Abstract
Background
The weight-adjusted waist index (WWI) is a new way to measure central obesity, reflecting body fat distribution more effectively. The association between WWI and depression in individuals with diabetes is still not well understood. This study explores this association in diabetic patients.
Methods
This study involved adult participants diagnosed with diabetes mellitus in NHANES 2009–2018. We explored the association between WWI and depression through multivariate logistic regression analysis, subgroup analyses, interaction tests, and smoothed curve-fitting methods.
Results
In Model 3, this study found a significant positive association between WWI and depression among patients with diabetes (OR = 1.38, 95% CI: 1.17–1.63). In the smoothed curve fitting analysis, a nonlinear relationship between WWI and depression was observed, with an inflection point at 12.31. Subgroup analysis indicated that this association was influenced by the poverty income ratio (PIR), with a stronger association observed among diabetic patients with PIR ≥ 1, < 3, and PIR ≥ 3.
Conclusions
Among patients with diabetes, WWI was significantly positively associated with the prevalence of depression. This association was more pronounced among diabetic patients with PIR ≥ 1, < 3, and PIR ≥ 3. These findings suggest that WWI may be a relevant factor for physicians to consider when assessing depressive symptoms in patients with diabetes.
Keywords: Weight-adjusted waist index, Obesity, Depression, Diabetes, NHANES
Introduction
Diabetes is a chronic disease where prolonged high blood sugar damages the eyes, kidneys, nerves, and cardiovascular system [1]. Among its risk factors, obesity plays a critical role, contributing significantly to disease onset and progression [2]. In 2017, around 425 million adults between the ages of 20 and 79 had diabetes globally. Projections suggest that this number will rise by 48%, reaching 629 million by 2045 [3].
Depression is a widespread mental health condition and a major contributor to global disability [4]. Over 264 million individuals worldwide are affected by major depressive disorder, with the prevalence rate increasing by nearly 50% over the past three decades and lifetime recurrence rates as high as 75–90% [5]. Nearly 90% of individuals who die by suicide have a diagnosed mental disorder, according to numerous studies, with depression accounting for half to two-thirds of these cases [6–8]. Severe depression places a considerable health and financial strain on medical systems and individuals globally [9]. Consequently, identifying high-risk factors for depression and implementing effective interventions are crucial for reducing suicide rates.
Diabetes and depression share a reciprocal relationship [10]. People living with diabetes face a 24% greater likelihood of developing depression, whereas those with depression are 60% more likely to develop diabetes [11, 12]. Globally, the percentage of individuals with diabetes experiencing depression rose from 20% in 2007 to 32% in 2018, highlighting its growing impact on public health [13]. Depression and obesity exacerbate the burden of diabetes, affecting blood sugar control, complications, quality of life, healthcare costs, and mortality risk [14, 15]. Therefore, recognizing modifiable factors linked to depressive symptoms in individuals with diabetes is essential for early intervention.
Obesity, particularly visceral fat accumulation, plays a crucial role in both diabetes and depression. Traditional indicators like body mass index (BMI) and waist circumference (WC) have constraints in accurately capturing obesity-related risks. Recently, the WWI has been introduced as a promising metric for evaluating central obesity, independent of body weight [16, 17]. Unlike BMI, which assesses overall obesity, and WC, which does not consider body shape variations, WWI offers a more precise measurement of central fat distribution—a key factor in metabolic health [17]. Multiple cross-sectional studies based on the U.S. NHANES database have consistently demonstrated a strong positive association between WWI and the prevalence of depression in the general population [18, 19]. Compared to the general population, individuals with diabetes are more prone to depressive symptoms, with the incidence of depression in diabetic patients being approximately twice that of the general population [20–22]. Given the close association between central obesity and insulin resistance, WWI may serve as a particularly relevant anthropometric index for this population.
There are no reported studies that have investigated the association between WWI and depression prevalence in populations with diabetes. Our research fills the gap by examining the association between WWI and depression prevalence in individuals ≥ 20 years old with diabetes, using data from the 2009–2018 National Health and Nutrition Examination Survey (NHANES).
Materials and methods
Study population
The National Health and Nutrition Examination Survey (NHANES) is a nationwide program conducted by the National Center for Health Statistics (NCHS) to assess the health and nutritional status of the U.S. population. All study procedures were approved by the NCHS Ethics Review Board, and informed consent was obtained from all participants. Information on the study design and datasets is publicly available on the official NHANES website.
This study analyzed data from the 2009–2018 NHANES cycles to explore the association between WWI and depression symptoms among individuals with diabetes. After applying inclusion and exclusion criteria, 3,375 individuals aged 20 to 80 were selected for the final analysis. The following exclusion criteria were strictly adhered to: (1) participants younger than 20 years of age; (2) pregnant women; (3) non-diabetic patients; (4) missing data on WWI; (5) missing data on Patient Health Questionnaire-9 (PHQ-9); and (6) missing data on covariates (Fig. 1).
Fig. 1.
Flow Chart for Participant Inclusion and Exclusion. NHANES, National Health and Nutrition Examination Survey; PIR, poverty-income ratio
Weight-adjusted waist index
Trained health technicians conducted controlled physical measures of weight and waist circumference in a mobile examination center (MEC). Participants were measured in a fasting state during the early morning, with shoes and outer clothing removed. WC was measured at the highest point of the right hip bone, aligned with the right side of the body, and recorded after a normal exhalation while the participant was standing. A soft tape measure was used for this measurement. WWI was calculated by dividing WC (cm) by the square root of body weight (kg) [23].
Diabetes
In this study, diabetes was identified if participants met any of the following criteria: self-reported diagnosis, use of insulin or oral antidiabetic medications, fasting plasma glucose ≥ 126 mg/dL or HbA1c ≥ 6.5% [24].
Assessment of depression
In this study, depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), which evaluates symptoms over the past two weeks across nine items. Each item is scored from 0 to 3, yielding a total score between 0 and 27. A PHQ-9 score ≥ 10 was used to define the presence of depressive symptoms [25]. This screening tool demonstrated good sensitivity and specificity, both at 88% [26].
Study variables
Potential confounding variables that may influence the association between depression and WWI were included in this study. The analysis considered a variety of covariates, including age, gender, race, marital status, poverty-to-income ratio (PIR), education level, alcohol use, smoking, hypertension, physical activity, energy (kcal), protein (g), total fat (g), carbohydrates (g), dietary fiber (g), calcium (mg), total folic acid (µg), and vitamin B12 (µg). Physical activity data were obtained from the NHANES Physical Activity Questionnaire (PAQ). The following formula was used to calculate physical activity in MET-hours per week (MET-h/wk): MET × weekly frequency × duration per session (minutes). Based on the calculated MET-h/wk values, participants were categorized into vigorous, moderate, and light physical activity groups. Dietary information was obtained from two 24-hour dietary recall interviews at NHANES, and data from the two interviews were averaged. Participants were asked if they had ever smoked 100 or more cigarettes in their lifetime. Those who responded “yes” were classified as smokers, while those who answered “no” were considered non-smokers. Alcohol use was assessed by asking whether they consumed alcohol at least 12 times per year. Participants who said “yes” were identified as drinkers, whereas those who said “no” were categorized as non-drinkers [27]. Hypertension status was determined based on whether a doctor had ever diagnosed the participant with high blood pressure. Responses were documented as either “yes” or “no” [28].
Statistical analysis
With R (version 4.2) and EmpowerStats (version 2.0), data analysis and graph generation were conducted. A P value below 0.05 in a two-tailed test was interpreted as statistically significant. Diabetic participants were divided into tertiles based on WWI, low (T1), medium (T2), and high (T3), and their baseline characteristics were compared. We further assessed whether WWI exhibited a dose-response relationship with depression. To maintain data accuracy, participants with missing values in key variables were excluded. The distribution of continuous variables was assessed with the Shapiro-Wilk test and histogram plots. Participants with missing values for key variables were excluded to maintain data accuracy. The distribution of continuous variables was assessed using the Shapiro-Wilk test and histograms. Continuous variables were expressed as mean ± standard deviation (SD) and analyzed by analysis of variance (ANOVA) or Student’s t-test. Categorical variables were analyzed using chi-square tests and expressed as frequencies (n) and percentages (%). Multiple logistic regression analyses were performed using three models to explore the relationship between WWI and depression. Model 1 was unadjusted. Model 2 was adjusted for age, gender, and race. Model 3 was fully adjusted for all potential confounders, including marital status, PIR, education level, alcohol consumption, smoking status, hypertension, physical activity, total energy intake, protein, total fat, carbohydrates, dietary fiber, calcium, total folic acid, and vitamin B12, in addition to variables included in Model 2. Additionally, we applied smoothed curve fitting and threshold effect analysis to examine the potential nonlinear association between WWI and depression prevalence in diabetic patients. Subgroup analyses were performed, and potential effect modifiers were identified using interaction terms in multivariate logistic regression models. Finally, without adjusting for any covariates, we compared the predictive abilities of three obesity indices (WWI, BMI, and WC) for depression using ROC curve analysis to identify the best predictor.
Results
Baseline characteristics
This analysis included 3,375 participants, with a mean age of 59.97 ± 13.30 years. Among them, 53.21% were male, and 46.79% were female. Additionally, 427 participants had a PHQ-9 score of 10 or higher, with a prevalence of 12.65%. They were categorized into three WWI-based groups: T1 (8.91–11.30), T2 (11.31–11.91), and T3 (11.91–14.37). Compared with the lowest WWI group (T1), diabetic patients in the highest WWI group (T3) were likely to be older, have a lower PIR, lower education level, be more often female, single, not drink alcohol, and be mildly physically active; have lower intakes of total energy, protein, total fat, carbohydrates, dietary fiber, calcium, total folate, and vitamin B12; and be predominantly other Hispanic or non-Hispanic white. They were more likely to have hypertension and depressive symptoms (P < 0.05) (Table 1).
Table 1.
Baseline characteristics of diabetic participants stratified by WWI tertile
| Variables | Total (N = 3375) | weight-adjusted waist circumference index (WWI) | P-value | ||
|---|---|---|---|---|---|
| T1(8.91–11.30) N = 1125 |
T2(11.31–11.91) N = 1125 |
T3(11.91–14.37) N = 1125 |
|||
| Age(years) | 59.97 ± 13.30 | 55.86 ± 13.12 | 60.76 ± 13.23 | 63.29 ± 12.47 | < 0.001 |
| PIR | 2.32 ± 1.55 | 2.55 ± 1.60 | 2.30 ± 1.54 | 2.12 ± 1.48 | < 0.001 |
| Energy (kcal) | 1898.25 ± 798.91 | 2066.58 ± 863.92 | 1854.81 ± 781.11 | 1773.36 ± 716.62 | < 0.001 |
| Protein (g) | 77.35 ± 35.29 | 83.86 ± 39.21 | 76.27 ± 34.28 | 71.91 ± 30.85 | < 0.001 |
| Total fat (g) | 75.25 ± 38.92 | 81.69 ± 40.86 | 73.22 ± 38.62 | 70.83 ± 36.35 | < 0.001 |
| Carbohydrates (g) | 224.59 ± 98.21 | 241.75 ± 106.84 | 220.52 ± 97.56 | 211.32 ± 86.80 | < 0.001 |
| Dietary fiber (g) | 16.66 ± 9.23 | 17.82 ± 10.00 | 16.65 ± 9.42 | 15.49 ± 8.01 | < 0.001 |
| Calcium (mg) | 865.40 ± 460.52 | 917.08 ± 493.64 | 849.07 ± 471.30 | 830.04 ± 408.19 | < 0.001 |
| Total folic acid (µg) | 368.71 ± 197.69 | 392.62 ± 215.44 | 364.66 ± 192.99 | 348.85 ± 180.76 | < 0.001 |
| Vitamin B12 (µg) | 4.76 ± 6.15 | 5.04 ± 6.02 | 4.70 ± 5.85 | 4.55 ± 6.56 | 0.002 |
| Gender | < 0.001 | ||||
| Male | 1796 (53.21%) | 755 (67.11%) | 637 (56.62%) | 404 (35.91%) | |
| Female | 1579 (46.79%) | 370 (32.89%) | 488 (43.38%) | 721 (64.09%) | |
| Race | < 0.001 | ||||
| Mexican American | 605 (17.93%) | 163 (14.49%) | 225 (20.00%) | 217 (19.29%) | |
| Other Hispanic | 375 (11.11%) | 108 (9.60%) | 125 (11.11%) | 142 (12.62%) | |
| Non-Hispanic White | 1196 (35.44%) | 314 (27.91%) | 389 (34.58%) | 493 (43.82%) | |
| Non-Hispanic Black | 850 (25.19%) | 388 (34.49%) | 265 (23.56%) | 197 (17.51%) | |
| Other Race | 349 (10.34%) | 152 (13.51%) | 121 (10.76%) | 76 (6.76%) | |
| Education level | < 0.001 | ||||
| < High school | 999 (29.60%) | 268 (23.82%) | 332 (29.51%) | 399 (35.47%) | |
| High school | 781 (23.14%) | 270 (24.00%) | 244 (21.69%) | 267 (23.73%) | |
| >High school | 1595 (47.26%) | 587 (52.18%) | 549 (48.80%) | 459 (40.80%) | |
| Marital status | < 0.001 | ||||
| Non-single | 2038 (60.39%) | 705 (62.67%) | 717 (63.73%) | 616 (54.76%) | |
| Single | 1337 (39.61%) | 420 (37.33%) | 408 (36.27%) | 509 (45.24%) | |
| Alcohol use | < 0.001 | ||||
| Yes | 1993 (59.05%) | 725 (64.44%) | 667 (59.29%) | 601 (53.42%) | |
| No | 1382 (40.95%) | 400 (35.56%) | 458 (40.71%) | 524 (46.58%) | |
| Smoking | 0.231 | ||||
| Yes | 1687 (49.99%) | 539 (47.91%) | 576 (51.20%) | 572 (50.84%) | |
| No | 1688 (50.01%) | 586 (52.09%) | 549 (48.80%) | 553 (49.16%) | |
| Hypertension | < 0.001 | ||||
| Yes | 2206 (65.36%) | 671 (59.64%) | 734 (65.24%) | 801 (71.20%) | |
| No | 1169 (34.64%) | 454 (40.36%) | 391 (34.76%) | 324 (28.80%) | |
| Physical activity | < 0.001 | ||||
| light | 1623 (48.09%) | 466 (41.42%) | 532 (47.29%) | 625 (55.56%) | |
| moderate | 1153 (34.16%) | 359 (31.91%) | 412 (36.62%) | 382 (33.96%) | |
| vigorous | 599 (17.75%) | 300 (26.67%) | 181 (16.09%) | 118 (10.49%) | |
| Depressive symptoms | < 0.001 | ||||
| Yes | 427 (12.65%) | 92 (8.18%) | 138 (12.27%) | 197 (17.51%) | |
| No | 2948 (87.35%) | 1033 (91.82%) | 987 (87.73%) | 928 (82.49%) | |
Continuous variables were expressed as mean ± SD and analyzed by analysis of variance (ANOVA) or Student's t-test. Categorical variables were analyzed using chi-square tests and expressed as frequencies (n) and percentages (%); PIR poverty-income ratio
Association between WWI and depression in adults with diabetes
We applied multivariate logistic regression to evaluate the association between WWI and depressive symptoms in adults with diabetes (Table 2). In models 1, 2, and 3, depressive symptoms were positively associated with WWI. In the final model (Model 3), which adjusted for all confounding factors, each 1-unit increase in WWI was associated with a 38% higher likelihood of depressive symptoms among diabetic patients (OR = 1.38, 95% CI: 1.17–1.63). Further dividing WWI into tertiles, the prevalence of depression in the highest tertile group was 75% higher than in the lowest tertile group (OR = 1.75, 95% CI: 1.30–2.35).
Table 2.
Association between WWI and depression patients with diabetes
| Exposure | Model 1 OR (95% CI) |
Model 2 OR (95% CI) |
Model 3 OR (95% CI) |
|---|---|---|---|
| WWI (continuous) | 1.67 (1.45, 1.93) | 1.55 (1.32, 1.82) | 1.38 (1.17, 1.63) |
| WWI (categorized) | |||
| T1 (8.91–11.30) | 1.0(Reference) | 1.0(Reference) | 1.0 (Reference) |
| T2 (11.31–11.91) | 1.57 (1.19, 2.07) | 1.54 (1.16, 2.05) | 1.42 (1.06, 1.91) |
| T3 (11.91–14.37) | 2.38 (1.83, 3.10) | 2.11 (1.59, 2.81) | 1.75 (1.30, 2.35) |
| P for trend | < 0.0001 | < 0.0001 | 0.0002 |
WWI was converted from a continuous variable to a categorical variable (tertiles). T, tertiles. OR, Odds ratio. 95% CI, 95% confidence interval
Model 1 was unadjusted and did not control for any covariates
Model 2 included adjustments for age, gender, and race
Model 3 was fully adjusted, incorporating all potential confounders by further accounting for marital status, PIR, education level, alcohol use, smoking, hypertension, physical activity, energy, protein, total fat, carbohydrates, dietary fiber, calcium, total folic acid, and vitamin B12, in addition to the variables in Model 2
Nonlinear association between WWI and depression and comparative predictive performance of obesity indices
Smoothed curve fitting revealed a nonlinear association between WWI and depression in diabetic patients (Fig. 2A). The turning point was 12.31 by threshold effect analysis. Significant correlations were found before the turning point (OR = 1.67, 95% CI: 1.33–2.10). However, after the inflection point, the association between WWI and depressive symptoms lost statistical significance (OR = 0.71, 95% CI: 0.41–1.24) (Table 3). The results of receiver operating characteristic curve (ROC) analysis showed that the area under the curve (AUC) values for WWI, BMI, and WC were 0.613, 0.592, and 0.575, respectively. Among the three indicators, WWI showed the highest AUC. Comparing their ability to predict depression in patients with diabetes mellitus under the same conditions without adjusting for covariates, WWI demonstrated better predictive performance than both BMI and WC (Fig. 2B).
Fig. 2.
Smoothed curve fitting and ROC curve analysis. WWI, weight-adjusted waist index. WC, waist circumference. BMI, body mass index. ROC, receiver operating characteristic curve. AUC, area under the curve
Table 3.
Threshold effect analysis of WWI on depression using a two-piecewise linear regression model
| WWI | 0R (95% CI) |
|---|---|
| Fitting by the standard linear model | 1.38 (1.17, 1.63) |
| Fitting by the two-piecewise linear model | |
| Inflection point | 12.31 |
| WWI < 12.31 | 1.67 (1.33, 2.10) |
| WWI > 12.31 | 0.71 (0.41, 1.24) |
| P for log-likelihood ratio | 0.011 |
Adjust for age, gender, race, marital status, PIR, education level, alcohol consumption, smoking status, hypertension, physical activity, energy, protein, total fat, carbohydrates, dietary fiber, calcium, total folic acid, and vitamin B12.
Subgroup analysis
Subgroup analysis indicated that the association between WWI and the prevalence of depression in patients with diabetes was influenced by PIR (P for interaction < 0.05). Among diabetic patients, a marked positive association between WWI and depressive symptoms was observed in the subgroups with PIR ≥ 1, < 3 (OR = 1.55, 95% CI: 1.23–1.96), and PIR ≥ 3 (OR = 2.00, 95% CI: 1.29–3.08), while a non-significant positive association was found in the PIR < 1 subgroup (OR = 1.03, 95% CI: 0.78–1.35) (Fig. 3).
Fig. 3.
Subgroup analysis for the association between WWI and depression patients with diabetes
Discussion
In this cross-sectional study involving 3,375 participants, our study is the first to demonstrate a significant positive association between a novel anthropometric index of obesity, the WWI, and the likelihood of depressive symptoms in patients with diabetes. This association remained significant after dividing WWI into tertiles (T1-T3), with higher WWI levels corresponding to a greater likelihood of depressive symptoms. When WWI was below 12.31, the association between WWI and depressive symptoms was particularly strong. Without adjusting for covariates, the predictive abilities of WWI, BMI, and WC for depressive symptoms in patients with diabetes mellitus were compared, and WWI demonstrated better predictive performance than BMI and WC. Interestingly, the results also indicated that PIR modified the association between WWI and depressive symptoms, with a significant positive association observed in the subgroups with PIR ≥ 1, < 3, and PIR ≥ 3.
Fei et al. [29] analyzed 34,528 NHANES participants from 2005 to 2018 and reported that WWI outperformed BMI and WC in identifying depression, aligning with our findings. They also observed a nonlinear positive association between WWI and depression, with a threshold of 11.14, beyond which the association remained significant. In contrast, our study focused on diabetic patients and identified a higher WWI threshold (12.31), beyond which the association with depression became nonsignificant, suggesting the complexity of the relationship between WWI and depression in the context of specific metabolic diseases, highlighting the importance of considering disease-specific thresholds when assessing mental health. Zhang et al. [30] analyzed data from 4,524 participants with non-alcoholic fatty liver disease (NAFLD) from NHANES between 2017 and 2020, and found a positive association between WWI and depression in NAFLD, independent of BMI. While Zhang et al.‘s study focused on NAFLD patients, our research concentrated on a diabetic patient population. Both studies highlight a close relationship between WWI and depression in metabolic diseases, suggesting a potential interaction between metabolic health and mental health.
Notably, our study showed that PIR modified the relationship between WWI and depression in diabetic patients, with significant positive associations observed in individuals with PIR ≥ 1, <3, and PIR ≥ 3. Although higher income and social status are traditionally considered protective factors for mental health, a longitudinal study reported a “social reversal” phenomenon, wherein individuals with higher socioeconomic status were more prone to obesity-related depression [31]. We speculate that diabetes, as a chronic disease requiring long-term self-management, imposes a continuous burden of disease control and psychological stress even among high-income individuals. The presence of central obesity may further complicate disease management and elevate the likelihood of depressive symptoms. Additionally, greater demands for self-image and professional performance in high-income populations may exacerbate social embarrassment and reduced self-esteem associated with obesity, thereby further contributing to depressive symptoms. Although the specific mechanisms by which PIR influences this relationship remain unclear, PIR differences in the distribution of diabetes, depression, and obesity may be potential contributing elements. Our results imply that WWI could act as a potential marker for screening depressive symptoms in patients with diabetes, particularly among those with higher income levels.
Our study found a notable positive correlation between WWI and depressive symptoms in diabetic patients, with a threshold value of 12.31. Beyond this threshold, the association between WWI and depressive symptoms attenuated. This result differs from earlier research, including that of Fei et al. [29], which identified a threshold of 11.14, possibly reflecting the unique characteristics of diabetic patients in terms of baseline metabolic profiles, disease burden, and adipose tissue dysfunction. In individuals with diabetes, visceral fat secretes inflammatory cytokines, such as IL-6 and TNF-α, which can cross the blood-brain barrier, trigger neuroinflammation, impair neurotransmitter function and neuroplasticity, and ultimately promote depressive symptoms [32, 33]. HPA axis hyperactivation and elevated cortisol levels linked to visceral fat may further increase depressive vulnerability [34, 35]. Compared with the general population, diabetic patients may exhibit a distinct pattern of association between WWI and depressive symptoms due to more severe metabolic disturbances and adipose tissue dysfunction. Therefore, longitudinal studies are required to clarify the causal relationship between WWI and depressive symptoms in patients with diabetes, particularly to assess whether changes in WWI predict the onset or progression of depression. Clinicians can incorporate WWI into routine evaluations, especially in patients with metabolic abnormalities, to identify individuals at high risk for depression and intervene early.
One key strength of this study is its large and representative sample, complemented by subgroup analyses that enhance the assessment of the robustness of the association between WWI and depression among diabetic patients across various subgroups. However, several important limitations should be acknowledged. First, due to the cross-sectional design, this study cannot establish a causal relationship between WWI and the prevalence of depression in diabetic individuals. The temporal sequence between WWI elevation and the onset of depressive symptoms remains unclear, and reverse causality cannot be excluded. Second, although multiple covariates were adjusted for, the possibility of residual confounding factors influencing the observed associations cannot be ruled out. Third, depressive symptoms were assessed using the PHQ-9, a widely used and validated self-reported screening tool, but not confirmed through clinical diagnostic interviews. The exclusive reliance on PHQ-9 may compromise diagnostic accuracy, as the tool may both overestimate and underestimate true depression prevalence. Clinical confirmation by trained mental health professionals, such as psychiatrists or psychologists, would provide more precise diagnoses and should be incorporated in future studies. Fourth, the study population was derived from NHANES, and although it is nationally representative of the U.S., the generalizability of our findings to other populations may be limited. Future longitudinal studies are needed to verify the temporal association between WWI and depressive symptoms and to explore the association using both self-reported and clinically confirmed mental health assessments.
Conclusion
Among patients with diabetes, WWI was significantly positively associated with the prevalence of depression. Further analysis showed that PIR significantly influenced this association, with a stronger relationship observed among those with PIR ≥ 1, < 3, and PIR ≥ 3. These findings highlight the importance of monitoring abdominal obesity, and WWI may serve as a relevant factor for clinicians to consider when assessing depressive symptoms in patients with diabetes. Further large-scale prospective studies are needed to validate and refine these results.
Acknowledgements
We extend our gratitude to all the participants in NHANES.
Abbreviations
- BMI
Body mass index
- MEC
Mobile Examination Center
- NCHS
National Center for Health StatisticsNHANESNational Health and Nutrition Examination Survey
- PHQ-9
Patient Health Questionnaire-9
- PIR
Poverty-income Ratio
- ROC
Receiver operating characteristic curve
- WC
Waist circumference
- WWI
Weight-adjusted waist index
Authors’ contributions
Conceptualization, M.Y., J.Y., H.H., and D.C.; methodology, M.Y. and H.H; software, M.Y.and J.Y.; validation, M.Y.; data curation, M.Y; writing-original draft preparation, M.Y.and J.Y.; writing-review and editing, M.Y. All authors read and approved the final manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The survey data are publicly accessible online for researchers and data users worldwide (www.cdc.gov/nchs/nhanes/).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The survey data are publicly accessible online for researchers and data users worldwide (www.cdc.gov/nchs/nhanes/).