# Metabolic Periparturient Diseases in Small Ruminants: An Update
## 2 Pregnancy Toxemia and Lactational Ketosis
### Prevention  
The prevention of hyperketonemia is based, in the first instance, on the effective supply of a balanced diet in nutrients and energy density. In the critical periods of occurrence, there is adequate functioning of the rumen and its flora and, consequently, the maximization supply of metabolizable energy. The nutritional management of animals depends on their productive capacity. In dairy animals with low milk production, ruminal pH is normally above 6, which reduces the probability of encountering chronic ruminal acidosis in these herds, normally grazing, with a low concentrate/fiber feed ratio. However, when it becomes necessary to increase the energy density of the diet (up to a ratio of 1.5), rumen pH tends to drop to values below 5.8 for more than 6 to 8 h a day, causing sub-acute ruminal acidosis [82; 83]. The chemical action of low pH, in addition to being able to cause ruminitis, alters the local flora with imbalances in the production of volatile fatty acids (decrease in propionic acid) [82] with the formation of toxic amino acids such as histidine. An optimal body condition score should be reached before the susceptible period in order to avoid weak and obese females at this time [84].  
Despite the semiquantitative determination of some metabolites in milk (e.g., BHBA), the sample analysis is performed off-site at a laboratory, and even some on-farm handheld devices have been studied, such as BHBA meters for ketotic ewes [85] and does [86]. These handheld devices are based on electrochemical technology, allowing an accurate mensuration of BHBA in blood samples in late pregnancy in ewes [87] (including subclinical PT [88] and does [86]. Nonetheless, when blood glucose levels are measured, it was reported that the distinction between normo and hypoglycemia is not suitable [86]. New sensors and algorithms are continuously under research and validation for use in small ruminants, e.g., [PERSON] et al. [89] (amperometric-based glucose biosensor) and [PERSON] et al. [90], for glucose and creatine in does, respectively. Biochemistry assigns seems to be the complement of the decision tree given by the mentioned threshold and composite indices for the effective implementation of precision medicine.  
Another approach for herd health management of metabolic diseases is based on the combination of sensor data and health monitoring, such as described in dairy cows [PERSON] et al. [91], but this approach remains incipient, and more applied research is required. In a recent review, [PERSON] and [PERSON] [92] described biosensors and intelligent wearable devices for monitoring cattle health. Some of them that combine rumination time and physical activity or even feeding behavior are reliable in detecting metabolic diseases. After validation, these systems may be translated to sheep and goats (e.g., [93; 94]). Specific monitoring systems, based on sensing, Internet of Things and machine learning have been studied to improve small ruminants' welfare [95; 96; 97]. These last systems allow an automatization of surveillance and detection of abnormalities at individual and group levels. The readaptation of these systems for health monitoring can also contribute to the early detection of metabolic and other periparturient diseases in individuals and to their control and prevention in specific groups or flocks. Moreover, the interrelationships between animal well-being and animal health or animal production are demonstrated in domestic ruminants [98; 99], although integrated approaches are required [100].  
Omics and metabolomics are other promising tools driving the understanding of the molecular mechanisms of these pathologies for better medicine precision and prevention. Despite the relationship between gut microbiome and metabolomics (see [80; 81]), the research on molecular metabolic mechanisms, the discovery of new genes related to fertility and fat metabolism (see [PERSON] et al. [27]) can be used to predict animals at risk, etc.