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Review

Sleep Characteristics in Adults of African Descent at Risk for and with Cardiometabolic Conditions: A Systematic Review

by
Cherlie Magny-Normilus
1,*,
Stephanie Griggs
2,
Julie Sanders
1,
Youri Hwang
3 and
Catrina Longhurst
1
1
William F. Connell School of Nursing, Boston College, Chestnut Hill, Newton, MA 02467, USA
2
Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH 44106, USA
3
Yale School of Nursing, Yale University, Orange, CT 06477, USA
*
Author to whom correspondence should be addressed.
Endocrines 2023, 4(3), 502-520; https://doi.org/10.3390/endocrines4030036
Submission received: 20 April 2023 / Revised: 11 July 2023 / Accepted: 14 July 2023 / Published: 19 July 2023
(This article belongs to the Special Issue Advances in Diabetes Care)
Browse Figure
Versions Notes

Abstract

:
The purpose of this systematic review is to synthesize available studies on sleep health characteristics in adults of African descent with or at risk for cardiometabolic conditions. PubMed, PsycINFO, CINAHL, and Web of Science were searched for original research studies on subgroups of African descent with at least one cardiometabolic risk factor. Studies published in English with measured sleep characteristics were included. Studies focused on participants with severe psychiatric illness, night shift workers, or with a pharmacologic sleep treatment focus were excluded. The risk for bias was assessed using the NHLBI 2021 Quality Assessment Tool. Two reviewers independently synthesized the results before reaching a consensus. Out of 340 studies screened, 35 studies were included. There were 631,756 participants with an average age of 44.3 combined (SD = 16.5) (53% female and 22% Black). Disparities in sleep health characteristics and cardiometabolic health among African American adults were found. Markers of poor cardiometabolic health were associated with disordered sleep. While the studies in this review captured key factors, the study measurement methods were inconsistent, and African Caribbean Americans were underrepresented. The studies demonstrated the intersectionality of poor sleep characteristics, cardiometabolic risk factors, and racial/ethnic groupings. Clinicians should consider these findings when providing care.

1. Introduction

Cardiometabolic diseases, including endocrine, nutritional, and metabolic diseases (referred to herein as ENM conditions) (e.g., thyroid conditions, diabetes, hyperlipidemia, obesity), affect an estimated 47 million people in the United States) [1,2] and are one of the leading causes of death globally [3]. One-third of the United States population has metabolic syndrome, one in three have prediabetes, and 34.2 million have diabetes (10.5%) [4,5]. Racial cardiometabolic health disparities and cardiometabolic risk factors and outcomes have a higher prevalence at earlier ages in Non-Hispanic Black compared with Non-Hispanic White populations.
ENM conditions disproportionately affect historically underrepresented racial and ethnic groups based on a multitude of multi-ethnic studies from multiple countries, including the United States [6,7,8]. This is especially true for subgroups of African descent (e.g., Black, African American, African Caribbean American) in America. The African Caribbean American population accounts for over 5% of African Americans [9]. The African Caribbean population has a three-fold higher prevalence of ENM conditions compared to a European population and descents of other African ethnic groups [10,11]. People of African Caribbean descent with ENM conditions are also at greater risk of worse clinical diabetes outcomes than those of European descent [12].
There is an overwhelming gap in our understanding of ethnic disparities in ENM conditions, including prediabetes, as an ethnic background for persons of African descent is rarely delineated. Limited research focused on African subgroups leaves this population vulnerable to unrecognized health disparities. There is sparse research on those of African Caribbean descent, limiting our understanding of the unique biological, clinical, and cultural characteristics of ENM conditions in the African Caribbean American population.
Sleep health is comprised of multiple characteristics, including regularity, satisfaction, alertness, timing, efficiency, and duration [13,14]. Sleep health characteristics are essential to physical and psychological well-being and are a key modifiable factor affecting cardiometabolic disease onset, progression, and severity [15]. For instance, short sleep duration or experimental sleep deprivation led to overweight and obese weight status due to increased energy intake and reduced energy expenditure through reciprocal changes in hormonal appetite regulating responses (higher ghrelin, lower leptin) [15,16,17]. Obesity is a major contributing factor to incident cardiovascular risk factors, including dyslipidemia, type 2 diabetes, hypertension, and sleep disorders [17].
Adults living with ENM conditions or at risk of ENM conditions often have concurrent cardiometabolic risk factors, including but not limited to hypertension, obesity, and dyslipidemia, which require further management [16]. Experimental data have demonstrated a potential underlying pathway of developing ENM conditions in people with sleep disturbance, such as an increased appetite, altered glucose metabolism through increased nocturnal cortisol and growth hormones, and increased sympathetic nervous system activity [15,16,17,18,19]. The causal direction of the relationship between sleep and ENM conditions is unclear, yet there is compelling evidence that sleep is closely linked to the development and clinical outcomes of ENM conditions [15,20].
Poor sleep health characteristics can also adversely lead to unfavorable ENM-related clinical outcomes in people with or at risk for ENM by ineffective self-management behaviors (e.g., planning meals, physical activity, and regular medical appointments) [21,22,23,24,25,26]. African Caribbean American adults are at elevated risk for ENM and risk for poor sleep health, characterized by shorter sleep duration [10,12,24,25,26,27,28]. To the best of our knowledge, existing reviews have yet to synthesize the evidence on the potential role of sleep health characteristics for adults of African or African Caribbean descent in preventing and managing ENM disease. To fill this gap in the literature, we conducted a systematic review of sleep health characteristics in adults of African descent. We aimed to evaluate sleep characteristics in adults of African descent with cardiometabolic health risk factors or ENM disease.

2. Materials and Methods

The Preferred Reporting Items for Systematic Reviews and Meta-analyses Statement guidelines were followed for this systematic review [29]. Before implementing the search, we registered our protocol with the PROSPERO registry in the International Prospective Register of Systematic Reviews (Prospero; registration number CRD42021269533).

2.1. Literature Search Strategy

The primary aim of this systematic review was to synthesize available studies on sleep health characteristics (derived from self-report or objective via actigraphy or polysomnography) in adults of African descent with or at risk for cardiometabolic conditions (e.g., type 2 diabetes). A controlled vocabulary and keyword search of the following databases was conducted: PubMed, PsycINFO, CINAHL, and Web of Science. The search was limited to articles published in the English language. All searches covered the periods from the date of establishment of each database to 9 November 2021. The PubMed search terms are provided in Table 1. The search strategies were adjusted for syntax as appropriate for each database. The search was conducted under the guidance of a medical librarian (WA) and two investigators (CMN and SG).
We considered studies with persons of African descent (e.g., Black, African American, and African Caribbean) due to the lack of a uniform term when referring to Black individuals. The primary outcome measures investigated were sleep measures that represent at least one of the following sleep health characteristics: satisfaction or alertness measured via self-report, timing measured via self-report or actigraphy, efficiency and duration measured via actigraphy/polysomnography (PSG) or self-report, and sleep stages (rapid eye movement sleep and non-rapid eye movement sleep [stages 1, 2, and 3], as evaluated with PSG only).

2.2. Eligibility and Exclusion Criteria

Studies that met the following criteria were included in this review: (1) original research of adults identifying as Black, African, African American, African Caribbean, Haitian and who had at least one cardiometabolic risk factor (obesity, diabetes, hypertension, and dyslipidemia) that were published in English; and (2) in which sleep characteristics were measured using self-report questionnaires, wrist actigraphy, or PSG. Studies that focused on populations that met one of the following criteria were excluded: (1) people with severe comorbid psychiatric illness (e.g., bipolar disorder, schizophrenia); (2) night shift workers; and (3) all participants receiving pharmacological treatment for sleep. A sub-analysis was performed when possible if the participants of a study were not exclusively Black, African, African Caribbean, Haitian, or adults.

2.3. Study Selection

A total of 432 references were imported into Covidence (Veritas Health Information), and duplicates were removed. A total of 340 were screened through Covidence. Two reviewers independently screened all titles and abstracts with 87% agreement. Next, the reviewers assessed 53 full texts. A third researcher resolved any disagreements regarding eligibility when consensus was not reached among the first two reviewers. For any studies with the same cohort data, the study with the largest sample size was used to describe study characteristics in the results section. The study selection process is illustrated in Figure 1. We identified 35 studies that met our inclusion criteria. The results are based on the data published in these articles and supplemental materials.

2.4. Data Extraction and Synthesis

Data were extracted and recorded using customized spreadsheets by three reviewers. Recorded data included study characteristics (authors, title, year, country), participants’ characteristics (age, race/ethnicity, sex), and sleep measures used (self-report, PSG, or actigraphy). Based on the primary aim of the review, we extracted the sleep characteristics of each included study. We grouped them into categories adapted from the SATED framework: satisfaction, alertness, timing, efficiency, and duration [14]. We also extracted and recorded data on sleep disorders separately. Numeric results of sleep characteristics were recorded, and a pooled mean was calculated for results with greater than three studies. Results were then narratively described.

2.5. Risk of Bias

Two researchers independently assessed the risk of bias in the included studies using the Study Quality Assessment Tools for Observational Cohort and Cross-Sectional Studies [30]. The Study Quality Assessment Tools for Observational Cohort and Cross-Sectional Studies assess for clear, specific objectives and populations, high participation rate, the timing for exposures and measures, quality of variables, blinding, loss to follow-up, and adjustment of key confounding variables.

3. Results

3.1. Study Characteristics

A total of 31 cross-sectional and 4 cohort studies are included in this review. The studies comprise 631,756 participants with an average age of 44.3 years (SD = 16.5). A little over half (53%) of participants identify as women, and 22% identify as Black. In the total population, 8.8% of participants have type 2 diabetes. There are 470,162 reported cases of other ENM conditions, including hypertension, elevated BMI, obstructive sleep apnea, hypercholesterolemia, dyslipidemia, coronary artery disease or other heart conditions, stroke, heart attack, mild cognitive impairment, elevated cardiometabolic risk, peripheral artery disease. The characteristics of the included studies are presented in Table 2.
Sleep was measured via self-report questionnaires in all studies, actigraphy in five studies [31,32,33,34,35], EEG in one study [36], and PSG in three studies [32,37,38]. For the studies with actigraphy, the procedure was primarily conducted in the participants’ homes [32,33,34,35], except for one study completed in an inpatient general medicine setting [34]. For studies with PSG/EEG, the procedure was performed in a laboratory setting or at home [32,37,38].

3.2. Quality Appraisal

Each included paper had either a cross-sectional or observational design. None of the selected papers received a poor-quality rating in the quality assessment. Eleven of the papers were rated good, and twenty-five of the papers were rated fair. Most often, papers were rated fair due to the nature of cross-sectional and observational studies, including the lack of exposure measurement prior to measurement of outcomes and the lack of time frame between exposure and outcome. Otherwise, the most common reasons for being rated fair were the lack of sample size justification, power description, variance and effect estimates, and lack of reporting for blinding and participation rates. The findings of the quality assessment are summarized in Table 3.

3.3. Sleep Characteristics

3.3.1. Satisfaction

Self-report sleep satisfaction was reported in 14 studies [31,33,34,38,39,40,41,42,43,44,45,46,47,48,49,50]. A variety of measures were used across studies: the Pittsburgh Sleep Quality Index (PSQI) Global Score (n = 3) [41,44,50], the Berlin Sleep Questionnaire (n = 1) [34], the Sleep Index for Midlife Women (n = 1) [42], and the Caring for End Stage Renal Disease Health Experience Questionnaire sleep scale (n = 1) [31].
Sleep satisfaction across studies measured by PSQI ranged from 6.6 (SD = 3.0) to 7.0 (SD = 3.8) [43,50]. Over half of African Americans were classified as having low sleep satisfaction based on a cut-off score of 5 on the PSQI [41]. Individuals with or at risk for type 2 diabetes reported lower sleep quality [44]. To illustrate, in a study of 155 individuals (52% African American, 48% White), those with prediabetes had significantly poorer sleep satisfaction (PSQI M = 7.2, SD = 0.7) compared with those with standard glucose tolerance (PSQI M = 6.0, SD = 0.4) [44].
Lower sleep satisfaction was significantly associated with poorer type 2 diabetes clinical outcomes (i.e., HbA1c, insulin use) [50]. In a longitudinal study predicting the incidence of type 2 diabetes, restless sleep significantly predicted the development of cardiometabolic conditions, including type 2 diabetes, in Black women, not for Black men [39].

3.3.2. Alertness

There were limited data on alertness comparing racial, ethnic, or cardiometabolic risk differences. Daytime sleepiness is a measure of alertness. Daytime sleepiness was reported in three studies [44,48,51]. Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS) in one study [51] and by a single-item question in the other two studies [44,48]. About half of those with type 2 diabetes [51] (53%) had excessive daytime sleepiness (ESS > 10), but the prevalence was not statistically significantly different from those without type 2 diabetes (51%) [51]. There was limited significant data from this sample set on differences in alertness between those with cardiometabolic risk factors and those without, as well as racial or ethnic differences.

3.3.3. Efficiency

Efficiency was assessed in eight studies, with self-report in seven studies and objective measures in three studies [33,34,36,41,44,45,51,52]. Three of these studies used a wrist-worn research actigraph device [32,33,35]. Based on actigraphy data, African Americans had significantly lower sleep efficiency (M = 72.3% SD = 11.5%) than European Americans (M = 82.2%, SD = 8.7%) [33]. Furthermore, lower sleep efficiency was significantly associated with poorer health behaviors related to cardiometabolic risk, including diet, physical activity, and smoking [33]. Consistent with this finding, another study that employed actigraphy also found that lower sleep efficiency was significantly associated with higher glucose and HbA1c in individuals identifying as Black (n = 789) [36] and in the sample where 74% were Black (n = 148) [34].

3.3.4. Duration

Sleep duration was reported in 25 studies and was the most common dimension studied and reported on, of which 4 studies objectively assessed sleep duration using actigraphy and/or PSG [32,33,34,36]. Self-report sleep duration was measured in 19 studies with a single item [35,41,42,44,45,46,47,49,50,51,52,53,54,55,56,57,58,59,60]. Participants were categorized into short, intermediate, and prolonged sleep groups in nine studies [42,46,51,53,55,56,57,58,59]. The most common definition was ≤6 h/night for short sleep and ≥9 h/night for extended sleep. Short sleep was defined by studies at or between <5 h/night and ≤7 h/night. Long sleep was defined by studies at or between ≥6 and ≥9 h/night.
Black participants were more likely to have shorter sleep duration when compared with those of other racial/ethnic groups [32,33,42,45,53,54,55,61]. For example, African Americans had a 40 min shorter average sleep duration compared with European Americans (p < 0.001) [32].
Cardiometabolic health risk factors were associated with shorter sleep duration in African Americans [32,33,34,41,47,56,58,61]. For instance, African Americans with type 2 diabetes had shorter sleep duration than those without (p = 0.004) [41].

3.3.5. Sleep Disorders

Sleep disorders were determined by assessing snoring, sleep-disordered breathing, obstructive sleep apnea, and insomnia in 10 studies [32,34,35,36,45,48,51,62,63,64]. In four studies, home sleep apnea testing devices such as ARES® were used [36,48,51,65], and two studies used polysomnography [32,37]. Objective sleep measures, including >15 apnea events per hour and percent sleep with less than 90% oxygen saturation, were reported in 6 studies. Markers of poor cardiometabolic health were associated with sleep disorders [32,34,36,37,45,62,63,64]. To illustrate, Bakker and associates (2015) determined the number of AHI events to be higher in those with abnormal fasting glucose (M 27.0 SD 20.3) compared with those with average fasting glucose (M 20.4 SD 17.4) (p < 0.01). In 2017, Joseph and team identified African Americans with optimal modifiable diabetes risk factors were less likely to have a sleep-disordered breathing burden (68% prevalence) than those with poor (98%) or average (91%) modifiable risk factors (p < 0.001).
Black participants were more likely to have insomnia disorder when compared with those of other racial/ethnic groups in three studies [42,46,48]. Insomnia symptoms (e.g., difficulty falling asleep, difficulty staying asleep) were prevalent in the African American population. Up to 39% of the Black participants identifying as Black had difficulty falling asleep [41,45,47]; up to 43% had difficulty staying asleep [42,46,48]. A total of 3% (3 days or more per week) to 11% (at least one day per week) of the Black population were on sleep medication [42,46].

4. Discussion

The evidence gathered from this systematic review of 35 research studies provides an international snapshot of the significant global health problem of poor sleep health characteristics, cardiometabolic risk including type 2 diabetes, and the impact on the quality of life for African Americans, Blacks, and African Caribbeans. Multiple studies found disparities in sleep health characteristics and cardiometabolic health among adults of African descent across multiple studies.
This review synthesizes the evidence and supports similarities in sleep health characteristics among African Caribbean adults with cardiometabolic risk compared with other ethnicities in the Caribbean. When African Caribbean sleep characteristics were compared to African American sleep, however, a pattern emerged that Black Americans with cardiometabolic risk, including type 2 diabetes, may be at an elevated risk for poor sleep satisfaction, alertness, efficiency, duration, and sleep disorders compared to African Caribbeans with similar risk factors [66,67,68]. Due to the minimal concentration of African Caribbean data, future research is required to further our understanding of the physiological, social, and cultural phenomenon.
These results align with the literature outside of this search that described poorer sleep characteristics in Black American individuals compared with White American individuals [69,70,71,72] and that those with cardiometabolic disease and cardiometabolic risk factors such as type 2 diabetes have poor sleep characteristics [72,73,74]. The role of socioeconomic disadvantage plays and probably contributes to African decent adults’ insomnia disorder. The review by Ruiter et al. has commented on similar factors facing African descendants (e.g., Black, African American, African Caribbean). Obesity was associated with poorer sleep, negatively impacting their health [74]. This review connects the two concepts and suggests that the intersectionality of African descent in America and cardiometabolic risk may put individuals at heightened risk for poor sleep characteristics.
Previous research has shown that insomnia disorder is associated with various social, physical, and mental health conditions among Blacks [75,76,77]. While the primary focus of this review aimed to evaluate sleep characteristics in adults of African descent without directly emphasizing the impact of stress, sleep, and cardiometabolic conditions. The differences noted between the sleep duration of those of African descent living in the Caribbean and those in the United States may be due in part to the significant stressors associated with immigrating to and living in the United States [66]. Racialized stress and trauma gained through adjusting to life in the United States significantly influence the day-to-day life of Black immigrants [75]. Racial discrimination has been associated with poor sleep characteristics [75,76,77]. Poor sleep has been associated with type 2 diabetes severity, cardiovascular disease, obesity, and other chronic diseases [50,78,79]. Cardiometabolic conditions, sleep, and racial/ethnic factors significantly influence health. Many of these societal issues contributing to African-descent adults’ adverse health outcomes are modifiable. While detecting and treating African-descent insomnia disorder, clinicians need to be aware of and consider their mental and physical health and social determinants of health.
The lack of knowledge about the negative health impact of lower sleep satisfaction means that African descendants may need help to make informed sleep regime decisions. In this review, we gained some additional insight. In a sample of 1129 participants, Assari et al. noted that restless sleep was a predictor of cardiometabolic conditions, specifically in Black women, not Black men [39]. The study by Fuller-Rowell et al. has commentated that if individuals of African descent individuals perceived their suboptimal sleeping habits to be associated with unfavorable health conditions and they do not believe that they are not as resilient as people perceived them to be about their mental state, they may take measures, and develop good sleep habits [80]. Lower sleep efficiency was also significantly associated with cardiometabolic risk [33]. These findings indicate that lower sleep efficiency is significantly associated with higher glucose levels in individuals who identify as Black [76,77,78,79].
Additionally, the intersections of racial identity, sex, and gender identity seem to modify the residual effects of restless sleep over cardiometabolic conditions. Thus, clinicians interested in preventing comorbid sleep problems in individuals of African descent patients should consider a programmatic approach that includes education, frequent detection of poor sleep, building quality treatment modalities, and providing accessible resources. Based on the intersections of racial identity, sex, and gender identity, future research could examine the effect of unhealthy stress behaviors, comorbid sleep problems, and cardiometabolic risk among African descent men and women.

4.1. Clinical Implications

The revealed interconnected web of cardiometabolic disease, race, and sleep is beneficial for clinicians to understand and apply in their practice. Improvements in assessing sleep health behaviors and habits and screening for sleep disorders can be made to improve chronic disease self-care management techniques in at-risk populations. Primary care providers often agree that sleep assessments are essential but may need more training and guidance to address multiple sleep characteristics as well as treat sleep disorders based on work by Klingman and colleagues [81]. Clinicians should consider the intersectionality of poor sleep characteristics, cardiometabolic risk factors, and racial/ethnic groupings when creating care plans and providing care.

4.2. Limitations

The limitations of the 35 studies used in this systematic review related to missing demographic information on gender identity and the specific number of participants in each category. The use of epidemiological techniques for calculating and projecting from retrospective design and less use of prospective data. In several articles, the authors used national data sources with multiple variables older than five years. A dearth of studies specifically studied African Caribbeans, and most studies that delineated their results by race did not break down further to ethnicity.
Thirty-five of the 35 studies did not specify ethnic background for participants past generalized racial categories. Subsets of African descendants, such as African Caribbeans, have unique cultural, social, and potentially genetic factors that influence health and are therefore essential to study. Only with ethnic delineation is it possible to understand specific population health risks.
The researchers anticipated studies to assess the sleep stage via PSG; however, none of the studies assessed the sleep stage. No two papers had comparable methodologies, so a meta-analysis was not possible. The wide breadth of sleep characteristic measurement methodology provides a general understanding of elevated risk; however, the need for more consistency fails in concretely benchmarking sleep characteristics. Additional research is needed to create fair comparisons between the sleep characteristics of specific populations.

5. Conclusions

Sleep health is vital to cardiometabolic disease and stressors specific to race (e.g., discrimination). Practitioners should also consider assessing sleep behavior and screening for sleep disorders. In this systematic review, the intersectionality between African descent and cardiometabolic disease concerning poor sleep is described. Black Americans with cardiometabolic risk, including type 2 diabetes, may be at an elevated risk for poor sleep satisfaction, alertness, efficiency, duration, and sleep disorders compared to African Caribbeans with similar risk factors. This review contributes to the knowledge gap on sleep characteristics in adults of African descent and calls for further assessment of the racial stress factors associated with living in America that may influence sleep, metabolic factors, and general health. Clinicians should consider African descent and poor cardiometabolic markers as risk factors for disordered sleep and assess sleep as a health measure.

Author Contributions

Conceptualization, C.M.-N.; methodology, C.M.-N. and S.G.; software, J.S. and C.L.; validation, C.M.-N., J.S., Y.H. and C.L.; formal analysis, C.M.-N., S.G., Y.H. and J.S.; investigation, C.M.-N., S.G., J.S. and Y.H.; resources, C.M.-N.; writing—original draft preparation, C.M.-N.; writing—S.G., J.S., Y.H. and C.L.; review and editing, C.M.-N., S.G., J.S., Y.H. and C.L., funding acquisition, C.M.-N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Institute of Nursing Research of the National Institutes of Health under award numbers R00NR019323-03, K99NR019325-02, and R00NR018886.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Endocrines 04 00036 g001 550
Figure 1. PRISMA diagram illustrates our study search and selection process.
Figure 1. PRISMA diagram illustrates our study search and selection process.
Endocrines 04 00036 g001
Table
Table 1. Characteristics of PubMed Search Strategy.
Table 1. Characteristics of PubMed Search Strategy.
MEDLINE PubMedCINAHL, PsycINFO, Web of Science
1((((“Haiti”[Mesh]) OR (“African Continental Ancestry Group”[Mesh] OR “African Americans”[Mesh])) OR (“Caribbean Region”[Mesh])) (Haiti or Haitian or African continental ancestry group or African American or African Americans or Caribbean region or African Caribbean American)
2((((((((sleep habit* [tiab]) OR (sleep disturbance [tiab])) OR (reduced sleep [tiab])) OR (sleep loss [title/abstract])) OR (sleep loss [title/abstract])) OR (sleep loss [tiab])) OR (“Sleep”[Mesh])) OR (“Sleep Wake Disorders”[Mesh]))) (Sleep or sleep-wake disorder or sleep hygiene or sleep habits and patterns or sleep-wake cycle or sleep disturbance or reduced sleep or sleep loss)
3((“Diabetes Mellitus, Type 2”[Mesh]) OR (diabetes* [tiab])) OR (nutrition* [tiab]) OR (“metabolic* [tiab])) OR (“Endocrine “[Mesh]))) (Type 2 diabetes or diabetes or diabetes mellitus type 2 or diabetes mellitus or NIDDM or non-insulin dependent diabetes or nutrition or metabolic or endocrine)
4(((((((“Haiti”[Mesh]) OR (“African Continental Ancestry Group”[Mesh])) OR (“African Ameri-cans”[Mesh])) OR (“Caribbean Region”[Mesh])) OR (“African Caribbean Americans”[Mesh]))) OR (((((((((sleep habit* [tiab]) OR (sleep disturbance [tiab])) OR (reduced sleep [tiab])) OR (sleep loss [title/abstract])) OR (sleep loss [title/abstract])) OR (sleep loss [tiab])) OR (“Sleep”[Mesh])) OR (“Sleep Wake Disorders”[Mesh]))))) AND (((“Diabetes Mellitus, Type 2”[Mesh]) OR (diabe-tes* [tiab])) OR (nutrition* [tiab]) OR (“metabolic* [tiab])) OR (“Endocrine “[Mesh]))))(Haiti or Haitian or African continental ancestry group or African American or African Americans or Caribbean region or African Caribbean American) and (Sleep or sleep-wake disorder or sleep hygiene or sleep habits and patterns or sleep-wake cycle or sleep disturbance or reduced sleep or sleep loss) and (type 2 diabetes or diabetes or diabetes mellitus type 2 or diabetes mellitus or NIDDM or non-insulin dependent diabetes or nutrition or metabolic or endocrine)
Table
Table 2. Study Characteristics.
Table 2. Study Characteristics.
Authors, YearStudy TypeSleep MeasurementTotal NAge Mean (SD)Female N (%)Ethnic Breakdown% CMCTotal CMC NCountry
Rodriguez et al., 2013 [31]Cross-sectional/ObservationalSleep subscale of the Choices for Healthy Outcomes in Caring for ESRD Health Experience Questionnaire16862 (17)168 (100)70% Non-African American
30% African American		92% HTN
49% T2D
35% HF
32% CAD
18% PAD
18% Stroke		410USA
Bakker et al., 2015 [32]Cross-sectional/ObservationalPolysomnography and actigraphy215668.5 (9.2)1167 (54)37.2% White
27.3% African American
23.8% Hispanic
11.8% Chinese		77.3% OSA
40% T2D		2532USA
Curtis et al., 2017 [33]Cross-sectional/ObservationalWrist actigraphy42656.8 (11.3)260 (61)69% European American
31% African American 		NRNRUSA
DePietro et al., 2017 [34]Cohort study/ObservationalWrist actigraphy, Berlin Sleep Questionnaire21263.9 (22.2)127 (60)74% African American
26% “other”		61.3% Overweight
58% T2D 		252USA
Wagner et al., 2016 [35]Cross-sectional/ObservationalSelf-reported single question (how well did you sleep last night); altigraph for sleep and wake time7755.7 (11.8)77 (100)51% Black
49% White		100% T2D77USA
Yano et al., 2020 [36]Cross-sectional/ObservationalType 3 home sleep apnea testing device; Seven-day wrist actigraphy with a sleep diary78963 (11)584 (74) 100% Black25% T2D197USA
Mahmood et al., 2009 [37]Cross-sectional/ObservationalPSG—apnea-hypopnea index (AHI)100845 (14.6)540 (54)66% African American
18% White
15% Hispanic
3%Asian		53.2% HTN
27% T2D
19.4% HLD
7.8% CAD		1083USA
Andreozzi et al., 2019 [38]Cohort study/ObservationalPSG—apnea-hypopnea index (AHI)171752.8 (12.7)573 (33)62% White
34% Black
4% Other		50% HTN
22% T2D		1234USA
Assari et al., 2017 [39]Cohort study/ObservationalLikert question on restlessness112941 (11)727 (64)69% White
31% Black		NRNRUSA
Bermudez-Millan et al., 2016 [40]Cross-sectional/ObservationalA single question on sleep quality7755.8 (11.7)77 (100)51% Black
49% White		100% T2D77USA
Bidulescu et al., 2010 [41]Cross-sectional/ObservationalPSQI151547.5 (17)1096 (72)100% African Americans 56.7% HTN
33.7% High cholesterol
32.5% Obesity
17% T2D		2116USA
Gaston et al., 2018 [42]Cross-sectional/ObservationalSelf-report sleep duration and other self-report questions80,88042 (18)45,729 (57)70% White
30% Black		70% Overweight
39.3% Obesity
36.3% HTN
9.4% Heart disease
9% T2D
4.3% Stroke		132,637USA
Im et al., 2019 [43]Cross-sectional/ObservationalSleep Index for Midlife Women16449.9 (5.54)164 (100)26.8% White
26.2% Hispanic
24.4% Asian
22.6% African American		59.7% Overweight/obese
38% T2D		160USA
Iyegha et al., 2019 [44]Cross-sectional/ObservationalPSQI15537.9 (2)94 (61)52% Black48% White39% T2D60USA
Kalmach et al., 2016 [45]Cross-sectional/ObservationalIndividual questions based on insomnia DSM-5; TST, SOL, WASO using a single question; STOP-BANG391146 (13.3)2557 (65)65.4% White
25% Black Asian
4.22% Pacific Islander
1.74% Hispanic/Latino
2.33% Middle Eastern or Indian
1.31% other		25.9% HTN
24.5% HLD
8% T2D
1.4% MCI
1.5% Stroke		2734USA
Matthews et al., 2018 [46]Cross-sectional/ObservationalIndividual questions about sleep duration, SOL, sleep continuity, sleep med use, and subjective feeling31,724Categorical 15,369 (48)79% White
14% Black
7% Native Hawaiian		43% Obesity
27% HTN
9% CVD		27,917USA
Shankar et al., 2010 [47]Cross-sectional/ObservationalSelf-reported single question asking, “During the past 30 days, for about how many days have you felt you did not get enough rest or sleep?”372,144Categorical186,072 (50)69% White
10% Black
14% Mexican American
7% other		27.3% Obesity
37% Overweight
9% T2D
8.3% CVD 		303,298USA
Williams et al., 2018 [48]Cross-sectional/ObservationalARES questionnaire101362 (14)699 (69)100% Black94% HTN
74% DLD
67% Obesity
60% T2D
31% Heart disease		3303USA
Duester et al., 2011 [49]Cross-sectional/ObservationalLikert scale12931 (8.3) Black, 28.4 (5.6) White66 (51)65% Black
35% White		NRNRUSA
Knutson et al., 2006 [50]Cross-sectional/ObservationalPSQI20457.3 (12.5)119 (58)79% African American
19% White
2% “other”		100% T2D204USA
Ramos et al., 2015 [51]Cross-sectional/ObservationalARES, ESS, sleep duration with a single question, insomnia symptoms with questions101362 (14)588 (58)100% Non-Hispanic blacks 60% T2D
6% HTN
5% Dyslipidemia		1087USA
Picarsic et al., 2008 [52]Cross-sectional/ObservationalNap duration, prevalence, nighttime sleep duration, SOL, efficiency (total time in bed, nighttime sleep duration, SOL, efficiency from the PSQI Q1–4)41476.8 (4.2)285 (69)75% White
18% Black
7% Other Minority/Ethnic group		82.2% CVD
69.3% HTN
22% T2D
9.4% MI
5.6% CHF
4.8% Stroke		799USA
Gordon and Hsueh, 2021 [53]Cross-sectional/ObservationalSelf-reported sleep duration1,387,569CategoricalNR60.3% White
16% Latino
9.8% Black
8.3% Filipino
5.6% Chinese		NRNRUSA
Shadyab et al., 2015 [54]Cross-sectional/ObservationalSelf-reported two questions about nighttime sleep and daytime napping durations160967.3 (9.8)1609 (100)56% White
21% Filipina
23% Black 		37% T2D595USA
Beihl et al., 2009 [55]Cohort study/ObservationalA single question on sleep duration900NR510 (57)38% non-Hispanic White
34% Hispanic
29% African American		33% T2D
30.6% HTN		575USA
Gamaldo et al., 2015 [56]Cross-sectional/ObservationalSelf-reported sleep duration120747.34 (8.74)715 (59)50% Black
50% White		3% CAD
14% T2D		203USA
Hairston et al., 2010 [57]Cohort study/ObservationalSelf-reported sleep duration110741.7 (7.7)685 (62)74% Hispanic
26% African American		100% T2D1107USA
Jackson et al., 2013 [58]Cross-sectional/ObservationalSelf-reported sleep duration130,94350.6 (0.143)66,781 (51)87% White13% Black78% Overweight
44% Obese
10% T2D		173,005USA
Maskarinec et al., 2018 [59]Cohort study/ObservationalA single question about sleep duration151,691longitudinal82,594 (54)26% Japanese American
23% White
22% Latino
16% African American
7% Native Hawaiian
6% other		43.1% Heart attack/Stroke
6% T2D		73,874USA
Singh et al., 2005 [60]Cross-sectional/ObservationalSelf-reported questions about sleep habits, snoring, and sleep duration (24 h) on weekdays and weekends (the 2-week period immediately prior to the study)315841.6 (12.6)1570 (50)69% White
25% Black
6% “Other”		25% HTN
6.4% Heart Disease
6% T2D
1.5% Stroke		287USA
Zizi et al., 2012 [61]Cross-sectional/ObservationalSelf-reported sleep duration question29,81847.4 (17.8)16,698 (56)85% White
15% Black		33.7% Overweight
27% HTN
24.6% Obese
8% T2D
7.8% Heart disease		30,265USA
Joseph et al., 2017 [62]Cohort study/ObservationalSleep-disordered breathing burden/Berlin Sleep Questionnaire325253.3 (12.5)2081 (64)100% African Americans17% T2D560USA
Ramos et al., 2014 [63]Cohort study/ObservationalBerlin questionnaire to assess OSA risk17660.0 (12)
25–92		80 (45)44% Hispanic
44% Non-Hispanic Black
12% Non-Hispanic White		100% Acute ischemic stroke
84% HTN
38% T2D		391USA
Rosen et al., 2019 [64]Cross-sectional/ObservationalSelf-reported snoring questions449552.1 (12.7)2884 (64)100% African American59.7% HTN
31% Hypercholesteremia
16% T2D		4815USA
Ceide et al., 2015 [65]Cross-sectional/ObservationalARES score103564 (14)725 (70)100% Non-Hispanic Blacks 92% HTN
90% Overweight/obese
72% DLD
61% T2D		3260USA
Abbreviations: CMC: cardiometabolic conditions; T2D: type 2 diabetes; HTN: hypertension; OSA: obstructive sleep apnea; CVD: cardiovascular disease; MI: myocardial infarction; DLD: dyslipidemia; CHF: chronic heart failure; MCI: mild cognitive impairment; ESRD: End Stage Renal Disease; ARES: Apnea Risk Evaluation Systems; SOL: sleep onset latency; ESS: Epworth Sleepiness Scale; PSQI: Pittsburg Sleep Quality Index; DSM-5: Diagnostic and Statistical Manual of Mental Disorders; TST: total sleep time; WASO: wake after sleep onset; STOP-BANG: snoring history, tired during the day, observed stop breathing with sleep, high blood pressure, body mass index more than 35 kg/m2, age more than 50 years, neck circumference more than 40 cm, and male sex at birth; AHI: Apnea-hypopnea index; NHANES: National Health and Nutrition Examination Survey; USA: United States of America; UK: United Kingdom.
Table
Table 3. Quality Assessment.
Table 3. Quality Assessment.
StudyRodriguez et al., 2013 [31]Bakker et al., 2015 [32]Curtis et al., 2017 [33]DePietro et al., 2017 [34]Wagner et al., 2016 [35]Yano et al., 2020 [36]Mahmood et al., 2009 [37]Andreozzi, F. et al., 2019 [38]Assari et al., 2017 [39]Bermudez-Millan et al., 2016 [40]Bidulescu et al., 2010 [41]Gaston et al., 2018 [42]Im et al., 2019 [43]Iyegha et al., 2019 [44]Kalmach et al., 2016 [45]Matthews et al., 2018 [46]Shankar et al., 2010 [47]Williams et al., 2018 [48]Deuster, P. et al., 2011 [49]Knutson et al., 2006 [50]Ramos et al., 2015 [51]Picarsic et al., 2008 [52]Gordon and Hsueh, 2021 [53]Shadyab et al., 2015 [54]Beihl et al., 2009 [55]Gamaldo et al., 2015 [56]Hairston et al., 2010 [57]Jackson et al., 2013 [58]Maskarinec et al., 2018 [59]Singh et al., 2005 [60]Zizi et al., 2012 [61]Joseph et al., 2017 [62]Ramos et al., 2014 [63]Rosen et al., 2019 [64]Ceide et al., 2015 [65]
Was the research question or objective in this paper clearly stated?YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
Was the study population clearly specified and defined?YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
Was the participation rate of eligible persons at least 50%?NRNRNRNRNRNRNRYNRNRNRNRNRYNRNRNRNRNNRNRNRNRNRNRNRNRNRYNRYNRNRNRNR
Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants?YYYYYYYNYYYYYYNNYYNYYYYYYYYNNYNYYYY
Was a sample size justification, power description, or variance and effect estimates provided?NNNNNNNNNNNNYNNNNNYNNNNNNNNNNNNNNNN
For the analyses in this paper, were the exposure(s) of interest measured prior to the outcome(s) being measured?NNNNNNNYYNNNNNNYNNYNNNNNYNNNNNNYNNN
Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed?NNNNNNNYYNNNNNNNNNYNNNNNYNNNNNNYNNN
For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome (e.g., categories of exposure or exposure measured as continuous variable)?YYYYYYYYYYYYYYYYYYYYNYYYYYYYYYYYYYY
Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants?YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
Was the exposure(s) assessed more than once over time?NNNNYYNYYYNNNNNNNNYNNNNNYNNNYNNNNNN
Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants?YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
Were the outcome assessors blinded to the exposure status of participants?NRNRNRNRNRNRNRNNRNRNRNRNRNRNRNRNRNRNNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNR
Was the loss to follow-up after baseline 20% or less?NRNRNRNRNRNRNRNRYYNRNRNRYNRNRNRNRNRNRNRNRNRNRYNRNRNRYNRYNRNRNRNR
Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)?YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
Team rankingFairFairFairFairgoodgoodFairgoodgoodgoodFairFairgoodgoodFairFairFairFairgoodFairFairFairFairFairgoodFairFairFairgoodFairgoodgoodFairFairFair
Key: Y: yes; N: no; CD: cannot determine; NA: not applicable; NR: not reported label as good, fair, or poor.
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Magny-Normilus, C.; Griggs, S.; Sanders, J.; Hwang, Y.; Longhurst, C. Sleep Characteristics in Adults of African Descent at Risk for and with Cardiometabolic Conditions: A Systematic Review. Endocrines 2023, 4, 502-520. https://doi.org/10.3390/endocrines4030036

AMA Style

Magny-Normilus C, Griggs S, Sanders J, Hwang Y, Longhurst C. Sleep Characteristics in Adults of African Descent at Risk for and with Cardiometabolic Conditions: A Systematic Review. Endocrines. 2023; 4(3):502-520. https://doi.org/10.3390/endocrines4030036

Chicago/Turabian Style

Magny-Normilus, Cherlie, Stephanie Griggs, Julie Sanders, Youri Hwang, and Catrina Longhurst. 2023. "Sleep Characteristics in Adults of African Descent at Risk for and with Cardiometabolic Conditions: A Systematic Review" Endocrines 4, no. 3: 502-520. https://doi.org/10.3390/endocrines4030036

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