Abstract
Background
Prolongation of the corrected QT interval (QTc) reflects delayed ventricular repolarization and predisposes patients to malignant arrhythmias and sudden cardiac death. Hypertension is strongly associated with QTc abnormalities, yet the influence of habitual sleep patterns remains underexplored.
Methods
We conducted a cross-sectional analysis of 1,338 hypertensive participants from the Fasa Adult Cohort Study. Sleep parameters were assessed using the Pittsburgh Sleep Quality Index, and 12-lead electrocardiograms were obtained under standardized conditions. QTc was calculated using Bazett’s formula, with prolongation defined as > 450 ms in men and > 460 ms in women based on the latest AHA/ACCF/HRS recommendations. Logistic regression models, adjusted for demographic, clinical, and medication-related confounders, evaluated associations and interactions between sleep behaviors and QTc prolongation.
Results
QTc prolongation was observed in 20.5% of participants. While prolonged nighttime sleep (≥ 9 h) and daytime napping alone were not independently associated with QTc prolongation, their combination significantly increased the risk (OR = 3.54; 95% CI: 1.29–9.71; p = 0.014). Interaction analysis confirmed a statistical interaction between prolonged night sleep and regular napping (OR = 4.36; 95% CI: 1.43–13.27; p = 0.009), independent of antihypertensive medication use. Conversely, short nighttime sleep (≤ 6 h) accompanied by daytime napping showed no statistically significant association with QTc prolongation; with point estimate suggesting lower odds.
Conclusions
Excessive total sleep duration, distributed across both night and day, is strongly associated with QTc prolongation in hypertensive adults. This combined sleep phenotype may represent a novel behavioral risk factor for impaired cardiac repolarization, highlighting the importance of incorporating sleep assessment into cardiovascular risk stratification. Moreover, hypertensive individuals with long nighttime sleep may mitigate their risk of QTc prolongation by reducing additional daytime naps, potentially lowering the likelihood of arrhythmic events. These findings identify a sleep phenotype associated with QTc prolongation and highlight the need for longitudinal studies to clarify causality.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-026-05670-7.
Keywords: Sleep, Sleep duration, QTc, Hypertension, QT interval, Corrected QT prolongation, Midday napping
Introduction
The QT interval on the surface electrocardiogram (ECG), which extends from the start of the QRS complex to the end of the T wave, indicates the complete period of ventricular depolarization and repolarization. Since the QT interval changes with heart rate, the corrected QT interval (QTc) is commonly used as a standard measure of cardiac repolarization. Prolongation of the QTc reflects delayed ventricular repolarization [1]. It indicates impaired myocardial refractoriness, creating a substrate for dangerous ventricular arrhythmias like torsade de pointes, which can develop into ventricular fibrillation and sudden cardiac death (SCD) [2–4]. Population-based and prognostic studies indicate that QTc abnormalities are associated with cardiovascular death in patients with heart failure, myocardial infarction, coronary artery disease, systemic hypertension, and other cardiovascular conditions [5–8]. Research indicates that QTc prolongation is especially common among hypertensive patients, occurring more frequently in those with poorly controlled blood pressure compared to those with well-controlled hypertension [9–12]. Because hypertension itself is a recognized cardiovascular risk factor, it’s crucial to identify other factors that may further predispose this high-risk population to arrhythmic events. Known risk factors for QTc prolongation are female sex, older age, hypertension, electrolyte imbalance, thyroid disorders, liver dysfunction, obesity, diabetes, and certain medications [8, 13, 14]. Despite recognizing traditional clinical and demographic risk factors, lifestyle-related causes of QTc prolongation are less studied. Notably, sleep and circadian rhythm disturbances have been linked to cardiac repolarization abnormalities [15, 16], suggesting a potentially important but under-researched connection in people with hypertension. Therefore, evaluating how sleep impacts QTc prolongation could offer important insights into arrhythmic risk assessment and prevention.
Sleep is a dynamic physiological process with important effects on cardiovascular regulation, autonomic balance, metabolism, and hormonal rhythms [17]. Disturbances in sleep quantity and quality have been associated with various adverse outcomes, such as hypertension, coronary artery disease, diabetes, and increased mortality [18, 19]. Growing evidence further suggests that sleep may influence cardiac electrophysiology, with previous studies showing associations between sleep deprivation and QTc prolongation [8, 20–22]. The mechanisms linking sleep to QTc prolongation are not fully understood, but several pathways have been proposed. Sleep influences the autonomic nervous system balance, with alterations in sympathetic and parasympathetic tone affecting ventricular repolarization. In addition, sleep modulates electrolyte homeostasis, circadian regulation of ion channel expression, and hormonal rhythms, all of which play key roles in cardiac electrophysiology [15, 23–25]. These factors together suggest that habitual sleep patterns may contribute to QTc variability and arrhythmic susceptibility.
Most existing studies on QTc have focused on sleep-disordered breathing [26–28] or the effects of acute sleep deprivation [20–22], with far less attention given to the role of habitual, chronic sleep patterns. Prior work has shown associations between poor sleep quality, extremes in nighttime sleep duration, and QTc prolongation in elderly and general populations [8]. However, little is known about how multidimensional sleep behaviors interact to influence QTc in hypertensive patients, a group at particularly high cardiovascular risk. In this context, investigating for the first time the combined effects of nighttime sleep duration and daytime napping on QTc prolongation in hypertensive patients, while accounting for medication-specific effects, represents a novel approach that addresses critical gaps in current cardiovascular risk stratification strategies.
Materials and methods
Study design and data collection
This cross-sectional study utilized data from the Fasa Adult Cohort Study (FACS), a regional subset of the broader PERSIAN cohort, comprising 10,138 participants. The FACS cohort was conducted in Sheshdeh, a rural district in Fasa, Iran, along with 24 surrounding villages, enrolling adults aged 35 to 70 years. During the initial phase of data collection (2015–2016), participants provided comprehensive information through structured surveys covering demographic characteristics, anthropometric measurements, socioeconomic status, dietary habits, lifestyle factors, medication use, and medical history. Written informed consent was obtained from all participants, and the collected data were systematically stored in an electronic database following the FACS data management protocol [29]. The inclusion and exclusion criteria for this study are illustrated in Fig. 1. Participants with missing data on primary exposure (sleep duration or daytime napping) or outcome (QTc interval) were excluded from analysis. For covariates, missingness was low (< 5% for all variables). Complete-case analysis was therefore performed without imputation. Individuals with invalid sleep duration values (negative calculated durations), or implausible outliers identified using stem-and-leaf plots and box plots (≤ 0.5 h or ≥ 13 h of nighttime sleep), were excluded from the analysis. These thresholds were selected to remove physiologically implausible values while preserving the full range of realistic habitual sleep durations.
Fig. 1.
Study’s enrollment flowchart
Blood pressure measurement and hypertension definition
Blood pressure (BP) was measured after participants rested for 15 min. Two readings were taken at five-minute intervals, and the average systolic and diastolic BP (measured in mmHg) was recorded. Hypertension was defined as either systolic BP (SBP) ≥ 140 mmHg, diastolic BP (DBP) ≥ 90 mmHg on two or more measurements, or current use of antihypertensive medications following a prior diagnosis [30].
Covariates
An online questionnaire was used to collect demographic and health-related data for adjustment of potential confounders in multivariable analyses. These covariates were selected based on an extensive literature review and expert consensus. These included demographic factors, cardiometabolic variables, lifestyle behaviors, psychiatric status, and medication use known to influence QTc. Key variables included gender, age, physical activity (expressed in metabolic equivalent of task [MET] units over 24 h), body mass index (BMI; weight in kilograms divided by height in meters squared), estimated glomerular filtration rate (eGFR, calculated using the CKD-EPI 2021 formula in mL/min), alcohol consumption (grams per day), smoking status (current regular smoker or non-smoker), and illicit drug use (defined as using illicit substances at least once per week for a minimum of six months).
Additionally, medication history was documented, particularly drugs known to prolong the QT interval. A comprehensive list was compiled from CredibleMeds, encompassing 187 cardiac and non-cardiac drugs associated with Torsade de Pointes [31]. These included selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants (TCAs), antipsychotics, macrolide antibiotics, antifungals, anti-tuberculosis drugs, and bronchodilators, among others. Nutrient intake, particularly sodium, potassium, and calcium (measured in mg/day), was estimated using a validated food frequency questionnaire [32]. Medication use within two weeks before enrollment was recorded, with a particular focus on blood pressure-lowering agents such as ACE inhibitors, angiotensin receptor blockers (ARBs), diuretics, aldosterone antagonists, and beta-blockers. Exact timing relative to ECG acquisition was unavailable.
Sleep factors measurements
Sleep patterns were assessed using two components of the Pittsburgh Sleep Quality Index (PSQI) [33], evaluating participants’ typical sleep behaviors over the past year: “What time do you regularly go to bed at night?“; “What time do you regularly wake up in the morning?“; and “How long does it take for you to fall asleep after lying in bed?” (Sleep latency).
Nighttime sleep duration sleep duration was calculated using the following formula [34]:
 |
The primary independent variable was the frequency of daytime naps, with regular napping defined as at least three times per week. Additional sleep-related variables included regular use of sleeping pills (defined as use more than twice per week), night shift work (working at least six hours between 9 PM and 6 AM at least once a month in the past year), sleep disturbances due to leg restlessness, and daytime drowsiness (defined as unintentional dozing during periods of inactivity), which was considered a proxy indicator of excessive daytime sleepiness. To minimize exposure misclassification, sleep duration values were rigorously screened for plausibility prior to analysis.
ECG recording and QTc interval measurement
In the fourth phase of the FACS protocol, electrocardiograms (ECGs) were obtained under standardized conditions. Participants lay in a supine position for 15 min, remaining relaxed, awake, and refraining from movement or conversation. A 12-lead ECG was recorded using the Cardiax®28 system, a digital paperless device with a 2000 Hz sampling rate and a resolution of 0.04 µV/bit (24-bit). The software automatically analyzed ECG parameters, including QT interval (ms) and heart rate (bpm), and results were subsequently reviewed and validated by cardiologists for accuracy [31]. QT intervals were derived using automated ECG software from standard 12-lead recordings and reviewed by a cardiologist for quality control. Measurements were based on algorithm-defined QT intervals, with manual correction performed when necessary. ECG acquisition and interpretation followed standardized cohort procedures with centralized quality control. Because QT values were derived from routine cohort ECG recordings, formal inter-observer and intra-observer reliability assessments were not performed.
The QT interval was corrected for heart rate (QTc) using Bazett’s formula [35], consistent with routine clinical practice and large epidemiological studies. Bazett’s formula was applied uniformly across all participants to maintain internal comparability, with prolonged QTc defined as > 450 ms in men and > 460 ms in women, based on the latest AHA/ACCF/HRS recommendations [36].
Statistical analyses
Descriptive statistics were applied to summarize data, with continuous variables expressed as means and standard deviations and categorical variables as frequencies and percentages. Given the large sample size, parametric methods were considered appropriate in accordance with the central limit theorem.
Univariable analyses assessed associations with QTc prolongation using the chi-square test for categorical variables and the independent t-test for continuous variables. Multivariable analyses adjusted for significant literature-based confounders (p < 0.2 in univariable tests) [37] using multiple logistic regression models. Covariates were selected based on prior knowledge and biological plausibility regarding their associations with sleep behaviors and QTc prolongation. Univariable screening (p < 0.2) was used only as an additional step to avoid overfitting and assess model parsimony, but did not determine primary confounder selection. To evaluate robustness, we additionally fitted fully adjusted sensitivity models including all literature-based confounders irrespective of univariable significance. To account for the potential impact of nighttime sleep duration, subgroup analyses stratified participants into three categories: short sleepers (≤ 6 h per night), normal sleepers (6–9 h), and long sleepers (≥ 9 h) [38, 39].
Interaction analyses
To formally evaluate whether the association between daytime napping and QTc prolongation differed by prolonged nighttime sleep and antihypertensive medication use, we conducted multivariable logistic regression analyses using the Enter (forced-entry) method. For these analyses, nighttime sleep duration was treated as a binary variable (long sleep ≥ 9 h vs. <9 h), and daytime napping was defined as regular versus none. Antihypertensive medication use was initially considered broadly, but subsequent analyses focused on diuretic use due to potential differential effects on QTc.
The primary interaction model included:
Main effects: daytime napping, prolonged nighttime sleep (≥ 9 h), and diuretic use.
Two-way interactions: nap × night, nap × diuretic, and night × diuretic.
Three-way interaction: nap × night × diuretic.
All models were adjusted for relevant chosen covariates including age, sex, employment status, physical activity, hyperlipidemia, ischemic heart disease, thyroid disease, chronic lung disease, family history of myocardial infarction (second-degree relatives), fatty liver index, sodium intake, night shift work, and use of sleeping pills.
The primary interaction of interest was nap × night, which was evaluated within the Enter logistic regression framework.
All statistical analyses were performed using SPSS 23.0 (IBM Corp., Armonk, NY, USA), with a significance threshold set at p < 0.05.
Results
Study population and univariable analysis
A total of 1,338 participants were included in the analysis, comprising 322 men (24.1%) and 1,016 women (75.9%), with an average age of 54.79 ± 8.64 years. The mean QTc interval was recorded at 439.80 ± 34.69 milliseconds, with QTc prolongation observed in 274 individuals (20.5%). Table 1 presents the demographic and clinical characteristics of the study population.
Table 1.
Hypertensive population characteristics considering QTc prolongation
| Variable | Total (N = 1338) | QTc prolongation | P-value* | |||
|---|---|---|---|---|---|---|
| No (n = 1064) | Yes (n = 274) | |||||
| Gender | Male | 322 (24.1) | 271 (25.5) | 51 (18.6) | 0.018 | |
| Female | 1016 (75.9) | 793 (74.5) | 223 (81.4) | |||
| Age (years) | 54.79 ± 8.64 | 54.53 ± 8.65 | 55.79 ± 8.51 | 0.032 | ||
| BMI (kg/m2) | 27.54 ± 4.91 | 27.54 ± 4.85 | 27.54 ± 5.13 | 0.996 | ||
| Physical activity (METs) | 38.76 ± 8.31 | 38.94 ± 8.55 | 38.03 ± 7.24 | 0.075 | ||
| Socioeconomic status (asset index) | -0.38 ± 1.90 | -0.35 ± 1.94 | -0.49 ± 1.75 | 0.295 | ||
| Marital status | Single (Never married) | 10 (0.7) | 9 (0.8) | 1 (0.4) | 0.549** | |
| Married | 1124 (84.0) | 896 (84.2) | 228 (83.2) | |||
| Widowed | 187 (14.0) | 144 (13.5) | 43 (15.7) | |||
| Divorced | 17 (1.3) | 15 (1.4) | 2 (0.7) | |||
| Academic degree | No | 1332 (99.6) | 1058 (99.4) | 274 (100.0) | 0.357** | |
| Yes | 6 (0.4) | 6 (0.6) | 0 (0) | |||
| Having a job | No | 954 (71.3) | 749 (70.4) | 205 (74.8) | 0.149 | |
| Yes | 384 (28.7) | 315 (29.6) | 69 (25.2) | |||
| Daily calorie intake (kilocalorie) | 2850.68 ± 1130.39 | 2832.85 ± 1089.35 | 2920.15 ± 1277.66 | 0.301 | ||
| Smoker | Yes | 204 (15.2) | 168 (15.8) | 36 (13.1) | 0.276 | |
| No | 1134 (84.8) | 896 (84.2) | 238 (86.9) | |||
| Illicit drug user | No | 1207 (90.2) | 961 (90.3) | 246 (89.8) | 0.789 | |
| Yes | 131 (9.8) | 103 (9.7) | 28 (10.2) | |||
| Alcohol intake (grams/day) | 0.03 ± 0.08 | 0.04 ± 0.09 | 0.03 ± 0.06 | 0.679 | ||
| Fatty liver index | 52.12 ± 27.42 | 51.55 ± 27.25 | 54.35 ± 28.03 | 0.132 | ||
| GFR (ml/min/1.73m2) | 74.75 ± 12.11 | 74.66 ± 12.09 | 75.09 ± 12.19 | 0.598 | ||
| Hyperlipidemia | No | 232 (17.3) | 192 (18.0) | 40 (14.6) | 0.179 | |
| Yes | 1106 (82.7) | 872 (82.0) | 234 (85.4) | |||
| Diabetes | No | 962 (71.9) | 767 (72.1) | 195 (71.2) | 0.763 | |
| Yes | 376 (28.1) | 297 (27.9) | 79 (28.8) | |||
| Ischemic heart disease | No | 985 (73.6) | 792 (74.4) | 193 (70.4) | 0.181 | |
| Yes | 353 (26.4) | 272 (25.6) | 81 (29.6) | |||
| Thyroid disease | No | 1144 (85.5) | 917 (86.2) | 227 (82.8) | 0.162 | |
| Yes | 194 (14.5) | 147 (13.8) | 47 (17.2) | |||
| Depression | No | 1222 (91.3) | 969 (91.1) | 253 (92.3) | 0.507 | |
| Yes | 116 (8.7) | 95 (8.9) | 21 (7.7) | |||
| Other psychiatric disorders | No | 1166 (87.1) | 922 (86.7) | 244 (89.1) | 0.290 | |
| Yes | 172 (12.9) | 142 (13.3) | 30 (10.9) | |||
| Chronic lung disease | No | 1290 (96.4) | 1022 (96.1) | 268 (97.8) | 0.163 | |
| Yes | 48 (3.6) | 42 (3.9) | 6 (2.2) | |||
| Gastroesophageal reflux disease | No | 1083 (80.9) | 857 (80.5) | 226 (82.5) | 0.467 | |
| Yes | 255 (19.1) | 207 (19.5) | 48 (17.5) | |||
| MI in a first degree relative | No | 1003 (75.0) | 805 (75.7) | 198 (72.3) | 0.247 | |
| Yes | 335 (25.0) | 259 (24.3) | 76 (27.7) | |||
| MI in a second degree relative | No | 1217 (91.0) | 961 (90.3) | 256 (93.4) | 0.109 | |
| Yes | 121 (9.0) | 103 (9.7) | 18 (6.6) | |||
| Calcium channel blocker use | No | 1080 (80.7) | 864 (81.2) | 216 (78.8) | 0.359 | |
| Yes | 254 (19.0) | 196 (18.4) | 58 (21.2) | |||
| Betablocker use | No | 895 (66.9) | 714 (67.1) | 181 (66.1) | 0.743 | |
| Yes | 443 (33.1) | 350 (32.9) | 93 (33.9) | |||
| Diuretic use | No | 1205 (90.1) | 966 (90.8) | 239 (87.2) | 0.079 | |
| Yes | 133 (9.9) | 98 (9.2) | 35 (12.8) | |||
| QT-prolonging medication use | No | 1190 (88.9) | 949 (89.2) | 241 (88.0) | 0.561 | |
| Yes | 148 (11.1) | 115 (10.8) | 33 (12.0) | |||
| Blood pressure lowering medication use | No | 493 (36.8) | 403 (37.9) | 90 (32.8) | 0.124 | |
| Yes | 845 (63.2) | 661 (62.1) | 184 (67.2) | |||
| Regular daytime naps | No | 551 (41.2) | 444 (41.7) | 107 (39.1) | 0.422 | |
| Yes | 787 (58.8) | 620 (58.3) | 167 (60.9) | |||
| Night sleep duration | Too short | 641 (47.9) | 495 (46.5) | 146 (53.3) | 0.024 | |
| Adequate | 565 (42.2) | 469 (44.1) | 96 (35.0) | |||
| Too long | 132 (9.9) | 100 (9.4) | 32 (11.7) | |||
| Involuntary daytime dozing off | No | 825 (61.7) | 662 (62.2) | 163 (59.5) | 0.407 | |
| Yes | 513 (38.3) | 402 (37.8) | 111 (40.5) | |||
| Sleeping pill use | No | 1147 (85.7) | 919 (86.4) | 228 (93.2) | 0.182 | |
| Yes | 191 (14.3) | 145 (13.6) | 46 (16.8) | |||
| Night shift work | No | 1242 (92.8) | 980 (92.1) | 262 (95.6) | 0.044 | |
| Yes | 96 (7.2) | 84 (7.9) | 12 (4.4) | |||
| Leg restlessness | Yes | 451 (33.7) | 358 (33.6) | 93 (33.9) | 0.960 | |
| No | 887 (66.3) | 706 (66.4) | 181 (66.1) | |||
| Sleep latency (minutes) | 32.07 ± 43.84 | 32.61 ± 44.01 | 29.98 ± 43.34 | 0.892 | ||
| Sodium intake (milligrams/day) | 4620.92 ± 1931.76 | 4577.76 ± 1863.30 | 4789.15 ± 2173.75 | 0.107 | ||
| Potassium intake (milligrams/day) | 3860.18 ± 1585.63 | 3850.24 ± 1560.54 | 3898.92 ± 1682.30 | 0.651 | ||
| Calcium intake (milligrams/day) | 1331.31 ± 639.66 | 1328.73 ± 636.54 | 1341.38 ± 652.77 | 0.771 | ||
| Magnesium intake (milligrams/day) | 362.77 ± 137.02 | 362.97 ± 137.44 | 361.98 ± 135.62 | 0.915 | ||
Categorical variables are presented as frequencies (percentages), and continuous variables are presented as mean ± standard deviation
*Association with the outcome (QTc prolongation) is computed using the chi-square test for qualitative variables and the Independent T-test test for quantitative variables, Significance level for the tests is set at 0.05
**calculated using the Fisher’s exact test
Univariable analysis revealed that QTc prolongation was significantly associated with gender (p = 0.018), nighttime sleep duration (p = 0.024), and night shift work (p = 0.044). Additionally, individuals with prolonged QTc intervals tended to be older (p = 0.032).
Multivariable logistic regression analysis
To further investigate the associations identified in the univariable analysis, a multivariable logistic regression model was employed, adjusting for key confounders with p-values < 0.2. These confounders included gender, age, employment status, physical activity, hyperlipidemia, ischemic heart disease, thyroid disease, chronic lung disease, a family history of myocardial infarction (second-degree relatives), fatty liver index, diuretic use, sodium intake, night shift work, and the use of sleeping pills.
Among hypertensive individuals who had prolonged nighttime sleep (≥ 9 h), those who regularly napped during the day exhibited significantly increased odds of QTc prolongation, with the elevated odds ratio (OR) of 3.54 (95% CI: 1.29–9.71, p = 0.014). In contrast, daytime napping had no significant effect on QTc prolongation among hypertensive individuals who had adequate nighttime sleep (6–9 h), with only a 20% increase in odds. Similarly, in hypertensive individuals with short nighttime sleep (≤ 6 h), daytime napping was associated with a nonsignificant 4% reduction in the likelihood of QTc prolongation. Overall, napping was not significantly associated with QTc prolongation in participants with adequate (6–9 h) or short (≤ 6 h) nighttime sleep (Table 2).
Table 2.
Multiple logistic regression analysis of the association between regular daytime napping and QTc prolongation in hypertensive populations categorized by night sleep duration
| Variables | Night sleep duration | |||||
|---|---|---|---|---|---|---|
| Too short (n = 641) | Adequate (n = 565) | Too long (n = 132) | ||||
| Adjusted OR (95% CI) # | p-value* | Adjusted OR (95% CI) # | p-value* | Adjusted OR (95% CI) # | p-value* | |
| Regular daytime naps (Ref = no) | 0.96 (0.66, 1.42) | 0.853 | 1.20 (0.73, 1.99) | 0.476 | 3.54 (1.29, 9.71) | 0.014 |
| Gender (Ref = male) | 1.36 (0.75, 2.47) | 0.315 | 1.37 (0.70, 2.70) | 0.360 | 2.15 (0.51, 9.08) | 0.297 |
| Age (years) | 1.01 (0.98, 1.03) | 0.627 | 1.02 (1.00, 1.05) | 0.107 | 1.04 (0.98, 1.11) | 0.198 |
| Having a job (Ref = no) | 1.53 (0.90, 2.62) | 0.118 | 1.01 (0.50, 2.06) | 0.971 | 0.55 (0.09, 3.50) | 0.523 |
| Physical activity (METs) (kcal/kg/hour) | 0.98 (0.96, 1.01) | 0.231 | 0.98 (0.95, 1.02) | 0.431 | 1.02 (0.94, 1.11) | 0.611 |
| Hyperlipidemia (Ref = no) | 0.91 (0.52, 1.59) | 0.742 | 1.35 (0.70, 2.61) | 0.377 | 1.11 (0.30, 4.07) | 0.878 |
| Ischemic heart disease (Ref = no) | 1.10 (0.72, 1.69) | 0.648 | 1.08 (0.64, 1.83) | 0.779 | 1.58 (0.55, 4.52) | 0.398 |
| Thyroid disease (Ref = no) | 1.29 (0.78, 2.14) | 0.319 | 1.03 (0.53, 2.01) | 0.929 | 1.78 (0.49, 6.53) | 0.382 |
| Chronic lung disease (Ref = no) | 0.42 (0.12, 1.48) | 0.176 | 0.71 (0.20, 2.52) | 0.593 | 0.00 (0.00, 0.00) | 0.999 |
| MI in a second degree relative (Ref = no) | 0.76 (0.39, 1.46) | 0.405 | 0.53 (0.20, 1.42) | 0.209 | 0.00 (0.00, 0.00) | 0.999 |
| Fatty Liver Index (FLI) | 1.00 (1.00, 1.01) | 0.529 | 1.00 (0.99, 1.01) | 0.969 | 1.01 (1.00, 1.03) | 0.162 |
| Diuretic use (Ref = no) | 1.25 (0.67, 2.32) | 0.478 | 1.72 (0.87, 3.40) | 0.122 | 1.07 (0.29, 4.02) | 0.918 |
| Sodium Intake (mg/day) | 1.00 (1.00, 1.00) | 0.025 | 1.00 (1.00, 1.00) | 0.964 | 1.00 (1.00, 1.00) | 0.178 |
| Night shift work (Ref = no) | 0.52 (0.19, 1.45) | 0.214 | 0.98 (0.31, 3.05) | 0.971 | 1.51 (0.08, 26.92) | 0.779 |
| Sleeping pill use (Ref = no) | 1.33 (0.82, 2.15) | 0.253 | 1.01 (0.50, 2.04) | 0.981 | 0.66 (0.14, 3.15) | 0.607 |
Too short, 6 h or less; Adequate, between 6 to 9 h; Too long, 9 h or more
Abbreviations: MI Myocardial infarction
#Adjusted for the chosen confounders most related to QTc prolongation from the univariable analyses, including gender, age, having a job, physical activity, hyperlipidemia, ischemic heart disease, thyroid disease, chronic lung disease, MI in a second degree relative, fatty liver index, diuretic use, sodium intake, night shift work, and sleeping pill useThe long-sleep group comprised a relatively small proportion of the cohort (n=132), and the number of QTc prolongation events within this stratum was limited, contributing to wide confidence intervals around interaction estimates.Multivariable models were fit separately within each sleep-duration stratum using identical covariate sets.Odds ratios reported as 0.00 (0.00–0.00) reflect complete or quasi-complete separation due to zero QTc prolongation events in those categories. These estimates should be interpreted cautiously
*Significance level at 0.05
To formally test whether the effect of napping differed by nighttime sleep duration, and whether the nap–long sleep effect among hypertensive patients is just because long sleepers are also diuretic users, a logistic regression model including daytime napping, prolonged nighttime sleep, and their interaction, along with relevant covariates, was performed in the full hypertensive population. The interaction between daytime napping and prolonged nighttime sleep was statistically significant (OR = 4.36; 95% CI: 1.43–13.27, p = 0.009), confirming that the association of napping with QTc prolongation was strongest in long sleepers. The main effects of daytime napping alone, and prolonged nighttime sleep, as well as additional interaction terms with diuretic use, were not statistically significant, indicating that the observed effect is independent of antihypertensive medication class (Table 3).
Table 3.
Association between daytime napping, night sleep duration, diuretic use, and QTc prolongation among hypertensive participants: results of multivariable logistic regression including main effects and interaction terms
| Variable | OR (95% CI) | P-value |
|---|---|---|
| Gender (Ref = male) | 1.49 (0.98, 2.28) | 0.062 |
| Age (years) | 1.02 (1.00, 1.03) | 0.055 |
| Having a job (Ref = no) | 1.23 (0.82, 1.85) | 0.314 |
| Physical activity (METs) (kcal/kg/hour) | 0.99 (0.97, 1.01) | 0.419 |
| Hyperlipidemia (Ref = no) | 1.09 (0.73, 1.62) | 0.670 |
| Ischemic heart disease (Ref = no) | 1.07 (0.78, 1.46) | 0.691 |
| Thyroid disease (Ref = no) | 1.27 (0.87, 1.46) | 0.691 |
| Chronic lung disease (Ref = no) | 0.51 (0.21, 1.23) | 0.135 |
| MI in a second degree relative (Ref = no) | 0.63 (0.37, 1.08) | 0.093 |
| Fatty Liver Index (FLI) | 1.00 (1.00, 1.01) | 0.426 |
| Sodium Intake (mg/day) | 1.00007 (1.000002, 1.0001) | 0.042 |
| Night shift work (Ref = no) | 0.74 (0.36, 1.53) | 0.419 |
| Sleeping pill use (Ref = no) | 1.23 (0.84, 1.79) | 0.288 |
| Regular daytime naps (Ref = no) | 1.01 (0.74, 1.38) | 0.966 |
| Long night sleep duration (≥ 9 h) (Ref = no) | 0.45 (0.17, 1.18) | 0.106 |
| Diuretic use (Ref = no) | 1.94 (0.90, 4.19) | 0.090 |
| Regular daytime naps × Long night sleep duration | 4.36 (1.43, 13.27) | 0.009 |
| Regular daytime naps × Diuretic use | 0.94 (0.35, 2.52) | 0.899 |
| Long night sleep duration × Diuretic use | 2.13 (0.26, 17.27) | 0.478 |
| Regular daytime naps × Long night sleep duration × Diuretic use | 0.27 (0.02, 3.77) | 0.329 |
Model included main effects and interaction terms: Regular daytime naps × Long night sleep duration, Regular daytime naps × Diuretic use, Long night sleep duration × Diuretic use, Regular daytime naps × Long night sleep duration × Diuretic use
Adjusted for age, sex, employment, physical activity, hyperlipidemia, ischemic heart disease, thyroid disease, chronic lung disease, second degree family history of MI, fatty liver index, sodium intake, and sleeping pill use
Only the nap × night interaction was significant (OR = 4.36, p < 0.05); all other terms were non-significant
Reference categories were coded as 0 for short/normal sleep, no regular napping, and no diuretic use. Interaction terms were constructed using binary coding (0/1)
Sodium intake showed a statistically significant but very small association with QTc prolongation (OR 1.001 per 1 mg/day). When rescaled per 1,000 mg/day, the effect size remained modest, indicating limited clinical relevance
Abbreviations: MI Myocardial infarction
Sex-stratified analyses were not performed due to limited statistical power within certain exposure subgroups, and exploratory sex-interaction analyses did not demonstrate significant effect modification by sex (sex × sleep phenotype p = 0.789).
Supplementary analyses
Baseline associations between sleep characteristics and QTc Prolongation
Among hypertensive participants, 274 individuals exhibited QTc prolongation. Regular daytime napping was not significantly associated with QTc prolongation at the univariable level (p = 0.422). QTc prolongation was observed in 39.1% of non-nappers and 60.9% of regular nappers.
Nighttime sleep duration, however, showed a significant association with QTc prolongation (p = 0.024). Participants with too short sleep (≤ 6 h) constituted 53.3% of QTc prolongation cases compared with 46.5% in the non-prolonged group. Adequate sleep (6–9 h) was less frequent among those with QTc prolongation (35.0%) compared to those without (44.1%). Long sleep duration (≥ 9 h) accounted for 11.7% of QTc prolongation cases versus 9.4% among those without QTc prolongation (Table S1).
When evaluating the combined exposure (regular daytime napping plus long sleep duration), QTc prolongation was significantly more common in participants with both exposures (9.1%) compared to those without the combined phenotype (4.8%) (p = 0.006) (Table S2). These cell counts indicate that the long-sleep group was relatively small, with limited QTc events in some exposure strata. Accordingly, absolute prevalence estimates are presented alongside odds ratios to aid interpretability.
Heart rate analysis
Mean heart rate did not differ significantly by regular daytime napping status (73.01 ± 13.27 bpm vs. 74.01 ± 12.46 bpm; p = 0.161) nor by sleep duration categories (p = 0.129). However, heart rate was significantly higher among individuals with QTc prolongation (79.03 ± 12.56 bpm) compared to those without prolongation (72.20 ± 12.50 bpm; p < 0.001) (Table S3).
Sensitivity analyses
Using Fridericia formula
When QTc was calculated using the Fridericia formula, regular daytime napping was not significantly associated with QTc prolongation across sleep strata (short sleep: OR 0.84; p = 0.545; adequate sleep: OR 1.23; p = 0.520; long sleep: OR 1.50; p = 0.512) (Table S4).
The interaction between napping and long sleep was also non-significant (OR 1.87; 95% CI 0.50–7.02; p = 0.352) (Table S5).
Only 14 participants in the long sleep group had QTc prolongation using Fridericia correction (10.6%), limiting statistical power (Table S6).
Additional adjustment for heart rate
After additional adjustment for heart rate, the association in long sleepers remained significant and slightly stronger (OR 3.97; 95% CI 1.38–11.42; p = 0.010), supporting robustness of the primary finding (Table S7).
Fully adjusted model (literature-based covariates)
In the fully adjusted sensitivity model including extensive cardiometabolic, psychiatric, diet, and medication covariates, regular daytime napping remained significantly associated with QTc prolongation among long sleepers (OR 5.82; 95% CI 1.66–20.40; p = 0.006), while remaining non-significant in short and adequate sleep groups (Table S8).
Discussion
To the best of our knowledge, this is the first study to simultaneously examine the association of nighttime sleep duration and daytime napping with QTc prolongation in hypertensive patients. These findings suggest a synergistic effect of prolonged nighttime sleep and regular daytime napping on QTc prolongation among hypertensive adults. While daytime napping alone and prolonged nighttime sleep defined as nighttime sleep duration ≥ 9 h assessed using PSQI-derived estimates alone were not significantly associated with QTc prolongation, their combination markedly increased the odds, indicating that excessive total sleep duration, distributed across both day and night, may contribute to impaired cardiac repolarization. The association was independent of antihypertensive medication use, including diuretics, demonstrating that the observed risk is driven by sleep behavior itself rather than pharmacologic treatment. In contrast, short nighttime sleep with daytime napping showed an opposite, though non-significant, trend, indicating that prolonged rather than reduced sleep duration may contribute to impaired cardiac repolarization.
Only one prior study, conducted by Wang et al. [8], has directly examined the association between chronic sleep parameters and QTc prolongation, focusing on Chinese elderly adults. The study reported that both poor sleep quality and extremes of sleep duration (≤ 6 h or > 10 h) were associated with QTc prolongation. However, Wang’s work was limited to a general elderly population and did not adjust for medication use. In contrast, our work extends these findings to hypertensive adults across a wider age range, a population with elevated baseline cardiovascular risk, and uniquely demonstrates an interaction between nighttime sleep and daytime napping. This highlights a novel behavioral pathway linking excessive sleep duration to cardiac repolarization abnormalities.
Although evidence directly linking combined sleep duration and napping with QTc prolongation is scarce, several studies have investigated their joint effects on cardiovascular and systemic outcomes. Yang et al. [40] showed that long sleep (≥ 10 h) combined with extended napping (> 90 min) increased coronary heart disease risk in Chinese adults. Wang et al. [41] reported higher mortality when long napping co-occurred with either short or long nighttime sleep. Additionally, Meta-analyses [42] also, suggest that daytime napping increases the risk of major cardiovascular events and mortality for individuals who sleep more than 6 h each night, but not for those who sleep less than 6 h per night. These findings together suggest that excessive cumulative sleep, whether distributed across day and night, may adversely influence cardiovascular regulation and possibly contribute to repolarization abnormalities. These findings are also consistent with prior studies linking hypersomnia and excessive sleep duration with adverse cardiovascular outcomes [43–47], although direct comparisons are limited by differences in sleep assessment and study design.
Excessive sleep duration, particularly when distributed across both nighttime and daytime periods, may promote QTc prolongation through several converging biological pathways. Sleep regulatory systems demonstrate dynamic coupling between the central and autonomic nervous systems, a relationship that becomes disrupted during altered sleep patterns. Autonomic conflict, defined as the simultaneous activation of sympathetic and parasympathetic inputs to the heart, serves as a pro-arrhythmic trigger that worsens long QT–related ventricular arrhythmias and contributes to prolonged cardiac repolarization. Sleep, therefore, plays a critical role in arrhythmogenesis by modulating autonomic mechanisms that influence cardiac electrophysiology. In particular, vagus nerve stimulation affects cardiovascular function both through direct modulation of cardiac repolarization and through regulation of ion channel activity [15, 48–52]. Long sleep duration is associated with elevated markers of systemic inflammation, particularly interleukin-6 (IL-6) and C-reactive protein (CRP). IL-6 plays a key pathogenic role in QTc prolongation by directly inhibiting the rapid delayed rectifier potassium current (IKr), thereby prolonging the action potential duration in ventricular myocytes. Dysfunction of cardiac potassium channels, especially the delayed rectifier currents (IKs and IKr), is central to repolarization, and their impairment contributes directly to QT interval abnormalities [53–56]. At the same time, circadian clock disruption from irregular sleep–wake patterns interferes with the molecular regulation of key repolarizing potassium channels, further diminishing repolarization reserve [57, 58]. In hypertensive patients, however, the convergence of autonomic dysregulation, inflammation, RAAS activation, metabolic abnormalities, and structural heart changes [59–61] creates a synergistic environment that magnifies the arrhythmic potential of excessive total sleep distributed across both night and day. Thus, long sleep duration and daytime napping may reflect unfavorable sleep patterns that contribute to autonomic imbalance and ventricular repolarization abnormalities. Alternatively, long nighttime sleep and frequent daytime napping in hypertensive adults may not represent causal behavioral exposures but rather phenotypic markers of underlying sleep fragmentation, excessive daytime sleepiness, or undiagnosed sleep disorders such as obstructive sleep apnea (OSA), which is common among overweight individuals with hypertension and is independently associated with QTc prolongation, nocturnal hypoxemia, and excessive daytime sleepiness [62]. In this context, the observed association between combined long nighttime sleep and daytime napping with QTc prolongation may be partially driven by unmeasured sleep-disordered breathing rather than a direct causal effect of sleep duration itself. Although statistically significant associations were observed, the wide confidence intervals, particularly for interaction estimates, indicate limited precision and suggest that the magnitude of effect should be interpreted cautiously. The small size of certain exposure subgroups may have contributed to this imprecision. Among antihypertensive classes, diuretics were specifically considered in interaction analyses because of their known capacity to alter potassium and magnesium balance, which may influence ventricular repolarization. Other antihypertensive classes primarily affect autonomic tone or blood pressure but have less direct electrophysiologic impact on QT interval [63].
Emerging mechanistic evidence also links circadian blood pressure regulation (“nocturnal dipping”) to vascular inflammatory pathways that may be relevant to sleep-cardiovascular phenotypes. Experimental studies have identified the volume-regulated anion channel subunit LRRC8A in vascular smooth muscle as a nodal mediator of angiotensin-II–induced oxidative stress and inflammation. In hypertensive mouse models, smooth-muscle-specific LRRC8A deletion reduces vascular inflammation and preserves physiological nocturnal blood-pressure dipping despite angiotensin-II exposure, suggesting that LRRC8A-dependent inflammatory signaling contributes to impaired circadian vascular tone regulation [64, 65]. These data support the concept that nondipping hypertension reflects underlying vascular inflammatory dysregulation rather than solely hemodynamic load. Although nocturnal blood-pressure patterns were not available in the present cohort, sleep phenotypes such as prolonged nocturnal sleep and daytime napping may partly capture underlying circadian and inflammatory vascular alterations linked to nondipping physiology. Future studies integrating sleep architecture, ambulatory blood-pressure monitoring, and molecular inflammatory markers are needed to clarify these relationships in hypertensive populations.
This study has several notable strengths. First, it was conducted in a relatively large hypertensive population, providing sufficient power to detect meaningful associations between sleep parameters and QTc prolongation. Moreover, the combination of prolonged nighttime sleep and regular daytime napping was linked to nearly a four-fold increase in the odds of QTc prolongation, although the confidence interval was relatively wide, suggesting uncertainty in the magnitude of the association despite statistical significance. These findings underscore both the clinical relevance of the results and the robustness of the observed effect in a well-characterized hypertensive cohort. Second, the analyses were rigorously adjusted for a wide range of demographic, clinical, lifestyle, and medication-related confounders, including antihypertensive agents known to affect the QT interval. This reduces the likelihood of residual confounding and strengthens the validity of our findings. Third, by focusing exclusively on hypertensive adults, a group with heightened cardiovascular risk, we were able to evaluate the impact of sleep behaviors in a clinically vulnerable population where repolarization abnormalities have important prognostic implications. Finally, our approach accounted for both nighttime sleep duration and daytime napping, enabling the novel identification of a combined sleep phenotype that may influence cardiac electrophysiology.
Despite these strengths, several limitations should be acknowledged. First, sleep parameters were self-reported using the Pittsburgh Sleep Quality Index. Although subject to recall bias, this method is widely used and validated in epidemiological studies, providing a reliable measure of habitual sleep behaviors. Second, information on congenital or major structural heart disease and significant valvular abnormalities was not available. However, these conditions are relatively uncommon in large community-based cohorts, such as ours, and their absence is unlikely to substantially affect our findings. Although, future studies incorporating echocardiographic data may strengthen the conclusions. Third, electrolyte abnormalities were not systematically assessed. Nevertheless, in a non-hospitalized outpatient population, the prevalence of clinically significant electrolyte disturbances is expected to be low, and severe derangements sufficient to alter QTc are rare. Forth, objective assessment or clinical diagnosis of sleep-disordered breathing, particularly obstructive sleep apnea, was not available. Given the overweight mean BMI and hypertensive status of the cohort, residual confounding by undiagnosed sleep apnea is plausible and may partly explain the observed associations. Fifth, although QT-prolonging medications were comprehensively identified, information on dosage, treatment duration, and temporal proximity to ECG recording was unavailable, which may have limited the ability to detect medication-related QTc effects. Sixth, multiple statistical tests were performed across main effects, subgroup analyses, and interaction models, and no post-hoc adjustment for multiple comparisons was applied. Therefore, some statistically significant findings may represent false-positive results, and the findings should be considered hypothesis-generating rather than confirmatory. Also, daytime napping was classified based on frequency without information on nap duration, which may have obscured heterogeneous cardiovascular effects of short versus prolonged naps.
QTc was primarily corrected using Bazett’s formula, which remains widely used in clinical and epidemiologic research despite known rate-dependent bias (overestimation at higher heart rates and underestimation at lower rates). In our cohort, heart rate did not differ systematically across sleep phenotypes, reducing concern for differential misclassification. Nevertheless, individuals with QTc prolongation had higher mean heart rate overall. To address potential heart-rate–related bias, we performed two sensitivity analyses. First, QTc was recalculated using Fridericia’s correction. However, direct RR-interval data were unavailable and RR had to be derived from heart rate, requiring assumptions of regular rhythm. Moreover, only 14 participants in the long-sleep group had QTc prolongation under Fridericia correction, substantially limiting statistical power and yielding imprecise estimates. Second, we additionally adjusted the primary Bazett-based regression models for heart rate, and the association between long sleep and QTc prolongation remained materially unchanged. Together, these analyses suggest that heart-rate variability and QT correction method are unlikely to fully account for the observed association, although some residual QTc measurement bias cannot be excluded.
Finally, the cross-sectional design precludes causal inference; longitudinal studies are required to determine whether modifying sleep behaviors could reduce QTc prolongation and associated arrhythmic risk. Moreover, several key sleep-disorder–related variables were unavailable in the FACS baseline dataset, including snoring, witnessed apneas, standardized daytime sleepiness scales, neck circumference, and objective nocturnal hypoxemia measures. Consequently, we could not directly assess obstructive sleep apnea or sleep fragmentation. Although we adjusted for body mass index and incorporated involuntary dozing off as a proxy indicator of excessive daytime sleepiness, substantial residual confounding by undiagnosed OSA or other sleep pathology remains likely. Reverse causation is also possible due to the cross-sectional design, whereby individuals with poorer cardiovascular health or autonomic dysfunction may experience greater fatigue, prolonged sleep, or daytime napping.
The observed interaction should be interpreted cautiously. The relatively small number of participants and QTc prolongation events in the long-sleep stratum increases susceptibility to sparse-data bias, which can inflate effect estimates and widen confidence intervals in logistic regression models. As such, the magnitude of the interaction effect may be overestimated. Accordingly, this interaction should be considered exploratory and hypothesis-generating rather than confirmatory, and requires replication in larger cohorts with adequate exposure cell counts. Interaction analyses were conducted on the multiplicative scale using logistic regression. Additive interaction metrics (e.g., relative excess risk due to interaction, attributable proportion, or synergy index) were not assessed; therefore, conclusions regarding biological or public health synergy cannot be drawn. The predominantly female composition of the cohort may limit generalizability, particularly given known sex differences in QTc duration, QTc thresholds, and sleep behaviors. Cultural, occupational, and environmental factors specific to this rural Iranian population may further influence sleep patterns and cardiometabolic risk. Therefore, extrapolation to male-predominant, urban, or Western populations should be made with caution. Given the cross-sectional design, reverse causation cannot be excluded. QTc prolongation may reflect subclinical cardiac or autonomic dysfunction that contributes to fatigue, prolonged nighttime sleep, and increased daytime napping, rather than these sleep behaviors acting as causal drivers. Sleep behaviors were derived from PSQI components and are subject to misclassification, particularly for daytime napping frequency and unmeasured nap duration.
Clinically, hypertensive individuals exhibiting both long nighttime sleep and regular daytime napping may represent a high-risk group, warranting closer monitoring through electrocardiographic evaluations and personalized counseling on sleep habits. These results underscore the importance of considering joint sleep behaviors rather than isolated sleep metrics in cardiovascular risk assessment.
Future studies should replicate these findings in larger and more diverse cohorts to establish the generalizability of this combined sleep phenotype as a risk factor for QTc prolongation. Prospective cohort studies are needed to clarify temporal directionality and to determine whether prolonged nighttime sleep and regular daytime napping precede changes in QTc duration or reflect underlying disease burden. Incorporation of objective sleep assessments (e.g., actigraphy or polysomnography), heart rate variability measures, inflammatory biomarkers, serum electrolyte levels, and echocardiographic evaluation would help elucidate potential mechanisms, including autonomic imbalance, metabolic dysfunction, electrolyte disturbances, and structural cardiac abnormalities that may mediate the observed interaction. In addition, interventional studies are warranted to evaluate whether modifying sleep patterns in this high-risk subgroup can influence QTc duration or reduce arrhythmic events, thereby informing targeted preventive strategies for hypertensive populations.
Conclusion
The combination of prolonged nighttime sleep and regular daytime napping creates a synergistic effect that significantly increases QTc prolongation risk in hypertensive patients. This finding suggests that excessive total sleep duration distributed across day and night may impair cardiac repolarization through mechanisms involving autonomic dysregulation and circadian rhythm disruption. Hypertensive individuals with this combined sleep phenotype may require closer cardiovascular monitoring and targeted sleep counseling as part of arrhythmic risk management. Furthermore, hypertensive patients with long nighttime sleep may reduce their likelihood of QTc prolongation by limiting additional daytime naps, thereby lowering the potential risk of arrhythmic events. Although, these findings are observational and hypothesis-generating and require confirmation in longitudinal or interventional studies. The observed associations between combined sleep behaviors and QTc prolongation should be interpreted in light of potential exposure misclassification and warrant confirmation using objective sleep measurements. Moreover, causal inferences cannot be drawn due to the cross-sectional design, and prospective studies are needed to determine temporal directionality.
Supplementary Information
Acknowledgements
The authors have no acknowledgments to declare.
Informed consent statement
Informed consent was obtained from all participants involved in the study.
Authors’ contributions
Conceptualization: Z.M., S.B.; Methodology: S.B.; Software: S.B.; Validation: S.B., R.T., M.A; Formal analysis: S.B.; Investigation: Z.M. and S.B.; Resources: R.T., S.K.; Data curation: S.B.; Writing – original draft: Z.M. and S.B.; Writing – review and editing: S.B., Z.M., M.A., S.K.; Visualization: S.B.; Supervision: R.T., M.A., S.K.; Project administration: R.T. and S.B. All authors have read and approved the final version of the manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
Our institutional policy does not specify that the data must be made public, nor does a data and material transfer agreement allow for further data transfer without the provider’s prior written consent. However, the data can be requested from the corresponding author, who is part of this team. Additionally, the dataset produced for this study is available upon request to the Fasa Non-Communicable Diseases Research Center management team. They can be reached by telephone at +987153314068 or by email at ncdrc.fums.ac.ir@gmail.com .
Declarations
Ethics approval and consent to participate
The study was conducted by the guidelines of the Declaration of Helsinki. The research protocol received approval from the Institutional Review Board of Fasa University of Medical Sciences (ethical approval number: IR.FUMS.REC.1403.098).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Supplementary Materials
Data Availability Statement
Our institutional policy does not specify that the data must be made public, nor does a data and material transfer agreement allow for further data transfer without the provider’s prior written consent. However, the data can be requested from the corresponding author, who is part of this team. Additionally, the dataset produced for this study is available upon request to the Fasa Non-Communicable Diseases Research Center management team. They can be reached by telephone at +987153314068 or by email at ncdrc.fums.ac.ir@gmail.com .