Factors Influencing Blood Lactate Concentration During Exercise: A Narrative Review With a Lactate Shuttle Perspective

ABSTRACT

Aim

Blood lactate concentration ([La⁻]), usually measured in mmol/L, is one of the most frequently measured parameters during clinical exercise tests as well as during performance assessments of athletes. Therefore, the purpose of this review is to examine the methodological and biological factors that influence [La⁻] in order to improve the accuracy and interpretation of its measurement during clinical, research, and athletic testing.

Methods

A narrative review of the scientific literature was conducted, focusing on studies addressing the biological as well as methodological variables that may affect the measurement of [La⁻].

Results

According to the lactate shuttle theory, blood [La⁻] depends on production, transport, and consumption. Both methodological and biological factors can substantially alter these processes and, subsequently, [La⁻], potentially leading to misinterpretation when comparing data across sessions or individuals.

Conclusion

Since lactate is commonly measured in research, medical, and training testing, it is important to understand these factors to avoid misinterpretation. The main recommendation is to control all these factors when measuring [La⁻] and to carry out the measurements under the same conditions when monitoring the evolution of a specific person or comparing different individuals.

1. Introduction

Blood lactate concentration ([La⁻]), usually measured in mmol/L or in mg/dL to a lesser extent, is one of the most often measured parameters during clinical exercise testing as well as during performance assessment of athletes [1]. Its frequent measurement is due to the valuable information provided by this metabolite, fulfilling three crucial functions in the body: (1) a major energy source, (2) a gluconeogenic precursor, and (3) a signaling molecule [2]. These functions are possible thanks to the proper transport of lactate through the cell membranes by the MonoCarboxylate Transporters (MCTs) [3]. MCTs cotransport monocarboxylates (such as lactate) coupled with a proton in a 1:1 ratio. This cotransport of lactate and protons is passive and bidirectional, requiring a gradient difference between two compartments separated by a membrane [4]. According to the lactate shuttle theory [2], during exercise, lactate is released from the producing cells (mainly active muscle cells) to the surrounding cells and circulation. In the blood, lactate is distributed between plasma and erythrocytes and it is transported throughout the whole body [5]. Once in the circulation, it is taken up by the consuming cells (less active muscle cells, heart, brain, liver or kidney). These consuming cells take up lactate from the blood to use it as an energy source or for gluconeogenesis. That means lactate is highly interchangeable between the producer and the consumer cells (Figure 1) [6]. Therefore, it is important to keep in mind when we are measuring lactate that the concentration of lactate in the blood is the balance between the production and the consumption [6].

FIGURE 1.

Representation of lactate as an energy source through the lactate shuttle theory. The width of the arrow represents the relevance of a specific process. Continuous lines indicate direct reactions, and dashed lines indicate the presence of intermediary reactions. MCT1 and 4, monocarboxylate transporter 1 and 4, respectively; MPC, mitochondrial pyruvate carrier.

Literature combined with our experience investigating lactate metabolism have showed that several factors modify [La⁻]. It is crucial to be aware of these factors to prevent misinterpretation. Thus, the aim of this study is to review the methodological and biological factors affecting [La⁻] with the intention to control them when analyzing this metabolite.

2. Methodological Factors

Those factors independent of the person are classified as methodological factors. These factors can be controlled independently of the individual.

2.1. Exercise Intensity

Exercise intensity is one of the most famous aspects affecting [La⁻]. During an incremental test, blood [La⁻] remains stable during the first minutes. However, a slow initial increment in blood [La⁻] occurs once a certain intensity is reached. As intensity continues to increase, a second exponential increase in [La⁻]. The first and the second breakpoints in blood [La⁻] plotted against intensity are called lactate threshold 1 (LT1) and 2 (LT2), respectively [1, 7]. Below LT1, mainly type I fibers are recruited. These fibers are primarily oxidative, so lactate production is minimal [8]. Above LT1, type IIa fibers begin to be recruited, which have an intermediate oxidative‐glycolytic metabolism increasing lactate production inside the cell [8]. Lactate starts to increase in the cytoplasm because the production of lactate in glycolysis is faster than the consumption by the mitochondria. As lactate accumulates in the cytoplasm, it is released to the blood by MCTs resulting in the increment of [La⁻] in the blood causing LT1 [9]. Finally, when the intensity is very high, type IIx fibers are recruited, which are mainly glycolytic increasing lactate production considerably inside the cell. Subsequently, lactate will be released from the cell by MCTs increasing blood [La⁻] abruptly causing LT2 [8]. It is important to note that once lactate is released into the blood it begins to be taken up by the consuming tissues through MCTs modifying the blood [La⁻]. Hence, LTs are indicators of the lactate production/consumption ratio, especially of the active muscle cells (Figure 2). It is noteworthy to say that these increments in [La⁻] are transient rather than sudden, making it difficult to establish specific points. Despite its difficulty, different concepts and exercise protocols exist to identify LT1 and LT2, delivering different results [7, 11, 12]. A useful online app developed by F.M. Mattioni and led by J.M. Murias has been created to determine the different LT concepts [13]. Given the wide variety of concepts and protocols available to calculate LT1 and LT2, we recommend always using the same methodology when assessing the evolution of a subject or comparing different subjects.

FIGURE 2.

Representative illustration of the lactate threshold 1 (LT1) and 2 (LT2). The width of the arrow represents the relevance of a specific process. Continuous lines indicate direct reactions, and dashed lines indicate the presence of intermediary reactions. MCT1 and 4, monocarboxylate transporter 1 and 4, respectively; MPC, mitochondrial pyruvate carrier. Lower graph modified from an individual from [10].

During constant tests at low intensity, lactate remains stable close to baseline values. The highest intensity during a constant test that can be maintained without an increase in blood [La⁻] above baseline levels coincides with LT1, previously explained. Hence, the first spectrum of intensities is characterized by a basal [La⁻] and a recruitment of type I fibers. If the intensity is higher, a specific intensity is reached at which [La⁻] increases above resting levels remaining stable during the test. Therefore, the second spectrum of intensities is defined by a stable [La⁻] above baseline values and a recruitment of type IIa fibers. If intensity is even higher during a constant test, a specific intensity is reached at which lactate starts to increase during the test despite intensity being constant. Thereby, the third spectrum of intensities is indicated by a gradual increase in [La⁻] despite intensity being constant and a recruitment of type IIx fibers. The highest intensity during a constant test that can be maintained with a stable blood [La⁻] above baseline values is defined as the maximal lactate steady state (MLSS) [1, 14, 15, 16, 17] (Figure 3). In this aspect, it is important to differentiate between the blood [La⁻] (MLSSc) and the workload (MLSSw) obtained at this intensity [14]. Several methodologies have been proposed to identify the MLSS (for further review see [7]). A stable blood [La⁻] during a constant test means that the release of lactate by the producer cells is similar to the removal by the lactate consumer cells [17]. Since the main destination of blood lactate during exercise is being used as an energy source, or gluconeogenesis to a lesser extent [2, 19, 20], a stable [La⁻] would imply most of the energy is coming from the mitochondrial pathway (oxidation) [21]. However, if blood [La⁻] starts to increase during a constant test, it means that the release is higher than the removal [17]. A gradual increase in blood [La−] during a constant test suggests that energy coming from the glycolytic pathway is becoming more relevant [21], as lactate is the end product of glycolysis [22]. Since, MLSS is the intensity at which the release of lactate is similar to the consumption by the whole body [14], MLSS could be used as an indirect marker of the intensity eliciting the maximal whole‐body oxidation during a particular exercise without a great contribution from the glycolytic pathway.

FIGURE 3.

Representative illustration of the blood [La⁻] during a constant test performed below the first lactate threshold (LT1), between LT1 and maximal lactate steady state (MLSS) and above MLSS. The width of the arrow represents the relevance of a specific process. Continuous lines indicate direct reactions, and dashed lines indicate the presence of intermediary reactions. MCT1 and 4, monocarboxylate transporter 1 and 4, respectively; MPC, mitochondrial pyruvate carrier. Left graph modified from [18].

During a maximal effort of short duration (~30 to 120 s), it is possible to measure maximal [La⁻] [1]. Blood [La⁻] at the end of the exercise test, final [La⁻], is not the highest. In fact, the highest blood [La⁻], maximal [La⁻], is observed a few minutes later (3–8 min) [1]. This is because there is a delay between lactate production in the active myocyte and its distribution in the blood through the body until the measurement site. Therefore, it is important to differentiate between final and maximal blood [La⁻] (Figure 4). Maximal lactate accumulation rate (VLamax) is typically used as an indicator of the glycolytic capacity (for further review see [24, 25]). However, this interpretation should be taken with caution because blood [La⁻] is the balance between the release and the removal; it is not just the result of lactate production [1, 2, 6].

FIGURE 4.

Blood [La⁻] through a maximal effort of 30 s. Baseline [La⁻]: blood lactate concentration before exercise; Final [La⁻]: blood lactate concentration immediately after exercise; Maximal [La⁻]: Highest blood lactate concentration. Graph modified from [23].

During recovery from exercise performed above LT1 (i.e., at intensities that raise [La⁻] above baseline), lactate clearance increases as recovery intensity approaches LT1, reducing blood [La⁻]. Menzies et al. found active recovery near LT1 (≈80% to 100% LT, 10%/min–11%/min) produced greater clearance than moderate intensities (40%–60% LT, 7%/min–8%/min) and passive recovery (≈5%/min) [26]. In fact, the metabolic clearance rate of lactate reaches an apex just below LT1 [27]. Blood [La⁻] decreases faster as the intensity approaches LT1 because active muscle cells uptake lactate to use it as an energy source, considering the other lactate‐consuming tissues remove the same amount of lactate independently of the intensity of the recovery [28].

2.2. Type of Blood

Since lactate is allocated through the whole body, it is important to consider the type of blood analyzed (venous, capillary, or arterial blood). Previous studies have found different [La⁻] comparing different types of blood [29, 30, 31, 32, 33, 34]. According to the lactate shuttle theory [1, 2, 6], the different [La⁻] depending on the type of blood is a result of the lactate consumption/production balance. Producer cells release lactate to capillary blood, moving to venous blood where it travels until the heart (systemic circulation). This type of blood is the most representative of muscle metabolism [31, 32, 33, 34]. Subsequently, it makes a round journey from the heart to the lungs (pulmonary circulation). Finally, it is distributed from the heart through the body in arterial blood (systemic circulation). Once lactate is released by the producer cells, it can be removed by the consumer cells (such as less active muscle cells, heart, brain, liver, or kidney) [35]. Therefore, lactate will be lower the more it has circulated through the different tissues because of the removal by the consumer cells. Several studies have documented lactate uptake by different tissues (for an excellent review, see [36]). Van Hall et al. demonstrated that during arm exercise (double poling) at 72% VO_2max, the arms predominantly released lactate (−5.48 mmol/min), while the legs simultaneously took it up (7.4 mmol/min) [32]. This finding highlights the capacity of less active muscle groups (legs) to utilize lactate produced by more active ones (arms). In a subsequent study by the same group, cycling at 75% VO_2max induced lactate release from the legs (−1.88 mmol/min), whereas the brain showed net lactate uptake (0.25 mmol/min) [37], suggesting that the brain can directly use lactate generated by contracting muscles. Similarly, Gertz et al. reported that during cycling at 40% VO_2max for 25–30 min, myocardial lactate uptake increased substantially (97.5 μmol/min) compared with resting conditions (34.9 μmol/min) [38], suggesting that the heart readily oxidizes lactate, likely derived from skeletal muscle. Finally, Ahlborg et al. observed a rise in the arterial‐hepatic venous lactate difference from rest (0.24 mmol/L) to exercise (0.72 mmol/L) performed during 240 min at 30% VO_2max [39], implying that the liver markedly increases lactate uptake under exercise conditions. All that means that the highest [La⁻] is found in the capillary and venous blood coming from active muscles, and the lowest [La⁻] will be found in arterial blood returning to the active muscles [31, 32, 33]. For example, after knee extension exercise until exhaustion (~3 min at ~65 W), venous [La⁻] was 17 mmol/L and arterial [La⁻] was 11 mmol/L [33]. Hence, it is crucial to consider the type of blood analyzed and the circulation of lactate from the place of production to the place of measurement (Figure 5). Based on that, the lactate arterio‐venous difference of the active muscles during exercise could be used as an indirect indicator of lactate clearance by the consumer cells.

FIGURE 5.

Representative illustration of the lactate circulation through the body. The width of the arrow indicates the expected amount of lactate in the blood.

2.3. Blood Compartment

It is well known that blood can be divided into two main compartments: plasma and erythrocytes. Lactate can be transported through the membrane of erythrocytes by three different pathways: (1) free diffusion of the undissociated acid, (2) exchange with inorganic anions (band three system), and (3) MCT1 [40]. MCT1 is the specific carrier in erythrocytes, being responsible for > 90% of the transport [33].

Red blood cells are purely glycolytic cells as they lack mitochondria [41]. Hence, red blood cells cannot use lactate as an energy source, they can only transport it (Figure 1). Despite being glycolytic cells, they have MCT1 which is a bidirectional transporter with a high affinity for lactate specialized for uptake [4]. This shows that the exchange of lactate across the erythrocyte membrane is very relevant, especially for two reasons. On the one hand, lactate transport in both plasma and erythrocytes increases the total capacity of the blood to transport lactate. This would allow a higher distribution of lactate through the body to be used as an energy source, as a gluconeogenic precursor and to meet its signaling functions. On the other hand, in capillaries surrounding active myocytes, the transport of lactate from plasma to erythrocytes reduce the [La⁻] in plasma. That increases the gradient between the active myocyte and plasma allowing for a higher release of lactate from the active myocyte [42].

Most studies performing a test of constant or incremental intensity observed lower [La⁻] in erythrocytes compared to plasma [30, 33, 43, 44, 45, 46, 47]. Just as an example, Smith et al. reported 30 min at ~70% VO_2max resulted in a maximal plasma [La⁻] of 8.75 mmol/L and an erythrocyte [La⁻] of 4.81 mmol/L [44]. To our knowledge, only one study by Tomschi et al. using an interval test consisting of two bouts at 95% of maximum running speed reported higher [La⁻] in erythrocytes than in plasma (15 and 9 mmol/L, respectively) [5]. The explanation of these apparent contradictory results could reside in the different methodologies of the studies. Presumably, during an incremental or a constant test above LT1, active muscle cells produce and release lactate to the capillary and venous plasma continuously. Once in the plasma, lactate will be taken by erythrocytes. Therefore, if the sample is analyzed in the venous blood coming from active muscles, lactate will be higher in plasma compared to erythrocytes. As occurs in those studies finding a higher [La⁻] in plasma than erythrocytes [30, 33, 43, 44, 45, 46, 47]. Thereafter, this venous blood coming from active muscles will circulate through the body and consumer cells will remove lactate from plasma reducing [La⁻] in plasma. Consequently, lactate will leave erythrocytes as plasma lactate declines. Then, if the sample is analyzed in the blood once it has circulated through the body, the difference between plasma and erythrocytes will be reduced [33]. Even, [La⁻] can be higher in erythrocytes compared to plasma. This may be found when the consumption of lactate is higher than the production, that is, during the rests of an intervallic protocol or during a final recovery. As occurs in the study finding a higher [La⁻] in erythrocytes compared to plasma [5].

It is remarkable to take in mind the blood processing by the analyzer. Since some of them analyze plasma lactate, while others lyse erythrocytes and measure plasma and erythrocytes' lactate together (whole blood). In this sense, if plasma lactate is higher than erythrocyte lactate, measuring blood lactate using an analyzer lysing erythrocytes (whole blood) will result in a lower [La⁻] compared to using an analyzer that only analyzes plasma lactate. Conversely, if plasma lactate is lower than that inside the erythrocyte, measuring blood lactate using an analyzer lysing erythrocytes (whole blood) will result in a higher [La⁻] compared to using an analyzer that only analyzes plasma lactate. This is perfectly illustrated by Mentzoni et al. in their study comparing several lactate analyzers [48].

2.4. Type of Exercise

Considering the ability of the non‐muscle‐consuming tissues to clear lactate is unaffected by the type of exercise, the amount of muscle mass involved during exercise is the predominant source of lactate turnover during exercise [28, 49]. Accordingly, Beneke showed different MLSSc in different sports (rowing: 3.1 mmol/L, cycling: 5.4 mmol/L, and skating: 6.6 mmol/L) [50]. In fact, some authors have concluded that the different MLSSc may correspond to the sport‐specific mass of working muscle [50]. Aligned with that, the amount of muscle mass involved in a specific exercise plays a major role in blood lactate production, the subsequent release to the blood and its removal [51]. Exercise involving a low amount of muscle mass implies fewer lactate‐producing muscle fibers and low lactate production. Therefore, there is a considerable amount of muscle fibers able to uptake lactate reducing blood [La⁻]. In contrast, whole‐body exercises involve a considerable amount of muscle mass, that is, a greater number of lactate‐producing muscle fibers and high lactate production. Hence, the amount of lactate‐consuming muscle fibers able to reduce lactate will decrease. Therefore, skeletal muscle mass involved during exercise should be considered when measuring blood [La⁻]. Concretely, when comparing different types of exercise, participants with different body compositions or the evolution of a person suffering changes in body composition should be taken into account.

2.5. Blood Flow Restriction

Several situations may produce blood flow restriction, such as tourniquets for blood extraction, blood pressure measurement and blood flow restriction training. To understand the effect of blood flow restriction on [La⁻], it is primarily necessary to consider the site of [La⁻] measurement, the main site of lactate production, and both the location and duration of blood flow restriction application. The application of blood flow restriction to an area at rest for several minutes increases [La⁻] if the measurement is taken within the area where the blood flow restriction is applied [52, 53, 54]. For example, Benzon et al. showed applying a tourniquet to the mid–upper arm at rest for ~36 min increased [La⁻] in the antecubital vein (within the area of tourniquet application) from 1 to 3.11 mmol/L [52]. If blood flow restriction is very long during resting, it may reduce arterial blood flow causing local hypoxia in the specific area. Since hypoxia increases blood [La⁻] (see Section 4.3), the local hypoxia produced in the area by blood flow restriction will increase [La⁻] in this area. Additionally, blood flow restriction prevents the distribution of venous blood from this area to the rest of the body [55]. This would reduce lactate removal increasing [La⁻] within the area affected by blood flow restriction. However, shorter application tourniquet time at rest does not seem to affect [La⁻] [56, 57]. As an example, Chiew et al. described that after 15 min of tourniquet application for blood extraction at rest, [La⁻] of the tourniquet versus non‐tourniquet arm was 0.91 versus 0.89 mmol/L, respectively [56]. On the other hand, during exercise, [La⁻] may be lower in the region affected by blood flow restriction if the restriction is applied to an area other than the one performing the exercise. This was illustrated in the same study by Chiew et al. [56] in which participants performed intense exercise on a cycle ergometer, and a tourniquet was applied to one randomly selected arm immediately post‐exercise. [La⁻] was lower in the tourniqueted arm compared with the non‐tourniqueted arm at 2.5 min (mean difference −1.28 mmol/L) and 5 min (mean difference −1.07 mmol/L) after tourniquet application. This is likely because lactate produced in the active muscle fibers cannot circulate effectively to the blood flow restricted region where measurement is taken, thereby reducing [La⁻]. Therefore, it is highly recommended to prevent measuring blood [La⁻] within the area affected by the blood flow restriction when its application is prolonged during resting or during exercise.

3. Biological Factors

Those factors inherent to the individual are classified as biological factors. These factors are totally dependent on the individual. This does not mean these factors cannot be controlled methodologically.

3.1. Modifiable Factors

3.1.1. Training Status

It is well known the effect of training on blood [La⁻] is (1) a reduction in blood [La⁻] at a given external work [49, 58, 59], or (2) a higher external work at a given blood [La⁻] [58]. This is because endurance training decreases whole body and working‐muscle lactate production and increases clearance by active muscle [49]. The effect of training on blood [La⁻] is commonly observed during an incremental exercise test. Hence, the predictable effect of training on blood lactate is a rightward shift on the lactate‐load relationship [59, 60]. This is manifested as a higher workload achieved at LT1 and LT2 [60]. In line with that, a huge difference has been found in blood [La⁻] between participants with different training status, observing lower [La⁻] the higher the training status at the same absolute workloads [61]. It is hypothesized that this different lactate response across populations with widely ranging metabolic capabilities is mainly due to their different mitochondrial characteristics [61, 62]. During exercise, lactate produced in glycolysis is introduced into the mitochondria for its oxidation, as it is an important mitochondrial substrate. However, those individuals with poor mitochondrial lactate oxidation will release lactate to the blood. Thus, a premature increase in blood [La⁻] during exercise would indicate a poor ability of the mitochondria to oxidize lactate [62, 63] (Figure 6). Subjects with the highest blood [La⁻] before training present the most marked reduction during the physical training period [59]. This suggests that the lower the training status of the participants, the higher the effectiveness of training in reducing blood [La⁻].

FIGURE 6.

Blood [La⁻] difference between a trained and untrained person at the same absolute intensity. The width of the arrow represents the relevance of a specific process. Continuous lines indicate direct reactions, and dashed lines indicate the presence of intermediary reactions. MCT1 and 4, monocarboxylate transporter 1 and 4, respectively; MPC, mitochondrial pyruvate carrier. Lower graph modified from [61].

The effect of training is usually evaluated through constant tests determining MLSS as well. The goal is to achieve a stable blood [La⁻] above baseline levels at a higher workload (i.e., higher MLSSw) [14, 64]. Six weeks of training at the MLSSw before training during ~30 min three times per week increased MLSSw from 150 to 171 W [64]. This is desirable due to the relationship between MLSSw and performance [14]. A higher MLSSw would imply an enhancement in intensity eliciting the maximal whole‐body oxidation during a particular exercise without a great contribution of the glycolytic pathway. In contrast, MLSSc presents a high inter‐individual variability not being changed by training or related to performance [14, 64, 65].

Regarding maximal [La⁻], it appears that training increases this value [59, 66]. After endurance training performed 2–3 times per week during 8–10 weeks, maximal [La⁻] increased from 12.8 to 14 mmol/L [59]. The main reason could reside in the higher glycolytic flux observed with training allowing one to achieve a higher workload [66]. In accordance, VLamax is positively correlated to performance [66].

3.1.2. Nutrition

It is reasonable to assume that as one of the sources of lactate is muscle glycogen [22], the lower the level of muscle glycogen the lower the production of lactate. Several studies have modified the previous glycogen levels combining diet and exercise showing lower [La⁻] with low glycogen levels during an incremental test [67, 68, 69, 70, 71, 72, 73, 74], during a constant test [75, 76] and during recovery after a constant test [77]. For instance, Benítez‐Muñoz indicated [La⁻] was 2.52 mmol/L with high glycogen levels and 1.94 mmol/L with low glycogen levels at 150 W during an incremental test [74]. These consequences just mentioned caused by a low glycogen level state on lactate response could be wrongly understood as an improvement in performance because training produces similar adaptations [7, 17]. The mainly response differing between a low glycogen level state and an adaptation to training is the maximal lactate production because this parameter increases with training (see Section 4.1) [59, 66], whereas it decreases with low glycogen levels [71, 72, 73]. Benítez‐Muñoz et al. reported maximal [La⁻] decreased from 12.45 mmol/L with high glycogen levels to 10.94 mmol/L with low glycogen levels during an incremental test (data not published). Therefore, it is highly recommended to measure the maximal [La⁻] while testing to differentiate between the adaptations to training and a low glycogen level situation.

However, not all studies have found different lactate responses with different glycogen levels. In the study of Hargreaves [78], subjects performed a 75 s all‐out exercise in two different conditions: after exercise and a high carbohydrate diet (80% carbohydrates) or after exercise and a low carbohydrate diet (25% carbohydrates). Maximal [La⁻] did not differ between conditions despite muscle glycogen content being reduced after exercise and a low carbohydrate diet. However, net glycogen utilization was not significantly different between conditions. This could be the reason why maximal [La⁻] did not differ between conditions. Mikulski et al. [79] exposed the subjects to four incremental tests on different visits. On the first visit, the control incremental test was performed. In the following three visits, on the previous day, a test of 90 min at 70% maximal heart rate was performed to reduce muscle glycogen content. 2 h before the subsequent three incremental tests subjects were given a high‐carbohydrate meal (4% proteins, 1% fat, 95% carbohydrates), a low‐carbohydrate meal (35% proteins, 64% fat, 1% carbohydrates) or remained fasted, respectively. No differences in [La⁻] during the tests and LT were found. Probably, performing 90 min at 70% maximal heart rate the previous day and a meal before the test is not enough to reduce muscle glycogen content and affect [La⁻]. This is because the exercise protocol designed to reduce muscle glycogen includes high‐intensity interval exercise in addition to low moderate intensity exercise [80].

Studies trying to reduce glycogen levels with diet exclusively have opposite results. In the study by Quirion et al. [81], the following protocol was administered: a 48‐h period of mixed dieting (53% carbohydrates, 30% lipids, 17% proteins) preceding the first incremental test, immediately followed by a 48‐h period of either a high carbohydrate diet (68% carbohydrates, 23% lipids, 9% proteins) or a high fat diet (19% carbohydrates, 57% lipids, 26% proteins) leading to the second incremental test and separated from the third incremental test by a 12‐day period. The results showed that these dietary modifications had no significant effect on lactate response. Most likely, a dietary alteration during 48 h without previous exercise is not enough to affect glycogen levels and the consequent lactate production. To our knowledge, in the only previous study by Yoshida et al. [82] that has used a similar diet without exercise to reduce glycogen levels, the diet was modified for 4 days finding differences in the lactate response. In this study [82], subjects were exposed to three different diets: a mixed diet for 3 days (50% carbohydrates, 30% fat, and 20% protein), a low carbohydrates diet for 4 days (30% carbohydrates, 50% fat and 20% protein) and a high carbohydrates diet for 3 days (70% carbohydrates, 20% fat, and 10% protein). The results revealed that absolute and relative VO₂ decreased at 4 mmol/L of blood [La⁻] after a high carbohydrate diet, indicating a leftward shift of the lactate‐ VO₂ relationship in this situation. It would be interesting to know which nutrition protocol can reduce glycogen levels by itself, with the intention of avoiding such an intense exercise session and the consequent fatigue as conducted in other studies.

It has been found that there is a close relationship between adrenaline [72] or noradrenaline [73] and [La⁻] and between adrenaline threshold and LT [72]. Catecholamines bind to ß‐adrenergic receptors on the skeletal muscle membrane initiating a cascade of events leading to glycogen breakdown which ultimately increases lactate production [72]. Podolin et al. found lower catecholamines concentrations and the subsequent lower [La⁻] with low glycogen levels [72], suggesting that the glycogen levels could have an impact on catecholamines response. Gollnick et al. [80] were one step further when their subjects performed two‐legged cycle ergometer exercise in which one leg was glycogen depleted and the other one was normal. Glycogen depletion of one leg was produced by one‐legged exercise on a previous day followed by the consumption of a low carbohydrate diet. The results of this experiment showed that the low glycogen leg extracted lactate from the blood and the normal leg released lactate to the blood. That means that lactate transport through the membrane cell by the MCTs is dependent on glycogen levels. These results were observed with the same catecholamines concentrations in both legs suggesting that glycogen levels per se have a greater impact on [La⁻] than catecholamines.

3.1.3. Hypoxia

The most common intervention to study the effects of hypoxia is altitude. In this aspect, it is important to differentiate between an acute and a chronic response due to acclimation. In the first hours in hypoxia, a leftward shift in the lactate‐load relationship [83, 84, 85, 86, 87] and in the lactate‐oxygen consumption relationship [88, 89] has been observed without changes in the maximal [La⁻] [86, 89, 90, 91]. For example, Lühker et al. reported [La⁻] increased from a median of ~5 mmol/L in normoxia to ~10 mmol/L in hypoxia at 200 W during an incremental test; while maximal [La⁻] at the end of the incremental test was similar in both conditions, with a median value of ~17 mmol/L [86]. With acclimation, a re‐establishment of the lactate‐load relationship [83, 84, 92] and in the lactate‐oxygen consumption relationship [88] approaching normoxia levels occurs with a concomitant decrease in maximal [La⁻] [90, 91, 92, 93, 94]. In acclimatized subjects, an inverse relationship between hypoxia and maximal [La⁻] has been reported [95]. It is noteworthy to say that the reduced maximal [La⁻] due to acclimation to hypoxia is not observed in phosphagen dependent efforts like high intensity exercise with a short duration (≈10 s), while it is observed in more glycolysis‐dependent efforts like high intensity exercise with a longer duration (≈30 s) and in more oxidative phosphorylation‐dependent efforts like incremental exercise test [94]. This occurs because the contribution of the glycolysis and the subsequent lactate production in short duration efforts (≈10 s) is less relevant.

However, not all studies have found an acclimation of lactate to hypoxia showing that the leftward shift in the lactate‐load relationship persists with acclimation [85, 96] and the [La⁻] during a constant test is still elevated in chronic hypoxia, similar to acute hypoxia, compared to normoxia [97, 98]. It is important to emphasize that in these studies not showing an acclimation of lactate to hypoxia, a decrease in body weight was produced because the diet was not controlled. This reduction in body weight is probably accompanied by a reduction in muscle mass. As mentioned in the Section 2.4, the amount of muscle mass is a methodological aspect to consider. Since muscle mass is a major consumer of lactate [32], this reduction in weight could have reduced muscle mass diminishing the uptake of lactate preventing the reduction in blood [La⁻] with acclimatization. The reduced lactate removal would counteract the expected rightward shift of the lactate‐load relationship with acclimation to hypoxia. Another explanation could be that the relative intensity of each muscle fiber is higher if there is a loss of muscle mass because a lesser amount of muscle fibers would be developing the same absolute work. This would produce more lactate due to the close lactate‐load relationship [83], preventing the rightward shift of the lactate‐load relationship expected with chronic altitude. However, it cannot be overlooked that one study found the rightward shift of the lactate‐load relationship with chronic altitude despite a reduction in body weight [92], suggesting it is possible to find adaptations to hypoxia despite weight loss. On the other hand, regarding maximal [La⁻], some contradictory results can be found in the literature too. A study has shown that it increases with acclimation returning to normoxia values [91], others do not find differences between acute or chronic hypoxia and normoxia [85, 96] and another reports an increase with acute hypoxia compared to normoxia [99].

Some studies have observed that total oxygen consumption [31, 87, 88, 92, 97, 98, 100, 101, 102, 103, 104, 105, 106], arterial oxygen delivery to the legs [88, 89, 103] and leg oxygen uptake [31, 89, 98] do not differ between acute, chronic altitude exposure or sea level at the same absolute submaximal intensity. This phenomenon gives rise to the lactate paradox. It was considered paradoxical as the changes in lactate with acclimation to hypoxia occur despite the finding that the oxygen response does not differ between hypoxic conditions. However, it is currently known that lactate production is independent of intracellular oxygen [89], which means that the lactate paradox does not have to be paradoxical. Although, some studies have observed a lower oxygen consumption in hypoxic conditions [89, 99, 100].

Since [La⁻] depends on removal and production, it is important to know which of the two factors are affected by hypoxia. Whole body lactate oxidation and lactate oxidation by the legs are increased with chronic hypoxia compared to normoxia [98]. Also, the metabolic clearance rate of lactate is enhanced in acute and chronic hypoxia compared to normoxia, being higher in the former [104]. Moreover, chronic hypoxia increases MCT1 content in erythrocytes enhancing the transport capacity of lactate by erythrocytes [42]. This improves the distribution of lactate through the body increasing lactate uptake by the consumer cells. All these results suggest that lactate removal is improved with hypoxia, most notably chronic hypoxia. On the other hand, lactate released by the legs [31, 85, 89, 96, 100, 102], the rate of appearance of lactate [102, 104] and muscle lactate [100] are higher in acute hypoxia compared to normoxia which suggests that the production of lactate increases with acute hypoxia. Regarding chronic hypoxia, some studies have shown a lactate released by the legs [100, 102], the rate of appearance of lactate [102, 104] and muscle lactate [100] close to normoxia. This indicates that the production of lactate approaches normoxia values with chronic hypoxia. On the contrary, some studies have not observed this acclimation in the production of lactate with chronic hypoxia [85, 96]. These are the same studies mentioned above that did not observe a re‐establishment of the lactate‐load relationship with acclimation. As previously explained, a considerable weight loss was produced in these studies. A body weight reduction could imply a loss of muscle mass increasing the relative intensity of each muscle fiber if the same absolute intensity is performed. This higher relative intensity of the muscles would produce more lactate due to the close relationship between lactate and intensity [83].

Some studies have tried to investigate the mechanisms behind the lactate kinetics in hypoxia. In an incremental maximal test breathing oxygen‐enriched gas it was reversed the typical leftward shift observed in the lactate‐load relationship in acute altitude [84, 87, 88, 92, 96]. That suggests that the lack of oxygen is responsible for the leftward shift in the lactate‐load relationship at acute altitude. With acclimation to altitude, the lactate‐load relationship was very similar between chronic altitude, altitude breathing oxygen‐enriched gas and sea level [84, 92]. This indicates that adaptations to chronic altitude have reversed the effects produced by oxygen reduction on the lactate‐load relationship at acute altitude. On the contrary, maximal [La⁻] was similar between breathing oxygen‐enriched gas at altitude and one, three or 5 weeks at altitude, all being lower than at sea level [92]. This reduction in the maximal lactate at altitude was not counteracted by breathing oxygen‐enriched gas. Therefore, maximal [La⁻] does not seem to be oxygen‐dependent. Another possibility explaining the effect of altitude on lactate response is the different workload achieved in each situation. However, performing an incremental maximal test after one and 5 weeks at altitude breathing oxygen‐enriched gas increased peak power output compared to acute altitude without changes in maximal [La⁻] [92]. This suggests that the peak workload is not responsible for the decrease in maximal [La⁻].

Hypoxia has been shown to increase adrenal epinephrine existing in an inverse relationship between arterial oxygen saturation and epinephrine concentration [106]. During an incremental test in normoxia and acute hypoxia it has been observed a close relationship between adrenaline [84, 89, 99] or noradrenaline [84, 99] and [La⁻]. Also, this relationship between adrenaline or noradrenaline and [La⁻] has been observed in chronic hypoxia [84]. Pooling acute and chronic hypoxia values of adrenaline, it is also found a close relationship between adrenaline and [La⁻]. Regarding adrenaline at acute altitude, most of the studies have observed an increased concentration of adrenaline [31, 85, 89, 97, 98, 104, 105, 106], but not all [96, 99]. At chronic altitude, some studies have showed a diminished adrenaline concentration compared with acute altitude [97, 104, 105, 106], while others have observed an increment [85, 96]. Regarding noradrenaline, at acute altitude, most studies have observed an increased concentration [85, 97, 98, 99, 105, 107], but others have not [31, 96, 104]. With chronic altitude, an increased [85, 96, 104, 105, 107] or a similar [97] noradrenaline concentration has been found compared to acute altitude, with no studies showing a reduction. It has been suggested that during acute altitude, epinephrine would produce a higher activation of glycolysis, rising lactate production. The return of adrenaline to sea level values with chronic hypoxia would explain the reduction in lactate production [105]. Moreover, studies with ß‐blocked subjects preventing the rise in epinephrine have shown a decrease in [La⁻] at sea level, in acute altitude and in chronic altitude compared to a control group [106]. However, ß‐blockade did not completely prevent the rise in blood [La⁻] on acute altitude suggesting additional mechanisms are involved in this process [106].

3.1.4. Temperature

Exercise in the heat in unacclimated individuals results in a greater reliance on carbohydrate metabolism [108, 109]. Since lactate is the end product of glycolysis [22], the greater reliance on carbohydrate metabolism in a hot environment produces an increase in lactate production and a subsequent higher blood [La⁻] [74, 108, 109, 110, 111, 112, 113, 114, 115, 116]. For example, Benítez‐Muñoz et al. showed [La⁻] increased from 3.11 mmol/L at ~21°C to 5.32 mmol/L at 36°C–38°C at 180 W during an incremental test [74]. In addition, it has been observed a relationship between muscle temperature and muscle lactate [117]. Furthermore, heat acclimation with the intention to decrease the rise in temperature in hot conditions has been effective in reducing muscle [La⁻] [114, 118, 119]. Also, using cooling strategies during exercise in a thermoneutral environment [117] decreases [La⁻] by preventing the rise in temperature.

Different mechanisms could explain the increased lactate production during heat exercise. Catecholamines seem to play a key role in this mechanism. Heat increases circulating epinephrine enhancing the glycolytic rate rising lactate production [108, 120, 121]. It has been observed that epinephrine infusion at 20°C to similar levels observed during exercise at 40°C increases glycogen use and the subsequent lactate production without changes in muscle temperature compared to control [122]. Furthermore, heat acclimation decreases epinephrine levels reducing [La⁻] [108]. All these studies demonstrate the close relationship between lactate and catecholamines in the heat. On the other hand, it has been suggested that the activity of the key enzymes involved in carbohydrate metabolism, such as glycogen phosphorylase, phosphofructokinase and pyruvate dehydrogenase, is affected by temperature per se via a Q ₁₀ effect [114, 123]. Therefore, it could be that the mentioned enzymes could increase their activity in heat conditions, rising lactate production and the subsequent blood [La⁻].

Regarding cold exposure, Cypess et al. transported volunteers to a room maintained at 20°C, where they wore a surgeon's cooling vest with circulating water set at 14°C for 60 min. Under these conditions, blood [La⁻] increased (0.94 mmol/L) compared with measurements obtained at room temperatures above 23°C (0.77 mmol/L) [124]. In the study by No et al., participants cycled for 20 min at 60% VO_2max, and then exercise intensity was increased at a rate of 0.5 kp/2 min until exhaustion at three different environmental conditions (22°C, 10°C, and 35°C). Blood [La⁻] was lower at 22°C than at 10°C or 35°C at rest and during 20 min at 60% VO_2max (22°C: 1.92 mmol/L, 10°C: 2.65 mmol/L, 35°C: 2.44 mmol/L) suggesting cold and heat both increase blood [La−]. Conversely, blood [La⁻] was higher at 22°C (7.17 mmol/L) compared to 10°C (6 mmol/L) and 35°C (5.38 mmol/L) during maximal exercise, likely due to the higher time to exhaustion in this situation [113]. In line with that, Beelen et al. reported a higher [La⁻] during a constant test at 70% of the VO_2max after immersing the legs for 45 min in a stirred water bath at 12°C compared to control [125]. In agreement, Imai et al. [126] showed that whole body skin surface cooling while wearing a water‐perfusion suit (water at 10°C) during 1 h before and during an incremental exercise test at an ambient temperature of 10°C increases lactate accumulation. Conversely, this effect was not observed at an ambient temperature of 25°C. This might be because only prior cooling combined with a low ambient temperature was effective in reducing skin temperature raising noradrenaline before exercise, which increases lactate production during the subsequent exercise [126].

A possible explanation for the higher [La⁻] at low temperatures could reside in the effect of cold on muscle fiber recruitment patterns. Studies in animals have shown a greater recruitment of fast‐twitch fibers at low temperatures [127, 128]. In line with that, studies in humans have shown a higher amplitude of electromyography after cold water immersion [129]. This suggests that a decrease in temperature could reduce the conduction velocity of the nerve and the muscle fibers. This could be compensated for by an increase in muscle fiber recruitment [129]. Since fast‐twitch fibers are more glycolytic, the increased recruitment of these fibers in cold conditions could increase lactate production.

Contrary, not all studies have observed a higher [La⁻] with cold. Therminarias et al. observed a lower [La⁻] during an incremental test at −2°C compared to 24°C [130]. These results are supported by another study of the same research group showing a lower [La⁻] during an incremental test at 10°C compared to 30°C [131]. Finally, a previous study compared the LT during an incremental test in a thermoneutral situation (20°C) with a cold situation (0°C), showing a higher power output at 0°C indicating a rightward shift of the lactate‐load relationship with cold [132]. Therefore, based on the present studies, apparent contradictory results are found regarding cold and [La⁻]. Likely, due to the different acclimation protocols used before exercise and the broad ranges of ambient temperatures studied.

3.1.5. Hydration

Hydration state has been proposed to influence muscle metabolism and the vast majority of studies have concluded that dehydration increases [La⁻] [121, 133, 134, 135, 136, 137, 138, 139, 140]. This was illustrated by Hargreaves et al. finding a higher [La⁻] without fluid ingestion (2.81 mmol/L) compared to fluid ingestion (2.34 mmol/L) during 120 min at 67% VO_2max [133]. It is well known the close relationship between dehydration and temperature. However, most of the studies have not observed the effect of isolated hydration matching temperature [110, 121, 133, 134, 135, 136, 137, 138, 139, 141]. Therefore, they usually suggest that the elevated muscle temperature is the most plausible mechanism to explain the increased muscle glycogenolysis and the subsequent lactate production during dehydrated exercise [138].

On the contrary, some studies have managed to dissociate the effect of temperature and hydration on [La⁻]. England et al. [140] developed a study where subjects performed an incremental test in a 5% weight loss hypohydration state and in a normal hydration state. Previous hypohydration was induced by sauna exposure and the test was performed when the rectal temperature returned to the pre‐sauna baseline value. In both situations, the rectal temperature was the same during the test avoiding the confusing effect of this variable. [La⁻] was higher in the hypohydration situation compared to the normal hydration situation (e.g., 4.4 and 3.4 mmol/L at 150 W, respectively), suggesting that dehydration per se increased [La⁻]. Conversely, several studies have observed opposite results. González‐Alonso et al. compared 30 min at 72% VO_2max in heat (35°C) and cold (8°C) situations with different hydration conditions (euhydration, 1.5%, 3%, and 4.2% dehydration achieved by 120 min of exercise while ingesting different volumes of fluid) [115]. They found a similar blood [La⁻] of ~2 mmol/L in the cold situations regardless of the hydration conditions [115], indicating hydration status per se has no effect on [La⁻]. In line with that, in the study by Papadopoulos et al. [110], subjects underwent four maximal incremental tests in four different conditions: in a hypohydrated condition (≈2.5% dehydration) at room temperature (≈22°C) and in a warm chamber (≈37°C) and in euhydrated conditions at room temperature (≈22°C) and in a warm chamber (≈37°C). They found that during the test in the hot condition, the LT occurred at a significantly earlier stage and with a lower oxygen consumption compared to the thermoneutral condition independently of the hydration state. Finally, Armstrong et al. [141] compared participants in a euhydrated situation and a 5% hypohydration situation (refraining the previous day from any fluid intake and performing a 1 h run at 75% VO_2max). They found a similar immediately post [La⁻] between experimental conditions after running 10 min at 70% and 85% VO_2max.

Due to the close relationship between temperature and hydration, the vast majority of studies have not observed the effect of isolated hydration matching temperature [110, 133, 134, 135, 136, 137, 138, 139, 141]. However, those studies trying to isolate the effect of hydration have observed that hydration per se seems not to affect blood [La⁻].

3.1.6. Sleep

Sleep seems to affect many physiological functions in the body. During submaximal exercise at the same absolute intensity, a partial sleep loss of ≈3 h seems to increase [La⁻] compared to a reference night [142, 143]. For example, [La⁻] was higher after a partial sleep loss of ≈3 h (4.92 mmol/L) compared to a control night (3.91 mmol/L) at 75% VO_2max determined before experimental trials [143]. To further understand the interaction between sleep and lactate, it is necessary to pay attention to the effect of sleep on the relationship between workload and [La⁻]. Thus, it is important to observe changes in workload when evaluating the effect of sleep on [La⁻]. Souissi et al. found blood [La⁻] did not differ between the control situation and partial sleep deprivation condition when a submaximal self‐selected intensity is performed [144]. Nevertheless, the velocity was lower in the partial sleep deprivation condition, which means that the same blood [La⁻] is found at a lower exercise intensity with partial sleep deprivation. Skein et al. found similar [La⁻] during an intermittent‐sprint protocol after 30 h sleep deprivation compared to a control night. It is important to highlight that it was observed that a lower performance occurred after sleep deprivation, indicating that the same blood [La⁻] was reached at a lower workload after sleep deprivation [145].

Regarding maximal [La⁻], in the studies by Mejri et al. [146] and Romdhani et al. [147], it was found that partial sleep deprivation (≈3–4 h) reduces maximal [La⁻] in a maximal effort. This reduction in maximal [La⁻] was accompanied by a decline in the maximal workload achieved after partial sleep deprivation [146]. Similar results were found by Romdhani et al. [147], who found a lower final [La⁻] associated with a lower performance during a repeated sprint ability test after 4 h of partial sleep loss. Also, Keramidas et al. found that maximal [La⁻] was lower following a constant test until exhaustion performed after a military expedition where subjects slept ≈5 h distributed in 51 h compared to before the expedition. This reduction in the [La⁻] could be explained by the reduction in the time to exhaustion observed after the expedition compared to before [148]. On the other hand, Mougin et al. [143] showed that the maximal [La⁻] was higher in a 3 h sleep‐deprived night compared to the control night. Maximal workload was similar between conditions indicating that the [La⁻] was higher in the sleep‐deprived night at the same workload compared to the control night [143]. In another study by Mougin et al. [142], maximal [La⁻] did not differ between conditions. However, maximal workload was lower in the partial sleep loss condition compared with the reference night. This indicates that the maximal [La⁻] found during the partial sleep loss condition was produced at a lower workload [142]. In agreement with that, Souissi et al. [149] exposed the subjects to four Wingate tests after two different conditions (reference night vs. sleep deprivation night). After the reference night, subjects performed the Wingate tests 1 h after and 13 h after 7 h of sleep. After the sleep deprivation night, the Wingate tests were performed after 24 h and 36 h of sleep deprivation. [La⁻] was not different in the four different Wingate tests. On the contrary, mean power and peak power were lower after 36 h of sleep deprivation, indicating that the same [La⁻] was reached at a lower absolute workload after 36 h of sleep deprivation.

On the contrary, not all studies have shown an effect of sleep on lactate response and performance. Goodman et al. [150] found that time to exhaustion during an incremental test and maximal [La⁻] did not differ after 60 h of sleep deprivation. Also, Symons et al. [151] found no significant differences in performance and blood [La⁻] in a Wingate test after 60 h of sleep deprivation. In line with this, Mougin et al. [152] did not observe an effect of ≈4 h of sleep restriction on either exercise performance or [La⁻] during a Wingate test. These studies showing no effect of sleep on [La⁻] do not observe changes in performance either. This supports the idea that the effect of sleep on lactate response could be mediated by the changes in performance.

3.1.7. Circadian Rhythms

The circadian rhythms refer to the daily fluctuations in some parameters suffered by the organisms [153]. That means the behavior of different variables is different depending on the moment of the day.

Most of the studies have shown a higher [La⁻] in the evening than in the morning [154, 155, 156, 157, 158, 159, 160, 161]. Reilly et al. found a higher [La⁻] in the evening (9.83 mmol/L) than in the morning (8.15 mmol/L) after cycling until exhaustion at 95% VO_2max [161]. This increased [La⁻] in the evening compared to morning is accompanied by a higher temperature [154, 155, 157, 158, 159, 160, 161]. As previously explained, a higher temperature reached during the evening could affect the higher [La⁻]. In addition, the higher [La⁻] during the evening than morning is coupled with a higher performance (higher peak power output [154, 155, 157, 159], higher time to exhaustion [156, 161], higher distance covered [158] or lower time trial performance [160]). In this sense, due to the close relationship between load and [La⁻], the higher [La⁻] observed during the evening could be due to the higher performance at this moment of the day as well.

However, some studies have not shown an effect of time of the day on [La⁻]. In a study by Fernandes et al. [162] performing a 1 km cycling time trial it was not observed a different [La⁻] in the morning compared to evening despite finding differences in other variables (such as a lower insulin, cortisol and free and total testosterone and a higher growth hormone and plasma glucose in the evening). In this study, performance was higher in the evening, so the different load between situations could be affecting the results due to the close lactate‐load relationship. Something similar was found by Souissi et al. developing a Wingate test in the evening and in the morning [163], where similar [La⁻] were found. However, a higher performance, temperature and oxygen consumption were found in the evening, all of these variables being closely related to lactate. Therefore, changes in these variables could be confounding the effect of time of day on lactate. Finally, something to consider is the habitual time of training of the participant. For example, Dolton et al. found that lactate and performance were similar at different times of the day, although temperature was higher in the evening performing a 15 min time trial [164]. It is noteworthy to say that subjects in this study usually trained in the morning. This could modify the effects of the time of the day, since adaptations to training are greater at the time of day at which training is regularly performed [165]. The same occurred in Ammar et al. where subjects usually trained at the same time of the day (3:30–5:30 p.m.), showing a similar [La⁻] between morning, afternoon and early evening after a resistance training session. On the contrary, it was found a time of the day effect for creatine kinase, testosterone and cortisol [166].

3.2. Unmodifiable Factors

3.2.1. Sex

Although sex‐based differences in substrate oxidation have been extensively studied, [La⁻] has received less attention. Benítez‐Muñoz et al. compared both sexes during an incremental test at the same power outputs finding [La⁻] was higher in females than in males [10]. However, final [La⁻] was similar between males and females [10]. Unfortunately, both sexes were not matched by training status, and this is one of the main factors affecting blood [La⁻], as previously explained (see Section 4.1). In contrast, in a following study, Benítez‐Muñoz found similar [La⁻] between sexes during an incremental test at the same power outputs (e.g., 2.5 mmol/L in males and 2.84 mmol/L in females at 150 W), as well as final [La⁻] (12.42 mmol/L in males and 11.69 mmol/L in females), matching both sexes by training status (similar VO_2max relative to fat‐free mass) [167].

Additionally, no sex differences were found by Horton et al. in blood [La⁻] at 40% VO_2max during 2 h [168]. Similarly, no sex differences were found in blood [La⁻] during 90 min at 58% VO_2max matching both sexes by training status (similar VO_2max relative to lean body mass) by Roepstorff et al. [169]. However, in the latter two studies, the exercise intensity was too low to significantly increase [La⁻] above basal levels.

3.2.2. Age

Aging affects several physiological functions, being [La⁻] one of them. Importantly, the effect of age on [La⁻] at a specific power output seems to be influenced by training status [170]. In this sense, Massé‐Biron et al. reported similar [La⁻] between highly trained master athletes (1.76 mmol/L) and young athletes (1.98 mmol/L), but higher in untrained elderly participants (2.86 mmol/L), at 150 W during an incremental test [170]. Regarding final [La⁻], values were lowest in highly trained master athletes (3.9 mmol/L), intermediate in untrained elderly participants (5.36 mmol/L), and highest in young athletes (10.29 mmol/L) [170]. Aligned with that, a curvilinear relationship with an inverted u‐shape was found between blood [La⁻] analyzed at the end of sprint events (100, 200 and 400 m) and age by Korhonen et al. [171], indicating age reduces maximal blood [La⁻] specially at advanced ages. In line with that, it was found that [La⁻] after an incremental test until exhaustion was lower the higher the age [172]. Hence, there is compelling evidence that final or maximal [La⁻] is reduced with age.

3.2.3. Genetic

McArdle disease (glycogenosis type V) is an autosomal recessive condition caused by a total inherited deficiency of the muscle isoform of glycogen phosphorylase (myophosphorylase), an enzyme encoded by the phosphorylase (PYGM) gene [173, 174]. Given that myophosphorylase catalyzes the breakdown of glycogen, the absence of glycogen breakdown in these patients results in a very low [La⁻] since glycogen is one of the main precursors of lactate, as mentioned above. This phenomenon is observed in several studies [174, 175, 176]. Valenzuela et al. reported a [La⁻] of ~0.81 mmol/L throughout an incremental test in these patients, whereas it increased to 10.37 mmol/L at the end of the test in healthy individuals [174].

On the other hand, it has been identified the presence of a single‐nucleotide polymorphism (SNP) in the gene that codifies MCT1, the T1470A (rs1049434), resulting in an aspartic acid to glutamic acid change in codon 490 [177]. This genetic variant results in a deficiency of lactate transport, although no clinical effects have been reported [177]. However, this genetic modification could be functionally relevant during exercise at high intensity due to the high gradient difference of lactate between muscles and plasma and between plasma and erythrocytes. There is compelling evidence that the polymorphism T1470A affects [La⁻] [28, 178, 179, 180, 181, 182], due to the lower lactate transport through the MCT1 with the T allele. As an example, Cupeiro et al. found a higher maximal [La⁻] in carriers (21.3 mmol/L) than in non‐carriers (16.3 mmol/L) of the A1470T polymorphism in the MCT1 gene during a strength training circuit [180]. Although this is not a universal finding because some studies have observed no significant differences [183, 184].

4. Less Explored Factors and Future Research Directions

This review covers several factors that can affect [La⁻]. However, some aspects that have received less attention are addressed in this section, and the door is opened for future studies.

Traditionally, blood [La⁻] is considered to depend on its production, transport, and consumption. However, the role of excretion has received less attention. Lactate has been detected in external body fluids such as sweat [185], urine [186], and saliva [187], and its concentration in these fluids has been compared with blood [La⁻]. Thus, future studies should also address the potential contribution of lactate excretion to blood [La⁻] regulation, in addition to the established roles of consumption, transport, and removal.

While the differences between males and females have been relatively underexplored, the impact of various hormonal cycles (such as the menstrual cycle and different contraceptive methods) and life stages in females on lactate metabolism has received even less attention. It is essential to encourage research in this area to better understand their influence.

An often overlooked factor is sample volume. While low volumes ease and speed collection, they also increase susceptibility to contamination (e.g., sweat) and amplify the impact of any contaminant on measured [La⁻]. We are not aware of studies that systematically quantify the effect of sample volume on [La⁻], so no specific volume can be recommended. We therefore encourage targeted investigations of sample volume to improve the accuracy of blood [La⁻] measurements.

5. Conclusion

According to the lactate shuttle theory, blood [La⁻] depends on production, transport and consumption. Therefore, it is important to keep in mind that many methodological and biological factors affect these processes and the subsequent measurement of [La⁻] (Figure 7). Since lactate is usually measured in research, medical and training testing, it is important to understand these factors to avoid misinterpretation. The main recommendation is to control all these factors when measuring [La⁻] and perform the measurement in the same conditions when monitoring the evolution of a specific person or comparing different individuals.

FIGURE 7.

Schematic illustration of the identified factors affecting blood [La⁻].

Author Contributions

José Antonio Benítez‐Muñoz: conceptualization; investigation; writing – original draft; writing – review and editing; visualization. Rocío Cupeiro: conceptualization; investigation; writing – original draft; writing – review and editing; visualization; supervision.

Ethics Statement

The authors have nothing to report.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Benítez‐Muñoz J. A. and Cupeiro R., “Factors Influencing Blood Lactate Concentration During Exercise: A Narrative Review With a Lactate Shuttle Perspective,” Acta Physiologica 241, no. 12 (2025): e70131, 10.1111/apha.70131.

Data Availability Statement

The authors have nothing to report.