Low-Fat vs Personalized Diet: Which Is Better for Weight Loss?

Sticking to a personalized diet focused on a reduction in postprandial glycemic response (PPGR) wasn’t any better for weight loss than a run-of-the-mill low-fat diet, the Personal Diet Study found.

Among 204 adults with obesity and abnormal glucose metabolism, those randomized to a standardized low-fat diet lost 4.31% of body weight after 6 months while those in the personalized diet group lost 3.26% (between-group difference of 1.05%, 95% CI -0.40 to 2.50, P=0.16), reported Collin Popp, PhD, MS, RDN, of NYU Langone Health in New York City, and colleagues.

This equated to an average 4.16 kg (9.17 lb) drop for the low-fat dieters and a 3.14 kg (6.9 lb) drop for the personalized diet group, missing the trial’s primary endpoint.

“Overall, weight losses did not reach a threshold considered to be clinically meaningful (e.g., 5%); however, moderate weight loss of 3% to 5% has been reported to improve health outcomes,” the researchers wrote in JAMA Network Open.

In addition, there were also no significant between-group differences when it came to changes in body composition and adaptive thermogenesis, with numbers favoring the control group:

  • Fat mass: 0.82 kg difference (95% CI -0.48 to 2.13, P=0.21)
  • Fat-free mass: 0.79 kg (95% CI -0.05 to 1.63, P=0.07)
  • Body fat percentage: 0.25% (95% CI -0.71 to 1.21, P=0.61)
  • Respiratory quotient: 0.04 (95% CI -0.04 to 0.11, P=0.26)
  • Adaptive thermogenesis: 67.8 kcal/d (95% CI -18.7 to 154.4, P=0.12)

The only significant difference seen was that the low-fat dieters had a greater change in resting energy expenditure compared with the personalized diet group (difference between groups 92.3 kcal/day, 95% CI 0.9-183.8, P=0.05). Popp’s group said this may be related to the trend toward a greater reduction in fat-free mass seen with the low-fat diet (-1.30 kg vs -0.51 kg), though this difference was not statistically significant.

The 97 participants randomized to the low-fat diet were instructed to keep their total fat intake to less than 25% of total energy and keep saturated fat intake to less than 7% of total energy intake.

For the 102 participants randomized to the personalized diet group, the researchers utilized a machine-learning algorithm developed by researchers in Israel. The algorithm uses anthropometrics, blood tests like HbA1c, lifestyle factors, plus microbiome abundances to create a tailored diet aimed at reducing PPGR. Participants on this personalized diet received PPGR feedback in the form of color-coded meal scores via a mobile app indicating a range of very bad to excellent PPGR.

At baseline, the participants (ages 18 to 80) had body mass indexes of 27 to 50 and HbA1c levels of 5.7% to 8.0%. To avoid recruiting participants with advanced type 2 diabetes, individuals taking medications other than metformin, as well as those with chronic kidney disease, were excluded from enrollment. Over half the participants were white, a quarter were Black, and 18.1% were Hispanic.

Despite the fact that this personalized dietary algorithm didn’t perform any better than a regular low-fat diet for weight loss, Popp’s group suggested that future research should continue, taking the idea of the personalized diet plan further.

“Future studies are needed to develop and test a weight loss-specific algorithm that incorporates key characteristics central to body weight regulation as well as features of the energy balance model, including appetitive hormones (e.g., leptin, glucagonlike peptide 1), total energy expenditure, and fat mass,” the team suggested.

  • Kristen Monaco is a staff writer, focusing on endocrinology, psychiatry, and nephrology news. Based out of the New York City office, she’s worked at the company since 2015.


The study was supported by the American Heart Association, National Center for Advancing Translational Sciences, NIH, and the Health Resources and Services Administration.

Popp reported a relationship with Renaissance Periodization, and a co-author disclosed serving as a consultant for DayTwo.

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