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Lactate Threshold

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Some of the tests could be "p-hacking", where the study looks at a sufficiently large number of variables that some correlation occurs randomly<ref name="HeadHolman2015"/>.

[[File:LactateComp.jpg|none|thumb|500px|The correlation (or lack thereof) between MLSS and the lactate levels at the MLSS intensity seen during 3 or 5 minute incremental power tests<ref name="Heck-1985"/>.]]

=Factors =Lactate Threshold And Near Infrared Spectroscopy==A promising technology for measuring Lactate Threshold is Near-infrared spectroscopy (NIRS) which shines infrared light into the skin above an active muscle and measures the reflected light. NIRS measures the oxygen saturation in the capillaries of the muscle and has the potential to test for Lactate Threshold without any blood sampling. Because NIRS can monitor continually, it is possible that it may be able to determine the Lactate Threshold during an incremental test rather than requiring the multiple tests of MLSS. ===Introduction to NIRS and SmO2===Near-infrared spectroscopy (NIRS) has been shown to measure the oxygen saturation of blood in muscle (SmO₂) or other body tissues (StO₂)<ref name="Kek-2008"/><ref name="Torricelli-2004"/><ref name="Mancini-1994"/>. (This works on similar principles to a [[Pulse Oximeter]].) Medical NIRS systems for monitoring StO₂ use Infrared LED or Lasers at 2, 3, or 4 frequencies<ref name="Hyttel-Sorensen-2011"/>. SmO₂ reflects the balance of oxygen delivery and consumption during exercise<ref name="Chance-1992"/> and there are some initial indications that relative SmO₂ may reflect changes in performance capacity<ref name="Neary-2005"/>. There is generally a four phase response of smo2 during incremental exercise from rest to maximum intensity and the following recovery<ref name="Belardinelli-1995a"/>:# An initial increase in SmO₂ above resting levels to supply the now active muscles. (This may influence be due to increased blood flow<ref name="Bhambhani-1997"/>, but computer models do not support this<ref name="Fuglevand-1997"/>.) # SmO₂ decreases linearly or exponentially with increasing intensity, followed by a leveling off as the subject approaches maximum intensity. There is some evidence of a breakpoint where the rate of decline increases (see below). # During the first 1-2 minutes of recovery there is a rapid increase in SmO₂ which usually exceeds resting levels. # SmO₂ then declines to resting levels over a further few minutes. Skin should not impact SmO₂ readings more than 5%<ref name="Hampson-1988"/>, but surface fat can interfere with smo2<ref name="van Beekvelt-2001"/><ref name="Homma1996"/><ref name="BenaronMatsushita1998"/><ref name="BenaronYamamoto1998"/>. Because the penetration depth of NIRS is about 50-60% of the distance between the emitter and receiver, the site must be selected so that the fat layer is much thinner than this depth<ref name="Bhambhani-2004"/>.===SmO₂ Breakpoint===As the intensity increases during incremental exercise SmO₂ will remain constant or decline, with the rate of decline being greater near the Lactate Threshold<ref name="Belardinelli-1995a"/><ref name="Bhambhani-2004"/><ref name="Belardinelli-1995b"/>. This has led to several studies using the concept of an SmO₂ "breakpoint"<ref name="Grassi-1999"/>. This breakpoint is a change in the slope of the line plotting SmO₂ against work intensity in an incremental intensity test. This increase in the rate of desaturation can either be visually determined or based on bilinear regression. (The bilinear regression iterates over different combinations of two regression lines to find the lowest sum of squares of the residuals. I could not find the details of the constraints placed on this approach.) [[File:SmO2 Breakpoint1.jpg|none|thumb|500px|A graph showing the SmO₂ breakpoint.]]===SmO₂ and Lactate Threshold===A number of studies have looked at the relationship between SmO₂ and Lactate or Lactate Threshold* A study of five mountain climbers found a relationship between the "point of inflection of lactate" and the SmO₂ breakpoint during an incremental cycling test<ref name="Grassi-1999"/>. The definition they were using for the inflection of lactate was a blood lactate reading that is more than 0.5 below the subsequent value, with typical lactate levels below 2 mmol/l. This is closer to the "aerobic threshold" concept than the typical Lactate Threshold. The study did not find any correlation between the SmO₂ breakpoint and the 4 mmol/l value of blood lactate. The breakpoint was determined from bi-linear regression. * A study of 40 sedentary undergraduates showed a correlation between SmO₂ returning to resting levels and Ventilatory Threshold (VT) in 65% of subjects during an incremental cycling test<ref name="Bhambhani-1997"/>. While the text refers to Lactate Threshold as the point at which Lactate rises above resting levels (aerobic threshold) the method used to determine VT appears to be the anaerobic threshold. This study did not use the breakpoint mentioned above, but the point where the SmO₂ drops below the level detected at rest. * A study of 11 subjects of varying fitness levels showed a correlation between SmO₂ breakpoint determined visually and Ventilatory Threshold (VT) during an incremental cycling test<ref name="Belardinelli-1995a"/>.* A comparison between 12 healthy subjects and 7 suffering from chronic heart failure (CHF) showed a correlation between the SmO₂ breakpoint and Ventilatory Threshold (VT) during an incremental cycling test<ref name="Belardinelli-1995c"/>. * A 2012 study compared the results from NIRS on the calf (Gastrocnemius Lateralis) and quads (Vastus Lateralis) in 31 active but not highly trained college students during an incremental cycling test<ref name="Wang-2012"/>. The study found a correlation between Lactate Threshold (determined from the log-log method<ref name="Davis-2007"/>) and the SmO₂ breakpoint (determined from bi-linear regression) in both locations, but the quads corresponded better. (I suspect the results from running could be quite different.)* A 2009 study used MLSS (the gold standard for Lactate Threshold) with running to evaluate NIRS<ref name="Snyder-2009"/>. The 16 athletes performed between 2 and 5 tests of 30 minutes each to determine MLSS, separated by at least 48 hours each. The subjects then performed an incremental treadmill test using 6 minute stages with the 4th stage at the pace they estimated they could maintain for an hour (around Lactate Threshold). The first 3 stages where then 0.66, 0.44, & 0.22 meter/second slower, and the subsequent stages were 0.22 meters/second faster each time. SmO₂ breakpoint was defined as the workload immediately prior to a drop of 15% that lead to a continuous decline in smo2. A Lactate breakpoint was also determined based on the incremental test using the workload prior to an increase of 1 mmol/l as the criteria. Both the SmO₂ and Lactate breakpoint were determined visually. Of the 16 subjects, 1 did not reach MLSS, 2 did not have both a SmO₂ breakpoint or a Lactate breakpoint (based on the criteria used) and 1 did not have either. The study found that SmO₂ is as effective as Lactate breakpoint tests for determining true Lactate Threshold (MLSS). The table below shows the values for each of 12 subjects, with the paces shown as KPH, then min/mile, then the error as a percentage. This shows that while smo2 is as good as the lactate breakpoint, the individual differences from MLSS are not insignificant. For instance, subject 5 had an MLSS of 6:21, but an SmO₂ breakpoint of 6:57 and lactate breakpoint of 6:59, which is a big difference. {| class="wikitable" ! Subject! MLSS! SmO₂ ! Lb! MLSS! SmO₂ ! Lb! SmO₂ err! Lb err|-| 1| 13.5| 12.9| 13.7| 7:09| 7:29| 7:03| 4.4%| -1.5%|-| 2| 14.3| 14.2| 13.4| 6:45| 6:48| 7:12| 0.7%| 6.3%|-| 3| 9| 9.5| 9.5| 10:44| 10:10| 10:10| -5.6%| -5.6%|-| 4| 13.4| 12.9| 12.9| 7:12| 7:29| 7:29| 3.7%| 3.7%|-| 5| 15.2| 13.9| 13.8| 6:21| 6:57| 6:59| 8.6%| 9.2%|-| 6| 12.9| 12.7| 13.5| 7:29| 7:36| 7:09| 1.6%| -4.7%|-| 7| 15.4| 14.5| 15.3| 6:16| 6:40| 6:19| 5.8%| 0.6%|-| 8| 11.5| 11.7| 10.9| 8:24| 8:15| 8:52| -1.7%| 5.2%|-| 9| 10.8| 10.9| 10.8| 8:56| 8:52| 8:56| -0.9%| 0.0%|-| 10| 14.2| 14.2| 13.2| 6:48| 6:48| 7:19| 0.0%| 7.0%|-| 11| 11.6| 12.2| 12.2| 8:19| 7:55| 7:55| -5.2%| -5.2%|-| 12| 14.8| 14.6| 13.8| 6:31| 6:37| 6:59| 1.4%| 6.8%|-| Mean| 13.05| 12.85| 12.75| | | | | |-| SD| 1.88| 1.51| 1.55| | | | | |}===Thoughts on SmO2 and Lactate Threshold===I think that the currently available research indicates that NIRS and SmO₂hold promise for simplifying the measurement of Lactate Threshold. However, the research is at a fairly early stage, with only one study using the gold standard of MLSS. There are also indications in the research that the indicators of Lactate Threshold are not always evident in all subjects. There is no indication if this is a problem that occurs in specific subjects so that they will never get a valid test result, or if it's a problem that simply occurs randomly. Currently there are two consumer products available; [[BSX]] and [[Moxy]]. BSX is a fully automated approach to analyzing the data and estimating Lactate Threshold, whereas the Moxy is intended to provide the end-user with the underlying data to evaluate.=Factors That May Influence Lactate Threshold=

There are a few factors that may change the Lactate Threshold (other than training)

* Because lactate is produced from the metabolism of carbohydrate, a reduction in carbohydrate intake (or [[Glycogen]] depletion) will shift the lactate curve to the right<ref name="Reilly-1999"/><ref name="Yoshida-1984"/><ref name="Maassen-1989"/><ref name="McLellan-1989"/>.

<ref name="Lehmann-1983">M. Lehmann, A. Berg, R. Kapp, T. Wessinghage, J. Keul, Correlations between laboratory testing and distance running performance in marathoners of similar performance ability., Int J Sports Med, volume 4, issue 4, pages 226-30, Nov 1983, doi [http://dx.doi.org/10.1055/s-2008-1026039 10.1055/s-2008-1026039], PMID [http://www.ncbi.nlm.nih.gov/pubmed/6654546 6654546]</ref>

<ref name="Allen-1985">WK. Allen, DR. Seals, BF. Hurley, AA. Ehsani, JM. Hagberg, Lactate threshold and distance-running performance in young and older endurance athletes., J Appl Physiol (1985), volume 58, issue 4, pages 1281-4, Apr 1985, PMID [http://www.ncbi.nlm.nih.gov/pubmed/3988681 3988681]</ref>

<ref name="Kek-2008">KJ. Kek, R. Kibe, M. Niwayama, N. Kudo, K. Yamamoto, Optical imaging instrument for muscle oxygenation based on spatially resolved spectroscopy., Opt Express, volume 16, issue 22, pages 18173-87, Oct 2008, PMID [http://www.ncbi.nlm.nih.gov/pubmed/18958095 18958095]</ref>

<ref name="Torricelli-2004">A. Torricelli, V. Quaresima, A. Pifferi, G. Biscotti, L. Spinelli, P. Taroni, M. Ferrari, R. Cubeddu, Mapping of calf muscle oxygenation and haemoglobin content during dynamic plantar flexion exercise by multi-channel time-resolved near-infrared spectroscopy., Phys Med Biol, volume 49, issue 5, pages 685-99, Mar 2004, PMID [http://www.ncbi.nlm.nih.gov/pubmed/15070196 15070196]</ref>

<ref name="Hyttel-Sorensen-2011">S. Hyttel-Sorensen, LC. Sorensen, J. Riera, G. Greisen, Tissue oximetry: a comparison of mean values of regional tissue saturation, reproducibility and dynamic range of four NIRS-instruments on the human forearm., Biomed Opt Express, volume 2, issue 11, pages 3047-57, Nov 2011, doi [http://dx.doi.org/10.1364/BOE.2.003047 10.1364/BOE.2.003047], PMID [http://www.ncbi.nlm.nih.gov/pubmed/22076266 22076266]</ref>

<ref name="Chance-1992">B. Chance, MT. Dait, C. Zhang, T. Hamaoka, F. Hagerman, Recovery from exercise-induced desaturation in the quadriceps muscles of elite competitive rowers., Am J Physiol, volume 262, issue 3 Pt 1, pages C766-75, Mar 1992, PMID [http://www.ncbi.nlm.nih.gov/pubmed/1312785 1312785]</ref>

<ref name="Bhambhani-2004">YN. Bhambhani, Muscle oxygenation trends during dynamic exercise measured by near infrared spectroscopy., Can J Appl Physiol, volume 29, issue 4, pages 504-23, Aug 2004, PMID [http://www.ncbi.nlm.nih.gov/pubmed/15328597 15328597]</ref>

<ref name="Belardinelli-1995a">R. Belardinelli, TJ. Barstow, J. Porszasz, K. Wasserman, Changes in skeletal muscle oxygenation during incremental exercise measured with near infrared spectroscopy., Eur J Appl Physiol Occup Physiol, volume 70, issue 6, pages 487-92, 1995, PMID [http://www.ncbi.nlm.nih.gov/pubmed/7556120 7556120]</ref>

<ref name="Belardinelli-1995b">R. Belardinelli, TJ. Barstow, J. Porszasz, K. Wasserman, Skeletal muscle oxygenation during constant work rate exercise., Med Sci Sports Exerc, volume 27, issue 4, pages 512-9, Apr 1995, PMID [http://www.ncbi.nlm.nih.gov/pubmed/7791581 7791581]</ref>

<ref name="Belardinelli-1995c">R. Belardinelli, D. Georgiou, TJ. Barstow, Near infrared spectroscopy and changes in skeletal muscle oxygenation during incremental exercise in chronic heart failure: a comparison with healthy subjects., G Ital Cardiol, volume 25, issue 6, pages 715-24, Jun 1995, PMID [http://www.ncbi.nlm.nih.gov/pubmed/7649420 7649420]</ref>

<ref name="Neary-2005">JP. Neary, DC. McKenzie, YN. Bhambhani, Muscle oxygenation trends after tapering in trained cyclists., Dyn Med, volume 4, issue 1, pages 4, Mar 2005, doi [http://dx.doi.org/10.1186/1476-5918-4-4 10.1186/1476-5918-4-4], PMID [http://www.ncbi.nlm.nih.gov/pubmed/15790400 15790400]</ref>

<ref name="Grassi-1999">B. Grassi, V. Quaresima, C. Marconi, M. Ferrari, P. Cerretelli, Blood lactate accumulation and muscle deoxygenation during incremental exercise., J Appl Physiol (1985), volume 87, issue 1, pages 348-55, Jul 1999, PMID [http://www.ncbi.nlm.nih.gov/pubmed/10409594 10409594]</ref>

<ref name="Bhambhani-1997">YN. Bhambhani, SM. Buckley, T. Susaki, Detection of ventilatory threshold using near infrared spectroscopy in men and women., Med Sci Sports Exerc, volume 29, issue 3, pages 402-9, Mar 1997, PMID [http://www.ncbi.nlm.nih.gov/pubmed/9139181 9139181]</ref>

<ref name="Wang-2012">B. Wang, G. Xu, Q. Tian, J. Sun, B. Sun, L. Zhang, Q. Luo, H. Gong, Differences between the Vastus Lateralis and Gastrocnemius Lateralis in the Assessment Ability of Breakpoints of Muscle Oxygenation for Aerobic Capacity Indices During an Incremental Cycling Exercise., J Sports Sci Med, volume 11, issue 4, pages 606-13, 2012, PMID [http://www.ncbi.nlm.nih.gov/pubmed/24150069 24150069]</ref>

<ref name="Mancini-1994">DM. Mancini, L. Bolinger, H. Li, K. Kendrick, B. Chance, JR. Wilson, Validation of near-infrared spectroscopy in humans., J Appl Physiol (1985), volume 77, issue 6, pages 2740-7, Dec 1994, PMID [http://www.ncbi.nlm.nih.gov/pubmed/7896615 7896615]</ref>

<ref name="Fuglevand-1997">AJ. Fuglevand, SS. Segal, Simulation of motor unit recruitment and microvascular unit perfusion: spatial considerations., J Appl Physiol (1985), volume 83, issue 4, pages 1223-34, Oct 1997, PMID [http://www.ncbi.nlm.nih.gov/pubmed/9338432 9338432]</ref>

<ref name="Hampson-1988">NB. Hampson, CA. Piantadosi, Near infrared monitoring of human skeletal muscle oxygenation during forearm ischemia., J Appl Physiol (1985), volume 64, issue 6, pages 2449-57, Jun 1988, PMID [http://www.ncbi.nlm.nih.gov/pubmed/3403428 3403428]</ref>

<ref name="Homma1996">Sachiko Homma, Influence of adipose tissue thickness on near infrared spectroscopic signal in the measurement of human muscle, Journal of Biomedical Optics, volume 1, issue 4, 1996, pages 418, ISSN [http://www.worldcat.org/issn/10833668 10833668], doi [http://dx.doi.org/10.1117/12.252417 10.1117/12.252417]</ref>

<ref name="van Beekvelt-2001">MC. van Beekvelt, MS. Borghuis, BG. van Engelen, RA. Wevers, WN. Colier, Adipose tissue thickness affects in vivo quantitative near-IR spectroscopy in human skeletal muscle., Clin Sci (Lond), volume 101, issue 1, pages 21-8, Jul 2001, PMID [http://www.ncbi.nlm.nih.gov/pubmed/11410110 11410110]</ref>

<ref name="BenaronMatsushita1998">David A. Benaron, Kenichi Matsushita, Sachiko Homma, Eiji Okada, Britton Chance, Marco Ferrari, <title>Influence of adipose tissue on muscle oxygenation measurement with an NIRS instrument</title>, volume 3194, 1998, pages 159–165, ISSN [http://www.worldcat.org/issn/0277786X 0277786X], doi [http://dx.doi.org/10.1117/12.301048 10.1117/12.301048]</ref>

<ref name="BenaronYamamoto1998">David A. Benaron, Katsuyuki Yamamoto, Masatsugu Niwayama, Ling Lin, Toshikazu Shiga, Nobuki Kudo, Makoto Takahashi, Britton Chance, Marco Ferrari, <title>Accurate NIRS measurement of muscle oxygenation by correcting the influence of a subcutaneous fat layer</title>, volume 3194, 1998, pages 166–173, ISSN [http://www.worldcat.org/issn/0277786X 0277786X], doi [http://dx.doi.org/10.1117/12.301049 10.1117/12.301049]</ref>

<ref name="Davis-2007">JA. Davis, R. Rozenek, DM. DeCicco, MT. Carizzi, PH. Pham, Comparison of three methods for detection of the lactate threshold., Clin Physiol Funct Imaging, volume 27, issue 6, pages 381-4, Nov 2007, doi [http://dx.doi.org/10.1111/j.1475-097X.2007.00762.x 10.1111/j.1475-097X.2007.00762.x], PMID [http://www.ncbi.nlm.nih.gov/pubmed/17944661 17944661]</ref>

<ref name="Snyder-2009">AC. Snyder, MA. Parmenter, Using near-infrared spectroscopy to determine maximal steady state exercise intensity., J Strength Cond Res, volume 23, issue 6, pages 1833-40, Sep 2009, doi [http://dx.doi.org/10.1519/JSC.0b013e3181ad3362 10.1519/JSC.0b013e3181ad3362], PMID [http://www.ncbi.nlm.nih.gov/pubmed/19675475 19675475]</ref>

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