Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51368, first published .
Design and Rationale of Prolonged Nightly Fasting for Multiple Myeloma Prevention (PROFAST): Protocol for a Randomized Controlled Pilot Trial

Design and Rationale of Prolonged Nightly Fasting for Multiple Myeloma Prevention (PROFAST): Protocol for a Randomized Controlled Pilot Trial

Design and Rationale of Prolonged Nightly Fasting for Multiple Myeloma Prevention (PROFAST): Protocol for a Randomized Controlled Pilot Trial

Original Paper

1Department of Medicine, Massachusetts General Hospital, Boston, MA, United States

2Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States

3Harvard Medical School, Boston, MA, United States

4Center for Early Detection and Interception of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA, United States

5Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, United States

6College of Health Solutions, Arizona State University, Phoenix, AZ, United States

Corresponding Author:

David J Lee, MD, MPH, MMSc

Department of Medicine

Massachusetts General Hospital

55 Fruit St.

Boston, MA, 02114

United States

Phone: 1 6177242450

Fax:1 6177247441

Email: DJLee@mgh.harvard.edu


Background: Obesity is an established, modifiable risk factor of multiple myeloma (MM); yet, no lifestyle interventions are routinely recommended for patients with overweight or obesity with MM precursor conditions. Prolonged nightly fasting is a simple, practical dietary regimen supported by research, suggesting that the synchronization of feeding-fasting timing with sleep-wake cycles favorably affects metabolic pathways implicated in MM. We describe the design and rationale of a randomized controlled pilot trial evaluating the efficacy of a regular, prolonged nighttime fasting schedule among individuals with overweight or obesity at high risk for developing MM or a related lymphoid malignancy.

Objective: We aim to investigate the effects of 4-month prolonged nightly fasting on body composition and tumor biomarkers among individuals with overweight or obesity with monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), or smoldering Waldenström macroglobulinemia (SWM).

Methods: Individuals with MGUS, SMM, or SWM aged ≥18 years and a BMI of ≥25 kg/m2 are randomized to either a 14-hour nighttime fasting intervention or a healthy lifestyle education control group. Participants’ baseline diet and lifestyle patterns are characterized through two 24-hour dietary recalls: questionnaires querying demographic, comorbidity, lifestyle, and quality-of-life information; and wrist actigraphy measurements for 7 days. Fasting intervention participants are supported through one-on-one telephone counseling by a health coach and automated SMS text messaging to support fasting goals. Primary end points of body composition, including visceral and subcutaneous fat (by dual-energy x-ray absorptiometry); bone marrow adiposity (by bone marrow histology); and tumor biomarkers, specifically M-proteins and serum free light-chain concentrations (by gel-based and serum free light-chain assays), are assessed at baseline and after the 4-month study period; changes therein from baseline are evaluated using a repeated measures mixed-effects model that accounts for the correlation between baseline and follow-up measures and is generally robust to missing data. Feasibility is assessed as participant retention (percent dropout in each arm) and percentage of days participants achieved a ≥14-hour fast.

Results: The PROlonged nightly FASTing (PROFAST) study was funded in June 2022. Participant recruitment commenced in April 2023. As of July 2023, six participants consented to the study. The study is expected to be completed by April 2024, and data analysis and results are expected to be published in the first quarter of 2025.

Conclusions: PROFAST serves as an important first step in exploring the premise that prolonged nightly fasting is a strategy to control obesity and obesity-related mechanisms of myelomagenesis. In evaluating the feasibility and impact of prolonged nightly fasting on body composition, bone marrow adipose tissue, and biomarkers of tumor burden, this pilot study may generate hypotheses regarding metabolic mechanisms underlying MM development and ultimately inform clinical and public health strategies for MM prevention.

Trial Registration: ClinicalTrials.gov NCT05565638; http://clinicaltrials.gov/ct2/show/NCT05565638

International Registered Report Identifier (IRRID): DERR1-10.2196/51368

JMIR Res Protoc 2024;13:e51368

doi:10.2196/51368

Keywords



Multiple myeloma (MM) is the second most common hematologic malignancy in the United States and is preceded by well-defined precursor conditions called monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM) [1,2]. MGUS and SMM are asymptomatic conditions that have an annual progression rate to overt MM of 1% and 10%, respectively, but can be as high as 58% in 20 years in certain risk groups [3,4]. Despite the increasingly strong interest in intervening at the precursor stage for the early interception and prevention of MM, safe and cost-effective interventions are lacking. Although clinical trials have shown that earlier initiation of anti-MM therapies at the precursor stage (eg, lenalidomide-based regimen for high-risk SMM [5]) may alter the natural disease course and improve survival [5,6], early treatment remains highly controversial due to high costs and toxicity risks to patients, limiting its use to only a subset of patients at the highest risk strata [7-10].

Lifestyle interventions targeting excess adiposity and metabolic health in precursor patients may have an important role in MM prevention. Obesity is a well-established, potentially modifiable risk factor of MM [11], and there is accumulating evidence that obesity may also increase the risk of MGUS and its progression to overt MM [12-14]. Although current dietary and weight control guidelines for cancer prevention focus largely on calorie restriction and optimizing intake of specific food groups [15], challenges related to their integration into individuals’ daily lives for a sustained period of time remain a consideration [16-19]. Time-restricted feeding, a form of intermittent fasting whereby ad libitum energy consumption is constrained to a window of time (typically between 4 and 12 hours daily), may be a simple, feasible alternative for weight loss and cancer risk reduction [16,20]. When food intake timing occurs during the wake phase of the 24-hour day, time-restricted feeding may benefit metabolic health and cancer risk by synchronizing feeding-fasting regimens with daily circadian rhythms, which, in turn, improve oscillations in the circadian clock expression of numerous genes important for glucose metabolism and overall cellular homeostasis (eg, autophagy and DNA damage repair) [16,21,22].

The PROlonged nightly FASTing (PROFAST) study is a randomized controlled pilot trial investigating the clinical benefit of a 4-month prolonged nightly fasting regimen in individuals with overweight or obesity with MGUS, those with SMM, and those with smoldering Waldenström macroglobulinemia (SWM). The intervention is supported by evidence that prolonged nighttime fasting is not only a simple and sustainable behavior change [16,23] but also improves metabolism and body weight regulation [24-29]. Here, we describe the study design, rationale, and framework for assessing the potential clinical significance of a low-risk, cost-effective lifestyle intervention in patients with MM precursor conditions.


Design of PROFAST

PROFAST is a pilot randomized controlled trial of our 4-month prolonged nightly fasting intervention in patients with overweight or obesity with MGUS, SMM, or SWM. The goal of the trial is to acquire preliminary outcome data for the efficacy of prolonged nightly fasting on body composition and clinical markers of disease progression in precursor patients. Participants are randomly assigned to (1) a theory-based intervention designed to promote a 14-hour fast during the nighttime hours or (2) the healthy lifestyle education control group.

Ethical Considerations

All study procedures and materials have been approved by the institutional review board at the Dana-Farber Cancer Institute (22-071).

Participants and Eligibility

Eligible participants are at least 18 years old, have a BMI of ≥25 kg/m2, and have a documented diagnosis of MGUS, SMM, or SWM via the review of their electronic medical records. As shown in Textbox 1, inclusion criteria include individuals who (1) are currently fasting for <14 hours per night as assessed via self-report and using 24-hour food recalls and (2) own a cell phone and are capable of sending and receiving SMS text messages comfortably. Exclusion criteria are patients with (1) overt MM; (2) other cancers requiring active therapy; (3) diabetes mellitus, which may increase the risk of hypoglycemia with a prolonged fast, unless the physician who manages their clinical care provides consent that they may enroll; or (4) any other condition or circumstance that, in the investigators’ judgment, would be a contraindication to nightly fasting or interfere with trial participation (eg, night shift work, night eating syndrome, taking weight loss medication, and participation in another weight loss program). Participants are primarily recruited from the Dana-Farber Cancer Institute Center for Early Detection and Interception of Blood Cancers, a clinic focused on evaluating patients diagnosed with precursor conditions of hematologic malignancies and which works with patients to manage their risk of disease progression.

Textbox 1. Study eligibility criteria.

Inclusion criteria

  • Age ≥18 years
  • BMI ≥25 kg/m2
  • Diagnosis of monoclonal gammopathy of undetermined significance, smoldering multiple myeloma, or smoldering Waldenström macroglobulinemia
  • Currently fasting for <14 hours per night, as assessed by 24-hour food recalls
  • Owns a cell phone capable of sending and receiving SMS text messages comfortably
  • Ability to understand and willing to sign a written informed consent document

Exclusion criteria

  • Diagnosis of overt multiple myeloma
  • Diagnosis of another malignancy requiring active therapy
  • Diagnosis of diabetes mellitus, unless consent from the patient’s physician managing the participant’s clinical care
  • Any medical or lifestyle condition contraindicated in or would interfere with study intervention (eg, night eating syndrome and night shift work)
  • Currently taking medications intended for weight loss or participating in other weight loss programs

Clinic Visits and Randomization

On completion of the consent form, participants attend a baseline clinic visit that includes laboratory, self-report, and physical assessments, as shown in Figure 1. In particular, participants have their height and weight measured and receive a dual-energy x-ray absorptiometry (DXA) scan to evaluate body composition. Participants provide biospecimen samples (ie, blood and bone marrow aspirate or biopsy). For each blood draw, approximately 30 to 60 mL of blood per participant is collected into EDTA and serum separator tubes for immediate plasma and serum preparation, respectively, and aliquots of plasma and serum in 1.8-mL cryovials are placed in a –80 °C freezer for storage. As for bone marrow samples, approximately 20 mL of bone marrow is collected into EDTA tubes for immediate processing, and the processed samples are placed in cryovials in a slow freezing cryostorage for 1 to 7 days before moving to storage in a liquid nitrogen tank.

Participants’ current diet and lifestyle patterns are characterized through two 24-hour dietary recalls: (1) questionnaires ascertaining demographic, comorbidity, lifestyle, and quality-of-life information; and (2) the wearing of an ActiGraph accelerometer (GT3XP-BTLE; ActiGraph Corp) on their wrists for 7 days for baseline assessment of sleep, physical activity, and circadian rhythm.

Within 28 days of completing baseline assessments, participants are randomized in a 1:1 manner to either the nighttime 14-hour fast intervention group or the healthy lifestyle education comparison group. Once randomized, participants initiate the intervention or control condition ideally no later than 28 days of baseline screening assessments.

A clinic visit is scheduled 4 months after initiating and ideally within 7 days of completing the intervention or control condition. At this final visit, participants’ weight, biospecimens and clinical labs, DXA scan, and self-reported outcome measures are reobtained.

Figure 1. Schematic of the study design. Timeline of participant recruitment, baseline visit, randomization to intervention or control arm, and end-of-study visit, with assessments that include tumor biomarkers, body composition, 24-hour food recalls, quality-of-life and lifestyle survey, and ActiGraph.

Details of the Intervention

The intervention’s goal is to achieve a 14-hour nightly fast. Fasting goals during the initial weeks of the study are individualized, allowing participants to gradually adopt a 14-hour fasting duration on or before the third week of study. A 14-hour window was selected because it has been demonstrated to be an achievable intervention target in other populations of adults with chronic health conditions, and studies to date suggest that it is a sustainable intervention target [29,30]. Specifically, previous interventions have reported elective continuation of the intervention beyond the intervention period, resulting in sustained health benefits including weight loss [29,30]. The overnight fasting period is defined as the longest interval of time overnight in which no calorie-containing foods or beverages are consumed. Given prior findings that fasting may be more beneficial when aligned with the biological night [26], participants are encouraged to begin their nightly fast by 8 PM. To maximize acceptability and adherence to the intervention protocol, participants are allowed to consume noncaloric beverages including water, plain coffee, plain tea, zero-calorie sodas, and calorie-free sweeteners.

The intervention delivery consists of one-on-one telephone counseling by a health coach and an SMS text messaging system to support this target. Participants are supported through telephone counseling during the initial phase (weeks 1-3) of the interventional period and gradually transition to activities that promote self-reliance for behavioral maintenance from week 4 until the end of the study. The intervention incorporates a number of theory-based behavior change strategies in its design [31-33], including those grounded in social cognitive theory and its central tenet of self-efficacy [31].

SMS Text Messaging System

An SMS text messaging system was developed in collaboration with Mosio, Inc, and is introduced to participants during week 3 of the study for the purpose of self-monitoring, tracking adherence, and encouraging maintenance of prolonged nightly fasting. Participants are asked to text the study team to indicate when they began and ended their overnight fast. Participants receive an automated response to their SMS text messages, with positive reinforcement if the fasting goal was successfully achieved or corrective tools, such as a behavioral strategy, that may help them achieve a longer fast. The messages sent back to participants also contain feedback about their cumulative adherence to the prolonged overnight fasting pattern, calculated as the percentage of the successful overnight fasts during the past week. Participants also receive an SMS text message each day to remind them of their target end time of the overnight fast as well as encouraging SMS text messages to promote adherence that are customized to past performance. An example of SMS text messages sent to and received from participants through the automated SMS text messaging system is provided in Table 1.

Table 1. Example of an interaction in which a participant in the intervention arm submits SMS text messages to indicate the beginning and end of overnight fast and, in turn, is provided with automated, encouraging messages promoting cumulative adherence and achievement of the fasting goal.
Time stampMessage statusMessage content
February 17, 2013, 6:00 PMSentRemember: Calories break a fast! Once you start, you need be calorie-free
February 17, 2013, 7:28 PMReceivedFAST
February 17, 2013, 7:29 PMResponseThanks! You are now running calorie free! Check back in the morning for your end time.
February 18, 2013, 7:00 AMSentYour fast ends at 9.28 am. You can do it!
February 18, 2013, 9:55 AMReceivedEAT
February 17, 2013, 10:05 AMResponseCongratulations! Your success rate is 100%. Nice work!

Healthy Lifestyle Education Control

Participants in the control arm receive educational information regarding healthy lifestyle to enhance retention of participants randomized to this group. At baseline, control participants receive an educational session with the health coach and a workbook containing brief information about topics about a healthy lifestyle (eg, sun safety, sleep, hydration, and sitting less). It is notable that research indicates that a single education session has only modest and temporary impacts on behaviors among most individuals [34,35]. To support sustained engagement throughout the 4-month study period, control group participants also receive 1 email and 1 SMS text message per week by the same SMS text messaging system used for participants in the interventional arm. These SMS text messages contain a mixture of educational information and potential personalized touch points to help participants feel valued.

Primary End Points

A challenge in assessing the clinical benefit of short-term interventions in MGUS, SMM, and SWM is that precursor patients are expected to have a relatively small number of progression events over months to years, limiting the ability to evaluate these events as the outcome. In this study, we focus on body composition, bone marrow adipose tissue, and biomarkers of tumor burden, which are accessible outcome measures in evaluating the efficacy of our lifestyle intervention.

Body Composition and Bone Marrow Adiposity

Most investigations evaluating the association of obesity and MGUS and MM have used BMI as a surrogate measure of obesity [12,13,36,37], and although BMI is a convenient, inexpensive measurement [38], it has low specificity for identifying excess adipose tissue and does not account for the type and distribution of fat (visceral and subcutaneous fat) [39,40]. These shortcomings of BMI limit our understanding of the relationship between obesity and the development of MM, as different adipose tissue compartments are known to have differential influences on obesity-related diseases [41-43]. For example, visceral fat is a metabolically active tissue type that releases fatty acids and proinflammatory substances and, in comparison with subcutaneous tissue, is more strongly associated with components of metabolic syndrome [41-43]. Thus, in our study, DXA scan (Horizon W DXA; Hologic, Inc) is used to measure body composition, differentiating not only fat mass from lean mass but also among adipose tissue types (visceral and subcutaneous fat). Furthermore, bone marrow adipose tissue is an understudied adipose depot with endocrine and paracrine signaling functions linked with the proliferation of nearby MM cells [44]. In our study, total marrow lipid content is quantified in bone marrow aspirate samples. Adipocyte-lipid content is analyzed using histology, flow cytometry, and lipidomics on adipocyte-enriched fractions. By measuring and comparing these adiposity measures between baseline and end of study, our study is able to evaluate whether prolonged nightly fasting possibly improves the body composition profile of participants.

Clinically Available Tumor Biomarkers

Serum protein electrophoresis supplemented by immunofixation (SPEP/IFX) and serum-free light chains (SFLCs) are the most commonly used clinical tests used to monitor patients at all stages of the disease continuum (MGUS, SMM, and MM). Monoclonal immunoglobulin (M-protein) and light-chain concentrations, as measured by SPEP/IFX and SFLCs, correlate with overall tumor burden and importantly decrease after treatment with a range of mild and aggressive MM therapies [45-47]. These biomarkers are sensitive and measurable variables of tumor response to treatment [48] and, therefore, serve as clinically relevant disease end points for our lifestyle-related intervention pilot study.

Other End Points

Metabolomics

Metabolomics—the high-throughput identification and quantification of small-molecule metabolites—is the study of metabolic changes in biological systems and provides the small-molecule fingerprints that reflect the complex relationships among diet, lifestyle (obesity), genes, and disease processes [49]. Metabolomics can yield novel insights into pathogenesis and risk of cancer and chronic disease [49,50]. Particularly important categories of metabolites in cancer and metabolic disease are metabolites of glycolysis and the tricarboxylic acid cycle [51,52]. These are of interest to this study because of their critical role in tumor growth and because they have been shown to be related to obesity [53]. Metabolomics-based data of MGUS, SMM, and MM are limited to only a small number of studies, including one by Ludwig et al [54] that identified 25 bone marrow metabolites that differed between 10 patients with MGUS and 10 patients with MM [55-59]. Of all molecular entities in the body (eg, genes, transcripts, proteins, and metabolites), metabolites as the final products of biochemical processing have the closest relationship to expressed phenotype [60]. Thus, we believe that metabolomics offers a unique lens to study progression of MM precursors.

Quality of Life

Given the high rates of anxiety and distress document in individuals with MGUS and SMM and lack of evidence-based risk reduction strategies available for preventing disease progression to MM [61,62], we aim to evaluate the psychosocial benefits of offering a low-risk lifestyle intervention to this patient population as an exploratory objective. Quality of life is assessed using the well-validated PROMIS (Patient-Reported Outcomes Measurement Information System) global health survey [63-65]. Cancer worry is assessed using a 4-item scale adapted from Lerman et al [66].

Baseline Dietary and Lifestyle Assessment

Participants undergo two 24-hour dietary recalls to assess food and beverage consumption (ideally for 1 weekday and 1 weekend day), which are conducted through the Behavioral Measurement and Interventions Shared Resource at the University of Arizona Cancer Center. Participants also complete a baseline survey querying sociodemographic variables (eg, age, sex, race, ethnicity, highest level of education attained, and annual family income), medical comorbidities (eg, hypertension, hyperlipidemia, and diabetes mellitus), and lifestyle factors (eg, smoking, physical activity, sleep impairment, and disturbance) measured by well-validated instruments [67-72]. Finally, participants are asked to wear an ActiGraph accelerometer (GT3XP-BTLE; ActiGraph Corp) on their wrists for 7 days for a baseline assessment of physical activity, sleep, and circadian rhythm parameters [73-75].

Feasibility, Acceptability, Fidelity, and Safety

Feasibility is assessed as participant retention (percent dropout in each arm) and percentage of days participants achieved a ≥14-hour fast. Acceptability is assessed as perceived effectiveness of intervention components, plans to continue to engage in a prolonged nightly fast, elements of intervention liked or disliked, and satisfaction with program delivery and staff. Items are rated on a scale from 1 to 5. Finally, fidelity is assessed as percentage of days participants recorded fasting duration via the SMS text messaging system, percentage of SMS text messages read, and percentage of calls with the health coach.

Safety is monitored and assessed by the number and severity of adverse events, according to the revised National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Individuals with any medical or lifestyle condition (eg, diabetes mellitus) that is deemed to elevate their risk of adverse events from the intervention are excluded from the study (Textbox 1). Participants are informed of expected side effects related to fasting (eg, lightheadedness, headaches, restlessness, irritability, and low blood glucose), and adverse events that vary in nature, intensity, and frequency from what is expected are reported.

Analysis Plan

The key outcomes evaluated in this trial are changes in body composition (with a focus on visceral and subcutaneous adiposity), M-protein concentrations, and bone marrow adiposity. Changes in these end points are evaluated from baseline to follow-up in the 2 groups using a repeated measures mixed-effects model that accounts for the correlation between baseline and follow-up measures and is generally robust to missing data. The baseline values of the dependent variables (eg, weight and M-protein level) and disease subtype are included as covariates in the regression models. Group-by-time interaction terms are included as fixed effects in the regression model. Model fit is assessed using standard methods.


The PROFAST Study was funded in June 2022. Participant recruitment commenced in April 2023, after developing and testing the text messaging system. As of July 2023, a total of 6 participants consented to the study. The study is expected to be completed by April 2024, and data analysis and results are expected to be published in the first quarter of 2025.


The PROFAST study provides important preliminary data regarding the impact of prolonged nightly fasting on body composition, bone marrow adipose tissue, and biomarkers of tumor burden (SPEP/IFX and SFLCs) in patients with MM precursor conditions, thereby generating hypotheses on how targeting obesity-related mechanisms of carcinogenesis may help prevent MM development.

Obesity is a well-established, potentially modifiable risk factor of MM [11], and there is accumulating evidence that obesity may also increase the risk of MGUS and its progression to overt MM [12-14]. A study of 3 large prospective cohorts of US-based adults followed over 5 million person-years observed a 17% increase in MM risk per 5 kg/m2 increase in BMI [36]. In another study of 7818 patients with MGUS in the US Veterans Health Administration database, being overweight and obese were associated with an increased risk of transformation to MM (hazard ratio [HR] for overweight 1.55, 95% CI 1.16-2.06; HR for obesity 1.98, 95% CI 1.47-2.68) [12]. Concordant with these findings, a more recent analysis of patients with MGUS identified through a population-based screening study in Olmstead County, Minnesota, between 1995 and 2003 observed that having a BMI ≥25 was also associated with increased progression to MM or other plasma cell or lymphoid disorders in univariate analysis (HR 2.14, 95% CI 1.05-4.36) and in a multivariable model accounting for clinical factors [13].

These epidemiological studies are supported by mechanistic evidence that obesity may lead to chronic low-grade inflammation and dysregulation of endogenous growth factors linked to myelomagenesis [76,77], together suggesting that weight control may be an effective MM prevention strategy. Indeed, according to a compelling 2016 consensus statement, an expert panel convened by the International Agency for Research on Cancer concluded that there is sufficient mechanistic evidence that a preventative relationship has been established between the “absence of body fatness” and MM [76]. In obesity, adipose tissue, including adipocytes in the bone marrow, is altered in ways that may promote carcinogenesis, including by creating an unfavorable tumor microenvironment in which MM can engraft and grow [44,78]. Notably, the adipose tissue of obese individuals leads to excess free fatty acids as well as altered levels of proinflammatory cytokines (eg, tumor necrosis factor α and interleukin 6), adipokines (eg, adiponectin and leptin), and metabolic peptide hormones (eg, insulin and insulin-like growth factor 1) [44,78,79]. Dysregulated levels of these biological compounds may fuel tumor initiation and influence the genetic characteristics of MM cells in ways that increase cell proliferation [80], reduce apoptosis [81], and contribute to immune cell evasion [82,83]. The implication of these factors is that weight loss may curb the contribution that excess adiposity-associated chronic inflammation and metabolic dysregulation have on MM development.

Beyond weight loss, the PROFAST study tests the hypothesis that “when we eat,” not just “what or how much we eat,” is relevant to cancer prevention [24,25,84,85]. There is evidence suggesting that chronic exposure to circadian rhythm disturbances may lead to metabolic dysregulation, upregulation of proinflammatory cytokines, and abnormal cell proliferation [21,86]. As food intake contributes to the setting of circadian clock rhythms in peripheral organs, such as by inducing changes in body temperature and through the action of hormones such as insulin and nutrient-sensing enzymatic and nuclear receptor signaling systems [21,87], the synchronization of feeding-fasting patterns with circadian rhythms may be important for preventing chronic diseases [24,26,27]. This hypothesis is supported by a large prospective study of patients with early-stage breast cancer, which observed that a short nightly fasting duration (<13 hours per night), compared with a nightly fasting of ≥13 hours, was associated with an increased risk of breast cancer recurrence [25]. In that same study, each 2-hour increase in the nightly fasting duration was associated with lower hemoglobin A1c levels and a longer duration of nighttime sleep [25]. These findings are consistent with the results of population-based studies [24,26,27], including a large case-control study in Spain that observed that diurnal eating patterns, specifically in maintaining longer time intervals between the last meal of the day and initiation of sleep, were inversely associated with risk of breast and prostate cancer [27]. Similarly, two US-based studies of 2009-2010 National Health and Nutrition Examination Survey data evaluating biomarkers of breast cancer risk observed that a longer duration of nighttime fasting was associated with improved measures of glycemic control and systemic inflammation [24,26], aligning with pathophysiological mechanisms underlying cancer risk [76].

Results from these human observational studies are consistent with rodent studies demonstrating that mice subjected to a time-restricted (16-hour) fasting regimen of a high-fat diet during the sleep phase were protected against weight gain, abnormal glucose metabolism, and inflammation, all of which were associated with cancer outcomes [22,88-91]. Notably, protective effects were observed despite these mice having the same caloric intake as those that had ad libitum access to food and ate frequently throughout day and night [22], suggesting that the beneficial effects of this fasting regimen were partly mediated through mechanisms independent of calorie restriction. Together, these data provide a strong basis of our intervention aimed to curb the inflammatory and metabolic mechanisms shown to contribute to myelomagenesis [44,77].

Finally, and importantly, prolonged nightly fasting represents a lifestyle modification that is safe, practical, and acceptable for patients who otherwise are managed by a “watchful waiting” strategy. Patients with MM precursor conditions are reported to have diminished quality of life, increased comorbidities, and heighted anxiety and a sense of loss of control regarding their MM risk [61,62,92,93]. There is, therefore, a need to identify lifestyle-based interventions that patients can safely and practically adopt into their daily lifestyles and ultimately help curb disease progression. Fasting, as a practice, developed independently among different people groups and religions (eg, Ramadan in Islam) around the world [94], and intermittent fasting has become one of the most common dietary patterns reported in the United States [95]. Prolonged nightly fasting is attractive because of its simplicity and feasibility, as supported by evidence from clinical trials of time-restricted eating strategies [16,23], and, thus, may be a potentially effective disease prevention strategy at the population level [16].

The limitations of this study include the relatively small sample size of the study and the potential lack of sociodemographic and geographic diversity of participants due to recruitment occurring at a single academic institution, thus, possibly impacting generalizability of findings. Also, participants willing to participate in a 4-month prolonged overnight fasting intervention may not be representative of the general population of patients with overweight or obesity with MM precursors, and selection bias for patients who are motivated to participate in PROFAST could increase the study adherence rate. Furthermore, although participants in the intervention group are prescribed solely a prolonged overnight fasting regimen with no other recommendations regarding lifestyle patterns, it is theoretically possible that participants may change their dietary, physical activity, and sleep habits due to paying closer attention to the timing of their eating and evaluations of their body composition before and after the intervention. These changes may also impact outcomes.

In summary, the study described herein serves as an important first step in exploring the premise that prolonged nightly fasting is a practical, effective strategy to control obesity and intercept disease progression in individuals with MM precursor conditions. By evaluating the impact of this lifestyle intervention on relevant biomarkers of excess adiposity and myeloma tumor burden, this pilot study may generate hypotheses and inform further investigations in identifying clinical and public health strategies for MM prevention.

Acknowledgments

This study was funded in part by the National Institutes of Health (R25AI147393, R38HL150212, R21 CA256644, K22 CA251648, R25 AI147393, R38 HL150212) and the Paula and Roger Riney Foundation.

Data Availability

Data sharing is not applicable to this article as no data sets were generated or analyzed during this study.

Conflicts of Interest

IMG has served as a consultant for AbbVie, Adaptive, Amgen, Aptitude Health, Bristol-Myers Squibb, GlaxoSmithKline, Janssen, Menarini Silicon Biosystems, Novartis, Pfizer, Regeneron, Sanofi, Standard Biotools, Takeda, The Binding Site, and Window Therapeutics; received honoraria from Vor Biopharma and CurioScience; and received support for attending meetings from Amgen, Bristol-Myers Squibb, Novartis, Menarini Silicon Biosystems, and Pfizer. IMG’s spouse, William Savage, is Chief Medical Officer and equity holder at Disc Medicine.

  1. Howlader N, Noone AM, Krapcho M, Miller D, Bishop K, Altekruse SF, et al. Previous version: SEER cancer statistics review, 1975-2013. National Cancer Institute: Surveillance, Epidemiology, and End Results Program. Bethesda, MD. National Cancer Institute; 2016. URL: https://seer.cancer.gov/archive/csr/1975_2013/ [accessed 2023-12-15]
  2. Kyle RA, Therneau TM, Rajkumar SV, Larson DR, Plevak MF, Offord JR, et al. Prevalence of monoclonal gammopathy of undetermined significance. N Engl J Med. Mar 30, 2006;354(13):1362-1369. [FREE Full text] [CrossRef] [Medline]
  3. Landgren O. Monoclonal gammopathy of undetermined significance and smoldering multiple myeloma: biological insights and early treatment strategies. Hematology Am Soc Hematol Educ Program. 2013;2013:478-487. [FREE Full text] [CrossRef] [Medline]
  4. Kyle RA, Durie BGM, Rajkumar SV, Landgren O, Blade J, Merlini G, et al. International Myeloma Working Group. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management. Leukemia. Jun 2010;24(6):1121-1127. [FREE Full text] [CrossRef] [Medline]
  5. Mateos MV, Hernández MT, Giraldo P, de la Rubia J, de Arriba F, López Corral L, et al. Lenalidomide plus dexamethasone for high-risk smoldering multiple myeloma. N Engl J Med. Aug 01, 2013;369(5):438-447. [FREE Full text] [CrossRef] [Medline]
  6. Korde N, Roschewski M, Zingone A, Kwok M, Manasanch EE, Bhutani M, et al. Treatment with carfilzomib-lenalidomide-dexamethasone with lenalidomide extension in patients with smoldering or newly diagnosed multiple myeloma. JAMA Oncol. Sep 2015;1(6):746-754. [FREE Full text] [CrossRef] [Medline]
  7. Caers J, Fernández de Larrea C, Leleu X, Heusschen R, Zojer N, Decaux O, et al. The changing landscape of smoldering multiple myeloma: a European perspective. Oncologist. Mar 2016;21(3):333-342. [FREE Full text] [CrossRef] [Medline]
  8. Ghobrial IM, Landgren O. How I treat smoldering multiple myeloma. Blood. Nov 27, 2014;124(23):3380-3388. [FREE Full text] [CrossRef] [Medline]
  9. Rajkumar SV, Landgren O, Mateos MV. Smoldering multiple myeloma. Blood. May 14, 2015;125(20):3069-3075. [FREE Full text] [CrossRef] [Medline]
  10. Kumar SK. Timing of treatment of smoldering myeloma: delay until progression. Blood Adv. Nov 13, 2018;2(21):3050-3053. [FREE Full text] [CrossRef] [Medline]
  11. Castaneda-Avila MA, Ulbricht CM, Epstein MM. Risk factors for monoclonal gammopathy of undetermined significance: a systematic review. Ann Hematol. Apr 2021;100(4):855-863. [FREE Full text] [CrossRef] [Medline]
  12. Chang SH, Luo S, Thomas TS, O'Brian KK, Colditz GA, Carlsson NP, et al. Obesity and the transformation of monoclonal gammopathy of undetermined significance to multiple myeloma: a population-based cohort study. J Natl Cancer Inst. 2017;109(5):djw264. [FREE Full text] [CrossRef] [Medline]
  13. Kleinstern G, Larson DR, Allmer C, Norman AD, Muntifering G, Sinnwell J, et al. Body mass index associated with Monoclonal Gammopathy of Undetermined Significance (MGUS) progression in Olmsted County, Minnesota. Blood Cancer J. 2022;12(4):67. [FREE Full text] [CrossRef] [Medline]
  14. Thordardottir M, Lindqvist EK, Lund SH, Costello R, Burton D, Korde N, et al. Obesity and risk of monoclonal gammopathy of undetermined significance and progression to multiple myeloma: a population-based study. Blood Adv. 2017;1(24):2186-2192. [FREE Full text] [CrossRef] [Medline]
  15. Rock CL, Thomson C, Gansler T, Gapstur SM, McCullough ML, Patel AV, et al. American Cancer Society guideline for diet and physical activity for cancer prevention. CA Cancer J Clin. 2020;70(4):245-271. [FREE Full text] [CrossRef] [Medline]
  16. Patterson RE, Laughlin GA, LaCroix AZ, Hartman SJ, Natarajan L, Senger CM, et al. Intermittent fasting and human metabolic health. J Acad Nutr Diet. 2015;115(8):1203-1212. [FREE Full text] [CrossRef] [Medline]
  17. Donaldson MS. Nutrition and cancer: a review of the evidence for an anti-cancer diet. Nutr J. 2004;3:19. [FREE Full text] [CrossRef] [Medline]
  18. Tsofliou F, Vlachos D, Hughes C, Appleton KM. Barriers and facilitators associated with the adoption of and adherence to a mediterranean style diet in adults: a systematic review of published observational and qualitative studies. Nutrients. 2022;14(20):4314. [FREE Full text] [CrossRef] [Medline]
  19. Clifton KK, Ma CX, Fontana L, Peterson LL. Intermittent fasting in the prevention and treatment of cancer. CA Cancer J Clin. 2021;71(6):527-546. [FREE Full text] [CrossRef] [Medline]
  20. Liu D, Huang Y, Huang C, Yang S, Wei X, Zhang P, et al. Calorie restriction with or without time-restricted eating in weight loss. N Engl J Med. 2022;386(16):1495-1504. [FREE Full text] [CrossRef] [Medline]
  21. Sulli G, Lam MTY, Panda S. Interplay between circadian clock and cancer: new frontiers for cancer treatment. Trends Cancer. 2019;5(8):475-494. [FREE Full text] [CrossRef] [Medline]
  22. Hatori M, Vollmers C, Zarrinpar A, DiTacchio L, Bushong EA, Gill S, et al. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 2012;15(6):848-860. [FREE Full text] [CrossRef] [Medline]
  23. Termannsen AD, Varming A, van Elst C, Bjerre N, Nørgaard O, Hempler NF, et al. Feasibility of time-restricted eating in individuals with overweight, obesity, prediabetes, or type 2 diabetes: a systematic scoping review. Obesity (Silver Spring). 2023;31(6):1463-1485. [FREE Full text] [CrossRef] [Medline]
  24. Marinac CR, Natarajan L, Sears DD, Gallo LC, Hartman SJ, Arredondo E, et al. Prolonged nightly fasting and breast cancer risk: findings from NHANES (2009-2010). Cancer Epidemiol Biomarkers Prev. 2015;24(5):783-789. [FREE Full text] [CrossRef] [Medline]
  25. Marinac CR, Nelson SH, Breen CI, Hartman SJ, Natarajan L, Pierce JP, et al. Prolonged nightly fasting and breast cancer prognosis. JAMA Oncol. 2016;2(8):1049-1055. [FREE Full text] [CrossRef] [Medline]
  26. Marinac CR, Sears DD, Natarajan L, Gallo LC, Breen CI, Patterson RE. Frequency and circadian timing of eating may influence biomarkers of inflammation and insulin resistance associated with breast cancer risk. PLoS One. 2015;10(8):e0136240. [FREE Full text] [CrossRef] [Medline]
  27. Kogevinas M, Espinosa A, Castelló A, Gómez-Acebo I, Guevara M, Martin V, et al. Effect of mistimed eating patterns on breast and prostate cancer risk (MCC-Spain Study). Int J Cancer. 2018;143(10):2380-2389. [FREE Full text] [CrossRef] [Medline]
  28. Wang JB, Patterson RE, Ang A, Emond JA, Shetty N, Arab L. Timing of energy intake during the day is associated with the risk of obesity in adults. J Hum Nutr Diet. 2014;27(Suppl 2):255-262. [FREE Full text] [CrossRef] [Medline]
  29. Wilkinson MJ, Manoogian ENC, Zadourian A, Lo H, Fakhouri S, Shoghi A, et al. Ten-hour time-restricted eating reduces weight, blood pressure, and atherogenic lipids in patients with metabolic syndrome. Cell Metab. 2020;31(1):92-104.e5. [FREE Full text] [CrossRef] [Medline]
  30. Gill S, Panda S. A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits. Cell Metab. 2015;22(5):789-798. [FREE Full text] [CrossRef] [Medline]
  31. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ. Prentice Hall; 1986.
  32. Carver CS, Scheier MF. Control theory: a useful conceptual framework for personality-social, clinical, and health psychology. Psychol Bull. 1982;92(1):111-135. [FREE Full text] [Medline]
  33. Patrick K, Marshall SJ, Davila EP, Kolodziejczyk JK, Fowler JH, Calfas KJ, et al. Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART). Contemp Clin Trials. 2014;37(1):10-18. [FREE Full text] [CrossRef] [Medline]
  34. Dansinger ML, Tatsioni A, Wong JB, Chung M, Balk EM. Meta-analysis: the effect of dietary counseling for weight loss. Ann Intern Med. 2007;147(1):41-50. [FREE Full text] [CrossRef] [Medline]
  35. Tsai AG, Wadden TA. Treatment of obesity in primary care practice in the United States: a systematic review. J Gen Intern Med. 2009;24(9):1073-1079. [FREE Full text] [CrossRef] [Medline]
  36. Marinac CR, Birmann BM, Lee IM, Rosner BA, Townsend MK, Giovannucci E, et al. Body mass index throughout adulthood, physical activity, and risk of multiple myeloma: a prospective analysis in three large cohorts. Br J Cancer. 2018;118(7):1013-1019. [FREE Full text] [CrossRef] [Medline]
  37. Shah UA, Whiting K, Devlin S, Ershler R, Kanapuru B, Lee DJ, et al. Extreme body mass index and survival in newly diagnosed multiple myeloma patients. Blood Cancer J. 2023;13(1):13. [FREE Full text] [CrossRef] [Medline]
  38. de Gonzalez AB, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010;363(23):2211-2219. [FREE Full text] [CrossRef] [Medline]
  39. Prentice AM, Jebb SA. Beyond body mass index. Obes Rev. 2001;2(3):141-147. [FREE Full text] [CrossRef] [Medline]
  40. Heymsfield SB, Peterson CM, Thomas DM, Heo M, Schuna JM. Why are there race/ethnic differences in adult body mass index-adiposity relationships? A quantitative critical review. Obes Rev. 2016;17(3):262-275. [FREE Full text] [CrossRef] [Medline]
  41. Zhang M, Hu T, Zhang S, Zhou L. Associations of different adipose tissue depots with insulin resistance: a systematic review and meta-analysis of observational studies. Sci Rep. 2015;5(1):18495. [FREE Full text] [CrossRef] [Medline]
  42. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881-887. [FREE Full text] [CrossRef] [Medline]
  43. De Larochellière E, Côté J, Gilbert G, Bibeau K, Ross MK, Dion-Roy V, et al. Visceral/epicardial adiposity in nonobese and apparently healthy young adults: association with the cardiometabolic profile. Atherosclerosis. 2014;234(1):23-29. [FREE Full text] [CrossRef] [Medline]
  44. Falank C, Fairfield H, Reagan MR. Signaling interplay between bone marrow adipose tissue and multiple myeloma cells. Front Endocrinol (Lausanne). 2016;7:67. [FREE Full text] [CrossRef] [Medline]
  45. Golombick T, Diamond TH, Manoharan A, Ramakrishna R. Monoclonal gammopathy of undetermined significance, smoldering multiple myeloma, and curcumin: a randomized, double-blind placebo-controlled cross-over 4g study and an open-label 8g extension study. Am J Hematol. 2012;87(5):455-460. [FREE Full text] [CrossRef] [Medline]
  46. Ross DM, To LB, Horvath N. Assessment of early paraprotein response to vincristine-doxorubicin-dexamethasone chemotherapy may help guide therapy in multiple myeloma. Intern Med J. 2004;34(9-10):576-578. [CrossRef] [Medline]
  47. Schaar CG, Kluin-Nelemans JC, le Cessie S, Franck PFH, te Marvelde MC, Wijermans PW. Early response to therapy and survival in multiple myeloma. Br J Haematol. 2004;125(2):162-166. [FREE Full text] [CrossRef] [Medline]
  48. Shah J, Bladé J, Sonneveld P, Harousseau JL, Lantz K, Londhe A, et al. Rapid early monoclonal protein reduction after therapy with bortezomib or bortezomib and pegylated liposomal doxorubicin in relapsed/refractory myeloma is associated with a longer time to progression. Cancer. 2011;117(16):3758-3762. [FREE Full text] [CrossRef] [Medline]
  49. Kim YS, Maruvada P, Milner JA. Metabolomics in biomarker discovery: future uses for cancer prevention. Future Oncol. 2008;4(1):93-102. [FREE Full text] [CrossRef] [Medline]
  50. Beger RD. A review of applications of metabolomics in cancer. Metabolites. 2013;3(3):552-574. [FREE Full text] [CrossRef] [Medline]
  51. Anderson NM, Mucka P, Kern JG, Feng H. The emerging role and targetability of the TCA cycle in cancer metabolism. Protein Cell. 2018;9(2):216-237. [FREE Full text] [CrossRef] [Medline]
  52. Hsu PP, Sabatini DM. Cancer cell metabolism: warburg and beyond. Cell. 2008;134(5):703-707. [FREE Full text] [CrossRef] [Medline]
  53. Doerstling SS, O'Flanagan CH, Hursting SD. Obesity and cancer metabolism: a perspective on interacting tumor-intrinsic and extrinsic factors. Front Oncol. 2017;7:216. [FREE Full text] [CrossRef] [Medline]
  54. Ludwig C, Williams DS, Bartlett DB, Essex SJ, McNee G, Allwood JW, et al. Alterations in bone marrow metabolism are an early and consistent feature during the development of MGUS and multiple myeloma. Blood Cancer J. 2015;5(10):e359. [FREE Full text] [CrossRef] [Medline]
  55. Piszcz J, Lemancewicz D, Dudzik D, Ciborowski M. Differences and similarities between LC-MS derived serum fingerprints of patients with B-cell malignancies. Electrophoresis. 2013;34(19):2857-2864. [FREE Full text] [Medline]
  56. Kuliszkiewicz-Janus M, Baczyński S. Chemotherapy-associated changes in 31P MRS spectra of sera from patients with multiple myeloma. NMR Biomed. 1995;8(3):127-132. [FREE Full text] [CrossRef] [Medline]
  57. Lodi A, Tiziani S, Khanim FL, Günther UL, Viant MR, Morgan GJ, et al. Proton NMR-based metabolite analyses of archived serial paired serum and urine samples from myeloma patients at different stages of disease activity identifies acetylcarnitine as a novel marker of active disease. PLoS One. 2013;8(2):e56422. [FREE Full text] [CrossRef] [Medline]
  58. Puchades-Carrasco L, Lecumberri R, Martínez-López J, Lahuerta JJ, Mateos MV, Prósper F, et al. Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission. Clin Cancer Res. 2013;19(17):4770-4779. [FREE Full text] [CrossRef] [Medline]
  59. Steiner N, Müller U, Hajek R, Sevcikova S, Borjan B, Jöhrer K, et al. The metabolomic plasma profile of myeloma patients is considerably different from healthy subjects and reveals potential new therapeutic targets. PLoS One. 2018;13(8):e0202045. [FREE Full text] [CrossRef] [Medline]
  60. Rattray NJW, Deziel NC, Wallach JD, Khan SA, Vasiliou V, Ioannidis JPA, et al. Beyond genomics: understanding exposotypes through metabolomics. Hum Genomics. 2018;12(1):4. [FREE Full text] [CrossRef] [Medline]
  61. Maatouk I, He S, Hummel M, Hemmer S, Hillengass M, Goldschmidt H, et al. Patients with precursor disease exhibit similar psychological distress and mental HRQOL as patients with active myeloma. Blood Cancer J. 2019;9(2):9. [FREE Full text] [CrossRef] [Medline]
  62. Hildebrandt MAT, Callender RA, Belachew AA, Classen C, Lee HC, Patel KK, et al. Quality of life and cancer worry in a follow-up cohort of patients with asymptomatic monoclonal gammopathies. J Clin Oncol. 2018;36(15_suppl):-8049. [FREE Full text] [CrossRef]
  63. Shim J, Hamilton DF. Comparative responsiveness of the PROMIS-10 global health and EQ-5D questionnaires in patients undergoing total knee arthroplasty. Bone Joint J. 2019;101-B(7):832-837. [FREE Full text] [CrossRef] [Medline]
  64. Wilford J, Osann K, Hsieh S, Monk B, Nelson E, Wenzel L. Validation of PROMIS emotional distress short form scales for cervical cancer. Gynecol Oncol. 2018;151(1):111-116. [FREE Full text] [CrossRef] [Medline]
  65. Schalet BD, Pilkonis PA, Yu L, Dodds N, Johnston KL, Yount S, et al. Clinical validity of PROMIS depression, anxiety, and anger across diverse clinical samples. J Clin Epidemiol. 2016;73:119-127. [FREE Full text] [CrossRef] [Medline]
  66. Lerman C, Schwartz MD, Lin TH, Hughes C, Narod S, Lynch HT. The influence of psychological distress on use of genetic testing for cancer risk. J Consult Clin Psychol. Jun 1997;65(3):414-420. [CrossRef] [Medline]
  67. Hoos T, Espinoza N, Marshall S, Arredondo EM. Validity of the Global Physical Activity Questionnaire (GPAQ) in adult Latinas. J Phys Act Health. 2012;9(5):698-705. [FREE Full text] [CrossRef] [Medline]
  68. Armstrong T, Bull F. Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). J Public Health. 2006;14(2):66-70. [FREE Full text] [CrossRef]
  69. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Health. 2009;6(6):790-804. [FREE Full text] [CrossRef] [Medline]
  70. Kenfield SA, Wei EK, Rosner BA, Glynn RJ, Stampfer MJ, Colditz GA. Burden of smoking on cause-specific mortality: application to the Nurses' Health Study. Tob Control. 2010;19(3):248-254. [FREE Full text] [CrossRef] [Medline]
  71. Cao Y, Willett WC, Rimm EB, Stampfer MJ, Giovannucci EL. Light to moderate intake of alcohol, drinking patterns, and risk of cancer: results from two prospective US cohort studies. BMJ. 2015;351:h4238. [FREE Full text] [CrossRef] [Medline]
  72. Patient Reported Outcomes Measurement Information System (PROMIS). URL: https://www.healthmeasures.net/explore-measurement-systems/promis [accessed 2023-01-11]
  73. Mitchell JA, Quante M, Godbole S, James P, Hipp JA, Marinac CR, et al. Variation in actigraphy-estimated rest-activity patterns by demographic factors. Chronobiol Int. 2017;34(8):1042-1056. [FREE Full text] [CrossRef] [Medline]
  74. Quante M, Mariani S, Weng J, Marinac CR, Kaplan ER, Rueschman M, et al. Zeitgebers and their association with rest-activity patterns. Chronobiol Int. 2019;36(2):203-213. [FREE Full text] [CrossRef] [Medline]
  75. Feliciano EMC, Quante M, Weng J, Mitchell JA, James P, Marinac CR, et al. Actigraphy-derived daily rest-activity patterns and body mass index in community-dwelling adults. Sleep. 2017;40(12):zsx168. [FREE Full text] [CrossRef] [Medline]
  76. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K, et al. International Agency for Research on Cancer Handbook Working Group. Body fatness and cancer—viewpoint of the IARC Working Group. N Engl J Med. 2016;375(8):794-798. [FREE Full text] [CrossRef] [Medline]
  77. Carson KR, Bates ML, Tomasson MH. The skinny on obesity and plasma cell myeloma: a review of the literature. Bone Marrow Transplant. 2014;49(8):1009-1015. [FREE Full text] [CrossRef] [Medline]
  78. van Kruijsdijk RCM, van der Wall E, Visseren FLJ. Obesity and cancer: the role of dysfunctional adipose tissue. Cancer Epidemiol Biomarkers Prev. 2009;18(10):2569-2578. [FREE Full text] [CrossRef] [Medline]
  79. Singla P, Bardoloi A, Parkash AA. Metabolic effects of obesity: a review. World J Diabetes. 2010;1(3):76-88. [FREE Full text] [CrossRef] [Medline]
  80. Sprynski AC, Hose D, Kassambara A, Vincent L, Jourdan M, Rossi JF, et al. Insulin is a potent myeloma cell growth factor through insulin/IGF-1 hybrid receptor activation. Leukemia. 2010;24(11):1940-1950. [FREE Full text] [CrossRef] [Medline]
  81. Reseland JE, Reppe S, Olstad OK, Hjorth-Hansen H, Brenne AT, Syversen U, et al. Abnormal adipokine levels and leptin-induced changes in gene expression profiles in multiple myeloma. Eur J Haematol. 2009;83(5):460-470. [CrossRef] [Medline]
  82. Brocke-Heidrich K, Kretzschmar AK, Pfeifer G, Henze C, Löffler D, Koczan D, et al. Interleukin-6-dependent gene expression profiles in multiple myeloma INA-6 cells reveal a Bcl-2 family-independent survival pathway closely associated with Stat3 activation. Blood. 2004;103(1):242-251. [FREE Full text] [CrossRef] [Medline]
  83. Colotta F, Allavena P, Sica A, Garlanda C, Mantovani A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis. 2009;30(7):1073-1081. [FREE Full text] [CrossRef] [Medline]
  84. Hutchison AT, Heilbronn LK. Metabolic impacts of altering meal frequency and timing—does when we eat matter? Biochimie. 2016;124:187-197. [FREE Full text] [CrossRef] [Medline]
  85. Mattson MP, Allison DB, Fontana L, Harvie M, Longo VD, Malaisse WJ, et al. Meal frequency and timing in health and disease. Proc Natl Acad Sci U S A. 2014;111(47):16647-16653. [FREE Full text] [CrossRef] [Medline]
  86. Zhou L, Zhang Z, Nice E, Huang C, Zhang W, Tang Y. Circadian rhythms and cancers: the intrinsic links and therapeutic potentials. J Hematol Oncol. 2022;15(1):21. [FREE Full text] [CrossRef] [Medline]
  87. Mayeuf-Louchart A, Zecchin M, Staels B, Duez H. Circadian control of metabolism and pathological consequences of clock perturbations. Biochimie. 2017;143:42-50. [FREE Full text] [CrossRef] [Medline]
  88. Chaix A, Zarrinpar A, Miu P, Panda S. Time-restricted feeding is a preventative and therapeutic intervention against diverse nutritional challenges. Cell Metab. 2014;20(6):991-1005. [FREE Full text] [CrossRef] [Medline]
  89. Das M, Ellies LG, Kumar D, Sauceda C, Oberg A, Gross E, et al. Time-restricted feeding normalizes hyperinsulinemia to inhibit breast cancer in obese postmenopausal mouse models. Nat Commun. 2021;12(1):565. [FREE Full text] [CrossRef] [Medline]
  90. Yan L, Sundaram S, Mehus AA, Picklo MJ. Time-restricted feeding attenuates high-fat diet-enhanced spontaneous metastasis of lewis lung carcinoma in Mice. Anticancer Res. 2019;39(4):1739-1748. [FREE Full text] [CrossRef] [Medline]
  91. Sundaram S, Yan L. Time-restricted feeding mitigates high-fat diet-enhanced mammary tumorigenesis in MMTV-PyMT mice. Nutr Res. 2018;59:72-79. [FREE Full text] [CrossRef] [Medline]
  92. Jean-Baptiste M, Gries KS, Lenderking WR, Fastenau J. Symptom burden and health-related quality of life impacts of smoldering multiple myeloma: the patient perspective. J Patient Rep Outcomes. 2020;4(1):95. [FREE Full text] [CrossRef] [Medline]
  93. Lomas OC, Mouhieddine TH, Tahri S, Ghobrial IM. Monoclonal gammopathy of undetermined significance (MGUS)—not so asymptomatic after all. Cancers (Basel). 2020;12(6):1554. [FREE Full text] [CrossRef] [Medline]
  94. Kerndt PR, Naughton JL, Driscoll CE, Loxterkamp DA. Fasting: the history, pathophysiology and complications. West J Med. 1982;137(5):379-399. [FREE Full text] [Medline]
  95. 2023 Food and Health Survey. International Food Information Council. 2023. URL: https://foodinsight.org/2023-food-and-health-survey/ [accessed 2023-12-15]


CTCAE: National Cancer Institute Common Terminology Criteria for Adverse Events
DXA: dual-energy x-ray absorptiometry
HR: hazard ratio
MGUS: monoclonal gammopathy of undetermined significance
MM: multiple myeloma
PROFAST: PROlonged nightly FASTing
PROMIS: Patient-Reported Outcomes Measurement Information System
SFLC: serum-free light chain
SMM: smoldering multiple myeloma
SPEP/IFX: serum protein electrophoresis supplemented by immunofixation
SWM: smoldering Waldenström macroglobulinemia


Edited by A Mavragani; submitted 31.07.23; peer-reviewed by W Demark-Wahnefried, S Chakraborty, S Chaudhry; comments to author 13.10.23; revised version received 17.11.23; accepted 23.11.23; published 11.03.24.

Copyright

©David J Lee, Elizabeth K O'Donnell, Noopur Raje, Cristina Panaroni, Robert Redd, Jennifer Ligibel, Dorothy D Sears, Omar Nadeem, Irene M Ghobrial, Catherine R Marinac. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 11.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.