Genes, Environment, and African Ancestry in Cardiometabolic Disorders

Abstract

The past two decades have been characterized by a substantial global increase in cardiometabolic diseases, but the prevalence and incidence of these diseases and related traits differ across populations. African ancestry populations are among the most affected yet least included in research. Populations of African descent manifest significant genetic and environmental diversity and this underrepresentation is a missed opportunity for discovery and could exacerbate existing health disparities and curtail equitable implementation of precision medicine. Here, we discuss cardiometabolic diseases and traits in the context of African descent populations, including both genetic and environmental contributors and emphasizing novel discoveries. We also review new initiatives to include more individuals of African descent in genomics to address current gaps in the field.

Cardiometabolic Diseases: Inter-related Disorders with Complex Genetic and Environmental Determinants

Cardiovascular disease and metabolic dysregulations often occur concurrently in a complex web of risk factors, comorbidities and complications. This inter-relatedness has been codified in the definition of the metabolic syndrome; a clustering of risk factors associated with both increased risk of cardiovascular disease (CVD) and type 2 diabetes (T2D), including high blood pressure (BP), elevated fasting plasma glucose, abdominal obesity, high triglycerides, and low high-density lipoprotein (HDL) cholesterol. These traits, referred to here as cardiometabolic disorders, have complications beyond T2D and CVD, and affect risk of chronic kidney disease (CKD) and neurological conditions such as dementia. Importantly, each of these cardiometabolic traits varies significantly across ancestry groups. Individuals of African ancestry experience a disproportionately higher prevalence of many of these traits, fueling a greater burden of related conditions. [1–4].

Each of these cardiometabolic traits is complex, the result of both genetic and environmental factors and, although less well understood, their interplay; thus, the observed health disparities by ancestry have roots in either genetic or environmental/lifestyle differences by ancestry or both. Individuals of African ancestry have been historically underrepresented in research, thus disentangling these health disparities in cardiometabolic disorders has been out of reach. Unfortunately, failure to understand the environmental and genetic contributions to these traits can be consequential, from assuming that all increased risk is a result of lifestyle choices (and thus the fault of those individuals) to a fatalism associated with overestimating the genetic contribution.

A recent major advance in understanding the underpinnings of cardiometabolic traits in African ancestry individuals is the increasing contribution of genomic studies of African individuals. While there can be significant genetic overlap between better-studied African Americans and some African populations (particularly West Africans), the environmental background is dramatically different in a variety of ways. For instance, the presence of obesogenic conditions, context of racism, access to healthcare, diet, and prevalence of infectious disease. Thus, comparisons of distribution and determinants of cardiometabolic traits in populations with and without African ancestry across different environmental backgrounds can help us to understand observed health disparities and to develop strategies for equitable care for all individuals.

The goal of this review is to comprehensively describe the epidemiology and genetic determinants of each component of the metabolic syndrome and associated clinical complications in populations of African ancestry, emphasizing data from a range of African ancestry populations, where available. We will also analyze the implications of the lack of representation for health inequalities and discuss ongoing initiatives to improve representation of populations of the African ancestry in research.

In this Review, groupings of individuals are described in term of ancestry, that is, genetic similarity within historically isolated populations. Labeling of ethnic groups and ancestry is notoriously inconsistent across the scientific literature; thus, terms used in papers included in this review (e.g., non-Hispanic whites, Caucasians, Whites, European Americans) are replaced here with the corresponding ancestry descriptor (e.g., European ancestry) where possible. In other instances, the original descriptors used by the authors are retained for clarity and/or to avoid changing post hoc the intended meaning conveyed by the authors.

Epidemiology and genetic determinants of cardiometabolic disorders in populations of the African Diaspora

Hypertension

Hypertension is a common complex disease characterized by elevated diastolic and/or systolic blood pressure. It disproportionally affects individuals of African descent [5, 6]. On average, hypertension was 1.45 times higher in African Americans (AAs) than in European Americans (EAs) between 2015 and 2016 as described in the National Health and Nutrition Examination Survey (NHANES) and depicted in Figure 1 (note: NHANES characterizes all individuals of African ancestry as AA regardless of recent immigration status)[7]. Observational studies have shown that prevalence of hypertension is different between ethnic minority groups in the USA. [8]. Differences exist in the prevalence of hypertension among African ancestry individuals depending on their environmental background or immigration status. In the 2010–2016 National Health Interview Survey (NHIS), African immigrants had an overall hypertension prevalence of 28%, which is lower than the prevalence observed (33%) in the 2013 NHIS for AA [8] or the crude prevalence of hypertension in NHANES AAs (40.3%), but there was no significant difference with NHANES EAs (27.8%). [7] In African immigrant women, hypertension prevalence was higher than in NHANES EAs (Figure 1). The trend seen in African immigrant women in the USA has been reported in other high-income countries that is, individuals of African ancestry have higher prevalence and incidence of hypertension than the host populations [9, 10]. A consistent gradient in hypertension prevalence from low-/middle-income countries to high-income countries has also been described. Africans living in Nigeria, Cameroon, or Ghana have the lowest prevalence of hypertension compared with Africans living in the USA or Europe, or individuals of African descent living in Europe, USA, or the Caribbean [9, 11]. This gradient can be seen, for instance in the RODAM study of Ghanaians living in rural Ghana, in urban Ghana, or in urban areas in Europe (Figure 2) [9]. Recent data have also shown a rapid rise in the prevalence of hypertension in Sub-Saharan Africa (SSA). The age-adjusted prevalence of hypertension has steadily risen from 20.9% to 30.9% in men and from 20.3% to 36.3% in women in a decade (2000–2010) [6, 12], consistent with the prevalence reported in studies conducted between 2012 and 2015 in Ghana (Figure 2) [9]. Most individuals with hypertension live in an urban setting [9] indicating that environmental factors may contribute to the observed changes.

Figure 1.

Prevalence of cardiometabolic disorders in populations of African Ancestry living in the USA compared to a European Ancestry population.

The percentage of individuals with hypertension, obesity, or type 2 diabetes among African Americans (green, data sources [7, 32], African immigrants in the USA (blue; data source [8], and Americans of European ancestry (yellow, data sources [7, 32].

#For African immigrants in the USA, obesity prevalence includes all participants with BMI≥25 kg/m² (i.e., overweight and obesity), unlike the prevalence estimates, which only include participants with obesity (BMI≥30 kg/m²). Abbreviations: AA, (African American); EA, (European American); MOA/WAM, Migrants of African origin/West African migrants.

Figure 2.

Prevalence of Controlled Hypertension in African Americans and European Americans The percentage of African American (blue) and European American individuals (orange) whose hypertension is controlled, as reported in [7]. Abbreviations: AA, African American; EA, European American.

Salt-sensitive hypertension is reportedly more common in populations of African origin (75% in AAs and 50% in EAs) [13]. Salt sensitivity is heightened in SSA and is associated with low renin and aldosterone levels [6]. This phenomenon may be partially due to variants in the epithelial sodium channel (ENaC) that cause an increase in activity of the channel, resulting in sodium and water retention. The increased activity in ENaC could contribute to vascular stiffness, which leads to difficulty in blood pressure control [6]. The increased presence of these variants may have arisen due to selective pressure for sodium retention in historically low-sodium diets [6]. Additionally, the association between blood aldosterone:renin ratio and high salt intake suggests that an inadequate suppression of aldosterone is involved in salt sensitivity in AAs [13]. The immune system also contributes to the pathogenesis of hypertension through the regulation of sodium [13]. Some factors for hypertension (e.g., obesity) are not only more prevalent in individuals of African origin but also can elicit immune-mediated responses, which can explain the higher prevalence of salt-sensitive hypertension in these populations. Other potential biological mechanisms for inducing salt-sensitive hypertension include the inflammasome, cytokines, and oxidative stress [13]. Additionally, psychosocial factors such as stress and racial discrimination have been associated with hypertension in individuals of African origin [14].

In addition to the higher prevalence of hypertension, it is often not well controlled in individuals of African descent, resulting in high rates of complications [6]. The prevalence of controlled hypertension was lower in AAs than in EAs (44.6% vs 50.8%)[7]. Similarly, African immigrants in Europe were less likely to have controlled blood pressure compared with the European ancestry individuals in that country (odds ratio 0.56) [10]. This poorer control in African immigrants is present despite a greater likelihood of being aware of their diagnosis (odds ratio 1.26) and being treated for it (odds ratio 1.49) [10]. Although a similar prevalence of hypertension was observed in AA women and men (39.9 and 40.6%, respectively), controlled hypertension was more frequent in AA women than men (48.5 and 40.1%) [7] (Figure 2).

Similar to many complex diseases, BP and hypertension are highly heritable [15]. However, only a few genomic studies of BP in populations of African descent have been conducted thus far and most were in AAs [16–20]. Studying admixed individuals like AAs enables admixture mapping analyses, which have been used successfully to identify loci specific to hypertension in AAs (e.g., NPR3)[19]. Although AAs have a shared genetic background with West Africans, and some loci discovered in AAs were replicated in a West African cohort, it is imperative to study the ancestral population that retains more genetic diversity. Several BP/hypertension loci discovered in AAs, including CACNA1H, SLC24A4, and SLC25A42 (Table 1), failed to replicate [21], which may be attributed to small sample sizes, population differences in the distribution of risk alleles, and/or gene-environment interactions. The largest BP genome-wide association study (GWAS) meta-analysis of African ancestry populations included AAs and Yoruba in Nigeria with replication in EA and East Asians and identified a common set of genes that regulate BP across human populations [19, 20] (Table 1). GWASs have not been successful in identifying genetic variants contributing to the observed hypertension disparity; however, candidate gene studies in biological pathways associated with BP regulation appear to identify loci that may explain ethnic differences in hypertension. In addition to the ENaC variants already mentioned, variants in the renin - angiotensin - aldosterone system have been associated with salt-sensitive hypertension [21, 22]. The genetic factors underlying the hypertension disparity in African ancestry populations may be identified by targeting narrow and specific phenotypes that are key in the pathogenesis of hypertension. Such an approach has been used for treatment-resistant hypertension, which is also more prevalent among AAs [23]. In a study conducted by the CHARGE consortium including AAs, the most significant loci associated with treatment-resistant hypertension in AAs was in an intronic region of myosin-Vb (MYO5B); a locus that was not previously associated with hypertension in any ancestral population [24].

Table 1.

Loci associated with cardiometabolic traits in Populations of the African Diaspora

Traits	Gene Symbol	SNP ID or chromosome Position	Population (n)	Replication Population (n)	Authors	Refs
Hypertension/BP	P4HA2	rs9791170	AA (1,017)	West Africans (980)	Adeyemo A et al.	[16]
	(AC096631.2)	rs12757682
	ADH7	rs991316
	(AC096631.2)	rs12748299
	ALDH1A2	rs1550576
	(AL354747.12)	rs7902529
	(RP11–375 F2.1)	rs2146204
	SLC24A4	rs11160059
	YWHAZ	rs17365948
	PMS1	rs5743185
	IPO7	rs12279202
	CACNA1H	rs3751664
	SV2B	rs8039294
	NRXN3	rs10135446
	ABCC4	rs9590141
	PRC1	rs1867226
	GPR98/ARRDC3	rs10474346	AA (8,591)	AA (11,882) EA (69,899)	Fox E et al.	[18]
	C21orf91	rs2258119
	SLC25A42	rs2012318
	HLA-B	rs2523586
	RSPO3	rs13209747	AA (28,190) Nigerians (1,180)	AA (5,577) Ghanaians (3,420) Seychellois (1,389) EA (69,395) East Asian (19,601)	Franceshini N et al.	[20]
	PLEKHG1	rs17080102
	EVX1-HOXA	rs17428471
	ULK4	rs1717027
	SOX6	rs1401454
Obesity related traits (BMI, WC, WHR)	FTO	rs708262 (Intron 8)	West Africans (517), AA (968)	NA	Adeyemo, A et al.	[48]
	FTO	rs11076022 (3’UTR)
	FTO	rs9932411 (Intron 8)
	FTO	rs7191513 (Intron 8)
	FTO	rs11076017 (Intron 8)
	FTO	rs9933611 (Intron 1)
	TCF7L2/HABP2	rs116718588	AA (41,696 and Africans (1,056) for BMI AA (20,384) for WHRadjBM	AA (10,143 [BMI]; 2,711 [WHRadjBMI])	Ng, Maggie et al.	[49]
	SPRYD7/DLEU2	rs2472591
	SSX2IP	rs140858719
	PDE3B	rs185693786
	IRX4/IRX2	rs112778462
	INTS10/LPL	rs149352150
	MLC1	rs56330886
	SEMA-4 D	rs80068415	West Africans (1570)	AA (9,020) West Africans (1,411)	Chen, G; Doumatey, a et al.	[151]
	PRKCA	rs115012414	Black South Africans (1,926)
	WARS2	rs56750694		NA	Sahibdeen V et al.	[163]
		rs17023092
	PDL5/SDCCAG8	rs12405634	Ugandans, South Africans, Nigerians, Ghanaians, and Kenyans (14,126)	NA	Gurdasani D et al.	[51]
	TAS2R	rs7798566
Type 2 Diabetes	TCF7L2	rs17746147	South Africans, Nigerians, Ghanaians, and Kenyans (4,347)	NA	Chen, Ji et al	[52]
	AGMO	rs73284431
	ZRANB3	rs1465146591	Nigerians, Ghanaians, and Kenyans (5,231)	Black South Africans (2,578)	Adeyemo, et al.	[54]
	ALG10B	rs7315028	AA (56,150)	NA	Vujkovic, et al	[53]
	KIF5A	rs11172254
	NA	rs10745460
Lipids and related traits	RP11–678G14.3	19:21749298	Ugandans, South Africans, Nigerians, Ghanaians, and Kenyans (14,126)	NA	Gurdasani, et al	[51]
	TIMD4	5:156378584
	APOC1	rs12721054
	RP11–230B22.1	rs569795903
	FAM49A-AC008069.1	rs798383	Admixed Africans or Africans (99,432)	NA	Graham, et al	[82]
	AC010096.2-AC019055.1	rs12478269
	CISD1P1-LINC00954	rs10184466
	NCOA1	rs11884143
	FOSL2	rs11689770
	ALK	rs2631990
	CREB3L2	rs73729085
	LPPR1	rs2246594
	RNU6–329P-AL391867.1	rs6479096
	SLC44A1	rs7032034
Lipids and related traits (cont)	RP11–196E1.3	rs12364363
	MGST1	rs9332891
	KRT80	rs7296443
	RP11–1008C21.2-	rs4924216
	RP11–346D14.1
	RP11–351A20.1	rs7197514
	Intergenic	rs138282551	Nigerians, Ghanaians Kenyans, and AA (13,859)	NA	Bentley, et al	[83]
	PGBD5	rs1468291761
	CD80	rs148194085
	SLC44A1	rs79922971
	TLL2	rs147706369
	ORC5	rs7797481	Nigerians, Ghanaians, and AA (13,125)	NA
	NA	20:60973327	Nigerians, Ghanaians, and Kenyans (4,317)	AA (9,542)
	CDC73	rs77612115
	DDX1	rs62122280
	GBE1	rs138202830
	ARAP2	rs183141928
	RP11–72L22.1	5:86213255
	MACROH2A1	rs62383172
	MIR877	6:30561321
	TINAG	rs141894016
	TINAG	rs188701119
	ENPP1	rs9375831
	GPNMB	rs706014
	COL1A2	rs144563873
Lipids and related traits (cont)	VIRMA	rs75741534
	PBX3	rs140987192
	LOC112268059	rs140568748
	C10orf35	rs72801121
	THY1	11:11933568
	NXF1	rs536925627
	MMP3	11:102724515
	ANAPC5	rs116964268
	TMEM117	rs150742617
	ERICH6B	rs75579422
	HTR2A	rs111590558
	KCNJ2	17:70312798
	CDH2	rs75360819
	DCC	rs56946445
	SMARCA4	rs1157494226
	APP	rs7281821
	LARGE	rs144086909

Obesity and T2D: The twin epidemics

The last few decades have been characterized by a global increase in the prevalence of obesity and T2D. The distribution of the prevalence and incidence of these two disorders has been uneven across populations [25–31]. The prevalence of obesity is 7.4% higher in AAs than in EAs. This difference is mainly driven by AAs women with a prevalence of 56.9% compared to EAs women (39.8%), while the prevalence among men is more similar [32] (Figure 1). An overall steady increase in obesity prevalence has been observed in the USA, with the largest increase in obesity and severe obesity prevalence seen in AAs women [31]. The increase in obesity and T2D prevalence and the observed sexual dimorphism is widely seen across the African diaspora [33–35]. In Jamaica, obesity prevalence was 46% in women and 8% among men. In the Seychelles, obesity prevalence was 11% higher in women compared to men (32% vs 21%) [34]. The prevalence of obesity has been described to follow a stepwise increase from rural West Africa to the USA, mimicking the degree of urbanization and westernization [30, 34].

In high-income countries, differences in obesity prevalence can be seen across ethnicity, sociodemographic groups, and sex; however, in low-/middle-income countries, such as countries in SSA, differences are more evident between individuals living in rural vs. urban areas (although differences attributed to sociodemographic factors and sex are also seen) [30, 36] (Figure 3). The higher prevalence of obesity in urban areas is thought to stem from the transition to a westernized, high-calorie diet and sedentary lifestyle from the low-calorie, healthier diet and physical labor in rural areas [37]. Although the prevalence of obesity and obesity-related traits remains lower in SSA compared with high-income countries [30, 34, 38], projections in these regions are alarming, with an expected 101 and 33 million individuals at risk of or living with obesity by 2030 (https://www.diabetesatlas.org, https://www.worldobesityday.org/assets/downloads/World_Obesity_Atlas_2022_WEB.pdf)

Figure 3.

Prevalence of cardiometabolic disorders in populations of African Ancestry living in sub-Saharan Africa and Europe. Shown is the percentage of individuals with hypertension, obesity, type 2 diabetes, and dyslipidemia in rural (blue) and urban (orange) sub-Saharan Africa (SSA), as well as West African migrants to Berlin (gray), Amsterdam (gold), and London (light blue), as described in [9, 30, 164]. Abbreviations: WAM (West African Migrants).

The increase in prevalence of obesity coincides with the increase in T2D prevalence, leading to obesity and T2D being labeled the twin epidemics in high-income countries [27, 37, 39, 40]. Overall T2D prevalence, although lower (Figures 1 & 3), followed a similar trend as obesity prevalence [34], and the recent sharp increase in obesity prevalence is accompanied by a concurrent increase in prevalence in T2D and other CVDs [41, 42]. The projections of SSA individuals at risk of or living with T2D are expected to soar to 33 million individuals by 2030 (https://www.diabetesatlas.org, https://www.worldobesityday.org/assets/downloads/World_Obesity_Atlas_2022_WEB.pdf). Paradoxically, even though women had a higher prevalence of obesity than men had, the prevalence of T2D was not significantly different in women and men of African ancestry (Figures 1 & 3). Differences in fat distribution by sex may explain this observation. Women tend to accumulate fat below the waistline, where it is less metabolically active, while men tend to accumulate it in the visceral area. Visceral adiposity has been shown to be more pathogenic than subcutaneous adiposity [30, 35, 43].

Despite the strong etiological links between these twin epidemics, this phenomenon is more nuanced in some African ancestry populations. For instance, in SSA and the Caribbean, T2D is also observed in individuals without obesity and has been coined lean diabetes, Jamaica-type Diabetes, malnutrition-related diabetes mellitus [44, 45]. Lean diabetes has been associated with malnutrition in early years of life and poor socioeconomic status [45] and is more common in the rural environment [44]. The reported coexistence of lean diabetes and obesity related T2D in SSA could trigger a dormant public health crisis characterized by the presence of T2D in apparently healthy individuals in rural or low socioeconomic status settings and in individuals with overweight and obesity in urban or high socioeconomic status areas.

Both obesity and T2D have genetic components in addition to these environmental risk factors. Although there is evidence of monogenic forms of both conditions, we focus here on polygenic obesity and T2D. To date GWAS have identified ≥ 1,100 independent loci for obesity and related traits [46, 47]. Although the majority were conducted and replicated in European ancestry populations (Figure 4), some loci were transferable across ancestries. One of these is the most replicated BMI locus, FTO, for which a study of African ancestry individuals identified novel associations within the same locus [48] (Table 1). Other established BMI loci successfully replicated in AA, including SEC16B, GALNT10, MC4R, and TMEM18 [48]. Despite the limited number of genomic studies conducted in populations of African ancestry, some loci were first identified in populations of African ancestry: TCF7L2/HABP2 (BMI), SPRYD7/DLEU2 (waist-to-hip ratio adjusted for BMI: WHR_adjBMI), SSX2IP (BMI), PDE3B (WHR_adjBMI in women), IRX4/IRX2 (BMI in women), INTS10/LPL (BMI in men) and MLC1 (BMI in men) [47, 49]. The few studies focused on SSA identified novel associations for variants that were rare or absent in other populations (SEMA4D (BMI), PRKCA (BMI, percent fat mass, hip circumference), WARS2 (hip circumference, WHR), PDL5/SDCCAG8 (BMI), TAS2R (BMI) (Table 1) [50, 51].

Figure 4.

Genome-wide association studies (GWASs) by phenotype and ancestry. Shown is the percentage of individuals of different ancestry that are included either in the GWAS discovery stage (Panel A) or replication stage (Panel B) for studies of cardiometabolic traits. Represented ancestry groups: European (pink), Asian (blue), African (orange), African American or Afro-Caribbean (yellow), Hispanic or Latin American (purple), other/mixed (teal). Data drawn from the GWAS Diversity Monitor [165]

Over 400 common variants have been associated with T2D across EA and Asians. However, their importance across all ethnic groups has not been assessed. One of the handful of GWASs conducted in populations of African ancestry identified variants in the widely replicated T2D risk locus TCF7L2 in SSA but showed by fine-mapping that there is a distinct African-specific signal in this locus in addition to the shared signal between EA and Africans. They also identified a novel signal near AGMO [52]. As with obesity traits, a number of T2D risk loci have been shown to be shared in African and non-African populations [50, 52]. A recent multi-ethnic meta-analysis of 1.4 million participants, including 56,150 AAs, identified 286 novel autosomal variants associated with T2D. Of these, three variants on chromosome 12 were exclusively identified in AAs and their effects on T2D seemed specific to AAs (Table 1); an additional novel variant was also identified in AAs only on chromosome X [53]. Importantly, although the number of novel loci identified in populations of African ancestry is still modest, these discoveries do contribute to our understanding of pathophysiology. For instance, a GWAS conducted in ~ 5,000 Africans identified a novel T2D loci, ZRANB3 (Table 1). ZRANB3 was shown to be a modulator of beta-cell mass and insulin response [50, 54].

Dyslipidemia

Serum lipids are established biomarkers for risk of cardiometabolic diseases and are routinely used in public health screening. Considerable heterogeneity in the distribution of serum lipids has been observed between African and non-African ancestry individuals. The serum lipids with the greatest evidence for differences across ancestry groups are TGs and HDL cholesterol, which are discussed further below.

Individuals of predominantly West African ancestry (which includes AAs and Afro-Caribbeans) are generally observed to have lower mean TG compared with other populations [55], differences that have been attributed in part to differences in fat deposition [56]. A recent in-depth analysis of the contributions of environment and ancestral background to TG distributions noted important sources of heterogeneity in these distributions [57]. An effect of both the environment and ancestry were observed. For instance, TG concentrations differed among those of West African ancestry, demonstrating some effect of environmental factors: those living in Europe had the lowest TG concentrations, with those living in SSA intermediate, and AAs the highest. However, within each of the regions, those of West African ancestry had markedly lower TG concentrations than other ancestry groups, demonstrating a strong genetic component on this trait. For instance, AAs had a much lower mean TG (0.93 mmol/L) compared with EA (1.29 mmol/L); West African migrants in Europe had a mean 0.62 mmol/L compared with 0.84 mmol/L among Europeans. Urban East Africans, however, had the highest mean TG of all populations considered (1.61 mmol/L) [57]. The high TG concentrations among Kenyans are in agreement with previous reports of both urban [58–60] and rural [61, 62] East African populations.

HDL concentrations among African ancestry populations are less consistent across geographical region. While AAs tend to have higher HDL compared with other populations [63–67], this difference is not observed in SSA, where the distribution is either similar [68, 69] or lower [70, 71]. High HDL concentrations are expected to co-occur with low TG concentrations, both components of a healthy lipid profile, yet low concentrations of both TG and HDL are often observed in SSA. While the presence of elevated TG was low (~5–10%), the presence of low HDL was reported to be 60.3% in rural Ghanaians [72], 28.4% in urban Ghanaian schoolchildren [73], 49.8% in Angolan workers [74], and 32.9% in urban Malawians [75]. Increasing prevalence of low HDL in Cape Town, South Africa has been attributed to increasing adiposity and urbanization [76], yet given the generally high HDL among AAs in the presence of a high prevalence of obesity and urbanization, the relationship with contributing environmental factors is likely to be complex.

The relationship between serum lipids and cardiometabolic outcomes appears to vary somewhat compared to what is observed among those of European ancestry. For instance, TG and the ratio of TG to HDL are considered markers for cardiometabolic health, yet these markers perform poorly among AAs and African immigrants to the USA [77, 78]. The association between TG and adiposity and hypertension was stronger in those of European versus African ancestry [57]. In a study of ≥ 9,000 individuals aged 40–60 years in Ghana, Burkina Faso, Kenya, and South Africa, total cholesterol was not associated with subclinical atherosclerosis, perhaps because of the low distribution of total cholesterol, with 87% of participants in the ideal range for total cholesterol [79].

Most studies of the genomic factors associated with the distribution of serum lipids in African ancestry individuals have been conducted in AAs. Some notable studies with AAs include the PAGE study and the Million Veteran Program (MVP). The PAGE study recently discovered novel genetic loci, as well as novel non-European ancestry-specific independent signals in known loci and found a heterogeneity of allelic effects between European and non-European ancestry populations (largely Hispanic and AA) [80]. MVP found a high correlation between effect size estimates among veterans of African, Hispanic, and European ancestry, and a better correlation of effect allele frequencies between those of European and Hispanic ancestry than of European and African ancestry, consistent with expectations of population migration history [81].

The most comprehensive study of the genomics of serum lipids that included AAs is a recent meta-analysis of 1.65 million individuals which included ~99,000 individuals of African ancestry (predominantly AAs), and found that 15 ancestry-specific lipids loci, more than were found in any other non-European ancestry group included, which the authors attributed to the larger allele frequency differences between African and European ancestry populations and to the greater genetic diversity among those of African ancestry. In these analyses, those of African and Hispanic ancestry identified the most loci per genotyped participant (Figure 5), consistent with expectations given higher genetic diversity. Notably, only 61% of index variants in African ancestry findings had sufficient power in at least one other ancestry group assuming similar sample sizes, fixed effect sizes, and observed allele frequencies from the other ancestry groups. In contrast 88% of South Asian index variants were well-powered in at least one other ancestry group [82].

Figure 5.

Comparison of power to detect genome-wide association study (GWAS) loci across ancestry groups. In a recent large, multi-ancestry lipids GWAS [82], the proportion of index variants identified from each ancestry-specific meta-analysis that would be well powered to detect an association of the same effect size but with ancestry-specific frequencies in the other ancestry groups was calculated. Dark blue regions represent index variants that would only be well-powered for detection in that ancestry-specific GWAS, while white regions indicate index variants that would be well-powered for detection in all of the ancestry groups. Image reproduced with permission from [82].

Studies of Africans (SSA) are also now becoming more available. A study of ≥ 4,000 Africans (Nigeria, Ghana, and Kenya), identified novel lipid loci, but also found additional loci in analyses stratified by region (West vs. East Africa), with different associations being observed across strata for some loci [83]. These data suggest that grouping all Africans together as a uniform group may not be appropriate in the context of lipids genomics. A study of Ugandans found higher heritability for low density lipoproteins (LDL) among Ugandans (54%) than in individuals of European ancestry (20–43%); the authors speculate that the obesogenic environment and dietary intake may reduce the impact of genetic factors on this phenotype. Meta-analysis of data from 14,126 individuals from Ghana, Kenya, Nigeria, South Africa, and Uganda, identified a novel HDL locus as well as distinct signals in known loci [51].

Clinical complications of cardiometabolic disorders

Beyond the burden of the above cardiometabolic disorders themselves, a key reason for interest in these traits is as they predict clinical complications. As expected, the ancestry-related differences in the distribution of these traits have implications for the distribution of related clinical complications. Hypertension contributes directly to the significant disparities in stroke, heart failure, and peripheral artery disease in African versus non-African ancestry individuals [84]. The higher prevalence of cardiometabolic factors is one of the reasons for the relatively earlier age of onset of CVD among African ancestry individuals, which often results in poorer outcomes and may also explain the higher susceptibility to CVD and CKD in these individuals compared to their European ancestry counterparts[84]. Hypertension is the second most common cause of CKD worldwide but the leading cause in SSA. In SSA, the prevalence of poor hypertension control leads to rapid progression of CKD to end-stage kidney disease (ESKD) [85]. Hypertension in CKD also leads to CVD outcomes, such as heart failure, pulmonary edema, and stroke, which are the leading causes of death in patients with CKD. Obesity-mediated CKD may be due to coexisting hypertension and obesity, but a direct effect through adiposity-stimulated inflammation, increased oxidative stress, and activation of the renin angiotensin-aldosterone system has also been recognized [86]. Diabetic kidney disease is the leading cause of ESKD, and it occurs in 40% of patients with diabetes, with albuminuria as a first sign, which often precedes decline in glomerular filtration rate [87]. Lastly, dyslipidemia is increasingly recognized to be associated with initiation and progression of CKD [88, 89]. Recent epidemiological studies suggest high cholesterol, TGs, and LDLs are independently associated with incident CKD, and a fat- and sugar-rich diet may increase the odds of CKD by 46% [90]. Here we describe some of the clinical consequences of cardiometabolic disorders, including CVDs, neurological diseases, and CKD, with a particular focus on African ancestry populations.

CVDs (heart failure and ventricular hypertrophy)

Left ventricular hypertrophy (LVH) refers to the thickening and enlargement of the left ventricle and is associated with increased risk of major cardiovascular events, including heart failure (HF). In a study of older adults in a community-based study in the USA, AAs had greater LV wall thickness, greater LV concentric remodeling, and worse LV function compared with EAs, and these differences remained after adjustment for cardiovascular comorbidities [91]. Population studies have also demonstrated that AAs have greater left ventricular mass (LVM) than EAs have [92], and a recent study demonstrated that these differences are even observed among children, with AAs having greater central hemodynamic load and cardiac target organ damage [93]. In a study in the Democratic Republic of the Congo comparing hypertensive patients with and without LVH, the prominent role of hypertension in predicting LVH and LVM was confirmed, as well as an independent role of obesity and insulin resistance for both of these quantities[94]. A recent whole exome sequencing study has identified three genes (MYRIP, TRAPPC11, and SLC27A6) associated with LVH among AAs [95].

The burden of HF is disproportionately higher in African ancestry populations with an earlier age of onset, greater severity, and poorer mortality outcomes [96–98]. The prevalence of HF is higher among AAs (3.8% in men and 3.3% in women) compared with EAs (2.9% in men and 1.6% in women) [98]. A recent study using harmonized data from six longitudinal population-based cohorts in the USA estimated the population attributable fraction (PAF) for HF and found differing contributions of cardiometabolic risk factors by race/sex strata. The highest PAF for AAs was for hypertension (25.8%- for women and 28.3% for men), while the PAF for EAs for hypertension was lower (17.3% for women and men). The PAF for diabetes was higher for AAs (16.4% in women and 9.2% in men) than EAs (4.4% in women and 6.1% in men) and lower/similar for obesity in AAs (13.1% in women and 16.2% in men) than EAs (17.9% in women and 21.0% in men)[99]. Strikingly, the INTERHF study reports an annual HF mortality rate in SSA of 34%, twice the world average (16.5%). The authors attribute this disparity to prevalence of hypertension and LVH, as well as a worse burden of co-morbidities, including CKD, left ventricular remodeling, and infections [97]. Genomic determinants and DNA methylation profiles associated with differential mortality outcomes in African ancestry populations have been described [97, 100] and include variants in genes encoding endothelial Nitric Oxide, Aldosterone Synthase, and β1 Adrenergic Receptor. A single genome wide significant locus associated with rheumatic heart disease, a common cause of heart failure among SSA was also recently described [101].

Neurological disorders: stroke/dementia

Stroke is the clinical culmination of complex processes and interacting pathways which involve several genetic and environmental factors [102]. Compared with European ancestry populations, African ancestry individuals experience an increased incidence of stroke, a younger age of onset, worse outcomes, and a higher proportion of small vessel disease and hemorrhagic stroke[103–107]. The determinants of this disparity include differences in socioeconomic factors, conventional cardiometabolic risk factors, lifestyle, and the interplay between these factors and genetic background.

In a comparison of the Stroke Investigative Research and Education Network (SIREN) study among SSA and the population-based Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study of AAs and EAs, a younger age of onset was observed among SSA and AAs compared with EAs[108]. Hemorrhagic stroke was more frequent among SSA (27%) compared to AAs (8%) and EAs (5.4%), while small vessel disease ischemic stroke was more prevalent in SSA (47.1%), followed by AAs (35.1%) and EAs (21.0%). The prevalence of hypertension and T2D, both risk factors for stroke, was higher in SSA and AAs compared with EAs, and lifestyle risk factors (i.e., smoking, high alcohol intake, and physical inactivity) were higher among AAs and EAs than in SSA. [108] These findings underscore the need to conduct research that disentangles the various contributions of lifestyle, behavioral, genetic, and other factors to stroke disparities [107].

Recent advances are helping to understand the genetic architecture of stroke in African ancestry populations. A recent GWAS meta-analysis of stroke in >22,000 individuals of African ancestry identified a novel locus near the HNF1A gene, while the field awaits the first stroke GWAS in SSA [107, 109]. Given the high level of variation in genomes of individuals across the African continent, the inclusion of African populations is vital to progress in the field. This will enhance trans-ancestry meta-analysis with implications for fine-mapping known stroke-associated loci, uncovering novel loci, establishing causal variants, designing genetic risk prediction algorithms, and facilitating personalized stroke preventive and therapeutic solutions for those of African ancestry as well as other global populations [110, 111].

Alzheimer’s disease (AD) and related dementias continue to be of great public health significance with huge impact on individuals, families, and society [112, 113]. The incidence of dementia has declined by 13% per decade over the past 25 years in Western Europe and North America[114] due to improved general living conditions, higher cognitive reserve from better education, and better control of cardiometabolic factors, especially hypertension [115]. In contrast, the burden of dementia has been projected to increase in low-and middle-income countries, including African countries [116]. Within the USA, there are ethnic disparities in the burden of dementia, with recent estimates from the Veterans Health Administration reporting the age-adjusted incidence of dementia to be 19.4 per 1000 personyears for AAs compared with 11.5 for EAs, despite all participants receiving care at Veteran’s Health Administration medical centers [117]. Higher burden of cardiometabolic risk factors, lower socioeconomic status, and higher exposure to air pollution are among factors driving the higher burden among African ancestry populations in North America [118, 119]. A recent GWAS meta-analysis for AD among AAs identified novel loci in genes involved in intracellular glycoprotein trafficking, immune response, recruitment glutamatergic receptors, and glutamate neurotoxicity. This study found broad overlap with pathways described in studies of EAs, such as immunity, lipid processing, and intracellular trafficking pathways, underlying AD risk, although the loci within those pathways varied. Additionally, this study identified a new pathway relating to the kidney system that needs further exploration [120].

Some studies involving multiple ancestry groups in the USA have provided neuropathological evidence of the reported disparities. In a report from the Rush Study [121], AA were less likely to have AD pathology as a single dementia pathology compared with EA (19.5% vs 42.0%). However, they were more likely to have AD pathology mixed with an additional pathology (70.7% vs 50.6%), particularly Lewy bodies and infarcts. Furthermore, AA also had more severe arteriolar sclerosis and atherosclerosis [121]. Similarly, a multiracial Brazilian neuropathological study showed a comparable reduction in AD pathology but higher vascular pathology in the brains of those of African ancestry. [122] A difference in white matter rarefaction by ancestry has also been reported, with nearly 40% of AA showing white matter rarefaction compared to 16% of EA and Hispanic [123].

Chronic Kidney Disease (CKD)

CKD is a huge burden on global health and is a leading cause of disability and premature death, especially in low-and middle-income countries [4, 124]. Worldwide, the prevalence varies substantially across regions from 3% to 17% [4, 124–128]. There is a wide variability in SSA of CKD prevalence, with a systematic review finding the highest prevalence in West/Central West Africa (16%) while East Africa was lowest (11%)[129]. Separate studies have reported prevalence in the general population as 23% in Africa [130] and 13.9% in Tanzania [126]. The prevalence is even higher in certain subpopulations; for example, CKD occurs in up to 34% of high-risk patients with T2D, HIV infection, and hypertension [129]. T2D, hypertension, HIV infection, and obesity have been identified as CKD risk factors in Tanzania [126], and a study including cases from across Africa identified the leading causes of cases as hypertension (16%), diabetes (15%), and glomerulonephritis (13%), while a fifth of individuals had CKD of unknown cause [129]. [130] In Africa 84% of adults with newly diagnosed ESKD discontinued dialysis, and 95% of adults and children with ESKD who could not access dialysis either died or were presumed to have died [131]. This CKD burden in SSA is unlike that observed elsewhere.

AAs also have a high CKD burden: the prevalence of CKD is 18.8% among AAs compared with 14.1% among EAs [132]. In the USA, ESKD is four-fold higher among AAs than EAs [132]. People of African descent also have a faster CKD progression compared with EAs [133, 134]. This excess CKD risk and progression is largely explained by risk variants in apolipoprotein L1 gene (APOL1) located on chromosome 22q12 [135–139]. Other factors that predict increased risk of CKD progression in AAs compared with EAs include proteinuria, albuminuria, higher treated blood pressure, and poor glycemic control [134]. The majority of studies of CKD mortality did not find significant differences in risk between AAs and Afro-Caribbean compared with EAs, with others mixed in terms of direction [134].

Gene-lifestyle Interactions

As the cardiometabolic traits being discussed are all influenced by both genomic and lifestyle factors, it is reasonable to expect that gene-lifestyle interactions may also influence their distribution. It has long been observed that individuals with the same lifestyle exposures can react to them in different ways, and, thus, the idea of gene-lifestyle interactions is fairly intuitive. Discovering gene-lifestyle interactions is also attractive as they suggest relatively straightforward mechanisms to alter risk compared to main effect genetic associations: by making changes in the lifestyle factor.

This need for genome-wide, unbiased, large-scale genome-wide interaction studies that include common environmental factors and cardiometabolic traits and include African ancestry populations has been addressed by recent initiatives, notably by the CHARGE Gene-Lifestyle Interactions Working Group [140]. Many of these efforts have found gene-lifestyle interactions that differ by ancestry. Allele frequency differences among populations explain some of these inter-ancestry differences. For example, an interaction between rs141588480 (SNTA1) and physical activity was discovered in individuals of African and Hispanic ancestry only, as this indel is polymorphic only in these groups [141]. However, many projects also identified interactions that were identified only among African ancestry individuals, even though the associated allele was common in other ancestry groups. One example is an interaction between rs12740061 and current smoking on HDL. At this locus, a strong interaction was observed among African ancestry individuals (p=7.4 × 10⁻⁹), yet there was no evidence of an interaction in any other ancestry group, despite higher allele frequencies [29]. The reasons for such differences are unclear, though they may be expected to either relate to uncaptured differences by ancestry in either the genetic or the lifestyle factor. Regarding the genetic factor, beyond allele frequency differences, the associated variant may be tagging the causal variant in the ancestry in which the association was found but not in other ancestry groups due to linkage disequilibrium patterns, which are well-known to differ by ancestry. This possibility was explored for the example of rs12740061, by investigating variants that are in linkage disequilibrium (LD) (r²>0.2) with this variant among those of African ancestry that were not in linkage disequilibrium (LD) in the other ancestry groups, yet each of these had been directly tested in the analysis of non-African ancestry groups and showed no association. [29] Relative to the environmental factor, a difference by ancestry in the number of exposed individuals might cause an association to be identifiable in one population, but not another. In the discussed example, the prevalence of current smoking was similar across ancestries (22% in African ancestry and 17% among European ancestry). Another possibility, which could not be tested in that study, is that there are qualitative differences in the lifestyle exposure by ancestry that are not captured in the parameter used. As the measure used in the example was relatively crude, smoking status as a binary variable, it is reasonable that it may not capture aspects of smoking exposure that are known to have a biological impact: cigarettes per day, years of smoking, use of mentholated versus non-mentholated cigarettes, characteristics of inhalation, age at initiation, etc. Additionally, the tested exposure may correlate differently with a causative environmental factor across ancestries, such that the tested exposure would only appear to be associated with the outcome in the groups in which they are tightly correlated.

Recent publications have confirmed the utility of using a gene-lifestyle approach to identify novel variants. These studies have found clear evidence of interactions that could not have been identified in a standard GWAS of main effect. This is particularly evident in interactions in which a different direction of effect is seen depending on lifestyle stratum [142]. For instance, a strong interaction between the A allele for rs73453125 and current smoking on LDL was observed (β = −10.3 mg/dl, P = 2.4 × 10⁻⁸) in AA, such that there was a positive association among non-smokers, and a negative association among current smokers. In a model of the same individuals without including an interaction term, the association was not detected (β = 0.29 mg/dl, P = 0.70), and adding a variable to the model to adjust for current smoking did not make a difference (β = 0.23 mg/dl, P = 0.76)[29].

A comparable or greater number of interaction associations passed the thresholds set for statistical significance for African ancestry individuals compared to those of European ancestry, despite markedly smaller sample sizes [29, 143–145]. For instance, in a recent genome-wide gene-smoking interaction study of incident T2D analyzing 52,561 European ancestry and 7,897 African ancestry individuals, there were three genome-wide statistically significant interactions were found in the European ancestry meta-analysis and two in the African ancestry meta-analysis [143]. In a study of gene-smoking interaction on serum lipids in which 30,965 out of 387,272 were of African ancestry, there were 13 novel loci discovered, from the following meta-analyses: African ancestry only (n=5), African and trans-ancestry (n=4), European and trans-ancestry (n=2), and trans-ancestry only (n=2) [29]. The explanation for this phenomenon is unclear. One potential explanation is that many of these findings may represent novel main effect associations in African ancestry individuals, as a 2 degree of freedom joint test of the main effect and interaction has become a common practice to increase statistical power [146]. The large sample sizes of African ancestry participants in these interaction studies compared to previous standard GWAS associations may mean that these are capturing novel main effect loci. While this phenomenon has been observed in some studies [29, 145], accompanying findings using a 1 degree of freedom interaction test demonstrate results that are driven by interaction, and not main effect, suggesting that other explanations for the high number of findings among African ancestry individuals are needed.

Although recent publications have identified a growing number of gene-lifestyle interactions, there is more work to be done in this area. Although tested sample sizes have now reached the level at which genome-wide interactions are identifiable, the patterns of interactions generally observed are those that are the easiest to detect statistically, suggesting that still greater sample sizes are necessary to detect interactions in which genotype alters the association in more subtle ways, like changing the magnitude of an association with the same direction of effect (discussed further in [29]). A greater variety of and more refined environmental exposures need to be tested. As we have observed differences in gene-lifestyle interactions by ancestry, it is also likely that there will be differences in gene-lifestyle interactions by sex and aging and even interactions between environmental factors, although identification of such complexities presents even greater challenges related to statistical power. Finally, differences by ancestry in gene-lifestyle interactions should be further explored, as the imprecision of attributing differences to broad ancestral groups is unsatisfying given the uncertainty as to whether ancestry here represents genetic or environmental differences. Studies of African diasporan populations, with similar genetic ancestry but different environmental exposures, such as West Africans compared to African Americans or African Caribbeans, could be particularly illustrative in this regard.

From Epidemiology to Genetics: implications for health disparities

Advances in research on cardiometabolic traits are currently hamstrung by a lack of representation across ancestry populations. This challenge is particularly salient for those of African ancestry, whose genomic diversity and wide variety of lifestyles, cultures, and environments greatly limit the degree to which we can expect research done in other populations to represent them. The underrepresentation of African ancestry individuals in research has implications for scientific discovery. Africa has been described as the most informative place on earth for uncovering genomic influences on complex traits [147] because of the greater amount of genetic variation because of human migration history. Although a recent analysis found that only 2.4% of the individuals in the GWAS catalog were of African ancestry, 7% of all associations in this database were found in those individuals [28]. Increasing numbers of African ancestry individuals in research are meeting the promise of novel scientific discoveries [148], with recent findings for serum lipids [51, 82, 83], T2D, and glycemic traits [52–54, 149], stroke [150], adiposity traits [151–153], and kidney function and disease[154, 155]. The lack of representation of African ancestry individuals in genomic research also has profound implications for clinical care. Although advances from genomic discoveries are often applicable to all populations, it is the case that those who are the most represented in science benefit the most from it [55]. Those of African ancestry are underrepresented in important clinical databases [156], limiting the ability of clinicians to make accurate diagnoses for those of African ancestry [157, 158].

Pharmacogenomics is an area where the impact of under-representation is straightforward: the presence and frequency of variants with an effect on drug response cannot be determined without studying that population. Importantly, continental African ancestry is not a useful grouping for determining likelihood of carrying pharmacogenomic loci; due to genetic diversity, wide differences in frequency of pharmacogenomic loci have been observed even within one African country [159]. Polygenic Risk Scores (PRSs) based on large genomic studies are a promising clinical tool for identifying individuals at risk of cardiometabolic outcomes. Unfortunately, PRSs derived from studies of European-ancestry individuals, by far the most well-powered genomic studies, perform poorly in those of African ancestry: prediction accuracy was an average of 4.5-fold lower across 17 quantitative traits in African ancestry individuals. A recent large-scale GWAS of serum lipids with notable inclusion of non-European ancestry participants found that a trans-ancestry polygenic risk score from this diverse sample performed best or comparably with an ancestry-specific PRSs in all populations evaluated, suggesting a strategy for future work with PRSs [82].

Under-representation spans all aspects of genomic research. Paucity of genomic researchers of African ancestry may limit the investigation of research questions of particular interest to these individuals. Fewer than 2% of all principal investigators on NIH-funded research awards are Black/African American [160]. The efforts required to improve the diversity of the research biomedical faculty and leadership in the USA have been described as no less than a culture change [160]. Even functional inference of association loci may be limited because of a comparative lack of well-characterized multi-omics data from African ancestry individuals.

It is important to note that the collapsing of all populations with ancestry on the continent of Africa into a single category is insufficient, given the genomic diversity among individuals of African ancestry. In an analysis of genomic data from a global sampling of humans, it was determined that there were 21 ancestries worldwide, and over half of these (11) were present in Africa [161]. In complex disease risk, this insufficiency is exacerbated by the environmental and socioeconomic diversity across the continent of Africa and the African Diaspora [156]. Additionally, African Americans and Afro-Caribbeans have varying degrees of non-African ancestry genetic admixture, which led the authors of a standardized framework for representation of ancestry in genomic studies to characterize them as a separate ancestry group [162], despite the prevailing practice of considering these groups as part of a monolithic African ancestry category in analyses. Thus, although African ancestry individuals taken as a single group are already under-represented, it is likely that an even greater degree of representation will be necessary to capture the heterogeneity among these individuals.

Concluding remarks and Future Perspectives

Although epidemiological evidence shows that cardiometabolic disorders are of considerable and rising impact for African ancestry populations, the research into these disorders in African ancestry individuals remains limited. Given the genomic diversity among those of African ancestry and the wide variety of environmental contexts among those on the continent and in the diaspora, the lack of engagement of these populations is hindering our understanding of biology and if not urgently addressed will exacerbate already unacceptable health inequalities. There are a number of scientific questions that can only be answered with increased study of African ancestry populations (see Outstanding Questions). As has been demonstrated in this Review, the clustering of individuals as African ancestry is not appropriate for describing cardiometabolic risk. For instance, the lean diabetes phenomenon described among SSA populations is not prominent in AAs, and the distribution of serum TGs is higher among East compared with West Africans. Thus, there is a critical need to increase the study of the genomics of cardiometabolic traits among those of African ancestry in general, and with attention to representation of the diversity of African ancestry populations and their environments.

Outstanding Questions.

Can we identify additional novel loci for cardiometabolic disorders by increasing both the number of individuals of African ancestry included in genomic research as well as the breadth of populations across Africa that are included?

Given the genomic diversity among individuals of African descent, it is likely that increasing sample sizes will lead to the identification of novel discoveries that could not be made in even very well-powered studies of individuals without African ancestry. Importantly, a great deal of the vast genomic richness across the continent has not yet been evaluated relative to cardiometabolic risk.

How can ‘omics data help us to understand the interplay between genetic and environmental factors in the etiology of cardiometabolic disorders?

‘Omics data represents an opportunity to explore both genetic and environmental influences on cardiometabolic traits through intermediates in the pathways between genetic and environmental exposures and outcomes.

How can African ancestry populations be meaningfully subdivided relative to cardiometabolic risk?

Efforts to combine all individuals with African ancestry into a monolithic group in assessments of cardiometabolic risk are problematic given the diversity among African ancestry populations.

Highlights.

• Cardiometabolic diseases are rising in populations of African descent and have become the leading causes of death and disability in these populations.

• Cardiometabolic diseases arise from the complex interplay between genetic and environmental factors. Despite the genetic and environmental diversity attributed to populations of African descent, cardiometabolic diseases are under-studied in these populations due to the lack of representation in research.

• The failure to include individuals of African descent in research and to understand the role of genetic and environmental factors in these diseases could hamper our efforts to address health disparities and to implement precision medicine.

• Recent global initiatives toward better representation in research have led to new discoveries and expanded our understanding of the molecular basis of cardiometabolic disorders.