The Association Between Sleeping Time and Metabolic Syndrome Features,Among Older Adults Living in Mediterranean Region: The MEDIS Study

Abstract

Background:

Metabolic syndrome (MetS) as a combination of features has been known to significantly increase cardiovascular disease risk, while MetS presence is linked to lifestyle parameters, including physical activity and dietary habits; recently, the potential impact of sleeping habits has also become an issue under consideration. The aim of this study was to investigate the role of sleep quantity in several MetS components.

Methods:

Design: a cross-sectional observational study. Setting: 26 Mediterranean islands (MEDIS) and the rural Mani region (Peloponnesus) of Greece. Participants: during 2005–2017, 3130 older (aged 65–100 years) Mediterranean residents were voluntarily enrolled. Measurements: dietary habits (including MedDietScore assessment), physical activity status, sociodemographic characteristics, lifestyle parameters (sleeping and smoking habits), and clinical profile aspects, including MetS components [i.e., waist circumference, systolic and diastolic blood pressure, fasting glucose, triglycerides, and low-density lipoprotein (LDL) and high-density lipoprotein cholesterol (HDL-C)], were derived through standard procedures.

Results:

The number of daily hours of sleep was independently associated with greater waist circumference [b coefficient/hr = 0.91, 95% confidence interval (CI): 0.34–1.49], higher LDL-cholesterol levels (b/hr = 3.84, 95% CI: 0.63–7.05), and lower diastolic blood pressure levels (b/hr = −0.98, 95% CI: −1.57 to −0.39) after adjusting for participants' age, gender, body mass index, daily walking time, level of adherence to Mediterranean diet, and smoking status. No association was revealed between hours of sleep per day and fasting glucose, triglycerides, HDL-C, and systolic blood pressure.

Conclusions:

Increased hours of sleep is an indicator of metabolic disorders among elderly individuals, and further research is needed to identify the paths through which sleep quantity is linked to MetS features in different age groups.

Introduction

The metabolic syndrome (MetS) has been defined as “multiple, interrelated factors that raise cardiovascular disease (CVD) risk.”^1
–3 The classic definition includes presence of several metabolic risk factors, such as insulin resistance, central obesity, dyslipidemia, and elevated blood pressure levels, with recent research expanding these to include chronic stress, inflammation processes, and epigenetic interactions.² This has brought into question the clinical value of confirming a diagnosis of MetS to identify individuals at risk of CVD, as it may exclude people with increased risk who do not present with a minimum of three or more risk factors.⁴ Thus, suggesting that identifying and managing risk associated with the individual isolated characteristics of MetS could be of equal if not greater clinical importance than diagnosing MetS.⁵

The role of lifestyle factors particularly those beyond diet⁵ and physical activity⁶ in relation to CVD risk and MetS is an area of increasing interest. This includes associations between social interaction, depression, and risk of CVD⁷ and has included investigations that found associations between sleeping pattern, including daytime sleeping, and MetS risk.⁸ Increasing evidence of the potential role of sleep in MetS components has recently emerged in the literature; a meta-analysis in 2015 of 21 studies found a robust and consistent negative association between insufficient sleep and waist circumference.⁹ This aligns with a separate review, which found an increased risk for MetS in short, but not in longer duration sleepers.¹⁰ However, the mechanism of how sleep duration may influence MetS risk is unclear. Moreover, elevated blood pressure and glucose dysregulation have been proposed as a primary driver behind the excess in mortality risk in short-duration sleepers.¹¹ The impact of sleep deprivation on the endocrine system is complex and includes decreased insulin sensitivity and dysregulation of hormonal pathways, including cortisol, leptin, and insulin-like growth factor-1.¹² Furthermore, sleep deprivation modifies inflammatory and cholesterol pathways associated with increased CVD risk at both the transcriptome level and in the circulating lipid profile.^13,14 This implies that the effect of sleep deprivation impacts on the component factors of MetS and as such merits investigation.

Despite understanding the impact of lack of sleep on metabolic risk, little is known regarding the association of sleep quantity on MetS features, especially in an older adult population. Moreover, older people residing in the Mediterranean region have attracted considerable scientific and public interest, surrounding their lifestyle and dietary factors, as potentially preventative and curative for several health conditions.^15

–19 Recently, the Mediterranean Islands (MEDIS) group has shown that sleeping during the day (siesta) is positively associated with odds of hypertension.⁷ To our knowledge, no study has investigated the relationship between the quantity of sleep and the individual component factors of MetS in elderly individuals. Thus, the aim of the present work was to evaluate the associations between sleep quantity and MetS features in elderly individuals from the Mediterranean region.

Materials and Methods

Methodology

The MEDIS study is an ongoing, large-scale, multinational epidemiological project that is exploring the association between lifestyle habits, psychosocial characteristics, and living environment, on cardiometabolic factors, among older people (>65 years) residing in the Mediterranean area.

The study's sample

Between 2005 and 2017, a random population-based, multistage sampling method [i.e., age group, 3 levels (65–75, 75–85, 85+) and 2 sex levels] was used to voluntarily enroll older men and women people from 26 MEDIS, including Malta Republic (n = 250), Sardinia (n = 60), Sicily (n = 50), Mallorca and Menorca (n = 111), Republic of Cyprus (n = 300), Gökçeada (n = 55) in Turkey, and the Greek islands of Lesvos (n = 142), Samothraki (n = 100), Cephalonia (n = 115), Crete (n = 131), Corfu (n = 149), Limnos (n = 150), Ikaria (n = 76), Syros (n = 151), Naxos (n = 145), Zakynthos (n = 103), Salamina (n = 147), Kassos (n = 52), Rhodes and Karpathos (n = 149), Tinos (n = 129), Ai-Stratis (n = 30), Spetses (n = 92), Aegina (n = 59), Paros (n = 90), as well as the rural region of East Mani (n = 295, 157 men aged 75 ± 7 years and 138 women aged 74 ± 7 years), a Greek peninsula in the southeast, continental area of Europe, with a total population of 13,005 people (census 2011), which has morphological and cultural specificities that are not common across the rest of Greece. Individuals who resided in assisted-living centers had a clinical history of CVD or cancer, or had left the island for a considerable period of time during their lifetime (i.e., >5 years), were excluded from participating in the study; these criteria were applied because the study aimed to assess lifestyle patterns that were not a response of individuals modifying how they live due to existing chronic healthcare conditions or by environmental factors, other than their living milieu. The participation rate varied according to the region, from 75% to 89%. Thus, information from 3130 individuals, 1,574 men, aged 75 ± 8 years and 1,556 women, aged 74 ± 7 years, was analyzed.

A multidisciplinary group of health scientists (physicians, dietitians, public health nutritionists, and nurses) with experience in field investigation collected all the required information using a quantitative questionnaire and standard procedures.

Bioethics

The study followed the ethical considerations provided by the World Medical Association (52nd WMA General Assembly, Edinburgh, Scotland; October 2000). The Institutional Ethics Board of Harokopio University approved the study design (16/19-12-2006), as well as the regional offices of the participated institutions. Participants were informed about the aims and procedures of the study and provided their consent before being interviewed.

Evaluation of clinical characteristics

All of the measurements taken in the different study centers were standardized, and the questionnaires were translated into all the cohorts' languages following the World Health Organization (WHO) translation guidelines for tools assessment (www.who.int/substance_abuse/research_tools/translation/en). Height and weight were measured using standard procedures to attain body mass index (BMI) scores (kg/m²). Waist circumference (cm) was measured at the midpoint between the 12th rib and the iliac crest. Fasting blood lipid levels [high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides] and fasting glucose levels were also recorded. Blood pressure was either self-reported or, in most islands, measured by trained physicians or nurses with participants in a sitting position and calm and the average of three measurements was calculated. The International Diabetes Federation (IDF) Epidemiology Task Force group definition of MetS was used to identify individuals with MetS.³

Evaluation of lifestyle and sociodemographic characteristics

Sleep was assessed estimating the amount of sleeping hours on a typical day while interviewing the participants using the self-reported wake after sleep onset (WASO). The frequency and the hours of sleeping during the day, as well as the wake-up and going-to-sleep time were also recorded according to individual self-reporting. Dietary habits were assessed through a semiquantitative, validated, and reproducible food-frequency questionnaire.²⁰ Trained dietitians estimated the mean daily energy intake and the mean percentage of total energy derived from dietary carbohydrates. To evaluate the level of adherence to the Mediterranean diet, the MedDietScore (possible range 0–55) was used.²¹ Higher values for this diet score being indicative of greater adherence to the Mediterranean diet. Participants were also encouraged to report the duration of following their dietary pattern (i.e., the number of years this pattern had been in place). Basic sociodemographic characteristics such as age, sex, as well as lifestyle characteristics, such as smoking habits and physical activity status, were also recorded. Current smokers were defined as smokers at the time of the interview, whereas former smokers were defined as those who previously smoked but had not done so for a year or more. Current and former smokers were defined as had “ever smokers.” The remaining participants were assigned as occasional or nonsmokers. Physical activity was evaluated in MET-min/week, using the shortened, translated, and validated into Greek, version of the self-reported International Physical Activity Questionnaire (IPAQ).^22,23 Frequency (times/week), duration (min/session), and intensity of physical activity during sports, occupation, and leisure activities were assessed. Participants were instructed to report only episodes of activity lasting at least 10 min since this is the minimum required to achieve health benefits. Physically active individuals were defined as those who reported at least 3 MET-minutes. Daily walking time was calculated by using the IPAQ about walking (times/week and average time spent).

Further details about the MEDIS study have extensively been published elsewhere.^24,25

Statistical analysis

Continuous variables are expressed as mean ± standard deviation for variables following assessing for normal distribution, or median (interquartile range) for variables not following a normal distribution. Normality was tested using P-P plots. Differences in continuous variables between males and females were evaluated with the Student's t-test for normally distributed parameters and the Mann–Whitney test for nonparametric variables. Correlations between continuous variables were tested using Pearson's r when both variables were normally distributed or Spearman's rho when at least one of them did not have a normal distribution. Nominal variables are presented as frequencies and relative frequencies (%). Pearson's chi-squared test was used to assess the association between two nominal variables.

Linear regression models were used to evaluate the association between sleep duration, other participants' characteristics (i.e., age, sex, BMI, physical activity, MedDietScore, and smoking habits), and levels of the MetS components (fasting glucose levels, waist circumference, systolic and diastolic arterial blood pressure, triglycerides, and LDL and HDL levels). Logarithmic transformation was used for the dependent variable that did not have a normal distribution (triglycerides and HDL-C). Results are expressed as b coefficients and 95% confidence intervals (CIs). Type I error was predefined at 0.05. Statistical analysis was carried out in IBM SPSS version 23.0 (IBM Corp., Armonk, NY).

Results

Mean sleep duration time was 8.30 ± 1.76 hrs/day, and specifically 8.30 ± 1.75 hrs for men and 8.20 ± 1.77 hrs for women (P = 0.52). Moreover, sleep duration did not differ between retired and nonretired individuals (P = 0.244), with the latter consisting 20.9% of the total sample. Sleep duration was positively associated with waist circumference (Pearson's r = 0.12, P = 0.01) and LDL-C (Pearson's r = 0.23, P = 0.001) and inversely associated with diastolic arterial blood pressure (Pearson's r = −0.15, P = 0.002). No association was observed between sleep and fasting glucose levels (P = 0.20), systolic arterial blood pressure (P = 0.59), fasting triglycerides (P = 0.44), and HDL-C (P = 0.47). MetS prevalence according to IDF criteria was 65.3% and did not differ between genders (P = 0.49) and was not associated with hours of sleep (odds ratio = 1.13, 95% CI: 0.853–1.50).

Mean BMI was 28.3 ± 4.67 kg/m², while the level of adherence to the Mediterranean diet was 32.5 ± 4.99 out of 55 (or 59% of ideal adherence), as calculated via the MedDietScore. Regarding the MetS individual components, mean waist circumference 101 ± 14.0 cm, mean fasting glucose 116 ± 39.5 mg/dL, and mean LDL-C 126 ± 41.3 mg/dL with median HDL-C 50 mg/dL and median triglyceride levels 119 mg/dL. Participants' mean systolic and diastolic arterial blood pressures were 135 ± 21.9 mmHg and 77.6 ± 13.0 mmHg, respectively. Descriptive characteristics of the study sample, divided into two groups with respect to their gender, are summarized in Table 1.

Table 1.

Lifestyle, Psychosocial, and Clinical Characteristics of the MEDIS Study Participants (n = 3130) in Respect to Their Gender

Characteristics	All	Males (n = 1574)	Females (n = 1556)	P
Age (years)	74.2 ± 7.34	74.8 ± 7.49	73.6 ± 7.14	<0.001
Daily sleep (hours)	8.30 ± 1.76	8.30 ± 1.75	8.20 ± 1.77	0.53
Body mass index (kg/m²)	28.3 ± 4.67	27.8 ± 4.12	28.9 ± 5.10	<0.001
Smoking (current), yes (%)	15.6	26.1	5.2	<0.001
MedDietScore (0–55)	32.5 ± 4.99	32.5 ± 4.98	32.5 ± 5.00	0.88
Daily walking time (min)^a	60 (30–120)	60 (30–120)	60 (25–120)	0.24
Waist circumference (cm)	101 ± 14.0	102 ± 12.3	100 ± 15.1	0.001
Fasting glucose levels (mg/dL)	116 ± 39.5	116 ± 37.7	115 ± 41.2	0.72
Diabetes mellitus, yes (%)	22.3	22.8	21.8	0.571
Diabetes treatment (disk and/or insulin), yes (%)	13.38	14.2	12.6	0.283
Triglycerides (mg/dL)^a	119 (92–160)	119 (95–160)	118 (91–156)	0.55
LDL-C (mg/dL)	126 ± 41.3	123 ± 39.3	129 ± 22.6	0.03
HDL-C (mg/dL)^a	50 (43–60)	46 (40–54)	55 (46–63)	<0.001
Hyperlipidemia, yes (%)	47.7	40.8	54.9	<0.001
Hyperlipidemic treatment, yes (%)	30.3	35.3	25.5	<0.001
Systolic blood pressure (mmHg)	135 ± 21.9	135 ± 19.8	134 ± 23.6	0.86
Diastolic blood pressure (mmHg)	77.6 ± 13.0	77.8 ± 12.0	77.3 ± 14.0	0.46
Hypertension, yes (%)	62.3	55.7	68.9	0.012
Hypertension treatment, yes (%)	54.6	47.4	62.1	0.02

Values are presented as median (25th–75th percentiles). P values derived from Student's t-test or nonparametric Mann–Whitney test (^a) for noncontinuous variables and chi-squared test for nominal variables.

HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

As presented in Table 1, females had higher BMI than males (28.9 ± 5.10 kg/m² vs. 27.8 ± 4.12 kg/m², respectively, P < 0.001), but their smoking prevalence was fivefold lower compared to men (5.2% vs. 26.1%, respectively, P < 0.001). No differences were revealed for their level of adherence to Mediterranean diet (P = 0.88), daily walking time (P = 0.24), nor their daily hours of sleep (P = 0.53). As expected, females had lower waist circumference than men (100 ± 15.1 cm vs. 102 ± 12.3 cm, respectively, P = 0.001), higher HDL-C levels [55 (46–63) mg/dL vs. 46 (40–54) mg/dL, respectively, P < 0.001], and lower LDL-C levels (129 ± 22.6 mg/dL vs. 123 ± 39.3 mg/dL, respectively, P = 0.026). Interestingly, no differences were detected for triglyceride levels (P = 0.55), fasting glucose levels (P = 0.72), and systolic (P = 0.86) and diastolic (P = 0.46) arterial blood pressure levels.

Characteristics of the participants according to their MetS status are presented in Table 2. As expected, subjects with MetS had higher waist circumference than MetS-free subjects (107 ± 10.4 cm vs. 96.8 ± 12.5 cm, respectively, P < 0.001), lower HDL-C levels [49 (42–58) mg/dL vs. 56 (49–62) mg/dL, respectively, P < 0.001), higher LDL-C levels (130 ± 40.2 mg/dL vs. 115 ± 44.1 mg/dL, respectively, P = 0.003), higher BMI (30.8 ± 4.37 kg/m² vs. 28.2 ± 3.81 kg/m², respectively, P < 0.001), higher fasting glucose levels (126 ± 36.9 mg/dL vs. 101 ± 36.1 mg/dL, respectively, P < 0.001), higher triglyceride levels [132 (102–177) mg/dL vs. 100 (86–119) mg/dL, respectively, P < 0.001], and higher systolic (138 ± 15.4 mmHg vs. 123 ± 14.2 mmHg, respectively, P < 0.001) and diastolic arterial blood pressure levels (79.5 ± 9.62 mmHg vs. 74.9 ± 9.96 mmHg, respectively, P < 0.001), as well as less daily walking time [60 (30–120) min/day vs. 120 (30–240) min/day, respectively, P < 0.001]. Interestingly, no differences were detected for gender (P = 0.49), age (P = 0.50), daily hours of sleep (P = 0.42), smoking status (P = 0.76), nor their level of adherence to Mediterranean diet (P = 0.53). No significant interaction between gender and sleep duration was detected when MetS presence is regarded.

Table 2.

Lifestyle, Psychosocial, and Clinical Characteristics of the MEDIS Study Participants (n = 3130) in Respect to Their Metabolic Syndrome Status

Characteristics	MetS (65.3%)	No MetS (34.7%)	P
Age (years)	74.3 ± 6.62	74.8 ± 7.36	0.50
Gender, male (%)	29.6	33.3	0.49
Daily sleep (hours)	7.91 ± 1.82	7.59 ± 1.56	0.42
Body mass index (kg/m²)	30.8 ± 4.37	28.2 ± 3.81	<0.001
Smoking (current), yes (%)	8.5	7.5	0.76
MedDietScore (0–55)	33.0 ± 5.16	32.5 ± 6.57	0.53
Daily walking time (min)^a	60 (30–120)	120 (30–240)	0.03
Waist circumference (cm)	107 ± 10.4	96.8 ± 12.5	<0.001
Fasting glucose levels (mg/dL)	126 ± 36.9	101 ± 36.1	<0.001
Triglycerides (mg/dL)^a	132 (102–177)	100 (86–119)	<0.001
LDL-C (mg/dL)	130 ± 40.2	115 ± 44.1	0.003
HDL-C (mg/dL)^a	49 (42–58)	56 (49–62)	<0.001
Systolic blood pressure (mmHg)	138 ± 15.4	123 ± 14.2	<0.001
Diastolic blood pressure (mmHg)	79.5 ± 9.62	74.9 ± 9.96	<0.001

MetS, metabolic syndrome.

Table 3 and Fig. 1 present the multivariable linear regression models that were implemented with the MetS individual component factors (waist circumference, fasting glucose, LDL-C and HDL-C, and triglyceride levels, and systolic and diastolic arterial blood pressure) as dependent variables. Total daily hours of sleep was independently associated with greater waist circumference in the age- and gender-adjusted model (b/hr = 0.70, 95% CI: 0.07–1.32), which remained significant and became stronger after adjusting for lifestyle factors such as smoking, daily walking, MedDietScore, and BMI (b/hr = 0.91, 95% CI: 0.34–1.49). When LDL-C levels are regarded, the daily hours of sleeping was a significant independent variable in the age- and gender-adjusted model (b/hr = 5.14, 95% CI: 2.10–8.19), while in the final model it remained significant, but the effect size decreased (b/hr = 3.84, 95% CI: 0.63–7.05). Total daily hours of sleep were independently and equally associated with lower diastolic blood pressure levels in the age- and gender-adjusted model (b/hr = −0.92, 95% CI: −1.49 to −0.34) and the multiadjusted model (b/hr = −0.98, 95% CI: −1.57 to −0.39). No associations were revealed between hours of sleep per day and fasting glucose, triglyceride, and systolic arterial blood pressure levels in any of the multivariable models.

FIG. 1.

Multivariable linear logistic regression model coefficients for the role of hours of total sleep in metabolic syndrome components (n = 3130). Model 1: all models have been adjusted for age and gender. Model 2: all models have been adjusted for age, gender, body mass index, daily walking, MedDietScore, and smoking status. Logarithmic transformation has been used to normalize the dependent variables HDL-C and triglyceride levels. HDL-C, high-density lipoprotein cholesterol; LDL, low-density lipoprotein.

Table 3.

Multivariable Linear Logistic Regression Model for the Role of Hours of Total Sleep in Metabolic Syndrome Components (n = 3130)

Dependent variable	b Coefficient (per hr)	Standard error	95% Confidence interval	P
Waist circumference (cm) (Model 1)	0.70	0.32	0.07 to 1.32	0.03
Waist circumference (cm) (Model 2)	0.91	0.29	0.34 to 1.49	0.002
Fasting glucose levels (mg/dL) (Model 1)	1.23	1.03	−0.79 to 3.25	0.23
Fasting glucose levels (mg/dL) (Model 2)	0.73	1.13	−1.51 to 2.96	0.52
Triglycerides (mg/dL)^a (Model 1)	0.001	0.01	−0.03 to 0.03	0.95
Triglycerides (mg/dL)^a (Model 2)	0.006	0.02	−0.02 to 0.04	0.69
LDL-C (mg/dL) (Model 1)	5.14	1.54	2.10 to 8.19	0.001
LDL-C (mg/dL) (Model 2)	3.84	1.62	0.63 to 7.05	0.02
HDL-C (mg/dL)^a (Model 1)	−0.001	0.01	−0.02 to 0.02	0.92
HDL-C (mg/dL)^a (Model 2)	−0.005	0.01	−0.03 to 0.02	0.67
Systolic blood pressure (mmHg) (Model 1)	0.24	0.38	−0.50 to 0.98	0.52
Systolic blood pressure (mmHg) (Model 2)	0.32	0.39	−0.46 to 1.09	0.42
Diastolic blood pressure (mmHg) (Model 1)	−0.92	0.29	−1.49 to −0.34	0.002
Diastolic blood pressure (mmHg) (Model 2)	−0.98	0.30	−1.57 to −0.39	0.001

Model 1: all models have been adjusted for age and gender. Model 2: all models have been adjusted for age, gender, body mass index, daily walking, MedDietScore, and smoking status.

Indicates that logarithmic transformation has been used to normalize the dependent variable.

LDL, low-density lipoprotein.

Discussion

This analysis has demonstrated that self-reported sleep duration can have variable effects on the individual component factors used in the diagnosis of MetS in a relatively healthy elderly cohort residing in the Mediterranean area. Using a component analysis of sleep quantity, individuals with greater duration of total sleep are more likely to have a higher waist circumference and LDL-C. More specifically, for every hour increase in total sleep, waist circumference is expected to rise per 1 cm and LDL-C per ∼4 mg/dL, even when important confounders were considered. From a clinical point of view, these findings could provide the clinicians an important lifestyle parameter to assess for elderly individuals. On the contrary, increased total sleep hours were found to be associated with slight decrease in diastolic blood pressure, but not of clinical importance. Interestingly, no associations were observed between sleep duration with respect to fasting glucose, triglyceride, and HDL-C levels and systolic blood pressure. This is suggestive of a mixed effect of sleep quantity on features of MetS, with four of the seven features not being influenced by sleep duration and this can explain the lack of association between hours of sleep and the MetS as an entity.

Over the last decade, there has been a growth in research describing the impact of short sleep duration,^10,26
–28 yet few have attempted to elucidate the risks associated with oversleeping. In studies inclusive of all adults, longer sleep duration may be protective of MetS.^29,30 However, this is believed to be the first study examining the association between the individual component features of MetS and sleep quantity in a relatively healthy elderly cohort. In the Mediterranean area, MetS is estimated to affect 20%–25% of individuals,³¹ with prevalence as high as 46.8% using NCEP ATP III criteria.³² These data highlight the need to understand the optimal sleep range to promote positive health and well-being relative to the components of MetS in an aging population and the need for sleep duration to be assessed in the clinical setting. Furthermore, this needs to be incorporated as part of a holistic preventative lifestyle approach, considering social factors alongside physical activity, diet, and mental well-being.⁷

The association of waist circumference to CVD and diabetes risk factors has been well described.³³ In this cohort, the association of an increased waist circumference for each hour of sleep was demonstrated independent of other CVD risk factors such as age, gender, BMI, and lifestyle characteristics. These findings highlight that an increased waist circumference and the presence of visceral adiposity could indicate the presence of insulin resistance and chronic low-grade inflammation. The production of adipocytokines from the central adipose tissue is implicated in atherogenic dyslipidemia such as high serum triglycerides and low HDL-C,³⁴ however, this was not associated with sleep duration in this cohort. In research using participants with obstructive sleep apnea, each hour of additional sleep was associated with a 7% increase in interleukin-6 (IL-6) and an 8% increase in C-reactive protein (CRP).³⁵ The Women's Health Study³⁶ found both IL-6 and CRP to be associated with increased waist circumference, BMI, and waist-to-hip ratio. Other adipocytokines, including leptin, resistin, tumor necrosis factor-α, and angiotensin II, have also been related to insulin resistance and visceral fat accumulation.³⁷

The role of a genetic predisposition toward obesity, waist circumference, and BMI has been observed in a U.K. cohort, which suggested this effect was moderated by sleep among other lifestyle characteristics.³⁸ This study found short and long sleep duration to compound the influence of a genetic predisposition toward obesity. Collectively, these findings indicate a need for a focus on reversing central adiposity, which is associated with inflammation. This research supports the view that clinician should consider sleep management alongside other lifestyle advice such as diet and physical activity in the treatment and prevention of MetS and CVD risk.

The link between MetS and CVD risk in older adults of the Mediterranean region has been previously reported, with an increase in the likelihood of CVD by 83% in individuals from Athens, Greece.² Elevated triglycerides and LDL-C, as along with lower levels of HDL-C, are associated with CVD risk, although the presented model only found an association between sleep duration per hour and increased LDL-C. Previously, high waist circumference has been demonstrated to be associated with elevated oxidized LDL-C independent of BMI in healthy older adults from Spain.³⁹ This again suggests that low-grade chronic inflammation may induce oxidative stress through the release of adipocytokines. While optimal sleep increases the ability to process moderate oxidative stress, these data may be explained by diminishing returns in the presence of higher than optimal sleep quantity.

While these findings suggest that extra sleep may have detrimental effects in this cohort, it also poses the question as to why individuals with these risk factors may be sleeping more. This analysis includes a relatively healthy cohort, evident by adherence to a Mediterranean diet and 60 min (median) of daily walking time. Adherence to a Mediterranean has been inversely associated with the risk of MetS, impaired fasting glucose, and insulin resistance.⁴⁰ It is plausible that a reverse cause/effect may be occurring with individuals living with symptoms of MetS sleeping more, possibly including during the day.⁷ This highlights the need for greater identification of sleep habits and behaviors in clinical practice due to the potential moderating effect on MetS symptoms, preferably with more objective methods such as polysomnography that could also assess sleep quality.⁴¹

Questions remain as to whether MetS should be treated on an individual basis or whether the emphasis on a full lifestyle intervention is suitable to reduce disease risk.⁴² Reaven suggested that the clustering of components of the MetS occurs only in insulin-resistant individuals and that focus on diagnosing MetS is unnecessary.⁴ Others contend that the identification of markers for MetS is crucial to treating the complex interaction between each component.³⁷ The results from this healthy cohort support the contention that each component, such as LDL-C, has individual importance, however, it the lifestyle variable of sleep quantity that appeared to moderate these component features differently, suggesting that individual components of MetS need to be considered separately, even if treatment is holistic. While the model presented is relative to sleep quantity, it did account for other lifestyle factors. However, it cannot be ignored that broad lifestyle recommendations can improve MetS symptoms and CVD risk^7,21,43 and along with an adjunctive benefit that may be derived by sleep quantification in the clinical setting.

Future research should aim to identify the reasons underlying the relationship between sleep quantity and the biochemical pathways impacting LDL-C and reduced diastolic blood pressure. Furthermore, the link between insulin resistance and oversleeping requires further investigation to be able to make evidence recommendations based on the optimal sleeping time. As the current middle-aged population progress ages, future research will also need to consider the impact of increased nocturnal light and electronic device exposure and interactions between circadian entrainment and MetS.

Strengths and limitations

It is important to note that this is a cross-sectional survey and therefore lacks the ability to infer causal relationships. The measurements have been performed once and may be prone to measurement and reporting errors. However, this methodology is commonly used in this field and this study used validated instruments and suitably qualified and trained staff, making the results comparable to other studies. The sleeping habits have been assessed only regarding quantity and not quality or patterns (e.g., daytime nap duration), which could be equally important; this was used as the measuring method is easier to implement and could be implemented in routine clinical practice. Furthermore, sleep duration was self-reported and not objectively measured (e.g., via polysomnography); however, in an outpatient environment, sleep data will also be self-reported and thus this information can be of practical importance. Moreover, the data on sleep were not obtained separately for weekends and weekdays, although it is common among the elderly to adopt the same pattern every day, this could increase the robustness of the data.

The use of individual component factors rather than a global assessment of MetS could also be viewed as a limitation, as well as the high MetS prevalence in the study sample, which is common among elderly though. However, with the different classifications of MetS and the inclusion of raised markers or treatment, it was felt that in this analysis, considering each feature in isolation would provide a clearer view of CVD risk. In addition, without considering the separate features it would not be possible to elucidate the differing effects of sleep quantity on the component features.

Conclusions

Increasing sleep duration has a variable effect on component features of MetS in an elderly population, with changes to waist circumference and LDL-C potentially increasing risk and reductions in diastolic blood pressure reducing risk, but may increase risk of other conditions. Sleep duration appears to influence markers of metabolic health in apparently healthy older adults; however, more work is required to elucidate mechanisms and how aging influences the role of sleep duration on health. It is logical that clinicians as part of lifestyle assessment, including quantifying sleep in subjects with existing MetS risk factors, should become an integral part of clinical practice, especially taking into account that MetS is a CVD risk factor of great significance.

Footnotes

Acknowledgments

We are, particularly, grateful to the men and women from the islands of Malta, Sardinia, Sicily, Mallorca, Menorca, Cyprus, Gökçeada, Lesvos, Samothraki, Crete, Corfu, Lemnos, Zakynthos, Cephalonia, Naxos, Syros, Ikaria, Salamina, Kassos, Rhodes, Karpathos, Tinos, Ai-Stratis, Spetses, Aegina, Paros, and the rural area of Mani, who participated in this research. The MEDIS Study Group is: M. Tornaritis, A. Polystipioti, M. Economou (field investigators from Cyprus), A. Zeimbekis, K. Gelastopoulou, I. Vlachou (field investigators from Lesvos), I. Tsiligianni, M. Antonopoulou, N. Tsakountakis, K. Makri (field investigators from Crete), E. Niforatou, V. Alpentzou, M. Voutsadaki, M. Galiatsatos (field investigators from Cephalonia), K. Voutsa, E. Lioliou, M. Miheli (field investigators from Corfu), S. Tyrovolas, G. Pounis, A. Katsarou, E. Papavenetiou, E. Apostolidou, G. Papavassiliou, P. Stravopodis (field investigators from Zakynthos), E. Tourloukis, V. Bountziouka, A. Aggelopoulou, K. Kaldaridou, E. Qira (field investigators from Syros and Naxos), D. Tyrovolas (field investigator from Kassos), I. Protopappa (field investigator from Ikaria), C. Prekas, O. Blaserou, K.D. Balafouti (field investigators from Salamina), S. Ioakeimidi (field investigator from Rhodes and Karpathos), A. Foscolou (field investigator from Tinos), A. Foscolou, T. Paka, P. Drepanidis (field investigators from Gökçeada), A. Mariolis, E. Petropoulou, A. Kalogerakou, K. Kalogerakou (field investigators from Mani), S. Piscopo (field investigator from Malta), J.A. Tur (field investigator from Mallorca and Menorca), G. Valacchi, B. Nanou (field investigators from Sardinia and Sicily), E. Votsi (field investigator from Ai-Stratis), A. Foscolou, K. Katsana, P. Drepanidis, S. Iosifidis (field investigators from Spetses), A. Foscolou, K. Gouvas, G. Soulis, K. Katsana (field investigators from Aegina), A. Foscolou, K. Gouvas, K. Katsas, P. Kaloudi, E. Papachristou, K. Stamouli (field investigators from Paros) for their substantial assistance in the enrollment of the participants. The study has been funded by the Hellenic Heart Foundation, the Graduate program of the Department of Nutrition and Dietetics, Harokopio University in Athens, Greece, and the Rutgers, The State University of New Jersey. S.T.'s work was funded through a scholarship from the Foundation for Education and European Culture (IPEP). J.-A.T. was funded by grants PI14/00636, CIBERobn CB12/03/30038, and CAIB/EU 35/2001.

Author Disclosure Statement

No competing financial interests exist.

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