Category: 8. Health

New results from a prospective surveillance study published in the Journal of the American Medical Association indicate a significant burden of hospitalizations due to community-acquired pneumonia (CAP) among US adults, with the largest burden of disease found in adults ages 65 years or older. Furthermore, the investigators observed that a sizable portion of CAP was caused by Streptococcus pneumoniae, specifically from serotypes included in the 21-valent pneumococcal conjugate vaccine (PCV21; V116, Capvaxie; Merck).¹

V116 Can Prevent Severe Illness, But Serotypes Still Infect Patients

Since 2024, following its approval by the FDA, V116 has been recommended by the Advisory Committee on Immunization Practices (ACIP) for adults ages 65 years and older and those with specific risk factors for the prevention of invasive pneumococcal disease and pneumonia due to Streptococcus pneumoniae. Clinical trials affirm the capability of V116 to demonstrate a robust immunogenic response in patients ages 18 to 64 years at heightened risk of pneumococcal infection.^2-4

Although the introduction of effective PCVs has drastically reduced the burden of pneumococcal disease and CAP, there is still a risk of Streptococcus pneumoniae serotypes not covered in a vaccine infecting individuals. It remains critical to properly monitor the burden of pneumococcal pneumonia in addition to the serotypes causing the illness. A myriad of challenges prevent such effective, thorough serotype monitoring from taking place, including pitfalls of traditional diagnostic techniques and highly sensitive pneumococcal cultures.¹

Novel techniques, including the use of serotype-specific urinary antigen detection (SSUAD) assays, have become helpful in identifying numerous pneumococcal serotypes in patients with noninvasive pneumonia. Use of SSUAD assays could allow for a more comprehensive assessment of the incidence of adult hospitalizations due to pneumonia. Therefore, the authors initiated a multicenter, prospective, active surveillance study to determine the incidence of hospitalizations for all-cause CAP, pneumococcal CAP, and pneumococcal CAP caused by serotypes included in the V116 vaccine.^1,5

Older Adults Remain Burdened by CAP Hospitalizations

This analysis was part of the ongoing Pneumococcal Pneumonia Epidemiology, Urine Serotyping, and Mental Outcomes (PNEUMO) program, which was designed by investigators at Vanderbilt University Medical Center to examine the epidemiology of hospitalizations due to CAP among adults. Participating hospitals identified adults hospitalized with clinical and radiographic evidence of CAP. Those enrolled between September 2018 and October 2022 were included in the analysis.¹

In total, 2016 patients hospitalized for all-cause CAP were included in the study population. According to the authors, the overall estimated annual incidence of hospitalizations due to all-cause CAP was 340 (95% CI, 323–358) per 100,000 adults. Notably, incidence was found to consistently increase with age. For pneumococcal CAP and pneumococcal CAP due to V116 serotypes, the annual incidence of hospitalizations was 43 (95% CI, 41-46) and 30 (95% CI, 29-32) per 100,000 adults, respectively.¹

The investigators also sought to determine the distribution of detected pneumococcal serotypes. Going by each included year in the study, SSUAD testing detected the most pneumococcal serotypes during year 1 of the study (100 serotypes), with the lowest during year 4 (28 serotypes). In the most recent study year (year 4), SSUAD testing detected 12 serotypes included in the 15-valent PCV (PCV15), 20-valent PCV (PCV20), and V116 vaccines; 4 serotypes not included in V116; 4 serotypes included in both PCV20 and V116; and 8 serotypes included in V116 but not in PCV15 or PCV20.1

Greater Uptake of V116 Could Prevent Hospitalizations

Ultimately, the authors determined that 14% of hospitalizations due to CAP had evidence of Streptococcus pneumoniae infection, and many detected pneumococcal serotypes corresponded to those included in the V116 vaccine. Furthermore, the incidence of CAP hospitalizations increased significantly with age. The data indicates a high burden of hospitalizations among older adults, but many of the serotypes causing these infections are covered in V116, necessitating greater vaccine proliferation and distribution.¹

A key aspect of this study was it taking place during the COVID-19 pandemic, which caused numerous disruptions across society and corresponding alterations in the circulation of respiratory illnesses. The authors noted that they maintained active surveillance throughout the pandemic, a key aspect of ensuring reliable results from this trial.¹

Pneumococcal CAP clearly remains a critical cause of hospitalizations, especially among older adults, in the US. Pharmacists and primary care providers should note the increased burden of hospitalizations among this population and target this group with interventions designed to increase uptake of a pneumococcal vaccine.¹

REFERENCES

1. Grijalva CG, Johnson KD, Resser JJ, et al. All-Cause and Pneumococcal Community-Acquired Pneumonia Hospitalizations Among Adults in Tennessee and Georgia. JAMA Netw Open. 2025;8(8):e2524783. doi:10.1001/jamanetworkopen.2025.24783

2. Gallagher A. FDA Approves V116 for Prevention of Invasive Pneumococcal Disease and Pneumonia. Pharmacy Times. Published June 18, 2024. Accessed August 7, 2025. https://www.pharmacytimes.com/view/fda-approves-v116-for-prevention-of-invasive-pneumococcal-disease-and-pneumonia

3. Halpern L. ACIP Votes to Recommend Pneumococcal 21-Valent Conjugate Vaccine for Adult Populations. Pharmacy Times. Published July 2, 2024. Accessed August 7, 2025. https://www.pharmacytimes.com/view/acip-votes-to-recommend-pneumococcal-21-valent-conjugate-vaccine-for-adult-populations

4. Halpern L. Pneumococcal 21-Valent Conjugate Vaccine Generates Positive Response in Adults at Increased Risk of Disease. Pharmacy Times. Published October 21, 2024. Accessed August 7, 2025. https://www.pharmacytimes.com/view/pneumococcal-21-valent-conjugate-vaccine-generates-positive-response-in-adults-at-increased-risk-of-disease

5. Wunderink RG, Self WH, Anderson EJ, et al. Pneumococcal Community-Acquired Pneumonia Detected by Serotype-Specific Urinary Antigen Detection Assays. Clin Infect Dis. 2018;66(10):1504-1510. doi:10.1093/cid/cix1066

Patient advocacy groups are urging the South African government to reopen an antitrust investigation into Vertex Pharmaceuticals over allegations the company misled authorities into closing a high-profile case last year over access to a cystic fibrosis treatment.

Last December, the Competition Commission ruled that the company had sufficiently provided access to its medication after a patient filed a complaint alleging that Vertex violated the South African Constitution. The petition cited human rights failures — such as a basic right to health — as one justification for the complaint, as well as claims that the company abused its patent status.

Notably, the petition maintained that Vertex, which is the leading purveyor of cystic fibrosis drugs, including a highly effective treatment called Trikafta, had failed to register the medication with regulators. And since Vertex held patent rights in the country, the only available route to obtain Trikafta was to import the medicine from the U.S. at a prohibitive cost, given a list price exceeding $300,000.

Participants

Fourteen healthy TS participants (female = 7, age = 31.71 ± 8.20 years, right-handedness = 13) underwent MRI scans at four different machines (two at the University of Fukui, one at Osaka University, and one at Chiba University) over a three-month period. The study used the TS dataset from the Child Developmental MRI (CDM) project [5] to address measurement bias in each MRI machine. Participants with ADHD were recruited from hospitals of the University of Fukui, Osaka University, and Chiba University in Japan. Children with TD were recruited from the local community and assessed to ensure that none of them had developmental delays, received any special support education, or had a history of epilepsy or other psychiatric disorders. Participants with ADHD fulfilled the diagnostic criteria for ADHD according to the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5). Participants in the current study participated in the experiments from 2014 to 2022. None of the participants had a history of severe head trauma, neurological illness, or potential for hazards associated with MRI examinations (such as the presence of metal on the body surface or internal structures, pregnancy, claustrophobia, or fear of the dark). The demographic data of the participants with ADHD and TD in each MRI machine are summarized in Tables 1 and S1.

MRI data acquisition

Participants were scanned with T1-weighted imaging at the University of Fukui, Osaka University, or Chiba University using a 3T GE Signa PET/MR scanner (General Electric HealthCare, Chicago, Illinois, USA; University of Fukui), 3T GE Discovery MR750 scanner (General Electric HealthCare; University of Fukui or Chiba University), or 3T GE Signa Architect scanner (General Electric HealthCare; Osaka University). The scanning parameters are provided in Table S2.

MRI analysis

The fully automated segmentation procedure implemented in FreeSurfer version 7.3.8 was used to estimate the gray matter volumes of the cortical and subcortical regions (http://surfer.nmr.mgh.harvard.edu/). The structural data were obtained using a standardized processing pipeline. The analysis used the Desikan-Killiany atlas for classifying cortical regions (68 brain regions) and for segmenting subcortical regions (14 brain regions, such as thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens). Details of the segmentation method are provided by Fischl et al. [19].

Harmonization methods

We followed the TS harmonization method reported by Yamashita et al. [5], which extends a general linear model harmonization using the TS dataset. Python was used to estimate the measurement bias of each MRI machine using the TS dataset and reduce measurement bias from the CDM dataset. We first utilized the TS dataset to calculate scanner differences using ridge regression. The model included dummy variables for both the 4 scanners and the 14 TS participants as follows:

Here, m signifies the measurement bias (4 machines × 1), and p signifies the TS participant factor (14 TS participants × 1).

There is no sampling bias in the TS participants, as participants across different MRI machines do not differ. The TS harmonization method only estimates variations between MRI scanners. Once we estimated the machine differences using the model above, we applied them to the CDM dataset to correct the measurement bias.

ComBat harmonization was also used to control measurement bias for comparison. ComBat was initially developed to correct the batch effect in genomics [20] and has recently been applied to MRI datasets [18]. ComBat corrects a type of multivariate dataset using an empirical Bayesian estimation approach and can be used to analyze datasets obtained using different scanning machines. In the current study, we used the module “neuroCombat” to correct structural brain data using Python [21]. We used ComBat harmonization in the TS dataset and CDM dataset individually. In the TS dataset, we included age, sex, and handedness as covariates for data correction. Whereas, in the CDM dataset, we included age, sex, handedness, and diagnosis (ADHD or TD) as covariates.

Measurement and sampling biases of different harmonization methods

To quantitatively investigate the validity of different harmonization methods in structural brain data, we calculated measurement biases, sampling biases, and disorder factors, following recommendations from Yamashita et al. [17]. We estimate the measurement and sampling biases using the following model:

where m represents the measurement bias (4 machines × 1), s represents the sampling bias of TD (3 sites × 1) and ADHD (3 sites × 1), d represents the disorder factor (ADHD × 1), and p represents the participant factor (43 participants with repeated measures × 1). We used ridge regression to calculate the parameters. We also assessed measurement bias and sampling bias by excluding or including participants as a random intercept in the model, detailed in the Supplementary Material. The brain structures were normalized for ridge regression. The model was tested on raw CDM data, TS-corrected CDM data, and ComBat-corrected CDM data to analyze measurement and sampling bias before and after harmonization. Measurement bias was calculated as the average of the effect sizes of the brain structures across different MRI scanners. The sampling biases in participants with TD and patients with ADHD were defined separately as the average effect sizes of the brain structures across different sites.

Statistical analyses

We used R (version 4.3.1; The R Foundation for Statistical Computing, Vienna, Austria) and Python (version 3.11.6; Python Software Foundation, Wilmington, DE, USA) for statistical analyses. First, we examined the necessity and validity of harmonization. We used a repeated-measures analysis of variance (ANOVA) on the TS dataset to examine the necessity of harmonization. Additionally, we computed the intraclass correlation coefficient (ICC) of the harmonized structural brain of the TS dataset, a descriptive statistic that can be used when quantitative measurements are made on units organized into groups (the individuals in this study) to examine validity [22]. We compared the ICC among the raw, TS-corrected, and ComBat-corrected data of the TS dataset using ANOVA, followed by a post hoc test using Tukey’s Honest Significant Difference (HSD) method with family-wise error rate (FWE) correction. We subsequently adapted TS and ComBat to correct the brain structure data in 178 children with TD and 116 children with ADHD from the CDM project. We calculated the measurement and sampling biases for TD and ADHD and compared these biases among TS-corrected, ComBat-corrected, and raw data using ANOVA and a post hoc test using Tukey’s HSD method with FWE correction.

Additionally, we examined the association between brain structures and ADHD in CDM dataset. First, we adapted a linear mixed-effects model to examine the relationship between brain structures harmonized by TS and ADHD. We analyzed this model using the R-package “lmerTest”. For the mixed-effects model with a group (ADHD or TD) as the independent variable and brain structures as the dependent variable, we considered participants’ age, sex, handedness, intelligence quotient (IQ) measured using the Wechsler Intelligence Scale for Children (WISC), and intracranial volume of the brain as covariates. As some participants participated in the experiment multiple times, the subject ID (used to distinguish whether it was the same person) was modeled as a random effect. Raw brain structural data and brain structures harmonized using ComBat were also used in the mixed-effects model to compare children with ADHD and TD. Additionally, considering the differences in age, sex, and handedness between the ADHD and TD groups, we adapted the propensity score matching method to match the age, sex, and handedness of the TD group with the ADHD group (N = 94) by using the R package “Matching” with caliper = 0.25, and analyzed them similarly [23, 24]. Specifically, after matching, we conducted mixed-effects regressions to examine the differences between ADHD and TD, with group (ADHD or TD) as the independent variable and brain structures as the dependent variable, controlling for IQ and ICV as covariates. In the current study, for analyses involving brain structures, we applied false discovery rate (FDR) correction to 82 brain regions, 68 cortical regions and 14 subcortical regions, for multiple comparisons correction [25].

Managing weight is crucial to maintaining cardiovascular health, according to study authors of research published in The Journal of the American College of Cardiology (JACC).^1,2 In these studies, the authors discuss actionable guidance to manage obesity, therefore improving individual cardiovascular health. Amid a new era of obesity management, there is an “ever-expanding set of tools” that can be used to help patients navigate diagnosis and weight reduction and mitigate the risk of cardiovascular disease.^1,2

Weight Management Strategies

Obesity is a chronic disease that affects over 1 billion adults worldwide. Across 3 decades, the rates of obesity in adults have doubled, and in children and adolescents, they have quadrupled internationally. Specifically, approximately 40.3% of US adults have obesity (defined as a body mass index [BMI] of ≥ 30 kg/m²), and 9.4% have severe obesity (BMI: ≥ 40 kg/m²).¹

“Obesity remains a stigmatized condition, and clinicians should be aware that some individuals may experience discomfort with being weighed, abdominally measured, or talking about weight during a medical visit,” study author Michelle Kittleson, MD, PhD, director of education in Heart Failure and Transplantation and professor of medicine at Smidt Heart Institute, Cedars-Sinai, said in a written interview with Pharmacy Times^®. “Diagnosis is important as the first step to treatments, as addressing obesity can improve [heart failure] symptom burden, functional capacity, and quality of life.”

The newest generation of obesity medications are nutrient-stimulated hormone (NuSH) therapies, which represent a broad treatment category that targets metabolic pathways while helping to control the patient’s appetite. Current targets of FDA-approved NuSH therapies include glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) receptor agonists.^1,2

It is important to note that although BMI is an inexpensive, easily acquired, and readily reproducible metric that is strongly embedded in research and clinical practice, significant limitations remain because this measurement does not take into account excess adiposity, the location of said adiposity, or applicability to diverse populations. Therefore, additional assessments of certain anthropometric criteria (eg, waist circumference) may be necessary to identify clinical obesity. Despite this, current indications for currently available antiobesity medications rely on BMI as a measurement.

“Regarding diagnosis of HF, this is encouraging individuals to think outside the ‘BMI box’ for diagnosis of obesity, including consideration of fat distribution, muscle mass, or sex/racial differences,” study author Olivia N. Gilbert, MD, MSc, FACC, associate professor at Wake Forest Baptist Medical Center and vice chair of quality and service in the Department of Cardiovascular Medicine, said in the written Q&A.

Regarding pharmacological interventions, Kittleson explained there are FDA-approved, second-generation antiobesity medications, including orlistat (Xenical; H2 Pharma, LLC), phentermine/topiramate (Qsymia; Vivus, Inc.), and naltrexone/bupropion (Contrave; Currax Pharmaceuticals); however, these are reported to have minimal efficacy and are limited by adverse effects (AEs), notably in patients with HF. Conversely, third-generation agents such as semaglutide (Ozempic, Wegovy; Novo Nordisk) and tirzepatide (Zepbound; Eli Lilly) are more effective than lifestyle interventions and less risky than invasive procedures, with increasing evidence of cardiovascular benefit in individuals with HF with preserved ejection fraction.

Pharmacists Are Crucial in Helping Patients With Weight Management

In the implementation of solution sets for obesity management, pharmacists can collaborate in a multidisciplinary setting to ensure patients are educated and aware of potential adverse events, as well as adherent to treatments to gain the best outcomes. Outside of education, pharmacists can also help patients navigate insurance-related challenges or the prior authorization process. Above all, Gilbert emphasized that as insurance coverage expands, there is a greater opportunity for accessibility to broaden beyond weight management and be used for cardiovascular outcomes.

“There is a key role for pharmacists in managing cardiometabolic clinics. Cardiometabolic clinics could serve as a dedicated, multidisciplinary platform for the early detection, prevention, and comprehensive management of the interrelated conditions of obesity, type 2 diabetes, dyslipidemia, and metabolic dysfunction,” Kittleson explained. “The role of the pharmacist would be to provide personalized treatment strategies, including lifestyle interventions, pharmacotherapy (such as titrating and tapering medications), and metabolic procedures, and provide structured follow-up to improve long-term outcomes.”

On the Horizon

There are some promising innovations coming up in the pipeline, said Kittleson and Gilbert. For instance, agents that are not only GLP-1 and GIP receptor agonists but also glucagon receptor agonists, and these triple agonists may provide even more potent efficacy. Further, there are several novel agents that have dual and triple mechanisms of action that target other NuSH therapies that are currently undergoing development.^1,2

Alongside pharmacological innovations, the need for guidelines is also required. In her paper, Gilbert emphasizes the benefits of Concise Clinical Guidance (CCG) documents and how they help fill the gaps for practical management of specific conditions, particularly in diseases in which none exist.¹

“The intent [of CCGs] is to make the information accessible and digestible with an emphasis on figures, tables, and checklists. As the evidence base evolves, it is expected that information within a CCG may be further addressed in more comprehensive clinical practice guidelines,” said Gilbert.

REFERENCES

1. Gilbert O, Gulati M, Gluckman T, et al. Concise Clinical Guidance: An ACC Expert Consensus Statement on Medical Weight Management for Optimization of Cardiovascular Health: A Report of the American College of Cardiology Solution Set Oversight Committee. JACC. Published online 2025, June 20. doi:10.1016/j.jacc.2025.05.024

2. Kittleson M, Benjamin E, Blumer V, et al. 2025 ACC Scientific Statement on the Management of Obesity in Adults With Heart Failure: A Report of the American College of Cardiology. JACC. Published online 2025, June 13. doi:10.1016/j.jacc.2025.05.008

Hong Kong reported a new imported chikungunya fever case and another probable patient on Friday, bringing the number of confirmed cases to five in a week.

Health authorities said the latest confirmed case involved a 66-year-old woman with a chronic illness who lived in Kwai Tsing district.

A preliminary investigation by the Centre for Health Protection found that the woman travelled alone to visit relatives in Foshan, Guangdong province, from July 24 to Tuesday this week.

The patient could not recall being bitten by a mosquito, the centre said.

The woman developed a fever, rash and joint pain on Wednesday and went to see a doctor at Yan Chai Hospital in Tsuen Wan the next day. Her blood test results came back positive for the chikungunya virus.

The centre said the woman’s condition was stable.

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Researchers at Dana-Farber Cancer Institute have developed a blood-based diagnostic method for multiple myeloma (MM) and its precursor conditions.

The approach, called single-cell whole-genome and full-length transcriptome sequencing (SWIFT-seq), profiles circulating tumor cells (CTCs) in blood samples. The method offers a non-invasive alternative to bone marrow biopsies, which are currently used to assess disease stage and genetic risk.

The research is published in Nature Cancer.

A non-invasive alternative to bone marrow biopsies

MM is a bone marrow cancer often preceded by conditions such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Current risk assessment methods typically involve bone marrow biopsies and fluorescence in situ hybridization (FISH) analysis. These procedures can be painful, infrequent, and may not provide complete genetic information.

“It would be amazing if we had a blood-based test that can outperform FISH and that works in the majority of patients – we think SWIFT-seq may just be that test,” said Dr. Romanos Sklavenitis-Pistofidis, co-first author.

SWIFT-seq uses single-cell sequencing to identify and genetically profile CTCs without the need for invasive sampling. Unlike existing techniques such as flow cytometry, SWIFT-seq does not depend on cell surface markers. Instead, it detects CTCs through tumor-specific molecular barcodes.

“A lot of work has gone into the identification of genomic and transcriptomic features that predict worse outcome in MM, but we are still lacking the tests to measure them in our patients,” said senior author, Dr. Irene M. Ghobrial. “As a clinician, this is the type of next-generation test that I would want to order for my patients.”

Study results and detection rates

The study included 101 participants, ranging from healthy donors to patients with MGUS, SMM and newly diagnosed MM. SWIFT-seq detected CTCs in:

• 90% of patients with MGUS, SMM, or MM

• 95% of patients with SMM

• 94% of patients with newly diagnosed MM

These latter two results are particularly interesting, the researchers say, as these groups are the most likely to benefit from improved risk stratification and genomic surveillance.

In addition to counting CTCs, SWIFT-seq characterizes the tumor’s genetic alterations, estimates its growth rate, and assesses gene expression signatures linked to prognosis. This multi-layered analysis can be performed from a single blood sample, providing a broader picture of disease biology than standard tests.

Not only can SWIFT-seq measure multiple clinically relevant features directly from a blood sample, but it can also the provide novel insights into the biology of tumor cell circulation.

“We identified a gene signature that we believe captures the tumor’s circulatory capacity and may partly explain some of the unexplained mysteries of myeloma biology,” said Dr. Elizabeth D. Lightbody, co-first author. “This can have a tremendous impact in how we think about curtailing tumor spread in patients with myeloma and could lead to the development of new drugs for patients.”

Reference: Lightbody ED, Sklavenitis-Pistofidis R, Wu T, et al. SWIFT-seq enables comprehensive single-cell transcriptomic profiling of circulating tumor cells in multiple myeloma and its precursors. Nat Cancer. 2025:1-19. doi: 10.1038/s43018-025-01006-0

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Ultraprocessed foods or UPFs are a growing concern due to their widespread consumption and impact on potential health risks. Most UPFs, particularly those commonly seen in U.S. dietary patterns, are high in saturated fat, added sugars and sodium (salt), the combination of which is often abbreviated as HFSS, and contribute to excess calories. These include sugar-sweetened drinks, ultraprocessed meats, refined grains, candy and commercial baked goods, among others. A limited number of ultraprocessed foods, such as certain commercial whole grains, low-fat-low-sugar dairy, and some plant-based items, have positive nutritional value and, therefore, can be part of an overall healthy dietary pattern. This overlap is confusing for health care professionals and the public.

A new Science Advisory from the American Heart Association, “Ultraprocessed Foods and Their Association with Cardiometabolic Health: Evidence, Gaps and Opportunities,” summarizes current knowledge about UPFs and their impact on cardiometabolic health, and outlines opportunities for research, policy and regulatory reform to improve dietary intake and overall health. The manuscript published today in Circulation, the flagship journal of the American Heart Association.  

“The relationship between UPFs and health is complex and multifaceted,” said Maya K. Vadiveloo, Ph.D., R.D., FAHA, volunteer chair of the writing group for this Science Advisory. “We know that eating foods with too much saturated fat, added sugars and salt is unhealthy. What we don’t know is if certain ingredients or processing techniques make a food unhealthy above and beyond their poor nutritional composition. And if certain additives and processing steps used to make healthier food like commercial whole grain breads have any health impact.”

The rapid rise in UPF consumption since the 1990s disrupted traditional dietary patterns, potentially contributing to adverse health effects. It is estimated that 70% of grocery store products in the U.S. contain at least one ultraprocessed ingredient. As detailed in a CDC report published yesterday, 55% of calories consumed by people ages 1 and older in the U.S. were UPFs. Among youth ages 1-18 years of age, total UPF calories jumped to nearly 62%, and among adults ages 19 and older total UPF calories was 53%. In addition, families with lower mean income had a higher percentage of UPFs consumed per day: 54.7% for the lowest income group vs. 50.4% for highest income group.

UPFs are relatively inexpensive, convenient for use and aggressively marketed, particularly toward youth and under-resourced communities, often displacing healthier alternatives. This shift resulted in lowering the overall nutritional quality of typical eating patterns in the U.S. and is misaligned with the American Heart Association’s dietary guidance.

This new Science Advisory reinforces current dietary guidelines from the American Heart Association to:

• Reduce the intake of most UPFs, especially those high in saturated fat, added sugars and sodium, and those that contribute to excessive calories; and
• Replace UPF consumption with healthier options like vegetables, fruits, whole grains, beans, nuts, seeds and lean proteins.

How are ultraprocessed foods classified?

UPFs are multi-ingredient foods containing additives (likely intended to enhance shelf life, appearance, flavor or texture) widely used in industrial food production and not commonly used in home cooking. Human diets are increasingly including more industrially processed foods, leading to various systems for classifying foods based on processing criteria. Multiple food classification systems exist currently; this Science Advisory focuses on the Nova framework for food classification. The Nova system, the most widely used, is based on the nature, extent and purpose of the food’s industrial food processing. However, the Nova categorization does not consider the nutritional quality of foods. Certain types of industrial food processing are beneficial for preservation and safety, and/or lowering cost, such as techniques that extend shelf life, control microbial growth, mitigate chemical toxicants, preserve functional, nutritional and sensory (taste) qualities, and reduce food loss and waste.

Efforts to understand UPFs are hindered by differing definitions, limitations in dietary assessment tools and food composition databases, which often lack detailed information on additives and processing methods. Currently, U.S. manufacturers are not required to disclose processing techniques or cosmetic additive quantities, which contributes to the variability in risk estimates and confusion for consumers.

The writing group cautions that an overreliance on the degree of processing as a proxy for healthfulness of foods could sway the food industry to reduce or remove the markers of ultraprocessing from foods that are high in saturated fats, added sugars and sodium and promote them as “better-for-you alternatives.”

Health impact of UPFs

A meta-analysis of prospective studies cited in the advisory found a dose-response relationship between UPF consumption and cardiovascular events, such as heart attack, transient ischemic attack and stroke, Type 2 diabetes, obesity and all-cause mortality. High versus low UPF intake was linked to a 25%-58% higher risk of cardiometabolic outcomes and a 21%-66% higher risk of mortality. More research is needed to understand the appropriate thresholds for daily consumption of UPFs—what a safe amount is and the incremental risks of eating more UPFs.

Research has also found that there may be underlying mechanisms that affect eating behaviors and obesity for some people, and that UPFs may promote obesity. UPFs frequently contain combinations of ingredients and additives that are uncommon in whole foods to enhance palatability and reduce cost, and these may influence reward-related brain activity. For example, ingredients like artificial flavors may mimic sweetness without sugar, and this disruption in flavor-nutrient relationships often leads to irregular eating habits, and results in weight gain.

Opportunities for research and policy

Balancing multiple priorities, including the practical need for a nutrient-dense, affordable food supply, current evidence supports the following key research and policy changes to improve public health and reduce risks related to UPFs:

1. Introduce approaches for individuals, food manufacturers and the retail industry that help shift eating patterns away from UPFs high in saturated fat, added sugars and sodium toward patterns high in vegetables, fruits, nuts, seeds, legumes, whole grains, nontropical liquid plant oils, fish and seafood, low-fat-low-sugar dairy, and, if personally desired, lean poultry and meats.
2. Enact multipronged policy and systems-change strategies (e.g., front-of-package labels) to help reduce intake of HFSS products.
3. Increase research funding to explore critical questions about UPFs: To what extent is it the ultraprocessing itself that makes a UPF unhealthy vs. the fact that ultraprocessed foods tend to have unhealthy ingredients? Most UPFs overlap with HFSS foods that are already targeted for cardiometabolic risk reduction, so a better understanding of the root causes of UPFs’ link to poor health is fundamental to effective reduction strategies.
4. Enhance ongoing efforts to improve food additive science, including streamlined and efficient evaluation and regulation of food additives.

”More research is needed to better understand the mechanisms of how UPFs impact health. In the meantime, the Association continues to urge people to cut back on the most harmful UPFs that are high in saturated fats, added sugars and sodium, and excessive calories and instead follow a diet rich in vegetables, fruits, nuts, seeds and whole grains, low-fat-low-sugar dairy, and lean proteins like fish, seafood or poultry—for better short- and long-term health,” said Vadiveloo.

Reference: Vadiveloo MK, Gardener CD, Bleich SN, et al. Ultraprocessed foods and their association with cardiometabolic health: evidence, gaps, and opportunities: A science advisory from the American Heart Association. Circ. 2025. doi: 10.1161/CIR.0000000000001365

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.