eISSN: 2720-5371
ISSN: 1230-2813
Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii
Current issue Archive Manuscripts accepted About the journal Scientific board Abstracting and indexing Subscription Contact Instructions for authors Ethical standards and procedures
Editorial System
Submit your Manuscript
SCImago Journal & Country Rank
vol. 32
Review paper

Body mass index and sleep disturbances: a systematic review and meta-analysis

Sohrab Amiri

Medicine, Quran and Hadith Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
Adv Psychiatry Neurol 2023; 32 (2): 96–109
Online publish date: 2023/06/28
Article file
- PPN-00221-Amiri.pdf  [0.21 MB]
Get citation
PlumX metrics:


Obesity has become an epidemic in the world, with rates tripling between 1975 and 2016 [1]. Accordingly, in 1997 the World Health Organization recognized obesity as a pandemic that is a major cause of health problems [2]. The World Health Organization’s 2016 report shows that 1.9 billion people over the age of 18 worldwide are overweight and the more than 650 million are obese; this means that 39% of people over the age of 18 are over-weight and 13% are obese [3]. The Institute of Global Burden of Disease report also shows that the body mass index (BMI) of 25 kg/m2 and above in adults between 1980 and 2013 in men increased from 28.8% to 36.9%, while the increase in women was from 29.8% to 38% [4]. There is also a significant increase in overweight and obesity among children and adolescents in both developed and developing countries [4]. In this regard, the increase in BMI in the form of overweight and obesity is associated with a range of diseases that are threats to health, such as mortality [5-7], cancer [8-11], diabetes [12-14], reduced quality of life [15-17], depression [18, 19], work-related issues [20-22], and other mental health issues [23, 24]. One of the health dimensions that can be affected by over-weight and obesity is sleep disturbances [25].

The International Classification of Sleep Disorders includes a variety of conditions [26]. The most prevalent type of sleep disorder is insomnia, with an estimated 30% of the adult population having trouble starting and continuing sleep, and between 5-10% having insomnia syndrome [27-29]. Another type of sleep disorder is obstructive sleep apnea, with a prevalence of 9% to 38% in the general population and is more prevalent in men [30]. It is estimated that among adults aged 30-69, 936 million worldwide have mild symptoms of obstructive sleep apnea and 425 million have moderate-to-severe obstructive sleep apnea [31]. Other types of sleep disorder can be noted as excessive daytime sleepiness; the overall prevalence of this disorder is reported to be 10.4% in men and 13.6% in women [32]. There are other types of sleep disturbance, such as poor sleep quality, sleep complaints, and so on. Several factors have been reported in connection with sleep disturbances, including aging [30, 33], sex, depressed mood [34], smoking [35], and alcohol use [36].

In addition, sleep disturbances have negative effects on various aspects of health and work such as increasing the risk of sick leave [37], dementia [38], cardiovascular disease [39], back pain [40], physical impairment [41], and mental disoders [42-44].

Studies have examined the risk factors associated with sleep disturbances [45-49]. The relationship between these and BMI and has been investigated in recent studies [30, 49, 50]. Accordingly, systematic review and meta-analysis studies have shown that BMI and weight loss are associated with a range of sleep disturbances, including the dia gnosis and symptoms of insomnia [51], obstructive sleep apnoea [30, 52], and restless legs syndrome [53]. It is clear that overweight and obesity have widespread negative effects on various dimensions of health, and one of these dimensions is sleep. Three systematic reviews and meta-analyses have examined the relationship between BMI and sleep disturbances [30, 51-53]; however, reviewing this group of studies reveals several critical points and necessitates the need for a new systematic review and meta-analysis. First, out of the total number of these studies, only three [49, 51, 53] have meta-analyzed the relationship between BMI and sleep disturbances. Second, each of these three studies addressed only one type of sleep disturbance and did not provide a comprehensive meta-analysis of the association between BMI and sleep disturbances. Third, these studies of the results of research often relied on cross-sectional studies, although cross-sectional studies are not able to determine the causality between BMI and sleep disturbances. Although some studies have examined sex differences in the relationship between BMI and sleep disturbances, this has not been the case for a variety of other causes of sleep disturbances.

Based on what has been stated, this study sought to examine the risk of sleep disturbances in overweight and obesity based on longitudinal studies. This association was also examined separately for overweight and obesity. The third goal was to examine sex differences in the association between BMI and sleep disturbances. Finally, associations between BMI and three types of sleep disorders including insomnia, obstructive sleep apnea, and excessive daytime sleepiness were investigated.


Search strategy

In this project, to guide the systematic review and meta-analysis process the steps mentioned in the PRISMA [54] protocol were used. Three sources of scientific information, namely PubMed, Scopus, and Embase, were selected as scientific databases, and based on a complete syntax of keywords listed in Appendix 1 each of these sources of information was fully searched; articles published in English until May 2021 were included. A manual search on references in related articles was also done. In cases where the articles were ambiguous, the authors were contacted via email to provide the required information.

Selection criteria

A set of inclusion and exclusion criteria were used for this study. Studies were included in the project if they met the following requirements: 1) overweight and obesity were considered as exposure variables; 2) sleep disturbances such as insomnia, restless legs syndrome, obstructive sleep apnea, snoring, sleep complaints, poor sleep quality, and excessive daytime sleepiness were the outcome variables; 3) study design was longitudinal studies, and cross-sectional, retrospective, time series, clinical trial studies, and case studies – review studies were excluded. Studies were excluded from the project with one of the following conditions: 1) studies in which the study subjects were children; 2) studies that did not provide the necessary information for the odds ratio and making this calculation was not possible; 3) studies on women during pregnancy or following childbirth; 4) studies that were from the same database and in this case only one article with the highest quality was selected.

Data extraction

A framework for the information extraction process was created. Accordingly, types of information were extracted for each of the eligible studies and these included information sets including the authors of each study and year of publication, population, country, length of time until fellow-up, demographic indicators of the study population such as age and sex, BMI, sleep disturbance and its measurement, and finally the result of each study.

Qualitative measure

In the qualitative evaluation section, as an essential component of a systematic review, the EPHPP tool [55, 56] was used. In the present study, according to the nature of this research, five adjusted dimensions of this tool were used to assess the quality of each of the studies selected.


This research involved a systematic review as well as a statistical analysis in the form of meta-analysis; the steps taken to complete the meta-analysis are described below. The odds ratio and confidence interval were extracted for each study. In cases where the risk ratio was reported, this ratio was converted into the odds ratio [57]. In cases where the hazard ratio was reported, this ratio was pooled separately. In some studies, there were dependent outcomes that used existing procedures [58] to combine them, and in independent outcomes, a fixed-effects method was used to combine them. Therefore, this information was extracted from each study and then the pooled odds ratio was calculated on the basis of the random- effects method. The analyzes were conducted at several levels. The first analysis was the association between BMI ≥ 25 and sleep disturbances, and then this odd ratio was calculated based on overweight and obesity, sex, and sleep disturbances categories. The degree of heterogeneity in the meta-analysis was studied based on two tests and their results were reported; the first test is I2, and the results of this test were reported for all analyzes, and the second was χ2 test [59, 60]. Publication bias was investigated using tests (Egger [61], the trim-and-fill [62]), as well as graphical representations (Funnel plot). The results were analyzed with Stata-14 (Stata Corp. College Station, TX).


Selected studies

After searching the sources, 17,582, 27,315, and 61,199 articles were obtained from each of the information platforms PubMed, Scopus, and Embase respectively. After screening the studies based on Figure I and removing the duplicate studies and the unqualified studies based on the titles and abstracts, 819 articles were downloaded and collected in full text. After reading all these full texts, 534 articles were excluded. Out of the remaining 285 articles, 264 were removed on the basis of the reasons mentioned in Figure I, and finally 21 longitudinal studies [63-83] remained for this project.

Figure I

Selection flow diagram


Quality of selected studies

Table 1 describes the qualitative status of studies in five dimensions: selection bias, confounder’s bias, performance bias, data collection bias, and withdrawals/dropouts and missing data bias. For selection bias, the results showed that a total of 14 studies had a low bias, while 3 articles were moderate in bias and the rest displayed high bias. When it came to confounder’s bias, the frequency of high bias studies was higher. In terms of performance bias, all of the studies had a low and moderate bias. In the data collection method, six studies showed that the bias was low, and the rest showed moderate and high bias. In the component related to the withdrawals/ dropouts and missing data, thirteen studies had a low bias, and the rest of the articles showed moderate and high bias.

Table 1

Studies included in the meta-analysis

First author and year of publi- cationCountryPopulationFollow- up timeAge rangeSexSam- ple sizeBody mass indexSleep-related issuesQuality assessment: Risk of biasResultsAdjustment for other covariates
Data collection
Withdrawals and dropouts
Batool- Anwar, 2016USAGeneral population6 years25-7576.04%55,540Self- report- edInternational Restless Legs Study Group criteria (restless legs syndrome)LowLowMode- rateLowLowOdd ratio; reference (BMI < 23) Men: 1. Overweight, BMI: 25–26.9;
1.41 (CI: 1.00-1.97); BMI: 27–29.9; 1.54 (CI 1.10-
2.20); 2. Obesity, BMI > 30; 1.46 (CI: 0.96-2.20)
Women: 1. Overweight, BMI: 25–26.9;
1.12 (CI: 0.91-1.38); BMI: 27–29.9; 1.66 (CI:
1.37-2.01); 2. Obesity, > 30; 1.64 (CI: 1.37-1.96)
Pooled BMI ≥ 25: 1.48 (CI: 1.34-1.63)
Pooled overweight: 1.41 (CI: 1.25-1.59)
Pooled obesity: 1.61 (CI: 1.37-1.90)
Pooled BMI ≥ 25 for men: 1.47 (CI: 1.19-1.81)
Pooled BMI ≥ 25 for women: 1.48 (CI: 1.32-1.65)
Race, the Crown- Crisp anxiety score, antidepressant medication, use
of iron specific sup- plement at base- line, menopausal status, chronic illnesses
Boulou- kaki, 2011GreekGeneral population6 years50.7
± 12.2
2,690Mea- suredApnea + hypo- pnea index (obstructive sleep apnea)LowHighLowLowLowOdd ratio
Total*: 5.08 (CI: 4.37-5.91)
Men: 6.86 (CI: 5.74-8.19)
Women: 4.09 (CI: 3.01-5.54)
Chan, 2016ChinaCoronary artery disease3 years> 219%
587Mea- suredApnoea-hy- popnea index (obstructive sleep apnea)Mode- rateHighLowLowLowOdd ratio Reference: 1.11 (CI: 1.06-1.17)Smoking, hypertension
Chen, 2017TaiwanGeneral population7 years≥ 1846.6%
12,728Un- knownClinically diagnosed (insomnia)LowMode- rateMode- rateLowLowHazard ratio Reference (BMI: 18.5-23.9)
Overweight, BMI 24-26.9: 0.87 (CI: 0.73-1.03)
Obesity, BMI ≥ 27: 0.99 (CI: 0.82-1.19)
Pooled BMI ≥ 25: 0.92 (CI: 0.81-1.05)
Multivariate adjustment
Elomaa, 2013FinlandHistory of depres- sive symp-
7 years25-6466.1%
127Mea- suredSelf-reported (sleep distur- bance)HighHighLowHighHighOdd ratio Reference (without reference):
0.92 (CI: 0.84-1.00)
Smoking, use of sleep or lipidlowering medication
Gureje, 2011NigeriaElderly1 year≥ 6550.5%
1,307Mea- suredWorld Health Organization Composite International Diagnostic Interview (insomnia)LowHighLowMode- rateMode- rateOdd ratio Reference: BMI < 18.5
Overweight, BMI 25.0-29.9: 0.90 (CI: 0.30-2.70)
Obesity, BMI ≥ 30: 1.70 (CI: 0.50-6.00)
Pooled BMI ≥ 25: 1.19 (CI: 0.52-2.71)
Sex, age
Janson, 2001SwedenGeneral population10 years30-69Men2,602Self- report- edQuestionnaire (insomnia)Mode- rateMode- rateMode- rateMode- rateMode- rateOdd ratio Reference (BMI < 27)
BMI > 27: 1.35 (CI: 1.01-1.81)
Medical disorder, BMI, physically inactive, alcohol dependent
Jaussent 2011FranceOlder adultsUn- known≥ 6554.6%
5,886Self- report- edQuestionnaire (sleep complaints) (insomnia)LowMode- rateMode- rateMode- rateLowOdd ratio Reference: BMI < 25
Difficulty with initiating sleep
Men: Overweight, BMI 25-29: 1.00 (CI: 0.51-1.96);
Obesity, BMI > 30: 2.70 (CI: 1.15-6.34)
Women: Overweight, BMI 25-29: 0.91 (CI: 0.61-
1.37); Obesity, BMI > 30: 0.57 (CI: 0.30-1.07)
Pooled BMI ≥ 25: 0.95 (CI: 0.71-1.27)
Pooled overweight: 0.93 (CI: 0.66-1.32)
Pooled obesity: 0.99 (CI: 0.60-1.65)
Pooled BMI ≥ 25 for men: 1.46 (CI: 0.86-2.48)
Pooled BMI ≥ 25 for women: 0.80 (CI: 0.57-1.12)
Kim, 2004KoreaMiddle- agedUn- known40-6932.3%
457Un- knownApnea- hypopnea index (obstructive sleep apnea)HighHighMode- rateLowHighOdd ratio Reference: 3.96 (CI: 2.27-6.91)Age, sex, hypertension
Knuiman, 2006Austra- liaGeneral population14 years25-7463%
967Mea- suredQuestionnaire (snoring)LowMode- rateLowMode- rateLowOdd ratio Reference: 1.44 (CI: 1.18-1.75)Multivariate- adjusted
Mokhlesi, 2012USAPolycystic ovary syn- dromeUn- known18-40Women78Un- knownBerlin questionnaire (obstructive sleep apnea)HighHighMode- rateMode- rateHighOdd ratio 1.23 (CI: 1.11-1.36)Unknown
Morgan, 2003UKElderly8 yearsUnknownBoth410Un- knownInterview assessments (insomnia)Mode- medMode- medMode- medHighHighOdd ratio
BMI > 25: 0.80 (CI: 0.50-1.40)
Pedraza, 2012USAElderly3 years≥ 7562%
1,085Mea- suredSelf-reported (sleep complaints, poor sleep quality)LowLowLowMode- rateLowOdd ratio; reference (BMI < 25)
Trouble Falling
Overweight, BMI 25 < 30: 1.14 (CI: 0.72-1.81);
Obesity, BMI 30 > 35: 1.11 (CI: 0.65-1.90);
Over obesity, BMI ≥ 35: 1.73 (CI: 0.86-3.45)
Wake up several times per night: Overweight, BMI 25 < 30: 1.11 (CI: 0.80-1.54);
Obesity, BMI 30 > 35: 1.51 (CI: 1.03-2.21);
Over obesity, BMI ≥ 35: 2.33 (CI: 1.37-3.96)
Trouble Staying
Overweight, BMI 25 < 30: 1.02 (CI: 0.63-1.69);
Obesity, BMI 30 > 35: 1.57 (CI: 0.92-2.68);
Over obesity, BMI ≥ 35: 1.20 (CI: 0.55-2.62)
Wake up after your usual amount of sleep. Feeling tired and worn:
Overweight, BMI 25 < 30: 1.28 (CI: 0.73-2.22);
Obesity, BMI 30 > 35: 1.58 (CI: 0.86-2.91);
Over obesity, BMI ≥ 35: 1.91 (CI: 0.86-2.24)
Poor quality of sleep: Overweight, BMI 25 < 30: 0.93 (CI: 0.61-1.41);
Obesity, BMI 30 > 35: 1.25 (CI: 0.79-1.99);
Over obesity, BMI ≥ 35: 1.20 (CI: 0.62-2.32)
Pooled BMI ≥ 25: 1.32 (CI: 0.95-1.83)
Pooled overweight: 1.53 (CI: 0.82-2.84)
Pooled obesity: 1.16 (CI: 0.70-1.92)
Baseline socio- demographics, medical conditions, high depressive symptoms, cognitive
impairment and ADL disability
Phillips 2005USAGeneral popu- lation9 years45-6955.2%
13,564Un- knownMaastricht Questionnaire (sleep complaints)LowLowMode- rateMode- rateLowOdd ratio Reference: BMI 20-24 Difficulty falling asleep:
Overweight, BMI 25-29: 0.80 (CI: 0.80-1.00);
Obesity, BMI ≥ 30: 0.90 (CI: 0.80-1.01)
Sleep continuity disturbance: Overweight, BMI 25-29: 1.00 (CI: 0.90-1.10);
Obesity, BMI ≥30: 1.10 (CI: 0.96-1.20)
Non-restorative sleep: Overweight, BMI 25-29: 1.00 (CI: 0.90-1.10);
Obesity, BMI ≥ 30: 1.00 (CI: 0.90-1.10)
Pooled BMI ≥ 25: 0.97 (CI: 0.88-1.07)
Pooled overweight: 0.93 (CI: 0.80-1.08)
Pooled obesity: 1.00 (CI: 0.88-1.12)
Age, sex, meno- pausal status, race, education level, income, body mass index, depres- sion, presence of cardiac disease, lung function status, presence of hypertension, alcohol intake, diabetes, and hypnotic use
Quan, 2005USAElderly4 years≥ 65Both4,467Mea- suredQuestionnaire (excessive daytime sleepiness)LowMode- rateLowMode- rateLowOdd ratio Reference: 1.03 (CI: 1.01-1.05)Age, sex, race, time interval between exami- nations
Singa- reddy, 2012USAGeneral population7.5 years≥ 2047.6%
1,741Self- report- edSelf-reported (Insomnia)LowLowMode- rateMode- rateLowOdd ratio Obesity: 1.24 (CI: 0.80-1.93)Age, gender, obesity, race, cigarettes, alcohol, caffeine use, physical health problems, mental health problems, poor sleep
Skarp- snno, 2018NorwayGeneral population11 years≥ 2054.5%
21,847Mea- suredSelf-reported (Insomnia)LowLowLowMode- rateLowRisk ratio; reference (normal BMI) Men: Overweight: 1.22 (CI: 0.97-1.53),
Obesity: 1.48 (CI: 1.08-2.01)
Women: Overweight: 1.15 (CI: 0.97-1.35)
Obesity: 1.25 (CI: 1.00-1.56)
Odd ratio*
Men: Overweight: 1.21 (CI: 0.97-1.50)
Obesity: 1.45 (CI: 1.08-1.94)
Women: Overweight: 1.14 (CI: 0.97-1.32)
Obesity: 1.23 (CI: 1.00-1.51)
Pooled BMI ≥ 25: 1.21 (CI: 1.09-1.34)
Pooled overweight: 1.16 (CI: 1.03-1.32)
Pooled obesity: 1.30 (CI: 1.10-1.54)
Pooled BMI ≥ 25 for men: 1.29 (CI: 1.08-1.54)
Pooled BMI ≥ 25 for women: 1.17 (CI: 1.04-1.33)
Age, leisure time physical activity, education, smoking, shift work, alcohol consumption, HADS
Theorell- Haglöw, 2015SwedenGeneral population10 years43.7
± 15.2
Women7,051Self- report- edUppsala Sleep Inventory;
self-reported (excessive daytime sleepiness)
LowHighMode- rateMode- rateLowOdd ratio
Obesity, BMI ≥ 28: 1.28 (CI: 0.62-2.66)
Age, development in all covariates, somatic disease
Tom, 2010UKGeneral populationUnknown48-54Women962Un- knownSelf-reported (sleep difficulty)HighHighMode- rateHighMode- rateOdd ratio, reference (BMI < 18.5) Overweight, BMI 25-29.9: 1.52 (CI: 0.80-2.89)
Obesity, BMI ≥ 30: 1.82 (CI: 0.91-2.66)
Pooled BMI ≥ 25: 1.69 (CI: 1.12-2.55)
Vahtera, 2009FranceRetired employees7 years35-50
at base- line
14,714Self- report- edQuestionnaires (sleep disturbances)LowHighMode- rateMode- rateLowOdd ratio, reference (BMI ≤ 24.9) Overweight, BMI 25-29.9: 1.03 (CI: 0.96-1.11)
Obesity, BMI ≥ 30: 1.10 (CI: 0.97-1.24)
Pooled BMI ≥ 25: 1.05 (CI: 0.98-1.12)
Sex, age
Wilton, 2018USARheuma- toid arthritis≥ 14.8
≥ 1868%
813Un- knownMedical record (obstructive sleep apnea)LowMode- rateMode- rateLowLowHazard ratio 4.99 (CI: 2.98-8.36)Age, sex, calendar year

* Calculated by author(s)

Body mass index and risk of sleep disturbances

Figure II shows the association between BMI ≥ 25 and sleep disturbances. The odds ratio in this regard was 1.33 and the confidence interval was between 1.16-1.51 (n = 19; p < 0.001; I2 = 96.6%). For 2 studies that reported hazard ratio (HR), pooled HR was 2.10 and the confidence interval was between 0.40-11.01 (n = 2; p = 0.379; I2 = 97.4%).

Figure II

The association between BMI ≥ 25 and sleep disturbances


Figure III shows the association between overweight and obesity and sleep disturbances. The odds ratio was 1.12 and the confidence interval was between 0.98-1.29 (n = 8; p = 0.100; I2 = 75.8%) in overweight. The odds ratio was 1.24 and the confidence interval was between 1.07-1.44 (n = 10; p = 0.005; I2 = 66.7%) in obesity.

Figure III

The association between overweight and obesity and sleep disturbances


Figure IV shows the association between BMI ≥ 25 with sleep disturbances in men and women. The odds ratio was 1.92 and the confidence interval was between 0.89-4.15 (n = 5; p = 0.095; I2 = 98.2%) in men. The odds ratio was 1.47 and the confidence interval was between 1.15-1.89 (n = 7; p = 0.002; I2 = 91.7%) in women.

Figure IV

The association between BMI ≥ 25 and sleep disturbances in men and women


Figure V shows the association between BMI ≥ 25 and different types of sleep disturbance. The odds ratio was 2.23 and the confidence interval was between 1.17-4.26 (n = 4; p = 0.015; I2 = 99.2%) in obstructive sleep apnea. The odds ratio was 1.17 and the confidence interval was between 1.05-1.30 (n = 6; p = 0.004; I2 = 7.7%) in insomnia. The odds ratio was 1.03 and the confidence interval was between 1.01-1.05 (n = 2; p = 0.003; I2 = 0%) in excessive daytime sleepiness.

Figure V

The association between BMI ≥ 25 and different types of sleep disturbance


Heterogeneity and publication bias

The results of the heterogeneity test showed that this index is at a high level [59] and equal to I2 = 96.6%, and the c2 test showed this result 537.23 (df = 18; p < 0.001) [31]. The funnel test in Figure VI showed that there was publication bias. The Egger (p = 0.071) test was also significant, indicating a publication bias [61]. The trim-and-fill [62] method imputed 6 missing studies, and the odds ratio increased to 1.59 and CI = 1.29-1.96 (Figure VI).

Figure VI

Funnel plot for BMI ≥ 25 and sleep disturbances



This study aimed to evaluate the risk of an individual experiencing sleep disturbances based on overweight and obesity, which was performed using systematic review and a meta-analysis of longitudinal meta-analyses. The first result of this meta-analysis was that a BMI above 25 increases the risk of sleep disturbances by up to 33%. This finding indicates the significant role that high BMI plays in the long term in increasing the risk of sleep disturbances. Also, discerning the relationship between overweight and obesity with the risk of sleep disturbances showed that with increasing BMI, the risk of sleep disturbances increases, so that obesity doubles the risk of sleep disturbances compared to overweigh. There seems to be a direct linear relationship between BMI and sleep disturbances; as the BMI increases, so does the risk of sleep disturbances. This finding is consistent with previous studies that have shown that increasing BMI increases the risk of sleep disturbances [49, 53]. In the relationship between overweight and obesity and sleep disturbances, we need to pay attention to other mechanisms. One mechanism is the effect of overweight and obesity on the increased risk of diabetes [84, 85]; diabetes, in turn, is recognized as a risk factor for sleep disorders [86, 87].

Another finding of the study showed that the risk of sleep disturbances in men with overweight and obesity is almost twice as high compared to men with normal weight. In women with overweight and obesity, the risk of sleep disturbances was almost half that of men. This indicates that the risk of sleep disturbances is higher in men than in women based on overweight and obesity. A previous meta-analysis showed that women with obesity were more likely than men to suffer from restless legs syndrome [53]. It has also been shown that the prevalence of insomnia is higher in women than in men [88, 89]. What is clear is that in the current meta-analysis, the focus was also on obstructive sleep syndrome, so this difference in results could be due to the varying prevalence of different types of sleep disturbances in men and women, as studies have shown that obstructive sleep syndrome has a higher prevalence in men compared to women [90-93]. On the other hand, it should be noted that the current study examined sex differences in sleep disturbances based on BMI, and therefore the results may be different from those that have dealt with the prevalence of sleep disturbances in men and women.

Another finding on the relationship between BMI and types of sleep disturbance showed that overweight and obesity have the highest risk for obstructive sleep apnea syndrome, doubling the risk of it. In the next ranks were insomnia and excessive daytime sleepiness. This high risk of obstructive sleep syndrome in people with obesity has been shown in studies to be up to three times higher than in normal-weight people [94-96]. Mechanisms through which obesity can increase the risk of obstructive sleep syndrome are listed [49], including an accumulation of fat in the respiratory tract and in the chest and abdomen that can cause problems with the respiratory system [49, 97, 98].

It should be noted that this study was able to provide a systematic review and meta-analysis of the association between BMI and sleep disturbances. On the other hand, overweight and obesity were studied separately, and also the results were studied based on sex and type of sleep disturbance. Due to the limitations of the existing literature, it was not possible to study other types of sleep disturbance. A methodological limitation was the high degree of heterogeneity in the studies included in the meta-analysis. Some studies included in the meta-analysis did not control confounder’s variables well, which is a limitation.


The present study showed that with increasing BMI, the risk of sleep disturbances increases. Considering that the role of overweight and obesity in many health problems has been identified, paying attention to the impact of overweight and obesity on sleep disturbances can play a vital role in levels of prevention as well as treatment. Therefore, this finding could provide insights into how to reduce the existing levels of sleep disturbances.

Conflict of interest


Financial support




Haththotuwa RN, Wijeyaratne CN, Senarath U. Worldwide epidemic of obesity. In: Mahmood TA, Arulkumaran S, Chervenak FA (eds.). Obesity and Obstetrics. 2nd ed. Elsevier; 2020, p. 3-8.


World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. Geneva: World Health Organization; 2000.


World Health Organization. Noncommunicable diseases country profiles 2018. 2018; https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.


Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384: 766-781.


Martínez J, Johnson CD, Sánchez-Payá J, de Madaria E, Robles-Díaz G, Pérez-Mateo M. Obesity is a definitive risk factor of severity and mortality in acute pancreatitis: an updated meta-analysis. Pancreatology 2006; 6: 206-209.


Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 2013; 309: 71-82.


Aune D, Sen A, Prasad M, et al. BMI, and all-cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants. BMJ 2016; 353: i2156. DOI: 10.1136/bmj.i2156.


Moghaddam AA, Woodward M, Huxley RJCE, Biomarkers P. Obesity and risk of colorectal cancer: a meta-analysis of 31 studies with 70,000 events. Cancer Epidemiol Biomarkers Prev 2007; 16: 2533-2547.


Larsson SC, Wolk A. Obesity and the risk of gallbladder cancer: a meta-analysis. Br J Cancer 2007; 96: 1457-1461.


Larsson S, Wolk AJ. Overweight, obesity and risk of liver cancer: a meta-analysis of cohort studies. Br J Cancer 2007; 97: 1005-1008.


Yang Y, Dong J, Sun K, et al. Obesity and incidence of lung cancer: a meta-analysis. Int J Cancer 2013; 132: 1162-1169.


Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health 2009; 9: 88. DOI: 10.1186/1471-2458-9-88.


Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabet Res Clin Pract 2010; 89: 309-319.


Torloni MR, Betrán AP, Horta BL, et al. Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obesity Rev 2009; 10: 194-203.


Pimenta FBC, Bertrand E, Mograbi DC, Shinohara H, Landeira-Fernandez J. The relationship between obesity and quality of life in Brazilian adults. Front Psychol 2015; 6: 966. DOI: 10.3389/fpsyg.2015.00966.


Ul-Haq Z, Mackay DF, Fenwick E, Pell JP. Meta-analysis of the association between body mass index and health-related quality of life among adults, assessed by the SF-36. Obesity 2013; 21: E322-E327. DOI: 10.1002/oby.20107.


Khairy SA, Eid SR, El Hadidy LM, Gebril OH, Megawer AS. The health-related quality of life in normal and obese children. Egypt Pediatr Assoc Gazette 2016; 64: 53-60.


Luppino FS, de Wit LM, Bouvy PF, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67: 220-229.


Amiri S, Behnezhad S, Nadinlui KB. Body mass index (BMI) and risk of depression in adults: a systematic review and meta-analysis of longitudinal studies. Obesity Med 2018; 12: 1-12.


Sanchez Bustillos A, Vargas KG, Gomero-Cuadra R. Work productivity among adults with varied body mass index: results from a Canadian population-based survey. J Epidemiol Global Health 2015; 5: 191-199.


Ku B, Phillips KE, Fitzpatrick JJ. The relationship of body mass index (BMI) to job performance, absenteeism and risk of eating disorder among hospital-based nurses. Appl Nurs Res 2019; 49: 77-79.


Amiri S, Behnezhad SJ. Body mass index and risk of sick leave: a systematic review and meta-analysis. Clin Obes 2019; 9: e12334. DOI: 10.1111/cob.12334.


Amiri S, Behnezhad S. Obesity and anxiety symptoms: a systematic review and meta-analysis. Neuropsychiatr 2019; 33: 72-89.


Gariepy G, Nitka D, Schmitz N. The association between obesity and anxiety disorders in the population: a systematic review and meta-analysis. Int J Obes (Lond) 2010; 34: 407-419.


Chan WS, Levsen MP, McCrae C. A meta-analysis of associations between obesity and insomnia diagnosis and symptoms. Sleep Med Rev 2018; 40: 170-182.


American Academy of Sleep Medicine. International Classification of Sleep Disorders. 3rd ed. Darien, IL: American Academy of Sleep Medicine; 2014.


Morin CM, LeBlanc M, Daley M, Gregoire JP, Mérette C. Epidemiology of insomnia: prevalence, self-help treatments, consultations, and determinants of help-seeking behaviors. Sleep Med 2006; 7: 123-130.


Ohayon MM. Epidemiology of insomnia: what we know and what we still need to learn. Sleep Med Rev 2002; 6: 97-111.


Lichstein KL, Durrence HH, Taylor DJ, Bush AJ, Riedel BW. Quantitative criteria for insomnia. Behav Res Ther 2003; 41: 427-445.


Senaratna CV, Perret JL, Lodge CJ, et al. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med Rev 2017; 34: 70-81.


Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med 2019; 7: 687-698.


Hayley AC, Williams LJ, Kennedy GA, Berk M, Brennan SL, Pasco JA. Prevalence of excessive daytime sleepiness in a sample of the Australian adult population. Sleep Med 2014; 15: 348-354.


Acquavella J, Mehra R, Bron M, Suomi JM, Hess GP. Prevalence of narcolepsy and other sleep disorders and frequency of diagnostic tests from 2013-2016 in insured patients actively seeking care. J Clin Sleep Med 2020; 16: 1255-1263.


Smagula SF, Stone KL, Fabio A, Cauley JA. Risk factors for sleep disturbances in older adults: evidence from prospective studies. Sleep Med Rev 2016; 25: 21-30.


Amiri S, Behnezhad S. Smoking and risk of sleep-related issues: a systematic review and meta-analysis of prospective studies. Can J Public Health 2020; 111: 775-786.


Britton A, Fat LN, Neligan A. The association between alcohol consumption and sleep disorders among older people in the general population. Sci Rep 2020; 10: 5275. DOI: 10.1038/s41598-020-62227-0.


Amiri S, Behnezhad S. Sleep disturbances and risk of sick leave: systematic review and meta-analysis. Sleep Biol Rhythms 2020; 18: 283-295.


Shi L, Chen SJ, Ma MY, et al. Sleep disturbances increase the risk of dementia: a systematic review and meta-analysis. Sleep Med Rev 2018; 40: 4-16.


Sofi F, Cesari F, Casini A, Macchi C, Abbate R, Gensini GF. Insomnia and risk of cardiovascular disease: a meta-analysis. Eur J Prev Cardiol 2020; 21: 57-64.


Amiri S, Behnezhad S. Sleep disturbances and back pain: systematic review and meta-analysis. Neuropsychiatr 2020; 34: 74-84.


Amiri S, Behnezhad S. Sleep disturbances and physical impairment: a systematic review and meta-analysis. Phys Occup Ther Geriatr 2021; 39: 258-281.


Harris LM, Huang X, Linthicum KP, Bryen CP, Ribeiro JD. Sleep disturbances as risk factors for suicidal thoughts and behaviours: a meta-analysis of longitudinal studies. Sci Rep 2020; 10: 13888. DOI: 10.1038/s41598-020-70866-6.


Pigeon WR, Pinquart M, Conner K. Meta-analysis of sleep disturbance and suicidal thoughts and behaviors. J Clin Psychiatry 2012; 73: e1160-e1167. DOI: 10.4088/JCP.11r07586.


Marino C, Andrade B, Campisi SC, et al. Association between disturbed sleep and depression in children and youths: a systematic review and meta-analysis of cohort studies. JAMA Netw Open 2021; 4: e212373. DOI: 10.1001/jamanetworkopen.2021.2373.


Smagula SF, Stone KL, Fabio A, Cauley JA. Risk factors for sleep disturbances in older adults: evidence from prospective studies. Sleep Med Rev 2016; 25: 21-30.


Zeng J, Wei M, Li T, et al. Risk of obstructive sleep apnea in Parkinson’s disease: a meta-analysis. PloS One 2013; 8: e82091. DOI: 10.1371/journal.pone.0082091.


Xiaolin Gu MM. Risk factors of sleep disorder after stroke: a meta-analysis. Top Stroke Rehabil 2017; 24: 34-40.


Bartel KA, Gradisar M, Williamson P. Protective and risk factors for adolescent sleep: a meta-analytic review. Sleep Med Rev 2015; 21: 72-85.


Dong Z, Xu X, Wang C, Cartledge S, Maddison R, Islam SMS. Association of overweight and obesity with obstructive sleep apnoea: a systematic review and meta-analysis. Obesity Med 2020; 17: 100185.


Andersen IG, Holm JC, Homøe PJ. Obstructive sleep apnea in obese children and adolescents, treatment methods and outcome of treatment–a systematic review. Int J Pediatr Otorhinolaryngol 2016; 87: 190-197.


Chan WS, Levsen MP, McCrae CS. A meta-analysis of associations between obesity and insomnia diagnosis and symptoms. Sleep Med Rev 2018; 40: 170-182.


Wong AM, Barnes HN, Joosten SA, et al. The effect of surgical weight loss on obstructive sleep apnoea: a systematic review and meta-analysis. Sleep Med Rev 2018; 42: 85-99.


Lin S, Zhang H, Gao T, et al. The association between obesity and restless legs syndrome: a systemic review and meta-analysis of observational studies. J Affect Disord 2018; 235: 384-391.


Moher D, Liberati A, Tetzlaff J, Altman DG, The PG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6: e1000097. DOI: 10.1371/journal.pmed.1000097.


Armijo-Olivo S, Stiles CR, Hagen NA, Biondo PD, Cummings GG. Assessment of study quality for systematic reviews: a comparison of the Cochrane Collaboration Risk of Bias Tool and the Effective Public Health Practice Project Quality Assessment Tool: methodological research. J Eval Clin Pract 2012; 18: 12-18.


Thomas H. Quality assessment tool for quantitative studies. In: Effective public health practice project; 2003.


Zhang J, Yu KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA 1998; 280: 1690-1691.


Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Multiple outcomes or time-points within a study. Introduction to meta-analysis. John Wiley & Sons; 2009.


Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002; 21: 1539-1558.


Ioannidis JP, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ 2007; 335: 914-916.


Egger M, Smith GD, Schneider M, Minder CJB. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629-634.


Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455-463.


Batool-Anwar S, Li Y, De Vito K, Malhotra A, Winkelman J, Gao X. Lifestyle factors and risk of restless legs syndrome: prospective cohort study. J Clin Sleep Med 2016; 12: 187-194.


Bouloukaki I, Kapsimalis F, Mermigkis C, et al. Prediction of obstructive sleep apnea syndrome in a large Greek population. Sleep Breath 2011; 15: 657-664.


Chan PF, Tai BC, Loo G, et al. Optimal body mass index cut-offs for identification of patients with coronary artery disease at high risk of obstructive sleep apnoea. Heart Lung Circ 2016; 25: 847-854.


Chen LJ, Steptoe A, Chen YH, Ku PW, Lin CH. Physical activity, smoking, and the incidence of clinically diagnosed insomnia. Sleep Med 2017; 30: 189-194.


Elomaa AP, Koivumaa-Honkanen H, Niskanen L, et al. Self-reported sleep disturbance is associated with elevated levels of PAI-1 in individuals with a recorded history of depressive symptoms. Prog Neuropsychopharmacol Biol Psychiatry 2013; 47: 46-51.


Gureje O, Oladeji BD, Abiona T, Makanjuola V, Esan OJS. The natural history of insomnia in the Ibadan study of ageing. Sleep 2011; 34: 965-973.


Janson C, Lindberg E, Gislason T, Elmasry A, Boman GJS. Insomnia in men–a 10-year prospective population based study. Sleep 2001; 24: 425-430.


Jaussent I, Dauvilliers Y, Ancelin ML, et al. Insomnia symptoms in older adults: associated factors and gender differences. Am J Geriatr Psychiatry 2011; 19: 88-97.


Kim J, In K, Kim J, et al. Prevalence of sleep-disordered breathing in middle-aged Korean men and women. Am J Respir Crit Care Med 2004; 170: 1108-1113.


Knuiman M, James A, Divitini M, Bartholomew HJC. Longitudinal study of risk factors for habitual snoring in a general adult population: the Busselton Health Study. Chest 2006; 130: 1779-1783.


Mokhlesi B, Scoccia B, Mazzone T, Sam S. Risk of obstructive sleep apnea in obese and nonobese women with polycystic ovary syndrome and healthy reproductively normal women. Fertil Steril 2012; 97: 786-791.


Morgan K. Daytime activity and risk factors for late-life insomnia. J Sleep Res 2003; 12: 231-238.


Pedraza S, Al Snih S, Ottenbacher KJ, Markides KS, Raji MA. Sleep quality and sleep problems in Mexican Americans aged 75 and older. Aging Clin Exp Res 2012; 24: 391-397.


Phillips B, Mannino D. Correlates of sleep complaints in adults: the ARIC study. J Clin Sleep Med 2005; 1: 277-283.


Quan SF, Katz R, Olson J, et al. Factors associated with incidence and persistence of symptoms of disturbed sleep in an elderly cohort: the Cardiovascular Health Study. Am J Med Sci 2005; 329: 163-172.


Singareddy R, Vgontzas AN, Fernandez-Mendoza J, et al. Risk factors for incident chronic insomnia: a general population prospective study. Sleep Med 2012; 13: 346-353.


Skarpsno ES, Nilsen TI, Sand T, Hagen K, Mork P. Do physical activity and body mass index modify the association between chronic musculoskeletal pain and insomnia? Longitudinal data from the HUNT study, Norway. J Sleep Res 2018; 27: 32-39.


Theorell-Haglöw J, Åkerstedt T, Schwarz J, Lindberg EJS. Predictors for development of excessive daytime sleepiness in women: a population-based 10-year follow-up. Sleep 2015; 38: 1995-2003.


Tom SE, Kuh D, Guralnik JM, Mishra GJM. Self-reported sleep difficulty during the menopausal transition: results from a prospective cohort study. Menopause 2010; 17: 1128-1135.


Vahtera J, Westerlund H, Hall M, et al. Effect of retirement on sleep disturbances: the GAZEL prospective cohort study. Sleep 2009; 32: 1459-1466.


Wilton KM, Matteson EL, Crowson CS. Risk of obstructive sleep apnea and its association with cardiovascular and noncardiac vascular risk in patients with rheumatoid arthritis: a population-based study. J Rheumatol 2018; 45: 45-52.


Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabet Res Clin Pract 2010; 89: 309-319.


Bell JA, Kivimaki M, Hamer M. Metabolically healthy obesity and risk of incident type 2 diabetes: a meta-analysis of prospective cohort studies. Obesity Rev 2014; 15: 504-515.


Khalil M, Power N, Graham E, Deschênes SS, Schmitz N. The association between sleep and diabetes outcomes–a systematic review. Diabet Res Clin Pract 2020; 161: 108035. DOI: 10.1016/j.diabres.2020.108035.


Khandelwal D, Dutta D, Chittawar S, Kalra S. Sleep disorders in type 2 diabetes. Indian J Endocrinol Metab 2017; 21: 758-761.


Zeng LN, Zong QQ, Yang Y, et al. Gender difference in the prevalence of insomnia: a meta-analysis of observational studies. Front Psychiatry 2020; 11: 577429-577429.


Tsou MT. Gender-specific correlations of insomnia and attitudes toward treatment among community-dwelling elderly in Northern Taiwan. Int J Gerontol 2018; 12: 200-204.


Douglas NJ. Clinicians’ guide to sleep medicine. Arnold; 2002.


Ancoli-Israel S, Kripke DF, Klauber MR, Mason WJ, Fell R, Kaplan O. Sleep-disordered breathing in community-dwelling elderly. Sleep 1991; 14: 486-495.


Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328: 1230-1235.


Quintana-Gallego E, Carmona-Bernal C, Capote F, et al. Gender differences in obstructive sleep apnea syndrome: a clinical study of 1166 patients. Respir Med 2004; 98: 984-989.


Barone JG, Hanson C, DaJusta DG, Gioia K, England SJ, Schneider D. Nocturnal enuresis and overweight are associated with obstructive sleep apnea. Pediatrics 2009; 124: e53-e59. DOI: 10.1542/peds.2008-2805.


Tishler PV, Larkin EK, Schluchter MD, Redline S. Incidence of sleep-disordered breathing in an urban adult population: the relative importance of risk factors in the development of sleep-disordered breathing. JAMA 2003; 289: 2230-2237.


Hanis CL, Redline S, Cade BE, et al. Beyond type 2 diabetes, obesity and hypertension: an axis including sleep apnea, left ventricular hypertrophy, endothelial dysfunction, and aortic stiffness among Mexican Americans in Starr County, Texas. Cardiovasc Diabetol 2016; 15: 86. DOI: 10.1186/s12933-016-0405-6.


Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med 2005; 353: 2034-2041.


Ramar K, Dort LC, Katz SG, et al. Clinical practice guideline for the treatment of obstructive sleep apnea and snoring with oral appliance therapy: an update for 2015. J Clin Sleep Med 2015; 11: 773-827.

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). License allowing third parties to download and share its works but not commercially purposes or to create derivative works.
Quick links
© 2023 Termedia Sp. z o.o.
Developed by Bentus.