BackgroundMultiple studies in various parts of the world have analysed the association of nutritional status on malaria using anthropometric measures, but results differ due to the heterogeneity of the study population, species of the parasite, and other factors involved in the host and parasite relationship. The aim of this study was to perform a systematic review on the inter-relationship of nutritional status based on anthropometry and malarial infection.MethodsTwo independent reviewers accessed the MEDLINE and LILACS databases using the same search terms related to malaria and anthropometry. Prospective studies associating anthropometry and malaria (incidence or severity) were selected. References from the included studies and reviews were used to increase the review sensitivity. Data were extracted using a standardized form and the quality of the prospective studies was assessed. Selected articles were grouped based on exposures and outcomes.ResultsThe search identified a total of 1688 studies: 1629 from MEDLINE and 59 from LILACS. A total of 23 met the inclusion criteria. Five additional studies were detected by reading the references of the 23 included studies and reviews, totaling 28 studies included. The mean sample size was 662.1 people, ranging from 57 to 5620. The mean follow-up was 365.8 days, ranging from 14 days to 1 year and 9 months, and nine studies did not report the follow-up period. Prospective studies assessing the relationship between malaria and malnutrition were mostly carried out in Africa. Of the 20 studies with malarial outcomes, fifteen had high and five had average quality, with an average score of 80.5 %. Most anthropometric parameters had no association with malaria incidence (47/52; 90.4 %) or parasite density (20/25; 80 %). However, the impact of malnutrition was noted in malaria mortality and severity (7/17; 41.2 %). Regarding the effects of malaria on malnutrition, malaria was associated with very few anthropometric parameters (8/39; 20.6 %).ConclusionsThis systematic review found that most of the evidence associating malaria and malnutrition comes from P. falciparum endemic areas, with a significant heterogeneity in studies’ design. Apparently malnutrition has not a great impact on malaria morbidity, but could have a negative impact on malaria mortality and severity. Most studies show no association between malaria and subsequent malnutrition in P. falciparum areas. In Plasmodium vivax endemic areas, malaria was associated with malnutrition in children. A discussion among experts in the field is needed to standardize future studies to increase external validity and accuracy.

We aimed to analyze the association between dietary iron intake and obesity assessed by BMI after adjustment for nutrient intake (macronutrients and fiber) and food groups. The study design was cross-sectional. Patients with type-2 diabetes (n = 1567; 63.1% males; mean age 62.3 ± 11.6 years) were included in the study. To assess diet, consumption of typical food groups was determined by a food frequency questionnaire. Obesity was defined as BMI ≥ 25 kg/m2. We performed a binary regression analysis between quartiles of iron intake and obesity by quartiles of age group. A direct linear association was found for the highest quartile of iron intake and obesity in the younger age group of 30 to 54 years (OR = 3.641, 95% CI = 1.020–12.990; p trend = 0.011). Multivariate analysis using food groups as opposed to nutrients revealed a positive trend for obesity in the younger age group after adjusting for lifestyle factors, energy intake and bean and vegetable intake (p trend = 0.023). In all participants, an inverse association was observed before adjustment by vegetable intake (OR = 0.453, 95% CI = 0.300–0.684; p trend = 0.001). Higher iron intake was associated with obesity independent of macronutrient and fiber intake but only in the youngest quartile of age group examined.

Although weight management plays a central role in diabetes treatment, the association between body weight and diabetic complications is not fully elucidated. Therefore, we investigated the relationship between BMI and risk of diabetic complications in patients with diabetes mellius (DM) using nationwide claims data. Analyzed were 91097 patients with DM without prior treatment-requiring diabetic eye disease (TRDED), initiation of dialysis (dialysis), coronary artery disease (CAD), cerebrovascular disease (CVD), heart failure (HF) or amputation (mean age 52 y, HbA1c 7.2%, median follow-up 4.5 y). Participants were divided into 6 groups according to BMI (BMI<20.0, 20.0-22.4, 22.5-24.9, 25.0-27.4, 27.5-29.0, ≥30.0). Risk of diabetic complications was examined by Cox regression analysis. There was an inverse correlation between the risk of dialysis and BMI; hazard ratio (HR) for dialysis in patients with BMI ≥30.0 was 0.46 (0.29-0.74). There was a U-shaped relationship between the risk of HF and BMI; HRs for HF in patients with BMI <20.0 and ≥30.0 were 1.97 (1.15-3.37) and 1.76 (1.27-2.45), respectively (Figure). High BMI was significantly associated with a higher risk of HF, but lower risks of TRDED and dialysis. The relationship between BMI and diabetic complications was inconsistent among complications, suggesting that the target weight should be individualized according to diabetic complications. Disclosure Y.Yaguchi: None. H.Sone: Research Support; Novo Nordisk, Astellas Pharma Inc., Kyowa Kirin Co., Ltd., Taisho Pharmaceutical Holdings Co., Ltd., Ono Pharmaceutical Co., Ltd., Eisai Co., Ltd., Takeda Pharmaceutical Co., Ltd. K.Fujihara: None. L.Khin: None. S.Wu: None. E.D.Ferreira: None. T.Sato: None. C.Horikawa: None. Y.Matsubayashi: None. K.Kato: None.

Although DR are important in managing diabetes, the traditional method, such as weighing foods and calculating nutrients, is burdensome to both patients and medical care providers. Although photo DR with smartphones using AI to identify the dish and calculate nutrients can be expected to reduce this burden, accuracy not been clarified. Furthermore, it is not clear to what extent accuracy improves when users and dietitians adjust the name of the dish and the portion consumed compared with AI only. In this study, photo DR taken with the smartphone app were analyzed by AI only, then adjusted by the users followed by further adjustments by a dietitian. We compared the nutrients calculated by AI, users, and dietitians to those by the traditional weighing method. Analyzed were 180 participants (mean age 58.7 y, BMI 26.7 kg/m2, 94/86 women/men, 22 with diabetes, energy intake [EI] 1640 kcal/day) The EIs (kcal/day) were calculated as 1502, 1619, and 1674 for AI, users, and dietitians, respectively, those were lower than that with the weighing method Spearman's rank correlation coefficients compared to the weighing method were 0.48, 0.88, and 0.88 for AI, users and dietitians, respectively, showing a weak correlation for AI and a strong correlation for users and dietitians. The user's adjustments greatly improved accuracy without increasing the burden on the dietitians. Disclosure M.Kitazawa: None. H.Sone: Research Support; Novo Nordisk, Astellas Pharma Inc., Kyowa Kirin Co., Ltd., Taisho Pharmaceutical Holdings Co., Ltd., Ono Pharmaceutical Co., Ltd., Eisai Co., Ltd., Takeda Pharmaceutical Co., Ltd. C.Horikawa: None. I.Ikeda: None. Y.Takeda: None. E.D.Ferreira: None. M.Takeuchi: None. S.Y.Morikawa: None. K.Fujihara: None. S.Kodama: None. Funding Grants-in-Aid for Scientific Research (21K17637)