Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis.

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-ß deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Blood glucose minimum predicts maximal lactate steady state on running.

This study analyzed if the running speed corresponding to glucose minimum (GM) could predict the maximal lactate steady state (MLSS). Thirteen physically active men (25.2+/-4.2 years, 73.4+/-8.0 kg, 180.0+/-1.0 cm) completed three running tests on different days: 1) a 1 600-m time trial to calculate the average speed; 2) after 10-min of recovery from a 150-m sprint to elevate [lac], participants performed 6 series of 800-m respectively at 78, 81, 84, 87, 90 and 93% of the 1 600-m speed to identify the lactate minimum (LM) and GM speeds and 3) 2-4 constant intensity exercise sessions for the MLSS. Repeated measures ANOVA showed no differences between running speeds associated to the GM (201.7+/-23.8 m.min (-1)), LM (200.0+/-23.9 m.min (-1)) and MLSS (201.5+/-23.1 m.min (-1)), with high correlation between GM vs. LM (r=0.984), GM vs. MLSS (r=0.947) and LM vs. MLSS (r=0.961) (P<0.01). Bland and Altman plots showed good agreement [Bias (+/-95% CI)] for MLSS and GM [0.2(15.3) m.min (-1)], MLSS and LM [-1.4(13.2) m.min (-1)], as well as for LM and GM [1.7(8.5) m.min (-1)]. These running speeds occurred at approximately 84.4% of 1 600-m speed, which would have practical applications for exercise prescription. We concluded that GM running speed is a good predictor of the MLSS for physically active individuals.

Polynomial modeling for the identification of lactate minimum velocity by different methods.

AIM: The lactate minimum (LM) protocol has been used to assess aerobic fitness and to predict exercise intensity associated with the maximal blood lactate steady state. The aim of this study was to compare different methods to identify the lactate minimum velocity (LMV) on cycling. METHODS: Fourteen male cyclists (26.8+/-4.5 years; 173.2+/-6.1 cm; 67.3+/-5.2 kg; 5,8+/-2.9 years of training) performed the LM test in a velodrome. The protocol consisted of an all out 2 km time trial to elevate blood lactate (bLAC), followed by 8 min of recovery and then 6 bouts of 2 km starting 5 kmxh(-1) below the individual mean velocity for the 6 km performance. The velocity was incremented by 1 kmxh(-1) at each bout with 25 microL of capillary blood being collected for bLAC measurements (YSI 2700 STAT). The LMV was identified visually (vLMV), and by applying a second grade polynomial function on 6 (pLMV(6)) and 3 (pLMV(3)) incremental bouts. Additionally, a method where the bLACx work velocity(-1) quotients (LMVQ) were plotted against the correspondent velocity during the incremental test, identified the LMV by considering 6 (LMVQ(6)) or 3 bouts (LMVQ(3)). RESULTS: ANOVA showed no differences between vLMV (33.1+/-2.5 kmxh(-1)), pLMV(6) (32.9+/-2.5 kmxh(-1)), pLMV(3) (33.2+/-2.3 kmxh(-1)), LMVQ(6) (32.8+/-2.5 kmxh(-1)) and LMVQ(3) (33.4+/-2.3 kmxh(-1)), with high correlation among them. CONCLUSIONS: It was possible to identify the LMV by the methods proposed in the present study, even when the results of only 3 bouts of the test were modeled by polynomial function. Such an approach enables a more practical and economical test in addition to minimizing the discomfort due to several blood collections.