Category: 8. Health

  • Long-term weight change among breast cancer patients treated with chemotherapy: a longitudinal study over 13 years | Breast Cancer Research

    Long-term weight change among breast cancer patients treated with chemotherapy: a longitudinal study over 13 years | Breast Cancer Research

  • Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63.

    Article 
    PubMed 

    Google Scholar 

  • Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391:1023–75.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Playdon MC, Bracken MB, Sanft TB, Ligibel JA, Harrigan M, Irwin ML. Weight gain after breast Cancer diagnosis and All-Cause mortality: systematic review and Meta-Analysis. J Natl Cancer Inst. 2015;107:djv275.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chan DSM, Vieira R, Abar L, Aune D, Balducci K, Cariolou M, et al. Postdiagnosis body fatness, weight change and breast cancer prognosis: global Cancer update program (CUP global) systematic literature review and meta-analysis. Int J Cancer. 2022. https://doi.org/10.1002/ijc.34322.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jung W, Park SH, Park Y-MM, Song Y-M, Park JH, Yu J, et al. Weight change and cardiovascular disease incidence in breast cancer survivors: a nationwide cohort study. Breast Cancer Res Treat. 2025. https://doi.org/10.1007/s10549-024-07594-2.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vance V, Mourtzakis M, McCargar L, Hanning R. Weight gain in breast cancer survivors: prevalence, pattern and health consequences. Obes Rev Off J Int Assoc Study Obes. 2011;12:282–94.

    Article 
    CAS 

    Google Scholar 

  • Lambert LK, Balneaves LG, Howard AF, Gotay CC. Patient-reported factors associated with adherence to adjuvant endocrine therapy after breast cancer: an integrative review. Breast Cancer Res Treat. 2018;167:615–33.

    Article 
    PubMed 

    Google Scholar 

  • Wells KJ, Pan TM, Vázquez-Otero C, Ung D, Ustjanauskas AE, Muñoz D, et al. Barriers and facilitators to endocrine therapy adherence among underserved hormone-receptor-positive breast cancer survivors: a qualitative study. Support Care Cancer Off J Multinatl Assoc Support Care Cancer. 2016;24:4123–30.

    Google Scholar 

  • van den Berg MMGA, Winkels RM, de Kruif JTCM, van Laarhoven HWM, Visser M, de Vries JHM et al. Weight change during chemotherapy in breast cancer patients: a meta-analysis. BMC Cancer. 2017;17.

  • Bhimani J, O’Connell K, Ergas IJ, Foley M, Gallagher GB, Griggs JJ, et al. Trends in chemotherapy use for early-stage breast cancer from 2006 to 2019. Breast Cancer Res BCR. 2024;26:101.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Vaz-Luis I, Cottu P, Mesleard C, Martin AL, Dumas A, Dauchy S, et al. UNICANCER: French prospective cohort study of treatment-related chronic toxicity in women with localised breast cancer (CANTO). ESMO Open. 2019;4:e000562.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pedersen B, Delmar C, Lörincz T, Falkmer U, Grønkjær M. Investigating changes in weight and body composition among women in adjuvant treatment for breast cancer: A scoping review. Cancer Nurs. 2019;42:91–105.

    Article 
    PubMed 

    Google Scholar 

  • Heideman WH, Russell NS, Gundy C, Rookus MA, Voskuil DW. The frequency, magnitude and timing of post-diagnosis body weight gain in Dutch breast cancer survivors. Eur J Cancer Oxf Engl 1990. 2009;45:119–26.

    CAS 

    Google Scholar 

  • Trédan O, Bajard A, Meunier A, Roux P, Fiorletta I, Gargi T, et al. Body weight change in women receiving adjuvant chemotherapy for breast cancer: A French prospective study. Clin Nutr. 2010;29:187–91.

    Article 
    PubMed 

    Google Scholar 

  • Puklin LS, Li F, Cartmel B, Zhao J, Sanft T, Lisevick A, et al. Post-diagnosis weight trajectories and mortality among women with breast cancer. NPJ Breast Cancer. 2023;9:98.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fukui J, White K, Frankland TB, Oshiro C, Wilkens L. Weight changes according to treatment in a diverse cohort of breast cancer patients. BMC Cancer. 2021;21:1005.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nyrop KA, Williams GR, Muss HB, Shachar SS. Weight gain during adjuvant endocrine treatment for early-stage breast cancer: what is the evidence? Breast Cancer Res Treat. 2016;158:203–17.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Sadim M, Xu Y, Selig K, Paulus J, Uthe R, Agarwl S, et al. A prospective evaluation of clinical and genetic predictors of weight changes in breast cancer survivors. Cancer. 2017;123:2413–21.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Reddy SM, Sadim M, Li J, Yi N, Agarwal S, Mantzoros CS, et al. Clinical and genetic predictors of weight gain in patients diagnosed with breast cancer. Br J Cancer. 2013;109:872–81.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Shang L, Hattori M, Fleming G, Jaskowiak N, Hedeker D, Olopade OI, et al. Impact of post-diagnosis weight change on survival outcomes in black and white breast cancer patients. Breast Cancer Res BCR. 2021;23:18.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Rey D, Touzani R, Bouhnik A-D, Rousseau F, Monet A, Préau M, et al. Evolution of physical activity and body weight changes in breast cancer survivors five years after diagnosis– VICAN 2 & 5 French National surveys. Breast. 2021;59:248–55.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lei Y-Y, Ho SC, Kwok C, Cheng A, Cheung KL, Lee R, et al. Weight and waist-to-hip ratio change pattern during the first five years of survival: data from a longitudinal observational Chinese breast cancer cohort. BMC Cancer. 2021;21:839.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nyrop KA, Deal AM, Shachar SS, Park J, Choi SK, Lee JT, et al. Weight trajectories in women receiving systemic adjuvant therapy for breast cancer. Breast Cancer Res Treat. 2020;179:709–20.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Uhelski A-CR, Blackford AL, Sheng JY, Snyder C, Lehman J, Visvanathan K, et al. Factors associated with weight gain in pre- and post-menopausal women receiving adjuvant endocrine therapy for breast cancer. J Cancer Surviv Res Pract. 2023. https://doi.org/10.1007/s11764-023-01408-y.

    Article 

    Google Scholar 

  • Raghavendra A, Sinha AK, Valle-Goffin J, Shen Y, Tripathy D, Barcenas CH. Determinants of weight gain during adjuvant endocrine therapy and association of such weight gain with recurrence in Long-term breast Cancer survivors. Clin Breast Cancer. 2018;18:e7–13.

    Article 
    PubMed 

    Google Scholar 

  • Nyrop KA, Deal AM, Lee JT, Muss HB, Choi SK, Wheless A, et al. Weight gain in hormone receptor-positive (HR+) early-stage breast cancer: is it menopausal status or something else? Breast Cancer Res Treat. 2018;167:235–48.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Anbari AB, Deroche CB, Armer JM. Body mass index trends and quality of life from breast cancer diagnosis through seven years’ survivorship. World J Clin Oncol. 2019;10:382–90.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Qin B, Kim K, Goldman N, Rundle AG, Chanumolu D, Zeinomar N, et al. Multilevel factors for adiposity change in a Population-Based prospective study of black breast Cancer survivors. J Clin Oncol. 2022;40:2213–23.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pedersini R, Laganà M, Bosio S, Zanini B, Cosentini D, di Mauro P, et al. Is weight gain preventable in women with early breast cancer undergoing chemotherapy? A real-world study on dietary pattern, physical activity, and body weight before and after chemotherapy. Breast Cancer Res Treat. 2023;202:461–71.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Irwin ML, McTiernan A, Baumgartner RN, Baumgartner KB, Bernstein L, Gilliland FD, et al. Changes in body fat and weight after a breast Cancer diagnosis: influence of demographic, prognostic, and lifestyle factors. J Clin Oncol. 2005;23:774–82.

    Article 
    PubMed 

    Google Scholar 

  • Sella T, Zheng Y, Tan-Wasielewski Z, Rosenberg SM, Poorvu PD, Tayob N, et al. Body weight changes and associated predictors in a prospective cohort of young breast cancer survivors. Cancer. 2022;128:3158–69.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Liu L-N, Wen F-H, Miaskowski C, Lin Y-C, Wang J-S, Jeng C, et al. Weight change trajectory in women with breast cancer receiving chemotherapy and the effect of different regimens. J Clin Nurs. 2014;23:2757–68.

    Article 
    PubMed 

    Google Scholar 

  • Saquib N, Flatt SW, Natarajan L, Thomson CA, Bardwell WA, Caan B, et al. Weight gain and recovery of pre-cancer weight after breast cancer treatments: evidence from the women’s healthy eating and living (WHEL) study. Breast Cancer Res Treat. 2007;105:177–86.

    Article 
    PubMed 

    Google Scholar 

  • Hurtado MD, Saadedine M, Kapoor E, Shufelt CL, Faubion SS. Weight gain in midlife women. Curr Obes Rep. 2024;13:352–63.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ekenga CC, Wang X, Pérez M, Schootman M, Jeffe DB. A prospective study of weight gain in women diagnosed with Early-Stage invasive breast cancer, ductal carcinoma In Situ, and women without breast Cancer. J Womens Health. 2020;29:524–33.

    Article 

    Google Scholar 

  • Gross AL, May BJ, Axilbund JE, Armstrong DK, Roden RB, Visvanathan K. Weight change in breast cancer survivors compared to cancer-free women: a prospective study in women at Familial risk of breast cancer. Cancer Epidemiol Biomark Prev. 2015;24:1262–9.

    Article 
    CAS 

    Google Scholar 

  • Freedman RJ, Aziz N, Albanes D, Hartman T, Danforth D, Hill S, et al. Weight and body composition changes during and after adjuvant chemotherapy in women with breast cancer. J Clin Endocrinol Metab. 2004;89:2248–53.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Sedjo RL, Hines LM, Byers T, Giuliano AR, Marcus A, Vadaparampil S, et al. Long-term weight gain among Hispanic and non-Hispanic white women with and without breast cancer. Nutr Cancer. 2013;65:34–42.

    Article 
    PubMed 

    Google Scholar 

  • Sternfeld B. Physical activity and changes in weight and waist circumference in midlife women: findings from the study of women’s health across the Nation. Am J Epidemiol. 2004;160:912–22.

    Article 
    PubMed 

    Google Scholar 

  • Banack HR, Bea JW, Chen Z, Blew RM, Nicholas S, Stefanick M, et al. Longitudinal patterns of abdominal visceral and subcutaneous adipose tissue, total body composition, and anthropometric measures in postmenopausal women: results from the women’s health initiative. Int J Obes. 2023;47:288–96.

    Article 

    Google Scholar 

  • Greendale GA, Sternfeld B, Huang M, Han W, Karvonen-Gutierrez C, Ruppert K, et al. Changes in body composition and weight during the menopause transition. JCI Insight. 2019;4:e124865.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Purcell SA, Marker RJ, Cornier M-A, Melanson EL. Dietary intake and energy expenditure in breast Cancer survivors: A review. Nutrients. 2021;13:3394.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Van Soom T, El Bakkali S, Gebruers N, Verbelen H, Tjalma W, van Breda E. The effects of chemotherapy on energy metabolic aspects in cancer patients: A systematic review. Clin Nutr. 2020;39:1863–77.

    Article 
    PubMed 

    Google Scholar 

  • Buttiron Webber T, Briata IM, DeCensi A, Cevasco I, Paleari L. Taste and smell disorders in Cancer treatment: results from an integrative rapid systematic review. Int J Mol Sci. 2023;24:2538.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Walker J, Joy AA, Vos LJ, Stenson TH, Mackey JR, Jovel J, et al. Chemotherapy-induced weight gain in early-stage breast cancer: a prospective matched cohort study reveals associations with inflammation and gut dysbiosis. BMC Med. 2023;21:178.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pistilli B, Lohrisch C, Sheade J, Fleming GF. Personalizing adjuvant endocrine therapy for Early-Stage hormone Receptor–Positive breast Cancer. Am Soc Clin Oncol Educ Book. 2022;:60–72.

  • Connolly RM, Miller KD. Back to the beginning: the role of ovarian suppression in management of hormone sensitive breast Cancer in premenopausal women. J Clin Oncol. 2023;41:1339–41.

    Article 
    PubMed 

    Google Scholar 

  • Dahl AK, Reynolds CA. Accuracy of recalled body weight–a study with 20-years of follow-up. Obes Silver Spring Md. 2013;21:1293–8.

    Article 

    Google Scholar 

  • Kyulo NL, Knutsen SF, Tonstad S, Fraser GE, Singh PN. Validation of recall of body weight over a 26-year period in cohort members of the adventist health study 2. Ann Epidemiol. 2012;22:744–6.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tehard B, van Liere MJ, Com NC, Clavel-Chapelon F. Anthropometric measurements and body silhouette of women: validity and perception. J Am Diet Assoc. 2002;102:1779–84.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Burke MA, Carman KG. You can be too thin (but not too tall): social desirability bias in self-reports of weight and height. Econ Hum Biol. 2017;27:198–222.

    Article 
    PubMed 

    Google Scholar 

  • Marell PS, Vierkant RA, Olson JE, Herrmann J, Larson NL, Lebrasseur NK, et al. Changes in amount and intensity of physical activity over time in breast cancer survivors. JNCI Cancer Spectr. 2023;7:pkad056.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Brown JC, Caan BJ, Cespedes Feliciano EM, Xiao J, Weltzien E, Prado CM, et al. Weight stability masks changes in body composition in colorectal cancer: a retrospective cohort study. Am J Clin Nutr. 2021;113:1482–9.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Brown JC, Cespedes Feliciano EM, Caan BJ. The evolution of body composition in oncology-epidemiology, clinical trials, and the future of patient care: facts and numbers. J Cachexia Sarcopenia Muscle. 2018;9:1200–8.

    Article 
    PubMed 

    Google Scholar 

  • Delrieu L, Blanc D, Bouhamama A, Reyal F, Pilleul F, Racine V, et al. Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients. Front Nucl Med. 2024;3:1292676.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

Continue Reading

  • Physicians Call for American Dietary Guidelines to Prioritize Legumes as a Protein Source – vegconomist

    Physicians Call for American Dietary Guidelines to Prioritize Legumes as a Protein Source – vegconomist

    134 physicians have called on the Department of Health and Human Services and the U.S. Department of Agriculture to prioritize beans, peas, and lentils as a protein source in the next Dietary Guidelines for Americans.

    In a letter sent on June 24, the doctors — all members of the Physicians Committee for Responsible Medicine (PCRM) — said that promoting legume consumption would help to prevent and reduce chronic disease. In contrast, they claim that red and processed meats are strongly associated with cardiovascular disease, diabetes, and certain cancers.

    Furthermore, legumes are whole foods and are rich in fiber, which most Americans are deficient in. Some types are sourced from American farmers, who could benefit from an increase in legume consumption.

    Photo: Polina Tankilevitch on Pexels

    “Important and appropriate emphasis”

    Recently, the 2025 Dietary Guidelines Advisory Committee recommended that federal nutrition guidelines be modified to move legumes from the Vegetables Food Group to the Protein Food Group. It also suggested that Beans, Peas, and Lentils should be listed as the first protein subgroup, followed by Nuts, Seeds, and Soy Products. Seafood would come third, while Meats, Poultry, and Eggs would be last on the list.

    In their letter, the PCRM physicians support these recommendations, noting that the reorganization would “more accurately classify these foods as a major protein source in many Americans’ diets”. They also say the change would educate people on the nutritional value of legumes, while dispelling the myth that plant-based foods are an incomplete source of protein.

    “The Dietary Guidelines Advisory Committee’s report put important and appropriate emphasis on beans and other plant-based foods,” said Neal Barnard, MD, FACC, president of the Physicians Committee. “Overwhelming evidence supports the role of these foods in supporting cardiovascular health, promoting a healthy body weight, and reducing the risk of type 2 diabetes, cancer, and other serious conditions. It is vital that the next Dietary Guidelines for Americans prioritize these nutritious sources of protein.”

    Continue Reading

  • Study illuminates caffeine’s longevity effects at the cellular level

    Study illuminates caffeine’s longevity effects at the cellular level

    A new paper reveals caffeine might play a role in slowing down the aging process at a cellular level — by tapping into an ancient cellular energy system. The study of fission yeast — a single-celled organism “surprisingly similar” to human cells — found that caffeine helps cells sustain life.

    Caffeine has long been linked to potential health benefits, including reduced risk of age-related diseases. But the researchers say until now, how it works inside our cells, and what exactly are its connections with nutrient and stress responsive gene and protein networks has remained a mystery.

    A few years ago, the same research team found that caffeine helps cells live longer by acting on a growth regulator called TOR (“Target of Rapamycin”). TOR is a biological switch that tells cells when to grow, based on how much food and energy is available.

    This switch has been controlling energy and stress responses in living things for over 500 million years.

    New research suggests caffeine helps cells age slower by activating their internal energy sensor, rather than directly influencing their growth.In the latest study, the scientists discovered caffeine doesn’t act on this growth switch directly. Instead, it works by activating another important system called AMPK, a cellular “fuel gauge” that is evolutionarily conserved in yeast and humans.

    “When your cells are low on energy, AMPK kicks in to help them cope,” explains the study’s senior author, Dr. Charalampos Rallis, a reader in Genetics, Genomics and Fundamental Cell Biology at Queen Mary University of London. “And our results show that caffeine helps flip that switch.”

    Caffeine’s effects at the cellular level

    Interestingly, AMPK is also the target of metformin, a common diabetes drug that’s being studied for its potential to extend human lifespan together with rapamycin, backed by advocates including longevity influencer Bryan Johnson and OpenAI CEO Sam Altman.

    Using their yeast model, the researchers showed that caffeine’s effect on AMPK influences how cells grow, repair their DNA, and respond to stress — all of which are tied to aging and disease.

    “These findings help explain why caffeine might be beneficial for health and longevity,” says Dr. John-Patrick Alao, the postdoctoral research scientist leading this study. 

    “And they open up exciting possibilities for future research into how we might trigger these effects more directly — with diet, lifestyle, or new medicines.”

    The study findings are published in the journal Microbial Cell.

    Previous research has spotlighted caffeine’s effects on human life quality, particularly for infants. A Rutgers Health study revealed that it may protect babies by preventing dangerous drops in oxygen that can cause death. Sudden Unexpected Infant Death is the leading cause of infant deaths between one and 12 months old.

    Continue Reading

  • Clinical courses and outcomes of cerebral toxoplasmosis in HIV-positive patients in Shiraz, Southern Iran: a retrospective study | BMC Infectious Diseases

    Clinical courses and outcomes of cerebral toxoplasmosis in HIV-positive patients in Shiraz, Southern Iran: a retrospective study | BMC Infectious Diseases

    CTX is among the most common opportunistic infections in patients with HIV/AIDS [3, 13]. The pathogenesis of the disease is attributed to the reactivation of the latent T. gondii infection, particularly in patients with immunocompromising conditions, such as HIV/AIDS [17]. This study investigated the prevalence, clinical course, and mortality rate of CTX in hospitalized HIV-positive patients. Our study showed a prevalence of 4% for toxoplasmosis and 2.17% for CTX among all patients with HIV/AIDS admitted over ten years to two main hospitals of Shiraz University of Medical Sciences. In 2007, Davarpanah et al. reported the seroprevalence of toxoplasmosis among patients with HIV/AIDS at 18.2% in Shiraz [15]. Additionally, the authors addressed a 10.4% prevalence of CTX in these patients. The relatively smaller sample size and the shorter period of the study by Davarpanah et al. may partly explain the differences between the findings of these two studies. However, the most important difference is that our study was focused on hospitalized patients with HIV/AIDS, while in the study by Davarpanah et al., the patients were included from an outpatient setting.

    Although several previous studies did not address this [3, 15, 18,19,20], our findings revealed a significantly higher prevalence of both toxoplasmosis (as a clinical cause for hospitalization) and CTX among HIV-positive females compared to HIV-positive males (4.32% vs. 1.59% and 7.03% vs. 3.18%, respectively). Previous research has highlighted the seroprevalence of toxoplasmosis among men and women with HIV/AIDS [21, 22]; however, our study focused on toxoplasmosis as a clinical diagnosis that necessitated hospital admission, rather than mere seropositivity. This distinction may explain the discrepancy in prevalence between our study and others. Thus, future systematic reviews and meta-analyses are needed to provide a more comprehensive and reliable conclusion.

    An important finding of our study was that more than half (57.89%) of the CTX patients were newly diagnosed with HIV infection. This aligns with findings from a case series study in Brazil, where CTX was reported as the first manifestation of HIV infection in 48.21% of patients [20]. Our study further revealed that the odds of developing CTX in HIV-positive patients increased as age decreased. Additionally, the mean age of CTX patients was 36.13 ± 9.20 years, compared to 40.25 ± 11.30 years in patients with a prior HIV diagnosis. Although the small sample size within these two subgroups limits the reliability of statistical analysis, these observations highlight the need for effective HIV screening programs, targeting at-risk young adults. Furthermore, similar to previous studies [8], the four most common symptoms among our patients were FND, decreased LOC, headache, and fever. Although with such symptoms, other differential diagnoses, such as brain stroke, encephalitis, meningitis, or bacterial brain abscess are at the top of the differential diagnosis list, special consideration should be given to HIV infection and CTX, particularly in young adults who are not previously diagnosed with HIV infection.

    Brain MRI is a more sensitive tool for diagnosing CTX lesions [8]; however, brain CT scans are more widely available as an initial imaging modality. The typical appearance of CTX lesions in brain CT scans may consist of ring-enhanced lesions with peripheral vasogenic edema and mass effect, particularly in basal ganglia and frontal and parietal lobes. In the brain MRI, typical lesions may appear as “eccentric target sign”, with an enhanced eccentric core and hypointense intermediate zones, surrounded by a hyperintense enhanced rim in a T1-weighted image. The lesions in T2-weighted MRI images are seen as “concentric target sign” with a concentric zone of hypo and hyperintensity [8, 23]. There are few case reports on unusual radiological findings of CTX, such as multiple hemorrhagic abscess lesions and diffuse white matter involvement with ependymal enhancement [24, 25]. Along with related neurological symptoms and physical exam findings, all of our patients underwent neuroimaging. While all of the patients had evidence of single or multiple brain lesions, in only eight initial imaging reports (42.11%), CTX was listed as a probable differential diagnosis by radiologists. Additionally, in six patients (31.58%), lymphoma/malignancies were reported as a suspected diagnosis.

    The suspicion of CTX is primarily based on a compatible clinical history, physical examination, neuroimaging findings, and serological evidence. Moreover, a positive radiological response to anti-Toxoplasma treatment also augments the primary diagnosis. A useful classification for diagnosing CTX has been proposed by Dian et al [7]. The four categories include histology- and laboratory-confirmed CTX, as well as probable and possible CTX. Histology- and laboratory-confirmed CTX require a compatible clinical syndrome, the presence of lesions in neuroimaging plus evidence of T. gondii tachyzoites in brain biopsy or its DNA in CSF-PCR, respectively. Probable CTX consists of a compatible clinical syndrome, presence of lesions in neuroimaging, and anti-Toxoplasma IgG seropositivity or radiological improvement in response to 10-14 days of empirical treatment. Finally, possible CTX applies in cases of death or absence of radiologic confirmation. We demonstrated that CTX was confirmed in the majority of the cases based on imaging and serological workups; however, in seven patients (36.84%) CSF analysis or brain biopsy (or both) was performed, probably due to a high suspicion for other diagnoses, such as lymphoma, or fungal and mycobacterial infections. In other words, our CTX cases were mostly (89.47%) diagnosed using probable CTX classification, that is, they were diagnosed based on compatible clinical presentation, presence of radiological lesions, and anti-T. gondii IgG seropositivity. However, only two patients could be labeled as histology- and laboratory-confirmed CTX, who had positive brain biopsy and CSF-PCR results for T. gondii.

    Interestingly, one of our patients was seronegative for T. gondii IgG, and the diagnosis was established based on radiological findings and clinical improvement following anti-Toxoplasma therapy. This observation aligns with previous reports indicating that a small but significant subset of patients with CTX may be IgG seronegative [26]. Additionally, we encountered another patient who presented with clinical features consistent with CTX and was seropositive for both IgG and IgM antibodies. The diagnosis was further confirmed by a positive brain biopsy and CSF-PCR for T. gondii. This case may reflect a primary infection leading to CTX, particularly in light of the patient’s markedly low CD4+ T-cell count (50 cells/μL). However, given the rarity of primary T. gondii infections among patients with CTX [27], a false-positive IgM result cannot be ruled out. A much less likely possibility is reinfection with a different strain of T. gondii [4, 28].

    A three-drug regimen of pyrimethamine (50 mg/day), sulfadiazine (4 g/day), and folinic acid (25 mg/day) for six weeks is the most effective and preferred treatment for CTX in patients with HIV/AIDS [8, 29]. In our study, all nineteen patients were treated with three-drug anti-Toxoplasma regimens during hospitalization. Some studies have shown that TMP-SMX could be as effective as pyrimethamine-sulfadiazine in curative treatment. Moreover, TMP-SMX prophylaxis is recommended for Patients with HIV/AIDS with a CD4+ count of less than 200 cells/µL. Thus, in low- and middle-income counties, where pyrimethamine is unavailable or expensive, TMP-SMX is a good choice for induction and maintenance treatment. It has been shown that TMP-SMX has fewer toxic or adverse reaction, less cost, and is better tolerated by patients compared to pyrimethamine-sulfadiazine [6, 7, 30,31,32]. In our study, TMP/SMX was added to the treatment of nine patients who had CD4+ counts less than 100 cells/µL. Clindamycin plus pyrimethamine is a reasonable alternative for sulfadiazine in patients with an allergy to sulfadiazine [8]. Additionally, TMP/SMX and clindamycin were replaced with sulfadiazine in one patient due to a new onset drug allergy to sulfadiazine and in another patient due to the temporary unavailability of pyrimethamine and sulfadiazine. Discontinuing maintenance therapy could be considered based on the clinical improvements and in patients with CD4+ above 200 cells/µL who received cART for at least 6 months on maintenance treatment [6].

    We showed an in-hospital mortality rate of 21.05% in patients with CTX. A prospective study of 55 HIV+/AIDS patients with CTX in Brazil, showed that after 6 weeks of treatment, 42% and 46% of patients had complete and partial response to therapy, respectively, while 13% died [18]. Two other similar studies in Taiwan and Mali also reported 16.7% (3 out of 18) and 15.4% (4 out of 26) mortality rates, respectively [33, 34]. It should be noted that various factors affect the prognosis and mortality rates of CTX, and thus they may explain the observed differences between studies. First, the number of included patients with CTX in studies is usually few and the calculated mortality rates might not represent the actual population. Second, in our study, all four dead patients were brought to the hospital with decreased LOC, indicating the severity and progression of the disease. In addition, the age, comorbid conditions, and timing of treatment initiation are not the same among the studies. Three of our deceased patients suffered from other comorbid infections, including hepatitis C virus (HCV) infection, and one had concomitant pulmonary TB. Finally, all of these four patients did not receive cART within the first two weeks of CTX diagnosis. According to our findings, six patients who had CD4+ counts below 100 cells/µL received cART. It has been shown that early initiation of cART within two weeks of anti-Toxoplasma therapy could significantly reduce the mortality of CTX and improve its prognosis [8, 18, 20]. For example, a study in Brazil showed a significant reduction in mortality rates from over 90% in the pre-cART era to less than 30% in recent years with cART [19].

    It is important to note that our study has several limitations. The findings could be influenced by various confounding factors, including limited access to detailed clinical and paraclinical data due to the study’s retrospective design. Improving medical record management during admission and archiving would enhance the accuracy and completeness of future data analysis and research. Despite the ten-year duration of our study, the relatively low prevalence of CTX may have affected the statistical analyses, potentially limiting the generalizability of the results and reducing the statistical power to detect significant associations. Finally, the patients were recruited from two referral hospitals of Shiraz University of Medical Sciences, which may not fully represent the outpatient population. Thus, a potential selection bias from including patients from tertiary referral hospitals may have impacted the results, as these institutions typically manage more severe or complex cases. Future studies with larger sample sizes and more diverse clinical settings could help address these limitations and provide more robust conclusions.

    Continue Reading

  • An Unexpected Diagnosis of Kawasaki Disease in a Three-Month-Old Infant: A Diagnostic Trap

    An Unexpected Diagnosis of Kawasaki Disease in a Three-Month-Old Infant: A Diagnostic Trap


    Continue Reading

  • MHRA to investigate links between genetics, GLP-1 drugs and pancreatitis : Clyde & Co

    MHRA to investigate links between genetics, GLP-1 drugs and pancreatitis : Clyde & Co

    The Medicines and Healthcare products Regulatory Agency (MHRA) has launched an investigation into whether an individual’s genes may increase their risk of developing acute pancreatitis when taking GLP-1 drugs for weight loss and Type 2 diabetes.

    The investigation follows reports submitted to the MHRA’s Yellow Card scheme purportedly linking GLP-1 drugs to numerous deaths, and adverse reactions, the bulk of which were comprised of gastrointestinal disorders. The reports break down as follows:

    • Semaglutides (with brand names including Ozempic and Wegovy): 18,046 adverse reactions, 1,765 serious reports and 16 fatal outcomes.

    • Tirzepatides (with brand names including Mounjaro): 20,882 adverse reactions, 3,116 serious reports and 21 fatal outcomes.

    • Liraglutide (with brand names including Saxenda and Victoza): 2,905 adverse reactions, 688 serious reports and 18 fatal outcomes.

    Extracted from Yellow Card website, 27 June 2025 – What is being reported | Making medicines and medical devices safer

    Whilst it should be stressed that these reports are unverified, reflecting suspected or potential links between GLP-1 drugs and adverse outcomes, the reports raise necessary questions in the context of the risk/benefit of GLP-1 drugs and broader risk landscape, which includes:

    • A huge increase in the use of GLP-1 drugs, with estimates suggesting that 1.5M people in the UK may be taking privately funded weight loss injections, with a further 220,000 expected to receive Mounjaro, after it recently became available via the NHS.

    • The suggestion of a causative relationship between GLP-1 drugs and pancreatitis, including:

      • A research paper published in the BMJ linking GLP-1 drugs to an increased risk of gastrointestinal events, including pancreatitis, gastroparesis, and bowel obstruction.

      • The US Food and Drug Administration’s Adverse Events Reporting System (FAERS) receiving 908 reports of Ozempic users developing pancreatitis.

    • The developing litigation in the US, which is centred around a large multi-district litigation against Eli Lilly (the manufacturer of Trulicity and Mounjaro) and Novo Nordisk (the manufacturer of Ozempic, Wegovy, and Rybelsus) in relation to alleged personal injuries. Whilst the alleged injuries include pancreas damage, the litigation is more strongly focused on other injuries, chiefly, gastroparesis, bowel blockage and vision problems. The claims comprise various causes of action, including failure to warn, negligence, misrepresentation and breaches of consumer protection legislation/unfair trade practices.

    • The extent to which, if at all, the MHRA’s investigation might impact clinical trials, including an ongoing NHS trial, which seeks to measure the “real-world” public health impact of weight loss drugs, including their impacts on prospects of employment and number of sick days taken. The trial involves around 3,000 people in the Greater Manchester area, and is set to take place over a 5 year period.

    Pending further clarification of the role (if any) that an individual’s genetics may play in the development of pancreatitis, there would be no obvious need for any or any immediate change in underwriters’ approach to GLP-1 drugs. The Market will, no doubt, be monitoring the position with interest.

    Further detail on the MHRA’s investigation is available here: If you take a GLP-1 medicine and have been hospitalised by acute pancreatitis, the Yellow Card Biobank wants to hear from you  – GOV.UK

    View all our ‘weight loss drugs’ content here

    Continue Reading

  • 3D-Printed Smart Pen Helps Diagnose Parkinson’s

    3D-Printed Smart Pen Helps Diagnose Parkinson’s


    Register for free to listen to this article

    Thank you. Listen to this article using the player above. ✖


    Want to listen to this article for FREE?


    Complete the form below to unlock access to ALL audio articles.

    Every year, tens of thousands of people with signs of Parkinson’s disease go unnoticed until the incurable neurodegenerative condition has already progressed.

    Motor symptoms, such as tremors or rigidity, often emerge only after significant neurological damage has occurred. By the time patients are diagnosed, more than half of their dopamine-producing neurons may already be lost. This kind of diagnostic delay can limit treatment options and slow progress on early-stage interventions. While there are existing tests to detect biomarkers of Parkinson’s, including cell loss in the brain and inflammatory markers in blood, they typically require access to specialists and costly equipment at major medical centers, which may be out of reach for many.

    Led by Jun Chen, an associate professor of bioengineering at the UCLA Samueli School of Engineering, researchers have developed a seemingly simple yet effective tool: a smart, self-powered magnetoelastic pen that could help detect early signs of Parkinson’s by analyzing a person’s handwriting.

    The highly sensitive diagnostic pen, described in a UCLA-led study and published as a cover story in the June issue of Natural Chemical Engineering, features a soft, silicon magnetoelastic tip and ferrofluid ink — a special liquid containing tiny magnetic particles. When the pen’s tip is pressed against a surface or moved in the air, the pen converts both on-surface and in-air writing motions into high-fidelity, quantifiable signals through a coil of conductive yarn wrapped around the pen’s barrel. Although not intended for writing, the pen is self-powered leveraging changes in the magnetic properties of its tip and the dynamic flow of the ferrofluid ink to generate data.

    To test the pen’s diagnostic potential, the team conducted a pilot study with 16 participants, three of whom had Parkinson’s disease. The pen recorded detailed handwriting signals, which were then analyzed by a neural network trained to detect motor patterns associated with the disease. The model was able to distinguish participants with Parkinson’s from healthy individuals with an average accuracy of 96.22%.

    “Detection of subtle motor symptoms unnoticeable to the naked eye is critical for early intervention in Parkinson’s disease,” said Chen, who is the study’s corresponding author. “Our diagnostic pen presents an affordable, reliable and accessible tool that is sensitive enough to pick up subtle movements and can be used across large populations and in resource-limited areas.”

    The researchers anticipate that this pen could transform early detection of Parkinson’s and other neurodegenerative conditions. Rather than waiting for symptoms to become disruptive, primary care physicians or geriatric specialists could administer a quick handwriting test during routine visits and use the data to inform earlier referrals or treatment.

    Reference: Chen G, Tat T, Zhou Y, et al. Neural network-assisted personalized handwriting analysis for Parkinson’s disease diagnostics. Nat Chem Eng. 2025;2(6):358-368. doi: 10.1038/s44286-025-00219-5

    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.

    Continue Reading

  • Mental health treatment and its impact on survival outcomes in patients with comorbid mental health and cardiovascular diseases: a retrospective cohort study | BMC Psychiatry

    Mental health treatment and its impact on survival outcomes in patients with comorbid mental health and cardiovascular diseases: a retrospective cohort study | BMC Psychiatry

    Study setting

    This study was conducted at four major healthcare facilities in Northwest Ethiopia including Debre Markos Comprehensive Specialized Hospital, Tibebe Gihon Comprehensive Specialized Hospital, University of Gondar Comprehensive Specialized Hospital, and Felege Hiwot Comprehensive Specialized Hospital.

    Study period and design

    The study was conducted, from January 1, 2023, to May 31, 2023. A retrospective cohort design was employed to assess existing medical records, using a one year dataset.

    Source and study population

    The study population consisted of patients diagnosed with comorbid mental health and cardiovascular diseases who received care at the participating hospitals. Patients were identified through hospital admission and discharge records, outpatient clinic logs, and electronic health records.

    Eligibility criteria

    The inclusion criteria for participants were:

    • A confirmed diagnosis of comorbid mental health and cardiovascular disease in medical records.

    • Age 18 years or older at the time of diagnosis.

    • Available medical records for the duration of the study period.

    The exclusion criteria for participants were:

    • Patients with incomplete medical records,

    • Those who had prior cardiovascular surgeries.

    • Individuals with terminal illnesses unrelated to comorbid mental health and cardiovascular diseases.

    Study variables

    The dependent variables are hospital readmission and emergency department visits. The independent variables included mental health treatment, age, sex, and residence.

    Sample size determination

    This study included all patients who met the eligibility criteria during the study period. As the study design was based on medical record review, no a priori sample size or power calculation was performed. Instead, the full population of eligible patients included to maximize statistical power and ensure generalizability.

    A total of 319 patients with comorbid mental health and cardiovascular diseases between January 2018 and December 2022 were identified from four healthcare institutions in Northwest Ethiopia. These institutions were selected in simple random approaching method.

    Sampling technique

    To ensure the sample was representative of the eligible population across the participating hospitals, a proportional simple random sampling technique was employed. The total number of eligible patients at each hospital during the study period was first identified through a manual review of patient records. Proportional allocation was then used to determine the number of patients to include from each hospital based on its share of the total eligible patient population.

    The formula used for proportional allocation was: ni = (Ni / N) × n.

    Where:

    • ni = sample size from hospital i.

    • Ni = number of eligible patients in hospital i.

    • N = total number of eligible patients across all hospitals.

    • n = total sample size (319).

    Based on estimated eligible patient numbers from hospital records (N = 1,100), the sample was allocated as follows:

    • Debre Markos Comprehensive Specialized Hospital: n1 = (360 / 1100) × 319 ≈ 104 patients.

    • University of Gondar Comprehensive Specialized Hospital: n2 = (300 / 1100) × 319 ≈ 87 patients.

    • Felege Hiwot Comprehensive Specialized Hospital: n3 = (240 / 1100) × 319 ≈ 70 patients.

    • Tibebe Gihon Comprehensive Specialized Hospital: n4 = (200 / 1100) × 319 ≈ 58 patients.

    After determining the number of participants per hospital, simple random sampling was applied within each hospital. Eligible patient lists were prepared, and random numbers were generated using a computer-based random number generator to select participants independently.

    Data collection procedure

    This study employed a structured questionnaire, developed after an extensive review of relevant literature. The data collection instrument designed to capture sociodemographic characteristics, clinical parameters, and medication-related variables, with all data extracted from patient medical records. Comorbid conditions including diabetes mellitus, hyperlipidemia, hypertension, and other chronic physical conditions were identified based on clinician-documented diagnoses in the medical charts. Comorbidity was considered present if it was recorded in the patient’s medical history, diagnostic summary, or treatment plan during admission or follow-up visits. These conditions were categorized as binary variables (present or absent), and no additional thresholds related to disease severity, duration, or laboratory values were applied due to variability in documentation across sites.

    Multiple methodologies were employed to assess the receipt of mental health treatment. Pharmacy refill records were used to determine whether patients actively received prescribed mental health medications during the study period. The duration of these prescriptions was also assessed as a measure of adherence to treatment regimens. Patient charts were systematically examined for indications of mental health treatment, including therapist notes, treatment plans, and mental health evaluations. Specific diagnosis codes associated with mental health conditions were identified to establish a clear connection between diagnosis and treatment. In addition to assessing the receipt of treatment, clinical outcomes related to mental health treatment were analyzed. Indicators such as psychiatric symptoms, changes in diagnoses, and hospitalization rates for mental health crises were assessed. To examine emergency department visits, patient medical records were reviewed throughout the study period. Details such as the reason for each visit, clinical diagnoses, and related mental health assessments were recorded. Visits were categorized based on their connection to comorbid mental health and cardiovascular diseases, mental health crises, or other health complications. For hospital readmissions, a similar review of patient medical records was conducted to track subsequent admissions within a specified follow-up period after discharge. Diagnosis dates were extracted from electronic health records, inpatient and outpatient medical charts, and physician notes. Diagnosis dates were extracted from electronic health records, inpatient and outpatient medical charts, and physician notes. For psychiatric disorders, clinical evaluations, mental health treatment initiation records, and International Classification of Diseases (ICD-10) codes were reviewed, with specific codes. For cardiovascular conditions, diagnostic imaging reports, laboratory results, and physician-confirmed diagnoses, along with corresponding ICD-10 codes, were examined. When exact diagnosis dates were unavailable, the earliest documented evidence of the condition, based on clinical evaluations or treatment initiation was recorded. Given the study’s focus on comorbid mental health and cardiovascular diseases, special attention was given to cases where the timing of psychiatric and cardiovascular diagnoses differed. For patients with pre-existing psychiatric disorders, the timing of the CVD diagnosis was recorded as the key event indicating the onset of a comorbid mental health and cardiovascular diseases. Conversely, for patients with pre-existing CVD, the timing of the psychiatric disorder diagnosis was recorded as the key event. In instances where both conditions were diagnosed simultaneously (e.g., during a single hospital admission), this date was recorded as the timing for both conditions. For patients with multiple episodes of the same condition, such as recurrent depressive episodes or repeated cardiovascular events, the first documented diagnosis within the study period was used.

    Operational definitions

    • Comorbid mental health and cardiovascular diseases are health conditions that involve both cardiovascular disorders and psychiatric disorders.

    • Mental health treatment refers to interventions aimed at alleviating symptoms and improving the well-being of patients with diagnosed mental health conditions.

    • Hospital readmission is defined as any unplanned admission to the hospital. In this study, readmissions included those which are related to comorbid mental health and cardiovascular diseases.

    • Emergency department visit is any encounter in the emergency department requiring immediate medical attention. In this study, emergency department visits included those related to comorbid mental health and cardiovascular diseases.

    • Event Occurred: refers patients who experienced the outcome of interest during the study period, including those who had a hospital readmission or an emergency department visit.

    • Censored: refers patients who did not experience the outcome of interest (hospital readmission or emergency department visit) during the follow-up period. These individuals remained under observation but did not have the event occur before the study’s conclusion or were lost to follow-up.

    • Survival time (time to event): This is the duration from the start to the event.

    Data quality assurance

    To ensure the integrity and reliability of the data collected in this study, several quality assurance measures were implemented throughout the data collection process. A structured questionnaire was initially developed based on a comprehensive review of relevant literature, to facilitate standardized data capture across all participating institutions. The questionnaire was pre-tested on a small sample of medical records to identify ambiguities, improve clarity, and refine variable definitions prior to full-scale implementation.

    Trained research assistants, all of whom were clinical pharmacists, conducted the data extraction. These data collectors underwent rigorous training on the study protocol, ethical considerations, operational definitions, and standard procedures for interpreting medical records. To assess and enhance inter-rater reliability, a pilot exercise was conducted in which 10% of patient charts were independently reviewed by two data collectors. Discrepancies were discussed and resolved through consensus, leading to adjustments in the protocol where necessary. Throughout the data collection period, Periodic supervisory audits were performed. The principal investigator and hospital-based site coordinators randomly reviewed approximately 10% of extracted data to verify accuracy and adherence to protocol. Any inconsistencies were addressed through targeted feedback and retraining sessions with the data collectors. Throughout the study period to ensure compliance with data collection protocols and to address any potential issues promptly. To minimize information bias, diagnoses were confirmed using multiple sources of documentation. Psychiatric disorders were validated by cross-referencing ICD-10 codes with therapist notes, treatment plans, and prescription records. Cardiovascular diagnoses were corroborated using physician-confirmed diagnoses, laboratory and imaging reports, and treatment documentation. In cases where exact diagnosis dates were missing, the earliest documented clinical evidence such as first mention of symptoms or treatment initiation was used as a proxy. To reduce the impact of missing data, records lacking essential variables (e.g., confirmed diagnoses or outcome data) were excluded from analysis. For less critical variables, a complete-case analysis was performed. Given the low frequency of missing data in those variables, imputation methods were not necessary. When feasible, missing details were recovered through triangulation across multiple record sources. To mitigate selection bias, a total population sampling strategy was used. All eligible patients with coexisting psychiatric and cardiovascular conditions who met the inclusion criteria and received care at any of the four participating hospitals were included. Additionally, all medical records and documentation were verified against the entries in the database to confirm accuracy and completeness.

    Data processing and analysis

    Data processing and analysis for this study were conducted using statistical software to ensure accurate interpretation of the findings. Following data collection, all questionnaires and medical record entries were reviewed for completeness and consistency. The data were then coded and entered into a secure electronic database to facilitate analysis. Descriptive statistics were generated to summarize the demographic and clinical characteristics of the study population. Categorical variables were described using frequency distributions and percentages, while continuous variables were summarized using means and standard deviations.

    To identify factors influencing survival outcomes, Cox proportional hazards regression analysis was performed. The primary outcomes were the time to hospital readmission and the time to the first emergency department visit, both measured in days from the date of discharge or study entry. The follow-up period spanned one year from the date of the first diagnosis or discharge, with censoring applied at the end of the study period or upon loss to follow-up. The assumptions of the Cox proportional hazards model were evaluated using the Schoenfeld residual test. To examine the relationship between baseline variables and patient survival, a two-step approach was employed. Initially, each baseline variable that satisfied the assumptions of the Cox proportional hazards model was analyzed individually using separate Cox regression models. Subsequently, variables with a P-value of less than 0.25 in the bivariate analysis were included in the multivariable analysis. However, final inclusion was not based solely on statistical criteria. We also incorporated variables based on their clinical relevance, biological plausibility, and established evidence from prior studies on mental health and cardiovascular outcomes. The Cox regression model was utilized to identify factors associated with the time to hospital readmission and emergency department visit. The results were reported as crude hazard ratios (CHR) and adjusted hazard ratios (AHR) with corresponding 95% confidence intervals, and statistical significance was determined at a P-value threshold of < 0.05. Additionally, multicollinearity among the independent variables was assessed using the variance inflation factor to detect and eliminate redundant variables that could bias the estimates. The overall mean VIF was calculated to be 1.21, which falls within the acceptable range of 1 to 5. Survival analysis was further conducted using Kaplan-Meier survival curves to illustrate survival functions, and the log-rank test was applied to compare survival distributions between patients who received mental health treatment and those who did not.

    Continue Reading

  • A network analysis of the depression and anxiety comorbidity: a nationwide survey among Chinese adolescents during the normalization phase of COVID-19 pandemic prevention and control | BMC Psychiatry

    A network analysis of the depression and anxiety comorbidity: a nationwide survey among Chinese adolescents during the normalization phase of COVID-19 pandemic prevention and control | BMC Psychiatry

  • Meherali S, Punjani N, Louie-Poon S, Abdul Rahim K, Das JK, Salam RA, et al. Mental health of children and adolescents amidst COVID-19 and past pandemics: A rapid systematic review. Int J Environ Res Public Health. 2021;18(7):3432.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Seligman LD, Ollendick TH. Comorbidity of anxiety and depression in children and adolescents: an integrative review. Clin Child Fam Psychol Rev. 1998;1(2):125–44.

    CAS 
    PubMed 

    Google Scholar 

  • Cummings CM, Caporino NE, Kendall PC. Comorbidity of anxiety and depression in children and adolescents: 20 years after. Psychol Bull. 2014;140(3):816–45.

    PubMed 

    Google Scholar 

  • Konac D, Young KS, Lau J, Barker ED. Comorbidity between depression and anxiety in adolescents: Bridge symptoms and relevance of risk and protective factors. J Psychopathol Behav Assess. 2021;43(3):583–96.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Choi KW, Kim YK, Jeon HJ. Comorbid anxiety and depression: clinical and conceptual consideration and transdiagnostic treatment. Adv Exp Med Biol. 2020;1191:219–35.

    CAS 
    PubMed 

    Google Scholar 

  • Cramer AO, Waldorp LJ, van der Maas HL, Borsboom D. Comorbidity: a network perspective. Behav Brain Sci. 2010;33(2–3):137–93.

    PubMed 

    Google Scholar 

  • Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91–121.

    PubMed 

    Google Scholar 

  • He Y, Wu C, LeMoult J, Huang J, Zhao Y, Liang K, et al. Exploring symptom-level associations between anxiety and depression across developmental stages of adolescence: a network analysis approach. BMC Psychiatry. 2023;23(1):941.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Cai H, Bai W, Liu H, Chen X, Qi H, Liu R, et al. Network analysis of depressive and anxiety symptoms in adolescents during the later stage of the COVID-19 pandemic. Transl Psychiatry. 2022;12(1):98.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu R, Chen X, Qi H, Feng Y, Su Z, Cheung T, et al. Network analysis of depressive and anxiety symptoms in adolescents during and after the COVID-19 outbreak peak. J Affect Disord. 2022;301:463–71.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu D, Yu M, Zhang X, Cui J, Yang H. Adolescent anxiety and depression: perspectives of network analysis and longitudinal network analysis. BMC Psychiatry. 2024;24(1):619.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang Y, Li Z, Cao X. Investigating the network structure and causal relationships among Bridge symptoms of comorbid depression and anxiety: A bayesian network analysis. J Clin Psychol. 2024;80(6):1271–85.

    PubMed 

    Google Scholar 

  • Zou H, Gao J, Wu W, Huo L, Zhang W. Which comes first? Comorbidity of depression and anxiety symptoms: A cross-lagged network analysis. Soc Sci Med. 2024;360:117339.

    PubMed 

    Google Scholar 

  • Xue S, Lu A, Chen W, Li J, Ke X, An Y. A latent profile analysis and network analysis of anxiety and depression symptoms in Chinese widowed elderly. J Affect Disord. 2024;366:172–80.

    PubMed 

    Google Scholar 

  • Zhang P, Wang L, Zhou Q, Dong X, Guo Y, Wang P, et al. A network analysis of anxiety and depression symptoms in Chinese disabled elderly. J Affect Disord. 2023;333:535–42.

    PubMed 

    Google Scholar 

  • Yohannes AM, Murri MB, Hanania NA, Regan EA, Iyer A, Bhatt SP, et al. COPDGene investigators. Depressive and anxiety symptoms in patients with COPD: A network analysis. Respir Med. 2022;198:106865.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Tang Q, Wang R, Niu H, Li Y, Li Y, Hu Z, et al. Mapping network connection and direction among symptoms of depression and anxiety in patients with chronic gastritis. Psych J. 2024;13(5):824–34.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ren L, Wang Y, Wu L, Wei Z, Cui LB, Wei X, et al. Network structure of depression and anxiety symptoms in Chinese female nursing students. BMC Psychiatry. 2021;21(1):279.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Sun HL, Zhao YJ, Sha S, Li XH, Si TL, Liu YF, et al. Depression and anxiety among caregivers of psychiatric patients during the late stage of the COVID-19 pandemic: A perspective from network analysis. J Affect Disord. 2024;344:33–40.

    PubMed 

    Google Scholar 

  • van Borkulo C, Boschloo L, Borsboom D, Penninx BW, Waldorp LJ, Schoevers RA. Association of symptom network structure with the course of depression. JAMA Psychiatry. 2015;72(12):1219–26.

    PubMed 

    Google Scholar 

  • Wang S, Hou W, Tao Y, Ma Z, Li K, Wang Y, et al. Mapping network connection among symptoms of anxiety, depression, and sleep disturbance in Chinese high school students. Front Public Health. 2022;10:1015166.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang Z, Yang H, Elhai JD. Are there gender differences in comorbidity symptoms networks of problematic social media use, anxiety and depression symptoms? Evidence from network analysis. Pers Indiv Differ. 2022;195:111705.

    Google Scholar 

  • Li K, Ren L, Zhang L, Liu C, Zhao M, Zhan X, et al. Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis. Int J Soc Psychiatry. 2023;69(4):916–27.

    PubMed 

    Google Scholar 

  • Altemus M, Sarvaiya N, Neill Epperson C. Sex differences in anxiety and depression clinical perspectives. Front Neuroendocrinol. 2014;35(3):320–30.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Bangasser DA, Cuarenta A. Sex differences in anxiety and depression: circuits and mechanisms. Nat Rev Neurosci. 2021;22(11):674–84.

    CAS 
    PubMed 

    Google Scholar 

  • Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212.

    PubMed 

    Google Scholar 

  • Shang B, Wang Y, Luo C, Lv F, Wu J, Shao X. Core and Bridge symptoms in self-perceived aging, depression, and anxiety among the elderly with multiple chronic conditions in Chinese communities: a network analysis perspective. BMC Public Health. 2025;25(1):859.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.

    PubMed 

    Google Scholar 

  • Borsboom D. A network theory of mental disorders. World Psychiatry. 2017;16(1):5–13.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. Qgraph: network visualizations of relationships in psychometric data. J Stat Softw. 2012;48(4):1–18.

    Google Scholar 

  • Bassi G, Mancinelli E, Salcuni S, Gori A, Musetti A. Failure in reflective functioning as a key factor in the association between problematic social networking sites use, attachment and childhood maltreatment: A network analysis approach on gender differences. Dev Psychopathol. 2023;1–9.

  • Jones PJ, Ma R, McNally RJ. Bridge centrality: A network approach to Understanding comorbidity. Multivar Behav Res. 2021;56(2):353–67.

    Google Scholar 

  • Jones PJ, Mair P, McNally RJ. Visualizing psychological networks: A tutorial in R. Front Psychol. 2018;9:1742.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lo Buglio G, Pontillo M, Cerasti E, Polari A, Schiano Lomoriello A, Vicari S, et al. A network analysis of anxiety, depressive, and psychotic symptoms and functioning in children and adolescents at clinical high risk for psychosis. Front Psychiatry. 2022;13:1016154.

    PubMed 
    PubMed Central 

    Google Scholar 

  • van Borkulo CD, van Bork R, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, et al. Comparing network structures on three aspects: A permutation test. Psychol Methods. 2023;28(6):1273–85.

    PubMed 

    Google Scholar 

  • Liu CH, Stevens C, Wong SHM, Yasui M, Chen JA. The prevalence and predictors of mental health diagnoses and suicide among U.S. College students: implications for addressing disparities in service use. Depress Anxiety. 2019;36(1):8–17.

    PubMed 

    Google Scholar 

  • Wang RQ, Wei RF, Zhang LT, Huang N, Wei QZ, Peng YY. Study on the comorbidity state of anxiety and depression among medical undergraduates and postgraduates. China Continuing Med Educ. 2019;11(18):57–9.

    Google Scholar 

  • Cénat JM, Dromer E, Mistry S, Villarreal DG, Moshirian Farahi SMM, Dalexis RD, et al. Prevalence and determinants of anxiety, depression and comorbid anxiety-depression symptoms among adolescents in Ebola-affected zones. BJPsych Open. 2023;9(6):e196.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Cavanagh D, Hart LM, Basden S, Lu S, Reavley N. Prevalence estimates of depression and anxiety symptoms among adolescents in bermuda, according to age, gender and race. Soc Psychiatry Psychiatr Epidemiol. 2025. https://doi.org/10.1007/s00127-025-02829-z

    Article 
    PubMed 

    Google Scholar 

  • Catling JC, Bayley A, Begum Z, Wardzinski C, Wood A. Effects of the COVID-19 lockdown on mental health in a UK student sample. BMC Psychol. 2022;10(1):118.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Beard C, Millner AJ, Forgeard MJ, Fried EI, Hsu KJ, Treadway MT, et al. Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychol Med. 2016;46(16):3359–69.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5). Washington, DC: American Psychiatric Pub; 2013.

    Google Scholar 

  • Andreescu C, Lenze EJ, Dew MA, Begley AE, Mulsant BH, Dombrovski AY, et al. Effect of comorbid anxiety on treatment response and relapse risk in late-life depression: controlled study. Br J Psychiatry. 2007;190:344–9.

    PubMed 

    Google Scholar 

  • Wei Z, Ren L, Wang X, Liu C, Cao M, Hu M, et al. Network of depression and anxiety symptoms in patients with epilepsy. Epilepsy Res. 2021;175:106696.

    PubMed 

    Google Scholar 

  • Bai W, Xi HT, Zhu Q, Ji M, Zhang H, Yang BX, et al. Network analysis of anxiety and depressive symptoms among nursing students during the COVID-19 pandemic. J Affect Disord. 2021;294:753–60.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • McElroy E, Fearon P, Belsky J, Fonagy P, Patalay P. Networks of depression and anxiety symptoms across development. J Am Acad Child Adolesc Psychiatry. 2018;57(12):964–73.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lu G, Zhu L, Huang R, Lai P, Wang C. The association between adaptability and the symptoms of depression and anxiety in early adolescents: a network analysis in a longitudinal design. BMC Psychiatry. 2025;25(1):117.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Winarsunu T, Iswari Azizaha BS, Fasikha SS, Anwar Z. Life skills training: can it increases self esteem and reduces student anxiety? Heliyon. 2023;9(4):e15232.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Werner AM, Tibubos AN, Rohrmann S, Reiss N. The clinical trait self-criticism and its relation to psychopathology: A systematic review – Update. J Affect Disord. 2019;246:530–47.

    PubMed 

    Google Scholar 

  • Zhang Z, Qiu A, Zhang X, Zhao Y, Yuan L, Yi J, et al. Gender differences in the mental symptom network of high school students in shanghai, china: a network analysis. BMC Public Health. 2024;24(1):2719.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu S, Ren H, Li Y, Liu Y, Fu S, Han ZR. Gender difference in the onset of adolescent depressive symptoms: A Cross-Lagged panel network analysis. Res Child Adolesc Psychopathol. 2025;53(1):113–23.

    CAS 
    PubMed 

    Google Scholar 

  • Kim HJ, Lee SH, Pae C. Gender differences in anxiety and depressive symptomatology determined by network analysis in panic disorder. J Affect Disord. 2023;337:94–103.

    PubMed 

    Google Scholar 

  • Su Z, Yang X, Hou J, Liu S, Wang Y, Chen Z. Gender differences in the co-occurrence of anxiety and depressive symptoms among early adolescents: A network approach. J Psychiatr Res. 2024;179:300–5.

    PubMed 

    Google Scholar 

  • Wang W, Wang J, Zhang X, Pei Y, Tang J, Zhu Y, et al. Network connectivity between anxiety, depressive symptoms and psychological capital in Chinese university students during the COVID-19 campus closure. J Affect Disord. 2023;329:11–8.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Li W, Zhao N, Yan X, Xu X, Zou S, Wang H, et al. Network analysis of depression, anxiety, posttraumatic stress symptoms, insomnia, pain, and fatigue in clinically stable older patients with psychiatric disorders during the COVID-19 outbreak. J Geriatr Psychiatry Neurol. 2022;35(2):196–205.

    PubMed 

    Google Scholar 

  • Isvoranu AM, Epskamp S, Cheung MW. Network models of posttraumatic stress disorder: A meta-analysis. J Abnorm Psychol. 2021;130(8):841–61.

    PubMed 

    Google Scholar 

    • Continue Reading

  • Metabolite Succinate Linked to IBD Progression

    Metabolite Succinate Linked to IBD Progression


    Register for free to listen to this article

    Thank you. Listen to this article using the player above. ✖


    Want to listen to this article for FREE?


    Complete the form below to unlock access to ALL audio articles.

    Northwestern Medicine investigators have identified a surprising culprit in the progression of inflammatory bowel disease: a naturally occurring metabolic compound in the gut, according to a study published in Nature Immunology.

    Inflammatory bowel disease (IBD), a chronic condition that includes Crohn’s disease and ulcerative colitis, is characterized by persistent inflammation of the gastrointestinal tract. It affects millions worldwide and can lead to debilitating symptoms such as abdominal pain, diarrhea, fatigue and weight loss. While the exact causes of IBD remain unclear, it is widely believed to be influenced by a mix of genetic, environmental and immune factors.

    The new study revealed that elevated levels of the metabolite succinate may actively contribute to the disease by disrupting the function of regulatory T-cells (Tregs), which are essential for maintaining immune balance and preventing runaway inflammation.

    The findings shed light on a previously unknown mechanism that could open new avenues for treatment, said Deyu Fang, PhD, the Hosmer Allen Johnson Professor of Pathology, who was senior author of the study.

    “Succinate is a normal metabolite we all have, but levels are increased in the blood, gut and stool of colitis patients and those with other inflammatory diseases,” Fang said. “We’ve known this for years. But how succinate causes inflammation, we don’t know much about.”

    In the study, Fang and his collaborators observed mice that consumed succinate in their drinking water. They found that higher succinate levels were associated with more severe symptoms of colitis, according to the findings.

    Next, investigators administered succinate to cultured Treg cells from mice. They found that succinate impairs the expression of FOXP3, a key protein essential for the suppressive function of Tregs. This disruption makes FOXP3 more vulnerable to degradation. As a result, Tregs lose their ability to control inflammation, leading to more severe colitis in mouse models.

    Further experiments demonstrated that deleting the gene Dlst mimicked the effects of high succinate levels, resulting in reduced FOXP3 expression, impaired Treg function and increased gut inflammation. However, restoring FOXP3 levels in these cells reversed the damage, highlighting the central role of this protein in immune regulation.

    The study also examined samples from people with IBD and found that their Treg cells had lower levels of FOXP3, which correlated with higher succinate levels and more severe inflammation.

    “This gives us a better understanding of why people have colitis,” Fang said. “One of the reasons is that increased succinate impairs the Treg immunosuppressive function through a direct mechanism. That’s the clinical implication that will help us to understand the pathogenesis of the disease.”

    The discovery could pave the way for new therapeutic strategies aimed at restoring Treg function or targeting succinate metabolism to treat IBD more effectively, Fang said.

    Next, Fang and his colleagues will examine other immune cells in patients with IBD to understand how and why succinate levels are heightened in the disease, he said.

    “The bacteria that make succinate are actually ‘good’ bacteria and probiotic in the gut microbiome, not the bad ones, so it’s really puzzling the field,” Fang said. “We don’t know exactly why succinate levels increase in active disease and return to normal in recovery, but this study may provide a clue for us to understand.”

    Reference: Wang H, Hu D, Cheng Y, et al. Succinate drives gut inflammation by promoting FOXP3 degradation through a molecular switch. Nat Immunol. 2025;26(6):866-880. doi: 10.1038/s41590-025-02166-y

    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.

    Continue Reading