glucose tolerance tests are well known and may be used to test a variety of disorders of glucose metabolism and hormone secretory disorders . basically , glucose is ingested in the form of a high glucose concentration beverage or as a carbohydrate rich food . glucose concentrations are then monitored periodically ( often every hour ) for a period of 3 - 5 hours , depending upon the suspected diagnostic endpoint . the shape of the glucose profile of the resulting data set may then be utilized to further identify the medical condition . for example , diabetes is diagnosed based upon the overall increase in glucose concentration from the initial fasting condition and the amount of time required for the glucose concentration to drop to a normal physiological glucose concentration of 80 - 120 mg / dl . in this invention , the glucose response profile shape as a function of time relative to a glucose challenge is utilized as input data to an algorithm that evaluates the profile and then classifies the profile that outputs a screening response indicating that the subject being tested either has diabetes , igt ( impaired glucose tolerance ), a normal physiological response , or abnormally low glucose tolerance ( lgt ). the input values may be those of blood glucose determinations collected once every ten to thirty minutes . in keeping with the object of providing a convenient , inexpensive screening method , it is preferable that the glucose measurements be made with a non - invasive analyzer , however minimally invasive and invasive devices are entirely suitable for practice of the invention . fig1 shows representative glucose concentration profiles for a diabetic 101 , a subject with igt 102 , a subject with a normal physiological glucose response 103 , and a low glucose response 104 as a function of time . the algorithm is executed on processing device appropriately programmed using conventional computer programming techniques . the typical diabetic profile shape 101 is often observed to start off at a higher fasting glucose concentration , rise to higher concentrations ( typically above 180 mg / dl ) often at a faster rate , maintain higher glucose concentrations for a longer period of time , and take longer to return towards a normal physiological glucose concentration of 80 to 120 mg / dl . after the peak , the rate of decrease of the glucose concentration may be minimal versus a subject with igt or with normal physiological glucose response . the igt profile shape 102 has a response that starts with normal fasting glucose levels , rises quickly to levels between 140 - 200 mg / dl , and then falls back to normal . however , the return to normal glucose concentration typically occurs with a slower negative rate of change compared to a normal physiological response . a normal glucose response profile 103 has a shape that shows a slight increase in glucose levels to & lt ; 140 mg / dl and generally returns within two hours to normal levels . the shape may be quite angular with very quick rates of glucose change indicating normal insulin function . the final segment of the profile is generally flat in the normal ranges . low glucose tolerance 104 ( lgt ) or hyperinsulinemia produces a shape or profile that starts with low to normal fasting glucose levels . the shape then shows a sharp increase in glucose response . the peak of the shape is usually dramatic , as glucose levels rarely linger in the elevated range . a shape with a peak at two hours might be indicative of a different phase two insulin response than that of a peak at three to four hours . the decrease continues through the normal range to blood glucose levels typically below 60 mg / dl . hypoglycemia triggers the adrenergic response causing the shape of the response to rise again into normal ranges . in a first embodiment of the invention , a simple comparison algorithm is provided that compares selected parameters from a subject &# 39 ; s profile with predetermined thresholds for the various conditions . the thresholds may be determined from standard diagnostic criteria for the various conditions . for example , a diabetic has a fasting plasma glucose level greater than or equal to 140 mg / dl or a 2 - hour post challenge glucose level greater than or equal to 200 mg / dl . a subject with impaired glucose tolerance has a fasting plasma glucose level less than 126 mg / dl and / or a 2 - hour post challenge glucose level between 140 mg / dl and 200 mg / dl . a person with normal physiological tolerance has a fasting plasma glucose concentration of less than 140 mg / dl and / or a two - hour post challenge glucose concentration less than 140 mg / dl . an individual suffering from lgt typically has a fasting plasma glucose level less than 85 mg / dl and / or a 2 - hour post challenge glucose level between 140 mg / dl and 200 mg / dl , and a 3 - 4 hour post challenge glucose level less than 70 mg / dl . the algorithm compares the values of the subject &# 39 ; s profile with the predetermined thresholds , and on the basis of the comparison classifies the profile ( and thus , the subject ) as normal , diabetic , having igt , or having lgt . the above parameters are exemplary only . one skilled in the art will appreciate other parameters and combinations that are consistent with the spirit and scope of the invention . once a classification has been made ( diabetic , igt , normal , lgt ), information about related diabetic diseases / symptoms may be presented to the subject . for example , if a subject is classified as having impaired glucose tolerance , then the subject would be made aware that they are at risk for heart disease , stroke , kidney disease , neuropathy , retinopathy , diabetic ketoacidosis , skin conditions , gum disease , impotence , and / or a shorter lifespan . the subject may be counseled to seek the advice of their healthcare practitioner . in an alternate embodiment , glucose concentration values as a function of time are input to a fuzzy mathematical algorithm that evaluates the series to determine if the range of values screens the subject as a diabetic , as having igt , normal physiological function , or lgt . a number of parameters may be utilized individually or in combination to make this determination . these parameters are identified in fig2 . the first parameter 201 is the initial glucose concentration ( fig2 : initial ). an increased initial glucose concentration is diagnostic of diabetes . the ada ( american diabetes association ) states that an initial fasting glucose concentration of greater than 126 mg / dl is an indication of diabetes . the ada also states , in the absence of external insulin injections , a fasting glucose concentration less than 123 mg / dl is indicative of normal physiological function but could also be igt . however , in this fuzzy algorithm more extreme numbers are assigned to a diabetic and normal state so that a range of weights from 0 to 1 can be assigned to intermediate levels . for example , a fasting glucose concentration & gt ; 140 mg / dl is a very strong indication of diabetes and could be assigned a value of 1 , as are all fasting glucose concentrations above 140 mg / dl . a fasting glucose concentration of 80 mg / dl is an indication of normal physiological function and could be assigned a value of 0 , as are all glucose concentrations less than 80 mg / dl . a linear or nonlinear scale can then be applied between the two values . thus , on a linear scale , a glucose concentration of 120 is assigned a weight of 0 . 66 . this indicates a reasonable likelihood of igt whereas a weight of 1 is indicative of diabetes and a weight of 0 is indicative of normal physiological function . for lgt screening , a fasting glucose concentration less than 50 mg / dl is an indication of lgt and would be assigned a value of 0 . a linear or non - linear scale can then be applied between the values of & lt ; 50 mg / dl and 80 mg / dl . with a linear scale , a value of 65 mg / dl would be assigned a value of 0 . 55 . prior to an evaluation of lgt , additional parameters would be necessary . alternately , a single scale can be employed to diagnose all conditions . in this case , a fasting glucose concentration of 50 mg / dl , indicative of lgt has a weight of 0 , a normal blood glucose concentration of 80 mg / dl has a weight of 0 . 33 and the diabetic value of 140 mg / dl still has a weight of 1 . a second parameter 202 is the rate at which the glucose concentration rises ( fig2 : m 1 ). in general , a higher slope is indicative of diabetes while smaller slopes indicate igt and still smaller slopes are indicative of a normal physiological response . initial slopes indicative of diabetes may range from 1 to 7 mg / dl / min ; whereas , normal physiological function results in rates of change from 0 to 2 mg / dl / min . intermediate rates are indicative of igt . due to the fact that the rates from each cluster overlap , only more extreme values could lead to an accurate classification , based on evaluation of the rate of change . as described above , high slopes ( above 3 mg / dl / min ) may be assigned a weight of 1 while low slopes ( less than 0 . 5 mg / dl / min ) may be assigned a value of zero . again using a linear scale , a slope of 2 . 5 mg / dl / min would be assigned a weight of 0 . 8 and would be interpreted as a positive screening for diabetes . a third parameter 203 is the maximum monitored glucose concentration ( fig2 : max ). glucose levels peaking above 220 mg / dl are an indication of diabetes , and may be assigned a weight of 1 . only a slight rise above the high end of the normal glucose concentration of 120 mg / dl is indicative of normal physiological activity . thus , glucose concentrations of 120 mg / dl or below may be assigned a weight of 0 . elevated but not grossly high glucose concentrations ( 160 to 220 mg / dl ) are indicative of igt and are then assigned intermediate weights . a positive correlation is known to exist between the diagnosis of normal , igt , or diabetes with the peak glucose concentration monitored . this correlation is well known and accepted ; therefore , this parameter may be given a larger weighting function . a fourth parameter 204 is the duration that the glucose concentration remains elevated ( fig2 : duration ). the longer the duration above a given threshold , the more indicative the data are of diabetes . for example , 15 minutes above 200 mg / dl may indicate igt while 1 hour above 200 mg / dl is indicative of diabetes . a fifth parameter 205 is the rate of decrease of the glucose concentration after the peak glucose concentration ( fig2 : m 2 ). typically , the sharper the decrease , the more on the continuum the data is toward normal physiological function . as observed in fig1 there exists a decent spread of rate of changes after the peak glucose concentration for subjects ranging from diabetic to normal , making this parameter a particularly sensitive indicator for diabetes or for igt . thus , this parameter may be then be given a larger weighting function . a sixth parameter 206 is the minimum glucose concentration obtained after the maximum ( fig2 : final ). glucose values that fall below 120 mg / dl without a dose of insulin are indicative of normal physiological response whereas glucose concentrations that stay above 150 mg / dl are indicative of diabetes . glucose values that fall below 80 mg / dl could be indicative of lgt . as with the first parameter , values below 50 mg / dl would be assigned a value of 0 and at 150 mg / dl a value of 1 . one or more of these parameters may be utilized to determine if the subject is diabetic , has impaired glucose tolerance , has a normal physiological response , or low glucose tolerance according to equation 1 , where sf is the screening factor , p ( 1 - 6 ) are parameters , and w ( 1 - 6 ) are weights : sf = ( p 1  w 1 + p 2  w 2 + p 3  w 3 + p 4  w 4 + p 5  w 5 + p 6  w 6 ) ( w 1 + w 2 + w 3 + w 4 + w 5 + w 6 ) ( 1 ) one or more of the parameters may be utilized to compute the screening factor and weights for each parameter may range from 0 to 1 . essentially , the screening factor is a weighted average of the individual scaled parameters . an average or a weighted final score can be computed from the individual score ( s ). thresholds can then be determined to classify the subject into one of the three clusters . any number of limits defining diabetic or non - diabetic may be established . similarly linear or nonlinear axes may be established for any of the scores . these parameters may be established based on the most current diagnostic criteria provided by bodies such as , for example , the american diabetes association . sf 1 = ( p 1  w 1 + p 6  w 6 ) ( w 1 + w 6 ) ; and sf 2 = ( p 2  w 2 + p 3  w 3 + p 4  w 4 + p 5  w 5 ) ( w 2 + w 3 + w 4 + w 5 ) ; any additional combination would indicate the likelihood of a medical condition related to insulin and glucose tolerance exists , but is not readily defined in the individual &# 39 ; s current physiological state . such a screening outcome suggests that the individual &# 39 ; s primary care physician performs addition tests . other algorithms for providing the same information will occur to those skilled in the art and all are entirely within the scope of the invention . as the understanding of diabetes and diabetes screening increases , it is expected that the criteria set forth by the ada and who will change , thus making it necessary to adjust the threshold values to meet current diagnostic criteria . it is noted here that a complete glucose profile is not required for this approach to function . missing data points can be overcome , as the data points are not independent from one another . thus , some of the data from each parameter can be absent . in fact , if all of the data from some parameters is absent the algorithm may still function by setting the weighting function for that parameter to zero . inasmuch as glucose profiles tend to reproduce from day to day , partial data from each day may be utilized in the function . while this will decrease the precision of the screening factor , it allows historical data to be utilized in place of a glucose or meal tolerance test . this would minimize the pain involved with invasive or minimally invasive glucose testing . in some instances , such as when a subject has good record keeping of meal , glucose concentrations and / or insulin dosages , this data could be utilized as the input data minimizing data collection time . importantly , actual glucose concentrations are not required if relative glucose concentrations are available . as it is the shape of the response that is utilized in the screening , differences in glucose concentration can be utilized to obtain a screening factor . for example , if a noninvasive or minimally invasive glucose testing procedure shows a relative increase in glucose concentration between the fasting level and the maximum concentration , then parameters 1 ( fasting ) and 3 ( maximum ) may be utilized to determine the screening factor without actual glucose concentrations . subjects may be screened in an obstetric setting for relative change in glucose concentration as an early screen for gestational diabetes . actual numbers would not be required as the response or shape could easily be identified as impaired . some clinicians may choose to initiate early interventions such as diet and self - blood glucose monitoring based upon the detection of the impairment . additional time to schedule diagnostic procedures may be precious because the pregnancy may already be at a relatively advanced stage . in high - risk settings , for example , hiv or pulmonary clinics could use this bloodless approach to screen patients who might develop glucose abnormalities as a response to certain drug treatment therapies . the work place setting could use routine employee screenings for either glucose impairment or relative risk of complications . within a glucose profile , the individual data points are not independent . this allows outliers to be determined . utilizing an individual glucose reading , only gross outliers may be detected . for example , a glucose reading of 20 with a conscious subject would be determined to be an outlier . however , with multiple data points , small outliers may be determined . for example , if every 20 minutes the glucose readings are 80 , 100 , 120 , 140 , 160 , 180 , 142 , 220 , 240 mg / dl then the data point 142 is an outlier . using a traditional two point test at fasting and at 2 hours , the 80 mg / dl would be the fasting and the 142 mg / dl would be the 2 - hour point . this person would be screened as having a normal physiological glucose response due to the outlier when in fact they are diabetic . hence , the algorithm has built in safeguards against some poor screening hazards . the screening algorithm of equation 1 allows early detection of igt . complications associated with diabetes may thus be discovered earlier . early treatment can then be initiated . being made aware of the condition which is largely due to environmental factors and to parameters such as body fat will allow the individual to mitigate or prevent future diabetes related complications . the above embodiments have dealt with the obtaining actual values of blood glucose . screening can also be done based on relative blood glucose values . parameter 1 would be dropped ( i . e . standardized to a given value , say 100 mg / dl ), while parameter 3 would be adjusted to focus on the range of blood glucose values , rather than the maximum . generally , individuals having normal glucose tolerances will not experience a change greater than 60 mg / dl , while someone suffering from igt or lgt will see a change greater than 60 mg / dl , but unlikely to experience a change greater than 100 mg / dl . people suffering from diabetes often experience changes greater than 100 mg / dl . thus the fuzzy logic would apply a weighting factor of 0 to range values & lt ; 60 mg / dl and a weighting factor of 1 to range values greater than 100 mg / dl . parameter 6 would then need to be modified to account for lgt . this would be achieved by assigning a weighting factor of 0 to range values & gt ;− 30 mg / dl from the standardized value and a weighting factor of 2 to a range values & gt ; 30 mg / dl from the standardized value at the 3 - 4 hour mark of the tolerance test . the invention finds application in healthcare facilities including , but not limited to : physician offices , hospitals , clinics , and long - term healthcare facilities . alternatively , this technology could be utilized in public settings such as shopping malls and the workplace , or in private settings such as the subject &# 39 ; s home . although the invention has been described herein with reference to certain preferred embodiments , one skilled in the art will readily appreciate that other applications may be substituted for those set forth herein without departing from the spirit and scope of the present invention . accordingly , the invention should only be limited by the claims included below .