XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Medications which bind to opioid receptors are increasingly being prescribed for the treatment of multiple and diverse chronic painful conditions. Their use for acute pain or terminal pain is well accepted. Their role in the long-term treatment of chronic noncancer pain is, however, controversial for many reasons. One of the primary reasons is the well-known phenomenon of psychological addiction that can occur with the use of these medications. Abuse and diversion of these medications is a growing problem as the availability of these medications increases and this public health issue confounds their clinical utility. Also, the extent of their efficacy in the treatment of pain when utilized on a chronic basis has not been definitively proven. Lastly, the role of opioids in the treatment of chronic pain is also influenced by the fact that these potent analgesics are associated with a significant number of side effects and complications. It is these phenomena that are the focus of this review. Common side effects of opioid administration include sedation, dizziness, nausea, vomiting, constipation, physical dependence, tolerance, and respiratory depression. Physical dependence and addiction are clinical concerns that may prevent proper prescribing and in turn inadequate pain management. Less common side effects may include delayed gastric emptying, hyperalgesia, immunologic and hormonal dysfunction, muscle rigidity, and myoclonus. The most common side effects of opioid usage are constipation (which has a very high incidence) and nausea. These 2 side effects can be difficult to manage and frequently tolerance to them does not develop; this is especially true for constipation. They may be severe enough to require opioid discontinuation, and contribute to under-dosing and inadequate analgesia. Several clinical trials are underway to identify adjunct therapies that may mitigate these side effects. Switching opioids and/or routes of administration may also provide benefits for patients. Proper patient screening, education, and preemptive treatment of potential side effects may aid in maximizing effectiveness while reducing the severity of side effects and adverse events. Opioids can be considered broad spectrum analgesic agents, affecting a wide number of organ systems and influencing a large number of body functions.

/pdf/opioid-complications-and-side-effects-29996mciak.pdf

Opioid complications and side effects.

Sleep-induced apnea and disordered breathing refers to intermittent, cyclical cessations or reductions of airflow, with or without obstructions of the upper airway (OSA). In the presence of an anat...

Pathophysiology of Sleep Apnea

正しい方法がわかる臨床基本手技 : from the New England journal of medicine

Obesity has become the major health problem in many industrialized countries. But why do so many people – who are facing an abundant food offer – stay slim? All organs in the human organism like the heart, liver, kidney lose 40% of their weight during inanition, except the brain, which loses 1% or less (Krieger, 1921). According to the Selfish Brain theory, the brain uses its stress system, i.e., the sympathetic nervous system (SNS) and the hypothalamus–pituitary–adrenal (HPA) system, to actively demand energy from the body (Peters et al., 2011b). In this way, the brain can satisfy its high energy needs, while the rest of the body is only sparsely supplied. The function of the stress system to actively procure the brain with energy is called “brain-pull” function. It has been shown analytically that in the cerebral supply chain a competent brain-pull function protects against body mass gain, even if there is an abundant food offer available (Peters and Langemann, 2009). And why do other people become obese? If the brain-pull function is incompetent, then energy accumulates in the cerebral supply chain: accumulation of energy in the body stores leads to obesity, accumulation of energy (glucose) within the blood vessels culminates in type 2 diabetes (Peters and Langemann, 2009). Thus, the Selfish Brain theory states that people with incompetent brain-pull have to eat more in order to cover the energetic need of their brain, although their body stores are already overfull.

Tanya Zilberter refers to the Selfish Brain theory in her article entitled “carbohydrate-biased control of energy metabolism” (Zilberter, 2011). At the same time, she refers to an apparently related idea proposed by the psychiatrist DuPont (1997), who has used the term “selfish brain” in the context of addiction. Zilberter discusses in her opinion paper the role of carbohydrate addiction as a potential cause of obesity and calls this aspect “darker side of the selfish brain.” She considers addiction as being “highly non-homeostatic” and concludes that “energy intake beyond rigid homeostatic regulation relies on behavior with hedonic rewarding and addictive nuances more characteristic for carbohydrates than for fat.”

Here I would first like to pose the question whether carbohydrate addiction really affects the organism in a non-homeostatic way. Second, I would like to question whether carbohydrate addiction does result in obesity at all. Carbohydrate (sugar) addiction, including tolerance and withdrawal, has been demonstrated in rodents but not in humans (Garber and Lustig, 2011). Bartley G. Hoebel and his team have carried out ground-breaking animal experiments on this theme (Avena et al., 2008). The researchers have induced sugar addiction in rats by exposing them to a 20-days-experimental paradigm, the so-called “daily intermittent sugar and chow” regimen. In fact, the animals fed in this way enhanced their sugar intake. However, these rats regulated their caloric intake by decreasing their chow intake to compensate for the extra calories obtained from sugar, which results in a normal body weight (Colantuoni et al., 2002; Avena and Hoebel, 2003). These experiments clearly demonstrate that homeostatic control is maintained in the animals, which displayed signs of sugar addiction. Thus, there is no experimental evidence that sugar addiction affects metabolism in a non-homeostatic way, nor that sugar addiction is a cause of obesity.

How did “sugar addiction” develop in the experiments, which used the “daily intermittent sugar and chow” paradigm? Animals were food-deprived for 12 h, and food was offered only 4 h after onset of dark, which is the usual time of their first meal (Colantuoni et al., 2002; Avena and Hoebel, 2003). In principle, food deprivation constitutes a stressor, which threatens brain and body energy supply. The stressful effects of caloric restriction become evident to its full extent when the restriction lasts longer; then the brain has to strongly activate the SNS and the HPA-system in order to safeguard brain energy content and mass. In fact, long term caloric restriction in rats leads to a dose-dependent increase in serum corticosterone (Levay et al., 2010), and the animal's brain mass is conserved, while its body mass decreases (Greenberg and Boozer, 2000). Such brain-mass-preserving effects have also been observed in humans who were on weight reduction diet (Peters et al., 2011a). If now, in the “daily intermittent sugar and chow” paradigm, energy is offered to the rats with a 4-h delay, the prevailing cerebral energy crises can be most quickly resolved by the ingestion of sugar. The unexpected sudden resolution of the difficulties in cerebral energy procurement by intake of sugar prompts a striatal dopamine release as a rewarding signal. The characteristic of the rewarding system is that dopamine release is triggered by unpredicted successes (Schultz, 2007). Dopamine release in the nucleus accumbens helps to acquire and consolidate a behavioral strategy (Kelley, 2004), which safeguards brain energy homeostasis and allows to shut off the stress response; the strategy includes the choice and immediate intake of sugar. In this way, the “daily intermittent sugar and chow” paradigm favors acquisition and consolidation of feeding strategies, which are very effective in maintaining cerebral energy homeostasis in times of food insecurity.

In conclusion, I don't see any evidence supporting the view that carbohydrate addiction really causes obesity. As mentioned above, the Selfish Brain theory states that the underlying cause of obesity is a brain-pull incompetence. There are many known causes for such an incompetence (i.e., reduced responsiveness) of the brain-pull system, e.g., the habituation to chronic psychosocial stress (Peters and Langemann, 2009; Peters et al., 2011b). The findings on “sugar addiction” in animals should not be linked to human obesity in an overhasty manner, since such ideas might be taken by others to offend those people who have gained weight. In this respect, scientists and clinicians should be particularly cautious, because humans with high body weight do already suffer from severe weight discrimination (Puhl and Heuer, 2009). These humans are known to exert even more rigid cognitive control over their eating behavior than slim subjects do, and these data contradict the notion that a lack of cognitive control causes weight gain (Timko and Perone, 2005; de Lauzon-Guillain et al., 2006; Snoek et al., 2008; Gallant et al., 2010). But even despite such scientific evidence people with high body weight are still accused of being week-willed and hedonistic – only striving at the satisfaction of their lust. The recent progresses in the field of brain energy metabolism, showing that the people who have gained weight just strive at covering their cerebral energy needs (Peters et al., 2011b), can be helpful to relieve them from the burden of weight discrimination.

/pdf/does-sugar-addiction-really-cause-obesity-4t57br9qs3.pdf

Does sugar addiction really cause obesity

Summary
Objective Hypoglycaemia-induced decreases in male LH and testosterone concentrations are possibly mediated by activation of the hypothalamus–pituitary–adrenal (HPA) axis or by an increase in PRL. As counterregulatory stress hormone release is attenuated during recurrent hypoglycaemia, we questioned whether the gonadotrophic axis and PRL adapt similarly.

Design  We performed two consecutive hypoglycaemic clamps on day 1 and one clamp on the following day in 15 healthy men. Blood concentrations of gonadotrophins, PRL, testosterone, ACTH and cortisol were measured during the first and the third clamp, taking place at the same time of day.

Results  During hypoglycaemia, serum concentrations of LH and testosterone decreased (P   0·28 for both) whereas the increase in PRL, ACTH and cortisol was markedly attenuated during the third clamp (P < 0·001).

Conclusions  LH and testosterone responses do not adapt to recurrent hypoglycaemia, whereas the increase in PRL is attenuated, indicating adaptation. Considering the marked decrease in the responses of PRL and the HPA axis after antecedent hypoglycaemia, the data suggest that the hypoglycaemia-induced decreases in LH and testosterone concentrations, not adapting to recurrent hypoglycaemia, are mediated independently, probably by blood glucose itself.

Preserved inhibitory effect of recurrent hypoglycaemia on the male gonadotrophic axis

Responses of the LHPA-system to CRH stimulation in subjects with type 1 diabetes mellitus and obesity

Low plasma lactate concentration as a biomarker of an incompetent brain-pull: A risk factor for weight gain in type 2 diabetes patients

Hypoglycemia is known to decrease the body temperature and to stimulate counterregulatory hormone secretion. Although it is well established that antecedent hypoglycemia reduces the hormonal response to subsequent hypoglycemia, the effects of antecedent hypoglycemia on the subsequent hypothermic response are obscure. In the present study, body temperature was measured orally during a total of 90 glucose clamp experiments in 45 healthy men. The clamps lasted 6 hours and were performed under 6 different experimental conditions: a euglycemic clamp with a low rate of insulin infusion, 1.5 mU/kg x min (low insulin-eu), a euglycemic clamp with a high rate of insulin infusion, 15.0 mU/kg x min (high insulin-eu), a hypoglycemic clamp with a low rate of insulin infusion, 1.5 mU/kg x min (low insulin-hypo), a hypoglycemic clamp with a high rate of insulin infusion, 15.0 mU/kg x min (high insulin-hypo), and 2 hypoglycemic clamps following an antecedent 2.5-hour hypoglycemia (56 mg/dL) induced by either a low (1.5 mU/kg x min, low insulin-ante-hypo) or a high (15.0 mU/kg x min, high insulin-ante-hypo) rate of insulin infusion. Plasma glucose was maintained normal during the euglycemic clamps and was decreased stepwise during the hypoglycemic clamps (76 --> 66 --> 56 --> 46 mg/dL). During the hypoglycemic clamps, body temperature decreased by 0.26 degrees +/- 0.09 degrees C in low insulin-hypo, 0.28 degrees +/- 0.09 degrees C in high insulin-hypo, 0.29 degrees +/- 0.09 degrees C in low insulin-ante-hypo, and 0.41 + 0.11 degrees C in high insulin-ante-hypo (all P .1 for all comparisons). In contrast, body temperature remained unchanged during the euglycemic clamps, so the changes in body temperature differed significantly during the euglycemic clamps versus the hypoglycemic clamps (P < .05 for all comparisons). The data show that the body temperature decreases during hypoglycemia and this decrease is influenced neither by antecedent hypoglycemia nor by circulating insulin levels.

Preserved hypothermic response to hypoglycemia after antecedent hypoglycemia.

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