Weight of Clinical and Social Determinants of Metabolic Syndrome in People Living with HIV

Abstract

Background. Comorbidities in people living with HIV (PLWH) represent a major clinical challenge today, and metabolic syndrome (MTBS) is one of the most important. Objective. Our objective was to assess the prevalence of MTBS and the role of both clinical/socio-behavioral risk factors for MTBS in a cohort of PLWH. Methods. All PLWH, over 18 years of age, attending all Infectious Disease Units in Calabria Region (Southern Italy) for their routine checks from October 2019-January 2020 were enrolled. MTBS was defined by NCEP-ATP III criteria. Logistic regression analysis was performed to assess factors significantly associated with the main outcome (MTBS). Results. We enrolled 356 PLWH, mostly males (68.5%), with a mean age of 49 years (standard deviation: 12), including 98 subjects with and 258 without MTBS. At logistic regression analysis, a statistically significant association was found between MTBS and alcohol use, osteoporosis, polypharmacy, and a history of AIDS. Conclusions. Identifying and addressing risk factors, including those that are socio-behavioral or lifestyle-related, is crucial to prevent and treat MTBS. Our results suggest the importance of implementing educational/multidimensional interventions to prevent MTBS in PLWH, especially for those with particular risk factors (alcohol abuse, osteoporosis, previous AIDS events, and polypharmacy). Moreover, alcohol consumption or abuse should be routinely investigated in clinical practice.

Full text

Citation: Mazzitelli, M.; Fusco, P.;
Brogna, M.; Vallone, A.; D’Argenio,
L.; Beradelli, G.; Foti, G.; Mangano,
C.; Carpentieri, M.S.; Cosco, L.; et al.
Weight of Clinical and Social
Determinants of Metabolic Syndrome
in People Living with HIV. Viruses
2022,14, 1339. https://doi.org/
10.3390/v14061339
Academic Editor: Sonia Moretti
Received: 10 April 2022
Accepted: 9 June 2022
Published: 20 June 2022
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4.0/).
viruses
Article
Weight of Clinical and Social Determinants of Metabolic
Syndrome in People Living with HIV
Maria Mazzitelli 1, 2, * , Paolo Fusco 1, Michele Brogna 3, Alfredo Vallone 3, Laura D’Argenio 3,
Giuseppina Beradelli 4, Giuseppe Foti 5, Carmelo Mangano 5, Maria Stella Carpentieri 5, Lucio Cosco 6,
Paolo Scerbo 6, Armando Priamo 6, Nicola Serrao 7, Antonio Mastroianni 8, Chiara Costa 1,
Maria Teresa Tassone 1, Vincenzo Scaglione 1, Francesca Serapide 1, Enrico Maria Trecarichi 1and Carlo Torti 1
1Unit of Infectious and Tropical Diseases, Department of Medical and Surgical Sciences,
“Magna Graecia” University, 88100 Catanzaro, Italy; paolofusco89@gmail.com (P.F.);
costach65@gmail.com (C.C.); mariateresatassone90@gmail.com (M.T.T.);
vincenzo.scaglione91@gmail.com (V.S.); francescaserapide@gmail.com (F.S.); em.trecarichi@unicz.it (E.M.T.);
torti@unicz.it (C.T.)
2Infectious and Tropical Diseases Unit, Padua University Hospital, 35128 Padua, Italy
3Jazzolino Hospital, 89900 Vibo Valentia, Italy; brognam@libero.it (M.B.); alfredo.vallone@aspvv.it (A.V.);
laura.dargenio@aspvv.it (L.D.)
4Unit of Infectious and Tropical Diseases, “Giovanni Paolo II” Hospital, 88046 Lamezia Terme, Italy;
giuseppina.berardelli@asp.cz.it
5
Unit of Infectious and Tropical Diseases, “Bianchi-Melacrino-Morelli” Hospital, 89121 Reggio Calabria, Italy;
fotigiuseppe@tin.it (G.F.); mangano.carmelo@alice.it (C.M.); carpentieri.mariastella@gmail.com (M.S.C.)
6Unit of Infectious and Tropical Diseases, “Pugliese-Ciaccio” Hospital, 88100 Catanzaro, Italy;
lucio.cosco@alice.it (L.C.); scr2002@hotmail.com (P.S.); armando1956@alice.it (A.P.)
7Unit of Infectious and Tropical Diseases, “San Giovanni di Dio” Hospital, 88900 Crotone, Italy;
n.serrao@libero.it
8Unit of Infectious and Tropical Diseases, “Annunziata” Hospital, 87100 Cosenza, Italy;
antoniomastroianni@yahoo.it
*Correspondence: m.mazzitelli88@gmail.com
Abstract: Background.
Comorbidities in people living with HIV (PLWH) represent a major clinical
challenge today, and metabolic syndrome (MTBS) is one of the most important.
Objective.
Our
objective was to assess the prevalence of MTBS and the role of both clinical/socio-behavioral risk
factors for MTBS in a cohort of PLWH.
Methods.
All PLWH, over 18 years of age, attending all
Infectious Disease Units in Calabria Region (Southern Italy) for their routine checks from October 2019–
January 2020 were enrolled. MTBS was defined by NCEP-ATP III criteria. Logistic regression analysis
was performed to assess factors significantly associated with the main outcome (MTBS).
Results.
We enrolled 356 PLWH, mostly males (68.5%), with a mean age of 49 years (standard deviation:
12), including 98 subjects with and 258 without MTBS. At logistic regression analysis, a statistically
significant association was found between MTBS and alcohol use, osteoporosis, polypharmacy, and
a history of AIDS.
Conclusions.
Identifying and addressing risk factors, including those that are
socio-behavioral or lifestyle-related, is crucial to prevent and treat MTBS. Our results suggest the
importance of implementing educational/multidimensional interventions to prevent MTBS in PLWH,
especially for those with particular risk factors (alcohol abuse, osteoporosis, previous AIDS events,
and polypharmacy). Moreover, alcohol consumption or abuse should be routinely investigated in
clinical practice.
Keywords:
HIV; PLWH; metabolic syndrome; non-communicable diseases; diabetes; dyslipidemia; AIDS
1. Introduction
Due to increased life expectancy and efficacy of newer antiretrovirals, the burden
of non-infectious comorbidities in people living with HIV (PLWH) is increasing [
1
,
2
].
Viruses 2022,14, 1339. https://doi.org/10.3390/v14061339 https://www.mdpi.com/journal/viruses

Viruses 2022,14, 1339 2 of 8
Indeed, cardiovascular disease, metabolic complications, cancer, and bone disorders are the
most frequent comorbidities in this population [
3
,
4
]. Among these, metabolic syndrome
(MTBS) is one of the most frequent [
5
]. Therefore, HIV became a chronic disease for which
management of non-communicable diseases (NCDs) remains to date the major clinical
challenge [
6
]. One of the most important issues is the management of the metabolic disease
because MTBS is not only the main drivers of major cardiovascular events, but it is also
associated with an increased risk of respiratory disorders and malignancies [
7
,
8
] and
possible side effects due to polypharmacy [
9
]. This is the reason why dedicated clinics and
services for a multidimensional approach to ageing PLWH have been implemented over
time [10].
Data about prevalence of the metabolic syndrome in people with HIV are not definitive.
Indeed, some data reported the prevalence of MTBS to be about 30%, comparable with the
prevalence of MTBS in the general population, while other studies reported that prevalence
was slightly higher in PLWH than in the general population [
11
,
12
]. Beyond the above-
mentioned criteria, social factors and lifestyle have been identified as contributors to the
risk of MTBS, and control of some social habits was also associated with prevention of
MTBS [
11
]. Moreover, recently it has been demonstrated that socioeconomic and lifestyle
differences between people with and without HIV could lead to a 2.5-fold increased life-
year loss [
13
], and for PLWH, specific factors such as chronic inflammation and type of
antiretroviral therapy could contribute to increases risk of metabolic alterations leading to
other chronic diseases [14,15].
In this study, we aimed at assessing prevalence of MTBS in PLWH in southern Italy
and both clinical and social determinants associated with its presence.
2. Materials and Methods
This observational study was coordinated by the Infectious and Tropical Diseases Unit
of “Mater Domini” teaching hospital in Catanzaro (Italy) and was conducted in accordance
with the Declaration of Helsinki and the principles of Good Clinical Practice [
16
]. The local
ethical committee (Calabria Region) approved the study protocol on 19 July 2018. Written
informed consent was obtained from all subjects enrolled. Participation in the survey was
proposed to all PLWH older than 18 years, attending the Infectious and Tropical Diseases
Units (ITDUs) in Calabria (cities of Catanzaro—two centers—, Cosenza, Crotone, Lamezia
Terme, Reggio Calabria, and Vibo Valentia) for their routine clinical checks from 1 October
2019 to 31 January 2020. Pregnant women and people aged under 18 years were excluded.
The study population was divided into two groups: PLWH with MTBS and PLWH without
MTBS. According to NCEP-ATP III criteria, metabolic syndrome (MTBS) was defined by
the presence of three or more of the following parameters: waist circumference greater
than 102 cm in males and 88 in females, blood pressure higher than 135/80 mmHg, fasting
blood glucose greater than 100 mg/dL, HDL lower than 50 mg/dL for men and 40 mg for
women, and triglycerides level higher than 150 mg/dL [17].
Data regarding demographics (age, gender, country of origin), clinical history, HIV-
related characteristics (viral load, CD4 + T cell count, AIDS-defining illnesses in the past
medical history) and all comorbidities, co-medications, risk factors and lifestyle-related
characteristics (smoking habit, alcohol consumption, physical exercise), and blood test
results were collected. Data on the level of education were collected, setting up a highest
level of education up to 16 years (primary school, 5 years; secondary school, 3 years; high
school, 5 years; university, 3 or more years). Data on comorbidities were retrieved by
clinical health records. Hypertension was defined by its presence in the medical history or
by anti-hypertensive agents among comedication. Physical activity was assessed by using
WHO definitions according to age [
18
]. Chronic kidney disease was considered if men-
tioned in the medical history or in subject with an estimated glomerular filtration rate below
90 mL/min [
19
]. Excessive alcohol intake was measured by using definitions of Italian
Ministry of Health: intake of 2 or more or 1 or more alcoholic units/day (
1 units = 12 g
of
alcohol) for men and women, respectively, or experiencing episodes of binge drinking (in-

Viruses 2022,14, 1339 3 of 8
take of 5 or more or 4 or more alcoholic units at once for men and women, respectively) [
20
].
Weight and height to calculate body mass index (BMI) and waist circumferences (to estab-
lish MTBS criteria) were measured during clinical check. Polypharmacy was defined as
the intake of 5 or more medications in the same patient [
21
]. Each participant was given a
unique study identification number, and data regarding each patient were transferred onto
an Excel database.
Continuous variables were compared by Student’s t-test for normally distributed
variables and the Mann–Whitney U test for non-normally distributed variables. Categorical
variables were evaluated using the
χ2
or two-tailed Fisher’s exact test. Odds ratios (ORs)
and 95% confidence intervals (CIs) were calculated to evaluate the strength of any asso-
ciation that emerged. Values are expressed as mean (
±
standard deviation) (continuous
variables) or as percentages of the group from which they were derived (categorical vari-
ables). Two-tailed tests were used to determine statistical significance; a p-value of <0.05
was significant. Multivariate analysis was used to explore any possible correlation with the
main outcome (MTBS). For this analysis, we used logistic regression and incorporated vari-
ables found to be significant in univariate testing. All statistical analyses were performed
using the Intercooled Stata program, version 11, for Windows (Stata Corporation, College
Station, TX, USA).
3. Results
Over the study period, we enrolled 356 PLWH, namely 98 (27.5%) subjects with
MTBS, and 258 (72.5%) without MTBS, mainly of male gender (244/356, 68.5%) and with a
mean age of 49 years (standard deviation, SD: 12). Demographics, lifestyle, and clinical
characteristics of the study population are depicted in Table 1according to the presence of
metabolic syndrome (PLWH with MTBS and PLWH without MTBS). In the MTBS group,
PLWH had a mean age of 53 years (SD: 10), were mainly of male gender (76.5%), and
experienced AIDS events in almost 90% cases. PLWH without MTBS had a mean age of
47.6 (SD: 11.6) and were mainly of male gender (65.5%). Prevalence of previous AIDS
events in this groups was 27.5%.
Table 1. Baseline characteristics by presence of metabolic syndrome.
Variable
No. PLWH with
MTBS (%)
98 (100)
No. PLWH without
MTBS (%)
258 (100)
p
Age, mean (SD) 53.1 (10.3) 47.6 (11.6) <0.001
Male gender 75 (76.5) 169 (65.5) 0.04
Country (Italy) 93 (94.9) 217 (84.1) 0.006
Highest level of education 12 (12.1) 54 (20.9) 0.05
Living alone 54 (55.1) 153 (59.3) 0.47
Being retired 15 (15.3) 22 (8.5) 0.05
Being smoker 57 (58.2) 130 (50.4) 0.18
Doing regular exercise 25 (25.5) 89 (34.5) 0.104
Excessive alcohol intake 51 (52) 88 (34.1) 0.019
Chronic kidney disease 10 (10.2) 20 (7.7) 0.45
Cirrhosis 3 (3.1) 5 (1.9) 0.52
COPD 15 (15.3) 17 (6.6) 0.01
Malignancies 3 (3.1) 5 (1.9) 0.52
Psychiatric disorders 24 (24.5) 65 (25.2) 0.89
Neurological disorders 21 (21.4) 19 (7.4) 0.002
Osteoporosis 28 (28.6) 27 (10.5) <0.01
Thyroid diseases 4 (4.1) 11 (4.3) 0.93
HBV coinfection 7 (7.1) 21 (8.1) 0.75
HCV coinfection 27 (27.5) 59 (22.9) 0.35
HBV/HCV coinfection 4 (4.1) 5 (1.9) 0.249
Polypharmacy 18 (18.4) 4 (1.5) <0.01
CD4/CD8 ratio > 1 20 (20.1) 79 (30.6) 0.05

Viruses 2022,14, 1339 4 of 8
Table 1. Cont.
Variable
No. PLWH with
MTBS (%)
98 (100)
No. PLWH without
MTBS (%)
258 (100)
p
Previous AIDS events 88 (89.9) 71 (27.5) <0.01
HIV RNA > 50 copies/mL 5 (5.1) 13 (5.1) 0.98
Years with HIV, mean (SD) 15.9 (0.6) 14.2 (0.6) 0.9
Last CD4 T cell count, mean (SD) 669 (21) 705 (37) 0.8
CD4 T cell count nadir, mean (SD) 310 (15) 277 (23) 0.13
cART *
2NRTI + INI 47 (47.9) 118 (45.7) 0.7
2NRTI + NNRTI 13 (13.2) 53 (20.5) 0.2
2NRTI + PI 18 (18.4) 48 (19.8) 0.9
INI + PI 7 (7.3) 22 (8.5) 0.7
Dual 0 (0) 5 (1.9) 0.2
SD, standard deviation; PLWH, people living with HIV; MTBS, metabolic syndrome; COPD, chronic obstructive
pulmonary disease; cART, combination antiretroviral therapy; NRTI, nucleos(t)ide reverse transcriptase inhibitors;
NNRTI, non-nucleos(t)ide reverse transcriptase inhibitors; INI, integrase inhibitors; PI, protease inhibitors. *, 13
subjects in the MTBS group and 12 in the group without MTBS were receiving cART not present in the listed
combinations.
At the univariate analysis (Table 2), factors significantly associated with MTBS were
age (53.1 vs. 47.6, p< 0.001), male gender (OR: 0.58, 95% CI: 0.3–1.1, p= 0.04), excessive
alcohol intake (OR: 2.1, 95% CI: 1.3–3.5, p= 0.019), chronic pulmonary disease (OR: 2.56, 95%
CI: 1.1–5.7, p= 0.01), neurological diseases (OR: 3.4, 95% CI: 1.6–7.1, p= 0.002), osteoporosis
(OR: 3.42, 95% CI 1.8–6.4, p< 0.01), polypharmacy (OR: 14.3, 95% CI 4.4–59.2, p< 0.01),
and AIDS events in the past medical history (OR: 23.1, 95% CI 11.1–52, p< 0.01). At the
multivariable model, (Table 2), significant association was maintained only for alcohol
consumption (OR: 3.1, 95% CI 1.4–6.6; p< 0.01), osteoporosis (OR: 3.1, 95% CI 1.8–7.3,
p< 0.01
), polypharmacy (OR: 7.1, 95% CI: 1.85–27.6; p< 0.01), and history of AIDS events
(OR: 21, 95% CI 10.9–44.1, p< 0.01).
Table 2.
Univariate and multivariate analyses of risk factors associated with metabolic syndrome
in PLWH.
Variable
No. PLWH
with MTBS (%)
98 (100)
No. PLWH without
MTBS (%)
258 (100)
Univariable Analysis Multivariable Analysis
Odds Ratio
(95% CI) pOdds Ratio
(95% CI) p
Age, mean (SD) 53.1 (10.3) 47.6 (11.6) - <0.001
Male gender 75 (76.5) 169 (65.5) 0.58 (0.3–1.1) 0.04
Country (Italy) 93 (94.9) 217 (84.1) 3.5 (1.32–11.7) 0.006
Highest level of education 12 (12.1) 54 (20.9) 0.52 (0.24–1.1) 0.05
Living alone 54 (55.1) 153 (59.3) 0.84 (0.51–1.4) 0.47
Being retired 15 (15.3) 22 (8.5) 1.9 (0.88–4.1) 0.05
Being smoker 57 (58.2) 130 (50.4) 1.36 (0.8–2.25) 0.18
Doing regular exercise 25 (25.5) 89 (34.5) 0.66 (0.36–1.1) 0.104
Excessive alcohol intake 51 (52) 88 (34.1) 2.1 (1.3–3.5) 0.019 3.1 (1.4–6.6) <0.01
Chronic kidney disease 10 (10.2) 20 (7.7) 1.35 (0.54–3.2) 0.45
Cirrhosis 3 (3.1) 5 (1.9) 1.59 (0.24–8.4) 0.52
COPD 15 (15.3) 17 (6.6) 2.56 (1.1–5.7) 0.01
Malignancies 3 (3.1) 5 (1.9) 1.59 (0.24–8.4) 0.52
Psychiatric disorders 24 (24.5) 65 (25.2) 0.96 (0.53–1.7) 0.89
Neurological disorders 21 (21.4) 19 (7.4) 3.4 (1.6–7.1) 0.002
Osteoporosis 28 (28.6) 27 (10.5) 3.42 (1.8–6.4) <0.01 3.6 (1.8–7.3) <0.01
Thyroid diseases 4 (4.1) 11 (4.3) 0.95 (0.21–3.3) 0.93
HBV coinfection 7 (7.1) 21 (8.1) 0.86 (0.3–2.1) 0.75
HCV coinfection 27 (27.5) 59 (22.9) 1.28 (0.7–2.24) 0.35
HBV/HCV coinfection 4 (4.1) 5 (1.9) 2.1 (0.41–10.2) 0.249
Polypharmacy 18 (18.4) 4 (1.5) 14.3 (4.4–59.2) <0.01 7.1 (1.85–27.6) <0.01
CD4/CD8 ratio > 1 20 (20.1) 79 (30.6) 0.58 (0.31–1.1) 0.05
Previous AIDS events 88 (89.9) 71 (27.5) 23.1 (11.1–52) <0.01 21 (10.9–44.1) <0.01

Viruses 2022,14, 1339 5 of 8
Table 2. Cont.
Variable
No. PLWH
with MTBS (%)
98 (100)
No. PLWH without
MTBS (%)
258 (100)
Univariable Analysis Multivariable Analysis
Odds Ratio
(95% CI) pOdds Ratio
(95% CI) p
HIV RNA > 50 copies/mL 5 (5.1) 13 (5.1) 1.01 (0.27–3.13) 0.98
Years with HIV, mean (SD) 15.9 (0.6) 14.2 (0.6) - 0.9
Last CD4 T cell count, mean (SD) 669 (21) 705 (37) - 0.8
CD4 T cell count nadir, mean (SD) 310 (15) 277 (23) - 0.13
cART *
2NRTI + INI 47 (47.9) 118 (45.7) 1.1 (0.66–1.78) 0.7
2NRTI + NNRTI 13 (13.2) 53 (20.5) 0.5 (0.3–1.2) 0.2
2NRTI + PI 18 (18.4) 48 (19.8) 0.9 (0.5–1.8) 0.9
INI + PI 7 (7.3) 22 (8.5) 0.8 (0.3–2.1) 0.7
Dual 0 (0) 5 (1.9) 0 (0–2) 0.2
SD, standard deviation; PLWH, people living with HIV; MTBS, metabolic syndrome; COPD, chronic obstructive
pulmonary disease; cART, combination antiretroviral therapy; NRTI, nucleos(t)ide reverse transcriptase inhibitors;
NNRTI, non-nucleos(t)ide reverse transcriptase inhibitors; INI, integrase inhibitors; PI, protease inhibitors.
*, 13 subjects in the MTBS group and 12 in the group without MTBS were receiving cART not present in the listed
combinations.
4. Discussion
We found that approximately one-third (27.5%) of PLWH in our cohort from southern
Italy had MTBS. This result in the middle of the range of prevalence of MTBS in the general
population, which is from 15% to 29% in Italy [
22
–
24
]. As for PLWH, in comparison with
other cohorts from Mediterranean area, (i.e., Spain), where the prevalence was 11.4%, our
prevalence of MTBS was higher [
25
], while it was lower than that recently described by
a multicenter Italian cohort reporting a prevalence of MTBS of 29.3–35% in PLWH over
10 years [
12
]. According to the latter study, prevalence of MTBS in PLWH residing in
Italy decreased from 2005 to 2015. However, since then, a new class of antiretroviral, the
integrase inhibitors, which are strictly associated with weight gain, is available. Whether
the advent of this new class influenced the prevalence of MTBS in PLWH across Italy
remains to be investigated.
Due to the residency of our patients in the Mediterranean area, we would have
expected a far lower prevalence of MTBS, similar to that found in other Mediterranean
cohorts (15–21%) [
22
,
23
]. A plausible explanation for this discrepancy could be the indirect
effect of globalization that has also changed people’s eating habits (increased consumption
of “junk food “and sweet/carbonated drinks) [
26
]. On the other hand, more likely, it could
be due to social determinants recently identified as determinants of MTBS in the Italian
Obesity Barometer Report 2019 [
27
]. Herein, it is demonstrated that 30% of Italians are
overweighted/obese and that proportions of obese/overweighted people is greater in the
south compared to the north of Italy [
27
]. This difference is due to sedentary lifestyle, lower
level of education, and high caloric intake [27].
This risk factors are represented also in PLWH [
28
,
29
] and confirmed by our results.
Moreover, in PLWH, some lifestyle behaviors increasing the risk of MTBS (such as alcohol
abuse) are overrepresented, increasing the risk of metabolic disorders further [
30
–
32
]. In
our cohort, people with excessive alcohol intake were 3.1-fold more likely to have MTBS
when compared to those who did not report any alcohol consumption. Moreover, alcohol
consumption is a part of the nutritional habit; hence, it is likely that these factors may
influence each other. Therefore, educational interventions to avoid and control alcohol
abuse should be promoted.
Another crucial tool to prevent MTBS is performing regular exercise, which also
prevents other comorbidities such as bone disorders, specifically osteoporosis, and could
contribute to keeping ageing people fit.
Polypharmacy was significantly associated with MTBS in our cohort, and this could
be easily explained by the fact that polypharmacy is a proxy of comorbidities. Moreover,
the use of specific medications (protease inhibitors, antidepressants agents, corticosteroids,
oral contraceptives) may increase the risk of the development of the metabolic syndrome

Viruses 2022,14, 1339 6 of 8
by either promoting weight gain or altering lipid or glucose metabolism [
33
,
34
]. Healthcare
providers should promptly recognize, systematically review, and assess the risk associated
with some medications more than others and appropriately change/switch off medications
contributing to the burden of metabolic disease. Moreover, careful attention to the drug
choices should be paid in patients who are overweight or have other risk factors for diabetes
or cardiovascular disease.
Our data showed a significant association between MTBS and previous AIDS events.
Furthermore, in our analysis, a trend to significance was found for CD4/CD8 ratio: PLWH
with a low CD4/CD8 ratio (<1) were more likely to have MTBS (p= 0.05). A low CD4/CD8
ratio has been linked to ageing and acts both as a predictor of mortality in the general
population and a biomarker of inflammation in PLWH [
35
]. It would therefore seem that
both inflammation and immunosuppression play a role in metabolic diseases.
It should be noted that our analysis shows that (even if not statistically) the most
educated people tend to have half the risk of developing MTBS. A possible explanation is
that highly educated people take much more care regarding quality of food and lifestyles;
by contrast, people with lower levels of education may eat larger amounts of unhealthy,
calorically dense food than those with a higher education level [
36
]. This result is also in
line with data from the general Italian population previously mentioned.
This study is somewhat limited by its cross-sectional nature, by the lack of a control
group, by the low number of participants, and by the possible bias connected with a
retrospective collection of data from clinical health records (missing information such as
underreporting of comedications and comorbidities, etc.). Furthermore, categorizations
of some variables in a dichotomous way could have had an impact on our results [
37
].
Moreover, it is well-recognized that there are prominent sex differences in MTBS [
38
,
39
].
Given the prominent sex differences in the pathogenesis of metabolic syndrome, it is
possible that the risk factors associated with MTBS may also be altered by different gender
distribution, and this can also be seen as a limitation of our study in terms of generalizability
of results.
Our study suggests that prevalence of MTBS in our cohort is high (27.5%); therefore, it
is important to both identify risk factors and implement educational/multidimensional
interventions to prevent MTBS in PLWH, especially for those with particular risk factors
(previous AIDS events or polypharmacy). Moreover, some behaviors, such as alcohol
consumption, should be routinely investigated in clinical practice, and campaigns should
be implemented to promote a change in the lifestyle of patients by promoting healthy diets,
weight loss, and physical activity. Lastly, since available data are still debated, more recent
and updated data are necessary to establish the actual prevalence of MTBS in PWLH.
Author Contributions:
Conceptualization, M.M. and C.T.; methodology, M.M., C.T. and E.M.T.;
software, E.M.T.; formal analysis, E.M.T.; investigation, M.M.; data curation, M.B.; A.V.; L.D.; G.F.;
C.C.; M.T.T.; C.M.; M.S.C.; V.S.; P.F.; F.S.; L.C.; A.P.; A.M.; N.S.; P.S.; G.B.; writing—original draft
preparation, M.M.; writing—review and editing, M.M. and C.T.; project administration, M.M. All
authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Ethics Committee of Calabria Region on 19 July 2018
(n. 201).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: All data of interest are herein reported.
Acknowledgments:
We want to thank all our patients for their participation in this study and the
CalabrHIV Study Group. We want to also thank Peter Reiss (Netherlands) for his support and advice
in manuscript draft preparation.

Viruses 2022,14, 1339 7 of 8
Conflicts of Interest:
The authors declare no conflict of interest. Maria Mazzitelli was supported as a
Ph.D. student by the European Commission (FESR FSE 2014–2020) and by the Calabria region (Italy).
The European Commission and Calabria region cannot be held responsible for any use that may be
made of information contained therein. She was also supported in developing this research by the
EACS mentorship program 2021 (Peter Reiss, The Netherlands). Results of this paper were presented
in part during a poster session at the EACS conference 2021, in London.
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