The association between triglyceride glucose index-related indices and depression among adults in the United States: NHANES 2005–2018

doi:10.21203/rs.3.rs-4929325/v1

Background

It has been established that the triglyceride-glucose (TyG) index is associated with depression. Nonetheless, it remains unclear whether combining TyG with obesity indices could show a stronger association with depression.

Methods

Based on the 2005–2018 National Health and Nutrition Examination Survey (NHANES), this large cross-sectional study analyzed a total of 14,821 adults. Four main indices were used in the present study: the TyG index, TyG-WC (with waist circumference), TyG-WHtR (with waist-to-height ratio), and TyG-BMI (with body mass index). Using logistic regression, restricted cubic splines (RCS), and stratified analysis, we rigorously examined the association between TyG-related indices and symptoms of depression. Diagnostic values were assessed using receiver operating characteristic (ROC) curves. Finally, sensitivity analyses were performed to determine whether the association was robust and consistent.

Results

A positive association was identified between TyG, TyG-WHtR, TyG-WC, and TyG-BMI and depression. This association is particularly pronounced among women, individuals with higher education levels, and those with hypertension and diabetes. Compared to the other three indices, TyG-WHtR demonstrated the most significant association with depression after adjusting for all covariates (odds ratio (OR):1.21, 95% confidence interval (CI):1.07–1.37). ROC curves indicated that TyG-WHtR had the highest diagnostic efficacy (AUC: 0.601, 95% CI 0.575–0.610). Sensitivity analyses consistently confirmed the robustness and reliability of the association between TyG-related indices and depressive symptoms.

Conclusions

Among American adults, TyG-WC, TyG-WHtR, and TyG-BMI showed a positive and non-linear association with depressive symptoms. Furthermore, our analysis revealed that TyG-WHtR offered superior precision and sensitivity in evaluating this association compared to TyG alone, TyG-WC, and TyG-BMI.

TyG

TyG-WHtR

TyG-WC

TyG-BMI

Depression

Obesity

NHANES

Depression, a prevalent mental health disorder, significantly diminishes the quality of life for individuals worldwide and can potentially lead to suicidal ideation in those with severe symptoms [1]. According to the World Health Organization (WHO), approximately 5% of the global population is affected by depression, making it the third most prevalent contributor to the global disease burden. Despite advancements in therapeutic interventions, the rate of depression recurrence remains high [2]. Furthermore, over 75% of individuals in low- and middle-income countries do not receive treatment for mental disorders [3]. Thus, the early identification of depression is of paramount importance.

Insulin resistance (IR) has been recognized as a potential independent risk factor for depression, affecting both diabetic and non-diabetic patients [4, 5]. The glucose clamp technique, a quantitative method for assessing insulin secretion and resistance, faces implementation challenges in regions with limited access to medical facilities [6]. Alternatively, the homeostatic model assessment of insulin resistance (HOMA-IR) method has limitations in evaluating insulin resistance in lean individuals [7]. The Triglyceride-Glucose index (TyG), which combines fasting glucose and triglyceride levels [8], addresses these shortcomings. A study has confirmed that individuals with elevated TyG index levels are notably more prone to experiencing depressive symptoms among U.S. adults [9].

Obesity has emerged as a widespread global health issue, with associated metabolic conditions, such as impaired glucose tolerance and insulin resistance, contributing to increased rates of depression and anxiety [10]. Evidence suggests that obesity and depression share similar biological mechanisms [11]. A recent study indicated that TyG, in combination with obesity indices, could offer enhanced precision and sensitivity in evaluating the association with depression compared to using TyG alone [12]. However, that study was conducted in a small sample size of premenopausal women, and few studies have been conducted in American adults. Herein, we investigated the association between the TyG index, in combination with obesity indices, and depression in adults using the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2018.

Data source and study population

The National Health and Nutrition Examination Survey provides extensive data on Americans' eating habits and health. Data collection protocols were approved by the NCHS ethical review board, and each participant provided informed consent. NHANES information is available at http://www.cdc.gov/nchs/nhanes/index.htm.

The total sample size of NHANES from 2005 to 2018 was 70,190. Final participants were identified based on the following exclusion criteria: 1) Age under 20; 2) Missing data on depression diagnosis, TyG index, and obesity indices; 3) Pregnant individuals; 4) Missing data on outcomes or covariates, including hypertension, diabetes, and total cholesterol. Figure 1 illustrates the study enrollment process.

Assessment of TyG, TyG‑WHtR, TyG‑WC, and TyG‑BMI

The fasting blood glucose (FSG) and triglycerides of participants were measured at baseline. Height, weight, and waist circumference (WC) were obtained at a mobile examination center. Waist-to-height ratio (WHtR) and body mass index (BMI) were calculated. TyG-WC, TyG-WHtR, and TyG-BMI were determined using the following formulas: TyG = ln [fasting triglycerides (mg/dl) × fasting glucose (mg/dl) / 2]; BMI = body mass (kg) / height² (m²); WHtR = WC / height; TyG-WC = TyG × WC; TyG-WHtR = TyG × WHtR; TyG-BMI = TyG × BMI.

Participants were divided into four groups (Q1, Q2, Q3, and Q4) based on the quartiles of TyG, TyG-WHtR, TyG-WC, and TyG-BMI, respectively, with Q1 serving as the reference group.

Assessment of depression

A nine-item Patient Health Questionnaire (PHQ-9), based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), was used to identify symptoms of depression. Each question was scored from 0 ("not at all") to 3 ("nearly every day"), with a total score range of 0–27. At the standard cut-off value of 10, the PHQ-9 had a sensitivity of 0.85 (95% confidence interval (CI): 0.77 to 0.89) and a specificity of 0.85 (95% CI: 0.82 to 0.87) [13].

Covariates

To investigate whether TyG-related indices were associated with depression, we adjusted for potential confounders, including sociodemographic factors, lifestyle variables, and health status [6]. Sociodemographic variables encompassed age (20–39 years, 40–59 years, ≥ 60 years), sex (male and female), race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Races), and education level (lower than high school education, high school education, above high school education) [14]. Marital status was assigned to six categories (married, never married, widowed, divorced, separated, living with partner) [15]. Income levels, based on the poverty-income ratio (PIR), were divided into three categories (< 1.3, 1.3–1.8, ≥ 1.8) [16].

Smoking status, alcohol consumption, and physical activity were considered lifestyle-related confounders. Participants were categorized as never, former, or current smokers [15]. Current smokers were those who reported smoking now and having smoked at least 100 cigarettes, while former smokers were those who had quit smoking. Alcohol intake was classified according to the National Institute on Alcohol Abuse and Alcoholism (NIAAA) criteria as never (< 12 drinks in a lifetime), former (≥ 12 drinks in a lifetime with no consumption in the past year), mild or moderate (1 drink per day for women or 1–2 drinks per day for men), or heavy (2 drinks per day for women or 3 drinks per day for men). Physical activity levels were classified as vigorous, moderate, or less than moderate, following the Physical Activity Guidelines for Americans [17].

BMI, total cholesterol, and history of hypertension and diabetes were considered health status variables. BMI was classified into three categories: underweight (< 25.0 kg/m²), overweight (25.0-29.9 kg/m²), and obese (≥ 30.0 kg/m²) [16]. Cholesterol analysis was conducted by the University of Minnesota, with detailed procedures outlined in the NHANES Laboratory Procedures Manual. Indices of hypertension included a physician's diagnosis, a measured average systolic blood pressure of at least 140 mmHg, a measured average diastolic blood pressure of at least 90 mmHg or a history of antihypertensive medication [18]. Diabetes was indicated by a self-reported diagnosis, a glycosylated hemoglobin level of 6.5% or higher, and/or the use of anti-diabetic medication [19].

Statistical analysis

In accordance with CDC (Centers for Disease Control and Prevention) recommendations, the complex multistage cluster survey design was considered in the statistical analysis. Appropriate NHANES sampling weights were used. Continuous variables were presented as weighted means and standard deviations (SD), while categorical variables were expressed as weighted percentages with 95% confidence intervals (CI).

To identify differences in baseline characteristics between participants with and without depression, continuous variables were analyzed using weighted linear regression, while categorical variables were assessed with the chi-square test. Binary weighted logistic regression analyses were used to explore the association between TyG-related indices and depression. For robustness, continuous variables were also categorized into quartiles. Results were expressed as odds ratios (OR) with 95% CI across three models. Model 1 was not adjusted for any covariates. Model 2 was adjusted for age, sex, and race/ethnicity. Model 3 included further adjustments for marital status, PIR, education level, BMI, total cholesterol, smoking status, alcohol consumption, physical activity, hypertension, and diabetes.

Moreover, we employed a restricted cubic splines (RCS) analysis, adjusted for the same variables as in Model 3, to investigate the potential dose-response association (both linear and nonlinear) between TyG, TyG-WC, TyG-WHtR, TyG-BMI, and depression. RCS analysis was conducted at the 5th, 50th, and 95th percentiles of the distribution to mitigate the potential impact of outliers [20].

Receiver operating characteristic (ROC) curves were used to evaluate diagnostic efficacy, with the area under the curve (AUC) computed to quantify the association between the TyG index and obesity-related indices as predictors of depression.

Furthermore, subgroup analyses were performed to determine if the results were consistent across different factors, including age, sex, race/ethnicity, PIR, education level, BMI, history of hypertension, and history of diabetes. Nonlinearity was assessed using the likelihood ratio test. Sensitivity analyses were conducted by excluding participants with missing data on marital status, PIR, education level, smoking status, alcohol consumption, and physical activity [12]. Subsequently, binary logistic regression was used under three models to examine the association between TyG, TyG-WHtR, TyG-WC, TyG-BMI, and depression to assess the reliability and coherence of the findings.

All statistical analyses were performed using EmpowerStats (http://www.empowerstats.com), STATA 16.0 (StataCorp, College Station, TX, USA), and R (version 4.2.2). P-values less than 0.05 were considered statistically significant.

Baseline characteristics of participants

Table 1 displays the weighted baseline characteristics of the participants. In a sample of 14,821 individuals (7,451 males and 7,370 females), 1,286 (8.29%) were diagnosed with depression, with a mean age of 47.8 ± 15.7 years. The depressed group had significantly higher values of TyG, TyG-WHtR, TyG-WC, and TyG-BMI compared to the non-depressed group (P < 0.001). Within the depressed group, participants were more likely to be between the ages of 40 and 60, female, Non-Hispanic White, have a higher level of education, be married, wealthier, current smokers, heavy alcohol consumers, engage in higher levels of physical activity, have a higher BMI, and exhibit higher levels of fasting glucose, triglycerides, and cholesterol.

Table 1

Weighted baseline characteristics of included participants (n = 14821) with or without depression in NHANES 2005–2018.
	Total (n = 14821)	Without depression (n = 13535)	With depression (n = 1286)	P value
Age(years)	47.5 ± 16.8	47.4 ± 16.9	47.8 ± 15.7	0.502
20–39	36.0(34.9,37.0)	36.3(35.2,37.4)	31.9(28.6,35.4)	<0.001
40–59	37.8(36.7,38.9)	37.3(36.2,38.5)	43.8(40.2,47.6)
≥ 60	26.2(25.3,27.1)	26.4(25.4,27.3)	24.3(21.4,27.3)
Gender				<0.001
Male	49.6(48.5,50.7)	50.6(49.5,51.7)	37.3(33.8,40.9)
Female	50.4(49.3,51.5)	49.4(48.3,50.5)	62.7(59.1,66.2)
Race/ethnicity				<0.001
Mexican American	8.4(8.0,8.8)	8.4(8.0,8.9)	7.6(6.4,9.0)
Other Hispanic	5.5(5.2,5.9)	5.3(5.0,5.7)	7.9(6.6,9.4)
Non-Hispanic White	68.3(67.4,69.2)	68.7(67.8,69.6)	63.1(59.9,66.3)
Non-Hispanic Black	10.7(10.3,11.2)	10.4(10.0,10.9)	14.5(12.7,16.5)
Other Races^a	7.1(6.6,7.6)	7.1(6.6,7.7)	6.9(5.2,9.0)
Education level				<0.001
Less than high school	15.7(15.1,16.4)	14.9(14.2,15.5)	26.5(23.7,29.5)
High school or Equivalent	23.5(22.6,24.5)	23.3(22.3,24.3)	26.5(23.3,30.0)
Above high school	60.7(59.7,61.8)	61.8(60.7,62.9)	47.0(43.3,50.7)
Not recorded	0.0(0.0,0.0)	0.0(0.0,0.0)	0.0(0.0,0.0)
Marital status				<0.001
Married	55.7(54.6,56.8)	57.2(56.0,58.3)	37.6(34.1,41.4)
Widowed	5.4(5.0,5.8)	5.2(4.8,5.6)	7.4(6.0,9.2)
Divorced	10.3(9.7,11.0)	9.7(9.0,10.4)	18.8(16.1,21.8)
Separated	2.3(2.0,2.6)	2.0(1.7,2.2)	6.3(5.0,8.1)
Never married	17.7(16.9,18.6)	17.6(16.7,18.5)	19.8(17.0,22.9)
Living with partner	7.1(6.5,7.6)	7.0(6.4,7.6)	8.1(6.4,10.2)
Not recorded	1.5(1.2,1.8)	1.4(1.2,1.8)	1.9(1.0,3.6)
Poverty-income ratio				<0.001
<1.3	19.2(18.4,19.9)	17.5(16.8,18.2)	40.1(36.7,43.6)
1.3–1.8	9.1(8.5,9.6)	8.9(8.3,9.5)	11.5(9.4,13.9)
≥ 1.8	65.7(64.7,66.6)	67.5(66.5,68.5)	42.2(38.4,46.0)
Not recorded	6.1(5.7,6.6)	6.1(5.6,6.6)	6.3(4.9,7.9)
Smoking status				<0.001
Never	54.2(53.1,55.2)	55.6(54.4,56.7)	36.5(33.0,40.1)
Former	25.7(24.7,26.6)	25.8(24.8,26.9)	23.3(20.4,26.6)
Now	20.1(19.3,21.0)	18.5(17.7,19.4)	40.1(36.6,43.8)
Not recorded	0.0(0.0,0.1)	0.0(0.0,0.1)	0.0(0.0,0.3)
Alcohol status				<0.001
Never	10.3(9.7,10.9)	10.3(9.7,10.9)	10.4(8.7,12.5)
Former	8.7(8.1,9.2)	8.5(7.9,9.1)	11.0(9.0,13.4)
Mild or moderate	31.0(30.0,32.1)	32.0(30.9,33.1)	19.0(16.2,22.2)
Heavy	46.6(45.5,47.7)	45.9(44.7,47.0)	55.4(51.7,59.0)
Not recorded	3.5(3.1,3.8)	3.4(3.1,3.8)	4.2(3.0,5.9)
Physical activity				<0.001
Less than moderate	25.3(24.4,26.2)	24.2(23.3,25.1)	39.4(35.9,43)
Moderate	18.8(18.0,19.7)	18.9(18.0,19.8)	18.0(15.3,21.1)
vigorous	55.8(54.7,56.8)	56.8(55.7,58.0)	42.3(38.7,46)
Not recorded	0.1(0.1,0.2)	0.1(0.0,0.2)	0.2(0.1,0.7)
BMI(kg/m²)	29.0 ± 6.8	28.9 ± 6.7	30.3 ± 7.7	<0.001
<25	30.0(29.0,31.0)	30.1(29.1,31.2)	28.6(25.2,32.1)
25–30	32.9(31.9,34.0)	33.7(32.6,34.7)	23.8(20.9,27.0)
≥ 30	37.1(36.0,38.1)	36.2(35.1,37.3)	47.6(44.0,51.3)
Fasting glucose, mmol/L	5.9 ± 1.7	5.9 ± 1.6	6.2 ± 2.1	<0.001
Triglyceride, mmol/L	1.4 ± 1.2	1.4 ± 1.2	1.6 ± 1.4	<0.001
Glycohemoglobin, %	5.6 ± 0.9	5.6 ± 0.9	5.8 ± 1.1	<0.001
Cholesterol, mmol/L	5.0 ± 1.1	5.0 ± 1.1	5.1 ± 1.2	0.153
TyG	8.6 ± 0.7	8.6 ± 0.6	8.7 ± 0.7	<0.001
TyG-WC	860.6 ± 179.8	857.6 ± 178.1	897.9 ± 196.6	<0.001
TyG-WHtR	5.1 ± 1.1	5.1 ± 1.0	5.4 ± 1.2	<0.001
TyG-BMI	251.5 ± 67.5	250.2 ± 66.5	267.2 ± 77.7	<0.001
Hypertension				<0.001
No	67.2(66.2,68.2)	67.9(66.8,68.9)	58.2(54.5,61.7)
Yes	32.8(31.8,33.8)	32.1(31.1,33.2)	41.8(38.3,45.5)
Diabetes				<0.001
No	89.1(88.5,89.7)	89.5(88.9,90.1)	83.7(8.01,86.0)
Yes	10.9(10.3,11.5)	10.5(9.9,11.1)	16.3(14.0,19.0)
Mean ± SD (standard deviation) for continuous variables: the P-value was calculated by the weighted linear regression model. Weighted % (95% confidence interval) for categorical variables: the P-value was calculated by the weighted chi-square test.
NHANES, National Health and Nutrition Examination Survey; BMI, Body mass index; Other Races^a, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, and multiracial persons.

Association between TyG, TyG‑WHtR, TyG‑WC, TyG‑BMI, and depression

Table 2 presents the results of the binary weighted logistic regression analysis, which identified associations between TyG-related indices and depression in U.S. adults. The analysis of models 1 and 2 showed that continuous variables such as TyG, TyG-WHtR, TyG-WC, and TyG-BMI were independently positively associated with depression. Furthermore, compared to the first quartile (Q1), there was a statistically significant positive association with depressive symptoms in the third (Q3) and fourth quartiles (Q4) of TyG-WC and TyG-WHtR. In contrast, TyG and TyG-BMI were positively correlated with depression only in the fourth quartile (Q4).

Table 2

Association between TyG, TyG-WHtR, TyG-WC, TyG-BMI and depression among U.S. adults in NHANES 2005–2018.
	Model 1		Model 2		Model 3
	OR(95%CI)	P-value	OR(95%CI)	P-value	OR(95%CI)	P-value
TyG	1.36(1.21,1.52)	<0.001	1.48(1.31,1.67)	<0.001	1.19(1.02,1.37)	0.023
Q1	Reference		Reference		Reference
Q2	0.95(0.76,1.20)	0.668	1.02(0.81,1.29)	0.865	0.83(0.65,1.06)	0.137
Q3	1.15(0.92,1.44)	0.229	1.30(1.02,1.65)	0.051	0.96(0.74,1.25)	0.755
Q4	1.61(1.30,1.99)	<0.001	1.87(1.48,2.36)	<0.001	1.20(0.92,1.57)	0.189
P for trend		<0.001		<0.001		0.111
TyG-WC	1.0012(1.0008,1.0016)	<0.001	1.0015(1.0010,1.0019)	<0.001	1.0008(1.0001,1.0015)	0.032
Q1	Reference		Reference		Reference
Q2	1.09(0.86,1.39)	0.458	1.17(0.92,1.50)	0.202	1.30(0.94,1.79)	0.110
Q3	1.36(1.08,1.71)	0.009	1.56(1.21,1.99)	<0.001	1.63(1.14,2.35)	0.008
Q4	1.68(1.36,2.08)	<0.001	1.93(1.54,2.43)	<0.001	1.53(1.01,2.31)	0.043
P for trend		<0.001		<0.001		0.047
TyG-WHtR	1.32(1.23,1.42)	<0.001	1.32(1.23,1.41)	<0.001	1.21(1.07,1.37)	0.002
Q1	Reference		Reference		Reference
Q2	1.11(0.87,1.41)	0.416	1.15(0.90,1.48)	0.270	1.35(0.98,1.84)	0.065
Q3	1.38(1.09,1.74)	0.008	1.45(1.13,1.85)	0.004	1.69(1.18,2.43)	0.005
Q4	2.14(1.73,2.65)	<0.001	2.15(1.72,2.69)	<0.001	1.95(1.29,2.95)	0.002
P for trend		<0.001		<0.001		0.001
TyG-BMI	1.0031(1.0024,1.0042)	<0.001	1.0032(1.0021,1.0042)	<0.001	1.0020(1.0002,1.0038)	0.027
Q1	Reference		Reference		Reference
Q2	0.79(0.62,1.01)	0.063	0.83(0.65,1.07)	0.153	1.13(0.77,1.66)	0.538
Q3	1.16(0.92,1.46)	0.197	1.20(0.95,1.53)	0.132	1.71(1.08,2.72)	0.023
Q4	1.63(1.32,2.00)	<0.001	1.61(1.30,2.00)	<0.001	1.88(1.12,3.17)	0.018
P for trend		<0.001		<0.001		0.016
Model 1 did not adjust for any covariates;
Model 2 adjusted for age, sex, and race/ethnicity;
Model 3 further adjusted for PIR, education level, smoking status, alcohol status, physical activity, hypertension, diabetes, and serum total cholesterol based on Model 2.

Subsequent analyses using model 3 revealed that TyG, TyG-WHtR, TyG-WC, and TyG-BMI all exhibited positive associations with depression. However, TyG, when considered a categorical variable, was not statistically significant in the trend test compared to TyG-WC, TyG-WHtR, and TyG-BMI (P > 0.05). Additionally, the OR for TyG-WHtR was higher than for the other three indices, whether analyzed as a continuous variable or in quartiles. This indicates that elevated TyG-related indices are independent predictors of depressive symptoms in American adults, with TyG-WHtR emerging as the most precise and sensitive marker.

Figure 2 presents a visual analysis of the associations between TyG, TyG-WHtR, TyG-WC, and TyG-BMI with depression using RCS. After adjusting for all covariates in model 3, a linear association between TyG and depression was observed (P-overall < 0.05, P-nonlinear > 0.05), aligning with previous studies [9]. Meanwhile, TyG-WC, TyG-WHtR, and TyG-BMI demonstrated nonlinear associations with depression (P-overall < 0.05, P-nonlinear < 0.05).

Receiver operating characteristic curves, stratified analyses, and sensitivity analyses

As shown in Fig. 3, TyG-WHtR exhibited the highest diagnostic efficacy for depression, with an AUC of 0.601 (95% CI: 0.575–0.610), followed by TyG-BMI, with an AUC of 0.576 (95% CI: 0.558–0.593), and TyG-WC, with an AUC of 0.573 (95% CI: 0.556–0.590).

The stratified analysis results revealed that significant associations between TyG, TyG-WHtR, TyG-WC, TyG-BMI, and depression were more likely to occur among women, individuals with higher education levels, and those with hypertension and diabetes, as shown in Fig. 4. Among the four indices, TyG-WHtR exhibited a consistent association across most subgroups.

Further sensitivity analysis was conducted across three models, excluding participants with incomplete covariates, as detailed in Table S1. The results consistently demonstrated stability and consistency in the association between TyG-related indices and depressive symptoms.

The association between triglyceride-glucose related indices and depression among American adults was examined using the NHANES dataset from 2005 to 2018. We found a positive association between TyG, TyG-WHtR, TyG-WC, and TyG-BMI and depression. This association was particularly pronounced among women, individuals with higher education levels, and those with hypertension and diabetes. TyG-WHtR demonstrated superior accuracy and sensitivity in diagnosing depression compared to the other three indices. Furthermore, in stratified analyses adjusted for all covariates, TyG-WHtR showed consistent associations across the majority of subgroups.

TyG, a biomarker, is increasingly favored as a cost-effective and sensitive alternative to measuring insulin resistance [21, 22]. Amir Hossein Behnoush et al. [23]demonstrated a significant association between the TyG index and depression in a systematic review, supporting the hypothesis that disturbances in sugar and lipid metabolism may contribute to increased depression and related events [24]. In our study, we examined the association between the TyG index combined with obesity indices and depression. Across all three models, a positive association was found between TyG, TyG-WHtR, TyG-WC, and TyG-BMI and depression as continuous variables. In model 3, as categorical variables, TyG-BMI, TyG-WC, and TyG-WHtR consistently showed an independent positive association with depressive symptoms in American adults, while TyG was no longer statistically significant. This finding is consistent with prior research on premenopausal women in the United States [12]. Yuri Milaneschi et al. [11] discussed the interplay of obesity and depression through genetic factors, the hypothalamic-pituitary-adrenal (HPA) axis, immune-inflammatory activation, neuroendocrine regulators of energy metabolism, and the microbiome. They concluded that biological pathways may interact with depression and obesity either as shared underlying mechanisms affecting susceptibility or as mediators in their causal associations [25, 26]. Thus, integrating TyG with obesity indices may offer a more precise understanding of their association with depression in American adults.

The RCS curves revealed a linear association between TyG and depression. In contrast to prior research, TyG-WC, TyG-WHtR, and TyG-BMI exhibited nonlinear associations with depression. This discrepancy may be due to differences in study populations, relatively large sample sizes, variations in the calibration of RCS curve parameters and the selection of appropriate modeling techniques.

Compared to the other three indices, our study demonstrated that TyG-WHtR exhibited superior accuracy and sensitivity in predicting depression among American adults across all three models. The ROC curves further confirmed that TyG-WHtR had the best diagnostic efficacy for depression. WHtR is a reliable indicator of visceral fat accumulation [27]. Excessive visceral fat has been linked to the development of chronic diseases, including depression, which is closely related to glucose and lipid metabolism, as well as insulin resistance [28]. A prospective cohort study found that obesity, particularly visceral fat, is associated with an increased risk of depression [29]. Additionally, dynamic abdominal obesity has been correlated with a heightened risk of depressive symptoms, especially in individuals under 60 years of age [30]. Our findings suggest that TyG-WHtR could serve as a significant biomarker for depression in individuals with abdominal obesity.

In the stratified analysis results, the combination of TyG and obesity indices showed a stronger association with depression in individuals with hypertension and diabetes. The incidence of depression has significantly increased among those with chronic conditions, including diabetes [31] and cardiovascular diseases such as hypertension [32]. The association between chronic diseases and depression is complex. A notable positive association was found between TyG-related indices and depression, particularly among women. It is commonly believed that women are more susceptible to depression than men, possibly due to hormonal fluctuations, especially estrogen. Estrogen can influence insulin secretion by pancreatic islet beta cells through various molecular pathways [33]. Additionally, our study indicated that the associations between TyG-related indices and depression were more pronounced among individuals with higher education levels. Highly educated individuals often engage in more sedentary mental work, which can disrupt glucose and lipid metabolism, lead to insulin resistance, and ultimately contribute to depression [34, 35]. Therefore, these four indices could be useful for screening depressive symptoms among women, individuals with higher education levels, and those with hypertension and diabetes.

This large cross-sectional study utilized a substantial, nationally representative sample of the adult population in the United States. We employed a sophisticated multi-stage probability sampling methodology and calculated sampling weights to accurately represent the entire country. Nevertheless, there are several limitations to this study. Firstly, the cross-sectional design of the NHANES study limits the ability to establish causality, necessitating follow-up cohort studies to validate the observed associations. Secondly, despite efforts to adjust for various potential confounding variables, certain residual covariates, such as medications affecting glucose and lipid levels and depression, may not have been fully accounted for. Thirdly, this study focused on adults in the United States, and its applicability to adult populations in other countries remains uncertain. Therefore, future prospective multicenter studies with large sample sizes are needed to further validate the association between TyG, TyG-WC, TyG-WHtR, TyG-BMI, and depression.

Our analysis reveals a significant positive association between TyG-related indices and depression among American adults. This association is particularly pronounced among women, individuals with higher education, and those diagnosed with hypertension and diabetes. Compared to the other three indices, TyG-WHtR shows higher accuracy and sensitivity in diagnosing depression. Therefore, we propose that TyG-WHtR could be utilized in depression screening.

TyG	Triglyceride-glucose
NHANES	National Health and Nutrition Examination Survey
WC	Waist circumference
WHtR	Waist-to-height ratio
BMI	Body mass index
TyG-WC	Glucose triglyceride-waist circumference
TyG-WHtR	Glucose triglyceride-waist height ratio
TyG-BMI	Glucose triglyceride-body mass index
RCS	Restricted cubic spline
ROC	Receiver operating characteristic
IR	Insulin resistance
PHQ-9	A nine-item Patient Health Questionnaire
DSM	Diagnostic and Statistical Manual of Mental Disorders
PIR	ratio of family income to poverty
NIAAA	National Institute on Alcohol Abuse and Alcoholism
CDC	Centers for Disease Control and Prevention
SD	Standard deviation
CI	Confidence interval
AUC	area under the curve
HPA	Hypothalamic-pituitary-adrenal

Acknowledgments

None

Authors’ contributions

All authors thank the NHANES 2005-2018 participants for their invaluable contributions. YY. S designed the study. YY. S and L.L and participated in data collection and analysis. YY. S and JM. Z were involved in the visualization of the results. YY. S and KK.W drafted the manuscript. All authors actively participated in the research process made substantial contributions to manuscript revisions, and carefully reviewed and approved the final version.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not -for -profit sectors.

Availability of data and materials

Raw data supporting the obtained results are available at the corresponding author.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Author details

Corresponding author: Kangkang Wei ([email protected])

¹Children's Hospital of Soochow University, Suzhou 215000, China.

²Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.

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No competing interests reported.

TableS1.docx

The association between triglyceride glucose index-related indices and depression among adults in the United States: NHANES 2005–2018

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Data source and study population

Assessment of TyG, TyG‑WHtR, TyG‑WC, and TyG‑BMI

Assessment of depression

Covariates

Statistical analysis

Results

Baseline characteristics of participants

Association between TyG, TyG‑WHtR, TyG‑WC, TyG‑BMI, and depression

Receiver operating characteristic curves, stratified analyses, and sensitivity analyses

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1