Category Archives: Diabetes

Living with diabetes demands that you have the right information, alongside your diet and lifestyle changes; be it in the form of education, self-awareness or daily monitoring, that is, knowing your numbers (blood sugar level).

Contrary to popular opinion, your blood sugar level is not the only important number. There are other numbers you should be conversant with and equally track. Being aware of numbers associated with the condition helps you and your doctor feel more in control and able to maintain it better.

Know your blood glucose number: Your blood glucose level is the measure of the amount of glucose in your blood at a particular time. Two blood glucose testsfasting blood glucose and random blood glucose testscan be done to monitor the levels of glucose in the blood.

The random blood glucose level can be checked at any time, while the fasting blood glucose level is recommended to be checked before 10am and is best checked first thing in the morning. Some recommended times to check your blood glucose levels include immediately you wake up/early in the morning (between 6am and 8am); before a meal; two hours after a meal and before bedtime.

For people living with diabetes, the target is that their fasting blood glucose falls between 80-120mg/dl. Two hours after eating, levels less than 180mg/dl are ideal targets.

Know your HbA1C number: When you visit the clinic for a routine health check, your doctor might require that you do the HBA1C (haemoglobin A1C) test, which tracks your average blood sugar levels over the past three months by measuring the glucose attached to haemoglobin found on red blood cells. This helps to check how you have been managing your glucose levels and the effectiveness of your treatment plan.

Red blood cells typically survive for around three and four months. The more glucose there is in the blood, the more glucose that will be available to attach to haemoglobin.

The normal range of HbA1C is less than 5.7%. Levels above 6.5% indicate diabetes while levels between 5.7% and 6.4% indicate prediabetes. If you have type 2 diabetes, a good target range should be 7% and lower. However, this is subject to your treatment plan, age and the goals you set with your doctor.

Ideally, everyone should know their normal blood pressure range and monitor it from time to time, but it is significantly more important for people living with diabetes, who should work to keep their blood pressure levels less than 130/80mmHg.

Blood pressure monitoring is very important because hypertension might complicate your diabetes management plan and increase your financial burden. High blood pressure also means youre at risk for other diseases such as kidney diseases, stroke, aneurysms and vascular dementia.

Monitoring your weight can give insights into how much you abide by good diet and lifestyle choices. An unhealthy weight or being obese is an important risk factor for metabolic diseases. Apart from the fact that obesity can contribute to the development of type 2 diabetes, an unhealthy weight can make you susceptible to other diseases and make diabetes harder to control.

Your doctor would usually recommend that you lose around 5% of your total weight if you are overweight, which is favourable in reducing insulin resistance, blood pressure and consequently the amount of diabetes medications needed.

Cardiovascular diseases are a leading cause of death in adults living with diabetes. The link between cholesterol levels and heart problems has been established and diabetes increases the risk of heart disease by almost four times.

Monitoring your cholesterol levels, with directives from your doctor, keeps you a step ahead. Total cholesterol levels should be at less than 200 mg/dl.

The two major types of cholesterolHDL (good) and LDL (bad)cholesterol are measured independently. While HDL cholesterol should be above 40-50 mg/dl, LDL cholesterol is ideal below 110mg/dl. A distortion in these values indicates strong risks for cardiovascular diseases.

If you have type 2 diabetes, youre likely planning or adhering to a healthy diet plan. A common practice in dieting is counting calories. Counting calories might be better than counting food nutrients: carbs, vitamins and proteins. This is because calories are actually what determines your weight outcomes. If you eat more calories, without losing some in exercise, you will gain weight and vice versa.

As a start, you can know the calories in popular foods around you or know the foods that seem healthy but are very high in calories.

Seeing a dietitian is the best way to learn about calories and what foods are best for you. Sometimes, it is not just about calories, some foods might be more likely to impact blood sugar levels and drastic weight loss by being in a calorie deficit state may not be the best option for you depending on your health outcome.

Self-monitoring is a critical part of the diabetes journey but you shouldnt have to figure it out alone. Joining a diabetes community is very beneficial to educate, guide and support you, on how to best stay alert and manage the condition to prevent any complications.

Mojisola Agabato is a certified Diabetes Nurse Educator, she can be reached on 07012362694

Excerpt from:
Staying healthy with diabetes: Numbers to know - Punch Newspapers

Kaiser Permanente study finds racial and ethnic differences in medication initiation

By Sue Rochman

For adults newly diagnosed with diabetes, getting blood sugar levels under control is the first goal. Guidelines recommend diabetes medications to help patients meet their target blood sugar range. Yet a new Kaiser Permanente study found that adults of certain racial and ethnic groups are less likely to start medication within the first year of diagnosis.

We know there are race and ethnic disparities in diabetes-related health outcomes and that many factors contribute to these differences, said the studys co-lead author Anjali Gopalan, MD, MS, a research scientist at theDivision of Researchand a senior physician with The Permanente Medical Group. With our growing awareness of the lasting benefits of early glycemic control, and the important role of medications in lowering blood sugar levels, we wanted to look at whether there are differences in early medication initiation by race and ethnicity and there are.

The study, published August 4in the Journal of General Internal Medicine, included 77,199 members of Kaiser Permanente Northern California newly diagnosed with type 2 diabetes between 2005 and 2016. The researchers used the patients electronic medical records to determine if they were dispensed any diabetes medication during the year following their diagnosis. Self-reported race and ethnicity information was used to separate the members into 12 groups.

The study showed that, overall, 47% (36,283) of the adults started a glucose-lowering medication during the first year following their diagnosis. However, initiation rates varied widely by group: 32% of Chinese adults and 35% of Japanese adults started taking a diabetes medication compared to 44% of white adults, 50% of Black adults, 56% of Hispanic/Latinx adults, and 58% of individuals of other racial or ethnic groups.

Seeing that Black and Latinx adults had higher than average initiation rates intrigued us because, overall, these groups tend to have higher rates of diabetes-related complications, said Gopalan. Our findings also suggest we need to better understand the factors that may be keeping Chinese and Japanese adults from starting medication after their initial diagnosis.

The study found that among adults newly diagnosed with diabetes who had high HbA1c (blood sugar) levels a group for whom it is clear medication should be started there was little difference in medication initiation between race and ethnic groups. In addition, race and ethnic differences in medication initiation did not meaningfully differ among individuals based on their age at diagnosis, body mass index (BMI), socioeconomic status, and the presence of other ongoing health problems.

Diabetes treatment is tricky as it is not one size fits all, said Gopalan. But if we dont understand what is driving the treatment differences we are seeing, we dont know if the decisions are clinically appropriate or if they are rooted in provider implicit biases or patient misconceptions about the risks and benefits of medication.

As a physician, I want everypatient to understand that having to take medication is not an indicator of how bad your diabetes is, or a sign that you have failed at making important behavior changes, she added. Medications are just another tool in diabetes management thatcan help keep patients healthy and preventcomplications.

The study was funded by the National Institute on Minority Health and Health Disparities, the National Institute of Diabetes and Digestive and Kidney Diseases, and the National Institute on Aging.

Co-authors include co-first author Aaron N Winn, PhD, of the Medical College of Wisconsin, Milwaukee; Andrew J Karter, PhD, of the Division of Research, and Neda Laiteerapong MD, MS, of The University of Chicago.

# # #

About the Kaiser Permanente Division of Research

The Kaiser Permanente Division of Research conducts, publishes and disseminates epidemiologic and health services research to improve the health and medical care of Kaiser Permanente members and society at large. It seeks to understand the determinants of illness and well-being, and to improve the quality and cost-effectiveness of health care. Currently, DORs 600-plus staff is working on more than 450 epidemiological and health services research projects. For more information, visitdivisionofresearch.kaiserpermanente.orgor follow us @KPDOR.

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Not all adults newly diagnosed with diabetes equally likely to start treatment NCAL Research Spotlight - Kaiser Permanente Division of Research

Diabetes: Benefits of onions.

Diabetes is one of the most common diseases in the world. Though it is thought to be benign, if blood sugar isn't controlled, it can cripple life and can even cause death. Currently, there isn't a cure for diabetes but patients can live normally with the condition if they can keep their blood sugar levels in check. Diabetes home remedies can also help control blood sugar. One of them is onion juice.

Onion can play a crucial role in fighting diabetes. This contains a substance that can control blood sugar. It also contains chromium in a proper amount that helps regulate glucose in the body. Onion's glycemic index is also low which means it gets absorbed in the body slowly, resulting in a low sugar spike in the blood.

The juice of Onion has other benefits as well. It improves digestion. Patients must start their day with onion juice. It can improve the digestive system and blood sugar simultaneously.

Onions are also good for the health of hair. It contains sulfur which is good for the growth of hair. If you apply onion juice to your hair and scalp even two days a week, the quality of your hair is likely to improve.

Onion also helps boost immunity.

How to prepare onion juice for consumption? Blend onions in a blender and add water, salt and lemon.

See the rest here:
Diabetes home remedy: Benefits of Onion in controlling blood sugar, diabetes; details - DNA India

Over half of Americans between 18 and 34 years old are living with a chronic medical condition, according to a recent report from the Centers for Disease Control and Prevention (CDC).

These conditions include obesity, depression, high blood pressure, and asthma. The findings were published July 29 in the CDCs Morbidity and Mortality Weekly Report (MMWR).

According to CDC researchers, data from 2019 show over half of young adults currently live with at least one chronic condition, and nearly one in four lives with two or more.

The study also found for adults under 35 that:

This data was based on telephone surveys conducted in 2019 and included over 67,000 18- to 34-year-olds across the U.S.

Many of these chronic health conditions are what we call society-driven risk factors, Dr. Alex Li, Deputy Chief Medical Officer at L.A. Care Health Plan, told Healthline.

For example, some of the society-driven risk factors include an increased prevalence of a sedentary lifestyle and easy access to processed food, he continued. As well as decreased time spent on physical and mental wellness activities.

The CDC findings show that depression affected 27 percent of young adult women, compared with only about 16 percent of men.

Not surprisingly, depression rates were highest for those who were unemployed, at 31 percent.

Dr. Alex Dimitriu, double board-certified in Psychiatry and Sleep Medicine and founder of Menlo Park (California) Psychiatry & Sleep Medicine and BrainfoodMD, said previous research also finds womens rate of depression often exceeds that of men.

According to Dimitriu, the reasons for this difference between men and women may be attributed to biological factors that include hormone changes after puberty and post-partum depression.

All possibly pointing to a hormone-mediated increase in sensitivity to stress, with a possible variation in serotonin sensitivity, he said. Psychologically, women have also been found to be more likely to internalize feelings, and have greater sensitivity to interpersonal relationships.

Li pointed out younger generations are facing higher levels of depression than previous generations.

It is less clear to me, and probably less well-studied, as to why we have such a high incidence of depression in our Gen Z and millennial or 18- 35-year-old cohort as compared to prior generations, said Li.

He said his hypothesis is that young adults see a future that is less bright.

[They] are more likely to be burdened by heavy debt, face an increasing number of existential crises such as global warming, and a host of other factors, said Li.

Among survey findings was that race and where you live was associated with increased risk for obesity, the leading chronic health condition identified.

According to the CDC report, roughly one-third of young adults living in rural areas were obese, but only about one-fourth of city residents were affected.

Black Americans were also more likely to live with obesity than whites; with almost 34 percent affected, compared to nearly 24 percent of whites.

Dr. Louis Morledge, an internist at Lenox Hill Hospital in New York, pointed out a sedentary lifestyle can increase the risk of obesity. According to Morledge, the COVID-19 pandemic shifted activities from outside to inside, and affected lifestyle choices for people.

Many have spent the past two years indoors, in front of a computer, he said. And this age group has experienced the most glaring shift from experiencing social engagement in a variety of educational and professional settings to instead being stationary and alone.

Morledge said long-term health risks for obesity include hypertension, high cholesterol, diabetes, osteoarthritis, sleep apnea, and some cancers.

Fortunately, chronic conditions like obesity, high blood pressure, and cholesterol, which accounts for about of a quarter of our young adults, is modifiable with lifestyle changes, said Li.

He explained said it may be possible to reverse some of these conditions by making healthy eating choices, eating smaller portions of food, and increasing our physical activity levels.

Li warned that the life-long impact of chronic health conditions on this age group is staggering.

In addition to lifestyle factors that can help lessen the impact of these conditions, there are medications that can help keep cholesterol and high blood pressure in check.

The CDC recently reported that 2019 data shows over half of 18- to 34-year-olds live with at least one chronic health condition.

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Depression, Diabetes, Hypertension: 1 in 2 Young People in the U.S. Have a Chronic Condition - Healthline

Introduction

Medication adherence is the process by which people take their medications as prescribed with respect to the timing, dose, and frequency.1 It has been described as comprising three different phases or behaviors including initiation, which occurs when an individual takes their first dose of prescribed medication; implementation, or the extent to which an individuals dosing corresponds to the prescribed regimen from initiation to the final dose; and discontinuation, which occurs when an individual decides, for whatever reason, to stop taking the prescribed medication.2

Optimization of treatment outcomes in people with type 2 diabetes (T2D) requires, firstly, that therapy is initiated in a timely manner, since therapeutic inertia can result in a prolonged period during which blood glucose levels are not at target leading to negative clinical, economic, and health-related quality of life (HRQoL) outcomes.35 Once on an appropriate medication, it is important that patients follow the dosing recommendations of the healthcare team. Substantial evidence suggests that not using medication as advised is associated with suboptimal glycemic control, increased use of healthcare resources, and higher costs.6,7 Finally, it is crucial that a person with diabetes (PwD) stays on (persists with) an effective treatment and does not discontinue prematurely. However, it has frequently been reported and extensively reviewed that discontinuation rates are high among people with T2D, and this has a negative impact on clinical and economic outcomes.8,9

The treatment journey of PwD can be influenced by multiple different factors at initiation of therapy, during treatment itself, and at the point of discontinuation.6,10,11 These factors may include patient, therapy, healthcare system, economic, support system, and psychosocial factors. Since delivery of patient-centered care that respects individual preferences and barriers is key in improving treatment outcomes,12,13 it is of interest to understand which factors are particularly important to PwD; prescribing therapies that better meet their needs and expectations may facilitate medication adherence and persistence.

While there is considerable evidence regarding patient preferences and beliefs with respect to T2D therapies, the explicit linking of these with the different phases of medication-taking behaviors from the perspective of the PwD is less well studied. The aim of this review was, therefore, to identify studies reporting data that make the link between PwD-expressed treatment-related attributes and their impact on medication-taking behaviors to produce a resource that consolidates evidence on this topic.

This review was conducted according to a protocol designed to limit the impact of reviewer bias, promote transparency and accountability, and improve the likelihood of accurate data extraction by describing the proposed approach, objectives, search strategy, study selection criteria, methods for data extraction and synthesis, and outcomes of interest that were specified a priori.

Searches were undertaken in the EMBASE and PubMed bibliographic databases. In addition, abstracts presented at the most recent (2020 or 2021) congresses of the Professional Society for Health Economics and Outcomes Research (ISPOR, Global and European), the European Association for the Study of Diabetes (EASD), and the American Diabetes Association (ADA) were searched electronically on EMBASE or by hand if not indexed therein. A hand-search of the reference lists of eligible studies identified in the main review was also conducted.

Searches were run in May 2021 and included studies published since 1 January 2005. The main structure of the search consisted of seven concepts combined as follows: T2D AND medication-taking behaviors AND (non-specific drug therapy OR non-specific diabetes therapy OR specific named drug therapy groups) AND study types of interest AND PwD. The detailed syntax that was developed for each database to capture these general concepts is provided in the online Supplementary Materials Tables S1 and S2.

Studies published in English were eligible for inclusion if they reported data regarding the link (or reported that there was no link) between PwD-expressed diabetes treatment attributes and medication-taking behaviors (eg initiation, taking, switching/changing, or discontinuation of a particular therapy) that came directly from adults (18 years) with T2D and not from a healthcare professional (HCP) or another individual. Studies could have incorporated open-ended questions or included a list of reasons for certain medication-taking behaviors that were directly put to PwD.

Interventions were where there were any pharmacologic therapy for T2D; however, a decision was made following full-text review to exclude studies solely focused on treatment with insulin, although if insulin was one of several treatments included in a study, the findings were summarized. The variety of insulin formulations, regimens, and methods of delivery described in studies across a wide range of studies from both developed and less developed countries introduced a range of concepts, several of which had implications beyond the treatment itself. Furthermore, many of these studies did not reflect therapies currently used in clinical practice as the search included publications from 2005 onwards. Thus, the decision to exclude insulin was made to keep the review focused but does suggest a similar exploration of the published literature on peoples attitudes to insulin use is warranted.

Eligible study types included those based on one-to-one or focus group interviews, surveys, other questionnaire-based studies, qualitative research, and patient diary studies. These types of study could be embedded in a broader investigation but were required to be a standalone component focused on the topic of interest of a relevant design. Discrete-choice experiments (DCE) or patient preference/satisfaction studies were only included if the link between treatment characteristics and medication-taking behaviors was explicitly explored.

Two independent reviewers assessed the results obtained by the search strategy. Initial screening involved a broad review of the title and/or abstract of results to identify studies meeting or possibly meeting eligibility criteria. This was followed by full-text review of studies identified at screening. Records excluded at this stage were assigned an exclusion code and reported in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. Discrepancies between reviewers were discussed until consensus was reached.

Data extraction was performed on a standardized data extraction form by two reviewers, with quality checking by a third. Variables extracted included study population, interventions, study type and methods of data collection, medication-taking behaviors impacted, and treatment attributes evaluated.

After de-duplication, the search identified 6464 records, of which 6347 were excluded at the first stage of review. Of 117 full-text publications, 101 were excluded (Figure 1) mainly because they did not report on medication-taking behaviors associated with treatment-related attributes. No additional studies were identified by searching relevant congress abstracts or citation searching of included publications. Thus, the final review included 16 studies.

Figure 1 Study selection.

Abbreviation: T2D, type 2 diabetes.

Notes: aOther reasons included reporting of psychologic rather than treatment-related factors in relation to medication-taking behaviors; study focused on preferences rather than adherence; literature review; not on prespecified medication-taking behaviors.

An overview of the characteristics of the 16 included studies is provided in Table 1. Studies were conducted across a wide range of geographies, with the USA being the most represented country (n = 8 studies, including three multinational). Medication-taking behaviors evaluated included initiation of T2D medication (n = 2 studies), on-treatment adherence (n = 9), and discontinuation (n = 2). One study reported on both initiation and adherence behaviors, and two reported on reasons for both adherence and discontinuation. It became clear upon reviewing the evidence base that several of the studies evaluated the impact of treatment attributes, not on behaviors per se, but rather on indicators of initiation, adherence, and discontinuation. Thus, studies were also categorized according to whether PwD linked attributes to actual behavior (n = 10 studies) or to how they believe they would behave (n = 6 studies). These assessments have been based on our best understanding of how the studies were conducted.

Both oral and injectable T2D medications were evaluated across studies (Table 1). As per search inclusion criteria, all study populations comprised people with T2D, and sample sizes ranged from 22 to 2173 (Table 1). Mean age of participants was generally similar across studies (55.166.0 years) and in most, study populations comprised ~4560% men.

Of the included studies, 10 employed structured questionnaires (including one with mixed qualitative and quantitative methods), four were qualitative studies, and two were DCEs. Study participants were queried regarding the attributes influencing their actual or hypothetical behaviors using both closed-ended questions (n = 9 studies), wherein they were provided with a list of possible alternatives from which they picked those relevant to their decision-making process, and open-ended questions where participants were free to voice any attributes that affected their decision-making (n = 7). Questionnaires that included closed-ended questions/lists were often generated from information gained by literature review and from clinical trials or product labels, then refined through pretesting in relevant patient populations.

In studies using a structured questionnaire, most reported the proportion of PwD indicating that a particular attribute impacted indicators of medication taking; two of these included Likert scales to determine the impact level of different attributes.14,15

Formal measures of adherence were employed in four studies: these included the Morisky Medication Adherence Scale (MMAS 4- or 8-item),14,16 5-item Medication Adherence Report Scale (MARS-5),17 and the Adherence to Refills and Medications Scale for Diabetes score.18 The remaining adherence studies did not employ formal measures: participants were simply asked questions regarding how their adherence may be affected by different factors. Most studies did not include any statistical analyses of the data.

Several different treatment-related attributes were reported across studies that, in the opinion of the PwD, had an impact on indicators of medication taking (Table 1). These included glycemic efficacy (n = 9), weight change (n = 9), dose frequency (n = 9), hypoglycemia (n = 8), gastrointestinal (GI) adverse events (AEs) (n = 8), dose complexity (n = 6), route of administration (n = 3), and cardiovascular (CV) risk (n = 1). Some attributes were described under umbrella terms. Where this was the case, these attributes are summarized as part of the review, provided they were specifically noted by the study authors as being included in a broader category. With respect to AEs, these were only summarized if their nature (eg GI, hypoglycemia) was specifically described.

Overviews of the key findings according to attributes are provided in Tables 24.

Table 2 Overview of Main Findings from Studies That Evaluated Glycemic Efficacy as a Treatment-Related Attribute

Table 3 Overview of Main Findings from Studies That Evaluated Adverse Events of T2D Medications as a Treatment-Related Attribute

Table 4 Overview of Main Findings from Studies That Evaluated Dosing Characteristics (Frequency, Complexity, and Route) of T2D Medications as a Treatment-Related Attribute

Nine studies included evidence that the glycemic efficacy of T2D medications impacted medication-taking behaviors from the point of view of PwD. One study considered impact on initiation of treatment,15 one on initiation and adherence,19 five on adherence,17,18,2022 and two on discontinuation of therapy.23,24 An overview of the key results regarding glycemic efficacy as an attribute influencing behavior indicators is provided in Table 2.

Two studies indicated that when PwD are thinking about initiating a new treatment, efficacy is a key consideration.15,19 For example, in a US online survey that provided a list of positive or negative characteristics of a once-weekly (QW) injectable, over 40% of study participants responded on a 5-point Likert scale that they would hypothetically be very or extremely likely to initiate such a treatment if it could help reduce blood sugar spikes.15

In qualitative studies in which PwD reported on their actual experiences with medications, blood glucose lowering was identified as a major motivation for medication adherence.18,20 Similarly, in a cross-sectional survey, effective reduction of elevated blood sugar levels was chosen by PwD already on an oral antidiabetes drug (OADs) as a motivation to continue using prescription medication.19 An exploratory analysis of a new patient-reported outcome measure embedded in a randomized controlled trial also reported that improved glucose levels/control was the most common reason for being willing to continue treatment with a dual glucagon-like peptide 1 (GLP-1R)/glucagon receptor (GCGR) agonist if it were offered.22 However, in a UK general practice survey it was demonstrated that while a high proportion of study participants agreed or strongly agreed with the beliefs that taking OADs regularly would keep their blood sugar under control or that regular OADs would keep their diabetes under control, these beliefs were not found to be significantly correlated with self-reported adherence as measured using MARS-5.17 In addition, a DCE that directly linked preferences for treatment attributes to the likelihood of people with T2D missing or skipping doses of a hypothetical OAD also found that the attribute of glucose control had no impact on this indicator of adherence.21

PwD who had discontinued medication in the previous 6 months commonly cited drug efficacy issues including no perceived positive benefit and inadequate blood glucose control as reasons for which they stopped treatment of OADs and injectable medications (Table 2).23,24

Weight change was evaluated in nine studies as an attribute influencing medication-taking indicators. Two studies evaluated its potential impact on indicators of initiation,15,25 four on indicators of adherence,17,2022 two on discontinuation,23,24 and one on both adherence and discontinuation.26 An overview of key findings from studies reporting on weight change is provided in Table 3.

Studies indicate that AEs associated with T2D medications such as weight gain could be an important consideration for PwD at initiation of therapy. For example, over half of PwD included in a US cross-sectional survey reported that hypothetically they would be very or extremely willing to take a QW injectable if it could help avoid weight gain or promote weight loss.15

In several studies, PwD also indicated that weight gain was a barrier17,21,22,26 or that weight loss was a motivator20,22 to treatment adherence. Indeed, in a UK cross-sectional survey, the belief that taking OADs regularly would lead to weight gain was the only belief evaluated that was correlated with lower adherence (MARS-5) (Spearmans r = 0.25; p < 0.01) although it was not correlated with intention to take medication (r = 0.12).17 Similarly, in a DCE, Hauber et al21 demonstrated that medication-related weight gain was one of the only treatment attributes of a hypothetical OAD that was significantly associated with a higher likelihood of PwD missing or skipping doses (p-value not provided) (Table 3).

Discontinuation of T2D medications may also be influenced by weight changes, as reported in three cross-sectional surveys.23,24,26 For example, in a study that used data from the Adelphi Diabetes Disease Specific Programme (DSP), two of the reasons for which previous GLP-1 RA users said they had stopped therapy in the prior 6 months were that treatment had not helped them to lose weight or had caused weight gain.24

Nine studies included an evaluation of GI AEs as a treatment-related attribute impacting medication-taking indicators. Attributes were predefined in five of these studies, while in the remaining four, participants indicated attributes of importance themselves. Table 3 provides an overview of study results with respect to GI AEs.

No studies were identified that linked GI AEs with the decision to initiate T2D medication, but several evaluated the impact of GI AEs on adherence indicators (n = 5). For example, in two qualitative studies, PwD stated that in their experience GI AEs had been barriers to use of both oral and injectable medications, with some individuals indicating that they had reduced doses to avoid these AEs.18,20 Another two studies, however, failed to demonstrate that GI AEs impacted adherence indicators. Farmer et alreported that, while nearly one-third of PwD agreed or strongly agreed with the belief that taking OADs regularly would cause unpleasant side effects such as feeling sick or bloated, the belief was not significantly correlated with either medication adherence or intention to take medication (Spearmans r = 0.08 and 0.03, respectively).17 It was also demonstrated in a DCE that the attribute of mild stomach upset had no effect on the likelihood of PwD missing or skipping doses of a hypothetical OAD.21

Three studies demonstrated that GI AEs had been the reason for discontinuation of medication in 1864% of people, depending on the study (Table 3).23,24,26 Gater et al reported that nausea and vomiting were the most common reasons for which PwD who had received a dual GCG/GLP-1 RA in an RCT would be unwilling to continue treatment with the same medication.22

Eight studies provided evidence that hypoglycemia may influence the way people with T2D take their medications. The impact of hypoglycemia on indicators of initiation was evaluated in two studies,15,25 on adherence indicators in four,16,18,21,26 and on discontinuation in three.23,24,26 Table 3 provides a summary of the main findings from these studies.

Hypoglycemia was frequently included under a broader category rather than reported as a single attribute. For example, one study that retrospectively evaluated the concerns that PwD currently taking injectable therapy had upon initiation of that treatment found that over one-third of participants were worried about AEs of injection therapy, and this included both hypoglycemia and weight gain.25 However, in another study, PwD indicated that having a lower risk of hypoglycemia specifically would hypothetically make them very or extremely willing to take a QW injectable medication (Table 3).15

With respect to indicators of adherence, three studies included hypoglycemia as an example under a broader category of attributes that were viewed by PwD as a barrier to adherence.16,18,26 However, in their DCE, Hauber et al21 found that hypoglycemia only negatively impacted adherence with a hypothetical OAD if it occurred more than twice per month.21 In the two discontinuation studies that specifically included hypoglycemia as an attribute, only a relatively small proportion of PwD indicated that it had been a reason for their stopping therapy.23,24

A single study evaluated the impact of CV risk on medication taking.21 Along with weight gain, CV risk was the only other attribute studied in the DCE by Hauber et al21 that had a significantly negative impact on the likelihood of PwD missing or skipping doses of a hypothetical OAD. A 1% increase in the risk of heart attack resulted in a 16.5% (95% CI 16.1, 17.0) reduction in likely OAD adherence.

Dose frequency was reported as a treatment attribute impacting medication-taking indicators in nine studies. The influence of dose frequency on indicators of initiation was evaluated in two studies, on adherence indicators in six studies, and on discontinuation in two studies. Key results from each of these studies are detailed in Table 4.

When considering initiation of medication, treatment-nave PwD indicated that they had concerns regarding the inconvenience of frequent dosing schedules and that less frequent schedules were a motivating factor.19,25 Similarly, dose frequency was also cited as a barrier to medication adherence in both qualitative studies27,28 and cross-sectional surveys.16,19,26 In addition, a DCE that linked treatment attributes of a hypothetical OAD to an indicator of medication adherence demonstrated that people with T2D were increasingly likely to miss or skip OAD doses as dose frequency and pill burden increased.29 Two cross-sectional surveys also provided evidence that the inconvenience of frequent injections had led to some PwD discontinuing medication.24,26

Regimen complexity was described in a variety of ways across the six studies in which it was evaluated as a feature influencing medication taking. Features such as convenience, ability to reduce doses of other glucose-lowering agents, and complexity of administration or medication container were all assumed to reflect regimen complexity in some way. Two studies reported on the influence of complexity on indicators of treatment initiation15,19 and five on adherence indicators.14,16,18,19,29 The main findings from these studies are presented in Table 4.

Convenience, ease of use, and reduction in other medications were all cited as important motivations for initiating treatment.15,19 For example, Polonsky et al reported that a substantial proportion of PwD agreed or strongly agreed that they would be hypothetically willing to take a QW injectable with such features (Table 4).15 Studies evaluating the impact of treatment attributes on indicators of adherence reported similar findings.14,16,18,29 For example, in a qualitative study, PwD stated that complexity of regimen comprising type, frequency, quantity, and size of medication had negatively impacted their adherence to medication.18 Furthermore, in a prospective observational study, participants cited complexity of medication regimen as a cause of self-reported non-adherence.16 Two more studies revealed that adherence was hypothetically impeded by regimens of greater complexity.14,29

Findings from three studies indicated that route of administration could have an impact on how PwD take their T2D medications (Table 4).

No studies were identified that explored the impact of route of administration on the decision to initiate medication. However, in two qualitative studies PwD reported that in their experience, injection was a barrier to adherence.27,28 In addition, nearly 40% of PwD who discontinued GLP-1 RAs indicated that they did so because of a preference for the oral over injectable route of administration.24

This review identified a range of different treatment-related attributes that people with T2D directly or indirectly implicated in medication taking across all parts of the treatment journey. These findings are both consistent with and add to the broader evidence base on the aspects of treatment that people with T2D value.

Treatment efficacy emerged as an important consideration for PwD in deciding whether to initiate a medication and as a motivating factor to take their medication; studies also suggested that inadequate glycemic efficacy may cause PwD to discontinue therapy. Similar findings have been more recently reported in a study that, while meeting eligibility criteria for this review, was published outside of the date cut-off. In a non-interventional, cross-sectional qualitative study in people with T2D who had received treatment with 1 GLP-1 RA, improvements in, or control of, blood glucose were cited as facilitators to adherence in 50.0% and 18.8% of study participants who continued treatment with a GLP-1 RA, respectively.30 Furthermore, among participants who discontinued treatment, 25% did so due to no improvement and 5% because of worsening of blood glucose. Taken together, the findings on direct elicitation of PwD opinions on reasons for medication-taking behaviors align with other published studies that employed formal patient preference assessments to demonstrate that glycemic management is an important driver of patient preference.31,32

Weight was also shown to be a factor that may influence the decision to initiate, continue treatment with, or discontinue a T2D medication. These observations are largely consistent with other evidence demonstrating an association between weight loss and better medication adherence3335 or lower rates of discontinuation in people with T2D.36,37 It should, however, be noted that although the relationship between weight and discontinuation appears to be relatively straightforward, this might not always be the case with weight and adherence, as suggested in a recent narrative review.38 Formal patient preference assessments have also demonstrated that people with T2D value therapies with weight-loss properties.3941 In fact, in a Spanish DCE (willingness-to-pay approach), it was reported that avoiding weight gain of 3 kg per 6 months was the most highly valued treatment attribute of oral or injectable therapies.42 Similarly, in another study, avoiding a 5-kg weight gain was 1.52.3-fold more important than achieving moderate glycemic control among people with T2D from Germany and Sweden.43

The weight profile of any given medication is likely to be an important attribute to people with T2D: excess weight has been linked to several negative sequelae, including worse glycemic management44 and increased risk of microvascular and macrovascular complications.45,46 Indeed, a recent expert opinion review by Lingvay et al reinforces the clinical importance of weight reduction for people with T2D and confirms that weight loss should be a primary approach in many individuals.47 Furthermore, individuals with T2D and higher body weight may have worse HRQoL.4850 In addition, weight gain has been reported to be significantly associated with lower rates of overall treatment satisfaction.48 This observation is consistent with findings from the study by Polonsky et al included in the current review, where it was demonstrated that people with T2D and low treatment satisfaction on their current OAD medication were more willing to initiate treatment with a hypothetical QW injectable therapy if it contributed to weight loss, compared with study participants who were highly satisfied with their current medication.15

GI AEs are commonly experienced by PwD treated with T2D medications,51 and we identified several studies suggesting that these symptoms influence medication adherence and discontinuation indicators. These findings are again consistent with those from formal preference assessments wherein people with T2D place greater value on treatments with lower rates of GI AEs.31,32 Indeed, the DCE by Hauber et al21 included here demonstrated that people with T2D preferred OADs that were not associated with stomach upset (although this was less important to them than other issues such as glycemic control and weight gain).

In many of the studies that reported on hypoglycemia and its impact on medication-taking indicators identified in this review, hypoglycemia was included in a broader category of attributes.16,25,26 Nevertheless, where it was specifically described, symptoms of hypoglycemia appeared to affect the likelihood of treatment initiation,15 adherence,18,21 and discontinuation.23,24 The negative impact of hypoglycemia on medication-taking indicators might be explained by its association with poorer HRQoL: hypoglycemia has been reported to detrimentally affect various aspects of well-being and functioning, as well as relationships and work performance.52,53 In addition, people with T2D who experience hypoglycemia report worse treatment satisfaction compared with individuals without hypoglycemia.54 The presence of symptoms has also been shown to be associated with increased rates of fear of hypoglycemia (FOH),54 which can itself have a negative impact on HRQoL, particularly with respect to psychosocial functioning, daily living, and sleep quality.55

The studies identified also indicated that dose frequency and regimen complexity affected indicators of medication-taking. This is consistent with other evidence clearly demonstrating that people with T2D prefer medications with a lower dosing frequency31,32,56 and that less frequent dosing is associated with better medication adherence.57,58 Similarly, treatment adherence has been reported to decrease as the regimen complexity increases.59

The current review is subject to some limitations. Even though a robust and reproducible protocol was used to identify studies, relevant research may have been published that were missed and some could have been published outside the date cut-off. In addition, we employed a two-stage approach for reviewing the search results such that at the first stage, the decision to include or exclude a publication is made based on review of the title/abstract and not on a comprehensive review of the full text of the article. It is, therefore, possible that potentially relevant studies were excluded at this stage due to lack of detail in the title or abstract.

After full-text review of potentially eligible studies, a decision was made to exclude those focused on insulin for reasons outlined in the methods. Medication-taking indicators in insulin-treated individuals are influenced by a broad range of concepts, many of which were outside of the scope of this review. However, insulin is an important treatment option for people with T2D and there is evidence that insulin treatment-related attributes do impact medication-taking behaviors in these individuals, suggesting that an in-depth review of these relationships would be of interest.6062

Another limitation is that it was often difficult to interpret exactly what attributes were being referred to within each study, which was particularly true of qualitative studies. For example, an individual mentioning regimen complexity could have been referring to a number of aspects including the complexity of the regimen itself, the device, or method of administration; interference with daily activities; or psychosocial issues, such as stigma. Similarly, attributes were sometimes described under what appeared to be an umbrella term wherein multiple factors were included under a broader category. This was particularly true for the attributes of GI AEs and hypoglycemia. For example, in the study by Chen et al, one of the attributes put to study participants was worries about AEs of injection therapy, such as hypoglycemia and weight gain. These results are included in the review because specific issues were explicitly mentioned, but individuals agreeing with this catch-all statement will obviously be experiencing a range of AEs that are not restricted to the examples provided. Similarly, in their study, Spain et al included an attribute of burden/inconvenience, which again could comprise a broad range of issues. This category was described in the study methods as including aspects of treatment such as dose frequency, and so was included in the current review.

Six of the included studies also evaluated AEs as a general treatment attribute without specifying their nature. In these instances, data were not summarized in the review as we were only interested in specific AEs such as those associated with GI function, hypoglycemia, and weight gain. Clearly, AEs are an issue in general and are a major influence on medication-taking behaviors, but the evidence base would have been richer had the nature of AEs been more explicit.

One major limitation of the evidence base is that we cannot readily compare or combine findings across studies or across different attributes, and it is not possible to reach any conclusions regarding which, if any, attribute has more influence over another on medication-taking behaviors. This is because studies varied considerably with respect to populations, drugs evaluated, stage of treatment journey or disease, and designs. Furthermore, study numbers reporting findings for specific attributes at different parts of the treatment journey were often low.

Another important consideration is that the attributes that influence behaviors may vary according to different PwD characteristics, attitudes, and previous treatment or disease experiences. For example, with respect to weight, there is evidence that, in general, women are more dissatisfied with their weight63 and so may value weight loss more highly than men. In addition, people with overweight or obesity may be more likely to respond that weight loss is an important treatment attribute influencing their behavior, while those of a healthier weight are less likely to do so. Indeed, in the DCE by Hauber et al,21 it was found that participants who had experienced weight gain with their current medication would be more likely to indicate that medication attributes would negatively affect adherence. Polonsky et al also demonstrated that users of injectable medications with worse HRQoL were more willing to initiate a hypothetical QW injectable if it helped avoid weight gain, reduce blood glucose spikes, or lower risk of hypoglycemia compared with individuals who had better HRQoL.15 Users of OADs who viewed blood glucose control as more problematic were also more willing to initiate a new QW injectable therapy compared with individuals with whose glycemia was well managed.15 It could also be that previous experience of hypoglycemia or FOH may lead to the behaviors of people with T2D being more heavily influenced by hypoglycemia as a treatment-related attribute. For example, it has been reported that insulin dose omission or mistiming occur more frequently among people with T2D who have previously experienced hypoglycemia.64

The different methods by which participants were presented attributes could also have influenced study findings. In some cases, studies used questionnaires that presented a list of attributes from which participants picked those that were relevant to them with respect to any medication-taking behavior. Often, the list of attributes was informed by previous qualitative research or literature searches, but it is possible these lists may not have included all factors important or relevant to the chosen study population. Indeed, the lists of attributes presented, or the description or wording used, varied from one study to another. It is possible that the different phrasing across studies, wherein similar attributes were framed as concerns/barriers to medication taking in some studies but as motivations in others, may have influenced participants perceptions (eg weight gain or loss are flip sides of the same attribute, but people with T2D may feel more strongly about one over the other).

Finally, it should be noted that many of the studies do not evaluate the impact of treatment-related attributes on medication-taking behaviors directly and rather use proxy measures such as, for example, willingness to take a medication or the likelihood of missing or skipping doses.15,21 Furthermore, in several studies, participants indicate that treatment-related attributes influence a hypothetical behavior; for example, they might suggest that an attribute is a motivator or barrier to adherence but the prospective association between that attribute and actual adherence at a later time point is not measured. Studies designed to prospectively evaluate this relationship are, therefore, warranted.

This review evaluated research in which people with T2D directly indicated the treatment-related attributes associated with their decision to initiate a medication, to stay on a medication, or to discontinue treatment. The included studies represent a consolidation of research on this topic and are a useful resource. Several treatment-related attributes including glycemic efficacy, effect on weight, hypoglycemia, GI AEs, dose frequency, and regimen complexity all appeared to play a role in how people with T2D took their medication at different points of the treatment journey.

The findings from this review may contribute to a greater understanding of the attributes that impact behaviors and could assist HCPs and people with T2D to make more informed treatment decisions. Each PwD will likely have a unique set of beliefs and attitudes towards different medications, and it is advisable that HCPs should routinely inquire about such perceptions and take them into account when making any treatment recommendations such that medications are chosen that individual PwD are comfortable initiating and persisting with for longer periods of time and that show efficacy in achieving standard-of-care treatment outcomes. In addition, the insights from this review may help to develop strategies and interventions for the support of medication taking that better meet the needs of PwD.

Data sharing is not applicable to this article as, since this is a review, no datasets were generated or analyzed.

This article is a review and analysis of previously published studies and does not include any new studies on human or animal subjects performed by any of the authors.

The authors thank Mick Arber (York Health Economic Consortium) for assistance with the literature search, and Alison Terry for assistance with editing the manuscript.

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

This study (including development of study design, conduct of the research, and medical writing services) was funded by Eli Lilly and Company (Indianapolis, IN, USA).

Tracy J Sims and Kristina S Boye are employees and minor shareholders of Eli Lilly and Company. Dr Susan Robinson reports grants from Eli Lilly and Company, during the conduct of the study. Tessa Kennedy-Martin and Susan Robinson are employees of KMHO, who received funding from Eli Lilly for time spent conducting this research and fees for other project work undertaken for Lilly outside the submitted work. The authors report no other conflicts of interest in this work.

1. ISPOR, The Professional Society for Health Economics and Outcomes Research. Medication compliance and persistence: terminology and definitions (2019). Available from: https://www.ispor.org/heor-resources/good-practices/article/medication-compliance-and-persistence-terminology-and-definitions/ (accessed 25 July 2022).

2. Vrijens B, De Geest S, Hughes DA, et al. A new taxonomy for describing and defining adherence to medications. Br J Clin Pharmacol. 2012;73:691705. doi:10.1111/j.1365-2125.2012.04167.x

3. Khunti K, Millar-Jones D. Clinical inertia to insulin initiation and intensification in the UK: a focused literature review. Prim Care Diabetes. 2017;11:312. doi:10.1016/j.pcd.2016.09.003

4. Khunti S, Khunti K, Seidu S. Therapeutic inertia in type 2 diabetes: prevalence, causes, consequences and methods to overcome inertia. Ther Adv Endocrinol Metab. 2019;10:111. doi:10.1177/2042018819844694

5. Pantalone KM, Misra-Hebert AD, Hobbs TM, et al. Clinical inertia in type 2 diabetes management: evidence from a large, real-world data set. Diabetes Care. 2018;41(7):e113e114. doi:10.2337/dc18-0116

6. Polonsky WH, Henry RR. Poor medication adherence in type 2 diabetes: recognizing the scope of the problem and its key contributors. Patient Pref Adherence. 2016;10:12991307. doi:10.2147/PPA.S106821

7. Kennedy-Martin T, Boye KS, Peng X. Cost of medication adherence and persistence in type 2 diabetes mellitus: a literature review. Patient Prefer Adherence. 2017;11:11031117. doi:10.2147/PPA.S136639

8. Guerci B, Chanan N, Kaur S, et al. Lack of treatment persistence and treatment nonadherence as barriers to glycaemic control in patients with type 2 diabetes. Diabetes Ther. 2019;10:437449. doi:10.1007/s13300-019-0590-x

9. Lee DSU, Lee H. Adherence and persistence rates of major antidiabetic medications: a review. Diabetol Metab Syndr. 2022;14(1):12. doi:10.1186/s13098-022-00785-1

10. Khunti K, Gomes MB, Pocock S, et al. Therapeutic inertia in the treatment of hyperglycaemia in patients with type 2 diabetes: a systematic review. Diabetes Obes Metab. 2018;20:427437.

11. Shields BM, Hattersley AT, Farmer AJ. Identifying routine clinical predictors of non-adherence to second-line therapies in type 2 diabetes: a retrospective cohort analysis in a large primary care database. Diabetes Obes Metab. 2020;22:5965. doi:10.1111/dom.13865

12. Davies MJ, DAlessio DA, Fradkin J, et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2018;41:26692701. doi:10.2337/dci18-0033

13. Draznin B, Aroda VR, Bakris G; American Diabetes Association (ADA). Facilitating behavior change and well-being to improve health outcomes. Standards of medical care in diabetes 2022. Diabetes Care. 2022;45(Suppl 1):S60S82. doi:10.2337/dc22-S005

14. Barba EL, de Miguel MR, Hernndez-Mijares A, et al. Medication adherence and persistence in type 2 diabetes mellitus: perspectives of patients, physicians and pharmacists on the Spanish health care system. Patient Pref Adherence. 2017;11:707718. doi:10.2147/PPA.S122556

15. Polonsky WH, Fisher L, Hessler D, Bruhn D, Best JH. Patient perspectives on once-weekly medications for diabetes. Diabetes Obes Metab. 2011;13:144149. doi:10.1111/j.1463-1326.2010.01327.x

16. Sajith M, Pankaj M, Pawar A, Modi A, Sumariya R. Medication adherence to antidiabetic therapy in patients with type 2 diabetes mellitus. Int J Pharm Pharma Sci. 2014;6(Suppl 2):564570.

17. Farmer A, Kinmonth AL, Sutton S. Measuring beliefs about taking hypoglycaemic medication among people with Type 2 diabetes. Diabetic Med. 2006;23:265270. doi:10.1111/j.1464-5491.2005.01778.x

18. Huang YM, Pecanac KE, Shiyanbola OO. Why am I not taking medications? Barriers and facilitators of diabetes medication adherence across different health literacy levels. Qual Health Res. 2020;30:23312342. doi:10.1177/1049732320945296

19. Kubo T, Okuyama K, Zhao X, Singh SS, Tokita S. Factors associated with reluctance to initiate or continue oral antihyperglycemic agent (OAHA) treatments in type 2 diabetes mellitus patients in Japan: an observational patient-reported study. Diabetes Met Syndr Clin Res Rev. 2019;13:12011207. doi:10.1016/j.dsx.2019.01.034

20. Flory JH, Keating S, Guelce D, Mushlin AI. Overcoming barriers to the use of metformin: patient and provider perspectives. Patient Pref Adherence. 2019;13:14331441. doi:10.2147/PPA.S211614

21. Hauber AB, Mohamed AF, Johnson FR, Falvey H. Treatment preferences and medication adherence of people with type 2 diabetes using oral glucose-lowering agents. Diabetic Med. 2009;26:416424. doi:10.1111/j.1464-5491.2009.02696.x

22. Gater A, Reaney M, Findley A, et al. Development and first use of the Patients Qualitative Assessment of Treatment (PQAT) questionnaire in type 2 diabetes mellitus to explore individualised benefitharm of drugs received during clinical studies. Drug Saf. 2020;43:119134. doi:10.1007/s40264-019-00877-4

23. de Climens AR, Pain E, Boss A, Shaunik A. Understanding reasons for treatment discontinuation, attitudes and education needs among people who discontinue type 2 diabetes treatment: results from an online patient survey in the USA and UK. Diabetes Ther. 2020;11:18731881. doi:10.1007/s13300-020-00843-9

24. Sikirica MV, Martin AA, Wood R, et al. Reasons for discontinuation of GLP1 receptor agonists: data from a real-world cross-sectional survey of physicians and their patients with type 2 diabetes. Diabetes Met Syndr Obesity. 2017;10:403412. doi:10.2147/DMSO.S141235

25. Chen T, Zhou L, Wang K, et al. The unmet medical needs of current injectable antidiabetic therapies in China: patient and health care professional perspectives. Clin Ther. 2020;42:15491563. doi:10.1016/j.clinthera.2020.06.006

26. Spain CV, Wright JJ, Hahn RM, Wivel A, Martin AA. Self-reported barriers to adherence and persistence to treatment with injectable medications for type 2 diabetes. Clin Ther. 2016;38:16531664. doi:10.1016/j.clinthera.2016.05.009

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29. Hauber AB, Han S, Yang JC, et al. Effect of pill burden on dosing preferences, willingness to pay, and likely adherence among patients with type 2 diabetes. Patient Pref Adherence. 2013;7:937949. doi:10.2147/PPA.S43465

The rest is here:
Type-2 Diabetes and Medication-Taking Behaviour | PPA - Dove Medical Press

1. In this randomized controlled trial, a comprehensive telehealth intervention was linked to a greater improvement in HbA1c% level at 12 months follow up in patients with persistently poorly controlled diabetes (PPDM) when compared to a simpler telehealth model.

2. In patients with PPDM, comprehensive telehealth intervention when compared to a simpler telehealth model was associated with greater improvement in diabetes distress, diabetes self-care, and self-efficacy, but was no associated with greater improvement in depressive symptoms or BMI.

Evidence Rating Level: 1 (Excellent)

Persistently poorly controlled diabetes is defined as HbA1C greater than 8.5% despite receiving clinic-based type 2 diabetes (T2DM) care. Drivers of PPDM include unavailable blood glucose data, medication non-adherence, suboptimal diet or activity, complex medication regimen, and depression, which are factors that are challenging to address in clinic. Given that PPDM is associated with disproportionately negative outcomes, it is important to consider optimal care delivery for this patient population. Telehealth has been found previously to improve outcomes in PPDM, but there is inconsistent data with respect to multicomponent T2D interventions. This randomized controlled trial compared the effect of a comprehensive telehealth intervention and a simpler telehealth approach on patient HbA1c level. Patients were randomized to receive either a comprehensive telehealth intervention, which consisted of an extensive multidisciplinary team including multiple nurses, diabetes physicians, and psychiatry based on issues that needed to be addressed for individual patients, or a simpler telehealth approach with telemonitoring and care coordination. 200 patients recruited from December 2018 to January 2020 in 2 Veterans Affairs healthcare systems were randomized to either the comprehensive telehealth intervention or the simple telehealth group. The primary outcome was patient HbA1c level, and secondary outcomes included diabetes distress, diabetes self-care, self-efficacy, BMI, and depression symptoms. After one year of follow up, the estimated difference of HbA1c change between the two groups was -0.61%, which was statistically significant favouring comprehensive telehealth (P=.02). With respect to secondary outcomes, the comprehensive telehealth group resulted in greater improvement in diabetes distress, diabetes self-care, and self-efficacy, while there was no statistically significant difference in depressive symptoms at 12 months or BMI at 6 months following initiation of the interventions. With respect to limitations, the results may have limited generalizability to healthcare systems that do not have funding or capacity for telehealth, especially in the comprehensive interventions group. Overall, this article suggests that a comprehensive telehealth approach is associated with a greater improvement in HbA1c level. With the rise of telehealth following the COVID-19 pandemic, reassessment of care delivery systems will be important to determine optimal management of PPDM going forwards.

Click to read the study in JAMA Internal Medicine

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2022 2 Minute Medicine, Inc. All rights reserved. No works may be reproduced without expressed written consent from 2 Minute Medicine, Inc. Inquire about licensing here. No article should be construed as medical advice and is not intended as such by the authors or by 2 Minute Medicine, Inc.

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Comprehensive telehealth intervention effective for reducing HbA1c in poorly controlled diabetes - 2 Minute Medicine

SALINAS, Calif. (KION)- A local fitness trainer is helping the community stay in shape beyond just the gym.

With the Summertime here, many have a goal. Everyone wants the perfect body, whether it's reaching their goal weight or being swimsuit ready.For trainer Nimsi Pulido working out is more than just an activity to like what we see in the mirror.

"We're trying to have people feel comfortable with themselves not just physically but mentally," said Pulido.

In order to start the mental process, a healthy diet is step one. Nimsi herself works a lot with the local Latino community to educate them on proper eating habits.

According to the American Diabetes Association, people of Hispanic origin in the United States have the second highest rate of diagnosed diabetes.

Pulido, who has had a family history of diabetes, wants to make the community aware of how to stay healthy.

"I myself deal with diabetes issues at home with my family its both sides, and so I've seen my grandparents pass away because of that, and this is why I want to help the community."

Take trainee Marth Armenta. After having a family history of diabetes, she knew it was time to get in shape but did it for a specific reason.

"I need to do something for my health for my kids to show them if I could do it, they could do it too."

Another trainee Nimsi works closely with comes with a unique story. Ana Garcia, who is currently deaf, doesnt let her disability stop her inside or outside the gym.

"I offered three times per week training if I was to go and pick her up during my lunch, and she agreed, "said Pulido.

And the two have figured out an innovative system to communicate.

"We communicate through text messages. I dont know sign language, so it's very difficult for me, but I do the best I can"

With the ongoing pandemic, many people suffering from mental illness need a gateway to relief, which is just what the gym offers.

Gym member Crystal Hunt comes to the gym to help ease her mental health.

"I was diagnosed when I was nineteen with panic attacks and anxiety; it helps me with everything my anxiety it helps me sleep, it helps me just feel better,"said Hunt.

Its a twenty-four-seven weekly job for Nimsi, but the work never stops here.

"Im a single mom. I have two kids. Im trying to build to show them that if we could be better for them, we can be better for ourselves."

But for everyone she works with, she wants this common goal.

"fitness to me, not only does it mean health, but it means confidence in myself,"said Gallaga.

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Local fitness coach helps bridge the gap between diabetes and the Latino population KION546 - KION

There was no significant increase in the post-COVID pandemic monthly rate of incident diabetes in children and youth in Ontario, Canada, compared with the pre-pandemic rate, in new research.

This contrasts with findings from a US study and a German study, but this is "not the final word" about this possible association, lead author Rayzel Shulman, MD, admits, since the study may have been underpowered.

The population-based, cross-sectional study was published recently as a Research Letter in JAMA Open.

The researchers found a nonsignificant increase in the monthly rate of new diabetes during the first 18 months of the COVID-19 pandemic compared with the 3 prior years (relative risk [RR] 1.09, 95% CI).

This differs from a Morbidity and Mortality Weekly Report from the US Centers for Disease Control and Prevention in which COVID-19 infection was associated with a significant increase in new onset of diabetes in children during March 2020 through June 2021, "although some experts have criticized the study methods and conclusion validity," Shulman and colleagues write.

Another study, from Germany, reported a significant 1.15-fold increase in type 1 diabetes in children during the pandemic, they note.

The current study may have been underpowered and too small to show a significant association between COVID-19 and new diabetes, the researchers acknowledge.

And the 1.30 upper limit of the confidence interval shows that it "cannot rule out a possible 1.3-fold increase" in relative risk of a diagnosis of diabetes related to COVID, Shulman explained to Medscape Medical News.

It will be important to see how the rates have changed since September 2021 (the end of the current study), added Shulman, an adjunct scientist at the Institute for Clinical Evaluative Sciences (ICES) and a physician and scientist at the Hospital for Sick Children, in Toronto, Ontario, Canada.

The current study did find a decreased (delayed) rate of diagnosis of new diabetes during the first months of the pandemic when there were lockdowns, followed by a "catch-up" increase in rates later on, as has been reported earlier.

"Our study is definitely not the final word on this, Shulman summarized in a statement from ICES. "However, our findings call into question whether a direct association between COVID-19 and new-onset diabetes in children exists."

The researchers analyzed health administrative data from January 2017 to September 2021.

They identified 2,700,178 children and youth in Ontario who were under age 18 in 2021, who had a mean age of 9.2, and about half were girls.

Between November 2020 and April 2021, an estimated 3.3% of children in Ontario had a SARS-COV-2 infection.

New diagnoses of diabetes in this age group are mostly type 1 diabetes, based on previous studies.

The rate of incident diabetes was 15%-32% lower during the first 3 months of the pandemic, March-May 2020 (1.67-2.34 cases per 100,000), compared with the pre-pandemic monthly rate during 2017, 2018, and 2019 (2.54-2.59 cases per 100,000).

The rate of incident diabetes was 33% to 50% higher during February to July 2021 (3.48-4.18 cases per 100,000) compared with the pre-pandemic rate.

The pre-pandemic and pandemic monthly rates of incident diabetes were similar during the other months.

The group concludes: "The lack of both an observable increase in overall diabetes incidence among children during the 18-month pandemic restrictions [in this Ontario study] and a plausible biological mechanism call into question an association between COVID-19 and new-onset diabetes."

More research is needed. "Given the variability in monthly [relative risks], additional population-based, longer-term data are needed to examine the direct and indirect effects of COVID-19 and diabetes risk among children, the authors write.

This study was supported by ICES (which is funded by the Ontario Ministry of Health) and by a grant from the Canadian Institutes of Health Research. Shulman reported receiving fees from Dexcom outside the submitted work, and she and three other authors reported receiving grants from the Canadian Institutes of Health Research outside the submitted work.

JAMA Open. Published online July 25, 2022. Article.

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Ongoing Debate Whether COVID Links to New Diabetes in Kids - Medscape

This transcript has been edited for clarity.

Diabetes treatment is currently focused on lowering blood glucose to reduce the risk for complications, particularly microvascular complications. Treatment approaches are also available to reduce the cardiovascular disease risk in people who are at high risk for or with established cardiovascular or kidney disease.

Not everyone with type 2 diabetes has obesity, but most people with type 2 diabetes do have abnormally functioning fat cells or unhealthy fat. That's what drives the metabolic process that results in diabetes.

People whose diabetes is primarily driven by unhealthy fat would benefit from prioritizing weight management in the treatment of their diabetes. That means not only using medications that control the blood glucose but also thinking about which medications might help with weight management and what other interventions might help with weight management, such as lifestyle interventions, changes in diet, changes in physical activity, and prioritizing medications that are going to both treat their diabetes and help them reduce weight.

We need to think about this in the majority of our patients with diabetes because most of them, even if they don't have obesity (as defined by a body mass index above 30), will have their unhealthy adipose tissue contribute to the disease process.

Over the past few years, it's become much easier to treat people with diabetes and excess weight with medications that are becoming increasingly available and increasingly effective at managing weight, diabetes, and other aspects of metabolic health.

We need to consider the weight impact of the treatment program that we use in our patients with diabetes and we should prioritize helping them with their weight management and focusing on treating diabetes.

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Any views expressed above are the author's own and do not necessarily reflect the views of WebMD or Medscape.

Cite this: Priya Sumithran,Mark Harmel.What Is the Weight Impact of Type 2 Diabetes Treatment?-Medscape-Aug01,2022.

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What Is the Weight Impact of Type 2 Diabetes Treatment? - Medscape

Patients with chronic kidney disease (CKD) or end-stage kidney disease (ESKD) face increased health burdens and are at increased risk for cardiovascular events and mortality. The most common cause of CKD is type 2 diabetes, and both diabetes and CKD are associated with greater risk of all-cause mortality and increased rates of infection and cardiovascular events. The increased mortality is attributable in part to cardiovascular or infection-related events.

Both glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT-2) inhibitors are associated with improved blood pressure control, greater reduction in body weight, lower mortality, and lower incidence of cardiovascular events in the general population with diabetes. Guidelines from the American Diabetes Association recommend GLP-1 receptor agonist treatment for patients with diabetes and CKD with an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 who are at risk for cardiovascular disease.

The Kidney Disease: Improving Global Outcomes 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease suggests GPL-1 receptor agonist treatment for patients unable to use metformin or SGLT-2 inhibitors; GLP-1 receptor agonists can be used in patients with advanced CKD or ESKD. Results of a recent meta-analysis found potentially different clinical outcomes following use of GLP-1 receptor agonists and use of dipeptidyl peptidase-4 (DPP-4) inhibitors in the general population with diabetes. In that study, there was an association between use of GLP-1 receptor agonists and improved survival compared with use of DPP-4 inhibitors.

According to Jia-Jin Chen, MD, and colleagues, previous randomized clinical trials have excluded or included only small numbers of patients with advanced CKD or ESKD. In addition, there are no real-world data available on comparisons of GLP-1 receptor agonists with DPP-4 inhibitors for the treatment of patients with advanced CKD or ESKD. The researchers conducted a retrospective cohort study among patients with diabetes and advanced stage CKD or ESKD to examine whether use of GLP-1 receptor agonists in that population would be associated with better outcomes compared with the use of DPP-4 inhibitors. Results of the study were reported online in JAMA Network Open [doi:10.1001/jamanetworkopen.2022.1169].

The study utilized data on patients with type 2 diabetes and stage 5 CKD or ESKD from the National Health Insurance Research Database of Taiwan. The study was conducted between January 1, 2012, and December 31, 2018. Data analyses were conducted from June 2020 to July 2021.

The study exposures were treatment with GFLP-1 receptor agonists compared with treatment with DPP-4 inhibitors. The outcomes were all-cause mortality, sepsis- and infection-related mortality, and mortality related to major adverse cardiovascular and cerebrovascular events (MACCE) in patients treated with GLP-1 receptor agonists compared with those in patients treated with DPP-4 inhibitors.

Covariates included age, sex, area of residence (urban or rural), income level, occupation, and 10 comorbidities (hypertension, dyslipidemia, cirrhosis, systemic lupus erythematosus, atrial fibrillation, peripheral arterial disease, coronary artery disease or ischemic heart disease, heart failure, cerebrovascular disease, and ESKD requiring dialysis). The imbalance among covariates between the groups was mitigated using propensity score weighting.

A total of 75,556 patients with type 2 diabetes and stage 5 CKD or ESKD requiring dialysis were identified during the study period. Of those, 48,277 were excluded, resulting in a study cohort of 27,279 patients. Of those, 26,578 were in the DPP-4 group (45.34% [n=14,443] were male; mean age 65 years) and 701 were in the GLP-1 receptor agonist group (49.36% male [n=346], mean age 59 years).

Prior to propensity score weighting, the DPP-4 group was older, concentrated in rural areas, and included fewer patients receiving dialysis and more patients receiving an angiotensin-converting enzyme inhibitor, diuretics, and insulin compared with the GLP-1 group. After propensity score weighting, the two groups were balanced in all analyzed covariates.

After propensity score weighting, the DPP-4 group included 26,568 patients and the GLP-1 receptor group included 603 patients. Mean age was 66 years in the DPP-4 inhibitor group and 65 years in the GLP-1 agonist group. In the DPP-4 group, 54.25% (n=14,414) were male; in the GLP-1 group 52.90% (n=319) were male. The most common comorbidity in the total cohort was hypertension (84.20% [n=22,369] in the DPP-4 inhibitor group and 83.92% [n=506] in the GLP-1 group).

The rate of all-cause mortality in the DPP-4 group was 7.95 per 100 person-years (95% CI, 7.76-8.15 per 100 person-years); in the GLP-1 group the rate was 6.10 per 100 person-years (95% CI, 4.76-7.45 per 100 person-years). Following propensity score weighting, there was an association between use of GLP-1 receptor agonists and lower all-cause mortality compared with use of DPP-4 inhibitors (hazard ratio [HR], 0.79; 95% CI, 0.63-0.98; P=.03).

The rate of sepsis- or infection-related mortality was 3.01 per 100 person-years (95% CI, 2.88-3.13 per 100 person-years) in the DPP-4 inhibitor group and 1.80 per 100 person-years (95% CI, 1.07-2.53 per 100 person-years) in the GLP-1 receptor group. The risk for sepsis- or infection-related mortality was lower in the GLP-1 receptor agonist group than in the DPP-4 inhibitor group (HR, 0.61; 95% CI, 0.40-0.91; P=.02).

The rate of MACCE-related mortality was 2.56 per 100 person-years in the DPP-4 inhibitor group and 2.64 per 100 person-years in the GLP-1 receptor agonist group. MACCE-related mortality in the GLP-1 receptor agonist group was similar to that in the DPP=4 inhibitor group (HR, 1.07; 95% CI, 0.76-1,51; P=.69).

In subgroup analyses, there was an association between use of GLP-1 receptor agonists with a lower risk of mortality compared with use of DPP-4 inhibitors among patients with cerebrovascular disease (HR, 0.33; 95% CI, 0.12-0.86) than among those without cerebrovascular disease (HR, 0.89; 95% CI, 0.871-1.12) (P=.04 for interaction).

The authors cited some limitations to the study findings, including the lack of detailed data on clinical factors and other possible confounders, the inability to examine the dose effect or to evaluate adherence to the medication, the relatively small sample size that resulted in an inability to assess the differences in treatment effects across subgroups, and pooling patients with stage CKD not receiving dialysis with those with ESKD who were receiving dialysis. Associates like Nephrology & Hypertension Medical Associates can help discuss more treatment options.

In conclusion, the researchers said, In this cross-sectional study, in patients with type 2 diabetes and stage 5 CKD or ESKD, use of GLP-1 receptor agonists was associated with better outcomes, including all-cause mortality and sepsis- and infection-related mortality, compared with use of DPP-4 inhibitors. Additional large-scale prospective studies are needed to examine our results.

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