Published online Nov 19, 2026. doi: 10.5498/wjp.123981
Revised: July 30, 2026
Accepted: September 21, 2026
Published online: November 19, 2026
Processing time: 147 Days and 0.1 Hours
Sleep problems commonly coexist with insulin resistance and abdominal adipo
To investigate associations of TyG-ABSI with sleep problems and sleep duration and evaluate depressive symptoms using statistical analysis.
This cross-sectional study included 12642 adults from the 2005-2014 National Health and Nutrition Examination Survey. TyG-ABSI was analyzed continuously and by tertiles. Sleep problems were defined by affirmative responses to either of two self-reported questionnaire items; sleep duration was analyzed continuously. Survey-weighted multivariable logistic and linear regressions assessed the associations. Each sleep item was examined separately in sensitivity analyses. Bootstrap statistical decomposition estimated total, direct, and indirect effects using binary depressive status and continuous Patient Health Questionnaire-9 scores.
Survey-weighted analyses showed that a 1-unit increase in TyG-ABSI was associated with higher odds of self-reported sleep problems (odds ratio = 1.17; 95%CI: 1.06-1.30; P = 0.002). Among those reporting sleep problems, TyG-ABSI was positively associated with sleep duration (β = 0.31 hours per unit; 95%CI: 0.05-0.57; P = 0.020), whereas the association was not statistically significant among those without sleep problems (β = 0.00). Restricted cubic splines suggested a nonlinear association with sleep duration (P for nonlinearity < 0.05). Decomposition using continuous depressive scores yielded a negative indirect estimate (β = -0.044; 95%CI: -0.082 to -0.016; P < 0.001) and a positive direct estimate, consistent with statistical suppression.
Higher TyG-ABSI is associated with self-reported sleep problems and with longer sleep duration among parti
Core Tip: This population-based study used survey-weighted data from United States adults to assess the association of the triglyceride-glucose index combined with a body shape index with self-reported sleep problems and sleep duration. Higher triglyceride-glucose index combined with a body shape index was associated with greater odds of sleep problems, while analyses suggested a possible nonlinear association with sleep duration among participants with sleep problems. Depressive symptoms exhibited a statistical suppression effect rather than conventional mediation. These findings extend evidence on metabolic-sleep associations by integrating metabolic and body-shape information and warrant prospective validation.
- Citation: Zhang YC, Yu ZZ, Zheng WT, Wang C. Triglyceride-glucose index combined with a body shape index in relation to sleep problems and sleep duration. World J Psychiatry 2026; 16(11): 123981
- URL: https://www.wjgnet.com/2220-3206/full/v16/i11/123981.htm
- DOI: https://dx.doi.org/10.5498/wjp.123981
Sleep disorders represent a growing global health concern, affecting nearly one-third of the adult population and adver
IR commonly coexists with systemic inflammation, oxidative stress, endothelial dysfunction, and neuroendocrine abnormalities, which have also been implicated in impaired sleep and circadian regulation[3-5]. Because direct measurement of IR is difficult in large population studies, the triglyceride-glucose (TyG) index has been widely used as a simple surrogate marker. Pei et al[3] reported that a higher TyG index was associated with sleep disorders among United States adults. More recently, Liu et al[6] examined TyG and its combined indices, including TyG-body mass index (BMI) and TyG-waist circumference (WC), in relation to sleep disorders using National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study and further demonstrated that depressive symptoms partially mediated these associations. These studies established an important link between TyG-related metabolic indicators and sleep health[3,6].
However, TyG primarily reflects glucolipid-related IR and does not directly account for body fat distribution. Although TyG-BMI and TyG-WC incorporate general or central obesity, the BMI does not adequately reflect abdominal fat distribution, while WC is strongly influenced by overall body size[7]. A body shape index (ABSI), which was proposed by Krakauer and Krakauer[8], standardizes WC according to height and BMI, thereby providing information on the abdominal body shape that is relatively independent of overall body size. TyG-ABSI combines the TyG index with ABSI and may capture both IR and standardized abdominal adiposity. Thus, compared with TyG alone, TyG-ABSI may provide complementary information on obesity-related metabolic burden rather than merely representing another mathematical extension of the TyG index[7,9].
Although previous studies have examined TyG, TyG-BMI, and TyG-WC in relation to sleep disorders[3,6], evidence regarding TyG-ABSI remains limited. In particular, it remains unclear whether incorporating ABSI-derived body-shape information provides additional information beyond TyG alone and whether depressive symptoms mediate the associations of TyG-ABSI with different sleep outcomes. Therefore, using data obtained from the NHANES, the present study aimed to examine the associations of TyG-ABSI with self-reported physician-diagnosed sleep problems and sleep duration, while investigating the mediating role of depressive symptoms. Furthermore, the present study assessed the potential nonlinear relationships and subgroup differences to more comprehensively characterize these associations.
This cross-sectional study utilized data from NHANES, a nationally representative program conducted by the United States Centers for Disease Control and Prevention[10]. NHANES employs a complex, multistage probability sampling design to collect health and nutritional information from the civilian, noninstitutionalized United States population[10].
Data from five consecutive cycles (2005-2014) were included because this period represented the longest continuous timeframe during which the variables required for TyG-ABSI calculation and sleep assessment were collected using consistent protocols. Participants aged ≥ 18 years with complete data on fasting triglycerides, fasting plasma glucose, height, weight, WC, and sleep outcomes were eligible. Participants missing information required to calculate TyG-ABSI or define the sleep outcomes were excluded (Figure 1). After applying these criteria, 12642 participants were included in the final analysis. Appropriate NHANES fasting subsample weights, strata, and primary sampling units were incorporated into the analyses to account for the complex survey design.
Fasting plasma glucose and serum triglyceride concentrations were measured according to standardized NHANES laboratory protocols. Height, weight, and WC were measured by trained health technicians following standardized procedures, and BMI was obtained from the NHANES examination data. In the calculation of ABSI, both WC and height were expressed in centimeters. The TyG index and its derived indices were calculated as follows[7,11,12]: TyG-ABSI = TyG × ABSI.
Assessment of sleep outcomes: Sleep information was obtained from the NHANES Sleep Disorders Questionnaire (SLQ). Two self-reported questions were used to define sleep problem status: (1) “Have you ever told a doctor or other health professional that you have trouble sleeping?” (SLQ050); and (2) “Have you ever been told by a doctor or other health professional that you have a sleep disorder?” (SLQ060). The sleep questions were completed during household interviews conducted by trained personnel using the Computer-Assisted Personal Interviewing system. A participant was considered to have sleep problems when either question was answered affirmatively; participants answering “No” to both questions formed the comparison group.
The two questions captured different aspects of sleep health. One asked whether the participant had ever reported trouble sleeping to a health professional, whereas the other referred to a sleep disorder identified by a health professional. We therefore treated the combined outcome as a broad indicator of self-reported sleep problems rather than as evidence of a clinically confirmed diagnosis. Responses may also have depended on access to healthcare and the likelihood of seeking professional help. To examine whether the findings were driven by either component, the two questions were analyzed separately in sensitivity analyses.
Usual sleep duration was obtained from the question, “How much sleep do you usually get at night on weekdays or workdays?” (SLD010H). The reported number of hours was retained as a continuous variable.
Assessment of depressive symptoms: Depressive symptoms were measured with the nine-item Patient Health Questionnaire. Responses to each item were assigned a score of 0-3, producing a total Patient Health Questionnaire-9 (PHQ-9) score between 0 and 27. Higher scores represented a greater burden of depressive symptoms. The total score was entered as a continuous variable in the exploratory analysis examining whether depressive symptoms acted as a mediator or statistical suppressor in the relationship between TyG-ABSI and sleep duration.
Covariates: Potential confounders were identified from previous studies[3,6,13,14] and from their plausible associations with metabolic health or sleep. The sociodemographic variables were age, sex, race and ethnicity, educational attainment, and poverty income ratio. Race and ethnicity were classified as non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, or other race. Education was grouped as less than high school, high school, or above high school.
Smoking status was categorized as never, former, or current smoking. Alcohol consumption was classified as never, former, mild, moderate, or heavy. The clinical and biochemical covariates comprised systolic and diastolic blood pressure, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, serum creatinine, total bilirubin, total cholesterol, high-density lipoprotein cholesterol, lactate dehydrogenase, and triglycerides. Glucose metabolism was classified as normal glucose regulation, impaired fasting glycemia, impaired glucose tolerance, or diabetes. We also accounted for hyperlipidemia, atherosclerotic cardiovascular disease, congestive heart failure, myocardial infarction, and angina pectoris. Measurements and questionnaire data were collected under the standardized procedures used in NHANES. Missing values for covariates were imputed using multiple imputation by chained equations.
All analyses were conducted using EmpowerStats and R version 4.4.2. NHANES sampling weights, strata, and primary sampling units were incorporated to account for the complex survey design. Continuous variables were presented as survey-weighted means with 95%CIs, and categorical variables as survey-weighted proportions. Between-group differences were assessed using survey-weighted t tests or χ² tests, as appropriate.
TyG-ABSI was analyzed on its original scale as a continuous variable, with estimates reported per 1-unit increase, and categorically in tertiles (T1-T3), with T1 as the reference group. Survey-weighted logistic regression was used to examine its association with the composite sleep-problem outcome. In sensitivity analyses, SLQ050 and SLQ060 were evaluated separately using survey-weighted logistic regression with the same covariate adjustments. Survey-weighted linear regression was used to assess the association between TyG-ABSI and continuous sleep duration, including analyses stratified by sleep-problem status. Model 1 was unadjusted; model 2 was adjusted for age, sex, and race; and model 3 was further adjusted for the socioeconomic, lifestyle, clinical, and laboratory covariates described above.
Restricted cubic spline models were used to assess potential nonlinear associations between TyG-ABSI and sleep duration. When evidence of nonlinearity was identified, two-piecewise linear regression was used to estimate the optimal change point. Linear and two-piecewise models were compared using the Akaike information criterion and log-likelihood ratio test. The distribution of TyG-ABSI was also summarized according to sleep-problem status. Subgroup analyses were conducted by sex, age, and race.
Exploratory mediation analyses were conducted using depressive status and the continuous PHQ-9 score as alternative mediators. Statistical total, direct, and indirect effects were estimated using the mediation package in R, with 5000 bootstrap iterations used to calculate bias-corrected 95%CIs. All tests were two-sided, with P < 0.05 considered statistically significant.
NHANES protocols were approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent. The present study utilized publicly available, de-identified data and was therefore exempt from additional institutional review.
A total of 12642 adults from the NHANES 2005-2014 cycles were included in this study, of whom 972 (7.7% of the analytic sample) were classified as having sleep problems based on an affirmative response to either questionnaire item. As shown in Table 1, compared with participants without sleep problems, those with sleep problems were older, more likely to be non-Hispanic White, and had a less favorable cardiometabolic profile, including higher blood pressure and triglyceride levels and higher weighted prevalences of diabetes mellitus, hyperlipidemia, and atherosclerotic cardiovascular disease (all P < 0.05). They also had higher mean TyG-ABSI values (7.27 vs 6.98) and shorter mean sleep duration (6.44 hours vs 6.93 hours; both P < 0.001). No significant differences were observed in sex, education level, or poverty-income ratio (all P > 0.05).
| Characteristic | Overall, n = 12642 | Sleep problems | P value | |
| No, n = 11670 (92.3%) | Yes, n = 972 (7.7%) | |||
| Age (years) | 46.30 (45.70-46.90) | 45.85 (45.22-46.49) | 51.41 (50.45-52.38) | < 0.0001 |
| Gender | 0.1135 | |||
| Male | 48.79 (47.85-49.73) | 48.48 (47.57-49.39) | 52.32 (47.54-57.05) | |
| Female | 51.21 (50.27-52.15) | 51.52 (50.61-52.43) | 47.68 (42.95-52.46) | |
| Race/ethnicity | < 0.0001 | |||
| Non-Hispanic White | 68.62 (65.62-71.47) | 68.06 (65.03-70.95) | 74.96 (71.30-78.29) | |
| Non-Hispanic Black | 10.59 (9.21-12.15) | 10.56 (9.17-12.15) | 10.84 (9.07-12.92) | |
| Mexican American | 8.69 (7.32-10.28) | 9.03 (7.63-10.65) | 4.77 (3.43-6.62) | |
| Other Hispanic | 5.33 (4.28-6.62) | 5.40 (4.33-6.72) | 4.52 (3.27-6.22) | |
| Other race | 6.78 (5.90-7.77) | 6.94 (6.03-7.98) | 4.90 (3.65-6.54) | |
| Education level | 0.5355 | |||
| Under Highschool | 5.93 (5.31-6.61) | 5.96 (5.35-6.63) | 5.60 (4.44-7.03) | |
| Highschool | 35.55 (33.49-37.67) | 35.71 (33.52-37.95) | 33.80 (30.19-37.61) | |
| Above Highschool | 58.46 (56.20-60.69) | 58.27 (55.91-60.60) | 60.61 (56.54-64.54) | |
| Poverty income ratio | 2.96 (2.88-3.05) | 2.97 (2.88-3.05) | 2.90 (2.72-3.08) | 0.4002 |
| Smoking status | < 0.0001 | |||
| Never | 52.90 (51.21-54.59) | 53.76 (52.14-55.37) | 43.09 (38.20-48.11) | |
| Former | 23.93 (22.66-25.26) | 23.46 (22.16-24.83) | 29.28 (25.79-33.03) | |
| Current | 20.55 (19.14-22.03) | 20.02 (18.60-21.51) | 26.65 (23.38-30.20) | |
| Alcohol use | < 0.0001 | |||
| Never | 10.41 (9.36-11.56) | 10.41 (9.43-11.48) | 10.34 (7.65-13.84) | |
| Former | 14.21 (13.16-15.32) | 13.58 (12.54-14.68) | 21.40 (18.22-24.98) | |
| Mild | 32.51 (30.93-34.13) | 32.64 (31.04-34.29) | 31.00 (26.77-35.57) | |
| Moderate | 15.28 (14.31-16.30) | 15.20 (14.20-16.25) | 16.20 (12.98-20.04) | |
| Heavy | 19.52 (18.48-20.60) | 19.84 (18.72-21.00) | 15.92 (13.07-19.25) | |
| Diastolic blood pressure (mmHg) | 69.17 (68.71-69.62) | 69.09 (68.62-69.55) | 70.09 (69.16-71.03) | 0.0387 |
| Systolic blood pressure (mmHg) | 120.09 (119.56-120.62) | 119.87 (119.34-120.41) | 122.52 (121.43-123.60) | < 0.0001 |
| ALT (U/L) | 25.51 (25.11-25.91) | 25.36 (24.93-25.80) | 27.20 (25.64-28.77) | 0.0340 |
| AST (U/L) | 25.62 (25.25-25.99) | 25.50 (25.11-25.88) | 26.98 (25.38-28.58) | 0.0849 |
| Blood urea nitrogen (mg/dL) | 12.87 (12.73-13.00) | 12.78 (12.64-12.91) | 13.92 (13.50-14.34) | < 0.0001 |
| Creatinine (mg/dL) | 0.88 (0.88-0.89) | 0.88 (0.87-0.89) | 0.94 (0.91-0.96) | < 0.0001 |
| Bilirubin, total (mg/dL) | 0.77 (0.76-0.78) | 0.77 (0.76-0.78) | 0.73 (0.70-0.75) | 0.0025 |
| Cholesterol, total (mg/dL) | 194.37 (193.25-195.49) | 194.57 (193.40-195.74) | 192.06 (188.41-195.72) | 0.2050 |
| Direct HDL-cholesterol (mg/dL) | 54.12 (53.65-54.58) | 54.42 (53.94-54.91) | 50.65 (49.47-51.83) | < 0.0001 |
| LDH (U/L) | 127.54 (126.82-128.26) | 127.16 (126.38-127.94) | 131.85 (129.49-134.20) | 0.0006 |
| Triglycerides (mg/dL) | 128.87 (125.50-132.24) | 126.69 (123.52-129.86) | 153.82 (136.60-171.05) | 0.0028 |
| Diabetes mellitus | < 0.0001 | |||
| No | 68.43 (66.94-69.89) | 69.61 (68.09-71.09) | 55.09 (51.28-58.84) | |
| Diabetes mellitus | 14.99 (13.99-16.04) | 13.80 (12.83-14.83) | 28.45 (24.85-32.34) | |
| Impaired fasting glycaemia | 7.88 (7.20-8.61) | 7.79 (7.12-8.53) | 8.80 (6.81-11.31) | |
| Impaired glucose tolerance | 8.71 (8.06-9.40) | 8.80 (8.11-9.54) | 7.66 (5.82-10.01) | |
| Hyperlipidemia | < 0.0001 | |||
| No | 28.85 (27.57-30.17) | 29.74 (28.40-31.12) | 18.67 (15.96-21.72) | |
| Yes | 71.15 (69.83-72.43) | 70.26 (68.88-71.60) | 81.33 (78.28-84.04) | |
| Atherosclerotic cardiovascular disease | < 0.0001 | |||
| No | 91.87 (91.02-92.64) | 92.79 (92.02-93.49) | 81.61 (78.46-84.40) | |
| Yes | 8.13 (7.36-8.98) | 7.21 (6.51-7.98) | 18.39 (15.60-21.54) | |
| Congestive heart failure | < 0.0001 | |||
| No | 97.46 (97.01-97.83) | 97.90 (97.52-98.22) | 92.52 (90.00-94.45) | |
| Yes | 2.54 (2.17-2.99) | 2.10 (1.78-2.48) | 7.48 (5.55-10.00) | |
| Myocardial infarction | < 0.0001 | |||
| No | 96.36 (95.81-96.84) | 96.84 (96.34-97.28) | 90.97 (87.90-93.32) | |
| Yes | 3.64 (3.16-4.19) | 3.16 (2.72-3.66) | 9.03 (6.68-12.10) | |
| Angina pectoris | < 0.0001 | |||
| No | 97.75 (97.39-98.07) | 98.08 (97.72-98.37) | 94.17 (92.42-95.54) | |
| Yes | 2.25 (1.93-2.61) | 1.92 (1.63-2.28) | 5.83 (4.46-7.58) | |
| Sleep duration (hours) | 6.89 (6.85-6.93) | 6.93 (6.89-6.97) | 6.44 (6.32-6.57) | < 0.0001 |
| TyG-ABSI | 7.01 (6.98-7.03) | 6.98 (6.96-7.01) | 7.27 (7.20-7.33) | < 0.0001 |
Table 2 showed that, among participants with sleep problems, higher TyG-ABSI tertiles were associated with older age, a higher proportion of men, higher systolic blood pressure, alanine aminotransferase and triglyceride levels, lower high-density lipoprotein cholesterol levels, and higher weighted prevalences of diabetes mellitus, hyperlipidemia, and atherosclerotic cardiovascular disease (all P < 0.001). Mean sleep duration increased numerically across TyG-ABSI tertiles from 6.20 hours to 6.61 hours, although the difference was not statistically significant (P = 0.0904).
| Characteristic | TyG-ABSI | P value | ||
| T1 (≤ 6.66) | T2 (< 7.34) | T3 (≥ 7.34) | ||
| Participant | 217 | 334 | 421 | |
| Age (years) | 42.45 (39.87-45.03) | 51.05 (49.13-52.98) | 56.32 (54.83-57.80) | < 0.0001 |
| Gender | < 0.0001 | |||
| Male | 31.76 (24.00-40.67) | 50.49 (44.49-56.49) | 64.37 (57.28-70.87) | |
| Female | 68.24 (59.33-76.00) | 49.51 (43.51-55.51) | 35.63 (29.13-42.72) | |
| Race/ethnicity | < 0.0001 | |||
| Non-Hispanic White | 67.78 (61.29-73.66) | 71.86 (66.02-77.04) | 81.14 (76.12-85.30) | |
| Non-Hispanic Black | 20.76 (16.08-26.39) | 11.93 (9.14-15.42) | 4.87 (3.30-7.12) | |
| Mexican American | 5.04 (2.85-8.76) | 4.72 (2.78-7.91) | 4.67 (2.96-7.32) | |
| Other Hispanic | 3.49 (1.98-6.06) | 5.47 (3.59-8.25) | 4.30 (2.71-6.78) | |
| Other race | 2.92 (1.27-6.59) | 6.03 (3.71-9.65) | 5.02 (3.01-8.26) | |
| Education level | 0.1148 | |||
| Under Highschool | 1.66 (0.67-4.05) | 6.65 (3.93-11.03) | 6.78 (4.92-9.27) | |
| Highschool | 30.67 (23.23-39.28) | 33.05 (26.69-40.10) | 36.00 (29.80-42.71) | |
| Above Highschool | 67.66 (59.09-75.19) | 60.30 (52.28-67.81) | 57.21 (50.29-63.87) | |
| Poverty income ratio | 2.95 (2.71-3.18) | 2.97 (2.69-3.25) | 2.82 (2.57-3.08) | 0.6353 |
| Smoke | < 0.0001 | |||
| Never | 55.46 (46.85-63.76) | 45.93 (37.86-54.22) | 34.44 (27.84-41.69) | |
| Former | 20.01 (13.87-27.97) | 24.09 (18.92-30.14) | 38.22 (32.24-44.58) | |
| Now | 21.87 (16.56-28.31) | 28.91 (23.41-35.12) | 27.30 (22.15-33.14) | |
| Alcohol use | 0.0017 | |||
| Never | 11.77 (7.24-18.58) | 8.87 (5.15-14.87) | 10.79 (7.78-14.78) | |
| Former | 10.59 (6.51-16.77) | 22.62 (17.49-28.73) | 26.00 (20.30-32.65) | |
| Mild | 26.14 (19.63-33.89) | 31.14 (23.17-40.41) | 33.38 (27.05-40.38) | |
| Moderate | 26.38 (18.33-36.40) | 15.40 (10.62-21.79) | 11.61 (7.88-16.77) | |
| Heavy | 18.06 (12.16-25.96) | 18.28 (13.49-24.29) | 12.92 (9.08-18.06) | |
| Diastolic blood pressure (mmHg) | 68.90 (67.17-70.64) | 70.05 (68.49-71.62) | 70.73 (69.02-72.45) | 0.3273 |
| Systolic blood pressure (mmHg) | 117.80 (115.78-119.82) | 120.46 (118.16-122.76) | 126.59 (124.74-128.43) | < 0.0001 |
| ALT (U/L) | 22.39 (20.31-24.47) | 25.33 (23.46-27.20) | 31.22 (28.25-34.19) | 0.0001 |
| AST (U/L) | 23.96 (21.77-26.14) | 26.46 (23.22-29.70) | 28.97 (26.70-31.24) | 0.0134 |
| Blood urea nitrogen (mg/dL) | 13.16 (12.32-13.99) | 13.11 (12.53-13.70) | 14.96 (14.34-15.58) | < 0.0001 |
| Creatinine (mg/dL) | 0.88 (0.85-0.91) | 0.91 (0.88-0.95) | 0.99 (0.94-1.03) | 0.0002 |
| Bilirubin, total (mg/dL) | 0.73 (0.67-0.79) | 0.72 (0.68-0.76) | 0.73 (0.68-0.77) | 0.9687 |
| Cholesterol, total (mg/dL) | 181.50 (174.02-188.98) | 194.00 (188.25-199.75) | 195.96 (189.73-202.20) | 0.0222 |
| Direct HDL-cholesterol (mg/dL) | 59.62 (56.80-62.45) | 53.43 (51.24-55.63) | 43.80 (42.29-45.32) | < 0.0001 |
| LDH (U/L) | 129.37 (124.38-134.37) | 132.71 (128.40-137.03) | 132.43 (128.78-136.08) | 0.5165 |
| Triglycerides (mg/dL) | 73.57 (66.58-80.55) | 114.43 (107.92-120.94) | 226.72 (190.74-262.69) | < 0.0001 |
| Diabetes mellitus | < 0.0001 | |||
| No | 83.02 (77.27-87.55) | 63.15 (57.00-68.90) | 34.35 (28.13-41.17) | |
| Diabetes mellitus | 9.42 (6.15-14.16) | 13.91 (9.85-19.29) | 49.77 (43.02-56.53) | |
| Impaired fasting glycaemia | 2.03 (0.80-5.06) | 12.03 (7.97-17.76) | 9.71 (6.78-13.73) | |
| Impaired glucose tolerance | 5.53 (2.91-10.27) | 10.91 (7.19-16.22) | 6.16 (3.72-10.04) | |
| Hyperlipidemia | < 0.0001 | |||
| No | 42.98 (35.28-51.03) | 18.26 (13.59-24.08) | 6.47 (3.95-10.44) | |
| Yes | 57.02 (48.97-64.72) | 81.74 (75.92-86.41) | 93.53 (89.56-96.05) | |
| Atherosclerotic cardiovascular disease | 0.0001 | |||
| No | 91.71 (86.19-95.14) | 85.37 (79.93-89.52) | 73.59 (66.71-79.48) | |
| Yes | 8.29 (4.86-13.81) | 14.63 (10.48-20.07) | 26.41 (20.52-33.29) | |
| Congestive heart failure | 0.0361 | |||
| No | 97.68 (91.30-99.41) | 92.50 (88.18-95.32) | 89.97 (85.65-93.09) | |
| Yes | 2.32 (0.59-8.70) | 7.50 (4.68-11.82) | 10.03 (6.91-14.35) | |
| Myocardial infarction | 0.0001 | |||
| No | 96.82 (93.77-98.40) | 92.55 (88.00-95.47) | 86.79 (81.91-90.51) | |
| Yes | 3.18 (1.60-6.23) | 7.45 (4.53-12.00) | 13.21 (9.49-18.09) | |
| Angina pectoris | 0.0004 | |||
| No | 98.25 (95.92-99.26) | 95.83 (92.84-97.60) | 90.84 (86.86-93.71) | |
| Yes | 1.75 (0.74-4.08) | 4.17 (2.40-7.16) | 9.16 (6.29-13.14) | |
| Sleep duration (hours) | 6.20 (5.90-6.49) | 6.40 (6.20-6.59) | 6.61 (6.40-6.82) | 0.0904 |
Association between the TyG-ABSI and sleep problems
As shown in Table 3, higher TyG-ABSI was associated with greater odds of sleep problems. In the fully adjusted model, each 1-unit increase in TyG-ABSI was associated with 17% higher odds of sleep problems [odds ratio (OR) = 1.17; 95%CI: 1.06-1.30; P = 0.002]. Sensitivity analyses presented in Supplementary Table 1 showed a significant association with SLQ050-defined trouble sleeping (OR = 1.19; 95%CI: 1.07-1.31; P = 0.001), but not with the SLQ060-defined sleep-disorder outcome (OR = 1.04; 95%CI: 0.89-1.20; P = 0.648).
| Variables | Model 1 | Model 2 | Model 3 | |||
| OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | |
| Sleep problem | 1.335 (1.269-1.404) | < 0.001 | 1.308 (1.225-1.395) | < 0.001 | 1.173 (1.063-1.295) | 0.0019 |
As shown in Table 4, survey-weighted linear regression analyses identified a positive association between TyG-ABSI and sleep duration among participants with sleep problems. In the fully adjusted model (model 3), each 1-unit increase in TyG-ABSI was associated with a 0.31-hour longer sleep duration (β = 0.31; 95%CI: 0.05-0.57; P = 0.020). In contrast, no significant association was observed among participants without sleep problems (β = 0.00; 95%CI: -0.06 to 0.07; P = 0.902). These estimates represent average associations across the observed TyG-ABSI distribution.
| Group | Participant with sleep disorder | Participant without sleep disorder | ||||
| Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | Model 3 | |
| Total | Ref | 0.13 (-0.02 to 0.29) 0.0864 | 0.31 (0.05-0.57) 0.0201 | Ref | -0.03 (-0.07 to 0.01) 0.1213 | 0.00 (-0.06 to 0.07) 0.9024 |
| Sex | ||||||
| Male | Ref | 0.06 (-0.15 to 0.27) 0.5742 | 0.12 (-0.27 to 0.50) 0.5523 | Ref | -0.10 (-0.16 to -0.04) 0.0004 | -0.05 (-0.15 to 0.05) 0.3490 |
| Female | Ref | 0.20 (-0.03 to 0.43) 0.0864 | 0.50 (0.10-0.90) 0.0140 | Ref | 0.03 (-0.02 to 0.08) 0.3060 | 0.05 (-0.03 to 0.13) 0.2544 |
| Age | ||||||
| ≥ 18 | Ref | 0.18 (-0.15 to 0.52) 0.2870 | 0.21 (-0.51 to 0.93) 0.5634 | Ref | -0.02 (-0.08 to 0.04) 0.5068 | -0.08 (-0.19 to 0.03) 0.1765 |
| 40-59 | Ref | 0.04 (-0.20 to 0.27) 0.7675 | 0.47 (0.03-0.91) 0.0391 | Ref | -0.07 (-0.13 to -0.01) 0.0249 | 0.00 (-0.10 to 0.10) 0.9967 |
| ≥ 60 | Ref | 0.22 (-0.03 to 0.47) 0.0841 | -0.01 (-0.43 to 0.41) 0.9745 | Ref | 0.00 (-0.07 to 0.07) 0.9678 | 0.06 (-0.06 to 0.17) 0.3133 |
| Ethnicity | ||||||
| Non-Hispanic White | Ref | 0.04 (-0.16 to 0.23) 0.7078 | 0.03 (-0.31 to 0.37) 0.8441 | Ref | -0.07 (-0.12 to -0.01) 0.0130 | -0.03 (-0.12 to 0.06) 0.5677 |
| Non-Hispanic Black | Ref | 0.14 (-0.22 to 0.50) 0.4427 | 0.20 (-0.49 to 0.89) 0.5678 | Ref | -0.09 (-0.18 to -0.00) 0.0402 | -0.04 (-0.20 to 0.13) 0.6672 |
| Mexican American | Ref | 0.59 (0.08-1.09) 0.0258 | 1.35 (0.26-2.44) 0.0196 | Ref | 0.15 (0.06-0.23) 0.0009 | 0.08 (-0.08 to 0.24) 0.3360 |
| Other Hispanic | Ref | 0.05 (-0.61 to 0.70) 0.8924 | 1.60 (-0.16 to 3.36) 0.0848 | Ref | 0.11 (-0.02 to 0.24) 0.0868 | 0.15 (-0.07 to 0.37) 0.1726 |
| Other race | Ref | 0.44 (-0.19 to 1.08) 0.1790 | 0.78 (-0.43 to 1.98) 0.2339 | Ref | -0.09 (-0.20 to 0.03) 0.1436 | 0.11 (-0.11 to 0.33) 0.3373 |
Among participants with sleep problems, positive associations were observed in females (β = 0.50; 95%CI: 0.10-0.90; P = 0.014), participants aged ≥ 40 years (β = 0.47; 95%CI: 0.03-0.91; P = 0.039), and Mexican American participants (β = 1.35; 95%CI: 0.26-2.44; P = 0.020). No significant associations were observed in the remaining demographic subgroups or among participants without sleep problems. Mean sleep duration also did not differ significantly across TyG-ABSI tertiles among participants with sleep problems (Table 2; P = 0.090). Potential variation across the TyG-ABSI distribution was further evaluated in the subsequent nonlinear analyses.
Restricted cubic spline analysis suggested a nonlinear association between TyG-ABSI and sleep duration in the overall population (P for nonlinearity < 0.05; Figure 2). Sleep duration generally increased across the lower and intermediate ranges of TyG-ABSI, whereas the association attenuated at higher values. Exploratory two-piecewise linear regression identified a data-derived inflection point at approximately 8.6; detailed threshold estimates are provided in Supplementary Table 2.
Statistical mediation analyses were conducted using depressive status as a binary mediator and the PHQ-9 score as a continuous mediator (Figure 3 and Supplementary Table 3). For depressive status, the total effect of TyG-ABSI on sleep duration was positive (estimate = 0.247, 95%CI: 0.052-0.451, P = 0.014). The indirect effect was negative (estimate = -0.046, 95%CI: -0.087 to -0.009, P = 0.002), whereas the direct effect was positive and larger than the total effect (estimate = 0.293, 95%CI: 0.092-0.491, P = 0.002).
Similar results were observed when the continuous PHQ-9 score was used. The total effect was 0.251 (95%CI: 0.051-0.454, P = 0.014), the indirect effect was -0.044 (95%CI: -0.082 to -0.016, P < 0.001), and the direct effect was 0.296 (95%CI: 0.085-0.497, P = 0.002). In both analyses, the indirect effect was opposite in direction to the total and direct effects, indicating a statistical suppression pattern rather than conventional mediation.
The present study suggests that TyG-ABSI is associated with sleep outcomes in a nonlinear and threshold-dependent manner, indicating a complex relationship between metabolic status and sleep regulation. As an index that integrates IR and central adiposity, TyG-ABSI may capture metabolic abnormalities that coexist with altered sleep, while sleep disturbances may be accompanied by adverse metabolic changes.
TyG-ABSI was positively associated with the composite definition of sleep problems. However, when the two questionnaire items were separately examined, the association remained for SLQ050, but was not observed for SLQ060 (Table 3 and Supplementary Table 1). This difference indicates that the overall result was mainly driven by participants who reported trouble sleeping to a health professional. Although the two items are related, these do not measure the same outcome. SLQ050 reflects a relatively broad sleep complaint, and may include insomnia symptoms, as well as other nonspecific sleep difficulties. By comparison, SLQ060 refers to a sleep disorder identified by a health professional, and may include several clinically distinct conditions, such as sleep apnea, restless legs syndrome, and narcolepsy. Combining these two items allowed a wider range of sleep problems to be identified, and grouped conditions with different underlying mechanisms.
However, the interpretation of these outcomes requires caution, because both were based on self-report, and depended on previous contact with a health professional. Participants with easier access to medical care, greater awareness of sleep symptoms, or a stronger tendency to seek help may have been more likely to report a positive response. This may have introduced ascertainment bias[15,16]. In addition, the absence of polysomnography, actigraphy, or other objective assessments meant that individual sleep disorders cannot be distinguished. The lack of an association with SLQ060 may thereby be correlated to its more restrictive definition, the smaller number of positive responses, and the clinical diversity of the included disorders. Furthermore, it should not be taken as evidence that metabolic abnormalities are unrelated to clinically recognized sleep disorders.
Among participants reporting sleep problems, higher TyG-ABSI was associated with longer self-reported sleep duration, with a possible change in slope at approximately 8.6 (Table 4, Figure 2, and Supplementary Table 2). The association was positive below this value but was no longer statistically significant above it. Because 8.6 corresponded to approximately the 90.8% of the overall distribution, relatively few participants were included in the upper range, and the negative coefficient above the turning point should not be interpreted as evidence of a true reversal. This turning point was derived from the observed data and was not based on a prespecified biological hypothesis. Previous studies have also reported data-derived TyG-ABSI inflection points, including 6.42 for incident stroke[17] and approximately 7.75 for carotid plaque[18]; however, these estimates were obtained for different outcomes and populations and therefore do not externally validate the value of 8.6 for sleep duration. The association below 8.6 may also reflect residual confounding from unmeasured or incompletely measured clinical and lifestyle factors, including undiagnosed obstructive sleep apnea, comorbidity burden, medication use, and physical activity. Moreover, longer reported sleep duration in participants with sleep problems may indicate fatigue, prolonged time in bed, non-restorative sleep, or compensatory sleep rather than better sleep health[13,19]. Thus, 8.6 should be considered an exploratory statistical finding rather than a clinically applicable cut-off.
Subgroup analyses identified different turning points and association patterns across demographic groups (Supplementary Table 2). However, these estimates were based on smaller samples and, in some groups, sparse observations at high TyG-ABSI levels. They should therefore not be interpreted as subgroup-specific biological thresholds and require confirmation in larger prospective studies.
Our analysis identified depressive symptoms as a suppressor rather than a conventional mediator of the association between TyG-ABSI and sleep duration. The indirect effect through depressive symptoms was negative, whereas the
Previous studies support close relationships among metabolic dysfunction, depressive symptoms, and sleep, although the direction of these relationships is not uniform[6,20,21]. Liu et al[6] recently analyzed NHANES and CHARLS data and reported conventional mediation by depressive symptoms in the associations of the TyG index and its combined indices with sleep disorders. In contrast, our analysis focused on self-reported sleep duration among participants with sleep problems and incorporated abdominal body-shape information through TyG-ABSI. Differences in the exposure construct, study population, sleep outcome, and statistical pathway may partly explain the contrasting mediation patterns. One possible interpretation is that depressive symptoms alter reported sleep duration through changes in sleep continuity, perceived sleep quality, fatigue, or time spent in bed, thereby obscuring rather than simply transmitting the observed metabolic-sleep association[21]. Nevertheless, this explanation remains speculative and should be tested in longitudinal studies with objective sleep measurements.
Our findings provide preliminary support for a metabolic-mood-sleep framework, but not for a fully established biological axis. Depressive symptoms were the only psychological factor examined, and the analysis did not include other potential pathways linking metabolic dysfunction to sleep. Inflammatory markers such as C-reactive protein and interleukin-6, oxidative stress indicators, neuroendocrine and autonomic factors, and gut microbiota-related metabolites may all contribute to this relationship[21,22]. Future longitudinal studies incorporating repeated metabolic and mood assessments, objective sleep measurements, and these biological markers are needed to test and refine a more comprehensive multidimensional pathway model.
The association between TyG-ABSI and sleep duration differed across several demographic subgroups, although these findings were exploratory (Figure 2 and Supplementary Table 2). A clearer positive association was observed among women and middle-aged adults. Differences in body-fat distribution, insulin sensitivity, and sleep physiology may contribute to these patterns[23]. The association between TyG-ABSI and sleep duration differed across several demographic subgroups, although these findings were exploratory (Figure 2 and Supplementary Table 2). A clearer positive association was observed among women and middle-aged adults. Differences in body-fat distribution, insulin sensitivity, and sleep physiology may contribute to these patterns[24,25]. However, information on menopausal status and sex hormone levels was unavailable. The different curve patterns observed in men and women at higher TyG-ABSI values may also reflect small numbers within the upper ranges and subgroup-specific, data-derived turning points.
A relatively large coefficient was observed among Mexican American participants, but this estimate was based on a small subgroup and had a wide confidence interval (β = 1.35; 95%CI: 0.26-2.44; Supplementary Table 2). Previous studies have reported differences in sleep duration and related behavioral factors across racial and ethnic groups[26-28], but the present data do not allow these mechanisms to be distinguished. Similar instability was seen among other Hispanic participants and in the subgroup-specific spline curves. These results may therefore be attributable to sparse data, residual confounding, multiple comparisons, or chance and should be regarded as hypothesis-generating rather than evidence of race-specific or ethnicity-specific associations.
Several limitations should be acknowledged. First, the cross-sectional design does not allow the temporal sequence or causal relationship between TyG-ABSI and sleep outcomes to be determined. Second, sleep problems and sleep duration were self-reported, and the composite outcome combined trouble sleeping with a sleep disorder recognized by a health professional. Although this definition identified a broader range of sleep problems, it may also have introduced recall bias, healthcare-related ascertainment bias, and heterogeneity across sleep conditions. The lack of polysomnography or actigraphy further prevented the identification of specific sleep disorders. Third, residual confounding remains possible despite adjustment for multiple covariates, particularly because information on undiagnosed sleep apnea, medication use, diet, physical activity, and shift work was incomplete or unavailable. Fourth, the estimated turning point of 8.6 was derived from the observed data and was located near the upper end of the TyG-ABSI distribution. Estimates above this value and in several demographic subgroups were based on relatively small numbers and should therefore be interpreted cautiously. Finally, depressive symptoms were the only psychological factor examined in the suppression analysis. Longitudinal studies using objective sleep measurements are needed to confirm these findings, clarify their direction, and compare TyG-ABSI directly with established metabolic and anthropometric indices before its additional clinical value can be determined.
In this cross-sectional study, we found that participants with higher TyG-ABSI were more likely to report sleep problems. Among those with sleep problems, higher TyG-ABSI was also associated with longer sleep duration. The results differed according to sleep definition and demographic subgroup, and depressive symptoms appeared to have a suppression effect rather than a conventional mediation effect. Further longitudinal research is needed to determine the direction of these associations and their potential clinical relevance.
We sincerely thank all staff members involved in the National Health and Nutrition Examination Survey for their dedicated efforts in data collection and management. We are also deeply grateful to all participants for their valuable contributions.
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