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    A normal circadian rhythm cortisol pattern is one in which there is a rise before waking (before 7-8 AM), and then a gradual decline throughout the rest of the day(26). Twelve patients had recognized circadian patterns of cortisol fluctuation, which will hereafter be referred to as Cortisol Pattern 1 (CP1). Of the twelve with CP1 whom were classified in the “normal” pattern, six had normal values at all four test time points, as well as normal total cortisol values or burden. Thus, of the 29 total charts reviewed, only 21% of the patients would be classified with normal cortisol values as well as normal patterns. The remaining six patients with a “normal” pattern of cortisol secretion (high waking and then decreasing over the rest of the day) had abnormal values at one or more time points.

    Seventeen of the 29 total patients fell into our classification of dysregulated circadian cortisol patterns.

    This consisted of patients with cortisol patterns that did not decrease in slope over the course of the day. Fourteen of these 17 patients had cortisol values out of normal range at one or more time points, or in total cortisol burden. The dysregulated patients’ cortisol plots fell into three distinct patterns, which we will hereafter call Cortisol Pattern 2 (CP2), Cortisol Pattern 3 (CP3), and Cortisol Pattern 4 (CP4).
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    Patients grouped into CP1 begin with a burst of cortisol between 7 and 8 AM (between 13-23 nM), and drop off slowly throughout the day. Normal values are considered 4-8 nM between 11 AM and noon, 4-8 nM between 4 and 5 PM, and 1-3 nM between 11 PM and midnight. It is notable that with the group of patients who presented with normal patterns, there was a large range in cortisol levels they excreted at any specific point. Those patients classified with CP2 tended to have abnormal circadian cortisol levels, higher in the late morning (11 AM to noon), compared to higher waking levels. Their levels then dropped off or stayed the same later in the day and at midnight. In contrast, patients grouped into CP3 had peaks between 7-8 AM and a more significant drop at noon. This created a second “mini” peak on the graph between 4-5 PM, though the levels are actually closer to normal. Patients assigned with CP4 fell into a distinct pattern of increasing between 11 PM and midnight. While a normal value falls between 1-3 nM for this time point, these patients averaged 15.4 nM (SD 8.3). One of the patients had extremely high cortisol levels at two time points, including time point 4 (between 11 PM and midnight). If the data is reanalyzed without this patient, the average cortisol value is still high with an average of 6.75 nM (SD 2.06).

    Typically, the highest cortisol value of the day occurs at the waking time point. Yet, of the patient charts reviewed, if levels were low at all, they were low at this time point. One patient with low total cortisol had consistently low cortisol levels, except the late night point, which is typically the lowest cortisol level. There were three patients who had consistently high cortisol levels, and two of these also had high total cortisol values.

    In healthy individuals, cortisol levels exhibit a circadian pattern, peaking in the morning and decreasing the rest of the day. Studies are inconclusive as to the relationship between high total cortisol levels and obesity, prediabetes, and type 2 diabetes. Since cortisol levels are circadian, many naturopathic physicians use a salivary test performed at four time points during the day to measure the overall pattern of secretion, rather than relying upon a blood draw from a single time point. Some physicians hypothesize that a dysregulated pattern of cortisol is more indicative of diabetes risk than a high mean cortisol level. A retrospective chart review was performed on obese, prediabetic, and type 2 diabetes patients in order to test this theory. The goals of this study were to determine whether people with, or at risk for, type 2 diabetes have abnormal circadian cortisol patterns and dehydroepiandrosterone (DHEA) levels. The chart review demonstrated four patterns of cortisol secretion, one of which is circadian, in this population.

    Type 2 diabetes is a multi-factorial disease characterized by mild to severe glucose dysregulation with associations to increased mortality, and the development of polyneuropathy, nephropathy, and heart disease. Diagnoses of obesity and/or prediabetes increases the risk of developing type 2 diabetes.(1,2) In healthy individuals, blood glucose concentrations are maintained between 80 and 100 mg/ dL. Ingestion of carbohydrates causes an increase in blood glucose concentration and subsequent release of insulin by beta-islet cells within the pancreas. Insulin lowers blood glucose both by decreasing hepatic and adipose glucose production, and by accelerating the uptake of glucose into peripheral tissues. One of the probable first steps in the development of type 2 diabetes is insulin resistance, defined by impaired sensitivity to a normal concentration of insulin. Insulin resistance is a common factor of obesity, prediabetes, and type 2 diabetes.

    In human studies, high cortisol has been shown to contribute to insulin resistance and is likely involved in the development of type 2 diabetes, as well as the persistence of high glucose levels. Cortisol is a glucocorticoid hormone produced by the adrenal cortex that is involved in the regulation of mineralocorticoids, blood pressure, immune function and metabolism. Conditions that involve excess cortisol are hypertension, hypercholesterolemia, central obesity, and glucose intolerance. In fact, one of the likely methods by which cortisol contributes to these diseases is by inducing a state of insulin resistance. As the primary glucocorticoid released during stress, cortisol has a variety of actions: 1) impairs insulin-dependent glucose uptake in the periphery, 2) enhances gluconeogenesis in the liver, and 3) inhibits insulin secretion from pancreatic b-islet cells. All of these actions contribute to elevated glucose levels. Dysregulated cortisol levels have been shown in persons with insulin resistance, prediabetes, and type 2 diabetes. Prediabetes is characterized by a fasting plasma glucose between 100-126 mg/dL. This is also known as Impaired Fasting Glucose (IFG) or Impaired Glucose Tolerance (IGT). Beyond 126 mg/dL is diagnostic of type 2 diabetes. Cortisol normally follows a circadian pattern of secretion, peaking 30 minutes after waking followed by a gradual decrease throughout the rest of the day. Cortisol should be lowest in the evening, allowing sleep at night. Due to the circadian nature of cortisol secretion, identification of cortisol dysregulation may not appear if measuring only total cortisol levels in blood at a single time point.

    Cortisol and dehydroepiandrosterone (DHEA) are produced in closely related metabolic pathways. DHEA is an additional factor to consider in the development of type 2 diabetes. The production of both DHEA and cortisol is regulated by the release of adrenocorticotropic hormone (ACTH) from the adrenal cortex. DHEA and DHEA-sulfate (S) are metabolic intermediates in the formation of the active sex steroids testosterone, dihydrotestosterone, and estrogen. In human studies, exogenously administered glucocorticoids reduce basal and ACTH-stimulated blood levels of DHEA and DHEA-S.14 Several studies have suggested that DHEA and DHEA-S are related to glucose and insulin regulation. A decrease of DHEA and DHEA-S is observed when humans are rendered hyperinsulinemic. In addition, a reduction in serum insulin is associated with an increase in serum DHEA and DHEA-S.17 Cortisol and DHEA can also be measured in saliva, and salivary levels have been found to correlate with plasma levels. In clinical practice, some physicians order salivary cortisol and DHEA tests for patients who have or are at risk for diabetes, or to diagnose and monitor adrenal function.

    The salivary cortisol test requires patients to collect saliva samples at home four times during one day, in the morning, noon, afternoon, and at night. Things that may compromise the sample are discouraged, such as specific behaviors. For example, smoking, posture, and eating can all influence salivation and may thus introduce artifact into the sample. As a result, patients are given very specific directions for collecting their saliva sample (described in detail in Methods section of this paper). A chart review was performed to examine the patterns of cortisol secretion and levels of DHEA in patients suspected of dysregulated glucose metabolism.
    Data was reviewed and collected from 29 patient charts from a naturopathic primary care clinic in Portland, OR. Informed consent for records review was obtained upon admission to the clinic. All data was coded to remove any identifiable information. In naturopathic clinics, individuals who are suspected of cortisol/DHEA dysregulation for reasons related to prediabetes or type 2 diabetes often undergo a clinical laboratory test called the Adrenal Stress Index™ (ASI™, Diagnos-Techs, Kent WA). ASI™ measures cortisol, DHEA, sIgA, and anti-gluten antibodies from saliva collected during the day. According to Diagnos-Techs, the analytic sensitivity observational of this test is 0.8 nM to 1.0 nM and the specificity of the immunoassay to cortisol is at 99% or greater.(25) In diabetics and those at risk for diabetes, naturopathic physicians often do this test to help establish etiology of the disease. The chart review was done on patients who were predominantly untreated or uncontrolled diabetics, and patients presenting with symptoms that indicated insulin resistance and/or adrenal dysfunction whom had an ASI recorded in their chart. Additional patient care, number of visits, and information on medication intake was unobtainable for review.

    Patients were instructed to follow the prementioned salivary collection protocol, but compliance was not assessed in the charts. Salivary samples were self-collected by the patient at four intervals in one day (between 7-8 AM, 11 AM-12 PM, 4-5 PM, and 11 PM-midnight). Patients were instructed not to eat or drink, use antacids, bismuth or mouthwash, or brush their teeth or smoke for 30-60 minutes before collecting the sample. They were also instructed not to eat more than one tablespoon of chocolate, onions, garlic, cabbage, cauliflower, or broccoli, or to drink coffee, tea, or caffeine on the day of collection. Patients were instructed to maintain a typical exercise regimen and activity level to obtain representative daily results. A sample consisted of saliva collected on a cotton roll held in the mouth until saturated and then placed in a 5 mL tube. Samples are refrigerated and mailed within 3 days. Samples are considered stable for a week at room temperature. The ASI™ tests were evaluated at Diagnos-Techs’ lab in Kent, WA.

    The saliva samples were analyzed for cortisol by ELISA. DHEA and DHEA(S) were analyzed by ELISA using pooled samples from the noon and afternoon time points. Data were entered and analyzed in Microsoft Excel. A total of 29 ASI™ tests in 29 patients were found. For 28 of these patients, DHEA levels were also available. Serum fasting blood glucose levels were available for 20 of these patients.

    Patients who forgo medications for both diabetes and chronic pain appear to be influenced primarily by economic pressures, whereas patients who cut back selectively on their diabetes treatments are influenced by their mood and medication beliefs. Our findings point toward more targeted strategies to assist diabetic patients who experience CRN.

    Prescription drug spending in 2007 was >750 USD per capita in the U.S., of which patients must pay a growing share through medication copayments. Nine of 10 older adults use prescription medications, and those with Medicare Part D take five prescriptions per month on average. Even among low-income patients, most take their medications despite copayments; however, one-fifth or more of all patients may cut back because of cost concerns. Cost-related nonadherence to medications (CRN) has been associated with increased rates of serious adverse events, emergency department visits, hospitalizations, and poorer health.

    Empirical studies have implicated financial, attitudinal, mood, and provider influences in CRN, although their relative effects are not well understood. Most of the variance in patients’ reports of CRN remains unexplained by financial measures. Although economic pressures drive these decisions, noncost factors appear to modify the effect of medication cost at a given level of ability to pay.

    Most survey-based studies of CRN have used a single global question to ascertain adherence and, therefore, could not discern whether patients cut back uniformly across their medications or selectively. Studies using administrative data indicate that patients vary in their adherence across medications, but these studies could not explore fully the influences of factors such as patients’ mood and medication-related beliefs.

    Building on our theoretical model of factors that influence patients’ elasticity of demand for prescription drugs, in the present study we explored further how cost and noncost factors influence patients’ adherence to prescription medications for two chronic conditions: type 2 diabetes and chronic pain. We hypothesized that although some patients would cut back on medications for both conditions, others would cut back selectively, and sought to understand the factors associated with these behaviors.

    These analyses are important for clinical care because most efforts to address CRN have targeted patients’ ability to pay exclusively, for example, through government assistance (e.g., Medicare Part D), pharmaceutical industry programs, and prescribing of less expensive therapeutic alternatives. Physicians are now called upon to incorporate discussions of medication cost pressures into their routine patient interactions. Because insufficient time may be the greatest barrier to such provider-initiated discussions, it is essential that we distinguish patients for whom ability to pay, as opposed to other factors, constitutes the dominant challenge to adherence.