The results of our analysis have demonstrated that the per capita fat supply is a very good predictor for the prevalence of overweight and obesity at the country level. The link was found to be linear, with a substantial association between per capita fat supply and the incidence of both overweight and obesity. The correlation we have found in this study between fat intake and overweight and obesity is compatible with that demonstrated in epidemiological studies [28,29,30], and in clinical studies [31, 32] which also shows the positive relationship between dietary fat consumption and increase of body-weight. Furthermore, in the pooled analysis, our findings on the link between fat consumption and the dependent variables of overweight and obesity demonstrate a substantial positive correlation.

However, the pattern of association of per capita fat supply to overweight and obesity differs according to income strata. According to that, a significant correlation between per capita fat supply and variables of both overweight and obesity was noted in the lower-middle-income group. At the same time, a significant correlation was also noted in high-income strata as well, but only for the overweight prevalence. Though the correlations were not significant in other sub-categories, that effect significantly changed for several sub-groups after removing few outliers. For example, the correlation coefficient was significantly noted for both variables of overweight and obesity in the low-income group after removing data from Yemen and Haiti, which were considered outliers. And, linear regression models between the per capita fat supply and prevalence of both overweight and obesity also increased after removing those two outliers. Moreover, correlation changed as significant for obesity in the upper-middle-income group after removing one outlier (China). The lack of significant correlation in the remaining two sub-categories (overweight in upper-middle-income and obesity in high-income groups) may have been due to the insufficient data points, with a smaller number of countries.

The joint WHO/FAO consultation on fats and oils proposed that dietary fat should supply a minimum of 15% of TEI, but not exceed 30–35% of TEI for most adults [33]. The country-specific analysis of the current study has found a range of 10.5–41.6% of fat energy ratio between 2014–16. According to our analysis, seven countries fell below the minimum recommendation of 15% of dietary energy supply from fat, all of which were in the low-income (Ethiopia, Ghana, Cambodia, and Lao People’s Democratic Republic) and lower-middle-income (Madagascar, Rwanda, and Afghanistan) categories. Thirteen countries exceeded the 35% maximum, with twelve countries being in the high-income group and one in the upper-middle-income group. It appears that the countries with an excess of per capita fat supply are generally economically developed countries.

When analyzing the factors that influence fat consumption patterns, assessments of fat consumption statistics suggest that persons in the lowest socioeconomic level in most developed countries take greater fatty foods [34]. Studies have shown that gender [35] and age [36] differences were also found in consumption of fatty foods. Moreover, urbanization is also strongly associated with the increasing consumption of fat in developing countries [37]. In addition to that, the physical environment, level of education, sociological, and individual factors also affect the altitude of fat consumption. Therefore, this entire phenomenon is part of an overall change in food habits and then determines the total quantity of fat availability at the country level.

Obesity caused by a high-fat diet is explained by a number of physiological processes. These include low satiating effects, as well as changes in hormones involved in energy balance [38]. More dietary fat leads to higher obesity because fat contains 9 kcal/g of energy compared to 4 kcal/g for carbs and protein [39]. It is evident that high-fat meals have a high energy density, and so the overall fat content of the diet is an important determinant in energy balancing. Furthermore, weaker satiety signals from fats than from carbohydrate and protein have been proposed to involved in fat-rich diet overconsumption of calories [40]. The extra eating caused by fat-rich diets is due to their post-ingestion effect, which may increase food intake by conditioning sensory preference [41]. Furthermore, protein and carbohydrate stimulate significant auto-regulatory modifications in oxidation in response to variations in intake, but fat is at the bottom of an oxidative hierarchy that controls fuel choices [42].

Due to unavailability from relevant UN organizations, all information on the two variables we utilized in our research was not equally available for all nations throughout the world. As a result, the number of countries used in this analysis was limited to those possessing relevant data. FAO, WHO, and the World Bank are international institutions that provide specialist information in their respective areas. Before they were released, they analyzed these data in terms of their potential uses, such as scientific research and decision making. This indicates that while mistakes have been decreased, certain inaccuracies relating to reporting quality may still exist in the data.


It must be noted that there are several limitations to this study. Firstly, there may be some possible confounding variables (e.g., prevalence of physical inactivity, absolute total calories, and fat consumption or food wastage) that were not included in our study and may have influenced the association we discovered. However, it is impossible to determine what such factors may be in the current investigation. Second, we could only utilize an international food database that measures per capita calorie and fat supply, not actual human consumption. However, there are no direct assessments of actual human intake that can account for food waste and give exact statistics of food consumption globally. Third, because the data studied are computed per capita in each country, we could only find the correlation at the country level, which does not always equate to the same associations at the individual level. Fourth, BMI values of 25 kg/m22 and 30 kg/m2 were used as the cut-off points for classifying overweight and obesity in this study cohort. However, different cut-off points can be employed to define overweight and obesity among different ethnic groups. We used WHO country reports for this study to possibly reduce substantial discrepancies between countries, although more recently published data on the prevalence of overweight and obesity are available for some countries. Finally, the findings cannot be extended to the individual level because this is an ecological study.

Future perspectives

Prospective cohort studies and intervention studies are recommended in each country to investigate this link further. Furthermore, assessing heterogeneity in various amounts of animal fat and plant fat is important for determining a true depiction of the connection at the national level. Country-specific nutrition education messages that warn consumers about the consequences of a high-fat diet and how to restrict sources of fat consumption to maintain a healthy body weight are critical. Relevant authorities in the countries should implement food regulations, active initiatives to raise awareness of the consequences of high fat consumption and its sources, and related taxes on food industry based on the amount of fat used as an ingredient so that the public would make rational decisions.

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