Individual milk fatty acids are potential predictors of enteric methane emissions from dairy cows fed a wide range of diets: Approach by meta-analysis.

There is a need to quantify methane (CH4) emissions with alternative methods. For the past decade, milk fatty acids (MFA) could be used as proxies to predict CH4 emissions from dairy cows because of potential common rumen biochemical pathways. However, equations have been developed based on a narrow range of diets and with limited data. The objectives of this study were to (1) construct a set of empirical models based on individual data of CH4 emissions and MFA from a large number of lactating dairy cows fed a wide range of diets; (2) further increase the models' level of complexity (from farm to research level) with additional independent variables such as dietary chemical composition (organic matter, neutral detergent fiber, crude protein, starch, and ether extract), dairy performance (milk yield and composition), and animal characteristics (days in milk or body weight); and (3) evaluate the performance of the developed models on independent data sets including measurements from individual animals or average measurements of groups of animals. Prediction equations based only on MFA [C10:0, iso C17:0 + trans-9 C16:1,cis-11 C18:1, and trans-11,cis-15 C18:2 for CH4 production (g/d); iso C16:0, cis-11 C18:1, trans-10 C18:1, and cis-9,cis-12 C18:2 for CH4 yield (g/kg of dry matter intake, DMI); and iso C16:0, cis-15 C18:1, and trans-10 + trans-11 C18:1 for CH4 intensity (g/kg of milk)] had a root mean squared error of 65.1 g/d, 2.8 g/kg of DMI, and 2.9 g/kg of milk, respectively, whereas complex equations that additionally used DMI, dietary neutral detergent fiber, ether extract, days in milk, and body weight had a lower root mean squared error of 46.6 g/d, 2.6 g/kg of DMI, and 2.7 g/kg of milk, respectively). External evaluation with individual or mean data not used for equation development led to variable results. When evaluations were performed using individual cow data from an external data set, accurate predictions of CH4 production (g/d) were obtained using simple equations based on MFA. Better performance was observed on external evaluation with individual data for the simple equation of CH4 production (g/d, based on MFA), whereas better performance was observed on external evaluation mean data for the simple equation of CH4 yield (g/kg of DMI). The performance of evaluation of the models is dependent on the domain of validity of the evaluation data sets used (individual or mean).

Effects of diet and exercise interventions on diabetes risk factors in adults without diabetes: meta-analyses of controlled trials.

BACKGROUND AND AIMS: Fasting insulin (FI), fasting glucose (FG), systolic blood pressure (SBP), high density lipoproteins (HDL), triacylglycerides (TAG), and body mass index (BMI) are well-known risk factors for type 2 diabetes. Reliable estimates of lifestyle intervention effects on these factors allow diabetes risk to be predicted accurately. The present meta-analyses were conducted to quantitatively summarize effects of diet and exercise intervention programs on FI, FG, SBP, HDL, TAG and BMI in adults without diabetes. MATERIALS AND METHODS: MEDLINE and EMBASE were searched to find studies involving diet plus exercise interventions. Studies were required to use adults not diagnosed with type 2 diabetes, involve both dietary and exercise counseling, and include changes in diabetes risk factors as outcome measures. Data from 18, 24, 23, 30, 29 and 29 studies were used for the analyses of FI, FG, SBP, HDL, TAG and BMI, respectively. About 60% of the studies included exclusively overweight or obese adults. Mean age and BMI of participants at baseline were 48 years and 30.1 kg/m(2). Heterogeneity of intervention effects was first estimated using random-effect models and explained further with mixed-effects models. RESULTS: Adults receiving diet and exercise education for approximately one year experienced significant (P <0.001) reductions in FI (-2.56 ± 0.58 mU/L), FG (-0.18 ± 0.04 mmol/L), SBP (-2.77 ± 0.56 mm Hg), TAG (-0.258 ± 0.037 mmol/L) and BMI (-1.61 ± 0.13 kg/m(2)). These risk factor changes were related to a mean calorie intake reduction of 273 kcal/d, a mean total fat intake reduction of 6.3%, and 40 minutes of moderate intensity aerobic exercise four times a week. Lifestyle intervention did not have an impact on HDL. More than 99% of total variability in the intervention effects was due to heterogeneity. Variability in calorie and fat intake restrictions, exercise type and duration, length of the intervention period, and the presence or absence of glucose, insulin, or lipid abnormalities explained 23-63% of the heterogeneity. CONCLUSIONS: Calorie and total fat intake restrictions coupled with moderate intensity aerobic exercises significantly improved diabetes risk factors in healthy normoglycemic adults although normoglycemic adults with glucose, insulin, and lipid abnormalities appear to benefit more.

Anti-methanogenic effects of monensin in dairy and beef cattle: a meta-analysis.

Monensin is a widely used feed additive with the potential to minimize methane (CH4) emissions from cattle. Several studies have investigated the effects of monensin on CH4, but findings have been inconsistent. The objective of the present study was to conduct meta-analyses to quantitatively summarize the effect of monensin on CH4 production (g/d) and the percentage of dietary gross energy lost as CH4 (Ym) in dairy cows and beef steers. Data from 22 controlled studies were used. Heterogeneity of the monensin effects were estimated using random effect models. Due to significant heterogeneity (>68%) in both dairy and beef studies, the random effect models were then extended to mixed effect models by including fixed effects of DMI, dietary nutrient contents, monensin dose, and length of monensin treatment period. Monensin reduced Ym from 5.97 to 5.43% and diets with greater neutral detergent fiber contents (g/kg of dry matter) tended to enhance the monensin effect on CH4 in beef steers. When adjusted for the neutral detergent fiber effect, monensin supplementation [average 32 mg/kg of dry matter intake (DMI)] reduced CH4 emissions from beef steers by 19±4 g/d. Dietary ether extract content and DMI had a positive and a negative effect on monensin in dairy cows, respectively. When adjusted for these 2 effects in the final mixed-effect model, monensin feeding (average 21 mg/kg of DMI) was associated with a 6±3 g/d reduction in CH4 emissions in dairy cows. When analyzed across dairy and beef cattle studies, DMI or monensin dose (mg/kg of DMI) tended to decrease or increase the effect of monensin in reducing methane emissions, respectively. Methane mitigation effects of monensin in dairy cows (-12±6 g/d) and beef steers (-14±6 g/d) became similar when adjusted for the monensin dose differences between dairy cow and beef steer studies. When adjusted for DMI differences, monensin reduced Ym in dairy cows (-0.23±0.14) and beef steers (-0.33±0.16). Monensin treatment period length did not significantly modify the monensin effects in dairy cow or beef steer studies. Overall, monensin had stronger antimethanogenic effects in beef steers than dairy cows, but the effects in dairy cows could potentially be improved by dietary composition modifications and increasing the monensin dose.

A mathematical model for determining age-specific diabetes incidence and prevalence using body mass index.

PURPOSE: Few models have been developed specifically for the epidemiology of diabetes. Diabetes incidence is critical in predicting diabetes prevalence. However, reliable estimates of disease incidence rates are difficult to obtain. The aim of this study was to propose a mathematical framework for predicting diabetes prevalence using incidence rates estimated within the model using body mass index (BMI) data. METHODS: A generic mechanistic model was proposed considering birth, death, migration, aging, and diabetes incidence dynamics. Diabetes incidence rates were determined within the model using their relationships with BMI represented by the Hill equation. The Hill equation parameters were estimated by fitting the model to National Health and Nutrition Examination Survey (NHANES) 1999-2010 data and used to predict diabetes prevalence pertaining to each NHANES survey year. The prevalences were also predicted using diabetes incidence rates calculated from the NHANES data themselves. The model was used to estimate death rate parameters and to quantify sensitivities of prevalence to each population dynamic. RESULTS: The model using incidence rate estimates from the Hill equations successfully predicted diabetes prevalence of younger, middle-aged, and older adults (prediction error, 20.0%, 9.64%, and 7.58% respectively). Diabetes prevalence was positively associated with diabetes incidence in every age group, but the associations among younger adults were stronger. In contrast, diabetes prevalence was more sensitive to death rates in older adults than younger adults. Both diabetes incidence and prevalence were strongly sensitive to BMI at younger ages, but sensitivity gradually declined as age progressed. Younger and middle aged adults diagnosed with diabetes had at least a two-fold greater risk of death than their nondiabetic counterparts. Nondiabetic older adults were found to be under slightly higher death risk (0.079) than those diagnosed with diabetes (0.073). CONCLUSIONS: The proposed model predicts diagnosed diabetes incidence and prevalence reasonably well using the link between BMI and diabetes development risk. Ethnic group and gender-specific model parameter estimates could further improve predictions. Model prediction accuracy and applicability need to be comprehensively evaluated with independent data sets.

Isoleucine and leucine independently regulate mTOR signaling and protein synthesis in MAC-T cells and bovine mammary tissue slices.

Understanding the regulatory effects of individual amino acids (AA) on milk protein synthesis rates is important for improving protein and AA requirement models for lactation. The objective of this study was to examine the effects of individual essential AA (EAA) on cellular signaling and fractional protein synthesis rates (FSR) in bovine mammary cells. Omission of L-arginine, L-isoleucine, L-leucine, or all EAA reduced (P < 0.05) mammalian target of rapamycin (mTOR; Ser2448) and ribosomal protein S6 (rpS6; Ser235/236) phosphorylation in MAC-T cells. Phosphorylation of mTOR and rpS6 kinase 1 (S6K1; Thr389) decreased (P < 0.05) in the absence of L-isoleucine, L-leucine, or all EAA in lactogenic mammary tissue slices. Omission of L-tryptophan also reduced S6K1 phosphorylation (P = 0.01). Supplementation of L-leucine to media depleted of EAA increased mTOR and rpS6 and decreased eukaryotic elongation factor 2 (Thr56) phosphorylation (P < 0.05) in MAC-T cells. Supplementation of L-isoleucine increased mTOR, S6K1, and rpS6 phosphorylation (P < 0.05). No single EAA considerably affected eukaryotic initiation factor 2-α (eIF2α; Ser51) phosphorylation, but phosphorylation was reduced in response to provision of all EAA (P < 0.04). FSR declined when L-isoleucine (P = 0.01), L-leucine (P = 0.01), L-methionine (P = 0.02), or L-threonine (P = 0.07) was depleted in media and was positively correlated (R = 0.64, P < 0.01) with phosphorylation of mTOR and negatively correlated (R = -0.42, P = 0.01) with phosphorylation of eIF2α. Such regulation of protein synthesis will result in variable efficiency of transfer of absorbed EAA to milk protein and is incompatible with the assumption that a single nutrient limits protein synthesis that is encoded in current diet formulation strategies.

Essential amino acids regulate both initiation and elongation of mRNA translation independent of insulin in MAC-T cells and bovine mammary tissue slices.

Current nutrient requirement models assume fixed efficiencies of absorbed amino acid (AA) conversion to milk protein. Regulation of mammary protein synthesis (PS) potentially violates this assumption by changing the relationship between AA supply and milk protein output. The objective of this study was to investigate the effects of essential AA (EAA) and insulin on cellular signaling and PS rates in bovine mammary cells. MAC-T cells were subjected to 0 or 100% of normal EAA concentrations in DMEM/F12 and 0 or 100 µg insulin/L in a 2 × 2 factorial arrangement of treatments. Lactogenic bovine mammary tissue slices (MTS) were subjected to the same treatments, except low-EAA was 5% of normal DMEM/F12 concentrations. In MAC-T cells, EAA increased phosphorylation of mammalian target of rapamycin (mTOR; Ser2448), ribosomal protein S6 kinase 1 (S6K1; Thr389), eIF4E binding protein 1 (4EBP1; Thr37/46), and insulin receptor substrate 1 (IRS1; Ser1101), and reduced phosphorylation of eukaryotic elongation factor 2 (eEF2; Thr56) and eukaryotic initiation factor (eIF) 2-α (Ser51). In the presence of insulin, phosphorylation of Akt (Ser473), mTOR, S6K1, 4EBP1, and IRS1 increased in MAC-T cells. In MTS, EAA had similar effects on phosphorylation of signaling proteins and increased mammary PS rates. Insulin did not affect MTS signaling, perhaps due to inadequate levels. Effects of EAA and insulin were independent and additive for mTOR signaling in MAC-T cells. EAA did not inhibit insulin stimulation of Akt phosphorylation. PS rates were strongly associated with phosphorylation of 4EBP1 and eEF2 in MTS. EAA availability affected translation initiation and elongation control points to more strongly regulate PS than insulin.