Waist circumference and low high-density lipoprotein cholesterol as markers of cardiometabolic risk in Kenyan adults

Daniel Faurholt-Jepsen; Henrik Friis; David L Mwaniki; Michael K Boit; Lydia U Kaduka; Inge Tetens; Dirk L Christensen

doi:10.1371/journal.pone.0247600

. 2021 Feb 25;16(2):e0247600. doi: 10.1371/journal.pone.0247600

Waist circumference and low high-density lipoprotein cholesterol as markers of cardiometabolic risk in Kenyan adults

Daniel Faurholt-Jepsen ¹, Henrik Friis ², David L Mwaniki ³, Michael K Boit ⁴, Lydia U Kaduka ³, Inge Tetens ², Dirk L Christensen ^5,^*

Editor: Giacomo Pucci⁶

PMCID: PMC7906307 PMID: 33630976

Abstract

Background

Abdominal obesity predict metabolic syndrome parameters at low levels of waist circumference (WC) in Africans. At the same time, the African lipid profile phenotype of low high-density lipoprotein (HDL) cholesterol without concomitant elevated triglyceride levels renders high triglyceride levels detrimental to cardiometabolic health unsuitable for identifying cardiometabolic risk in black African populations.

Objectives

We aimed to identify simple clinical measures for cardiometabolic risk based on WC and HDL in an adult Kenyan population in order to determine which of the two predictors had the strongest impact.

Methods

We used linear regression analyses to assess the association between the two exposure variables WC and HDL with cardiometabolic risk factors including ultrasound-derived visceral (VAT) and subcutaneous adipose tissue (SAT) accumulation, fasting and 2-h venous glucose, fasting insulin, fasting lipid profile, and blood pressure in adult Kenyans (n = 1 370), and a sub-population with hyperglycaemia (diabetes and pre-diabetes) (n = 196). The same analyses were performed with an interaction between WC and HDL to address potential effect modification. Ultrasound-based, semi-quantitative hepatic steatosis assessment was used as a high-risk measure of cardiometabolic disease.

Results

Mean age was 38.2 (SD 10.7) (range 17–68) years, mean body mass index was 22.3 (SD 4.5) (range 13.0–44.8) kg/m², and 57.8% were women. Cardiometabolic risk was found in the association between both WC and HDL and all outcome variables (p<0.05) except for HDL and SAT, fasting and 2-h venous glucose. Additive cardiometabolic risk (WC and HDL interaction) was found for SAT, low-density lipoprotein cholesterol, and triglycerides. No differences in the association between WC and HDL and the outcome variables were found when comparing the full study population and the hyperglycaemia sub-population. Increase in WC and HDL were both associated with hepatic steatosis (OR 1.09, p<0.001, and OR 0.46, p = 0.031, respectively).

Conclusion

In adult Kenyans, increasing WC identified more cardiometabolic risk factors compared to HDL.

Introduction

The concept of the hypertriglyceridemic waist phenotype and the potential of using a combination of simple clinical measures (waist circumference (WC) and plasma triglyceride) as a marker of elevated cardiovascular risk was introduced by Després and co-workers in 2000 [1, 2]. An African diaspora population was included in one of the studies [1], and the authors suggested that for a given WC, this population compared to a North American white population had a generally more cardio-protective plasma lipoprotein profile, including lower plasma triglyceride levels, due to lower visceral adipose tissue (VAT) accumulation and higher plasma lipoprotein lipase activity. Low triglyceride levels have also been shown in studies carried out in black South Africans [3, 4] as well as in rural Kenyans [5]. In the latter, we showed that the prevalence of dyslipidaemia was high (~37%) in both men and women, and that almost 9 in 10 had isolated hypoalphalipoproteinemia, i.e. low high-density lipoprotein cholesterol (HDL) dyslipidaemia. Furthermore, Delisle and co-workers reported low HDL across body mass index groups including underweight individuals in Benin [6]. It is of note that Després and co-workers used plasma triglyceride levels of ≥2.0 mmol/L and WC values of ≥90 cm as cut-offs for elevated levels [1]. Neither of the two cut-offs match the black African lipid or body composition profile phenotype; few exceed the 1.7 mmol/L cut-off for dyslipidaemia [5, 7], and a recent meta-analysis in a Pan African population (~25 000 individuals) on finding WC cut-offs for having at least two metabolic syndrome traits showed that cut-offs were similar in men and women at 81.2 and 81.0 cm, respectively [8].

Thus, replacing triglyceride with HDL in combination with lower WC cut-off than used by Deprés and colleagues seems more appropriate as cardiometabolic risk markers in black, African populations.

The aim of this study was to identify simple clinical outcome variables of cardiometabolic risk in adult Africans, including similar analyses in a sub-group of people with hyperglycaemia, based on body composition (WC) and lipid (HDL) phenotypes suitable for black African populations.

Methods

A sample of 1 449 adult rural and urban Kenyans participated in a study on cardiometabolic risk factors [5, 9]. In brief, the study population was based on a convenience sample, even though random sampling in the rural area and sampling of urban biological family members of the rural participants was attempted, but failed. The rural population consisted of agro-fishing (Luo), agriculture (Kamba), and agro-pastoralist (Maasai) people, while the urban population consisted of the aforementioned ethnic groups as well as culturally related ethnic groups. All participants were informed about the study in writing as well as orally. Informed consent was signed or given as thumb print in case of illiteracy. Exclusion from this particular sub-study was due to missing data on WC and HDL (n = 79). None were on lipid-lowering therapy, while 22 individuals were on oral hypoglycaemic agents or insulin therapy. Thus, 1 370 individuals (1 117 rural, 253 urban) were included in the study. Following an overnight (>8-h) fast, standard anthropometric measurements were carried out of which WC was measured with a measuring tape midway between the iliac crest and the costal margin following a quiet expiration. Ultrasound scanning of abdominal fat distribution, i.e. visceral (VAT) and subcutaneous (SAT) adipose tissue was measured using a standardised protocol [10]. Fasting plasma lipids were collected. Enzymatic colorimetric tests using the GPO-PAP [11], and the CHOD-PAP methods [12] were used to measure plasma triglycerides (TG) and total cholesterol (TC), respectively. The analysis was done using a Hitachi 912 System (Roche Diagnostics GmbH, Mannheim, Germany). A homogeny enzymatic colorimetric test was used for measuring plasma HDL-C, with HDL-C plus 2^nd generation without pre-treatment being applied using a Hitachi 912 System (Roche Diagnostics GmbH, Mannheim, Germany). Plasma very low-density lipoprotein (VLDL) concentration was calculated according to the following equation [13]: VLDL = TG/2.2, while plasma low-density lipoprotein cholesterol (LDL) concentration was calculated as: LDL = TC—VLDL—HDL [13]. Venous whole blood glucose was analysed according to the blood glucose dehydrogenase method using haemolysation and deproteinisation using a B-HemoCue 201+ device (HemoCue AB, Ängelholm, Sweden). Subsequently, a 75-g oral glucose tolerance test was performed to determine 2-h glucose levels. Serum insulin was measured by a 1235 AutoDELFIA automatic immunoassay system (sensitivity 0.5 lU/ml) using time-resolved fluoro-immunoassay technique (kit no. BO80-101, PerkinElmer Life and Analytical Sciences, Wallac Oy, Turku, Finland). Systolic and diastolic blood pressures were measured twice on the right upper arm using a full-automatic device (Omron M6, HEM-7001-E, Kyoto, Japan), while the participant was seated. Central obesity was defined as WC ≥81.2 cm for men and 81.0 cm for women according to Ekoru and co-workers cut-offs [8], and low HDL values were defined as <1.0 mmol/L for men and <1.3 mmol/L for women [7]. Glucose tolerance status was classified according to World Health Organization/International Diabetes Federation criteria [14]. In a sub-group (n = 756), liver fat accumulation was assessed using ultrasound liver scans. This method is semi-quantitative and allows to distinguish between normal liver (score ≤ 4), mild (score between 5 and 7), moderate (score between 8 and 10) and severe (score ≥ 11) steatosis according to standardised criteria. For detailed methodological description, see [15]. Ethical approval was given by the National Ethical Review Committee in Kenya (SSC Protocol No. 936), and consultative approval was given by the Danish National Committee on Biomedical Research Ethics.

Statistics

Descriptive data are presented as mean (SD) if normally distributed and as median (IQR) if skewed. Associations of WC and HDL were tested against cardiometabolic risk factors in unadjusted linear regression with WC and HDL as continuous and dichotomous variables and with interaction terms on WC and HDL. Furthermore, combined groups of low and high WC and HDL were tested against cardiometabolic risk factors using age and sex adjusted linear regression with combined low WC and high HDL as reference group. Skewed variables were log-transformed, thus the back-transformed coefficient e^B should be interpreted as a ratio. P-values <0.05 were considered statistically significant. All analyses were carried out using Stata 14.2 (IC version, Stata, College Station, USA).

Results

The study population had a mean age of 38.2 (SD 10.7) years with 57.8% being women. Characteristics of the study population on anthropometry and body composition, biochemistry, and blood pressure are presented in Table 1. In age and sex adjusted linear regression analyses, HDL was correlated with VAT, LDL, TC, triglycerides, systolic and diastolic blood pressure, and insulin, while WC was correlated with all assessed cardiometabolic risk factors. Additive cardio-metabolic risk HDL and WC interaction was found for SAT, LDL, and triglycerides. For details, see Table 2. There was no difference in estimates in unadjusted analyses.

Table 1. Characteristics of adult Kenyans (n = 1 370).

	Mean/n(%)	SD
Age (years)	38.2	10.7
Female sex	792 (57.8)
*Anthropometry and body composition*
Body mass index (kg/m²)	22.3	4.5
Waist circumference (cm)	79.5	11.4
Visceral adipose tissue (cm)	5.9	1.7
Subcutaneous adipose tissue (cm)	1.5	1.2
Hepatic steatosis, n (%)^*	116 (15.3%)
Mild hepatic steatosis, n (%)	108 (93.1%)
Moderate hepatic steatosis, n (%)	8 (6.9%)
Severe hepatic steatosis, n (%)	0 (0.0%)
*Biochemistry*
Fasting venous glucose (mmol/L)	4.6	1.5
2-h blood venous glucose (mmol/L)	5.4	2.6
Fasting serum insulin (pmol/L)^**	23	15;36
Fasting plasma total Cholesterol (mmol/L)	3.9	1.0
Fasting plasma HDL (mmol/L)	1.1	0.3
Fasting plasma total cholesterol/HDL ratio^**	3.5	2.9;4.4
Fasting plasma LDL (mmol/L)	2.3	0.8
Fasting plasma VLDL (mmol/L)	0.5	0.2
Fasting plasma triglyceride (mmol/L)^**	0.9	0.7;1.2
*Blood pressure (mmHg)*
Systolic	120	16
Diastolic	74	10

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Abbreviations: HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, VLDL: very low-density lipoprotein cholesterol, SD: standard deviation.

*Based on ultrasound scanning semi-quantitative liver fat score (n = 756).

**Denoting median (interquartile range).

Table 2. Association between high-density lipoprotein cholesterol (HDL) and waist circumference with body composition, biochemistry, and blood pressure in adult Kenyans (n = 1 370).

	HDL	Waist circumference	HDL * Waist circumference
Dependent variable	B (95% CI)	B (95% CI)	p for interaction
Visceral adipose tissue (cm)	-0.8 (-1.0; -0.5) ^**	0.1 (0.1; 0.1) ^**	0.903
Subcutaneous adipose tissue (cm)	0.1 (-0.1; 0.2)	0.1 (0.1; 0.1) ^**	0.018
Fasting venous glucose (mmol/L)	-0.2 (-0.4; 0.04)	0.02 (0.01; 0.03) ^**	0.880
2-h venous glucose (mmol/L)	-0.4 (-0.8; 0.01)	0.04 (0.03; 0.1) ^**	0.286
Low-density lipoprotein cholesterol (mmol/L)	0.3 (0.2; 0.4) ^**	0.02 (0.02; 0.03) ^**	0.004
Total cholesterol (mmol/L)	1.1 (1.0; 1.3) ^**	0.03 (0.02; 0.03) ^**	0.147
Systolic blood pressure (mmHg)	4.6 (2.2; 7.0) ^**	0.4 (0.4; 0.5) ^**	0.134
Diastolic blood pressure (mmHg)	2.0 (0.3; 3.6) ^**	0.3 (0.2; 0.3) ^**	0.312
Dependent variable	e^B (95% CI)	e^B (95% CI)	p for interaction
Triglyceride (mmol/L)^*	0.75 (0.71; 0.81) ^**	1.01 (1.01; 1.01) ^**	0.008
Fasting serum insulin (pmol/L)^*	0.89 (0.80; 1.00) ^**	1.02 (1.02; 1.02) ^**	0.945

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Data are linear regression analyses adjusted for age and sex.

*Log-transformed.

**denotes p<0.05.

Both high vs. low WC and low vs. high HDL were individually correlated with all assessed cardio-metabolic risk factors (Table 3). The combined high WC and low HDL group had higher VAT, fasting and 2-h blood glucose levels compared to the combined low WC and high HDL, combined low WC and low HDL, as well as the combined high WC and high HDL groups (Table 4). The combined high WC and low HDL group had higher SAT, fasting insulin and systolic as well as diastolic blood pressure compared to the combined low WC and high HDL, and the combined low WC and low HDL only. LDL and total cholesterol were highest in the high WC and high HDL group.

Table 3. Association between low high-density lipoprotein cholesterol (HDL) and high waist circumference with body composition, biochemistry, and blood pressure in adult Kenyans (n = 1 370).

	HDL Low vs. high	Waist circumference High vs. low	HDL * Waist circumference
Dependent variable	B (95% CI)	B (95% CI)	p for interaction
Visceral adipose tissue (cm)	0.3 (0.1; 0.5)^**	1.7 (1.5; 1.9)^**	0.296
Subcutaneous adipose tissue (cm)	0.4 (0.2; 0.5)^**	1.6 (1.5; 1.7)^**	0.012
Fasting venous glucose (mmol/L)	0.3 (0.1; 0.4)^**	0.3 (0.2; 0.5)^**	0.850
2-h venous glucose (mmol/L)	0.6 (0.3; 0.8)^**	0.9 (0.6; 1.2)^**	0.865
Low-density lipoprotein cholesterol (mmol/L)	-0.2 (-0.2; -0.1)^**	0.6 (0.5; 0.7)^**	0.099
Total cholesterol (mmol/L)	-0.5 (-0.7; -0.4)^**	0.7 (0.6; 0.8)^**	0.557
Systolic blood pressure (mmHg)	-2.8 (-4.5; -1.1)^**	9.3 (7.6; 11.0)^**	0.266
Diastolic blood pressure (mmHg)	-1.1 (-2.7; 0.002)^**	6.1 (5.0; 7.3)^**	0.755
Dependent variable	e^B (95% CI)	e^B (95% CI)	p for interaction
Triglyceride (mmol/L)^*	1.11 (1.06; 1.16)^**	1.33 (1.27; 1.39)^**	0.556
Fasting serum insulin (pmol/L)^*	1.18 (1.09; 1.27)^**	1.69 (1.57; 1.82)^**	0.703

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Data are univariate linear regression analyses.

*Log-transformed.

**denotes p<0.05.

Table 4. Association between combined low/high waist circumference and high/low high-density lipoprotein cholesterol (HDL) with body composition, biochemistry, and blood pressure by adjusted means (95% CI) in adult Kenyans (n = 1 370).

Dependent parameter	Low WC and high HDL (reference group)	Low WC and low HDL	High WC and high HDL	High WC and low HDL
N (%)	399 (29.1)	525 (38.3)	139 (10.2)	307 (22.4)
Visceral adipose tissue (cm)	5.1 (5.0; 5.3)	5.5 (5.4; 5.7)^a	6.6 (6.4; 6.9)^a^,^b	7.2 (7.0; 7.4)^a^,^b^,^c
Subcutaneous adipose tissue (cm)	1.1 (1.0; 1.2)	0.9 (0.9; 1.0)^a	2.5 (2.3; 2.6)^a^,^b	2.6 (2.5; 2.7)^a^,^b
Fasting venous glucose (mmol/L)	4.4 (4.2; 4.5)	4.6 (4.4; 4.7)^a	4.6 (4.3; 4.9)	4.9 (4.7; 5.0)^a^,^b
2-h venous glucose (mmol/L)	5.0 (4.7; 5.2)	5.3 (5.1; 5.5)^a	5.6 (5.2; 6.1)^a	6.0 (5.7; 6.3)^a^,^b
Low-density lipoprotein cholesterol (mmol/L)	2.3 (2.2; 2.3)	2.1 (2.0; 2.2)^a	2.9 (2.8; 3.0)^a^,^b	2.6 (2.5; 2.7)^a^,^b^,^c
Total cholesterol (mmol/L)	4.1 (4.0; 4.1)	3.4 (3.3; 3.5)^a	4.8 (4.6; 4.9) ^a^,^b	4,1 (4.0; 4.2) ^b^,^c
Systolic blood pressure (mmHg)	118 (117; 120)	116 (114; 117)^a	126 (123; 128)^a^,^b	125 (124; 127)^a^,^b
Diastolic blood pressure (mmHg)	73 (72; 74)	72 (71; 72)	78 (77; 80)^a^,^b	77 (76; 79)^a^,^b
Triglyceride (mmol/L)^*	0.8 (0.8; 0.8)	0.9 (0.9; 0.9)^a	1.0 (0.9; 1.0)^a	1.1 (1.1; 1.2)^a^,^b^,c
Fasting serum-insulin (pmol/L)^*	18.7 (17.5; 19.9)	18.9 (17.8; 19.9)	33.7 (30.2; 37.5)^a^,^b	35.0 (32.6; 37.6)^a^,^b

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Data are sex- and age adjusted means (95% confidence interval) with low WC and high HDL as reference group.

^aDenotes significant difference from low WC/high HDL group

^bDenotes significant difference from low WC/low HDL group

^cDenotes significant difference from high WC/high HDL group

*Back-transformed adjusted means.

In a sub-group with hyperglycaemia (n = 196), mean age of 41.8 (SD 10.8) years, and with 63.8% being women (background characteristics shown in S1 Table), the same analyses showed similar results except for lack of correlation between HDL with DBP and insulin. No WC-HDL interaction was found in the sub-group analyses (S2 Table).

We tested for confounding between WC and HDL in mutually adjusted models, but found no change in effect size after adjustment. As for VAT accumulation comparing the group with normoglycaemia vs. the hyperglycaemia group, no WC-HDL interaction was found, and this was sustained when repeating the analysis without the 22 participants on anti-hyperglycaemia medication.

WC and HDL increase were significantly associated with hepatic steatosis, OR = 1.09 (95% CI, 1.07; 1.11, p<0.001), and OR = 0.46 (95% CI, 0.23; 0.93, p = 0.031), and for every unit increase in WC and HDL, respectively. In the sub-group with hyperglycaemia, 90 participants were ultrasound scanned for liver steatosis assessment, and 30 (33.3%) had hepatic steatosis. In these, OR for every unit increase in WC was 1.10 (95% CI, 1.05; 1.16, p<0.001), and OR for unit increase in HDL was 0.38 (95% CI, 0.09; 1.60, p = 0.188). We identified 26 individuals with self-reported alcohol consumption that exceeded criteria for AFLD [16]. Excluding these individuals from the hepatic steatosis analyses did not alter the results.

Discussion

In light of the growing evidence of a black African phenotype when it comes to cholesterol levels and central obesity, this study attempted to investigate whether central obesity measured as WC or HDL was the main “predictor” of an adverse cardiometabolic profile consisting of high values of ultrasound-measured VAT, simple biochemistry measures and blood pressure in adult, black Africans with hyperglycaemia. Hepatic steatosis was used to substantiate cardiometabolic risk assessment. In order to determine whether results differed in a cardiometabolic disease high risk group, we did the same analyses in a sub-group with hyperglycaemia.

In contrast to WC, HDL was not significantly correlated with all anthropometric, biochemical and blood pressure variables included in this study, suggesting that central obesity identifies more predictors for cardiometabolic risk. An interaction between WC and HDL on cardiometabolic risk was only found in three outcome variables, of which two were LDL and triglyceride. As their values are closely inter-correlated with HDL, the effect of combined HDL-WC measurements on cardiometabolic risk is of limited, clinical value.

Overall, in the high/low dichotomous and combined measurements, the strongest association with a less favourable cardiometabolic profile was found in the high WC and low HDL group. VAT was highest in the high WC and low HDL group, which is of concern as it may signify an atherosclerotic profile beyond the simple clinical data presented here. Although both high WC and low HDL were individually associated with determinants of cardiometabolic risk, we did not see any change in risk, when the two measures were combined in the analyses with mutual adjustment. Thus, in this context there was no confounding between central obesity and HDL on cardiometabolic risk.

Sam and colleagues have shown that people with type 2 diabetes and combined high WC and high TG had higher VAT accumulation and coronary artery calcium compared to groups with less adverse WC-TG combinations [17]. Our study supported these findings as far as VAT accumulation is concerned. Low HDL (replacing high TG) or high WC were both associated with VAT whether in the total study population or in the sub-group with hyperglycaemia. As we were not able to assess coronary artery calcium accumulation by computed tomography technique, or even carotid artery stiffness by ultrasound scanning technique for a broader cardiometabolic risk assessment, including stroke, we used assessment of hepatic steatosis as an additional risk factor for cardiometabolic disease. There is evidence that individuals with hepatic steatosis are at high risk of cardiovascular disease [18], and our results showed that WC increase was significantly associated with hepatic steatosis whether in the main study population or in the hyperglycaemia sub-group. This was only the case for HDL increase and hepatic steatosis in the main study population.

It is of note that standard plasma measurement of HDL may not capture the full range of HDL effects; therefore, particle concentrations of especially low and intermediate HDL subclasses may be better predictors of the anti-atherogenic properties of HDL mediated through (intra-abdominal) obesity [19]. In this study, when comparing the dichotomous and high/low combined measurements, we used different Pan-African cut-offs for low HDL compared to a previous study on dyslipidaemia in the same population (1.0 vs. 0.9 mmol/L for men and 1.3 vs. 1.0 mmol/L for women) [20], which by inference categorised a larger proportion of the current study population with hypoalphalipoproteinemia.

In regards to specific disease outcomes due to cardiometabolic risk, ischaemic heart disease remains relatively uncommon in sub-Saharan African populations as shown in a comprehensive review report by Onen [21], while increased central obesity and low HDL remains the dominant risk factors for stroke, and especially ischaemic stroke in these populations.

We acknowledge several limitations to this study. First, we did not test for hepatitis B or C which is a limitation to our hepatic measurements. Another limitation concerning the hepatic steatosis results we need to emphasize is the relatively low proportion (15.3% of which 93.1% was mild steatosis, and none had severe steatosis) we found in a sub-group of the study population. This could partly be explained by the low triglyceride levels seen in African populations, and such an association has been reported in African Americans compared to white and Hispanic US populations [22]. Furthermore, by focusing on WC and HDL and thus indirectly insulin resistance, we do not consider those at risk of diabetes due to pancreatic beta-cell failure as the predominant mechanism for dysglycaemia. Lastly, we acknowledge that the study population may not be representative of all Kenyans even though we did sample according to different and common dietary practices as well as in rural and urban areas.

In conclusion, our data suggest that measuring WC better predicts cardiometabolic risk factors compared to using HDL as a cardiometabolic exposure variable. The trend was similar whether based on results in a large study group including low-to-high cardiometabolic risk individuals, or in a high risk sub-group of individuals with hyperglycaemia. Significant associations with hepatic steatosis, a high-risk measure for cardiometabolic disease, and WC whether in the general or in the hyperglycaemia study groups further substantiated WC as a better measure for cardiometabolic disease risk than HDL. Furthermore, combined WC-HDL sustained the conclusion of WC better predicting cardiometabolic risk factors.

Supporting information

S1 Table. Characteristics of adult Kenyans with impaired glucose tolerance (n = 196).

(DOCX)

Click here for additional data file.^{(15.7KB, docx)}

S2 Table. Association between high-density lipoprotein cholesterol (HDL) and waist circumference with body composition, biochemistry, and blood pressure in adult Kenyans with impaired glucose tolerance (n = 196).

(DOCX)

Click here for additional data file.^{(16.3KB, docx)}

Acknowledgments

We thank all participants, local chiefs, councils, politicians, and research teams responsible for data generation. Special thanks go to Professor Knut Borch-Johnsen, Copenhagen University Hospital (Holbaek, Denmark) for his invaluable contribution to the Kenya Diabetes Study in general. We acknowledge the permission by the Director of KEMRI to publish this manuscript.

Data Availability

Data cannot be shared publicly without permission from the Kenyan health authorities (KEMRI). In order to request for data access, contact: Kebenei Enock Kipchirchir Acting Head of the Scientific and Ethics Review Unit (SERU) kisacheienock@gmail.com.

Funding Statement

DLC: (J. no. 104.DAN.8-871, RUF project no. 91202); Cluster of International Health, University of Copenhagen (no grant no); Beckett Foundation (no grant no); Dagmar Marshall Foundation (no grant no); Dr. Thorvald Madsen's Grant (no grant no); Kong Christian den Tiende's Foundation (no grant no); Brdr. Hartmann Foundation (no grant no) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0247600.r001

Decision Letter 0

Giacomo Pucci

4 Nov 2020

PONE-D-20-26651

Waist circumference and hypoalphalipoproteinemia as markers of cardiovascular risk in Kenyan adults

PLOS ONE

Dear Dr. Christensen,

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: No

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General Comment - in this paper, Christensen et al. explore the effect of combination of HDL cholesterol values and waist circumference on other features of metabolic syndrome, such as fasting blood glucose, blood pressure, triglycerides, insulin reistance etc... in a large population of rural and urban Kenyans. The topic of increasing prevalence of metabolic syndrome in developing countries is relevant and hits my own interest. However, this paper does not add any relevant information on this topic. Basically, the main result of the paper is that the common definition of metabolic syndrome can be applied to Kenyans too, using appropriate cut-off levels for WC and HDLc (which were previously described). This is a quite trivial result. In my opinion, the main conceptual flaw of the paper is dealing WC and HDLc as independent factors, whereas their association is well estabilished since they are both criteria of metabolic syndrome. Similarly, the high probability of finding an additional feature of MetS in subjects with one feature of MetS, and even higher in subjects with two features of MetS, is a well estabilished notion and part of the definition of MetS itself (definitely not a peculiar feature of Kenyan population).

Title and Abstract - the endpoints of the study cannot be described as "cardiovascular risk" since they are not cardiovascular events, neither are they surrogate cardiovascular endpoints (such as carotid IMT, FMD, aortic stiffness, coronary calcium score, coronary stenosis etc...) Reported endpoints are instead metabolic parameters; so it would be more proper to describe them as "metabolic risk" or "cardiometabolic risk". This applies to the rest of the abstract and the text.

Results reported in the Abstract are too detailed. It is not clear what is the main finding of your research, whereas an abstract should clearly communicate this information.

Introduction - Introduction is accurate, but it does not explain the link between the background and the aim of the study. In other words: what is the unanswered question that you are addressing with your research?

Non-alcoholic fatty liver disease (NAFLD) is considered the liver manifestation of metabolic syndrome. I Authors agree to define their outcomes as "metabolic", rather than "cardiovascular", ther would be non necessity to define it as a separate analysis.

African populations share genetic determinants of HDL cholesterol values and HDL particles functionality (e.g. apolipoproteins L1). The impact of genetic variants should go beyond the prevalence of metabolic syndrome, as well as being correlated to other features of MetS. These aspects cannot be overcome in the introduction and should be at least briefly discussed in discussion.

Methods -

Since data about liver steatosis are reported, Authors should report if patients with other known causes of liver disease were excluded and if alcohol consumption was evaluated. If this was not, it would be a severe limitation for the validity of the study.

Page 8, Line 113 "HDL values were defined as <1.0 for men and <1.3 for women" Measure Units are missing.

Tables -

Table 1 should report also grades of liver steatosis.

Table 2 - data in columns should all be reported as beta (95%CI) or p-value or both.

Table 3 does not provide any additive information compared to Table 2

Reviewer #2: The manuscript by Faurholt-Jepsen et al. entitled: Waist circumference and hypoalphalipoproteinemia as markers of cardiovascular risk in Kenyan adults is well-written and brings forth important insight on the on WC – HDL relationship in the identification of CVD risk.

However, there are issues that need clarification:

1) The authors could consider changing the title from WC and “hypoalphalipoproteinemia” … to: WC and “low levels of high density lipoprotein-cholesterol”.

Technically the word “hypoalphalipoproteinemia” is correct. But throughout the manuscript they use “low HDL”.

2) The abstract should provide the age range. SD is already provided for age, but age range would be a very valuable addition. They should also add BMI with SD and range to the abstract. It is critical baseline data.

3) The authors do not present the data separately for men and women. They should consider this. Also their cohort has a mild predominance of women, they should state what they found in comparing pre and post menopausal women. Were the results the same? If they do not the menopausal status of the women, this should be stated as a limitation. But they could still arbitrarily divide the women into 2 groups (ie. above and below 48y). This is one reason why adding age range is so important.

4) They use hepatic steatosis as an end organ marker of cardiovascular risk. However, they need to recognize that this particular marker represents very advances disease in African descent populations. Hepatic steatosis is most well-studied in African Americans (AA). AA have much less hepatic steatosis than whites or Hispanics. And it is low levels of hepatic steatosis, which may explain why TG are low in the presence of insulin resistance. The authors are referred to: Guerrero, R. et al. Ethnic differences in hepatic steatosis: an insulin resistance paradox? Hepatology 2009; 49: 791-801.

In short, using hepatic steatosis as an end organ marker of CVD risk, will leave many people of African descent with CVD risk undiagnosed (just as using triglyceride does-triglyceride levels are closely linked to hepatic steatosis and VAT).

5) The methods section should describe how the participants were identified and recruited. It is not sufficient to refer to previous publications. In addition, they should add a limitation section. In the limitation section they need to state why they believe their sample is representative of Kenyans in general.

6) In the limitation section, they also need to acknowledge that their emphasis on high WC and low HDL represents a focus on insulin resistance as the cause of abnormal glucose tolerance and CVD. But in many African countries and low and middle income countries globally, abnormal glucose tolerance and the associated CVD is linked to relative beta-cell failure. The authors are referred to: Staimez L et al. Tale of Two Indians: Heterogeneity in Type 2 Diabetes Pathophysiology 2019; e3192.

7) Another limitation is the use of WC from the Ekoru et al article. The meta-analyses by Ekoru included studies which had people with diabetes in the prediction sample. WC is suppose to predict insulin resistance and who will get diabetes. So by including people with diabetes in the prediction of WC of risk, means people with diabetes are included in both the numerator and denominator. Further by including people with diabetes to predict the WC of risk-the results could be confounded: (a) uncontrolled or poorly controlled people with diabetes lose weight-and could confound the results by leading to spuriously low WC, (b) by including people with diabetes you are including people with diabetes due to beta-cell failure (which is associated with a lower BMI and a lower WC than when insulin resistance is the predominant cause) and again confounding results and leading to spuriously low WC.

Examples of studies done in Africa -which excluded people with diabetes and provide a much different picture and higher WC than the Ekoru study:

1) El Mabchour A, Delisle H, Vilgrain C, et al. Specific cut-off points for waist circumference and waist-to-height ratio as predictors of cardiometabolic risk in Black subjects: a cross-sectional study in Benin and Haiti. Diabetes Metab Syndr Obes 2015;8:513–23.

2) Peer N, Steyn K, Levitt N. Differential obesity indices identify the metabolic syndrome in Black men and women in Cape Town: the CRIBSA study. J Public Health 2016;38:175–82.

3) Prinsloo J, Malan L, de Ridder JH, et al. Determining the waist circumference cut off which best predicts the metabolic syndrome components in urban Africans: the SABPA study. Exp Clin Endocrinol Diabetes 2011;119:599–603.

So it is perfectly acceptable to use the Ekoru et al. But the controversy about the Ekoru study needs to be cited as a limitation.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Stefano Ministrini

Reviewer #2: No

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PLoS One. 2021 Feb 25;16(2):e0247600. doi: 10.1371/journal.pone.0247600.r002

Author response to Decision Letter 0

8 Jan 2021

RESPONSE: We are building our study on the early attempt by Després and colleagues and later on Sam and colleagues in DM patients to identify two simple clinical parameters for detecting (increased) risk of CVD/CMD. They used central obesity in combination with triglycerides. However, as WHO standard cut-off for central obesity may not fit African populations, and elevated triglycerides are uncommon as a dyslipidemic feature in African individuals, we combined central obesity based on newly published central obesity cut-offs for increased CVD/CMD risk, and the common low HDL cholesterol dyslipidemic feature in Africans. We agree that some of the data we have presented are MetS features. However, this does not apply for VAT and SAT measurements as well as for liver steatosis.

RESPONSE: We have changed “cardiovascular risk” to “cardiometabolic risk” in the title, abstract, key words, and main text (where appropriate).

Results reported in the Abstract are too detailed. It is not clear what is the main finding of your research, whereas an abstract should clearly communicate this information.

RESPONSE: We have shortened the Abstract accordingly – it turned out to be too long to begin with.

RESPONSE: Apart from adding/changing a few words, we have added the following sentence to make the aim of the study more clear:

“Thus, replacing triglyceride with HDL in combination with lower WC cut-off than used by Deprés and colleagues seems more appropriate as cardiometabolic risk markers in black, African populations”.

RESPONSE: We agree that NAFLD is a result of hyperglycaemia, and thereby MetS. We have therefore removed NAFLD from the aim of the study. However, we have kept the analysis in order to show the proportion of NAFLD.

RESPONSE: We acknowledge that APOL1 allele frequencies (G1 and G2 variants) are important in an African context. However, in the current context, they are primarily related to non-diabetic kidney disease, and we believe it is not possible to briefly discuss this genetic determinant in the context of HDL values in a meaningful way. It would require an introduction to the HDL cholesterol and APOL1 relationship before referring to the studies on for example CKD. Thus, we have decided not to include the proposed genetic aspects in the discussion.

Methods -

RESPONSE: We have identified the few individuals (n=26) who qualify for AFLD due to their relatively high alcohol consumption. However, excluding these from the analyses make no substantial difference, and therefore we have kept them in the analyses:

“We identified 26 individuals with excessive, self-reported alcohol consumption that exceeded criteria for AFLD (16). Excluding these individuals from the hepatic steatosis analyses did not alter the results (not shown)”.

We did not measure any other causes of possible liver disease, and have therefore added the following sentence as a study limitation:

“We acknowledge several limitations to this study. First, we did not test for hepatitis B or C which is a limitation to our hepatic measurements”.

Page 8, Line 113 "HDL values were defined as <1.0 for men and <1.3 for women" Measure Units are missing.

RESPONSE: We have added mmol/L as units behind the HDL values.

Tables -

Table 1 should report also grades of liver steatosis.

RESPONSE: We have added three grades of hepatic steatosis: mild, moderate, and severe.

Table 2 - data in columns should all be reported as beta (95%CI) or p-value or both.

RESPONSE: All data in columns are already reported as beta (95% CI).

Table 3 does not provide any additive information compared to Table 2

RESPONSE: We prefer to keep it, as the results are based on cut-points used in clinical practice. However, if the reviewer insists, we will remove Table 3

REPONSE: We thank the Reviewer for the positive comments.

However, there are issues that need clarification:

1) The authors could consider changing the title from WC and “hypoalphalipoproteinemia” … to: WC and “low levels of high density lipoprotein-cholesterol”. Technically the word “hypoalphalipoproteinemia” is correct. But throughout the manuscript they use “low HDL”.

RESPONSE: We have followed the suggestion by the Reviewer and changed “hypoalphalipoproteinemia” to “low high-density lipoprotein cholesterol” in the title of manuscript.

RESPONSE: Even though this would be in conflict with the comments of Reviewer#1 who has asked us to remove results from the Abstract, we have decided to comply with these comments and added the requested information. However, we have decided to replace SD with range as both results are not essential in the Abstract in our view, and it would require removal of other information due to word restrictions.

RESPONSE: We have already included several stratifications, and we have therefore chosen to adjust instead of stratifying for sex. We do not have information on pre- or postmenopausal status in the women, and have mentioned this as a limitation. We have adjusted rather than stratified for age, and the results were the same as in unadjusted analyses of the estimates. Thus, the interpretation of the results would not change. In brief, sex and age do not change the results.

RESPONSE: We thank the reviewer for adding the reference. While we agree that there is a paucity hepatic steatosis data in black African populations, and therefore we currently need to lean on data in African American populations, it important to acknowledge that using African Americans as a surrogate black African population is not without problems/limitations due to genetic admixture. It is also of note that the vast majority of individuals in our study with NAFLD had mild steatosis. Nevertheless, we have added the concerns raised here by the Reviewer as a limitation:

“Another limitation concerning the hepatic steatosis results we need to emphasize is the relatively low proportion (15.3 % of which 93.1 % was mild steatosis, and none had severe steatosis) we found in a sub-group of the study population. This could partly be explained by the low triglyceride levels seen in African populations, and such an association has been reported in African Americans compared to white and Hispanic US populations (22)”.

RESPONSE: We have added the requested information in the main text including a separate limitation section.

RESPONSE: We acknowledge this potential limitation to our study results, even though IFG – a marker of beta-cell failure as the predominant factor for pre-DM – was very low in the current study population as opposed to IGT – where (peripheral) insulin resistance is the predominant factor for pre-DM – which was relatively high. Accordingly, we have added a sentence to the Limitations section.

Examples of studies done in Africa -which excluded people with diabetes and provide a much different picture and higher WC than the Ekoru study:

2) Peer N, Steyn K, Levitt N. Differential obesity indices identify the metabolic syndrome in Black men and women in Cape Town: the CRIBSA study. J Public Health 2016;38:175–82.

So it is perfectly acceptable to use the Ekoru et al. But the controversy about the Ekoru study needs to be cited as a limitation.

RESPONSE: We agree, and the cut-points are still up for debate. We decided to include those with diabetes as it is a continuum of dysglycaemia. We also ran the analyses with and without those on treatment (n=22), which did not affect the results. Thus, we prefer to keep the participants in the model. If the reviewer insists, we will revise the data and leave them out.

________________________________________

PLoS One. doi: 10.1371/journal.pone.0247600.r003

Decision Letter 1

Giacomo Pucci

2 Feb 2021

PONE-D-20-26651R1

Waist circumference and low high-density lipoprotein cholesterol as markers of cardiometabolic risk in Kenyan adults

PLOS ONE

Dear Dr. Christensen,

Thank you for submitting your manuscript to PLOS ONE. The reviewers have considered positively your changes made on the original manuscript. However, there is still a question raised by reviewer #2 that needs to be solved. Therefore, we invite you to submit a revised version of the manuscript that addresses this specifical point.

Please submit your revised manuscript by Mar 19 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Giacomo Pucci

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: My comments have been adequately addressed. I'm still concerned about the limited scientific and practical relevance of the results.

Reviewer #2: There is only one necessary change. For BMI and age both SD and range have to be included.

SD gives an idea of skewness of data and does not offset the need for range and vice aversa.

The Reviewer has never seen an occasion where range was allowed eliminate the need for range.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Stefano MInistrini

Reviewer #2: No

PLoS One. 2021 Feb 25;16(2):e0247600. doi: 10.1371/journal.pone.0247600.r004

Author response to Decision Letter 1

4 Feb 2021

Reviewer #2: There is only one necessary change. For BMI and age both SD and range have to be included.

SD gives an idea of skewness of data and does not offset the need for range and vice aversa.

The Reviewer has never seen an occasion where range was allowed eliminate the need for range.

Response: Point taken and SD has been included for BMI. In order to ensure uniform reporting of results in the Abstract section, we have also added SD for age.

PLoS One. doi: 10.1371/journal.pone.0247600.r005

Decision Letter 2

Giacomo Pucci

10 Feb 2021

Waist circumference and low high-density lipoprotein cholesterol as markers of cardiometabolic risk in Kenyan adults

PONE-D-20-26651R2

Dear Dr. Christensen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Giacomo Pucci

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0247600.r006

Acceptance letter

Giacomo Pucci

17 Feb 2021

PONE-D-20-26651R2

Waist circumference and low high-density lipoprotein cholesterol as markers of cardiometabolic risk in Kenyan adults

Dear Dr. Christensen:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

Dr. Giacomo Pucci

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Characteristics of adult Kenyans with impaired glucose tolerance (n = 196).

(DOCX)

Click here for additional data file.^{(15.7KB, docx)}

(DOCX)

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Data Availability Statement

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PERMALINK

Waist circumference and low high-density lipoprotein cholesterol as markers of cardiometabolic risk in Kenyan adults

Daniel Faurholt-Jepsen

Henrik Friis

David L Mwaniki

Michael K Boit

Lydia U Kaduka

Inge Tetens

Dirk L Christensen

Roles

Abstract

Background

Objectives

Methods

Results

Conclusion

Introduction

Methods

Statistics

Results

Table 1. Characteristics of adult Kenyans (n = 1 370).

Table 2. Association between high-density lipoprotein cholesterol (HDL) and waist circumference with body composition, biochemistry, and blood pressure in adult Kenyans (n = 1 370).

Table 3. Association between low high-density lipoprotein cholesterol (HDL) and high waist circumference with body composition, biochemistry, and blood pressure in adult Kenyans (n = 1 370).

Table 4. Association between combined low/high waist circumference and high/low high-density lipoprotein cholesterol (HDL) with body composition, biochemistry, and blood pressure by adjusted means (95% CI) in adult Kenyans (n = 1 370).

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Giacomo Pucci

Roles

Author response to Decision Letter 0

Decision Letter 1

Giacomo Pucci

Roles

Author response to Decision Letter 1

Decision Letter 2

Giacomo Pucci

Roles

Acceptance letter

Giacomo Pucci

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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