Effect of probiotic fermented milk on blood pressure: a meta-analysis of randomised controlled trials

Previous studies have suggested that probiotic fermented milk may possess blood pressure (BP)-lowering properties. In the present study, we aimed to systematically examine the effect of probiotic fermented milk on BP by conducting a meta-analysis of randomised controlled trials. PubMed, Cochrane library and the ClinicalTrials.gov databases were searched up to March 2012 to identify eligible studies. The reference lists of the obtained articles were also reviewed. Either a fixed-effects or a random-effects model was used to calculate the combined treatment effect. Meta-analysis of fourteen randomised placebo-controlled trials involving 702 participants showed that probiotic fermented milk, compared with placebo, produced a significant reduction of 3·10mmHg (95% CI −4·64, −1·56) in systolic BP and 1·09mmHg (95% CI −2·11, −0·06) in diastolic BP. Subgroup analyses suggested a slightly greater effect on systolic BP in hypertensive participants than in normotensive ones (−3·98 v. −2·09mmHg). Analysis of trials conducted in Japan showed a greater reduction than those conducted in European countries for both systolic BP (−6·12 v. −2·08mmHg) and diastolic BP (−3·45 v. −0·52mmHg). Some evidence of publication bias was present, but sensitivity analysis excluding small trials that reported extreme results only affected the pooled effect size minimally. In summary, the present meta-analysis suggested that probiotic fermented milk has BP-lowering effects in pre-hypertensive and hypertensive subject

Growth and development in Chinese pre-schoolers with picky eating behaviour: a cross-sectional study.

OBJECTIVE:To explore the associations between picky eating behaviour and pre-schoolers' growth and development. Corresponding potential mechanisms, such as nutrient and food subgroup intake, as well as micronutrients in the blood, will be considered. METHODS:Picky eating behaviour was present if it was reported by parents. From various areas of China, 937 healthy children of 3-7 years old were recruited using a multi-stage stratified cluster sampling method. Children and their mothers' socio-demographic information and children's anthropometry, intelligence, blood samples, one 24-hour dietary intake record and food frequency questionnaire were collected. Z-scores and intelligence tests were used to evaluate growth and development (cognitive development). Multilevel models were employed to verify the associations between picky eating behaviour and growth and development. RESULTS:The prevalence of picky eating as reported by parents was 54% in pre-schoolers. Compared with the non-picky eaters, weight for age in picky eaters was 0.14 z-score (95% CI: -0.25, -0.02; p = 0.017) lower while no significant difference was found in intelligence (p > 0.05). Picky eating behaviour lasting over two years was associated with lower weight for age, as was nit-picking meat (the prevalence from parents' perception was 23% in picky eaters) (p 0.05). CONCLUSIONS:Picky eating behaviour is reported by parents in half of the Chinese pre-schoolers, which is negatively associated with growth (weight for age). Lower protein and dietary fibre as well as lower iron and zinc intakes were associated with picky eating as were lower intakes of vegetables, fish and cereals

Socio-demographic characteristics of mother-pre-schooler dyads of the subjects.

<p>^a indicates significant differences between non-picky eating and picky eating groups, <i>p</i> < 0.05.</p><p>^b SE = standard error.</p><p>Socio-demographic characteristics of mother-pre-schooler dyads of the subjects.</p

Z-scores for height, weight, and BMI of pre-schoolers with picky eating habits of different durations, and adjusted associations between z-scores and duration of picky eating behaviours.

<p>^† All of the models were constructed by using multilevel (two levels) mixed-effects liner regression with the iterative generalised least-squares estimation method. Results of the z-scores from the regression models with adjustment for child’s birth weight and feeding pattern during the first four months after birth, mother’s education, and family’s per capita monthly income. Results of intelligence from the regression models with adjustment for child’s age, gender, birth weight, and feeding pattern during the first four months after birth, mother’s education, and family’s per capita monthly income.</p><p>^‡ β represents the difference in mean z-scores between the non-picky eating group and the picky eating group after adjusting for the covariates listed above.</p><p>^a SE = standard error,</p><p>^b CI, confidence interval.</p><p>Z-scores for height, weight, and BMI of pre-schoolers with picky eating habits of different durations, and adjusted associations between z-scores and duration of picky eating behaviours.</p

Parameters of growth and development of non-picky eating and picky eating groups.

<p>^a Values were given as Mean <b>±</b> SE.</p><p>^† All of the models were constructed by using multilevel (two levels) mixed-effects liner regression with the iterative generalised least-squares estimation method. Results of the measurements from the regression models with adjustment for child’s age, gender (female, male), birth weight, and feeding pattern during the first four months after birth (exclusive breastfeeding, mixed feeding, artificial feeding, and unclear), mother’s education (middle school or below, high school, college or above, and unclear), and family’s per capita monthly income (< 2000, 2000–4000, > 4000, and unclear, Yuan). Results of z-scores from the regression models with adjustment for child’s birth weight and feeding pattern during the first four months after birth, mother’s education, and household income.</p><p>^‡ β represents the difference in mean parameters of growth and development between non-picky eating and picky eating groups after adjusting for the covariates listed above.</p><p>Parameters of growth and development of non-picky eating and picky eating groups.</p

Influence of nit-picking of subgroups of food on growth and development of pre-schoolers.

<p>^† All of the models were constructed by using multilevel (two levels) mixed-effects liner regression with the iterative generalised least-squares estimation method. Results of the z-scores from the regression models with adjustment for child’s birth weight and feeding pattern during the first four months after birth, mother’s education, family’s per capita monthly income and other kinds of food related with children’s growth listed above.</p><p>^‡ β represents the difference in mean z-scores between the non-picky eating group and the picky eating group after adjusting for the covariates listed above.</p><p>^a CI, confidence interval.</p><p>Influence of nit-picking of subgroups of food on growth and development of pre-schoolers.</p

Comparison of the micronutrient content in whole blood of pre-schoolers in non-picky and picky eating groups.

<p>^a SE = standard error.</p><p>Comparison of the micronutrient content in whole blood of pre-schoolers in non-picky and picky eating groups.</p

Socio-demographic characteristics of mother-pre-schooler dyads of the subjects.

<p>^a indicates significant differences between non-picky eating and picky eating groups, <i>p</i> < 0.05.</p><p>^b SE = standard error.</p><p>Socio-demographic characteristics of mother-pre-schooler dyads of the subjects.</p

Comparison of intake (g/day) of various groups of food between pre-schoolers in non-picky and picky eating groups.

<p>^a indicates significant differences between non-picky eating and picky eating groups, <i>p</i> < 0.05.</p><p>^b SE = standard error.</p><p>^† Results of food intake from covariance analysis with adjustment for child’s gender and age.</p><p>Comparison of intake (g/day) of various groups of food between pre-schoolers in non-picky and picky eating groups.</p