Improvements in feed intake of dairy cows entering the early lactation period potentially decrease the risk of metabolic disorders, but before developing approaches targeting the intake level, mechanisms controlling and dysregulating energy balance and feed intake need to be understood. This review focuses on different inflammatory pathways interfering with the neuroendocrine system regulating feed intake of periparturient dairy cows. Subacute inflammation in various peripheral organs often occurs shortly before or after calving and is associated with increased pro-inflammatory cytokine levels. These cytokines are released into the circulation and sensed by neurons located in the hypothalamus, the key brain region regulating energy balance, to signal reduction in feed intake. Besides these peripheral humoral signals, glia cells in the brain may produce pro-inflammatory cytokines independent of peripheral inflammation. Preliminary results show intensive microglia activation in early lactation, suggesting their involvement in hypothalamic inflammation and the control of feed intake of dairy cows. On the other hand, pro-inflammatory cytokine-induced activation of the vagus nerve transmits signalling to the brain, but this pathway seems not exclusively necessary to signal feed intake reduction. Yet, less studied in dairy cows so far, the endocannabinoid system links inflammation and the hypothalamic control of feed intake. Distinct endocannabinoids exert anti-inflammatory action but also stimulate the posttranslational cleavage of neuronal proopiomelanocortin towards β-endorphin, an orexigen promoting feed intake. Plasma endocannabinoid concentrations and hypothalamic β-endorphin levels increase from late pregnancy to early lactation, but less is known about the regulation of the hypothalamic endocannabinoid system during the periparturient period of dairy cows. Dietary fatty acids may modulate the formation of endocannabinoids, which opens new avenues to improve metabolic health and immune status of dairy cows.

Childhood obesity has multiple causes, most of them capable of explaining only one part of the problem. The population-wide impact of sedentary lifestyles and availability of energy-dense food is undeniable, but substantial individual differences in body weight persist, suggesting that individuals respond differently to the ‘obesogenic’ environment. One plausible mechanism for this variation is the early expression of appetitive traits, including low responsiveness to internal satiety signals, high responsiveness to external food cues, high subjective reward experienced when eating liked foods and preferences for energy-dense foods. Case–control studies support the existence of abnormalities in these traits among obese children compared with normal-weight children, and correlations between psychometric measures of child appetite and child weight suggest that appetitive trait profiles may not only promote obesity but also protect against it. The origins of appetitive traits are as yet uncharted, but will include both genetic and environmental influences. Parental feeding style may affect the development of appetite but the exact nature of the relevant behaviours is unclear and many studies are cross-sectional or begin late in childhood, obscuring causal relationships. Future research should explore determinants and biological mechanisms by using prospective designs beginning early in life, measuring relevant biomarkers such as gut hormones and incorporating neuroimaging and genotyping technologies. Potential clinical applications include the identification of ‘at risk’ children early in life and interventions to modify appetitive traits or ameliorate their impact on intake and weight.

The aim of this work was to develop an index describing the relative intake of the total diet by dairy cows, and hence the ability to predict intake responses to changes in both forage and concentrate variables. An evaluation of concentrate factors affecting silage dry matter (DM) intake of dairy cows was conducted based on dietary treatment means from milk production experiments. The data were divided into four subsets according to concentrate treatments used within the experiments: the amount of concentrate supplementation (n = 217), protein supplementation (n = 336), carbohydrate composition (n = 114) and fat concentration of the concentrate (n = 29). The data were subjected to mixed-model regression analysis. Increased concentrate DM intake (CDMI) decreased silage DM intake (SDMI) quadratically. The substitution rate (substitution of silage DM for concentrate DM) increased with improved silage intake potential. SDMI increased quadratically with concentrate protein intake, the response being negatively related to the effective protein degradability (EPD) of concentrates. Replacement of starchy concentrate ingredients with fibrous supplements had a small positive effect on silage intake, whereas increased concentrate fat concentration slightly decreased SDMI. The outcome of concentrate factors influencing total DM intake (TDMI) was used to create a relative CDMI index as follows: CDMI index = 100 + 10 × [(CDMI − 0.1629 × CDMI − 0.01882 × CDMI2 − 5.49) + ((0.9474 × CCPI − 0.4965 × CCPI2) − 2.017 × (CEPD − 0.74)) + 0.00225 × (CNDF − 250) − 0.0103 × (40 − Cfat) − 0.00058 × (CDMI − 8.0) × (SDMI index − 100)], where CDMI = concentrate DM intake (kg/day), CCPI = supplementary concentrate CP intake (kg/day; CP>170 g/kg DM), CEPD = concentrate EPD (g/g), CNDF = concentrate NDF concentration (g/kg DM), Cfat = concentrate fat concentration (g/kg DM) and SDMI index is the relative intake potential of silage (Huhtanen, Rinne and Nousiainen 2007. Animal 1, 758–770). TDMI index was calculated as SDMI index + CDMI index − 100 to describe the relative intake potential of the total diet. For the whole data set (n = 943), one TDMI index unit was equivalent to 0.095 kg/day DM intake, i.e. close to the default value of 0.100 kg. The CDMI index explained proportionally 0.88 of the variation in TDMI within a study with a 0.27 kg/day residual mean-square error (n = 616). The corresponding values for the TDMI index were 0.81 and 0.37 kg/day (n = 943), respectively. The residual mean-square errors in cross-validation were marginally higher. The developed TDMI index can be used to estimate the intake responses to diet changes. It provides an improved basis for practical dairy cow ration formulation and economic evaluation.