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.

Máxima velocidade aeróbia calculada pelo custo da frequência cardíaca: relação com a performance / Velocidad aeróbica máxima calculada por el costo del ritmo cardíaco: relación con el rendimiento / Maximal aerobic speed calculated by heart rate cost: relationship with performance

Objetivos. Comparar a máxima velocidade aeróbia (MVA) calculada pelo custo de oxigênio (vVO2max) e o custo da frequência cardíaca (vFCmax) com a medida direta da MVA (Vpico) e verificar a relação entre a vFCmax e a performance em provas de 10 e 15 km de corredores recreacionais de meia idade. Método. Participaram 21 corredores (idade: 30‐49 anos), subdivididos em 2 grupos a partir da idade (G1 e G2). Os participantes foram submetidos a um teste incremental contínuo máximo em laboratório para determinação do consumo máximo de oxigênio. A MVA foi determinada a partir das propostas apresentadas na literatura com base no vVO2max e no vFCmax. Além disso, foram realizadas 2 performances em pista de atletismo (10 e 15 km). Resultados. A menor diferença entre as médias observada para a Vpico foi em relação à vFCmax de Lacour et al. (0,0 km h–1; p > 0,05). A maior diferença foi em relação à vFCmax de Di Prampero (1,55 km h–1; p < 0,05). O mesmo padrão de diferenças foi observado quando analisado o G1 e G2. A vFCmax determinada a partir de 2 diferentes métodos propostos na literatura se correlacionou com as provas de 10 e 15 km (0,55 ≤ r ≤ 0,82; p < 0,05). Conclusões. A vFCmax em corredores recreacionais de meia idade tem elevada relação com as performances de 10 e 15 km e não foi diferente da Vpico (para vFCmax de Lacour et al.), apresentando resultados semelhantes aos observados pelos métodos baseados no custo de oxigênio (AU)

Objetivo: Comparar la velocidad aeróbica máxima (VAM), calculada a través del costo de oxígeno (vVO2max) y del costo de la frecuencia cardíaca (vFCmáx), con la medida directa de la VAM (Vpico) y verificar la relación entre la vFCmax y la performance de 10 e 15 km de corredores recreativos de mediana edad. Método: Participaron 21 corredores recreativos (edades: 30-49 años) subdivididos en 2 grupos por edad (G1 y G2). Los participantes se sometieron a un test incremental continuo máximo en laboratorio para la determinación del consumo máximo de oxígeno. La MVA fue determinada a través de las propuestas presentadas en la literatura basada en el vVO2max y el vFCmáx. Además, se realizaron 2 pruebas en pista de atletismo (10 e 15 km). Resultados: La menor diferencia entre las medias observadas para la Vpico fue en relación con la vFCmax de Lacour et al. (0,0 km h-1; p > 0,05). La mayor diferencia fue en relación con la vFCmax de Di Prampero (1,55 km h-1; p < 0,05). El mismo patrón de diferencias fue observado cuando se analizaron el G1 y G2. La vFCmáx determinada a través de 2 distintos métodos propuestos en la literatura se correlacionó con las pruebas de 10 y 15 km (0,55 ≤ r ≤ 0,82; p < 0,05). Conclusiones: La vFCmáx, en corredores recreativos de mediana edad, tiene alta correlación con las pruebas de 10 y 15 km y no fue diferente de la Vpico (para vFCmáx de Lacour et al.), presentando resultados similares a los observados por los métodos basados en el costo de oxígeno

Objectives. To compare maximal aerobic speed (MAS) calculated by oxygen cost (vVO2max) and heart rate cost (vHRmax) with the direct measurement of MAS (Vpeak) and to verify the relationship between vHRmax and 10‐ and 15‐km performance in middle‐age recreationally runners. Method. Twenty one recreationally runners participated in this study (age: 30 to 49 years), allocated in two groups according to age (G1 and G2). Participants were submitted to an incremental continuous test of maximal effort in laboratory to determine maximal oxygen uptake. MAS was determined according to proposes presented in literature based on vVO2max and vHRmax. Besides, it was performed two performances in field track (10 and 15 km). Results. The lowest difference between the mean values observed and Vpeak was in relation to vHRmax from Lacour et al. (0.0 km h–1; p > 0.05). The highest was in relation to vHRmax from di Prampero (1.55 km h–1; p < 0.05). The same pattern of differences was observed when G1 and G2 were analyzed. The vHRmax determined according to two different methods presented in literature showed to be correlated with 10 and 15 km performances (0.55 ≤ r ≤ 0.82; p < 0.05). Conclusions. The vHRmax in middle‐aged recreational runners has elevated correlation with 10 and 15 km performances and was not different from Vpeak (to vHRmax from Lacour et al.) showing similar results than the method based on oxygen cost (AU)

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.