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Fig. 27.1 Criteria for the diagnosis of diabetesDCCT Diabetes Control and Complications Trial,
FPG Fasting plasma glucose, OGTT Oral glucose tolerance test, WHO World Health Organization,
2-h PG 2-h plasma glucose. *In the absence of unequivocal hyperglycemia, diagnosis requires two
abnormal test results from the same sample or in two separate test samples
C. S. Froján et al.
Fig. 27.2 Criteria dening prediabetes*FPG Fasting plasma glucose, IFG Impaired fasting glu-
cose, IGT Impaired glucose tolerance, OGTT Oral glucose tolerance test, 2-h PG 2-h plasma glucose. *For all three tests, risk is continuous, extending below the lower limit of the range and
becoming disproportionately greater at the higher end of the range
diagnosed based on plasma glucose criteria, either the fasting plasma glucose (FPG)
value or the 2-h plasma glucose (2-h PG) value during a 75-g oral glucose tolerance
test (OGTT), or glycated hemoglobin (A1C) criteria (see Fig.27.1). Prediabetes is
the term used for individuals whose glucose levels do not meet the criteria for diabetes yet have abnormal glucose metabolism (see Fig.27.2) [21].
Insulin resistance or impaired insulin sensitivity is a complex temporary or
chronic condition in which muscle, liver, and fat cells don’t respond as they should
to insulin. Genetic predisposition, overweight or obesity (especially central obesity),
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Fig. 27.3 Equations for the calculation of insulin resistance and insulin sensitivity indices
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and a sedentary lifestyle contribute to the development of insulin resistance, favoring
the elevation of plasma glucose [22]. Insulin resistance plays a pathophysiological
role in T2D but is also associated with other entities such as obesity, hypertension
and dyslipidemia, conditions that dene the metabolic syndrome. There are various
laboratory tests to assess the degree of insulin resistance being the gold standard the
hyperinsulinemic-euglycemic glucose clamp. But this test is expensive and technically complex, so in epidemiological studies or daily clinical practice, the most suitable are the homeostatic model assessment for insulin resistance (HOMA-IR), the
quantitative insulin sensitivity check index (QUICKI), and Matsuda index.
The calculation of HOMA-IR is simple from fasting glucose and insulin.
HOMA-IR has a good correlation with hyperinsulinemic-euglycemic glucose
clamp. The higher the HOMA-IR, the higher the insulin resistance. However, there
is signicant variability in the threshold HOMA-IR levels to dene insulin resistance, and there is no universal consensus on the reference values for the indices
described, but published studies usually set it at a value >2–2.5 (see Fig.27.3) [23].
QUICKI is an empirically derived mathematical transformation of fasting blood
glucose and plasma insulin concentrations that provide a reliable, reproducible, and
accurate insulin sensitivity index. It is derived simply from fasting glucose and insulin values. It correlates well with the hyperinsulinemic-euglycemic glucose clamp
and measures insulin sensitivity, the inverse of insulin resistance. Thus, higher
QUICKI levels are related to higher insulin sensitivity and lower levels of insulin
resistance (see Fig.27.3) [24].
Matsuda index is an insulin sensitivity index that reects a composite estimate of
hepatic and muscle insulin sensitivity determined from OGTT data. It is more complex than the previous one since it requires the performance of an OGTT, but this is
not a particularly expensive or difcult test to perform and is frequently used in
daily clinical practice (see Fig.27.3) [24].
27.4 Metabolic Syndrome andLipid Metabolism
Metabolic syndrome is a term applied to the coexistence in the same individual of
abdominal overweight/obesity, dyslipidemia, type 2 diabetes, and high blood pressure. This combination of factors increases cardiovascular risk. Although there are
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different diagnostic criteria for metabolic syndrome, it is estimated that up to a third
of the adult population may present it. Obstructive sleep apnea has been associated
with metabolic syndrome or its core components. The pathophysiological mechanisms previously described in the relationship between obesity, diabetes, and OSA
are also found in the relationship with metabolic syndrome. Thus, the benecial
effects of CPAP on glucose metabolism and insulin resistance in patients with OSA
are not constant in all the studies carried out [25].
Data from the European Sleep Apnea Database Cohort (ESADA) showed that
OSA severity was independently associated with cholesterol and triglycerides concentrations [26]. Furthermore, OSA was positively associated with serum triglyceride levels in men with a normal waist circumference [27].
Although there are different diagnostic criteria, according to the guidelines from
the National Heart, Lung, and Blood Institute (NHLBI) and the American Heart
Association (AHA), metabolic syndrome is diagnosed when a patient has at least
three of the following ve conditions [28]:
• Fasting glucose ≥100mg/dL (or receiving drug therapy for hyperglycemia)
• Blood pressure ≥130/85mmHg (or receiving drug therapy for hypertension)
• Triglycerides ≥150mg/dL (or receiving drug therapy for hypertriglyceridemia)
• HDL cholesterol <40mg/dL in men or <50mg/dL in women (or receiving drug
therapy for reduced HDL cholesterol
• Waist circumference ≥102cm (40 in) in men or ≥88cm (35 in) in women; if
Asian American, ≥90cm (35 in) in men or ≥80cm (32 in) in women
C. S. Froján et al.
Dyslipidemias are lipid metabolism alterations with altered lipid concentrations, both by excess (hyperlipidemia) and by defect (hypolipidemia).
Dyslipidemia is diagnosed routinely by measuring the serum lipid prole in fasting state that includes total cholesterol, triglycerides, HDL cholesterol, and LDL
cholesterol. Unlike the other elements of the lipid prole, which are measured
directly, LDL cholesterol is calculated from the Friedewald formula: LDL cholesterol=Total cholesterol−[HDL cholesterol+(triglycerides/5)]. A triglyceride level above 400 mg/dL invalidates the use of this formula. The main
therapeutic target is LDL cholesterol since, it is the main cardiovascular risk
factor within the lipid prole. However, although severe, hypertriglyceridemia
(>500–1000mg/dL) can be associated with pancreatitis and constitutes a therapeutic priority when it appears. Other parameters that study lipid metabolism and
of diagnostic and therapeutic interest are Apolipoprotein B, non-HDL cholesterol, and Lipoprotein(a) [29].
27.5 Adipokines
Adipokines or adipocytokines are peptides and proteins secreted mainly by adipocytes and play diverse roles in body homeostasis. Adipose tissue has emerged as a
metabolically active tissue implicated in many processes such as metabolism,
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inammation, and cardiovascular diseases. Current evidence suggests that adipokines may play a role in the complex relationship between OSA and metabolic
disorders.
Leptin is a major adipokine that promotes satiety and is secreted mainly by the
adipocytes of the white adipose tissue. Leptin is positively correlated with fat mass.
Data suggest that long-term exposure to chronic intermittent hypoxia, as occurs in
OSA, may contribute to leptin resistance, which negatively affects the control of
food intake [30].
Adiponectin is another adipokine that improves insulin sensitivity and cardiovascular health. Patients with severe OSA have been shown to have lower levels of
adiponectin. Also, improvement in sleep quality is associated with increased serum
adiponectin levels [31].
An adipokine abundantly expressed in visceral fat, Visfatin, has an insulinmimetic effect. Circulating visfatin levels are elevated in obesity, diabetes, or metabolic syndrome. In patients with severe OSA, visfatin levels were correlated
positively with sleep latency and negatively with total sleep time and percentage of
stage 2 and REM sleep [32].
Chemerin, among other actions, regulates adipogenesis and inammation.
Several studies have shown that chemerin levels are an independent determinant of
OSA and correlated with the severity of OSA [31].
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27.6 Urinary Parameters
First, it should be remembered that OSA can affect the urinary pattern, making the
collection of samples for analysis more complicated. For example, Nocturia (waking up to urinate one or more times during the night) is more frequent in patients
with moderate or severe OSA than in patients with mild OSA [33]. In addition,
overactive bladder prevalence rates range from 49.6% to 79.3% in patients with
OSA, and CPAP treatment can improve symptoms [34].
OSA is highly prevalent in patients with chronic kidney disease and is associated
with accelerated renal dysfunction through several mechanisms such as hypertension, activation of the renin-angiotensin system, or hypoxia. In patients with endstage renal disease, uid overload contributes signicantly to OSA [35].
Microalbuminuria is a marker of renal damage and is used as a diagnostic tool
for early kidney dysfunction. OSA is associated with increased microalbuminuria,
as indexed by the urinary albumin-to-creatinine ratio, depending on the severity of
the disease and hypoxemia [36].
The relationship between OSA and hypertension may be produced by activation
of the sympathetic nervous system induced by hypoxic stress and mediated by the
release of catecholamines. Thus, OSA is associated with increased urinary concentrations of metabolites of catecholamines (normetanephrine and metanephrine),
suggesting increased sympathoadrenal activity [37]. CPAP treatment signicantly
reduces urinary or plasma catecholamines and their metabolites, suggesting an
intermediary role in the relationship between OSA and hypertension [38].
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C. S. Froján et al.
Sestrin2 is a crucial factor involved in oxidative stress. One study showed a
higher urinary level of Sestrin2in OSA patients and increased OSA severity, while
it reduced with CPAP treatment [39].
Lipocalin-type prostaglandin D synthase is responsible for the biosynthesis of
prostaglandin D2 and has been reported to be associated with cardiovascular disease
and sleep regulation. Urinary Lipocalin-type prostaglandin D synthase has been
linked with the AHI [40].
Take-Home Messages
• A signicant positive correlation between RDW and apnea-hypopnea index
(AHI) and oxygen desaturation index (ODI) has been found.
• Several studies have reported increased levels of CRP, IL-6, Tumor Necrosis
Factor Alpha (TNF-α), Fibrinogen, interleukin 8 (IL-8), intercellular adhesion
molecule (ICAM), vascular cell adhesion molecule (VCAM), and selectins in
OSA patients.
• OSA is associated with changes in glucose, lipid metabolism, adipokines and
urinary parameters.
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C. S. Froján et al.
Genetics ofObstructive Sleep Apnea
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28
AnaPatiño-García
28.1 General Aspects ofObstructive Sleep Apnea Genetics
The prevalence of obstructive sleep apnea (OSA) is high, and its effects are potentially severe, leading to cardiometabolic disorders and increased cardiovascular
risks. However, the unavailability of biomarkers for OSA makes diagnosing this
disease an unmet need. In addition, ideal biomarkers should be able to identify the
disease, correlate with severity, and give information about treatment outcomes and
potential complications/comorbidities. These biomarkers can be different: DNA
(single-nucleotide polymorphisms, SNPs, mutations, etc.), RNA (gene expression),
miRNA (microRNA, posttranscriptional regulation), epigenetic modications
(methylation for gene expression regulation) and/or proteins, and their different
nature and applications will be discussed in this chapter (Fig.28.1).
OSA is a very complex trait from the clinical and genetic points of view. It is
most probably conditioned by a plethora of low-risk genes, their interactions, and
their interplay with a network of environmental factors. There is increasing evidence that OSA is a heritable, but maybe not an inherited, trait. Heritability is often
dened as the variation of a given trait that can be attributed to genetic variation. It
is used to estimate the risk of traits conditioned by multiple low-risk genetic variants
interacting with complex multifactorial clinical variables. The estimation of heritability in complex traits such as OSA is highly inuenced by the design of the analyses and the nature of the trait, mainly by the sample size, the different genetic
backgrounds, and by the variability in the included phenotypes. Ovchinsky et al.
performed a study with 445 rst-degree relatives of 115 children with OSA. They
concluded that 12.2% of the pediatric relatives had symptoms suggestive of OSA,
A. Patiño-García (*)
Department of Pediatrics and Medical Genomics Unit, Clínica Universidad de Navarra,
Pamplona, Spain
e-mail: apatigar@unav.es
© The Author(s), under exclusive license to Springer Nature
Switzerland AG 2023
P. M. Baptista et al. (eds.), Obstructive Sleep Apnea,
https://doi.org/10.1007/978-3-031-35225-6_28
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Fig. 28.1 Types of molecules that can be considered as biomarkers for OSA or OSA-related
phenotypes
A. Patiño-García
supporting the heritability of this trait [1]. Other research indicated that OSA prevalence in rst-degree relatives of patients with OSA ranges from 22% to 84%. The
OR of a rst-degree relative having OSA ranges from 2 to 46 [2].
In addition, several studies suggest that around 40% of the apnea–hypopnea
index (AHI) variance can be explained by genetic factors, and twin and family studies indicate that related phenotypes like ventilatory responsiveness to either hypoxemia or hypercapnia, obesity, craniofacial morphology also have heritabilities
ranging from 30% to 70% [3].
28.2 Types ofAnalysis Aimed totheIdentification ofSleep
Apnea Biomarkers andResults Obtained
In general, the types of studies that aim for the identication of associations between
genes and OSA can be included in different categories (Fig.28.2):
1. Linkage Analyses: these studies rely on analyzing a high (or low) number of
markers in pedigrees segregating a given complex trait. The design can include
or not a segregation model in the family, being the model-free linkage analysis
the most frequently used for complex traits with unknown inheritance patterns
like OSA.Other approaches are gradually substituting this type of study.
Palmer and colleagues [4] conducted a genome-wide analysis of 349 subjects
belonging to 66 pedigree families sampled from the Cleveland Family Study
(ref). They performed a multipoint model-free linkage analysis and identied
candidate regions with evidence for linkage with AHI in chromosome regions
1p, 2p, 12p, and 19. Only the 2p region remained signicant after adjusting for
body mass index (BMI). They concluded that there is shared and unshared
genetic variability underlying both OSA and obesity and that there may be
shared pathways regulating both AHI and BMI.
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Fig. 28.2 Types of analysis and study designs for the identication of associations between
genes and OSA
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2. Genome-wide association studies (GWAS) have a case/control design with a
high number of patients and controls to identify genetic variants that are differentially enriched between groups. This design relies on testing common genetic
variants (MAF ≥ 5%). This hypothesis-free approach consists of scanning
high- density markers distributed across the genome to identify genetic loci associated with a complex disease. GWAS ndings are often not validated in further
analyses due, among other factors, to the strict signicance threshold. It is suggested that a gene should be considered positive if it reaches a genome-wide
signicance in any GWAS, either if it is validated or not [5].
In addition, as a range of factors inuence OSA, genes underlying OSA can
affect one or more of these factors, making it critical to carefully consider which
elements to include in any GWAS approach for OSA.
The International Sleep Genetic Epidemiology Consortium (ISGEC) has
already completed a study investigating the risk of moderate/severe OSA by
conducting a GWAS in case and control samples from 9 independent European
ancestry cohorts. In total, 8336 cases and 76,663 controls were investigated and
although several analyses are ongoing, results have not yet been published.
The GWAS database, known as the “GWAS catalog,” hosted by the NHGRIEBI, is a publicly available resource of published human GWAS ([6] https://
www.ebi.ac.uk/gwas/downloads). This database, accessed on April 2,2022,
includes three publications and ve traits in its last data release on March
23, 2022:
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