Comparison of Two Approaches for the Metataxonomic Analysis of the Human Milk Microbiome.

Recent work has demonstrated the existence of large inter-individual and inter-population variability in the microbiota of human milk from healthy women living across variable geographical and socio-cultural settings. However, no studies have evaluated the impact that variable sequencing approaches targeting different 16S rRNA variable regions may have on the human milk microbiota profiling results. This hampers our ability to make meaningful comparisons across studies. In this context, the main purpose of the present study was to re-process and re-sequence the microbiome in a large set of human milk samples (n = 412) collected from healthy women living at diverse international sites (Spain, Sweden, Peru, United States, Ethiopia, Gambia, Ghana and Kenya), by targeting a different 16S rRNA variable region and reaching a larger sequencing depth. Despite some differences between the results obtained from both sequencing approaches were notable (especially regarding alpha and beta diversities and Proteobacteria representation), results indicate that both sequencing approaches revealed a relatively consistent microbiota configurations in the studied cohorts. Our data expand upon the milk microbiota results we previously reported from the INSPIRE cohort and provide, for the first time across globally diverse populations, evidence of the impact that different DNA processing and sequencing approaches have on the microbiota profiles obtained for human milk samples. Overall, our results corroborate some similarities regarding the microbial communities previously reported for the INSPIRE cohort, but some differences were also detected. Understanding the impact of different sequencing approaches on human milk microbiota profiles is essential to enable meaningful comparisons across studies. Clinical Trial Registration: www.clinicaltrials.gov, identifier NCT02670278.

Multipathogen Analysis of IgA and IgG Antigen Specificity for Selected Pathogens in Milk Produced by Women From Diverse Geographical Regions: The INSPIRE Study.

Breastfeeding provides defense against infectious disease during early life. The mechanisms underlying this protection are complex but likely include the vast array of immune cells and components, such as immunoglobulins, in milk. Simply characterizing the concentrations of these bioactives, however, provides only limited information regarding their potential relationships with disease risk in the recipient infant. Rather, understanding pathogen and antigen specificity profiles of milk-borne immunoglobulins might lead to a more complete understanding of how maternal immunity impacts infant health and wellbeing. Milk produced by women living in 11 geographically dispersed populations was applied to a protein microarray containing antigens from 16 pathogens, including diarrheagenic E. coli, Shigella spp., Salmonella enterica serovar Typhi, Staphylococcus aureus, Streptococcus pneumoniae, Mycobacterium tuberculosis and other pathogens of global health concern, and specific IgA and IgG binding was measured. Our analysis identified novel disease-specific antigen responses and suggests that some IgA and IgG responses vary substantially within and among populations. Patterns of antibody reactivity analyzed by principal component analysis and differential reactivity analysis were associated with either lower-to-middle-income countries (LMICs) or high-income countries (HICs). Antibody levels were generally higher in LMICs than HICs, particularly for Shigella and diarrheagenic E. coli antigens, although sets of S. aureus, S. pneumoniae, and some M. tuberculosis antigens were more reactive in HICs. Differential responses were typically specific to canonical immunodominant antigens, but a set of nondifferential but highly reactive antibodies were specific to antigens possibly universally recognized by antibodies in human milk. This approach provides a promising means to understand how breastfeeding and human milk protect (or do not protect) infants from environmentally relevant pathogens. Furthermore, this approach might lead to interventions to boost population-specific immunity in at-risk breastfeeding mothers and their infants.

Critical windows for nutritional interventions against stunting.

An analysis of early growth patterns in children from 54 resource-poor countries in Africa and Southeast Asia shows a rapid falloff in the height-for-age z score during the first 2 y of life and no recovery until ≥5 y of age. This finding has focused attention on the period -9 to 24 mo as a window of opportunity for interventions against stunting and has garnered considerable political backing for investment targeted at the first 1000 d. These important initiatives should not be undermined, but the objective of this study was to counteract the growing impression that interventions outside of this period cannot be effective. We illustrate our arguments using longitudinal data from the Consortium of Health Oriented Research in Transitioning collaboration (Brazil, Guatemala, India, Philippines, and South Africa) and our own cross-sectional and longitudinal growth data from rural Gambia. We show that substantial height catch-up occurs between 24 mo and midchildhood and again between midchildhood and adulthood, even in the absence of any interventions. Longitudinal growth data from rural Gambia also illustrate that an extended pubertal growth phase allows very considerable height recovery, especially in girls during adolescence. In light of the critical importance of maternal stature to her children's health, our arguments are a reminder of the importance of the more comprehensive UNICEF/Sub-Committee on Nutrition Through the Life-Cycle approach. In particular, we argue that adolescence represents an additional window of opportunity during which substantial life cycle and intergenerational effects can be accrued. The regulation of such growth is complex and may be affected by nutritional interventions imposed many years previously.

Antropometría como predictor de diabetes gestacional: Estudio de cohorte / Anthropometry as predictor of gestational diabetes mellitus

Background: Gestational diabetes mellitus (GDM) is a high incidence disease. Easily measured predictor factors could help to implement preventive policies and early detection tests. Aim: To measure recognizable risk factors for GDM such as skinfolds and analyze the association between these factors and the development of GDM in a cohort of pregnant women. Material and Methods: Evaluation of 76 mothers that developed gestational diabetes, aged 32.2 ± 0.6 years and 324 control mothers that did not develop the disease, aged 30.1 ± 0.3 years. Weight, height, arm circumference, tricipital, bicipital, subscapular, suprailiac, knee, costal and mid-thigh skinfolds were measured in the pre-diseased stage. History of diabetes, fasting glucose and insulin levels were also evaluated. Results: Age, body mass index (BMI), fasting blood glucose, the homeostasis model assessment of insulin resistance (HOMA), bi-cipital, tricipital, costal, subscapular, suprailiac, and knee skinfolds were associated with GDM development. Age, fasting blood glucose and subscapular skinfolds were independent predictors in the logistic regression model. The odds ratio for a subs-capular skinfold over percentile 90 was 1.7 (95 percent confdence intervals: 1.07-3.04). Conclusions: Age and fasting blood glucose are independent risk factors for GDM. Subscapular skinfold is also an independent risk factor and could be used to detect high risk pregnant women and implement preventive policies.

Gestational diabetes, comparison of women diagnosed in second and third trimester of pregnancy with non GDM women: Analysis of a cohort study.

UNLABELLED: Pregnant women are normally screened for Gestational diabetes (GDM) at week 24 of pregnancy. However some women develop the disease later on their pregnancies. No study has analyzed women developing GDM later in pregnancy. OBJECTIVE: To analyze data on a cohort study and compare women diagnosed with GDM in second and third trimester of pregnancy with women without GDM. RESULTS: GDM women diagnosed during their first two trimesters of pregnancy were older (p = 0.0008) and had higher body mass index (BMI) (p = 0.0007) than non GDM women. However, the only risk factor in women diagnosed in their third trimester of pregnancy was having first degree relatives with type 2 DM and this was independent of age and BMI (OR of 2.7, 95% CI 1.2 - 6.0). CONCLUSIONS: Women who develop GDM in their second trimester of pregnancy have known risk factors for diabetes mellitus such as age and higher BMI, however, the only recognised risk factor between non GDM women and women developing GDM late in pregnancy is family history of type 2 DM. Two populations of GDM may exist and future studies should focus on analysing short and long term complications of these women to support the need to diagnosed and treat them all.

Gestational diabetes, comparison of women diagnosed in second and third trimester of pregnancy with non GDM women: Analysis of a cohort study / Diabetes gestational, comparación entre mujeres diagnosticadas en el segundo y tercer trimestre del embarazo con mujeres sin diabetes gestacional: Análisis de un estudio de cohorte

Pregnant women are normally screenedfor Gestational diabetes (GDM) at week 24 of pregnancy. Howeversome women develop the disease later on their pregnancies. No study has analyzed women developing GDM later in pregnancy. Objective: To analyze data on a cohort study and compare women diagnosed with GDM in second and third trimester of pregnancy with women without GDM. Results: GDM women diagnosed during their first two trimesters of pregnancy were older (p = 0.0008) and had higher body mass Índex (BMI) (p = 0.0007) than non GDM women. However, the only risk factor in women diagnosed in their third trimester of pregnancy was having first degree relatives with type 2 DM and this was independent of age and BMI (OR of2.7, 95 percent CI 1.2 - 6.0). Conclusions: Women who develop GDM in their second trimester of pregnancy have known risk factors for diabetes mellitus such as age and higher BMI, however, the only recognised risk factor between non GDM women and women developing GDM late in pregnancy is family history of type 2 DM. Two populations ofGDM may exist andfuture studies should focus on analysing short and long term complications ofthese women to support the need to diagnosed and treat them all.

La pesquisa para diabetes gestacional (DG) se realiza normalmente en la semana 24 de embarazo. Sin embargo, muchas mujeres desarrollan la enfermedad más tardíamente durante el embarazo. No hay estudios analizando DG en tercer trimestre del embarazo. Objetivo: Analizar los datos de una cohorte para comparar mujeres con DG diagnosticada en segundo y tercer trimestre del embarazo con mujeres sin DG. Resultados: Las mujeres diagnosticadas en los primeros dos trimestres del embarazo eran mayores (p = 0,0008) y tenían mayor índice de masa corporal (IMC) (p = 0,0007) que las mujeres sin DG. El único factor de riesgo en mujeres diagnosticadas en el tercer trimestre del embarazo fue tener antecedentes familiares de DM, lo cual fue independiente de la edad e IMC (OR: 2,7, 95 por ciento CI 1,2 - 6,0). Conclusiones: Mujeres con DG diagnosticada en el segundo trimestre del embarazo tienen distintos factores de riesgo que mujeres diagnosticadas más tardíamente. Es posible que existan dos poblaciones de DG según el período de diagnóstico, por lo que debiera estudiarse si las complicaciones de estos dos subgrupos justifican el diagnóstico y tratamiento de ambos.

[Anthropometry as predictor of gestational diabetes mellitus]. / Antropometría como predictor de diabetes gestacional: Estudio de cohorte.

BACKGROUND: Gestational diabetes mellitus (GDM) is a high incidence disease. Easily measured predictor factors could help to implement preventive policies and early detection tests. AIM: To measure recognizable risk factors for GDM such as skinfolds and analyze the association between these factors and the development of GDM in a cohort of pregnant women. MATERIAL AND METHODS: Evaluation of 76 mothers that developed gestational diabetes, aged 32.2 ± 0.6 years and 324 control mothers that did not develop the disease, aged 30.1 ± 0.3 years. Weight, height, arm circumference, tricipital, bicipital, subscapular, suprailiac, knee, costal and mid-thigh skinfolds were measured in the pre-diseased stage. History of diabetes, fasting glucose and insulin levels were also evaluated. RESULTS: Age, body mass index (BMI), fasting blood glucose, the homeostasis model assessment of insulin resistance (HOMA), bi-cipital, tricipital, costal, subscapular, suprailiac, and knee skinfolds were associated with GDM development. Age, fasting blood glucose and subscapular skinfolds were independent predictors in the logistic regression model. The odds ratio for a subscapular skinfold over percentile 90 was 1.7 (95% confdence intervals: 1.07-3.04). CONCLUSIONS: Age and fasting blood glucose are independent risk factors for GDM. Subscapular skinfold is also an independent risk factor and could be used to detect high risk pregnant women and implement preventive policies.