Association between cardiometabolic health and objectively-measured, free-living sleep parameters in a rural African setting: a pilot study.

Objectives To investigate the relationship between objectively-measured, free-living sleep quantity and quality, and cardiometabolic health, in a rural African setting in 139 adults ( ≥ 40 years, female: n=99, male: n=40). Wrist-mounted, tri-axial accelerometry data was collected over nine days. Measures of sleep quantity and quality, and physical activity were extracted from valid minute-by-minute data. Self-reported data included behavioural, health and socio-demographic variables. Biological data included body composition, resting blood pressure and fasting blood glucose, insulin and lipids. Logistic regression models were constructed with insulin resistance (IR) and Cardiometabolic (CM) risk, as dependent variables, adjusting for socio-demographic, behavioural and biological factors. Results Nocturnal sleep time was longer in females (p=0.054) and sleep quality was better in males (p ≤ 0.017). Few participants slept >9 hours/night (4-5%), and 46-50% slept <7 hours/night. IR and CM risk was higher in females (p ≤ 0.006). In adjusted models, sleep variables were independently associated with IR (p<0.05). Sleep quantity was non-linearly associated with CM risk (p ≤ 0.0398), and linearly associated with IR (p ≤ 0.0444). Sleep quality was linearly related with CM risk and IR (p ≤ 0.0201). In several models, sleep quantity and sleep quality measures were concurrently and signicantly associated with IR (p ≤ 0.044).


Introduction
Sleep health is closely linked to metabolic health [1], and while there is extensive literature from industrialised settings [2], there is a paucity of data from African settings, especially free-living, objective measures of sleep [3]. Within the South African context, self-reported long sleep duration is associated with poor cardiometabolic health in mainly urban settings [4,5]. Given the lack of objectively-measured, free-living sleep parameters in any South African setting, the objective of this study was to use wristactigraphy to investigate the association between sleep parameters and cardiometabolic health in a rural African setting during a cross-sectional survey, and thus extend the ndings of self-report sleep duration and cardiometabolic health [4][5][6] Methods Dikgale Health and Demographic Surveillance System site (DHDSS) sample A convenience sample of 167 adults was recruited from the DHDSS site [7]. These participants formed part of a larger study cohort (≥40 years) [6,8]. Trained eld workers collected self-reported and measured data from participants by means of questionnaires, anthropometry, oscillometric blood pressure measurement, ultrasound scans, and venipuncture [6,8]. We calculated body mass index (BMI, kg/m 2 ), and Conicity Index (CI) [9]. Questionnaire data included behavioural, health and socio-demographic variables [6,8]. Nine day, free-living, wrist-mounted accelerometry data was collected [10]. The ultrasound scans were not considered for this analysis.
Blood sample collection and analysis A registered nurse collected fasting blood samples. The samples were analysed centrally; procedures and calculations are described in detail elsewhere [8]. The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was calculated from fasting blood glucose and insulin [11].

Criteria for Metabolic Syndrome
In accordance with the harmonized Joint Interim Statement (JIS) de nition [12], the presence of the Metabolic Syndrome (MetS) required three of the following components, with waist circumference not a prerequisite: elevated waist circumference (WC): females ≥92 cm, males ≥86 cm; elevated triglycerides (TG): ≥1.7 mmol/l; reduced high-density lipoprotein cholesterol (HDL-C): men <1.0 mmol/l, women <1.3 mmol/l; elevated resting blood pressure ≥130/85mmHg or on hypertension treatment; and elevated fasting glucose (GC) ≥5.6 mmol/l or on diabetes treatment. For this study population-speci c WC cutpoints were chosen [13].
Using the ve criteria from the JIS de nition for MetS (JIS-MetS), we calculated sex-speci c z-scores for HDL-C, TG, GC, WC and MAP (Mean Arterial Pressure), which were summed to create a MetS z-Score (MetSz) [14].

Statistical analysis
Descriptive statistics comprised means (one standard deviation), medians (inter-quartile range), variances (maximum, minimum) and frequencies. Relationships between categorical variables were examined through Fisher's Exact Test. For continuous data, independent t tests and Levene's test examined differences between sexes and risk groups. Where required a non-parametric test was employed. Bi-variate relationships were examined using linear regression.
Forced-entry binary logistic regression models were constructed to examine the relationship between MetS risk (low/high) according to the JIS de nition [12], and tertiles (Q1=low/Q2/Q3=high) of sleep quantity and quality variables. Models were also constructed to examine the relationship between MetS risk and sleep quantity and quality categories [23][24]. Models were adjusted for socio-demographic (age, socio-economic status), behavioural (fruit and vegetable intake, sugar-sweetened beverages, tobacco and alcohol usage, physical activity) and biological (sex, HIV status) variables. CI was not included as an independent variable because WC formed part of the JIS risk de nition.
Forced-entry ordinal logistic regression models were constructed to examine the relationship between HOMA-IR levels (tertiles), and tertiles of sleep quantity and quality variables. Additional models were constructed to examine the relationship between HOMA-IR tertiles and sleep quantity and quality categories [23][24]. Models were adjusted for socio-demographic (age, socio-economic status), behavioural (fruit and vegetable intake, sugar-sweetened beverages, tobacco and alcohol usage, physical activity) and biological (sex, CI, HIV status) variables..
All covariates were entered as quantiles. Regression coe cients were expressed as odds ratios (OR±95% con dence intervals).
Multicollinearity was assessed using Variance In ation Factors (VIF) and Tolerance.
Post-hoc contrasts were run for binary and ordinal logistic regression models to test for linear and nonlinear trends, and pairwise comparison of groups (Bonferroni correction).
Data were analysed using appropriate statistical software (Stata/SE for Windows: Release 15.1. College Station, TX: StataCorp LP, 2020). Signi cance for all inferential statistics was set at p<0.05.

Results
Of the 167 raw accelerometry data les, 157 had valid data for at least one weekday and one weekend day. Once combined with the questionnaire and biological data, 139 participants had complete data.
Females consumed more sugar-sweetened beverages (SSB), experienced poorer sleep quality, and were more physically active and insulin resistant ( Table 1, p≤0.07). Males were leaner, used more alcohol and tobacco products and experienced lower people-to-bedroom density (p≤0.040). The JIS high risk group had a higher socio-economic status (SES), a lower HIV+ prevalence, were mostly obese, more insulin resistant, and likely to be in a partnered relationship (Table 1, p≤0.040).
In binary logistic regression models ( Figure 1A-C) males had signi cantly lower odds for JIS-MetS high risk (OR≈0.15, p≤ 0.006), and similarly for HIV+ status in the NST and SFI models (OR=0.25, p≤0.0035). Once adjusted for adiposity (CI), sex was no longer a signi cant factor (p≥0.097). However, HIV+ status remained a signi cant factor in most models (OR≈0.26, p≤0.048) ( Figure 1A-C).
There was a signi cant non-linear trend (U-shaped) between JIS-MetS risk and NST (p=0.0196) ( Figure  1A), such that for NST Q2, there was 80% less likelihood of being at high risk for JIS-MetS. There was also a signi cant difference in frequencies between NST Q1 and Q2 (p=0.021) ( Figure 1A).
[Insert Figure 1 here] There was a signi cant positive, linear trend between JIS-MetS risk and SFI (p=0.0001) ( Figure 1C). The odds of high risk JIS-MetS for SFI Q3 was high (OR= 69.81, 8.44; 577.63), however the con dence intervals were wide. There were signi cant differences between the frequencies for Q3 versus Q1 and Q2 (p<0.001) ( Figure 1C).
Being a current user of both alcohol and tobacco products carried a signi cant higher odds for JIS-MetS high risk in the NST (OR=4.51, p=0.034) and SFI (OR=6.55, p =0.023) models ( Figure 1A-C).
In ordinal logistic regression models ( Figure 1D-F), being in the VM Q2 level, signi cantly decreased the odds of being in HOMA-IR Q3 by a factor of ≈0.31 (p≤0.039). In contrast, for both CI Q2 and Q3 levels, the odds of being in HOMA-IR Q3 were signi cantly increased; Q2 OR≈2.9 and Q3 OR≈3.79, respectively (p≤0.045) ( Figure 1D-F).
There was a signi cant linear trend between TST and HOMA-IR levels (p=0.0444) ( Figure 1D). Compared to TST Q1 and Q2, being in TST Q3 (longest sleep time) increased the odds of being in the highest HOMA-IR level (Q3) by a factor of 2.84 (p=0.044) ( Figure 1D).
In the TST, NST, SE, WASO and AC models ( Figure 1D-E), being in SSB Q2 and Q3, increased the likelihood for being in HOMA-IR Q3 by a factor of 3.51 to 4.77, but did not reach statistical signi cance (p=0.068 to p=0.098).
Sleep quantity and quality measures featured concurrently and signi cantly in the TST, SE and SFI models ( Figure 1D-F) (p≤0.044).
Expressing sleep quantity and quality parameters in terms of sleep health guidelines, we found signi cant non-linear associations with JIS-MetS risk (p≤0.0308) (Figure 2A-F). The relationship between sleep categories for both TST and NST, and JIS-MetS risk were U-shaped, the nadir at 7-9 hours of sleep (Figure 2A-B).
In the TST, NST and SE models ( Figure 2D and 2F), being in SSB Q2, increased the odds for being in HOMA-IR Q3 by a factor of 3.24 to 3.65, but did not reach statistical signi cance (p=0.074).

Discussion
This analysis is novel in that, as far as the authors are aware, this is the rst free-living, actigraphymeasured sleep and cardiometabolic health study from a South African setting.
The main ndings of this analysis were rst that sleep quality and quantity measures were independently associated with HOMA-IR, and to lesser extent JIS-MetS. Second, we found linear and non-linear (Ushaped) relationships between categories of sleep quantity and cardiometabolic risk.. Third, all sleep quality measures were consistently associated with HOMA-IR.
Our ndings are in agreement with the linear relationship between sleep duration and HOMA-IR in black, urban women, although we found far more women had short sleep time compared with self-report measures [4]. A recent study from the METS group found long sleep duration in a black, urban South African sample [5].
Unadjusted sleep quantity did not differ signi cantly across the sexes, which is contrast to self-report measures [4]. Sleep quality was poorer in females, although sex did not reach signi cance in most HOMA-IR models.
Some have speculated that poor sleep quality might be underpinning the long self-reported sleep durations in South African settings and hence the poorer cardiometabolic health associated with long sleep [4,5]. Our results suggest that fragmented, poor sleep quality, independent of sleep duration might be more important than sleep duration. Future analyses need to explore the effect of the interaction between sleep quantity and quality, and cardiometabolic health [26].
In contrast with some self-reported PA studies [4], we found PA volume to be signi cantly and independently related to HOMA-IR, but not associated with JIS-MetS risk as in other self-report studies [5]. Interestingly, other lifestyle factors such as concurrent alcohol and tobacco use, and the consumption of SSB were independently associated with poor cardiometabolic health. The concurrent use of alcohol and tobacco is associated with poorer cardiometabolic health [27]. Although the association with SSB did not quite reach statistical signi cance, there is evidence that the consumption of SSB is linked with poor cardiometabolic health, even within rural environments [28].
In conclusion, we report the signi cant, independent importance of objectively-measured sleep quantity and quality in relation to cardiometabolic health in a rural South African setting.

Limitations
Due to the small sample size and cross-sectional, convenience sampling in this study, the results cannot be readily generalized, nor can causality be shown. . The participants recruited into the original studies were informed about the study objectives, expected outcomes, bene ts and the risks associated with it. Written informed consent was obtained from the participants prior to interviews and measurements.

Availability of data and material
The dataset analysed during the current study is available from the corresponding author on reasonable request.