Abstract
Anemia is a prevalent public health problem associated with nutritional and socio-economic factors that contribute to iron deficiency. To understand the complex interplay of risk factors, we investigated a prospective population sample from the Jiangsu province in China. At baseline, three-day food intake was measured for 2849 individuals (20 to 87 years of age, mean age 47 ± 14, range 20–87 years, 64% women). At a five-year follow-up, anemia status was re-assessed for 1262 individuals. The dataset was split and age-matched to accommodate cross-sectional (n = 2526), prospective (n = 837), and subgroup designs (n = 1844). We applied a machine learning framework (self-organizing map) to define four subgroups. The first two subgroups were primarily from the less affluent North: the High Fibre subgroup had a higher iron intake (35 vs. 21 mg/day) and lower anemia incidence (10% vs. 25%) compared to the Low Vegetable subgroup. However, the predominantly Southern subgroups were surprising: the Low Fibre subgroup showed a lower anemia incidence (10% vs. 27%), yet also a lower iron intake (20 vs. 28 mg/day) compared to the High Rice subgroup. These results suggest that interventions and iron intake guidelines should be tailored to regional, nutritional, and socio-economic subgroups.
Highlights
Anemia is a condition characterised by the reduction of red blood cell mass which leads to an inferior oxygen supply to tissues [1], affecting 25% of the global population [2] with the highest prevalence in Africa but a larger absolute number of cases in Asia [3]
(10% vs. 27%), yet a lower iron intake (20 vs. 28 mg/day) compared to the High Rice subgroup. These results suggest that interventions and iron intake guidelines should be tailored to regional, nutritional, and socio-economic subgroups
Our results provide an important data-driven insight into the complex nutritional and demographic diversity that contributes to anemia susceptibility in different subpopulations
Summary
Anemia is a condition characterised by the reduction of red blood cell mass which leads to an inferior oxygen supply to tissues [1], affecting 25% of the global population [2] with the highest prevalence in Africa but a larger absolute number of cases in Asia [3]. The most common global risk factor for anemia is iron deficiency from malnutrition, malabsorption, or both, leading to a decreased production of red blood cells [2]. It is difficult to identify individuals at high risk for anemia due to the complex relationships between anemia and the different nutritional intakes, in addition to other genetic and environmental factors including socio-economic and political factors [1]. We sought to use a systematic approach to identify nutritional intake patterns of prevalent and incident anemia. Population-based cohorts are unlikely to show an intrinsic clustering structure due to genetic diversity, environmental factors, and the wide age range, which makes conventional approaches less practical. Our results provide an important data-driven insight into the complex nutritional and demographic diversity that contributes to anemia susceptibility in different subpopulations
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