Weight Loss for Patients With Obesity: An Analysis of Long-Term Electronic Health Record Data

Weight Loss Treatment and Longitudinal Weight Change Among Primary Care Patients With Obesity

Importance

Among individuals with obesity, 5% or greater weight loss can improve health. Weight management treatments (WMT) include nutrition counseling, very low-calorie meal replacement (MR), antiobesity medications (AOM), and bariatric surgery; however, little is known about how these WMT are associated with weight change among individual patients and populations.

Objective

To characterize weight status and WMT use among primary care patients and assess associations between WMT and weight trajectories.

Design, Setting, and Participants

Retrospective, population-based cohort study of primary care patients from 1 academic health system in Michigan between October 2015 and March 2020 using cross-sectional analysis to compare obesity prevalence and WMT utilization. For patients with obesity and WMT exposure or matched controls, a multistate Markov model assessing associations between WMT and longitudinal weight status trajectories was used. Data were analyzed from October 2021 to October 2023.

Exposures

Cross-sectional exposure was year: 2017 or 2019. Trajectory analysis exposures were WMT: nutrition counseling, MR, AOM, and bariatric surgery.

Main Outcomes and Measures

Cross-sectional analysis compared mean body mass index (BMI), obesity prevalence, and, among patients with obesity, prospective WMT use. The trajectory analysis examined longitudinal weight status using thresholds of ±5% and 10% of baseline weight with primary outcomes being the 1-year probabilities of 5% or greater weight loss for each WMT.

Results

Adult patients (146 959 participants) consisted of 83 636 female participants (56.9%); 8940 (6.1%) were Asian, 14 560 (9.9%) were Black, and 116 664 (79.4%) were White. Patients had a mean (SD) age of 49.6 (17.7) years and mean (SD) BMI of 29.2 (7.2). Among 138 682 patients, prevalence of obesity increased from 39.2% in 2017 to 40.7% in 2019; WMT use among patients with obesity increased from 5.3% to 7.1% (difference: 1.7%; 95% CI, 1.3%-2.2%). In a multistate model (10 180 patients; 33 549 patient-years), the 1-year probability of 5% or greater weight loss without WMT exposure was 15.6% (95% CI, 14.3%-16.5%) at reference covariates. In contrast, the probability of 5% or greater weight loss was more likely with year-long exposures to any WMT (nutrition counseling: 23.1%; 95% CI, 21.3%-25.1%; MR: 54.6%; 95% CI, 46.5%-61.2%; AOM: 27.8%; 95% CI, 25.0%-30.5%; bariatric surgery: 93.0%; 95% CI, 89.7%-95.0%).

Conclusions and Relevance

In this cohort study of primary-care patients with obesity, all WMT increased the patient-level probability of achieving 5% or greater weight loss, but current rates of utilization are low and insufficient to reduce weight at the population level.

Early weight loss is an independent risk factor for shorter survival and increased side effects in patients with metastatic colorectal cancer undergoing first-line treatment within the randomized Phase III trial FIRE-3 (AIO KRK-0306)

Body weight loss is frequently regarded as negatively related to outcomes in patients with malignancies. This retrospective analysis of the FIRE-3 study evaluated the evolution of body weight in patients with metastatic colorectal cancer (mCRC). FIRE-3 evaluated first-line FOLFIRI (folinic acid, fluorouracil and irinotecan) plus cetuximab or bevacizumab in mCRC patients with RAS-WT tumors (ie, wild-type in KRAS and NRAS exons 2-4). The prognostic and predictive relevance of early weight loss (EWL) regarding patient outcomes and treatment side effects were evaluated. Retrospective data on body weight during first 6 months of treatment were evaluated (N = 326). To correlate with efficacy endpoints and treatment side effects, patients were grouped according to clinically significant EWL ≥5% and <5% at Month 3. Age constituted the only significant predictor of EWL following a linear relationship with the corresponding log odds ratio (P = .016). EWL was significantly associated with the incident frequencies of diarrhea, edema, fatigue, nausea and vomiting. Further, a multivariate analysis revealed EWL to be an independent negative prognostic factor for overall survival (32.4 vs 21.1 months; hazard ratio [HR]: 1.64; 95% confidence interval [CI] = 1.13-2.38; P = .0098) and progression-free survival (11.8 vs 9.0 months; HR: 1.72; 95% CI = 1.18-2.5; P = .0048). In conclusion, EWL during systemic treatment against mCRC is significantly associated with patient age. Patients exhibiting EWL had worse survival and higher frequencies of adverse events. Early preventative measures targeted at weight maintenance should be evaluated, especially in elderly patients being at highest risk of EWL.

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records

BACKGROUND

Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).

METHODS

In this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions.

FINDINGS

We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.

INTERPRETATION

A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.

FUNDING

The British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.

Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study

Background

Studies have found associations between increasing BMIs and the development of various chronic health conditions. The BMI cut points, or thresholds beyond which comorbidity incidence can be accurately detected, are unknown.

Objective

The aim of this study is to identify whether BMI cut points exist for 11 obesity-related comorbidities.

Methods

US adults aged 18-75 years who had ≥3 health care visits at an academic medical center from 2008 to 2016 were identified from eHealth records. Pregnant patients, patients with cancer, and patients who had undergone bariatric surgery were excluded. Quantile regression, with BMI as the outcome, was used to evaluate the associations between BMI and disease incidence. A comorbidity was determined to have a cut point if the area under the receiver operating curve was >0.6. The cut point was defined as the BMI value that maximized the Youden index.

Results

We included 243,332 patients in the study cohort. The mean age and BMI were 46.8 (SD 15.3) years and 29.1 kg/m², respectively. We found statistically significant associations between increasing BMIs and the incidence of all comorbidities except anxiety and cerebrovascular disease. Cut points were identified for hyperlipidemia (27.1 kg/m²), coronary artery disease (27.7 kg/m²), hypertension (28.4 kg/m²), osteoarthritis (28.7 kg/m²), obstructive sleep apnea (30.1 kg/m²), and type 2 diabetes (30.9 kg/m²).

Conclusions

The BMI cut points that accurately predicted the risks of developing 6 obesity-related comorbidities occurred when patients were overweight or barely met the criteria for class 1 obesity. Further studies using national, longitudinal data are needed to determine whether screening guidelines for appropriate comorbidities may need to be revised.

Association Between Medicaid Status, Social Determinants of Health, and Bariatric Surgery Outcomes

OBJECTIVE

To compare outcomes after bariatric surgery between Medicaid and non-Medicaid patients and assess whether differences in social determinants of health were associated with postoperative weight loss.

BACKGROUND

The literature remains mixed on weight loss outcomes and healthcare utilization for Medicaid patients after bariatric surgery. It is unclear if social determinants of health geocoded at the neighborhood level are associated with outcomes.

METHODS

Patients who underwent laparoscopic sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) from 2008 to 2017 and had ≥1 year of follow-up within a large health system were included. Baseline characteristics, 90-day and 1-year outcomes, and weight loss were compared between Medicaid and non-Medicaid patients. Area deprivation index (ADI), urbanicity, and walkability were analyzed at the neighborhood level. Median regression with percent total body weight (TBW) loss as the outcome was used to assess predictors of weight loss after surgery.

RESULTS

Six hundred forty-seven patients met study criteria (191 Medicaid and 456 non-Medicaid). Medicaid patients had a higher 90-day readmission rate compared to non-Medicaid patients (19.9% vs 12.3%, P < 0.016). Weight loss was similar between Medicaid and non-Medicaid patients (23.1% vs 21.9% TBW loss, respectively; P = 0.266) at a median follow-up of 3.1 years. In adjusted analyses, Medicaid status, ADI, urbanicity, and walkability were not associated with weight loss outcomes.

CONCLUSIONS

Medicaid status and social determinants of health at the neighborhood level were not associated with weight loss outcomes after bariatric surgery. These findings suggest that if Medicaid patients are appropriately selected for bariatric surgery, they can achieve equivalent outcomes as non-Medicaid patients.