Gender Differences in Sleep Apnea: A Study of the Catalan Population

doi:10.21203/rs.3.rs-5019262/v1

Objective

To analyse the distribution of Obstructive Sleep Apnea (OSA) and key comorbidities among men and women over 18 years old without a prior diagnosis of cardiovascular disease, seen by the teams of the Catalan Institute of Health (ICS) in both Primary Care (PC) and hospitalization according to Body Mass Index (BMI).

Methods

Cross-sectional, observational, and retrospective study with a cohort of 3,886 people, stratified by sex. Data were obtained from the SIDIAP database covering all individuals diagnosed with OSA without previous cardiovascular events from 01/01/2009-31/12/2016. The variables were age, socioeconomic deprivation index (MEDEA), BMI, smoking, alcohol consumption, and other comorbidities. Cardiovascular risk was calculated using the REGICOR function. Chi-square tests were used to compare variables by sex.

Results

Of the 3,886 people, 28.8% were women. The majority of individuals (92.6%) were > 40 years old. Women reported a higher BMI and a different comorbidity profile. They also showed a higher prevalence of Class III obesity. On the other hand, men presented higher alcohol and tobacco consumption and more metabolic conditions such as hypertriglyceridemia and hyperuricemia. They also showed a higher cardiovascular risk compared to women.

Conclusion

There are gender differences in OSA without prior cardiovascular disease according to gender and BMI. This has clinical implications to be considered in diagnosis and treatment.

Health sciences/Diseases/Respiratory tract diseases

Health sciences/Health care/Diagnosis

Health sciences/Health care/Disease prevention

Health sciences/Health care/Health services

Health sciences/Health care/Public health

Sleep Apnea

Gender Differences

Obstructive Sleep Apnea

Gender and Health

Obstructive Sleep Apnea (OSA) is the most common of sleep disorders and is defined as a condition characterized by recurrent episodes of obstruction in the upper airways during sleep ^1,2. OSA manifests through the repetitive collapse of the pharynx, which can be total (resulting in apnea) or partial (linked to hypopnea) ^3,4. These obstructions compromise gas exchange, causing oxygen desaturation, increased carbon dioxide in the blood, and sleep fragmentation ¹. OSA is among the risk factors for developing Hypertension (Ht) and Cardiovascular Events (CVE) and cerebrovascular events. ^5,6 y Diabetes Mellitus (DM) 2 ⁷. Additionally, the excessive daytime sleepiness associated with OSA has been linked to an increase in accidents and a decrease in quality of life ⁸.

There is evidence that the manifestations of OSA differ between genders ⁹. The prevalence has been reported differently between men (22%) and women (17%) ¹⁰. The clinical expression of OSA in women is not characterized by typical symptoms such as loud snoring, excessive daytime sleepiness, or choking awakenings during sleep. In women, OSA symptoms more frequently include insomnia, fatigue, headaches, mood disorders, and lack of energy, which can delay the diagnosis of OSA and may bias its prevalence in females ¹¹. Additionally, women exhibit different characteristics from men in polysomnographic parameters, which are less severe in women. Women also have a higher prevalence of apnea events related to REM sleep, a lower prevalence of positional OSA, and a predominance of hypopneas ¹². Currently, mechanisms that could influence the development or progression of OSA in women are being investigated, including factors such as menopause, which has been shown to have a relationship with this condition ¹³.

Given that OSA can be considered a cardiovascular risk factor, (16) it is crucial to detect, diagnose, and treat OSA early to prevent cardiovascular events. This should be done differently between sexes, as there is a high rate of under diagnosis of OSA that is potentially also sex-specific. This under diagnosis can delay the appropriate treatment of the syndrome and the management of its complications, and even create inequalities in treatment between men and women. Following that under-research of OSA in women could be associated with a higher risk of CVE, Ht and DM through the delay of the diagnosis and/or treatment of OSA. Investigating these differences is crucial to improving diagnostic and treatment strategies, better understanding the specific symptoms and cardiovascular risk relationship in women. This study analyses the differences in prevalence and comorbidities of OSA between men and women without cardiovascular disease, aiming to generate useful information for optimizing the diagnosis and treatment of OSA in Catalonia.

Study Design & Data Source

Retrospective cross-sectional study of a cohort of individuals with OSA without prior cardiovascular disease (3,886) stratified by sex. Information was obtained from the SIDIAP (Information System for the Development of Research in Primary Care) database, a computerized database that stores anonymized patient records from the 5.8 million patients of the 279 primary and hospital care centres of the ICS - covering 80% of the Catalan population. The ICS uses the clinical software eCAP to record clinical information. Health professionals collect data using ICD-10 codes and forms to gather variables such as smoking history or Body Mass Index (BMI). SIDIAP integrates electronic medical records with data from other databases and records (such as the CMBD, medication dispensed in pharmacies, or the National Mortality Registry). IDIAP Jordi Gol Research Ethics Committee approved (registration number P13/069) the study.

Variables

To define the SIDIAP OSA cohort, all individuals (≥ 18 years) who met the eligibility criteria for OSA cases were included, and all cases with OSA without prior cardiovascular disease were excluded. Data from 01/01/2009 to 31/12/2016 were analysed. Individuals with OSA in the SIDIAP hospital were considered (operationally) those with a primary diagnosis of OSA recorded in the CMBD AH-ICS (OSA Group, ICD9 Domain). Individuals with OSA in Primary Care (PC) were operationally considered those with OSA Group, ICD10 Domain. Institutionalized users (INSTITUT Group, ICD10 Domain) were excluded, as well as individuals with previously diagnosed cardiovascular disease in the SIDIAP hospital or PC. The excluded diagnostic groups here are: 1) AVC_tots Group, ICD10 Domain; 2) AVC_tots Group, ICD9 Domain. Additionally, individuals with other sleep disorders were excluded, including the groups 1) TRSON, ICD10 Domain; 2) TRSON, ICD9 Domain. Moreover, individuals with a hospital diagnosis of OSA where OSA was not the main diagnosis were excluded. Also excluded were individuals with unconfirmed suspected OSA (Snorer Group, ICD10 Domain) and terminal patients identified as complex chronic patients or patients with advanced chronic disease in PC.

Cardiovascular risk was calculated using the REGICOR score based on age (years), sex, smoking status, DM, total cholesterol, HDL cholesterol, and blood pressure (diastolic and systolic). The REGICOR provides the probability of suffering a myocardial infarction or angina pectoris (among other cardiovascular events) in the next 10 years ¹⁴. For our study, the risk was classified into the following levels: low risk (< 5%), moderate risk (5-9.9%), high risk (10-14.9%), and very high risk (15% or more) ¹⁴.

The study considers the following variables of interest. A variable was age (categorized: >40 years and ≤ 40 years). Then, the socioeconomic deprivation index Medea ¹⁵ (five quintiles, from Quintile 1 (Q1) to Quintile 5 (Q5), from least to most deprived). After, BMI which was categorized into three ranges according to the level of obesity ¹⁶ measured through BMI (< 35 kg/m² (Class I: CI), between 35.0 kg/m² and 39.9 kg/m² (Class II: CII), and ≥ 40.0 kg/m² or more (Class III: CIII)]. Also, tobacco use (dichotomous variable: smokers and non-smokers). Ht, hypercholesterolemia, hypertriglyceridemia, DM, chronic hyperglycemia (HbA1c ≥ 6.5%), and hyperuricemia evaluated binarily (yes/no). Finally, to assess the risk of alcohol consumption, a classification was used [based on the risk consumption recorded in the primary care medical history ¹⁷] based on three risk levels (R0, R1, and R2), differentiated between men and women according to the consumption of Standard Drink Units (SDU). The risk classification is summarized as follows: Risk 0 means "Non-drinker"; Risk 1 equates to low-risk consumption differentiated between men and women [Men < 28 SDU/week and women < 17 SDU/week]; and Risk 2 equates to risky drinking also differentiated by sex [Men ≥ 28 SDU/week and women ≥ 17 SDU/week].

Analysis

The analysis was carried out differently by sex. These variables included age, categorized into two groups: over and under 40 years. The Medea Index was also evaluated, from least to most deprive. Alcohol consumption was classified into drinkers (1–2) and non-drinkers (0). Additionally, dichotomous variables (yes/no) were included for tobacco use, the presence of Ht, hypercholesterolemia, hypertriglyceridemia, DM, HbA1c levels greater than or equal to 6.5%, and hyperuricemia. The chi-square test was used for comparison between sexes, and a P value of < 0.05 was considered statistically significant. Statistical analyses were performed using version 4.3.2 of the R statistical software ¹⁸.

Table 1. Distribution of OSA between Men and Women in Catalonia (2009–2016) Shows the differences in the distribution of OSA diagnoses between men and women in Catalonia during 2009–2016. Of 3,886 people, 28.8% were women and 71.2% men. Both women and men had the highest percentage of individuals in the group of over 40 years old [women: 94.11%; men: 92.04%]. Those under 40 years old were mostly men (7.96%). Regarding the Medea Index, the highest percentage was in the lowest deprivation quintile (Q3-5) [women: 59.1%; men: 51.2%]. Alcohol consumption showed that more men drank than women [women: 9.90%; men: 28.82% (p < 0.001)]. Ht affected a higher percentage of women [women:60.12%; men:53.24% (p < 0.001)], while hypercholesterolemia did not show significant differences between genders [women:44.87%; men:42.10%]. Men had a higher percentage of hypertriglyceridemia (4.85%) compared to women (1.78%). The prevalence of DM did not show significant differences [women:25.78%; men:22.42% (p = 0.028)]. Regarding HbA1c levels ≥ 6.5%, it was found in 16.77% of women and 11.97% of men (p = 0.001). Hyperuricemia was more prevalent in men [women:9.01%; men:17.32% (p < 0.001)]. Cardiovascular risk (REGICOR) showed that 24.17% of women had low risk (< 5%) compared to 14.94% of men. The very high risk (≥ 15%) was 0.27% in women and 1.30% in men (p < 0.001).

Table 1

Distribution of OSA between Men and Women in Catalonia (2009–2016).
Variable	Women		Men		P-value test
Variable	N	%	N	%	P-value test
Total	1121	28,84%	2765	71,15%	< 0.001
Age
>40	1055	94,11%	2545	92,04%	0.030
<=40 years	66	5,89%	220	7,96%
Missing	0	0,00%	0	0,00%
Total	1121	100,00%	2765	100,00%
Index Medea*
Quintile 1	94	8,39%	248	8,97%	< 0.001
Quintile 2	139	12,40%	433	15,66%
Quintile 3	208	18,55%	489	17,69%
Quintile 4	237	21,14%	545	19,71%
Quintile 5	218	19,45%	384	13,89%
Missing	225	20,07%	666	24,09%
Total	1121	100,00%	2765	100,00%
Alcohol Consumption Classification (ALRIS)**
Drinker (ALRIS 1–2)	111	9,90%	797	28,82%	< 0.001
Non-drinker (ALRIS 0)	1010	90,10%	1968	71,18%
Missing	0	0,00%	0	0,00%
Total	1121	100,00%	2765	100,00%
Tobacco consumption
Yes	284	25,33%	1347	48,72%	< 0.001
NO	837	74,67%	1418	51,28%
Missing	0	0,00%	0	0,00%
Total	1121	100,00%	2765	100,00%
Hypertension
Yes	674	60,12%	1472	53,24%	< 0.001
NO	447	39,88%	1293	46,76%
Missing	0	0,00%	0	0,00%
Total	1121	100,00%	2765	100,00%
Hypercholesterolemia
Yes	503	44,87%	1164	42,10%	0.122
NO	618	55,13%	1601	57,90%
Missing	0	0,00%	0	0,00%
Total	1121	100,00%	2765	100,00%
Hypertriglyceridemia
Yes	20	1,78%	134	4,85%	< 0.001
NO	1101	98,22%	2631	95,15%
Missing	0	0,00%	0	0,00%
Total	1121	100,00%	2765	100,00%
Diabetes Mellitus
Yes	289	25,78%	620	22,42%	0.028
NO	832	74,22%	2145	77,58%
Missing	0	0,00%	0	0,00%
Total	1121	100,00%	2765	100,00%
HbA1c > = 6.5%
Yes	188	16,77%	331	11,97%	0.001
NO	183	16,32%	487	17,61%
Missing	750	0,00%	1947	0,00%
Total	1121	100,00%	2765	100,00%
Hyperuricemia
Yes	101	9,01%	479	17,32%	< 0.001
NO	511	45,58%	928	33,56%
Missing	509	0,00%	1358	0,00%
Total	1121	100,00%	2765	100,00%
REGICOR
Low Risk: <5%	271	24,17%	413	14,94%	< 0.001
Moderate Risk: 5–9,9%	92	8,21%	355	12,84%
High Risk: 10–14,9%	9	0,80%	83	3,00%
Very high Risk: 15% or more	3	0,27%	36	1,30%
Missing	746	66,55%	1878	67,92%
Total	1121	100,00%	2765	100,00%
MEDEA = Deprivation Index. *ALRIS = Alcohol Consumption Classification.

Table 2. Distribution of OSA between Women in Catalonia (2009–2016) according Body Mass Index (BMI) distribution. In Table 2. 69.40% had a BMI ≤ 35 (CI), 30.60% had a BMI between 35-39.9 (CII), and 46.12% had a BMI ≥ 40 (CIII) (p < 0.001). Most women in all classes were over 40 years old: CI: 98.76%, CII: 95.77%, and CIII: 90.65% (p = 0.007). There were no significant differences in the Medea Index among obesity classes (p = 0.473), with higher representation in Q3. Regarding alcohol consumption, 16.77% of women in CI were drinkers, compared to 8.45% in CII and 8.41% in CIII (p = 0.066). Tobacco consumption was higher in CI: 32.30% compared to CII: 19.72% and CIII: 22.43%. Ht was reported in 63.35% of women in CI, 70.42% in CII, and 70.09% in CIII (p = 0.405). Hypercholesterolemia affected 54.04% of women in CI, 100% in CII, and 43.93% in CIII (p = 0.171). Hypertriglyceridemia was more prevalent in CII: 5.63% compared to CI: 0.62% and CIII: 0% (p = 0.004). DM was more common in CII: 35.21% and CIII: 33.64% compared to CI: 17.39% (p = 0.002). HbA1c levels ≥ 6.5% were found in 13.04% of women in CI, 22.54% in CII, and 20.56% in CIII (p = 0.749). Hyperuricemia was more prevalent in CIII: 14.95% compared to CII: 12.68% and CI: 4.97% (p = 0.015). According to the REGICOR index, 34.78% of women in CI had low risk, compared to 29.58% in CII and 23.36% in CIII (p = 0.43).

Table 2

Distribution of OSA between Women in Catalonia (2009–2016) according Body Mass Index (BMI) distribution.
Variable	Class I (< 35 IMC)		Class II (35-39.9 IMC)		Class III ( > = 40 IMC)		p-value
Variable	N	%	N	%	N	%	p-value
Total	161	69,40%	71	30,60%	107	46,12%	< 0.001
Age
>40	159	98,76%	68	95,77%	97	90,65%	0.007
<=40 years	2	1,24%	3	4,23%	10	9,35%
Missing	0	0,00%	0	0,00%	0	0,00%
Total	161	100,00%	71	100,00%	107	100,00%
Index Medea*
Quintile 1	19	11,80%	2	2,82%	7	6,54%	0.473
Quintile 2	17	10,56%	9	12,68%	16	14,95%
Quintile 3	30	18,63%	16	22,54%	23	21,50%
Quintile 4	24	14,91%	14	19,72%	21	19,63%
Quintile 5	28	17,39%	14	19,72%	21	19,63%
Missing	43	26,71%	16	22,54%	19	17,76%
Total	161	100,00%	71	100,00%	107	100,00%
Alcohol Consumption Classification ***
Drinker (1–2)	27	16,77%	6	8,45%	9	8,41%	0.066
Non-drinker (0)	134	83,23%	65	91,55%	98	91,59%
Missing	0	0,00%	0	0,00%	0	0,00%
Total	161	100,00%	71	100,00%	107	100,00%
Tobacco consumption
Yes	52	32,30%	14	19,72%	24	22,43%	0.069
NO	109	67,70%	57	80,28%	83	77,57%
Missing	0	0,00%	0	0,00%	0	0,00%
Total	161	100,00%	71	100,00%	107	100,00%
Hypertension
Yes	102	63,35%	50	70,42%	75	70,09%	0.405
NO	59	36,65%	21	29,58%	32	29,91%
Missing	0	0,00%	0	0,00%	0	0,00%
Total	161	100,00%	71	100,00%	107	100,00%
Hypercholesterolemia
Yes	87	54,04%	71	100,00%	47	43,93%	0.171
NO	74	45,96%	0	0,00%	60	56,07%
Missing	0	0,00%	0	0,00%	0	0,00%
Total	161	100,00%	71	100,00%	107	100,00%
Hypertriglyceridemia
Yes	1	0,62%	4	5,63%	0	0,00%	0.004
NO	160	99,38%	67	94,37%	107	100,00%
Missing	0	0,00%	0	0,00%	0	0,00%
Total	161	100,00%	71	100,00%	107	100,00%
Diabetes Mellitus
Yes	28	17,39%	25	35,21%	36	33,64%	0.002
NO	133	82,61%	46	64,79%	71	66,36%
Missing	0	0,00%	0	0,00%	0	0,00%
Total	161	100,00%	71	100,00%	107	100,00%
HbA1c > = 6.5%
Yes	21	13,04%	16	22,54%	22	20,56%	0.749
NO	44	27,33%	31	43,66%	51	47,66%
Missing	96	59,63%	24	33,80%	34	31,78%
Total	161	100,00%	71	100,00%	107	100,00%
Hyperuricemia
Yes	8	4,97%	9	12,68%	16	14,95%	0.015
NO	96	59,63%	49	69,01%	64	59,81%
Missing	57	35,40%	13	18,31%	27	25,23%
Total	161	100,00%	71	100,00%	107	100,00%
REGICOR
Low Risk: <5%	56	34,78%	21	29,58%	25	23,36%	0,43
Moderate Risk: 5–9,9%	14	8,70%	7	9,86%	12	11,21%
High Risk: 10–14,9%	1	0,62%	0	0,00%	1	0,93%
Very high Risk: 15% or more	0	0,00%	1	1,41%	2	1,87%
Missing	90	55,90%	42	59,15%	67	62,62%
Total	161	100,00%	71	100,00%	107	100,00%
MEDEA = Deprivation Index. IMC = Body Mass Index. ** Alcohol Consumption Classification.

Table 3. Distribution of OSA between men in Catalonia (2009–2016) according Body Mass Index (BMI) distribution. In Table 3. 74.10% had a BMI CI, 25.90% had a BMI CII, and 12.74% had a BMI CIII (p < 0.001). The majority were over 40 years old: 94.34% in CI, 94.41% in CII, and 76.14% in CIII (p < 0.001). The Medea Index showed higher representation in Quintile 4 across all classes (p = 0.012). Alcohol consumption was higher in CI (44.73%) and lower in CIII (30.68%) (p = 0.037). Tobacco consumption did not show significant differences. Ht was more prevalent in CII (65.36%) and CIII (61.36%). Hypercholesterolemia was similar across classes, being lower in CIII (31.82%). Hypertriglyceridemia did not show significant differences. DM was more common in CII: 32.96% and CIII: 28.41%. HbA1c levels ≥ 6.5% were higher in CII: 20.67%. Hyperuricemia showed a higher percentage in CIII: 34.09% (p = 0.001). The REGICOR risk was similar across classes, with the highest low risk in CI: 23.44%.

Table 3

Distribution of OSA between men in Catalonia (2009–2016) according Body Mass Index (BMI) distribution.
Variable	Class I (< 35 IMC)		Class II (35-39.9 IMC)		Class III ( > = 40 IMC)		p-value
Variable	N	%	N	%	N	%	p-value
Total	512	74,10%	179	25,90%	88	12,74%	< 0.001
Age
>40	483	94,34%	169	94,41%	67	76,14%	< 0.001
<=40 years	29	5,66%	10	5,59%	21	23,86%
Missing	0	100%	0	100%	0	100%
Total	512	100,00%	179	100,00%	88	100,00%
Index Medea*
Quintile 1	55	10,74%	7	3,91%	6	6,82%	0.012
Quintile 2	86	16,80%	30	16,76%	6	6,82%
Quintile 3	81	15,82%	32	17,88%	16	18,18%
Quintile 4	98	19,14%	36	20,11%	19	21,59%
Quintile 5	63	12,30%	33	18,44%	19	21,59%
Missing	129	25,20%	41	22,91%	22	25,00%
Total	512	100,00%	179	100,00%	88	100,00%
Alcohol Consumption Classification***
Drinker(1–2)	229	44,73%	71	39,66%	27	30,68%	0.037
Non-Drinker (0)	283	55,27%	108	60,34%	61	69,32%
Missing	0	100%	0	100%	0	100%
Total	512	100,00%	179	100,00%	88	100,00%
Tobacco consumption
Yes	259	50,59%	86	48,04%	52	59,09%	0.227
NO	253	49,41%	93	51,96%	36	40,91%
Missing	0	100%	0	100%	0	100%
Total	512	100,00%	179	100,00%	88	100,00%
Hypertension
Yes	283	55,27%	117	65,36%	54	61,36%	0.051
NO	229	44,73%	62	34,64%	34	38,64%
Missing	0	100%	0	100%	0	100%
Total	512	100,00%	179	100,00%	88	100,00%
Hypercholesterolemia
Yes	232	45,31%	81	45,25%	28	31,82%	0.056
NO	280	54,69%	98	54,75%	60	68,18%
Missing	0	100%	0	100%	0	100%
Total	512	100,00%	179	100,00%	88	100,00%
Hypertriglyceridemia
Yes	21	4,10%	7	3,91%	4	4,55%	0.970
NO	491	95,90%	172	96,09%	84	95,45%
Missing	0	100%	0	100%	0	100%
Total	512	100,00%	179	100,00%	88	100,00%
Diabetes Mellitus
Yes	123	24,02%	59	32,96%	25	28,41%	0.061
NO	389	75,98%	120	67,04%	63	71,59%
Missing	0	100%	0	100%	0	100%
Total	512	100,00%	179	100,00%	88	100,00%
HbA1c > = 6.5%
Yes	70	13,67%	37	20,67%	12	13,64%	0.226
NO	44	8,59%	31	17,32%	51	57,95%
Missing	398	77,73%	111	62,01%	25	28,41%
Total	512	100,00%	179	100,00%	88	100,00%
Hyperuricemia
Yes	105	20,51%	34	18,99%	30	34,09%	0.001
NO	96	18,75%	49	27,37%	64	72,73%
Missing	311	60,74%	96	53,63%	6	6,82%
Total	512	100,00%	179	100,00%	88	100,00%
REGICOR
Low Risk: <5%	120	23,44%	35	19,55%	18	20,45%	0,38
Moderate Risk: 5–9,9%	88	17,19%	37	20,67%	16	18,18%
High Risk: 10–14,9%	20	3,91%	4	2,23%	1	1,14%
Very high Risk: 15% or more	7	1,37%	4	2,23%	3	3,41%
Missing	277	54,10%	99	55,31%	50	56,82%
Total	512	100,00%	179	100,00%	88	100,00%
MEDEA = Deprivation Index. IMC = Body Mass Index. ** Alcohol Consumption Classification.

The results show differences between men and women diagnosed with OSA without cardiovascular disease in Catalonia. Men and women differ significantly in factors such as age, BMI, tobacco and alcohol consumption, metabolic biomarkers, and comorbidities. The differences in the distribution of OSA by sex have important clinical and epidemiological implications that should be considered in the diagnosis and treatment of OSA.

The differences between men and women with OSA can possibly be explained by anatomical differences; hormonal differences; differential clinical presentation and/or social determinants ^19–21. Anatomical differences could be related to the length of the soft palate in men could influence the progression of OSA or the larger dimension of the airway and susceptibility to collapse. Hormonal differences might play a protective role against OSA, and hormonal status could affect the prevalence and severity of OSA ^19–21. Additionally, women tend to present with OSA at an older age and with a higher BMI, and they often report "atypical" symptoms (or less studied symptoms since historically OSA has been studied more in men than in women) more frequently than men ^22,23. In the study, most individuals with OSA under 40 years old are men. This aligns with the possibility that men are diagnosed earlier due to classic symptoms, while women might be underdiagnosed ²⁴. Women are more likely to present with "atypical" symptoms, which can lead to a lower diagnostic suspicion ²⁵. Additionally, epidemiological factors suggest sex-specific mechanisms: in women, physical inactivity, Ht, and daytime sleepiness are associated with OSA ²⁶.

In our study, we observe a higher prevalence of Class III obesity or greater in women. Obesity is a risk factor for OSA, and its high prevalence in women may indicate differences in biological, hormonal, or behavioral factors more associated with gender patterns. The analysis of alcohol and tobacco consumption shows that men have a higher prevalence of these risk behaviors, which is consistent with gender-specific behavior patterns in the general population. Since alcohol and tobacco consumption are associated with poorer health outcomes and can exacerbate OSA symptoms, these findings are significant.

Men with OSA present a more unfavorable metabolic profile, with a higher prevalence of hypercholesterolemia, hypertriglyceridemia, hyperuricemia, and elevated HbA1c. In particular, hypertriglyceridemia and hyperuricemia are significantly more common in men, possibly due to differences in diet, physical activity, genetic factors, and gender ²⁷. The above findings are in line with the high prevalence of Ht and DM2 in men compared to women, highlighting the burden of comorbidities associated with OSA ^28,29. In the case of Ht, it is an important risk factor for cardiovascular disease, and its higher prevalence in men with OSA could partly explain the gender differences in cardiovascular mortality and morbidity observed in the OSA population. Traditionally, clinical presentation differences between men and women with OSA include a higher presence of Ht in women, highlighting a specific difference in the Catalan population ^23,30,31.

Regarding cardiovascular risk, the results of our study reveal differences in the prevalence of 10-year cardiovascular risk (evaluated using the REGICOR model) between men and women diagnosed with OSA, even after excluding all individuals with prior cardiovascular disease. In our study, men had a higher moderate and above-moderate risk compared to women, who presented more low-risk cases. These findings align with studies showing that men have a higher prevalence of risk factors associated with increased cardiovascular risk ^32,33. Similarly, cardiovascular risk according to the level of obesity in women was lower (low risk) in women with Class I obesity (CI). Higher risks (moderate, high, and very high) were detected in a smaller proportion regardless of the degree of obesity (CI-III), with only a slight increase in Class III obesity (CIII). This finding suggests that although obesity is a known risk factor for the development of OSA, its impact on the 10-year cardiovascular risk in women with OSA may not be as pronounced as observed in other studies ³⁴. In men, cardiovascular risk according to the level of obesity showed a pattern where moderate risk was similar across all obesity classes. The lack of significant differences in the distribution of risk among obesity levels in men reinforces the idea that obesity may not be the primary determinant of cardiovascular risk in men in this specific population ^32,34.

DM, although less prevalent than Ht in the study, also shows an unfavorable distribution in men with OSA. This aligns with other studies that find a high prevalence of OSA in patients with DM2 and a significant association between the severity of OSA and glycemic control, especially in men with DM2 ^35,36. However, in our study, we observed higher levels of HbA1c in women, reflecting suboptimal glycemic control. This is consistent with other studies showing that women with OSA have significantly higher HbA1c levels, suggesting poorer glycemic control ^37,38.

Some limitations of the study include the high proportion of missing data (Medea Index, HbA1c, hyperuricemia, and REGICOR), which may affect the results. The use of BMI presents limitations in differentiating between body fat and lean mass, impacting the accuracy of estimating health effects ³⁹. Future longitudinal studies could provide a better understanding of the dynamics between OSA and cardiometabolic risk factors in men and women.

This study highlights gender differences in cardiometabolic risk factors and health behaviors in patients with OSA without cardiovascular disease, emphasizing the need to consider BMI levels differences between men and women. It underscores the importance of differentiated diagnostic and therapeutic approaches between men and women, especially to improve the identification and management of OSA in women.

Ethical Declarations

This study did not involve the direct collection of data from human participants, as data were obtained from secondary sources. The ethics committee approved the use of these secondary databases (SIDIAP). In compliance with the Declaration of Helsinki, all data were anonymized to protect the privacy and confidentiality of the participants. Furthermore, the ethics committee reviewed and approved the research, aiming to benefit the Catalan population by contributing to potential improvements in the diagnosis, treatment, and/or health policies related to obstructive sleep apnea.

Funding Declaration

This research received no specific funding from public, commercial, or non-profit funding agencies.

Author Contribution

B.B.L.G., and P.T.M. conceptualized the study. B.B.L.G. and P.T.M. developed the methodology. B.B.L.G. drafted the main manuscript text. M.M.R.A, P.T.M., J.P.S., E.B.T., and S.E. conducted a critical review of the manuscript, providing key and relevant insights. J.R.A. performed all statistical analyses, prepared the tables, and contributed to the interpretation of the results. All authors reviewed and approved the final manuscript.

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No competing interests reported.

Gender Differences in Sleep Apnea: A Study of the Catalan Population

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Abstract

Objective

Methods

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Conclusion

Introduction

Methodology

Study Design & Data Source

Variables

Analysis

Results

Discussion

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Author Contribution

References

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Version 1