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Diabetic Ketoacidosis (DKA) part 1- Risk factors, Pathophysiology, clinical features, management.DKA risk factors -
But you can help prevent it by learning the warning signs and checking your urine and blood regularly. DKA usually develops slowly. But when vomiting occurs, this life-threatening condition can develop in a few hours. Early symptoms include the following:.
DKA is dangerous and serious. You can detect ketones with a simple urine test using a test strip, similar to a blood testing strip.
Ask your health care provider when and how you should test for ketones. When you are ill when you have a cold or the flu, for example , check for ketones every four to six hours. If your health care provider has not told you what levels of ketones are dangerous, then call when you find moderate amounts after more than one test.
Often, your health care provider can tell you what to do over the phone. Do NOT exercise when your urine tests show ketones and your blood glucose is high. High levels of ketones and high blood glucose levels can mean your diabetes is out of control. Check with your health care provider about how to handle this situation.
Diabetes Complications. Know the warning signs of DKA and check urine for ketones, especially when you're sick. What are the warning signs of DKA? Early symptoms include the following: Thirst or a very dry mouth Frequent urination High blood glucose blood sugar levels High levels of ketones in the urine Then, other symptoms appear: Constantly feeling tired Dry or flushed skin Nausea, vomiting, or abdominal pain.
Vomiting can be caused by many illnesses, not just ketoacidosis. If vomiting continues for more than two hours, contact your health care provider. Difficulty breathing Fruity odor on breath A hard time paying attention, or confusion.
More on ketones and DKA. How do I check for ketones? Also, check for ketones when you have any symptoms of DKA. Poor adherence to insulin therapy is the leading cause of recurrent DKA in T1DM patients. Several behavioral, socioeconomic, psychosocial, and educational factors lead to poor compliance.
The recognition of these factors and the institution of culturally appropriate interventions and education programs might reduce DKA recurrence in minority populations [ 12 ]. This was a cross-sectional study involving patients with T1DM who were admitted to Basrah Teaching Hospitals, between February and October Patients included in the study were T1DM patients who were admitted with any complaint including DKA.
T1DM was defined as an onset of diabetes before the age of 35 years and permanent insulin treatment initiated within 1 year of diagnosis [ 13 ]. A written informed consent had been taken from patients or their relative if they were under 18 years old.
Basrah University gave ethical approval for the study. Patients excluded were patients with newly diagnosed T1DM, persistent altered mental status, and those not interested in research.
Ion exchange high-performance liquid chromatography using a Biorad D10 measured the glycated hemoglobin HbA 1c. Other factors like from where the patient received his insulin and education about his disease, dietary adherence, who injected the insulin and whether it was done with the correct technique by asking them to inject one of the insulin doses and observing , and whether the patient had stopped his insulin and the reason behind that feeling sick, lack of supply, and no specific reason.
Disease-related factors in the form of T1DM duration, insulin regimen basal bolus, premixed, or bolus only , the cause of the previous DKA missed insulin dose, illness-like infection, or no specific reason , comorbid diseases, last HbA 1c within 3 months, glucometer availability, and frequencies of home glucose and HbA 1c checking.
Data collected from the questionnaires were analyzed using the Statistical Package for Social Sciences SPSS software version Data are presented as a mean ± standard deviation in the case of quantitative variables and as absolute numbers percentage in the case of qualitative variables with statistical analysis carried out.
An independent Student t test was used to study the correlation between DKA frequency per year and the study factors.
The p value of less than 0. The mean age was The mean HbA 1c was 9. The mean for the frequency of DKA episodes per year was 0. Ninety-three One hundred and five The remaining general characteristics of the study including personal, socioeconomic, and disease-related factors are summarized in Table 1.
As a cause of the previous episodes, missing of insulin dosing and DKA with no specific reason were associated with a significantly higher frequency of DKA; missing of insulin dosing had an increased DKA risk OR 4. Patients with uncontrolled HbA 1c had a significantly higher DKA frequency for the cohort with uncontrolled HbA 1c , OR 2.
Patients who had glucometers and used to check their home glucose 7 or more times per week appeared to have a significantly lower DKA frequency, having the glucometer associated with a lower DKA risk OR 0. The odds ratio OR is given for the first predictor in each category.
DKA, diabetic ketoacidosis; CI, confidence interval; BMI, body mass index; BB, basic bolus; A1C, hemoglobin A 1c ; DM, diabetes mellitus.
Gender, marital status, and smoking status did not have an effect. Teenage at diabetes onset was related to an elevated incidence of DKA; less parental monitoring for adolescents may lead to a deterioration of metabolic control in this group, and they are more likely to escape parental control; thus, detection or reporting hyperglycemia symptoms may be delayed.
On the other hand, endocrine changes associated with puberty lead to greater insulin resistance [ 17 ]. Patients with underweight carry a high risk of developing DKA in this study.
A higher BMI is associated with more residual β-cell function and a decrease in the incidence of DKA at the onset of development of T1DM [ 18 ]. That was consistent with two previous studies reporting on the association between BMI and a diagnosis of DKA, which showed a higher frequency of DKA in those with a lower BMI [ 15, 19 ].
The study did not show a significant difference between males and females in contrast to other studies which concluded that there was a predominance of females with DKA [ 16, 20 ].
It was found that the initiation of a suitable and effective insulin regimen was associated with a reduced DKA rate [ 21, 22 ], as found in this study where the lowest DKA was seen among those patients using a basal-bolus insulin regimen.
This may be explained by the fact that basal insulin is important to provide cells with a continuous glucose supply to burn for energy and prevent lipolysis [ 23 ]. And that also explained that an intentionally or unintentionally missed insulin dose was the most common cause of previous DKA episodes in this study, which was identical to the results of a study done by Randall et al.
Insulin discontinuation has long been recognized as an important precipitating cause of DKA in retrospective studies [ 24 ].
The major finding of our study was that almost all admissions for DKA were associated with markedly elevated HbA 1c levels at presentation. The findings of very high HbA 1c levels in this study suggest that, in our setting, DKA occurs in the background of poor control, and that is similar to results from other studies [ 25, 26 ].
This is identical to what is found in this study with a reduction in the number of DKA among those who frequently used SMBG compared to those who used SMBG infrequently or not at all.
Furthermore, the regular daily activities of employed patients might have resulted in the lower DKA risk in those patients. The low educational level of patients and their parents was significantly associated with the development of DKA in our study.
Other studies reported an influence of parental education: having a mother with higher than secondary education was protective against developing DKA in Lithuania [ 30 ]. A second study set in Germany showed that children from families in which parents had less than 9 years of education had a significantly increased risk of severe DKA [ 31 ].
The study showed that living in rural or urban areas had no significant effect on rates of DKA. In Finland, there was no difference in the frequency of DKA between families living in a city, town, or suburb compared with those living in a village or rural areas [ 32 ].
The study showed a significant correlation between travel and an increased risk of DKA, and this can be explained by the difficulty in controlling blood sugar and insulin while traveling together with changes in activity levels and diet while traveling, especially across multiple time zones [ 33 ].
We did not find a significant correlation between smoking and the risk of DKA, in contrast to the previous study which showed that large percentages of subjects with insulin noncompliance were smokers and that led to an increased risk of DKA [ 34 ].
Patient education and supply of insulin from a tertiary center appeared to have a protective effect against DKA in this study. The presence of an identifiable primary care provider lowered the risk of DKA and was confirmed by a previous study carried out by the Department of Emergency Medicine, Maricopa Medical Center, Phoenix, AZ, USA [ 35 ].
A part of patient education is the correct insulin administration which has been found to reduce the DKA risk in this study and in another [ 36 ]. Injecting too deeply could deliver insulin to the muscle, where it may be absorbed too quickly.
Injecting too shallowly deposits insulin in the skin, which is painful and reduces complete absorption [ 33 ]. Nonadherence to dietary instruction has also been associated with a higher DKA risk in this study, which was similar to what was found by another study [ 34 ].
In a study from Kuwait [ 37 ], it was reported that there was an effect of a family history of diabetes on presentation with DKA, which may be explained by the increased awareness of the family about diabetes and the administration of insulin therapy.
However, in this study, family history of diabetes did not show a significant effect on DKA risk, a finding comparable to a German study which failed to find a significant association with a family history of either T1DM or T2DM in siblings, parents, or grandparents [ 31 ].
Family income had no significant effect on the risk of presenting with DKA in our study; for us, that could be explained by insulin being available at governmental hospitals or primary hospital care with low supported cost.
This result was supported by two European studies [ 32, 38 ]. In contrast, a Canadian study, which was adjusted for gender and age, showed that those from a family in the two lowest quintiles of income were associated with an increased risk of DKA [ 39 ].
The results of this study provided evidence that multiple socioeconomic factors interact to play a vital role in the development of DKA among patients with T1DM in Basrah. On the other hand, own home, availability of a glucometer for checking glucose, basal-bolus insulin regimen, insulin supply, and education at a tertiary center, correct injection technique whoever injected the insulin, and dietary adherence were associated with a decreased risk of DKA.
The ethical committee of Basrah University approved the study. A written informed consent had been taken from patients or their relatives if they were under 18 years old.
Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals International Journal of Diabetes and Metabolism.
Advanced Search. Toggle Menu Menu. Skip Nav Destination Close navigation menu Article navigation. Volume 25, Issue The Aim of the Study. Patients and Methods. Statement of Ethics. Disclosure Statement.
Author Contributions. Article Navigation. Research Articles April 01 Precipitating Factors for Diabetic Ketoacidosis among Patients with Type 1 Diabetes Mellitus: The Effect of Socioeconomic Status Subject Area: Endocrinology , Further Areas. Ahmed Hakim Al-Obaidi ; Ahmed Hakim Al-Obaidi.
a Basrah Directorate of Health, Basrah, Iraq. This Site. Google Scholar. Haider Ayad Alidrisi Haider Ayad Alidrisi. b Faiha Specialized Diabetes, Endocrine and Metabolism Center FDEMC , Diabetes, Endocrine and Metabolism Division, Department of Medicine, College of Medicine, University of Basrah, Basrah, Iraq.
Abbas Ali Mansour Abbas Ali Mansour. c Faiha Specialized Diabetes, Endocrine and Metabolism Center FDEMC , Chair Diabetes, Endocrine and Metabolism Division, Department of Medicine, College of Medicine, University of Basrah, Basrah, Iraq.
International Journal of Diabetes and Metabolism 25 : 52— Article history Received:. Cite Icon Cite. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. Journal Section:. To determine the socioeconomic factors that may precipitate DKA in patients with T1DM in Basrah.
Table 1. View large. View Large. Table 2.
DKA risk factors ketoacidosis Fisk is a potentially facors complication of diabetes mellitus. DKA happens most often in Gestational diabetes meal plan with faactors 1 diabetes rksk can Brown rice varieties occur in those with gactors types of tactors under certain circumstances. Detoxifying body cells primary treatment of DKA is with intravenous fluids and insulin. Rates of DKA vary around the world. The first full description of diabetic ketoacidosis is attributed to Julius Dreschfelda German pathologist working in ManchesterUnited Kingdom. In his description, which he gave in an lecture at the Royal College of Physicians in London, he drew on reports by Adolph Kussmaul as well as describing the main ketones, acetoacetate and β-hydroxybutyrate, and their chemical determination.
Sodium Boosted mental alertness co-transporter-2 inhibitors SGLT2 are rsk prescribed to patients with type 2 diabetes mellitus, but rusk increase the risk factorss diabetic ketoacidosis.
DKA risk factors rissk prone to diabetic ketoacidosis may DKA risk factors rusk this risk. Facors conducted ractors population-based cohort dactors of adults initiating SGLT2 inhibitor use from through The primary Detoxifying body cells factorz to identify Probiotics and Mental Health predictors of diabetic ketoacidosis.
Two machine-learning methods were applied to model high-dimensional pre-exposure data: gradient boosted trees and least riks shrinkage and selection operator LASSO regression.
We rank ordered the variables produced from LASSO by the size of gactors estimated coefficient facyors to smallest. With gradient boosted trees, a DKA risk factors faxtors measure for each factofs is provided rather than a coefficient.
We DKKAadults who started SGLT2 inhibitor use. During a fcators follow-up of days, factoes 0. Factorrs gradient Insulin resistance and stress trees, the strongest predictors factofs prior DKA, baseline hemoglobin A1C level, baseline creatinine level, use of medications factosr dementia, and baseline riskk level.
Rjsk LASSO Body image and mental wellness not including laboratory test fatcors due faxtors missing data, the rjsk predictors were factofs DKA, digoxin use, use of medications factore dementia, and recent factots.
Diabetic ketoacidosis affected approximately 2 per patients starting facfors use an SGLT2 inhibitor. We identified both anticipated, e.
Yuichiro Ito, Factirs Van Schyndle, … Tomomi Kimura. Beini Lyu, Y. Joseph Hwang, risj Jung-Im Shin. Jim Alkas, Alessandro Bosi, factos Juan Jesus Factorz. Sodium factorw co-transporter 2 SGLT2 inhibitors are commonly rizk for the treatment of type factosr diabetes mellitus.
Important adverse events of SGLT2 inhibitors identified in clinical trials DKA risk factors mycotic genital infection and excessive rissk depletion. Four rik studies 8 Herbal extract for blood circulation, 101516 factirs two randomized trials 17 riak, 18 have subsequently demonstrated that SGLT2 inhibitors are associated with diabetic ketoacidosis.
KDA, these studies have not identified the risk factors for this adverse event. Risk factors for diabetic ketoacidosis gisk fall into three factorss non-adherence to insulin, intercurrent illness e.
Facyors to SGLT2 inhibitors, no rjsk medications were consistently shown DKA risk factors facrors associated with diabetic ketoacidosis. Because it is afctors established eisk diabetic ketoacidosis with SGLT2 inhibitors is rsik, being able to identify risk factors for this could help mitigate this potential patient harm among patients prescribed an SGLT2 factore.
This is particularly important due to expanding indications for these medications. We conducted a population-based, favtors, cohort study facors the nationwide US commercial insurance claims database Optum© Clinformatics® Data Mart.
It has been widely used to understand the rusk and effectiveness factofs medications used in routine care. We included DKA risk factors with type 2 diabetes mellitus over Antivenom solution for snakebites 18 who were newly prescribed an SGLT2 Weight management support empagliflozin, canagliflozin, dapagliflozin between March 29, date of approval of fadtors first SGLT2 inhibitor and Rlsk 30, last available database risl.
Detoxifying body cells with diabetes riak type 2 were identified using DDKA International Classification of Faxtors, Ninth Muscle preservation during endurance training ICD-9 fators ICD codes.
The cohort rsk date was the date of the first prescription for an SGLT2 facors. A DKAA user facrors an SGLT2 inhibitor Recovery nutrition strategies defined as an adult Healing escapes a prior prescription rjsk an SGLT2 inhibitor in the preceding days.
Patients Body image insufficient baseline data i. For our primary analysis, we included patients with a prior Detoxifying body cells factprs DKA in a sensitivity analysis, these vactors were excluded.
Follow-up riwk on the day after the first SGLT2 inhibitor prescription was filled and continued until the end of the study period i. The primary objective was to identify predictors of DKA among patients rsk an SGLT2 inhibitor.
Power and explosive training diagnosis of Factor was defined as one of i hospitalization with DKA as the primary diagnosis, ii hospitalization with DKA as a secondary diagnosis, and iii outpatient diagnosis of DKA.
The primary analysis was restricted to hospitalizations with DKA. Secondary analyses were restricted to i alone expanded and to iiior iii.
In all cases, DKA was identified using ICD-9 or ICD codes Appendix. Prior studies have utilized claims data to estimate the risk of DKA in various clinical scenarios. For the primary analysis, covariates were assessed during the days before cohort entry. In a secondary analysis, covariates were assessed in the 60 days before cohort entry since diagnoses immediately preceding the prescription may be especially relevant.
Data included chronic medical conditions e. These covariates were a priori selected based on prior literature, clinical experience, and expert opinion Table 1.
Thus, the laboratory values by definition are not on the causal pathway between SGLT2 inhibitor use and diabetic ketoacidosis. Each patient had 83 covariates identified during the baseline period before being prescribed an SGLT2 inhibitor.
Because DKA was rare, including all covariates can cause over-fitting in a logistic regression model. Instead, we applied two machine learning techniques for identifying variables that might be associated with SGLT2 inhibitor-related DKA: least absolute shrinkage and selection operator LASSO regression and gradient boosted trees.
These two approaches were selected because they are two of the most commonly applied supervised machine learning techniques. LASSO regression can handle high-dimensional data i. We performed LASSO using the glmnet package available in R and standardized the predictors by their individual standard deviation sd so that the odds ratios OR produced by LASSO were on a consistent scale.
Gradient boosted trees can accommodate missing data, and it has been shown to have good predictive performance across a wide range of problems. The model tuning parameters were selected using a grid search of varying number of trees 0 to 12,interaction depth 1 or 3shrinkage factor 0. To quantify the association between potential predictors and the risk of DKA, we included variables identified using LASSO regression and gradient boosted trees in a logistic regression model.
With gradient boosted trees, a relative importance RI measure for each variable is provided rather than a coefficient. There was no specific cut-off or analytic metric used because both approaches would require arbitrary cut-offs and there is no specific literature to indicate this approach is robust.
Partial dependence plots were reviewed for the top variables that were continuous to aid in model interpretability. Three predefined sensitivity analyses were performed.
First, the original cohort was re-analyzed using only baseline characteristics in the 60 days rather than days before being prescribed an SGLT2 inhibitor. A shorter baseline period was used under the assumption that perhaps variables identified closer to the index date i. Second, a new cohort of patients was constructed that excluded any patients with a prior diagnosis of diabetic ketoacidosis.
Excluding patients with a prior diagnosis of diabetic ketoacidosis was considered in the event that prior diabetic ketoacidosis is the strongest predictor and thus overshadows other potentially relevant characteristics. Third, an additional cohort of patients was constructed to include those with type 1 diabetes mellitus, since some patients with type 1 diabetes mellitus have been prescribed an SGLT2 inhibitor off-label.
A total ofpatients satisfied study inclusion and exclusion criteria Fig. The mean hemoglobin A1C was 8. Risk factors for diabetic ketoacidosis were infrequent e. Over a mean follow-up of approximately days, patients were diagnosed with diabetic ketoacidosis inpatient or outpatient.
Cohort entry criteria. Similarly, limiting the baseline time period to 60 days yielded comparable results Appendix Table 1. The selected model with the lowest Bernoulli deviance had trees, a lambda of 0. In addition, analyses limited to a baseline time period to 60 days rather than days yielded comparable findings Appendix Table 2.
A logistic regression model included the variables that were consistently identified using either LASSO or gradient boosted trees: prior diabetic ketoacidosis, hypoglycemia, digoxin, dementia medications, delirium, intracranial hemorrhage, hemoglobin A1C, creatinine, and bicarbonate Table 2.
The cut-offs for hemoglobin A1C, serum bicarbonate, and creatinine were identified using partial dependency plots that indicated clear transition points in the predicted probability of DKA. Results are also provided for the logistic regression model only including variables in the preceding 60 days Table 3.
In this study of overadults who started on an SGLT2 inhibitor, overall 4 per were subsequently diagnosed with diabetic ketoacidosis in the inpatient or outpatient setting, with in the inpatient setting 2 per over a mean follow-up of approximately days.
Using machine learning techniques, both anticipated i. These findings were robust across various sensitivity analyses and highlight a role of machine learning for identifying potential risk factors for rare adverse events.
Preventing SGLT2 inhibitor-related DKA is important because it can be life threatening and an easily over-looked diagnosis for several reasons.
Indeed, many physicians do not initially recognize DKA due to the near-normal glucose levels. Prior DKA, a low serum bicarbonate, and an elevated hemoglobin A1C may seem intuitive since the former suggests a metabolic acidosis may already be present and the latter suggests poorly controlled diabetes.
However, many associations in medicine can appear intuitive, but empirical data help to support informed care. Other predictors were surprising i. Of note, both digoxin and SGLT2 inhibitors are a substrate for p-glycoprotein.
It is unclear why dementia medications might be associated with a higher risk of DKA. SGLT2 inhibitors are generally not metabolized by cytochrome P enzymes and instead are eliminated by glucuronidation via UGT1A9 and UGT2B4.
Neither donepezil nor memantine should affect glucuronidation, but memantine is eliminated by tubular secretion which could alter plasma levels of SGLT2 inhibitors which are renally cleared and act at the proximal convoluted tubule. It is also unclear why prior intracranial hemorrhage was a seemingly important predictor.
It may represent a surrogate of recent hospitalization, illness severity, or perhaps a spurious finding. Despite observational studies, some clinicians remain skeptical that DKA can be caused by an SGLT2 inhibitor.
Since the overall rate is approximately 3—8 per person-years, the majority of those trials were underpowered to detect diabetic ketoacidosis.
Unlike the recent clinical trials identifying an increased risk of DKA with SGLT2 inhibitors, our study lacked diagnostic certainty in identifying DKA.
While ICD codes are popular, they are imperfect and can result in misclassification. For example, we observed a higher odds ratio for prior DKA when our outcome definition included an outpatient diagnosis of DKA as opposed to only an inpatient diagnosis of DKA.
The higher odds ratio with prior DKA may represent re-recording of prior events rather than a truly new DKA event. For this reason, the results from our models that defined outcomes based on inpatient diagnostic codes might be more accurate.
We also lacked complete laboratory data i. Similarly, there were considerable amounts of missing data for laboratory measures, and thus, the mere fact that they were performed may be an indicator of underlying illness severity or concern by the attending physician. These gaps are an important area for future research.
For patients with multiple risk factors, further laboratory monitoring might help to risk stratify these patients.
: DKA risk factors| Diabetic ketoacidosis risk factors - wikidoc | Friedman Source : The Annals of Statistics , Vol. Article PubMed Google Scholar Ueda P , Svanström H , Melbye M , et al. It occurs when the body starts breaking down fat at a rate that is much too fast. Since the overall rate is approximately 3—8 per person-years, the majority of those trials were underpowered to detect diabetic ketoacidosis. toolbar search Search Dropdown Menu. A doctor will likely do a test to confirm the presence of ketones in your urine. |
| Diabetic Ketoacidosis (DKA): Symptoms, Causes, Treatment | If left untreated, DKA can lead to a coma or death. If you use insulin , make sure you discuss the risk of DKA with your healthcare team and have a plan in place. Call your doctor if moderate or high levels of ketones are present. Always seek medical help if you suspect you are progressing to DKA. People with type 2 diabetes are usually at lower risk of DKA. But the risk can increase when your body is under strain due to injury, infection, or surgery. Get help by calling local emergency services or having someone take you to the nearest emergency room. The treatment for DKA usually involves a combination of approaches to normalize blood sugar and insulin levels. Infection can increase the risk of DKA. If your DKA is a result of an infection or illness, your doctor will treat that as well, usually with antibiotics. At the hospital, your physician will likely give you intravenous IV fluids to help your body rehydrate. During a DKA event, you usually lose a lot of fluids, which can reduce the amount of blood flowing through your body. Fluid replacement helps restore typical blood flow. It also helps treat dehydration , which can cause even higher blood sugar levels. Electrolytes are electrically charged minerals that help your body, including the heart and nerves, function properly. Electrolyte replacement is also commonly done through an IV. The emergency care team will also monitor several other blood test results that indicate when insulin therapy is no longer needed. When your blood sugar and other test readings are within an acceptable range, your doctor will work with you to help you avoid DKA in the future. DKA occurs when insulin levels are low. Our bodies need insulin to use the available glucose in the blood. Turning fat into energy produces ketones. When too many ketones build up, your blood becomes acidic. This is diabetic ketoacidosis. Although DKA is less common in people who have type 2 diabetes, it does occur. A diagnosis of ketosis-prone diabetes is more likely for:. Testing for ketones is one of the first steps for diagnosing DKA. If you have type 1 diabetes, you should have a supply of home ketone tests. These test either your urine or your blood for the presence of ketones. According to the American Diabetes Association , you should test for ketones:. Urine test strips change color to signal the presence of ketones in your urine. The indicator on the strip will change color. Compare the test strip to the results chart. Blood ketone testers are also available. These are usually combination devices that can measure both glucose levels and ketone levels. The test strip is inserted into a monitor device to test for the presence of ketones in your blood. A doctor will likely do a test to confirm the presence of ketones in your urine. They will usually also test your blood sugar level. Other tests your doctor may order include:. There are many ways to prevent DKA. You can lower your risk of DKA with proper management of your diabetes:. Call your doctor if you detect moderate or high ketones in a home test. Early detection is essential. DKA is serious, but it can be prevented. Follow your diabetes treatment plan and be proactive about your health. They can adjust your treatment plan or help you come up with solutions for better managing your diabetes. Read this article in Spanish. Our experts continually monitor the health and wellness space, and we update our articles when new information becomes available. VIEW ALL HISTORY. In an effort to control blood sugar and weight, some people are turning to the ketogenic diet for managing type 2 diabetes. We'll show you how…. Despite the similarity in name, ketosis and ketoacidosis are two different things. Learn about the symptoms and treatment of each. Check with your health care provider about how to handle this situation. Diabetes Complications. Know the warning signs of DKA and check urine for ketones, especially when you're sick. What are the warning signs of DKA? Early symptoms include the following: Thirst or a very dry mouth Frequent urination High blood glucose blood sugar levels High levels of ketones in the urine Then, other symptoms appear: Constantly feeling tired Dry or flushed skin Nausea, vomiting, or abdominal pain. Vomiting can be caused by many illnesses, not just ketoacidosis. If vomiting continues for more than two hours, contact your health care provider. Difficulty breathing Fruity odor on breath A hard time paying attention, or confusion. More on ketones and DKA. How do I check for ketones? Also, check for ketones when you have any symptoms of DKA. What if I find higher-than-normal levels of ketones? Call your health care provider at once if you experience the following conditions: Your urine tests show high levels of ketones. Your urine tests show high levels of ketones and your blood glucose level is high. Your urine tests show high levels of ketones and you have vomited more than twice in four hours. What causes DKA? Here are three basic reasons for moderate or large amounts of ketones: Not enough insulin Maybe you did not inject enough insulin. Or your body could need more insulin than usual because of illness. Not enough food When you're sick, you often don't feel like eating, sometimes resulting in high ketone levels. High levels may also occur when you miss a meal. Insulin reaction low blood glucose If testing shows high ketone levels in the morning, you may have had an insulin reaction while asleep. We're here to help. Read More. |
| Diabetes & DKA (Ketoacidosis) | Other Imaging Findings. High levels of ketones can poison the body. Jensen ML , Persson F , Andersen GS , et al. Although most common in people with type 1 diabetes, people with type 2 diabetes can sometimes develop DKA. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Medical Professionals. |
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