COMPUTERIZED IDENTIFICATION OF NORMAL AND ABNORMAL DIURNAL CORTISOL SECRETION PATTERNS AND LEVELS OF TESTOSTERONE FROM HUMAN SALIVA SAMPLES

Abstract: Normal and abnormal diurnal cortisol secretion patterns are identified from human saliva samples by an in vitro method. First and second saliva samples are taken from one human individual at first and second predefined times during the same day. A first cortisol concentration is determined in the first saliva sample, and a second cortisol concentration is determined in the second saliva sample. An abnormal secretion pattern I sthen compared to a normal secretion pattern with the help of a fuzzy logic algorithm. A function of the hypothalamic-pituitary-adrenal (HPA) axis is then determined for the human individual. Optionally, a testosterone level is determined from one of the samples and is used in combination with the cortisol concentrations to provide a refined determination.

Description: COMPUTERIZED IDENTIFICATION OF NORMAL AND ABNORMAL DIURNAL CORTISOL SECRETION PATTERNS AND LEVELS OF TESTOSTERONE FROM HUMAN SALIVA SAMPLES Technical Field The present invention relates to identification of normal and abnormal diurnal cortisol secretion patterns and levels of testosterone from human saliva samples, so as to determine the function of the hypothalamic-pituitary- adrenal (HPA) axis in individual subjects. Background Art The hypothalamus is a small, central part of the brain with several important regulatory functions, for example as regards physiologic response to external stress, body weight (especially the metabolically unfavorable abdominal obesity) , the sleep-wake cycle and mood fluctuations. As shown in FIG 1, the hypothalamus 2 is connected to the pituitary 3 as part of the central nervous system 1 and controls the secretion of different hormones from this endocrine gland. One of these hormones is ACTH (adenocorticotropic hormone) , the secretion of which is activated through secretion of corticotropin releasing hor- mone (CRH) from the hypothalamus 2, as indicated by reference numeral 7 in FIG 1. In response, this will activate secretion of the stress-related hormone cortisol 6 from the cortex of the adrenal glands 5 (see reference numerals 8 and 9 in FIG 1) . Cortisol 6 circulates in the bloodstream and activates specific receptors, such as the glucocorticoid receptor (GR) , in another small part of the brain, the hippocampus 4. This activation is indicated as 10 in FIG 1 and leads to inhibition of the release of ACTH (reference numeral 11) as well as CRH (reference numeral 12) . 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Φ r ø CQ 0 ) 0 0 ) <i tr Φ Φ m 01 rt CQ oo Ω 3 ø tr P< rt Φ o . Q μ- tr 0 Oi rt to rt φ CD CQ 0 Φ 0 ø 0 • Φ tr tr 0 1 0 ø rt φ 0 rt P- CD rt 0 ø Φ H, μ- rt 3 μ- CQ 00 3 rt Φ Ω r. tr ii 0 ) • Φ CQ tr 0 Ω rt - 1 μ- μ, 3 μ- ≤ μ- μ- 3 P. Hi tr Φ μ tr Φ μ- ø φ 0i m μ- ø P- pi CQ o Φ H μ- ø rt rt Φ - 0 ii rt Oi Ω 0 rt Ω Φ H tr rr ø φ Φ < ø 0 ) 0 ≥! 3 α P. Φ tr rt ^ μ- CD φ Φ ø Φ 3 0 H μ- φ μ- • rt ii CQ 0 ) M 3 tr rt 0 ) ø Hi φ rt 0 0 ) 0 ) tr 3 CD 0 Φ CO • ; rt ø μ- 0 • tr CD CQ rt Φ Oi CQ 0 μ- μ- μ- TJ Oi i rt Φ μ- "< H 1 3 rt CD 0 tr rr φ Φ <! o tr ø Φ tr φ O φ ø φ φ Φ t to ,_-_ o o o u. 5 > tr φ Φ ø Φ tr Ω f-3 Φ Φ Φ μ- CQ CQ ø ω rt CQ Oi O fi CQ oi ^ rt ø Φ P. Oi fi O Oi CQ 0 O Φ O 0i Hi Ω rt O Oi μ- μ 0 co Hi ≤ tr ; 0 Φ μ- 3 ø Φ 3 CQ ø φ Oi CO CQ φ μ- 0 X ø ϋ Φ O Pi μ- fi Hi P. μ- φ Φ ø • fi CO rt Oi O μ- 3 <. 0 ) 01 μ- 0 rt ø Oi CQ μ- μ- ø 0 CQ CQ Φ r ø tr Φ φ ii 0i to t o o o μ- ø tr μ- Ω tr •• FIG 1 is a schematic illustration of the hypothala- mic-pituitary-adrenal (HPA) axis, FIGs 2a-2c are graphical representations of diurnal cortisol variation according to three different main categories of HPA axis function, FIG 3 is a schematic overview of a computer system environment in which the computer program according to the invention may be executed, when used in an Internet application, FIG 4 is a flowchart of the main steps of the method according to a preferred embodiment, FIG 5 is a flowchart of an Internet use case for remote test and evaluation of diurnal cortisol secretion patterns from human saliva samples, and FIG 6 is a graphical representation of a diurnal cortisol variation known as inverted HPA axis function. Detailed Disclosure of the Invention As will be described in detail in the following, the main features of the invention involve: 1. The use of only two measurements of saliva cortisol concentration, sampled at defined times the same day. 2. A cortisol analysis algorithm, preferably implemented as a computer program, which analyses the measurement values and evaluates if the HPA axis function is normal or abnormal. If the HPA axis is abnormal, the program may also determine the type of abnormality. 3. Use of the above-mentioned computer program for remote distribution of tests and test results over the Internet . Preferably, the invention also involves a testosterone evaluation algorithm, advantageously implemented as part of the computer program, which evaluates the level of to t o o o to to » __ o on o 0» 0 OJ t to (— - o o on o on OJ < Oi Pi OJ rt TJ ø 0 tr tr μ- 0 Φ ii ø P. μ- Hi 01 ϋ Φ fi Ω Φ Hi 3 CQ φ Ω tr CQ φ K. μ- Φ 0i 0 ϋ CD ø 0 fi μ- φ μ- O OJ rr μ- rt CQ Ω ø CQ rt rt Φ ra 0 Ω tr Φ P. rt Ω rt i φ <! Φ . μ- 0 tr rt 0i ii μ- Ω 0 φ 1 tr fi 3 ø Ω Ω Φ μ- μ- < OJ 0 rt 0J CQ Oi OJ to ii 0 $ tr φ • μ- Ω Φ ~ en 0 rr 1 Φ Φ rt Φ tr μ> 0 CD μ- Φ en 3 - ø TJ • φ Oi r rt ø tr 0 0J o μ- Pi φ Oi to Hi Ω 3 O Ω Hi φ rt 0 3 0 μ- Φ 3 Φ φ ϋ fi ^ TJ tr Ω 01 rt co Φ Φ 0 1 rt Ω ! H en Ω O co Φ en 0 0 OJ fi μ- 0 ) Hi 0 0 ,rt rt ø ϋ Ω Pi 1 tr * ! rt 1 Ω Φ rt tr to CQ O tr Φ — Φ 0 TJ Φ OJ - Ω ,o ii ø <J O 1 0 Φ 0 ) OJ 0 to CQ X Φ fi 0 P. H Φ fi μ- P- O 0i Ω fi 0J 3 T 3 0 O tr 0 Φ rt ii 0 OJ Ω μ- Φ i CD 01 0 3 CD , rt 0 ■ 0 Φ 1 li tr Ω cn Ω O Φ μ- 0 Ξ CQ 0 O 3 Ω H" μ- OJ fi 0 Φ tr 01 CQ rt ,o ø rt φ 1 1 rt μ- Φ Ω _3 CQ CQ μ- 0 CD < CO 0 0) OJ rt rt Ω 0 fi tr 3 1 *« μ- φ O μ> 1 A set of three constants, alpha, beta and gamma, is predefined in the program for each of the two analysis variables, i.e. cort_conc_slope and cort_conc_mean. These constants are retrieved in a step 74. Two indices, which are decimal numbers between 0.00 and 1.00, are calculated in a step 75 for each variable. The first index, high_ind, defines to which degree the value of the variable can be regarded as high. The second index, low_ind, similarly defines to which degree the value of the variable can be regard as low. Preferably, it is calculated as the complement of high_ind (i.e., low_ind=l- high_ind) . These index calculations are based on the three above-mentioned constants, alpha, beta and gamma, for each indexed variable. The values of these constants have been empirically derived before execution of the program. In one implementation made by the inventors the constants are based on a survey of the scientific literature combined with information from a study population, consisting of 550 randomly selected middle-aged Swedes. The constants are based on statistical correlations between the measurement values and the different types of HPA axis function. Correlations with metabolic and physiologic risk factors for heart disease were used to define the abnormalities of the HPA axis . The constants, alpha, beta and gamma, are used in the following way in step 75 when calculating the first index, high_ind, for each of the variables: If the variable's value is lower than (or equal to) constant alpha, the index is set to 0.00. If the variable's is larger than (or equal to) constant gamma, the index is set to 1.00. If the variable's value is larger than constant alpha and smaller than (or equal to) constant beta, a program function calculates an index between 0 . 00 and 0.50. More specifically, the index is set to 2* ( ( (x - alpha) / (gamma - alpha)) 2), where x is the value of the variable. If the variable's value is larger than (or equal to) constant beta and smaller than constant gamma, a program function calculates an index between 0.50 and 1.00. More specifically, the index is set to 1 - (2* ( ( (x - gamma) / (x - alpha)) 2)), where x is the value of the variable. The calculated indices are stored in a step 76. Each possible HPA axis is characterized by a particular combination of the calculated two indices. These combinations are defined in a basic group definition matrix: This matrix has four possible categories, labeled A- D. Categories A and B = normal HPA axis, category C = activated HPA axis, and category D = blunted HPA axis. The calculated indices from the tested individual are used to assign the individual to a particular classification group in the following way: A membership index is calculated for each classification group, as defined in the above table, by retrieving the lowest index value in the corresponding column of the group definition matrix, as shown in a step 77. Thus, for each group (A-D) the membership index is defined as min (index_for_cort_conc_slope, index_for_cort_conc_mean) . This minimum value is stored in a step 78. Then, the tested subject is assigned to the group with the highest membership index in a step 79. The above may be exemplified as follows: Example In a normal subject, the value of cort_conc_slope should be high and the value of cort_conc_mean may be either high or low. This appears from the leftmost portion of the graph in FIG 2a. The corresponding indices for a subject could then be as follows: The high_index for cort_conc_slope might be 1.00 (corresponds to a high test value in the morning sample at 07.00 am and a low test value in the second sample at 11.00) . The high_ind for cort_conc_mean might be 0.6. This subject belongs to the group with normal HPA axis (group A in the group definition matrix) with a membership value of 0.6 (i.e. the minimum of 1.00 and 0.6) . The membership value of any other group would be less than this value. * * * Borderline cases, i.e. two classification groups with identical membership indices, may occur. These subjects are labeled as borderline normal/activated, borderline normal/- blunted or borderline activated/blunted as appropriate with regard to the indices . In a step 80, once the program has been able to either determine a single classification group for the subject, or a borderline case as described above, the result is output through the output means 25. The evaluation is refined by the addition of the information about testosterone level. The testosterone value is evaluated as follows : 1. In males: If the value is below a threshold value, the level is labeled as low. If the value is above this threshold value, the level is labeled normal. 2. In females: If the testosterone value is above a threshold value it is labeled high, otherwise it is labeled normal . Advantageously, the program is adapted to evaluate whether the second cortisol concentration (cort_conc_2) is higher than the first cortisol concentration (cort_conc_l) . Such a situation will be labeled as inverted HPA axis and is illustrated in FIG 6. This abnormality is associated with disturbed sleep pattern and/or diurnal rhythm. Internet use The procedure described above may preferably be used over the Internet, as will be explained with reference to FIGs 3 and 5. 1. The customer (user 30) uses a web client 31 to access a web site on a web server 27, which presents relevant information. 2. The customer orders a test kit (see dot and dash line i in FIG 3) and pays using a standard electronic payment procedure, which is not described further herein. 3. The customer receives a test kit to his/her address, using standard postal delivery 60 (see line ii in FIG 3) . This test kit contains an ID code, which is unique for each test kit. 4. The customer sends the test kit, which now contains the first and second saliva samples, to a contracted laboratory 40, using a preaddressed envelope, delivered to the customer together with the test kit. See line iii in FIG 3. 5. The laboratory performs the analyses and reports the results together with the test ID to the test database 24, which is connected to the web server 27 and the test computer 20. See line iv in FIG 3. 6. The customer accesses the web site a second time and enters the test kit ID code. The program described above retrieves the test results from the database by means of the ID code, performs the evaluation as described above and presents the conclusion to the customer, who has the option to obtain a print-out if he/she so wishes. See line v in FIG 3. Preferably, the conclusion involves not only the results from the measurement evaluation (i.e. the determined category of HPA axis functionality) but also a prediction of the risk of deranged health for the customer. The flow is depicted in FIG 5 and may be carried out for cortisol concentration measurements only, for both cortisol concentration measurements and a testosterone measurement, or for a testosterone measurement alone. At present, however, a combination of cortisol concentration measurements and a testosterone measurement is believed to provide the highest accuracy in the analysis of the HPA axis function and the prediction of the risk of deranged health for the customer. Program implementation An embodiment of the described program is implemented using the syntax module in Statistical Package for Social Sciences (SPSS) and also using VBScript for use over the Internet. The program can, however, be implemented in any other general purpose programming or scripting language, including but not limited to C++, Java, Visual Basic, Delphi or JavaScript . to t (— * ! __ o on O n o on tr μ- Ω tr ■ Oi en tr μ- CQ tr Φ ii μ- ø r tr Φ CQ fi 0 ø TJ CQ £ μ- rt tr 0 ) tr ø o fi 3 OJ K W OJ X μ- en • Hence, the method apparently identifies individuals with rather discrete deviations from normality. The evaluation is further refined by the addition of the information about testosterone levels. This results in a doubling of the number of groups, since the testosterone value may be either normal or abnormal, i.e. abnormal when high in women or low in men, and otherwise normal . Advantages The invention allows determination of the functional state of the HPA axis, based on only two measurements of the concentration of cortisol in saliva, performed the same day at defined times, optionally in addition to one simultaneously performed measurement of testosterone level in saliva. A computer program performs the interpretation of the test results. The invention facilitates practical use of measurements of saliva cortisol for determination of the function of the HPA axis, since only two saliva tests are needed from each subject. The use of a computer program for evaluation of the measurement values guarantees that all subjects are evaluated in an identical way in all examinations. The derangements in HPA axis function, identified by the program, are relevant for the health of individual subjects, and the computer-based analysis of the measurement values gives new information, hitherto not available. Use over the Internet provides access to the system to a large number of individuals. The addition of testosterone values improves the relationship between the diagnostic groups and risk factors for e.g. cardiovascular disease. The present invention has been described above with reference to a preferred embodiment. However, other embodiments than the one disclosed above are equally possible within the scope of the invention, as defined by the attached independent patent claims .

Claims: CLAIMS 1. An in vitro method of identifying normal and abnormal diurnal cortisol secretion patterns from human saliva samples, wherein first and second saliva samples are taken from one human individual at first and second predefined times during the same day, a first cortisol concentration is determined in the first saliva sample, and a second cortisol concentration is determined in the second saliva sample, characterized by the steps of providing first and second variables (cort_conc_slope, cort_conc_mean) ; assigning values to the first and second variables which are associated with the determined first and second cortisol concentrations; providing a set of predefined constants (alpha, beta, gamma) for each variable; calculating, for each variable, a first fuzzy logic index (high_ind) to represent a degree to which the variable has a high value in relation to the predefined constants; calculating, for each variable, a second fuzzy logic index (low_ind) to represent a degree to which the variable has a low value in relation to the predefined constants; matching different combinations of the first and second fuzzy logic indices, as calculated for the first and second variables, with predetermined definitions of a normal diurnal cortisol secretion pattern and at least one abnormal diurnal cortisol secretion pattern; and selecting the definition where the best correspondence occurs . 2. A method as in claim 1, wherein the first variable (cort_conc_slope) is calculated as a difference between the determined first and second cortisol concentrations, and wherein the second variable (cort_conc_mean) is calculated as an arithmetic mean of the determined first and second cortisol concentrations. 3. A method as in claim 1 or 2 , wherein the first and second fuzzy logic indices (high_ind, low_ind) are decimal values between 0.0 and 1.0. 4. A method as in claim 1, 2 or 3, wherein the set of predefined constants (alpha, beta, gamma) comprises three constants and wherein the first fuzzy logic index (high_ind) for each of the first and second variables (cort_conc_slope, cort_conc_mean) is set to: 0.0, if the variable is smaller than or equal to a first constant (alpha) in the set of predefined constants; 1.0, if the variable is larger than or equal to a third constant (gamma) in the set of predefined constants; a value between 0.0 and 0.5, if the variable is larger than the first constant (alpha) and smaller than or equal to a second constant (beta) in the set of predefined constants; a value between 0.5 and 1.0, if the variable is larger than the second constant (beta) and smaller than the third constant (gamma) . 5. A method as in any of claims 1-4, wherein the second fuzzy logic index (low_ind) is calculated as the complement of the first fuzzy logic index (high_ind) . 6. A method as in any of claims 1-5, comprising the additional step of determining a function of the HPA (hypothalamic-pituitary-adrenal) axis for said human individual in response to the selected diurnal cortisol secretion pattern definition, said function being one of the following: normal HPA axis function; activated HPA axis function; and blunted HPA axis function. 7. A method as in claim 6, comprising the further steps of : evaluating a level of testosterone in at least one of said first and second saliva samples, and using the result of the testosterone evaluation when determining the function of the hypothalamic-pituitary- adrenal (HPA) axis. 8. A method as in any of claims 1-7, comprising the further step of predicting a risk of deranged health for said human individual based on the selected diurnal cortisol secretion pattern definition. 9. A method as in any of claims 1-8, wherein the difference between said first and second predefined times during the same day is 4 hours. 10. A method as in claim 9, wherein said first predefined time is at 07.00 am and said second predefined time is at 11.00 am during the same day. 11. A computer program product directly loadable into an internal memory (22) of a computer (20) , comprising program code for performing the steps of any of claims 1-8 when executed on said computer. 12. A computer program product as defined in claim 9, embodied on a computer-readable medium (23) . 13. A computer having a memory (22) and a processor (21) , the memory containing program code for performing the steps of any of clams 1-8 when executed by said processor. 14. A method for remote test and evaluation of diurnal cortisol secretion patterns from human saliva samples, characterized by the steps of providing a server computer (27) , from which a user (30) may order a diurnal cortisol secretion saliva test using a client computer (31) , in response to receiving an order from the user, dispatching a test kit to the user, said test kit being adapted to receive first and second samples of saliva taken from the user at first and second predefined times during the same day, providing a computer (20) having a computer program capable of differentiating between a normal and an abnormal diurnal cortisol secretion pattern based on the first and second samples of saliva, storing, in a database (24) , an output from an execution of said computer program, and providing a server computer (27) , coupled to the database (24) and adapted to present a test result associated with the output stored in the database (24) to said user (30) . 15. A method as in claim 14, wherein said computer program is adapted to perform, upon execution, the steps of providing first and second variables (cort_conc_slope, cort_conc_mean) ; assigning values to the first and second variables which are associated with the cortisol concentrations in said first and second samples of saliva; providing a set of predefined constants (alpha, beta, gamma) for each variable; calculating, for each variable, a first fuzzy logic index (high ind) to represent a degree to which the variable has a high value in relation to the predefined constants; calculating, for each variable, a second fuzzy logic index (low_ind) to represent a degree to which the variable has a low value in relation to the predefined constants; matching different combinations of the first and second fuzzy logic indices, as calculated for the first and second variables, with predetermined definitions of a normal diurnal cortisol secretion pattern and at least one abnormal diurnal cortisol secretion pattern; and selecting, as said output, the definition where the best correspondence occurs . 16. A method as in claim 14 or 15, wherein said test result is one of the following: normal HPA (hypothalamic- pituitary-adrenal) axis function for said user (30) ; activated HPA axis function for said user (30) ; and blunted HPA axis function for said user (30) . 17. A method as in claim 16, wherein said computer program in addition is capable of evaluating a level of testosterone in at least one of said first and second samples of saliva.

Inventors: MARTIN LENE, WANGER PETER, MARTIN, LENE, WANGER, PETER
Applicants: DIAGNO INTERNAT BV, MARTIN LENE, WANGER PETER, DIAGNO INTERNATIONAL BV, MARTIN, LENE, WANGER, PETER
Application year: 2001
Main classification: G01N33
IPC codes: G01N33, G06F19