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Do Precision and Personalised Nutrition Interventions Improve Risk Factors in Adults with Prediabetes or Metabolic Syndrome? A Systematic Review of Randomised Controlled Trials

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This review aimed to synthesise existing literature on the efficacy of personalised or precision nutrition (PPN) interventions, including medical nutrition therapy (MNT), in improving outcomes related to glycaemic control (HbA1c, post-prandial glucose [PPG], and fasting blood glucose), anthropometry (weight, BMI, and waist circumference [WC]), blood lipids, blood pressure (BP), and dietary intake among adults with prediabetes or metabolic syndrome (MetS). Six databases were systematically searched (Scopus, Medline, Embase, CINAHL, PsycINFO, and Cochrane) for randomised controlled trials (RCTs) published from January 2000 to 16 April 2023. The Academy of Nutrition and Dietetics Quality Criteria were used to assess the risk of bias. Seven RCTs (n = 873), comprising five PPN and two MNT interventions, lasting 3–24 months were included. Consistent and significant improvements favouring PPN and MNT interventions were reported across studies that examined outcomes like HbA1c, PPG, and waist circumference. Results for other measures, including fasting blood glucose, HOMA-IR, blood lipids, BP, and diet, were inconsistent. Longer, more frequent interventions yielded greater improvements, especially for HbA1c and WC. However, more research in studies with larger sample sizes and standardised PPN definitions is needed. Future studies should also investigate combining MNT with contemporary PPN factors, including genetic, epigenetic, metabolomic, and metagenomic data.
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Citation: Robertson, S.; Clarke, E.D.;
Gómez-Martín, M.; Cross, V.; Collins,
C.E.; Stanford, J. Do Precision and
Personalised Nutrition Interventions
Improve Risk Factors in Adults with
Prediabetes or Metabolic Syndrome?
A Systematic Review of Randomised
Controlled Trials. Nutrients 2024,16,
1479. https://doi.org/10.3390/
nu16101479
Received: 28 March 2024
Revised: 6 May 2024
Accepted: 10 May 2024
Published: 14 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
nutrients
Systematic Review
Do Precision and Personalised Nutrition Interventions Improve
Risk Factors in Adults with Prediabetes or Metabolic Syndrome?
A Systematic Review of Randomised Controlled Trials
Seaton Robertson 1, Erin D. Clarke 1,2, María Gómez-Martín1,2, Victoria Cross 1, Clare E. Collins 1,2
and Jordan Stanford 1, 2, *
1School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle,
Callaghan, NSW 2308, Australia; clare.collins@newcastle.edu.au (C.E.C.)
2Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights,
NSW 2305, Australia
*Correspondence: jordan.stanford@newcastle.edu.au
Abstract: This review aimed to synthesise existing literature on the efficacy of personalised or
precision nutrition (PPN) interventions, including medical nutrition therapy (MNT), in improving
outcomes related to glycaemic control (HbA1c, post-prandial glucose [PPG], and fasting blood
glucose), anthropometry (weight, BMI, and waist circumference [WC]), blood lipids, blood pressure
(BP), and dietary intake among adults with prediabetes or metabolic syndrome (MetS). Six databases
were systematically searched (Scopus, Medline, Embase, CINAHL, PsycINFO, and Cochrane) for
randomised controlled trials (RCTs) published from January 2000 to 16 April 2023. The Academy of
Nutrition and Dietetics Quality Criteria were used to assess the risk of bias. Seven RCTs (n= 873),
comprising five PPN and two MNT interventions, lasting 3–24 months were included. Consistent
and significant improvements favouring PPN and MNT interventions were reported across studies
that examined outcomes like HbA1c, PPG, and waist circumference. Results for other measures,
including fasting blood glucose, HOMA-IR, blood lipids, BP, and diet, were inconsistent. Longer,
more frequent interventions yielded greater improvements, especially for HbA1c and WC. However,
more research in studies with larger sample sizes and standardised PPN definitions is needed. Future
studies should also investigate combining MNT with contemporary PPN factors, including genetic,
epigenetic, metabolomic, and metagenomic data.
Keywords: prediabetes; metabolic syndrome; personalized nutrition; precision nutrition; medical
nutrition therapy; systematic review; randomized controlled trial
1. Introduction
Prediabetes is a metabolic state characterised by disruptions in glucose regulation and
insulin resistance, wherein blood glucose levels exceed normal thresholds but do not reach
the diagnostic criteria for Type 2 Diabetes Mellitus (T2DM) [
1
3
]. Conversely, metabolic
syndrome (MetS) is a cluster of metabolic abnormalities that includes hypertension, central
obesity, insulin resistance, and atherogenic dyslipidaemia [
4
,
5
]. As of 2022, the global
prevalence of impaired fasting glucose is estimated at 10.6% (541 million individuals) [
6
],
while MetS prevalence ranges from 12.5% to 31.4% [7].
The pathology for both conditions is complicated, with lifestyle, environmental, and
genetic factors involved in disease progression [
2
,
8
,
9
]. Shared lifestyle risk factors for
prediabetes and MetS include poor dietary habits, sedentary behaviour, obesity, smoking,
and inadequate sleep [
10
]. If left untreated, prediabetes stands out as a pivotal risk factor
for the eventual development of T2DM, with around 70% of individuals progressing from
prediabetes to T2DM [
11
]. Similarly, MetS amplifies the risk not only for T2DM but also for
cardiovascular disease (CVD), stroke, and myocardial infarction [
2
,
12
]. Consequently, early
Nutrients 2024,16, 1479. https://doi.org/10.3390/nu16101479 https://www.mdpi.com/journal/nutrients
Nutrients 2024,16, 1479 2 of 18
interventions addressing these shared risk factors are imperative to prevent adverse health
outcomes associated with these conditions.
A healthy diet is widely recognised as a crucial factor in reducing the risk of predia-
betes, MetS, and other non-communicable diseases [
13
,
14
]. However, current approaches
to providing universal dietary recommendations or guidelines do not consider individual
variations in dietary response. Personalised and precision nutrition approaches aim to
improve health and well-being by leveraging dietary interventions that accommodate
human variability [
15
]. For example, research has shown that individuals consuming the
same meal may experience different glycaemic responses, highlighting the limitations
of generic approaches [
16
]. Machine learning algorithms have also been developed to
accurately predict personalised post-prandial glucose response to foods [
16
]. The algorithm
was evaluated using a dietary intervention RCT that demonstrated a significantly lower
post-prandial blood glucose response in participants after consuming lower carbohydrate,
higher fibre, or higher fat-to-carbohydrate ratio meals, but this response was not consistent
between individuals [16].
Currently, there is no universally agreed-upon definition for personalised and preci-
sion nutrition, and these terms are often used interchangeably. Efforts have been made to
clarify these terms, with personalised nutrition defined as incorporating various informa-
tion, including genetics, phenotypic, medical, nutritional, and other relevant information,
to provide tailored nutritional guidance for individuals [
15
]. It also allows for interven-
tions to be tailored based on an individual’s behaviour, preferences, lifestyle, and health
objectives. These principles align with Medical Nutrition Therapy (MNT) [
17
], a category
of personalised nutrition provided exclusively by registered and accredited practising di-
etitians. MNT involves a nutritional diagnosis and counselling services to facilitate lifestyle
changes. On the other hand, precision nutrition is suggested to take a more dynamic
approach, integrating genetic, metabolic, and environmental factors to develop compre-
hensive recommendations for individuals or subpopulation groups, utilising cutting-edge
technologies such as metabolomics, metagenomics, and epigenetics [
15
]. In the context
of this review, personalised and precision nutrition (PPN) are used as an umbrella term
to encompass approaches that utilise one or more of the abovementioned components to
tailor interventions to individuals.
To date, no systematic review has summarised the evidence of MNT and PPN inter-
ventions in adults with prediabetes or MetS. Therefore, the aim of this systematic review is
to consolidate current literature from randomised controlled trials investigating the effec-
tiveness of PPN interventions, including MNT, on outcomes related to glycaemic control,
anthropometry, blood lipids, blood pressure, and dietary intake among individuals with
prediabetes or MetS. Findings from this review may inform future treatment and research
in prediabetes or MetS through the use of PPN and/or MNT.
2. Materials and Methods
2.1. Protocol and Registration
This systematic review was conducted following the PRISMA (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses) guidelines (Table S1) [
18
]. The protocol
for this systematic review was registered on Open Science Frame (OSF) (https://doi.org/
10.17605/OSF.IO/9Z8TE, accessed on 20 April 2024) [19].
2.2. Database and Search
The search strategy was developed with the help of a research librarian. Medical
Subject Headings and keywords were used, including terms like “prediabetes” or “risk
of diabetes” and “nutrition therapy” or “personalised diet”. The search was carried out
systematically across six databases (Scopus, Medline, Embase, CINAHL, PsycINFO, and
Cochrane) and included articles published between January 2000 and 16 April 2023 to
account for significant advancements made in PPN and MNT methods within this time
and ensuring that the results of this review reflect the most up-to-date knowledge available.
Nutrients 2024,16, 1479 3 of 18
The search was also restricted to include only randomised controlled trials (RCTs), articles
published in English, and studies involving human subjects. The complete search string for
all databases can be found in Supplementary Materials, Figures S1–S6.
2.3. Study Selection Criteria
The inclusion of studies was determined according to the Population, Intervention,
Comparison, Outcomes, and Study (PICOS) framework (as detailed in Table 1). The study
population comprised adults diagnosed with prediabetes or MetS who participated in
an RCT that reported the effect of a personalised nutrition-based dietary intervention,
including MNT. Valid comparator groups comprised those receiving usual or standard care
or engaging in non-personalised dietary interventions.
Table 1. PICOS criteria for inclusion of final studies in this systematic review.
Category Inclusion Criteria Exclusion Criteria
Population Adults (18 yrs) diagnosed with prediabetes or
metabolic syndrome
Study participants diagnosed with a chronic disease
(e.g., type 1 or 2 diabetes, cardiovascular disease,
chronic kidney disease, or gestational diabetes)
Intervention MNT (provided by a registered or accredited practising
dietitian) or PPN
If the intervention included medications, surgeries,
supplements, physical activity, or another lifestyle
component, where the impact of the PPN
intervention could not be isolated
Comparison
Standard care, habitual diet, or non-personalised dietary
intervention
If the comparator or control was anything other than
standard care or non-personalised/individualised
dietary intervention
Outcome
Measures of glycaemic control [HbA1c, fasting blood
glucose levels, post-prandial glucose/OGTT,
Homeostatic Model Assessment for Insulin Resistance
(HOMA-IR), insulin levels, and insulin sensitivity],
anthropometry [weight, waist circumference, body mass
index (BMI)], blood lipids, blood pressure, and
reporting of dietary outcomes (nutrients, food groups,
dietary patterns, diet quality)
Did not measure any outcome of interest relating to
glycaemic control
Study design Randomised control trials (RCTs) published after the
year 2000
Studies that were not an RCT; studies not published
in English
The primary outcome measures were focused on glycaemic control indicators, includ-
ing HbA1c levels, fasting blood glucose concentrations, post-prandial glucose/results from
oral glucose tolerance tests (OGTT), and more (Table 1). The secondary outcome measures
encompassed anthropometric parameters (weight, waist circumference, and body mass
index [BMI]) as well as assessments of blood lipids, blood pressure, and dietary intake.
2.4. Study Selection
Studies from the search results were managed using the Covidence 2.0 platform [Covidence
systematic review software, Melbourne] [
20
]. Duplicates were removed before at least two
reviewers independently screened titles, abstracts, and full texts for inclusion in this review.
Discrepancies were resolved by consensus or adjudication by other research team members.
2.5. Risk of Bias and Study Quality Assessment
Two independent reviewers assessed the methodological quality of the included full-
text articles. The risk of bias was assessed using the Academy of Nutrition and Dietetics
Quality Criteria checklist [
21
]. This tool was selected because it has a higher inter-observer
agreement than ROB 2.0 [22]. Differences were resolved by discussion and consensus.
Nutrients 2024,16, 1479 4 of 18
2.6. Data Extraction
A standardised template, implemented within a Microsoft Excel (Version 2402; Build
16.0.17328.20282) spreadsheet, was used to extract data from the included articles. The
standardised template was first piloted with three articles to ensure that all information
relevant to this systematic review was collected. Data extraction was carried out by a
singular reviewer (SR), and accuracy was confirmed by a second reviewer.
2.7. Synthesis of Results
Due to the heterogeneity in study design, population, and interventions, a meta-analysis
was not performed. The results were synthesised narratively and summarised by study charac-
teristics (study design, intervention, and control type) and outcome measure results. Wherever
possible, results for outcome measures included both the confidence interval and p-value for
within-group or between-group differences. The summary also included the consistency of the
reported significant differences (p< 0.05) between the intervention and comparator groups for
each outcome, whether the differences were increases or decreases.
3. Results
3.1. Search Results
The initial search yielded 7396 studies. After removing 1186 duplicates, 6040 articles
were excluded based on their title and abstract. A total of 170 full-text articles were re-
trieved, and 163 studies were excluded during screening. The primary reason for exclusion
was incorrect intervention type (n = 85), with most excluded interventions lacking person-
alisation or comprehensive lifestyle interventions involving both diet and physical activity
that could not identify separate dietary impacts (Figure 1). A total of seven articles met all
inclusion criteria and were included in this review [2329].
Nutrients 2024, 16, x FOR PEER REVIEW 5 of 18
Figure 1. PRISMA ow diagram for the literature search and the study selection process [30].
3.2. Characteristics of the Included Studies
Characteristics of the included studies are presented in Table 2. All seven of the in-
cluded papers were parallel RCTs [2329]. The sample size of participants varied from 46
[26] to 225 [24], with a mean age range of 50 to 60 years. Across the included articles, there
was a total of 873 participants [2329]. The portion of males ranged from 28% [27] to 100%
[23], and in total, across the included studies, 462 participants were male, and 411 were
female [2329]. Two studies were based in the USA [27,28], while others were from Brazil
[29], Finland [26], Italy [25], Israel [24], and Japan [23]. Two papers met the inclusion cri-
teria for prediabetes [24,28], two papers included individuals with MetS [25,26], two pa-
pers included those with impaired glucose tolerance [23,29], and one included partici-
pants with an HbA1c range of 66.9% [27].
Identification
Studies screened (n = 6210)
Studies sought for retrieval (n = 170)
Studies assessed for eligibility (n = 170)
References removed (n = 1186)
Duplicates identified manually (n = 493)
Duplicates identified by Covidence (n = 693)
Marked as ineligible by automation tools (n =
0)
Other reasons (n = )
Studies excluded (n = 6040)
Studies not retrieved (n = 0)
Studies excluded (n = 163)
Wrong language (n = 2)
Wrong comparator (n = 6)
Wrong intervention (n = 85)
Wrong study design (n = 6)
Inadequate information (n = 1)
Wrong patient population (n = 45)
Not a full-text journal article (n = 18)
Studies included in review (n = 7)
Screening
Studies from databases/registers (n = 7396)
Cochrane (n = 4179)
Scopus (n = 1209)
Embase (n = 1006)
MEDLINE (n = 699)
CINAHL (n = 294)
PsycINFO (n = 9)
Identification of studies via databases and registers
Figure 1. PRISMA flow diagram for the literature search and the study selection process [30].
Nutrients 2024,16, 1479 5 of 18
3.2. Characteristics of the Included Studies
Characteristics of the included studies are presented in Table 2. All seven of the
included papers were parallel RCTs [
23
29
]. The sample size of participants varied from
46 [
26
] to 225 [
24
], with a mean age range of 50 to 60 years. Across the included articles,
there was a total of 873 participants [
23
29
]. The portion of males ranged from 28% [
27
]
to 100% [
23
], and in total, across the included studies, 462 participants were male, and
411 were female [
23
29
]. Two studies were based in the USA [
27
,
28
], while others were
from Brazil [
29
], Finland [
26
], Italy [
25
], Israel [
24
], and Japan [
23
]. Two papers met the
inclusion criteria for prediabetes [
24
,
28
], two papers included individuals with MetS [
25
,
26
],
two papers included those with impaired glucose tolerance [
23
,
29
], and one included
participants with an HbA1c range of 6–6.9% [27].
3.3. Quality Assessment of Studies
The results of the quality assessment are summarised in Table S2 in Supplementary
Materials. Most evaluations (n = 6) received positive ratings, with just one study rated as
neutral. The neutral assessment was attributed to factors such as the absence of a power
calculation and lack of blinding in the study design.
3.4. Interventions
Personalisation varied between studies, with four studies personalising based on
dietary and nutritional information [
23
,
25
27
], one employed a published algorithm, which
integrates clinical and gut microbiome features to predict personal post-prandial glycaemic
responses to meals [
24
], and two were MNT interventions (Table 2) [
28
,
29
]. Four interven-
tions were conducted by nutritionists [
23
,
25
,
26
,
29
], two by dietitians [
24
,
28
], and one by an
interventionist with unreported qualifications [
27
]. Four interventions were delivered face-
to-face [
25
,
26
,
28
,
29
], one used a combination of face-to-face and phone [
27
], another used a
combination of face-to-face and mail [
23
], and one used a combination of face-to-face, email,
and phone [
24
]. The length of the included studies ranged from 6 months [
27
] to 2 years [
25
].
The frequency of each intervention session ranged from once-off [
23
] to 36 sessions [
29
],
while the duration of interventions ranged from 3 months [
28
] to 2 years [
25
]. One study
compared individual MNT (considered the intervention) to group MNT (considered the
comparator) [28].
3.5. Comparators
The comparator groups also varied among the studies (Table 2). The most common
control group was a usual care comparator or standardised generic information [
23
,
25
,
27
].
Another study implemented a Mediterranean diet, which included specific nutrient targets
based on percentage energy intake. Additionally, meals were assessed and scored based on
the recommendations of four independent dietitians, and participants’ dietary preferences
were also considered [
24
]. Among the remaining studies, one encouraged participants of
the control group to continue with their usual diet and physical activity [
26
], and another
had participants attend group nutrition sessions facilitated by a dietitian [
28
]. One study
provided insufficient details regarding the control group, with the authors stating that
participants did not receive any advice [29].
3.6. Outcome Measures
3.6.1. Glycaemic Control Outcomes
All included studies evaluated blood glucose levels (BGLs), of which six studies
reported examining fasting BGLs [
23
,
24
,
26
29
]. Two studies used continuous glucose
monitors (CGMs) to examine mean glucose [
24
,
27
], while three examined post-prandial
glucose (Tables 2and S3) [23,24,29].
Nutrients 2024,16, 1479 6 of 18
Table 2. Study characteristics and results summary of studies included in the systematic review examining the effect of PPN and MNT on adults with prediabetes.
First Author,
Year,
Country
Primary
Risk
Participant
Characteristics
RCT Design
and Study
Duration
Outcomes
Measured
Intervention
Prescribed by
Intervention
Group(s)
Conditions
Comparison
Group(s)
Conditions
Glycaemic
Control
Outcomes
Anthropometry
Outcomes Blood Lipids
Blood Pressure
Diet
Ben-Yacov
et al., 2021,
Israel [24]
Prediabetes
N = 225
(35–70 yrs;
41% male)
Parallel, 1 year
Glycaemic
control (FBGL,
2-h and 5-h
PPG (CGM),
mean CGM
glucose,
HbA1c,
HOMA-IR,
insulin, and
fructosamine),
anthropometry
(weight,
BMI, waist
circumference
[WC]), blood
lipids, blood
pressure (SBP,
DBP), and diet
(daily food log
via smart-
phone app)
Intervention:
one-on-one
counselling
with a dietitian
Control:
one-on-one
counselling
with a dietitian
A tailored diet
was created
based on the
participant’s
personal
predicted
glucose
responses
using an
algorithm that
integrated
clinical and gut
microbiome
features.
Monthly
in-person
meetings with
a dietitian
during 6
months of
follow-up and
interim contact
via telephone
or email with a
dietitian as
needed.
Diet recom-
mendations
were
administered
as menus
Monthly
in-person
meetings with
a dietitian
during 6
months of
follow-up and
interim contact
via telephone
or email with a
dietitian as
needed.
The
participants
were
encouraged to
consume a
Mediterranean
diet consisting
of whole foods
and
discouraged
from
consuming
processed
foods. Diet rec-
ommendations
were
administered
as menus
Greater
improvements
in CGM mean
above 140 (95%
CI 1.29 to
0.66 h/day,
p< 0.001),
HbA1c (0.14
to 0.02%
[1.5 to 0.2
mmol/mol],
p= 0.007), 5-h
PPG excursions
(95% CI 12.3
to 7.6 mg/dL
×h, p< 0.001),
and mean
CGM glucose
(95% CI
7.0 to
3.22 mg/dL
[0.39 to
0.18 mmol/L],
p< 0.001) in
intervention
group
compared to
control at 6
months. No
significant
difference
between
groups for
fasting BGL,
insulin,
HOMA-IR, and
2-h PG (OGTT)
No significant
difference
between
groups for BMI,
weight, and fat
(%) at 6 months
Greater
improvements
in triglycerides
(95% CI 0.36
to
0.07 mmol/L
[31.51 to
6.11 mg/dL],
p< 0.003) and
HDL (95% CI
0.02–
0.13 mmol/L
[0.77–
4.9 mg/dL],
p= 0.003) in
intervention
group
compared to
control at 6
months. No
significant
difference for
LDL and total
cholesterol
No significant
difference
between
groups
Significant difference
between groups, with
lower carbohydrate
intake (93.2, 95% CI
101.9 g to
84.4 g/d,
p< 0.001), fibre intake
(10.8, 95% CI 12.9
to 8.7 g/day,
p< 0.01), greater
protein intake (+5.1,
95% CI +0.5 to
+10.8 g/d,
p< 0.001), and fat
intake (+37.1, 95% CI
+32.1 to +42.1 g/d,
p< 0.001) and
saturated fat intake
(+11.5, 95% CI +9.7 to
+13.2 g/d,
p< 0.001) observed in
the intervention group
compared to the
control group at 6
months. No
significant difference
between groups for
energy intake.
Reported participants’
top 10 most popular
logged foods for the
intervention and
control diets, which
differed; however, the
authors did not report
statistical
between-group
differences.
Nutrients 2024,16, 1479 7 of 18
Table 2. Cont.
First Author,
Year,
Country
Primary
Risk
Participant
Characteristics
RCT Design
and Study
Duration
Outcomes
Measured
Intervention
Prescribed by
Intervention
Group(s)
Conditions
Comparison
Group(s)
Conditions
Glycaemic
Control
Outcomes
Anthropometry
Outcomes Blood Lipids
Blood Pressure
Diet
Cole et al.,
2013, USA
[28]
Prediabetes
N = 65
(18 yrs;
54% male)
Parallel, 1 year
Glycaemic
control (FBGL,
HbA1c),
anthropometry
(weight, BMI),
blood lipids,
and blood
pressure (SBP,
DBP)
Intervention:
one-on-one
counselling
session with a
dietitian
Control: small
medical
appointments
(SMA) with a
dietitian,
diabetes
educator, and
behaviour
specialist or
nurse
Participants
attended at
least one
45–60-min
individualised
counselling
session with a
dietitian
following an
initial 3-h
prediabetes
class. The
dietitian
discussed
patients’
clinical
outcomes and
progress made
in achieving
lifestyle
medication
since the initial
class, assisted
in the
development
of SMART
goals, and
scheduled
follow-up
appointments
if the patient
desired
Participants
participated in
3 90-min SMA
that
accommodated
6–8
participants
and were
supported by
dietitians,
diabetes
educators, and
behaviour
specialists or
nurses. Each
participant also
received
10 min of
individual time
to discuss
clinical and
biochemical
measures,
challenges, and
smart goals
No significant
difference
between
groups for
FBGL and
HbA1c
No significant
difference
between
groups for
weight and
BMI
No significant
difference
between
groups for total
cholesterol,
HDL, LDL, and
triglycerides
No significant
difference
between
groups for
systolic and
diastolic BP
n/a
Nutrients 2024,16, 1479 8 of 18
Table 2. Cont.
First Author,
Year,
Country
Primary
Risk
Participant
Characteristics
RCT Design
and Study
Duration
Outcomes
Measured
Intervention
Prescribed by
Intervention
Group(s)
Conditions
Comparison
Group(s)
Conditions
Glycaemic
Control
Outcomes
Anthropometry
Outcomes Blood Lipids
Blood Pressure
Diet
Dorans et al.,
2022, USA
[27]
HbA1c
6.0–6.9%
N = 150
(40–70 yrs;
28% male)
Parallel, 6
months
Glycaemic
control (FBGL,
mean 24-h
CGM glucose,
HbA1c,
HOMA-IR, and
fasting insulin),
anthropometry
(weight, waist
circumference
[WC]), blood
lipids, blood
pressure (SBP,
DBP), and diet
(24-h recall)
Intervention:
one-on-one
counselling
from an
interventionist
Control:
received
written
information
from the
interventionist
Phase 1: The
participant
received
behavioural
counselling
and key
supplemental
food with a
carbohydrate
target of less
than 40 g. This
phase involved
weekly
individual
sessions for 4
weeks,
followed by 4
small group
sessions every
other week and
4 telephone
follow-ups.
Phase 2: net
carbohydrate
target was less
than 60 g.
During this
phase,
participant
attended three
monthly group
sessions and
three telephone
follow-ups
Participants
received
written
information
with standard
dietary advice
and did not
receive
ongoing recom-
mendations.
Participants
were offered
optional
monthly
educational
sessions on
topics
unrelated to
diet
Greater
improvements
in HbA1c
(0.23, 95% CI
0.32 to
0.14%,
<0.001), FBGL
(10.3, 95% CI
15.6 to
4.9 mg/dL,
p= 0.001),
fasting insulin
(6.2, 95% CI
10.5 to
2µIU/mL,
p= 0.004),
HOMA-IR
(2.4, 95% CI
3.7 to 1.1,
p< 0.001) and
mean 24-h
CGM glucose
(7, 95% CI
13.8 to
0.1 mg/dL,
p< 0.05) in
intervention
group
compared to
control at 6
months
Greater
improvements
in weight
(5.9, 95% CI
7.4 to
4.4 kg,
p< 0.001) and
waist
circumference
(4.7, 95% CI
6.7 to
2.6 cm,
p< 0.001) in
intervention
group
compared to
control at 6
months
No significant
difference in
HDL, LDL, and
total-to-HDL
between
groups
No significant
difference
between
groups for
systolic and
diastolic blood
pressure
Did not report on
difference between
groups
Nutrients 2024,16, 1479 9 of 18
Table 2. Cont.
First Author,
Year,
Country
Primary
Risk
Participant
Characteristics
RCT Design
and Study
Duration
Outcomes
Measured
Intervention
Prescribed by
Intervention
Group(s)
Conditions
Comparison
Group(s)
Conditions
Glycaemic
Control
Outcomes
Anthropometry
Outcomes Blood Lipids
Blood Pressure
Diet
Esposito
et al., 2004,
Italy [25]
Metabolic
syndrome
N = 180
(18 yrs;
55% male)
Parallel, 2
years
Glycaemic
control (BGL,
HOMA-IR,
fasting insulin),
anthropometry
(weight, BMI,
waist
circumference
[WC]), blood
lipids, blood
pressure (SBP,
DBP), and diet
(3-day food
record)
Intervention:
one-on-one
counselling
provided by
nutritionist
Control:
One-on-one
visits with
study
personnel
Patients were
given detailed
advice through
monthly group
sessions.
Received
education on
reducing
dietary calories,
goal-setting,
and self-
monitoring
using food
diaries.
Behavioural
and
psychological
counselling
was also
offered. The
dietary advice
was tailored to
each patient on
the basis of
3-day food
records. The
recommended
composition of
the dietary
regimen was as
follows:
carbohydrates,
50% to 60%;
proteins, 15%
to 20%; total
fat, less than
30%; saturated
fat, less than
10%; and
cholesterol
consumption,
less than 300
mg per day.
Patients were
advised to
increase intake
of fruit,
Patients were
given general
oral and
written
information
about health
food choices at
visits but were
offered no
specific
individualized
program. The
general recom-
mendation for
macronutrient
composition of
the diet was
carbohydrates,
50–60%;
proteins,
15–20%; and
total fat, <30%.
Received
guidance on
increasing their
level of
physical
activity
Greater
improvements
in BGL (6,
95% CI 11 to
2 mg/dL.
p< 0.001),
insulin (3.5,
95% CI 6.1 to
1.7 µIU/mL,
p= 0.01), and
HOMA score
(1.1, 95% CI
1.9 to 0.3,
p< 0.001) in
intervention
group
compared to
control at 2
years
Greater
improvements
in weight
(2.8, 95% CI
5.1 to
0.5 kg,
p< 0.001), BMI
(0.8, 95% CI
1.4 to
0.2 kg/m2,
p= 0.01), and
waist
circumference
(2, 95% CI
3.5 to
0.5 cm,
p= 0.01) in
intervention
group
compared to
control at 2
years
Greater
improvements
in total
cholesterol (
9,
95% CI 17 to
1 mg/dL,
p= 0.02), HDL
(+3, 95% CI 0.8
to 5.2 mg/dL,
p= 0.03), and
triglycerides
(19, 95% CI
32 to
6 mg/dL,
p= 0.001) in
intervention
group
compared to
control at 2
years
Greater
improvement
in systolic
blood pressure
(
3, 95% CI
5
to 1 mmHg,
p= 0.01) and
diastolic blood
pressure (2,
95% CI 3.5 to
0.5 mmHg,
p= 0.03) in
intervention
group
compared to
control at 2
years
Greater reductions in
energy intakes (100,
95% CI 178 to 21
kcal/d, p< 0.001), fat
(1.4, 95% CI 2.8 to
0.2%, p= 0.02), SFA
(5.3, 95% CI 9.5 to
2.0%, p< 0.001),
omega 6/3 ratio (
4.3,
95% CI 8.3 to 1,
p< 0.001), and dietary
cholesterol (80, 95%
CI 135 to 25 mg/d,
p< 0.001) in
intervention group
compared to control.
Greater increases in
carbohydrate intake
(+0.6, 95% CI +0.1 to
+1.1%, p= 0.02),
complex carbohydrate
(+7, 95% CI +4 to
+12%, p< 0.001), fibre
(+16, 95% CI +4 to
+30 g/day, p< 0.001),
MUFA (+3, 95% CI
+1.0 to +5.0%,
p< 0.001), PUFA (+0.9,
95% CI +0.3 to +1.5,
p= 0.01), and Omega 3
FA (+0.86, +0.25 to
+1.4 g/day, p< 0.001)
in intervention group
compared to control at
2 years. No significant
differences between
groups for protein
intake.
At the food group
level, significant
improvement in olive
oil (+8.2, 95% CI +3.3
to +12.4 g/d,
p< 0.001), fruits,
vegetables, nuts and
legumes (+274, 95% CI
+176 to +372 g/d,
p< 0.001),
Nutrients 2024,16, 1479 10 of 18
Table 2. Cont.
First Author,
Year,
Country
Primary
Risk
Participant
Characteristics
RCT Design
and Study
Duration
Outcomes
Measured
Intervention
Prescribed by
Intervention
Group(s)
Conditions
Comparison
Group(s)
Conditions
Glycaemic
Control
Outcomes
Anthropometry
Outcomes Blood Lipids
Blood Pressure
Diet
vegetables,
whole grains,
walnuts, and
olive oil.
Received
guidance on
increasing their
level of
physical
activity
and wholegrains
(+103, 95% CI +45 to
+159 g/d, p< 0.001)
reported in the
intervention group
compared to the
control group at 2
years. No significant
between-group
differences for alcohol
consumption
Kolehmainen
et al., 2007,
Finland [26]
Impaired
fasting
glycemia
or
impaired
glucose
tolerance
and
features of
metabolic
syndrome
N = 46
(40–70 yrs;
43% male)
Parallel, 33
weeks
Glycaemic
control (FBGL,
fasting insulin,
insulin
sensitivity
index, acute
insulin
response,
glucose
effectiveness
index),
anthropometry
(weight, BMI,
waist
circumference
[WC]), and diet
(4-day food
record)
Intervention:
One-on-one
counselling
provided by a
nutritionist
Control:
instructions by
research
personnel
Subjects
underwent
12-week
intensive
weight
reduction
program
followed by
~20-week
weight
maintenance.
Received
individual
counselling
from
nutritionist
based on food
records, aiming
to decrease
energy intake.
Follow-up
meeting with
nutritionist to
check food
record and
discuss any
difficulties.
Subjects asked
to maintain
physical
activity levels
Subjects were
advised to
continue
normal lifestyle
and to keep
diet and
exercise habits
unchanged
No significant
difference
between
groups for
fasting FBGL,
fasting insulin,
insulin
sensitivity
index, acute
insulin
response, and
glucose
effectiveness
index
Greater
improvements
in weight
(p= 0.0002),
BMI
(p= 0.0002),
and waist
circumference
(p= 0.0001) in
intervention
group
compared to
control at 33
weeks.
Paper did not
report mean
differ-
ence/change
between
groups but
reported mean
change within
groups from
baseline
n/a n/a
Significant decrease in
MUFA intake (%
energy) in the
intervention versus
the control (p= 0.013).
No significant
between-group
difference for energy,
protein, fat, SFA,
PUFAs, carbohydrates,
dietary cholesterol,
fibre, and calcium
intake
Nutrients 2024,16, 1479 11 of 18
Table 2. Cont.
First Author,
Year,
Country
Primary
Risk
Participant
Characteristics
RCT Design
and Study
Duration
Outcomes
Measured
Intervention
Prescribed by
Intervention
Group(s)
Conditions
Comparison
Group(s)
Conditions
Glycaemic
Control
Outcomes
Anthropometry
Outcomes Blood Lipids
Blood Pressure
Diet
Pimentel
et al., 2010,
Brazil [29]
Impaired
glucose
tolerance
and one
other risk
factor for
T2DM
N = 51
(18 yrs
33–38% male at
baseline)
Parallel, 1 year
Glycaemic
control (FBGL,
PPG and
post-prandial
insulin, HbA1c,
HOMA-IR,
fasting insulin),
anthropometry
(weight, BMI),
blood lipids,
and diet (7-day
food record)
Intervention:
one-on-one
counselling
and group
counselling
from
nutritionist
Control: no
information
provided
Participants
received
individual and
group
counselling
from team of
nutritionists.
Consisted of
discussion-
format group
sessions twice
a month and
individual
sessions one
per month.
Instructions to
improve diet
quality
provided orally
and in written
form
Control:
No information
provided.
Greater
improvements
in HbA1c
(p< 0.05), PPG
(p< 0.05),
post-prandial
insulin
(p< 0.05), and
FBGL (p< 0.05)
in the
intervention
group
compared to
control at 1
year.
No significant
difference
between
groups for
fasting insulin
and HOMA-IR.
Paper did not
report mean
differ-
ence/change
between
groups but
reported
significance
No significant
difference
between
groups for
weight and
BMI
Greater
improvements
in total
cholesterol
(p< 0.05) in
intervention
group
compared to
control at 1
year.
No significant
difference
reported
between
groups for
HDL, LDL, and
triglycerides
Paper did not
report mean
differ-
ence/change
between
groups but
reported
significance
n/a
Greater improvements
in dietetic (dietary)
cholesterol (p< 0.05)
in intervention group
compared to control at
1 year.
No significant
difference between
groups for energy,
carbohydrate, protein,
fat, and saturated fat.
Paper did not report
mean
difference/change
between groups but
reported significance
Nutrients 2024,16, 1479 12 of 18
Table 2. Cont.
First Author,
Year,
Country
Primary
Risk
Participant
Characteristics
RCT Design
and Study
Duration
Outcomes
Measured
Intervention
Prescribed by
Intervention
Group(s)
Conditions
Comparison
Group(s)
Conditions
Glycaemic
Control
Outcomes
Anthropometry
Outcomes Blood Lipids
Blood Pressure
Diet
Watanabe
et al., 2003,
Japan [23]
Borderline
diabetes
(patients
with 1-h
PG
10 mmol/L)
N = 156
(35–70 yrs;
100% male)
Parallel, 1 year
Glycaemic
control (FBGL,
1-h, and 2-h
PPG) and diet
(FFQW65)
Intervention:
one-on-one
counselling
was provided
by a
nutritionist,
and additional
resources were
sent via post
Control:
Provided with
oral and
written
information by
the
interventionist
Phase 1:
Individualised
counselling
was received
using a booklet
explaining the
concepts of the
new dietary
education
(NDE)
program.
Information
was provided
on dietary
intake based
on a food
frequency
questionnaire
and motivation
to improve
dietary
practices.
Phase 2: The
following
resources were
sent via post:
letter
encouraging
the subject to
improve
dietary habits,
examples of
menus
corresponding
to the subject’s
RDA and
information to
confirm the
necessity of
blood glucose
control
General oral
and written
information
about results of
health
examination
and food
frequency
questionnaire
without
detailed
explanation.
Received
conventional
group
counselling
using leaflet
with general
information on
prevention of
lifestyle-
related
diseases
Greater
improvement
in 2-h PPG
(15.2%, 95%
CI 22.0 to
8.4%,
p< 0.001) in the
intervention
group
compared to
control at 1
year. No
significant
difference for
fasting BGL
and 1-h PPG
between
groups
n/a
Measured at
baseline but
not reported at
follow-up visit
n/a
Measured at
baseline but
not reported at
follow-up visit
n/a
Measured at
baseline but
not reported at
follow-up visit
Greater improvement
in daily absolute value
of “overin-
take/underintake
fraction” for total
energy intake (%)
(6.0%, 95% CI 9.8
to 2.2%, p= 0.002)
and dinner (15.3%,
95% CI 24.6 to
6.0%, p= 0.002) in
the intervention group
compared to control at
1 year.
No significant
difference for absolute
value of “overin-
take/underintake
fraction” for total
energy intake (%)
during breakfast and
lunch between groups
BGL (Blood Glucose Level), BMI (Body Mass Index), BP (Blood Pressure), FBGL (Fasting Blood Glucose Level), HbA1c (Glycated Haemoglobin), HDL (High-Density Lipoprotein), HOMA-
IR (Homeostatic Model Assessment for Insulin Resistance), LDL (Low-Density Lipoprotein), MUFA (Mono-Unsaturated Fatty Acid), N/A (Not Available), PUFA (Poly-Unsaturated
Fatty Acid), SFA (Saturated Fatty Acid), Yrs (Years), PPG (Post-Prandial Glucose).
Nutrients 2024,16, 1479 13 of 18
Four studies examining fasting BGLs reported significant decreases (p< 0.05) in the
intervention group compared to baseline [
26
29
]. Two of these papers reported significant
differences between the intervention and comparison groups (Figure 2) [
27
,
29
]. Another
study also reported a significant difference in BGLs between the intervention and control
groups but did not specify whether these measures were taken in a fasted state [25].
Nutrients 2024, 16, x FOR PEER REVIEW 13 of 18
Figure 2. Number of studies that reported statistically signicant dierences between the interven-
tion and comparison groups for each outcome of interest. The blue bars represent reported increases
in the intervention group compared to the control group, while the red bars denote decreases. Stud-
ies that examined the outcome but did not report a signicant between-group dierence are not
included in this gure (see Table S3 for further information). Esposito et al. [25] did not specify if
blood/plasma glucose levels (counted under BGL outcome) were measured in a fasted state. Pimen-
tel et al. [29] did not report the timing for when post-prandial glucose or insulin were measured,
while Ben-Yacov et al. [24] calculated post-prandial glucose from continuous glucose monitor
(CGM) data. BGL (Blood Glucose Level), BMI (Body Mass Index), HbA1c (Glycated Haemoglobin),
HOMA-IR (Homeostatic Model Assessment of Insulin Resistance).
Both papers that measured mean glucose using a CGM reported signicant dier-
ences between groups in favour of the intervention groups [24,27]. Interestingly, Dorans
et al. also noted signicant improvements in CGM night-time glucose in the intervention
relative to the control [27].
Among the three studies that evaluated the impact on post-prandial glucose, all re-
ported a signicant dierence between the intervention and control groups, although at
Figure 2. Number of studies that reported statistically significant differences between the intervention
and comparison groups for each outcome of interest. The blue bars represent reported increases in the
intervention group compared to the control group, while the red bars denote decreases. Studies that
examined the outcome but did not report a significant between-group difference are not included in
this figure (see Table S3 for further information). Esposito et al. [
25
] did not specify if blood/plasma
glucose levels (counted under BGL outcome) were measured in a fasted state. Pimentel et al. [
29
] did
not report the timing for when post-prandial glucose or insulin were measured, while Ben-Yacov
et al. [
24
] calculated post-prandial glucose from continuous glucose monitor (CGM) data. BGL (Blood
Glucose Level), BMI (Body Mass Index), HbA1c (Glycated Haemoglobin), HOMA-IR (Homeostatic
Model Assessment of Insulin Resistance).
Nutrients 2024,16, 1479 14 of 18
Both papers that measured mean glucose using a CGM reported significant differences
between groups in favour of the intervention groups [
24
,
27
]. Interestingly, Dorans et al.
also noted significant improvements in CGM night-time glucose in the intervention relative
to the control [27].
Among the three studies that evaluated the impact on post-prandial glucose, all
reported a significant difference between the intervention and control groups, although at
varying times (Table 2, Figure 2, and Table S3). One study found a significant difference at
2 h after a 75 g oral glucose challenge but not at 1 h [
23
], while the other did not specify a
time frame or dose [
29
]. The third study, which used CGM data, noted a difference between
groups at 5 h but not 2 h following an at-home 75 g oral glucose challenge [24].
Four included studies examined HbA1c, of which three reported significant between-
group differences in favour of the intervention group (Figure 2and Table S3) [
24
,
27
,
29
].
Notably, in the one study that reported no difference, the intervention involved only two
sessions with participants [
28
] compared to interventions with 8 [
24
], 10 [
27
], and 36 [
29
]
sessions in the other studies. Ben-Yacovet et al. [
24
] was the only study that measured
fructosamine (a shorter-term measure reflecting 2–3 week changes in blood glucose) and
found a significant decrease in the intervention compared to the control group.
Multiple papers reported outcomes related to insulin, including HOMA-IR and fasting
insulin (Tables 2and S3) [
24
27
,
29
]. Four studies examined HOMA-IR using the homeosta-
sis model of assessment [
24
,
25
,
27
,
29
], of which two reported a significantly greater decrease
in the intervention group compared to the control group (Figure 2) [
25
,
27
]. Similarly, two
of the five studies examining fasting insulin levels [
24
27
,
29
] also reported a significant
between-group difference in favour of the intervention [
25
,
27
]. Pimentel et al. [
29
] also
reported a significant decrease in post-prandial insulin levels in the intervention compared
to the control group but did not indicate the timing of these measurements.
3.6.2. Anthropometric Outcomes
Six of the included studies reported anthropometric data (Table 2) [
24
29
]. All six
studies examined body weight [
24
29
]. Three of these studies reported significant between-
group differences in favour of the intervention group (Figure 2) [
25
27
]. Four studies
examined waist circumference changes [
24
27
], three of which reported significant differ-
ences between the intervention and control groups [
25
27
]. Five studies examined changes
in BMI [
24
26
,
28
,
29
], two of which reported a significant decrease in the intervention group
and a significant difference between the intervention and control groups [25,26].
3.6.3. Blood Lipids
Five studies examined changes in blood lipid levels (Table 2) [
24
,
25
,
27
29
]. All five
studies reported changes in total cholesterol [
24
,
25
,
27
29
]. Of those five, two reported
significant differences between the intervention and control groups (Figure 2) [
25
,
29
]. All
five studies reported changes in HDL cholesterol [
24
,
25
,
27
29
]. Of those five studies, two
reported a significantly greater increase in HDL in the intervention group compared to
the control group [
24
,
25
]. Four papers examined LDL cholesterol, and all papers reported
non-significant between-group differences [
24
,
27
29
]. Four papers reported changes in
triglyceride levels [
24
,
25
,
28
], and two reported a significant decrease in the intervention
compared to control groups [24,25].
3.6.4. Blood Pressure
Five of the included studies examined changes in systolic and diastolic blood pressure
(Table 2) [
24
28
]. Only one study reported significant differences in systolic and diastolic
blood pressure between the intervention and control groups, with the intervention group
showing a significant decrease (Figure 2) [
25
]. Another study reported a significant differ-
ence between the intervention and control groups at the midway assessment (3 months)
but not at the final follow-up [27].
Nutrients 2024,16, 1479 15 of 18
3.6.5. Dietary Outcomes
The methods for assessing dietary outcomes and adherence varied among the studies
(Table 2). One study employed daily food logs via a smartphone app [
24
], offering a selec-
tion of over 7000 foods. One paper utilised a food frequency questionnaire (FFQW65) [
23
],
while another used 24-h recalls [
27
]. However, the most common method across studies
was the use of food records [25,26,29], typically spanning 3 [25]to7[29] days.
Among the seven studies, five evaluated nutrient composition (Table 2) [
24
27
,
29
].
Of these, only one study investigated data at the food group level between groups [
25
].
Additionally, one study reported the top 10 most logged foods by participants based on
whether they received the intervention or control intervention but did not report or quantify
differences in intake between the groups [
24
]. Another study solely reported on the absolute
value of the proportion of over/under intake fraction for estimated total energy [23].
4. Discussion
This review summarises the current evidence on the effectiveness of PPN and MNT
in improving various outcomes related to glycaemic control, anthropometry, blood lipids,
blood pressure, and diet among adults with prediabetes or MetS. Comparing interventions
to standard care or non-personalised approaches showed evidence supporting PPN and
MNT in improving certain glycaemic response outcomes like HbA1c, post-prandial glu-
cose, and waist circumference, where the majority of studies (at least 75%) investigating
these outcomes reported a significant between-group difference favouring the intervention
(Figure 2, Table S3). However, mixed results were found for other outcomes such as fasting
BGL, HOMA-IR, fasting insulin, BMI, weight, blood pressure, and blood lipids (Figure 2
and Table S3). Positive findings were identified for certain outcomes, such as mean CGM
glucose, but were measured in only two studies. Variations in study design, including the
types of PPN interventions and comparison groups utilised, as well as the frequency and
duration of interventions, appeared to influence the magnitude of the reported changes.
The findings suggest that more intense and longer interventions seemed to have
a greater positive effect, especially on outcomes like HbA1c and waist circumference.
For instance, interventions with eight or more sessions or lasting 6 months or longer
showed significant differences in HbA1c levels [
24
,
27
,
29
] and waist circumference [
25
27
]
compared to less intensive and shorter studies. These results are not surprising, as HbA1c
is a long-term marker of blood sugar levels, and longer interventions are expected to have
a more notable impact on HbA1c results. Previous research has demonstrated that multiple
encounters of MNT interventions are necessary to achieve desired outcomes in individuals
with diabetes, such as HbA1c levels [
31
]. Similar findings have been observed for waist
circumference, with longer interventions and more frequent sessions leading to greater
weight loss [
25
27
]. These results are consistent with previous studies indicating that
more than 28 sessions resulted in significantly better improvements in weight, BMI, waist
circumference, HbA1c, and fasting blood glucose levels compared to those who received
fewer sessions [
32
]. Given that obesity is a major risk factor for prediabetes and weight loss
can reduce the risk of developing type 2 diabetes, personalised interventions and MNT
may be effective in managing prediabetes and preventing its progression. However, more
longer-term studies are necessary to better understand the relationship between the dose
of intervention and its response in treating prediabetes.
Studies that compared mean CGM glucose levels [
24
,
27
] showed a significant de-
crease in favour of the PPN intervention groups. However, for other outcomes such as
HOMA-IR, fasting insulin, fasting BGL, weight, BMI, total cholesterol, and blood pressure,
significant differences favouring the intervention were reported in some studies, but the
results were more mixed, as not all studies reported significant results. These findings are
similar to a systematic review focusing on MNT and prediabetes, which found that MNT
compared to standard care significantly improved HbA1c, fasting BGL, anthropometric
measures, cholesterol levels, and blood pressure [
33
]. A study using MNT provided by
dietitians reported a significant decrease within the intervention group in fasting BGL,
Nutrients 2024,16, 1479 16 of 18
weight, and HbA1c [
28
]. Differences in comparison groups and the level of precision and
personalisation in the intervention were major sources of heterogeneity and may explain
the inconsistencies across the studies in this review. As expected, studies that used standard
care or generic information as the comparator tended to report a greater magnitude of
difference in favour of the intervention group [
23
,
25
27
,
29
]. This is in contrast to studies
where comparators, involving some level of guidance from a dietitian or personalisation,
reported smaller differences between the control and intervention groups [
24
,
28
]. For in-
stance, comparison groups that received small group sessions facilitated by a dietitian [
28
],
or a personalised Mediterranean diet but did not receive the same level of precision and
personalisation relating to the microbiome and other clinical or biological markers as the
intervention group [
24
]. Additionally, the purpose of the intervention seemed to impact
the outcomes differently. For example, a low-carbohydrate diet [
27
] successfully produced
significant improvements in glycaemic and anthropometric-related measures relative to the
comparison group but not for other clinical measures, such as blood pressure and blood
lipids. This was in contrast to a study that investigated a tailored Mediterranean diet [
25
]
provided by a nutritionist, which produced significant improvements in all glycaemic, an-
thropometric, and clinical measures and is likely explained by the manipulation of multiple
dietary components in the diet, resulting in a more widespread effect.
4.1. Strengths and Limitations
A major strength of the current review is the inclusion of only RCTs, which are the
highest-ranked study designs in terms of evidence hierarchy. To minimise confounding
effects on the results, studies were excluded if participants were also given medications,
supplements, and physical activity as part of the intervention, thereby focusing solely on
the effectiveness of dietary interventions. Another strength is that six out of the seven
studies had positive ratings regarding quality assessment [
23
28
], therefore having a
lower risk of bias. The limited evidence base and considerable variation in study design,
interventions, comparison groups, and sample populations pose a challenge for drawing
definitive conclusions and generalising results. For example, only one included paper
personalised intake on factors other than diet and health data [24].
4.2. Recommendations
Further research is needed to determine the effectiveness of PPN among diverse pop-
ulation groups, especially since only two studies have investigated PPN interventions in
individuals with MetS [
25
,
26
]. Moreover, different approaches to precision and person-
alisation need to be further explored. For example, only one study integrated advanced
precision data, such as clinical and gut microbiome information, to predict individual post-
prandial glycaemic responses to meals and further tailor their dietary intervention [
24
].
Advances in multi-omic technologies, including genomics and metabolomics, coupled with
sophisticated data analysis techniques, have improved our understanding of individual
variability and led to the identification of novel disease subgroups (subphenotypes) that
impact clinical practice and disease understanding [
2
,
34
]. However, this review highlights
the need for exploration and validation in the use of this information to guide PPN for
people with pre-diabetes and MetS. At the same time, behavioural, psychological, and
sociocultural factors are essential components of dietary prescription, which are core to
MNT and key determinants of patient adherence and should not be underestimated in
future PPN interventions [
35
]. Future studies should aim for larger sample sizes to enhance
study power and detect statistically significant between-group differences more effectively.
This will improve the generalisability of findings to a broader population. Finally, a major
challenge identified during the paper selection process was lack of a standardised definition
for PPN interventions. Establishing a universal definition for PPN will promote consistency
and comparability across research within this field.
Nutrients 2024,16, 1479 17 of 18
5. Conclusions
The current systematic review suggests that the use of PPN and MNT for managing
prediabetes and MetS is promising, especially for interventions of longer duration and that
offer more frequent sessions or contact with interventionists. More consistent improvements
favouring PPN and MNT interventions were reported across studies examining outcomes
such as HbA1c, post-prandial glucose, and waist circumference. However, further research
is essential to enhance our understanding of PPN as a treatment for prediabetes and MetS.
Different approaches to precision and personalisation should be further explored, for
example, through combining MNT with contemporary factors such as genetic, epigenetic,
metabolomic, and metagenomic data.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/nu16101479/s1, Table S1: PRISMA Reporting Guidelines Check-
list; Table S2: Academy of Nutrition and Dietetic Quality criteria checklist for included studies
in the systematic review examining the effect of PN and MNT for adults with prediabetes; Table
S3: Summary of between-group differences between the intervention and comparison groups for
outcomes of interest (Source data for Figure 2in the manuscript). Tables S4–S9: Search string for
each database.
Funding: This research received no external funding.
Informed Consent Statement: Not applicable.
Data Availability Statement: Given that this was a systematic review, all data used for this study are
already published and publicly available.
Acknowledgments: The authors would like to express their gratitude to Research Librarian Nicole
Faull-Brown for her assistance in refining the search strategy.
Conflicts of Interest: C.E.C. is funded by an Australian National Health and Medical Research
Council Leadership Investigator Grant (APP2009340). The authors declare that they have no conflicts
of interest to disclose.
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... Identifying exogenous and endogenous metabolites is likely to play a key role in the development and implementation of personalized nutrition interventions. Studies have shown that dietary intake improves to a greater extent in individuals who received personalized nutrition advice compared with that in those with generalized dietary advice [8,9]. Specifically, the pan-European Food4Me RCT demonstrated that compared with generalized dietary advice, targeted personalized nutrition advice based on dietary intake, phenotype, and genotype was more effective for improving food group, nutrient intakes, and overall dietary patterns [10,11]. ...
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Context Dietary modifications can improve cardiovascular disease (CVD) risk factors. Personalized nutrition (PN) refers to individualized nutrition care based on genetic, phenotypic, medical, behavioral, and/or lifestyle characteristics. PN may be beneficial in improving CVD risk factors, including diet. However, this has not been reviewed previously. Objective The aim was to evaluate the effectiveness of PN interventions on CVD risk factors and diet in adults at elevated CVD risk. Data Sources Six databases were searched for randomized controlled trials published between 2000 and 2023 that tested the impact of PN interventions on CVD risk factors in people at elevated risk. Data Extraction Risk of bias was assessed using the Academy of Nutrition and Dietetics Quality Criteria checklist. Data synthesis of eligible articles included participant characteristics, intervention details, and change in primary CVD risk factor outcomes, including blood pressure (BP), plasma lipids, and CVD risk score, and secondary risk factors, including anthropometric outcomes and diet quality. Random-effects meta-analyses were conducted to explore weighted mean differences (WMDs) in change or final mean values for studies with comparable data (studies with dietary counseling interventions) for outcomes including BP, blood lipids, and anthropometric measurements. Data Analysis Of 7676 identified articles, 16 articles representing 15 studies met the inclusion criteria. Studies included between 40 and 563 participants and reported outcomes for CVD risk factors, including hyperlipidemia (n = 5), elevated BP (n = 3), overweight/obesity (n = 1), and multiple risk factors (n = 6). Risk of bias was low. Results suggested potential benefit of PN on systolic BP (WMD: −1.91; 95% CI: −3.51, −0.31 mmHg) and diastolic BP (WMD: −1.49; 95% CI: −2.39, −0.58 mmHg) and dietary intake in individuals at high CVD risk. Results were inconsistent for plasma lipid and anthropometric outcomes. Conclusion Results were promising for PN interventions that used dietary counseling on CVD risk factors in at-risk individuals. However, further evidence for other personalization methods is required, including improving methodological quality and longer study duration in future PN interventions. Systematic Review Registration OpenScience Framework (https://doi.org/10.17605/OSF.IO/SHVWP).
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