Evidence map›Paper›PMID 42523796›Full record

ArticleFrontiers in nutrition2026

Tracking the nutrition transition in Tanzania, Ghana, South Africa, and Malaysia: analysis of per capita supply of energy, protein, and fat from 1982 to 2022.

Nyabasi Makori, Akwilina Mwanri, Daniela Weible, Ee Von Goh, Johanna Schott, Trylee Nyasha Matongera, Wendy Geza, Geoffrey Adebayo Asalu, Maxwell Mudhara, Festo Massawe and 2 more

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Nyabasi MakoriDepartment of Human Nutrition and Consumer Sciences, Sokoine University of Agriculture, Morogoro, Tanzania.
Akwilina MwanriDepartment of Human Nutrition and Consumer Sciences, Sokoine University of Agriculture, Morogoro, Tanzania.
Daniela WeibleJohann Heinrich von Thünen Institute - Federal Research Institute for Rural Areas, Forestry and Fisheries, Braunschweig, Germany.
Ee Von GohSchool of Biological and Environmental Sciences, University of Nottingham Malaysia, Semenyih, Malaysia.
Johanna SchottJohann Heinrich von Thünen Institute - Federal Research Institute for Rural Areas, Forestry and Fisheries, Braunschweig, Germany.
Trylee Nyasha MatongeraSchool of Biological and Environmental Sciences, University of Nottingham Malaysia, Semenyih, Malaysia.
Wendy GezaCentre for Transformative Agricultural and Food Systems, University of KwaZulu-Natal, Pietermaritzburg, South Africa.
Geoffrey Adebayo AsaluDepartment of Industry and Service, Science and Technology Policy Research Institute, Accra, Ghana.
Maxwell MudharaCentre for Transformative Agricultural and Food Systems, University of KwaZulu-Natal, Pietermaritzburg, South Africa.
Festo MassaweSchool of Biological and Environmental Sciences, University of Nottingham Malaysia, Semenyih, Malaysia.
Tafadzwanashe MabhaudhiSchool of Biological and Environmental Sciences, University of Nottingham Malaysia, Semenyih, Malaysia.
Joyce KinaboDepartment of Human Nutrition and Consumer Sciences, Sokoine University of Agriculture, Morogoro, Tanzania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Globally, dietary patterns have shifted over recent decades due to economic growth and urbanization, contributing to nutritional improvement and emerging diet related health challenges. However, few studies have compared long-term food supply trends across countries at different stages of economic development and the nutrition transition. We examined trends in energy supply (2010-2022), protein, and fat (1982 and 2022) supply in Tanzania, Ghana, South Africa, and Malaysia. Methods: Data from the Food and Agriculture Organization - Food Balance Sheets were used to assess national-level energy supply, analyzed in Stata version 18 using descriptive statistics, correlation analysis, and linear regression to evaluate trends in nutrient supply and their association with GDP per capita. Results: Economic growth was associated with increases in food supply across the four countries. Malaysia, with the highest GDP, reported an increase in energy (2,822.8 kcal/capita/day), protein (77.4 g/capita/day), and fat (89.6 g/capita/day) supply. Ghana, despite having the second lowest GDP, reported the highest energy supply (2,863.8 kcal/capita/day) and a 101% increase in protein supply, suggesting improvement in dietary diversity. In contrast, Tanzania, which experienced the lowest GDP growth, recorded the lowest energy (2,274.8 kcal/capita/day) and protein (55.6 g/capita/day) supply. Fat emerged as the secondary energy source in Malaysia and South Africa, both exhibited high sugar supply. Across all countries, animal protein supply increased more rapidly than plant protein supply. However, South Africa experienced an 18.5% decline in plant-based protein supply, indicating a gradual shift away from traditional dietary patterns. Correlation analysis showed a statistical significant relationship ( Conclusion: Trends in per capita energy, protein, and fat supply reveal uneven changes across the four countries, reflecting different stages of economic development and the nutrition transition. The divergent trends call for country-specific strategies to promote healthier, more sustainable diets and reduce the growing burden of diet-related non-communicable diseases.

Indexed as

dietary patternfood balance sheetfood systemgross domestic productnutrition transitionper capita supply

Identifiers

PMID42523796
PMCPMC13407377

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.