Similar effects of leucine rich and regular dairy products on muscle mass and functions of older polymyalgia rheumatica patients: A randomized crossover trial

Objectives  

Leucine-rich milk and whey proteins have been suggested for prevention of age related loss of muscle mass and strength i.e. sarcopenia. The effects of milk protein supplementation and low intensity home based physical exercise on body composition and muscle functions were investigated.     

GAD Antibody Positivity Predicts Type 2 Diabetes in an Adult Population

OBJECTIVE

To evaluate the significance of GAD antibodies (GADAs) and family history for type 1 diabetes (FH_T1) or type 2 diabetes (FH_T2) in nondiabetic subjects.

RESEARCH DESIGN AND METHODS

GADAs were analyzed in 4,976 nondiabetic relatives of type 2 diabetic patients or control subjects from Finland. Altogether, 289 (5.9%) were GADA⁺—a total of 253 GADA⁺ and 2,511 GADA⁻ subjects participated in repeated oral glucose tolerance tests during a median time of 8.1 years. The risk of progression to diabetes was assessed using Cox regression analysis.

RESULTS

Subjects within the highest quartile of GADA⁺ (GADA⁺_high) had more often first-degree FH_T1 (29.2 vs. 7.9%, P < 0.00001) and GADA⁺ type 2 diabetic (21.3 vs. 13.7%, P = 0.002) or nondiabetic (26.4 vs. 13.3%, P = 0.010) relatives than GADA⁻ subjects. During the follow-up, the GADA⁺ subjects developed diabetes significantly more often than the GADA⁻ subjects (36/253 [14.2%] vs. 134/2,511 [5.3%], P < 0.00001). GADA⁺_high conferred a 4.9-fold increased risk of diabetes (95% CI 2.8–8.5) compared with GADA⁻—seroconversion to positive during the follow-up was associated with 6.5-fold (2.8–15.2) and first-degree FH_T1 with 2.2-fold (1.2–4.1) risk of diabetes. Only three subjects developed type 1 diabetes, and others had a non–insulin-dependent phenotype 1 year after diagnosis. GADA⁺ and GADA⁻ subjects did not clinically differ at baseline, but they were leaner and less insulin resistant after the diagnosis of diabetes.

CONCLUSIONS

GADA positivity clusters in families with type 1 diabetes or latent autoimmune diabetes in adults. GADA positivity predicts diabetes independently of family history of diabetes, and this risk was further increased with high GADA concentrations.Latent autoimmune diabetes in adults (LADA) was introduced nearly 2 decades ago to separate a GAD antibody (GADA)-positive subgroup of adult patients initially diagnosed with type 2 diabetes (1,2). Using this definition with the add-on criteria of no exogenous insulin during the first 6–12 months, the prevalence of LADA among unselected “type 2 diabetic patients” is ∼25% in subjects younger than 35 years and between 4 and 13% in subjects older than 35 years at diagnosis in populations of European origin (3–9). In follow-up studies, a progressive defect in insulin secretion was observed in ∼50–60% of LADA patients within 6–10 years (3,10), which led to the inclusion of these patients as a slowly progressing form of type 1 diabetes in the last World Health Organization (WHO) classification of diabetes (11). However, both the existence of LADA as a distinct subgroup of diabetes and the criteria that should be used to diagnose it have been challenged (e.g., (12,13). The LADA group is heterogeneous, and most studies have been cross-sectional, whereas prospective studies including patients at or before diagnosis and population-based studies are few (3,4,14–16). Genetic background, especially for type 1 diabetes, may be a confounding factor, and we have shown that LADA was more frequent in families with both type 1 and type 2 diabetes than in families with type 2 diabetes only (17). Moreover, some data support that type 1 and type 2 diabetes cluster in same families (17–20), although this has been contradicted in a large U.K. study on parents of type 1 diabetic patients (21).In children, progression to diabetes has been associated with high antibody levels and early development of multiple autoantibodies, whereas subjects with a later appearance of antibodies had a slower progression (22–25). We have previously hypothesized that GADAs would be a marker of a subclinical autoimmune process and showed that GADA positivity was associated with a decrease in maximal insulin secretory capacity in nondiabetic subjects (26). If that is the case, GADAs should also be a predictor of future diabetes in adults. This was not supported by two studies on the general population (16,27), but a Swedish study reported a sixfold increased risk for diabetes in GADA⁺ subjects (15).In a prospective follow-up study of a large cohort of relatives of type 2 diabetic patients and population control subjects from Finland, we have now evaluated the predictive value of GADAs and family history for type 1 or type 2 diabetes in conjunction with the traditional risk factors for diabetes.     

Multi-detector computed tomography demonstrates smoke inhalation injury at early stage

A multitrauma victim was transported to our trauma centre. Smoke inhalation injury was suspected based on trauma history and clinical examination. The first trauma computer tomography (CT) obtained 2.8 h after the injury revealed subtle ground-glass opacifications with mainly peribronchial distribution and patchy peribronchial consolidations centrally in the left lung. A repeated scan showed a more distinctive demarcation of the peribronchial opacities, further substantiating the clinically verified smoke inhalation injury. The golden standard for diagnosing smoke inhalation injury still is fibroptic bronchoscopy examination. This paper shows that lesions typical to smoke inhalation injury appear much earlier than previously reported. Whether assessment of smoke inhalation injury severity using CT could clinically benefit patients is controversial and still requires further research. Multi-detector computed tomography is readily available in trauma centres and to simply neglect its potential as a diagnostic tool in some inhalation injury would be unwise.     

Metabolomic approaches to phenotype characterization and applications to complex diseases

Metabolites are the key regulators of systems homeostasis. As such, concentration changes of specific groups of metabolites may reflect systemic responses to environmental, therapeutic or genetic interventions. Thus, the study of metabolites is a powerful tool for the characterization of complex phenotypes as well as for the development of biomarkers for specific physiological responses. Therefore, metabolomics is a valuable platform for studies of complex diseases and the development of new therapies, both in nonclinical disease model characterization and clinical settings.