Improving Vascular Function in Overweight Postmenopausal Women
Among 53 postmenopausal women recruited from the Step by Step Program, 47 attended the final clinical exploration and completed the study protocol (Figure 1). The mean attendance at PA sessions was 74%. After the intervention, an increase in METs/hour/week (7.3 [2.9] vs 14.2 [1.1], P < 0.001) was observed. Anthropometric measurements, Mediterranean diet score, body fat distribution, and general biochemical characteristics did not change during the intervention ( Table 1 ).
(Enlarge Image)
Figure 1.
Participants' flow diagram.
Variations in antioxidant enzymes and vascular determinants between baseline and the end of the intervention are shown in Table 2 . We observed significant increases in intraerythrocyte and plasma superoxide dismutase concentrations (SODc; 2,075 [874] vs 3,331 [1,055] U/grHb, P < 0.001) and intraerythrocyte GPXc (9,506 [3,488] vs 12,628 [2,472] µmol/min/grHb, P < 0.001). saRHI increased (1.97 [0.51] vs 2.26 [0.77], P = 0.043, nonsignificant) according to a significant decrease in ADMA levels (0.48 [0.09] vs 0.40 [0.08] µmol/L, P < 0.001). Oxidized low-density lipoprotein cholesterol did not change significantly (83 [29] vs 77 [32] U/L, P = 0.291). RHR decreased by 6.6% (9.8%) after the intervention (P < 0.001).
Univariate tests showed that the increase in PA was positively associated with the increases in saRHI (r = 0.330, P = 0.027; Figure 2A) and GPXc (r = 0.299, P = 0.05). Moreover, PA inversely correlated with changes in glucose (r = −0.310, P = 0.038), body weight (r = −0.451, P = 0.002), and RHR (r = −0.297, P = 0.047).
(Enlarge Image)
Figure 2.
A: Association between changes in small artery reactive hyperemia index (saRHI) and changes in physical activity. Univariate associations were derived from Spearman correlation analysis.
B: Association between changes in small artery reactive hyperemia index and changes in resting heart rate. Univariate associations were derived from Spearman correlation analysis. MET, metabolic equivalent task.
Changes in saRHI inversely correlated to changes in RHR (r = −0.364, P = 0.021; Figure 2B). Participants with greater increases in saRHI had greater increases in SODc levels compared with participants who had smaller increases in saRHI (2,134 [411] vs 326 [286] U/grHb, P = 0.04).
A multiple stepwise logistic regression test was performed to assess the main determinants of endothelial function improvement, using saRHI increase as the dependent variable. The independent variables included age, smoking, increase in METs/hour/week, decrease in waist circumference, GPXc increase, SODc increase, changes in RHR, and ADMA decrease. After adjustment for interactions, the best predictor model (R Nagelkerke = 0.427; 81.4% of correctly prognosticated values) included smoking, increase in METs/hour/ week, changes in RHR, and increase in GPXc. When this test was forced, the increase in METs/hour/week (β = 2.63; 95% CI, 1.24-4.19; P = 0.019), changes in RHR (β = 1.96; 95% CI, 1.01-5.03; P = 0.048), and increase in GPXc (β = 2.64; 95% CI, 1.18-5.08; P = 0.021) remained independent predictors of saRHI improvement (Figure 3).
(Enlarge Image)
Figure 3.
Determinants of small artery reactive hyperemia. Dependent variable: small artery reactive hyperemia index (saRHI) changes between baseline and postintervention. Independent variables: smoking, increase in metabolic equivalent tasks (METs)/hour/week, changes in resting heart rate, and increase in glutathione peroxidase erythrocyte lysate (GPXc). R Nagelkerke = 0.42; 81.4% of correctly prognosticated values.
Results
Among 53 postmenopausal women recruited from the Step by Step Program, 47 attended the final clinical exploration and completed the study protocol (Figure 1). The mean attendance at PA sessions was 74%. After the intervention, an increase in METs/hour/week (7.3 [2.9] vs 14.2 [1.1], P < 0.001) was observed. Anthropometric measurements, Mediterranean diet score, body fat distribution, and general biochemical characteristics did not change during the intervention ( Table 1 ).
(Enlarge Image)
Figure 1.
Participants' flow diagram.
Variations in antioxidant enzymes and vascular determinants between baseline and the end of the intervention are shown in Table 2 . We observed significant increases in intraerythrocyte and plasma superoxide dismutase concentrations (SODc; 2,075 [874] vs 3,331 [1,055] U/grHb, P < 0.001) and intraerythrocyte GPXc (9,506 [3,488] vs 12,628 [2,472] µmol/min/grHb, P < 0.001). saRHI increased (1.97 [0.51] vs 2.26 [0.77], P = 0.043, nonsignificant) according to a significant decrease in ADMA levels (0.48 [0.09] vs 0.40 [0.08] µmol/L, P < 0.001). Oxidized low-density lipoprotein cholesterol did not change significantly (83 [29] vs 77 [32] U/L, P = 0.291). RHR decreased by 6.6% (9.8%) after the intervention (P < 0.001).
Univariate tests showed that the increase in PA was positively associated with the increases in saRHI (r = 0.330, P = 0.027; Figure 2A) and GPXc (r = 0.299, P = 0.05). Moreover, PA inversely correlated with changes in glucose (r = −0.310, P = 0.038), body weight (r = −0.451, P = 0.002), and RHR (r = −0.297, P = 0.047).
(Enlarge Image)
Figure 2.
A: Association between changes in small artery reactive hyperemia index (saRHI) and changes in physical activity. Univariate associations were derived from Spearman correlation analysis.
B: Association between changes in small artery reactive hyperemia index and changes in resting heart rate. Univariate associations were derived from Spearman correlation analysis. MET, metabolic equivalent task.
Changes in saRHI inversely correlated to changes in RHR (r = −0.364, P = 0.021; Figure 2B). Participants with greater increases in saRHI had greater increases in SODc levels compared with participants who had smaller increases in saRHI (2,134 [411] vs 326 [286] U/grHb, P = 0.04).
A multiple stepwise logistic regression test was performed to assess the main determinants of endothelial function improvement, using saRHI increase as the dependent variable. The independent variables included age, smoking, increase in METs/hour/week, decrease in waist circumference, GPXc increase, SODc increase, changes in RHR, and ADMA decrease. After adjustment for interactions, the best predictor model (R Nagelkerke = 0.427; 81.4% of correctly prognosticated values) included smoking, increase in METs/hour/ week, changes in RHR, and increase in GPXc. When this test was forced, the increase in METs/hour/week (β = 2.63; 95% CI, 1.24-4.19; P = 0.019), changes in RHR (β = 1.96; 95% CI, 1.01-5.03; P = 0.048), and increase in GPXc (β = 2.64; 95% CI, 1.18-5.08; P = 0.021) remained independent predictors of saRHI improvement (Figure 3).
(Enlarge Image)
Figure 3.
Determinants of small artery reactive hyperemia. Dependent variable: small artery reactive hyperemia index (saRHI) changes between baseline and postintervention. Independent variables: smoking, increase in metabolic equivalent tasks (METs)/hour/week, changes in resting heart rate, and increase in glutathione peroxidase erythrocyte lysate (GPXc). R Nagelkerke = 0.42; 81.4% of correctly prognosticated values.
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