The relationship between composite lipid indices and Baveno classification in patients with obstructive sleep apnoea

The relationship between composite lipid indices and Baveno classification in patients with obstructive sleep apnoea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The relationship between composite lipid indices and Baveno classification in patients with obstructive sleep apnoea Andreea-Roxana Florescu, Stefan Mihaicuta, Stefan Marian Frent, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4807363/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Obstructive Sleep Apnoea (OSA) is associated with the development of cardiovascular disease (CVD); however, the risk is only weekly related to OSA severity traditionally determined by the apnoea-hypopnoea index. The Baveno classification was developed to improve patient selection who would benefit from OSA treatment in terms of symptoms improvement and CVD risk reduction. However, it is unclear how the classification relates to biomarkers of CVD risk. Composite lipid indices were evaluated in various populations and showed good predictive value for incident CVD. Methods One thousand two hundred and forty-two patients with OSA were included in the study. Composite lipid indices, including the atherogenic index of plasma (AIP), visceral adiposity index (VAI), lipid accumulation product (LAP) and cardiometabolic index (CMI) were compared between different Baveno groups (A – minor symptoms, minor comorbidities, B – severe symptoms, minor comorbidities, C – minor symptoms, severe comorbidities, D – severe symptoms, severe comorbidities). Results All composite lipid indices were higher in group D versus groups A and C. Only LAP (86.93 /58.40–119.49/ vs. 75.73 /48.37–99.04/) and CMI (1.03 /0.69–1.42/ vs. 0.91 /0.63–1.12/) were significantly different between the minimally symptomatic groups (C vs. A), and solely LAP was significantly higher (110.69 /76.76–147.22/ vs. 82.32 /60.35–113.50/) when the highly symptomatic groups (D vs. B) were compared. Conclusion The Baveno classification correlates with cardiovascular risk estimated with composite lipid indices. Composite lipid indices may help better stratifying cardiovascular risk in both minimally- and highly symptomatic patients. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Introduction Obstructive sleep apnoea (OSA) is a very common disease that is characterised by repetitive collapse of the upper airways during sleep. Due to the resulting chronic intermittent hypoxaemia, sleep fragmentation and changes in the intrathoracic pressures, OSA is associated with a significant risk for the development of cardiovascular diseases [ 1 ]. However, the treatment with continuous positive airway pressure (CPAP) did not reduce cardiovascular events in large randomised controlled trials (RCTs) [ 2 – 4 ]. This suggests that the OSA-associated cardiovascular risk cannot be generalised at individual patient level, and biomarkers that can predict future cardiovascular risk to better tailor treatment are warranted in clinical practice. Apnoea-hypopnoea index (AHI) is a measure of the frequency of the respiratory events [ 5 ]. It is necessary for the diagnosis, but is poorly correlated with cardiovascular outcomes [ 5 , 6 ]. In contrast, sleep apnoea-specific hypoxic burden was reported to predict cardiovascular events in population-based studies [ 7 , 8 ] and post-hoc analyses of a RCT [ 9 ]; however, its calculation requires specific analysis that is not widely available in clinical practice. Obstructive sleep apnoea leads to dyslipidaemia through several mechanisms that include consumption of lipid-rich foods, lipid mobilisation from adipose tissue, increased production of triglycerides as well as impaired reverse cholesterol transport from the atherosclerotic vessels [ 10 ]. Dyslipidaemia is known to increase the risk for cardiovascular diseases, and higher triglyceride as well as lower high-density lipoprotein cholesterol (HDL-C) levels independently relate to this increased risk [ 11 , 12 ]. Furthermore, there is a dynamic exchange of cholesterol and tryglycerides between lipoprotein particles [ 13 ]; therefore, individual lipid or lipoprotein levels cannot fully explain the cardiovascular risk. To address this, and taking into account the potential additive value of visceral fat distribution for the cardiovascular risk [ 14 ], several composite indices were introduced, such as the atherogenic index of plasma (AIP), lipid accumulation product (LAP), visceral adiposity index (VAI) and cardiometabolic index (CMI). The differences between the indices include the impact of individual lipid values, the impact of biological sex, body mass index and waist circumference. These indices predicted cardiovascular diseases better than the individual lipid components [ 15 – 18 ]. They have been thoroughly evaluated and also compared in OSA [ 19 – 29 ]. Most studies, except two evaluating VAI [ 28 , 29 ], reported a significant relationship with OSA severity, as determined by the AHI. The incidence of cardiovascular events was evaluated in relation to baseline AIP [ 30 ] and CMI [ 18 ]. Analysing more than 2000 patients, both studies have shown a significant relationship [ 18 , 30 ]. Finally, significant relationships between the prevalence of cardiovascular diseases and lipid indices were reported in a systematic review and meta-analysis [ 22 ], indicating that they may serve as predictive biomarkers to quantify cardiovascular risk in patients with OSA. Recently, the Baveno classification has been developed as a feasible method to group patients based on their burden of symptoms and comorbidities in an attempt to better select those who could benefit from CPAP [ 31 ]. The four Baveno groups include patients with minor symptoms and low comorbidity burden (group A), severe symptoms and low comorbidity burden (group B), minor symptoms and high comorbidity burden (group C) and severe symptoms and high comorbidity burden (group D). However, the number of studies validating the Baveno classification is limited. Serino et al. have concluded significant temporal variability of the classification in the same patient [ 32 ]. In a small study, the adherence to CPAP was significantly higher in group D than in any other group [ 33 ]. In the European Sleep Apnoea Database (ESADA) study, CPAP improved blood pressure only in patients who were in groups C and D and patients in groups B and D perceived the highest symptomatic benefit from CPAP treatment [ 34 ]. In contrast, in another large observational cohort study, the Baveno groups did not differ in predicting cardiovascular mortality [ 35 ]. To better understand the validity of the Baveno classification, we aimed to correlate it with cardiovascular risk estimated by composite lipid indices. We also explored which index is better related to Baveno classification. Methods Study design and subjects We assessed the data of 2937 patients participating in the ESADA study from the Timisoara site. The patients were classified into Baveno groups as described previously [ 34 ]. Following the exclusion of patients with incomplete lipid data, 1242 patients were included to the current analysis. Hypertension, ischaemic heart disease (stable angina, previous cardiovascular event), cerebrovascular disease (previous cerebrovascular event) and type 2 diabetes were defined based on medical history and relevant medications. Venous blood samples were taken for measuring the level of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). We recorded the blood pressure values as well as heart rates in the morning; height, weight, neck circumference (NC) and waist circumference (WC) were measured, and the participants filled out the Epworth Sleepiness Scale (ESS). The study was approved by the local Ethics Committee (22/2014/24.07.2019 University of Medicine and Pharmacy Victor Babes Timisoara) and all methods used, were performed in accordance with all the relevant guidelines and regulations. Patients gave their informed consent before participating. Composite lipid indices AIP was calculated as \(\:{Log}_{10}\left(\frac{Tg}{HDL-C}\right)\) , VAI was calculated as \(\:\frac{WC}{(39.68+(1.88\times\:BMI)}\times\:\frac{TG}{1.03}\times\:\frac{1.31}{HDL-C}\) for men and \(\:\frac{WC}{36.58+(1.89\times\:BMI)}\times\:\frac{TG}{0.81}\times\:\frac{1.52}{HDL-C}\) for women, LAP was calculated as \(\:(WC-65)\times\:TG\) for men and \(\:(WC-58)\times\:TG\) for women, and CMI was calculated as \(\:\frac{Tg}{\text{H}\text{D}\text{L}-\text{C}}\times\:\frac{WC}{\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}}\) where WC and height were both expressed in cm and TGs and HDL-C were both expressed in mmol/l. Sleep studies Cardiorespiratory polygraphy was performed according to the recommendations of the American Academy of Sleep Medicine (AASM) [ 36 ]. Apnoea was defined as at least 90% reduction in the nasal airflow that lasted for at least 10 seconds. Hypopnoea was defined as at least 30% reduction in the nasal airflow that lasted for at least 10 seconds accompanied by at least 3% drop in oxygen saturation. An AHI ≥ 5/h was diagnostic for OSA. Based on the sleep study patients were grouped into mild (AHI 5-14.9/h), moderate AHI (15-29.9/h) and severe (AHI ≥ 30/h) groups. Apart from the AHI, we recorded the time spent with oxygen saturation below 90% (T90%). Statistical analysis The JASP 0.14.1 (University of Amsterdam, Amsterdam, Netherlands) software was used for statistical analysis. Data were compared along severities and Baveno groups using Chi-square and Kruskal-Wallis tests. Composite lipid indices were compared along severities and Baveno groups with Kruskal-Wallis test followed by the Dunn’s test and Bonferroni’s correction. Analyses on composite lipid indices were also performed following adjustment for age, sex, BMI and AHI. Data are presented as median /interquartile range/. A p value < 0.05 was considered significant. Results Clinical characteristics along severity categories and Baveno groups The three severity groups differed in age, blood pressure, heart rate, WC, NC, AHI, T90%, ESS values and HDL-C, LDL-C and triglyceride levels (all p < 0.05). Interestingly, there was only a statistical trend for total cholesterol (p = 0.07). The prevalence of males, hypertension, ischaemic heart disease and type 2 diabetes increased in parallel with worsening severity. In contrast, there was no difference in the prevalence of cerebrovascular disease or smokers between the three groups (all p > 0.05, Table 1 ). With increasing severity, we noticed that the ratio of different Baveno groups changed. There were more Baveno A patients in the mild group and more Baveno D patients in the severe group. Table 1 Clinical characteristics along OSA severities Mild OSA (n = 183) Moderate OSA (n = 363) Severe OSA (n = 696) P value Age (years) 51 /43–61/ 55 /46–64/ 56 /48–65/ < 0.01 Sex (males %) 56 67 76 < 0.01 BMI (kg/m2) 30.0 /26.6–34.4/ 31.9 /27.9–35.4/ 35.1 /31.4–39.2/ < 0.01 SBP (mmHg) 130 /121–140/ 132 /125–140/ 135 /128–141/ < 0.01 DBP (mmHg) 79 /72–85/ 80 /73–89/ 80 /77–90/ < 0.01 Heart rate (/min) 75 /69–84/ 78 /71–83/ 78 /72–86/ 0.02 WC (cm) 107 /97–119/ 112 /104–120/ 120 /111–129/ < 0.01 NC (cm) 40 /37–42/ 42 /39–44/ 44 /42–46/ < 0.01 Hypertension (%) 61 71 82 < 0.01 Ischaemic heart disease (%) 7 14 19 < 0.01 Cerebrovascular disease (%) 4 5 7 0.21 Type 2 Diabetes (%) 14 16 24 < 0.01 Smokers (%) 24 26 27 0.68 ESS 7 /4–10/ 8 /5–11/ 10 /7–13/ < 0.01 AHI (/h) 11.0 /8.0–13.1/ 22.0 /18.7–25.8/ 49.8 /37.4–66.8/ < 0.01 T90% (mins) 0.7 /0.1–4.3/ 7.5 /1.2–27.0/ 33 /8.1–84.4/ < 0.01 TC (mmol/l) 5.21 /4.68–5.66/ 5.20 /4.49–5.78/ 5.43 /4.55–5.97/ 0.07 LDL-C (mmol/l) 3.18 /2.61–3.93/ 3.28 /2.61–4.06/ 3.54 /2.67–4.19/ < 0.01 HDL-C (mmol/l) 1.13 /1.06–1.32/ 1.11 /1.03–1.27/ 1.06 /1.03–1.22/ < 0.01 TG (mmol/l) 1.68 /1.15–2.04/ 1.71 /1.25–2.12/ 1.84 /1.37–2.25/ < 0.01 Baveno group (A/B/C/D%) 19/8/52/21 16/6/53/25 9/6/41/44 < 0.01 AHI – apnoea-hypopnoea index, BMI – body mass index, DBP – diastolic blood pressure, ESS – Epworth Sleepiness Scale, HDL-C – high-density lipoprotein cholesterol, LDL-C – low-density lipoprotein cholesterol, NC – neck circumference, SBP – systolic blood pressure, T90% – time spent with oxygen saturation below 90%, TC – total cholesterol, TG – triglycerides, WC – waist circumference. There were significant differences between the four Baveno groups for most investigated variables, except for the HDL-C levels (Table 2 ). The age and, as expected, the prevalence of smoking and comorbidities were higher in groups C and D. Interestingly, the lipid levels were higher in the symptomatic (B and D) groups. Whilst there was no difference in OSA severity between the C and D groups, the prevalence of mild OSA was the highest in group A and the prevalence of severe OSA was the highest in group D. Table 2 Clinical characteristics among Baveno groups Group A (n = 157) Group B (n = 78) Group C (n = 571) Group D (n = 436) P value Age (years) 46 /40–51/ 46 /40–52/ 58 /50–66/ 57 /48–65/ < 0.01 Sex (males %) 78 76 67 71 0.02 BMI (kg/m2) 29.8 /27.2–33.2/ 32.0 /28.7–34.8/ 32.7 /28.7–36.3/ 36.0 /32.2–40.6/ < 0.01 SBP (mmHg) 125 /121–131/ 130 /122–135/ 135 /126–141/ 137 /130–145/ < 0.01 DBP (mmHg) 79 /72–81/ 79 /72–82/ 80 /75–90/ 81 /77–90/ < 0.01 Heart rate (/min) 78 /72–85/ 78 /73–89/ 77 /70–82/ 79 /72–87/ < 0.01 WC (cm) 108 /100–118/ 111 /102–119/ 114 /106–122/ 122 /113–131/ < 0.01 NC (cm) 41 /39–43/ 42 /39–44/ 42 /39–45/ 44 /41–47/ < 0.01 Hypertension (%) 0 0 93 94 < 0.01 Ischaemic heart disease (%) 2 1 14 26 < 0.01 Cerebrovascular disease (%) 0 0 7 9 < 0.01 Type 2 Diabetes (%) 0 0 11 7 < 0.01 Smokers (%) 0 0 23 28 < 0.01 ESS 6 /4–8/ 12 /11–14/ 7 /5–9/ 13 /11–14/ < 0.01 AHI (/h) 27.2 /16–40.1/ 30.2 /20.5–49.7/ 29.5 /17.9–44.1/ 42.4 /26.6–67.8/ < 0.01 T90% (min) 4.7 /0.3–21.2/ 10.2 /1.7–40.0/ 10.0 /2.0–39.8/ 38 /7.8–96.0/ < 0.01 TC (mmol/l) 5.22 /4.73–5.70/ 5.48 /4.97–5.92/ 5.20 /4.34–5.79/ 5.41 /4.52–6.01/ 0.02 LDL-C (mmol/l) 3.39 /2.79–3.98/ 3.60 /3.03–4.14/ 3.30 /2.56–4.06/ 3.54 /2.69–4.20/ < 0.01 HDL-C (mmol/l) 1.09 /1.03–1.22/ 1.09 /1.04–1.18/ 1.09 /1.03–1.25/ 1.06 /1.01–1.24/ 0.74 TG (mmol/l) 1.69 /1.29–1.94/ 1.82 /1.46–2.23/ 1.72 /1.26–2.15/ 1.90 /1.39–2.27/ 0.01 Mild/Moderate/Severe OSA (%) 22/36/42 19/30/51 17/33/50 9/21/70 < 0.01 AHI – apnoea-hypopnoea index, BMI – body mass index, DBP – diastolic blood pressure, ESS – Epworth Sleepiness Scale, HDL-C – high-density lipoprotein cholesterol, LDL-C – low-density lipoprotein cholesterol, NC – neck circumference, SBP – systolic blood pressure, T90% – time spent with oxygen saturation below 90%, TC – total cholesterol, TG – triglycerides, WC – waist circumference. Composite lipid indices along severity categories All composite lipid indices were related to disease severity; however, post-hoc tests revealed significant differences only between severe vs. mild and severe vs. moderate groups (Table 3 ). These results were unchanged following adjustment for age, sex, BMI and AHI. Table 3 Composite lipid indices along OSA severities Mild OSA (n = 183) Moderate OSA (n = 363) Severe OSA (n = 696) P value AIP 0.15 /-0.03–0.27/ 0.18 /0.01–2.92/ 0.23 /0.07–0.34/ #¶ < 0.01 VAI 2.41 /1.57–3.15/ 2.37 /1.65–3.30/ 2.65 /1.85–3.59/ #¶ < 0.01 LAP 73.60 /42.96–107.16/ 81.56 /54.09–110.29/ 103.56 /74.46–142.20/ #¶ < 0.01 CMI 0.90 /0.56–1.23/ 0.97 /0.65–1.34/ 1.20 /0.81–1.58/ #¶ < 0.01 AIP – atherogenic index of plasma, CMI – cardiometabolic index, LAP – lipid accumulation product, VAI – visceral adiposity index. # p < 0.05 vs. group mild OSA, ¶ p < 0.05 vs. moderate OSA. Composite lipid indices among Baveno groups Using the Kruskal-Wallis test all composite lipid indices were significantly different between the four Baveno groups. However, post hoc tests showed that AIP and VAI were different only between groups D and A as well as groups D and C, whilst LAP and CMI were different between groups C and A, groups D and A as well as groups D and C. Only LAP was significantly different between groups D and B (Table 4 ). These results were unchanged following adjustment for age, sex, BMI and AHI. Table 4 Composite lipid indices among Baveno groups Group A (n = 157) Group B (n = 78) Group C (n = 571) Group D (n = 436) P value AIP 0.18 /0.02–0.27/ 0.23 /0.11–0.32/ 0.19 /0.02–0.31/ 0.24 /0.06–0.34/ #@ < 0.01 VAI 2.35 /1.65–2.80/ 2.55 /1.96–3.34/ 2.50 /1.70–3.41/ 2.71 /1.85–3.65/ #@ < 0.01 LAP 75.73 /48.37–99.04/ 82.32 /60.35–113.50/ 86.93 /58.40–119.49/ # 110.69 /76.76–147.22/ #¶@ < 0.01 CMI 0.91 /0.63–1.12/ 1.07 /0.77–1.40/ 1.03 /0.69–1.42/ # 1.24 /0.80–1.62/ #@ < 0.01 AIP – atherogenic index of plasma, CMI – cardiometabolic index, LAP – lipid accumulation product, VAI – visceral adiposity index. # p < 0.05 vs. group A, ¶ p < 0.05 vs. group B, @ p < 0.05 vs. group C. Discussion In this study we compared composite lipid indices along OSA severity as assessed by the AHI and by the Baveno classification in a large cohort of OSA patients. We reported that AIP, VAI, LAP and CMI were all related to severe disease. Using Baveno groups A (minor symptoms and low comorbidity burden) and C (minor symptoms and high comorbidity burden) as references, all indices were higher in group D (severe symptoms and high comorbidity burden); however only LAP and CMI were higher in group C than A and only LAP was different between groups B (severe symptoms and low comorbidity burden) and D. Although the composite lipid indices were analysed in OSA in several studies [ 19 – 27 ], only few compared them. Our workgroup has recently evaluated AIP, VAI and LAP in OSA [ 19 ]. We reported that the association between OSA and these indices was the strongest with LAP which also predicted the presence of hypertension, diabetes and cardiovascular disease better than the other parameters [ 19 ]. Comparing LAP and VAI, two studies have concluded that the former is more strongly related to OSA [ 25 , 29 ]. In contrast, the CMI was compared to LAP and was found to better predict OSA and metabolic syndrome in OSA [ 27 ]. In the current analysis, only the LAP and CMI was different between the minimally symptomatic groups and only LAP was different between the highly symptomatic groups. These results suggest the CMI and LAP could help predicting cardiovascular disease both in sleepy and non-sleepy patients irrespectively of knowing about their comorbidity profile. In contrast, all composite lipid indices were higher in group D compared to group C. This suggest that lipid parameters and the corresponding cardiovascular risk is driven by OSA symptoms. This confirms the previous reports concluding that patients with OSA who report excessive daytime sleepiness [ 37 ] and insomnia [ 38 ] are at higher risk for development of cardiovascular disease. Patients with severe OSA had the highest values for all composite lipid indices. In addition, there was a progressive increase in the number of traditional cardiovascular risk factors, such as obesity, blood pressure values and diabetes along increasing OSA severity. This suggests that clinicians should still strongly consider treating severe OSA to reduce further cardiovascular events irrespectively from the Baveno classification. Whether the treatment would consist of CPAP, weight loss [ 39 ], medication [ 40 ] or their combination is up to the clinicians and patients’ decision. Similarly to the previous studies [ 34 , 35 ], patients in groups C and D were older and had higher BMI than in groups A and B. In addition, patients in C and D groups had higher blood pressure values [ 34 , 35 ]. Not surprisingly, patients in group D had more severe OSA in the previous studies [ 34 , 35 ], including ours. However, in the study of Jorquera et al. [ 35 ] patients in group C had more severe overnight hypoxaemia which can potentially drive higher triglyceride and cholesterol levels [ 6 ]. In contrast, in our study, there was no clinically significant difference in OSA severity between groups B and C. Yet, lipid values tended to be higher in group B than C. This suggests, that apart from hypoxaemia, lipid values may be associated with daytime symptoms. The causality between the symptoms burden and dyslipidaemia cannot be concluded from this study, however evidence suggest that the relationship could be bidirectional [ 10 , 41 ]. The link between OSA and its comorbidities, such as cardiovascular disease and dyslipidaemia is affected by the investigated population, as often uncontrolled genetic, epigenetic, dietary and lifestyle factors can affect this relationship [ 42 ]. Additionally, the symptoms’ burden shows high inter-population variability [ 43 ]. Therefore, multicentre studies evaluating Baveno classification maybe misleading due to the biases above. We believe the large-size, single centre study design is a strength rather than a limitation; however, we acknowledge that the generalisability of our results is limited. The study has limitations. First, it had a cross-sectional nature. Therefore, the causality between the composite lipid indices as well as symptoms and comorbidities cannot be concluded. We believe that our data could serve basis to design prospective studies evaluating this relationship. Second, we used cardiorespiratory polygraphy rather than polysomnography. Whilst the American Academy of Sleep Medicine endorses this diagnostic tool [ 44 ], it tends to underestimate OSA severity compared to polysomnography, as hypopneas associated with arousal but without oxygen desaturation are not scored. In addition, it may further underestimate OSA severity in patients with insomnia. As the primary analysis of our study did was irrespective from AHI, hence we believe that this limitation did not significantly affect the validity of our results. Third, we did not record lifestyle factors, such as diet and exercise apart, that could affect the lipid values. Day to day variations in lipid values depending on the last meal are plausible [ 45 ], and potential inter-day variations in the composite lipid values need to be thoroughly investigated before using them in clinical practice for cardiovascular risk stratification in OSA. In addition, due to the aforementioned factors, normal values for composite lipid indices in each population need to be determined. In summary, both severe OSA and OSA associated with high burden of symptoms and comorbidities (Baveno group D) were related to high levels of composite lipid indices. These groups may therefore require extra attention and encouragement to utilise OSA treatments. Of the investigated indices, the cardiometabolic index as well as the lipid accumulation product seem to be beneficial to identify the excessive cardiovascular risk in minimally symptomatic patients (groups A and C). For highly symptomatic patients (groups B and D) we recommend the use of LAP for the same purpose. Declarations Author Contributions: Conceptualization, S.M., S.M.F and A.B.; methodology, S.M., O.C.D., and A.B.; formal analysis, S.M., A.R.F. and A.B.; investigation, A.R.F., M.R.B., S.M.F and A.R.M.; resources, O.C.D, M.R.B., A.M.P and A.R.M; data curation, L.G. and S.M.F; writing—original draft preparation, S.M., A.B. and A.R.F; writing - review and editing, A.B., A.M.P., L.G. and A.R.M.; visualization, L.G. and A.M.P.; supervision, S.M.F, A.B. and M.R.B.; project administration, S.M.F., S.T.F. and M.R.B. All authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript. 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Applicability of visceral adiposity index in predicting metabolic syndrome in adults with obstructive sleep apnea: a cross-sectional study. BMC pulmonary medicine 2016: 16: 37. Wang D, et al . Application Value of Cardiometabolic Index for the Screening of Obstructive Sleep Apnea with or Without Metabolic Syndrome. Nature and science of sleep 2024: 16: 177-191. Mazzuca E, et al . Gender-specific anthropometric markers of adiposity, metabolic syndrome and visceral adiposity index (VAI) in patients with obstructive sleep apnea. Journal of sleep research 2014: 23(1): 13-21. Zhao X, et al . Abdominal Obesity Is More Strongly Correlated with Obstructive Sleep Apnea than General Obesity in China: Results from Two Separated Observational and Longitudinal Studies. Obesity surgery 2019: 29(8): 2535-2547. Cai XT, et al. A J-shaped relationship between the atherogenic index of plasma and new-onset myocardial infarction in hypertensive patients with obstructive sleep apnea: a cohort study. European review for medical and pharmacological sciences 2022: 26(21): 8022-8029. Randerath W, et al . Challenges and perspectives in obstructive sleep apnoea: Report by an ad hoc working group of the Sleep Disordered Breathing Group of the European Respiratory Society and the European Sleep Research Society. The European respiratory journal 2018: 52(3). Serino M, et al . OSA patients not treated with PAP - Evolution over 5 years according to the Baveno classification and cardiovascular outcomes. Sleep medicine 2021: 88: 1-6. Sousa SR, Caldeira JN, Moita J. Beyond Apnea-Hypopnea Index: how clinical and comorbidity are important in obstructive sleep apnea. Advances in respiratory medicine 2022. Randerath WJ, et al . Evaluation of a multicomponent grading system for obstructive sleep apnoea: the Baveno classification. ERJ open research 2021: 7(1). Jorquera J, et al . Clinical Application of the Multicomponent Grading System for Sleep Apnea Classification and Incident Cardiovascular Mortality. Sleep science (Sao Paulo, Brazil) 2023: 16(4): e446-e453. Kushida CA, et al . Practice parameters for the indications for polysomnography and related procedures: an update for 2005. Sleep 2005: 28(4): 499-521. Mazzotti DR, Keenan BT, Lim DC, Gottlieb DJ, Kim J, Pack AI. Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes. American journal of respiratory and critical care medicine 2019: 200(4): 493-506. Luyster FS, et al . Association of comorbid obstructive sleep apnea and insomnia with risk of major adverse cardiovascular events in sleep medicine center patients. Sleep health 2024: 10(3): 335-341. Chirinos JA, et al . CPAP, weight loss, or both for obstructive sleep apnea. The New England journal of medicine 2014: 370(24): 2265-2275. Malhotra A, et al . Tirzepatide for the treatment of obstructive sleep apnea: Rationale, design, and sample baseline characteristics of the SURMOUNT -OSA phase 3 trial. Contemporary clinical trials 2024: 141: 107516. Vgontzas AN, et al . Sleep apnea and daytime sleepiness and fatigue: relation to visceral obesity, insulin resistance, and hypercytokinemia. The Journal of clinical endocrinology and metabolism 2000: 85(3): 1151-1158. Bikov A, et al . The Burden of Associated Comorbidities in Patients with Obstructive Sleep Apnea-Regional Differences in Two Central-Eastern European Sleep Centers. Journal of clinical medicine 2020: 9(11). Bonsignore MR, et al . Excessive Daytime Sleepiness in Obstructive Sleep Apnea Patients Treated With Continuous Positive Airway Pressure: Data From the European Sleep Apnea Database. Frontiers in neurology 2021: 12: 690008. Kapur VK, et al . Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 2017: 13(3): 479-504. Nordestgaard BG, et al . Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points-a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine. European heart journal 2016: 37(25): 1944-1958. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4807363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":347457597,"identity":"003a754a-64ed-46dc-8744-b59c3e1fea7b","order_by":0,"name":"Andreea-Roxana Florescu","email":"","orcid":"","institution":"Institutul de Pneumoftiziologie \"Marius Nasta\"","correspondingAuthor":false,"prefix":"","firstName":"Andreea-Roxana","middleName":"","lastName":"Florescu","suffix":""},{"id":347457598,"identity":"ec6a0c29-c45e-4369-b78b-1ac1d7253e37","order_by":1,"name":"Stefan Mihaicuta","email":"","orcid":"","institution":"University of Medicine and Pharmacy “Victor Babeș”","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Mihaicuta","suffix":""},{"id":347457599,"identity":"4378c308-1b97-4aea-a073-740a8918de9b","order_by":2,"name":"Stefan Marian Frent","email":"","orcid":"","institution":"University of Medicine and Pharmacy “Victor Babeș”","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"Marian","lastName":"Frent","suffix":""},{"id":347457600,"identity":"36bab67c-1942-438a-9a25-5cac73405dda","order_by":3,"name":"Oana-Claudia Deleanu","email":"","orcid":"","institution":"Somnolog Clinic","correspondingAuthor":false,"prefix":"","firstName":"Oana-Claudia","middleName":"","lastName":"Deleanu","suffix":""},{"id":347457601,"identity":"f340abbb-7a21-4c7d-b7a7-156d5150313b","order_by":4,"name":"Andras Bikov","email":"","orcid":"","institution":"Manchester University NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Andras","middleName":"","lastName":"Bikov","suffix":""},{"id":347457602,"identity":"79e14d1f-ef75-4d3b-acb8-fd803d21ead8","order_by":5,"name":"Mariela Romina Birza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYJCCA0CcwAZifWBg4CdNC+MMBgbJHmJtSgARzDzEaDFv7048XFDDkMcnffzhZ9u2bRL2DLwPH+DTInPm7IbDM44xFLPxJSRL57bdluBhYDc2wKdFQiJ3w2EeNobENh6GAyAtdTwMbGwShLX8A2lhbP5tCbaFGC28bSAtzGzSjERp4QH6hbdPopiNh43NsuccUMthNmb8fmHv3fyZ55tNnnwP++MbP8puS7C3tzE+wKcFphOJzUyE+lEwCkbBKBgF+AEALOA9Xn9CEJ8AAAAASUVORK5CYII=","orcid":"","institution":"Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, Timisoara, Romania","correspondingAuthor":true,"prefix":"","firstName":"Mariela","middleName":"Romina","lastName":"Birza","suffix":""},{"id":347457603,"identity":"ba5e548e-c420-4ae5-99d2-cd416e07599e","order_by":6,"name":"Alina Mirela Popa","email":"","orcid":"","institution":"Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, Timisoara, Romania","correspondingAuthor":false,"prefix":"","firstName":"Alina","middleName":"Mirela","lastName":"Popa","suffix":""},{"id":347457604,"identity":"3f8ebca1-09dc-434d-b6d5-a220cc1e683d","order_by":7,"name":"Andrei Raul Manzur","email":"","orcid":"","institution":"Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, Timisoara, Romania","correspondingAuthor":false,"prefix":"","firstName":"Andrei","middleName":"Raul","lastName":"Manzur","suffix":""},{"id":347457605,"identity":"e9d6f66e-acd2-48db-b158-1d9f89834777","order_by":8,"name":"Loredana Gligor","email":"","orcid":"","institution":"Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, Timisoara, Romania","correspondingAuthor":false,"prefix":"","firstName":"Loredana","middleName":"","lastName":"Gligor","suffix":""}],"badges":[],"createdAt":"2024-07-26 10:17:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4807363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4807363/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-80113-x","type":"published","date":"2024-12-05T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70964772,"identity":"3b85a4de-d7ce-4f69-96d2-51556756a1c5","added_by":"auto","created_at":"2024-12-09 16:15:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":720652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4807363/v1/62d8dd9e-a105-46b6-978b-001fc74bea72.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between composite lipid indices and Baveno classification in patients with obstructive sleep apnoea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObstructive sleep apnoea (OSA) is a very common disease that is characterised by repetitive collapse of the upper airways during sleep. Due to the resulting chronic intermittent hypoxaemia, sleep fragmentation and changes in the intrathoracic pressures, OSA is associated with a significant risk for the development of cardiovascular diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the treatment with continuous positive airway pressure (CPAP) did not reduce cardiovascular events in large randomised controlled trials (RCTs) [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This suggests that the OSA-associated cardiovascular risk cannot be generalised at individual patient level, and biomarkers that can predict future cardiovascular risk to better tailor treatment are warranted in clinical practice. Apnoea-hypopnoea index (AHI) is a measure of the frequency of the respiratory events [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is necessary for the diagnosis, but is poorly correlated with cardiovascular outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast, sleep apnoea-specific hypoxic burden was reported to predict cardiovascular events in population-based studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and post-hoc analyses of a RCT [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; however, its calculation requires specific analysis that is not widely available in clinical practice.\u003c/p\u003e \u003cp\u003eObstructive sleep apnoea leads to dyslipidaemia through several mechanisms that include consumption of lipid-rich foods, lipid mobilisation from adipose tissue, increased production of triglycerides as well as impaired reverse cholesterol transport from the atherosclerotic vessels [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Dyslipidaemia is known to increase the risk for cardiovascular diseases, and higher triglyceride as well as lower high-density lipoprotein cholesterol (HDL-C) levels independently relate to this increased risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, there is a dynamic exchange of cholesterol and tryglycerides between lipoprotein particles [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; therefore, individual lipid or lipoprotein levels cannot fully explain the cardiovascular risk. To address this, and taking into account the potential additive value of visceral fat distribution for the cardiovascular risk [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], several composite indices were introduced, such as the atherogenic index of plasma (AIP), lipid accumulation product (LAP), visceral adiposity index (VAI) and cardiometabolic index (CMI). The differences between the indices include the impact of individual lipid values, the impact of biological sex, body mass index and waist circumference. These indices predicted cardiovascular diseases better than the individual lipid components [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. They have been thoroughly evaluated and also compared in OSA [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Most studies, except two evaluating VAI [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], reported a significant relationship with OSA severity, as determined by the AHI. The incidence of cardiovascular events was evaluated in relation to baseline AIP [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and CMI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Analysing more than 2000 patients, both studies have shown a significant relationship [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Finally, significant relationships between the prevalence of cardiovascular diseases and lipid indices were reported in a systematic review and meta-analysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], indicating that they may serve as predictive biomarkers to quantify cardiovascular risk in patients with OSA.\u003c/p\u003e \u003cp\u003eRecently, the Baveno classification has been developed as a feasible method to group patients based on their burden of symptoms and comorbidities in an attempt to better select those who could benefit from CPAP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The four Baveno groups include patients with minor symptoms and low comorbidity burden (group A), severe symptoms and low comorbidity burden (group B), minor symptoms and high comorbidity burden (group C) and severe symptoms and high comorbidity burden (group D). However, the number of studies validating the Baveno classification is limited. Serino et al. have concluded significant temporal variability of the classification in the same patient [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In a small study, the adherence to CPAP was significantly higher in group D than in any other group [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the European Sleep Apnoea Database (ESADA) study, CPAP improved blood pressure only in patients who were in groups C and D and patients in groups B and D perceived the highest symptomatic benefit from CPAP treatment [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast, in another large observational cohort study, the Baveno groups did not differ in predicting cardiovascular mortality [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo better understand the validity of the Baveno classification, we aimed to correlate it with cardiovascular risk estimated by composite lipid indices. We also explored which index is better related to Baveno classification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and subjects\u003c/h2\u003e \u003cp\u003e We assessed the data of 2937 patients participating in the ESADA study from the Timisoara site. The patients were classified into Baveno groups as described previously [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Following the exclusion of patients with incomplete lipid data, 1242 patients were included to the current analysis.\u003c/p\u003e \u003cp\u003eHypertension, ischaemic heart disease (stable angina, previous cardiovascular event), cerebrovascular disease (previous cerebrovascular event) and type 2 diabetes were defined based on medical history and relevant medications. Venous blood samples were taken for measuring the level of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). We recorded the blood pressure values as well as heart rates in the morning; height, weight, neck circumference (NC) and waist circumference (WC) were measured, and the participants filled out the Epworth Sleepiness Scale (ESS).\u003c/p\u003e \u003cp\u003e The study was approved by the local Ethics Committee (22/2014/24.07.2019 University of Medicine and Pharmacy Victor Babes Timisoara) and all methods used, were performed in accordance with all the relevant guidelines and regulations. Patients gave their informed consent before participating.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eComposite lipid indices\u003c/h2\u003e \u003cp\u003eAIP was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Log}_{10}\\left(\\frac{Tg}{HDL-C}\\right)\\)\u003c/span\u003e\u003c/span\u003e, VAI was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{WC}{(39.68+(1.88\\times\\:BMI)}\\times\\:\\frac{TG}{1.03}\\times\\:\\frac{1.31}{HDL-C}\\)\u003c/span\u003e\u003c/span\u003e for men and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{WC}{36.58+(1.89\\times\\:BMI)}\\times\\:\\frac{TG}{0.81}\\times\\:\\frac{1.52}{HDL-C}\\)\u003c/span\u003e\u003c/span\u003e for women, LAP was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(WC-65)\\times\\:TG\\)\u003c/span\u003e\u003c/span\u003e for men and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(WC-58)\\times\\:TG\\)\u003c/span\u003e\u003c/span\u003e for women, and CMI was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Tg}{\\text{H}\\text{D}\\text{L}-\\text{C}}\\times\\:\\frac{WC}{\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e where WC and height were both expressed in cm and TGs and HDL-C were both expressed in mmol/l.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSleep studies\u003c/h2\u003e \u003cp\u003eCardiorespiratory polygraphy was performed according to the recommendations of the American Academy of Sleep Medicine (AASM) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Apnoea was defined as at least 90% reduction in the nasal airflow that lasted for at least 10 seconds. Hypopnoea was defined as at least 30% reduction in the nasal airflow that lasted for at least 10 seconds accompanied by at least 3% drop in oxygen saturation. An AHI\u0026thinsp;\u0026ge;\u0026thinsp;5/h was diagnostic for OSA. Based on the sleep study patients were grouped into mild (AHI 5-14.9/h), moderate AHI (15-29.9/h) and severe (AHI\u0026thinsp;\u0026ge;\u0026thinsp;30/h) groups. Apart from the AHI, we recorded the time spent with oxygen saturation below 90% (T90%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe JASP 0.14.1 (University of Amsterdam, Amsterdam, Netherlands) software was used for statistical analysis. Data were compared along severities and Baveno groups using Chi-square and Kruskal-Wallis tests. Composite lipid indices were compared along severities and Baveno groups with Kruskal-Wallis test followed by the Dunn\u0026rsquo;s test and Bonferroni\u0026rsquo;s correction. Analyses on composite lipid indices were also performed following adjustment for age, sex, BMI and AHI. Data are presented as median /interquartile range/. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics along severity categories and Baveno groups\u003c/h2\u003e \u003cp\u003eThe three severity groups differed in age, blood pressure, heart rate, WC, NC, AHI, T90%, ESS values and HDL-C, LDL-C and triglyceride levels (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interestingly, there was only a statistical trend for total cholesterol (p\u0026thinsp;=\u0026thinsp;0.07). The prevalence of males, hypertension, ischaemic heart disease and type 2 diabetes increased in parallel with worsening severity. In contrast, there was no difference in the prevalence of cerebrovascular disease or smokers between the three groups (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). With increasing severity, we noticed that the ratio of different Baveno groups changed. There were more Baveno A patients in the mild group and more Baveno D patients in the severe group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics along OSA severities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild OSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;183)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate OSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;363)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere OSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;696)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 /43\u0026ndash;61/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 /46\u0026ndash;64/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 /48\u0026ndash;65/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (males %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0 /26.6\u0026ndash;34.4/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.9 /27.9\u0026ndash;35.4/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.1 /31.4\u0026ndash;39.2/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 /121\u0026ndash;140/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 /125\u0026ndash;140/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 /128\u0026ndash;141/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 /72\u0026ndash;85/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 /73\u0026ndash;89/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 /77\u0026ndash;90/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 /69\u0026ndash;84/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 /71\u0026ndash;83/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 /72\u0026ndash;86/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 /97\u0026ndash;119/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 /104\u0026ndash;120/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 /111\u0026ndash;129/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 /37\u0026ndash;42/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 /39\u0026ndash;44/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 /42\u0026ndash;46/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschaemic heart disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 Diabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 /4\u0026ndash;10/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 /5\u0026ndash;11/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 /7\u0026ndash;13/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAHI (/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 /8.0\u0026ndash;13.1/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0 /18.7\u0026ndash;25.8/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.8 /37.4\u0026ndash;66.8/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT90% (mins)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 /0.1\u0026ndash;4.3/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5 /1.2\u0026ndash;27.0/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 /8.1\u0026ndash;84.4/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.21 /4.68\u0026ndash;5.66/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20 /4.49\u0026ndash;5.78/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.43 /4.55\u0026ndash;5.97/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.18 /2.61\u0026ndash;3.93/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28 /2.61\u0026ndash;4.06/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.54 /2.67\u0026ndash;4.19/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 /1.06\u0026ndash;1.32/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 /1.03\u0026ndash;1.27/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 /1.03\u0026ndash;1.22/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68 /1.15\u0026ndash;2.04/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71 /1.25\u0026ndash;2.12/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84 /1.37\u0026ndash;2.25/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaveno group (A/B/C/D%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19/8/52/21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16/6/53/25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/6/41/44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAHI \u0026ndash; apnoea-hypopnoea index, BMI \u0026ndash; body mass index, DBP \u0026ndash; diastolic blood pressure, ESS \u0026ndash; Epworth Sleepiness Scale, HDL-C \u0026ndash; high-density lipoprotein cholesterol, LDL-C \u0026ndash; low-density lipoprotein cholesterol, NC \u0026ndash; neck circumference, SBP \u0026ndash; systolic blood pressure, T90% \u0026ndash; time spent with oxygen saturation below 90%, TC \u0026ndash; total cholesterol, TG \u0026ndash; triglycerides, WC \u0026ndash; waist circumference.\u003c/p\u003e \u003cp\u003eThere were significant differences between the four Baveno groups for most investigated variables, except for the HDL-C levels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The age and, as expected, the prevalence of smoking and comorbidities were higher in groups C and D. Interestingly, the lipid levels were higher in the symptomatic (B and D) groups. Whilst there was no difference in OSA severity between the C and D groups, the prevalence of mild OSA was the highest in group A and the prevalence of severe OSA was the highest in group D.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics among Baveno groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;571)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup D\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;436)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 /40\u0026ndash;51/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 /40\u0026ndash;52/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 /50\u0026ndash;66/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 /48\u0026ndash;65/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (males %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.8 /27.2\u0026ndash;33.2/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.0 /28.7\u0026ndash;34.8/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.7 /28.7\u0026ndash;36.3/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.0 /32.2\u0026ndash;40.6/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 /121\u0026ndash;131/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 /122\u0026ndash;135/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 /126\u0026ndash;141/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137 /130\u0026ndash;145/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 /72\u0026ndash;81/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 /72\u0026ndash;82/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 /75\u0026ndash;90/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 /77\u0026ndash;90/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 /72\u0026ndash;85/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 /73\u0026ndash;89/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 /70\u0026ndash;82/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 /72\u0026ndash;87/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 /100\u0026ndash;118/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 /102\u0026ndash;119/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 /106\u0026ndash;122/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 /113\u0026ndash;131/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 /39\u0026ndash;43/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 /39\u0026ndash;44/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 /39\u0026ndash;45/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 /41\u0026ndash;47/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschaemic heart disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 Diabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 /4\u0026ndash;8/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 /11\u0026ndash;14/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 /5\u0026ndash;9/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 /11\u0026ndash;14/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAHI (/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.2 /16\u0026ndash;40.1/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2 /20.5\u0026ndash;49.7/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.5 /17.9\u0026ndash;44.1/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.4 /26.6\u0026ndash;67.8/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT90% (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7 /0.3\u0026ndash;21.2/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2 /1.7\u0026ndash;40.0/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 /2.0\u0026ndash;39.8/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 /7.8\u0026ndash;96.0/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.22 /4.73\u0026ndash;5.70/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.48 /4.97\u0026ndash;5.92/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.20 /4.34\u0026ndash;5.79/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.41 /4.52\u0026ndash;6.01/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.39 /2.79\u0026ndash;3.98/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.60 /3.03\u0026ndash;4.14/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.30 /2.56\u0026ndash;4.06/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.54 /2.69\u0026ndash;4.20/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 /1.03\u0026ndash;1.22/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 /1.04\u0026ndash;1.18/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 /1.03\u0026ndash;1.25/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 /1.01\u0026ndash;1.24/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69 /1.29\u0026ndash;1.94/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82 /1.46\u0026ndash;2.23/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72 /1.26\u0026ndash;2.15/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.90 /1.39\u0026ndash;2.27/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild/Moderate/Severe OSA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/36/42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/30/51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17/33/50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9/21/70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAHI \u0026ndash; apnoea-hypopnoea index, BMI \u0026ndash; body mass index, DBP \u0026ndash; diastolic blood pressure, ESS \u0026ndash; Epworth Sleepiness Scale, HDL-C \u0026ndash; high-density lipoprotein cholesterol, LDL-C \u0026ndash; low-density lipoprotein cholesterol, NC \u0026ndash; neck circumference, SBP \u0026ndash; systolic blood pressure, T90% \u0026ndash; time spent with oxygen saturation below 90%, TC \u0026ndash; total cholesterol, TG \u0026ndash; triglycerides, WC \u0026ndash; waist circumference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComposite lipid indices along severity categories\u003c/h2\u003e \u003cp\u003eAll composite lipid indices were related to disease severity; however, post-hoc tests revealed significant differences only between severe vs. mild and severe vs. moderate groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results were unchanged following adjustment for age, sex, BMI and AHI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposite lipid indices along OSA severities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild OSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;183)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate OSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;363)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere OSA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;696)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15 /-0.03\u0026ndash;0.27/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18 /0.01\u0026ndash;2.92/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23 /0.07\u0026ndash;0.34/ \u003csup\u003e#\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41 /1.57\u0026ndash;3.15/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.37 /1.65\u0026ndash;3.30/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.65 /1.85\u0026ndash;3.59/ \u003csup\u003e#\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.60 /42.96\u0026ndash;107.16/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.56 /54.09\u0026ndash;110.29/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103.56 /74.46\u0026ndash;142.20/ \u003csup\u003e#\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 /0.56\u0026ndash;1.23/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97 /0.65\u0026ndash;1.34/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20 /0.81\u0026ndash;1.58/ \u003csup\u003e#\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAIP \u0026ndash; atherogenic index of plasma, CMI \u0026ndash; cardiometabolic index, LAP \u0026ndash; lipid accumulation product, VAI \u0026ndash; visceral adiposity index. \u003csup\u003e#\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. group mild OSA, \u003csup\u003e\u0026para;\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. moderate OSA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eComposite lipid indices among Baveno groups\u003c/h2\u003e \u003cp\u003eUsing the Kruskal-Wallis test all composite lipid indices were significantly different between the four Baveno groups. However, post hoc tests showed that AIP and VAI were different only between groups D and A as well as groups D and C, whilst LAP and CMI were different between groups C and A, groups D and A as well as groups D and C. Only LAP was significantly different between groups D and B (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results were unchanged following adjustment for age, sex, BMI and AHI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposite lipid indices among Baveno groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;571)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup D\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;436)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18 /0.02\u0026ndash;0.27/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23 /0.11\u0026ndash;0.32/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19 /0.02\u0026ndash;0.31/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24 /0.06\u0026ndash;0.34/ \u003csup\u003e#@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.35 /1.65\u0026ndash;2.80/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.55 /1.96\u0026ndash;3.34/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50 /1.70\u0026ndash;3.41/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.71 /1.85\u0026ndash;3.65/ \u003csup\u003e#@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.73 /48.37\u0026ndash;99.04/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.32 /60.35\u0026ndash;113.50/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.93 /58.40\u0026ndash;119.49/\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110.69 /76.76\u0026ndash;147.22/ \u003csup\u003e#\u0026para;@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 /0.63\u0026ndash;1.12/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 /0.77\u0026ndash;1.40/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 /0.69\u0026ndash;1.42/\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24 /0.80\u0026ndash;1.62/ \u003csup\u003e#@\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAIP \u0026ndash; atherogenic index of plasma, CMI \u0026ndash; cardiometabolic index, LAP \u0026ndash; lipid accumulation product, VAI \u0026ndash; visceral adiposity index. \u003csup\u003e#\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. group A, \u003csup\u003e\u0026para;\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. group B, \u003csup\u003e@\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. group C.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we compared composite lipid indices along OSA severity as assessed by the AHI and by the Baveno classification in a large cohort of OSA patients. We reported that AIP, VAI, LAP and CMI were all related to severe disease. Using Baveno groups A (minor symptoms and low comorbidity burden) and C (minor symptoms and high comorbidity burden) as references, all indices were higher in group D (severe symptoms and high comorbidity burden); however only LAP and CMI were higher in group C than A and only LAP was different between groups B (severe symptoms and low comorbidity burden) and D.\u003c/p\u003e \u003cp\u003eAlthough the composite lipid indices were analysed in OSA in several studies [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], only few compared them. Our workgroup has recently evaluated AIP, VAI and LAP in OSA [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We reported that the association between OSA and these indices was the strongest with LAP which also predicted the presence of hypertension, diabetes and cardiovascular disease better than the other parameters [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Comparing LAP and VAI, two studies have concluded that the former is more strongly related to OSA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In contrast, the CMI was compared to LAP and was found to better predict OSA and metabolic syndrome in OSA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the current analysis, only the LAP and CMI was different between the minimally symptomatic groups and only LAP was different between the highly symptomatic groups. These results suggest the CMI and LAP could help predicting cardiovascular disease both in sleepy and non-sleepy patients irrespectively of knowing about their comorbidity profile. In contrast, all composite lipid indices were higher in group D compared to group C. This suggest that lipid parameters and the corresponding cardiovascular risk is driven by OSA symptoms. This confirms the previous reports concluding that patients with OSA who report excessive daytime sleepiness [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and insomnia [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] are at higher risk for development of cardiovascular disease.\u003c/p\u003e \u003cp\u003ePatients with severe OSA had the highest values for all composite lipid indices. In addition, there was a progressive increase in the number of traditional cardiovascular risk factors, such as obesity, blood pressure values and diabetes along increasing OSA severity. This suggests that clinicians should still strongly consider treating severe OSA to reduce further cardiovascular events irrespectively from the Baveno classification. Whether the treatment would consist of CPAP, weight loss [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], medication [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] or their combination is up to the clinicians and patients\u0026rsquo; decision.\u003c/p\u003e \u003cp\u003eSimilarly to the previous studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], patients in groups C and D were older and had higher BMI than in groups A and B. In addition, patients in C and D groups had higher blood pressure values [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Not surprisingly, patients in group D had more severe OSA in the previous studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], including ours. However, in the study of Jorquera et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] patients in group C had more severe overnight hypoxaemia which can potentially drive higher triglyceride and cholesterol levels [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast, in our study, there was no clinically significant difference in OSA severity between groups B and C. Yet, lipid values tended to be higher in group B than C. This suggests, that apart from hypoxaemia, lipid values may be associated with daytime symptoms. The causality between the symptoms burden and dyslipidaemia cannot be concluded from this study, however evidence suggest that the relationship could be bidirectional [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe link between OSA and its comorbidities, such as cardiovascular disease and dyslipidaemia is affected by the investigated population, as often uncontrolled genetic, epigenetic, dietary and lifestyle factors can affect this relationship [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, the symptoms\u0026rsquo; burden shows high inter-population variability [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, multicentre studies evaluating Baveno classification maybe misleading due to the biases above. We believe the large-size, single centre study design is a strength rather than a limitation; however, we acknowledge that the generalisability of our results is limited.\u003c/p\u003e \u003cp\u003eThe study has limitations. First, it had a cross-sectional nature. Therefore, the causality between the composite lipid indices as well as symptoms and comorbidities cannot be concluded. We believe that our data could serve basis to design prospective studies evaluating this relationship. Second, we used cardiorespiratory polygraphy rather than polysomnography. Whilst the American Academy of Sleep Medicine endorses this diagnostic tool [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], it tends to underestimate OSA severity compared to polysomnography, as hypopneas associated with arousal but without oxygen desaturation are not scored. In addition, it may further underestimate OSA severity in patients with insomnia. As the primary analysis of our study did was irrespective from AHI, hence we believe that this limitation did not significantly affect the validity of our results. Third, we did not record lifestyle factors, such as diet and exercise apart, that could affect the lipid values. Day to day variations in lipid values depending on the last meal are plausible [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and potential inter-day variations in the composite lipid values need to be thoroughly investigated before using them in clinical practice for cardiovascular risk stratification in OSA. In addition, due to the aforementioned factors, normal values for composite lipid indices in each population need to be determined.\u003c/p\u003e \u003cp\u003eIn summary, both severe OSA and OSA associated with high burden of symptoms and comorbidities (Baveno group D) were related to high levels of composite lipid indices. These groups may therefore require extra attention and encouragement to utilise OSA treatments. Of the investigated indices, the cardiometabolic index as well as the lipid accumulation product seem to be beneficial to identify the excessive cardiovascular risk in minimally symptomatic patients (groups A and C). For highly symptomatic patients (groups B and D) we recommend the use of LAP for the same purpose.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions: \u003c/strong\u003eConceptualization, S.M., S.M.F and A.B.; methodology, S.M., O.C.D., and A.B.; formal analysis, S.M., A.R.F. and A.B.; investigation, A.R.F., M.R.B., S.M.F and A.R.M.; resources, O.C.D, M.R.B., A.M.P and A.R.M; data curation, L.G. and S.M.F; writing\u0026mdash;original draft preparation, S.M., A.B. and A.R.F; writing - review and editing, A.B., A.M.P., L.G. and A.R.M.; visualization, L.G. and A.M.P.; supervision, S.M.F, A.B. and M.R.B.; project administration, S.M.F., S.T.F. and M.R.B. All authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement: \u003c/strong\u003eThe datasets analysed during the current study are not publicly available due general data protection policy, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical declaration:\u003c/strong\u003e The study was approved by the local Ethics Committee (22/2014/24.07.2019 University of Medicine and Pharmacy Victor Babes Timisoara). All methods used were performed in accordance with all the relevant guidelines and regulations. The patients gave their informed consent before participating.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRedline S, Azarbarzin A, Peker Y. 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The Baveno classification was developed to improve patient selection who would benefit from OSA treatment in terms of symptoms improvement and CVD risk reduction. However, it is unclear how the classification relates to biomarkers of CVD risk. Composite lipid indices were evaluated in various populations and showed good predictive value for incident CVD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOne thousand two hundred and forty-two patients with OSA were included in the study. Composite lipid indices, including the atherogenic index of plasma (AIP), visceral adiposity index (VAI), lipid accumulation product (LAP) and cardiometabolic index (CMI) were compared between different Baveno groups (A \u0026ndash; minor symptoms, minor comorbidities, B \u0026ndash; severe symptoms, minor comorbidities, C \u0026ndash; minor symptoms, severe comorbidities, D \u0026ndash; severe symptoms, severe comorbidities).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll composite lipid indices were higher in group D versus groups A and C. Only LAP (86.93 /58.40\u0026ndash;119.49/ vs. 75.73 /48.37\u0026ndash;99.04/) and CMI (1.03 /0.69\u0026ndash;1.42/ vs. 0.91 /0.63\u0026ndash;1.12/) were significantly different between the minimally symptomatic groups (C vs. A), and solely LAP was significantly higher (110.69 /76.76\u0026ndash;147.22/ vs. 82.32 /60.35\u0026ndash;113.50/) when the highly symptomatic groups (D vs. B) were compared.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Baveno classification correlates with cardiovascular risk estimated with composite lipid indices. Composite lipid indices may help better stratifying cardiovascular risk in both minimally- and highly symptomatic patients.\u003c/p\u003e","manuscriptTitle":"The relationship between composite lipid indices and Baveno classification in patients with obstructive sleep apnoea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 04:36:29","doi":"10.21203/rs.3.rs-4807363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-25T03:42:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-21T09:34:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-11T18:39:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141720929718862210933920102543896903156","date":"2024-09-04T09:36:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193730789287179278734175610655496229834","date":"2024-09-04T08:40:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-02T08:20:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-02T08:10:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-08T09:06:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-06T13:40:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-26T10:16:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf4a79e6-076a-4881-9652-b8e8832c4848","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36864125,"name":"Health sciences/Diseases"},{"id":36864126,"name":"Health sciences/Health care"},{"id":36864127,"name":"Health sciences/Medical research"},{"id":36864128,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-12-09T16:02:48+00:00","versionOfRecord":{"articleIdentity":"rs-4807363","link":"https://doi.org/10.1038/s41598-024-80113-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-05 15:57:31","publishedOnDateReadable":"December 5th, 2024"},"versionCreatedAt":"2024-09-02 04:36:29","video":"","vorDoi":"10.1038/s41598-024-80113-x","vorDoiUrl":"https://doi.org/10.1038/s41598-024-80113-x","workflowStages":[]},"version":"v1","identity":"rs-4807363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4807363","identity":"rs-4807363","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}