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COMMENTARY
A Systematic Approach to Treating Early Metabolic
Disease and Prediabetes
Nicholas W. Carris .Brian E. Bunnell .Rahul Mhaskar .
Christopher G. DuCoin .Marilyn Stern
Received: June 30, 2023 / Accepted: July 19, 2023
The Author(s) 2023
ABSTRACT
At least 70% of US adults have metabolic dis-
ease. However, less is done to address early dis-
ease (e.g., overweight, obesity, prediabetes)
versus advanced disease (e.g., type 2 diabetes
mellitus, coronary artery disease). Given the
burden of advanced metabolic disease and the
burgeoning pandemics of obesity and predia-
betes a systematic response is required. To
accomplish this, we offer several
recommendations: (A) Patients with over-
weight, obesity, and/or prediabetes must be
consistently diagnosed with these conditions in
medical records to enable population health
initiatives. (B) Patients with early metabolic
disease should be offered in-person or virtual
lifestyle interventions commensurate with the
findings of the Diabetes Prevention Program.
(C) Patients unable to participate in or other-
wise failing lifestyle intervention must be
screened to assess if they require pharma-
cotherapy. (D) Patients not indicated for,
refusing, or failing pharmacotherapy must be
screened to assess if they need bariatric surgery.
(E) Regardless of treatment approach or lack of
treatment, patients must be consistently
screened for the progression of early metabolic
disease to advanced disease to enable early
control. Progression of metabolic disease from
an overweight yet otherwise healthy person
includes the development of prediabetes, obe-
sity ±prediabetes, dyslipidemia, hypertension,
type 2 diabetes, chronic kidney disease, coro-
nary artery disease, and heart failure. Systematic
approaches in health systems must be deployed
with clear protocols and supported by stream-
lined technologies to manage their population’s
metabolic health from early through advanced
metabolic disease. Additional research is needed
to identify and validate optimal system-level
interventions. Future research needs to identify
strategies to roll out systematic interventions
for the treatment of early metabolic disease and
N. W. Carris (&)
Department of Pharmacotherapeutics and Clinical
Research, Taneja College of Pharmacy, University of
South Florida, 12901 Bruce B. Downs Blvd MDC 30,
Tampa, FL 33612, USA
e-mail: carris@usf.edu
B. E. Bunnell
Department of Psychiatry and Behavioral
Neurosciences, Morsani College of Medicine,
University of South Florida, Tampa, FL, USA
R. Mhaskar
Department of Internal Medicine, Morsani College
of Medicine, University of South Florida, Tampa, FL,
USA
C. G. DuCoin
Department of Surgery, Morsani College of
Medicine, University of South Florida, Tampa, FL,
USA
M. Stern
Department of Child and Family Studies, College of
Behavioral and Community Sciences, University of
South Florida, Tampa, FL, USA
Diabetes Ther
https://doi.org/10.1007/s13300-023-01455-9
to improve the metabolic health among the
progressively younger patients being impacted
by obesity and diabetes.
Keywords: Diabetes; Obesity; Overweight;
Population health; Prediabetes; Prevention
Key Summary Points
Diabetes and other metabolic diseases are
highly undesirable.
Prediabetes and obesity should be
consistently diagnosed to facilitate
population health initiates.
With patient-specific factors considered,
evidence supports treating prediabetes
and obesity with lifestyle intervention,
medications, and bariatric surgery.
Systematic approaches to preventing or
delaying the progression of metabolic
disease are needed, given the pandemics
of obesity and prediabetes.
Additional research is needed to identify
the best ways to roll out interventions to
treat early metabolic disease.
Additional research is needed to improve
interventions targeting the metabolic
health of youths.
A speaker asked for hands to be raised in an
auditorium where there were more held doc-
torate degrees than people and the average body
mass index (BMI) likely approximated 22 kg/
m
2
. ‘‘If you’d like to participate, please raise
your hand if you do not have type 2 diabetes.’’
Many hands went up. Then the speaker asked,
‘‘please keep your hand in the air if you are
indifferent to whether or not you develop
type 2 diabetes.’’ No hands remained in the air.
Next, the speaker asked, ‘‘please raise your hand
if you address new-onset type 2 diabetes in
clinic.’’ Many hands went up. Then the speaker
asked, ‘‘please keep your hand in the air if you
have seen a baseline glycated hemoglobin of at
least 8%.’’ No hands went down. ‘‘How about
9%?’’ No hands went down. ‘‘What about 10%?’’
Still, no hands went down. ‘‘Ok, 11%?’’ The
hands of a few of the younger clinicians went
down. Not until 14% were only a few hands
remaining. When this exercise is repeated, the
results are invariable. No one wants to develop
type 2 diabetes, and many patients present for
care after type 2 diabetes has had the opportu-
nity to cause irreparable damage to life [1], limb
[2], and a bank account [3].
Why, then does the American Diabetes
Association write, ‘‘[p]rediabetes should not be
viewed as a clinical entity in its own right but
rather as a risk factor for progression to diabetes
and cardiovascular disease’’ [4]? How is this
different from hypertension, which daily causes
no symptoms, but rather is a risk factor for
myocardial infarction, stroke, and heart failure
[5]? Why, as a health system, are we not doing
more to treat overweight, obesity, and predia-
betes? There appears to be a disconnect between
clinician preference for their health, their
interpretation of the evidence, and their
approach to patient care. This commentary will
reconcile this disconnect and propose a way
forward. This article is based on previously
conducted studies and does not contain any
new studies with human participants or animals
performed by any of the authors.
THE STATE OF THINGS
At least 70% of United States (US) adults have
metabolic disease (e.g., overweight, obesity,
hypertension, prediabetes, diabetes, coronary
artery disease), including over 100 million US
adults with prediabetes [5]. There is debate
among guidelines and nations regarding what
should be indicative of prediabetes; however, it
is somewhat trivial as it does not impact the
primary patient care approach. Whether a
patient has overweight, obesity, or prediabetes
(either defined as a fasting blood glucose of
100 mg/dL or 110 mg/dL), they would benefit
from improvements in their diet and exercise
habits, as would all patients with metabolic
disease [6].
Diabetes Ther
Disagreement becomes meaningful in
choosing pharmacotherapy. The American
Diabetes Association reserves medication ther-
apy, specifically metformin, for patients with
higher degrees of dysglycemia [6]. Metformin is
recommended to prevent type 2 diabetes in
adults aged 25–59 years, with BMI C35 kg/m
2
,
fasting glucose C110 mg/dL, glycated hemo-
globin C6.0%, and in women with prior ges-
tational diabetes [6]. The American Association
of Clinical Endocrinology does not require a
higher degree of dysglycemia for medication
therapy (i.e., metformin, pioglitazone, acar-
bose) and further recommends glucagon-like
peptide 1 (GLP-1) receptor agonists or phenter-
mine/topiramate extended-release among
patients with prediabetes also indicated for
weight loss pharmacotherapy (obesity or BMI
C27 kg/m
2
with weight-related comorbidity)
[7]. Conversely, others have advocated for no
pharmacotherapy for prediabetes [8–10]. Indeed
a state-of-the-art review from the Journal of the
American College of Cardiology concluded,
‘‘[w]ith regard to primary prevention of [car-
diovascular disease], it is clear that for [diabetes
mellitus]…there is no role for pharmacological
interventions in individuals without overt dis-
ease.’’ But was this clear then, and if so, is it still
now?
HARD OUTCOMES
AND THE DIABETES PREVENTION
PROGRAM
Improving lifestyle, noted by healthier diet and
more exercise, is the core approach to prevent-
ing new-onset type 2 diabetes [6]. Notable goals
and approaches to intensive lifestyle interven-
tion include achieving and maintaining a 7%
reduction in baseline body weight through
moderate-intensity exercise (C150 min/week)
and a healthy reduced-calorie diet (500–-
1000 kcal per day below the amount needed to
maintain body weight) [6]. While the Diabetes
Prevention Program focused diet change on
reducing total dietary fat and calories, several
patient-tailored approaches to diet can be used
for diabetes prevention including Mediter-
ranean-style, low-carbohydrate, vegetarian,
plant-based, and Dietary Approaches to Stop
Hypertension (DASH).
The Diabetes Prevention Program demon-
strated the impact of intensive lifestyle inter-
vention in preventing new-onset type 2
diabetes [11]. As such, the trial offered a group-
implemented lifestyle intervention to all par-
ticipants after the 3-year initial study of inten-
sive lifestyle intervention, metformin, and
placebo [12]. Yet, even at 21 years of follow-up,
both metformin and intensive lifestyle inter-
vention resulted in significantly fewer cases of
new-onset type 2 diabetes versus placebo (pla-
cebo 60%, metformin 55%, lifestyle interven-
tion 53%, p\0.01 for both interventions versus
placebo) [13]. Several important points can be
gleaned from these data.
First, an absolute reduction of 5–7% over
21 years in new-onset type 2 diabetes is not
dramatic [13]. Second, with the small difference
in the development of type 2 diabetes overall, it
is not surprising that the study did not
demonstrate a difference in microvascular or
macrovascular outcomes between treatment
groups [13,14]. Third, the smaller absolute dif-
ference is less surprising as all participants were
offered a group lifestyle intervention following
year 3. Fourth, the lifetime risk of developing
new-onset type 2 diabetes is high among adults
with prediabetes, even in a controlled trial with
optimized care. In addition to the findings by
treatment assignment, overall findings demon-
strated significant benefit from not developing
versus developing type 2 diabetes in the long-
term follow-up. At 15 years, not having diabetes
was associated with a 28% lower prevalence of
microvascular complications [14]. Additionally,
at an average follow-up of 22 years, not having
diabetes was associated with a 57% lower risk of
early eye changes, a 37% lower risk of kidney
disease, and a 39% lower risk of major cardio-
vascular disease endpoints [15].
A clinical treatment approach either values
preventing type 2 diabetes or it does not. Clin-
icians cannot abdicate the decision with a
patient in front of them. No decision to pro-
mote metabolic health is a decision not to
promote metabolic health. So, each clinician
must answer whether attempting to prevent
type 2 diabetes helps. The evidence suggests it
Diabetes Ther
does. Importantly, there is no economic ratio-
nale for withholding lifestyle intervention or
metformin as both are cost-saving in attempt-
ing to prevent of type 2 diabetes [16–18]. A key
point in the preceding sentence is the word
‘‘attempting’’ as adherence significantly waned
over time in the group assigned to receive
metformin [13]. Even so, diabetes prevalence
remained lower with metformin, and cost-sav-
ings were achieved [12,16,17]. Therefore, even
if the evidence suggesting a macro- and
microvascular benefit from not developing
type 2 diabetes is discounted, the primary
interventions to delay or prevent new-onset
type 2 diabetes are cost-saving, with the added
benefit of avoiding type 2 diabetes in some
patients as highlighted above. Indeed, as illus-
trated above and in the literature, type 2 dia-
betes impacts a range of patient-centered
outcomes [19]. Thus, lifestyle intervention and
metformin should be deployed systematically
to improve patient outcomes and reduce
healthcare spending.
In addition to the overall benefits observed
in the Diabetes Prevention Program, five key
points further highlight the expected benefit of
metformin for diabetes prevention in practice.
First, the benefit of metformin in preventing
type 2 diabetes is not simply masking type 2
diabetes. The majority of metformin’s benefit
was retained following metformin washout in
the Diabetes Prevention Program [20]. Second,
it is established that greater adherence to met-
formin improved its effect on reduced new-on-
set type 2 diabetes [21]. Thus, patients choosing
and staying on metformin can expect benefits
beyond those observed in the Diabetes Preven-
tion Program. Third, it is established that a
major health disparity exists in diabetes preva-
lence [22,23]. Diabetes prevention efforts must
account for health disparity and access to care
[22]. Not all patients have access to healthy
foods or intensive lifestyle interventions. As
such, patients should be offered metformin
when indicated as the ‘‘in practice’’ alternative
to metformin for some patients would be no
intervention, far below the level of care pro-
vided to patients in the placebo group of the
Diabetes Prevention Program. Fourth, key sub-
groups benefited from metformin more or less
in the Diabetes Prevention Program. These
subgroups are mirrored in the American Dia-
betes Association’s recommendations [6]. Thus,
targeting metformin therapy in practice toward
patients younger and more obese would gener-
ate a benefit greater than that observed in the
Diabetes Prevention Program as obesity is a
trigger for increasing insulin resistance and
subsequent new-onset type 2 diabetes [24].
Fifth, men, particularly younger men, are
underrepresented among those participating in
the National Diabetes Prevention Program [25].
In the Diabetes Prevention Program, men
experienced a reduction in coronary artery cal-
cium with metformin therapy [26]. Thus, the
patients least likely to participate in intensive
lifestyle interventions (i.e., younger patients
and men) are also the most likely to benefit
from metformin.
A WAY FORWARD
Overweight, obesity, and prediabetes must be
diagnosed for two key reasons. First, this
enables population health approaches to
improve patient care and outcomes [27]. With a
diagnosis, these efforts can be done efficiently.
Population health studies in hypertension and
diabetes have demonstrated improvements in
care, and patient identification is simplified
when International Classification of Diseases
(ICD) codes can be searched electronically
[28,29]. Without a diagnosis, population health
initiatives become less efficient if they must
start with screening [30,31]. Specific to predia-
betes, when patients are already screened for
type 2 diabetes in practice, the return on
investment of screening is diminished when
prediabetes is identified…but not diagnosed
[31]. Second, patients receiving a diagnosis
should receive a plan in a progress note for the
new problem identified as standard care.
Indeed, it has been demonstrated that diag-
nosed obesity prompts action by clinicians [32].
However, many patients with prediabetes
receive no diagnosis and no intervention [33].
Regardless of intervention, the diagnosis of
overweight, obesity, or prediabetes can be done
at no added cost (as screening is already being
Diabetes Ther
done) and can improve the process for contin-
ued screening for progression to overt metabolic
disease, including type 2 diabetes. By formaliz-
ing a follow-up plan for subsequent screening, it
may be possible to limit patients being lost to
follow-up and later presenting with significant
glucose elevations as their new baseline or other
advanced metabolic disease.
Recommendations for screening for predia-
betes and type 2 diabetes have been established
by multiple guidelines [4,6,34]. Patients lost to
follow-up with prediabetes who later present
with type 2 diabetes and significantly elevated
glucose appear to be at substantially higher risk
for complications [35–37]. Conversely, patients
benefit from the early treatment and control of
type 2 diabetes [35,38,39]. Moreover, the
benefit of early treatment and control of type 2
diabetes is likely underestimated as newer
medications for the treatment of type 2 diabetes
significantly reduce cardiovascular events
[40–44]. Given the benefit of early treatment
and control of type 2 diabetes [35], and the
harm associated with developing type 2 dia-
betes versus not developing type 2 diabetes
[14,15], we propose it is appropriate for clini-
cians to operate with the understanding that
patients significantly benefit when they do not
develop type 2 diabetes. Among various popu-
lations, the interventions which appear to have
the clearest data regarding diabetes prevention
and cardiovascular benefit are intensive lifestyle
intervention [11,15,16,45], metformin
[11,12,20,35,46], pioglitazone [47,48], and
liraglutide and semaglutide [40–42,49,50]. As
such, we propose these interventions should be
systematically deployed as supported by evi-
dence and guideline for the prevention of type 2
diabetes [6,7,34].
Patients with the early metabolic disease
(e.g., overweight) are at notable risk for the
continued progression of their metabolic dis-
ease, including obesity ±prediabetes, dyslipi-
demia, hypertension, chronic kidney disease,
coronary artery disease, heart failure, and death
[4,51–55]. Therefore, as population health ini-
tiatives across health systems seek to improve
care quality, prediabetes should be incorporated
into overall metabolic health for two reasons.
First, it is presently undertreated and a
necessary component of comprehensive meta-
bolic care. Second, it is an early diagnosis of
metabolic disease in addition to overweight and
obesity. By identifying prediabetes, there is a
greater chance to intervene to slow metabolic
disease progression overall. Particularly in
younger patients, treating overweight, obesity,
and prediabetes earlier and more aggressively
has to potential to support healthier aging.
Moreover, employer-based health insurance
tends to have better prescription drug coverage
versus Medicare, thus potentially enabling
greater access to novel medications and treat-
ments with greater effects in working-age
patients.
MORE IS NEEDED
Greater action is needed to systematically
deploy interventions known to be effective in
treating overweight, obesity, and prediabetes.
Two key approaches include health system
protocol and electronic health record decision
information support [56]. Health system proto-
col is needed to prompt clinician behavior. No
medication has a specific US Food and Drug
Administration-approved indication for predia-
betes, though medications are approved for
obesity and overweight (BMI C27 kg/m
2
with
weight-related comorbidity). However, evi-
dence and guideline strongly support ‘‘off-label’’
use for prediabetes. As such, health systems
must enact protocols to encourage evidence-
based treatments and destigmatize off-label
pharmacotherapy for prediabetes in addition to
streamlining the appropriate use of medications
for weight loss and standardize referrals to bar-
iatric surgery. Decision information support can
facilitate these efforts by promoting the con-
sistent diagnosis of overweight, obesity, and
prediabetes. Moreover, it can be paired with
one-click referral to community lifestyle inter-
ventions partnered with the National Diabetes
Prevention Program. It is in these programs
where patients can receive the education and
support needed to adopt healthy patterns of
diet and exercise [25]. Additionally, based on
predetermined parameters, decision informa-
tion support can identify patients that may be
Diabetes Ther
indicated for pharmacologic or surgical
treatment.
The health system approaches outlined
above can be implemented broadly today.
Research is needed to refine replicable methods
to deploy these interventions. However, the
20-plus-year trends in obesity and diabetes
suggest systemic factors negatively impacting
metabolic health [57–59]. One analysis identi-
fied Sugar Research Foundation sponsored
research as problematic [60], the timing of
which roughly corresponded to the start of the
increasing prevalence of obesity and diabetes
[57–59]. Now many commonly purchased food
items contain added sugars [61,62], a known
risk factor for cardiovascular disease mortality
[63]. The food supply must be examined and
addressed to enable healthy choices, without
the economic burden of ‘‘organic’’ items which
are also less accessible [64–66]. Through public
health approaches to improve the food supply it
may be possible to improve the metabolic
health of whole populations by addressing
excess (and empty) calories [67], insufficient
vitamin D [68], insufficient dietary fiber [69],
and insufficient healthy proteins [70].
The need to address metabolic health sys-
tematically is urgent. While some cling to BMI
as inaccurate with the hope that the metabolic
health of the USA is better than as measured by
BMI, recent data indicate that, if anything, BMI
underidentifies people with obesity and that the
metabolic health of the USA is even worse than
BMI would suggest [71,72]. And today, poor
metabolic health impacts younger adults, ado-
lescents, and children more than ever [73–76].
Indeed, the number of children developing and
maintaining an unhealthy lifestyle portends an
insurmountable wave of metabolic disease
[74–76]. Recent evidence suggests the trend of
increased new pediatric type 2 diabetes is not
reversing post-pandemic [77]. Moreover, body
image, depression, social media, gaming, screen
time, parental role, school role, and socioeco-
nomic status complicate reaching and posi-
tively impacting children and adolescents
[73,78,79], those most likely to benefit long-
term from adopting and maintaining a healthy
lifestyle. Therefore, we propose research needs
with the potential to improve the metabolic
health of youth without compromising, and
hopefully improving, mental health.
What is the Best Way to Start
Conversations with Children, Adolescents,
and Parents About Metabolic Health?
It is quite possible, and even likely, that the
answer to this question will vary by age, sex,
culture, and socioeconomic status. Addition-
ally, we expect various individuals to be able to
positively impact a child or adolescent’s life and
metabolic health, though each may need a dif-
ferent conversation starter. Parents and guar-
dians are a vital influence in a child’s life and
need to be equipped to address health with their
child in a positive way. Other key childhood
points of contact include teachers, pediatri-
cians, and dentists. The start of conversations
regarding metabolic health in all circumstances
is vital to promote healthy change in lifestyle,
while being cognizant of mental health issues,
body image/self-esteem issues, and identity. We
hypothesize empowerment and a focus on
wellness will be important in broadly support-
ing physical and mental health. Regarding par-
ents, particularly the parents of students who
are elementary-age or younger, research is nee-
ded on how to deliver nutrition and physical
activity education to parents and prompt par-
ents to impose healthy dietary and physical
activity habits on their children in an appro-
priate way.
What is the Best Way to Systematically
Increase Physical Activity Among Public
School Attendees?
Lifestyle interventions focusing on improving
eating and physical activity behaviors have
become standard approaches to improving
health, although generally yielding only mod-
est impact [80]. Unfortunately, sweat, stigma,
fitting in, time, and comparison may jeopardize
consistent participation in physical activity
among various groups [81–83]. Moreover,
training among school teachers in physical
activity, if any, is not standardized [84–86].
Limited time, resources for exercise, space, and
Diabetes Ther
personnel appear to be obstacles [81,82]. We
hypothesize these obstacles can be overcome
using current school resources and personnel
through additional targeted pieces of training
for school teachers, protocolized in-class physi-
cal activity curriculum, and directing teachers
to prioritize physical activity time [84,85].
What is the Best Way to Combat Social
Media, Screen Time, and Gaming Among
Children and Adolescents?
There appears to be a link between social media
and depression [78,87]. A bidirectional rela-
tionship between obesity and depression is
known [79,88]. Limited data suggest video
game time impairs physical health, having an
impact on total sedentary time [89]. As time has
been established as a key limiting factor in
physical activity, we pose time spent playing
video games as an important factor in poten-
tially limiting physical activity time. Indeed,
digital addiction has become a major problem
exacerbated by the pandemic [90]. Youth have
long been known to be particularly susceptible
to addiction based on neurodevelopment [91],
and screen time, social media, and gaming
appear to impact the same dopamine reward
pathway as opioids [92,93]. Together, there
appears to be a great but ill-defined harm asso-
ciated with screens in all forms, with a pro-
gressively worse impact as use increases and age
decreases, at the very least by promoting
increased sedentary time. More research is nee-
ded to clarify this harm and support children
and parents in methods to avoid this harm.
A CALL TO ACTION
Systematic approaches to promote metabolic
health are available but must be deployed
including the use of health system protocols
and technological approaches to streamline
metabolic care in an already burdened health
system [94]. However, these approaches are
limited to the extent they are an institutional
priority. Smaller physician groups may not have
an information systems team able to deploy
decision information support. Additionally,
depending on the physician group’s business
structure, there may not be a medical officer or
therapeutics committee able to enact and
monitor protocols. Even in larger health sys-
tems, approving protocols and building out the
needed support in electronic health records can
be a long process. Thus, institutional commit-
ment and incentive from insurers are needed. A
1-year time horizon for quality metrics and
shared savings is not likely to capture the ben-
efit of systematically treating early metabolic
disease. Thus, research, streamlined and repli-
cable systems, and updated policy are needed to
optimize a systematic approach to treating early
metabolic disease, its institutional rollout, and
its incentivization.
The systematic treatment of early metabolic
disease must include support for adopting a
healthy diet and regular exercise in addition to
the judicious, but systematic, use of medica-
tions and surgery [6,45,95]. While evidence
supports lifestyle intervention, medications,
and surgery, again more research is needed.
Specifically, research is needed to identify the
best way to get patients engaged in and adher-
ent to a lifestyle intervention program. Simi-
larly, research is needed to identify the best way
to identify and approach patients suitable for
metformin or other medication therapy for
early metabolic disease. Unfortunately, newer
medications are expensive and approaches to
increase patient access without overwhelming
the healthcare system need to be developed. As
part of this, the impact of higher-dose GLP-1
receptor agonists on cardiovascular outcomes
needs to be assessed in addition to assessing
their cardiovascular outcomes in patients with-
out type 2 diabetes. A Study of Tirzepatide on
the Reduction on Morbidity and Mortality in
Adults with Obesity (SURMOUNT-MMO) will
help shed light on this as it plans to enroll
15,000 patients without diabetes and will assess
the composite outcome of all-cause mortality,
nonfatal myocardial infarction, nonfatal stroke,
coronary revascularization, and heart failure
events [96].
Lifestyle diseases require a lifetime of action.
Indeed, the impact of metabolic disease on
human health begins prior to conception, as the
metabolic health of mothers impacts their
Diabetes Ther
offspring [97]. The impact of obesity and dia-
betes globally has escalated over the past
20 years and the projections are ominous as
obesity is expected to drive the global preva-
lence of diabetes to 1.3 billion by 2050 [98].
While food supply and societal factors need to
be improved, patients cannot wait. Continuing
the approach of diet and exercise only for
metabolic health is not patient-centered, pro-
motes health disparity, and is illogical as it has
failed for over 20 years [22,23,57–59].
ACKNOWLEDGEMENTS
Author Contribution. Nicholas W. Carris
wrote the first draft. All named authors
reviewed the manuscript critically for important
intellectual content, approved the submitted
version, and agree to be accountable for the
work.
Funding. No funding or sponsorship was
received for this study or publication of this
article.
Ethical Approval. This article is based on
previously conducted studies and does not
contain any new studies with human partici-
pants or animals performed by any of the
authors.
Conflict of Interest. Christopher DuCoin
declares consulting for Johnson & Johnson,
Medtronic, and Intuitive. Nicholas Carris, Brian
Bunnell, Rahul Mhaskar, and Marilyn Stern
have nothing to disclose.
Open Access. This article is licensed under a
Creative Commons Attribution-NonCommer-
cial 4.0 International License, which permits
any non-commercial use, sharing, adaptation,
distribution and reproduction in any medium
or format, as long as you give appropriate credit
to the original author(s) and the source, provide
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nc/4.0/.
REFERENCES
1. Raghavan S, Vassy JL, Ho YL, et al. Diabetes melli-
tus-related all-cause and cardiovascular mortality in
a national cohort of adults. J Am Heart Assoc.
2019;8: e011295. https://doi.org/10.1161/jaha.118.
011295.
2. Lin C, Liu J, Sun H. Risk factors for lower extremity
amputation in patients with diabetic foot ulcers: a
meta-analysis. PLoS ONE. 2020;15:e0239236.
https://doi.org/10.1371/journal.pone.0239236.
3. Gao CC, Espinoza Suarez NR, Toloza FJK, et al.
Patients’ perspective about the cost of diabetes
management: an analysis of online health com-
munities. Mayo Clin Proc Innov Qual Outcomes.
2021;5:898–906. https://doi.org/10.1016/j.
mayocpiqo.2021.07.003.
4. ElSayed NA, Aleppo G, Aroda VR, et al. 2. Classifi-
cation and diagnosis of diabetes: standards of care
in diabetes-2023. Diabetes Care. 2023;46:S19–40.
https://doi.org/10.2337/dc23-S002.
5. Tsao CW, Aday AW, Almarzooq ZI, et al. Heart
disease and stroke statistics-2023 update: a report
from the American Heart Association. Circulation.
2023;147:e93–621. https://doi.org/10.1161/cir.
0000000000001123.
6. ElSayed NA, Aleppo G, Aroda VR, et al. 3. Preven-
tion or delay of type 2 diabetes and associated
comorbidities: standards of care in diabetes-2023.
Diabetes Care. 2023;46:S41–8. https://doi.org/10.
2337/dc23-S003.
7. Samson SL, Vellanki P, Blonde L, et al. American
Association of Clinical Endocrinology Consensus
Statement: comprehensive type 2 diabetes man-
agement algorithm - 2023 update. Endocr Pract.
2023;29:305–40. https://doi.org/10.1016/j.eprac.
2023.02.001.
Diabetes Ther
8. Redberg RF. The medicalization of common con-
ditions. JAMA Intern Med. 2016;176:1863. https://
doi.org/10.1001/jamainternmed.2016.6210.
9. Shahraz S, Pittas AG, Kent DM. Prediabetes risk in
adult Americans according to a risk test. JAMA
Intern Med. 2016;176:1861–3. https://doi.org/10.
1001/jamainternmed.2016.5919.
10. Farr OM, Mantzoros CS. Treating prediabetes in the
obese: are GLP-1 analogues the answer? Lancet.
2017;389:1371–2. https://doi.org/10.1016/s0140-
6736(17)30315-x.
11. Knowler WC, Barrett-Connor E, Fowler SE, et al.
Reduction in the incidence of type 2 diabetes with
lifestyle intervention or metformin. N Engl J Med.
2002;346:393–403. https://doi.org/10.1056/
NEJMoa012512.
12. Knowler WC, Fowler SE, Hamman RF, et al. 10-year
follow-up of diabetes incidence and weight loss in
the Diabetes Prevention Program Outcomes Study.
Lancet. 2009;374:1677–86. https://doi.org/10.1016/
s0140-6736(09)61457-4.
13. Goldberg RB, Orchard TJ, Crandall JP, et al. Effects
of long-term metformin and lifestyle interventions
on cardiovascular events in the diabetes prevention
program and its outcome study. Circulation.
2022;145:1632–41. https://doi.org/10.1161/
circulationaha.121.056756.
14. Diabetes Prevention Program Research Group.
Long-term effects of lifestyle intervention or met-
formin on diabetes development and microvascular
complications over 15-year follow-up: the Diabetes
Prevention Program Outcomes Study. Lancet Dia-
betes Endocrinol. 2015;3:866–75. https://doi.org/
10.1016/s2213-8587(15)00291-0.
15. American Diabetes Association. New data from
diabetes prevention program outcomes study shows
persistent reduction of type 2 diabetes development
over 22-year average follow-up. https://diabetes.
org/newsroom/press-releases/2020/new-data-from-
diabetes-prevention-program-outcomes-study-
shows-persistent-reduction-of-t2d-development-
over-22-year-average-follow-up. Accessed 14 Jun
2023.
16. Herman WHES, Ratner RE, Montez MG, et al. The
10-year cost-effectiveness of lifestyle intervention
or metformin for diabetes prevention: an intent-to-
treat analysis of the DPP/DPPOS. Diabetes Care.
2012;35:723–30. https://doi.org/10.2337/dc11-
1468.
17. Carris NW, Cheng F, Kelly WN. The changing cost
to prevent diabetes: a retrospective analysis of the
Diabetes Prevention Program. J Am Pharm Assoc.
2003;2017(57):717–22. https://doi.org/10.1016/j.
japh.2017.05.015.
18. Centers for Medicare and Medicaid Services. Medi-
care Diabetes Prevention Program expansion.
https://www.cms.gov/Newsroom/
MediaReleaseDatabase/Fact-sheets/2016-Fact-
sheets-items/2016-07-07.html. Accessed 21 Jun
2018.
19. Nano J, Carinci F, Okunade O, et al. A standard set
of person-centred outcomes for diabetes mellitus:
results of an international and unified approach.
Diabet Med. 2020;37:2009–18. https://doi.org/10.
1111/dme.14286.
20. Diabetes Prevention Program Research Group.
Effects of withdrawal from metformin on the
development of diabetes in the diabetes prevention
program. Diabetes Care. 2003;26:977–80. https://
doi.org/10.2337/diacare.26.4.977.
21. Walker EA, Gonzalez JS, Tripputi MT, et al. Long-
term metformin adherence in the Diabetes
Prevention Program Outcomes Study. BMJ Open
Diabetes Res Care. 2020. https://doi.org/10.1136/
bmjdrc-2020-001537.
22. Spencer-Bonilla G, Rodriguez-Gutierrez R, Montori
VM. What we don’t talk about when we talk about
preventing type 2 diabetes-addressing socioeco-
nomic disadvantage. JAMA Intern Med. 2016;176:
1053–4. https://doi.org/10.1001/jamainternmed.
2016.2952.
23. Gaskin DJ, Thorpe RJ Jr, McGinty EE, et al. Dispar-
ities in diabetes: the nexus of race, poverty, and
place. Am J Public Health. 2014;104:2147–55.
https://doi.org/10.2105/ajph.2013.301420.
24. Wondmkun YT. Obesity, insulin resistance, and
type 2 diabetes: associations and therapeutic
implications. Diabetes Metab Syndr Obes. 2020;13:
3611–6. https://doi.org/10.2147/dmso.S275898.
25. Ely EK, Gruss SM, Luman ET, et al. A national effort
to prevent type 2 diabetes: participant-level evalu-
ation of CDC’s National Diabetes Prevention Pro-
gram. Diabetes Care. 2017. https://doi.org/10.2337/
dc16-2099.
26. Goldberg RB, Aroda VR, Bluemke DA, et al. Effect of
long-term metformin and lifestyle in the diabetes
prevention program and its outcome study on
coronary artery calcium. Circulation. 2017. https://
doi.org/10.1161/circulationaha.116.025483.
27. Williams J, Sachdev N, Kirley K, et al. Implemen-
tation of diabetes prevention in health care orga-
nizations: best practice recommendations. Popul
Health Manag. 2022;25:31–8. https://doi.org/10.
1089/pop.2021.0044.
Diabetes Ther
28. Dugdale DC, Khor S, Liao JM, Flum DR. Association
between a population health intervention and
hypertension control. J Gen Intern Med. 2022;37:
4095–102. https://doi.org/10.1007/s11606-022-
07522-4.
29. Tanumihardjo JP, Kuther S, Wan W, et al. New
frontiers in diabetes care: quality improvement
study of a population health team in rural critical
access hospitals. J Gen Intern Med. 2023;38:56–64.
https://doi.org/10.1007/s11606-022-07928-0.
30. Hoerger TJ, Hicks KA, Sorensen SW, et al. Cost-ef-
fectiveness of screening for pre-diabetes among
overweight and obese US adults. Diabetes Care.
2007;30:2874–9. https://doi.org/10.2337/dc07-
0885.
31. Chatterjee R, Narayan KM, Lipscomb J, et al.
Screening for diabetes and prediabetes should be
cost-saving in patients at high risk. Diabetes Care.
2013;36:1981–7. https://doi.org/10.2337/dc12-
1752.
32. Banerjee ES, Gambler A, Fogleman C. Adding obe-
sity to the problem list increases the rate of provi-
ders addressing obesity. Fam Med. 2013;45:629–33.
33. Mainous AG 3rd, Tanner RJ, Baker R. Prediabetes
diagnosis and treatment in primary care. J Am
Board Fam Med. 2016;29:283–5. https://doi.org/10.
3122/jabfm.2016.02.150252.
34. Davidson KW, Barry MJ, Mangione CM, et al.
Screening for prediabetes and type 2 diabetes: US
preventive services task force recommendation
statement. JAMA. 2021;326:736–43. https://doi.
org/10.1001/jama.2021.12531.
35. Svensson E, Baggesen LM, Johnsen SP, et al. Early
glycemic control and magnitude of HbA1c reduc-
tion predict cardiovascular events and mortality:
population-based cohort study of 24,752 met-
formin initiators. Diabetes Care. 2017;40:800–7.
https://doi.org/10.2337/dc16-2271.
36. Giraldez RR, Clare RM, Lopes RD, et al. Prevalence
and clinical outcomes of undiagnosed diabetes
mellitus and prediabetes among patients with high-
risk non-ST-segment elevation acute coronary syn-
drome. Am Heart J. 2013;165:918–925.e912.
https://doi.org/10.1016/j.ahj.2013.01.005.
37. Roberts RO, Kantarci K, Geda YE, et al. Untreated
type 2 diabetes and its complications are associated
with subcortical infarctions. Diabetes Care.
2011;34:184–6. https://doi.org/10.2337/dc10-0602.
38. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil
HA. 10-year follow-up of intensive glucose control
in type 2 diabetes. N Engl J Med. 2008;359:1577–89.
https://doi.org/10.1056/NEJMoa0806470.
39. UK Prospective Diabetes Study (UKPDS) Group.
Effect of intensive blood-glucose control with
metformin on complications in overweight patients
with type 2 diabetes (UKPDS 34). Lancet. 1998;352:
854–65.
40. Marso SP, Bain SC, Consoli A, et al. Semaglutide
and cardiovascular outcomes in patients with
type 2 diabetes. N Engl J Med. 2016;375:1834–44.
https://doi.org/10.1056/NEJMoa1607141.
41. Gerstein HC, Colhoun HM, Dagenais GR, et al.
Dulaglutide and cardiovascular outcomes in type 2
diabetes (REWIND): a double-blind, randomised
placebo-controlled trial. Lancet. 2019;394:121–30.
https://doi.org/10.1016/s0140-6736(19)31149-3.
42. Marso SP, Daniels GH, Brown-Frandsen K, et al.
Liraglutide and cardiovascular outcomes in type 2
diabetes. N Engl J Med. 2016;375:311–22. https://
doi.org/10.1056/NEJMoa1603827.
43. Zinman B, Wanner C, Lachin JM, et al. Empagli-
flozin, cardiovascular outcomes, and mortality in
type 2 diabetes. N Engl J Med. 2015;373:2117–28.
https://doi.org/10.1056/NEJMoa1504720.
44. Neal B, Perkovic V, Mahaffey KW, et al. Canagli-
flozin and cardiovascular and renal events in type 2
diabetes. N Engl J Med. 2017;377:644–57. https://
doi.org/10.1056/NEJMoa1611925.
45. Jensen MD, Ryan DH, Apovian CM, et al. 2013
AHA/ACC/TOS guideline for the management of
overweight and obesity in adults: a report of the
American College of Cardiology/American Heart
Association Task Force on Practice Guidelines and
The Obesity Society. Circulation. 2014;129:
S102–138. https://doi.org/10.1161/01.cir.
0000437739.71477.ee.
46. Han Y, Xie H, Liu Y, Gao P, Yang X, Shen Z. Effect of
metformin on all-cause and cardiovascular mortal-
ity in patients with coronary artery diseases: a sys-
tematic review and an updated meta-analysis.
Cardiovasc Diabetol. 2019;18:96. https://doi.org/
10.1186/s12933-019-0900-7.
47. DeFronzo RA, Tripathy D, Schwenke DC, et al.
Pioglitazone for diabetes prevention in impaired
glucose tolerance. N Engl J Med. 2011;364:1104–15.
https://doi.org/10.1056/NEJMoa1010949.
48. Kernan WN, Viscoli CM, Furie KL, et al. Pioglita-
zone after ischemic stroke or transient ischemic
attack. N Engl J Med. 2016. https://doi.org/10.1056/
NEJMoa1506930.
49. le Roux CW, Astrup A, Fujioka K, et al. 3 years of
liraglutide versus placebo for type 2 diabetes risk
reduction and weight management in individuals
with prediabetes: a randomised, double-blind trial.
Diabetes Ther
Lancet. 2017;389:1399–409. https://doi.org/10.
1016/s0140-6736(17)30069-7.
50. Wilding JPH, Batterham RL, Calanna S, et al. Once-
weekly semaglutide in adults with overweight or
obesity. N Engl J Med. 2021. https://doi.org/10.
1056/NEJMoa2032183.
51. Cefalu WT, Buse JB, Tuomilehto J, et al. Update and
next steps for real-world translation of interven-
tions for type 2 diabetes prevention: reflections
from a diabetes care editors’ expert forum. Diabetes
Care. 2016;39:1186–201. https://doi.org/10.2337/
dc16-0873.
52. Taba
´k AG, Herder C, Rathmann W, Brunner EJ,
Kivima
¨ki M. Prediabetes: a high-risk state for dia-
betes development. Lancet. 2012;379:2279–90.
https://doi.org/10.1016/s0140-6736(12)60283-9.
53. Ali MK, Bullard KM, Saydah S, Imperatore G, Gregg
EW. Cardiovascular and renal burdens of predia-
betes in the USA: analysis of data from serial cross-
sectional surveys, 1988–2014. Lancet Diabetes
Endocrinol. 2018. https://doi.org/10.1016/s2213-
8587(18)30027-5.
54. Cai X, Zhang Y, Li M, et al. Association between
prediabetes and risk of all cause mortality and car-
diovascular disease: updated meta-analysis. BMJ.
2020;370: m2297. https://doi.org/10.1136/bmj.
m2297.
55. Cai X, Liu X, Sun L, et al. Prediabetes and the risk of
heart failure: a meta-analysis. Diabetes Obes Metab.
2021;23:1746–53. https://doi.org/10.1111/dom.
14388.
56. Carris NW, Corvin JA. Prediabetes: an undiagnosed
pandemic. Endocr Pract. 2022;28:231–2. https://
doi.org/10.1016/j.eprac.2021.10.004.
57. Ellison-Barnes A, Johnson S, Gudzune K. Trends in
obesity prevalence among adults aged 18 through
25 years, 1976–2018. JAMA. 2021;326:2073–4.
https://doi.org/10.1001/jama.2021.16685.
58. Hales CM, Carroll MD, Fryar CD, Ogden CL.
Prevalence of obesity and severe obesity among
adults: United States, 2017–2018. NCHS Data Brief,
no 360. Hyattsville, MD: National Center for Health
Statistics. 2020.
59. Centers for Disease Control and Prevention.
National Diabetes Statistics Report website. https://
www.cdc.gov/diabetes/data/statistics-report/index.
html. Accessed 28 Jun 2023.
60. Kearns CE, Schmidt LA, Glantz SA. Sugar industry
and coronary heart disease research: a historical
analysis of internal industry documents. JAMA
Intern Med. 2016;176:1680–5. https://doi.org/10.
1001/jamainternmed.2016.5394.
61. Drewnowski A, Rehm CD. Consumption of added
sugars among US children and adults by food pur-
chase location and food source. Am J Clin Nutr.
2014;100:901–7. https://doi.org/10.3945/ajcn.114.
089458.
62. Acton RB, Vanderlee L, Hobin EP, Hammond D.
Added sugar in the packaged foods and beverages
available at a major Canadian retailer in 2015: a
descriptive analysis. CMAJ Open. 2017;5:E1–E6.
https://doi.org/10.9778/cmajo.20160076.
63. Yang Q, Zhang Z, Gregg EW, Flanders WD, Merritt
R, Hu FB. Added sugar intake and cardiovascular
diseases mortality among US adults. JAMA Intern
Med. 2014;174:516–24. https://doi.org/10.1001/
jamainternmed.2013.13563.
64. Li C, Ghiasi A, Li X, Chi G. Sociodemographics and
access to organic and local food: a case study of New
Orleans Louisiana. Cities. 2018;79:141–50. https://
doi.org/10.1016/j.cities.2018.03.003.
65. Sansom G, Hannibal B. Disparate access to nutri-
tional food; place, race and equity in the United
States. BMC Nutr. 2021;7:29. https://doi.org/10.
1186/s40795-021-00434-2.
66. Gundala RR, Singh A. What motivates consumers to
buy organic foods? Results of an empirical study in
the United States. PLoS ONE. 2021;16: e0257288.
https://doi.org/10.1371/journal.pone.0257288.
67. Zeng BT, Pan HQ, Li FD, Ye ZY, Liu Y, Du JW.
Comparative efficacy of different eating patterns in
the management of type 2 diabetes and predia-
betes: an arm-based Bayesian network meta-analy-
sis. J Diabetes Investig. 2023;14:263–88. https://doi.
org/10.1111/jdi.13935.
68. Zhang Y, Tan H, Tang J, et al. Effects of vitamin D
supplementation on prevention of type 2 diabetes
in patients with prediabetes: a systematic review
and meta-analysis. Diabetes Care. 2020;43:1650–8.
https://doi.org/10.2337/dc19-1708.
69. Reynolds AN, Akerman AP, Mann J. Dietary fibre
and whole grains in diabetes management: sys-
tematic review and meta-analyses. PLoS Med.
2020;17:e1003053. https://doi.org/10.1371/
journal.pmed.1003053.
70. Hansen TT, Astrup A, Sjo
¨din A. Are dietary proteins
the key to successful body weight management? A
systematic review and meta-analysis of studies
assessing body weight outcomes after interventions
with increased dietary protein. Nutrients. 2021.
https://doi.org/10.3390/nu13093193.
Diabetes Ther
71. Clasey JL, Easley EA, Murphy MO, et al. Body mass
index percentiles versus body composition assess-
ments: challenges for disease risk classifications in
children. Front Pediatr. 2023;11:1112920. https://
doi.org/10.3389/fped.2023.1112920.
72. Endocrine Society. BMI alone may not be a suffi-
cient indicator of metabolic health. https://www.
endocrine.org/news-and-advocacy/news-room/
2023/endo-2023-press-visaria. Accessed 21 Jun
2023.
73. Andes LJ, Cheng YJ, Rolka DB, Gregg EW, Impera-
tore G. Prevalence of prediabetes among adoles-
cents and young adults in the United States,
2005–2016. JAMA Pediatr. 2020;174: e194498.
https://doi.org/10.1001/jamapediatrics.2019.4498.
74. Sanyaolu A, Okorie C, Qi X, Locke J, Rehman S.
Childhood and adolescent obesity in the United
States: a public health concern. Glob Pediatr
Health. 2019. https://doi.org/10.1177/
2333794x19891305.
75. Badesha HS, Bagri G, Nagra A, Nijran K, Singh G,
Aiyegbusi OL. Tackling childhood overweight and
obesity after the COVID-19 pandemic. Lancet Child
Adolesc Health. 2021;5:687–8. https://doi.org/10.
1016/s2352-4642(21)00204-2.
76. Jha S, Mehendale AM. Increased incidence of obe-
sity in children and adolescents post-COVID-19
pandemic: a review article. Cureus. 2022;14:
e29348. https://doi.org/10.7759/cureus.29348.
77. Endocrine Society. Type 2 diabetes increased
among youth during and after COVID-19 pan-
demic. https://www.endocrine.org/news-and-
advocacy/news-room/2023/endo-2023-press-bell-
sambataro. Accessed 21 Jun 2023.
78. Boers E, Afzali MH, Newton N, Conrod P. Associa-
tion of screen time and depression in adolescence.
JAMA Pediatr. 2019;173:853–9. https://doi.org/10.
1001/jamapediatrics.2019.1759.
79. Mannan M, Mamun A, Doi S, Clavarino A.
Prospective associations between depression and
obesity for adolescent males and females—a sys-
tematic review and meta-analysis of longitudinal
studies. PLoS ONE. 2016;11: e0157240. https://doi.
org/10.1371/journal.pone.0157240.
80. Ali MK, Echouffo-Tcheugui J, Williamson DF. How
effective were lifestyle interventions in real-world
settings that were modeled on the Diabetes
Prevention Program? Health Aff (Millwood).
2012;31:67–75. https://doi.org/10.1377/hlthaff.
2011.1009.
81. Ferreira Silva RM, Mendonc¸a CR, Azevedo VD,
Raoof Memon A, Noll P, Noll M. Barriers to high
school and university students’ physical activity: a
systematic review. PLoS ONE. 2022;17: e0265913.
https://doi.org/10.1371/journal.pone.0265913.
82. Nally S, Ridgers ND, Gallagher AM, Murphy MH,
Salmon J, Carlin A. ‘‘When you move you have
fun’’: perceived barriers, and facilitators of physical
activity from a child’s perspective. Front Sports Act
Living. 2022;4: 789259. https://doi.org/10.3389/
fspor.2022.789259.
83. Yungblut HE, Schinke RJ, McGannon KR. Views of
adolescent female youth on physical activity during
early adolescence. J Sports Sci Med. 2012;11:39–50.
84. Abi Nader P, Hilberg E, Schuna JM, John DH,
Gunter KB. Teacher-level factors, classroom physi-
cal activity opportunities, and children’s physical
activity levels. J Phys Act Health. 2018;15:637–43.
https://doi.org/10.1123/jpah.2017-0218.
85. Pulling Kuhn A, Kim E, Lane HG, et al. Associations
between elementary and middle school teachers’
physical activity promoting practices and teacher-
and school-level factors. Int J Behav Nutr Phys Act.
2021;18:66. https://doi.org/10.1186/s12966-021-
01129-4.
86. Weaver RG, Webster CA, Beets MW, et al. Initial
outcomes of a participatory-based, competency-
building approach to increasing physical education
teachers’ physical activity promotion and students’
physical activity: a pilot study. Health Educ Behav.
2018;45:359–70. https://doi.org/10.1177/
1090198117731600.
87. Gao J, Zheng P, Jia Y, et al. Mental health problems
and social media exposure during COVID-19 out-
break. PLoS ONE. 2020;15: e0231924. https://doi.
org/10.1371/journal.pone.0231924.
88. Wang Y, Lynne SD, Witherspoon D, Black MM.
Longitudinal bidirectional relations between body
dissatisfaction and depressive symptoms among
Black adolescents: a cross-lagged panel analysis.
PLoS ONE. 2020;15:e0228585. https://doi.org/10.
1371/journal.pone.0228585.
89. Huard Pelletier V, Lessard A, Piche
´F, Te
´treau C,
Descarreaux M. Video games and their associations
with physical health: a scoping review. BMJ Open
Sport Exerc Med. 2020;6: e000832. https://doi.org/
10.1136/bmjsem-2020-000832.
90. Meng SQ, Cheng JL, Li YY, et al. Global prevalence
of digital addiction in general population: a sys-
tematic review and meta-analysis. Clin Psychol Rev.
2022;92: 102128. https://doi.org/10.1016/j.cpr.
2022.102128.
91. Hamidullah S, Thorpe HHA, Frie JA, McCurdy RD,
Khokhar JY. Adolescent substance use and the
Diabetes Ther
brain: behavioral, cognitive and neuroimaging
correlates. Front Hum Neurosci. 2020;14:298.
https://doi.org/10.3389/fnhum.2020.00298.
92. Cepni AB, Ledoux TA, Johnston CA. Screen media:
a powerful reinforcement. Am J Lifestyle Med.
2020;14:126–9. https://doi.org/10.1177/
1559827619892543.
93. Kosten TR, George TP. The neurobiology of opioid
dependence: implications for treatment. Sci Pract
Perspect. 2002;1:13–20. https://doi.org/10.1151/
spp021113.
94. Yarnall KS, Pollak KI, Østbye T, Krause KM, Mich-
ener JL. Primary care: is there enough time for
prevention? Am J Public Health. 2003;93:635–41.
https://doi.org/10.2105/ajph.93.4.635.
95. Schauer PR, Bhatt DL, Kirwan JP, et al. Bariatric
surgery versus intensive medical therapy for
diabetes—5-year outcomes. N Engl J Med.
2017;376:641–51. https://doi.org/10.1056/
NEJMoa1600869.
96. Eli Lilly and Company. A study of tirzepatide
(LY3298176) on the reduction on morbidity and
mortality in adults with obesity (SURMOUNT-
MMO). ClinicalTrials.gov Identifier: NCT05556512.
https://classic.clinicaltrials.gov/ct2/show/
NCT05556512. Accessed 12 Jul 2023.
97. Khan SS, Brewer LC, Canobbio MM, et al. Opti-
mizing prepregnancy cardiovascular health to
improve outcomes in pregnant and postpartum
individuals and offspring: a scientific statement
from the American Heart Association. Circulation.
2023;147:e76–91. https://doi.org/10.1161/cir.
0000000000001124.
98. Lancet T. Diabetes: a defining disease of the 21st
century. Lancet. 2023;401:2087. https://doi.org/10.
1016/s0140-6736(23)01296-5.
Diabetes Ther
