Type 2 diabetes is a frequent chronic disease. Given its strong positive association with older age, it is a significant public health issue in elderly populations. Furthermore, the aging of the population, driven by increasing life expectancy in high and middle-income countries leads to an increasing prevalence of diabetes.Although the same diagnostic criteria apply to the elderly and to younger people, there are unique aspects to the care for elderly type 2 diabetes patients. Both treatment goals and preferred medications, as well as non-pharmacological approaches should be adjusted in the elderly. For example, increasing the amount of physical activity may encounter difficulties, while introducing an appropriate diet may be more challenging. The patients\' therapeutic adherence requires special attention due to cognitive and physical limitations. The most important treatment goal is to avoid hypoglycemia. Frailty, social and economic issues, comorbidities and the consequent polypharmacy frequently causing drug-drug interactions, as well as the increased danger of drug toxicity due to renal failure are only some of the problems that make the health care for old diabetes patients extremely difficult. Adequate care requires cooperation from a multidisciplinary team of health care professionals.Acute diabetes complications have a higher mortality in the elderly, thus close attention must be paid to avoid them. Family members should be involved in the care of elderly diabetes patients, and it is recommended to educate them on clinical signs of complications. Regular care for the patients including feedback on quality of life and early signs of health issues are essential.

BACKGROUND: WHO indicates that diabetes will become the 7th leading reason for death by 2030. The physiopathology of dysfunctioning is associated with obesity, weight gain and predominantly insulin resistance in insulin-sensitive cells and continuous deterioration of pancreatic beta cell function..Imeglimin is an investigational novel oral anti-diabetic drug.
OBJECTIVE: The motive of the review is to comprehensively explore the chemistry, biological and analytical analysis of the Imeglimin hydrochloride.
METHODS: To enhance the understanding, a systematic review was conducted by forming a database of relevant existing studies from electronic resources like Web of Science, ScienceDirect and PubMed. The methodology is reflected in the PRISMA design.
RESULTS: The drug was approved in the year 2021 for therapeutic purposes in Japan. It is the novel and first approved drug for this type of Anti-diabetic treatment. It is a small molecular drug whose molecular weight is 191.6 grams per mole utilized for oral administration. Imeglimin is thought to have both activities, as the amount of glucose is dependent on insulin secretory impact and insulin sensitivity is increased.
CONCLUSIONS: Therapeutic, pharmacological, and analytical considerations for the novel drug Imeglimin hydrochloride are discussed in this review.

Diabetes is very common in people over 75. A broad arsenal of treatments is now available. It is important, however, to choose the right treatment regimen to suit the patient\'s specific glycemic targets.

To examine longitudinal BMI trajectory from birth to age 10 years in a clinical cohort after exposure to maternal pre-existing type 1 (T1D), type 2 (T2D), gestational diabetes managed with or without anti-diabetes medication, and no diabetes during pregnancy.
Data included 218 227 singleton children born in 2008-2015 from a population-based integrated healthcare system; 537 exposed to maternal T1D, 7836 to T2D, 6982 to medicated GDM and 12 576 to unmedicated GDM. Differences in BMI over time among groups were assessed by non-linear mixed-effects models adjusting for covariates.
Children\'s BMI was significantly lower 6-months after birth for all diabetes exposed groups compared to no diabetes. Beginning at approximately age 2.5 years, BMI was significantly higher for T1D, T2D and medicated GDM groups compared to the no diabetes group. At age 3, the growth pattern started separating with highest BMI in T1D and T2D groups, followed by medicated GDM, unmedicated GDM, and the no diabetes groups. By age 7, BMI was significantly higher for the unmedicated GDM group compared to the no diabetes group. Adjusted BMI was generally comparable between T1D and T2D groups for all ages. Starting at age 5, T1D, T2D and medicated GDM groups had BMI greater than one SD over the BMI in the no diabetes group.
In a clinical cohort with standard diabetes management approaches, a hierarchical BMI growth pattern exists in offspring exposed to different types of diabetes during pregnancy after adjusting for important covariates, starting as early as age 3 years.

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today\'s multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them \'personalized\' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.