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This study investigated the effect of diets negative in dietary cation–anion difference (DCAD) or restricted in Ca fed prepartum to dairy cows for three weeks on colostrum yield and composition, and the health and growth performance of their calves. Thirty-six pregnant non-lactating Holstein-Friesian cows were randomly assigned to three isoenergetic diets: (1) low Ca: 0.24% Ca, DCAD: +86 mEq/kg; (2) high Ca: 1.23% Ca, DCAD: +95 mEq/kg; and (3) low DCAD: 1.28% Ca, DCAD: −115 mEq/kg (all dry matter (DM) basis). While colostrum quality was not affected, low Ca supply prepartum tended to increase the colostrum yield compared to high Ca (low Ca = 8.81 vs. high Ca = 5.39 kg). However, calves from cows fed low DCAD showed higher serum concentrations of K, lower body weight (BW), starter feed intake and average daily weight gain before weaning compared to low Ca and high Ca calves (53.12 vs. 57.68 and 57.32 kg) but BW was similar postweaning (d 70). In addition, calves from dams fed low DCAD were more likely to develop diarrhea and had increased number of days with abnormal fecal scores. Consequently, calves from low DCAD dams had to be treated more frequently.

The Goettingen minipig developed at the University of Goettingen, Germany, is a special breed for medical research. As a laboratory animal it has to be as small and light as possible to facilitate handling during experiments. For achieving the breeding goal of small body size in the future, the growth pattern of the minipig was studied. This study deals with the analysis of minipig BW by modeling growth with linear and nonlinear functions and comparing the growth of the minipigs with that of normal, fattening pigs. Data were provided by Ellegaard Goettingen minipigs, Denmark, where 2 subpopulations of the Goettingen basis population are housed. In total 189,725 BW recordings of 33,704 animals collected from birth (d 0) to 700 d of age were analyzed. Seven nonlinear growth functions and 4 polynomial functions were applied. The growth models were compared by using the Akaike's information criterion (AIC). Regarding the whole growth curve, linear polynomials of third and fourth order of fit had the smallest AIC values, indicating the best fit for the minipig BW data. Among the nonlinear functions, the logistic model had the greatest AIC value. A comparison with fattening pigs showed that the minipigs have a nearly linear BW development in the time period from birth to 160 d. Fattening pigs have very low weight gains in their first 7 wk in relation to a specific end weight. After 7 wk, fattening pigs have increased growth, resulting in a growth curve that is more sigmoid than the growth curve of the minipig. Based on these results, further studies can be conducted to analyze the growth with random regression models and to estimate variance components for optimizing the strategies in minipig breeding.

Background The previously published “Dose Response Multicentre International Collaborative Initiative (DoReMi)” study concluded that the high mortality of critically ill patients with acute kidney injury (AKI) was unlikely to be related to an inadequate dose of renal replacement therapy (RRT) and other factors were contributing. This follow-up study aimed to investigate the impact of daily fluid balance and fluid accumulation on mortality of critically ill patients without AKI (N-AKI), with AKI (AKI) and with AKI on RRT (AKI-RRT) receiving an adequate dose of RRT. Methods We prospectively enrolled all consecutive patients admitted to 21 intensive care units (ICUs) from nine countries and collected baseline characteristics, comorbidities, severity of illness, presence of sepsis, daily physiologic parameters and fluid intake-output, AKI stage, need for RRT and survival status. Daily fluid balance was computed and fluid overload (FO) was defined as percentage of admission body weight (BW). Maximum fluid overload (MFO) was the peak value of FO. Results We analysed 1734 patients. A total of 991 (57 %) had N-AKI, 560 (32 %) had AKI but did not have RRT and 183 (11 %) had AKI-RRT. ICU mortality was 22.3 % in AKI patients and 5.6 % in those without AKI ( p  < 0.0001). Progressive fluid accumulation was seen in all three groups. Maximum fluid accumulation occurred on day 2 in N-AKI patients (2.8 % of BW), on day 3 in AKI patients not receiving RRT (4.3 % of BW) and on day 5 in AKI-RRT patients (7.9 % of BW). The main findings were: (1) the odds ratio (OR) for hospital mortality increased by 1.075 (95 % confidence interval 1.055–1.095) with every 1 % increase of MFO. When adjusting for severity of illness and AKI status, the OR changed to 1.044. This phenomenon was a continuum and independent of thresholds as previously reported. (2) Multivariate analysis confirmed that the speed of fluid accumulation was independently associated with ICU mortality. (3) Fluid accumulation increased significantly in the 3-day period prior to the diagnosis of AKI and peaked 3 days later. Conclusions In critically ill patients, the severity and speed of fluid accumulation are independent risk factors for ICU mortality. Fluid balance abnormality precedes and follows the diagnosis of AKI.

Obesity has been associated with a higher risk of mortality, whereas caloric restriction reduces the risk. In this study, we examined how body weight development during life affects lifespan in a mouse model for obesity. Therefore, mice of the Berlin Fat Mouse Inbred line were set on either a standard or a high-fat diet (HFD). Median lifespans of standard diet-fed mice were 525 and 539 days for males and female animals, respectively. HFD feeding further decreased lifespan by increasing the risk of mortality. Our data provide evidence that the highest body weight reached in lifetime has only a minor effect on lifespan. More important is the age when the highest body weight is reached, which was positively correlated with lifespan ( r =0.77, P <0.0001). Likewise, the daily gain of body weight was negatively correlated with the age of death ( r =−0.76, P <0.0001). These data indicate that rapid weight gain in early life followed by rapid weight loss affect lifespan more than the body weight itself. These data suggest that intervention strategies to prevent rapid weight gain are of high impact for a long lifespan.

White Leghorn roosters, 3-4 days after hatching, received single exposures to either 60 Co γ-rays or 200 kVp x-rays. Doses were in or immediately below the acute lethal range. Survivors at 35 days were caged individually and maintained for duration of life observations (85 control and 193 irradiated birds). Survival time, weight, and major pathologic findings were determined in control and in four irradiated groups (A, 24-hr γ-ray exposure to 700-1000 R, 30-day lethality <3%; B, 24-hr γ-ray exposure to 1200-2000 R, 30-day lethality 10-80%; C, 6-60 min γ-ray exposure to 700-1100 R, 30-day lethality 10-80%; D, 30-min x-ray exposure to 1000-1200 R, accompanied by prophylactic treatments that reduced acute lethality from 99% to 30-80%). Mean survival time of controls was about 6 years and maximum life span was 10.6 years. A single γ-ray exposure in the acute lethal range (groups B and C) resulted in a mean life shortening of about 50%, and the decrease in body weight (growth) was correlated with decrease in mean survival time. When the exposure time was increased from 1 hr to 24 hr, radiation effectiveness for life-span shortening and for weight was reduced. In x-irradiated birds protected against acute lethality by various prophylactic treatments, there was a differential effect on survival and growth; growth was more severely reduced than would be expected on the basis of 30-day mortality or life-span shortening. The principal radiation-induced lesions were those characteristic of cardiac failure; these were observed most frequently in birds that died before 5 years of age. Tumors were commonly testicular in origin; they were found with equal frequency in irradiated and unirradiated groups. The incidence of leukosis, a virus-induced neoplasia, was reduced in irradiated groups and was zero after the higher radiation exposures (groups B and D).

Abstract Obesity contributes to reduced life expectancy, impaired quality of life, and disabilities, mainly in those individuals who develop cardiovascular diseases, type 2 diabetes, osteoarthritis, and cancer. However, there is a large variation in the individual risk to developing obesity-associated comorbid diseases that cannot simply be explained by the extent of adiposity. Observations that a proportion of individuals with obesity have a significantly lower risk for cardiometabolic abnormalities led to the concept of metabolically healthy obesity (MHO). Although there is no clear definition, normal glucose and lipid metabolism parameters—in addition to the absence of hypertension—usually serve as criteria to diagnose MHO. Biological mechanisms underlying MHO lower amounts of ectopic fat (visceral and liver), and higher leg fat deposition, expandability of subcutaneous adipose tissue, preserved insulin sensitivity, and beta-cell function as well as better cardiorespiratory fitness compared to unhealthy obesity. Whereas the absence of metabolic abnormalities may reduce the risk of type 2 diabetes and cardiovascular diseases in metabolically healthy individuals compared to unhealthy individuals with obesity, it is still higher in comparison with healthy lean individuals. In addition, MHO seems to be a transient phenotype further justifying therapeutic weight loss attempts—even in this subgroup—which might not benefit from reducing body weight to the same extent as patients with unhealthy obesity. Metabolically healthy obesity represents a model to study mechanisms linking obesity to cardiometabolic complications. Metabolically healthy obesity should not be considered a safe condition, which does not require obesity treatment, but may guide decision-making for a personalized and risk-stratified obesity treatment. Graphical Abstract Graphical Abstract

Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks. Wellcome Trust, AstraZeneca Young Health Programme, EU.

Despite the well documented consequences of obesity during childhood and adolescence and future risks of excess body mass on non-communicable diseases in adulthood, coordinated global action on excess body mass in early life is still insufficient. Inconsistent measurement and reporting are a barrier to specific targets, resource allocation, and interventions. In this Article we report current estimates of overweight and obesity across childhood and adolescence, progress over time, and forecasts to inform specific actions. Using established methodology from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021, we modelled overweight and obesity across childhood and adolescence from 1990 to 2021, and then forecasted to 2050. Primary data for our models included 1321 unique measured and self-reported anthropometric data sources from 180 countries and territories from survey microdata, reports, and published literature. These data were used to estimate age-standardised global, regional, and national overweight prevalence and obesity prevalence (separately) for children and young adolescents (aged 5–14 years, typically in school and cared for by child health services) and older adolescents (aged 15–24 years, increasingly out of school and cared for by adult services) by sex for 204 countries and territories from 1990 to 2021. Prevalence estimates from 1990 to 2021 were generated using spatiotemporal Gaussian process regression models, which leveraged temporal and spatial correlation in epidemiological trends to ensure comparability of results across time and geography. Prevalence forecasts from 2022 to 2050 were generated using a generalised ensemble modelling approach assuming continuation of current trends. For every age-sex-location population across time (1990–2050), we estimated obesity (vs overweight) predominance using the log ratio of obesity percentage to overweight percentage. Between 1990 and 2021, the combined prevalence of overweight and obesity in children and adolescents doubled, and that of obesity alone tripled. By 2021, 93·1 million (95% uncertainty interval 89·6–96·6) individuals aged 5–14 years and 80·6 million (78·2–83·3) aged 15–24 years had obesity. At the super-region level in 2021, the prevalence of overweight and of obesity was highest in north Africa and the Middle East (eg, United Arab Emirates and Kuwait), and the greatest increase from 1990 to 2021 was seen in southeast Asia, east Asia, and Oceania (eg, Taiwan [province of China], Maldives, and China). By 2021, for females in both age groups, many countries in Australasia (eg, Australia) and in high-income North America (eg, Canada) had already transitioned to obesity predominance, as had males and females in a number of countries in north Africa and the Middle East (eg, United Arab Emirates and Qatar) and Oceania (eg, Cook Islands and American Samoa). From 2022 to 2050, global increases in overweight (not obesity) prevalence are forecasted to stabilise, yet the increase in the absolute proportion of the global population with obesity is forecasted to be greater than between 1990 and 2021, with substantial increases forecast between 2022 and 2030, which continue between 2031 and 2050. By 2050, super-region obesity prevalence is forecasted to remain highest in north Africa and the Middle East (eg, United Arab Emirates and Kuwait), and forecasted increases in obesity are still expected to be largest across southeast Asia, east Asia, and Oceania (eg, Timor-Leste and North Korea), but also in south Asia (eg, Nepal and Bangladesh). Compared with those aged 15–24 years, in most super-regions (except Latin America and the Caribbean and the high-income super-region) a greater proportion of those aged 5–14 years are forecasted to have obesity than overweight by 2050. Globally, 15·6% (12·7–17·2) of those aged 5–14 years are forecasted to have obesity by 2050 (186 million [141–221]), compared with 14·2% (11·4–15·7) of those aged 15–24 years (175 million [136–203]). We forecasted that by 2050, there will be more young males (aged 5–14 years) living with obesity (16·5% [13·3–18·3]) than overweight (12·9% [12·2–13·6]); while for females (aged 5–24 years) and older males (aged 15–24 years), overweight will remain more prevalent than obesity. At a regional level, the following populations are forecast to have transitioned to obesity (vs overweight) predominance before 2041–50: children and adolescents (males and females aged 5–24 years) in north Africa and the Middle East and Tropical Latin America; males aged 5–14 years in east Asia, central and southern sub-Saharan Africa, and central Latin America; females aged 5–14 years in Australasia; females aged 15–24 years in Australasia, high-income North America, and southern sub-Saharan Africa; and males aged 15–24 years in high-income North America. Both overweight and obesity increased substantially in every world region between 1990 and 2021, suggesting that current approaches to curbing increases in overweight and obesity have failed a generation of children and adolescents. Beyond 2021, overweight during childhood and adolescence is forecast to stabilise due to further increases in the population who have obesity. Increases in obesity are expected to continue for all populations in all world regions. Because substantial change is forecasted to occur between 2022 and 2030, immediate actions are needed to address this public health crisis. Bill & Melinda Gates Foundation and Australian National Health and Medical Research Council.

Background:Aerobic exercise is recommended for weight management but energy balance is often less negative than predicted from exercise energy expenditure (ExEE).Objective:To examine effects of active commuting and leisure-time exercise on fat loss in women and men with overweight and obesity.Methods:We randomized 130 younger, physically inactive women and men with overweight and obesity (body mass index: 25-35 kg m- 2 ) to 6 months of habitual lifestyle (control; CON, n=18), active commuting (BIKE, n=35) or leisure-time exercise of moderate (MOD, 50% VO2 peak reserve, n=39) or vigorous intensity (VIG, 70% VO2 peak reserve, n=38). The primary outcome was change in fat mass measured by dual-energy X-ray absorptiometry, which was analyzed intention-to-treat. Accumulated energy balance was calculated based on changes in body composition, and ExEE was calculated based on heart rate monitoring during exercise.Results:Testing at 3 and 6 months was completed by 95 and 90 participants, respectively. Fat mass was reduced after 3 and 6 months in BIKE (3 months: -3.6 (-5.5; -1.7) kg (mean (95% CI)); 6 months: -4.2 (-6.6; -1.9) kg; both: P<0.001), MOD (3 months: -2.2 (-3.9; -0.4) kg; 6 months: -2.6 (-4.8; -0.5) kg, both: P<0.02) and VIG (3 months: -3.4 (-5.2; -1.7) kg; 6 months: -4.5 (-6.6; -2.3) kg; both: P<0.001) compared with CON. Furthermore, fat loss was greater in VIG compared with MOD (6 months: -1.8 (-3.6; -0.1) kg, P=0.043). Based on the ExEE and the accumulated energy balance MOD compensated for the ExEE (77 (48; 106) %) but not BIKE (38 (-18; 95) %) and VIG (21 (-14; 55) %).Conclusions:A meaningful fat loss was obtained by 6 months of active commuting and leisure-time exercise, but fat loss was greater with vigorous compared with moderate intensity exercise. Active commuting is an alternative to leisure-time exercise in the management of overweight and obesity. The trial was registered at clinicaltrials.gov as NCT01962259 (main trial) and NCT01973686 (energy metabolism sub-study).

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight ( n  = 321,223) and offspring birth weight ( n  = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight–blood pressure association is attributable to genetic effects, and not to intrauterine programming. An expanded GWAS of birth weight and subsequent analysis using structural equation modeling and Mendelian randomization decomposes maternal and fetal genetic contributions and causal links between birth weight, blood pressure and glycemic traits.