A multivariate logistic regression equation to screen for dysglycaemia: development and validation

Aims  To develop and validate an empirical equation to screen for dysglycaemia [impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and undiagnosed diabetes]. Methods  A predictive equation was developed using multiple logistic regression analysis and data collected from 1032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, body mass index (BMI), post-prandial time (self-reported number of hours since last food or drink other than water), systolic blood pressure, high-density lipoprotein (HDL) cholesterol and random capillary plasma glucose as independent covariates for prediction of dysglycaemia based on fasting plasma glucose (FPG) ≥ 6.1 mmol/l and/or plasma glucose 2 h after a 75-g oral glucose load (2-h PG) ≥ 7.8 mmol/l. The equation was validated using a cross-validation procedure. Its performance was also compared with static plasma glucose cut-points for dysglycaemia screening. Results  The predictive equation was calculated with the following logistic regression parameters: P  = 1 + 1/(1 + e −X ) = where X = −8.3390 + 0.0214 (age in years) + 0.6764 (if female) + 0.0335 (BMI in kg/m 2 ) + 0.0934 (post-prandial time in hours) + 0.0141 (systolic blood pressure in mmHg) − 0.0110 (HDL in mmol/l) + 0.0243 (random capillary plasma glucose in mmol/l). The cut-point for the prediction of dysglycaemia was defined as a probability ≥ 0.38. The equation's sensitivity was 55%, specificity 90% and positive predictive value (PPV) 65%. When applied to a new sample, the equation's sensitivity was 53%, specificity 89% and PPV 63%. Conclusions  This multivariate logistic equation improves on currently recommended methods of screening for dysglycaemia and can be easily implemented in a clinical setting using readily available clinical and non-fasting laboratory data and an inexpensive hand-held programmable calculator. Diabet. Med. 22, 599–605 (2005)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75603/1/j.1464-5491.2005.01467.x.pd

Diabetes and cancer (2): evaluating the impact of diabetes on mortality in patients with cancer.

In this paper we address methodological aspects of aetiological importance in the link between diabetes and mortality in patients with cancer. We identified nine key points on the cancer pathway at which confounding may arise-cancer screening use, stage at diagnosis, cancer treatment selection, cancer treatment complications and failures, peri-treatment mortality, competing risks for long-term mortality, effects of type 2 diabetes on anti-cancer therapies, effects of glucose-lowering treatments on cancer outcome and differences in tumour biology. Two types of mortality studies were identified: (1) inception cohort studies that evaluate the effect of baseline diabetes on cancer-related mortality in general populations, and (2) cohorts of patients with a cancer diagnosis and pre-existing type 2 diabetes. We demonstrate, with multiple examples from the literature, that pre-existing diabetes affects presentation, cancer treatment, and outcome of several common cancer types, often to varying extents. Diabetes is associated with increased all-cause mortality in cancer patients, but the evidence that it influences cancer-specific mortality is inconsistent. In the absence of data that address the potential biases and confounders outlined in the above framework, we caution against the reporting of cancer-related mortality as a main endpoint in analyses determining the impact of diabetes and glucose-lowering medications on risk of cancer

Enzymatic reactions in confined environments

Within each biological cell, surface- and volume-confined enzymes control a highly complex network of chemical reactions. These reactions are efficient, timely, and spatially defined. Efforts to transfer such appealing features to in vitro systems have led to several successful examples of chemical reactions catalysed by isolated and immobilized enzymes. In most cases, these enzymes are either bound or adsorbed to an insoluble support, physically trapped in a macromolecular network, or encapsulated within compartments. Advanced applications of enzymatic cascade reactions with immobilized enzymes include enzymatic fuel cells and enzymatic nanoreactors, both for in vitro and possible in vivo applications. In this Review, we discuss some of the general principles of enzymatic reactions confined on surfaces, at interfaces, and inside small volumes. We also highlight the similarities and differences between the in vivo and in vitro cases and attempt to critically evaluate some of the necessary future steps to improve our fundamental understanding of these systems