<p>The interaction between nutritional status and malaria disease is complex and often controversial. Nutritional deficiencies (macro- or micro-nutrient) are thought to lead to malnutrition with subsequent susceptibility to malaria infection. On the other hand severe malaria or repeated malaria infections lead to malnutrition. While the cause and effect are difficult to attribute, micronutrient deficiencies such as iron deficiency and malaria infection often co-exist and show complex interactions leading to mutually reinforced detrimental clinical effects.</p><p>That iron deficiency has adverse effects on human health is widely recognized. Iron plays a crucial role in processes of growth and cell division and in the transport of oxygen throughout the body. It is also important for the proliferation of cells of the immune system as well as for microorganisms including the malaria parasite. Iron deficiency results in a decrease in hemoglobin concentrations and subsequent anemia. However, the etiology of anemia is multi-factorial and may be affected, in addition, by several factors including malaria and host factors, especially hemoglobinopathies such as alpha-thalassemia and sickle cell trait. These hemoglobinopathies are also common in malaria endemic areas.</p><p>In this thesis, we have investigated the relationship between nutritional status, immunity, hemoglobinopathies and <i>falciparum</i> malaria in a cohort of children less than 8 years old living on the coast of Kenya. We have found that malaria was associated with malnutrition in an age-dependent fashion. Malaria was associated with subsequent underweight or stunting in children under the age of 2 years, but this effect was not there in older children. Also, we observed that iron deficiency was associated with protection of children against clinical malaria. Children who were iron deficient had a lower incidence of malaria episodes as compared to those who were iron replete.</p><p>While studies on the effects of single micronutrient deficiencies on components of the immune system are difficult to design and interpret, there is ample evidence that micronutrient deficiencies, in general, affect all components of immunity. In line with this, we found that nutritional iron status was associated with certain malaria-specific immunoglobulins and interleukin-4 mRNA levels. Iron deficient children had lower levels of malaria-specific IgG2 and IgG4 but higher expression levels of IL-4 mRNA as compared to the iron replete children. Finally, we observed a tendency towards a higher prevalence of iron deficiency in children carrying either alpha-thalassemia or sickle cell trait.</p>

The interaction between nutritional status and malaria disease is complex and often controversial. Nutritional deficiencies (macro- or micro-nutrient) are thought to lead to malnutrition with subsequent susceptibility to malaria infection. On the other hand severe malaria or repeated malaria infections lead to malnutrition. While the cause and effect are difficult to attribute, micronutrient deficiencies such as iron deficiency and malaria infection often co-exist and show complex interactions leading to mutually reinforced detrimental clinical effects. That iron deficiency has adverse effects on human health is widely recognized. Iron plays a crucial role in processes of growth and cell division and in the transport of oxygen throughout the body. It is also important for the proliferation of cells of the immune system as well as for microorganisms including the malaria parasite. Iron deficiency results in a decrease in hemoglobin concentrations and subsequent anemia. However, the etiology of anemia is multi-factorial and may be affected, in addition, by several factors including malaria and host factors, especially hemoglobinopathies such as alpha-thalassemia and sickle cell trait. These hemoglobinopathies are also common in malaria endemic areas. In this thesis, we have investigated the relationship between nutritional status, immunity, hemoglobinopathies and falciparum malaria in a cohort of children less than 8 years old living on the coast of Kenya. We have found that malaria was associated with malnutrition in an age-dependent fashion. Malaria was associated with subsequent underweight or stunting in children under the age of 2 years, but this effect was not there in older children. Also, we observed that iron deficiency was associated with protection of children against clinical malaria. Children who were iron deficient had a lower incidence of malaria episodes as compared to those who were iron replete. While studies on the effects of single micronutrient deficiencies on components of the immune system are difficult to design and interpret, there is ample evidence that micronutrient deficiencies, in general, affect all components of immunity. In line with this, we found that nutritional iron status was associated with certain malaria-specific immunoglobulins and interleukin-4 mRNA levels. Iron deficient children had lower levels of malaria-specific IgG2 and IgG4 but higher expression levels of IL-4 mRNA as compared to the iron replete children. Finally, we observed a tendency towards a higher prevalence of iron deficiency in children carrying either alpha-thalassemia or sickle cell trait.

Introdução - Nos últimos anos diversas hipóteses foram investigadas sobre a relação entre gordura corporal e a massa óssea. Objetivo - O presente estudo visa avaliar a associação de índices de gordura corporal e massa óssea em adultos e idosos. Metodologia - O estudo foi desenvolvido com os dados obtidos do estudo transversal de base populacional intitulado Inquérito Domiciliar de Saúde no Município de São Paulo (ISA Capital 2015), realizada de janeiro de 2015 a maio de 2016. A amostra foi composta por 296 indivíduos, sendo 129 adultos (18 a 59 anos) e 167 idosos (60 anos ou mais), de ambos os sexos. Utilizando os dados antropométricos, foram calculados os índices: Índice de Massa Corpórea (IMC), Índice de Conicidade (IC), Índice de Circularidade Corporal (ICC), Índice de Formato Corporal (IFC), Índice de Adiposidade Corporal (IAC), Índice de Gordura Corporal (IGC) e Índice de Adiposidade Visceral (IAV). Além disso, foram avaliados os dados de gordura corporal (GC) em kg, gordura visceral (GV em gramas), porcentagem de gordura corporal ( por centoGC) e densidade mineral óssea de corpo total (DMO CT), coluna lombar (DMO L1- L4) e do colo do fêmur (DMO femoral), obtidos pelo DXA (modelo Lunar iDXA Advance, GE Healthcare, Madison, WI, USA). Foram calculadas estatísticas descritivas (média, desvio-padrão, percentis); a normalidade foi testada por Anderson- Darling, foi aplicado o teste Mann-Whitney e a correlações de Spearman. A GC (kg) foi ajustada por sexo e idade e a DMO CT, L-L4 e femoral foram ajustadas por gênero, classe etária, atividade física, ingestão de álcool e tabagismo com o uso de Modelos Lineares Generalizados. Uma vez identificado o modelo mais adequado a uma variável resposta, procurou-se reduzir o número de parâmetros com uso do Critério de Informação Akaike (AIC). Para realizar essas análises foi utilizado o software SPSS, 23.0 (SPSS Inc, Chicago IL, USA) e R (Projeto para estatística em sistema computacional) for Windows, versão 3.4.1. O nível de significância adotado foi de 5 por cento. Resultados - No artigo 1 é apresentado uma revisão sobre a relação entre os índices antropométricos e de gordura corporal com Doenças Crônicas Não-Transmissíveis (DCNT) como diabetes mellitus, hipertensão arterial sistêmica, síndrome metabólica entre outras. No artigo 2, foi observado baixa proporção de osteoporose nos participantes. Na relação entre os índices antropométricos com a GC (kg), verificamos que com exceção do IFC e IAV, os demais índices apresentaram correlação positiva e significante com a GC em kg (p<0,001). Entretanto, o modelo que apresentou o melhor ajuste e associação para a GC foi o IGC (89,97 por cento), seguido do IMC (83,93 por cento). Na associação dos índices com a DMO nos 3 sítios (DMO CT, L-L4 e femoral), observamos baixos valores de predição dos modelos avaliados, sendo que o modelo que apresentou melhor associação foi o IMC para DMO femoral. Conclusão O índice antropométrico que mais se aproximou da GC (kg) foi o IMC, como observado na análise de correlação e confirmado na análise inferencial, uma vez que as fórmulas para obtenção de ambos os índices são muito semelhantes. Na relação com a DMO, nenhum índice antropométrico é capaz de predizer a DMO nos 3 sítios avaliados na amostra estudada. / Introduction - In recent years several hypotheses have been investigated on the relationship between body fat and bone mass. Objective - The present study aims to evaluate the association of body composition indexes and bone mass in adults and the elderly. Methods - The study was conducted in a subsample from the populationbased cross-sectional study titled Health Study of São Paulo (ISA-Capital Study- 2015), held from january 2015 to may 2016. This 396 individuals, 129 adults (18 to 59 years) and 167 elderly (60 and over), of both sexes. Data on demographic, anthropometric [weight (kg), height (m), waist circumference and hip (cm)], body composition (body fat distribution) and bone (bone mineral density and composition) analyzed. The ratios was calculated, ranked and analyzed: Body Mass Index (BMI), Body Adiposity Index (BAI), Body Roundness Index (BRI), a Body Shape Index (ABSI) and the Conicity Index (C index) and as a comparison method was the Fat Mass Index (FMI) obtained by DXA. Bone mineral density (BMD) was evaluated in the lumbar spine L1-L4 and femoral neck through energy dual beam absorptiometry technique, issued by an X-ray source - DXA (dual-energy X-ray absorptiometry) Lunar model iDXA Advance (GE Healthcare, Madison, WI, USA). Descriptive statistics (mean, standard deviation, percentiles) were calculated; the normality was tested by Anderson-Darling, thus the Mann-Whitney test and Spearman correlations were applied. The BF (kg) was adjusted by gender and age and the CT, L-L4 and femoral BMD were adjusted for gender, age group, physical activity, alcohol intake e smoking, with the use of Generalized Linear Models. Once the most appropriate model was identified for a response variable, we attempted to reduce the number of parameters using the Akaike Information Criterion (AIC). The SPSS software, 23.0 (SPSS Inc, Chicago IL, USA) and R (Computer statistical system design) for Windows, version 3.4.1 for data analysis were used. The level of significance was set at 5 per cent. Results - In article 1, it presents revision about the relationship between anthropometric and body fat indexes with chronic-degenerative diseases (CDC) such as diabetes mellitus, systemic arterial hypertension, metabolic syndrome, among others. In article 2 a low proportion of osteoporosis was observed in the participants. In the relationship between the anthropometric indices with the BF (kg), we observed that, with the exception of the IFC and VAI, the other indexes presented a positive and significant correlation with the BF in kg (p <0.001). However, the model that presented the best fit and association for BF was FMI (89.97 per cent), followed by BMI (83,93 per cent). In the association of the indexes with BMD in the 3 sites, we observed low values of prediction of the models evaluated, and the model that presented the best association was the BMI for femoral BMD. Conclusion - The anthropometric index that most approached the BF (kg) was BMI, as observed in the correlation analysis and confirmed in the inferential analysis, since the formulas to obtain both indices are very similar. In relation to BMD, no anthropometric index is able to predict BMD at the 3 sites evaluated in the studied sample.

Darbo tikslas – įvertinti antropometrinių indeksų ir lėtinių ligų rizikos veiksnių ryšius. Uždaviniai: Įvertinti tiriamųjų antropometrinius indeksus ir jų tarpusavio sąsajas; nustatyti lėtinių ligų rizikos veiksnių paplitimą tiriamojoje populiacijoje; palyginti skirtingų antropometrinių indeksų ryšį su lėtinių ligų rizikos veiksniais. Tyrimo metodika: Tiriamieji – atsitiktinai atrinkti Kauno miesto gyventojai, gimę 1964 metais, pirmą kartą ištirti 1977 metais pagal Juvenilinės hipertenzijos programą (n=1082). 2012 metais sveikatos patikrinime dalyvavo 511 asmenų (64,4 proc. galėjusių atvykti). Tiriamiesiems buvo atlikti antropometriniai matavimai, matuotas arterinis kraujospūdis ir atlikti biocheminiai kraujo tyrimai. Skaičiuotas kūno masės indeksas – KMI=svoris (kg)/ūgis2(m2). Antsvoris nustatytas, kai KMI buvo 25-29,9 kg/m2, nutukimas – kai KMI >30 kg/m2. Vyrų liemens apimtis >94 cm, o moterų – >80 cm laikyta padidėjusia. Padidėjęs liemens ir klubų santykis vyrams buvo >1, moterims – >0,85. Padidėjęs liemens ir ūgio santykis buvo >0,51. Statistinė duomenų analizė atlikta naudojant SPSS 16.0 for Windows programą. Rezultatai: Net 69,1 proc. vyrų ir 56,1 proc. moterų turėjo per didelį KMI, 57,0 proc. tirtųjų – per didelę liemens apimtį, 23,2 proc. vyrų ir 13,0 proc. moterų – padidėjusį liemens ir klubų santykį, 63,5 proc. vyrų ir 41,1 proc. moterų – padidėjusį liemens ir ūgio santykį. Visi antropometriniai indeksai buvo tarpusavyje susiję. Didėjant antropometrinių indeksų... [toliau žr. visą tekstą] / The aim of the study is to assess the associations between anthropometric indexes and risk factors of chronic diseases. Objectives: to evaluate anthropometric indexes and their interrelationship in Kaunas cohort; to determine prevalence of the risk factors of chronic diseases in the study population; to compare strength of associations between anthropometric indexes and risk factors of chronic diseases. Methods: In 1977, a random sample of Kaunas schoolchildren born in 1964 (n=1082) was examined in the first cross-sectional survey. In 2012, 511 subjects participated in 35-year follow-up survey (64.4% response rate). Health examination involved measurements of blood pressure, anthropometric and biochemical parameters. The body mass index was calculated - BMI=weight (kg)/ūgis2 (m2). Overweight was defined when BMI was 25 - 29.9 kg/m2 and obesity – when BMI >30 kg/m2. Waist circumference >94 cm for men and >80 cm for women was considered as increased. Waist-to-hip ratio > 1 for men and >0.85 for women was defined as increased. Waist-to-height ratio >0.51 was considered as increased. Statistical analyzes were performed using SPSS 16.0 for Windows program. Results: The study has revealed that even 69.1% of men and 56.1% of women were overweight or obese, 57.0% of participants had increased waist circumference, 23.2% of men and 13.0% of women - increased waist-to-hip ratio, 63.5% of men and 41.1% of women - increased waist-to-height ratio. All anthropometric indexes were... [to full text]

Introdução - Nos últimos anos diversas hipóteses foram investigadas sobre a relação entre gordura corporal e a massa óssea. Objetivo - O presente estudo visa avaliar a associação de índices de gordura corporal e massa óssea em adultos e idosos. Metodologia - O estudo foi desenvolvido com os dados obtidos do estudo transversal de base populacional intitulado Inquérito Domiciliar de Saúde no Município de São Paulo (ISA Capital 2015), realizada de janeiro de 2015 a maio de 2016. A amostra foi composta por 296 indivíduos, sendo 129 adultos (18 a 59 anos) e 167 idosos (60 anos ou mais), de ambos os sexos. Utilizando os dados antropométricos, foram calculados os índices: Índice de Massa Corpórea (IMC), Índice de Conicidade (IC), Índice de Circularidade Corporal (ICC), Índice de Formato Corporal (IFC), Índice de Adiposidade Corporal (IAC), Índice de Gordura Corporal (IGC) e Índice de Adiposidade Visceral (IAV). Além disso, foram avaliados os dados de gordura corporal (GC) em kg, gordura visceral (GV em gramas), porcentagem de gordura corporal ( por centoGC) e densidade mineral óssea de corpo total (DMO CT), coluna lombar (DMO L1- L4) e do colo do fêmur (DMO femoral), obtidos pelo DXA (modelo Lunar iDXA Advance, GE Healthcare, Madison, WI, USA). Foram calculadas estatísticas descritivas (média, desvio-padrão, percentis); a normalidade foi testada por Anderson- Darling, foi aplicado o teste Mann-Whitney e a correlações de Spearman. A GC (kg) foi ajustada por sexo e idade e a DMO CT, L-L4 e femoral foram ajustadas por gênero, classe etária, atividade física, ingestão de álcool e tabagismo com o uso de Modelos Lineares Generalizados. Uma vez identificado o modelo mais adequado a uma variável resposta, procurou-se reduzir o número de parâmetros com uso do Critério de Informação Akaike (AIC). Para realizar essas análises foi utilizado o software SPSS, 23.0 (SPSS Inc, Chicago IL, USA) e R (Projeto para estatística em sistema computacional) for Windows, versão 3.4.1. O nível de significância adotado foi de 5 por cento. Resultados - No artigo 1 é apresentado uma revisão sobre a relação entre os índices antropométricos e de gordura corporal com Doenças Crônicas Não-Transmissíveis (DCNT) como diabetes mellitus, hipertensão arterial sistêmica, síndrome metabólica entre outras. No artigo 2, foi observado baixa proporção de osteoporose nos participantes. Na relação entre os índices antropométricos com a GC (kg), verificamos que com exceção do IFC e IAV, os demais índices apresentaram correlação positiva e significante com a GC em kg (p<0,001). Entretanto, o modelo que apresentou o melhor ajuste e associação para a GC foi o IGC (89,97 por cento), seguido do IMC (83,93 por cento). Na associação dos índices com a DMO nos 3 sítios (DMO CT, L-L4 e femoral), observamos baixos valores de predição dos modelos avaliados, sendo que o modelo que apresentou melhor associação foi o IMC para DMO femoral. Conclusão O índice antropométrico que mais se aproximou da GC (kg) foi o IMC, como observado na análise de correlação e confirmado na análise inferencial, uma vez que as fórmulas para obtenção de ambos os índices são muito semelhantes. Na relação com a DMO, nenhum índice antropométrico é capaz de predizer a DMO nos 3 sítios avaliados na amostra estudada. / Introduction - In recent years several hypotheses have been investigated on the relationship between body fat and bone mass. Objective - The present study aims to evaluate the association of body composition indexes and bone mass in adults and the elderly. Methods - The study was conducted in a subsample from the populationbased cross-sectional study titled Health Study of São Paulo (ISA-Capital Study- 2015), held from january 2015 to may 2016. This 396 individuals, 129 adults (18 to 59 years) and 167 elderly (60 and over), of both sexes. Data on demographic, anthropometric [weight (kg), height (m), waist circumference and hip (cm)], body composition (body fat distribution) and bone (bone mineral density and composition) analyzed. The ratios was calculated, ranked and analyzed: Body Mass Index (BMI), Body Adiposity Index (BAI), Body Roundness Index (BRI), a Body Shape Index (ABSI) and the Conicity Index (C index) and as a comparison method was the Fat Mass Index (FMI) obtained by DXA. Bone mineral density (BMD) was evaluated in the lumbar spine L1-L4 and femoral neck through energy dual beam absorptiometry technique, issued by an X-ray source - DXA (dual-energy X-ray absorptiometry) Lunar model iDXA Advance (GE Healthcare, Madison, WI, USA). Descriptive statistics (mean, standard deviation, percentiles) were calculated; the normality was tested by Anderson-Darling, thus the Mann-Whitney test and Spearman correlations were applied. The BF (kg) was adjusted by gender and age and the CT, L-L4 and femoral BMD were adjusted for gender, age group, physical activity, alcohol intake e smoking, with the use of Generalized Linear Models. Once the most appropriate model was identified for a response variable, we attempted to reduce the number of parameters using the Akaike Information Criterion (AIC). The SPSS software, 23.0 (SPSS Inc, Chicago IL, USA) and R (Computer statistical system design) for Windows, version 3.4.1 for data analysis were used. The level of significance was set at 5 per cent. Results - In article 1, it presents revision about the relationship between anthropometric and body fat indexes with chronic-degenerative diseases (CDC) such as diabetes mellitus, systemic arterial hypertension, metabolic syndrome, among others. In article 2 a low proportion of osteoporosis was observed in the participants. In the relationship between the anthropometric indices with the BF (kg), we observed that, with the exception of the IFC and VAI, the other indexes presented a positive and significant correlation with the BF in kg (p <0.001). However, the model that presented the best fit and association for BF was FMI (89.97 per cent), followed by BMI (83,93 per cent). In the association of the indexes with BMD in the 3 sites, we observed low values of prediction of the models evaluated, and the model that presented the best association was the BMI for femoral BMD. Conclusion - The anthropometric index that most approached the BF (kg) was BMI, as observed in the correlation analysis and confirmed in the inferential analysis, since the formulas to obtain both indices are very similar. In relation to BMD, no anthropometric index is able to predict BMD at the 3 sites evaluated in the studied sample.