Age of onset for type 1 diabetes mellitus,type 2 diabetes treatment success rate zones,what can someone with type 2 diabetes eat for lunch app - PDF Books


Send Home Our method Usage examples Index Statistics Advertise with us ContactWe do not evaluate or guarantee the accuracy of any content in this site. There are approximately 10 new type 1 patients under age 40 per 100.000 inhabitants per year in Belgium, which is comparable with neighbouring countries. In the age group between 15 and 40 years, there are more men than women with type 1 diabetes. There is no global increase in the number of new cases of type 1 diabetes under age 40 in Belgium. We have observed an increase in the number of new cases of type 1 diabetes in children, especially in boys, in parallel with a decrease in adults.
A possible cause of this earlier manifestation is the increase in the prevalence of overweight in the Belgian population, but this hypothesis still needs to be confirmed.
There were more new patients diagnosed in winter than during the warmer and sunnier summer months. However, the presence of a seasonal pattern at onset of type 1 diabetes seems to be limited to men above age 10 without genetic susceptibility. These differences can be attributed to a variable contribution of genetic and external factors to the disease process according to sex, age and season. Medical and health topics fully illustrated with pictures and videos for easy understanding. Diabetes mellitus includes a group of conditions characterized by a high level of blood glucose, commonly referred to as blood sugar. There are two types of chronic (lasts for life) diabetic conditions : type 1 diabetes and type 2 diabetes. How blood glucose is regulated ? A feedback loop is in place to ensure that glucose level in the blood is never too high or too low, i.e.
Type 1 diabetes = insulin dependent : The pancreas does not produce enough insulin due to lack of beta cells. This entry was posted in Endocrinology (diabetes) and tagged compare type 1 and 2 diabetes, diabetes mellitus, differences, different types of diabetes on December 23, 2013 by Alila Medical Media. Find us on YoutubeSubscribe FREE to our Youtube channel to watch new educational videos uploaded weekly. DisclaimerMedical information on this website is for educational purposes only and is NOT intended to be medical advice in any cases.
Cases with risk haplotype develop type 2 diabetes younger and at a lower BMI than non-carriers.
Extended Data Figure 5: Cases with risk haplotype develop type 2 diabetes younger and at a lower BMI than non-carriers. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.
Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, 01120 Mexico City, Mexico. Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston 02114, Massachusetts, USA. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA.
Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
The Genomics Platform, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, D-04103 Leipzig, Germany.
Palaeolithic Department, Institute of Archaeology and Ethnography, Russian Academy of Sciences, Siberian Branch, 630090 Novosibirsk, Russia. The Metabolite Profiling Platform, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA. Cancer Biology Program, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.
Duke National University of Singapore Graduate Medical School, Singapore 169857, Singapore.
Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597, Singapore.
Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Department of Medicine, Department of Genetics, Albert Einstein College of Medicine, Bronx, New York 10461, USA. Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas 78227, USA.
Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Department of Biochemistry, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA.
Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do, 200-702 South Korea.
Endocrinology and Metabolism Service, Hadassah-Hebrew University Medical School, Jerusalem 91120, Israel. Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.
National Heart and Lung Institute (NHLI), Imperial College London, Hammersmith Hospital, London W12 0HS, UK. Department of Medicine, University of Eastern Finland, Kuopio Campus and Kuopio University Hospital, FI-70211 Kuopio, Finland. Center for Genome Science, Korea National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do 363-951, South Korea. Department of Epidemiology and Public Health, National University of Singapore, Singapore 117597, Singapore. Centre for Molecular Epidemiology, National University of Singapore, Singapore 117456, Singapore.
Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore.
Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore 117456, Singapore.
Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore.
Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi 39216, USA.


Division of Nephrology, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, USA. Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, USA. Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, USA.
See the author list for details of author contributions.A list of participants and affiliations for the T2D-GENES Consortium and the Broad Genomics Platform is available in the Supplementary Information. Extended Data Figure 1: Principal component analysis (PCA) projection of SIGMA samples onto principal components calculated using data from samples collected by the Human Genome Diversity Project (HGDP) and 1000 Genomes Project.
Point colour indicates r2 to the most strongly associated site (rs7903146) and recombination rate is also shown, both based on the 1000 Genomes ASN population. Extended Data Figure 4: Regional plots for SLC16A11 conditional on associated missense variants of that gene. Extended Data Figure 6: Frequency distribution of the risk haplotype and dendrogram depicting clustering with Neanderthal haplotypes.
Latent Autoimmune Diabetes of Adults (LADA) is a slow developing form of autoimmune diabetes found in people over 35 years old that is often misdiagnosed as Type 2. Blood Sugar 101 does not control which products appear in Google Ads or endorse these products. It is manifested by the sudden onset of severe hyperglycemia, rapid progression to diabetic ketoacidosis, and death unless treated with insulin. Patients who developed the disease at an early age usually have a more severe form of diabetes and their tissues are exposed to high blood sugar levels for longer periods of time.
Pregnant women may acquire a transient form of the disease called gestational diabetes which usually resolves after the birth of baby. Point colour indicates r2 to the most strongly associated SNP (rs13342232) and recombination rate is also shown, both based on the 1000 Genomes ASN population.
Sample size for each tissue (n): adipose (394), adrenal (69), brain (1,990), breast (4,104), heart (178), kidney (675), liver (721), lung (1,442), pancreas (150), placenta (107), prostate (578), salivary gland (26), skeletal muscle (793), skin (947), testis (102), thyroid (108). Prediabetes is when your blood sugar is at the borderline : higher than normal, but lower than in diabetics. Insulin is a hormone produced by beta cells of the pancreas and is necessary for glucose intake by the target cells. Type 1 is characterized by early (juvenile) onset, symptoms commonly start suddenly and before the age of 20. The pancreas produces less insulin, liver and muscle cells absorb less glucose, glucose stays in the blood, blood sugar level increased. Click on image to see a larger version on Alila Medical Media website where the image is also available for licensing. The pancreas produces the same amount of insulin but organs are unresponsive, glucose can not be used and stays in the blood, blood sugar level increased.
P values from two-sample t-test between type 2 diabetes risk haplotype carriers and type 2 diabetes non-carriers. A list of sample identities and accession numbers are available in the Supplementary Information. Point colour indicates r2 to rs11564732 and recombination rate is also shown, both based on the 1000 Genomes ASN population. Nodes for modern human haplotypes are labelled in red or blue with the 1000 Genomes population in which the corresponding haplotype resides.
In other words, when insulin is deficient, muscle or liver cells won’t be able to use or store glucose and as a result, glucose will accumulate in the blood (Fig. Type 2 is characterized by adult onset, symptoms usually appear gradually and start after the age of 30. Archaic Neanderthal sequences are labelled in black and include the low-coverage Neanderthal sequence14 (labelled Vindija), and the unpublished Neanderthal sequence that is homozygous for the 5 SNP risk haplotype17 (Altai).
H1 includes haplotypes from MXL and FIN, and H2 and H3 both include haplotypes from CLM, MXL, CHB and ASW.
On the basis of negative controls, a normalized log2 expression of 4 is considered baseline and log2 expression values greater than 6 are considered expressed. Modern human sequences included are all 1000 Genomes Phase I samples that are homozygous for the 5 SNP risk haplotype (n = 15), and 16 non-risk haplotypes—four haplotypes (from two randomly selected individuals) from each of the CLM (Colombian in Medellin, Colombia), MXL (Mexican Ancestry in Los Angeles, California), CHB (Han Chinese in Beijing, China) and FIN (Finnish in Finland) 1000 Genomes populations (the populations with carriers of the 5 SNP haplotype). In target organs, insulin induces cells to take up glucose for use as energy or store for later use. The red subtree depicts the Neanderthal clade, with all risk haplotypes clustering with the Altai and Vindija sequences.
Spearman correlation coefficients were used to measure the association between both incidence and prevalence of type 1 diabetes and the selected indicators. As glucose is consumed by target organs, its concentration in the blood goes down and no more insulin is secreted from beta cells, insulin level goes down, glucose is no longer taken into cells, this prevents glucose level from going down further.
The dendrogram was generated by the R function hclust using a complete linkage clustering algorithm on a distance matrix measuring the fraction of SNPs called in the 1000 Genomes project at which a pair of haplotypes differs (the y axis represents this distance). Because haplotypes are unavailable for the archaic samples, we picked a random allele to compute the distance matrix.
This means that the oral drugs given to people with Type 2 diabetes often will have very little impact on the blood sugar of a person with LADA.
Regulation and ratio of these two hormones are vital for maintaining blood glucose levels within normal range. Further studies are needed to develop and test potential genetic and environmental hypotheses that could help to better understand the interplay between genetic susceptibility and environment in type 1 diabetes across different ethnic groups. Still, there is a lot of evidence that starting insulin early in Type 2 can make control much easier in the future--and since LADA combines genetic feature of both Type 1 and Type 2 diabetes, it is possible that some of the benefit seen in Type 2 may extend to people with LADA.
For now, there seems to be no compelling reason for a person with LADA NOT to start insulin early.
Also, several of the oral drugs used to treat Type 2 diabetes stimulate the beta cells to produce insulin, and because LADA involves an autoimmune attack which is stimulated by the production of insulin at the beta cells, stimulating insulin production by the beta cells with drugs may increase the ferocity of the attack, killing more beta cells. So it is very important to get a correct diagnosis so you can avoid the drugs that stimulate insulin production by the beta cells. These drugs include the sulfonylureas like Amaryl and Glipizide and may also include the incretin drugs, Byetta and Januvia because they also stimulate insulin production by the beta cells.
There is a genetic tendency towards developing autoimmune diabetes, so if you have a close family member who has autoimmune diabetes, it is more likely that you have that same genetic make up and the same tendency towards developing autoimmune diabetes.
The Presence of Other Autoimmune Conditions If you already have been diagnosed with another autoimmune condition, like Rheumatoid Arthritis or some Thyroid diseases, it is more likely that your diabetes is also caused by an autoimmune response.
Normal or Near Normal Weight Although there are, indeed, other forms of Type 2 diabetes that strike people of normal weight as well as non-autoimmune genetic forms of diabetes that are also misdiagnosed as Type 2 diabetes, most thin people who are incorrectly diagnosed with Type 2 diabetes turn out to have LADA.
So LADA should always be tested for in a thin or normal weight "Type 2," especially if blood sugars are extremely high at diagnosis. However, LADA is usually not a concern for normal weight people diagnosed with the mild blood sugar irregularity diagnosed as "prediabetes" unless they have a family history of Type 1 diabetes or other autoimmune conditions. Failure to Respond to Oral Drugs People with LADA often see swift deterioration in their blood sugars in the months after a Type 2 diagnosis.


If your blood sugars are getting worse, not better, despite taking oral drugs and cutting back on carbohydrates, a combination which is usually effective in Type 2 diabetes, you should demand that your doctor test you for LADA or send you to an Endocrinologist who will do this.How to Test for LADAThe most common test for LADA is one that looks for GAD (glutamic acid decarboxylase) antibodies.
Another issue is that very early on in the disease process there may be no detectable antibodies, but over time they may emerge.The other important test for LADA is the fasting C-peptide test.
A very low C-peptide result suggests that the beta cells have stopped making insulin, possibly because they are dead. Earlier reports had indicated that there was a polar-equatorial gradient in the incidence of type 1 diabetes, but additional research has cast some doubts on the strength of this association. Instead, the variation in type 1 diabetes incidence appears to follow an ethnic and racial distribution in the world population (4). In general, Europoid populations have a higher incidence of type 1 diabetes than do non-Europoid populations, although significant geographic differences are evident in incidence within each major ethnic group (4). Useful clues about environmental risk factors have been provided by a previous ecological analysis, which reported that the wide variation in type 1 diabetes incidence rates within Europe could be partially explained by indicators of national prosperity such as infant mortality rate and gross domestic product (8). The study also confirmed previously reported associations with milk consumption, coffee consumption, and geographical latitude (8). The DiaMond Project oversees the maintenance of registries of children with type 1 diabetes around the world and has monitored the worldwide type 1 diabetes incidence over the last 15 years (9). Genetic Similarities Between Latent Autoimmune Diabetes in Adults, Type 1 Diabetes, and Type 2 Diabetes. Due to the fact that specific data on cities or regions were seldom available, we used country-level data for this study. When diabetes incidence data were reported for a specific city or region in a country, the specific geographical latitude of that city or region was obtained; otherwise, the geographical latitude of the country's capital was used. Data on the human development index, which is a combined measure of adult literacy, life expectancy, and gross domestic product per capita, were obtained from the United Nations Development Program (15).
While the estimated prevalence of type 1 diabetes was available for almost all countries (19 of 20), incidence data were available for only about half of them (11 of 20).
In general, incidence is lower than that reported for Spain and Portugal (4), two southern European countries that significantly contributed to the population composition of most of today's Latin American countries. This strong negative correlation for both incidence and prevalence suggests that the reported lower incidence of type 1 diabetes in some Latin American countries could be partially explained by a low genetic susceptibility to diabetes in Amerindians, by low levels of exposure to unidentified risk factors, or by high levels of exposure to unidentified protecting factors in this population group.
The results of previous studies indicate that both Amerindian ancestry and haplotypes of Native American origin confer protection against type 1 diabetes (22, 23).
Similarly, an ecological study in Europe found a positive correlation between milk consumption and the incidence of type 1 diabetes (8). In addition, a direct association between cow milk intake and the incidence of type 1 diabetes was reported in Sardinia, an Italian island that has the world's highest reported incidence of type 1 diabetes (26).
Furthermore, a small harmful effect of the early introduction of cow's milk in infants was suggested by a meta-analysis of retrospective studies (27).
The above-mentioned meta-analysis of retrospective studies (27) suggests a small protective effect of breast-feeding. However, prospective studies conducted more recently have not found such an association (30, 31). Similar to these latter studies (30, 31), our study did not find a correlation between breast-feeding (either for less than 4 months or continued breast-feeding for 12 to 15 months) and type 1 diabetes. It is important to notice that breast-feeding data used in our analyses were collected five to eight years after the time for which incidence and prevalence data were reported.
The most recent report by the WHO DiaMond Project Group (4) includes incidence data from 100 registries in 50 countries, including the Latin American countries in this study. That research indicates that the earlier-reported polar-equatorial gradient in the incidence of type 1 diabetes in the Northern Hemisphere does not seem to be as strong as previously assumed. Furthermore, such a gradient has not been reported in the Southern Hemisphere, where several of the Latin American countries in this study are located. This discrepancy between incidence and prevalence could be related to the fact that data on incidence were not available for half the countries in our study. It could also be argued that incidence of type 1 diabetes is in general much lower and varies less strikingly in Latin America than in Europe.
Also, factors linked to industrialization and changes in lifestyles are still at a lower level in Latin American countries than they are in European nations. The limitations of ecologic data for making causal inferences have been reviewed in depth (33). One disadvantage of this kind of analysis is that the joint distribution of exposure and health remains unknown (ecological fallacy), which leads to possible distortion of association between exposure and outcome.
Also, for some countries some of the indicators were only available for years after the incidence data period. However, a recent study of the DNA of 52 human groups from around the world suggested that self-reported population ancestry likely provides a suitable proxy for genetic ancestry (34). In addition, our results confirm reports from other countries and regions of the world of the association between type 1 diabetes and milk consumption. Further studies are needed to develop and test potential genetic and environmental hypotheses that could help to better understand the interplay between genetic susceptibility and environment in type 1 diabetes across different ethnic groups.
A review of the recent epidemiological data on the worldwide incidence of type 1 (insulin-dependent) diabetes mellitus.
Sex difference in the incidence of insulin-dependent diabetes mellitus: an analysis of the recent epidemiological data.
HLA class II alleles and susceptibility and resistance to insulin dependent diabetes mellitus in Mexican-American families. Incidence of insulin-dependent diabetes mellitus in the IX region of Chile: ethnic differences. Genetic predisposition and environmental factors leading to the development of insulin-dependent diabetes mellitus in Chilean children.
Lack of association between duration of breast-feeding or introduction of cow's milk and development of islet autoimmunity. No major association of breast feeding, vaccinations and childhood viral diseases with early islet autoimmunity in the German BABYDIAB Study.
Is children's or parents' coffee or tea consumption associated with the risk for type 1 diabetes mellitus in children? An ecological analysis of childhood-onset type 1 diabetes incidence and prevalence in Latin America. Incidence of insulin-dependent diabetes mellitus (IDDM) in Iberian heritage populations: positive association with Caucasian ethnicity.
15th International Diabetes Federation Congress (Kobe, Japan, November 1994); abstract 08A3OP0702.



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