Determine the impact of long-term non-surgical weight loss maintenance on clinical relevance for osteoarthritis, cancer, opioid use, and depression/anxiety and healthcare resource utilization.
A cohort of adults receiving primary care within Geisinger Health System between 2001–2017 was retrospectively studied. Patients with ≥3 weight measurements in the two-year index period and obesity at baseline (BMI ≥30 kg/m2) were categorized: Obesity Maintainers (reference group) maintained weight within +/-3%; Weight Loss Rebounders lost ≥5% body weight in year one, regaining ≥20% of weight loss in year two; Weight Loss Maintainers lost ≥5% body weight in year one, maintaining ≥80% of weight loss. Association with development of osteoarthritis, cancer, opioid use, and depression/anxiety, was assessed; healthcare resource utilization was quantified. Magnitude of weight loss among maintainers was evaluated for impact on health outcomes.
In total, 63,567 patients were analyzed including 67% Obesity Maintainers, 19% Weight Loss Rebounders, and 14% Weight Loss Maintainers; median follow-up was 9.7 years. Time until osteoarthritis onset was delayed for Weight Loss Maintainers compared to Obesity Maintainers (Logrank test p <0.0001). Female Weight Loss Maintainers had a 19% and 24% lower risk of developing any cancer (p = 0.0022) or obesity-related cancer (p = 0.0021), respectively. No significant trends were observed for opioid use. Weight loss Rebounders and Maintainers had increased risk (14% and 25%) of future treatment for anxiety/depression (both <0.0001). Weight loss maintenance of >15% weight loss was associated with the greatest decrease in incident osteoarthritis. Healthcare resource utilization was significantly higher for Weight Loss Rebounders and Maintainers compared to Obesity Maintainers. Increased weight loss among Weight Loss Maintainers trended with lower overall healthcare resource utilization, except for hospitalizations.
Citation: Wood GC, Bailey-Davis L, Benotti P, Cook A, Dove J, Mowery J, et al. (2021) Effects of sustained weight loss on outcomes associated with obesity comorbidities and healthcare resource utilization. PLoS ONE 16(11): e0258545. https://doi.org/10.1371/journal.pone.0258545
Editor: David Meyre, McMaster University, CANADA
Received: May 11, 2021; Accepted: September 29, 2021; Published: November 3, 2021
Copyright: © 2021 Wood et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: This study involved patient data from within the Geisinger Health System. No data are available via public databases. Data will be available by contacting the primary author (Craig Wood, firstname.lastname@example.org) or by contacting the Director of the Geisinger IRB and HRPP (Debra L. Henninger, MHSA, BSN, RN, CCRC) at (570) 271-8663 or via email (email@example.com). None of the participant (de-identified) data collected in the study can be shared via public databases. Data contains potentially sensitive and identifying information. If you have questions or need assistance, please contact: • Geisinger IRB at (570) 271-8663 or via email (firstname.lastname@example.org). • Debra L. Henninger, MHSA, BSN, RN, CCRC, • Director, IRB Operations and HRPP.
Funding: Abhilasha Ramasamy, Neeraj N. Iyer, B. Gabriel Smolarz, and Neela Kumar are employed by Novo Nordisk, Inc., the study sponsor. Lisa Bailey-Davis, G Craig Wood, Peter Benotti, Adam Cook, James Dove, Jacob Mowery, and Christopher Still are employed by Geisinger Health, which received funding from Novo Nordisk, Inc. for work performed on this study. The funder (Novo Nordisk, Inc,) provided support in the form of salaries for authors [AR, NNI, BGS, NK]. The funder provided support for medical writing provided by Elizabeth Tanner of KJT Group. Inc. The funder was involved in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Abhilasha Ramasamy, Neeraj N. Iyer, B. Gabriel Smolarz, and Neela Kumar are employed by Novo Nordisk, Inc., the study sponsor. Lisa Bailey-Davis, G Craig Wood, Peter Benotti, Adam Cook, James Dove, Jacob Mowery, and Christopher Still are employed by Geisinger Health, which received funding from Novo Nordisk, Inc. for work performed on this study. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Obesity is a chronic disease that has been associated with a multitude of comorbidities, including cardiovascular disease, diabetes, certain cancers, joint diseases, mental health disorders, and sleeping disorders [1–11] with a negative impact on quality of life . As the prevalence of obesity continues to rise to more than 40% of the U.S. adult population , the health and societal effects will be substantial.
Weight loss achieved through a variety of lifestyle interventions, anti-obesity medications, or bariatric surgery can improve health outcomes and reduce the risk of mortality [14–20]. However, weight loss achieved through caloric restriction or bariatric surgery has many limitations including effects on basal metabolic rate and endocrine regulation, as well as frequent reoperations needed with bariatric surgery [21–26] which can make it challenging for many people with obesity to sustain weight loss over time [27, 28].
There is little in the literature describing the longer-term clinical impact of weight loss with regain and sustained weight loss on obesity . The aim of this study was to determine the relationship between long-term weight loss maintenance and clinical relevance across a range of comorbidities that can particularly impact the patients with obesity including osteoarthritis, cancer, opioid use, and depression/anxiety. In another report, we have summarized outcomes related to the cardiometabolic comorbidities of type 2 diabetes, hypertension, and cardiovascular disease (article in preparation). Time to development of each condition and the effect of varying magnitudes of weight loss was assessed in a broad patient population which sought care at a large integrated healthcare delivery system in the United States (U.S.) over a ten-year period. A secondary objective was to examine the relationship between obesity and long-term weight loss maintenance on health care resource utilization.
A retrospective observational study was performed with patients receiving primary care at Geisinger Health System between 2001 and 2017. The Geisinger Institutional Review Board reviewed the study, determined it qualified for exempt status, and granted a waiver of patient consent. Geisinger Health System is a Pennsylvania-based integrated delivery system (IDS) which includes a health plan, acute care hospitals, specialty hospitals, ambulatory surgery centers, and additional clinical services . There are numerous research units within the Geisinger Health System including the Obesity Institute, which provides resources supporting obesity research across the IDS .
The study population included adult patients who were at least 18 years of age and for whom three or more weight measurements were documented in the electronic health record (EHR) over a two-year period. This is denoted as the index period and included a baseline weight, a one-year weight (within 6–18 months), and a two-year weight (within 12–24 months). The index period was preceded by a lead-in period to establish medical history. Weight measurements within 15 months prior to baseline BMI measurement were excluded. Outcomes were observed following the index period only and weight changes were not analyzed following the index period. Any patient who had bariatric surgery prior to or during the index period or prevalent/history of cancer were excluded from the study. The weight measurements within six-months of pregnancy indicators were also excluded for women who were pregnant during the index period. Based on weight trends during each year of the index period, three study groups with a history of obesity were defined: 1) Obesity Maintainers: patients who maintained weight within ±3% margin from baseline; 2) Weight Loss Rebounders: patients who lost ≥5% weight via non-surgical methods and regained weight 20% or more of one-year weight loss from baseline (weight regain of 20% or more was selected as a boundary based on the King et al. study exploring weight regain measurements ); and 3) Weight Loss Maintainers: patients who lost ≥5% weight via non-surgical methods and maintained ≥80% of the one-year weight loss from baseline. All patients were censored at the time of the last visit in the health record.
To determine if health outcomes were impacted by the magnitude of weight loss, the Weight Loss Maintainers group was stratified by amount of initial weight loss (i.e., <7%, 7–10%, >10–15%, and >15%). Only patients meeting the definition of any of the three groups were included in the analysis.
Outcomes were analyzed post-index period and included a range of physical and mental domains of health. The outcomes reported in this study included osteoarthritis, cancer, opioid use, and depression/anxiety. Cardiometabolic outcomes have been reported elsewhere (article in preparation). Outcomes associated with osteoarthritis were defined by EHR documentation of International Classification of Diseases 10th edition (ICD-10) diagnosis codes on the problem list or at least two outpatient visits. Time until osteoarthritis was calculated as a new occurrence of an osteoarthritis diagnosis. Osteoarthritis diagnosis was defined as ICD-10 (ICD-10 M15-M19) on problem list or 2+ outpatient visits. Cancer diagnosis was defined as any in situ or malignant condition as previously defined by the Pennsylvania Cancer Registry and Commission on Cancer, categorized by ICD-10 code for the primary cancer site. To be included in the Geisinger tumor registry, cases are either diagnosed and/or treated for the condition within the Geisinger Health System. Analyses were conducted for all documented cancer types and then for a subset of cancer types that have been associated with obesity: breast, uterine, colon, kidney, pancreas, thyroid, liver, rectum, stomach, esophagus, ovary, gallbladder, and rectosigmoid junction (S1 Table) [8–10]. Time until cancer was calculated as a new occurrence of cancer in the Geisinger Health System tumor registry. Since males and females are predisposed to different cancer types, the analyses for time until cancer were stratified by sex. Opioid use was included as a proxy for pain. Time until opioid use was defined as a new occurrence of opioid use and was defined as two or more outpatient prescriptions for opioids. Time until treatment for depression/anxiety was defined as medication orders or active use of a depression/anxiety medication occurring after the end of the index period; medications included alpha-2 receptor antagonists (tetracyclics), benzodiazepines, monoamine oxidase inhibitors, selective serotonin reuptake inhibitors, serotonin modulators, serotonin-norepinephrine reuptake inhibitors, and tricyclic agents.
The classification of prevalence and incidence of each condition was evaluated based on the timing of the first signal that occurred within the index period; specifically, patients who met the diagnostic or treatment criteria prior to or during the index period were considered as having prevalent comorbidity, whereas those who met diagnostic criteria after the index period were considered as having incident comorbidity. To capture only incident disease, patients with prevalent disease were excluded from analysis related to that disease. For example, patients with prevalent osteoarthritis were excluded from the analysis that identified emergence of osteoarthritis. Data on weight measurements, socio-demographics, vital signs, laboratory tests, encounters, procedures, diagnostic codes, orders (pharmacological, diet, etc.) was extracted from Geisinger’s EPIC® EHR and data warehouse. Median height was calculated and used for all body mass index (BMI) measures. Weight measurements of <80 pounds and >700 pounds and height measurements of <42 inches and >90 inches were excluded as outliers.
A lead-in period to establish medical history was used by excluding weight measurements during the first 15 months that patients participated in primary care within the Geisinger Health System (Fig 1). To allow for adequate time to capture three EHR-recorded weight measures, specific ranges were defined. Time zero, or the beginning of the index period was defined as a baseline weight of BMI ≥30 kg/m2. A second weight measurement occurred approximately one year after baseline assessment within 6–18 months, followed by a third weight measurement at least one year later, within 12–24 months. This approximates a baseline, year one, and year two weight measurement. A follow-up visit was conducted at least 6 months after the last weight measure.
Overall healthcare utilization was characterized using three encounter types: 1) outpatient visits, 2) emergency department (ED) visits, and 3) hospitalizations. The encounters were defined and calculated as follows: total number of days with an outpatient encounter (limited to those involving contact with a care provider); total number of days with an ED visit and time until first ED visit; and total number of hospitalizations, number of hospitalized days, and time until first hospitalization. Utilization was limited to visits occurring within the Geisinger Health System’s clinics and hospitals.
Independent and joint associations of weight loss and weight maintenance on each clinical outcome were evaluated. To ensure that the final complex models were representative of associations found within underlying analysis, the statistical analyses proceeded from simple to complex, starting with descriptive statistics and unadjusted analysis, followed by adjusted regression modeling.
The simple analyses evaluated the unadjusted association of each clinical indictor with the weight loss groups (Obesity Maintainers, Weight Loss Rebounders, and Weight Loss Maintainers) using Cox regression for dichotomous outcomes (i.e., a time-to-event regression model). For each clinical indicator, time-to-outcome was calculated as the number of days between the initial baseline weight measurement until the outcome of interest occurred. For patients who did not develop the outcome of interest, the time was censored at the last follow-up visit. Testing for proportional hazard assumptions allowed for the examination of consistent effects in the short-term and the long-term.
The unadjusted analyses were followed by models that adjusted for selected patient characteristics and tested whether these characteristics influenced the effect of weight loss on the clinical outcomes using Obesity Maintainers as the reference group. The final models presented in this paper were adjusted for age, sex, BMI, diabetes, hypertension treatment, hyperlipidemia treatment, depression/anxiety treatment, osteoarthritis, asthma, and gastrointestinal reflux disease (GERD). The Charlson Index, a validated score combining multiple comorbidities into a 10-year survival predictor  was also used as a covariate as were other conditions not included in the Charlson Index. Comorbidities were based on diagnosis codes documented in the EHR and weighted higher for diseases with greater mortality risk based on the Charlson Index.
The cumulative incidence of osteoarthritis, cancer, time to opioid use, depression/anxiety were estimated by the Kaplan-Meier (KM) method and plotted over 10 years of follow-up for each patient sub-group. Additionally, Kaplan-Meier curves were used to compare time until outcome with the Weight Loss Maintainers group stratified by the amount of weight loss at the end of year 2 of the index period: <7%, 7–10%, >10–15%, and >15%. A minimum weight loss of 7% was examined based on research demonstrating the effect of weight loss of at least 7% preventing or delaying development of type 2 diabetes . Sensitivity analyses were conducted to determine if the results were influenced by the amount of weight loss during the first year or the amount of weight regain from year one to year two. Patients diagnosed with the outcome of interest prior to baseline or during the index period were excluded from the analysis. The analyses were conducted using SAS version 9.4 software.
Healthcare resource utilization was compared between groups using Poisson Regression. These analyses were conducted using unadjusted raw data and after adjusting for age, sex, BMI, Charlson Index, and selected/prevalent baseline comorbidities (diabetes, hypertension treatment, hyperlipidemia treatment, osteoarthritis, depression/anxiety treatment, asthma, and GERD).
The final study sample comprised of 63,567 patients, classified as Obesity Maintainers (67%), Weight Loss Rebounders (19%), and Weight Loss Maintainers (14%) with a median follow-up period of 9.7 years. Baseline descriptive statistics and disease status for the study population are presented in Table 1.
Specific weight loss interventions including visits with a weight loss specialist, visits with a registered dietitian or nutritionist, or anti-obesity medication use were documented for only a small portion of patients. Patients who received/utilized any of these weight loss treatments included 2.5% of Obesity Maintainers, 4.5% of Weight Loss Rebounders, and 3.9% of Weight Loss Maintainers.
Impact of obesity on health-related outcomes
The adjusted hazard ratios for the risk associated with developing the studied outcomes are displayed in Table 2. Weight Loss Maintainers had the longest time until an osteoarthritis diagnosis and Obesity Maintainers had the shortest time (Logrank test p <0.0001). Compared to Obesity Maintainers, Weight Loss Maintainers had a lower risk of incident osteoarthritis (Hazard Ratio, HR = 0.904); the difference between Obesity Maintainers and Weight Loss Rebounders was not significant. Females in the Weight Loss Maintainers group had a 19% lower risk of developing any cancer (p = 0.0022) and a 24% lower risk of developing obesity-related cancer (p = 0.0021) compared to those in the Obesity Maintainers group (Fig 2 and S1 Appendix). There were no significant differences in time to developing any type of cancer or obesity-related cancer among males.
A. All cancer types, female. B. All cancer types, male. C. Obesity-related cancer, female. D. Obesity-related cancer, male. OM, Obesity Maintainers; WLR, Weight Loss Rebounders; WLM, Weight Loss Maintainers.
Compared to Obesity Maintainers, Weight Loss Rebounders and Weight Loss Maintainers had a 14% and 25% higher risk of future treatment for depression/anxiety, respectively. Weight Loss Maintainers had the shortest time until treatment for depression/anxiety and Obesity Maintainers had the longest time until treatment for depression/anxiety (Logrank test p<0.0001) (Fig 3 and S1 Appendix).
Impact of magnitudes of weight loss on health-related outcomes
There was a significant effect modification of time associated with the relationship between amount of weight loss and time until osteoarthritis (failed the proportional hazards assumption). The Kaplan-Meier curve suggests a delayed effect signified by little difference between groups early in follow-up and diverging curves later in follow-up (Fig 4 and S1 Appendix). Thus, the adjusted Cox models were adapted to account for the time-varying covariate of weight loss amount. Specifically, the adjusted hazard ratios were calculated for the first four years of follow-up (where there was little difference between the weight loss groups) and then again for four years of follow-up onward (where the differences between weight loss groups were apparent). Compared to those who had <7% weight loss, patients with >15% weight loss had a 47% lower risk of incident osteoarthritis (p = 0.0006) starting at four years of follow-up.
WLM, Weight Loss Maintainers; WL, weight loss.
There was no association with the amount of weight loss in the Weight Loss Maintainers group and time until depression/anxiety or cancer in either sex. Although the association trended in the direction of decreased opioid use with increasing weight loss, there was no significant association.
Healthcare resource utilization
Healthcare resource utilization was significantly higher for Weight Loss Maintainers and Weight Loss Rebounders compared to the Obesity Maintainers for outpatient visits, ED visits, hospitalizations, and inpatient days (Table 3). Additionally, Weight Loss Maintainers and Weight Loss Rebounders had a significantly shorter time to ED visit as compared to Obesity Maintainers: HR = 1.106 for Weight Loss Maintainers, (p = 0.0001) and HR = 1.062 for Weight Loss Rebounders (p = 0.0094). Similar trends were seen for hospitalizations: HR = 1.158 for Weight Loss Maintainers (p <0.0001) and HR = 1.095 for Weight Loss Rebounders (p = 0.0003).
Among the Weight Loss Maintainers, greater magnitudes of weight loss were associated with lower overall healthcare utilization, except for hospitalizations (Table 4). When adjusting for baseline factors, patients with the greatest magnitude of sustained weight loss (>15%) had a lower number of outpatient visits per year. Patients with sustained weight loss >7% had fewer ED visits per year. Patients with sustained weight loss >10% had a higher number of inpatient days per year. There was no association between amount of weight loss and time until ED visit or until hospitalization.
This study demonstrates a robust sample size and long follow-up to assess the real-world clinical impact of weight loss trajectories on disease risk, progression, and prevention, as well as health care utilization. This study found varying associations between obesity and health-related outcomes in a population-based sample. The risk of developing osteoarthritis was lower for patients with sustained weight loss. Cancer-related risk was more nuanced with a positive association of sustained weight loss compared to obesity maintenance over a 10-year period for the rate of cancers in females; the relationship was similar for all cancers and obesity-related cancers. There were no significant correlations between cancer incidence and weight change patterns for males with obesity. There is a growing body of evidence that obesity and cancer are significantly linked. While obesity has been established as risk factor for a subset of cancers [8, 9], there is evidence that obesity may be a risk factor for additional cancer types . Our research is one of the few studies that demonstrates that non-surgical weight loss reduces the risk of developing cancer; the majority of the literature describes associations between reduced cancer incidence and weight loss resulting from bariatric surgery [36–39]. Chlebowski et al. showed reduced cancer incidence in breast cancer for women with non-surgical weight loss . Beyond this, there is little evidence and additional studies, such as ours, are needed to investigate if weight loss can prevent cancer incidence.
Compared to obesity maintenance, weight loss maintenance and weight loss regain were associated with a higher chance of future treatment for depression/anxiety. It has been suggested that there is a complex and bidirectional relationship between obesity and depression, where obesity could increase the risk of depression, and vice versa; this is further complicated in that weight or BMI gain has been associated with anti-depressant use [41, 42].
There was a time-dependent effect on the risk for developing osteoarthritis among Weight Loss Maintainers, with a significant relationship appearing beginning at four years of follow-up with the longest delay seen by the patients with weight loss of >15%. Similar results have been demonstrated by others, where the patients who lost more weight had increased relief of symptoms with larger weight loss outcomes [43, 44]. Meta-analyses of randomized clinical trials of weight loss interventions among patients with osteoarthritis showed that weight loss of 10% or more resulted in moderate-to-large effects on disability, pain, and function [45–47]. There were no statistically significant effects observed for varying magnitudes of weight loss among Weight Loss Maintainers with respect to cancer outcomes, opioid use, or depression outcomes.
Overall healthcare resource utilization (outpatient visits, ED visits, and hospitalizations) was higher for Weight Loss Maintainers and Weight Loss Rebounders compared to Obesity Maintainers though the differences are likely not clinically meaningful. Among Weight Loss Maintainers, those with the greatest weight loss had lower overall healthcare utilization, except for hospitalizations. Healthcare resource utilization did not demonstrate expected trends as it has been shown that obesity is associated with higher healthcare utilization [48, 49]. Future studies will need to explore the relationship between healthcare utilization and obesity. Other studies have shown decreased healthcare costs and utilization associated with weight loss [50, 51]. There are numerous routes for exploration: it is possible that the Weight Loss Maintainers and Weight Loss Rebounders are in better overall health and as such are eligible for elective procedures, such as joint replacement for osteoarthritis; patients who lost weight may have seen their healthcare provider more often to facilitate and maintain weight loss; perhaps these patients are exercising more and have associated injuries with the increased activity. A more detailed analysis that includes pharmacy utilization as well as detailed interventions and activity would be necessary to understand the observed associations with increased healthcare utilization.
While obesity is a major health concern in the U.S. , there has been little research into impact of weight loss with regain, and sustained weight loss, particularly at ten years. Current literature highlights the effects of weight loss on specific outcomes or physiological associations, but do not assess weight loss on an epidemiological scale [52–55].
Our retrospective observational study included robust longitudinal data over a median 9.7-year period with over 60,000 patients included in the sample. This study provides an example of large-scale analysis that can inform health outcomes. Prospective data acquisition, including formation of registries for monitoring weight loss efforts could provide deeper insights into the complex nature inherent in evaluating obesity-related outcomes and healthcare utilization patterns. Population level studies are important for understanding prevention and treatment of people with obesity over their lifetime.
Since no information was available on lifestyle behavioral change counseling in the EHR, it is plausible that physicians provided counseling or referred persons with obesity for treatment, consistent with clinical guidelines. Additional research into the techniques that providers use when offering counseling and what, if any, referrals are made, as well as the details of subsequent care is needed. Predictive modeling and observational studies suggest that engaging patients who have personal motivation in obesity management is associated with successful weight loss .
There are several limitations to this type of retrospective observational research including misclassification and confounding biases . Data included in the study are limited to visits within the Geisinger Health System, limiting generalizability to other health systems and regions of the U.S. Because the data for this study were based on observations of a primary care cohort receiving standard clinical care, there is some missing or unknown information. For example, a patient without a diabetes diagnosis and no diabetes treatment probably does not have diabetes even if a hemoglobin A1c is not present. When defining outcomes of interest, multiple signals were reviewed to reduce misclassification (e.g., for diabetes we reviewed multiple sources of diagnoses, medication use, and laboratory results). For the majority of the sample, we lack knowledge regarding weight loss strategies used to achieve weight loss in the index period and weight loss may not have been intentional. Many factors could contribute to overall health and the incidence of the outcomes analyzed including exercise, diet, sleep, smoking, alcohol consumption, or engaging in cancer screening or other health services; these factors are not considered in our analysis. The index period is the only time period when weight was monitored. Patients may have experienced weight changes during the observation period that are undocumented that affect the outcomes analyzed. Illness-related weight loss may confound the data related to overall and specific comorbidities in the weight loss group. In older adults, it has been shown that unintentional weight loss is more common than intentional weight loss .
The models were adjusted for selected patient characteristics such as baseline weight loss, treatment types, age, sex, and BMI, but there could be a potential for confounding for variables that impact disease trajectory that were not identified and therefore not tested. The generalizability of the study findings may be limited due to the racial/ethnic make-up of the study population. However, our results may be conservative due to the higher prevalence of obesity and cardiometabolic conditions in minority populations.
In people with obesity, sustained weight loss has greater clinical benefits, such as delayed onset of osteoarthritis and lower cancer incidence, than either regained short-term weight loss or no weight loss at all. Also, higher magnitudes of weight loss delayed onset of osteoarthritis and led to decreased healthcare utilization suggesting we should aim for greater magnitudes of weight loss for people with obesity. Supporting patients in the management of obesity by offering or referring for care and ensuring access to resources and treatments to sustain weight loss long-term will likely further reduce the development of obesity-related conditions in the future.
Writing assistance was provided by Elizabeth Tanner of KJT Group, Inc. Samantha M.R. Kling, PhD, contributed to the study design prior to completing a post-doctoral fellowship at Geisinger Health.
- 1. Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism—Clinical and Experimental. 2019;92:121–35. pmid:30445141
- 2. Meurling IJ, Shea DO, Garvey JF. Obesity and sleep: a growing concern. Current Opinion in Pulmonary Medicine. 2019;25(6):602–8. pmid:31589189
- 3. Pereira-Miranda E, Costa PRF, Queiroz VAO, Pereira-Santos M, Santana MLP. Overweight and Obesity Associated with Higher Depression Prevalence in Adults: A Systematic Review and Meta-Analysis. Journal of the American College of Nutrition. 2017;36(3):223–33. pmid:28394727
- 4. Rajan T, Menon V. Psychiatric disorders and obesity: A review of association studies. Journal of Postgraduate Medicine. 2017;63(3):182–90. pmid:28695871
- 5. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body Fatness and Cancer—Viewpoint of the IARC Working Group. New England Journal of Medicine. 2016;375(8):794–8. pmid:27557308.
- 6. Garg SK, Maurer H, Reed K, Selagamsetty R. Diabetes and cancer: two diseases with obesity as a common risk factor. Diabetes, Obesity and Metabolism. 2014;16(2):97–110. pmid:23668396
- 7. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Public Health. 2009;9(1):88. pmid:19320986
- 8. Centers for Disease Control and Prevention. Cancer and Obesity 2017 [updated October 3, 2017November 17, 2020]. https://www.cdc.gov/vitalsigns/obesity-cancer/index.html.
- 9. Massetti GM, Dietz WH, Richardson LC. Excessive Weight Gain, Obesity, and Cancer: Opportunities for Clinical Intervention. JAMA. 2017;318(20):1975–6. pmid:28973170
- 10. Steele CB, Thomas CC, Henley SJ, Massetti GM, Galuska DA, Agurs-Collins T, et al. Vital Signs: Trends in Incidence of Cancers Associated with Overweight and Obesity—United States, 2005–2014. MMWR Morb Mortal Wkly Rep. 2017;66:1052–8. http://dx.doi.org/10.15585/mmwr.mm6639e1External. pmid:28981482
- 11. Li Q, Blume SW, Huang JC, Hammer M, Ganz ML. Prevalence and healthcare costs of obesity-related comorbidities: evidence from an electronic medical records system in the United States. J Med Econ. 2015;18(12):1020–8. Epub 2015/07/03. pmid:26134917.
- 12. Kolotkin RL, Andersen JR. A systematic review of reviews: exploring the relationship between obesity, weight loss and health-related quality of life. Clinical Obesity. 2017;7(5):273–89. pmid:28695722
- 13. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity and Severe Obesity Among Adults: United States, 2017–2018. National Center for Health Statistics: 2020. Brief No 360. pmid:32487284
- 14. Look AHEAD Research Group, Yeh H-C, Bantle JP, Cassidy-Begay M, Blackburn G, Bray GA, et al. Intensive Weight Loss Intervention and Cancer Risk in Adults with Type 2 Diabetes: Analysis of the Look AHEAD Randomized Clinical Trial. Obesity. 2020;28(9):1678–86. pmid:32841523
- 15. Cohen JB. Hypertension in Obesity and the Impact of Weight Loss. Current Cardiology Reports. 2017;19(10):98. pmid:28840500
- 16. Xanthopoulos MS, Berkowitz RI, Tapia IE. Effects of obesity therapies on sleep disorders. Metabolism—Clinical and Experimental. 2018;84:109–17. pmid:29409812
- 17. Ma C, Avenell A, Bolland M, Hudson J, Stewart F, Robertson C, et al. Effects of weight loss interventions for adults who are obese on mortality, cardiovascular disease, and cancer: systematic review and meta-analysis. BMJ. 2017;359:j4849. pmid:29138133
- 18. Fruh SM. Obesity: Risk factors, complications, and strategies for sustainable long-term weight management. Journal of the American Association of Nurse Practitioners. 2017;29(S1):S3–S14. pmid:29024553
- 19. Kritchevsky SB, Beavers KM, Miller ME, Shea MK, Houston DK, Kitzman DW, et al. Intentional Weight Loss and All-Cause Mortality: A Meta-Analysis of Randomized Clinical Trials. PLOS ONE. 2015;10(3):e0121993. pmid:25794148
- 20. Fabricatore AN, Wadden TA, Higginbotham AJ, Faulconbridge LF, Nguyen AM, Heymsfield SB, et al. Intentional weight loss and changes in symptoms of depression: a systematic review and meta-analysis. International Journal of Obesity. 2011;35(11):1363–76. pmid:21343903
- 21. Leibel RL, Rosenbaum M, Hirsch J. Changes in Energy Expenditure Resulting from Altered Body Weight. New England Journal of Medicine. 1995;332(10):621–8. pmid:7632212.
- 22. Sumithran P, Prendergast LA, Delbridge E, Purcell K, Shulkes A, Kriketos A, et al. Long-Term Persistence of Hormonal Adaptations to Weight Loss. New England Journal of Medicine. 2011;365(17):1597–604. pmid:22029981.
- 23. Acosta A, Abu Dayyeh BK, Port JD, Camilleri M. Recent advances in clinical practice challenges and opportunities in the management of obesity. Gut. 2014;63(4):687–95. pmid:24402654
- 24. Kushner RF, Sorensen KW. Prevention of Weight Regain Following Bariatric Surgery. Curr Obes Rep. 2015;4(2):198–206. Epub 2015/12/03. pmid:26627215.
- 25. O’Brien PE, Hindle A, Brennan L, Skinner S, Burton P, Smith A, et al. Long-Term Outcomes After Bariatric Surgery: a Systematic Review and Meta-analysis of Weight Loss at 10 or More Years for All Bariatric Procedures and a Single-Centre Review of 20-Year Outcomes After Adjustable Gastric Banding. Obes Surg. 2019;29(1):3–14. Epub 2018/10/08. pmid:30293134.
- 26. Colquitt JL, Pickett K, Loveman E, Frampton GK. Surgery for weight loss in adults. Cochrane Database Syst Rev. 2014;(8):Cd003641. Epub 2014/08/12. pmid:25105982.
- 27. Ghanemi A, Yoshioka M, St-Amand J. Broken Energy Homeostasis and Obesity Pathogenesis: The Surrounding Concepts. Journal of Clinical Medicine. 2018;7(11):453. pmid:30463389
- 28. Soleymani T, Daniel S, Garvey WT. Weight maintenance: challenges, tools and strategies for primary care physicians. Obesity Reviews. 2016;17(1):81–93. pmid:26490059
- 29. Haase CL, Lopes S, Olsen AH, Satylganova A, Schnecke V, McEwan P. Weight loss and risk reduction of obesity-related outcomes in 0.5 million people: evidence from a UK primary care database. International Journal of Obesity. 2021. pmid:33658682
- 30. Paulus RA, Davis K, Steele GD. Continuous Innovation In Health Care: Implications Of The Geisinger Experience. Health Affairs. 2008;27(5):1235–45. pmid:18780906.
- 31. Geisinger Health. Obesity Research Institute 2020 [Nobember 13, 2020]. https://www.geisinger.edu/research/departments-and-centers/obesity-institute.
- 32. King WC, Hinerman AS, Belle SH, Wahed AS, Courcoulas AP. Comparison of the Performance of Common Measures of Weight Regain After Bariatric Surgery for Association With Clinical Outcomes. JAMA. 2018;320(15):1560–9. pmid:30326125
- 33. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases. 1987;40(5):373–83. pmid:3558716
- 34. Diabetes Prevention Program Research Group, Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, et al. Reduction in the Incidence of Type 2 Diabetes with Lifestyle Intervention or Metformin. New England Journal of Medicine. 2002;346(6):393–403. pmid:11832527.
- 35. Wilcock S, Haboubi N. Obesity and the Risk of Malignancy: An Evolving Saga. J R Coll Physicians Edinb. 2020;50(4):392–7. Epub 2021/01/21. pmid:33469614.
- 36. Schauer DP, Feigelson HS, Koebnick C, Caan B, Weinmann S, Leonard AC, et al. Association Between Weight Loss and the Risk of Cancer after Bariatric Surgery. Obesity (Silver Spring). 2017;25 Suppl 2(Suppl 2):S52–S7. pmid:29086527.
- 37. Zhang X, Rhoades J, Caan BJ, Cohn DE, Salani R, Noria S, et al. Intentional weight loss, weight cycling, and endometrial cancer risk: a systematic review and meta-analysis. Int J Gynecol Cancer. 2019;29(9):1361–71. Epub 2019/08/26. pmid:31451560.
- 38. Rodriguez C, Freedland SJ, Deka A, Jacobs EJ, McCullough ML, Patel AV, et al. Body mass index, weight change, and risk of prostate cancer in the Cancer Prevention Study II Nutrition Cohort. Cancer Epidemiol Biomarkers Prev. 2007;16(1):63–9. Epub 2006/12/21. pmid:17179486.
- 39. Sjöström L, Gummesson A, Sjöström CD, Narbro K, Peltonen M, Wedel H, et al. Effects of bariatric surgery on cancer incidence in obese patients in Sweden (Swedish Obese Subjects Study): a prospective, controlled intervention trial. The Lancet Oncology. 2009;10(7):653–62. pmid:19556163
- 40. Chlebowski RT, Luo J, Anderson GL, Barrington W, Reding K, Simon MS, et al. Weight loss and breast cancer incidence in postmenopausal women. Cancer. 2019;125(2):205–12. Epub 2018/10/08. pmid:30294816.
- 41. Lee SH, Paz-Filho G, Mastronardi C, Licinio J, Wong ML. Is increased antidepressant exposure a contributory factor to the obesity pandemic? Transl Psychiatry. 2016;6(3):e759–e. pmid:26978741.
- 42. Schwartz BS, Glass TA, Pollak J, Hirsch AG, Bailey-Davis L, Moran TH, et al. Depression, its comorbidities and treatment, and childhood body mass index trajectories. Obesity. 2016;24(12):2585–92. pmid:27804225
- 43. Hunter DJ, Beavers DP, Eckstein F, Guermazi A, Loeser RF, Nicklas BJ, et al. The Intensive Diet and Exercise for Arthritis (IDEA) trial: 18-month radiographic and MRI outcomes. Osteoarthritis Cartilage. 2015;23(7):1090–8. Epub 2015/04/19. pmid:25887362.
- 44. Atukorala I, Makovey J, Lawler L, Messier SP, Bennell K, Hunter DJ. Is There a Dose-Response Relationship Between Weight Loss and Symptom Improvement in Persons With Knee Osteoarthritis? Arthritis Care & Research. 2016;68(8):1106–14. pmid:26784732
- 45. Chu IJH, Lim AYT, Ng CLW. Effects of meaningful weight loss beyond symptomatic relief in adults with knee osteoarthritis and obesity: a systematic review and meta-analysis. Obesity Reviews. 2018;19(11):1597–607. pmid:30051952
- 46. Alrushud AS, Rushton AB, Kanavaki AM, Greig CA. Effect of physical activity and dietary restriction interventions on weight loss and the musculoskeletal function of overweight and obese older adults with knee osteoarthritis: a systematic review and mixed method data synthesis. BMJ Open. 2017;7(6):e014537–e. pmid:28600365.
- 47. Christensen R, Bartels EM, Astrup A, Bliddal H. Effect of weight reduction in obese patients diagnosed with knee osteoarthritis: a systematic review and meta-analysis. Ann Rheum Dis. 2007;66(4):433–9. Epub 2007/01/06. pmid:17204567.
- 48. Suehs BT, Kamble P, Huang J, Hammer M, Bouchard J, Costantino ME, et al. Association of obesity with healthcare utilization and costs in a Medicare population. Curr Med Res Opin. 2017;33(12):2173–80. Epub 2017/08/02. pmid:28760001.
- 49. Kamble PS, Hayden J, Collins J, Harvey RA, Suehs B, Renda A, et al. Association of obesity with healthcare resource utilization and costs in a commercial population. Curr Med Res Opin. 2018;34(7):1335–43. Epub 2018/04/14. pmid:29649917.
- 50. Smith KC, Losina E, Messier SP, Hunter DJ, Chen AT, Katz JN, et al. Budget Impact of Funding an Intensive Diet and Exercise Program for Overweight and Obese Patients With Knee Osteoarthritis. ACR Open Rheumatol. 2020;2(1):26–36. Epub 2020/01/17. pmid:31943972.
- 51. Schousboe JT, Kats AM, Langsetmo L, Taylor BC, Vo TN, Kado DM, et al. Associations of recent weight loss with health care costs and utilization among older women. PloS one. 2018;13(1):e0191642–e. pmid:29377919.
- 52. Nalliah CJ, Sanders P, Kalman JM. The Impact of Diet and Lifestyle on Atrial Fibrillation. Curr Cardiol Rep. 2018;20(12):137. Epub 2018/10/14. pmid:30315401.
- 53. Naik RD, Choksi YA, Vaezi MF. Impact of Weight Loss Surgery on Esophageal Physiology. Gastroenterol Hepatol (N Y). 2015;11(12):801–9. Epub 2016/05/03. pmid:27134597.
- 54. Duggan C, Tapsoba JD, Wang CY, Schubert KEF, McTiernan A. Long-Term Effects of Weight Loss and Exercise on Biomarkers Associated with Angiogenesis. Cancer Epidemiol Biomarkers Prev. 2017;26(12):1788–94. Epub 2017/10/19. pmid:29042415.
- 55. Lee S-H, Kim Y-S, Sunwoo S, Huh B-Y. A Retrospective Cohort Study on Obesity and Hypertension Risk among Korean Adults. J Korean Med Sci. 2005;20(2):188–95. pmid:15831985
- 56. Dhurandhar NV, Kyle T, Stevenin B, Tomaszewski K. Predictors of weight loss outcomes in obesity care: results of the national ACTION study. BMC Public Health. 2019;19(1):1422. Epub 2019/11/02. pmid:31666040.
- 57. Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML. Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report—Part II. Value Health. 2009;12(8):1053–61. Epub 2009/09/12. pmid:19744292.
- 58. Gaddey HL, Holder K. Unintentional weight loss in older adults. Am Fam Physician. 2014;89(9):718–22. Epub 2014/05/03. pmid:24784334.