# Patent

## Title
Assay for the detection of creatinine

## Abstract

This application is in part a continuation of my application Ser. No. 07/274,505, filed Nov. 11, 1988 now U.S. Pat. No. 5,104,639, which in turn is a divisional application of my Ser. No. 06/797,615, filed Nov. 13, 1985 (Now U.S. Pat. No. 4,863,873), which is in turn a continuation of my application Ser. No. 06/670,483, filed Nov. 13, 1984 (now abandoned), which in turn is in part a continuation of my application Ser. No. 06/579,401, filed Feb. 17, 1984 (now U.S. Pat. No. 4,511,659), which is in part a continuation of my application Ser. No. 06/472,387 filed Mar. 4, 1983 (now abandoned), and in part a continuation of my application Ser. No. 06/425,183 filed Sep. 28, 1982 (now abandoned), which is in turn a continuation of my application Ser. No. 06/111,917 filed Jan. 14, 1980 (now U.S. Pat. No. 4,404,065).

## Background
This invention relates to analytical and mathematical methods for diagnosing or categorizing disorders. The invention has particular utility for diagnosing or categorizing disorders in living animals from analysis profiles of biologically active materials such as neurotransmitters and other neurochemical substances in brain tissue, cerebrospinal fluid, plasma, serum, saliva, blood containing platelets, nasal mucosa, urine and the like, such as catecholamines, their precursors, cofactors and their metabolites. The invention is uniquely capable of differentiating a large number of compounds of biological, diagnostic and/or pharmaceutical significance and of using such differential for diagnosing disorders and will be described in connection with such utility although other uses are contemplated.

There is an extensive body of literature relating abnormalities in neurotransmitters, precursors, and metabolites to degenerative, neuropsychiatric and behavioral disorders, hypertension and certain carcinomas. See, for example, Schildkraut et al in The Brain, Biochemistry and Behavior, Proceedings of the Sixth, Arnold O. Beckman Conference in Clinical Chemistry, pages 47-68. Although the potential role of these compounds in a number of significant disorders has been established, their routine analysis has not yet achieved widespread clinical use. Two problems in the clinical utility of neurotransmitter measurements are related to the economic and technical limitations of current technology. First, there is felt to be a high degree of interlaboratory and intersample uncertainty in quantitative values. Second, it has been difficult to measure enough of the known metabolically related compounds of a particular neurotransmitter to fully describe its biochemical significance in an individual sample, or to detect, identify and measure unusual neurotransmitters--an important aspect of basic research in various disease states that is presently very expensive and specialized.

While a number of interlaboratory technique intercomparisons for a variety of neurotransmitters have been carried out, there has been no comprehensive study within and among different techniques and laboratories for neurotransmitters in typical samples of interest. In the absence of such studies, given the complexity of the analytical problem and the historically wide variation whenever an analyte has been subjected to rigorous interlaboratory testing, the current values for normal and abnormal neurotransmitter levels must be taken with unspecified and probably wide limits of confidence.

Although the analysis of single neurotransmitters or metabolites from a complex biochemical pathway has been shown to correlate with a number of disorders utilizing statistical analysis over a large number of samples, the analytical level of a single neurotransmitter in an individual sample, with a few exceptions, has had relatively low clinical diagnostic utility. Essentially the state of the field of biochemical correlates of disorders is that while between large populations of normal and abnormal individuals a correlation generally can be determined for a particular biochemical, the scatter that results from both analytical and biochemical phenomena typically does not permit the level of a particular biochemical to be utilized diagnostically for a particular single individual. Nor may a single biochemical value be utilized for the rational prescription or development of a pharmaceutical for that individual. This is not particularly surprising in that both the levels and effects of a particular neurotransmitter are modified by a number of other neurotransmitters, in the same, or parallel metabolic pathways. If, for instance, 5-HT (serotonin) is to be used as a diagnostic tool for depression, suicidal tendencies, or schizophrenia, it would be necessary and perhaps provide a route to definitive diagnosis and pharmaceutical specification or development, to simultaneously determine the approximately 40 other compounds that derive from tryptophan and significantly effect the indolaminergic neuronal system's activity.

In recent years, LCEC (Liquid Chromotography with Electrochemical Detection) has become a common tool for the determination of catecholamines biogenic amines and their metabolites in biological fluids. Because of sensitivity limitations (typically 20-50 pg) and the complexity of biological samples, both separation and concentration steps typically have been necessary. Heretofore, plasma catecholamine analysis typically required three steps. First, the sample is collected and the catecholamines separated and concentrated, for example, using the alumina extraction procedure of Anton and Sayre (See A. H. Anton and D. F. Sayre, J. Pharmacol, Exp. Ther., 138 (1962), p. 360-375). The analytes, norepinephrine, epinephrine and dopamine, along with the internal standard DHBH (dihydroxybenzylamine), then are separated chromatographically, and finally detected electrochemically. Typical sample size requirements are 1.0 ml plasma or serum. In routine clinical use, there have been numerous problems with conventional techniques (alumina absorption, ion exchange and extraction), due to a large number of poorly understood variables, in the overall analysis system of sample acquisition, storage, preparation and sensor response. These problems have quite likely confused the relationships that may exist between levels and distribution of the catecholamines and various physiological and behavioral phenomena and disease states.

In the analysis of complex biological materials such as blood, serum and cerebrospinal fluids which may contain numerous different constituents, the important (e.g. abnormal) metabolites such as neurotransmitters to be identified may be present in only parts per trillion. While a chromatographic column can achieve macro separation of the various constituents, it may not provide adequate spatial (in time) separation of the extremely small portion of metabolites of interest from the much larger percentage of the many other compounds coeluted from the column at the same time as the metabolites of interest. Many of these interfering coeluted materials are electrochemically active but electrochemically irreversible, while many metabolites such as neurotransmitters are both electrochemically active and electrochemically reversible. It has been found that the analytical problems of reliable measurements of neurochemicals and related compounds are complicated by the fact that interferences with conventional or prior technologies are disorder related. This was discussed in my prior publication, (Matson et al, Clinical Chemistry, Vol. 30, No. 9, 1984) (see U.S. Pat. No. 4,511,659) for dopamine, dopac and seratonin measurements in directly analyzed brain extract anti cerebrospinal fluid for normal, schizophrenics and Alzheimers. Recent work has indicated that even for the widely used and accepted technique of alumina extraction for plasma catecholamines that interferences may be disorder specific. Higher values for Norepinephrine (NE) and Epinephrine (EP) were observed following alumina extraction and analysis of a single energy conventional electrochemical detector than for a three cell redox detector on neonatal stress blood samples. Analysis of the neonate extracts on the sixteen channel chemical imaging system revealed several unexpected compounds that are potential interferences including dihydroxyphenylacetic acid (DOPAC), 3 hydroxykynurenamine (3-OHKYA) and 3-hydroxy-anthranilic acid (3-OHAN). These compounds have not been detected in normal adult plasma alumina extracts.

In my aforesaid U.S. Pat. No. 4,511,659, there is provided an electrochemical detection system comprising a plurality of coulometrically efficient electrochemical cells, in series, for sequentially oxidizing and reducing selected substances in a sample solution under controlled conditions prior to measurement on a downstream testing electrode or electrodes. More specifically, in accordance with the invention provided in my aforesaid U.S. Pat. No. 4,511,659, a sample solution (e.g. a body fluid) is passed through a suitable chromatographic column and the eluant is streamed in contact with a series of electrochemically isolated, in-line coulometric electrodes operated under conditions so as to establish a series of "gates" for the sequential oxidation and reduction of substances in the sample solution whereby to screen (remove) selected interfering and electrochemically irreversible substances contained in the sample solution, while passing selected electrochemically reversible products for detection and measurement on a downstream electrode. The gate electrode series is follows in-line by one or more, preferably an array of six or more coulometric measuring electrodes, each formed of porous electrode base material such as fritted graphite, fritted carbon or other conductive fritted material, for detecting and measuring the electrochemically reversible compounds of interest (e.g. neurotransmitters).

As reported in my aforesaid U.S. Pat. No. 4,511,659, there are several beneficial effects of this approach to electrochemical analysis. Long-term drift in response is effectively eliminated by acquiring essentially 100% of the signal. The capability of analyzing essentially 100% of a material allows the assay of compounds of unknown purity by relating them to the basic principles of electrochemical reaction embodied in Faraday's Law. Poisoning of the electrode, a dominant problem with electrochemical sensors, is effectively eliminated by the use of a much larger relative surface area for reaction. And, finally, and most important to the eventual development of array and gate cells, a coulometric electrode by virtue of its essentially 100% efficiency allows sequential oxidation and/or reduction of compounds at successive-in-line detectors. The improved sensitivity of the detection system as discussed in my aforesaid U.S. Pat. No. 4,511,659, particularly where two or more active testing electrodes follow the screening electrodes has given the ability to do direct injections of serum filtrates and has also allowed the generation of reproducible patterns of compounds with catecholamine like electrochemical behavior of a large number of resolvable components. This provides the possibility of performing pattern recognition for the diagnosis or perhaps even predictive diagnosis, of various disorders or disease states.

In my copending application Ser. No. 797,615 and its parent U.S. Pat. No. 4,863,873, I describe a system for resolving and detecting hundreds of compounds in a single sample at femtogram levels whereby to provide a small molecule inventory or metabolic pathway pattern of an individual. As taught in my aforesaid U.S. Pat. No. 4,863,873, the small molecule inventory may be considered to reflect the underlying activity and distribution of the enzymatic pathways of an individual and hence reflect an operational measure of the genome determining those enzymes. The small molecule inventory of an individual may thus be used to determine the health state of the individual and/or to diagnose disease states. Correlation of the patterns from a plurality of individuals provides an understanding of the mechanisms of disorders or disease states or conditions and, in turn, provides a rational route to pharmacological development leading to treatment, cure or suppression of such disorders, disease states or conditions.

The present invention is an improvement in the invention described in my aforesaid U.S. Pat. No. 4,863,873. More particularly, in the practice of my invention as described in my U.S. Pat. No. 4,863,873, I have observed that the biochemical analysis profiles of "normal" or healthy individuals may vary quite widely, while the biochemical profile analysis data of individuals having disorders is far less chaotic. More particularly, I have observed that the frequency distribution of certain biochemical compounds or ratios of compounds in individuals suffering from a disorder are far less chaotic than "normal" or healthy individuals. This leads to a general protocol for diagnosing, categorizing or differentiating individuals based on comparisons of biochemical analytical data of small molecule inventory against data bases of known or previously diagnosed cases. By way of example the process of the present invention may advantageously be employed in the differentiation of neurological degenerative dementing or affective disorders such as Alzheimer's disease, Huntington's disease, Parkinson's disease, schizophrenia, or Progressive Supernuclear Palsy from each other and neurologically normal controls. Moreover, by suitable selection of variables, the process of the present invention also is applicable to classification of tumors, carcinomas, cardiovascular abnormalities and other disorders. Similarly, the process of the present invention advantageously may be utilized to select therapy based on categories of known successful vs. unsuccessful outcomes.

While not wishing to be bound by theory, the two fundamental hypotheses underlying the process of the present invention are:

1. The underlying genetic makeup or predis-position of an individual will reflect through the proteins, enzymes, and other factors it determines in patterns of small molecules. Individual components within these patterns will be affected by environmental effects such as diet, stress or chemical inset; however, the overall pattern of relationships will reflect the underlying operation of the genome or the interference of a particular disorder. Among the small molecules are the transmitters, cofactors and metabolites that regulate neuronal and endocrine functions and the interactions of somatic and central nervous system processes. Thus, the compounds such as purines, tyrosine and tryptophan derived neurotransmitters, peptides, pterin and vitamine cofactors are highly relevant to the effect or etiology of neurological disorders, cardiovascular disfunction and certain tumors or carcinomas.

2. The relationships of these biochemical patterns from a disorder are less chaotic or more regular than those from healthy controls. All of the biochemical systems of small molecules are interconnected and interrelated in a complex web of feedback and response. These interactions are highly nonlinear and thus, depending on subtle differences in initial conditions, the response of individual components in a biochemical pattern will be highly variable. The overall system will thus behave in a mathematically chaotic fashion. In a disorder, elements within the biochemical pattern are over or underregulated, thus reducing the degrees of freedom or overall variability. Consequently, the presence of a disorder implies more regulated or less chaotic variability of compounds or relationships among compounds in patterns from disordered individuals.

These two fundamental hypotheses provide an approach to diagnostic categorization of disorders using frequency distributions of compounds and relationships from large data bases (which may be of epidermiologically significant size).

For a fuller understanding of the nature and objects of the present invention, reference should be had to the following detailed description taken in combination with the accompanying drawings wherein:

FIGS. 1a-1d are plots showing frequency measurement distributions;

FIG. 2 is a plot showing sequential calculations;

FIG. 3 is a plot showing probability analysis; and

FIG. 4 is a plot similar to FIG. 3, and showing probability analysis corrected for error.

280 cerebrospinal fluid (CSF) samples from the categories Alzheimer's Disease--AD (61 samples), Parkinson's Disease--PD (60 samples), schizophrenia--SC (60 samples), Huntington's Disease--HD (20 samples), Supernuclear Palsy--PSP (13 samples) and neurologically normal controls--C (68 samples), were electrochemically analyzed in accordance with the teachings of my aforesaid U.S. Pat. No. 4,863,873. Samples from normal and diseased individuals were prepared and flowed through a chromatographic column, and detected in an electrochemical cell using an NCA Chemical Analyzer, Model No. CEAS available from ESA, Inc., Bedford, Mass. All samples were from 7th or 8th mL aliquots of nostril caudal gradients. Pools were created for each category utilizing small subaliquots of the samples, and pools of all samples were created for analytical quality control and evaluation of unknowns. Samples were run under a variant of a standard reverse phase gradient procedure (Table 1) in the repetitive sequence Control Standard, Pool, 7 Samples, Control Standard, Pool, . . . as set forth in Table I: REAL TIME SETUP REVIEW TIME          DEVICE             FUNCTION       VALUE   TOTAL 1      0.00          FLOW               %B             5       1.20 2      0.20          CLEAN CELL         ON             960 3      0.50          CLEAN CELL         OFF 4      7.00          AUTO ZERO          ON 5      7.20          FLOW               %B             5       1.20 6      7.58          AUTO SAMPLER       INJECT 7      8.00          FILE               START 8      66.00         FLOW               %B             94  1.06 9      66.00         FLOW               %B             5       1.20 10  70.00         FILE               STOP 11  70.00         FLOW               %B             5       1.20 12  70.00         AUTO SAMPLER       STEP 13  70.00         END                               2 14  70.00         METHOD             M147 REVIEW OF LIVE METHOD Autorange Full Scale Current             P'stats                 On 10 uA  100 uA  10 uA   10 uA  -40 mV  25 mV  90 mV 155 mV 1 uA    1 uA      10 uA   1 uA    220 mV  285 mV  350 mV  415 mV  Floor 1 uA    1 uA       1 uA 1 uA    480 mV  545 mV  610 mV  675 mV  100 nA 1 uA    1 uA       1 uA 1 uA    740 mV  805 mV  870 mV  910 mV Cell Box Temp: 35.degree. C. UPPER LIMIT   LOWER LIMIT PUMP A:   350           0 PUMP B:   350           0 VALVE: POS1 Sat. Nov. 18 17:15:22 1990 Standard  std0001 Table: NOY8STD Study: CSFRUX01 # Identified Compounds in Standard: 38 # Missing Compounds in Standard: 0 File    Name # RT      Conc      1           2         3             4         5 6         7 1    std0001 ASC       1.717       1.700     1.725         1.725     1.750 1.733 1.725   10000.00  3872279     9625125   1.27e+07 1.60e+07 1.50e+07 1.14e+07 2    std0001 CYS 2.267 2.283   1000.00 129467 3    std0001 URIC                            2.333         2.342     2.350 2.350 2.350   0000.00                         5672          1324197 3193191       1019128 4    std0001 XAM 3.058   1000.00 5    std0001 HX 3.125   100.00 6    std0001 VMA                                           4.883     4.867 4.875     4.875 4.875   10.00                                         387       4959 16674     13957 7    std0001 GSH 4.875 4.900   500.00 13957 8    std0001 NE        5.625       5.642     5.642         5.650     5.650 5.642   10.00     334         18928     17463         1507      117 9    std0001 MHPG                                                    6.075 6.075     6.075 6.075   10.00                                                   749 13983     5545 File    Name # RT      Conc      8       9       10        11        12        13 14        15      16 1 2    std0001 CYS       2.267   2.258   2.267     2.275     2.283     2.292 2.283   1000.00   251043  528682  1298734   1601456   2250767 1062584 4    std0001 XAM               3.042   3.033     3.058     3.067     3.067 3.108 3.058   1000.00           47171   1393667   3304889   2039171   350667 110609 5    std0001 HX 3.108     3.117   3.125 3.125   100.00 110609    154070  161138 6 7    std0001 GSH       4.875   4.875   4.875     4.892     4.900     4.988 4.900   500.00    28075   51532   117237    133772    197987    85618 8 9    std0001 MHPG      6.075   6.083   6.092 6.075   10.00     1828    1718    1044 File    Name # RT      Conc      1           2         3             4         5 6         7 10   std0001 HGA       6.392       6.408 6.392   10.00     16607       4976 11   std0001 G 7.942   100.00 12   std0001 GR 9.150   500.00 13   std0001 LD        9.325       9.283     9.283         9.292     9.275 9.283   10.00     165         10336     14158         1156      562 14   std0001 MET 9.650   500.00 15   std0001 AM                                            10.008    10.008 10.017    10.000 10.008  100.00                                        33745     118573 17512     2128 16   std0001 EPI       10.167      10.142    10.150 10.142  10.00     367         14152     9783 17   std001  EPI       A 10.217    100.00 18   std0001 DOPAC     11.825      11.833    11.833        11.842    11.850 11.833 11.833  10.00     247         7794      9749          1412      445 251 File    Name # RT      Conc      8       9       10        11        12        13 14        15      16 10 11   std0001 G                 7.942   7.942     7.950     7.958     7.958 7.942   100.00            2395    122100    53287     4420      975 12   std0001 GR                        9.142     9.133     9.125     9.142 9.150     9.175   9.192 9.150   500.00                    859       26558     194121    430593 1054244   189936  111915 13 14   std0001 MET                       9.642     9.642     9.650     9.650 9.650     9.667   9.675 9.650   500.00                    986       6461      26013     33548 107832    86595   31459 15 16 17   std001  EPI       A 10.250  10.217 10.217  100.00 26046   36349 18 File    Name # RT      Conc      1           2         3             4         5 6         7 19   std0001 30HAN                 13.225    13.200        13.217    13.242 13.250 13.200  10.00                 1194      29905         8215      2107 1401 20   std001  30HXY                 13.225    13.200        13.217    13.242 13.250 13.200  10.00                 1194      29905         8215      2107 1401 21   std0001 4HPLA 13.450  100.00 22   std0001 XMN                                                     13.683 13.692    13.683 13.692  10.00                                                   1593 14780     3917 23   std0001 4HBAC 13.992  200.00 24   std0001 TYR 15.250 15.233  1000.00 232 25   std0001 5HTP                            15.583        15.592    15.617 15.592  10.00                           6994          16297     463 26   std0001 DA                    17.342    17.342        17.358    17.367 17.375 17.342  10.00                 17722     6301          305       144 8313964 27   std0001 SHIAA                 17.850    17.892        17.892    17.908 17.925 17.892  50.00                 233       9330          119759    14604 549 File    Name # RT      Conc      8       9       10        11        12        13 14        15      16 19 20 21   std0001 4HPLA             13.442  13.450    13.467    13.458 13.450  100.00            13364   125434    43118     2233 22 23   std0001 4HBAC                               13.983    13.992    13.992 14.008 13.992  200.00                              75738     207495    311864 170423 24   std0001 TYR.      15.242  15.225  15.233    15.250    15.250    15.292 15.233  1000.00   3866    102584  1181673   776764    117510 25 26 27 File    Name # RT      Conc      1           2         3             4         5 6         7 28   std0001 4HPAC 18.083 18.100  200.00 80 29   std0001 KYA 18.117  50.00 30   std0001 30MD                                          21.575    21.608 21.608    21.600 31.608  10.00                                         58        2183 11535     2176 31   std0001 5HTOL                 22.167    22.200        22.200    22.217 22.200  10.00                 47        1668          19401     1931 32   std00l  HVA                                           23.925    23.925 23.917    23.917 23.917  200.00                                        225       13994 237705    163964 33   std0001 KYX 24.258  100.00 34   std0001 TYRA 24.492  80.00 35   std0001 5HT                   30.742    30.783        30.792 30.792  10.00                 231       6955          9627 36   std0001 3HT                                           31.967    31.975 31.975    31.967 31.975  100.00                                        143       11501 33026     2425 File    Name # RT      Conc      8       9       10        11        12        13 14        15      16 28   std0001 4HPAC     18.117  18.092  18.100    18.125 18.100  200.00    1143    37258   461257    287050 29   std0001 KYA                                                     18.075 18.117    18.167  18.175 18.117  50.00                                                   59412 112086    54548   38306 30   std0001 30MD      21.592 143 31 32   std0001 HVA       23.925  23.925  23.950 47372   8852    2293 33   std0001 KYX                                 24.267    24.258    24.283 24.300    24.308  24.308 24.258  100.00                              101620    108998    55261 39928     21718   4609 34   std0001 TYRA              24.475  24.492    24.517 24.492  80.00             2368    28935     5390 35 36   std0001 3MT       31.975 171 File    Name # RT      Conc      1           2         3             4         5 6         7 37   std0001 TPOL 41.500 41.467  10.00 375 38   std0001 TRP                                                     45.350 45.342    45.388 45.292  700.00                                                  615 8371      23358 File    Name # RT      Conc      8       9       10        11        12        13 14        15      16 37   std0001 TPOL      41.475  41.458  41.467    41.492 41.467  10.00     2006    7109    9905      4810 38   std0001 TRP       45.300  45.275  45.292    45.325    45.350    45.350 45.292  700.00    248497  967515  2056846   1090677   187013    109610 Tue Dec 18 11:43:57 1990 Sample Report                           Page 1 Sample: POOL37 Standard: Table: POOL19A Study: CSF19A # Compounds identified: 22 # Known Compounds Not Found: 0 # Unknown Peak Clusters: 745 Compounds Identified Com- pound   Conc        RT      RT Error  Height    Ratio Accuracy *p01    104.413971  2.617   0.100     167971 ##EQU1## XAN     973.750732  2.900   0.117     756026 ##EQU2## *p02    101.869156  3.108   0.125     208822 ##EQU3## p03     131.687439  5.733   0.275      2747 p04     106.228569  8.242   0.325     142085 ##EQU4## TYR     979.260254  13.567  0.692     1375195 ##EQU5## *P05     99.553802 14.633  0.583      52515 ##EQU6## P09      89.990860 15.117  0.650      7632 ##EQU7## P07      92.967072 15.275  0.600      55664 ##EQU8## P08      96.659180 16.133  0.417      29579 ##EQU9## *P10     96.603462 21.875  0.942      42516 ##EQU10## HVA     195.139450  22.475  0.800      30508 ##EQU11## P11     102.689194  24.892  0.717      16233 ##EQU12## P12      91.103188 25.150  0.850     10652 ##EQU13## *p18     98.121086 26.850  1.308       470 *p19    121.804512  27.192  1.142       324 P13      96.415916 28.158  0.883      53718 ##EQU14## P14      97.315338 29.542  1.108      24489 ##EQU15## P15      94.104630 29.642  1.100      10631 ##EQU16## P16      88.826569 32.425  0.767      2937 ##EQU17## P17      96.782722 37.992  1.133      22787 ##EQU18## TRP     674.284607  42.150  1.583     277999 ##EQU19##

The analysis records were linked by a unique identifier to clinical data of clinical diagnosis, diagnostic criteria, age, pharmaceutical history, sex and race. Pools analyzed as samples against standards for known values were utilized to assess the precision of known compound values in the data base. Standards sequentially analyzed against identical standards were used as a measure of instrumental performance and pools sequentially analyzed against identical pools were utilized as a measure of the precision of unknown peaks.

Control standards analyzed against sequential control standards yielded precision values ranging from .+-.1%-.+-.4% CV (coefficient of variation) with no outlying values. Pools analyzed as samples gave precision values ranging from .+-.2-.+-.7% CV for compounds present at the 0.5 ng/mL level or greater and typically .+-.25-30% for compounds present at 2.times. the detection limit of 0.02-0.03 ng/mL (e.g. 5HT, EPI). Pools analyzed against sequential pools for unknowns gave values of .+-.3-.+-.15% coefficient of variation. Typically, the coefficient of variation of the pools was 5-25 fold less than the coefficient of variation of analytes in a group of samples. Essentially, the contribution of assay variability to the results is minimal.

The data base, upon completion, contained 280 samples by 57 analytes (17,000 records). Of these, 163 were null either because no peaks were detected at the sensitivity limits of the assay, or because a signal detected did not meet the qualitative criteria for purity.

Linear regression analysis and stepwise regression analysis were used in a preliminary evaluation of the data. Both raw and mean corrected data was evaluated.

Regression comparison of the AD group (61) vs. controls (60) setting AD=1 and C=0 gave a categorical separation regression equation with an S (standard error of estimate) value=0.39 and p (the probability that the sample belongs in one group and not in another) value=0.0041 for 27 of the most significant known compound variables identified in stepwise regression (Table I). Inclusion of 7 of the most significant variables (labeled with asterisks in Table II) from the pool analysed unknown peak data base gave values of S=0.382 and p=0.0037. Assuming a clinical diagnostic error rate in the order of 10%, seven AD samples with regression calculated values (from -1.2 to 0.01) were removed from the calculation. The regression characteristics were then S=0.352 and p=0.0031.

Regression of the AD group with AD=1 vs. all others (219)=0 for the same variable group yielded an equation with S=0.481 and p=0.0013.

Observations: Although the AD group is separated from other groups with a high degree of probability, there is too high a degree of overlap for a simple linear regression algorithm to accurately categorize an individual sample.

Cluster analysis procedures using nearest neighbor arid furthest neighbor approaches were applied to the data base. With both these approaches, the AD group tended to cluster, but controls were scattered relatively evenly, both outside and inside the AD region. Thus, the cluster analysis approach is not suitable as a categorization tool for this type of data.

Observations: The behavior of the data under cluster analysis protocols, and the observations that the standard deviations of compound values and of precursor/product ratios across metabolic pathways within a disorder group are smaller than within control groups is consistent with the hypothesis that the biochemical response of controls or normal individuals is more chaotic than that of disordered individuals.

The observations on the nature of the data distributions coupled with the technical ability to run large numbers of samples and variables offers an approach to categorization based on differences in the frequency distributions of variables in different disorder categories. This approach relies in basic probability considerations without any assumptions on the shape of a distribution curve of linearity of relationships.

The simplest question that I have investigated for the preliminary data base is that given an unknown sample, what is the probability (p) that that sample belongs in one group and not another.

For one variable, the question takes the form: ##EQU20##

where F(V.sub.n).sub.A or f(V.sub.n).sub.B =the frequency with which an unknown sample value (V.sub.n) occurs in category A or category B.

For multiple compounds, the expression expands: ##EQU21##

If all frequencies are the same, the P value is 0.5 or a 50/50 chance that the unknown sample is A and not B. A positive answer compresses the expression to a 1 and a negative answer to 0.

Like cluster procedures and unlike regression, the use of the algorithm is independent of the number of variables used.

Implementation of the Procedure:

The implementation of the procedure is by the following steps:

1. Frequency distributions (shown in FIGS. 1a-1d) were created by using a smoothing algorithm based on a 3 point polynomial expansion function that treats each point in the sparse data distribution with equal weighting as the means of a distribution with a width at half height proportional to its value. The use of smoothing functions is a necessary assumption until the number of samples in a particular category reaches approximately 300-400. The procedure used was to divide all data in the categories by the maximum value among categories X 85, apply the polynomial expression algorithm, and normalize the data distributions for the number of samples in each category. The frequency distributions in each category are then organized into look up tables for each variable (Table IV) DISTRIBUTION TABLE HVA                              MHPG AD          CONTROL              AD          CONTROL 1      0.010377    0.000205      1      1.2E-10     0.000000 2      0.045259    0.001702      2      0.000000    0.000003 3      0.126131    0.007945      3      0.000000    0.000026 4      0.242295    0.025793      4      0.000000    0.000140 5      0.379120    0.064431      5      0.000000    0.000576 6      0.503888    0.129976      6      0.000001    0.001932 38     0.798418    0.938551      38     1.117459    0.742461 39     0.761982    0.889793      39     1.160736    0.801194 40     0.730955    0.836392      40     1.196534    0.855713 83     0.006846    0.088021      83     0.225452    0.069938 84     0.005088    0.081885      84     0.208132    0.064130 85     0.003699    0.075893      85     0.190170    0.041177

2. A sample record such as that shown in Table 5 DATACASE RECORD C           DISTRIBUT AT0022      RANGE MT3            0.018080    1 OHAN3          0.018080    3 OHKY3          0.090402    4 3OMD           1.970763    44 HVA            58.03809  56 MHPG           8.714754    62 12             2911604     22 P01            5725159     9 P02            1359827     49 P03            1808040     1

is then inserted into the look up table. Individual values are divided by the range value X 85 and the frequencies for each variable for category A and B are sequentially calculated in the algorithm after subtracting the effect of that sample from the frequency table. The effect of a sequential calculation across a group of variables is shown in FIG. 2 for 3 AD and 3 C cases from a group of 61 AD and 44 controls. One of the major features of the algorithm is that no single variable predominates as a differentiation among a large group of samples.

For an initial test, conditions were set up such that each individual sample was evaluated as if the data base were set up without its contribution. The results of the initial scoring are shown in FIG. 3. The scoring of five of the 61 AD cases as controls (p=less than 0.01 that the sample is an AD and not a control) is not surprising given the probable diagnostic error rate in AD. The scoring of 4 of the controls as AD are of concern.

One possible explanation is that the AD data base is in effect contaminated by five cases that clearly do not match the overall AD profile and are probably not AD. When these five samples are removed from the AD data base and all samples, including the 5 removed, are scored, the control and AD groups are uniquely separated as shown in FIG. 4. The five samples that were removed from the AD scoring data group distribute in an equivocal region from 0.1 to 0.9. In subsequent application of the procedure and algorithm to AD samples vs. all other samples (PD, SC, HD, PSP and C) in the data base asking the question is this sample in the AD distribution and not in the distribution of all others yielded similar results scoring AD samples with p values=0.98 or greater. The distribution of scores of all others was scattered from 0.001 to 0.8 including the 5 AD samples which previously scored in this region vs. controls.

The invention has been described for use in diagnosing Alzheimer's Disease from CSF patient samples. It will be understood, however, that the invention advantageously may be used to diagnose and characterize other neurological, degenerative or defective disorders such as Huntington's Disease, Parkinson's Disease, schizophrenia, progressive supernuclear palsy, ALS amyotrophic lateral sclerosis (Lou Gehrig's Disease) and senile dementias. The invention also advantageously may be used to classify and diagnose tumors, carcinomas, cardiovascular abnormalities and other disorders, or for selection of therapy based on categories of known successful vs. unsuccessful outcomes. Moreover, both treatment protocols and new pharmaceuticals may be evaluated.

Still other changes and advantages will be obvious to one skilled in the art.

## Claims

1. In a method for diagnosing disorders in a test individual in which fluid samples from normal, unafflicted control individuals, afflicted, abnormal individuals, and said test individual are analyzed to generate electrical signal patterns representative of molecular constituents of said samples, the improvement which comprises creating a data base of electrical signal patterns representative of frequency distribution of a plurality of predetermined molecular constituents of fluid samples from an epidemiologically significant number of individuals having known categories of disorders and from said unafflicted control individuals, and comparing said electrical signal patterns in said data base for conformity to electrical signal patterns representative of frequency distribution of said predetermined molecular constituents of a fluid sample from said test individual.

2. A method according to claim 1, wherein said fluid samples comprise a body fluid.

3. A method according to claim 2, wherein said body fluid comprises cerebrospinal fluid.

4. A method according to claim 2, wherein said body fluid comprises plasma.

5. A method according to claim 2, wherein said body fluid comprises blood containing platelets.

6. A method according to claim 2, wherein said body fluid comprises nasal mucosa.

7. A method according to claim 2, wherein said body fluid comprises serum.

8. A method according to claim 2, wherein said body fluid comprises saliva.

9. A method according to claim 2, wherein said body fluid comprises urine.

10. A method according to claim 1, wherein said fluid samples comprise electrochemically active compounds, and wherein each electrical signal pattern representative of frequency distribution of said plurality of predetermined constituents of said fluid samples is generated by the following steps, comprising: passing each one of said fluid samples separately through a liquid chromatographic column for achieving time-space separation of the electrochemically active compounds of said fluid sample eluting in the column and generating electrical signals representative of the electrochemical pattern of said fluid sample using an electrochemical detection apparatus.

11. A method according to claim 1, wherein one of said known categories of disorders comprises Alzheimer's Disease.

12. A method according to claim 1, wherein one of said known categories of disorders comprises Parkinson's Disease.

13. A method according to claim 1, wherein one of said known categories of disorders comprises Huntington's Disease.

14. A method according to claim 1, wherein one of said known categories of disorders comprises schizophrenia.

15. A method according to claim 1, wherein one of said known categories of disorders comprises Progressive Supernuclear Palsy.

16. A method according to claim 1, wherein one of said known categories of disorders comprises amyotrophic lateral sclerosis.

17. A method according to claim 1, wherein one of said known categories of disorders comprises senile dementis.

18. A method according to claim 1, wherein one of said known categories of disorders comprises tumors.

19. A method according to claim 1, wherein one of said known categories of disorders comprises carcinomas.

20. A method according to claim 1, wherein one of said known categories of disorders comprises cardiovascular abnormalities.