WWI was treated as a continuous variable and designated as the exposed variable.

Assessment of depression

Depression samples of NHANES participants were obtained from questionnaire data (21). The Patient Health Questionnaire 9 (PHQ-9) has nine items and serves as a simple and effective self-assessment scale for depression disorders, with good reliability and validity, as demonstrated by a reported Cronbach’s α of 0.89 (22). A score of 0–4 indicates no depression, a score of 5–9 indicates mild depression, a score of 10–14 indicates moderate depression, and a score of ≥15 indicates severe depression (23).

Assessment of covariates of interest

Demographic and dietary covariates in our study included gender, age, race, education level, marital status, ratio of family income to poverty, energy (kcal), and dietary fiber (g). Several anthropometric and laboratory covariates included WC, BMI, and systemic immune-inflammation index (SII, calculated as the product of peripheral platelet count and neutrophil-to-lymphocyte ratio) (24, 25). Questionnaire variables composed of alcohol use, diabetes, minutes sedentary activity, and sleep disorders were also included. Detailed measurement processes of these variables are publicly available at www.cdc.gov/nchs/nhanes/.

Statistical analysis

In descriptive analysis, data description and statistical analysis were based on complex weighting. Continuous variables were summarized as the means with standard error (SE), while categorical parameters were presented as proportions. Multiple regression analysis was employed to analyze the relationship between WWI and depression. Three regression models were constructed, adjusting for different confounding factors. Among them, model 1 did not adjust for any confounding factors; model 2 adjusted for gender, age, and race; model 3 further adjustments were made to education level, marital status, ratio of family income to poverty, energy (kcal), dietary fiber (g), alcohol use, diabetes, minutes sedentary activity, and sleep disorders based on model 2. In sensitivity analysis, linear trend tests were conducted with WWI quartile groups as independent variables to evaluate their robustness (26). Subgroup analysis was performed using a stratified multivariable regression model, and a stratified factors included gender, age, race, education level, marital status, ratio of family income to poverty, alcohol use, diabetes, BMI, and sleep disorders. This study used smooth curve fitting to observe whether the relationship between WWI and depression is linear. If it is non-linear, threshold effect analysis was used to fit each interval and calculate the threshold. The study used receiver operating characteristic (ROC) curves to compare the area under the curve (AUC) values for each indicator to evaluate the correlation between WWI and other obesity parameters with depression. Finally, using mediation effect analysis, explore the role of lifestyle and endocrine factors in the relationship between WWI and depression. R Project and EmpowerStats were used for all analyses. Statistical significance was assessed at a two-sided value of P < 0.05.

Results

Baseline characteristics of participants

A total of 38,154 participants, with an average age of 47.58 ± 18.58 years were enrolled. The PHQ-9 score is divided into 4 levels, with Q1 representing 0–4 points, Q2 representing 5–9 points, Q3 representing 10–14 points, and Q4 representing ≥15 points. There is a tendency for women and individuals of non-Hispanic White background to have higher PHQ-9 scores frequently. The research results showed that depression severity is heightened among persons who concurrently face family income strain, possess a higher education level, have poor marital statuses, have a past history of alcohol intake, are obese, and have sleep disorders. There are significant differences in baseline characteristics among different levels of depression (P < 0.001). The degree of depression seems to become more severe as the minutes sedentary activity, DII, SII, and WWI increase (Table 1).

Table 1

Baseline characteristics of the study population based on the degree of depression.

Variable Total Depression (PHQ-9 score) P
Q1 (0–4) Q2 (5–9) Q3 (10–14) Q4 (≥15)
Age, years 47.58 ± 18.58 47.80 ± 18.73 46.71 ± 18.61 46.94 ± 17.66 47.80 ± 15.89 <0.001
Gender, % <0.001
 Male 18,853 (49.41%) 15,091 (52.46%) 2546 (41.77%) 792 (37.97%) 424 (35.22%)
 Female 19,301 (50.59%) 13,678 (47.54%) 3549 (58.23%) 1294 (62.03%) 780 (64.78%)
Ethnicity, % <0.001
 Mexican American 5987 (15.69%) 4523 (15.72%) 958 (15.72%) 326 (15.63%) 180 (14.95%)
 Other Hispanic 3715 (9.74%) 2622 (9.11%) 654 (10.73%) 256 (12.27%) 183 (15.20%)
 Non-Hispanic White 15,770 (41.33%) 11,917 (41.42%) 2527 (41.46%) 836 (40.08%) 490 (40.70%)
 Non-Hispanic Black 8578 (22.48%) 6442 (22.39%) 1381 (22.66%) 491 (23.54%) 264 (21.93%)
 Other race 4104 (10.76%) 3265 (11.35%) 575 (9.43%) 177 (8.49%) 87 (7.23%)
Family income, % <0.001
 0–4.98 28,633 (75.05%) 20,968 (72.88%) 4887 (80.18%) 1757 (84.23%) 1021 (84.80%)
 ≥5 6153 (16.13%) 5261 (18.29%) 693 (11.37%) 140 (6.71%) 59 (4.90%)
 Other 3368 (8.83%) 2540 (8.83%) 515 (8.45%) 189 (9.06%) 124 (10.30%)
Education level, % <0.001
 Less than 9th grade 3432 (9.00%) 2439 (8.48%) 559 (9.17%) 250 (11.98%) 184 (15.28%)
 9th–11th grade 4951 (12.98%) 3367 (11.70%) 942 (15.46%) 394 (18.89%) 248 (20.60%)
 High school graduate/GED or equivalent 8349 (21.88%) 6147 (21.37%) 1423 (23.35%) 487 (23.35%) 292 (24.25%)
 Some college or AA degree 10,823 (28.37%) 8073 (28.06%) 1803 (29.58%) 619 (29.67%) 328 (27.24%)
 College graduate or above 8375 (21.95%) 7082 (24.62%) 966 (15.85%) 224 (10.74%) 103 (8.55%)
 Other 2224 (5.83%) 1661 (5.77%) 402 (6.60%) 112 (5.37%) 49 (4.07%)
Marital status, % <0.001
 Married/living with partner 21,551 (56.48%) 17,077 (59.36%) 3046 (49.98%) 928 (44.49%) 500 (41.53%)
 Widowed/divorced/separated 4751 (12.45%) 3248 (11.29%) 897 (14.72%) 376 (18.02%) 230 (19.10%)
 Never married 10,140 (26.58%) 7182 (24.96%) 1833 (30.07%) 691 (33.13%) 434 (36.05%)
 Other 1712 (4.49%) 1262 (4.39%) 319 (5.23%) 91 (4.36%) 40 (3.32%)
Alcohol use, % <0.001
 Yes 25,202 (66.05%) 19,130 (66.50%) 3988 (65.43%) 1333 (63.90%) 751 (62.38%)
 No 6708 (17.58%) 4795 (16.67%) 1166 (19.13%) 455 (21.81%) 292 (24.25%)
 Other 6244 (16.37%) 4844 (16.84%) 941 (15.44%) 298 (14.29%) 161 (13.37%)
BMI, % <0.001
 Normal weight <25 11,318 (29.69%) 8816 (30.67%) 1666 (27.37%) 576 (27.63%) 260 (21.63%)
 Over weight (25, 30) 12,417 (32.58%) 9770 (33.99%) 1779 (29.23%) 537 (25.76%) 331 (27.54%)
 Obese ≥30 14,380 (37.73%) 10,156 (35.34%) 2641 (43.39%) 972 (46.62%) 611 (50.83%)
Diabetes, % <0.001
 Yes 4602 (12.06%) 3106 (10.80%) 888 (14.57%) 351 (16.83%) 257 (21.35%)
 No 32,677 (85.65%) 25,047 (87.06%) 5044 (82.76%) 1671 (80.11%) 915 (76.00%)
 Borderline 850 (2.23%) 602 (2.09%) 157 (2.58%) 61 (2.92%) 30 (2.49%)
 Other 25 (0.07%) 14 (0.05%) 6 (0.10%) 3 (0.14%) 2 (0.17%)
Sleep disorder <0.001
 Yes 2271 (5.95%) 1223 (4.25%) 586 (9.61%) 296 (14.19%) 166 (13.79%)
 No 5696 (14.93%) 4688 (16.30%) 759 (12.45%) 170 (8.15%) 79 (6.56%)
 Other 30,187 (79.12%) 22,858 (79.45%) 4750 (77.93%) 1620 (77.66%) 959 (79.65%)
Energy (kcal) 2044.67 ± 763.26 2052.11 ± 756.13 2045.23 ± 786.31 1995.20 ± 764.85 1949.72 ± 802.25 <0.001
Dietary fiber (g) 16.52 ± 8.27 16.88 ± 8.40 15.84 ± 7.87 14.80 ± 7.34 14.29 ± 7.56 <0.001
Minutes sedentary activity 357.86 ± 171.49 356.10 ± 169.39 359.68 ± 175.62 366.01 ± 176.62 376.80 ± 188.83 <0.001
WC (cm) 98.96 ± 16.71 98.16 ± 16.13 100.74 ± 17.98 101.96 ± 18.65 103.68 ± 18.17 <0.001
SII 538.79 ± 365.03 530.58 ± 361.88 552.28 ± 340.54 583.26 ± 454.23 589.79 ± 375.17 <0.001
DII 1.58 ± 1.29 1.53 ± 1.29 1.65 ± 1.28 1.75 ± 1.25 1.90 ± 1.21 <0.001
WWI 11.01 ± 0.88 10.96 ± 0.86 11.11 ± 0.90 11.21 ± 0.91 11.29 ± 0.87 <0.001

BMI, body mass index; DII, dietary inflammatory index; PHQ-9, Patient Health Questionnaire 9; SII, systemic immune-inflammation index; WWI, weight-adjusted-waist index; WC, waist circumference.

The association between weight-adjusted-waist index and depression

As shown in Table 2, regression analysis showed that a high level of WWI was significantly associated with an increased score of depression. After adjusting for all confounders, the above associations remained significant (β = 0.41, 95% CI: 0.36–0.47, P < 0.001). Sensitivity analysis was conducted after treating WWI as a categorical variable (quartile), and this association still holds significant statistical significance (β = 0.43, 95% CI: 0.36–0.49, P for trend < 0.001). The subgroup analysis by gender, age, race, education level, marital status, ratio of family income to poverty, alcohol use, diabetes, BMI, and sleep disorders showed statistical differences in gender, age, ratio of family income to poverty, education level, diabetes, BMI, and sleep disorders (Fig. 2).

Figure 2
Figure 2

Subgroup analysis of the effect of WWI on depression. WWI, weight-adjusted-waist index.

Citation: Endocrine Connections 13, 6; 10.1530/EC-23-0450

Table 2

Association between weight-adjusted-waist index and depression.

Model 1 Model 2 Model 3
β (95% CI) P β (95% CI) P β (95% CI) P
WWI 0.51 (0.46, 0.55) <0.0001 0.65 (0.59, 0.71) <0.0001 0.41 (0.36, 0.47) <0.0001
Quartiles of WWI
 Q1 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Q2 0.14 (0.02, 0.26) 0.0186 0.31 (0.19, 0.43) <0.0001 0.25 (0.13, 0.37) <0.0001
 Q3 0.38 (0.26, 0.50) <0.0001 0.63 (0.51, 0.76) <0.0001 0.42 (0.29, 0.54) <0.0001
 Q4 1.15 (1.03, 1.27) <0.0001 1.41 (1.27, 1.55) <0.0001 0.89 (0.75, 1.02) <0.0001
P for trend 0.55 (0.49, 0.61) <0.0001 0.69 (0.62, 0.75) <0.0001 0.43 (0.36, 0.49) <0.0001

Model 1: no covariates were adjusted; model 2: age, gender, and race were adjusted; model 3: age, gender, race, education level, marital status, ratio of family income to poverty, alcohol use, diabetes, energy (kcal), dietary fiber (g), minutes sedentary activity, and sleep disorder were adjusted.

WWI, weight-adjusted-waist index.

A nonlinear relationship between WWI and depression

Smooth curve fitting exhibited a nonlinear relationship between WWI and depression. (Fig. 3A). We further calculated the breakpoint (K), which was 11.14 (Table 3). On the left and right of the breakpoint, a positive relationship between WWI and depression was detected. Before and after the adjustment of covariates, the logarithmic likelihood ratio test showed statistical differences (P = 0.010). Figure 3B andC shows a U-shaped relationship between WC, BMI, and depression. As shown in Table 3, we further calculated the breakpoints (KWC = 81.50; KBMI = 21.63). Figure 3D shows that compared to other obesity indicators, WWI has a better correlation with depression (AUCWWI = 0.593; AUCBMI = 0.584; and AUCWC = 0.581). The relationship between WWI and depression in males followed an S-shaped curve (Fig. 3E).

Figure 3
Figure 3

Smooth curve fitting and ROC curve graph. WWI, weight-adjusted-waist index; WC, waist circumference; BMI, body mass index; ROC, receiver operating characteristic; AUC, area under curve. ‘1’ represents male, ‘2’ represents female. Age, gender, race, education level, marital status, ratio of family income to poverty, alcohol use, diabetes, energy, dietary fiber, minutes sedentary activity, and sleep disorder were adjusted (A, B, C, E).

Citation: Endocrine Connections 13, 6; 10.1530/EC-23-0450

Table 3

Threshold effect analysis of WWI on depression.

WWI WC BMI
β (95% CI) P β (95% CI) P β (95% CI) P
Model 1a 0.41 (0.36, 0.47) <0.0001 0.02 (0.02, 0.02) <0.0001 0.04 (0.04, 0.05) <0.0001
Model 2b
 Breakpoint (K) K = 11.14 K  = 81.50 K = 21.63
 OR1 (<K) 0.32 (0.22, 0.41) <0.0001 −0.03 (−0.04, −0.01) 0.0025 −0.22 (−0.28, −0.16) <0.0001
 OR2 (>K) 0.53 (0.42, 0.63) <0.0001 0.02 (0.02, 0.03) <0.0001 0.05 (0.05, 0.06) <0.0001
 OR2/OR1 0.21 (0.05, 0.37) 0.0103 0.02 (0.02, 0.03) 0.0004 0.27 (0.21, 0.33) <0.0001
Logarithmic likelihood ratio test 0.010 <0.001 <0.001

Age, gender, race, education level, marital status, ratio of family income to poverty, alcohol use, diabetes, energy (kcal), dietary fiber (g), minutes sedentary activity, and sleep disorder were adjusted.

aModel 1: Standard linear model.

bModel 2: Two-piecewise linear model.

BMI, body mass index; WWI, weight-adjusted-waist index; WC, waist circumference.

Mediation analysis of WWI and depression

Based on the above analysis, we analyzed the relationship between WWI and depression using DII, SII, and sedentary as a mediating variables. All mediator analyses were performed based on adjustment for age, gender, race, education level, marital status, ratio of family income to poverty, alcohol use, diabetes, energy, dietary fiber, and sleep disorder. As shown in Fig. 4, WWI was positively associated with DII, SII, sedentary, and depression. In addition, DII, SII, and sedentary were positively associated with depression. DII estimated that depression mediated 4.93% of the total association between WWI and depression. SII mediated 5.08%, and sedentary mediated 0.35% of the total association between WWI and depression.

Figure 4
Figure 4

Path diagram of the mediation analysis models. WWI, weight-adjusted-waist index; SII, systemic immune-inflammation index; DII, dietary inflammatory index.

Citation: Endocrine Connections 13, 6; 10.1530/EC-23-0450

Discussion

This study aims to evaluate the relationship between WWI and depression in the American population. In the cross-sectional study that recruited 38,154 participants, the nonlinear positive correlation between WWI and depression remains stable in a fully adjusted covariate model. Compared to other obesity indicators, WWI has a better correlation with depression. The results of our analysis confirm our hypothesis (27, 28). Based on the above analysis, we delved into the mutual influence between WWI and depression by introducing DII, SII, and sedentary behavior as mediating variables. The results showed that DII mediated 4.93%, SII mediated 5.08%, and sedentary mediated 0.35% of the total association between WWI and depression.

At present, most studies exploring the relationship between obesity and depression use simple obesity parameters such as BMI and WC, and there are few studies using WWI to measure central obesity (29). This study found a non-linear positive correlation between WWI and depression (KWWI  = 11.14), which may be mainly due to specific fat distribution (30). Moreover, a multitude of additional factors that can cause fluctuations in depression, including gender, age, ratio of family income to poverty, education level, diabetes, BMI, and sleep disorders (31). The relationship between WWI and depression. seems to be more gender specific. We found a linear relationship between WWI and female depression, while a nonlinear relationship exists in males. It could be attributed to the fact that women experience more hormonal fluctuations and heightened sensitivity to such changes compared to men (32). Some studies have shown that the relationship between depression and gender weight change has a unique pattern (33, 34). According to a large cross-sectional study, a U-shaped relationship between depression and BMI was found, especially in women or young age groups (9). The prevalence of depression and obesity varies by gender due to hormonal physiological processes and other factors (34). However, other studies have found that there was a more pronounced inverse association between abdominal adiposity and depressive symptoms in males compared to females. This counterintuitive link can be accounted for by the ‘jolly fat’ theory originally proposed by Crisp et al., which challenges the conventional understanding of the connection between obesity and depression (35). Therefore, the relationship between depression and obesity based on age and gender differences remains inconclusive. Generally speaking, women are more prone to depression and obesity than men, with some exceptions. The different association patterns between depression symptoms and obesity in different regions indicate the need for further investigation to gain a clear and comprehensive understanding of this relationship.

The study demonstrates a stronger relationship between WWI and depression compared to BMI and WC. WC, BMI, and WWI are the main indicators for determining systemic obesity and central obesity. According to reports, WWI is a newly developed obesity index used to evaluate central obesity, which has been explored in various fields, especially in relation to cardiovascular diseases (36, 37). Previous studies have also confirmed that these simple parameters of obesity are independent factors for cardiovascular disease, and this correlation is likely due to the accumulation of visceral fat (38). In a cohort study, WWI was the best evaluation indicator of cardiac metabolic disease and mortality compared to BMI and WC (39). Especially in the assessment of intra-abdominal fat, BMI and WC are not as accurate and reliable as WWI measurements (40). BMI is the most widely used measurement method in humans, but it cannot distinguish between lean weight and fat mass. WC is considered as an alternative indicator for indirectly assessing visceral fat gain (41). Similar to BMI, WC itself cannot distinguish visceral fat from subcutaneous fat (14). Adding BMI as an explanatory factor does improve WC's prediction of abdominal subcutaneous fat (27). Liu et al. found that the incidence rate of abdominal obesity was higher than that of general obesity (42). As an anthropometric indicator, WWI, due to its simple calculation and excellent performance in evaluating disease, is expected to be further explored. In brief, WWI can be used as an evaluation indicator of depression, and the obesity assessed by WWI may be more conducive to developing depression management strategies.

The study showed that DII, SII, and sedentary mediated the total association between WWI and depression. Previous studies have shown that depression may have a two-way relationship with obesity, diabetes, cardiovascular disease, and other chronic diseases (27, 28). The unhealthy lifestyle that obese individuals may have, such as lack of exercise and overeating, can affect their mental health. On the contrary, depression may also promote the formation of unhealthy lifestyle habits, forming a vicious cycle and having negative effects on other chronic diseases. On the other hand, the connection between obesity and depression has biological and social psychological foundations. From a biological perspective, obesity may be involved in the onset and progression of diabetes and insulin resistance, and the low level of inflammation caused by diseases may lead to depression (43, 44). Obesity can also lead to dysfunction of adipose tissue, with a large amount of ectopic accumulation of adipose tissue, disrupting normal physiological functions and participating in the occurrence and development of metabolic-related diseases (31, 45). Central obesity is a typical representative of ‘dysfunctional adipose tissue’ and is the ectopic accumulation of adipose tissue that poses the greatest health risk (46). Central obesity may lead to the occurrence and development of depression through neuroendocrine disorders in the hypothalamic–pituitary–adrenal cortex axis (47). In addition to biological mechanisms, social psychological mechanisms should also be given attention. The weight perception of being overweight or obese may increase psychological distress, dissatisfaction with one's body shape, and a decrease in self-esteem. The accumulation of psychological stress and weight-related shame in obese individuals can increase the risk of depression (48, 49).

The research was based on national data, and the results were widely applicable to the general population in the United States. The large sample size allowed us to conduct a sensitivity analysis to confirm the robustness of the results. The study further explored the non-linear issues of male participants, thereby demonstrating gender differences that are easily overlooked in daily clinical practice. Finally, the study showed that DII, SII, and sedentary mediated the total association between WWI and depression. However, there are still some restrictions that need to be declared. Although we have adjusted for some potential covariates, there are still many factors that affect depression, and we cannot completely rule out the effects of other potential confounding factors, such as other social and environmental variables. In addition, our research findings are based on one country and race, so it remains to be investigated whether these findings apply to other races or countries. Most importantly, due to the cross-sectional study design, causal relationships cannot be unraveled.

Conclusion

This study suggests that WWI is associated with a higher level of depression, and the correlation between WWI and depression. is stronger compared to other obesity biomarkers, indicating that obesity management evaluated by WWI may be beneficial for mental health. Given that factors such as gender, age, and lifestyle may affect the association between WWI and depression, future research should further analyze these potential interactive effects. According to this study, clinicians can consider WWI as an important indicator when evaluating depression.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the study reported.

Funding

This study was supported by the Wuxi Research Project of Wuxi Municipal Health Commission (FYKY202201) and Wuxi Science and Technology Development Fund Project (Y20212036).

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the Research Ethics Review Board of the NCHS. The patients/participants provided their written informed consent to participate in this study. All analyses were carried out in accordance with the Declaration of Helsinki.

Availability of data and materials

Publicly available datasets were analyzed in this study. These data can be found at www.cdc.gov/nchs/nhanes/.

Author contributions

FS: software, data analysis, and writing – original draft. ML and SH: writing – original draft, formal analysis, and methodology. RX and ZS: data analysis. HC and HB: formal analysis and methodology. ML and SH: conceptualization, funding acquisition, and writing – reviewing and editing. All authors approved the final version of the manuscript.

Acknowledgements

We thank all authors for assistance in the manuscript preparation.

References

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  • Collapse
  • Expand
  • Figure 1

    Flowchart of the sample selection from NHANES 1999–2020. A total of 107,614 participants were enrolled at first. After excluding those with depression as a binary variable (n = 31,125), missing data on depression (n = 37,027), and missing data on WWI (n = 1308), 38,154 eligible participants were included in our final analysis.

  • Figure 2

    Subgroup analysis of the effect of WWI on depression. WWI, weight-adjusted-waist index.

  • Figure 3

    Smooth curve fitting and ROC curve graph. WWI, weight-adjusted-waist index; WC, waist circumference; BMI, body mass index; ROC, receiver operating characteristic; AUC, area under curve. ‘1’ represents male, ‘2’ represents female. Age, gender, race, education level, marital status, ratio of family income to poverty, alcohol use, diabetes, energy, dietary fiber, minutes sedentary activity, and sleep disorder were adjusted (A, B, C, E).

  • Figure 4

    Path diagram of the mediation analysis models. WWI, weight-adjusted-waist index; SII, systemic immune-inflammation index; DII, dietary inflammatory index.

  • 1

    Anand A, Mathew SJ, & Hu B. Ketamine versus ECT for nonpsychotic treatment-resistant major depression. New England Journal of Medicine 2023 389 961962. (https://doi.org/10.1056/NEJMc2308757)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Liu Q, He H, Yang J, Feng X, Zhao F, & Lyu J. Changes in the global burden of depression from 1990 to 2017: findings from the global burden of disease study. Journal of Psychiatric Research 2020 126 134140. (https://doi.org/10.1016/j.jpsychires.2019.08.002)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Eccleston-Turner M, & Upton H. International collaboration to ensure equitable access to vaccines for COVID-19: the ACT-accelerator and the COVAX facility. Milbank Quarterly 2021 99 426449. (https://doi.org/10.1111/1468-0009.12503)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet 2020 396 12041222. (https://doi.org/10.1016/S0140-6736(2030925-9)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Li X, Tian Y, Phillips MR, Xiao S, Zhang X, Li Z, Liu J, Li L, Zhou J, & Wang X. Protocol of a prospective community-based study about the onset and course of depression in a nationally representative cohort of adults in China: the China depression cohort study-I. BMC Public Health 2023 23 1617. (https://doi.org/10.1186/s12889-023-16542-6)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    GBD Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, Marczak L, Mokdad AH, Moradi-Lakeh M, et al.Health effects of overweight and obesity in 195 countries over 25 years. New England Journal of Medicine 2017 377 1327. (https://doi.org/10.1056/NEJMoa1614362)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Rao WW, Zong QQ, Zhang JW, An FR, Jackson T, Ungvari GS, Xiang Y, Su YY, D'Arcy C, & Xiang YT. Obesity increases the risk of depression in children and adolescents: results from a systematic review and meta-analysis. Journal of Affective Disorders 2020 267 7885. (https://doi.org/10.1016/j.jad.2020.01.154)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Baldini I, Casagrande BP, & Estadella D. Depression and obesity among females, are sex specificities considered? Archives of Women’s Mental Health 2021 24 851866. (https://doi.org/10.1007/s00737-021-01123-6)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    He K, Pang T, & Huang H. The relationship between depressive symptoms and BMI: 2005–2018 NHANES data. Journal of Affective Disorders 2022 313 151157. (https://doi.org/10.1016/j.jad.2022.06.046)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Zhou Y, Yang G, Peng W, Zhang H, Peng Z, Ding N, Guo T, Cai Y, Deng Q, & Chai X. Relationship between depression symptoms and different types of measures of obesity (BMI, SAD) in US women. Behavioural Neurology 2020 22 9624106. (https://doi.org/10.1155/2020/9624106)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Tang R, Fan Y, Luo M, Zhang D, Xie Z, Huang F, Wang Y, Liu G, Wang Y, Lin S, et al.General and central obesity are associated with increased severity of the VMS and sexual symptoms of menopause among Chinese women: a longitudinal study. Frontiers in Endocrinology (Lausanne) 2022 13 814872. (https://doi.org/10.3389/fendo.2022.814872)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Lemieux S, Prud’homme D, Bouchard C, Tremblay A, & Després JP. A single threshold value of waist girth identifies normal-weight and overweight subjects with excess visceral adipose tissue. American Journal of Clinical Nutrition 1996 64 685693. (https://doi.org/10.1093/ajcn/64.5.685)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Michel S, Linder N, Linder A, Eggebrecht T, Schaudinn A, Blüher M, Dietrich A, Denecke T, & Busse H. Anthropometric estimators of abdominal fat volume in adults with overweight and obesity. International Journal of Obesity 2023 47 306312. (https://doi.org/10.1038/s41366-023-01264-x)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, Allison TG, Batsis JA, Sert-Kuniyoshi FH, & Lopez-Jimenez F. Accuracy of body mass index in diagnosing obesity in the adult general population. International Journal of Obesity 2008 32 959966. (https://doi.org/10.1038/ijo.2008.11)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Park Y, Kim NH, Kwon TY, & Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Scientific Reports 2018 8 16753. (https://doi.org/10.1038/s41598-018-35073-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Kim NH, Park Y, Kim NH, & Kim SG. Weight-adjusted waist index reflects fat and muscle mass in the opposite direction in older adults. Age and Ageing 2021 50 780786. (https://doi.org/10.1093/ageing/afaa208)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    Kim JY, Choi J, Vella CA, Criqui MH, Allison MA, & Kim NH. Associations between weight-adjusted waist index and abdominal fat and muscle mass: multi-ethnic study of atherosclerosis. Diabetes and Metabolism Journal 2022 46 747755. (https://doi.org/10.4093/dmj.2021.0294)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Ding C, Shi Y, Li J, Li M, Hu L, Rao J, Liu L, Zhao P, Xie C, Zhan B, et al.Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: a prospective cohort study. Nutrition, Metabolism, and Cardiovascular Diseases 2022 32 12101217. (https://doi.org/10.1016/j.numecd.2022.01.033)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    Cai S, Zhu T, Ding Y, Cheng B, Zhang A, Bao Q, Sun J, Li M, Liu X, & Wang S. The relationship between the weight-adjusted-waist index and left ventricular hypertrophy in Chinese hypertension adults. Hypertension Research 2023 46 253260. (https://doi.org/10.1038/s41440-022-01075-z)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Wang S, Zhang Q, Hou T, Wang Y, Han X, Song L, Tang S, Dong Y, Cong L, Du Y, et al.Differential associations of 6 adiposity indices with dementia in older adults: the MIND-China study. Journal of the American Medical Directors Association 2023 24 14121419.e4. (https://doi.org/10.1016/j.jamda.2023.06.029)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Patel JS, Oh Y, Rand KL, Wu W, Cyders MA, Kroenke K, & Stewart JC. Measurement invariance of the patient health questionnaire-9 (PHQ-9) depression screener in U.S. adults across sex, race/ethnicity, and education level: NHANES 2005–2016. Depression and Anxiety 2019 36 813823. (https://doi.org/10.1002/da.22940)

    • PubMed
    • Search Google Scholar
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