South Australian Health and Medical Research Institute (SAHMRI), Adelaide SA 5000, Australia.
School of Biological Sciences, University of Adelaide, Adelaide SA 5005, Australia.
Nutrients. 2017 Oct 21;9(10):1153. doi: 10.3390/nu9101153.
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 ( = 2526), prospective ( = 837), and subgroup designs ( = 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.
贫血是一个普遍存在的公共卫生问题，与营养和社会经济因素有关，这些因素导致缺铁。为了了解风险因素的复杂相互作用，我们调查了来自中国江苏省的一个前瞻性人群样本。在基线时，对 2849 个人（年龄在 20 至 87 岁之间，平均年龄为 47 ± 14 岁，范围为 20-87 岁，女性占 64%）进行了为期三天的饮食摄入测量。在五年的随访中，对 1262 个人的贫血状况进行了重新评估。数据集被分割并按年龄匹配，以适应横断面（n = 2526）、前瞻性（n = 837）和亚组设计（n = 1844）。我们应用了机器学习框架（自组织映射）来定义四个亚组。前两个亚组主要来自较不富裕的北方：高纤维亚组的铁摄入量（35 毫克/天与 21 毫克/天）较高，贫血发生率（10%与 25%）较低，而低蔬菜亚组则相反。然而，主要来自南方的亚组则令人惊讶：低纤维亚组的贫血发生率（10%与 27%）较低，但其铁摄入量（20 毫克/天与 28 毫克/天）也低于高水稻亚组。这些结果表明，干预措施和铁摄入量指南应根据地区、营养和社会经济亚组进行调整。
