Sally N. Adebamowo

Precision medicine is being enabled in high-income countries by the growing availability of health data, increasing knowledge of the genetic determinants of disease and variation in response to treatment (pharmacogenomics), and the decreasing costs of data generation, which promote routine application of genomic technologies in the health sector. However, there is uncertainty about the feasibility of applying precision medicine approaches in low- and middle-income countries, due to the lack of population-specific knowledge, skills, and resources. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive new research into the genetic and environmental basis for human diseases of relevance to Africans as well as to build capacity for genomic research on the continent. Precision medicine requires this capacity, in addition to reference data on local populations, and skills to analyze and interpret genomic data from the bedside. The H3Africa consortium is collectively processing samples and data for over 70,000 participants across the continent, accompanied in most cases by rich clinical information on a variety of non-communicable and infectious diseases. These projects are increasingly providing novel insights into the genetic basis of diseases in indigenous populations, insights that have the potential to drive the development of new diagnostics and treatments. The consortium has also invested significant resources into establishing high-quality biorepositories in Africa, a bioinformatic network, and a strong training program that has developed skills in genomic data analysis and interpretation among bioinformaticians, wet-lab researchers, and health-care professionals. Here, we describe the current perspectives of the H3Africa consortium and how it can contribute to making precision medicine in Africa a reality.

Gut dysbiosis has been associated with several disease outcomes including diabetes in human populations. Currently, there are no studies of the gut microbiome composition in relation to type 2 diabetes (T2D) in Africans. Here, we describe the profile of the gut microbiome in nondiabetic adults and investigate the association between gut microbiota and T2D in urban West Africans. Gut microbiota composition was determined in 291 Nigerians (98 T2D, 193 non-T2D controls) using fecal 16S V4 rRNA gene sequencing done on the Illumina MiSeq platform. Data analysis of operational taxonomic units (OTU) was conducted to describe microbiome composition and identify differences between T2D and controls. The most abundant phyla were Firmicutes, Actinobacteria, and Bacteroidetes. Clostridiaceae and Peptostreptococcaceaea were significantly lower in T2D cases than controls (p<0.001). Feature selection analysis identified a panel of 18 OTUs enriched in T2D that included Desulfovibrio piger, Prevotella, Peptostreptococcus, and Eubacterium. A panel of 17 OTUs that was enriched in the controls included Collinsella, Ruminococcus lactaris, Anaerostipes and Clostridium. OTUs with strainlevel annotation showing the largest fold-change included Cellulosilyticum ruminicola (log2FC= -3.1; p=4.2 x10 -5 ), Clostridium paraputrificum (log2FC= -2.5; p=0.005), and Clostridium butyricum (log2FC=-1.76; p=0.01), all lower in T2D cases. These findings are notable because supplementation with Clostridium butyricum and Desulfovibrio piger has been shown to improve hyperglycemia and reduce insulin resistance in murine models. This first investigation of gut microbiome and diabetes in urban Africans shows that T2D is associated with compositional changes in gut microbiota highlighting the possibility of developing strategies to improve glucose control by modifying bacterial composition in the gut.

Hypertension and obesity are the most important modifiable risk factors for cardiovascular diseases, but their association is not well characterized in Africa. We investigated regional patterns and association of obesity with hypertension among 30 044 continental Africans. We harmonized data on hypertension (defined as previous diagnosis/use of antihypertensive drugs or blood pressure [BP]≥140/90 mmHg/BP≥130/80 mmHg) and obesity from 30 044 individuals in the Cardiovascular H3Africa Innovation Resource across 13 African countries. We analyzed data from population-based controls and the Entire Harmonized Dataset. Age-adjusted and crude proportions of hypertension were compared regionally, across sex, and between hypertension definitions. Logit generalized estimating equation was used to determine the independent association of obesity with hypertension ( P value &lt;5%). Participants were 56% women; with mean age 48.5±12.0 years. Crude proportions of hypertension (at BP≥140/90 mmHg) were 47.9% (95% CI, 47.4–48.5) for Entire Harmonized Dataset and 42.0% (41.1–42.7) for population-based controls and were significantly higher for the 130/80 mm Hg threshold at 59.3% (58.7–59.9) in population-based controls. The age-adjusted proportion of hypertension at BP≥140/90 mmHg was the highest among men (33.8% [32.1–35.6]), in western Africa (34.7% [33.3–36.2]), and in obese individuals (43.6%; 40.3–47.2). Obesity was independently associated with hypertension in population-based controls (adjusted odds ratio, 2.5 [2.3–2.7]) and odds of hypertension in obesity increased with increasing age from 2.0 (1.7–2.3) in younger age to 8.8 (7.4–10.3) in older age. Hypertension is common across multiple countries in Africa with 11.9% to 51.7% having BP≥140/90 mmHg and 39.5% to 69.4% with BP≥130/80 mmHg. Obese Africans were more than twice as likely to be hypertensive and the odds increased with increasing age.

BACKGROUND: There is exponential growth in the interest and implementation of genomics research in Africa. This growth has been facilitated by the Human Hereditary and Health in Africa (H3Africa) initiative, which aims to promote a contemporary research approach to the study of genomics and environmental determinants of common diseases in African populations. OBJECTIVE: The purpose of this article is to describe important challenges affecting genomics research implementation in Africa. METHODS: The observations, challenges and recommendations presented in this article were obtained through discussions by African scientists at teleconferences and face-to-face meetings, seminars at consortium conferences and in-depth individual discussions. RESULTS: Challenges affecting genomics research implementation in Africa, which are related to limited resources include ill-equipped facilities, poor accessibility to research centers, lack of expertise and an enabling environment for research activities in local hospitals. Challenges related to the research study include delayed funding, extensive procedures and interventions requiring multiple visits, delays setting up research teams and insufficient staff training, language barriers and an underappreciation of cultural norms. While many African countries are struggling to initiate genomics projects, others have set up genomics research facilities that meet international standards. CONCLUSIONS: The lessons learned in implementing successful genomics projects in Africa are recommended as strategies to overcome these challenges. These recommendations may guide the development and application of new research programs in low-resource settings.