Wisniewski et al.
Journal of Physiological Anthropology(2024) 43:18
https://doi.org/10.1186/s40101-024-00365-4

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open AccessSustained metabolic dysregulationand the emergence of diabetes: associations
between HbA1c and metabolic syndrome
components in Tunisian diabetic
and nondiabetic groups
Adriana Wisniewski1* , Alicia M. DeLouize2, Tian Walker2, Somnath Chatterji3, Nirmala Naidoo3,
Paul Kowal4 and J. Josh Snodgrass2,5,6

Abstract
Introduction Metabolic Syndrome (MetS), diabetes, and other noncommunicable diseases (NCDs) have beena major focus of research in recent decades as the prevalence of these conditions continues to rapidly increase
globally. However, the timing and patterns of development from metabolic risk factors to disease states are less wellunderstood and are especially critical to understand in low- and middle-income countries (LMICs) and populations
undergoing epidemiological transitions.
Methods Nationally representative sociodemographic, anthropometric, and point-of-care biomarker datafrom the 2016 Tunisian Health Examination Survey (n = 8170) were used to determine the prevalence of diabetes
and MetS components in Tunisia and to investigate associations between glycated hemoglobin (HbA1c) and MetS
components (blood pressure [BP], HDL cholesterol [HDL], triglycerides [TG], and waist circumference [WC]) in participants aged 15-97 years old. To better understand how sustained metabolic dysregulation and disease states impact
these associations, diabetic and nondiabetic groups were analyzed separately.
Results The overall prevalence of diabetes based on measured HbA1c was 18.2%. The diabetic groups had a higher
prevalence of each individual MetS component, and significantly higher (BP, TG, WC, and HbA1c) and lower (HDL)
values than the nondiabetic groups. Yet, there were a higher number of significant associations between HbA1c
and MetS components found in nondiabetic women and men when compared to diabetic women and men. HbA1c
was positively associated with the cumulative number of MetS components, irrespective of diabetes status in men
and women.
Conclusions The prevalence of both diabetes and MetS components (particularly low HDL cholesterol and elevatedTG) is high among the Tunisian population. More MetS components were associated with HbA1c in nondiabetic
individuals, showing a strong connection between the development of MetS components and diabetes. However, once the diabetes disease state manifests, there is more variability in the relationships. These results show

*Correspondence:
Adriana Wisniewski
adrianaw@unc.edu
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Wisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 2 of 14

the potential for HbA1c to be an indicator of metabolic health below clinical disease cutoffs, which may allow insights
into the physiological changes that precipitate the emergence of diabetes.
Keywords Metabolic syndrome, Diabetes, Glycated hemoglobin, HbA1c, Noncommunicable disease, Global health,
Preventative medicine

BackgroundIn recent decades, the prevalence of noncommunicable diseases (NCDs) has increased worldwide as many
countries undergo rapid social, demographic, and epidemiological transitions. Currently, NCDs cause 74% ofdeaths globally, with 77% of deaths due to NCDs occurring in low- and middle-income countries (LMICs)[1]. Metabolic dysregulation results from physiologicalchanges that disrupt the body’s metabolism. One way toassess metabolic dysregulation is Metabolic Syndrome(MetS). MetS is not characterized by a single metabolic
abnormality; rather, it encompasses a group of metabolic
abnormalities that contribute to increased risk for NCDs,
such as cardiovascular disease (CVD) and type 2 diabetes(T2D) [2]. There is no single pathway or process between
shared metabolic risk factors and NCD development.
Instead, metabolic dysregulation can lead to a variety ofNCDs and is influenced by variations in the physiological patterns and timelines of processes from metabolic
dysregulation to energy imbalances that eventually culminate in the development of disease states. Steps canbe taken to alter lifestyle to prevent NCD development
or severity. Yet once a disease state is achieved, metabolic
dysregulation continues to increase risk for other NCDs
and comorbidities.
The purpose of the present study is to develop a better understanding of how a disease state, like diabetes,
impacts the prevalence and types of MetS components
and overall metabolic health within a population andto assess the ability of hemoglobin A1c (HbA1c), also
known as glycated hemoglobin, to indicate metabolic
health beyond glucose metabolism, including below
clinical cutoffs used to diagnose disease. HbA1c has beenused as a supplement to or in place of fasting blood glucose as a hyperglycemia criterion for MetS detection [3,4]. Rather than analyzing HbA1c as a potential component of MetS, we examine HbA1c as an outcome variable
by evaluating the relationships between HbA1c and metabolic syndrome components in people with and without
diabetes. There are known and well-studied relationships
between HbA1c and MetS and its components of bloodpressure, HDL cholesterol, triglycerides, and waist circumference, as NCDs and associated risk factors havebeen a large focus over the past few decades. Previous
studies have found positive associations between MetS
components and HbA1c in nondiabetic populations [5,6]. Associations in diabetic populations have also been
examined [7, 8]. Studies have also analyzed risk for metabolic syndrome and other related conditions in diabetics
vs nondiabetics, associations with HbA1c based upon
the presence or absence of another disease (e.g. cardiovascular disease), or clustering of components in diabetic vs nondiabetic populations, but the impact of MetS
on HbA1c has not been well examined when separating
a population based on the presence of diabetes [9–11].
Thus, we examine the degree to which MetS components
may impact HbA1c differently based upon disease statestatus (i.e., diabetic vs. nondiabetic) within one population (Tunisia) [12].
Defining and exploring metabolic syndrome and diabetes

There is no consensus on the defining criteria for MetS,
with multiple approaches available; however, in general MetS is characterized by a combination of glucose
intolerance/insulin resistance, central obesity, hypertension, and dyslipidemia [13]. In this way, it is closely
related to diabetes, CVD, and other NCDs, yet the variation between individuals in the relationships among
components has not often been differentially evaluatedin people with and without an NCD. The timing of thedevelopment of MetS components and numerous elements of MetS pathophysiology, including glucose andfat metabolism, insulin, adipocyte-derived hormones,
and cytokines, complicate the sequential and clustering relationships of components, and the physiologicalpathways that connect individual factors are all aspects of
MetS that require more research.
Diabetes has been shown to be particularly closely
associated with MetS. While the causes of MetS are notfully understood, there are strong associations between
the development of MetS and the presence of insulin resistance, suggesting a commonly shared pathwaythat links MetS to an increased risk for T2D [14]. SomeMetS diagnostic criteria require an element of insulin
resistance for MetS diagnosis [1]. The global prevalenceand burden of diabetes have been rising rapidly in both
LMICs, and high-income countries (HICs), and areprojected to grow by 20% in the next decade [15]. Bothtype 1 diabetes (T1D) and T2D are characterized byhyperglycemia, high levels of glucose within the blood,
and alterations to beta cell function, insulin secretion,

Wisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 3 of 14

cellular response to insulin, or both [16, 17]. The impactof this is inefficient uptake and metabolism of glucose by
the body’s cells, which results in increased levels of glucose in the blood and increased cellular fat and protein
metabolism to compensate for reduced glucose metabolism [16]. Once the disease state is achieved, those withdiabetes are at higher risk for other metabolic risk factors(MetS components) due to changes in how the body uses
and stores different types of energy (lipids and proteins)[18, 19].
Although diabetes has been a large focus of scientificand medical research for decades, much is still unknown
about the specific physiological pathways leading to disease development and progression. Diabetes is a disease with multifactorial etiology, meaning there is notjust one pathway leading to diabetes development, andthe exact pathways and relationships between pathways
are not well understood. There is additional complexity due to the considerable variation in disease progression between individuals. For example, sustained highblood sugar levels, commonly present in individuals
with undiagnosed diabetes or poorly managed diabetes,
can increase the risk for CVD [20, 21]. Diabetes can also
increase risk for other NCDs, including obesity, hyperlipidemia, chronic kidney disease, stroke, cognitive decline,
decreased quality of life, and premature death [22, 23].
The links between diabetes and other NCDs are in partdue to connections to obesity, chronic inflammation,oxidative stress, and insulin resistance [24, 25]. Obesity,
more specifically, central adiposity (measured using waist
circumference [WC]) and associated chronic systemic
inflammation, are also believed to be closely related toboth MetS factors, diabetes, and other NCDs.

furthermore, HbA1c can be measured in individuals who
are not fasted.

Defining and using HbA1c

Over the past several decades there has been an increasein the use of HbA1c for diabetes diagnosis and management. HbA1c measures the percentage of a hemoglobin compound produced by an irreversible reaction
between a glucose molecule and a hemoglobin molecule
within the blood [26]. HbA1c formation is proportional
to the concentration of glucose in the blood. Red blood
cells, which bind to hemoglobin, have an average lifespanof 120 days. Thus, HbA1c levels reflect the average blood
glucose level over the previous 2-3 months [26]. HbA1c isincreasingly being utilized in the screening for and diagnosis of diabetes, to evaluate disease progression, and tomonitor treatment and management of the disease [27,28]. A benefit of using HbA1c is the insight into metabolic health over a longer time frame compared to fasting
glucose values and other single time point biomarkers;

Beyond metabolic risk factors

MetS and diabetes are also influenced by environmental,
genetic, and lifestyle risk factors. Environmental factors,
including early life conditions, viral infection, immune
system and gut microbiome development, and diet, haveall been found to influence risk for disease [29]. Recently,
studies have shown connections between SARS-CoV-2
infection and increased prevalence of T1D and T2D,
increased rates of diabetic ketoacidosis at the time ofT1D diagnosis, and worse COVID-19 outcomes in thosewith diabetes [30–32]. Sedentary habits and changes todiet, such as increased carbohydrate consumption, as
seen during pandemic lockdowns, have been associated with an increased risk of T2D development [33].
Genetic and epigenetic influences on the risk of developing diabetes and multiple aspects of MetS have also beenwell-studied [34, 35]. Multiple genome-wide association
studies have led to the identification of multiple genes
involved in risk for T2D [35]. Gene-environment interactions such as with increased adiposity, may alter T2D risk
directly by influencing insulin action or secretion, butalso altering environmental interactions [35]. Epigenetic
changes due to the maternal environment and conditions
during early infancy can alter risk for chronic disease(including diabetes and metabolic syndrome) throughout
the life course [35]. Beyond individual-level physiology,
more research is also needed to understand better population-level factors that may contribute to MetS and its
relationships to NCDs.
The present study provides epidemiological data on theprevalence of clinically recognized (diagnosed) and undiagnosed diabetes and MetS components (blood pressure[BP], HDL cholesterol levels [HDL], blood triglyceride[TG] levels, and waist circumference [WC]) in Tunisia
using a nationally representative sample from the Tunisian Health Examination Survey (THES). Additionally, it
investigates relationships between MetS components andHbA1c levels in diabetic and nondiabetic Tunisian adults
to evaluate how many and which specific components of
MetS affect HbA1c levels within two different timelines
in terms of metabolic dysfunction. The relatively novel
approach of separating groups by disease state thereby
also provides insight into the potential for HbA1c to indicate the impact of achieving a disease state (i.e., diabetes),
while also assessing metabolic changes and risk in those
who do not meet or exceed clinical cutoff values for diabetes diagnosis. This allows the measurement of subtle
changes to glucose metabolism in nondiabetic individuals, a concept not common within the broader diabetes
literature.

Wisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Tunisia is a lower-middle income country located in
Northern Africa. It has been undergoing demographic,
epidemiologic, and nutritional transitions in recent
decades. As a result of these transitions, NCDs are an
increasing health concern. According to the WHO in
2016, NCDs were responsible for 86% of all deaths in
Tunisia (44% from cardiovascular diseases, 12% fromcancer, 4% from chronic respiratory diseases and 5%
from diabetes) as well as 16% of premature deaths
between 30-70 years [36]. With a large burden of NCDs
and a dynamic social and economic landscape, assessing
metabolic health and risk in Tunisia may contribute valuable insight into groups within the country that may be at
higher risk for developing NCDs. Additionally, this study
contributes to a growing body of literature with increased
attention to health within lower- and middle-income
countries on a global scale, as well as literature focusedon Tunisia specifically.
Hypotheses

We hypothesize that there will be positive associations
between HbA1c and 1) each individual component of
MetS and 2) the cumulative number of MetS components
among both diabetic and nondiabetic groups.

Methods
Tunisian health examination survey

The Tunisian Health Examination Survey (THES) is anationally representative study used to monitor population health and provide supporting evidence for healthpolicy and strategy development. The primary purpose was to analyze the state of population health andits determinants, disease consequences, and the use ofpoint-of-care testing (POCT). Some non-private dwellings, such as prisons, hospitals, hospices, dementia careunits, and some remote areas, were excluded from thesurvey population. The THES used a stratified multistage
cluster sampling design to monitor population healthand provide supporting evidence for health policy andstrategy development. Households are selected at random, and one individual per household is then selected
through a procedure using the Kish table method.
The present study uses sociodemographic characteristics, anthropometrics, biomarkers, risk factors, preventative health behaviors, and chronic conditions data fromthe individual THES questionnaire. Trained interviewers performed face-to-face computer-assisted personal
interviews and collected anthropometric, biomarker, andblood pressure measurements. The interviews took placefrom January to May 2016 in the participants’ homes.

Page 4 of 14

ParticipantsHouseholds (n = 5079) were selected in 3 stages with 7
regions, 24 governorates, and 351 districts included. This
study analyzes data from (n = 8,170) THES participants
after accounting for exclusions due to incomplete data (n= 964), pregnant individuals (n = 138), and individuals
with out-of-range biomarker data (n = 904) (i.e., defined
as above or below the following measurement ranges:
Total cholesterol [100-400 mg/dL], HDL cholesterol [20120 mg/dL], TG [50-500 mg/dL], blood glucose [20-600mg/d], HbA1c [4.0-13.0%]).
The sample was 52.3% women. Participants ranged
from 15 to 97 years old (M = 48.01, SD =17.33), andthe age range for individuals (adults) to participate in
the study was determined by Tunisian-based researchers. Formal education ranged from 0 to 34 years (M =7.44, SD = 5.97). Mean education was particularly low
because 25.1% of the sample had no formal education.
Most participants were married (73.7%) as opposed tobeing never married, widowed, or divorced, and the
majority of people lived in urban settings (64.5%). The
monthly household income ranged from 0 – 12,000 Tunisian dinars (M = 699.56, SD = 766.48).
Sociodemographic measures

Gender, age, marital status, education, urban/rural location, and perceived health were determined using standard survey measures. Questionnaire responses regarding
diabetes, dyslipidemia, and hypertension, answers tosymptom and medication use questions and participant
demographic information were used in the present study.
Anthropometrics and blood pressure
Waist and hip circumference

Waist and hip circumference were measured using a flexible measuring tape (Gulick II Measuring tape model –67020). WC was measured at the top of the iliac crest.
Hip circumference was measured around the maximum
circumference of the participant’s buttocks. Both waist
and hip circumference measurements were recorded to
the nearest 0.1 cm.
Blood pressure

Blood pressure was measured using an automatic wrist
blood pressure monitor (Omron M6 Comfort; Omron
Healthcare, Kyoto, Japan). Three blood pressure measurements were taken on the participants’ left wrist, which
was held steady at the approximate level of the heart. All
systolic and diastolic measurements were recorded inunits of mmHg. Accuracy of the device is ±3 mmHg forpressure and ±5% of display reading for pulse.

Wisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Biomarker collection

All biomarker data were collected using finger prick
capillary blood samples and POCT devices. Total cholesterol (100-400 mg/dL), HDL cholesterol (20-120 mg/dL), TG (50-500 mg/dL) and blood glucose (20-600 mg/dL) were measured using the CardioChek PA system(Polymer Technology Systems, Inc., Whitestown, IN) andvalues were recorded in mg/dL. The CardioChek PA system meets US National Cholesterol Education Program(NCEP) guidelines for accuracy and precision.
Percent hemoglobin A1c (HbA1c) (4.0-13.0%) was
measured using the A1CNow+ device (Polymer Technology Systems, Inc., Whitestown, IN). The overall imprecision of the A1cNow+ system is 3.00% CV at the low leveland 4.02% at the high level, meeting National Glycohemoglobin Standard Program certification. The accuracyof the A1c+Now system on average was 99% from fingerstick (capillary) comparative testing, the accuracy was on
average 99.7% from venous comparative testing.
Diabetes

Individuals with self-reported diabetes diagnosis or thosewith measured HbA1c values ≥ 6.5% were included in
the diabetic group. An HbA1c of 6.5 was used as partof the criteria for the diabetic group as 6.5 is the widely
accepted clinical cutoff value for diabetes diagnosis [37].
Those with measured HbA1c values < 6.5% and no selfreported diabetes diagnosis were placed in the nondiabetic group.
Metabolic syndrome

There is not one universally accepted MetS measurement
definition for use in clinical settings; instead, recognition of MetS is generally based on well-recognized signs,
including abdominal obesity, elevated TG, low HDL cholesterol, elevated BP, and/or high plasma glucose [13].
The MetS components analyzed for the present study
included BP, HDL cholesterol, TG, and WC. Blood glucose was excluded from our analyses due to not all participants having fasted when data were collected and thatboth measures (blood glucose and HbA1c) ultimately
measure glucose, though at different time scales.
We used the National Cholesterol Education Program(NCEP) Adult Treatment Panel (ATP) III diagnostic criteria for MetS (Table 1), which is one of the most widelyused. It does not require one specific component of MetS
for diagnosis and therefore does not incorporate any preconceived notion of an underlying cause of MetS (such as
insulin resistance or obesity) [38].
Statistical analyses

R version 4.3.0 was used for all statistical analyses andthe alpha level was set to 0.05. All statistical assumptions

Page 5 of 14

Table 1 ATP III MetS criteria
NCEP ATP III (2005)
Required

None

Hyperglycemia Blood glucose> 100.8 mg/dl (5.6 mmol/L)a

Dyslipidemia

HDL Cholesterol< 40 mg/dl (1.0 mmol/L) (men)a< 50 mg/dl (1.3 mmol/L)(women)a

Triglycerides> 150 mg/dl (1.7 mmol/L)a

Obesity

Waist Circumference > 102 cm (men)> 88 cm (women)

Hypertension

Blood Pressurea> 130/85 ­mmHga

Or drug treatment for specific criteria

were checked before analysis. HbA1c values were logtransformed for linear analysis due to non-normality(skew = 2.61, kurtosis = 8.29). Monthly householdincome was log-transformed due to non-normality(skew = 4.73, kurtosis = 39.02). Following standard practice, men and women were analyzed separately becauseof divergences in metabolic processes and risk factors
closely related to sex, which are also reflected in differences in MetS criteria. However, it should be noted thatsex differences in biological measures are typically minor,and distributions generally overlap considerably, withenvironmental factors having major effects [39]. In our
analyses, men and women were also further divided into
diabetic and nondiabetic groups.
All statistics were run separately on the four groups:
nondiabetic and diabetic men and women. Descriptive
statistics on HbA1c and MetS components are presented
along with inferential statistics on their pairwise comparisons. Spearman’s rank correlations were performed
to test associations between untransformed HbA1c andthe total number of MetS components. Multiple linear
regressions with maximum likelihood estimation wereused to evaluate the partial correlations between each
MetS component and log HbA1c, and then also the
cumulative number of MetS components and log HbA1c.
Fully adjusted models excluded participants who were on
medications for diabetes and controlled for age, years ofeducation, urban/rural household location, and monthly
household income (Tunisian dinars). The exponentials ofcoefficients are presented.

Results
Diabetes poses a significant burden to the health of people living in Tunisia with an estimated 17.6% of womenand 18.8% of men from this study with the condition(Table 2). Combined, 18.2% of participants had diabetes. In the nondiabetic population, the mean HbA1cvalue was 5.5% in both women and men. In the diabetic

Wisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 6 of 14

Table 2 Prevalence of diabetes in diabetic and nondiabetic women and men, and average HbA1c values
Diabetic

Nondiabeticn

M ± SEn

M ± SEt

Women

752 (17.6%)

7.94 ± 0.06

3517 (82.4%)

5.46 ± 0.01

-44.87***Men

734 (18.9%)

8.11 ± 0.07

3167 (81.1%)

5.45 ± 0.01

-44.38****** p < 0.001

Table 3 Prevalence of diabetes medication use and average
HbA1c in diabetic men and women
Women
n

Men
M ± SEn

M ± SENo Medication

39 (8.6%)

7.25 ± 0.31 33 (7.8%)

7.41 ± 0.39

Insulin78 (17.3%)

8.83 ± 0.20 87 (20.5%)

8.81 ± 0.21

Tablet296 (65.6%) 8.08 ± 0.11 269 (63.3%) 8.16 ± 0.12

Insulin & Tablet

38 (8.4%)

NA (No diagnosis) 301 (-)

9.55 ± 0.25 36 (8.5%)

8.60 ± 0.30

7.46 ± 0.08 309 (-)

7.90 ± 0.10

population, the mean HbA1c value was 7.9% in womenand 8.1% in men (Table 2).
Within the diabetes groups, 60.0% of women and 57.9%
of men had reported previous clinical diabetes diagnoses;
the rest had HbA1c values greater than or equal to 6.5%
without a diagnosis. Of those with a diabetes diagnosis,
91.4% of women and 92.2% of men were on medication(insulin, tablets [e.g., Metformin or Amaryl], or bothinsulin and tablets) (Table 3). Those without a diagnosis
did not utilize medication for diabetes.
Diabetic women with a diagnosis, on any type of medication for diabetes, had significantly higher mean HbA1cvalues than those not taking medication (M = 7.25).
Those on both insulin and tablets had the highest mean
HbA1c values (M = 9.55, t = -6.31, p < 0.001) followed byinsulin (M = 8.83, t = -4.54, p < 0.001), and then by tablets (M = 8.08, t = -2.77, p < 0.01) (Table 3).
Diabetic men with a diagnosis on insulin or insulin andtablets had significantly higher mean HbA1c values thanthose not taking medication (M = 7.41). Those on insulin

had the highest mean HbA1c values (M = 8.81, t = -3.32,
p < 0.01), followed by those on insulin and tablets (M =8.60, t = -2.80, p < 0.01), and then those on tablets (M =8.16, t = -2.02, p > 0.05) (Table 3).
The prevalence of individual MetS components BP,
HDL, TG, and WC were calculated for diabetic and nondiabetic women and men, as well as the total prevalenceby gender (Table 4).
The prevalence of the cumulative number of MetS
components and associated average HbA1c values by
number of components for diabetic and nondiabetic men
and women are shown in Table 5.
Associations between untransformed HbA1c and thecumulative number of MetS components were assessedusing Spearman’s rank correlation.
HbA1c was positively correlated with the total numberof MetS components in all groups: nondiabetic men (ρ =0.18, p < 0.001) and women (ρ = 0.19, p < 0.001), and diabetic men (ρ = 0.12, p < 0.05) and women (ρ = 0.09, p <0.05) (Table 5; Fig. 1A-D).
In women, the average values for diastolic BP, systolic
BP, TG, and WC were significantly higher, and the average value of HDL cholesterol was significantly lower, inthe diabetic population compared to the nondiabetic
population (Table 6).
In men, the average values of diastolic BP, systolic BP,
TG, and WC were significantly higher, and the averagevalue of HDL cholesterol was significantly lower in the
diabetic population compared to the nondiabetic population (Table 7).
Pairwise comparisons between the average HbA1cvalues for individuals with and without each MetS

Table 4 Prevalence of metabolic syndrome components in diabetic and nondiabetic women and men
Women
Diabetic
MetS Component

Men
Nondiabetic

Total

Diabetic

Nondiabetic

Total

BP

423 (56.6%)

932 (26.5%)1355 (31.8%)361 (49.3%)

749 (23.7%)1110 (28.5%)

HDL

462 (61.4%)

1739 (49.4%)2201 (51.6%)455 (62.0%)

1592 (50.3%)2047 (52.5%)

TG

572 (76.1%)

1884 (53.6%)

2456 (57.5%)

556 (75.7%)

1974 (62.3%)

2530 (64.9%)

WC

580 (82.3%)

1949 (56.6%)2529 (61.0%)273 (38.4%)

535 (17.2%)

808 (21.2%)

Components determined by values that exceed ATP III Metabolic Syndrome criteria thresholds (Table 1)Wisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 7 of 14

Table 5 Prevalence of and average HbA1c values by cumulative number of metabolic syndrome components# of MetS Components

Women

Men

Diabetic

Nondiabetic

Diabetic

Nondiabeticn

25 (3.6%)

462 (13.4%)

55 (7.8%)547 (17.6%)

HbA1c M ± SE

7.62 ± 0.32

5.34 ± 0.02

7.87 ± 0.26

5.30 ± 0.02

n63 (9.0%)915 (26.6%)

116 (16.4%)

997 (32.1%)

HbA1c M ± SE

7.54 ± 0.19

5.41 ± 0.02

7.84 ± 0.16

5.41 ± 0.01n173 (24.6%)

997 (29.0%)232 (32.7%)1031 (33.2%)

HbA1c M ± SE

8.01 ± 0.14

5.44 ± 0.02

8.01 ± 0.13

5.49 ± 0.01n

238 (33.9%)

770 (22.4%)

209 (29.5%)

420 (13.5%)

HbA1c M ± SE

7.83 ± 0.11

5.57 ± 0.02

8.33 ± 0.14

5.55 ± 0.02n

204 (29.0%)295 (8.6%)

112 (3.6%)

112 (3.6%)

HbA1c M ± SE

8.14 ± 0.13

5.63 ± 0.03

8.37 ± 0.18

5.59 ± 0.04

ρ

0.09

0.19

0.12

0.18

p

0.02 *< 2.2e-16 ***0.002 **

< 2.2e-16 ***

0
1
2
3
4

Components determined by values that exceed ATP III Metabolic Syndrome criteria thresholds (Table 1)*

p < 0.05; ** p < 0.01; *** p < 0.001

Fig. 1 A-D Correlation graphs for the Number of MetS components and HbA1c (%).Note. Nondiabetic women (A, p < 0.001), nondiabetic men (B, p< 0.001), diabetic women (C, p < 0.05) and diabetic men (D, p < 0.05). Significance was determined using Spearman’s rank correlations

component were assessed in all four groups (non-diabetic and diabetic, men and women). In nondiabetic
women log HbA1c was significantly associated withMetS components of BP (t = 9.45, p < 0.001), TG (t =5.48, p < 0.001), and WC (t = 8.20, p < 0.001) (Fig. 2A).
In diabetic women, no significant differences in HbA1c

Wisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 8 of 14

Table 6 Average values of metabolic syndrome components innondiabetic and diabetic womenLMICs such as Tunisia. Population-level studies that are
based on measured health data (rather than self-report)
are extremely valuable as they expand beyond projections and estimates and give us objectively measured
values without relying on participant memory or recall,or their access to healthcare. Relying solely upon clinical diagnoses for estimating diabetes prevalence would
result in the exclusion of a large portion of the diabetic
population due to lack of clinical diagnosis. We show that
HbA1c has different relationships to MetS components
based on diabetes disease status, which could stem fromthe metabolic regulation of varying components in the
disease state.

Diabetic
M ± SE

Nondiabetic
M ± SEt

Average Systolic Blood
Pressure

139.94 ± 0.79 125.46 ± 0.34

-16.80 ***Average Diastolic Blood
Pressure

84.58 ± 0.41

81.94 ± 0.19

-6.93

***

HDL

50.50 ± 0.52

52.07 ± 0.25

2.72

**

Triglycerides

217.78 ± 3.42 176.18 ± 1.43

-11.23 ***Waist Circumference

100.00 ± 0.53 91.03 ± 0.26

-15.27 *****p < 0.01; ***p < 0.001

Table 7 Average values of Metabolic Syndrome Components indiabetic and nondiabetic men
Diabetic and/or HbA1c ≥6.5
M ± SE

Nondiabetic t
M ± SE

Average Systolic
Blood Pressure

140.97 ± 0.78

129.38 ± 0.32

-13.71 ***Average Diastolic Blood
Pressure

83.29 ± 0.44

79.10 ± 0.19

-8.65

***

HDL

39.38 ± 0.46

41.89 ± 0.23

4.87

***

Triglycerides

226.83 ± 3.75

196.88 ± 1.71

-7.27

***

Waist Circumference

98.67 ± 0.52

90.25 ± 0.24

-14.68 ****** p < 0.001

were observed with any MetS components (Fig. 2C). Significantly higher HbA1c values were observed in nondiabetic men with MetS components of BP (t = 6.85, p <0.001), HDL (t = 4.22, p < 0.01), TG (t = 6.95, p < 0.001),
and WC (t = 3.60, p < 0.001) (Fig. 2B). Significantly
higher HbA1c values were observed in diabetic men with
the MetS components of HDL (t = 2.09, p < 0.05) and TG(t = 3.65, p < 0.001) (Fig. 2D).
Multiple linear regressions were performed on fourgroups (women and men who were nondiabetic andwomen and men who were diabetic and did not takemedications) to assess the association of metabolic variables with while also accounting for covariates of age,years of education, urban/rural location, and (monthly)household income (Table 8). Then linear regressions withthe cumulative number of MetS components with thesame covariates were also performed (Table 9).

Discussion
Our results provide unique insights into the prevalence ofdiabetes and MetS components in a large, nationally representative sample of Tunisian adults. Few studies on theprevalence of these conditions have been conducted in

Diabetes prevalence and access to healthcare in Tunisia

As of 2021, the prevalence of diabetes in Tunisia in theliterature was estimated to be 10.8% according to theInternational Diabetes Federation based upon a combination of oral glucose tolerance test (OGTT), fasting blood
glucose (FBG), HbA1c, self-reported diabetes, and medical record/clinical diagnosis data from existing diabetes
sources [15]. Our results show that 18.2% of our nationally representative sample from 2016 has diabetes, a value
much higher than both the 2021 estimation for Tunisianadults and the 10.5% estimation of the global prevalenceof diabetes [15]. Of those in our sample in the diabetic
group, 40.0% of women and 42.1% of men who have
HbA1c values greater than or equal to 6.5% do not havea formal diagnosis. This large proportion of the diabetic
population being undiagnosed likely indicates a recent
shift in the burden of disease, as well as a lack of screening resources for diabetes and access to healthcare. The
substantially higher prevalence of diabetes documented
in the present study has important social and economic
implications for Tunisia.
Those without a diabetes diagnosis did not utilize
medication. Of those with a diabetes diagnosis, 91.4% of
women and 92.2% of men were on medication (insulin,tablets [e.g., Metformin or Amaryl], or both insulin andtablets), indicating generally good access to healthcare
and medication once a diagnosis is received. This further supports the conclusion that the rapid increase inchronic disease prevalence is putting an increased burden on healthcare systems that are not fully prepared toaddress the growing prevalence of diabetes and, beyond
that, NCD prevention and treatment in general.
Previous studies conducted using the THES data haveindicated income-related inequalities in cardiovascular risk factors in those with a clinical diagnosis of diabetes [40]. Another study found healthcare access orrefusal of health care in the Tunisian diabetic population was driven primarily by financial reasons (i.e., lackof health insurance) [41]. Furthermore, associations haveWisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 9 of 14

Fig. 2 A-D Average HbA1c by the presence of Individual MetS components (BP, HDL, TG, and WC). Note. Nondiabetic women (A), nondiabetic men(B), diabetic women (C) and diabetic men (D). Significance was determined using t-tests with the log of HbA1c as the outcome. (“NS” p > 0.05; * p <0.05; ** p < 0.01; *** p < 0.001)

Table 8 β Values for multiple regression predicting log HbA1cbased upon individual MetS components and covariates (age,education, urban/rural location, and log monthly householdincome)
Women

Men

Diabetic Nondiabetic Diabetic NondiabeticBP

0.00750.011**

0.085***

0.0093*

HDL

0.010

0.00610.00120.014***

TG

0.023

0.00590.060*

0.017***

WC

0.020

0.011**-0.0270.0037Age-0.00016

0.012***-0.00019

0.0011***

Education-0.00033-0.000023

0.021

0.00041

Rural-0.028-0.0027-0.00140.0028Household Income 0.0028

0.00250.00960.0047*

Adj. R Squared

-0.01

0.07

0.04

0.07

Log-likelihood

108.753183.0255.53

2918.86*

p < .05, ** p < .01, *** p < .001; The diabetic groups were restricted toindividuals that were not taking medications

been found between inequalities in healthcare and social
determinants of health such as geographical location,
gender, education, and SES [42]. We found that many
participants did not have a diagnosis of diabetes, which isin line with previous research showing high rates of undiagnosed hypertension in the THES participants, in partdue to a lack of disease education and barriers to healthcare access [43].
Metabolic syndrome component prevalence in Tunisia

The prevalence of MetS components and populationspecific patterning has not been thoroughly assessed in
Tunisia at a population level. Results show that over halfof Tunisian women met or surpassed the MetS cutoff
values for HDL cholesterol, TG, and WC, and over halfof Tunisian men met or exceeded the cutoffs for HDLcholesterol and TG. These results indicate increased susceptibility to metabolic risk factors related to lipid metabolism in higher proportions of the Tunisian population.
Additionally, the results support that Tunisian women
were more susceptible to central adiposity and obesityWisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 10 of 14

Table 9 β Values for Multiple Regression predicting log HbA1c based upon cumulative number of MetS components and covariates(age, education, urban/rural location, and log monthly household income)
Women

Men

Diabetic

Nondiabetic

Diabetic

Nondiabetic

Number of MetS Components

0.015

0.0082***0.033**

0.012***

Age-0.00020

0.0012***0.000210.0011***

Education-0.00036-0.00023

0.000770.00039

Rural-0.028-0.00310.023

0.0029Household Income-0.0025

0.0026-0.0030

0.0045*

Adj. R Squared-0.002

0.07

0.02

0.06

Log-likelihood

108.583182.0749.70

2915.94*

p < .05, ** p < .01, *** p < .001; The diabetic groups were restricted to individuals that were not taking medications

than men, as nearly 60% of Tunisian women have waist
circumference values that surpassed the MetS cutoff,while only approximately 20% of men met or exceeded
the cutoff. However, anthropometric dimensions are
known to have population differences in cutoff points,
and it is possible these values do not indicate health status. These findings provide insights into the componentsof MetS that may disproportionately affect the general
Tunisian population.
Unsurprisingly, when assessing diabetic and nondiabetic groups separately, for both women and men, thediabetic groups had higher percentages of individuals
with MetS components than the nondiabetic groups. Not
only were MetS components more prevalent in the diabetic groups, but it is also essential to acknowledge thatin both women and men, the diabetic groups had poorer
metabolic health as indicated by statistically significant
higher values for HbA1c, systolic and diastolic BP, TG,
and WC, and lower HDL cholesterol values, supporting
the view that diabetes impacts not only metabolic healthrelated to glucose metabolism but also obesity and lipid
metabolism, which may increase the risk for other NCDs
like CVD.
The availability of information about which specific
MetS components disproportionately affect the Tunisianpopulation has the potential to provide insights that canbenefit the general population and healthcare professionals as the country continues to undergo social, demographic, and epidemiological changes, and the NCD
burden continues to grow.
Hypothesis testing (diabetic vs. nondiabetic)

When testing our first hypothesis, if HbA1c was positively associated with individual MetS components using
multiple regressions in diabetic (not on medication)and nondiabetic groups, it was surprising to find that in
nondiabetic women the presence of MetS components

of BP and WC were associated with significantly higher
HbA1c, age was also positively associated with HbA1c
in this model. The same was true for nondiabetic menwith the components of BP, HDL cholesterol, and TG,
in this model age and monthly household income werealso significantly positively associated with HbA1c. This
indicates that prior to reaching a diabetes disease state,
HbA1c may be tied to metabolic components; these relationships remain relatively unexplored.
Meanwhile, this relationship between MetS component and HbA1c was not as strong in the diabetic
groups. There were no significant associations in the
diabetic women group between HbA1c and any MetS
components or covariates. In the men, BP and TG were
the only variables significantly associated with HbA1c,
and they were positively associated. These results suggest that once the diabetic disease state was reached, theindividual MetS components alone were not significantly
associated with HbA1c, meaning having one component
did not necessarily indicate increased HbA1c once it had
surpassed the threshold of 6.5%. Alternatively, in thenondiabetic group, most MetS components were significantly positively associated with HbA1c, meaning below
the 6.5% HbA1c threshold widely utilized for clinical diabetes diagnosis; HbA1c may provide additional insight
into other forms of metabolic dysregulation apart fromglucose metabolism or insulin resistance below a certain
threshold or level of dysregulation.
When testing our second hypothesis, if HbA1c waspositively associated with the cumulative number of
MetS components, our multiple linear regression model
results showed that the number of MetS components was
significantly positively associated with HbA1c in diabeticmen (not on medications) and both nondiabetic groups(men and women). This indicates that overall metabolic
health worsens as indicated by the accumulation of more
MetS components, there is an association with increasedWisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 11 of 14

HbA1c. Thus, increased HbA1c values may be indicativeof changes to not only glucose metabolism and/or insulin
resistance that are known to be closely related to changes
in insulin signaling, production, absorption, or a combination of those factors, but also the physiological systems
closely related to the other MetS components.
While directional and causal relationships cannot beestablished due to a lack of longitudinal data, through thiscross-sectional analysis provides insight into an important finding that suggest that managing and treating
individual components may help reduce the cumulative
number of components that may have further implications on metabolic health beyond the specific component
being treated. It is important to note that while we foundsignificance in the nondiabetic groups, and less in the
diabetic groups, it does not mean that these physiological transitions and connections are not occurring in the
diabetic population. Rather, the severity of the degreeof dysregulation to glucose metabolism in the diabetic
groups may be hindering our ability to assess other formsof metabolic dysregulation, resulting in a lack of significance in our analyses.
It is not surprising that age was positively associatedwith increases in HbA1c in some groups when assessing both the impact of individual and cumulative number of components, although the significant results were
only found in nondiabetic women and men. This finding
is consistent with a variety of studies of age and HbA1c
in nondiabetic populations [44–47]. This association
between age and increasing HbA1c in nondiabetic populations has potential implications for the managementand diagnosis of diabetes with increasing age.
It is also important to note the lack of significance inboth regression models (assessing individual and cumulative MetS components) relating HbA1c to education
and urban/rural location in all groups and the lack ofassociation with household income except for the nondiabetic men group.

for further investigation of other metabolic markers on
an individual level before and after the development of adisease state.
Some MetS diagnostic criteria require a componentof insulin resistance or indications of changes to glucose
metabolism for diagnosis [38]. This was based on theprevious understanding that MetS development always
begins with insulin resistance before other MetS components accumulate [14]. Our results indicate that whileinsulin resistance may affect large portions of the population even prior to levels resulting in diabetes diagnosis, it
is not necessarily required for other forms of metabolic
dysregulation. In short, measurable insulin resistance is
not necessary for MetS development. While not all thosewith multiple MetS components had indications of someforms of insulin resistance or changes to glucose metabolism it is still likely that some extent of insulin resistanceis at play, but that the body is overcompensating via theother MetS components. The impact of MetS components is significant with or without changes to insulin
production, sensitivity, and target tissue resistance ordiabetes, further supporting that more than one patternor process of metabolic dysregulation contributes to thedevelopment of MetS components and disease statesover time.
While our findings indicate significant associations
between MetS components and HbA1c, the proximate/physiological understanding of these associations,
including the specific pathways that relate our variables,
are not yet well understood. Furthermore, how metabolic risk factors contribute to not just an initial diseasestate, but also subsequent comorbidities, are also not wellunderstood. This complexity is exemplified in the relationship between T2D and CVD. It is known that T2D
is a major risk factor for CVD while in some individuals
CVD may precede T2D development [48]. Additionally,treating these conditions independently may reduce risk
or prevent developing the other disease [49]. While diseases may share common physiological mechanisms andshared clinical risk factors, the outcomes of these factors
are widely varying between individuals and the pathways
from shared conditions to disease state may develop
either independently or via shared pathways [48, 50].
These findings provide further insight into the mismatch between evolved physiological mechanisms formetabolic regulation and contemporary environments.
Extremely high rates of metabolic disorders are emerging specifically in areas with higher income, urbanism,high-calorie diets, overly hygienic environments, andsedentary lifestyles [51]. Many of these characteristics of post-agricultural/post-industrial environments
directly increase the risk of MetS. For example, increased
sedentary time generally leads to weight gain, andFrom metabolic dysregulation to disease states
and beyond

Using our approach that divided the population based on
the presence of a disease state (diabetes), we were able
to find significant associations between MetS components and HbA1c in nondiabetic populations and a lackof significance in most analyses of the diabetic population. This indicates significant associations and metabolic changes are not only occurring well below disease
cutoff values but there are multiple metabolic influencesthat can be reflected using HbA1c prior to a disease state
being established. This supports the potential for HbA1c
to be used as a marker of metabolic health beyond diabetes diagnosis and management and may indicate a needWisniewski et al. Journal of Physiological Anthropology(2024) 43:18

Page 12 of 14

higher-calorie foods cause insulin bursts that wear the
pancreas down [52]. Brain insulin resistance is an adaptive response to famine and harsh, cold environments,
but in high-calorie, low-exercise circumstances, brain
insulin resistance leads to systematic insulin resistancethat leads to disease [53]. Beyond these direct pathways
to disease, contemporary environments are also missing
the richness in microbiota, helminths, and other symbionts that are required for optimal health. It is likely thatthe lack of microbiota in utero, at birth, in foods, and in
the physical environment led to physiological systems
that were designed to interact with a multitude of microbiota with only a fraction of microbiota to help balance
and maintain complex systems in which they play animperative role. This microbial disruption has effects on
the immune system, which can bring about physiologicalchanges that lead to MetS [54]. Importantly, the transition away from helminth infection has been identified asa potential contributor to metabolic diseases since they
consume glucose and lipids, heighten insulin sensitivity,and increase immune activation [55]. While speculative,
it is possible that some of the patterns associated with thediabetic group, such as increased inflammation, insulin
resistance, and fewer associations between blood glucose
and lipids, are the result of evolved responses to helminth
infection that lead to disease states in the absence of parasite exposure.
The complex nature of NCD development necessitates future research to continue refining our knowledge of deleterious physiological changes that influence
the patterns and timelines from metabolic dysregulation
to initial NCD development and beyond. This includes
assessing how lifestyle factors can impact physiologicalrisk factors and disease development and progression.
Future analysis should include additional confounding
factors beyond those included in this study, such as dietand physical activity, to further examine the complex
relationships between MetS components and HbA1c.
Also, observing the development of MetS components
over time and assessing the relationship to HbA1c would
be a valuable future direction for research as well.

physical activity data were not included as these data havebeen shown to have low validity in WHO surveys.
A second limitation is that the gender variable was not
self-reported; rather, the study interviewer estimated theparticipant’s “gender” based on their interpretation of
appearance and then recorded “Male” or “Female”. For
the present study, the variable was renamed from “sex” to“gender”, and the limitation of gender identification due tothe methodology should be appreciated. It is also important to note that gender is not limited to men and womenand thus fails to consider individuals who identify as nonbinary, transgender, or other gender identities. Additionally, gender is not necessarily representative of biologicalsex, as it fails to consider intersex individuals. Our analysis
separated women and men based upon the NCEP ATP III
guidelines for MetS diagnosis, which has some differences
based upon gender/sex; we used this as a proxy for hormonal differences that have marked impacts on the metabolic systems. However, it is important to note that while
we separated males and females for our analyses, differences between males and females or men and women are
not necessarily due to genetic factors but are also strongly
influenced by numerous environmental factors (e.g., parasite exposure, diet, etc.) that influence biology [39].
A further limitation is that the present study was unable
to differentiate between diabetes types. Increased HbA1clevels are present in both T1D and T2D, but the underlying pathological mechanisms that result in increased
HbA1c are different between the two conditions. T1D and
T2D have different pathophysiologies. T1D is a result of an
autoimmune response that destroys pancreatic beta-cells,
and T2D is due to sustained insulin resistance at a cellular level; the current study cannot distinguish between thecausal mechanisms that might produce the observed outcomes in this study. Thus, it is possible that the inability to
distinguish between the conditions may have increased thevariance of our analyses and decreased the significance ofour results. That said, it is expected that T2D is far morecommon in adult populations. Additionally, we are lacking
other important types of data related to diabetes beyond
type of diabetes, such as duration of disease and information related to diabetes management beyond medication,that may have provided a more nuanced understanding ofvariability in HbA1c in these groups.
In addition to biomarker values, medication use wasalso included in MetS criteria. Medication use variables
were self-reported by participants, which has implications
for the reliability of medication recall, use, and adherence.
Finally, HbA1c values can be affected by a variety of genetic,hematologic, and illness-related factors that influence erythropoiesis, hemoglobin structure, glycation, and erythrocyte
destruction and have been found in previous studies to cause
falsely high or low HbA1c results [27, 56] Some examples

Limitations

A main limitation of this study is the inability to assess the
directionality of these associations due to the cross-sectional study design. Therefore, while we can ascertain thatsignificant relationships exist between HbA1c and MetS
components, a lack of directionality and longitudinal
study design does not allow us to create causal pathways
or understand these relationships beyond their existence.
Additionally, the format of the THES does not provide alot of detailed, qualitative, or ethnographic data due to thesurvey-based nature of the study. Additionally, diet andWisniewski et al. Journal of Physiological Anthropology(2024) 43:18

include recent blood loss, erythropoietin treatment, hemodialysis or transfusion, iron-deficiency anemia, kidney failure,
and liver disease. Additionally, conditions that result in variation in hemoglobin type such as sickle cell anemia or a thalassemia may interfere with some HbA1c tests as well [27].

Conclusions
Diabetes was documented in approximately 18% of individuals in this nationally representative sample in Tunisia, yetapproximately 40% of those with diabetes had not received aclinical diagnosis, and therefore did not have access to medication. Of those with a diagnosis, 92% utilized medication,
providing insights into healthcare and medication access.
The diabetic men and women in this study had significantly
higher (HbA1c, BP, TG, and WC) and lower (HDL) values,
indicating worse metabolic health compared to the nondiabetic group. HbA1c was positively associated with moreindividual components of MetS in nondiabetic men (BP,
HDL, and TG) and women (BP, TG, and WC) compared todiabetic men (TG) and women (none). HbA1c was positively
associated with the number of cumulative MetS components
in all groups, but especially in nondiabetic men and women.
Our findings provide insights into the growing prevalence of metabolic risk factors and diabetes in Tunisia,
and potentially extend to other LMICs. By separating diabetic from nondiabetic participants, we are able to begin
to assess how the degree of metabolic dysregulation isaffected by the development of a disease state and haveshown that it alters the relationship between MetS components and HbA1c. As the prevalence of metabolic andlifestyle risk factors and NCDs continue to increase wewill require a better physiological understanding of how
metabolic dysregulation leads to disease states over time.
Abbreviations
NCDs	noncommunicable diseases
MetS	Metabolic Syndrome
T1D	Type 1 Diabetes
T2D	Type 2 Diabetes
CVD	Cardiovascular DiseaseTG	Triglycerides
HDL	High-density lipoprotein
WC	Waist circumferenceBP	Blood pressure
HbA1c	Hemoglobin A1c
HICs	High-income countries
LMICs	Low- and middle-income countries
Acknowledgments
We thank Olfa Saidi and Hajer Aounallah-skhiri for coordination of the Tunisian
Health Examination Survey, as well as the study team and survey participants.
Authors’ contributions
AW - drafted work, analysis of data, interpretation of data, conception/design.
AMD – analysis of data, interpretation of data, conception/design, revision. TW analysis of data, interpretation of data, conception/design, revision. SC – on the
THES technical committee with WHO (conception, acquisition of data, design).
NN – revision. PK – revision. JJS – conception/design of work, interpretation ofdata, revision. All authors read and approved the final manuscript.

Page 13 of 14

Funding
Financial support was provided by the Tunisian Ministry of Health, the World
Health Organization, the Laboratory of Epidemiology and Research on
Cardiovascular diseases, the Tunisian Society of Endocrinology, Diabetes andMetabolic Diseases, and the National Office for the Family and Population.
Availability of data and materials
The datasets generated during the current study are available from theauthors.

Declarations
Ethics approval and consent to participate
The main ethical implications applicable to any form of medical research,stemming from the declaration of Helsinki of the World Medical Association
were followed. The study protocol was approved by the Ministry of Health andthe World Health Organization institutional review boards. The protocol was
also approved by the Tunisian National Council of Statistics (Visa n ° 09-2015 of
10 November 2015) and by the National Council for the Protection of Personal
Data (Authorization to Process health data N ° 185-01 / 16 of 06 May 2016). All
participants took part in an informed consent process.
Consent for publication
This work has not been published before (except in the form of an abstract oras part of a published lecture, review, or thesis), it is not under consideration for
publication elsewhere, and the publication has been approved by all co-authors.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Anthropology, University of North Carolina at Chapel Hill,
Chapel Hill, USA. 2 Global Health Biomarker Lab, Department of Anthropology, University of Oregon, Eugene, USA. 3 World Health Organization, Geneva,
Switzerland. 4 Centre for Women’s Health Research, University of Newcastle,
Callaghan, Australia. 5 Center for Global Health, University of Oregon, Eugene,USA. 6 Global Station for Indigenous Studies and Cultural Diversity, Hokkaido
University, Sapporo, Japan.
Received: 16 October 2023 Accepted: 29 June 2024

References
1. Non communicable diseases [cited 2022 Dec 1]. Available from: https://​www.​who.​int/​news-​room/​fact-​sheets/​detail/​nonco​mmuni​cable-​disea​ses
2. Grundy SM, Hansen B, Smith SC, Cleeman JI, Kahn RA. Clinical management of metabolic syndrome. Circulation. 2004;109(4):551–6.
3. Siu PM, Yuen QS. Supplementary use of HbA1c as hyperglycemic criterion
to detect metabolic syndrome. Diabetol Metab Syndr. 2014;6:119.
4. Cavero-Redondo I, Martínez-Vizcaíno V, Álvarez-Bueno C, Agudo-Conde
C, Lugones-Sánchez C, García-Ortiz L. Metabolic syndrome including
glycated hemoglobin A1c in adults: Is it time to change? J Clin Med.
2019;8(12):2090.
5. Kim H, Cho J, Jang J, Kang J, Seo M, Keon H, et al. Correlation between
hemoglobin A1c and metabolic syndrome in adults without diabetes
under 60 years of age. Korean J Fam Pract. 2017;7(1):60–5.
6. Dilley J, Ganesan A, Deepa R, Deepa M, Sharada G, Williams OD,
et al. Association of A1C with cardiovascular disease and metabolic
syndrome in Asian Indians with normal glucose tolerance. Diab Care.
2007;30(6):1527–32.
7. Okosun IS, Annor F, Dawodu EA, Eriksen MP. Clustering of cardiometabolic risk factors and risk of elevated HbA1c in non-Hispanic White,
non-Hispanic Black and Mexican-American adults with type 2 diabetes.
Diabetes Metab Syndr. 2014;8(2):75–81.
8. Khan HA, Sobki SH, Khan SA. Association between glycaemic control andserum lipids profile in type 2 diabetic patients: HbA1c predicts dyslipidaemia. Clin ExperMed. 2007;7(1):24–9.

Wisniewski et al. Journal of Physiological Anthropology

9.
10.
11.
12.
13.
14.

15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.

33.
34.
35.
36.
37.
38.

(2024) 43:18

Page 14 of 14

Saely CH, Aczel S, Marte T, Langer P, Hoefle G, Drexel H. The Metabolic
Syndrome, Insulin Resistance, and Cardiovascular Risk in Diabetic andNondiabetic Patients. J Clin Endocrinol Metab. 2005;90(10):5698–703.
Singer DE, Nathan DM, Anderson KM, Wilson PWF, Evans JC. Associationof HbA1c With Prevalent Cardiovascular Disease in the Original Cohort of
the Framingham Heart Study. Diabetes. 1992;41(2):202–8.
Esteghamati A, Zandieh A, Khalilzadeh O, Meysamie A, Ashraf H. Clustering of metabolic syndrome components in a Middle Eastern diabetic andnon-diabetic population. Diabetol Metab Syndr. 2010;2(1):36.
Mandal GK, Bhatt SA. Glycated hemoglobin levels in metabolic syndrome
patients. Asian J Pharm Clin Res. 2020;13(11):101–3.
Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA,
et al. Diagnosis and management of the metabolic syndrome. Circulation. 2005;112(17):2735–52.
Kahn R, Buse J, Ferrannini E, Stern M. The metabolic syndrome: Time fora critical appraisal: Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diab Care.
2005;28(9):2289–304.
International Diabetes Federation. IDF Diabetes Atlas. 10th ed. Brussels,
Belgium: International Diabetes Federation; 2021.
Hall JE, Hall ME. Guyton and Hall Textbook of Medical Physiology. Elsevier
Health Sciences; 2020. p. 1156.
Liston A, Todd JA, Lagou V. Beta-cell fragility as a common underlying risk
factor in type 1 and type 2 diabetes. Trends Mol Med. 2017;23(2):181–94.
Felig P, Wahren J, Sherwin R, Palaiologos G. Amino acid and protein
metabolism in diabetes mellitus. Arch Intern Med. 1977;137(4):507–13.
Saudek CD, Eder HA. Lipid metabolism in diabetes mellitus. Am J Med.
1979;66(5):843–52.
Action to Control Cardiovascular Risk in Diabetes Study Group, Gerstein
HC, Miller ME, Byington RP, Goff DC, Bigger JT, et al. Effects of intensive
glucose lowering in type 2 diabetes. N Engl J Med. 2008;358(24):2545–59.
Diabetes Control and Complications Trial (DCCT). Results of feasibility
study. The DCCT Research Group. Diab Care. 1987;10(1):1–19.
DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al.
Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019.
Ducat L, Philipson LH, Anderson BJ. The mental health comorbidities ofdiabetes. JAMA. 2014;312(7):691–2.
Oguntibeju OO. Type 2 diabetes mellitus, oxidative stress and inflammation: examining the links. Int J Physiol Pathophysiol Pharmacol.
2019;11(3):45.
Dandona P, Aljada A, Chaudhuri A, Mohanty P, Garg R. Metabolic syndrome. Circulation. 2005;111(11):1448–54.
Freeman VS. Glucose and hemoglobin A1c. Lab Med. 2014;45(1):e21-4.
Gallagher EJ, Le Roith D, Bloomgarden Z. Review of hemoglobin A1c inthe management of diabetes. J Diab. 2009;1(1):9–17.
Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. Diab Res Clin Pract. 2011;93(3):299–309.
Norris JM, Johnson RK, Stene LC. Type 1 diabetes—early life origins andchanging epidemiology. Lancet Diab Endocrinol. 2020;8(3):226–38.
Caballero AE, Ceriello A, Misra A, Aschner P, McDonnell ME, Hassanein M,
et al. COVID-19 in people living with diabetes: An international consensus. J Diab Compl. 2020;34(9):107671.
Drucker DJ. Diabetes, obesity, metabolism, and SARS-CoV-2 infection: theend of the beginning. Cell Metab. 2021;33(3):479–98.
Rahmati M, Keshvari M, Mirnasuri S, Yon DK, Lee SW, Il Shin J, et al. The
global impact of COVID-19 pandemic on the incidence of pediatricnew-onset type 1 diabetes and ketoacidosis: A systematic review andmeta-analysis. J Med Virol. 2022;94(11):5112–27.
Mattioli AV, Pinti M, Farinetti A, Nasi M. Obesity risk during collective
quarantine for the COVID-19 epidemic. Obes Med. 2020;20:100263.
Baekkeskov S, Hansen B, eds. Human diabetes: genetic, environmental
and autoimmune etiology. Berlin, Heidelberg: Springer-Verlag; 1990.
Ali O. Genetics of type 2 diabetes. World J Diabetes. 2013;4(4):114–23.
Noncommunicable diseases country profiles 2018. [cited 2024 Apr 13].
Available from: https://​www.​who.​int/​publi​catio​ns-​detail-​redir​ect/​97892​
41514​620
HEARTS D: diagnosis and management of type 2 diabetes. [cited 2023
Jul 13]. Available from: https://​www.​who.​int/​publi​catio​ns-​detail-​redir​ect/​
who-​ucn-​ncd-​20.1
Huang PL. A comprehensive definition for metabolic syndrome. Dis
Model Mech. 2009;2(5–6):231–7.

39. DuBois LZ, Shattuck-Heidorn H. Challenging the binary: gender/sex andthe bio-logics of normalcy. Am J Hum Biol. 2021;33(5):e23623.
40. Saidi O, Zoghlami N, Bennett KE, Mosquera PA, Malouche D, Capewell S,
et al. Explaining income-related inequalities in cardiovascular risk factors
in Tunisian adults during the last decade: comparison of sensitivity analysis of logistic regression and Wagstaff decomposition analysis. Int J Equity
Health. 2019;18(1):177.
41. Rejaibi S, Cherif I, Ben Mansour N, Zoghlami N, Saidi O, Skhiri A, et al.
Health care renunciation in Tunisian diabetic patients, 2016. Eur J Pub
Health. 2020;30(Supplement 5):ckaa165.594.
42. Kotti AB, Cherif A, Elloumi A. The social roots of health inequity in Tunisia:
A preliminary study on the social determinants of health inequity. Adv
Soc Sci Res J. 2021;8(4):576–93.
43. Cherif I, Ben Mansour N, Rejaibi S, Zoghlami N, Saidi O, Skhiri A, et al.
Prevalence, awareness and control of hypertension among Tunisianadults, 2016. Eur J Pub Health. 2020;30(Supplement 5):ckaa165.094.
44. Pani LN, Korenda L, Meigs JB, Driver C, Chamany S, Fox CS, et al. Effectof Aging on A1C Levels in Individuals Without Diabetes: Evidence fromthe Framingham Offspring Study and the National Health and Nutrition
Examination Survey 2001–2004. Diabetes Care. 2008;31(10):1991–6.
45. Davidson MB, Schriger DL. Effect of age and race/ethnicity on HbA1clevels in people without known diabetes mellitus: Implications for the
diagnosis of diabetes. Diabetes Res Clin Pract. 2010;87(3):415–21.
46. Roth J, Müller N, Lehmann T, Heinemann L, Wolf G, Müller UA. HbA1c
and Age in Non-Diabetic Subjects: An Ignored Association? Exp Clin
Endocrinol Diabetes. 2016;124(10):637–42.
47. Dubowitz N, Xue W, Long Q, Ownby JG, Olson DE, Barb D, et al. Agingis associated with increased HbA1c levels, independently of glucose
levels and insulin resistance, and also with decreased HbA1c diagnostic
specificity. Diabetic Medicine. 2014;31(8):927–35.
48. Fernandes Silva L, Vangipurapu J, Laakso M. The, “common soil hypothesis” revisited: risk factors for type 2 diabetes and cardiovascular disease.
Metabolites. 2021;11(10):691.
49. Ceriello A, Motz E. Is oxidative stress the pathogenic mechanism underlying insulin resistance, diabetes, and cardiovascular disease? The common
soil hypothesis revisited. Arterioscler Thromb Vasc Biol. 2004;24(5):816–23.
50. Stern MP. Diabetes and cardiovascular disease: The “common soil” hypothesis. Diabetes. 1995;44(4):369–74.
51. Gluckman PD, Hanson MA, Low FM. Evolutionary and developmental
mismatches are consequences of adaptive developmental plasticity inhumans and have implications for later disease risk. Phil Trans Roy Soc B:
Biol Sci. 2019;374(1770):20180109.
52. Stearns SC. Evolutionary Medicine. Sunderland, Massachusetts: Sinauer
Associates, Inc., Publishers; 2016.
53. Tsatsoulis A, Mantzaris MD, Bellou S, Andrikoula M. Insulin resistance:
An adaptive mechanism becomes maladaptive in the current environment—An evolutionary perspective. Metabolism. 2013;62(5):622–33.
54. Rook G, Bäckhed F, Levin BR, McFall-Ngai MJ, McLean AR. Evolution, human-microbe interactions, and life history plasticity. Lancet.
2017;390(10093):521–30.
55. Gurven MD, Trumble BC, Stieglitz J, Blackwell AD, Michalik DE, Finch CE,
et al. Cardiovascular disease and type 2 diabetes in evolutionary perspective: A critical role for helminths? Ev Med Pub Heal. 2016;2016(1):338–57.
56. Radin MS. Pitfalls in hemoglobin A1c measurement: When results may be
misleading. J Gen Intern Med. 2014;29(2):388–94.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.