Aetna considers chemical hair analysis experimental and investigational, except for diagnosis of suspected chronic arsenic poisoning.
Note: Microscopic evaluation of hair structure (trichogram) may be medically necessary as part of the work-up of members with alopecia or abnormal-appearing or abnormally growing hair.
Note: Although hair samples may be used for verifying abuse of illicit substances in persons who wish to evade detection, its use in this situation is not considered treatment of disease.Background
Hair analysis has been proposed as an aid in the diagnosis of disorders such as mineral or protein deficiency or mineral toxicity. Hair analysis has not been proven to be effective in ascertaining mineral or metabolic imbalances or IgE-mediated allergic diseases. Hair analysis has also not been proven to be of use in either the diagnosis or treatment of autism.
Because arsenic is taken up and bound in hair and fingernails, analysis of hair and nails have been used to detect chronic arsenic exposure (Goldman, 2008). However, in a setting in which air exposure is a consideration, as in an industrial environment, it is very difficult to remove exogenous arsenic from hair, and therefore to get a reliable reading. Also a lack of standardization of analysis contributes to the variability of results from hair and nail testing. Goldman (2008) explained that commercial laboratory hair analyses for multiple elements including arsenic are highly inaccurate. Determination of arsenic in hair and nails has been most useful in epidemiological studies performed to evaluate environmental exposures of populations to inorganic arsenic; it is less useful in the evaluation of an individual patient (Goldman, 2008).
Microscopic evaluation of hair structure (trichogram) may be indicated as part of the work-up of members with alopecia or abnormal-appearing or abnormally growing hair. Hair may be examined under the microscope to determine the number of hairs that are actively growing (anagen phase) versus the resting phase (telogen). In addition, microscopic examination of clipped hair can reveal structural abnormalities of the hair bulb or shaft.
Rahman et al (2009) determined the concentration of trace elements present in scalp hair sample of schizophrenic patients and examined the relationship between trace elements level and nutritional status or socioeconomic factors. The study was conducted among 30 schizophrenic male patients and 30 healthy male volunteers. Hair trace element concentrations were determined by flame atomic absorption spectroscopy and analyzed by independent t-test, Pearson's correlation analysis, regression analysis, and analysis of variance (ANOVA). Mn, Zn, Ca, Cu, and Cd concentrations of schizophrenic patients were 3.8 +/- 2.31 microg/gm, 171.6 +/- 59.04 microg/gm, 396.23 +/- 157.83 microg/gm, 15.40 +/- 5.68 microg/gm, and 1.14 +/- 0.89 microg/gm of hair sample, while those of control subjects were 4.4 +/- 2.32 microg/gm, 199.16 +/- 27.85 microg/gm, 620.9 +/- 181.55 microg/gm, 12.23 +/- 4.56 microg/gm, and 0.47 +/- 0.32 microg/gm of hair sample, respectively. The hair concentration of Zn and Ca decreased significantly (p = 0.024; p = 0.000, respectively) and the concentration of Cu and Cd increased significantly (p = 0.021; p = 0.000, respectively) in schizophrenic patients while the concentration of Mn (p = 0.321) remain unchanged. Socioeconomic data reveals that most of the patients were poor, middle-aged and divorced. Mean body mass indices (BMIs) of the control group (22.26 +/- 1.91 kg/m(2)) and the patient group (20.42 +/- 3.16 kg/m(2)) were within the normal range (18.5 to 25.0 kg/m(2)). Pearson's correlation analysis suggested that only Ca concentration of patients had a significant positive correlation with the BMI (r = 0.597; p = 0.000) which was further justified from the regression analysis (R (2) = 44 %; t = 3.59; p = 0.002) and 1-way ANOVA test (F = 3.62; p = 0.015). A significant decrease in the hair concentration of Zn and Ca as well as a significant increase in the hair concentration of Cu and Cd in schizophrenic patients than that of its control group was observed, which may provide prognostic tool for the diagnosis and treatment of this disease. However, further work with larger population is suggested to examine the exact correlation between trace element level and the degree of disorder.
Guidelines from the National Institute for Health and Clinical Excellence (2011) recommended against the use of hair analysis in the diagnosis of food allergy. An NIAID expert panel (Boyce et a., 2010) made similar recommendations against the use of hair analysis in food allergy. Guidelines from the American Academy of Asthma, Allergy and Immunology (Wallace et al, 2008) state that hair analysis has no diagnostic validity in rhinitis. Autism guidelines from the Singapore Ministry of Health (2010) recommend against the use of hair mineral analysis for autism.
Albar et al (2013) stated that recently, hair cortisol has become a topic of global interest as a biomarker of chronic stress. Different research groups have been using different methods for extraction and analysis, making it difficult to compare results across studies. A critical examination of the reported analytical methods is important to facilitate standardization and allow for a uniform interpretation. These researchers qualitatively compared 4 published procedures from laboratories in Germany, the Netherlands, the United States of America and Canada. Multiple aspects of their procedures were compared. A major difference among the laboratories was the enzyme-linked immunosorbent assay (ELISA) kit used: the Canadian laboratory used the kit from ALPCO Diagnostics (Salem, MA), the American laboratory used the kit from DRG International (Springfield, NJ), the German laboratory used the kit from DRG Instruments GmbH (Marburg, Germany), or IBL (Hamburg, Germany), and the Dutch used the kit from Salimetrics (Suffolk, UK). In addition, there were noted differences in hair mass used as well as washing and extraction procedures. The range of hair cortisol levels determined in healthy volunteers by the 4 groups was within 2.3-fold: Koren, 46.1 pg/mg; Van Rossum, 29.72 pg/mg; Kirschbaum, 20 pg/mg and Laudenslager ~ 27 pg/mg. The authors concluded that the relative similarities in hair cortisol values in volunteers among the 4 laboratories should facilitate a quality assurance exchange program, as a necessary step toward clinical use of this novel test.
Karlen and colleagues (2013) examined cortisol concentrations in hair as biomarker of prolonged stress in young children (n = 100) and their mothers and the relation to perinatal and socio-demographic factors. Prolonged stress levels were assessed through cortisol in hair. A questionnaire covered perinatal and socio-demographic factors during the child's first year of life. Maternal hair cortisol during the 2nd and 3rd trimester and child hair cortisol at year 1 and 3 correlated. Child cortisol in hair levels decreased over time and correlated to each succeeding age, between years 1 and 3 (r = 0.30, p = 0.002), 3 and 5 (r = 0.39, p < 0.001), and 5 and 8 (r = 0.44, p < 0.001). Repeated measures gave a significant linear association over time (p < 0.001). There was an association between high levels of hair cortisol and birth weight (β = 0.224, p = 0.020), non-appropriate size for gestational age (β = 0.231, p = 0.017), and living in an apartment compared with a house (β = 0.200, p = 0.049). In addition, these investigators found high levels of cortisol in hair related to other factors associated with psychosocial stress exposure. The authors concluded that correlation between hair cortisol levels in mothers and their children suggested a heritable trait or maternal calibration of the child's hypothalamic-pituitary-adrenocortical axis. Cortisol output gradually stabilizes and seems to have a stable trait. They stated that cortisol concentration in hair has the potential to become a biomarker of prolonged stress, especially applicable as a non-invasive method when studying how stress influences children's health.
Russell et al (2014) noted that cortisol is assumed to incorporate into hair via serum, sebum, and sweat sources; however, the extent to which sweat contributes to hair cortisol content is unknown. In this study, sweat and saliva samples were collected from 17 subjects after a period of intensive exercise and analyzed by salivary ELISA. Subsequently, an in-vitro test on exposure of hair to hydrocortisone was conducted. Residual hair samples were immersed in a 50-ng/ml hydrocortisone solution for periods lasting 15 mins to 24 hrs, followed by a wash or no-wash condition. Hair cortisol content was determined using the authors’ modified protocol for a salivary ELISA. Post-exercise control sweat cortisol concentrations ranged from 8.16 to 141.7 ng/ml and correlated significantly with the log-transformed time of day. Sweat cortisol levels significantly correlated with salivary cortisol concentrations. In-vitro hair exposure to a 50-ng/ml hydrocortisone solution (mimicking sweat) for 60 mins or more resulted in significantly increased hair cortisol concentrations. Washing with isopropanol did not affect immersion-increased hair cortisol concentrations. The authors concluded that human sweat contains cortisol in concentrations comparable with salivary cortisol levels. The findings of this study suggested that perfuse sweating after intense exercise may increase cortisol concentrations detected in hair. This increase likely cannot be effectively decreased with conventional washing procedures and should be considered carefully in studies using hair cortisol as a biomarker of chronic stress.
Boscolo-Berto et al (2014) evaluated the diagnostic performance of ethyl glucuronide in the 3-cm proximal scalp hair fraction (HEtG) as a marker of chronic excessive drinking. In July 2012/May 2013, after a systematic search through the MEDLINE, OVID/EMBASE, WEB OF SCIENCE, and SCOPUS databases, 8 studies were included in the pooled analysis that reported raw single data on HEtG concentration and self-reported daily alcohol intake (SDAI). A receiver operating characteristic curve analysis and a Spearman rank-order correlation test were used. A meta-analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane recommendations, comprising quality and bias assessments. The pooled analysis showed that 30 pg/mg could be a useful cut-off value for HEtG to detect an SDAI greater than 60 g/d and demonstrated a parabolic direct correlation between HEtG and SDAI data [rho 0.79; 95 % confidence interval (CI): 0.69 to 0.87; p < 0.001]. The meta-analysis found an overall HEtG sensitivity of 0.96 (95 % CI: 0.72 to 1.00) and a specificity of 0.99 (95 % CI: 0.92 to 1.00); a nomogram to predict the post-test probability of exhibiting the targeted condition in the general population was built. Significant variability among the included studies was detected, which was mainly explained by true heterogeneity in the presence of publication bias. The authors concluded that HEtG is a promising marker for identifying chronic excessive drinking. Nonetheless, they stated that larger and well-designed population studies are needed to draw any definitive conclusions on the significance and appropriateness of its application in the forensic setting.
Mao et al (2014) stated that trace elements play an important role in maintaining the normal metabolic and immune function. The onset of recurrent respiratory tract infection (RRI) is associated with the immune function, genetic factors and nutritional status. However, the association between the levels of trace elements and RRI remains inconclusive. These researchers investigated the alterations of hair levels of zinc (Zn), copper (Cu) and iron (Fe) in Chinese children with RRI by performing a meta-analysis. A pre-defined electronic databases search was performed to identify eligible studies for the analysis of hair Zn, Cu or Fe levels in Chinese children with RRI. A total of 13 studies were included; RRI patients displayed significantly lower levels of hair Zn (13 studies, random effects SMD: - 1.215, 95 % CI: - 1.704 to - 0.725, p < 0.0001), Cu (11 studies, random effects SMD: - 0.384, 95 % CI: - 0.717 to - 0.052, p = 0.023) and Fe (12 studies, random effects SMD: - 0.569, 95 % CI: - 0.827 to - 0.312, p < 0.0001) compared with controls. No evidence of publication bias was observed. Sensitivity analysis did not change the results significantly. The authors concluded that deficiency of Zn, Cu and Fe may be contributing factors for the susceptibility of RRI in Chinese children. However, more studies in different ethnicities should be performed in the future.
Wei et al (2015) compared the hair cortisol levels between patients with depression and healthy controls. Hair cortisol levels of 22 first-episodic and 13 recurrent female patients with depression and 30 healthy controls were measured and compared using the electrochemiluminescence immunoassay. The relationship between hair cortisol levels and Hamilton depression scale (HAMD) or Hamilton anxiety scale (HAMA) scores were also examined. Before disease episode, no significant differences were observed among healthy controls, first-episodic patients and recurrent patients. In disease episode, the hair cortisol level in first-episodic patients was significantly higher than that in healthy controls or recurrent patients, while no significant difference was observed between recurrent patients and healthy controls. No significant correlation was found between HAMD or HAMA scores and hair cortisol levels in patients. The authors concluded that these findings indicated that hair cortisol levels increased in disease episode in first-episodic, but not recurrent patients with depression, which may suggest that episodes of disease have influence on cortisol levels. The main drawbacks of this study were: (i) long-term effects of anti-depressants on the results cannot be excluded without detailed medication information of the recurrent patients, and (ii) sample sizes were relatively small.
Liu et al (2015) examined the association between Zn levels and myocardial infarction (MI) using a meta-analysis approach. These investigators searched articles in the PubMed, OVID, and ScienceDirect published as of November 2014. A total of 13 eligible articles with 2,886 subjects from 41 case-control studies were identified. Overall, pooled analysis indicated that subjects with MI had lower Zn levels than healthy controls (standardized mean difference (SMD) = -1.848, 95 % CI: -2.365 to -1.331). Further subgroup analysis found that subjects with MI had lower Zn levels than healthy controls in serum (SMD = -1.764, 95 % CI: -2.417 to 1.112) and hair (SMD = -3.326, 95 % CI: -4.616 to -2.036), but not in toenail (SMD = -0.396, 95 % CI: -1.114 to 0.322). The subgroup analysis stratified by type of Zn measurement found a similar pattern in inductively coupled plasma-atomic absorption spectrometry (ICP-AAS) (SMD = -2.442, 95 % CI: -3.092 to -1.753), but not in neutron activation analysis (NAA) (SMD = -0.449, 95 % CI: -1.127 to 0.230]). Lower Zn levels in MI patients were found both in male (SMD = -3.350, 95 % CI: -4.531 to -2.169]) and female (SMD = -2.681, 95 % CI: -3.440 to -1.922]). And the difference of Zn levels according to MI in Asia (SMD = -2.555, 95 % CI: -3.267 to -1.844) was greater to that among the population in Europe (SMD = -0.745, 95 % CI: -1.386 to -0.104), but no difference was found in Oceania (SMD = -0.255, 95 % CI: -0.600 to 0.089]). The authors concluded that this meta-analysis indicated that there is a significant association between Zn deficiency and MI. They suggested that a community-based, long-term observation in a cohort design should be performed to obtain better understanding of causal relationships between Zn and MI, through measuring hair Zn at baseline to investigate whether the highest Zn category versus lowest was associated with MI risk.
|CPT Codes / HCPCS Codes / ICD-10 Codes|
|Information in the [brackets] below has been added for clarification purposes.  Codes requiring a 7th character are represented by "+":|
|ICD-10 codes will become effective as of October 1, 2015 :|
|Chemical hair analysis:|
|CPT codes covered if selection criteria are met:|
|83015||Heavy metal (e.g., arsenic, barium, beryllium, bismuth, antimony, mercury); screen|
|HCPCS codes not covered for indications listed in the CPB:|
|P2031||Hair analysis (excluding arsenic)|
|ICD-10 codes covered if selection criteria are met:|
|T37.8x1+ - T37.96x+||Poisoning by, adverse effect of and underdosing of other and unspecified systemic anti-infectives and antiparasitics|
|T57.0x1+ - T57.0x4+||Toxic effect of arsenic and its compounds|
|Z13.88||Encounter for screening for disorder due to exposure contaminants [suspected chronic arsenic poisoning]|
|ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):|
|D53.0||Protein deficiency anemia|
|E58, E59, E60, E61.0 - E61.9||Mineral deficiencies|
|E70.0 - E88.9||Metabolic disorders|
|J30.0 - J30.9||Vasomotor and allergic rhinitis|
|J45.20 - J45.998||Asthma|
|L20.0 - L20.9||Atopic dermatitis|
|L27.2||Dermatitis due to ingested food|
|L50.0 - L50.9||Urticaria|
|R82.5 - R82.6, R89.2 - R89.3||Other and unspecified abnormal findings in urine|
|T36.0x1+ - T37.5x6+, T38.0x1+ - T50.996+||Poisoning by, adverse effects of and underdosing of drugs, medicaments and biological substances|
|T63.001 - T63.94x+||Toxic effect of contact with venomous animals and plants|
|T78.00x+ - T78.2xx+
|Z13.21 - Z13.29||Encounter for screening for nutritional, metabolic and other endocrine disorders|
|Z91.010 - Z91.09||Allergy status, other than to drugs and biological substances|
|Microscopic evaluation of hair structure (trichogram):|
|CPT codes covered if selection criteria are met:|
|96902||Microscopic examination of hairs plucked or clipped by the examiner (excluding hair collected by the patient) to determine telogen and anagen counts, or structural hair shaft abnormality|
|ICD-10 codes covered if selection criteria are met:|
|L65.0 - L65.9||Other nonscarring hair loss [abnormal alopecia]|
|L67.0, L67.8 - L67.9||Hair color and hair shaft abnormalities|
|L73.1, L73.8 - L73.9||Other follicular disorders|