Primary Reference
Caciano, S., Inman, C., Gockel-Blessing, E., & Weiss, E. (2015). Effects of Dietary Acid Load on Exercise Metabolism and Anaerobic Exercise Performance. Journal of Sports Science and Medicine, 14.
Introduction: Variations and changes in an athlete’s diet can cause alterations in acid/base balance by as much as ~0.03 pH units. This change is accompanied by a urine pH alteration of ~1.0 pH unit. The influence of diet on acid load can be calculated by the potential renal acid load (PRAL) of the diet. The PRAL is influenced by the composition of the diet, with higher intakes of fruits and vegetables promoting systemic alkalinity and higher intakes of meats, grains, and cheeses promoting acidity. It has been previously hypothesized a low-PRAL diet increases bicarbonate availability, increasing the ability to produce non-metabolic CO2 during maximal exercise. Non-metabolic CO2 is responsible for increasing resting exchange ratio (RER) above 1.00 during maximal exercise. Thus, increases in bicarbonate availability will result in a higher RER. This increase in bicarbonate availability would be expected to improve anaerobic performance. Bicarbonate loading via sodium bicarbonate is a well-studied supplemental intervention which has demonstrated improvements in anaerobic performance. Currently, there is a lack of data examining dietary influences on performance through acid load manipulation. Therefore, the purpose of this study was to determine the effects of a short-term low-PRAL diet on RER and anaerobic performance.
The authors did an adequate job of detailing the background knowledge regarding diet, acid-load, and bicarbonate levels. They then connected the information by divulging the data concerning bicarbonate levels and metabolic CO2 and relating them to performance. It makes clinical sense if sodium bicarbonate ingestion improves performance, then diets which are more alkaline would probably improve performance. Additionally, it also makes clinical sense if increasing systemic alkalinity increases the ability to produce non-metabolic CO2, this would also cause RER to increase.
Subject Description: This study included men and women (n=10) between the ages of 18-60 years old. The subjects were considered healthy and at low risk for exercise complications as judged by the criteria provided by the American College of Sports Medicine. The subjects included both trained and untrained individuals.
Overall, I was not a huge fan of the authors’ subject description. There was no reporting of typical demographics, which includes things such as height, weight, VO2, etc. In short, there was a lack of baseline data. This makes it difficult for the reader to understand the population being studied.
Methods: The authors used a cross-over design, which increases the power of the study. Each subject participated in both arms of the study. The diet was written and provided by a dietitian whom gave the subjects specific instructions on how to modify their diet to achieve a low or high PRAL diet. The dietician contacted each subject daily either via phone or email to encourage compliance. The intervention to achieve a low PRAL was to increase the consumption of alkaline foods such as fruits and vegetables and to reduce acidity foods such as meats, cheeses, and grains. Specifically, 6-8 cups of vegetables along with > 4 servings of fruit were recommended. In order to balance calories, subjects were instructed to eat frequently and calorie-dense foods during the low-PRAL diet. The high-PRAL diet consisted of 3-4 servings of common grains (wheat, corn, oats), 3 servings of meat, and 3 servings of cheese each day with minimal fruit and vegetable intake. The high-PRAL diet received less counseling, as this diet resembled the average intake of the subjects. The goal of the low PRAL was to achieve a PRAL of ≤-1 mEq/d. The goal of the high-PRAL diet was to achieve a PRAL≥15 mEq/d. These were based off data cut points from the authors’ prior research. Food portions were adjusted to equalize calorie intake. Diets were a minimum of 4 days and continued until a fasted morning pH of ≥7.0 in the low-PRAL trial and ≤6.0 in the high-PRAL trial were achieved. Subjects were required to record 3 day food diaries.
Urine pH was measured to the nearest 0.5 pH using pH strips. This information was used to determine the duration of the diets and to confirm the diets adequately influenced acid load. Fasted morning urine pH was self-monitored by the participants each morning. On the morning of exercise testing, urine pH was again checked to ensure the subjects had achieved the desirable pH. Indirect calorimetry was performed during a graded treadmill exercise test to exhaustion. The graded exercise test (GXT) was initiated at a speed in which generated HR to ~ 70% age predicted maximal HR at a grade of 0%. The grade was increased 2 percentage points every 2 minutes until the subjects were unable to continue secondary to fatigue. This test was repeated following each intervention. Peak VO2 was considered true VO2 if measured HRmax ≥ age predicted HRmax minus 10 beats/min and rating of perceive exertion was ≥19 on the 6-20 point Borg scale. Age-predicted HR max was calculated using the formula 208-0.7 x age (year). All subjects met the criteria for VO2. Submaximal RER values were determined using linear regression for each test that described the relationship between oxygen uptake and RER. These subject-specific equations were used to determine RER at 70%, 80%, and 90% of VO2max. VO2 data was normalized to percentages of predicted VO2 based on the treadmill speed and grade. This is consistent with the recommendations from the American College of Sports Medicine. Upon completion of the GXT, the subjects rested for 10 minutes. Afterwards, subjects participated in the anaerobic exercise performance test. Performance measured was time-to-exhaustion while running on a treadmill with the speed set during the GXT with the addition of 2 percentage points steeper than the highest achieved. A priori power analysis was performed to determine the smallest detectable effect size. This calculation was carried out using an alpha of 0.05, power of 0.80, and with n=10. It was determined a treatment effect of ≥0.04 RERmax unit would be detectable.
The methods used in this study were decent, but left out some key issues in which I felt would have strengthened this study. It appears from the methods this study was probably considered a pilot study. The sample size is small and the authors were simply examining a short dietary intervention on exercise performance. The results could then be used to determine a better way to design a larger grandiose study. The authors did not include a familiarization session. Therefore, depending on the order of the diets, the subjects could experience a learning curve for the second test. During the second test the subjects may pace themselves better, thus withholding maximal performance. Another possibility is the subjects may perform better during the second test because they understand the expectation level better. A familiarization session would improve the methods. Additionally, there was no attempt to blind the subjects to the interventions. Therefore, the possibility of the placebo effect must be accounted for. It is plausible the subjects may feel they are being “healthier” in the fruits and vegetable groups, thus leading to better exercise performance. Another possibility for performance improvements may simply be the first exercise session results in improved adaptations, leading to better endurance and time to exhaustion in the second trial. Other factors also should have been taken under consideration. The amount of exercise the subjects regularly participated in should have been noted in the subject description, it was provided in the results. There was also no mention of avoiding strenuous activity 48 hours prior to the testing and avoiding alcohol consumption.
Results: All subjects completed the study in its entirety. Six participants were women and 3 were exercise trained (≥ 5 hours per week). Average age was 42 ± 5 yr (range: 19-60 yr). Baseline PRAL was 8.6 ± 5.7 mEq/day. During the high PRAL diet, PRAL was 33 ± 8 mEq/day. During the low PRAL diet, PRAL was -21 ± 4 mEq/day (p<0.001). Dietary intake of protein, phosphorus, potassium, and calcium differed between the diets. Total energy, carbohydrate, and fat intake did not differ. Carbohydrate intake exceeded the recommended dietary allowance of 130 g/day in the low PRAL (245 ± 20% of RDA) and high PRAL diets ((272 ± 49% of RDA) (p>0.0001, p>0.004). All subjects achieved the recommended urine pH goals during both interventions. On average, subjects took 4.3± 0.1 days to achieve the high PRAL pH and 6.8 ± 0.4 days for the low PRAL pH. Maximal respiratory exchange ratio was greater during the high PRAL treatment compared to the low PRAL treatment. Additionally, during submaximal testing, there were tendencies (p-values of 0.059 to 0.077) for higher RER in the high PRAL group. All subjects met the “true” criteria for VO2max. No differences between sessions was noted for VO2max. HRmax was 97% ± 2% of age-predicted maximum in the high PRAL and 98 ± 1% in the low PRAL. Mean oxygen pulse was 14.9 ± 1.2 mL/beat in the low-PRAL and 14.2 ± 1.1 mL/beat in the high-PRAL (p=0.153). Submaximal work efficiency did not differ (p=0.481). Time to exhaustion was 21% greater in the low PRAL compared to high PRAL (p=0.044). There was a tendency between the magnitude of difference between PRAL trials and the change in anaerobic exercise time to exhaustion (r=-0.58, p=0.082), although this did not reach statistical significance. A 10 unit greater reduction in PRAL resulted in an increase of 6 sec (5%) in time to exhaustion.
This section was presented well, the accompanying graphs painted a clear picture of the results. It was evident from the results the time to exhaustion in the low PRAL diet was significantly enhanced. Furthermore, it was easy to tell the results for RER were not as expected. RER actually decreased in the low PRAL diet, whereas it was predicted to increase.
Discussion: This study demonstrated that a short-term alkaline diet promotes enhanced anaerobic performance. Increases in systemic pH resulted in improved time to exhaustion. Contrary to predicted, RER actually decreased during the low PRAL trial. These results are unexpected as the majority of research supports increases in systemic pH increase RER. Studies examining the performance benefits of sodium bicarbonate have also shown increases in RER. Therefore, it is possible a short-term diet of low PRAL fails to raise RER. This could result from a lack of increase in sodium bicarbonate which would not have an effect on non-metabolic CO2 production and thus RER. Future research will need to determine the time-course relationship between RER and a low PRAL diet. The performance enhancements noted in this study may be due to the improved alkaline environment and possibly a rise in sodium bicarbonate. Sodium bicarbonate improves performance by promoting acid buffering. The main issue with sodium bicarbonate supplementation is the gastrointestinal distress associated with it. Therefore, achievement through diet would be an ideal method to improve performance while minimizing stomach discomfort. A secondary issue is the increase in sodium ingestion, which potentially could be harmful. It appears a low PRAL diet is a feasible method to improve time to exhaustion in a recreational population. Future work needs to confirm these findings in well-trained athletes.
Conclusion: In conclusion, a low PRAL short term diet can enhance anaerobic performance, and specifically time to exhaustion in a recreational population. Contrary to initially predicted, a low PRAL diet resulted in a reduction in RER, which requires future validation.
My thoughts: This research study has some really good points but also missed the mark on some of its methods. First, I though the descriptions of the subjects could have been much better. I would like to have had more data on activity, training experience, and body composition. Another detail missing was the differences in the diets and how they may have played an influence on the results. For instance, although not statistically significant, the high PRAL group consumed on average nearly 2,700 cals while the low PRAL group consumed only 2,200. Also, the low PRAL group consumed more potassium, and less phosphorus. Given the results did not improve RER, I think it is justifiable to believe the results may have come from a mechanism other than an increase in sodium bicarbonate. It is well known sodium bicarbonate increases RER. Second, the authors did do a good job of presenting the results. They used contrast graphs making it easy to see the differences between diets and RER and a line graph to demonstrate the changes in pH over the diet. All in all, this study was typical of a small sample pilot study. There were some really good points while some of the methods will need improved upon in order for future work to be validated.
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