Gut microbiome profiling of a rural and urban South African cohort reveals biomarkers of a population in lifestyle transition
Bibliographic record
Abstract
Abstract Background Comparisons of traditional hunter-gatherers and pre-agricultural communities in Africa with urban and suburban Western North American and European cohorts have clearly shown that diet, lifestyle and environment are associated with gut microbiome composition. Yet, little is known about the gut microbiome composition of most communities in the very diverse African continent. South Africa comprises a richly diverse ethnolinguistic population that is experiencing an ongoing epidemiological transition and concurrent spike in the prevalence of obesity, largely attributed to a shift towards more Westernized diets and increasingly inactive lifestyle practices. To characterize the microbiome of African adults living in more mainstream lifestyle settings and investigate associations between the microbiome and obesity, we conducted a pilot study, designed collaboratively with community leaders, in two South African cohorts representative of urban and transitioning rural populations. As the rate of overweight and obesity is particularly high in women, we collected single time-point stool samples from 170 HIV-negative women (51 at Soweto; 119 at Bushbuckridge), performed 16S rRNA gene sequencing on these samples and compared the data to concurrently collected anthropometric data. Results We found the overall gut microbiome of our cohorts to be reflective of their ongoing epidemiological transition. Specifically, we find that geographical location was more important for sample clustering than lean/obese status and observed a relatively higher abundance of the Melainabacteria , Vampirovibrio , a predatory bacterium, in Bushbuckridge. Also, Prevotella , despite its generally high prevalence in the cohorts, showed an association with obesity. In comparisons with benchmarked datasets representative of non-Western populations, relatively higher abundance values were observed in our dataset for Barnesiella (log 2 fold change (FC) = 4.5) , Alistipes (log 2 FC = 3.9), Bacteroides (log 2 FC = 4.2), Parabacteroides (log 2 FC = 3.1) and Treponema (log 2 FC = 1.6), with the exception of Prevotella (log 2 FC = − 4.7). Conclusions Altogether, this work identifies putative microbial features associated with host health in a historically understudied community undergoing an epidemiological transition. Furthermore, we note the crucial role of community engagement to the success of a study in an African setting, the importance of more population-specific studies to inform targeted interventions as well as present a basic foundation for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".