@misc{Schulze2018,
  author    = {Schulze, Matthias Bernd},
  title     = {Metabolic health in normal-weight and obese individuals},
  series = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  volume    = {62},
  journal   = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  number    = {4},
  publisher = {Springer},
  address   = {New York},
  issn      = {0012-186X},
  doi       = {10.1007/s00125-018-4787-8},
  pages     = {558 -- 566},
  year      = {2018},
  abstract  = {Cardiovascular complications are commonly associated with obesity. However, a subgroup of obese individuals may not be at an increased risk for cardiovascular complications; these individuals are said to have metabolically healthy obesity (MHO). In contrast, metabolically unhealthy individuals are at high risk of cardiovascular disease (CVD), irrespective of BMI; thus, this group can include individuals within the normal weight category (BMI 18.5-24.9kg/m(2)). This review provides a summary of prospective studies on MHO and metabolically unhealthy normal-weight (MUHNW) phenotypes. Notably, there is ongoing dispute surrounding the concept of MHO, including the lack of a uniform definition and the potentially transient nature of metabolic health status. This review highlights the relevance of alternative measures of body fatness, specifically measures of fat distribution, for determining MHO and MUHNW. It also highlights alternative approaches of risk stratification, which account for the continuum of risk in relation to CVD, which is observable for most risk factors. Moreover, studies evaluating the transition from metabolically healthy to unhealthy phenotypes and potential determinants for such conversions are discussed. Finally, the review proposes several strategies for the use of epidemiological research to further inform the current debate on metabolic health and its determination across different stages of body fatness.},
  language  = {en}
}
@article{SchibornSchulze2022,
  author    = {Schiborn, Catarina and Schulze, Matthias Bernd},
  title     = {Precision prognostics for the development of complications in diabetes},
  series = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  journal   = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  publisher = {Springer},
  address   = {New York},
  issn      = {0012-186X},
  doi       = {10.1007/s00125-022-05731-4},
  pages     = {16},
  year      = {2022},
  abstract  = {Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.},
  language  = {en}
}