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Triglyceride-glucose index is associated with poor sleep quality in apparently healthy subjects: A cross-sectional study

ABSTRACT

Objectives:

We aimed to evaluate the association between the triglyceride glucose index (TyG index) and sleep quality and to establish a cut-off value for the TyG index based on the prevalence of subjects with insulin resistance (IR).

Materials and methods:

This cross-sectional study involved Brazilian health professionals (20-59 years). A total of 138 subjects answered the Pittsburgh Sleep Quality questionnaire to evaluate sleep quality. They were categorized into two groups: good sleep quality (global score ≤ 5 points) and poor sleep quality (global score ≥ 6 points). Also, we classified the subjects as having a high (>8.08 or >4.38) or low TyG index (≤8.08 or ≤4.38).

Results:

The majority of the subjects (70%) with high TyG index values (>8.08 or >4.38) reported poor sleep quality (p ≤ 0.001). Those with poor sleep quality had a 1.44-fold higher prevalence of IR (TyG index >8.08 or >4.38) compared to those with good sleep quality, regardless of sex, total cholesterol, LDL/HDL ratio, insulin, complement C3, CRP, and adiponectin (p ≤ 0.001).

Conclusion:

Our data showed a positive and significant association between the TyG index and poor sleep quality. Thus, these findings support the association between poor sleep quality and IR.

Keywords:
Cardiometabolic risk; insulin resistance; ROC curve; sleep quality; TyG index

INTRODUCTION

Insulin resistance (IR) is a condition in which the molecular mechanisms of insulin uptake and degradation are impaired, leading to the development of type 2 diabetes (T2D) and cardiovascular diseases in the long term (11 Goldstein BJ. Insulin resistance as the core defect in type 2 diabetes mellitus. Am J Cardiol. 2002;90(5 Suppl):3-10.33 Tokarz VL, MacDonald PE, Klip A. The cell biology of systemic insulin function. J Cell Biol. 2018;217(7):2273-89.). More than 500 million individuals were living with T2D globally in 2018, and it is expected to have a high prevalence in low-income countries (44 Kaiser AB, Zhang N, der Pluijm WR van. Global Prevalence of Type 2 Diabetes over the Next Ten Years (2018-2028). Diabetes. 2018;67(Suppl 1).). Adults with diabetes have a higher risk for all-cause morbidity and mortality because they often present other major comorbidities such as cardiovascular, chronic lower respiratory, and kidney diseases (55 Li S, Wang J, Zhang B, Li X, Liu Y. Diabetes Mellitus and Cause-Specific Mortality: A Population-Based Study. Diabetes Metab J. 2019;43(3):319-41.). These complications are mediated by several inflammatory markers, including cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6), that trigger an inflammatory response (66 Rehman K, Akash MSH. Mechanisms of inflammatory responses and development of insulin resistance: how are they interlinked? J Biomed Sci. 2016;23(1):87.88 Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122.). Since IR can contribute to the pathogenesis of diabetes and its related comorbidities (33 Tokarz VL, MacDonald PE, Klip A. The cell biology of systemic insulin function. J Cell Biol. 2018;217(7):2273-89.,99 Rachdaoui N. Insulin: The Friend and the Foe in the Development of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020;21(5):1770.,1010 Arnold SE, Arvanitakis Z, Macauley-Rambach SL, Koenig AM, Wang HY, Ahima RS, et al. Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol. 2018;14(3):168-81.), understanding its mechanisms is of great importance.

Poor sleep quality is a common issue in modern society for several reasons, and growing evidence has linked it with IR (1111 Spiegel K, Knutson K, Leproult R, Tasali E, Van Cauter E. Sleep loss: A novel risk factor for insulin resistance and Type 2 diabetes. J Appl Physiol (1985). 2005;99(5):2008-19.1414 Li M, Li D, Tang Y, Meng L, Mao C, Sun L, et al. Effect of Diabetes Sleep Education for T2DM Who Sleep After Midnight: A Pilot Study from China. Metab Syndr Relat Disord. 2018;16(1):13-9.). For example, as a direct consequence of the COVID-19 pandemic, sleep problems have affected approximately 40% of people in general and healthcare populations (1515 Jahrami H, BaHammam AS, AlGahtani H, Ebrahim A, Faris M, AlEid K, et al. The examination of sleep quality for frontline healthcare workers during the outbreak of COVID-19. Sleep Breath. 2021;25(1):503-11.1818 Abdulah DM, Musa DH. Insomnia and stress of physicians during COVID-19 outbreak. Sleep Med X. 2020;2:100017.). While short sleep duration and metabolic impairments are strongly associated (1919 Stamatakis KA, Punjabi NM. Effects of sleep fragmentation on glucose metabolism in normal subjects. Chest. 2010;137(1):95-101.), their mechanisms remain largely unknown. There is some support for the roles of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic activation in glucose impairments and IR due to inadequate sleep quality (2020 Schmid SM, Hallschmid M, Jauch-Chara K, Wilms B, Lehnert H, Born J, et al. Disturbed Glucoregulatory Response to Food Intake After Moderate Sleep Restriction. Sleep. 2011;34(3):371-7.,2121 Tyrka A, Walters O, Price L, Anderson G, Carpenter L. Altered Response to Neuroendocrine Challenge Linked to Indices of the Metabolic Syndrome in Healthy Adults. Horm Metab Res. 2012;44(07):543-9.).

The triglyceride glucose index (TyG index) has been extensively used as a reliable marker for IR, expressed as the product of triglyceride and glucose levels (2222 Vasques ACJ, Novaes FS, de Oliveira M da S, Matos Souza JR, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: A hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011;93(3):8-10.,2323 Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299-304.). A meta-analysis showed a significant association between higher TyG values and T2D risk (2424 da Silva A, Caldas APS, Rocha DMUP, Bressan J. Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: A systematic review and meta-analysis of cohort studies. Prim Care Diabetes. 2020;14(6):584-93.). Recently, two cross-sectional studies have associated TyG with obstructive sleep apnea (OSA), a sleep breathing disorder that often involves IR (2525 Kang HH, Kim SW, Lee SH. Association between triglyceride glucose index and obstructive sleep apnea risk in Korean adults: A cross-sectional cohort study. Lipids Health Dis. 2020;19(1):1-8.,2626 Bikov A, Frent SM, Meszaros M, Kunos L, Mathioudakis AG, Negru AG, et al. Triglyceride-Glucose Index in Non-Diabetic, Non-Obese Patients with Obstructive Sleep Apnoea. J Clin Med. 2021;10(9):1932.). However, the relationship between TyG and sleep quality has not been previously studied.

Given the limited evidence on the association between TyG index and sleep quality, we aimed to establish a cutoff value for the TyG index based on the prevalence of IR patients under the homeostatic model assessment of IR (HOMA-IR) and evaluate the association between TyG index and sleep quality. We hypothesize that higher TyG index values are positively associated with poor sleep quality.

MATERIALS AND METHODS

This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. The STROBE checklist used is included in the Supplementary Material.

Study design and subjects

We analyzed data collected by a previous cross-sectional study in Viçosa, Brazil, that involved Brazilian health professionals between 20 and 59 years old (2727 De Carvalho Vidigal F, Ribeiro AQ, Babio N, Salas-Salvadó J, Bressan J. Prevalence of metabolic syndrome and pre-metabolic syndrome in health professionals: LATINMETS Brazil study. Diabetol Metab Syndr. 2015;7(1):1-9.). All subjects signed a consent form previously approved by the Human Research Ethics Committee of the Federal University of Viçosa (Ref. No. 005/2011; Viçosa, Brazil) under the principles of the Declaration of Helsinki. This study is not registered in “Plataforma Brasil” because it was approved on February 18, 2011, before “Plataforma Brasil” came into effect on January 2, 2012.

To be eligible for this study, health professionals (doctors, nurses, nutritionists, physical trainers, physiotherapists, dentists, pharmacists, biochemists, and psychologists) must work in health facilities or higher education institutions, and students must be in their last two years of courses in a health-related area. The recruitment was performed via phone calls, website disclosures, social networks, local radio, and pamphlets. Individuals who were pregnant, lactating, using corticosteroids, using antibiotics, had a cancer diagnosis within the last three years, or had any serious illness that required hospitalization at the time of this study, were excluded. Individuals who could not follow the measuring protocols such as weighing, blood pressure, or performing blood collection were also excluded. All data were collected between January 2012 and July 2013.

As a baseline, we surveyed 976 healthcare professionals in Viçosa, Brazil. The calculated sample size was 223 subjects, with a 95% confidence interval (CI), 5% sampling error, and an expected metabolic syndrome prevalence of 25%. However, our sample size is contingent on the subset of participants who had completed the Pittsburgh sleep quality index (PSQI) questionnaire (N = 138; Figure 1). Supplementary Table 1 shows the characteristics of participants who had and had not completed the PSQI questionnaire. Subjects who had not completed the questionnaire showed higher TyG index and very-low-density lipoprotein cholesterol (VLDL-c) values than those who completed the PSQI questionnaire.

Figure 1
Flowchart of origin of data used in this study.

Assuming a prevalence of TyG index > 8.08 or > 4.38 in the exposed (poor sleep quality) and non-exposed (good sleep quality) groups, respectively, the analysis had 89.49% power to detect a difference of this magnitude or larger, determined using the OpenEpi online software (2828 Dean AG, Sullivan KM, Soe MM. OpenEpi: Open Source Epidemiologic Statistics for Public Health [Internet]. 2013 [cited 2022 Jun 12]. Available from: www.OpenEpi.com
www.OpenEpi.com...
).

Collected data

Sleep Quality (Exposure)

Sleep quality was assessed by the adapted and validated Brazilian version of the PSQI (2929 Bertolazi AN, Fagondes SC, Hoff LS, Dartora EG, da Silva Miozzo IC, de Barba MEF, et al. Validation of the Brazilian Portuguese version of the Pittsburgh Sleep Quality Index. Sleep Med. 2011;12(1):70-5.). The information refers to the last month and contains nineteen items that cover seven components: subjective sleep quality (contentment at daily sleep), sleep latency (extended sleep onset time), sleep duration, habitual sleep efficiency (proportion of hours slept relative to total hours in bed), sleep disorders (disruption of sleep), use of sleeping medication, and daytime dysfunction (difficulty staying awake during social activities) (3030 Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research. 1989;28(2):193-213.). We assessed the global PSQI score from 0 to 21 points (3030 Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research. 1989;28(2):193-213.). Finally, volunteers were categorized into two groups: good sleep quality (global PSQI score ≤ 5) or poor sleep quality (global PSQI score ≥ 6).

Dietary intake assessment and lifestyle

Dietary intake was assessed by a semi-quantitative food frequency questionnaire, validated for a Spanish population, and adapted for Brazilian citizens, with 136 food items (3131 Fernández-Ballart JD, Piñol JL, Zazpe I, Corella D, Carrasco P, Toledo E, et al. Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain. Br J Nutr. 2010;103(12):1808-16.). Nutrient intake was estimated using ad hoc computer software specifically developed for this aim. In addition, updated information from Brazilian food composition tables was considered. A trained professional was responsible for administering the questionnaire to minimize potential bias. To evaluate physical activity, we used the international physical activity questionnaire (IPAQ), which is validated for the Brazilian population (3232 Matsudo S, Araújo T, Matsudo V, Andrade D, Andrade E, Oliveira LC, et al. Questionário internacional de atividade física (IPAQ): estudo de validade e reprodutibilidade no Brasil. Rev Bras Ativ Física Saúde. 2001;6(2):5-18.). Smoking habit was determined by asking the participants whether they were smokers, former smokers, or nonsmokers.

Anthropometric, body composition, and blood pressure

Weight and height were measured to calculate body mass index (BMI) by dividing the weight (kg) by height (m) squared. Overweight was classified as BMI ≥ 25 kg/m². Waist circumference (WC) was measured at the midpoint between the last rib and the iliac crest using a flexible and inelastic tape measure. Hip circumference (HC) was measured in the greater protuberance in the gluteal region. The waist-to-height ratio was calculated as the ratio of WC (cm) and height (cm). Body composition was evaluated with a BMI 310 Bioimpedance Analyzer (Biodynamic Research Corporation; San Antonio, TX, USA) according to standardized measurement conditions (2727 De Carvalho Vidigal F, Ribeiro AQ, Babio N, Salas-Salvadó J, Bressan J. Prevalence of metabolic syndrome and pre-metabolic syndrome in health professionals: LATINMETS Brazil study. Diabetol Metab Syndr. 2015;7(1):1-9.). The systolic and diastolic blood pressures were measured using an Omron HEM-742INT digital sphygmomanometer (Hoffman Estates, IL, USA) according to the protocol recommended by the European Society of Hypertension and the European Society of Cardiology (3333 Mancia G, De Backer G, Dominiczak A, Cifkova R, Fagard R, Germano G, et al. 2007 Guidelines for the Management of Arterial Hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens. 2007;25(6):1105-87.). Research team members were suitably trained to obtain these measurements.

Metabolic markers (Outcomes)

Venous blood samples were drawn following a 12-hour fast, centrifuged at 3500 rpm at 4 °C for 10 min (Megafuge 11R; Thermo Scientific, Waltham, MA, USA), and stored at -80 °C. A trained health professional was responsible for the blood collection. Triglyceride, total cholesterol, high-density lipoprotein cholesterol (HDL-c), and uric acid levels were determined by the enzymatic colorimetric method. Fasting glycemia was determined by the glucose oxidase method. Friedewald’s equation was used to calculate values of low-density lipoprotein cholesterol (LDL-c) and VLDL-c (3434 Friedewald WT, Levy RI, Fredrickson DS. Estimation of the Concentration of Low-Density Lipoprotein Cholesterol in Plasma, Without Use of the Preparative Ultracentrifuge. Clin Chem. 1972;18(6):499-502.). The TyG index was calculated as Ln [triglyceride (mg/dL) × glycemia (mg/dL)/2] and as Ln [triglycerides (mg/dL) × glycemia (mg/dL)]/2. We performed the calculation using both formulas because of discrepancies in TyG index cutoff values observed in the literature (3535 Hosseini SM. Triglyceride-Glucose Index Simulation. J Clin Basic Res. 2017;1(1):11-6.). The final division in the TyG formula is applied outside of the square brackets by some studies and not by other studies (3535 Hosseini SM. Triglyceride-Glucose Index Simulation. J Clin Basic Res. 2017;1(1):11-6.). Complement component 3 (C3) was obtained by the immunoturbidimetry method, and C-Reactive Protein (CRP) levels were determined by enzyme-linked immunosorbent assays (ELISA; Multiskan FC; Thermo Scientific) with the ultra-sensitive DSL-C-reactive protein kit. Plasma levels of different cytokines, tumor necrosis factor (TNF), interleukins (IL-1b, IL-6, IL-10), and adiponectin were determined by ELISA multiplex using a commercial kit (Biosource [Camarillo, CA, USA] or Sellex [São Paulo, Brazil]).

Statistical analysis

Statistical analyzes were performed using MedCalc (v.9.3; Ostend, Belgium) and the R statistical software (v.4.1.0). Subjects were classified into two groups according to the global PSQI score: good sleep quality (≤5) and poor sleep quality (≥6). Subjects with missing PSQI data were excluded from the analyses. A cutoff value for the TyG index was estimated, taking the presence of IR (HOMA-IR>2.71) as a reference (3636 Lyra R, Oliveira M, Lins D, Cavalcanti N, Gross JL, Maia FFR, et al.; Sociedade Brasileira de Diabetes. Vol. 5, Diabetes Mellitus Tipo 1 e Tipo 2. 2020. p. 709-17.). Then, the area under the curve (AUC) for receiving operating characteristic (ROC) curves was calculated to obtain sensitivity and specificity estimates. Variable normality was assessed by the Shapiro-Wilk test. The Student’s t-test or Pearson Chi-square test was used to compare the subject characteristics according to the TyG index cutoff values. The data are presented as mean with standard deviation (SD) and frequencies. The prevalence of subjects with TyG index values above the cutoff was greater than 10% in our data. For this reason, Poisson regression with robust variance was used to assess the association between the TyG index (categorical and dependent variable) and the sleep quality (categorical and independent variable). The variables associated with the TyG index by the hypothesis test were used to adjust the regression analysis. The variable VLDL-c was calculated using the triglyceride values. Therefore, we do not include it as an adjustment variable. The data are presented as prevalence ratio (95% CI). A 5% significance level was used for all tests performed.

RESULTS

Of the 138 subjects included in the study, 103 (75.7 %) were female with a mean age of 29.17 (SD = 7.23) years, 39.9 % presented poor sleep quality evaluated through PSQI, and 8.9% presented IR evaluated by HOMA-IR. We estimated cutoffs for the TyG index, taking IR presence as a reference. Therefore, subjects were classified as having a TyG index of ≤8.08 or >8.08, or ≤4.38 or >4.38 (Figure 2). 100% sensitivity and 51.2% specificity were found for the optimal TyG index cutoff (Supplementary Tables 3 and 4).

Figure 2
Cut-off for TyG index based on the presence of insulin resistance based on HOMA-IR. (A) TyG index calculated through the formula: Ln [triglycerides (mg/dL) * glycemia (mg/dL)/2]; (B) TyG index calculated through the formula: Ln [triglycerides (mg/dL) * glycemia (mg/dL)]/2. Area under the curve (AUC) = 0.766, Standard error = 0.0825, 95 % Confidence interval 95% = 0.685-0.834, z-statistic = 3.224, significance level p (Area=0.5) = 0.0013.

Higher levels of total cholesterol, VLDL-c, LDL-c, LDL/HDL ratio, insulin, complement C3, CRP, and lower adiponectin levels were found in subjects with TyG index >8.08 or >4.38 (Table 1). Characteristics of the participants according to sleep quality are presented in Supplementary Table 2. Briefly, subjects with poor sleep quality were mostly male and presented higher values of muscle mass, VLDL-c, insulin, and TyG index but lower percentages of body fat and carbohydrate intake than those with good sleep quality. However, while most subjects (70%) with high TyG index values (>8.08 or >4.38) were likely to have poor sleep quality, 41% had good sleep quality (Figure 3A). We found that subjects with poor sleep quality had a 1.44-fold higher prevalence of IR (TyG index > 8.08 or > 4.38) compared to those with good sleep quality, regardless of sex and total cholesterol, LDL/HDL ratio, insulin, complement C3, CRP, and adiponectin levels (Figure 3B).

Table 1
General characteristics of the subjects according to TyG index cut-off values
Figure 3
Association between the sleep quality measured by Pittsburgh Sleep Quality Index (PSQI) and cut-off values of TyG index.

Good sleep quality: PSQI score ≤ 5 points; and poor sleep quality: PSQI score ≥ 6 points.

A. Prevalence of subjects with good or poor sleep quality according to cut-off values of TyG index. Data are presented as relative frequency. P-value obtained through Pearson Chi-square test.

B. Prevalence ratio of subjects classified with TyG index > 8.08 or > 4.38 and poor sleep quality. Data are prevalence ratio and 95% CI obtained through Poisson regression with robust variance. Reference category: Subjects classified as having good sleep quality. Model 1: Crude; Model 2: Adjusted by sex, total cholesterol and LDL/HDL ratio; Model 3: Adjusted by variables in model 2 + insulin; Model 4: Adjusted by variables in model 3 + C3 complement, and C-reactive protein + Adiponectin.


Finally, we constructed a correlation matrix to explore the correlations of variables related to TyG index. TyG index was positively correlated with the PSQI score, waist circumference, waist-to-height ratio, body fat, diastolic blood pressure, cardiac frequency, total cholesterol, insulin, complement C3, and CRP. Conversely, higher TyG values were negatively associated with adiponectin levels (Figure 4).

Figure 4
Correlation matrix of variables related to the TyG index.

All variables have at least a statistically significant correlation with the TyG index in Pearson’s chi-square test.

R coefficient interpretation: positive values = direct correlation; negative values = inverse correlation; 0 = absence of correlation; <0.30 = weak correlation; ≤0.30 and ≤70 = moderate correlation; >70 = strong correlation.

TyG_index: triglyceride-glucose index; PSQI: Pittsburgh Sleep Quality Index; WC: waist circumference; WHR: waist-hip ratio; Body_fat_perc: percentage of body fat; Body_fat_kg: kilograms of body fat; DBP: diastolic blood pressure; CF: cardiac frequency; TC, total cholesterol; VLDL-c: very-low-density lipoprotein cholesterol; LDLc: low-density lipoprotein cholesterol; LDLc_HDLc: LDLc HDLc ratio; Complement_C3: complement component 3; CRP: C-reactive protein.


DISCUSSION

To the best of our knowledge, this is the first study to evaluate the association between the TyG index and sleep quality in ostensibly healthy adults. The two previous cross-sectional studies have associated TyG with OSA (2525 Kang HH, Kim SW, Lee SH. Association between triglyceride glucose index and obstructive sleep apnea risk in Korean adults: A cross-sectional cohort study. Lipids Health Dis. 2020;19(1):1-8.,2626 Bikov A, Frent SM, Meszaros M, Kunos L, Mathioudakis AG, Negru AG, et al. Triglyceride-Glucose Index in Non-Diabetic, Non-Obese Patients with Obstructive Sleep Apnoea. J Clin Med. 2021;10(9):1932.) but did not consider sleep quality. We found that subjects with poor sleep quality had a 1.44-fold higher risk of having a TyG index above the cutoff than those with good sleep quality, regardless of sex and total cholesterol, LDL/HDL ratio, insulin, complement C3, CRP, and adiponectin levels. A number of established mechanisms have addressed the link between metabolic disorder and IR, defined as a state that stimulates impairments in glucose uptake, particularly glycogen synthesis (22 Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):1-14.). This metabolic disorder causes hyperglycemia and leads to oxidative stress and inflammatory responses (22 Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):1-14.). IR also leads to dyslipidemia because adipocytes increase their release of free fatty acids, which are absorbed by the liver to form triglyceride-rich and VLDL-c particles in large circulating amounts (22 Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):1-14.,3636 Lyra R, Oliveira M, Lins D, Cavalcanti N, Gross JL, Maia FFR, et al.; Sociedade Brasileira de Diabetes. Vol. 5, Diabetes Mellitus Tipo 1 e Tipo 2. 2020. p. 709-17.).

Evidence has shown that sleep fragmentation can change glucose metabolism by reducing insulin sensitivity (1919 Stamatakis KA, Punjabi NM. Effects of sleep fragmentation on glucose metabolism in normal subjects. Chest. 2010;137(1):95-101.). Endocrine mechanisms underlie the influence of sleep on IR through inflammatory pathways and persistent activation of the sympathetic and HPA axis (3737 Briançon-Marjollet A, Weiszenstein M, Henri M, Thomas A, Godin-Ribuot D, Polak J. The impact of sleep disorders on glucose metabolism: Endocrine and molecular mechanisms. Diabetol Metab Syndr. 2015;7(1):1-16.). Poor sleep quality has been associated with high inflammatory marker (3838 Aggarwal B, Makarem N, Shah R, Emin M, Wei Y, St-Onge MP, et al. Effects of inadequate sleep on blood pressure and endothelial inflammation in women: Findings from the American Heart Association Go Red for Women Strategically Focused Research Network. J Am Heart Assoc. 2018;7(12):1-9.) and cortisol (3939 Bassett SM, Lupis SB, Gianferante D, Rohleder N, Wolf JM. Sleep quality but not sleep quantity effects on cortisol responses to acute psychosocial stress. Stress. 2015;18(6):638-44.) levels. Sympathetic and HPA axis activation has been reported to increase catecholamine and cortisol secretion (3737 Briançon-Marjollet A, Weiszenstein M, Henri M, Thomas A, Godin-Ribuot D, Polak J. The impact of sleep disorders on glucose metabolism: Endocrine and molecular mechanisms. Diabetol Metab Syndr. 2015;7(1):1-16.). Combined with a proinflammatory state, these factors could contribute to IR development. Additionally, poor sleep quality seems to have epigenetic effects and share genetic architecture with metabolic syndrome (4040 Jackson CL, Redline S, Emmons KM. Sleep as a potential fundamental contributor to disparities in cardiovascular health. Ann Rev Public Health. 2015;36:417-40.). A study found a genetic correlation between insomnia symptoms and HOMA-IR, suggesting the involvement of genetic variants (4141 Lane JM, Liang J, Vlasac I, Anderson SG, Bechtold DA, Bowden J, et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat Genet. 2017;49(2):274-81.).

Current data have indicated a close relationship between HOMA-IR and TyG index (2222 Vasques ACJ, Novaes FS, de Oliveira M da S, Matos Souza JR, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: A hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011;93(3):8-10.,4242 Guerrero-Romero F, Villalobos-Molina R, Jiménez-Flores JR, Simental-Mendia LE, Méndez-Cruz R, Murguía-Romero M, et al. Fasting Triglycerides and Glucose Index as a Diagnostic Test for Insulin Resistance in Young Adults. Arch Med Res. 2016;47(5):382-7.). In Brazil, a validated study concluded that the TyG index had a better performance than the HOMA-IR index for measuring IR in clinical practice (2222 Vasques ACJ, Novaes FS, de Oliveira M da S, Matos Souza JR, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: A hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011;93(3):8-10.). Furthermore, a population-based cross-sectional study found a correlation between TyG and other markers of IR, such as HOMA-IR and the hyperinsulinemic-euglycemic clamp (HIEC), in healthy subjects (4242 Guerrero-Romero F, Villalobos-Molina R, Jiménez-Flores JR, Simental-Mendia LE, Méndez-Cruz R, Murguía-Romero M, et al. Fasting Triglycerides and Glucose Index as a Diagnostic Test for Insulin Resistance in Young Adults. Arch Med Res. 2016;47(5):382-7.). A systematic review has found that the highest achieved sensitivity was 96% using HIEC (4343 Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, González-Nava V, Díaz González-Colmenero A, Solis RC, et al. Diagnostic Accuracy of the Triglyceride and Glucose Index for Insulin Resistance: A Systematic Review. Int J Endocrinol. 2020;2020:1-7.). The highest specificity was 99% using HOMA-IR, with a cutoff value of 4.68 (4343 Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, González-Nava V, Díaz González-Colmenero A, Solis RC, et al. Diagnostic Accuracy of the Triglyceride and Glucose Index for Insulin Resistance: A Systematic Review. Int J Endocrinol. 2020;2020:1-7.), close to the value of 4.38 in this study.

We estimated TyG index cutoffs to detect IR based on two different methods for calculating the TyG index. While the formulas are identical, some studies have applied the division outside of the square brackets, and others have not. Therefore, the TyG index cutoff values reported in the literature range from ~4 and ~8 (3535 Hosseini SM. Triglyceride-Glucose Index Simulation. J Clin Basic Res. 2017;1(1):11-6.). Kang and cols. (2525 Kang HH, Kim SW, Lee SH. Association between triglyceride glucose index and obstructive sleep apnea risk in Korean adults: A cross-sectional cohort study. Lipids Health Dis. 2020;19(1):1-8.) recently reported a cut-off for the TyG index of 8.83 (sensitivity: 61.6%; specificity: 69.0%; AUC: 0.688;P = 0.001) among subjects with suspected OSA. To estimate our cutoff values, we used HOMA-IR > 2.71 as a reference according to the Brazilian guidelines for diabetes (3636 Lyra R, Oliveira M, Lins D, Cavalcanti N, Gross JL, Maia FFR, et al.; Sociedade Brasileira de Diabetes. Vol. 5, Diabetes Mellitus Tipo 1 e Tipo 2. 2020. p. 709-17.). In this study, the TyG index > 8.08 or > 4.38 was the optimal value to identify IR in our samples, with 100% sensitivity and 51.2% specificity. These values suggest that subjects with a TyG index > 8.08 or > 4.38 have IR, with 0% false-positive cases. However, the TyG index is not a good measure to detect subjects without IR, with a high false-negative rate.

Previous studies have reported a positive association between the TyG index, IR, and related conditions such as T2D (2424 da Silva A, Caldas APS, Rocha DMUP, Bressan J. Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: A systematic review and meta-analysis of cohort studies. Prim Care Diabetes. 2020;14(6):584-93.) and cardiovascular events (4444 Li S, Guo B, Chen H, Shi Z, Li Y, Tian Q, et al. The role of the triglyceride (triacylglycerol) glucose index in the development of cardiovascular events: a retrospective cohort analysis. Sci Rep. 2019;9(1):7320.). Subjects in our study with IR determined by a TyG index > 8.08 or > 4.38 had worse metabolic profiles, with higher total cholesterol, VLDL-c, LDL-c, LDL/HDL ratio, insulin, complement C3, and CRP values and lower adiponectin levels than those with a TyG index lower ≤ 8.08 or ≤ 4.38. Moreover, we found simultaneous and positive correlations between the TyG index and cardiometabolic risk variables such as waist circumference, waist-to-height ratio, body fat, diastolic blood pressure, cardiac frequency, total cholesterol, and fractions (including VLDL-c, LDL-c and LDL/HDL ratio), insulin, complement C3, and CRP. These observations are consistent with previous studies that found a worse metabolic profile in subjects classified in the highest quartiles of the TyG index (2424 da Silva A, Caldas APS, Rocha DMUP, Bressan J. Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: A systematic review and meta-analysis of cohort studies. Prim Care Diabetes. 2020;14(6):584-93.,4444 Li S, Guo B, Chen H, Shi Z, Li Y, Tian Q, et al. The role of the triglyceride (triacylglycerol) glucose index in the development of cardiovascular events: a retrospective cohort analysis. Sci Rep. 2019;9(1):7320.).

Poor sleep quality and IR could contribute to chronic inflammation, but it remains challenging to manipulate factors such as diet and sleep that may affect inflammation experimentally (4545 Furman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25(12):1822-32.). Complement C3 and CRP are prominent biomarkers for IR (4646 Muscari A, Antonelli S, Bianchi G, Cavrini G, Dapporto S, Ligabue A, et al. Serum C3 Is a Stronger Inflammatory Marker of Insulin Resistance Than C-Reactive Protein, Leukocyte Count, and Erythrocyte Sedimentation Rate: Comparison study in an elderly population. Diabetes Care. 2007;30(9):2362-8.,4747 Uemura H, Katsuura-Kamano S, Yamaguchi M, Bahari T, Ishizu M, Fujioka M, et al. Relationships of serum high-sensitivity C-reactive protein and body size with insulin resistance in a Japanese cohort. Herder C, editor. PLoS One. 2017;12(6):e0178672.). As mentioned above, subjects with a TyG index > 8.08 or > 4.38 had higher values for these inflammation markers. Uemura and cols. have reported that higher serum CRP was associated with IR in a dose-dependent manner (4747 Uemura H, Katsuura-Kamano S, Yamaguchi M, Bahari T, Ishizu M, Fujioka M, et al. Relationships of serum high-sensitivity C-reactive protein and body size with insulin resistance in a Japanese cohort. Herder C, editor. PLoS One. 2017;12(6):e0178672.). In another study, complement C3 was strongly associated with IR, independent of the other components of metabolic syndrome (4646 Muscari A, Antonelli S, Bianchi G, Cavrini G, Dapporto S, Ligabue A, et al. Serum C3 Is a Stronger Inflammatory Marker of Insulin Resistance Than C-Reactive Protein, Leukocyte Count, and Erythrocyte Sedimentation Rate: Comparison study in an elderly population. Diabetes Care. 2007;30(9):2362-8.). Moreover, our results have shown a simultaneous correlation between the TyG index and adiponectin, a crucial modulator of insulin sensitivity and chronic inflammation (4848 Fasshauer M, Blüher M. Adipokines in health and disease. Trends Pharmacol Sci. 2015;36(7):461-70.).

This study used a cutoff value for the TyG index > 8.08 or > 4.38 as a surrogate marker to estimate IR. It was associated with poor sleep quality among apparently healthy adults, and its predictive significance also correlates with other important independent risk factors. Although the prevalence in women and men was not statistically different between the sleep quality and TyG index categories, selection bias potentially limits our study because of the high frequency of females in our data. In addition, since it is a cross-sectional study, it cannot establish a causal relationship. While the HOMA-IR test is not the gold standard for diagnosing IR, the euglycemic-hyperinsulinemic clamp is the gold standard, but it is impractical for use in large cohort studies. The PSQI questionnaire was available for all subjects in the study. However, 100 subjects did not respond to the questionnaire, creating additional selection biases in our study.

This study’s greatest benefit is its use of ostensibly healthy adults before the onset of chronic diseases to highlight the potential involvement of poor sleep quality in IR, even before it manifests clinically. Our findings reinforce the need for further research into using the TyG index as a surrogate marker of IR and its relationship with sleep. In addition, understanding modifiable risk factors for IR in adults may offer more effective primary prevention efforts in an at-risk population and expansion of interventions to improve sleep quality.

  • Financing: Brazilian National Council for Scientific and Technological Development (CNPq - Processes 481019/2012-0 and 444519/2014-9) and Minas Gerais State Research Foundation (Fapemig – CDS-APQ01609-10) supported this research.

Acknowledgments:

we thank all the subjects who participated in the study and the Capes for the scholarship conceded to AS.

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SUPPLEMENTARY MATERIALS

Supplementary Table 1
General characteristics of the subjects according to completion of the Pittsburgh Sleep Quality Index (PSQI) score
Supplementary Table 2
General characteristics of the subjects based on the Pittsburgh Sleep Quality Index (PSQI) score
Supplementary Table 3
Criterion values and coordinates of the ROC curve
Supplementary Table 4
Criterion values and coordinates of the ROC curve
STROBE Statement
a checklist of items that should be included in reports of observational studies

Publication Dates

  • Publication in this collection
    03 Oct 2022
  • Date of issue
    Feb 2023

History

  • Received
    20 Aug 2021
  • Accepted
    23 May 2022
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