Wernette, E.M.D, PhD., Granata, N., M.S.

The science of photobiomodulation (PBM) has a long history in medicine and robust literature on its efficacy for a variety of health concerns. Although this extensive body of work has clarified many questions on the biological effect of light, as science usually goes, it has also made our understanding less clear.

One of the major findings to come out of the PBM literature is the general characterization of the dose-response curve. The benefit of PBM follows the Arndt-Schulz law, producing a biphasic dose-response curve’, meaning that too little and too much light both produce a suboptimal effect, compared to a moderate, ‘optimal’ amount of light.

This insight motivated research to further elucidate the “optimal dose” of light, but the proposed hypotheses could not explain the mixed data, causing debate in the field. Still today, researchers and physicians struggle to understand the shape of PBM’s dose-response curve, making it difficult to test and recommend reliable dosing protocol for patients.

But what specifically about PBM’s dose-response curve is causing this widespread confusion? Below we will explore some of the main sources of controversy in the PBM literature.

1. Mischaracterizing the Biphasic Dose-Response Curve Hypothesis

One of the biggest sources of confusion is that there are three distinct dose-response hypotheses for PBM, but often only one generic hypothesis is cited. When presented graphically, with the x-axis representing “dose” and the y-axis representing “response”, this generic representation is a simple monotonic bell curve (Figure 1).

Moderate amounts of light provide more benefit than too little or too much light. However, a question remains as to the specific end behavior of this bell curve; in other words, how do extremely low or high doses affect the biological response to light? The three hypotheses are discussed in depth below.

Hypothesis 1: Too little and too much light lead to minimal or zero benefit, but not adverse effects.

The first, and seemingly most common, graphical representation of the dose-response curve in PBM has tails that reach a horizontal asymptote at the point of zero-benefit (Figure 2a; Sommer et al., 2001; Lopes et al., 2009; Gao et al., 2009). Thus, it is not possible to have a negative or adverse effect from light. However, we know this is not true, because too high doses of light can lead to adverse effects from burns to cell death to longer healing times (Hamblin et al., 2018de Lima et al., 2013); Gonclaves et al., 2007; Huang et al., 2011). As such, this hypothesis is ill-equipped to explain the observed adverse effects of extremely high doses, or overdoses, of light.   

Hypothesis 2: Too little light can result in minimal or zero benefit, but not adverse effects; whereas too much light can cause adverse effects.

The second hypothesis that is used to explain PBM is similar to the first, except that the end behavior differs based on the dose of light (Figure 2b; Rojas et al., 2011; Huang et al., 2009; Tumility et al., 2009). Here, low doses of light result in zero or minimal benefit and cannot result in adverse effects. High doses of light, on the other hand, can result in adverse effects, which is graphically represented as a tail that projects below the x-axis.

This hypothesis is more comprehensive than the first, in that it recognizes there are physiological limits to the dose of light. However, this hypothesis is also insufficient given there are data to suggest that too little light can also cause adverse effects.

Hypothesis 3: The light deprivation hypothesis: too little and too much light have adverse effects.

The most realistic shape for the hypothesized effect of light might be a parabola without asymptotes only at the level of total cell death (Figure 2c). This is because we know that “chronic light deprivation” can result in adverse effects, by depriving the cell of usable energy necessary for optimal functioning. For example, light deprivation has been linked to cognitive deficits (Asadian et al., 2022), appetite loss (Varela et al. 2014), mental health (Wilson et al., 2002; Gonzolez et al., 2008), and infertility (Reiter et al., 1976). Thus, the most comprehensive hypothesis of PBM acknowledges that light deprivation and overexposure can lead to cell death.

In summary, the theoretical framework a researcher designs their research with biases the outcome of the research: what questions are investigated, what outcome measures are chosen, and how the data is analyzed, modeled, and interpreted. Studies should specifically test these competing hypotheses by assessing the effects of extremely high and low doses of light.

2. Not having a standard operational definition of a “dose”.
A ‘dose’ of light in PBM is a product of four independent factors: power output, distance, time and spot/treatment size. This is partially because PBM is subject to the inverse square law of light, which describes how the intensity of light changes as a function of distance. Changing one of these factors, independently of the others, changes the dose of light (Zein et al., 2018; de Freitas & Hamblin, 2016). In theory, it should be easy to calculate a dose based on these parameters; however, converting dosage parameters to the same unit depends on what measures are reported in studies.

Unfortunately, it is not possible to compare doses between many studies because they use different and incomparable, measures of energy output. These measures include total power output (measured in watts); power density (measured in milliwatts per centimeter squared); total energy output (measured in Joules); and energy density, or fluence (measured in Joules per centimeter squared).

Importantly, it is not possible to retroactively calculate some measures of dose for direct comparison. For example, power density cannot be calculated from total power without the treatment spot size, and it is not useful to compare total power with power density. Similarly, measures of energy and fluence cannot be calculated from power without the treatment time.

As such, it is not effective to compare the effect of dose on a given outcome measure from multiple studies using different, incomparable measures of energy output. There needs to be a greater push for a standard unit for reporting PBM dosages in peer-reviewed articles, especially for use in meta-analyses and reviews.

3. Using various outcome measures.
Not only does the measure for dosage differ between studies, but so do the outcome measures. Importantly, not all effects of PBM follow a biphasic dose-response curve, and the effect of the dose depends on what biological response is being measured. Therefore, using the biphasic dose response as a blanket term for all PBM effects biases how PBM’s dose-response curve is modeled and analyzed. For example, the effect of PBM on reactive oxygen species (ROS) follows a triphasic dose-response curve (Prabhu et al., 2010; Huang et al., 2018).There is also evidence that different types of cells respond differently to light, even when the same outcome measure is used (Huang et al., 2010). Thus, PBM protocols must account for both the outcome measure chosen and the cell type for that outcome, and researchers should formulate their initial predictions based on these specifications.

4. A lack of randomized control clinical studies.
Clinical studies that experimentally test the effect of dose in humans are very sparse. Of the few studies that have been conducted so far, there is evidence that moderate doses of light produce the most health benefits, compared to low and high doses (Hashimoto et al., 1997; Dellagrana et al., 2017; Dellagrana et al., 2018). However, these studies present methodological challenges, including small sample sizes, and limited outcome measures. Therefore, there is an immediate need for experiments that directly test the clinical efficacy of PBM at different dosages.

5. Not measuring individual differences that might moderate PBM efficacy.
Thus far, PBM experiments have largely focused on manipulating light parameters. However, an unexplored area of PBM research is how environmental or individual differences affect the efficacy of PBM. For example, not only does the depth of light penetration depend on the wavelength, but it also likely depends on an individual’s body structure (e.g. BMI, fat, muscle, water content, etc.). Extra fat, muscle, and water provide extra depth and matter that light must travel through to reach a treatment area, which reduces the amount of energy that will reach that area.Furthermore, we know that skin tone moderates the efficacy of PBM because melanin itself is a chromophore that absorbs red and near-infrared light (Brondon et al., 2007; de Souza Contatori et al., 2022; Suh et al., 2020). As such, more melanin means a higher affinity towards absorbing certain wavelengths of light.

There is a strong need for consideration of individual differences in future studies to better understand how to properly dose light, or more importantly, how to avoid mis-dosing light, in diverse populations.

Main Takeaways

What can the scientific community conclude about PBM’s dose-response effect today? First, cells need light to survive; an absence of light will eventually result in cell death. There are also limits to the maximum amount of light that a cell can handle; an overdose of light is fatal. However, there is still a need for clinical studies that experimentally manipulate the dose of light, and that separately and repeatedly test dosing effects for various indications. One promising step towards clinical dosing studies and, thus, delivering more reliable PBM to patients, is providing researchers and clinicians with a PBM device that allows for experimental control of dosing. Thus, PBM device companies and researchers need to work collaboratively to achieve the shared goal of providing effective and precise treatment.

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