10-Year Weight Gain in 13,802 US Adults: The Role of Age, Sex, and Race

Tucker, Larry A.; Parker, Kayla

doi:10.1155/2022/7652408

Cited by 24 publications

(12 citation statements)

References 38 publications

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“…Consistent with prior studies, we found women who had an underweight or normal BMI at diagnosis had the greatest weight gain post-diagnosis 37 , 38 . The magnitude of weight gain we observed among certain subgroups in our cohort exceeds that of average adult females in the United States (0.54 kg/year) 39 . These findings highlight subgroups of breast cancer patients who are most at risk for large post-diagnosis weight gain and, in turn, support increasing awareness and monitoring of post-diagnosis weight change for these populations.…”

Section: Discussioncontrasting

confidence: 47%

Post-diagnosis weight trajectories and mortality among women with breast cancer

Puklin,

Li,

Cartmel

et al. 2023

npj Breast Cancer

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Weight gain after breast cancer diagnosis is associated with adverse health outcomes. Yet, few studies have characterized post-diagnosis weight change in the modern treatment era or populations most at risk for weight changes. Among women diagnosed with stages I–III breast cancer in the Smilow Care Network (2013–2019; N = 5441), we abstracted demographic and clinical characteristics from electronic health records and survival data from tumor registries. We assessed if baseline characteristics modified weight trajectories with nonlinear multilevel mixed-effect models. We evaluated body mass index (BMI) at diagnosis and weight change 1-year post-diagnosis in relation to all-cause and breast cancer-specific mortality with Cox proportional hazard models. Women had 34.4 ± 25.5 weight measurements over 3.2 ± 1.8 years of follow-up. Weight gain was associated with ER/PR−, HER2+ tumors, BMI ≤ 18.5 kg/m2, and age ≤ 45 years (+4.90 kg (standard error [SE] = 0.59), +3.24 kg (SE = 0.34), and +1.75 kg (SE = 0.10), respectively). Weight loss was associated with BMI ≥ 35 kg/m2 and age ≥ 70 years (−4.50 kg (SE = 0.08) and −4.34 kg (SE = 0.08), respectively). Large weight loss (≥10%), moderate weight loss (5–10%), and moderate weight gain (5–10%) 1-year after diagnosis were associated with higher all-cause mortality (hazard ratio [HR] = 2.93, 95% confidence interval [CI] = 2.28–3.75, HR = 1.32, 95% CI = 1.02–1.70 and HR = 1.39, 95% CI = 1.04–1.85, respectively). BMI ≥ 35 kg/m2 or BMI ≤ 18.5 kg/m2 at diagnosis were also associated with higher all-cause mortality. Weight change after a breast cancer diagnosis differed by demographic and clinical characteristics highlighting subgroups at-risk for weight change during a 5-year period post-diagnosis. Monitoring and interventions for weight management early in clinical care are important.

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Section: Discussioncontrasting

confidence: 47%

Post-diagnosis weight trajectories and mortality among women with breast cancer

Puklin,

Li,

Cartmel

et al. 2023

npj Breast Cancer

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“…The prevalence of obesity is increasing nationwide with few successful long-term interventions. 1 Self-monitoring and coaching are successful strategies for managing obesity, but they require significant time commitment from coaches. 2 , 3 Primary care is an important source of weight management care and support given the large number of individuals who visit primary care clinics.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of electronic health record-based tools to support a weight management program in primary care

Kukhareva,

Weir,

Cedillo

et al. 2024

JAMIA Open

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Objectives This paper reports on a mixed methods formative evaluation to support the design and implementation of information technology (IT) tools for a primary care weight management intervention delivered through the patient portal using primary care staff as coaches. Methods We performed a qualitative needs assessment, designed the IT tools to support the weight management program, and developed implementation tracking metrics. Implementation tracking metrics were designed to use real world electronic health record (EHR) data. Results The needs assessment revealed IT requirements as well as barriers and facilitators to implementation of EHR-based weight management interventions in primary care. We developed implementation metrics for the IT tools. These metrics were used in weekly project team calls to make sure that project resources were allocated to areas of need. Conclusion This study identifies the important role of IT in supporting weight management through patient identification, weight and activity tracking in the patient portal, and the use of the EHR as a population management tool. An intensive multi-level implementation approach is required for successful primary care-based weight management interventions including well-designed IT tools, comprehensive involvement of clinic leadership, and implementation tracking metrics to guide the process of workflow integration. This study helps to bridge the gap between informatics and implementation by using socio-technical formative evaluation methods early in order to support the implementation of IT tools. Trial registration clinicaltrials.gov, NCT04420936. Registered June 9, 2020.

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“…The prevalence of obesity has increased throughout the world over the last 50 years. According to the World Health Organization (WHO), its incidence has tripled since 1975 1 . Obesity results in low‐grade systemic chronic inflammation marked by increased circulating pro‐inflammatory cytokines (TNF‐α, IL‐1, IL‐6, and type‐1 interferons or IFNs) and different pro‐inflammatory immune cells (macrophages, mast cells, neutrophils, innate lymphoid cells [ILCs], and Th1 helper cells).…”

Section: Introductionmentioning

confidence: 99%

Obesity, bone marrow adiposity, and leukemia: Time to act

Kumar,

Stewart

2023

Obesity Reviews

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SummaryObesity has taken the face of a pandemic with less direct concern among the general population and scientific community. However, obesity is considered a low‐grade systemic inflammation that impacts multiple organs. Chronic inflammation is also associated with different solid and blood cancers. In addition, emerging evidence demonstrates that individuals with obesity are at higher risk of developing blood cancers and have poorer clinical outcomes than individuals in a normal weight range. The bone marrow is critical for hematopoiesis, lymphopoiesis, and myelopoiesis. Therefore, it is vital to understand the mechanisms by which obesity‐associated changes in BM adiposity impact leukemia development. BM adipocytes are critical to maintain homeostasis via different means, including immune regulation. However, obesity increases BM adiposity and creates a pro‐inflammatory environment to upregulate clonal hematopoiesis and a leukemia‐supportive environment. Obesity further alters lymphopoiesis and myelopoiesis via different mechanisms, which dysregulate myeloid and lymphoid immune cell functions mentioned in the text under different sequentially discussed sections. The altered immune cell function during obesity alters hematological malignancies and leukemia susceptibility. Therefore, obesity‐induced altered BM adiposity, immune cell generation, and function impact an individual's predisposition and severity of leukemia, which should be considered a critical factor in leukemia patients.

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10-Year Weight Gain in 13,802 US Adults: The Role of Age, Sex, and Race

Cited by 24 publications

References 38 publications

Post-diagnosis weight trajectories and mortality among women with breast cancer

Post-diagnosis weight trajectories and mortality among women with breast cancer

Design and implementation of electronic health record-based tools to support a weight management program in primary care

Obesity, bone marrow adiposity, and leukemia: Time to act

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