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A federal law capped the cost of insulin. Diabetic Kansans are already seeing crucial savings | KCUR

Diabetes News


Julie Cogley has lived with Type 2 diabetes for years. For a long time, paying for medication was never an issue.

But three years ago, Cogley needed surgery and was forced to retire earlier than expected, moving her to a fixed income. Shortly after, she learned she needed to start taking insulin, which would cost up to $700 out of pocket a month, not including additional medication.

Cogley is covered by Medicare, but by June or July she would be paying out of pocket. She asked her doctor and pharmacists for samples and cut back on other areas of her budget to cover the high costs.

In August 2022, Congress passed the Inflation Reduction Act, which caps out-of-pocket insulin costs for Medicare beneficiaries at $35. In response, the top three insulin-making drug companies in the U.S. pledged to reduce insulin prices by up to 75% or cap out-of-pocket costs at $35 a month for those privately insured, as well.

“I think I actually cried when I found out it had gone through because for six months, I would have had to come up with all that and I definitely couldn’t have afforded that,” Cogley said.

More than 37 million people in the United States, including one in four seniors, have diabetes. Of 59,000 diabetic residents in Kansas 3rd Congressional District, about 17,000 require a daily insulin shot, according to a report released by the office of Rep. Sharice Davids — the only member of the Kansas congressional delegation to vote for the measure.

More than 80% of adults with diabetes rely on medications to manage their condition, and 34% rely on daily insulin injections to manage their diabetes. Had these caps been in place in 2020, 15,657 people in Kansas would have saved roughly $650 per year in lower out-of-pocket costs, according to the report.

“For too long, Kansans have been forced to pay extremely high prices for insulin while drug companies rake in massive profits,” Davids said. “By capping the cost of insulin at $35 a month through the Inflation Reduction Act, we are not only lowering a major cost burden for tens of thousands of Kansans — we are saving lives.”

The act also instituted a new inflation rebate under Medicare that means drug companies cannot raise drug prices higher than inflation and allows Medicare to begin negotiating lower prices for prescription drugs, including insulin.

In 2017, individuals diagnosed with diabetes had an average of $16,750 in medical expenses. In that same year, the total cost of treating all Americans with diabetes was $327 billion, which includes $237 billion of direct medical expenses and $90 billion in lost productivity.

In 2019, diabetes was the seventh leading cause of death in the United States with 87,000 deaths.

“If your doctor says your body needs this much insulin to maintain and be able to be healthy, then you can’t cut corners on that sort of thing,” Cogley said. “I think people that aren’t diabetic need to be aware that it is literally a life or death kind of thing to have financial relief.”





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Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabet

Diabetes News


Introduction

The latest global diabetes mellitus (DM) map demonstrated by the International Diabetes Federation (IDF) showed that the incidence of DM patients (aged 20–79) would rise to 12.2% (783.2 million people) in 2045.1 China has the most DM currently, and the number is expected to exceed 174 million by 2045.1 Diabetic kidney disease (DKD), characterized by persistent microalbuminuria or decreased estimated glomerular filtration rate (eGFR), is one of the most significant microvascular complications of diabetes. DKD is now more common than primary glomerulonephritis and is the major cause of chronic kidney disease (CKD) in China, where it affects 40% of those with type 2 diabetes mellitus (T2DM) who are hospitalized.2–4

DKD is always asymptomatic until it develops to the advanced stage. Currently, the main methods for early diagnosis of DKD are urinary albumin creatinine ratio (UACR) and eGFR. However, on the one hand, these diagnostic methods have their limitations. UACR does not increase in the early stage (stages I and II) of DKD,5 meanwhile eGFR increases in the early stage.6 On the other hand, the increasing incidence of DKD is placing a burden on the healthcare system, making it essential to find an approach to screen DKD in the medical environment. Hence, early detection and treatment of DKD are critical.

Previous studies have demonstrated multiple clinically related factors of DKD, such as age, UACR, haemoglobin A1c (HbA1c) and high-density lipoprotein (HDL).7,8 Furthermore, related studies have shown that the development of DKD is strongly associated with insulin resistance (IR), which may accelerate the progression of DKD.9–11 Hyperinsulinemic euglycemic glucose clamp (HEGC), a significant method for evaluating IR, is expensive and difficult to operate.12 Additionally, the homeostasis model for insulin resistance (HOMA-IR) index is also a common tool to assess IR in clinical practice.13 However, serum insulin or C-peptide tests are not appropriate for all inpatients or outpatients owing to the impact of insulin treatment. Triglyceride-glucose index (TyG index), as well as triglyceride and fasting glucose products, demonstrates greater potential in evaluating IR.14,15 Previous clinical studies have demonstrated the performance of the TyG index in evaluating IR, and it was superior to the HOMA-IR index.10 In conclusion, these easily available clinical risk factors may be used to build an accurate prediction model of DKD development in individuals with newly diagnosed T2DM, facilitating DKD screening. In addition, we intended to use the least absolute shrinkage and selection operator (LASSO) regression to screen variables and construct a predictive model. LASSO regression, a compressive estimation method for selecting relevant features for various classes of data, helps deal with biased estimates with complex collinearity data.16 It can perform variable screening and complexity adjustment while fitting generalized linear models, and is widely applied in variable screening of various prediction models, including commonly used clinical variables and related omics data.17,18 LASSO regression has contributed to constructing some prediction models, such as tumors, metabolic diseases, kidney diseases, etc.19,20

In this study, we designed a long-term cohort trial in patients with newly diagnosed T2DM for more than 10 years, aiming to explore the risk factors related to DKD, building and validating the prediction model.

Materials and Methods

Study Population

This was a retrospective analysis on prospectively collected T2DM patients’ data from the Third Affiliated Hospital of Soochow University. We consecutively incorporated 863 newly diagnosed T2DM patients in our hospital from December 2010 to January 2014 for long-term follow-up.

Inclusion criteria: (1) meet the American Diabetes Association (ADA) classification criteria for T2DM and were newly diagnosed patients;14 (2) age > 18 years old.

Exclusion criteria: (1) a history of DKD or other kidney diseases; (2) UACR ≥ 30mg/g, eGFR < 60 mL/min/1.73m2 (CKD-EPI),21 or urine dipstick test ≥ 1 +; (3) presence of an acute infection; (4) presence of a malignancy or pregnancy; (5) incomplete clinical records.

The endpoint event was the occurrence of DKD,22 defined as (1) persistent albuminuria (UACR ≥ 30mg/g) over 3 months (2) eGFR < 60 mL/min/1.73 m2; (3) excluded other kidney diseases.

Participants were followed up until DKD was diagnosed for the first time. Otherwise, patients without DKD events were followed up until June 2022. Figure 1 shows the procession of subject inclusion.

Figure 1 Flow chart of participants.

Abbreviations: T2DM, type 2 diabetes mellitus; DKD, diabetic kidney disease.

Note: The flow chart shows the entire research process.

Data Collection

Baseline demographic data (sex and age) and related clinical data were from the electronic medical record system. The risk prediction model was trained using variables selected from medical record system and published literature,23,24 including age, sex, body mass index (BMI), history of smoking and hypertension, systolic blood pressure (SBP), fasting blood glucose (FBG), postprandial blood glucose (PBG), haemoglobin A1c (Hb1Ac), fasting C-peptide (FCP), white blood cells (WBC), hemoglobin (Hb), neutrophil to lymphocyte ratio (NLR), fibrinogen (FIB), D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum albumin (ALB), bile cid (BA), bicarbonate, blood urea nitrogen (BUN), uric acid (UA), eGFR, total cholesterol (TC), triglyceride (TG), HDL, low density lipoprotein (LDL), apolipoprotein a1 (Apoa1), apolipoprotein b (Apob), UACR, TyG index, HOMA-IR, thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free tetraiodothyronine (FT4), diagnosis of diabetic retinopathy (DR) and diabetic peripheral neuropathy (DPN), as well as antidiabetic and antihypertensive treatment. The superior indicator of the HOMA-IR and TyG index for predicting DKD was also determined by comparing the area under the curve (AUC).

Statistical Analysis

Statistical analyses were performed using SPSS software version 25.0, R software version 4.2.2 and GraphPad Prism software version 9.0.0. The median (interquartile range) [M (P25, P75)] was used to represent data with a non-normal distribution, whereas the mean ± standard deviation (SD) was used to express data with a quantitative normal distribution. The qualitative variable was selected as percentages (%). A random number table generated using R software was assigned to the training and validation set in a ratio of 7: 3. There was no statistical difference between the training and validation set for all variables, so the validation set can fully validate the prediction model based on the training set. We screened the variables and built the model based on the training set, and presented the model in a nomogram visualization. In addition, we utilized the validation set to internally validate the performance and clinical utility of the model constructed based on the training set. Spearman correlation was employed for correlation analysis. For further analysis, a nomogram was developed based on multivariate analysis. The logistic regression results were presented as odds ratio (OR), 95% confidence intervals (95% CI), and P values. To obtain the perfect prediction model, we used “glmnet” package for LASSO regression screening to to further filter the predictor variables derived from the traditional univariate logistic regression. The nomogram was drawn using the R package “regplot”, and receiver operating characteristic (ROC) analysis was performed to assess how well the nomogram predicted the risk of developing DKD. Use 1000 bootstrap replications for validation. Calibration curves were conducted using the “calibrate” R package. Draw a decision curve using the “rmda” R package. P < 0.05 was considered statistically significant.

Results

Baseline Characteristics

The final queue size was 521 persons after follow-up (Figure 1). The average follow-up time of the T2DM participants was 7.8 ± 1.8 years. There were 127 DKD events (24.4%) among the 521 individuals identified in our long-time follow-up cohort.

Table 1 presents the baseline demographic and medication of 521 T2DM patients, meanwhile, Table 2 presents the laboratory measurement indicators. The R package randomly divided the whole individuals into the training set and validation set in a ratio of 7: 3. 365 participants (70%) were randomly assigned to the training set and 156 participants (30%) to the validation set. In the training set, 90 patients (24.7%) progressed to DKD, while 37 patients (23.7%) progressed to DKD in the validation set. There was no significant difference in demographic and clinical characteristics between the training and the validation set (P > 0.05). However, variables of significance include DKD participants being on average older in age, having higher FCP, higher BMI, higher BA, higher TG, higher Apob, higher UACR, higher HOMA-IR, and TyG index, and having greater proportion with a history of smoking and hypertension, a higher incidence of DPN, and a higher utilization rate of some antidiabetic and antihypertensive drugs while having lower eGFR and HDL (Table 1 and Table 2). In addition, we also develop logistic and LASSO regression, aiming to identify the related factors with DKD.

Table 1 Demographic and Medication of Newly Diagnosed T2DM Patients

Table 2 Laboratory Measurement Indicators of Newly Diagnosed T2DM Patients

Comparison of TyG Index and HOMA-IR

To evaluate the performance of the HOMA-IR and TyG index in predicting DKD, we plotted AUC. HOMA-IR and TyG index are both tools to assess IR. We determined the AUCs for the TyG index and HOMA-IR in the training set, were 0.694 (95% CI 0.630 to 0.758) and 0.640 (95% CI 0.575 to 0.705), respectively (Figure 2). The findings were following those reported in other studies, which found that TyG index was more accurate than HOMA-IR at predicting the risk of IR and DM-related complications.25,26 In addition, multivariate analysis revealed that the HOMA-IR was no statistical significance after adjusting confounding variables (Table 3).

Table 3 Logistic Regression Analysis of DKD-Related Risk Factor

Figure 2 ROC curve of TyG index and HOMA-IR.

Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; TyG index, triglyceride-glucose index; HOMA-IR, homeostasis model assessment for insulin resistance.

Notes: ROC curve of TyG index and HOMA-IR drawn according to the training set. We calculated the AUC of the TyG index and HOMA-IR, which were 0.694 (95% CI 0.630 to 0.758) and 0.640 (95% CI 0.575 to 0.705), respectively.

LASSO Regression Analysis

To identify the independent significant factors, we used logistic regression and LASSO regression for analysis. We conducted a correlation analysis and univariate logistic regression (Figure 3 and Table 3). Correlation analysis found a strong collinearity correlation among some variables. LASSO regression, a greedy algorithm, can be used to select the most significant variables to avoid overfitting, then establish a more accurate linear regression model.27 According to previous reports and univariate analysis, we selected 37 variables related to DKD (age, gender, BMI, smoking, hypertension, SBP, HbA1c, FBG, PBG, ALB, BUN, UA, TC, TG, HDL, LDL, Apoa1, Apob, UACR, eGFR, FCP, FT4, TyG index, HOMA-IR, DR, DPN, antihypertensive and antidiabetic treatment) for LASSO analysis. Figure 4A shows the cross-validation curve result, and the coefficient distribution plot is shown in Figure 4B. Through 10-fold cross-validation, we chose the minimum value within 1 standard error and obtained 5 independent variables that were finally screened out (Table 4).

Table 4 LASSO Regression Analysis of DKD-Related Risk Factor

Figure 3 Clinical indicators correlation analysis.

Abbreviations: BMI, body mass index; SBP, systolic blood pressure; FBG, fasting blood glucose; PBG, postprandial blood glucose; HbA1c, haemoglobin A1c; FCP, fasting c-peptide; WBC, white blood cell; Hb, haemoglobin; NLR, neutrophil to lymphocyte ratio; FIB, fibrinogen; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; BA, bile acid; BUN, blood urea nitrogen; UA, uric acid; eGFR, estimated glomerular filtration rate; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Apoa1, apolipoprotein a1; Apob, apolipoprotein b; UACR, urinary albumin creatinine ratio; TyG index, triglyceride-glucose index; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free thyroxine; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; CCB, calcium channel blocker; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.

Notes: *P value < 0.05, **P value < 0.001, significant correlation between variables.

Figure 4 LASSO regression analysis.

Abbreviation: LASSO, less absolute shrinkage and selection operator.

Notes: (A) Cross-validation curve. (B) Coefficient distribution plot of 37 variables. The cross-validated error within one standard error of the minimum is shown on the right vertical line, while the minimum error is shown on the left vertical line.

Development of an Individualized Prediction Model

To display the prediction model vividly, we applied an individualized dynamic nomogram to visualize it (Figure 5). Then, we applied ROC to analyze the performance of the prediction model. Figure 6 shows the AUC of the prediction model. For the training set, the predicted AUC of the nomogram was 0.826 (95% CI 0.775 to 0.876), meanwhile, the AUC of the validation set was 0.803 (95% CI 0.719 to 0.887).

Figure 5 Dynamic nomogram developed for predicting DKD in patients with newly diagnosed T2DM.

Abbreviations: TG, triglyceride; eGFR, estimated glomerular filtration rate; UACR, urinary albumin creatinine ratio; TyG index, triglyceride-glucose index; DKD, diabetic kidney disease; T2DM, type 2 diabetes mellitus.

Notes: Dynamic nomogram can reflect the score of each variable of patients in actual situation, so as to obtain the corresponding DKD risk probability. The risk probability of DKD calculated for the first patient in the cohort by each variable score is shown in the figure above.

Figure 6 ROC curves of the predictive model.

Abbreviations: ROC, Receiver operating characteristic; AUC, area under the curve; V set, validation set; T set, training set.

Notes: For the training set, the predicted AUC of the nomogram was 0.826 (95% CI 0.775 to 0.876), meanwhile, the AUC of the validation set was 0.803 (95% CI 0.719 to 0.887).

Calibration of the Model

To validate the nomogram, we used 1000 bootstrap analysis and draw a calibration curve. The calibration curves of the model revealed a strong correlation between the predicted probability and the actual probability, indicating that our model had been properly calibrated (Figure 7).

Figure 7 Calibration curves of the nomogram.

Abbreviation: DKD, diabetic kidney disease.

Notes: (A) Calibration curve of the model in the training group. (B) Calibration curve of the model in the validation group. The calibration curve reflects the consistency between the predicted probability and the actual probability. Y-axis = actual probability of DKD. X-axis = predicted probability of DKD. The shadow line represents a perfect prediction by an ideal model. The red line represents the performance of our model, which coincides well with the shadow line.

Clinical Use of the Model

To prove the clinical practicability of the nomogram, we carried out a decision curve analysis (DCA). Figure 8 presents the DCA for the DKD nomogram. Between 20% and 40% of T2DM patients progressed to DKD,28 and within this cutoff, both decision curves were above the None and All lines. This demonstrated that predicting DKD in newly diagnosed T2DM using the nomogram model provides more clinical benefits than either treatment or no treatment for all patients.

Figure 8 DCA for the nomogram.

Abbreviations: DCA, decision curve analysis; DKD, diabetic kidney disease.

Notes: (A) Decision curve of the model in the training group. (B) Decision curve of the model in the validation group. The y-axis measures the net benefit. The solid red line represents the nomogram. The solid gray line represents the assumption that all patients have DKD. The solid black line represents the assumption that no patients have DKD.

Discussion

Our study was a single-center analysis of prospectively collected data from 521 newly diagnosed T2DM patients, 127 of whom had a DKD event. We screened variables through LASSO regression and incorporated 5 variables to construct a prediction model. Furthermore, we carried out internal validation, discrimination ability, calibration ability, and clinical practicability of the model. These results revealed that the prediction model performed well and had clinical applicability.

Independent prediction indicators were screened by logistic and LASSO regression, and finally, a DKD prediction model for predicting newly diagnosed T2DM was developed. Nomogram revealed that age, renal excretion, lipid metabolism, and IR may be potential good predictors of DKD. Age is recognized as an independent risk factor for T2DM and DKD.29 Persistent microalbuminuria (UACR > 30mg/g) can appear in the early stage of the DKD hyperfiltration state, and metabolic substances such as serum creatinine will accumulate with the decrease of renal filtration level in the later period.30 At present, some scholars considered that mitochondrial dysfunction played an indispensable role in DN, which will cause fatty acid oxidation disorder, resulting in abnormal lipid deposition in the kidney.31 Furthermore, many lipid metabolites have been confirmed to be related to the complications of DM. As an index of IR, the TyG index performs excellently in predicting complications of DM.32,33 TyG index can accurately evaluate whether IR exists only by combining two simple indexes: serum lipid and glucose. Moreover, an Australian study investigated the relationship between the TyG index and end-stage renal disease (ESRD). The results revealed a positive correlation between the TyG index and ESRD risk.9 Therefore, researchers should not ignore the relationship between IR and DKD in the future.

The onset of DKD is hidden, and it may be difficult for patients newly diagnosed with T2DM to realize the risk of developing DKD. However, if DKD occurs, we can only take symptomatic treatment (control albuminuria and serum glucose), then wait for ESRD to evolve slowly. Therefore, incorporating the clinical variables described above into the model and developing a nomogram as a screening tool can well identify newly diagnosed T2DM patients with DKD risk. For the identification of our model, the AUC of the training set and the validation set were 0.826 and 0.803, respectively. These results revealed that the model had an excellent predictive ability in identifying DKD and non-DKD in T2DM patients. The model’s calibration curve revealed a strong correlation between the predicted probability and the actual probability, indicating that our model had been properly calibrated. DCA results showed that using this model to predict DKD had clinical practicability and benefits patients.

In comparison to previous studies on developing DKD prediction models, our study had some strengths. First, our study was a retrospective cohort study with a long-time follow-up. Some patients with DKD have a DM duration of 10 years or more, so it may be difficult to collect enough participants with positive outcomes in short-term follow-up and underestimate the prevalence rate, which ultimately affects the predictive performance. Secondly, the participants in our study were newly diagnosed T2DM patients compared with other research focus individuals, who frequently overlooked the danger of developing DKD. Newly diagnosed T2DM patients are a concern in this study, and the prediction model of DKD is constructed to find all DKD events in DM patients earlier. Finally, to incorporate all related variables, we initially investigated the pathological process and risk factors for T2DM and its complications. IR is a major pathogenic factor and characteristic of T2DM, causing concomitant injury to various target organs, and DKD is no exception.34,35 It is crucial to find an index for assessing IR since it is the chief culprit of glucose metabolism disorders. The performance of the TyG index and the HOMA-IR as IR evaluation indexes varies throughout other studies. Hence, we used AUC to distinguish the performance of the TyG index from the HOMA-IR and finally revealed that the TyG index performs better. Therefore, we incorporated significant correlation factors including the TyG index, constructing a more accurate prediction model.

There were still some limitations in this trial. First, the lack of external validation was one of the important limitations of our study. To duplicate and externally verify the results of this study, more investigation is required. Secondly, this was a cohort study with limited sample size, so the endpoint event was defined as the occurrence of DKD, and no subgroup analysis was carried out. If DKD was divided into subgroups with different renal function levels according to eGFR stages, the research results would be more abundant. In addition, the family history of DM was missing from baseline data, and it is undeniable that it is also a significant factor in the development of DKD. Finally, since this was a retrospective cohort study, it may suffer from recollection bias and loss of follow-up. In this cohort study, there were 82 participants with loss to follow-up, which was controlled within 10% and had no significant impact on the outcome.

Conclusion

In conclusion, the new DKD progression model for T2DM patients constructed in this study had excellent identification and calibration ability, which was helpful for clinical practice. In the future, we need to integrate large-scale T2DM cohort studies to further verify this nomogram prediction model.

Data Sharing Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Ethics Statement

This research study was complied with the Declaration of Helsinki, and approved by the Ethics Committee of the Third Affiliated Hospital of Soochow University (2013#27). All individuals provided written informed consent for this research study.

Author Contributions

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.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82000684) and Changzhou Sci & Tech Program (CE20215024) and Top Talent of Changzhou “The 14th Five-Year Plan” High-Level Health Talents Training Project (2022260).

Disclosure

The authors report no conflicts of interest in this work.

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35. Wang Z, Xie J, Wang J, Feng W, Liu N, Liu Y. Association between a novel metabolic score for insulin resistance and mortality in people with diabetes. Front Cardiovasc Med. 2022;9:895609. doi:10.3389/fcvm.2022.895609



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Painful diabetic peripheral neuropathy | JPR

Diabetes News


Introduction

About 37 million people in the United States (US) have diabetes and as many as 96 million may have prediabetes.1 Diabetic neuropathy (DN) is the most common microvascular complication associated with diabetes. Diabetic peripheral neuropathy (DPN) affects about 50% of patients with diabetes,2 and symptomatic or painful DPN (pDPN) occurs in 30–50% of these patients.3–5 pDPN generally presents as a spontaneous burning pain of the feet, can be difficult to treat successfully, and has a significant impact on lifestyle and quality of life (QoL). The pathophysiology of pDPN is not well understood, but hyperglycemia-induced cellular damage in the nervous system results in hyperexcitable pain pathways, resulting in both peripheral and central sensitization.6 Thus, both primary afferent nociceptors and central neurons may contribute to neuropathic pain in pDPN.2,5

The management of pDPN is multifactorial, involving a combination of lifestyle factors (such as diet, exercise and weight loss) and pain medications. Because the pathogenesis of pDPN is poorly understood there is no mechanism-based approach to pharmacotherapy, resulting in doctors testing different treatments until something works.2,4 Glycemic control and risk factor management play an important role and, if well managed, can prevent or partially reverse DPN and modulate associated pain.6,7 There is a plethora of novel pharmacological agents that target a variety of pain pathways of potential therapeutic benefit in pDPN,7–9 although very few have gained FDA-approval for pDPN.2 Thus, the treatment of patients with pDPN is difficult, complex and frustrating for health-care professionals (HCPs), patients and caregivers.10,11

Whatever their diagnosis, patients are entitled to be informed about their condition and receive sound and clear advice about their treatment options. For patients with pDPN, the patient journey can consist of various stages: initially, because they may experience tingling and numbness, patients may not interpret what they feel in their feet as being pain, and this can delay the identification of foot pain; the search for information to obtain a diagnosis; the introduction of treatment; and thereafter, at any given time, evaluation of their current treatment, assessment of their current status (eg degree of ability/disability) and a review of their perspective of what the future holds.

The present survey was conducted to obtain information about the knowledge, experiences, emotions, and attitudes of patients with pDPN in the US, relating to their condition and their health care. The survey results will be used to investigate patient challenges, gaps in resources, treatment options, and to educate and equip patients with the information needed for them to become better advocates for themselves, as well as leading to more open discussion by patients about their pain and treatments.

Methods

Survey Method

This was a disease-level, patient-focused survey conducted in March 2021 as part of a program to increase awareness and discussion about pDPN in the US, leading up to National Neuropathy Awareness Week. The goal of the survey was to assess how to streamline the path to diagnosis and better disease management. To ensure credibility, relevance and resonance within the medical and patient advocacy community, the questionnaire was developed and conducted by SMS Research Advisors in collaboration with The Foundation for Peripheral Neuropathy and Averitas. Prior to conducting the survey, the questionnaire was reviewed by Averitas (legal, medical and regulatory review) and reviewed/validated by The Foundation for Peripheral Neuropathy. Averitas was not identified as a sponsor.

This qualitative market research study was conducted according to the ISPOR Code of Ethics.12 Clinical Research Ethics Committee or IRB approval for this type of qualitative market research is not required.

All respondents were screened against the following criteria, and only those fitting all the criteria (based on the information provided by each patient) were allowed to take the survey:

  • Age 18 years or older,
  • Diagnosis of diabetes with pDPN for at least 6 months,
  • Receiving a prescribed treatment from a physician for their diabetic nerve pain for at least 6 months.

Respondents were recruited from a general population sample across the US (SMS Research used a third-party company and, where necessary, advocacy groups, to identify potential respondents) and the survey was conducted via an online questionnaire with an anticipated duration of 12–15 min.

Survey Design

The online questionnaire had three sections.

The first set of questions (S1 – S8) were “screening questions” to ensure participants met the desired profile based on the selection criteria. These questions covered the age of the participant, a diagnosis of diabetes (and the type), whether they currently experienced nerve pain due to the diabetes, the location and severity of their pain, how long since their first diagnosis of diabetic nerve pain of the feet, whether or not they were being treated for the pDPN and, if so, that this was with more than over-the-counter pain medication.

The second set of questions (Q1 – Q31) represented the full survey questionnaire from which desired patient insights would be derived. The full list of questions is presented in Table 1. The options for the types of pain that could be selected (Question 3) were as follows: a burning sensation, a painful cold, a pain like electric shocks, tingling, like touching pins and needles, numbness, itching, loss of sensation in the area, pain so significant that it interfered with daily activities. The information sources that could be selected (Question 6) included a range of HCPs and healthcare associations and organizations, other patients, family and friends, the internet, social media, pain medication manufacturers, advocacy groups and advertising in its various forms. Options that could be selected for responding to the question about reasons for changing medication (Question 15) included: increased severity of symptoms, wanting to try a new drug, lack of effectiveness, QoL (related to side effects) and medication not covered by health insurance.

Table 1 Survey Questionnaire Questions Relating to Patient Insight

Supplementary treatments that could be considered (Question 19) were diet, exercise, vitamins and supplements, medical marijuana, pain medications prescribed for other illnesses, complimentary or alternative treatments, alternating heat and cold, avoidance of use or contact when the pain is bad and distracting. Options that could be selected for challenges to seeking prescribed pain medication (Question 20) were diverse, including insufficient pain, stigma associated with drug use, fear of addiction, cost, inadequacy of dosage, poor past experience, too busy or too difficult to get to the doctor, concern about wasting the doctor’s time, concern that the doctor would not take the patient’s symptoms seriously, fear of talking about pain medications, risk of side effects or drug interactions, fear of more tests, already overmedicated, concerns about impact on QoL. Patients were asked which of the following activities they could no longer perform (Question 23) or they wished they could perform (Question 24): driving, travel, need to rest, use of walking aids, visiting family and friends or socializing, ability to work, use of sleep medications, reliance on caregivers and engagement with exercise.

In Question 28, patients were asked who they trust most to give the correct information about their diabetic nerve pain (selecting only one): primary care physician, diabetes specialist, pain specialist, podiatrist, neurologist, pharmacist, alternative or functional medical specialist, friend/relative, another patient, search engines, specific websites, online patient forums or social media.

The final group of questions (Q32 – Q44) were general demographic questions. Patients were asked their gender and ethnicity; if they lived in an urban, suburban or rural location; which state they live in; what level of education they had completed; whether they have children and, if so, what age; their current marital status; the number of people in their household; the household income; and whether they receive any disability allowance.

No personally identifiable information was requested, captured or stored during the survey.

Statistical Analysis

Due to the qualitative design of this market research survey, no formal statistical analyses were conducted. Data generated from the survey are presented as the proportion of individuals providing responses to each survey question.

Results

A total of 3636 adults started to respond to the survey (screening questions). The survey was completed by 506 adults who had diabetes were diagnosed with pDPN affecting the feet for at least 6 months, and who had received a physician-prescribed treatment for at least 6 months.

Demographics and Medical Condition

Overall, 79% of respondents had type 2 diabetes, 60% were male, and 70% were aged <65 years. 82% were Caucasian, 6% African American, 6% Hispanic, 3% mixed race, 2% Asian American, and 1% other ethnicities. 60% of respondents were married, 7% were in other committed relationships, 14% were single, 11% divorced or separated, and 8% widowed. 59% had undertaken tertiary education to degree level, including 7% with professional or advanced degrees.

In terms of household demographics, 17% of respondents lived alone, 30% were in a household of two people, 66% in a household of 3 or 4 people, and 13% in a household of ≥5 people. 57% had children in the household, mostly (82%) in the age 8–13 years. 61% of respondents lived in urban or suburban areas, and 39% in a rural area. 48% lived in a household having an annual income ≤US$50,000 and 18% were in a household with an annual income >US$125,000. The states having the highest number of participants were New York, California, Texas, and Florida.

Comorbidities were common among respondents; 13% of respondents had no comorbidity. Amongst those with comorbidities, 50% had hypertension, 38% obesity, 22% obstructive sleep apnea, 22% diabetic retinopathy, 16% cardiovascular disease, 12% chronic kidney failure and 11% hyperlipidemia. Osteoporosis, metabolic syndrome, autoimmune disease and nonalcoholic fatty liver disease were each reported in ≤9% of respondents.

Diabetes Pain Profile

In addition to foot pain, 50% of patients had pain in the legs and/or ankles, 40% had pain in the hands, 20% had shoulder pain and 2% had pain elsewhere. 37% of respondents had first been diagnosed with pDPN of the feet ≥3 years ago, whilst 25% had their first diagnosis 6–12 months ago. Pain was assessed as moderate (pain scale 3–6) in 50% of respondents, significant (scale 7–8) in 37% and severe (scale 9–10) in 12%. 66% had disability due to nerve pain. Pain medication was being taken daily by 48% of respondents and a couple of times per week in a further 37%.

The most commonly reported feelings respondents had about their nerve pain were as follows: “it makes me feel old” (44%), “it’s more of an annoyance than anything else” (42%), “I’m not sure what else I can do to help with the pain” (38%), “I’m increasingly unable to do things I want to do” (37%), “it hurts, but I have more important health issues to be concerned about” (33%) and “I’m worried I will lose my independence and need a caregiver” (30%).

Diabetes Symptoms and Diagnosis

In most respondents, the pDPN diagnosis was made at the time of diabetes diagnosis (19%) or later (60%); 17% of respondents received their pDPN diagnosis prior to being diagnosed with diabetes. The nerve pain was communicated to the doctor within 3 months of the patient first experiencing it in most cases (78%). Figure 1 shows the different types of pain experienced by respondents and the percentage experiencing each type. The most commonly reported pain types were `like touching pins or needles’ (58%), tingling (56%), and numbness (50%). In 30% of respondents, the pain was sufficient to interfere with the completion of daily activities.

Figure 1 Type of pain experienced by respondents.

As shown in Figure 2, the majority of patients (61%) took 2 or more doctors to receive a correct diagnosis, whereas 39% of respondents had their diabetic nerve pain correctly diagnosed by the first doctor they consulted; for 18% of respondents, it took visits to 3 or more doctors to get a correct diagnosis. Most respondents (56%) were very comfortable talking with their doctor about the pain in their feet, whilst 23% were somewhat comfortable and 22% were not comfortable 85% of respondents considered their conversations with their doctor about the pain in their feet to be “great” or “good”, and that the doctor clearly or generally understood the impact of the pain on their life. 12% of the respondents were frustrated that their doctor, whilst understanding the condition, was of limited or no help in managing the pain.

Figure 2 Number of doctors visited before a correct diagnosis of diabetic nerve pain was made.

Treatment Aspects

Figure 3 presents the percentages of respondents reporting the previous (Figure 3A) and current (Figure 3B) use of different treatments. Anticonvulsants and over-the-counter pills and supplements were the most commonly used treatments, both previously and currently, with diet and exercise also playing significant roles. Prescribed topical creams and patches were currently used by 23% of respondents, an increase over what had been prescribed previously (15%). Similarly, the percentage of respondents receiving prescribed antidepressants was higher currently than in the past (22% versus 14%, respectively).

Figure 3 Previous (A) and current (B) treatments.

Since their initial diagnosis of pDPN involving the feet, 70% of respondents had taken ≥2 medications for the pain (33% had taken ≥3 medications). The reason for switching was a lack of effectiveness in 58%, worsening symptoms in 49% and declining QoL (generally too many side effects) in 46%.

A majority of respondents were not satisfied with their current treatment plan for their nerve pain (Figure 4). Amongst the 58% of respondents in this unsatisfied category, two-thirds were looking for greater pain relief and almost half for longer pain relief.

Figure 4 Overall treatment satisfaction (Question 16) rated on a scale from 0 (not enough pain relief) to 10 (pain successfully managed).

When asked about their challenges in seeking prescribed pain medication for their pDPN (Question 20) respondents most commonly reported that they: are already taking a lot of medications and did not want to add more (32%), were worried about the potential for more side effects (31%) or were afraid of the impact on their QoL (23%), of interaction with their other medications (22%) or that they would become addicted to the pain medication (22%). 13% said that the out-of-pocket expense was too high and 10% were concerned that the medication would not be covered by their insurance. Statements relating to a negative relationship between a respondent and their doctor were reported by relatively few respondents, eg, that the respondent was wasting the doctor’s time (13%), that the doctor did not focus on their pain issue (10%) or that the doctor did not take their pain symptoms seriously (9%).

Knowledge and Sources of Information

66% of respondents reported that they understood more than just the basic facts about their pDPN involving the feet or had a solid understanding. 29% said that they knew only the basic facts and 5% did not know anything.

By far the most popular source of information about their pDPN was a medical professional (62% of respondents; Figure 5). The most common type of physician that respondents saw for their foot pain was a podiatrist (29%). The American Diabetes Association was by far the most utilized advocacy group for more information (36%). The most trusted sources for information about pDPN were also medical professionals (primary care physician, diabetes specialist physician, pain management physician, neurologist or podiatrist/hand specialist) in 74% of respondents. However, only 26% of respondents recorded their primary care physician as the most trusted source.

Figure 5 Primary source of information for respondents about their diabetic nerve pain (Question 6); including only sources reported by ≥10% of respondents.

70% of respondents had no difficulty finding the information they wanted about pDPN, and only 10% found it very difficult. Among those who found it difficult (scoring ≥6 on the 10-point difficulty scale), 41.1% found the information too scientific or academic, 31.9% said that their HCP did not believe that their foot pain was related to their diabetes and 30% reported that their HCP knew very little. Despite this, 64% of respondents indicated that their doctor was the person they felt best understood their pain and its impact on their life, followed by their spouse (41%).

Figure 6 presents the types of information that respondents found most valuable. Information about medications to treat or manage pain was reported to be the most valuable (58%), followed by information about the causes of pain (42%); approximately one-third of respondents reported that information about ideas on how to cope or deal with pain was valuable and a similar proportion reported that information about activities used to alleviate pain was valuable.

Figure 6 Types of information reported as most valuable by respondents (Question 7).

Attitudes and Behavioral Changes

When asked which four emotions respondents felt in a typical day since their diagnosis of pDPN involving the feet (Question 29), the top four emotions reported were all negative: frustrated (46%), worried (33%), anxious (26%) and uncertain (26%). Isolation, hopelessness, anger and fear were each reported in 19–20% of respondents. 22% of respondents recorded that they felt hopeful, 21% determined, and 14% in control.

The attitudes of respondents to their diabetic nerve foot pain were assessed in Question 30 of the survey in which they were asked to rate, on a scale from 1 (do not agree at all) to 10 (completely agree), how much they agreed or disagreed with a list of statements. The statements and the percentage of respondents reporting a score of 9 or 10 on each are presented in Figure 7. Overall, respondents felt relatively comfortable talking about their diabetic nerve foot pain and were actively seeking a remedy for their pain. The frustration highlighted in the emotional assessment was supported by higher percentages of respondents eager to find new medications to relieve their pain, desperate for a cure and wishing they could find the right tools to better understand their condition.

Figure 7 Attitudes of respondents, as assessed by their agreement or disagreement (on a scale from 0 to 10) with a list of statements (Question 30). The percentage of those strongly agreeing with each statement (score 9–10) are shown.

Figure 8 shows the lifestyle changes respondents reported as a result of their pDPN (Question 23). Most commonly, as a result of their pain, respondents reported the need to rest more often, stop or reduce physical activity, take more medications for their pain and to get to sleep. Respondents who indicated that they had made a change in their activities because of their pain were asked what they wished they could do more of. 31% wished they could engage in more physical activity, 28% that they could be active longer without rest, 20% that they could travel or participate in general entertainment, 20% that they did not need sleep medications and 20% that they spent less time lying in bed. Only 13% wished they could work more often.

Figure 8 Lifestyle changes resulting from diabetic nerve pain (Question 23).

33% of respondents did not currently use a primary caregiver, 43% had their spouse or partner as a primary caregiver, 16% had a friend or relative and only 7% relied on a home health aide. Amongst those requiring a caregiver, the activities for which help was most commonly required were as follows: preparing food (37%), walking (36%), transportation (35%), bathing/showering (33%), getting in and out of chairs (23%), dressing (22%), getting in and out of bed (22%) and eating/drinking (18%).

Future Outlook

The final survey question (Question 31) asked about future expectations. The results are shown in Figure 9. Better treatments (71%) and freedom from pain (61%) were the overriding perspectives among respondents when thinking about the future. Expectations about additional financial support or less skeptical doctors were only shared by about one-third of respondents (32% and 31%, respectively).

Figure 9 Future expectations of patients with diabetic nerve pain of the feet (Question 31).

Discussion

The results of our online survey provide valuable qualitative information about the knowledge and experiences of 506 diabetic adults with pDPN in the USA. The high percentage of type 2 diabetics among respondents in the current study is consistent with other reports. In an observational study in England involving 15,692 diabetic patients, painful symptoms were present in 34%, with a diagnosis of pDPN in 21%. Painful neuropathic symptoms were twice as common in type 2 compared with type 1 diabetics.3 In that study, the risk of painful symptoms was 50% higher in females than males,3 whilst in a 2012 survey of patients with diabetes and symptoms of DPN (n=1004) in the US, 53% were female.13 The current survey included a higher proportion of males than females (60% vs 40%), indicating that the prevalence of pDPN may have been slightly under-represented compared with earlier studies.

The current survey also had a very high prevalence of Caucasian respondents (82%). This may have no reflection on the actual incidence of pDPN across ethnic groups. A study of the characteristics of pDPN symptoms in a multicultural population in the US had to use oversampling of Hispanics and African-Americans to recruit sufficient subjects, compared with Caucasians.14 Fewer Hispanics and African-Americans are diagnosed with pDPN in the US, possibly because of differences in perception of pain, but also because these patients are less willing to discuss pain, and generally less comfortable communicating, with their HCP.14

There may be a combination of reasons why many patients receive their diagnosis of pDPN after their diabetes diagnosis, including the subjective language patients use to describe their pain, some of which may not fall within the classic terminology for pDPN; a hesitancy of some patients to talk to their doctor about pain; the availability of too much information, with some sources not making the connection between foot pain and diabetes. In a community-based, cross-sectional study in type 2 diabetics in Taiwan (n=628), it was found that 30.6% had pDPN, but the primary health-care providers had paid little attention to pain symptoms or their treatment.15 In the US, a survey of patients with diabetes and symptoms consistent with DPN identified that, whilst 83% of the 1004 patients reported pDPN, only 41% had been diagnosed with DPN. 64% of HCPs never had patients complete DPN assessment questionnaires and did not perform specific diagnostic tests. Whilst 85% of patients reported that the pDPN impacted their daily life, their HCPs estimated that only 41% actually experienced pain and 38% had their daily life impacted.13

pDPN is associated with a substantial burden to patients, causing moderate-to-severe pain, which is often sub-optimally controlled, requiring polypharmacy, limiting physical activities and being associated with low levels of treatment satisfaction.16 Most patients genuinely struggle with finding a lasting solution to their pain. Despite most respondents in the present survey having tried two or more medications for their pain management, 58% remained unsatisfied with their treatment. Patients try multiple treatments, yet nothing seems to alleviate their pain. The lack of effectiveness could be because they are not just looking for a solution to their pain, but a solution to improving their lives. Patients tend to find temporary relief with one medication, then seek another treatment once the current treatment becomes ineffective (only 12% of patients in this survey stuck with the same treatment for a prolonged period). This is consistent with reviews of the literature which highlight the complexity of pharmacotherapy for the management of pain in pDPN patients.2,8,10

The language gap between laypersons (health consumers) and HCPs has long been recognized as a major barrier to effective health communication and health information comprehension.17 A recent study demonstrated that differences exist among health consumers with respect to the complexity of their language use when discussing health-related topics.18 A US survey of >1000 adults diagnosed with type 1 or type 2 diabetes who self-reported symptoms consistent with DPN concluded that, among patients and HCPs, misperceptions exist on the cause and management of pDPN, with additional disparities between the perspectives of patients and HCPs.13 The outcomes from that survey suggested a need for pDPN educational initiatives that target patients and HCPs, and the initiation of improved dialogue between patients and their HCPs for discussing appropriate management of pDPN that is distinct from treatment of the underlying diabetes. Nevertheless, in terms of access to information in the current study, most respondents reported that they knew what information they wanted (medication solutions), had no difficulty finding it and felt well-informed about their condition. This most likely reflects increased access to the internet globally, with 4.8 billion internet users as of January 2021,19 and the growing e-patient phenomenon.20 Moreover, 85% of respondents reported having great or good conversations with their doctor who they felt understood their pain and its impact on their life, possibly reflecting a sample bias due to the fact that most of the survey population was Caucasian with apparent good access to HCPs and information. However, for some patients there may be a lack of clear communication with their doctor, manifesting as a lack of trust in the doctor, and this may be a barrier for these patients. A significant disconnect in the doctor–patient relationship is highlighted by the finding that 64% of patients believe their doctor best understands their pain but only 26% mention their primary care physician as the person who they trust knows the most about their diabetic foot pain. This possibly suggests that while the doctor is sympathetic to the patient’s pain, they are not necessarily trusted to alleviate (or cure) the pain. Trust is the keystone of the patient-physician relationship and communication is fundamental to engendering trust.21–23 Effective communication improves satisfaction among patients and physicians, and patient adherence to medication and treatment regimens, decreases the number of medical errors, patient complaints and medical negligence claims, and positively impacts the overall clinical, physical and mental health outcomes for patients.24,25 A lack of clear patient/doctor communication in some cases may be confounded by the fact that many patients (34% of respondents in this survey) know only the basic facts or nothing about their condition, and the high incidence of worry, frustration, anxiety, anger and lack of hope among some patients. These emotions may fuel an inability to interpret properly what is being said and make it difficult for doctors to tell their patients things they may not want to hear. As a result, patients can feel they are not “being heard” by doctors and doctors feel their patients “don’t understand” what they are trying to say. Again, this highlights the need for improved educational material to address communication and knowledge gaps. Patients with pDPN and HCPs have previously acknowledged the value of learning more about pDPN.13

Pain catastrophizing26 has been shown to be significantly associated with pain disability, QoL, and physical activity decline in patients with DPN.27 Furthermore, depression, anxiety, pain acceptance, pain severity and sleep disturbance are also predictors for a declining QoL in patients with DPN.28,29 A report by the Danish Center for Strategic Research involved a questionnaire on neuropathy and pain completed by 5514 patients with type 2 diabetes. The prevalence of possible DPN and pDPN was 18% and 10%, respectively, somewhat lower than has been reported elsewhere. However, both DPN and pDPN were associated with lower QoL, poorer sleep, depression, and anxiety.30 Mental, as well as physical function, is impaired in pDPN patients compared with these functions in diabetic patients without pain.31 Early and effective diagnosis and management of pain in patients with DPN is, therefore, important to try to avoid pain catastrophizing and the emotional and other factors associated with a decline in QoL. In our study, the most common emotions reported in a typical day were all negative: frustrated (46%), worried (33%), anxious (26%), and uncertain (26%), with approximately 20% of respondents each reporting feelings of isolation, hopelessness, anger or fear.

Consistent with reported practice, respondents in this survey focused on two different paths to pain management: lifestyle changes and medical treatments. Among lifestyle changes, diet, vitamins and exercise were most popular. Diet, exercise and weight loss can not only help with remission of diabetes but they can also reverse DPN.2 Most of the reported changes in lifestyle in the current survey related to the ability to perform physical activities, the need for more rest and problems with sleeplessness. A desire to still work was less frequently reported, perhaps partly accounted for by 30% of respondents being aged >65 years. Not surprisingly, spouses/partners were the most common primary caregiver, when required. Activities requiring prolonged periods of standing, such as walking and food preparation, were the activities that required the most assistance from a caregiver. Whilst some patients accept that they need to moderate their current lifestyle, others had an expectation of a complete cure with about one-third of respondents being desperate for a cure for their diabetic nerve pain. This may help account for some of the doctor/patient communication difficulties and negative emotional profiles reported in this survey.

Tricyclic anti-depressants, serotonin-noradrenaline reuptake inhibitors, anticonvulsants, opioids and topical treatments are the approved and most commonly prescribed medications for pDPN.2,32 Although the previous and current use of prescribed anticonvulsants was reported most commonly in our survey, over-the-counter pills and foot creams/patches were also reported to be used commonly among respondents. Other prescribed medications included antidepressants, creams, patches, and opioids, generally reflecting the approved medications for pDPN. Interestingly, compared with previously, approximately 30% more respondents reported the current use of prescribed topical creams and patches.

Patients primarily want to see a future where better treatments become available for pDPN of the feet. They also want to see a day when they can be free from the side effects associated with their pain medication. Approximately one-third of patients indicated they hope for greater awareness of pDPN, that access to education improves and doctors become less skeptical about patients with pDPN.

One approach to improving treatment satisfaction amongst patients with pDPN (at least amongst the one-third who feel under-informed) could be to make it easier for them to find information about treatments. This could involve helping doctors to create a more focused treatment plan, as well as improving information about the link between diabetes and nerve foot pain. Another factor could be educating physicians about treatment options, particularly regarding differences in adverse event profiles. Topical treatments are considered a good option for patients with pDPN who are intolerant to conventional systemic therapies due to an unwillingness to use them because of dependence concerns, or adverse events.2 Additionally, topical treatments significantly reduce the risk of drug interactions, which is particularly important in patients with multiple co-morbidities and those experiencing polypharmacy;2 indeed, polypharmacy can pose a significant risk to patient health (eg renal issues and fractures) in elderly individuals who are often prescribed numerous medications.33 In the present survey, topical patches and creams were currently prescribed to only 23% of respondents, while 19% used over-the-counter (OTC) topical creams or patches. Of note, although numerous OTC creams and patches are available, the capsaicin 8% topical system (Qutenza®) is the only HCP-prescribed topical treatment, and only topical formulation of capsaicin, approved by the US Food and Drug Administration (FDA) to treat pDPN of the feet.34 A network meta-analysis of 25 randomized, controlled clinical trials involving the capsaicin 8% topical system versus other treatments for pDPN showed that the patches had similar efficacy to oral agents with fewer side effects.35

By design, qualitative surveys of this nature present limitations.36 To avoid potential bias associated with using an organization’s membership sample, the survey was conducted using a “general population” sample of individuals. However, it is important to note that the study sample was mostly Caucasian, male, educated, and with a high prevalence of comorbidities. Therefore, our findings may not be generalizable to the broader US population with diabetes and pDPN of the feet.

Conclusions

People with pDPN affecting the feet commonly experience sharp or tingling pain, which can be severe and may impact their lifestyle and emotional well-being. They generally report pain to their doctors promptly but may need to see several doctors before receiving a correct diagnosis. Treatment is multifactorial, involving diet, exercise, lifestyle changes and pharmaceuticals. Most patients have tried multiple therapies, remain unsatisfied with their current pain treatment, and struggle to find a lasting solution for their pain. Patients seek information from various sources, but predominantly from HCPs. Most are comfortable communicating with their doctor and find the information they are looking for, but about one-third feel insufficiently informed. Early identification and diagnosis of pain in patients with diabetes, and education about pain treatments, is important to reduce or avert the impact of pain on QoL and emotional well-being.

Abbreviations

DN, diabetic neuropathy; DPN, diabetic peripheral neuropathy; FDA, Food and Drug Administration; HCP, health-care professional; OTC, over-the-counter; pDPN, painful diabetic peripheral neuropathy; QoL, quality of life.

Acknowledgments

The authors acknowledge Lindsay Colbert, Executive Director, Foundation of Peripheral Neuropathy, who reviewed the questionnaire and provided insights before execution of the project. Professional medical writing support was provided by Jon Monk and David P. Figgitt PhD, ISMPP CMPP™, Content Ed Net, with funding from Averitas Pharma (A Grünenthal Company).

Author Contributions

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.

Funding

The research and survey were conducted by SMS Research Advisors with funding from Averitas Pharma (A Grünenthal Company). Panel respondents were compensated for their participation in the survey.

Disclosure

Alaa A Abd-Elsayed is a consultant for Medtronic, Avanos, Abbott, Sprint, and Averitas Pharma. Lizandra P Marcondes and Zachary B Loris are employees of Averitas Pharma (A Grünenthal Company). Dan Reilly was an employee of SMS Research Advisors. The authors report no other conflicts of interest in this work.

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Babysitter Quits Job Because Child Was Diabetic

Diabetes News
YourTango


Working as a babysitter and looking after children can sometimes be a grueling effort.

There are a lot of factors that are taken into account when hired for babysitting gigs, including making sure you’re aware of how to assist a child in cases of emergency.

One babysitter had to turn down a job after learning an important detail about a child she had been meant to look after.

Posting to the subreddit “r/AmItheA–hole” (AITA) — a forum where users try to figure out if they were wrong or not in an argument that has been bothering them — she described the events.

In her Reddit post, the 18-year-old woman wrote that she often babysits for multiple families in her neighborhood.

RELATED: Theme Park Nanny Hired By Parents Who Need Extra Help On Their Vacations Opens Up About Her Job

For her latest babysitting gig, one of the women she routinely babysits for recommended her to a woman that had just moved into their neighborhood.

“She contacted me on Wednesday asking if I could babysit her two kids on Friday evening through Saturday afternoon,” the teenager said.





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