Expert panelists discuss the essential role of primary care in early skin cancer detection, balancing clinical judgment with emerging AI artificial intelligence tools, and more.

Primary Care’s Role in Skin Cancer Detection

Genitourinary syndrome of menopause (GSM) encompasses a spectrum of genital and urinary symptoms resulting from hypoestrogenism after menopause. It affects more than half of postmenopausal women, yet most do not receive treatment, often due to stigma or underrecognition in clin

Symptoms are progressive rather than self-limiting, and their impact on sexual health, urinary comfort, and quality of life is comparable to other chronic conditions.⁴ Primary care clinicians are in a key position to identify, educate, and initiate effective management for GSM.

1. What is genitourinary syndrome of menopause? 

GSM describes structural and functional changes to the vulva, vagina, urethra, and bladder associated with declining estrogen and other sex steroids.¹,² The term, introduced by an international consensus in 2014, replaced “vulvovaginal atrophy” and “atrophic vaginitis” to better capture the broad range of genital and urinary symptoms and to use less stigmatizing language.¹  

GSM affects an estimated 50%–70% of postmenopausal women, but only about 1 in 4 seek care.³–⁵ Unlike vasomotor symptoms, which usually resolve, GSM is chronic and progressive, often worsening over time without treatment.¹,² Its burden on quality of life is substantial, interfering with intimacy, self-image, and daily comfort.⁶

2. What are the typical symptoms and underlying mechanisms of GSM?

 include dryness, irritation, itching, and dyspareunia, often accompanied by narrowing or loss of elasticity of the vaginal canal.³–⁵


 may include urgency, frequency, dysuria, and recurrent urinary tract infections (UTIs).⁶

Estrogen deficiency leads to thinning of the vaginal epithelium, loss of collagen, reduced vascularity, and decreased glycogen production.¹,² These changes increase vaginal pH from a physiologic 3.5–4.5 to 5.5–7.0, encouraging pathogenic overgrowth and recurrent infection.¹,² Although the vaginal microbiome shifts after menopause, studies suggest it is not the primary driver of symptoms.⁷–⁹

3. How should clinicians diagnose and screen for GSM?

GSM is a clinical diagnosis based on compatible symptoms, examination findings, and menopausal context.¹⁰ Symptoms and physical signs may not always correlate; the purpose of examination is as much to rule out alternative conditions—such as lichen sclerosus, vulvodynia, or infection—as to confirm hypoestrogenic changes.¹⁰

Because embarrassment often prevents disclosure, clinicians should inquire proactively during wellness or chronic care visits.³–⁵ Approximately half of women report self-treating with over-the-counter products such as barrier creams or topical anesthetics, which may worsen irritation.¹¹

4. What nonhormonal treatments are available?

Nonhormonal options are appropriate for mild GSM or when hormonal therapy is contraindicated or declined.

 (polycarbophil or hyaluronic acid formulations) used two to three times weekly can improve dryness and sexual comfort and may provide similar benefit to low-dose estrogen in some studies.¹²,¹³

 (water-, silicone-, or oil-based) are recommended for sexual activity; simpler formulations such as coconut oil or petroleum jelly minimize irritation.¹²

 can alleviate pain and improve sexual function when adherence is maintained, though long-term continuation rates are low.¹⁹,²⁰

, an oral selective estrogen receptor modulator, improve dyspareunia and vaginal function for moderate symptoms.¹⁴–¹⁶

 have not demonstrated benefit over sham procedures in randomized trials and should not be recommended.¹⁷,¹⁸

5. What are the hormonal treatment options?  

 remains the gold standard for moderate to severe GSM.¹⁴ Formulations include:

 (estradiol or conjugated estrogens), used twice weekly

 (estradiol), replaced every 3–4 months

Multiple randomized trials confirm that local estrogen effectively improves dryness, dyspareunia, and vaginal pH across formulations.²¹–²⁷ Despite proven efficacy, adherence is low—only 16%–44% of women continue therapy beyond the initial prescription.¹⁶ Addressing access, education, and comfort with self-application is crucial for sustained use.

6. How does local estrogen therapy affect urinary symptoms?

Vaginal estrogen reduces recurrent UTI incidence by roughly 50%, with some evidence suggesting creams may be more effective than tablets or rings.²⁸–³⁰ Topical therapy also improves urgency and incontinence symptoms, whereas systemic hormone therapy can worsen them.²⁸–³⁰ For postmenopausal women with recurrent UTI or overactive bladder, local estrogen should be considered first-line adjunctive therapy.

7. Is low-dose vaginal estrogen safe, including for breast cancer survivors?

Extensive data show no increased risk of cardiovascular disease, breast cancer, or endometrial pathology with low-dose vaginal estrogen.³³–³⁷ The Women’s Health Initiative observational data and several large cohort studies (>2 million women) found no elevation in cancer or cardiovascular events.³³,³⁴
Among breast cancer survivors, multiple population studies and meta-analyses show no increased recurrence risk with local therapy.³⁵ Given minimal systemic absorption, low-dose vaginal estrogen is considered safe and reasonable when GSM symptoms significantly affect quality of life.³⁵–³⁷

8. When should primary care clinicians refer?

Most GSM can be managed within primary care. Referral is appropriate for:

Persistent or refractory symptoms despite first-line therapy

Uncertain diagnosis (e.g., dermatoses, vulvodynia, malignancy)

Complex comorbidities or severe anatomical changes

Because only 25% of affected women receive treatment,³–⁵ proactive inquiry and early management in primary care can close the treatment gap and markedly improve quality of life.⁶

Articles

Genitourinary symptoms of menopause often are ignored--by patients and practitioners alike. Primary care clinicians are in a key position to identify, educate, and initiate treatment. 

Genitourinary Syndrome of Menopause (GSM): Clinical FAQ for Primary Care

, federal law has required all mammography facilities to notify patients if they have 

—a designation that affects nearly half of women undergoing screening and significantly impacts cancer detection rates.

 Dense breast tissue not only makes cancers harder to detect on standard mammography but is itself an independent risk factor for breast cancer. These changes have thrust primary care physicians (PCPs) to the forefront of counseling patients about supplemental screening options, risk stratification, and the latest guidelines from multiple professional societies. The complexities can be daunting: Which patients truly benefit from supplemental MRI? When should risk assessment begin? How do emerging technologies like abbreviated MRI and artificial intelligence (AI) tools factor into shared decision-making? 

The Menopause Society (TMS) 2025 Annual Meeting

, October 21-25, Donna Plecha, MD, a nationally known expert in breast imaging, presented on evidence-based approaches to 

® met with Plecha during the meeting to discuss practical strategies for risk assessment, the latest screening guidelines, cost-effective supplemental imaging options, and emerging AI technologies that may soon revolutionize personalized screening pathways. 

Plecha is chair and Theodore J. Castele professor of radiology at the Case Western Reserve University School of Medicine in Cleveland, Ohio, and the Ida and Irwin Haber and Wei-Shen Chin chair of the department of radiology at University Hospitals Medical Center.

The following transcript of the interview has been lightly edited for length, style, and flow.

Patient Care®: Dr Plecha, you are presenting during a symposium on breast health. Can you briefly describe your presentation?

 in patients with dense breast tissue. When we get a mammogram, we find out if they have dense breasts. I'm also talking about making sure that PCPs know recommendations from different guidelines—from the NCCN guidelines and the ACR guidelines—of how to screen patients to see if they are at high risk, even before they get a screening mammogram. We want to evaluate their risk at a young age, before they're even at the age of 40 when they should be starting screening mammography, in case they need to be screened earlier.

PC: What are one to two takeaways you hope clinicians will take away from your presentation?

 Number one is to do a risk assessment of your patients starting at the age of 25, which is hard to believe. But if you start at the age of 25, you might find with these risk assessment tools that we currently have—which may advance and get better in the future—you can identify patients that might need to be screened earlier, might need genetic counseling, might need genetic testing. All of those things that we can catch really early before it's too late, because some patients, and especially African American women, can get breast cancer at earlier ages. They're more aggressive and they're more likely to die from breast cancer. So it's good to be able to identify patients early if they're at risk.

Number two is to start screening routine patients at routine risk at the age of 40 and to screen every year, because you're more likely to save more lives if you do that.

The third thing is for those patients with dense breasts, to consider supplemental screening. Breast MRI has been shown to be the most sensitive tool, especially in patients with dense breast tissue. So those three points are the main takeaways.

PC: You mentioned screening recommendations. Can you walk through the most important screening recommendations, or what the differing recommendations are?

For additional coverage on women’s health and cancer screening, check out:

Donna Plecha, MD: Why You Should Start Breast Cancer Risk Assessment at Age 25

Breast Cancer Screening Updates: USPSTF Lowers Starting Age to 40

Menopausal VMS Education in Primary Care: Build Trust First, Says One Clinician

World Cancer Day 2025: 7 Screening Recommendations from the USPSTF

Cervical Cancer Screening Guidelines: Key Updates for Primary Care Physicians

3 Breast Screening Tips for Primary Care Physicians

 There are multiple different recommendations out there. I would say that they all agree that to save the most lives you have to start at the age of 40. Even the 

United States Preventive Services Task Force

 lowered their age of starting to screen to the age of 40.

 Now, to save the most lives, you should do it annually. Studies have shown that. If you think about the women in their 40s, if you're not going to screen them, those are the patients you're more likely to add really quality years of life onto if you screen them in their 40s, because they have more life to live. If you catch it early, you have a really good chance of curing.

The American College of Radiology and the NCCN guidelines both agree that starting at the age of 40 and using tomosynthesis, which is the 3D mammogram, is the best way to decrease mortality and morbidity from breast cancer.

PC: Can you walk us through your approach when a patient is told they have dense breasts? What is the decision-making process for determining who needs supplemental screening beyond standard mammography?

 Just to be clear, there is a national law that now states that every patient needs to be told if they have dense breast tissue or not when they get their mammogram. That went into effect in September of 2024. The FDA started this, that all mammogram facilities must provide all patients information to let them know if they have dense breast tissue or not.

On top of that, there are guidelines. Sometimes people are a little critical of the American College of Radiology because some people say, "Well, you're just trying to increase volumes and make money and tell patients that you have to keep screening." But NCCN guidelines, which is not a College of Radiology, recommends screening in extremely dense patients.

There are 4 groups of density: two which are non-dense and two which are dense. The two that are dense—there's one that's called heterogeneously dense, and then there's one that's extremely dense. There's enough evidence in the literature to recommend MRI screening in those patients with extremely dense breasts. Most of that was done in a prospective trial in the Netherlands that really supports screening those patients with supplemental MRI.

For the heterogeneously dense, the American Cancer Society, NCCN, ACR, and the European group would recommend considering—and it may not come down as hard as a complete recommendation, but consider MRI for those patients. In the ACR, we say if a patient wants supplemental screening, then we would recommend MRI. The NCCN says consider, and the European society says consider as well, either ultrasound or MRI, but MRI is the better test for supplemental screening.

The other thing to consider is that even if you have dense breasts and no other risk factors, just having dense breast tissue is a risk factor for cancer. It's not just that you can't find it; it's actually a risk factor. On top of that, if you have other risk factors, that would maybe push you more to recommend getting and ordering screening MRI. They're even recommending in patients who don't have any other risk factors and just have extremely dense breasts that they get an MRI.

The other thing to think about is that MRIs in the past can take up to 45 minutes. But multiple studies have shown that we now do what's called an abbreviated MRI, which is 10 minutes or less. A lot of institutions offer that on a cash basis, and we use that tool for screening, for supplemental screening in patients with dense breasts. There have been multiple studies done with that, and there was a large randomized controlled trial supported by ACRIN ECOG. Chris Comstock is the lead author on that, and it was a multi-site trial all over the country—and I think there were some patients in Germany too. It just showed that MRI can pick up way more cancers than a 3D mammogram. A 3D mammogram might pick up seven to nine cancers per 1000 women screened, but an MRI is going to pick up 17 to 25 cancers per 1000 women screened. So it's a much more sensitive tool for finding cancers.

PC: What do you see as the biggest barriers in implementing optimal screening for women with dense breasts?

 One of the barriers is that PCPs don't understand or know about supplemental screening. Number two, there's a lot of talk about all of the risks of screening. You might end up getting a biopsy that's not cancer. You might end up getting additional imaging that's unnecessary. But I think that if you look at the studies that have been done where they actually ask patients, "What would you rather have? Would you rather have some extra studies and maybe a negative biopsy, but find these cancers early when they're really treatable? Or would you rather wait because you're going to end up finding it at a later stage and it's going to be harder to treat?" Most patients are okay with the risks that are discussed associated with screening.

One of the other barriers to MRI screening is not all geographical areas in the US have good access to MRIs, and they are expensive. For example, if a person gets an MRI and is—assume I have the cancer gene, I have a BRCA-positive alteration—my insurance will cover my MRI. But assume my MRI costs over $1000 and I have over $1000 of deductibles, so my out-of-pocket cost, because I haven't reached my deductible yet, is over $1000. There are a lot of people with high-deductible insurance plans, if they have insurance plans. If you compare that to cash only, there are different ranges of how much these abbreviated MRI studies cost. I have seen up to $500 to $750. Ours is $250. So if I am out of pocket $250 versus $1000 or more, I'm going to pick the $250. It is going to be only 10 minutes. If I am claustrophobic, it is much easier to get through. And I am going to get very similar results for a test that is screening me with maybe increased risk, or normal findings in a patient with dense breast tissue.

Some of our high-risk patients were not getting MRIs in the past because they could not afford them, and now they're starting to get these abbreviated MRIs. They might not be able to afford it yearly, but maybe every other year or every third year is better than none.

PC: You mentioned primary care education. Research has shown that many PCPs feel like they need more education on breast health and breast density to properly counsel their patients. What is your approach to effective communication around this topic with patients, and where do you see the biggest need for education among PCPs?

 You have to let patients know—you have to notify them, by law—that they have dense breasts. In addition to that, we send a pamphlet to our patients educating them about breast density and talking about supplemental screening and talking about abbreviated MRI. That's our way of educating our patients.

In addition to that, when our patients come in, they fill out a form electronically with multiple questions, and then those questions go through a risk assessment calculation. We use Tyrer-Cuzick for our risk assessment tool, and that gives us a risk assessment of that patient. So we know that they have dense breasts, and we know what their risk is by the answers that they gave us.

If they are over the 20% of lifetime risk of breast cancer, then we put in our report to the PCP that we would recommend MRI in this patient. They could refer the patient to a high-risk clinic to be evaluated to see if they need genetic counseling, genetic testing. The nurse practitioners that see patients in these clinics will also order the supplemental screening that's needed. So that might be a way to help the PCPs deal with patients that are at increased risk.

When it comes to patients with dense breasts without any other risk factor, those are the patients that we need the PCPs to be able to have a discussion with. We need to have them say, "Okay, you are at increased risk because you have dense breast tissue, and studies have shown that supplemental screening is helpful in patients like you. Mammogram, even though it's a 3D mammogram, may not pick up breast cancers that an MRI will. And if you're extremely dense breasted, the NCCN guidelines 100% recommend—not just consider but recommend—supplemental screening with an MRI."

PC: What are any emerging research or technologies in this space that PCPs should be aware of?

 There's something new that I'm debating whether I'm putting it in my talk because it's getting long, but I think I should. There's some new literature out there that when you get a mammogram, there are some 

 that have been applied to multiple mammograms going backwards and trying to predict your chance of getting breast cancer in the next five years, just by doing AI calculations on your mammogram. You can't detect this by the human eye, and it's not that it's detecting the breast cancer early—it is actually figuring out your risk. They compared it to the Gail model, which is one of our risk assessment tools, and it's beating the Gail model.

In the future—this isn't FDA approved yet—the mammogram might be able to tell us this patient in five years has an increased risk of breast cancer. Does that mean that we screen her more often with MRI and mammogram in the next five years? Number two, it also could mean that we're going to give this patient medication that will decrease her risk of getting 

. Patients who have genetic susceptibility to breast cancer now may be put on oral medication to decrease their risk of breast cancer. Those patients—those studies have to be done, those haven't been done yet—might be put into a category of, "Hey, maybe we need to give you medication. Maybe we need to do genetic testing on you, even though your Tyrer-Cuzick risk score may not be big."

The other thing that's coming down the pipe is something called a polygenic risk score, and that is specific to you. It's by looking at your DNA—it'd be a blood sample—and you and a twin sister, or you and a sister, might have completely different polygenic risk scores. Those aren't ready for prime time yet either. But I think by looking at some of these tools and technologies down the road—and I think quicker than 10 years—we might be in a different place and be able to do what people have been trying to do in the past, which is really individualized screening pathways.

Somebody that we don't think is going to get breast cancer in the next five years, somebody that doesn't have dense breast tissue, maybe they don't get screened every year. Maybe they get screened less often with a mammogram. Maybe we do this risk assessment every two to three years on the mammogram, and then we see if there's some blip that's, "Oh, now it looks like maybe you might get breast cancer."

I just think that in the future, we're going to have a better way of really making our screening pathways for the patients really individualized to that patient. But we're not quite there yet. I'm excited that we have an opportunity in the future to do that.

US Food and Drug Administration. Important information: Final rule to amend the mammography quality standards act (MQSA). Revised September 10, 2024. Accessed October 31, 2025. 

https://www.fda.gov/radiation-emitting-products/mammography-quality-standards-act-mqsa-and-mqsa-program/important-information-final-rule-amend-mammography-quality-standards-act-mqsa

Task force issues final recommendation statement on screening for breast cancer. News release. US Preventive Services Task Force. April 30, 2024. Accessed October 31, 2025. 

https://www.uspreventiveservicestaskforce.org/uspstf/sites/default/files/file/supporting_documents/breast-cancer-screening-final-rec-bulletin.pdf

TMS 2025: Donna Plecha, MD, discusses how primary care physicians should approach breast cancer screening in women with dense breasts and use MRI effectively.

Screening for Breast Cancer in Patients With Dense Breast Tissue: A Q&A With Donna Plecha, MD

US obesity rates have shown a statistically significant decline for the first time in years, coinciding with a sharp rise in the use of GLP-1 receptor agonist drugs, according to new findings from Gallup’s National Health and Well-Being Index released this month.

After peaking at 39.9% in 2022, the adult obesity rate has dropped to 37.0%, a reduction representing approximately 7.6 million fewer adults classified as obese in just 3 years. This decrease follows the doubling in use of GLP-1 medications, such as semaglutide (brands Ozempic and Wegovy), prescribed specifically for weight loss since early 2024. The poll was based on surveys of 16,946 US adults during the first 3 quarters of 2025. 

Since February 2024, the proportion of adults using GLP-1 injections for weight loss increased from 5.8% to 12.4%. Use remains higher in women (15.2%) than men (9.7%), but rates have more than doubled in both groups over the past year.

Obesity rates dropped more in women (down 3.5 percentage points to 38.8%) than in men (down 2.3 points to 35.2%) since 2022, paralleling greater uptake of GLP-1 therapy among women.

The most notable declines in obesity occurred among adults:

Ages 40–49: down 4.3 points to 43.3%; 16.2% now use GLP-1 drugs for weight loss

Ages 50–64: down 5.0 points to 42.8%; 17.0% report using these medications

Minimal change was seen in the youngest adults and seniors, despite the latter population having the third-highest usage rate (11.1%) but reporting less weight loss benefit.

The data reveal a clear correlation, report authors noted: age groups with the highest GLP-1 usage rates show the greatest reductions in obesity. However, seniors present an anomaly. While their usage rate is the highest, at 11.1%, there is no observed corresponding reduction in obesity. "Previous Gallup research indicates that older Americans report significantly lower effectiveness from these medications compared with younger age groups," according to the report.

Another paradox of the findings, the percentage of Americans diagnosed with diabetes has reached 13.8%, the highest yet recorded in the Gallup surveys. Reductions in obesity have not yet yielded measurable declines in diabetes diagnoses, the researchers note, pointing out the reflection of diabetes as a lifetime disease. "Once diagnosed, patients remain in that category regardless of subsequent weight changes." Obesity continues to correlate with increased diabetes prevalence across all age groups, but is not the sole factor, authors added.

The survey also probed respondents' attitudes on the approval of GLP-1 drugs for weight loss and found signficant shifts in public opinion: 

Awareness of weight loss injectables soared to 89% nationally in 2025, up from 80% the previous year.

More than half (53%) of Americans see these drugs as helpful for those with obesity or weight-related conditions, although 62% oppose the use of semaglutide for people without such conditions.

GLP-1 medications are now covered by 13 states under Medicaid, with ongoing legislative efforts for broader coverage.

The sustainability of the obesity decline may depend on both improved access to pharmacotherapy and parallel progress in public health behaviors, authors suggested. Former users of GLP-1s report somewhat fewer obesity-related chronic conditions than current users, suggesting modest long-term benefit but not a replacement for healthier daily routines.

While GLP-1 uptake aligns with reduced obesity rates, the impact on new diabetes incidence remains unproven. The article cautions, "While GLP-1s support weight management, they should not be considered a cure-all for overall health or other disease states." Non-pharmacologic factors such as physical activity and healthy eating habits remain crucial for sustained health gains, and recent Gallup findings indicate that U.S. adults' overall health habits have declined compared to 2019. Genetic risk and sociodemographic differences continue to shape U.S. diabetes and obesity landscapes, as well. Considerations 

Gallup’s data draws from more than 16,900 adults surveyed in 2025. Obesity rates use self-reported height and weight, a method that may slightly underestimate true obesity rates due to social desirability, but nevertheless reflects consistent trend data. Since the initiation of ongoing Gallup measurement in 2008, US obesity rose by more than 14% through the era of COVID-19 before this recent downturn.

With nearly 9 in 10 Americans now aware of GLP-1 medications, 2025 may mark a transitional period both for clinical obesity management and national health strategy. Whether these advances translate into lasting improvement will depend on continued access, surveillance, and broader changes in public health behaviors, the authors concluded. 

 Witters D, James MP. Obesity rate declining in the US. Wellbeing. Gallup. October 2028, 2025. Accessed October 31, 2025. https://news.gallup.com/poll/696599/obesity-rate-declining.aspx

Use of GLP-1 receptor agonists for weight loss has more than doubled since early 2024 while 7.6 million fewer US adults qualify as obese in 2025. 

US Obesity Rates Decline in Tandem With Rise in Use of GLP-1 RA, New Gallup Index Shows 

Postdoctoral Research Fellow, Mayo Clinic College of Medicine and Science, Jacksonville, FL

Dr. Regina Castaneda discusses new research on menopause and obesity pharmacotherapy.

TMS 2025: Regina Castaneda, MD, on Tirzepatide, Menopause, and Hormone Therapy

At The Menopause Society’s 2025 Annual Meeting, Regina Castaneda, MD, a postdoctoral research fellow at the Mayo Clinic College of Medicine and Science in Jacksonville, FL, presented new data on how menopausal stage and hormone therapy use may influence weight loss outcomes with tirzepatide. In this interview with 

, Dr Castaneda explains what prompted the study, how the findings compare with prior research on semaglutide, and why understanding the interplay between estrogen, reproductive stage, and GLP-1–based therapies could help clinicians personalize obesity treatment for midlife women.

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Researchers at the University of Chicago have developed a comprehensive mathematical model that predicts diabetes complications up to 15 years in advance, offering clinicians a powerful new tool to understand how early treatment decisions shape patient outcomes. 

The Diabetes Outcome Model of the US (DOMUS), published in Diabetes Care, draws on data from 129,000 patients with type 2 diabetes (T2D) and tracks 17 distinct complications, from traditional cardiovascular events to increasingly recognized conditions such as depression and dementia.

The model addresses a critical gap in diabetes care planning, the authors noted. Unlike existing prediction models that rely heavily on data from the United Kingdom Prospective Diabetes Study, which included minimal representation of African American, Asian, Latino, and American Indian populations, DOMUS uses real-world data from Kaiser Permanente Northern California's multiethnic patient population. "We wanted to build a model that represented the people we actually treat in the US--a socioeconomically and racially diverse population," lead researcher Neda Laiteerapong, MD, MS, professor of medicine and chief of general internal medicine at the University of Chicago, said in a statement. The cohort reflects the demographic diversity of contemporary US practice, with 74% White, 24% Asian, 17% Hispanic, and 9% Black patients, followed quarterly over 12 years.

Laiteerapong said the project began with a patient's simple question: does early treatment really matter? "I wanted to say, 'Yes, absolutely,' but I didn't have any evidence to support that," she recalled. "The challenge with diabetes is that the benefits of treatment, like controlling blood sugar, blood pressure, cholesterol, weight, or quitting smoking, often don't show up until many years later." The DOMUS model now provides that evidence. "First-year HbA1c did, in fact, help predict long-term complications. So yes, those early months matter," Laiteerapong said. First-year hemoglobin A1c emerged as a predictor for most long-term complications, suggesting that the initial months after diagnosis carry lasting consequences--a finding with clear implications for the "wait and see" approach some clinicians take with newly diagnosed patients.

The research team built DOMUS as a microsimulation model with quarterly cycles, tracking patient trajectories through four interconnected modules: diabetes medications, seven biomarker trajectories (including HbA1c, blood pressure, cholesterol levels, and body mass index [BMI]), 17 diabetes-related health outcomes, and mortality. The model predicts not only established complications such as end-stage kidney disease, myocardial infarction, stroke, and amputation, but also conditions that have gained prominence in diabetes care: atrial fibrillation, depression, and dementia.

To ensure accuracy, the team split their data into 3 samples for development (50%), calibration (25%), and validation (25%). The validation process demonstrated that simulated and empirical data aligned closely across biomarkers, events, and mortality predictions. Cumulative failure curves for all complications and deaths fell within the 95% confidence intervals of observed data, confirming the model's predictive accuracy across diverse patient subgroups.

The model incorporates social determinants of health alongside clinical factors. Neighborhood deprivation index appeared as a predictor in heart failure, ischemic heart disease, and foot ulcer equations. English language proficiency influenced risk for myocardial infarction, angina, atrial fibrillation, and nonproliferative retinopathy. These findings underscore how structural inequities shape diabetes outcomes independent of individual clinical characteristics.

Researchers used instrumental variable analysis to account for treatment selection bias, employing primary care physician prescribing patterns as instruments. This methodologic approach helps disentangle the effects of medications from the clinical factors that drive prescribing decisions. The model tracked 15 common medication regimens that represented 99.5% of treatment choices in the cohort.

The median follow-up time reached 21 quarters, generating 748,582 person-years of data. Annual event rates varied widely, from 0.0003 for blindness to 0.0181 for nonproliferative retinopathy. At baseline, 25% of patients had depression, 8% had ischemic heart disease, and 5% each had atrial fibrillation, angina, and myocardial infarction.

Among the model’s limitations the authors noted the Kaiser Permanente population tends to be healthier than the general US population, with participants more than 10% less likely to be overweight or have hyperlipidemia and 9% less likely to have hypertension compared with national averages. The system's commitment to equitable care may underestimate how structural inequity affects diabetes progression in other settings. Additionally, the cohort's medication patterns predate widespread use of GLP-1 receptor agonists and SGLT2 inhibitors, though the model's structure allows researchers to incorporate newer agents by adjusting biomarker equations based on clinical trial data.

The limited 13-year follow-up constrains extrapolation beyond this timeframe, though the researchers used current age rather than time since diagnosis to measure time in survival models, which should improve predictions outside the sample period. External validation in other healthcare systems represents an important next step.

"Historically, when we ask policy questions about diabetes, we often can't do it using real people in real time," Laiteerapong explained. "We have to estimate or simulate the outcomes using mathematical models." DOMUS offers researchers, policymakers, and health system managers a tool to evaluate how interventions affect population health and health disparities. The model can inform cost-effectiveness analyses, guide clinical decisions about treatment intensification, and help quantify the long-term value of diabetes prevention programs. As healthcare systems work to eliminate persistent racial and ethnic disparities in diabetes outcomes, the study authors concluded that DOMUS provides a foundation for understanding how early clinical decisions and social determinants combine to shape patient trajectories. "This model can be used by scientists, policymakers, and health system managers to better understand how choices can affect population health and health disparities, including the broad diversity of U.S. races and ethnicities," they wrote.

The model predicts 14 different T2D complications that can develop over ~15 years, and models how the disease progresses by predicting change in risk factors over time. 