Discussion

Effect of Low-Fat vs Low-Carbohydrate Diet on 12-Month Weight Loss in Overweight Adults and the Association With Genotype Pattern or Insulin Secretion The DIETFITS Randomized Clinical Trial Christopher D. Gardner, PhD; John F. Trepanowski, PhD; Liana C. Del Gobbo, PhD; Michelle E. Hauser, MD; Joseph Rigdon, PhD; John P. A. Ioannidis, MD, DSc; Manisha Desai, PhD; Abby C. King, PhD

IMPORTANCE Dietary modification remains key to successful weight loss. Yet, no one dietary strategy is consistently superior to others for the general population. Previous research suggests genotype or insulin-glucose dynamics may modify the effects of diets.

OBJECTIVE To determine the effect of a healthy low-fat (HLF) diet vs a healthy low-carbohydrate (HLC) diet on weight change and if genotype pattern or insulin secretion are related to the dietary effects on weight loss.

DESIGN, SETTING, AND PARTICIPANTS The Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized clinical trial included 609 adults aged 18 to 50 years without diabetes with a body mass index between 28 and 40. The trial enrollment was from January 29, 2013, through April 14, 2015; the date of final follow-up was May 16, 2016. Participants were randomized to the 12-month HLF or HLC diet. The study also tested whether 3 single-nucleotide polymorphism multilocus genotype responsiveness patterns or insulin secretion (INS-30; blood concentration of insulin 30 minutes after a glucose challenge) were associated with weight loss.

INTERVENTIONS Health educators delivered the behavior modification intervention to HLF (n = 305) and HLC (n = 304) participants via 22 diet-specific small group sessions administered over 12 months. The sessions focused on ways to achieve the lowest fat or carbohydrate intake that could be maintained long-term and emphasized diet quality.

MAIN OUTCOMES AND MEASURES Primary outcome was 12-month weight change and determination of whether there were significant interactions among diet type and genotype pattern, diet and insulin secretion, and diet and weight loss.

RESULTS Among 609 participants randomized (mean age, 40 [SD, 7] years; 57% women; mean body mass index, 33 [SD, 3]; 244 [40%] had a low-fat genotype; 180 [30%] had a low-carbohydrate genotype; mean baseline INS-30, 93 μIU/mL), 481 (79%) completed the trial. In the HLF vs HLC diets, respectively, the mean 12-month macronutrient distributions were 48% vs 30% for carbohydrates, 29% vs 45% for fat, and 21% vs 23% for protein. Weight change at 12 months was −5.3 kg for the HLF diet vs −6.0 kg for the HLC diet (mean between-group difference, 0.7 kg [95% CI, −0.2 to 1.6 kg]). There was no significant diet-genotype pattern interaction (P = .20) or diet-insulin secretion (INS-30) interaction (P = .47) with 12-month weight loss. There were 18 adverse events or serious adverse events that were evenly distributed across the 2 diet groups.

CONCLUSIONS AND RELEVANCE In this 12-month weight loss diet study, there was no significant difference in weight change between a healthy low-fat diet vs a healthy low-carbohydrate diet, and neither genotype pattern nor baseline insulin secretion was associated with the dietary effects on weight loss. In the context of these 2 common weight loss diet approaches, neither of the 2 hypothesized predisposing factors was helpful in identifying which diet was better for whom.

TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01826591 JAMA. 2018;319(7):667-679. doi:10.1001/jama.2018.0245 Last corrected on April 24, 2018.

Supplemental content

CME Quiz at jamanetwork.com/learning and CME Questions page 715

Author Affiliations: Stanford Prevention Research Center, Department of Medicine, Stanford University Medical School, Stanford, California (Gardner, Trepanowski, Del Gobbo, Hauser, Ioannidis, King); Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California (Rigdon, Desai); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California (Ioannidis, Desai, King); Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California (Ioannidis, Desai); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California (Ioannidis, Desai).

Corresponding Author: Christopher D. Gardner, PhD, Stanford Prevention Research Center, Department of Medicine, Stanford University Medical School, 1265 Welch Rd, Stanford, CA 94305 ([email protected]).

Research

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O besity is a 21st-century major public health challenge.1,2Among many strategies studied for weight loss,a common contrast has been low-fat diets vs low- carbohydrate diets.3-5 Most diet trials have reported modest (ie, <5%) mean weight loss after 12 months and negligible mean weight loss differences between diet groups.6 In contrast, in- dividual weight losses have varied widely within diet groups in these studies, ranging from approximately 25 kg lost to ap- proximately 5 kg gained.3-5

The substantial variability of weight loss response sug- gests some strategies may work better for some individuals than others, and that no one diet should be recommended universally.7 Yet, interindividual differences in response to diet are poorly understood. Some studies have reported that geno- type variation could predispose individuals to differential weight loss that varies by diet type.8,9

In a preliminary retrospective study, a 3-fold differ- ence was observed in 12-month weight loss for initially over- weight women who were determined to have been appropri- ately matched (mean weight loss of 6 kg) vs mismatched (mean weight loss of 2 kg) to a low-fat or low-carbohydrate diet based on multilocus genotype patterns with single- nucleotide polymorphisms (SNPs) from 3 genes (PPARG, ADRB2, and FABP2) relevant to fat and carbohydrate metabo- lism (a putative low-fat–responsive genotype and a low- carbohydrate–responsive genotype). The participants with the low-fat–responsive genotype were observed to lose more weight when assigned to a low-fat diet than those assigned to a low-carbohydrate diet, and vice versa for those with the low-carbohydrate–responsive genotype.9,10

Similarly, several studies11-14 have reported that baseline insulin dynamics may explain differential weight loss suc- cess obtained via a low-fat diet vs a low-carbohydrate diet. For example, individuals with greater insulin resistance may have better success with low-carbohydrate diets due to the de- creased demand on insulin to clear a lower amount of dietary carbohydrate delivered to the circulation. However, these stud- ies were limited by relatively small sample sizes or post hoc analyses of the results.

The primary objective of the Diet Intervention Examin- ing The Factors Interacting with Treatment Success (DIETFITS) study was to test whether (1) a set of 3 SNP geno- type patterns or (2) baseline differences in insulin secretion (the blood insulin concentration at 30 minutes after a glu- cose challenge; INS-30),12,13 or both, predisposed individu- als to differential success in 12-month weight change while on a low-fat diet vs a low-carbohydrate diet.

Methods The Stanford University human subjects committee ap- proved the study. All study participants provided written in- formed consent.

Study Design This single-site, parallel-group, weight loss diet trial ran- domized individuals to a healthy low-fat diet or a healthy

low-carbohydrate diet for 12 months. Participant enrollment began on January 29, 2013, and continued through April 14, 2015. The date of final follow-up was May 16, 2016. Inter- ventions consisted primarily of class-based instruction. Five waves of recruitment (cohorts) had staggered start dates between March 2013 and March 2015. The primary outcome was 12-month weight change.

The first primary hypothesis was that there is a signifi- cant diet × genotype pattern interaction for weight loss. The second primary hypothesis was that there is a signifi- cant diet × insulin secretion interaction for weight loss. Sec- ondary outcomes included anthropometric measures, plasma lipid levels, insulin and glucose levels, and blood pressure levels. The protocol update and statistical analysis plan are included in Supplement 1 and the full study proto- col was published previously10 (the protocol included details regarding blood sampling, storage, and specific labo- ratory assays).

Participants We aimed to recruit 600 adults from the Stanford and San Francisco Bay areas of California using media advertise- ments and email lists from previous recruitment for nutri- tion studies conducted by our laboratory group. We consid- ered men and premenopausal women aged 18 to 50 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) of 28 to 40.

The major criteria for exclusion were having uncon- trolled hypertension or metabolic disease; diabetes; cancer; heart, renal, or liver disease; and being pregnant or lactating. Individuals were excluded if taking hypoglycemic, lipid- lowering, antihypertensive, psychiatric, or other medica- tions known to affect body weight or energy expenditure. Any medication type not noted was allowed if the individual had been stable while taking such medication for at least 3 months prior to baseline data collection.

Randomization to a healthy low-fat diet or a healthy low-carbohydrate diet was performed using an allocation sequence determined by computerized random-number

Key Points Question What is the effect of a healthy low-fat (HLF) diet vs a healthy low-carbohydrate (HLC) diet on weight change at 12 months and are these effects related to genotype pattern or insulin secretion?

Findings In this randomized clinical trial among 609 overweight adults, weight change over 12 months was not significantly different for participants in the HLF diet group (−5.3 kg) vs the HLC diet group (−6.0 kg), and there was no significant diet-genotype interaction or diet-insulin interaction with 12-month weight loss.

Meaning There was no significant difference in 12-month weight loss between the HLF and HLC diets, and neither genotype pattern nor baseline insulin secretion was associated with the dietary effects on weight loss.

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generation (Blockrand in R version 3.4.0; R Project for Sta- tistical Computing) in block sizes of 8 (with 4 individuals going to each diet) by a statistician not involved in interven- tion delivery or data collection. Participants did not learn of their diet group assignment until they completed all base- line measures and attended their first intervention class (Figure 1).

The original study design was a 2 × 2 factorial design (diet × genotype-pattern matching). However, near the onset of the study, the initial funding was more than doubled, allowing for a 50% increase in sample size, the addition of a second primary hypothesis for the assessment of a diet × insulin secretion interaction, and an expanded set of measurements. To test for both primary hypotheses, the study was changed to a simple parallel group design with testing for 2 interactions (described in further detail in eAppendix 1 in Supplement 2).

Weight Loss Intervention The protocol included a 1-month run-in period during which participants were instructed to maintain their habitual diet, physical activity level, and body weight. The intervention involved 22 instructional sessions held over 12 months in diet-specific groups of approximately 17 participants per class. Sessions were held weekly for 8 weeks, then every 2 weeks for 2 months, then every 3 weeks until the sixth month, and monthly thereafter. Classes were led by 5 regis- tered dietitian health educators who each taught 1 healthy low-fat class and 1 healthy low-carbohydrate class per cohort. Dietitians were blinded to all laboratory measures and genotype.

The dietary interventions were described previously.10

Briefly, the main goals were to achieve maximal differentia- tion in intake of fats and carbohydrates between the 2 diet groups while otherwise maintaining equal treatment

Figure 1. Flow of Participants Through the Diet Intervention Examining The Factors Interacting with Treatment Success Trial

1057 Individuals screened for eligibility

803 Attended study orientation and informed of study details

254 Excluded 69 Did not meet eligibility criteria

137 No longer interested 29 Discontinued communication 19 Other

60 Body mass index >40 or <28a 9 Blood glucose level >125 mg/dLb

171 Excluded 142 Not interested

29 Other

632 Randomized

241 Completed study 238 Completed study

305 Included in primary analysis 9 Excluded (withdrew prior to

receiving diet assignment)

304 Included in primary analysis 14 Excluded (withdrew prior to

receiving diet assignment)

314 Randomized to receive a healthy low-fat diet

318 Randomized to receive a healthy low-carbohydrate diet

305 Informed of diet assignment 304 Informed of diet assignment

24 Lost to follow-up 40 Discontinued intervention

21 Personal reasons 8 Scheduling conflict 6 Health issues unrelated to study 5 Unhappy with diet

29 Lost to follow-up 37 Discontinued intervention

13 Personal reasons 12 Scheduling conflict 11 Health issues unrelated to study

1 Unhappy with diet

9 Withdrew prior to receiving diet assignment 5 Scheduling conflict 4 Other reasons

14 Withdrew prior to receiving diet assignment 9 Scheduling conflict 5 Other reasons

a Body mass index is calculated as weight in kilograms divided by height in meters squared.

b To convert glucose to mmol/L, multiply by 0.0555.

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intensity and an emphasis on high-quality foods and bever- ages. Thus, participants were instructed to reduce intake of total fat or digestible carbohydrates to 20 g/d during the first 8 weeks. Higher priorities for reduction were given to specific foods and food groups that derived their energy content pri- marily from fats or carbohydrates. For example, the reduc- tion of edible oils, fatty meats, whole-fat dairy, and nuts was prioritized for the healthy low-fat group, whereas the reduc- tion of cereals, grains, rice, starchy vegetables, and legumes was prioritized for the healthy low-carbohydrate group.

Then individuals slowly added fats or carbohydrates back to their diets in increments of 5 to 15 g/d per week until they reached the lowest level of intake they believed could be maintained indefinitely. No explicit instructions for energy (kilocalories) restriction were given. Both diet groups were instructed to (1) maximize vegetable intake; (2) minimize intake of added sugars, refined flours, and trans fats; and (3) focus on whole foods that were minimally processed, nutrient dense, and prepared at home whenever possible. Other components of the emphasis on high-quality food for both diet groups are described elsewhere.10

Participants were encouraged to follow current physical activity recommendations.15 Health educators emphasized emotional awareness and behavior modification to support dietary adherence and weight loss. Behavioral modification strategies included empirically supported principles of self- regulatory behavior change (eg, goal setting, self-efficacy building, supportive environments, and relapse prevention) based on social cognitive theory and the transtheoretical model.10,16-18

Outcome Measurements All data were collected at baseline and at months 3, 6, and 12 for all cohorts unless noted otherwise. Staff who measured out- comes were blinded to diet assignment, genotype pattern, INS-30, and diet assignment. Dietary intake at each time point was assessed using 3 unannounced 24-hour multiple-pass re- call interviews (2 on weekdays and 1 on a weekend day).19

Total energy expenditure was assessed using the Stanford Seven-Day Physical Activity Recall questionnaire.20

Both the dietary intake and physical activity recall were self- reported measures. Weight was measured by digital scale at the Stanford Clinical Translational Research Unit and 12-month weight change was the primary outcome.

Genotype pattern and insulin secretion were assessed for interaction testing. The Affymetrix UK Biobank Axiom micro- array was used for analysis of 820 967 SNPs and insertions or deletions. The array included 2 of the SNPs from the original study design: PPARG (rs1801282) and ADRB2 (rs1042714). FABP2 (rs1799883) was imputed with an imputation quality score (r2 = 0.99). Additional details appear in eAppendix 2 in Supplement 2. The 3 SNP multilocus genotype patterns have been explored previously.9

Of 27 possible 3-locus genotypes that could arise from the combination of the 3 SNPs, 15 were observed with 1% or greater genotype frequency in previously studied samples of adults. The multilocus genotypes were grouped into those predicted to be more sensitive to fat (low-fat geno-

type; patterns 1-5), more sensitive to carbohydrates (low- carbohydrate genotype; patterns 6-14), or sensitive to nei- ther genotype (pattern 15). Additional details are available in eAppendix 3 in Supplement 2.

Before randomization and at months 6 and 12, each par- ticipant completed an oral glucose tolerance test of 75 g. This included measurement of insulin concentration 30 minutes after glucose consumption (ie, INS-30, which is a proxy mea- sure of insulin secretion).10,21,22 When this study was first designed, insulin sensitivity was to be measured and used as a predictor of differential weight loss success. After the study was initiated, reports were published12,13,23,24 indicating INS-30 was a successful predictor of weight loss in the con- text of low-carbohydrate diets or similar diets. There also was evidence25 that early-phase insulin secretion differed mark- edly between diets that were similar to those tested in the DIETFITS study. Prior to examining any data, we modified the primary hypothesis of our study and tested baseline INS-30 rather than a measure of insulin sensitivity as the putative effect modifier. No other glucose or insulin variables were tested for effect modification.

A set of related secondary outcomes was assessed. Con- centrations of plasma lipids, glucose, and insulin were mea- sured in fasting samples, waist circumference was assessed by measuring tape, blood pressure was measured via auto- mated device, and all of these were assessed using standard assessment techniques.10

Body composition was assessed by dual-energy x-ray absorptiometry and both respiratory exchange ratio (bounded by 0.7 [using solely fat for fuel] and 1.0 [using solely glucose for fuel]) and resting energy expenditure were assessed by metabolic cart (ie, measures respiratory exchange of oxygen and carbon dioxide while a participant is supine and resting) at baseline and at months 6 and 12 in cohorts 2 through 5. Adequate funding became available for dual-energy x-ray absorptiometry, respiratory exchange ratio, and resting energy expenditure only after cohort 1 was enrolled. The metabolic syndrome was determined using Adult Treatment Panel III guidelines from the National Cholesterol Education Program.26

Statistical Analysis Based on the original study design, assuming 100 partici- pants in each of the 4 relevant groups (genotype and dietary assignment), and normally distributed values of weight change at 12 months, there was 80% power to detect clini- cally meaningful differences in treatment effect by genotype (eg, whether dietary assignment had an effect on weight change at 12 months except for those assigned to the low- carbohydrate diet who have the low-carbohydrate genotype because such individuals lose 3.2 kg on average). This calcu- lation was based on simulations, and assumed a 2-sided Wald test conducted at the .05 level of significance.

Under similar assumptions regarding the statistical testing and type I error, and assuming a sample size of only 400 participants (200 in each treatment group), there was greater than 80% power to detect differences in the treat- ment effect with insulin secretion, including for example,

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if for every 1-unit increase in insulin secretion, weight loss at 12 months increases by 0.8 lb (0.36 kg). These power calculations were performed a priori for the originally plan- ned sample size of 400. As described in greater detail in eAppendix 1 in Supplement 2, after initially being funded by the National Institutes of Health and the National Institute of Diabetes and Digestive and Kidney Diseases in 2012, additional funding was received to augment the trial, which involved, among other modifications, increasing the sample size from 400 to 600, and adding INS-30 as a second vari- able for interaction testing. With the larger sample size, the study had even greater statistical power, estimated at 90% based on post hoc calculations.

The main hypotheses addressed 12-month weight change by diet, diet and genotype, and diet and baseline INS-30. All hypotheses were addressed using generalized, linear mixed- effects models.27,28 We applied modified intent-to-treat prin- ciples. This means that all participants who were randomized and had baseline information were included in the analysis and analyzed according to original treatment assignment, regardless of adherence or loss to follow-up (Figure 1). For the hypothesis about the effect of diet group on 12-month weight change, a linear mixed-effects model for weight that accounted for missing data under flexible assumptions regarding missingness was used with fixed effects for diet, time (baseline, 3, 6, and 12 months), and their interaction, along with a random effect for participant. For the hypoth- eses involving diet and genotype (or diet and baseline INS-30), an additional fixed effect was added for genotype (or baseline INS-30), along with all 2- and 3-way interactions (model appears in eAppendix 4 in Supplement 2).

The validity of such an analysis relies on the assumption that the missing outcome data measured at follow-up are un- related to unobserved values of weight conditional on ob- served variables such as treatment assignment and baseline and intermittent values of weight. The hypothesis about diet was tested using a Wald test for the 2-way interaction be- tween the 12-month time point and diet. The hypothesis about genotype (or baseline INS-30) was tested using a Wald test for the 3-way interaction between the 12-month time point, diet, and genotype (or baseline INS-30). Genotype was defined as matched for those participants with a 3-SNP combination sug- gesting success on a low-carbohydrate diet who were random- ized to the low-carbohydrate diet, or for those participants with a 3-SNP combination suggesting success on a low-fat diet who were randomized to the low-fat diet. Genotype was other- wise defined as mismatched and is described in eAppendices 2 and 3 in Supplement 2.

There were 185 individuals who were not classified as hav- ing either the low-fat genotype pattern or a low-carbohydrate genotype pattern (146 individuals with other 3-SNP patterns and 39 with missing or compromised genotyping data) who were excluded from the genotype analysis for the first hypothesis as originally planned.10 An additional diet- genotype analysis was performed, restricting the study popu- lation to whites only and focusing on only 1 ancestry group as originally planned.10 The second hypothesis was tested using a Wald test for the interaction among diet, 12-month

time point, and baseline INS-30. The INS-30 variable was analyzed as a continuous variable, but is presented as tertiles for ease of presentation in parallel to the presentation of genotype pattern data. The cutoffs for the tertiles were de- termined using the baseline insulin concentrations of all 609 participants.

A Satterthwaite approximation for denominator degrees of freedom was used in all Wald tests.29 All tests were 2-sided and conducted at the .05 level of significance. Formal hypoth- esis testing was performed only for the 2 primary hypoth- eses. All other P values that were generated were purely de- scriptive in nature and correspond to secondary and exploratory analyses. Statistical analyses were performed using R version 3.4.0 (R Project for Statistical Computing). Specifi- cally, the lme430 package was used for mixed-effects models and the lmerTest29 package was used for hypothesis tests in the mixed-effects models.

Results Among 609 participants randomized (mean age, 40 [SD, 7] years; 57% women; mean body mass index, 33 [SD, 3]; 244 [40%] had a low-fat genotype; 180 [30%] had a low- carbohydrate genotype; mean baseline INS-30, 93 μIU/mL), 481 (79%) completed the trial. The flow of the participants through the trial appears in Figure 1. Baseline characteristics by diet group appear in Table 1. Among participants in the healthy low-fat diet group, 130 (42.6%) had the low-fat geno- type and 83 (27.2%) had the low-carbohydrate genotype, whereas in the healthy low-carbohydrate group, 114 (37.5%) had the low-fat genotype and 97 (31.9%) had the low- carbohydrate genotype.

Of 22 assigned intervention instruction sessions for the full study sample, the mean number of sessions attended was 14.4 (SD, 5.3) for the healthy low-fat diet group and 14.6 (SD, 5.1) for the healthy low-carbohydrate diet group, which includes dropouts. Retention at 12 months, which was defined as par- ticipants who provided any data at 12 months, was 79% for both groups. Participant ratings for health educator enthusiasm and knowledge of material was high and similar between diet groups. The mean ratings were 4.6 to 5.0 on a scale of 1 to 5, with 5 as the highest rating.

Total energy intake was not different between diet groups at baseline or at any subsequent time point (P ≥ .10 for all; Table 2). Despite not being instructed to follow a specific energy (kilocalorie) intake restriction, the mean reported energy intake reduction relative to baseline was approxi- mately 500 to 600 kcal/d for both groups at each time point after randomization.

At baseline, there were no significant between-group differences for any nutrients examined. In contrast, there were significant between-group differences after random- ization at every time point (all P ≤ .001) for percentage of energy; intakes of carbohydrates, fat, protein, saturated fat, fiber, and added sugars; and glycemic index and glycemic load (Table 2). In the healthy low-fat diet vs the healthy low-carbohydrate diet, respectively, the mean 12-month

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Table 1. Baseline Demographics and Anthropometric and Metabolic Variables

Healthy Low-Fat Diet (n = 305)

Healthy Low-Carbohydrate Diet (n = 304)

Sex, No. (%)

Women 167 (54.8) 179 (58.9)

Men 138 (45.2) 125 (41.1)

Age, mean (SD), y 39.3 (6.8) 40.2 (6.7)

Highest level of education achieved, No. (%)a

<High school degree 2 (0.6) 2 (0.6)

High school degree 5 (1.6) 11 (3.6)

Some college 63 (20.7) 67 (22.0)

College degree 102 (33.4) 106 (34.9)

Some postgraduate school 25 (8.2) 12 (3.9)

Postgraduate degree 107 (35.1) 103 (33.9)

Race/ethnicity, No. (%)b

White 176 (57.7) 182 (59.9)

Hispanic 67 (22.0) 61 (20.1)

Asian 30 (9.8) 30 (9.9)

African American 10 (3.3) 13 (4.3)

American Indian, Alaskan Native, or Pacific Islander 3 (1.0) 0

Other 19 (6.2) 18 (5.9)

Weight, mean (SD), kg

Women 90.7 (11.5) 88.9 (12.5)

Men 105.7 (13.9) 106.8 (13.7)

Both sexes 97.5 (14.7) 96.3 (15.7)

Body mass index, mean (SD)c

Women 33.3 (3.4) 32.9 (3.4)

Men 33.5 (3.4) 33.8 (3.4)

Both sexes 33.4 (3.4) 33.3 (3.4)

Body fat %, mean (SD)d

Women 41.0 (3.9) 40.4 (4.0)

Men 29.9 (4.5) 30.3 (4.7)

Both sexes 36.3 (6.9) 36.5 (6.6)

Waist circumference, mean (SD), cme

Women 103.5 (10.4) 102.6 (10.5)

Men 111.8 (9.7) 112.7 (9.9)

Both sexes 107.2 (10.9) 106.7 (11.4)

Blood lipid level, mean (SD), mmol/L

High-density lipoprotein cholesterol 1.28 (0.23) 1.29 (0.24)

Low-density lipoprotein cholesterolf 2.89 (0.79) 2.94 (0.68)

Triglycerides 1.45 (0.80) 1.45 (1.03)

Blood pressure, mean (SD), mm Hgg

Systolic 122.9 (12.5) 122.9 (12.4)

Diastolic 81.0 (7.3) 81.2 (7.8)

Fasting glucose, mean (SD), mg/dL 98.6 (8.6) 98.5 (9.7)

Fasting insulin, mean (SD), μIU/mL 15.9 (13.5) 15.5 (8.0)

Insulin-30, mean (SD), μIU/mLh 95.1 (67.5) 91.8 (61.7)

Metabolic syndrome, No. (%)i 106 (34.8) 100 (32.9)

Respiratory exchange ratio, mean (SD)j,k 0.861 (0.065) 0.862 (0.058)

Resting energy expenditure, mean (SD), kcalj,k 1651 (283) 1629 (293)

Energy expenditure, mean (SD), kcal/kg/dj,l 32.6 (1.7) 32.5 (2.2)

Genotype, No. (%)m

Low fat 130 (42.6) 114 (37.5)

Low carbohydrate 83 (27.2) 97 (31.9)

Neither 70 (23.0) 76 (25.0)

SI conversion factors: To convert glucose to mmol/L, multiply by 0.0555; high-density and low-density lipoprotein cholesterol to mg/dL, divide by 0.0259; insulin to pmol/L, multiply by 6.945; triglycerides to mg/dL, divide by 0.0113. a There were missing data for 1

participant in the low-fat group and 3 participants in the low-carbohydrate group.

b Determined by self-report using fixed categories.

c Calculated as weight in kilograms divided by height in meters squared.

d Available only for cohorts 2 through 5 because additional funding became available for use of dual-energy x-ray absorptiometry and technician time. There were missing data for 77 participants in the low-fat group and 66 participants in the low-carbohydrate group. In addition, 37 participants in the low-fat group and 28 participants in the low-carbohydrate group declined being measured.

e There were missing data for 3 participants in the low-fat group and 2 participants in the low-carbohydrate group.

f Thereweremissingdatafor1participant in the low-carbohydrate group.

g There were missing data for 2 participants in the low-fat group and 1 participant in the low-carbohydrate group.

h Indicates the blood concentration of insulin at the 30-minute time point of an oral glucose tolerance test. There were missing data for 3 participants in the low-fat group.

i Defined by Adult Treatment Panel III guidelines from the National Cholesterol Education Program.26

j Available only for cohorts 2 through 5 because additional funding became available for metabolic cart and technician time.

k There were missing data for 41 participants in the low-fat group and 40 participants in the low-carbohydrate group.

l There were missing data for 23 participants in the low-fat group and 33 participants in the low-carbohydrate group.

mThere were missing data for 22 participants in the low-fat group and 17 participants in the low-carbohydrate group.

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Table 2. Dietary Intake by Time Point

Healthy Low-Fat Diet Healthy Low-Carbohydrate Diet

Mean Between-Group Difference (95% CI)a No. of Participants Mean (SD)

No. of Participants Mean (SD)

Total Energy Intake, kcal

Baseline 304 2148.1 (39.4) 304 2222.8 (37.5) −76.3 (−166.1 to 13.4)

3 mo 274 1515.0 (27.7) 275 1580.8 (29.1) −56.9 (−150.2 to 36.4)

6 mo 240 1624.4 (37.3) 251 1621.3 (33.2) 0.2 (−96.9 to 97.3)

12 mo 225 1716.1 (34.5) 224 1697.1 (32.1) 2.9 (−97.2 to 103.0)

Carbohydrates, g

Baseline 304 241.8 (5.0) 304 246.5 (4.5) −4.9 (−16.6 to 6.9)

3 mo 274 205.2 (4.3) 275 96.6 (3.4) 109.0 (96.8 to 121.2)

6 mo 240 211.2 (5.3) 251 113.2 (4.1) 95.6 (83.0 to 108.3)

12 mo 225 212.9 (5.0) 224 132.4 (4.2) 74.2 (61.2 to 87.2)

Carbohydrates, % kcal

Baseline 304 44.5 (0.5) 304 44.0 (0.4) 0.5 (−1.1 to 2.1)

3 mo 274 52.6 (0.6) 275 23.1 (0.7) 29.4 (27.8 to 31.0)

6 mo 240 50.8 (0.7) 251 26.5 (0.7) 24.1 (22.4 to 25.8)

12 mo 225 48.4 (0.7) 224 29.8 (0.7) 17.8 (16.0 to 19.5)

Fat, g

Baseline 304 87.0 (2.0) 304 92.6 (1.9) −5.6 (−10.4 to −0.8)

3 mo 274 42.0 (1.2) 275 88.8 (2.0) −46.2 (−51.2 to −41.2)

6 mo 240 50.3 (1.8) 251 86.6 (2.0) −36.0 (−41.2 to −30.8)

12 mo 225 57.3 (1.7) 224 86.2 (2.0) −28.4 (−33.8 to −23.0)

Fat, % kcal

Baseline 304 34.8 (0.4) 304 36.0 (0.3) −1.2 (−2.4 to 0.1)

3 mo 274 24.0 (0.5) 275 49.0 (0.5) −24.9 (−26.2 to −23.6)

6 mo 240 26.4 (0.6) 251 46.8 (0.6) −20.3 (−21.7 to −18.9)

12 mo 225 28.7 (0.5) 224 44.6 (0.6) −15.4 (−16.8 to −14.0)

Protein, g

Baseline 304 92.1 (1.7) 304 93.1 (1.6) −1.1 (−5.8 to 3.6)

3 mo 274 79.5 (1.6) 275 96.9 (2.0) −17.1 (−22.0 to −12.2)

6 mo 240 81.9 (1.9) 251 93.8 (1.9) −11.6 (−16.6 to −6.5)

12 mo 225 84.5 (1.8) 224 93.3 (2.0) −8.5 (−13.8 to −3.3)

Protein, % kcal

Baseline 304 17.9 (0.3) 304 17.3 (0.2) 0.6 (−0.4 to 1.5)

3 mo 274 21.5 (0.4) 275 25.9 (0.4) −4.4 (−5.4 to −3.5)

6 mo 240 20.8 (0.4) 251 24.3 (0.4) −3.5 (−4.5 to −2.5)

12 mo 225 20.6 (0.4) 224 22.9 (0.4) −2.1 (−3.2 to −1.1)

Saturated Fat, g

Baseline 304 28.9 (0.7) 304 30.8 (0.7) −1.9 (−3.7 to −0.1)

3 mo 274 12.7 (0.4) 275 29.1 (0.7) −16.2 (−18.1 to −14.3)

6 mo 240 15.3 (0.7) 251 27.9 (0.7) −12.4 (−14.4 to −10.5)

12 mo 225 18.2 (0.6) 224 28.2 (0.8) −9.8 (−11.8 to −7.8)

Saturated Fat, % kcal

Baseline 304 11.5 (0.2) 304 11.9 (0.2) −0.4 (−0.9 to 0.2)

3 mo 274 7.2 (0.2) 275 16.1 (0.3) −8.8 (−9.4 to −8.2)

6 mo 240 8.0 (0.2) 251 15.0 (0.3) −7.0 (−7.6 to −6.4)

12 mo 225 9.0 (0.2) 224 14.5 (0.3) −5.3 (−5.9 to −4.7)

Fiber, g

Baseline 304 22.0 (0.6) 304 21.6 (0.5) 0.4 (−1.2 to 1.9)

3 mo 274 24.2 (0.7) 275 16.7 (0.8) 7.5 (5.9 to 9.1)

6 mo 240 23.7 (0.7) 251 17.2 (0.5) 6.5 (4.8 to 8.2)

12 mo 225 23.0 (0.6) 224 18.6 (0.5) 4.1 (2.3 to 5.8)

(continued)

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macronutrient distributions were 48% vs 30% for carbohy- drates, 29% vs 45% for fat, and 21% vs 23% for protein.

Primary Outcome The mean 12-month weight change was −5.3 kg (95% CI, −5.9 kg to −4.7 kg) for the healthy low-fat diet group and −6.0 kg (95% CI, −6.6 kg to −5.4 kg) for the healthy low- carbohydrate diet group, which was not statistically different (Table 3). There was a similar range for weight change of approximately 40 kg within each group (−30 kg to 10 kg; eFigure 1 in Supplement 2).

Interaction Testing The test for the interaction among diet, genotype pattern, and the 12-month time point was not statistically sig- nificant. The interpretation of the beta coefficient for the 3-way interaction (beta coefficient, 1.38 [95% CI, −0.72 to 3.49], P = .20) is that 12-month weight change increases (estimated as 1.38 kg) when switching from a healthy low- carbohydrate diet and a low-carbohydrate genotype to a healthy low-fat diet and low-fat genotype beyond the main effects of switching from a healthy low-carbohydrate diet to a healthy low-fat diet and from a low-carbohydrate geno-

type to a low-fat genotype (Figure 2A). This indicates that there was no significant difference in weight change among participants matched vs mismatched to their diet assign- ment based on their 3-SNP genotype pattern. In analyses restricted to participants of European descent only, no sig- nificant interaction was observed by genotype pattern (the 3-way interaction for the main diet, genotype, and time yielded a beta coefficient of 2.58 [95% CI, −0.18 to 5.34]; P = .07).

Similarly, the test for interaction among diet, baseline insulin secretion (INS-30), and the 12-month time point was not statistically significant. The interpretation of the beta coefficient for the 3-way interaction (beta coefficient, 0.08 [95% CI, −0.13 to 0.28], P = .47) is that 12-month weight change increases (estimated as 0.08 kg) when switching from a healthy low-carbohydrate diet and x units of baseline INS-30 to a healthy low-fat diet and x + 10 units of baseline INS-30 beyond the effects of changing from a healthy low- carbohydrate diet to a healthy low-fat diet and increasing baseline INS-30 by 10 μIU/mL (Figure 2B). Weight change trajectories for the diet-genotype pattern subgroups are pre- sented in eFigure 2A and for diet and INS-30 tertile sub- groups in eFigure 2B in Supplement 2.

Table 2. Dietary Intake by Time Point (continued)

Healthy Low-Fat Diet Healthy Low-Carbohydrate Diet

Mean Between-Group Difference (95% CI)a No. of Participants Mean (SD)

No. of Participants Mean (SD)

Fiber, g/1000 kcal

Baseline 304 10.8 (0.2) 304 10.3 (0.2) 0.4 (−0.5 to 1.4)

3 mo 274 16.5 (0.4) 275 11.3 (0.6) 5.3 (4.3 to 6.3)

6 mo 240 15.4 (0.4) 251 11.2 (0.3) 4.2 (3.1 to 5.2)

12 mo 225 14.2 (0.4) 224 11.6 (0.3) 2.5 (1.4 to 3.5)

Added Sugars, g

Baseline 304 49.3 (2.0) 304 52.2 (2.0) −3.0 (−7.3 to 1.3)

3 mo 274 28.5 (1.3) 275 16.2 (1.2) 12.4 (8.0 to 16.9)

6 mo 240 31.5 (1.7) 251 18.9 (1.3) 12.2 (7.5 to 16.9)

12 mo 225 33.1 (1.7) 224 22.8 (1.6) 9.6 (4.7 to 14.5)

Sugar, g/1000 kcal

Baseline 304 22.4 (0.7) 304 23.2 (0.7) −0.8 (−2.7 to 1.1)

3 mo 274 18.4 (0.7) 275 9.8 (0.6) 8.6 (6.6 to 10.6)

6 mo 240 18.9 (0.8) 251 11.2 (0.7) 7.6 (5.5 to 9.7)

12 mo 225 18.7 (0.9) 224 12.9 (0.8) 5.6 (3.5 to 7.8)

Glycemic Indexb

Baseline 304 57.8 (0.3) 304 58.2 (0.3) −0.4 (−1.4 to 0.6)

3 mo 274 56.0 (0.3) 275 50.1 (0.4) 5.9 (4.9 to 7.0)

6 mo 240 56.2 (0.4) 251 51.4 (0.5) 4.7 (3.6 to 5.8)

12 mo 225 56.1 (0.4) 224 52.7 (0.5) 3.1 (2.0 to 4.3)

Glycemic Loadc

Baseline 304 128.2 (2.9) 304 132.4 (2.7) −4.3 (−11.1 to 2.6)

3 mo 274 102.1 (2.3) 275 43.1 (2.0) 59.0 (51.9 to 66.2)

6 mo 240 107.0 (3.1) 251 52.4 (2.5) 53.0 (45.6 to 60.4)

12 mo 225 108.0 (2.8) 224 62.9 (2.6) 41.5 (33.8 to 49.1) a Healthy low-fat diet minus healthy low-carbohydrate diet from linear

mixed-effects model. b Indicates ranking of foods according to the potential of 50 g of carbohydrates

from that food to raise blood glucose relative to 50 g of glucose (scale of 0-100; a score of 100 refers to the same rate as glucose).

c Indicates the actual amount of carbohydrates multiplied by the glycemic index.

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Secondary Outcomes There were improvements in the secondary outcomes for both diet groups. However, there were no significant between-group differences observed for body mass index, body fat percentage, and waist circumference (Table 3). At 12 months relative to baseline, both diets improved lipid profiles and lowered blood pressure, insulin, and glucose lev- els, with the exception of low-density lipoprotein cholesterol concentrations, which increased for participants in the healthy low-carbohydrate group (Table 3). The 12-month changes in low-density lipoprotein cholesterol concentrations signifi- cantly favored a healthy low-fat diet. High-density lipopro- tein cholesterol concentrations increased significantly more and concentrations of triglycerides decreased significantly more for the healthy low-carbohydrate diet group than for the healthy low-fat diet group. The decrease in the preva- lence of the metabolic syndrome was not significantly differ- ent between the diet groups.

Respiratory exchange ratio was not significantly differ- ent between the groups at baseline, but was lower for the healthy low-carbohydrate diet group than for the healthy low-fat diet group at each time point after randomization (P < .001; eTable 1 in Supplement 2). Resting energy expen- diture was not significantly different between groups at baseline or at 6 months or 12 months, but decreased sig- nificantly from baseline in both diet groups. Total energy expenditure was not significantly different between groups at baseline or any other time point. Relative to baseline, there was a small absolute mean increase in energy expen- diture for both diet groups that was not significantly differ- ent than baseline.

Adverse Events During the trial, there were 7 serious adverse events, all requiring hospitalization; 2 of these could have been related to the study (kidney stones and diverticulitis requiring

Table 3. 12-Month Change Estimates for Anthropometric Variables by Diet

12-mo Change Estimate (95% CI)a

Between-Group Difference (95% CI)b

Healthy Low-Fat Diet (n = 305)

Healthy Low-Carbohydrate Diet (n = 304)

Weight, kg −5.29 (−5.93 to −4.65) −5.99 (−6.63 to −5.35) 0.70 (−0.21 to 1.60)

Body mass indexc −1.75 (−1.97 to −1.52) −2.07 (−2.30 to −1.85) 0.33 (0.01 to 0.64)

Body fat %d −1.97 (−2.38 to −1.56) −2.15 (−2.54 to −1.75) 0.18 (−0.40 to 0.75)

Waist circumference, cm −3.74 (−4.64 to −2.84) −4.41 (−5.31 to −3.51) 0.67 (−0.60 to 1.94)

Lipid level, mg/dL

High-density lipoprotein cholesterol 0.40 (−0.37 to 1.18) 2.64 (1.87 to 3.41) −2.24 (−3.33 to −1.15)

Low-density lipoprotein cholesterol −2.12 (−4.70 to 0.47) 3.62 (1.04 to 6.19) −5.74 (−9.38 to −2.09)

Triglycerides −9.95 (−17.46 to −2.44) −28.20 (−35.67 to −20.72) 18.25 (7.65 to 28.84)

Blood pressure, mm Hg

Systolic −3.18 (−4.33 to −2.03) −3.72 (−4.86 to −2.58) 0.54 (−1.07 to 2.16)

Diastolic −1.94 (−2.65 to −1.22) −2.64 (−3.34 to −1.93) 0.70 (−0.31 to 1.71)

Fasting glucose, mg/dL −3.67 (−4.90 to −2.44) −2.10 (−3.32 to −0.87) −1.58 (−3.31 to 0.16)

Fasting insulin, μIU/mL −2.64 (−3.79 to −1.49) −2.33 (−3.48 to −1.19) −0.31 (−1.93 to 1.31)

Insulin-30, μIU/mLe −15.38 (−21.13 to −9.62) −11.48 (−17.18 to −5.78) −3.90 (−12.00 to 4.20)

Metabolic syndrome, No. (%)f

Had metabolic syndrome at baseline but not at 12 mo

36 (11.8) 36 (11.8)

Had metabolic syndrome at baseline and 12 mo 39 (12.8) 36 (11.8)

Did not have metabolic syndrome at baseline or 12 mo

128 (42.0) 137 (45.1)

Did not have metabolic syndrome at baseline but had metabolic syndrome at 12 mo

13 (4.3) 11 (3.6)

Respiratory exchange ratiog −0.008 (−0.018 to 0.002) −0.027 (−0.037 to −0.018) 0.020 (0.006 to 0.033)

Resting energy expenditure, kcalg −66.45 (−96.65 to −36.26) −76.93 (−106.68 to −47.19) 10.48 (−31.91 to 52.87)

Energy expenditure, kcal/kg/d 0.55 (0.20 to 0.90) 0.49 (0.13 to 0.84) 0.06 (−0.44 to 0.56)

SI conversion factors: To convert glucose to mmol/L, multiply by 0.0555; high-density and low-density lipoprotein cholesterol to mmol/L, multiply by 0.0259; insulin to pmol/L, multiply by 6.945; triglycerides to mmol/L, multiply by 0.0113. a Data were missing for 91 participants in the healthy low-fat diet group and

86 in the healthy low-carbohydrate diet group (almost exclusively due to dropout).

b Healthy low-fat diet minus healthy low-carbohydrate diet. c Calculated as weight in kilograms divided by height in meters squared. d There were missing data for 138 participants in the healthy low-fat diet group

and 123 in the healthy low-carbohydrate diet group. This was due to a combination of dropout and not having any data for cohort 1.

e Indicates the blood concentration of insulin at the 30-minute time point of an oral glucose tolerance test.

f Defined by Adult Treatment Panel III guidelines from the National Cholesterol Education Program.26

g There were missing data for 125 participants in the healthy low-fat diet group and 121 in the healthy low-carbohydrate diet group due to a combination of dropout and not having any data for cohort 1.

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surgery). There were 11 adverse events; 9 of these were related to the study or possibly related (eg, hypoglycemia following oral glucose tolerance test). Combined serious adverse events and adverse events were evenly distributed across the 2 diet groups.

Discussion In this clinical trial of 609 generally healthy overweight or obese adults without diabetes who were randomly assigned to a healthy low-fat vs a healthy low-carbohydrate diet,

there was no significant difference in weight loss at 12 months. In addition, there were no significant interactions between diet and 3 SNP multilocus genotype patterns or diet and baseline insulin secretion on 12-month weight loss. These results were observed in the context of similar mean 12-month weight loss in both diet groups that was greater than 5% of baseline body weight, and a similar and substan- tial range of weight change, reflecting approximately 40 kg within each diet group (from losing approximately 30 kg to gaining approximately 10 kg).

Dietary intake of fats and carbohydrates was well differ- entiated between the 2 diet groups, as confirmed by diet

Figure 2. Interaction Among Diet and Genotype and Diet and Insulin-30 Tertile at Baseline and 12-Month Weight Loss

–40 2010

12-mo Weight Change, kg –30 –20 –10 0

12-mo Weight loss by diet and genotypeA

Low-fat genotype

No. of Participants

Healthy low-fat diet 83

Healthy low-carbohydrate diet 70

Low-carbohydrate genotype Healthy low-fat diet 63

Healthy low-carbohydrate diet 81

Neither genotype Healthy low-fat diet 79

Healthy low-carbohydrate diet 60

–40 2010

12-mo Weight Change, kg –30 –20 –10 0

12-mo Weight loss by diet and insulin-30 tertile at baselineB

Lowest insulin-30 tertile Healthy low-fat diet 66

Healthy low-carbohydrate diet 85

Middle insulin-30 tertile Healthy low-fat diet 81

Healthy low-carbohydrate diet 71

Highest insulin-30 tertile Healthy low-fat diet 68

Healthy low-carbohydrate diet 64

No. of Participants

The black solid circle indicates the mean, the left and right borders of the box mark the first and third quartiles, the black vertical line indicates the median, the error bars indicate the 5th and 95th percentiles, and the hollow circles indicate the individuals whose values were outside the 5th or 95th percentiles. The No. of participants reflect data for the individuals who had weight data at both baseline and 12 months. Statistical analyses include data from all individuals randomized (described in the Statistical Analysis section).

A, Three-way interaction term among diet, genotype, and the 12-month time point was not statistically significant (beta coefficient, 1.38 [95% CI, −0.72 to 3.49]; P = .20). As described in Stanton et al,10 of all the possible combinations of variance in 3 single-nucleotide polymorphism multilocus genotype patterns, some were considered consistent with the low-fat genotype pattern, some with the low-carbohydrate genotype pattern, and some with neither of

these 2 genotype patterns. By design, as described in the initial National Institutes of Health grant application, those individuals with neither of the main 2 genotype patterns were not included in the main analyses. There were 39 participants who had compromised or missing DNA.

B, Three-way interaction term among diet, insulin, and the 12-month time point was not statistically significant (beta coefficient for 10-μIU/mL increase in insulin, 0.08 [95% CI, −0.13 to 0.28]; P = .47). Insulin-30 is the blood concentration of insulin 30 minutes after consuming 75 g of glucose as part of a standard oral glucose tolerance test. Insulin-30 was treated as a continuous variable in the statistical model. Tertiles were used in this Figure for ease of presentation. The mean for the lowest tertile was 40.8 μIU/mL (range, 7.3-60.6 μIU/mL); middle, 80.1 μIU/mL (range, 60.7-103.1 μIU/mL); and highest, 159.6 μIU/mL (range, 103.4-562.5 μIU/mL).

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assessment, and corroborated by changes in blood lipid para- meters and respiratory exchange ratio, indicating strong treat- ment fidelity. With the large sample size, good retention, substantial weight loss and weight loss variability, and good adherence to and differentiation of diets, the study was well positioned to detect significant interactions by the pri- mary variables of interest if they existed. However, no such ef- fects were observed. Differences in weight loss between the 2 groups were nonsignificant and not clinically meaningful.

Among the secondary outcomes, the clinical variables that were significantly different between the diet groups were the blood lipid results, which were more favorable in the healthy low-fat diet group for changes in low-density lipopro- tein cholesterol and were more favorable in the healthy low- carbohydrate diet group for changes in high-density lipopro- tein cholesterol and triglycerides. The magnitude of the between-group differences were 5% for low-density lipopro- tein cholesterol, 5% for high-density lipoprotein cholesterol, and 15% for triglycerides.

There is considerable scientific interest in identifying genetic variants that help explain interindividual differences in weight loss success in response to diet interventions,31,32

particularly diets with varying macronutrient compositions. Multiple secondary analyses of low-fat and low-carbohydrate weight loss diet trials, including the Preventing Over- weight Using Novel Dietary Strategies (POUNDS LOST) and the Nutrient-Gene Interactions in Human Obesity (NUGENOB) trials,8,32-34 have reported effect modification by SNPs on associations of dietary fat and carbohydrates with weight loss.

For example, Qi et al8 reported that individuals with the IRS1 rs2943641 CC genotype were more successful with weight loss than those without this genotype when assigned to a low-fat and high-carbohydrate diet vs a low-carbohydrate and high-fat diet. Grau et al32 reported that individuals with the FTO rs9939609 TT genotype had greater decreases in the homeostatic assessment model of insulin resistance on low- fat vs low-carbohydrate diets; however, the diet-genotype in- teraction for weight loss was not statistically significant. Most prior studies examined single SNPs, with few replication at- tempts. The intent in the current study was to replicate the post hoc findings from the A TO Z (Atkins, Traditional, Ornish, Zone) Weight Loss Study.3

The finding of no significant difference in weight loss in genotype-matched vs mismatched groups in the current study highlights the importance of conducting large, appropriately powered trials such as DIETFITS for validating early explor- atory analyses. Analyses of all the genomic data obtained are under way to evaluate whether other genetic signatures may demonstrate effect modification.

Several research groups previously reported observ- ing a differential effect of low-fat vs low-carbohydrate diets on weight loss by baseline insulin status. In both a 6-month feeding study with 32 participants and an 18-month free- living study with 56 participants, effect modification between diet assignment (low-fat vs low-carbohydrate or low glycemic load) and INS-30 was reported.12,13 Using fasting insulin cutoffs in a 4-month feeding study involving 20 par-

ticipants, Cornier et al11 observed a significant diet × fasting insulin interaction for weight loss. A post hoc analysis from the A TO Z Study revealed a significant diet × fasting insulin interaction on 12-month weight loss among a subset of 81 overweight and obese women.14

However, in a recent pilot study conducted in prepara- tion for the DIETFITS study, a significant effect modification was not detected for INS-30 status.35 In each case in which a significant interaction was reported, investigators proposed a mechanism involving insulin secretion status, insulin sensi- tivity, or insulin resistance interacting with glycemic load to differentially affect weight loss response with low-fat diets high in carbohydrates vs high-fat diets low in carbohydrates.12,36

In these studies, the consistent direction of the finding was that a lower carbohydrate diet was superior for those indi- viduals with higher insulin secretion or higher insulin resis- tance; the putative mechanism involves a lower demand or bur- den on insulin-mediated glucose disposal for those with impaired insulin metabolism while maintaining a lower car- bohydrate and higher fat diet. Despite mechanistic plausibil- ity, studies to date have involved relatively small sample sizes.

Effect modification claims observed in single random- ized trials are often spurious and this result is even more fre- quent when small sample sizes and post hoc analyses are in- volved; validation of such claims is infrequent.37-39 The current study with a larger sample, a low-carbohydrate diet that was also a low glycemic load diet, and using INS-30 could not rep- licate findings from prior studies using smaller numbers of pa- tients or those studies with a shorter duration. We consider the differences between the current findings and the studies cited to potentially involve diet quality beyond simply differenti- ating fat and carbohydrate intake. In this regard, refined grains are low in fat but considered of poor nutritional quality due to low-nutrient density relative to energy content. In con- trast, vegetables are high in nutrient density, and relatively high in proportional carbohydrate content, but low in calories. Both diet groups in the current study were instructed to minimize or eliminate refined grains and added sugars and maximize in- take of vegetables. We conclude that when equal emphasis is given to high dietary quality for both low-fat and low- carbohydrate eating plans, it is not helpful to preferentially di- rect an individual with high insulin secretion status who is seek- ing weight loss to follow a lower-carbohydrate eating plan instead of a lower-fat eating plan.

This study had several strengths. Study design strengths included the similarly intensive demands on both diet groups in making changes to baseline diets, similar focus on dietary quality, repeated major time points of data collection, and the extensive range of types of data collected. Strengths in study conduct included meeting and exceeding the sample size tar- get of 600 participants, the nearly equal proportions of women and men enrolled, high and equivalent retention for both diet groups, and comparability of change between groups in po- tentially important outcomes related to weight loss, such as physical activity. In addition, the collective loss of approxi- mately 3000 kg among study participants, and the wide indi- vidual variability of weight loss, provided the opportunity to meaningfully test for effect modification.

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Limitations The study also has several limitations. First, generalizability of the findings may be limited by the conduct of the study in a geographic area with individuals who have attained rela- tively high education levels, and have personal resources and high accessibility to high-quality food options. To address this, the study was broadly advertised and successfully enrolled par- ticipants with relatively good ethnic and racial diversity, and a range, albeit limited, of educational attainment.

Second, in regard to the possible role of insulin-glucose dy- namics as an effect modifier in low-fat vs low-carbohydrate studies, there are many possible indices to consider other than INS-30,36 a proxy measure of insulin secretion selected for rea- sons described elsewhere.12,13 But others have reported find- ing significant effect modification according to prestudy fast- ing insulin concentrations.11,14

Third, there were 3 missing secondary anthropometric and metabolic variables (percentage of body fat, resting energy expenditure, and respiratory exchange ratio) for the first 78 participants enrolled in the study due to inadequate initial funding. This funding situation subsequently changed (described in eAppendix 1 in Supplement 2), which allowed the addition of these measurements for the remaining par- ticipants enrolled.

Fourth, the Stanford 7-day Physical Activity Recall tool (which was used to determine total energy expenditure) pro- vides only a relatively crude assessment of total energy ex- penditure. Another method of measuring energy expendi- ture, such as the doubly labeled water method, would have provided greater accuracy; however, the overall cost and added

participant burden were determined to be beyond the scope of the study. In addition, self-reported diet assessment meth- ods are all known to have limited accuracy; therefore, we chose to use the Nutrition Data System for Research, which is rec- ognized as a top method.

Fifth, even though insulin sensitivity was well assessed in this study, assessment of genetic characteristics as effect modi- fiers of diet response need better and increased study in the future because there has been much progress in understand- ing the genetic architecture of metabolic phenotypes such as obesity since the current trial was designed. Other explana- tions for heterogeneity besides insulin dynamics and genetic characteristics also need to be assessed.

Sixth, by not randomizing or conducting stratification ac- cording to genotype or insulin secretion status, the level of causal inference to be drawn from the analyses of interac- tions was limited; however, this allowed us to test for 2 pri- mary interaction associations in the same study.

Conclusions In this 12-month weight loss diet study, there was no signifi- cant difference in weight change between a healthy low-fat diet vs a healthy low-carbohydrate diet, and neither genotype pat- tern nor baseline insulin secretion was associated with the di- etary effects on weight loss. In the context of these 2 com- mon weight loss diet approaches, neither of the 2 hypothesized predisposing factors was helpful in identifying which diet was better for whom.

ARTICLE INFORMATION

Accepted for Publication: January 17, 2018.

Correction: This article was corrected on April 3, 2018, to change the units of measure for the lipid level rows in Table 3 from mmol/L to mg/dL and reverse the corresponding SI conversion factor instructions from divide to multiply. This article was corrected on April 24, 2018, to change the Nutrition Science Initiative funding description.

Author Contributions: Dr Gardner had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Gardner, Rigdon, Ioannidis, Desai, King. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Gardner, Trepanowski, Del Gobbo, Hauser, Rigdon, Desai. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Gardner, Del Gobbo, Hauser, Rigdon, Ioannidis, Desai. Obtained funding: Gardner. Administrative, technical, or material support: Gardner, Hauser, King. Supervision: Gardner, Desai, King.

Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: This study was supported by grant 1R01DK091831 from the National Institute of Diabetes and Digestive and Kidney Diseases, funding from the Nutrition Science Initiative, grants 1K12GM088033 and T32HL007034 from the National Heart, Lung, and Blood Institute, and the Stanford Clinical and Translational Science Award.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the other funders.

Additional Contributions: This study would not have been possible without the work of the following individuals who were affiliated with Stanford University at the time of the study and who received compensation for their work: Jennifer Robinson, PhD, and Antonella Dewell, MS, RD (served as study coordinators), Rise Cherin, MS, RD, Susan Kirkpatrick, RD, CDE, Jae Berman, MS, RD, CSSD, Dalia Perelman, MS, RD, CDE, and Mandy Murphy Carroll, MPH, RD (health educators), Sarah Farzinkhou, MPH, Valerie Alaimo, BS, Margaret Shimer Lawton, MPH, and Diane Demis, BS (diet assessment team), Josephine Hau, MPH, RD, Erin Avery, MS, Alexandra Rossi, BS, Katherine Dotter, BS, RD, and Sarah Mummah, PhD

(involved in recruitment, screening, blood sample management, innovation, and other tasks), Ariadna Garcia, MS, FeiFei Qin, MPH, and Vidhya Balasubramanian, MS (involved in statistical support), Alana Koehler, BA (administrative support), and Lucia Aronica, PhD, Jennifer Hartle, DrPH, MHS, CIH, Lisa Offringa, PhD, Kenji Nagao, PhD, Marily Oppezzo, PhD, MS, RD, Benjamin Chrisinger, MUEP, PhD, and Michael Stanton, PhD (aided various phases of the study). We also acknowledge the 609 study participants without whom this investigation would not have been possible.

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