High-Protein Diet in Lactation Leads to a Sudden Infant Death-Like Syndrome in Mice

BACKGROUND: It is well accepted that reduced foetal growth and development resulting from maternal malnutrition are associated with a number of chronic conditions in later life. On the other hand such generation-transcending effects of over-nutrition and of high-protein consumption in pregnancy and lactation, a proven fact in all developed societies, are widely unknown. Thus, we intended to describe the generation-transcending effects of a high-protein diet, covering most relevant topics of human life like embryonic mortality, infant death, and physical health in later life. METHODS: Female mice received control food (21% protein) or were fed a high protein diet (42% protein) during mating. After fertilisation, females stayed on their respective diet until weaning. At birth, pups were put to foster mothers who were fed with standard food or with HP diet. After weaning, control diet was fed to all mice. All offspring were monitored up to 360 days after birth. We determined glucose-tolerance and measured cardiovascular parameters using a tip-catheter. Finally, abdominal fat amount was measured. RESULTS AND CONCLUSIONS: We identified a worried impact of high-protein diet during pregnancy on dams' body weight gain, body weight of newborns, number of offspring, and also survival in later life. Even more important is the discovery that high-protein diet during lactation caused a more than eight-fold increase in offspring mortality. The observed higher newborn mortality during lactation is a hitherto non-described, unique link to the still incompletely understood human sudden infant death syndrome (SIDS). Thus, although offspring of lactating mothers on high-protein diet might have the advantage of lower abdominal fat within the second half of life, this benefit seems not to compensate the immense risk of an early sudden death during lactation. Our data may implicate that both pregnant women and lactating mothers should not follow classica

The Queensland cloud seeding research program

In late 2006 the Queensland government decided to establish the Queensland Cloud Seeding Research Program (QCSRP) in southeastern Queensland to determine the feasibility of cloud seeding as a component of its long-term water management strategy. The Queensland water management strategy recognizes the need for a broad portfolio of water sources to account for the uncertainties and costs associated with each type of source. While it was not expected that cloud seeding would restore southeastern Queensland's water supply levels to pre-drought values, it seemed valuable to determine whether certain types of seeding techniques might impact rainfall and water supplies in the region and whether that impact could be quantified. The project was developed as a collaboration between a number of institutions from Australia, the United States, and South Africa, and included field measurements over the course of two wet seasons. A two-pronged approach was taken to a) conduct a randomized cloud seeding experiment and b) assemble state-of-the-art instrumentation systems to collect data on the complete physical process from cloud formation to seeding to precipitation

Survival rate and physiological parameters in adulthood.

<p>(<b>a</b>) Body weight at day 180; (<b>b</b>) Oral glucose tolerance test at day 150 (C-C: n = 10; HP-C: n = 7; C-HP: n = 8); (<b>c</b>) Kaplan Meier curve from weaning until end of experiment (d360); (<b>d</b>) Cardiovascular characterization of mice at day 360 via Tip-catheter (C-C: n = 8; HP-C: n = 7; C-HP: n = 7). Heart rate, left ventricular pressure (LVP), contractility (dP/dtmax), and mean arterial pressure (MAP); Data are the means ± SEM; group differences are calculated by one way of ANOVA (<b>4a, 4d</b>) *<i>P</i><0.05 <i>vs</i> control group (C-C); ^#<i>P</i><0.05 <i>vs</i> high protein-control (HP-C); or two way of ANOVA (<b>4b</b>), **<i>P</i><0.01; group differences (diet effects). For Kaplan-Meier analysis (<b>4c</b>) logrank calculation has been performed; ^$<i>P</i><0.05.</p

Scheme describing the generation of the different diet groups.

<p>Detailed description on the generation of the C-C, C-HP, and HP-C groups has been given in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017443#s2" target="_blank">Materials and Methods</a> and the Results parts. C  =  control diet, HP  =  high-protein diet.</p

Effect of diets during lactation on body weight, survival and milk composition.

<p>Litters were cross-fostered and standardized (8♂, 2♀) at birth. Resulting from dietary treatments 80 males and 20 females counted per group (control-control: C-C, high protein-control: HP-C, and control-high protein C-HP). (<b>a</b>) Pup body weight at weaning (21^st day of life) depending on diet group (C-C: 98 pups; HP-C: 98 pups; C-HP: 83 pups). (<b>b</b>) Number of death in lactation in the three examined groups shown in % (of 100 pups) for period from birth till weaning and (<b>c</b>) Number of death in lactation in the same period subdivided in five-day steps; (<b>d</b>) mRNA levels of whey acidic protein in mammary glands was measured in lactating mice on control diet (C; n = 8) or high-protein diet (HP; n = 9). Data are the means ± SEM; group differences are calculated by ANOVA (<b>3a</b>), Friedman test (<b>3c</b>; <i>P</i> = 0.018 C-HP <i>vs</i> C-C and HP-C) or Student's t-test (<b>3d</b>), *<i>P</i><0.05 <i>vs</i> control food (C), ***<i>P</i><0.001 <i>vs</i> control group (C-C).</p