Personalised nutrition needs to improve inclusivity and mechanistic insight

By Nicola Gordon-Seymour

- Last updated on GMT

Personalised nutrition needs to improve inclusivity and mechanistic insight

Related tags personalised nutrition Prebiotic Probiotic

Commercial predictive modelling for personalised nutrition requires proper validation to maximise inclusivity and achieve optimal health benefits, say experts.

To achieve this, stakeholders need to develop a set of standards and best practice to design and assess the efficacy of personalised interventions, they write in an overview of clinical approaches to microbiota-mediated personalised nutrition and health.

While they acknowledge personalised nutrition is a critical approach to optimise human health, they also highlight that many commercial interventions remain nascent and often over-estimate their predictive capabilities.

Current statistical modelling approaches are limited as they rely upon a training cohort and sometimes lack mechanistic insight, they say: “If new data come from individuals who are sufficiently different from the training cohort, predictions will begin to break down”.

“It is unclear whether or not precision nutrition models trained on relatively affluent developed-world cohorts are broadly applicable to the rest of the world, necessitating a sharper focus on running observational and interventional trials in indigenous, non-industrialised, and rural populations​.”

Personal evaluation

The international team of experts compiled the overview to discuss the implications of developing more diversity in precision nutrition strategies.

Their report, ‘Perspective: leveraging the gut microbiota to predict personalised responses to dietary, prebiotic, and probiotic interventions’​, describes a “vision of a precision nutrition and healthcare future”​ that leverages the gut microbiota to develop effective, individual-specific interventions.

Current precision nutrition models often ignore individual responses to dietary, lifestyle, and pharmacological interventions - and also exclude populations in underdeveloped regions.

In a highly under-regulated market, it is therefore “incumbent upon researchers to collect more evidence from well-designed, hypothesis-generating human observational studies and hypothesis-testing experimental intervention trials where dense phenotypic, clinical, and behavioural information is combined with gut microbiome profiling​”, they write.

Recent advances in host-microbiota metabolic in silico​ modelling have enabled high-throughput of personalised responses to dietary, prebiotic, and probiotic interventions, however results require proper in vitro​ and in vivo​ validation to “serve as a foundation for better mechanistic modelling”,​ they write.

In this way, emerging knowledge could be harnessed to build “statistical learning models​” to produce inclusive personalised predictions of phenotypic responses based on “causally-validated host-microbe and microbe-microbe interactions​”.

Clinical challenges

The authors note that both statistical and mechanistic ‘in silico’​ approaches are essential to assure progress in precision engineering of the gut microbiota. Data-intensive statistical methods are ideal for making predictions on host-microbiota mechanisms, but their interpretation is often “opaque​”.

Similarly, the scope of action for in vitro​ studies has widened due to improvements in culturing and isolation methods, however human bacteria can be difficult to culture, making it hard to simulate the digestive system and compromising the microbial community dynamics, which limits its application.

There are also challenges with in vivo​ dietary interventions. Studies require adequate variation in the target population, collection of relevant metadata and covariates, and robust measures for characterising response and outcomes. Additional challenges relate to timing, adherence, and sampling.

The costs and logistical hurdles of in vivo​ need to decrease to ensure sufficient representation of indigenous, non-industrialised, and rural populations in microbiome research “so that the societal benefits of precision nutrition and healthcare are more equitably distributed​”, the authors say.

Setting standards

The experts recognise the enormous engineering and optimisation challenges ahead and concede there is unlikely to be a “general-purpose algorithm​” to achieve specific outcomes for predicting personalised responses across a range of diverse contexts.

They comment: “We must move forward, one targeted application at a time, to build up an ecosystem of precision intervention tools.”

But even as clinical progress accelerates, commercial viability of personalised nutrition is hampered by market dynamics, they add.

“The ultimate timescales for translating microbiome science into personalised nutrition and healthcare will be limited by the availability of funding, by the size of the scientific workforce, by access to diverse human cohorts, and ultimately, by buy-in from healthcare systems.”

Source: Oxford University Press

Published online: doi/10.1093/advances/nmac075/6626013

‘Perspective: leveraging the gut microbiota to predict personalized responses to dietary, prebiotic, and probiotic interventions’

Authors: Gibbons, Sean M et al.

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