Category: 8. Health

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Targets underlying problem

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Association of Optometrists

‘Failing the public’

This year may prove to be an important year for advances in gene editing and gene therapies.

But what does that really mean, and how have we got to this point?

In what is claimed as a world first, doctors in the United States recently treated a baby with a rare genetic disease using a highly specific and personalised gene-editing technique.

What did this involve and how is the baby doing?

Baby boy KJ was born with a rare metabolic disease known as CPSI deficiency. This prevented his body from getting rid of ammonia during the metabolism of protein. And while he was put on a highly restricted protein diet and given drugs to remove protein from his blood, he remained at high risk of brain damage or even death.

Following the early diagnosis of KJ’s condition, gene-editing researchers at the University of Philadelphia worked with colleagues throughout the US to quickly develop a personalised gene-editing infusion to correct a genetic variant that led to the disorder.

KJ responded well to the three-dose regimen and is now reaching his developmental milestones. “Ultimately, we hope this has set a precedent where we have firmly entered a world of genetic cures on demand,” said Fyodor Urnov, scientific director of the Innovative Genomic Institute in the University of California, Berkeley, a member of the treatment team.

Have similar gene-editing techniques been used on patients in Ireland too and how do they differ from gene therapy?

The gene-editing platform known as Crispr is widely used by researchers in scientific laboratories in their search for new therapies for cancer and other diseases. Essentially, Crispr – whose inventors won the Nobel Prize in chemistry in 2020 – is a cheap and efficient way of finding and altering specific pieces of DNA within cells.

Gene therapy is slightly different in that it introduces new DNA materials into cells to replace or correct a gene or inactivate a target gene in the treatment of a specific disease.

Earlier this year, ophthalmologic surgeon Dr Max Treacy treated 20-year-old Maros Tomko, who had severe visual loss since birth, with gene therapy at the Royal Victoria Eye and Ear Hospital in Dublin. The once-off treatment replaced the faulty gene RPE65 with a healthy copy. This was done via a viral vector which carried the healthy copy of the gene into the cells of the eye.

“Within four weeks, the vision in his eyes had doubled. From not being able to see any letters, he could read the first and second lines. His visual field [the total area seen] also doubled in size,” explains ophthalmologist Dr Emma Duignan. Tomko adds that he is very grateful to the doctors for this opportunity to have surgery. “I can see people’s faces now and I can read the numbers on my bank card for the first time. It’s only three months since the surgery so it will take longer to get better,” he says.

A 31-year-old Sligo man with a similar congenital blindness got his sight back after being treated with ocular gene therapy at the Mater hospital in Dublin in 2024.

Gene therapies, however, remain very expensive and the Royal Victoria Eye and Ear Hospital treatment, which was paid for by the Health Service Executive, cost close to €800,000.

Dr Duignan says it took years for the team to get the funding in place, but such treatments now mean that Dublin will have two potential centres for gene therapy clinical trials. “Clinical trials of gene therapies for inherited eye conditions are desperately needed for the treatment of an estimated 5,000 patients on the island of Ireland affected by these diseases,” says Dr Duignan. Drugs used in clinical trials are made available free of charge.

Manipulating human DNA – the genetic material responsible for life – is like the stuff of science fiction. How is it even possible?

Research into gene therapy goes back to the 1940s and 1950s. The first studies began to recognise that DNA was a transforming substance capable of changing the living characteristics of individuals through a biochemical process. Later, studies identified that human stem cells (mainly found in the bone marrow) could be genetically modified to carry therapeutic DNA which could then differentiate into various cell types to correct genetic defects.

In the 1980s, researchers at Boston Children’s Hospital published a paper to show a virus could be used to insert genes into blood-forming stem cells. These so-called viral vectors later became an established way to deliver specific genetic material to human cells. In the meantime, the Human Genome Project was completed in 2003, paving the way for gene therapy to become a reality for multiple diseases, especially those caused by mutations in a single gene.

What specific diseases are we talking about?

The painful and life-threatening sickle cell disease, a rare genetic immune deficiency disorder called chronic granulomatous disease, and the rare childhood disease adrenoleukodystrophy have all been treated with gene therapy. Genetic diseases including cystic fibrosis, muscular dystrophy, sickle-cell anaemia and haemophilia could potentially be treated with gene therapy.

But are there risks to making such precise changes to the basic building blocks of life?

Yes, there are huge risks, which is why it has taken so long for gene therapy to reach clinical settings. In 1990, four-year-old Ashanthi de Silva became the first patient to be successfully treated with gene therapy. She was given a healthy adenosine deaminase (ADA) enzyme to cure the severe immunodeficiency disorder caused by the absence of ADA. Although she continues to take a drug to keep her condition under control, she leads an active life to this day.

[ Delving into the power of DNA for patientsOpens in new window ]

Throughout the 1990s, European researchers focused on other forms of severe immunodeficiency disorders, reporting the first cures in 2000. However, some years later, five of the 20 treated children developed cancer. The viral vector which had delivered the gene to their T cells (immune cells in the body) had also activated an oncogene, triggering leukaemia. Also in the US, an 18-year-old boy died after receiving gene therapy for a rare metabolic disorder.

These incidents delayed research into gene therapy for almost 10 years. But in the early 2010s, scientists developed better viral vectors that could more precisely target expressions of genes in specific cell types which don’t go astray in the body and don’t trigger an immune response. This spurred further developments in genetic therapies and several gene therapy drugs were approved for use.

The use of messenger RNAs (mRNAs) – as was used in Covid-19 vaccines – also represents a form of gene therapy. In this case, the mRNA vaccine introduces information that cells then use to make the coronavirus spike protein, which then stimulates the person’s immune system to develop antibodies to the virus.

What are the next steps for gene editing and gene therapy?

There are now hundreds of active gene therapy studies around the world and more than a dozen gene therapy drugs on the market, according to researchers at Boston Children’s Hospital.

A more finely tuned approach called base-editing, which uses Crispr technology to chemically change one “letter” of a gene’s code at a time, is deemed to be the next technological advance in genetic therapies. The small changes of base-editing can correct a “spelling error” mutation, silence a disease-causing gene or help activate a specific gene. However, while this approach hasn’t yet been tested in clinical trials, places such as Boston Children’s Hospital have several base-editing projects under way.

Dr Duignan adds that new so-called gene agnostic therapies, which will treat diseases caused by different gene mutations without having to develop a specific infusion for each mutation, represent the next frontier in gene therapy.

Picture this: you’re in the middle of a Zoom marathon or drowning in tied-up emails, your brain begging for five minutes of calm. You open the fridge, pull out a chilled Diet Coke, crack it open, feel the fizz on your tongue – and just like that, you’ve had a “fridge cigarette.”In the midst of Zoom fatigue, endless deadlines, and life in fast-forward, a new ritual has emerged: the “fridge cigarette.” Not a cigarette at all, but that cold crack-and-sip moment when you crack open a Diet Coke straight from the fridge. No smoke, no guilt, just that crisp ritual of pop you didn’t know you needed.TikTok’s Gen Z has turned this fizzy moment into a cultural wink – a mini smoke break with zero nicotine and full aesthetic energy. ‘Tis Gen Z’s latest obsession – a tongue-in-cheek break that’s half nostalgia, half self-care, and fully TikTok-viral. It’s not just fizzy drink culture – it’s a symbolic pause in a fast-paced digital life.

But just the part of omitting nicotine – does it help health-wise at all?Let’s unpack.

What’s a Fridge Cigarette?

The term “fridge cigarette” originated on TikTok, popularized by creator @reallyrachelreno, whose June video comparing cracking open a Diet Coke to lighting a cigarette went viral with near about 4M views. What she captured was a moment: the hiss, the fizz, the pause – and that psychological hit. It’s not about substances; it’s emotional.

On TikTok, fridge-cig posts feature ASMR-worthy fizz, minimalist setups, moody lighting, and captions like “time for a crispy ciggy.” One user described it as “main‑character energy” – a daily starring moment in everyday life. The ritual resonates because it blends irony, self-care, and aesthetic performance in a few crisp seconds.Gen Z reveres this ritual as a purposeful break from digital overload, recasting a smoke break into something healthier.. well, healthier-ish.

Why Diet Coke?

Diet Coke provides the quintessential chill experience: fizzy, caffeinated, calorie-free, and effortlessly cool. Users on TikTok have even playfully mapped Coke variants to cig brands – Diet Coke as Parliaments, Coke Zero as American Spirits, regular Coke as Marlboro Reds, full-fat glass-bottle Coke as a cigar.Interestingly, Coca‑Cola didn’t engineer this trend—it happened organically. Their marketing simply delivered delicious taste, and TikTok reshaped it into a cultural phenomenon.

Health trade‑offs: Less harm, but not harmless

No nicotine? Does that imply no health hazards at all?Well, that’s not true.Acidic erosion: Diet Coke has a pH of around 2.7–3.0, thanks to phosphoric and citric acids – enough to weaken enamel below the critical 4.0 level. Studies show that frequent soda exposure roughens enamel and accelerates erosion – punctual exposure may be manageable, but repeated sipping overwhelms saliva’s restorative ability.Artificial sweeteners: Though sugar-free, Diet Coke relies on aspartame and acesulfame K. WHO now labels aspartame “possibly carcinogenic,” and studies link diet sodas with heart disease, stroke, diabetes, and gut disruption.Dental and chemical concerns: Yes, it’s a vast improvement from tobacco, but Diet Coke isn’t innocent. Experts warn that its acidity can erode enamel and artificial sweeteners like aspartame may be linked to long-term health issues such as stroke, heart disease, and metabolic disruptions.Caffeine and bloating: Caffeine can cause jitters, disrupted sleep, and mild dehydration, while carbonation leads to bloating and digestive discomfort.Most professionals advise treating fridge cigarettes as occasional reminders, not all-day habits. Sip with water, use a straw, and maybe rotate in non-sweet beverages and mindful breaks.

The final sip: A balanced act

Yes – “fridge cigarettes” offer Gen Z a clever, safer stand-in for old-school smoke breaks. But they come with real risks: enamel erosion, enamel weakening, artificial sweetener exposure, and caffeine side effects. It’s one thing to enjoy the fizz and take a moment; it’s another to sip mindlessly all day.So, what’s the bottom line? Enjoy the crack and fizz, but make it intentional. A mindful fridge cigarette – sipped fast, chased with water, spaced out through the day – is a fun ritual. But replace it regularly with water, tea, or gum to safeguard your health. After all, you can pause without damaging your smile – or your wellness.

Introduction

For the past few decades, the incidence of thyroid cancer is increasing rapidly worldwide with papillary thyroid carcinoma (PTC) being the most common type of thyroid cancer.¹ Except for the actual increase in tumor occurrence, the increased prevalence of PTC is due to the increasing use of high-resolution ultrasound (US) imaging and US-guided fine-needle aspiration biopsies (FNAB).² The increased accuracy of pathological thyroid examinations also contributes to the increased diagnosis of PTC, especially the overdiagnosis of papillary thyroid microcarcinoma (PTMC),³ which is defined as a PTC with a diameter of ≤10 mm.⁴ Studies reported that most of PTMCs (over 70%) are diagnosed incidentally in autopsies and thyroidectomy specimens, and most PTMCs are benign with reported mortality from 0% to 1%.⁵ Therefore, the risks of overdiagnosis and overtreatment are highly possible for PTMCs, which causes potential complications, distress, and economic burdens for patients with PTMCs.⁶ Preoperative differentiation of PTMC from PTC is of great clinical significance to avoid overtreatment and to determine the appropriate treatment options for patients with PTMCs.⁷

US imaging of the thyroid and neck is usually the initial workup for a patient with a thyroid nodule, but the visual interpretation of US images in the diagnosis of PTC and PTMC is limited not only because the diagnosis highly depends on the radiologists’ experience but also the interobserver variation.⁸ Although the study of Ma et al demonstrated that combining conventional US, contrast-enhanced ultrasound (CEUS) and real-time elastography (RTE) is able to improve the diagnostic accuracy of PTMC, it is not a routine clinical practice for a patient with all three types of US exams.⁹ With the emergence of radiomics, studies reported that US-based radiomics is able to differentiate benign and malignant thyroid nodules.^10,11 Deep learning models with convolutional neural network (CNN) have also been investigated for the differentiation of benign and malignant thyroid nodules with US images and demonstrated excellent performance compared with radiologists.^12–14 However, few studies have addressed the application of radiomics and deep learning models in the differentiation of PTC and PTMC.

In this study, the feasibility and accuracy of US-based radiomics, deep learning, and combined deep learning radiomics models were investigated in the differentiation of PTMC and PTC to decrease the risk of overtreatment with patients’ data from two hospitals, one hospital for training and testing, and second hospital for external validation.

Materials and Methods

Patients

Patients diagnosed with papillary thyroid neoplasm in Hospital One from January 2018 to September 2020 were retrospectively reviewed according to the electronic medical records. Enrolled patients were randomly divided into training, validation, and independent testing sets. Fifty PTC cases acquired from Hospital Two were used as an independent external testing dataset. The inclusion criteria were as follows: 1) pathologically confirmed PTMCs and PTCs; 2) diagnosed by US images with detailed sonographic features described. The exclusion criteria included the followings: 1) with preoperative therapy (resection biopsy, neoadjuvant radiotherapy, or chemotherapy); 2) benign thyroid lesions; 3) missing important histopathological results (immunohistochemical results or lymph-nodule results); 4) incomplete information or images. Routine clinical tests and patients’ characteristics were also extracted from the records. Figure 1 shows the flowchart for patients’ enrollment. This study was approved by the ethics committee in Clinical Research (ECCR) of the First Affiliated Hospital of Wenzhou Medical University and conducted following the Declaration of Helsinki (ECCR no. 2019059) with confirmed patient confidentiality. The requirement of informed consent was waived by the ECCR according to the retrospective nature of this study.

Figure 1 The patient enrollment process for the training set and the two independent testing sets. The training cohort and the independent testing set 1 patient enrollment process are shown on the left, and the independent testing set 2 is shown on the right. These two datasets are from two different hospital patients.

US Examinations and Clinical Factors

US examinations of thyroid nodules were performed with high-frequency linear probes (5 MHz to 14 MHz) with a variety of US systems: Philips EPIQ7C (Philips Medical Systems, the Netherlands), GE Volume E8 (GE Medical Systems, USA), Siemens ACUSON OXANA 2 (Siemens Medical Solutions, USA), Esaote MyLab Class C (Esaote, Italy), Hitachi HI VISION Preirus (Hitachi-Aloka Medical, Japan) and Mindray Resona 7T (Mindray Medical International, China). The US images included both transverse and longitudinal sections of nodules. Clinical characteristics included basic information (age and sex) and ultrasound findings, which consist of composition, echogenicity, shape, margin, and echogenic foci, as well as stages scored according to the thyroid imaging reporting and data system (TI-RADS) criteria of the American College of Radiology.¹⁵

Radiomics Features Extraction and Modeling

Target volumes were contoured manually by one junior radiologist on the US images and confirmed by a senior radiologist with over 15 years of experience. Supplementary Figure 1 shows the typical US with contoured target volumes. Python (v. 3.7.0; https://www.python.org/) and package pyradiomics 2.2.0 (version 2.2) were used to extract radiomics features from the manually segmented target volumes. Based on different matrices that capture the spatial intensity distribution and wavelet filtering, a total of 1566 radiomics features were extracted, which includes 88 exponential features, 88 gradient features, 88 logarithm features, 70 square features, 352 wavelet features, and 880 log features, respectively. Radiomics features with a p <0.05 in Mann–Whitney U-tests were selected as potentially informative features, then the least absolute shrinkage selection operator (LASSO) was applied to identify optimal features for PTMC and PTC classification.¹⁶ A ten-fold cross validation was applied to tune the elastic net parameters to reduce the redundant information and to avoid over-fitting. A minimum standard deviation and maximum area under curves (AUC) were achieved by tuning coefficient λ. The linear combination of selected radiomics features with respective weights makes the final radiomics signature.

Deep Learning Models

In the preprocessing, a rectangular region of interest (ROI) was cropped from raw US images according to the tumor segmentation mask and resized to 224×224 pixels for normalization. Five deep learning networks, visual geometry group 13 (VGG13),¹⁷ VGG16, VGG19, AlexNet, and EfficientNet were pre-trained on Imagenet and then adopted as the deep learning networks in this study using python 3.7 programming language Tensorflow 2.4, and Keras 2.2.4 open-source programming packages.¹⁸ As shown in Supplementary Figure 2 the structures of different deep learning networks, VGG uses a pooling layer as a demarcation and has six block structures with each having the same number of channels. Because both the convolutional and fully connected layers have weight coefficients, they are also referred to as weight layers. The pooling layer does not involve weights. For the VGG CNN, its convolutional layers and pooling layers are responsible for feature extraction, and the final 3 fully connected layers are responsible for the classification task (×2 means that the module in brackets is repeated twice). The AlexNet network consists of 8 layers with the first 5 layers convolutional and the last 3 layers of fully connected. Each backbone of the EfficientNet contains 7 blocks with each having different numbers of sub-blocks.

In the training stage, rectangular ROIs were fed into the networks to update model parameters with classification results as the output. The loss function was calculated based on the cross-entropy of the outputs and labels. A learning rate of 1e-4, dropout parameter of 0.6, training Epoch of 100, the activation function ReLu, the classification function softmax, and the Adam optimizer were applied to update the model parameters with a batch size of 32 and a maximum iteration step of 300. Compiled prediction codes were generated based on the data weights obtained from training to obtain the predicted value of each test picture. Identical parameters were applied for the training of these deep learning networks. The computer environment configuration for deep learning modeling was Linux 64-bit operating system, GPU RTX2080, and video memory 6GB with all other applications shut down while the program is running.

Fused Models and Evaluation

Firstly, radiomics and deep learning models were developed to differentiate PTC and PTMC independently. In order to improve the classification performance, the prediction scores of radiomics and deep learning models were fused by applying an information fusion method.¹⁹ As shown in Figure 2, the prediction results were obtained independently by training each model in the early stage, then logistic regression was applied to fuse the outputs of each model to make a decision. The minimum loss value, positive predictive value (PPV), negative predictive value (NPV), recall rate, F1 score, and the AUCs of receiver operating characteristics (ROC) curves were calculated to evaluate and compare the performance of these models.²⁰

Figure 2 Schematic pipeline of the radiomics and deep learning modeling, as well as their combination for the differentiation of papillary thyroid tumor and papillary thyroid microcarcinoma. Abbreviations: Fc2, 2 fully connected layer; R_score, radiomics score.

Statistical Analysis

Detailed clinical differences between PTC and PTMC were compared by t-test, chi-square test, and Mann–Whitney U-test. LASSO regression model building was done using the “glmnet” package. Glmnet function in R language was applied for n cross validation (n = 10), which means that data was separated into 10 subsets. All statistics were two-sided and p-values less than 0.05 were considered to be statistically significant. Statistical analysis was performed using the R analysis platform (version 3.6.0), OriginPro2018, MedCalc (version 19.3.0), SPSS 19 software, and Python 3.7.

Results

A total of 549 patients with an average age of 46.55±11.33 years (ranges from 14 to 81 years) were enrolled in this study with confirmed 180 PTC and 436 PTMC nodules from Hospital One, as shown in Figure 1 the flowchart of patient selection. Patients were randomly divided into training cohort and validation cohort at a ratio of 8:2. There were 56 patients from Hospital One used as independent testing set 1. Fifty patients from Hospital Two with confirmed 25 PTC and 25 PTMC nodules were enrolled as independent testing set 2 for the external validation of these models. There were 205 PTC and 461 PTMC nodules with an average diameter of 15.80±7.15 mm and 6.89±3.25 mm, respectively. Detailed clinical characteristics of these patients were presented in Table 1. A total of 612 PTMC and 311 PTC US images were analyzed.

Table 1 The Clinical Characteristics of Enrolled Patients for Training and Testing

A total of 203 radiomics features were selected out of the extracted 1777 features according to the Mann–Whitney U-test with a p <0.05. A final 10 features were further screened out from the 203 features to build the radiomics signature using the LASSO logistic regression model, as shown in Supplementary Figure 3. These features included 5 first order features, 2 grey level run length matrix (GLRLM) features, 1 Gray Level Dependence Matrix (GLDM), and 2 Gray Level Size Zone Matrix (GLSZM). Detailed features and their corresponding non-zero coefficients were presented in Table S1. The ROC evaluation of radiomics signature in the differentiation of PTC and PTMC was shown in Figure 3 with an AUC of 0.908 (95% CI: 0.887–0.928), 0.826 (95% CI: 0.734–0.918), and 0.822 (95% CI: 0.710–0.936) in the validation cohort, independent testing set 1 and independent testing set 2, respectively.

Figure 3 The performance of radiomics signature with ROC curves in (a) training, (b) independent testing set 1, and (c) independent testing set 2.

Table 2 shows the performance of different deep learning models in the differentiation of PTC and PTMC with an accuracy of 0.800 (95% CI: 0.700–0.837), 0.850 (95% CI: 0.775–0.875), 0.850 (95% CI: 0.737–0.873), 0.850 (95% CI: 0.763–0.875) and 0.863 (95% CI: 0.787–0.932) in the validation cohort for AlexNet, VGG13, VGG16, VGG19, and EfficientNet, respectively. The ROC curves and the corresponding confusion matrices of different deep learning models were shown in Figure 4. As shown in Figure 4b the AUCs of AlexNet, VGG13, VGG16, VGG19, and EfficientNet were 0.800 (95% CI: 0.708–0.892), 0.850 (95% CI: 0.772–0.928), 0.846 (95% CI: 0.765–0.927), 0.890 (95% CI: 0.818–0.962), and 0.867 (95% CI: 0.789–0.945), respectively. Accordingly, VGG19 and EfficientNet were selected to combine with radiomics signature for further analysis. To further interpret the results of deep learning models, a heatmap of output features was generated by activating the visualization class. The generated heatmap was then overlaid onto the original image to generate the final deep learning visualization heatmap, as shown in Figure 4a the US images of 3 cases of PTMC and PTC, and their activated heatmap with VGG19. The heatmap could produce a coarse localization map highlighting the import regions for the classification targets.

Table 2 The Performance of Different Deep Learning Models in the Training and Validation Cohorts

Figure 4 (a) The network features and heatmaps of 3 cases of papillary microcarcinoma of the thyroid and papillary thyroid carcinoma. The ROC curves and the corresponding confusion matrices of different deep learning models, (b) AlexNet, VGG13, VGG16, VGG19, EfficientNet; (c) ROCs comparison.

Detailed comparison of the performance of radiomics signature, deep learning models, and combined deep learning radiomics models with the independent testing set 1 and set 2 were presented in Table 3. The accuracy of VGG19, EfficientNet, Radiomics, combined radiomics VGG19 (R_V_combined), and radiomics EffiecientNet combination (R_E_combined) were 0.829, 0.798, 0.766, 0.904, 0.851 and 0.680, 0.680, 0.740, 0.780, 0.900 with the independent testing set 1 and set 2, respectively. Figure 5 shows the ROC curves of these models with an AUC of 0.826 (95% CI: 0.734–0.918), 0.890 (95% CI: 0.818–0.962), 0.867 (95% CI: 0.790–0.945), 0.931 (95% CI: 0.870–0.993), 0.908 (95% CI: 0.849–0.966) and 0.822 (95% CI: 0.709–0.936), 0.698 (95% CI: 0.546–0.849), 0.899 (95% CI: 0.806–0.993), 0.874 (95% CI: 0.778–0.969), 0.946 (95% CI: 0.885–1.000) for Radiomics, VGG19, EfficientNet, R_V_combined, and R_E_combined with the independent testing set 1 and set 2, respectively.

Table 3 The Performance of Radiomics Signature, Deep Learning Models and Combined Deep Learning Radiomics Models with Independent Test 1 and 2

Figure 5 The performance of radiomics, deep learning, and combined deep learning radiomics models in differentiating PTMC from PTC with (a) independent testing set 1; (b) independent testing set 2.

Discussion

In this study, the feasibility and accuracy of radiomics, deep learning models, and combined deep learning radiomics models were investigated in the differentiation of PTMC from PTC using US images. The models were further verified with external validation cohorts from a second hospital. An AUC of 0.826, 0.890, 0.867, 0.931, 0.908 and 0.822, 0.698, 0.899, 0.874, 0.946 for Radiomics, VGG19, EfficientNet, R_V_combined, and R_E_combined models was achieved with the independent testing set 1 and set 2, respectively.

With the increasing prevalence of high-resolution US and other imaging modalities, the diagnosis of thyroid cancers has increased remarkably and continuously.²¹ Most of the newly diagnosed thyroid cancers are small PTCs including PTMCs.²² Studies reported that the average size of thyroid tumors decreased from 1.51 cm in the year 2000 to 1.02 cm in 2005 with 36.9% of thyroid cancers being small than 1 cm in 2000, but PTMCs account for 61.48% of all thyroid cancers in 2005.²³ Similarly, PTMCs account for about 70% of all cases enrolled in this study. However, more evidence demonstrated that most PTMCs have a very indolent nature and excellent outcomes.²⁴ The increasing awareness of the impact of overtreatment on PTMCs also changed dramatically the international guidelines.⁶ Therefore, non-invasive methods are urgently needed to differentiate PTMC from PTC preoperative to avoid overtreatment for patients with PTMC regardless of the increased diagnosis of thyroid cancers.

US is widely applied for preoperative imaging of PTC for diagnosis and staging. US features, such as irregular border, halo sign microcalcifications, macrocalcifications, isoechoic, and hypoechoic appearance, had been investigated to differentiate PTC from PTMC clinically.²⁵ An AUC of 0.97, a sensitivity of 88.6%, and a specificity of 94.6% were reported in the prediction of PTMC with combined conventional US, CEUS, and RTE.⁹ However, the accuracy of US diagnosis is easily affected by image quality and the experience of US technician and radiologists, who handles the probe and interprets the US images.²⁴ With the emergence of radiomics, the radiomics model had been proposed as a promising method to assess the risk of PTC metastasis using US images and achieved an AUC of 0.782 and an accuracy of 0.710, respectively.²⁶ The presence of extrathyroidal extension (ETE) of PTC was preoperatively predicted with a radiomics model using CT images and achieved an AUC of 0.812 in the validation cohort.²⁷ Radiomics models were also investigated to predict the presence of B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutation in PTC with an average AUC of 0.651, an accuracy of 64.3%, a sensitivity of 66.8%, and a specificity of 61.8% using US images, respectively.²⁸ However, to the best of our knowledge, this is the first study to investigate the feasibility of US-based radiomics and deep learning models in the differentiation of PTC and PTMC. After manual target segmentation, feature extraction, and selection, an AUC of 0.826 and 0.822 was achieved with radiomics signature in the independent testing set 1 and set 2, respectively, in this study. The performance of our radiomics model was promising in comparison with other radiomics studies for PTC as mentioned previously.^28–30 However, it was inferior to the direct prediction of PTC and PTMC with combined US, CEUS, and RTE.⁹

Five deep learning networks were adapted and trained in this study to further investigate the differentiation of PTMC from PTC in this study. As shown in Table 2 and Figure 4, VGG19 achieved a best AUC of 0.890 and EfficientNet achieved a best accuracy of 0.867, respectively. As shown in Table 3, the performance of radiomics was inferior to two deep learning models in accuracy and AUC with independent testing set 1; however, the accuracy of the radiomics model was better than those of two deep learning models in accuracy with an external validation set (independent testing set 2). However, advanced deep machine learning approaches have been developed to handle the challenging health problems and to diagnose thyroid cancer.^31,32 In this study, the application of deep learning networks did not guarantee superior differentiation ability for PTC and PTMC. On the other hand, the interpretability of deep learning models was weaker in comparison with radiomics features.³³ In this study, visualization class was activated to generate a heatmap after the differentiation with deep learning networks to increase the interpretability of our deep learning models. As shown in Figure 4a the heatmap corresponding to the boundary of the tumor and the low echo area inside the tumor were highlighted, which are the features corresponding to deep learning network features.

In this study, the differentiation ability and accuracy of these models were further improved by combining radiomics and deep learning networks, as shown in Table 3 and Figure 5. The best accuracy and AUC of 0.904, 0.900, and 0.931, 0.946 were achieved with the combination of VGG + radiomics (R_V_Combined) and EffiecientNet + radiomics (R_E_Combined) in the independent testing set 1 and set 2, respectively. Consistently, deep learning radiomics had been reported to improve the prediction ability using US images for breast cancers.^34,35 The combination of radiomics scores and deep learning prediction scores was usually fused by information fusion, which includes three strategies of early fusion, mid-term fusion, and late-stage fusion.¹⁹ However, only the late-stage fusion strategy was applied in this study. Although automatic segmentation on US images for cervical cancer and ovarian cancer had been intensively investigated, further study is still necessary to transfer the automatic segmentation methods to PTC and PTMC on US images.^29,30 On the other hand, the stability and reproducibility of radiomics features may be easily affected by the type of scan machines and the different automatic segmentation algorithms.^36,37 Another limitation of this study is that the influence of clinical factors in the differentiation of PTMC and PTC was not fully investigated.

Conclusions

Deep learning and radiomics combination models are promising in the noninvasively preoperative differentiation of PTMC and PTC to decrease the overtreatment of patients with PTMC and to minimize the complications caused by overtreatment.

Ethics Approval and Informed Consent

This study was approved by the ethics committee in Clinical Research (ECCR) of authors’ hospital and conducted following the Declaration of Helsinki (ECCR no. 2019059). The requirement of informed consent was waived by the ECCR.

Acknowledgments

We would like to thank Jianping Wu, who made a significant contribution in the revision of this manuscript.

Funding

This research was supported partially by a National Natural Science Foundation (12475352), a key project of Zhejiang Natural Science Foundation (LZ24A050008), a Key project of Zhejiang Provincial Health Science and Technology Program (WKJ-ZJ-2437), a Major project of Wenzhou Science and Technology Bureau (ZY2022016, ZY2020011), Zhejiang Engineering Research Center for innovation and application of Intelligent Radiotherapy Technology, Zhejiang-Hong Kong Precision Theranostics of Thoracic Tumors Joint Laboratory, and Wenzhou key Laboratory of basic science and translational research of radiation oncology, Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, Discipline Cluster of Oncology, Wenzhou Medical University.

Disclosure

No potential conflict of interest relevant to this article was reported. This study had been presented in the 2023 conference of the American Association of Physicists in Medicine.

References

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13. Li X, Zhang S, Zhang Q, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study [published correction appears in Lancet Oncol. 2020 Oct;21(10):e462. doi: 10.1016/S1470-2045(20)30546-5]. Lancet Oncol. 2019;20(2):193–201. doi:10.1016/S1470-2045(18)30762-9

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Within months of Pennsylvania’s medical cannabis program adding anxiety as a qualifying condition, that diagnosis quickly rose to become the most common for cannabis certifications, according to a study by researchers at the University of Pittsburgh and Johns Hopkins University. The study was published today in Annals of Internal Medicine.

To date, 39 states have medical cannabis programs, with chronic pain and post-traumatic stress disorder (PTSD) historically being the most common and second-most common qualifying diagnoses, respectively, among participants. In recent years, several states, including Pennsylvania, have incorporated anxiety into their programs.

We found that adding anxiety as a qualifying condition fundamentally changed the makeup of Pennsylvania’s medical cannabis program.”

Coleman Drake, Ph.D., corresponding author, associate professor in the Department of Health Policy and Management at Pitt’s School of Public Health

The researchers analyzed Pennsylvania Department of Health data on 1,730,600 medical cannabis certifications issued from November 2017, when anxiety disorders were added, to December 2023. These certifications require a physician visit and annual renewal.

The team found that the number of certifications issued each month nearly tripled during that time. Before the list of qualifying conditions was expanded, chronic pain comprised the lion’s share of diagnoses, at 67%, followed by 16% for PTSD. After anxiety was added, these numbers dropped to 41% and 11%, respectively, and anxiety quickly became the most common diagnosis, at 60%. Some certifications listed multiple conditions. The team was not able to determine how the overall size of the program was affected, nor how many participants had added or switched to anxiety from another diagnosis or were enrolling in the program for the first time.

Evidence supporting cannabis as an effective treatment for anxiety disorders is scant in comparison to other qualifying conditions, notably chronic pain, notes Drake.

“Adding anxiety to the program may inadvertently signal to patients that cannabis is effective for treating it, despite the lack of evidence, which is concerning,” he said. “At the same time, cannabis may improve some health outcomes, relative to alternative treatments, depending on the individual and their circumstances.” But, due to the red tape and funding scarcity that has historically restricted research on cannabis-as well as the lack of more granular data available from medical and adult-use cannabis programs-these unknowns persist, he said.

“The urgency in filling these knowledge gaps is pretty clear, given increases in cannabis use over the past decade, and the large changes in cannabis markets, like those we observed in this study,” said Drake.

Other authors on the study were Linh Tran and Matthew Eisenberg, Ph.D., of Johns Hopkins University Bloomberg School of Public Health.

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Introduction

Diabetes Mellitus (DM) is a chronic metabolic disorder characterized by elevated blood glucose levels due to either insulin deficiency or insulin resistance.¹ It is commonly classified into Type 1 (T1DM) and Type 2 (T2DM). T1DM is primarily caused by an autoimmune-mediated destruction of pancreatic β-cells, leading to a complete deficiency of insulin, while T2DM is characterized by insulin resistance and a progressive decline in insulin secretion.² Several risk factors increase the likelihood of developing T2DM, including a strong family history of diabetes, advancing age, obesity, and physical inactivity.³ In Saudi Arabia, the prevalence of diabetes is notably high, with 17.7% of adults living with the condition, according to the International Diabetes Federation.⁴ Alarmingly, the prevalence of T2DM has risen from 15.8% in 2016 to 18.2% in 2021, and if the trend continues, it is projected to exceed 20% by 2026.⁵

Effective management of DM requires significant lifestyle modifications, including structured meal planning and regular physical activity.⁶ In patients who are overweight or obese, even a modest reduction in body weight has been shown to improve glycemic control, lipid profiles, and blood pressure regulation.⁶ However, inadequate disease management and poor adherence to prescribed treatment regimens can lead to serious chronic complications. These complications are broadly categorized into macrovascular complications, such as coronary artery disease, stroke, and myocardial infarction, and microvascular complications, including diabetic retinopathy, nephropathy, and neuropathy.⁶ Among patients with T2DM in Saudi clinics, nephropathy was the most common microvascular complication (80.2%), followed by retinopathy (32.7%) and neuropathy (8.4%).⁷ Moreover, according to an analysis from the Saudi Health Interview Survey (SHIS), 3.5% of patients with diabetes had experienced a myocardial infarction, and 1.2% had suffered a stroke.⁸

Medication non-adherence is a well-documented challenge in the management of chronic diseases, particularly diabetes and hypertension, with global estimates indicating that up to 50% of patients do not adhere to their prescribed regimens.⁹ In Al-Ahsa, Saudi Arabia, previous studies have reported medication non-adherence rates as high as 65% among diabetic patients receiving primary care.⁹ Poor adherence to antidiabetic medications significantly compromises glycemic control, accelerates disease progression, and increases the risk of adverse health outcomes and complications.^9,10 Moreover, inadequate adherence is frequently associated with poor metabolic control, contributing to both immediate and long-term complications.¹¹ Despite the clinical significance of adherence, accurately assessing medication adherence remains a persistent challenge in diabetes management.¹²

The Adherence to Refills and Medications Scale (ARMS), comprising 12 items, is a widely utilized tool for assessing medication adherence. Its psychometric robustness, including reliability and validity, has been demonstrated in English-speaking populations,¹³ and it has been successfully translated and validated in several languages, such as Turkish,¹⁴ Korean,¹⁵ Chinese,¹⁶ Polish,¹⁷ and Arabic,¹⁸ particularly among patients with chronic conditions like diabetes and hypertension. Due to the lack of studies on refill adherence and the underrepresentation of certain populations, this is the first study to apply the ARMS tool to assess medication adherence among patients with T2DM in the Al-Ahsa region of Saudi Arabia. Using a stratified random sampling technique, the study aims to identify key factors influencing adherence in this population.

Methodology

Study Design and Sample Size

This is a cross-sectional study conducted over a three-month period, from May 2024 to July 2024, among T2DM patients in Al-Ahsa, Saudi Arabia. The primary aim was to assess adherence to refills and medications among patients with T2DM. Data were collected through phone call interviews using a structured questionnaire. The target population consisted of adult T2DM patients who visited governmental primary healthcare centers (PHCs), which provide free healthcare services, between October 2023 and March 2024. Patients diagnosed with T1DM and individuals who refused to participate or did not complete the study requirements were excluded.

A stratified random sampling method was employed to gather data. Initially, all PHCs in the Al-Ahsa region were categorized into four strata—Eastern, Middle, Northern, and Southern—based on their geographical locations. A specific sample size was allocated to each stratum, and participants were then randomly selected within each stratum using simple random sampling via Excel software (Figure 1). Table 1 shows the estimated sample size for each stratum, calculated using the formula with Z = 1.96 (95% confidence interval), E = 0.05 (5% margin of error), and P = 50%.

Table 1 Calculated Sample Size for Each Geographic Stratum

Figure 1 A Stratified Random Sampling Framework for Selecting Study Participants.

Data Collection

In this cross-sectional study, questionnaires were administered through phone call interviews, while weight, height, and HbA1C measurements were obtained from system records. For illiterate participants, the survey was completed with the assistance of their relatives. The self-reported questionnaire included sections on sociodemographic characteristics (age, gender, income level, marital status, level of education, occupation, and residency) and clinical profiles (family history of DM, age of onset of DM, disease duration, type and number of medications prescribed, duration of exercise, complications of DM, frequency of blood sugar monitoring, whether the physician provided information about medications, whether the physician provided adequate care, availability of assistance with medication, reasons for non-adherence to medication, number of doctor visits per year, and comorbid conditions and medications prescribed). The final section utilized the ARMS to assess medication adherence.¹⁸ Internal validity was evaluated using Cronbach’s alpha (α = 0.74), indicating acceptable internal consistency and reliability of the questionnaire within the study population.

Statistical Analysis

Data analysis for this study was conducted using SPSS version 27 (IBM Corp., 2020). Descriptive statistics were used to display the characteristics of the study population, including demographic, diabetes-related, health profile, and adherence-related variables. Continuous variables such as age and duration of diabetes were described using mean and standard deviation, while categorical variables such as gender, region, marital status, and diabetes treatment were summarized using frequency and percentage. For the analysis of factors influencing medication adherence and refill behaviors, bivariate tests, including Pearson’s Chi-square test, were used to examine relationships between independent variables (for example, demographics, diabetes control, and healthcare access) and the dependent variable of adherence. Significant associations were further explored using multiple logistic regression to identify the predictors of non-adherence to medication and refill behavior. Adjusted Odds ratios (OR) and confidence intervals (CI) were reported to assess the strength and direction of these associations. A P-value less than 0.05 was considered for statistical significance.

Ethical Statement

The confidentiality of all participants will be strictly maintained. Ethical approval has been obtained from the King Fahad Hospital-Al-Hofuf Ethics Committee (IRB-KFHH No. H-05-HS-065). The study will be thoroughly explained to potential respondents, and an informed consent will be obtained from all participants. The study will adhere to the principles outlined in the Declaration of Helsinki.

Results

Table 2 presents the bio-demographic characteristics of 732 type 2 diabetic patients in Saudi Arabia. Geographically, the study population was relatively evenly distributed across the regions, with the Eastern region at 27.9% (No=204), the Middle region at 26.5% (No=194), the Northern region at 25.7% (No=188), and the Southern region at 19.9% (No=146). Regarding gender, exact of 378 (51.6%) were females. The age distribution showed 223 (30.5%) aged 50–59 years and a mean age of 54.5 ± 12.7 years. In terms of body mass index, the majority of participants were either obese class I (30.7%, No=225) or overweight (27.7%, No=203), while only 15.0% (No=110) were within the normal weight range. A significant proportion of the participants reported a monthly income of less than 5000 SR (66.8; 489). The vast majority were married, accounting for 85.4% (No=625). Educational levels were varied, with basic education being the most common at 39.3% (No=288). Occupationally, nearly half were not working or were students, at 47.4% (No=347). Most participants resided in downtown areas, representing 71.4% (No=523). Notably, a substantial 86.6% (No=634) reported a family history of diabetes mellitus.

Table 2 Bio-Demographic Characteristics of the Study Type 2 Diabetic Patients in Saudi Arabia (N=732)

Table 3 shows the distribution of diabetes-related variables among 732 type 2 diabetic patients in Saudi Arabia. The age of diabetes onset was most frequently 50 years or older, comprising 31.4% (No=230) of the cases, while the lowest frequency was observed in those with onset between 30–39 years at 18.9% (No=138). Regarding treatment, oral pills were the most common modality, utilized by 48.2% (No=353) of patients, whereas diet and exercise alone represented the lowest at 0.7% (No=5). As for the duration of diabetes, the highest percentage being less than 5 years at 31.4% (No=230). About glycemic control, well-controlled HbA1c levels (<7%) were observed in 36.2% (No=265) of patients, while poor control (>8%) was seen in 31.6% (No=231). The most frequent blood glucose monitoring was more than once daily, at 28.7% (No=210), and the least frequent was every 3 months, at 0.4% (No=3). Notably, 57.8% (No=423) reported no diabetic complications, while retinopathy was the most prevalent complication at 31.3% (No=229), and stroke was the least at 1.8% (No=13).

Table 3 Distribution of Diabetes-Related Variables Among Type 2 Diabetic Patients in Saudi Arabia (N=732)

Table 4 illustrates the health profile of Type 2 diabetic patients in Saudi Arabia. Considering other comorbidities, the most common condition among the participants was hypertension, affecting 357 individuals (49.0%). Other comorbidities included rheumatic diseases, osteoarthritis, and others (132, 18.1%) and diseases of the digestive system such as inflammatory bowel disease (96, 13.2%). Regarding medication use, most participants were on multiple medications. A significant portion (192, 26.2%) received three medications, while six or more medications were prescribed to 142 participants (19.4%). Regarding smoking habits, 105 participants (14.3%) reported that they smoke. Finally, exercise habits show that a few numbers of participants (201, 27.5%) do not engage in any physical activity. Among those who do exercise, 294 participants (40.2%) reported engaging in less than 60 minutes of physical activity weekly, while 147 participants (20.1%) met the recommended 60–150 minutes of exercise per week.

Table 4 Health Profile of Type 2 Diabetic Patients in Saudi Arabia: Comorbidities, Medication, and Lifestyle Behaviors (N=732)

Table 5 clarifies data on healthcare access and medication support among 732 type 2 diabetic patients. A significant majority, 85.9% (No=629), reported receiving information about anti-diabetic medications from their physicians. Regarding the frequency of doctor visits, the highest percentage, 36.5% (No=267), reported visiting their doctor more than three times a year, and only 3.7% (No=27) reported visiting their doctor once a year. The vast majority of patients, 88.5% (No=648), confirmed they were continuing to receive healthcare from their doctors, with only 11.5% (No=84) reporting otherwise. In terms of medication assistance, a total of 79.9% (No=585) of patients reported managing their medications independently, while 20.1% (No=147) indicated they had someone who helped them.

Table 5 Healthcare Access and Medication Support in Type 2 Diabetic Patients (N=732)

Table 6 examines medication adherence and refill behaviors among 732 type 2 diabetic patients. A significant 33.9% of patients reported forgetting to take their medications at least sometimes. Similarly, 20.1% decided not to take their medications at least sometimes. Regarding medication refills, 13.4% forgot to refill their medications, and 14.2% ran out of medications at least sometimes. Furthermore, 16.7% skipped a dose without consulting their doctor, and 20.5% missed medications when feeling better. Notably, 6.6% missed medications when feeling sick, indicating a potential vulnerability during illness. Carelessness led to missed medications for 11.7%. A substantial 33.7% changed their medication dose to suit their needs, highlighting potential issues with medication management. Additionally, 20.1% forgot medications that they were supposed to take more than once a day. Finally, 4.1% put off their medications due to high costs. Conversely, a combined 58.6% reported refilling their medication before running out, indicating a reasonably high rate of proactive refill behavior.

Table 6 Adherence to Medication and Refill among Type 2 Diabetic patients (N=732)

Figure 2 displays the adherence rate to refills and medications among 732 type 2 diabetic patients. A total of 52.5% (n=384) of patients were classified as adherent and 47.5% (n=348) were classified as non-adherent.

Figure 2 Type 2 Diabetic Patients’ Adherence Rate to Re-fill and Medications (n=732).

Figure 3 presents the reasons that reduce medication adherence among Type 2 diabetic patients. The majority of participants (62.4%) reported no reasons for reducing their commitment to taking medication. Among those who reported barriers to adherence, the most common reason was forgetting to take medication (22.1%). Other reasons included stopping medications due to side effects (8.1%) and stopping medication when feeling well (8.6%). A smaller proportion of patients stopped taking medications because of their multiplicity (5.6%) or taking medication only when the disease worsens (5.1%). Financial constraints were less common, with only 1.4% of patients reporting inability to afford medication as a reason for non-adherence. Psychological factors and concerns about kidney and liver failure were reported by just 0.13% of patients each.

Figure 3 Reasons That Reduce Commitment to Taking Medication among Type 2 Diabetic Patients.

Table 7 examines factors associated with refill and medication adherence among type 2 diabetic patients. A highly significant association (p = 0.001) was observed with region. Patients residing in the Southern region demonstrated the lowest adherence rate, with only 22.6% being adherent, compared to higher adherence rates in the Eastern (62.3%), Middle (60.3%), and Northern (56.9%) regions. Monthly income also significantly impacted adherence (p = 0.001). Patients with a monthly income of less than 5000 SR exhibited the highest adherence rate at 61.1%, while those in higher income brackets showed substantially lower adherence. Marital status significantly influenced adherence (p = 0.002). Single patients had the lowest adherence rate, with only 32.6% being adherent, compared to 55.0% for married patients. Occupation showed a significant association with adherence (p = 0.008). Patients not working or students had a higher adherence rate of 56.5%, while retired patients had a lower rate of 42.9%. Residence type also showed a significant association (p=0.016). Patients who live in rural areas had a higher adherence rate of 56.1% compared to those who live in immigration areas (15.4%) or downtown (52%).

Table 7 Factors Associated with Diabetic Patient’s Re-fill and Medication Adherence

Table 7 presents other factors associated with diabetic patients’ medication refill and adherence. Significant factors influencing adherence include the age of onset of diabetes, with those diagnosed at age 50 or older showing the highest adherence (61.3%) compared to younger groups (p=0.001). Diabetic complications were also a key factor, with those without complications having better adherence (57.0%) than those with complications (46.3%, p=0.004). Diabetic control, as measured by HbA1c levels, showed that patients with well-controlled diabetes (<7%) had better adherence (58.1%) compared to those with poorly controlled diabetes (>8%, 45.9%, p=0.025). Monitoring blood glucose levels more frequently was positively associated with adherence. Co-morbidities also played a role; patients without other co-morbidities demonstrated better adherence (60.1%) compared to those with additional health conditions (48.2%, p=0.002). The number of medications prescribed influenced adherence as well. Patients on 3 medications showed the highest adherence (67.2%), while those on 6 or more medications had lower adherence (43.0%, p=0.068). Receiving information from a physician about anti-diabetic medications was another significant factor; those who received guidance had higher adherence (56.8%) compared to those who did not (26.2%, p=0.001). Doctor visits also correlated with adherence. Patients who visited their doctor more frequently (three or more times a year) had better adherence (66.3%) compared to those who only visited once a year (37.0%, p=0.001). Finally, the presence of a helper for medication was not significantly associated with adherence (p=0.134).

Table 8 presents the results of a multiple logistic regression analysis that identifies the predictors of medication non-adherence among type 2 diabetic patients. Patients from the Southern region were significantly more likely to be non-adherent compared to those from the Eastern region (OR=6.59, p=0.001). Additionally, higher monthly income was associated with better adherence (OR=1.40, p=0.021), indicating that patients with greater financial resources were more likely to follow their prescribed medication regimens. Regarding occupation, non-medical field occupations were linked to higher odds of non-adherence (OR=1.13, p=0.049. Patients living in downtown areas had a lower likelihood of non-adherence (OR=0.71, p=0.038). Furthermore, age at onset of diabetes was a significant factor, with older patients or those diagnosed later in life being less likely to be non-adherent (OR=0.98, p=0.009). The presence of diabetic complications was another significant predictor of non-adherence, as patients with complications were more likely to be non-adherent (OR=1.44, p=0.047). A key finding was the lack of physician-provided information about anti-diabetic medications, which significantly increased the odds of non-adherence (OR=2.15, p=0.005). More frequent doctor visits were associated with better adherence (OR=1.53, p=0.001). Finally, infrequent healthcare access was strongly associated with non-adherence (OR=2.34, p=0.003).

Table 8 Multiple Logistic Regression Model for Predictors of Type 2 Diabetic Patients Medications Non-adherence

Discussion

Medication adherence is defined by the Food and Drug Administration (FDA) as the extent to which patients take their medications as prescribed, in agreement with their healthcare provider. Optimal adherence to diabetes medication and lifestyle modifications has been shown to reduce emergency room visits, hospitalizations,¹⁹ and diabetes-related complications.²⁰ There are several factors that contribute to medication adherence in patients with DM, including family support, routine, side effects, complexity of the medication regimen, blood sugar levels, and forgetfulness. Additionally, one of the major factors influencing adherence is the relationship between healthcare professionals and their patients.⁹

Our study found that adherence to medication and refills was 52.5%, as measured by ARMS. In comparison, a previous study conducted in the Al Hasa region of Saudi Arabia in 2012 reported a non-compliance rate of 67.9%,⁹ which is significantly higher than our findings. The difference in adherence rates may be influenced by the passage of time and the impact of the COVID-19 pandemic on people’s behavior regarding medication adherence and refills. Nonetheless, the medication adherence rate observed in our study was satisfactory and higher than those reported in studies conducted in the Eastern Province of Saudi Arabia, Egypt, Switzerland, and Ethiopia, where adherence rates were 34.7%, 38.9%, 40%, and 51.3%, respectively.^21–24 However, it was lower than the rates observed in Tabuk²⁵ (76.4%). These variations in adherence levels could be attributed to several factors, including the availability of free medical services, varying levels of awareness regarding the importance of medication adherence, regional strategies to promote adherence, and differences in the measurement tools used across studies.²⁵

In this study, significant factors influencing medication adherence and refills included income level, marital status, occupation, and residency area. Similarly, a prior study conducted in Ethiopia identified financial difficulties as the primary external barrier to medication adherence.²⁴ Despite the availability of free healthcare in Saudi Arabia, issues such as transportation challenges⁹ and difficulties in scheduling clinic visits still impact adherence. Marital status also emerged as a significant factor, with a study by Alfulayw et al identifying a significant association between marital status and medication adherence.²¹ However, a study conducted in Al-Ahsa, Saudi Arabia, found no statistically significant relationship between marital status and adherence.⁹ Regarding residency, several studies have reported significant differences between rural and urban populations, with rural residents exhibiting higher adherence and refill rates.^9,26 However, a study by Alfulayw et al did not observe a significant association between place of residence and medication adherence.²¹ Consistent with our findings, occupation was also identified as a significant factor influencing adherence in the Saudi population, as reported by Alfulayw et al.²¹

This study found that age was not a significant factor in medication adherence and refills. However, earlier studies have suggested that adherence tends to increase with age.^21,23 On the other hand, Shams et al reported lower adherence among elderly patients, with good adherence rates of 28.1% in the elderly, 36.2% in middle-aged adults, and 51.8% in younger individuals.²² Gender was not significantly associated with medication adherence in our study, a finding consistent with several previous reports.^21,22,26 However, some studies have suggested that women are more likely to adhere to treatment regimens,^9,27 while others have reported higher adherence among men.^23,28 Additionally, our study found no significant association between educational level and adherence, which aligns with the findings of multiple prior studies.^21,26

Our study found a non-statistically significant association between smoking status and adherence to medications and refills. This result contrasts with findings from a study conducted in Shiraz, Iran, which reported a significant relationship between smoking and medication adherence.²⁹ Similarly, no significant association was observed between physical activity and medication adherence in our study. However, a study from Eastern Nepal demonstrated that daily jogging was significantly associated with improved adherence,³⁰ supporting the hypothesis that regular physical activity may enhance mood and motivation, thereby promoting better adherence—particularly among individuals following a structured exercise regimen.²¹ In terms of diabetes information, factors such as the age of onset, having diabetic complications, lower HbA1c levels, daily blood glucose level (BGL) checks, other comorbidities, receiving sufficient information from physicians about antidiabetic medications, the frequency of annual doctor visits, and the continuity of healthcare have a statistically significant relationship with adherence to medications and refills. For instance, a study conducted in Johor, Malaysia, found that medication adherence was significantly associated with good glycemic control.³¹ Similarly, a study in Shiraz, Iran, reported the same findings.²⁹ Additionally, a study in the Kurdistan Region of Iraq indicated that non-adherent participants were more likely to have higher HbA1c levels, reflecting poorer glycemic control.³² Nonadherence to medications and refills has been attributed to factors such as polypharmacy, financial constraints, or concerns about side effects.³³ Participants who were prescribed a glucometer for self-monitoring of BGLs experienced positive outcomes, such as lower BGLs.³⁴ Furthermore, a good relationship with the physician helped reduce concerns and issues related to medications, leading to more frequent visits to the same physician for regular check-ups, thereby ensuring continuity of care.^35–37

Our study found a significant association between the presence of chronic diseases and medication adherence and refill rates, whereas the number of medications was not statistically associated with adherence. In contrast, a study conducted at a tertiary hospital in Saudi Arabia involving 8932 adults with DM found that approximately 78% experienced polypharmacy, with a higher prevalence observed among women and patients with multiple comorbid conditions, including cardiovascular disease, chronic kidney disease, musculoskeletal disorders, respiratory issues, and mental health conditions.³⁸ Due to the numerous complications associated with DM, patients often require more medications, and factors such as the cost of polypharmacy, pill burden, and drug side effects contribute to polypharmacy, ultimately worsening the patient’s condition.³⁹

Limitations

Our study has several limitations, including recall bias, as participants may not accurately remember past treatments, and non-response bias, which may affect data accuracy. Additionally, selection bias is a concern since all participants were recruited from governmental PHCs offering free healthcare, excluding the private sector, which may impact the external validity and generalizability of the findings. The cross-sectional design prevents establishing causal relationships, requiring longitudinal studies for better insight into temporal associations. Furthermore, as the study was questionnaire-based, it is subject to self-reporting bias, which may lead to overestimation or underestimation of adherence behaviors.

Conclusion

This study highlights that nearly half of patients with T2DM in Al-Ahsa were non-adherent to their prescribed medications and refills, as measured by the ARMS, despite the availability of free healthcare services. The findings emphasize the multifactorial nature of medication adherence, with significant associations observed across sociodemographic, clinical, and healthcare-related variables. Key factors influencing adherence included income level, marital status, occupation, residency area, diabetic complications, glycemic control, frequency of blood glucose monitoring, and continuity of physician care. Importantly, multivariable analysis identified residence in the Southern region, presence of complications, lack of physician counseling, and limited access to healthcare as strong predictors of non-adherence.

The study underscores the need for targeted, patient-centered interventions aimed at improving medication adherence, especially among high-risk groups. Educational initiatives, enhanced communication between patients and healthcare providers, and better continuity of care are essential to address barriers to adherence. Future research should further explore behavioral and psychosocial determinants of adherence and assess the effectiveness of tailored interventions in improving long-term treatment outcomes for diabetic patients in different regions of Saudi Arabia.

Data Sharing Statement

All relevant data supporting the findings of this study are included within the manuscript.

Acknowledgment

The authors would like to express their sincere gratitude to all the patients who participated in this study.

Disclosure

The authors declare no conflicts of interest in this work.

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A study published in The Journals of Gerontology: Series A reports that a novel combination of brain stimulation and personalized coaching significantly increased physical activity in older adults and held steady for months. The results offer hopeful news for inactive older adults living in subsidized housing, who may experience several barriers to increased activity, including depression and a lack of motivation.

Regular physical activity is an extremely effective, safe, and modifiable means to improve health, especially in older adults. But more than 85% of adults aged 65 and above regularly fail to meet federal physical activity guidelines, and insufficient physical activity remains a global health issue.

In the randomized trial, researchers found that inactive older adults who received transcranial direct current stimulation (tDCS), a noninvasive technique that delivers low-level electrical currents to targeted areas of the brain, along with individualized behavioral coaching, increased their daily step count an average of 1,179 steps per day.

That’s more than twice the increase seen in those who received coaching and placebo stimulation. Importantly, this boost persisted for three months after the program ended, and adherence to both the stimulation and coaching was exceptionally high.

Participants in the tDCS group received 10 brief 20-minute sessions over two weeks, targeting the left dorsolateral prefrontal cortex, a region associated with motivation, planning, and goal-directed behavior. They also received a personalized behavioral program that continued for two months. The coaching, delivered by physical therapists, included regular phone check-ins, individualized step goals, and practical strategies – like marching during commercials or walking with friends – to help participants gradually increase their daily movement. Researchers tracked daily steps using Fitbits and continued monitoring participants for 12 weeks after the intervention ended.

Adherence was significant: 97% of tDCS sessions and 93% of coaching sessions were completed, with Fitbit usage remaining strong throughout the intervention. Even during the no-contact retention phase, many participants maintained increased activity, suggesting the behavioral changes had taken hold.

Beyond the physical gains, participants in the tDCS group also reported improved motivation and greater perceived walking ability. The study’s findings suggest that tDCS may enhance not just the drive to move but also the stickiness of behavior change, particularly when paired with accessible, goal-driven coaching.

While tDCS has been explored in clinical and lab settings to improve mood, memory, and motor function, this study is among the first to test its use to support real-world health behavior change in older adults, particularly those with limited resources.

Helping older adults build and maintain healthy habits is notoriously difficult, especially in underserved communities. This study provides early but exciting evidence that a short course of brain stimulation can ‘prime the pump’ – enhancing motivation and helping new behaviors stick – and is encouraging, especially given the setting. The program was delivered entirely within participants’ housing facilities, which removed barriers to access. That model could be a blueprint for future community-based interventions.”

On-Yee (Amy) Lo, PhD, assistant scientist II at the Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife

The authors caution that larger trials are needed to confirm the findings and explore how tDCS might be used to amplify other types of behavioral health programs. They also note the importance of exploring how cognitive function, baseline activity level, and social support might affect outcomes.

Still, for a population at high risk for inactivity-related decline, this study offers hope – and a potential new approach – for getting and keeping older adults moving.

The researchers included Levi Ask; Melike Kahya, PT, PhD, assistant professor of physical therapy at High Point University; Thomas Travison, PhD, senior scientist at the Marcus Institute; Lewis Lipsitz, MD, director of the Marcus Institute and chief academic officer of the Irving and Edyth S. Usen and Family Chair in Medical Research, Hebrew SeniorLife; and Brad Manor, PhD, senior scientist at the Marcus Institute.

The study, Modulating Brain Activity to Improve Goal-directed Physical Activity in Older Adults: A Pilot Randomized Controlled Trial, is published in The Journals of Gerontology: Series A, Volume 80, Issue 6, June 2025, glaf039.