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IRS-1 protein in blood, indicative of Alzheimer

Demonstrated brain insulin resistance in Alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline.

http://www.ncbi.nlm.nih.gov/pubmed/22476197

While a potential causal factor in Alzheimer’s disease (AD), brain insulin resistance has not been demonstrated directly in that disorder.

We provide such a demonstration here by showing that the hippocampal formation (HF) and, to a lesser degree, the cerebellar cortex in AD cases without diabetes exhibit markedly reduced responses to insulin signaling in the IR→IRS-1→PI3K signaling pathway with greatly reduced responses to IGF-1 in the IGF-1R→IRS-2→PI3K signaling pathway.

Reduced insulin responses were maximal at the level of IRS-1 and were consistently associated with basal elevations in IRS-1 phosphorylated at serine 616 (IRS-1 pS⁶¹⁶) and IRS-1 pS⁶³⁶/⁶³⁹.

In the HF, these candidate biomarkers of brain insulin resistance increased commonly and progressively from normal cases to mild cognitively impaired cases to AD cases regardless of diabetes or APOE ε4 status.

Levels of IRS-1 pS⁶¹⁶ and IRS-1 pS⁶³⁶/⁶³⁹ and their activated kinases correlated positively with those of oligomeric Aβ plaques and were negatively associated with episodic and working memory, even after adjusting for Aβ plaques, neurofibrillary tangles, and APOE ε4.

Brain insulin resistance thus appears to be an early and common feature of AD, a phenomenon accompanied by IGF-1 resistance and closely associated with IRS-1 dysfunction potentially triggered by Aβ oligomers and yet promoting cognitive decline independent of classic AD pathology.

About IRS-1

IRS-1, as a signalling adapter protein, is able to integrate different signalling cascades, which indicates its possible role in cancer progression.[34] IRS-1 protein is known to be involved in various types of cancer, including colorectal,[35] lung,[36] prostate and breast cancer[37] IRS-1 integrates signalling from insulin receptor (InsR),insulin-like growth factor-1 receptor (IGF1R) and many other cytokine receptors is elevated in β-catenin induced cells.

Some evidence shows that TCF/LEF-β-catenin complexes directly regulate IRS-1. IRS-1 is required for maintenance of neoplasmic phenotype in adenomatous polyposis coli (APC) – mutated cells, it is also needed for transformation in ectopically expressing oncogenic β-catenin cells. IRS-1 dominant-negative mutant functions as tumor suppressor, whereas ectopic IRS-1 stimulates oncogenic transformation. IRS-1 is upregulated in colorectal cancers (CRC) with elevated levels of β-catenin, c-MYC, InsRβ and IGF1R. IRS-1 promotes CRC metastasis to the liver.[35] Decreased apoptosis of crypt stem cells is associated with colon cancer risk. Reduced expression of IRS-1 in Apc (min/+) mutated mice showes increased irradiation-induced apoptosis in crypt. Deficiency in IRS-1 – partial (+/-) or absolute (-/-) – in Apc (min/+) mice demonstrates reduced amount of tumors comparing to IRS-1 (+/+)/ Apc (min/+) mice.[38]

In lung adenocarcinoma cell line A549 overexpression of IRS-1 leads to reduced growth. Tumor infiltrating neutrophils have recently been thought to adjust tumor growth and invasiveness. Neutrophil elastase is shown to degrade IRS-1 by gaining access to endosomal compartment of carcinoma cell. IRS-1 degradation induces cell proliferation in mouse and human adenocarcinomas. Ablation of IRS-1 alters downstream signalling through phosphatidylinositol-3 kinase (PI3K), causing an increased interaction of it with platelet-derived growth factor receptor (PDGFR). Therefore, IRS-1 acts as major regulator of PI3K in lung adenocarcinoma.[36]

Some evidence shows role of IRS-1 in hepatocellular carcinoma (HCC). In rat model, IRS-1 focal overexpression is associated with early events of hepatocarcinogenesis. During progression of preneoplastic foci into hepatocellular carcinomas expression of IRS-1 gradually decreases, which is characterises a metabolic shift heading towards malignant neoplastic phenotype.[39] Transgenic mice, co-expressing IRS-1 and hepatitis Bx (HBx) protein, demonstrate higher rate of hepatocellular displasia that results in HCC development. Expressed alone, IRS-1 and HBx are not sufficient to induce neoplastic alterations in the liver, though their paired expression switches on IN/IRS-1/MAPK and Wnt/β-catenin cascades, causing HCC transformation.[40]

LNCaP prostate cancer cells increase cell adhesion and diminish cell motility via IGF-1 independent mechanism, when IRS-1 is ectopically expressed in the cells. These effects are mediated by PI3K. Uncanonical phosphorylation of Serine 612 by PI3K of IRS-1 protein is due to hyper-activation of Akt/PKB pathway in LNCaP. IRS-1 interacts with integrin α5β1, activating an alternative signalling cascade. This cascade results in decreased cell motility opposing to IGF-1 – dependent mechanism. Loss of IRS-1 expression and PTEN mutations in LNCaP cells could promote metastasis.[41] Ex vivo studies of IRS-1 involvement in prostate cancer show ambiguous results. Down-regulation of IGF1R in bone marrow biopsies of metastatic prostate cancer goes along with down-regulation of IRS-1 and significant reduction of PTEN in 3 out of 12 cases. Most of the tumors still express IRS-1 and IGF1R during progression of the metastatic disease.[42]

IRS-1 has a functional role in breast cancer progression and metastasis. Overexpression of PTEN in MCF-7 epithelial breast cancer cells inhibits cell growth by inhibiting MAPK pathway. ERK phosphorylation through IRS-1/Grb-2/Sos pathway is inhibited by phosphatase activity of PTEN. PTEN does not have effect on IRS-1 independent MAPK activation. When treated with insulin, ectopic expression of PTEN in MCF-7 suppresses IRS-1/Grb-2/Sos complex formation due to differential phosphorylation of IRS-1.[43] Overexpression of IRS-1 has been linked to antiestrogen resistance and hormone independence in breast cancer. Tamoxifen (TAM) inhibits IRS-1 function, therefore suppressing IRS-1/PI3K signalling cascade in estrogen receptor positive (ER+) MCF-7 cell line. IRS-1 siRNA is able to reduce IRS-1 transcript level, thereby reducing protein expression in MCF-7 ER+ cells. Reduction of IRS-1 leads to decreased survival of these cells. siRNA treatment effects are additive to effects of TAM treatment.[44] IGFRs and estrogen coaction facilitates growth in different breast cancer cell lines, however amplification of IGF1R signalling can abrogate need of estrogen for transformation and growth of MCF-7 cells. IRS-1 overexpression in breast cancer cells decreased estrogen requirements. This decrease is dependent on IRS-1 levels in the cells.[45] Estradiol enhances expression of IRS-1 and activity of ERK1/2 and PI3K/Akt pathways in MCF-7 and CHO cells transfected with mouse IRS-1 promoter. Estradiol acts directly on IRS-1 regulatory sequences and positively regulates IRS-1 mRNA production.[46] Decreased anchorage- dependent/independent cell growth and initiation of cell death under low growth factor and estrogen conditions are observed in MCF-7 cells with down-regulated IRS-1.[47] mir126 is underexpressed in breast cancer cells. mir126 targets IRS-1 at transcriptional level and inhibits transition from G1/G0 phase to S phase during cell cycle in HEK293 and MCF-7 cells.[48] Transgenic mice overexpressing IRS-1 develop metastatic breast cancer.The tumors demonstrate squamous differentiation which is associated with β-catenin pathway. IRS-1 interacts with β-catenin both in vitro and in vivo.[49] IRS-1 and its homologue IRS-2 play distinct roles in breast cancer progression and metastasis. Overexpression of either one is sufficient to cause tumorogenesis in vivo. Frequency of lung metastasis in IRS-1 deficient tumor is elevated opposing to IRS-2 deficient tumor, where it is decreased. Basically, IRS-2 has a positive impact on metastasis of breast cancer whereas a stronger metastatic potential is observed when IRS-1 is down-regulated.[citation needed] IRS-1 is strongly expressed in ductal carcinoma in situ, when IRS-2 is elevated in invasive tumors. Increased IRS-1 makes MCF-7 cells susceptible to specific chemotherapeutic agents, such as taxol, etoposide, and vincristine.Therefore, IRS-1 can be a good pointer of specific drug therapies effectiveness for breast cancer treatment.

What is the best data mining method to predict dementia?

My answer to What is the best data mining method to predict dementia?

Answer by Connie b. Dellobuono:

Health data on Diabetes and Alzheimer’s

Diabetes Mellitus and Risk of Alzheimer's Disease and Dementia with Stroke in a Multiethnic Cohort

Data analysis

Prevalences of diabetes and other covariates were compared between subjects with and without Alzheimer's disease and between subjects with and without stroke-associated dementia. Continuous variables were compared by analysis of variance, and categorical variables were compared by χ test. Cox proportional hazards modeling was used for multivariate analyses. The time-to-event variable was age at onset of dementia; the models were stratified by year of entry into the cohort in order to control for period effects, as recommended for longitudinal studies. There was one model for each outcome mentioned above. All covariates were treated as time-constant covariates using the baseline values. In 26 of the 255 subjects with diabetes, the diagnosis was made after baseline, but these persons were treated as having had diabetes at baseline. An additional analysis was carried out treating diabetes as a time-dependent covariate taking into account the date of reporting of the diabetes diagnosis; this analysis was conducted to examine how the definition of diabetes (baseline vs. follow-up) affected the analysis. A similar analysis was carried out treating all variables as time-dependent covariates with the beginning of exposure used as the beginning of observation (or later for the 26 subjects diagnosed with diabetes after baseline), to compare the results with the time-constant covariate model. Subjects without the outcome were censored at the time of the last follow-up visit. Subjects with a type of dementia different than the one considered in the specific model were censored at the time of onset of dementia. For example, when Alzheimer's disease was examined as the outcome, persons with stroke-associated dementia were censored at the time of dementia onset. Additional analyses were performed using nondementia cognitive impairment without stroke and nondementia cognitive impairment with stroke as the outcomes; persons with nondementia cognitive impairment at baseline were excluded.

The population attributable risk (PAR) for diabetes in relation to dementia was calculated for each ethnic group using the formula PAR = Pr(HR − 1)/1 + Pr(HR − 1), where HR is the adjusted hazard ratio obtained from the multivariate models and Pr is the prevalence of diabetes in each ethnic group in the cohort; 95 percent confidence intervals were calculated for the population attributable risk using methods described for prospective studies. SAS for Windows, version 7 (SAS Institute, Inc., Cary, North Carolina), was used for all analyses.

What is the best data mining method to predict dementia?

Health data on Diabetes and Alzheimer’s

The mean age of the cohort was 75.6 years (standard deviation 5.9); 68.9 percent of the subjects were women. Forty-five percent of the subjects were Hispanic, and 32 percent were Black.

The prevalence of diabetes was 9.6 percent in Whites, 21.2 percent in Blacks, and 24.1 percent in Hispanics.

There were 213 incident cases of dementia in the cohort. Of these, 157 cases (74 percent) were due to Alzheimer’s disease, 36 cases (17 percent) were due to stroke, and 20 cases (9 percent) were due to other causes.

The incidence of dementia was 1.4 per 1,000 person-years in Whites (33 cases: 23 Alzheimer’s disease, four stroke-associated dementia, and six other), 2.4 per 1,000 person-years in Blacks (80 cases: 62 Alzheimer’s disease, 14 stroke-associated dementia, and four other), and 2.3 per 1,000 person-years in Hispanics (100 cases: 72 Alzheimer’s disease, 18 stroke-associated dementia, and 10 other).

The hazard ratio for nondementia cognitive impairment without stroke in persons with diabetes, as compared with persons without diabetes, was 1.3 (95 percent CI: 0.8, 1.9). The hazard ratio for nondementia cognitive impairment with stroke in relation to diabetes was 1.6 (95 percent CI: 0.6, 4.4) (table 2). Persons with nondementia cognitive impairment have an increased risk of developing Alzheimer’s disease compared with persons without nondementia cognitive impairment (40); therefore, we conducted an analysis examining the relation between diabetes and a composite outcome of Alzheimer’s disease and nondementia cognitive impairment (without stroke). The hazard ratio for this composite outcome in relation to diabetes, as compared with the absence of diabetes, was 1.6 (95 percent CI: 1.2, 2.1).

http://aje.oxfordjournals.org/content/154/7/635.full

diabetes AD

Table 1 shows a comparison of characteristics between all subjects in the sample and subjects with Alzheimer’s disease, stroke-associated dementia, nondementia cognitive impairment without stroke, and nondementia cognitive impairment with stroke. Persons with Alzheimer’s disease were older, had fewer years of education, had a higher proportion of Blacks, and had a higher prevalence of heart disease than persons without Alzheimer’s disease.

Persons with stroke-associated dementia were older and had a higher prevalence of diabetes, a higher level of low density lipoprotein cholesterol, a higher prevalence of hypertension, and a higher prevalence of heart disease than persons without stroke-associated dementia.

After the exclusion of 174 cases of nondementia cognitive impairment at baseline, there were 1,088 persons left for the analysis of nondementia cognitive impairment.

Persons with nondementia cognitive impairment without stroke were older and had fewer years of education, a higher proportion of ever smokers, and a higher proportion of Hispanics than persons without it.

Persons with nondementia cognitive impairment with stroke had a higher prevalence of hypertension and heart disease than persons without it.

diabetes stat

http://www.statisticbrain.com/diabetes-statistics/

How can I slow down my diabetes?

My answer to How can I slow down my diabetes?

Answer by Connie b. Dellobuono:

De-stress, adequate sleep, avoidance of toxins (drugs,alcohol,sugar,cigarette,medications) and taking whole foods rich in good fat (avocado,walnut,fish) and fiber (encapsulates fats and sugar out of the body). Add digestive enzymes (papaya, pineapple), prebiotic (raw garlic,raw carrots) and probiotic (pickled veggies) in your diet. Exercise at least 30min a day.

Diabetes and Herbal (Botanical) Medicine

Avoidance of environmental toxins: Exposure to pesticides/TCEs linked to 61% risk of diabetes and Parkinson

Type 2 diabetes linked to 20% risk of blood cancer

Agar in Jello – guar gum fights diabetes

Guava and water apple to fight diabetes

Which psychiatric medications are known to cause diabetes?

Take this German Diabetes Risk Score

Sucralose, GMOs, PCB,sugar,aspartame are health hazards for your liver by Dr Mercola

Sugary soda are harming our children

How can I slow down my diabetes?

What is the best measure of health?

My answer to What is the best measure of health?

Answer by Connie b. Dellobuono:

As we age, our body is not efficient in many bodily functions. We compare the health of our eyes with that of a healthy young person.

I will use genetic test (23andme), family history, comprehensive lab tests (blood,fecal,urine,pulmonary function test, eye test, mental and psychological tests, others) and doctor’s report (health assessment, physical exam,reflexes,tongue,skin color,others). My pulmonary function test shows a 60 yr old female. This is due to exposure to second hand smoking in the Philippines and air pollution. My blood test shows borderline diabetes although I consume whole foods and veggies. My family history is prone to heart disease and lung cancer.

Heart rate is the first thing that we measure. Ingestion of drugs or stimulants can increase heart rate.

  • Examine the environment that you live (absence of toxins, drugs or medications, alcohol,cigarette,noise and air pollution).
  • Monitor sleep status (8hrs of sleep) as the brain detoxes during sleep.
  • Monitor exercise routine (30 min of exercise per day).
  • Monitor whole food consumption vs junk food.
  • Compare all tests with a normal healthy person.

Note: For a newborn, we look at the flexion of the body, skin tone, loudness of cry, ear lobes alignment with eyes, descent of scrotum, and many more.

Note: Presence of infection in blood test: Blood test results indicating infection

Note: Sample blood test panel, women: Blood test panel, women

What is the best measure of health?

Drugs Used to Fight Hepatitis, Worm Infections Might Stop Zika

By Maggie Fox

They were delighted to find a drug already on the market and considered safe to use in pregnant women. “Niclosamide is an FDA-approved drug (trade name Niclocide) that has been used in humans to treat worm infections for nearly 50 years, and it is well tolerated,” the team wrote in their report, published in the journal Nature Medicine.

“It is known to inhibit several viruses in culture systems, including the Japanese encephalitis flavivirus.”

Japanese encephalitis is a relative of Zika and also carried by mosquitoes.

Tests on human brain stem cells in lab dishes showed it could interfere with Zika’s replication in those cells. “Niclosamide is a category B drug, which indicates that no risk to fetuses has been found in animal studies. It has low toxicity in mammals,” they added.

“EMRICASAN WAS WELL TOLERATED IN HUMAN TRIALS, AND THERE WERE NO SIGNIFICANT ADVERSE EVENTS.”

Another drug, called emricasan, helped prevent Zika from killing those cells.

“Emricasan is currently being evaluated in phase 2 clinical trials for the reduction of hepatic injury and liver fibrosis caused by chronic hepatitis C infection,” the team wrote.

Emricasan was well tolerated in human trials, and there were no significant adverse events.”

A third compound called PHA-690509 also helped stop Zika replicating and from killing nerve cells, they said.

While the drugs work well in lab dishes full of cells, “we don’t know if they can work in humans in the same way,” Song said.

Niclosamide works in the gut, but it’s not clear if it’s possible to get it into the brain, Song said.

The researchers think the drugs might work best as a cocktail, attacking the virus from several fronts — in much the same way as drug cocktails fight the human immunodeficiency virus (HIV) that causes AIDS.

Why some Google products failed?

By Lewis Lin

  1. Lack of vision. There are only so many people who can predict the future. Sundar Pichai was one of those rare individuals who saw the Chrome browser and Chromebook OS opportunity, despite daunting odds and endless customer naysaying.
  2. Lack of resources. When I was at Google, I believed Google Notebook had half an engineer working on it a few months out of the year. Hard to defend the fort if the guard tower is empty.
  3. Lack of insight. The Google Wave and Google Glass team worked hard, but both teams missed the critical insight that others realized. That is, Slack realized work messages belong to channels. And Google Glass was too dorky to wear in public.
  4. Lack of focus. Google+ included everything but the kitchen sink. It was an authentication service. And a commenting plug-in. And an address book. And a multi-user video conferencing feature. It felt and was designed by committee.
  5. Lack of trying. I believe Marissa Mayer once said, “There are great (product) ideas that are executed poorly.” In other words, we shouldn’t conclude an idea is flawed because it failed. After Google Answers shut down, it was wrong to conclude that the Internet didn’t want a Q&A service like http://www.QUORA.COM . It was more appropriate to conclude that Google Answers just implemented Q&A the wrong way.
  6. ———–

Connie’s comments: If the user designed the product, then the product will match what the user wants.  If the designer’s vision matches what is needed by the user, then the user wins. If the product is welcomed by of the user base, then it will be widely accepted and by word of mouth and will then go viral.

Dear Readers,

If you are caring for your parents, if you are a doctor and if you are like many of the consumers who wanted to reduce cost of chronic health care, what would you want to see in a Mobile health application?

Please email motherhealth@gmail.com of any features you want in the mobile health application such as video chat with health care providers, matching of care providers, monitoring of patient generated health data, genetic test health data and lab tests and more.

Also, I would like to invite all who wanted to be investors and participate in the development of this mobile health application by Motherhealth.

Our goal is to empower each patient to monitor and collect health data, learn from others, match care provider and reduce cost of chronic health care.

Regards,

Connie Dello Buono