Lack of association between loneliness, social isolation and inflammation in people living with HIV aged ≥50 years: Results from the sub-study “No One Alone-Gesida Study”

Jose-Ramon Blanco; Helena Albendin-Iglesias; Eugenia Negredo-Puigmal; Ana Mª Barrios-Blandino; Cristina Tomás-Jimenez; Isabel Sanjoaquin-Conde; María Saumoy; Verónica Pérez-Esquerdo; Inmaculada Gonzalez-Cuello; Ana María López-Lirola; María José Galindo; Noemí Cabello-Clotet; Jesica Abadía Otero; Dolores Merino-Muñoz; Joanna Cano-Smith; Magdalena Muelas-Fernandez; Javier De La Torre; Alicia González-Baeza; Lourdes Romero; Antonio Ocampo; Rafael Torres; Carmen Hidalgo; Herminia Esteban; Maria Angeles Fernandes-López; Jordi Puig; Lucio Garcia Fraile; Enrique Bernal Morell; Laura Perez-Martinez; Marta De Miguel Montero; Inma Jarrin; Julian Olalla; THE GESIDA 12021 STUDY GROUP

doi:10.1371/journal.pone.0335949

. 2025 Dec 17;20(12):e0335949. doi: 10.1371/journal.pone.0335949

Lack of association between loneliness, social isolation and inflammation in people living with HIV aged ≥50 years: Results from the sub-study “No One Alone-Gesida Study”

Jose-Ramon Blanco ^1,^2,^*, Helena Albendin-Iglesias ³, Eugenia Negredo-Puigmal ⁴, Ana Mª Barrios-Blandino ⁵, Cristina Tomás-Jimenez ⁶, Isabel Sanjoaquin-Conde ⁷, María Saumoy ⁸, Verónica Pérez-Esquerdo ⁹, Inmaculada Gonzalez-Cuello ¹⁰, Ana María López-Lirola ¹¹, María José Galindo ¹², Noemí Cabello-Clotet ¹³, Jesica Abadía Otero ¹⁴, Dolores Merino-Muñoz ¹⁵, Joanna Cano-Smith ¹⁶, Magdalena Muelas-Fernandez ¹⁷, Javier De La Torre ¹⁸, Alicia González-Baeza ¹⁹, Lourdes Romero ², Antonio Ocampo ²⁰, Rafael Torres ²¹, Carmen Hidalgo ²², Herminia Esteban ²³, Maria Angeles Fernandes-López ³, Jordi Puig ⁴, Lucio Garcia Fraile ⁵, Enrique Bernal Morell ⁶, Laura Perez-Martinez ², Marta De Miguel Montero ²³, Inma Jarrin ²⁴, Julian Olalla ¹⁸; THE GESIDA 12021 STUDY GROUP^¶

Editor: Stanley Chinedu Eneh²⁵

PMCID: PMC12711021 PMID: 41406145

Introduction

Inflammation is linked to multiple health conditions. Emerging evidence suggest it may play a role in the association between social isolation – loneliness and health outcomes. People living with HIV (PLWH) exhibit chronic inflammation even with viral suppression, likely due to ongoing immune activation. However, few studies have explored inflammatory biomarker in PLWH experiencing loneliness or social isolation. This study aimed to assess this association in PLWH aged 50 or older.

Materials and methods

This observational, multicenter, cross-sectional study was conducted by the AIDS Study Group of the Spanish Society of Infectious Diseases and Clinical Microbiology across 22 Spanish hospitals. Participants were classified based on whether they experienced loneliness (UCLA 3-item scale ≥6) and/or social isolation (Lubben Social Network Scale-6 ≤ 20). Blood samples were stored at −80°C, and cytokine levels were measured using the Olink Target 48 Cytokine panel. Multivariable median regression was used to assess differences in inflammatory biomarkers between those experiencing loneliness and/or social isolation and those experiencing neither.

Results

Among 199 PLWH, 33.2% reported loneliness/social isolation. Those without loneliness/social isolation were more likely to be male (p = 0.023), employed or students (p < 0.001) and had fewer prior AIDS events (p = 0.045) and comorbidities. CSF1 levels were elevated in unadjusted analysis (median difference 11.33; 95% CI 3.50–19.15; p = 0.005) but not in adjusted models.

Conclusion

No significant increase in inflammatory markers was found in PLWH experiencing loneliness and/or social isolation. Further research should prioritize longitudinal, multi–time-point designs with expanded biomarker panels and test whether connection-enhancing interventions modulate inflammation.

Introduction

Despite the success of antiretroviral therapy (ART), people living with HIV (PLWH) are at higher risk of noncommunicable diseases than age-matched HIV-negative individuals. The underlying causes of this elevated risk are not fully understood, though they may involve an accelerated or accentuated aging process [1–3]. This process is likely driven by a complex interplay of factors, including HIV infection, ART, chronic viral co-infections, and lifestyle factors.

Until recently, health has been characterized by eight biological hallmarks that influence overall health [4]. These hallmarks affect various biological system across the body. Recently, however, mental and socioeconomic factors have been proposed as additional critical hallmark of health [5]. In this context, the impact of mental state on biological aging is considered substantial, potentially exerting effects comparable to those of smoking [6].

Social isolation (SIL) and loneliness significantly impact the well-being, mental health, and quality of life (QoL) of PLWH [7,8]. SIL refers to objectively limited social contact, whereas loneliness reflects the subjective distress arising from a discrepancy between desired and actual social relationships [9]. Although these phenomena often co-occur, they are distinct: individuals may experience loneliness despite frequent social interactions [10]. Both have been linked to adverse health outcomes, including increased all-cause mortality, unhealthy behaviours, and mental health issues [11–16], with particularly strong impacts in older PLWH, who experience them more often than the general population [15,17].

Social disconnection triggers a biological stress response that disrupts endocrine, immune, metabolic, and cardiovascular systems and the gut–microbiome interface, with downstream effects on cortisol regulation, blood pressure, host defenses, and inflammatory activity [18]. SIL acts as a chronic stressor that increases mortality risk and the incidence of mental and cardiometabolic disorders through hyperactivation of the hypothalamic–pituitary–adrenal axis, glucocorticoid resistance, and inflammation; mitochondrial dysfunction serves as a central immunometabolic hub linking stress to disease. These changes promote depression, anxiety, and metabolic syndrome and, in turn, amplify social disconnection, reinforcing a self-perpetuating cycle [19,20]. Inflammation is a common pathway through which stress contribute to many chronic conditions. Evidence across disciplines implicates inflammatory process in a broad spectrum of physical and mental health outcomes [21,22], and accumulating data indicate that inflammation mediates, at least in part, the health effects of loneliness [23–27]. PLWH exhibit persistently elevated systemic inflammation compared to HIV-negative individuals, even with virological suppression, consistent with ongoing immune activation. [28,29].

SIL and loneliness are prevalent among PLWH and are associated with heightened inflammation [24,30,31], a key driver of HIV-associated comorbidities [32]. Although distinct [9], SIL and loneliness frequently co-occur [10] and may synergistically exacerbate inflammatory responses in PLWH. We conducted a cross-sectional study to evaluate whether loneliness, SIL, or their combination were associated with inflammatory markers in PLWH, hypothesizing that that reporting loneliness and/or SIL would exhibit higher concentrations of selected inflammatory markers compared to those without these experiences.

Materials and methods

Ethics statement

This study was approved by the Institutional Research Ethics Committee (Comité de Ética de Investigación con medicamentos de La Rioja [CEImLAR]). All participants provided written informed consent.

Study design and participants

The study design has been described previously [33,34]. Briefly, this was an observational, cross-sectional, multicenter study conducted in 22 Spanish hospitals (September 2022 – May 2023. Eligible participants were PLWH aged ≥50 years in active follow-up at participating centers; those with a life expectancy <1 year were excluded at the treating physician’s discretion.

Questionnaires and venous blood sampling were conducted as part of the same study. Participants provided serum samples for biomarker analyses and gave written informed consent before any procedures.

Data collection

Data were obtained via self-report (face-to-face interviews and self-administered questionnaires) and systematic chart review, and captured in REDCap. From records we abstracted sociodemographic characteristics (age, sex at birth, place of birth/residence, education, employment, personal and marital status) and clinical history. HIV-related variables included duration of infection, route of acquisition, prior AIDS-defining events, CD4 nadir, current CD4 count, HIV RNA (undetectable <50 copies/mL), HBV coinfection (HBsAg+) and HCV coinfection (RNA+). Adherence in the previous 4 weeks was self-report. Comorbidities across major organ systems were recorded [35]. Polypharmacy was defined as >5 non-ART medications.

Loneliness and social isolation

Loneliness was assessed with the 3-item UCLA Loneliness Scale; scores ≥6 classified participants as lonely [36]. Social isolation was assessed with the Lubben Social Network Scale–Revised (LSNS-R); scores ≤20 classified participants as socially isolated [37,38].

These scales were administered in their validated Spanish versions: UCLA [39] and Lubben [40]. Internal consistency for this cohort was previously reported (Cronbach’s α = UCLA 0.896; and Lubben 0.842 [33].

Psychosocial measures

Anxiety and depressive symptoms were assessed with the Hospital Anxiety and Depression Scale (HADS) (≥8 indicates elevated symptoms) [41]; HIV-related stigma with the Spanish-adapted HIV Stigma Scale (higher scores indicate greater stigma) [42]; and health-related quality of life with the EQ-5D-5L plus the EQ-5D visual analogue scale (higher scores indicate better health) [43,44]. Alcohol use was assessed with AUDIT-C (cut-offs >4 in men, > 3 in women) [45,46]. Tobacco and illicit drug use were self-reported.

These scales were administered in their validated Spanish versions: HADS and EQ-5D. Internal consistency for this cohort was previously reported (Cronbach’s α = HADS 0.896; EQ-5D 0.795) [33].

Inflammation biomarkers

Serum samples were obtained from blood drawn during a study visit and stored at −80° C. To assess inflammatory cytokine levels, the Olink Target 48 Cytokine panel was selected (https://olink.com/products/olink-target-48). Detailed information about the panel can be found in the S1 Table.

Statistical Analysis

Participants were classified based on the presence of loneliness (UCLA-3 scale ≥6) and/or social isolation (LUBBEN scale ≤20). Descriptive analysis included frequency tables for categorical variables, and mean and standard deviation (SD) or median and interquartile range (IQR) for quantitative variables with normal or non-normal distribution, respectively. Differences in participant characteristics by loneliness and/or social isolation status were assessed using chi-square test for categorical variables and either the Student t-test or the non-parametric Mann-Whitney U test for continuous variables with normal or non-normal distribution, respectively.

At the analyte level, we required ≥70% detectability. Inflammatory proteins with >30% of measurements below the assay-specific lower limit of detection (LOD) were excluded (IL-33, IL-2, IL-1β, IL-4, TSLP, IL-13, and CSF2) [47]. For retained biomarkers, sub-LOD values were set to the LOD; this applied to IL-17A and IL-17F. Given the skewed distribution of inflammatory markers, we calculated the medians (IQR) of markers by loneliness and/or social isolation status, and used quantile regression models to assess differences between individuals experiencing loneliness and/or social isolation and those without it. Multivariable models were adjusted for the following variables, which are commonly associated with inflammatory markers in PLWH. Briefly, demographic factors (age, sex) [48], HIV-related immune status (CD4/CD8 ratio, viral suppression, duration of infection) [49,50], health status (multimorbidity, polypharmacy) [51], behaviors (tobacco use) [52], mental health (clinically significant anxiety/depression) [53], and socioeconomic context (employment status) [54] have been associated with systemic inflammatory biomarkers in PLWH.

All statistical analysis were conducted using Stata software (version 17.0; Stata Corporation, College Station, TX, USA).

Results

A total of 199 PLWH were included in this analysis: 66.8% reported neither loneliness nor SIL, 13.6% loneliness only, 9.5% SIL only, and 10.1% both.

Loneliness and SIL were first analyzed as separate exposures, with participants initially classified into four mutually exclusive categories: neither loneliness nor social isolation, loneliness only, social isolation only, or both. Because some strata were small and no meaningful differences in inflammatory markers were observed among the loneliness only, social isolation only, and both groups, we combined these categories into a single exposure (loneliness and/or social isolation) to improve precision.

Socio-demographic data are shown in Table 1. Individuals experiencing loneliness and/or SIL were significantly more likely to be female (p = 0.023), unemployed/retired (p < 0.001), and living alone unwillingly (p = 0.006).

Table 1. Characteristics of 199 people with HIV included in the study.

	Loneliness and/or social isolation
	No	Yes	p-value	Total
	N = 133	N = 66		N = 199
Sex at birth [N (%)]			0.023
Male	103 (77.4)	41 (62.1)		144 (72.4)
Female	30 (22.6)	25 (37.9)		55 (27.6)
Age, years, mean (SD)	59.1 (5.6)	60.5 (6.2)	0.212	59.6 (5.8)
Age, years, [N (%)]			0.582
50-59	74 (55.6)	34 (51.5)		108 (54.3)
60+	59 (44.4)	32 (48.5)		91 (45.7)
Race [N (%)]			0.373
Caucasian	127 (95.5)	61 (92.4)		188 (94.5)
Other	6 (4.5)	5 (7.6)		11 (5.5)
Place of birth [N (%)]			0.538
Spain	117 (88.0)	56 (84.8)		173 (86.9)
Outside Spain	16 (12.0)	10 (15.2)		26 (13.1)
Place of residence [N (%)]			0.369
Urban	117 (88.0)	55 (83.3)		172 (86.4)
Rural (< 10.000 inhabitants)	16 (12.0)	11 (16.7)		27 (13.6)
Education level [N (%)]			0.078
Elementary or lower	38 (28.6)	29 (43.9)		67 (33.7)
Secondary or higher	77 (57.9)	28 (42.4)		105 (52.8)
Unknown	18 (13.5)	9 (13.6)		27 (13.6)
Employment status [N (%)]			<0.001
Student/Employed	80 (60.2)	19 (28.8)		99 (49.7)
Retired/Unemployed	48 (36.1)	45 (68.2)		93 (46.7)
Unknown	5 (3.8)	2 (3.0)		7 (3.5)
Living situation, [N (%)]			0.006
Not living alone/ Lives alone by choice	111 (83.5)	50 (75.8)		161 (80.9)
Lives alone unwillingly	3 (2.3)	9 (13.6)		12 (6.0)
Unknown	19 (14.3)	7 (10.6)		26 (13.1)
Marital Status [N (%)]			0.663
Married/Partnered/Separated/Widowed	77 (57.9)	34 (51.5)		111 (55.8)
Single	44 (33.1)	26 (39.4)		70 (35.2)
Unknown	12 (9.0)	6 (9.1)		18 (9.0)

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HIV-related data (Table 2) show that individuals without loneliness and/or social isolation had significantly fewer prior AIDS events (p = 0.045).

Table 2. Data related to HIV and coinfections, as a function of loneliness and/or social isolation.

	Loneliness and/or social isolation
	No	Yes	p-value	Total
	N = 133	N = 66		N = 199
Time since HIV diagnosis, years, [N (%)]			0.712
< 10	22 (16.5)	8 (12.1)		30 (15.1)
10-19	39 (29.3)	20 (30.3)		59 (29.6)
≥ 20	72 (54.1)	38 (57.6)		110 (55.3)
HIV acquisition route, [N (%)]			0.583
Heterosexual	43 (32.3)	20 (30.3)		63 (31.7)
Men who have sex with men	54 (40.6)	22 (33.3)		76 (38.2)
Intravenous drug users	23 (17.3)	17 (25.8)		40 (20.1)
Other	12 (9.0)	7 (10.6)		19 (9.5)
Unknown	1 (0.8)	0 (0.0)		1 (0.5)
CD4 nadir, mm³, [N (%)]			0.330
< 200	57 (42.9)	27 (40.9)		84 (42.2)
≥ 200	72 (54.1)	39 (59.1)		111 (55.8)
Unknown	4 (3.0)	0 (0.0)		4 (2.0)
Current CD4 count, mm³, [N (%)]			0.791
< 500	30 (22.6)	16 (24.2)		46 (23.1)
≥ 500	103 (77.4)	50 (75.8)		153 (76.9)
Current CD4/CD8 ratio, [N (%)]			0.118
≤ 1	80 (60.2)	32 (48.5)		112 (56.3)
> 1	53 (39.8)	34 (51.5)		87 (43.7)
Current HIV RNA < 50 copies/mL, [N (%)]			0.090
No	13 (9.8)	2 (3.0)		15 (7.5)
Yes	120 (90.2)	64 (97.0)		184 (92.5)
Prior AIDS event, [N (%)]			0.045
No	106 (79.7)	44 (66.7)		150 (75.4)
Yes	27 (20.3)	22 (33.3)		49 (24.6)
Lipoatrophy, [N (%)]			0.787
No	105 (78.9)	51 (77.3)		156 (78.4)
Yes	28 (21.1)	15 (22.7)		43 (21.6)
Lipohypertrophy, [N (%)]			0.518
No	119 (89.5)	57 (86.4)		176 (88.4)
Yes	14 (10.5)	9 (13.6)		23 (11.6)
Hepatitis B coinfection, (HBsAg+) [N (%)]			0.617
No	127 (95.5)	64 (97.0)		191 (96.0)
Yes	6 (4.5)	2 (3.0)		8 (4.0)
Hepatitis C coinfection (ARN+), [N (%)]			0.259
No	97 (72.9)	43 (65.2)		140 (70.4)
Previous (cured)	34 (25.6)	23 (34.8)		57 (28.6)
Active	2 (1.5)	0 (0.0)		2 (1.0)
ART Adherence in the last 4 weeks, [N (%)]			0.530
> 95	126 (94.7)	61 (92.4)		187 (94.0)
< 95	6 (4.5)	5 (7.6)		11 (5.5)
Unknown	1 (0.8)	0 (0.0)		1 (0.5)

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Comorbidity data (Table 3) reveal that those without loneliness and/or social isolation had significantly fewer musculoskeletal (p < 0.001) and neuro-psychiatric (p = 0.02) comorbidities, as well as lower rates of multimorbidity (p = 0.001) and polypharmacy (p = 0.002).

Table 3. Comorbidities, as a function of loneliness and/or social isolation.

	Loneliness and/or social isolation
	No	Yes	p-value	Total
	N = 133	N = 66		N = 199
Cardiovascular, [N (%)]			0.449
No	82 (61.7)	37 (56.1)		119 (59.8)
Yes	51 (38.3)	29 (43.9)		80 (40.2)
Digestive, [N (%)]			0.165
No	126 (94.7)	59 (89.4)		185 (93.0)
Yes	7 (5.3)	7 (10.6)		14 (7.0)
Endocrine, [N (%)]			0.885
No	78 (58.6)	38 (57.6)		116 (58.3)
Yes	55 (41.4)	28 (42.4)		83 (41.7)
Respiratory, [N (%)]			0.194
No	121 (91.0)	56 (84.8)		177 (88.9)
Yes	12 (9.0)	10 (15.2)		22 (11.1)
Musculoskeletal, [N (%)]			<0.001
No	115 (86.5)	42 (63.6)		157 (78.9)
Yes	18 (13.5)	24 (36.4)		42 (21.1)
Neuro-psychiatric, [N (%)]			0.020
No	100 (75.2)	39 (59.1)		139 (69.8)
Yes	33 (24.8)	27 (40.9)		60 (30.2)
Nephrological, [N (%)]			0.236
No	122 (91.7)	57 (86.4)		179 (89.9)
Yes	11 (8.3)	9 (13.6)		20 (10.1)
Multimorbidity, [N (%)]			0.001
No	101 (75.9)	35 (53.0)		136 (68.3)
Yes	32 (24.1)	31 (47.0)		63 (31.7)
Polypharmacy, [N (%)]			0.002
No	105 (78.9)	38 (57.6)		143 (71.9)
Yes	28 (21.1)	28 (42.4)		56 (28.1)
Neurosensorial deficit, [N (%)]			0.062
No	121 (91.0)	54 (81.8)		175 (87.9)
Yes	12 (9.0)	12 (18.2)		24 (12.1)

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Regarding tobacco, alcohol, and drug use (Table 4), no significant differences were observed between groups.

Table 4. Tobacco, alcohol and drug use as a function of loneliness and/or social isolation.

	Loneliness and/or social isolation
	No	Yes	p-value	Total
	N = 133	N = 66		N = 199
Have you ever been a regular tobacco user?, [N (%)]			0.693
No	27 (20.3)	15 (22.7)		42 (21.1)
Yes	106 (79.7)	51 (77.3)		157 (78.9)
Risk Alcohol consumption, [N (%)]			0.701
No	82 (61.7)	44 (66.7)		126 (63.3)
Yes	20 (15.0)	10 (15.2)		30 (15.1)
Unknown	31 (23.3)	12 (18.2)		43 (21.6)
Drug use in the last year, [N (%)]			0.293
No	100 (75.2)	54 (81.8)		154 (77.4)
Yes	33 (24.8)	12 (18.2)		45 (22.6)

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Data on quality of life, anxiety, depression, and HIV-related stigma are presented in Table 5. Across all dimensions of the EQ-5D-5L index, individuals with loneliness and/or social isolation reported significantly higher proportion of problems (p < 0.001 for each dimension). Similarly, HADS scale results indicated higher anxiety and depression levels in individuals with loneliness and/or social isolation (p < 0.001 for both). Finally, self-reported stigma, measured by the HSS Scale, was also significantly higher in the loneliness and/or social isolation group third tercile (p < 0.001).

Table 5. Quality of life, depression and anxiety, and stigma, as a function of loneliness and/or social isolation.

	Loneliness and/or social isolation
	No	Yes	p-value	Total
	N = 133	N = 66		N = 199
Quality of life – EQ-5D-5L scale
Any mobility issue, [N (%)]			<0.001
No	111 (83.5)	30 (45.5)		141 (70.9)
Yes	22 (16.5)	36 (54.5)		58 (29.1)
Any self-care issue, [N (%)]			<0.001
No	127 (96.2)	52 (78.8)		179 (90.4)
Yes	5 (3.8)	14 (21.2)		19 (9.6)
Any issue in performing usual activities, [N (%)]			<0.001
No	116 (87.9)	37 (56.1)		153 (77.3)
Yes	16 (12.1)	29 (43.9)		45 (22.7)
Any pain or discomfort issue, [N (%)]			<0.001
No	88 (66.2)	22 (33.3)		110 (55.3)
Yes	45 (33.8)	44 (66.7)		89 (44.7)
Any anxiety or depression issue, [N (%)]			<0.001
No	84 (63.2)	16 (24.2)		100 (50.3)
Yes	49 (36.8)	50 (75.8)		99 (49.7)
Anxiety and Depression – HADS Scale
Clinically significant depression, [N (%)]			<0.001
No	113 (85.0)	33 (50.0)		146 (73.4)
Yes	18 (13.5)	33 (50.0)		51 (25.6)
Unknown	2 (1.5)	0 (0.0)		2 (1.0)
Clinically significant anxiety, [N (%)]			<0.001
No	104 (78.2)	29 (43.9)		133 (66.8)
Yes	28 (21.1)	37 (56.1)		65 (32.7)
Unknown	1 (0.8)	0 (0.0)		1 (0.5)
HIV-Stigma – HSS Scale, [N (%)]			<0.001
T1/T2	97 (72.9)	27 (40.9)		124 (62.3)
T3	31 (23.3)	37 (56.1)		68 (34.2)
Unknown	5 (3.8)	2 (3.0)		7 (3.5)

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Note: T1: first tertile; T2: second tercile; T3: third tercile

Table 6 shows medians (IQR) and median differences (95% CI) of inflammatory markers comparing individuals with loneliness and/or social isolation versus those with neither loneliness nor social isolation. In the unadjusted analysis, only CSF1 showed significantly elevated levels among individuals with loneliness and/or social isolation [median: 151.2 (95% CI: 134.7; 165.2)] compared to those experiencing neither [median: 139.9 (95% CI: 126.0; 153.3)], resulting in a median difference of 11.33 (95% CI 3.50; 19.15; p = 0.005). However, in the adjusted analysis, no significant differences were observed in any of the biomarkers analyzed in relation to loneliness and/or social isolation

Table 6. Medians (RI) and Median Difference (95%CI) of inflammatory markers as a function of having loneliness and/or social isolation versus having neither loneliness nor social isolation. Entries are listed in alphabetical order by biomarker name.

	Loneliness and/or social isolation		Crude analysis		Adjusted analysis*
	No	Yes
	Median (IQR)	Median (IQR)	Difference in medians (yes vs. no) (95% CI)	p-value	Difference in medians (95% CI)	p-value
CCL11	190.1 (131.8 - 248.2)	176.9 (131.9 - 239.9)	−13.04 (−39.91; 13.84)	0.340	5.89 (−28.94; 40.72)	0.739
CCL13	174.8 (117.8 - 247.5)	163.5 (107.6 - 241.0)	−4.42 (−45.21; 36.37)	0.831	−13.24 (−56.27; 29.79)	0.545
CCL19	121.6 (93.0 - 159.8)	117.1 (86.3 - 154.2)	−4.42 (−23.78; 14.95)	0.653	−17.64 (−42.39; 7.10)	0.161
CCL2	625.4 (474.4 - 874.3)	611.5 (434.7 - 765.9)	−8.41 (−109.46; 92.64)	0.870	−46.33 (−159.07; 66.41)	0.419
CCL3	9.5 (7.6 - 12.2)	8.7 (7.1 - 10.6)	−0.67 (−1.82; 0.49)	0.256	−0.88 (−2.31; 0.54)	0.222
CCL4	129.9 (101.7 - 188.0)	122.4 (88.5 - 160.3)	−6.83 (−32.23; 18.56)	0.596	2.88 (−22.98; 28.75)	0.826
CCL7	1.3 (0.9 - 1.9)	1.3 (0.9 - 1.9)	−0.02 (−0.30; 0.26)	0.896	−0.16 (−0.47; 0.14)	0.296
CCL8	66.8 (41.9 - 89.5)	60.4 (40.6 - 83.9)	−5.54 (−17.93; 6.86)	0.379	−6.65 (−21.70; 8.40)	0.385
CSF1	139.9 (126.0 - 153.3)	151.2 (134.7 - 165.2)	11.33 (3.50; 19.15)	0.005	5.90 (−2.21; 14.02)	0.153
CSF3	117.2 (91.7 - 156.4)	122.3 (89.8 - 147.7)	5.57 (−10.74; 21.89)	0.501	12.22 (−5.81; 30.26)	0.183
CXCL10	108.7 (81.6 - 171.3)	120.0 (82.5 - 161.8)	12.52 (−15.58; 40.63)	0.381	11.88 (−19.11; 42.87)	0.450
CXCL11	69.4 (45.2 - 106.6)	64.6 (46.0 - 92.5)	−4.27 (−19.77; 11.22)	0.587	1.65 (−16.42; 19.72)	0.857
CXCL12	196.3 (171.7 - 233.1)	208.4 (166.7 −263.0)	13.44 (−5.65; 32.54)	0.167	19.84 (−4.14; 43.82)	0.104
CXCL8	18.0 (13.1 - 24.7)	16.7 (13.2 - 21.2)	−1.11 (−4.06; 1.83)	0.457	−1.79 (−5.87; 2.30)	0.389
CXCL9	88.1 (62.8 - 132.3)	87.6 (65.6 - 125.4)	−1.06 (−19.67; 17.55)	0.911	−12.34 (−31.93; 7.24)	0.215
EGF	320.3 (58.0 - 490.7)	200.3 (42.9 - 454.1)	−100.20 (−225.57; 25.17)	0.117	23.68 (−119.79; 167.14)	0.745
FLT3LG	129.0 (107.2 - 160.5)	131.8 (110.4 - 162.5)	2.95 (−13.09; 18.99)	0.717	−4.98 (−22.83; 12.86)	0.582
HGF	598.6 (464.8 - 792.5)	654.9 (476.5 - 857.4)	59.78 (−34.92; 154.49)	0.215	1.80 (−120.92; 124.52)	0.977
IFNG	0.2 (0.2 - 0.4)	0.3 (0.2 - 0.4)	0.01 (−0.05; 0.07)	0.770	−0.01 (−0.08; 0.05)	0.711
IL10	8.3 (5.6 - 13.9)	8.5 (5.4 - 10.7)	0.33 (−1.71; 2.36)	0.752	−1.11 (−3.37; 1.14)	0.332
IL15	14.4 (12.3 - 17.2)	14.9 (13.1 - 16.9)	0.59 (−0.62; 1.80)	0.339	1.06 (−0.36; 2.48)	0.143
IL17A	0.6 (0.3 - 1.2)	0.5 (0.3 - 1.0)	−0.09 (−0.37; 0.19)	0.529	0.02 (−0.29; 0.32)	0.916
IL17C	23.9 (16.8 - 38.1)	23.6 (18.5 - 34.8)	0.06 (−6.08; 6.21)	0.983	1.59 (−4.19; 7.38)	0.587
IL17F	0.7 (0.4 - 1.2)	0.6 (0.4 - 1.4)	−0.16 (−0.38; 0.05)	0.131	−0.09 (−0.33; 0.15)	0.454
IL18	352.7 (284.5 - 473.8)	375.8 (295.4 - 503.0)	23.31 (−25.07; 71.70)	0.343	34.83 (−29.85; 99.52)	0.289
IL27	9.2 (5.2 - 14.0)	9.0 (6.0 - 12.1)	−0.16 (−2.52; 2.20)	0.893	1.38 (−1.31; 4.07)	0.314
IL6	3.4 (2.5 - 5.9)	3.7 (2.5 - 6.2)	0.31 (−0.59; 1.21)	0.499	−0.50 (−1.52; 0.52)	0.337
IL7	5.9 (4.0 - 7.8)	5.8 (3.9 - 7.6)	−0.10 (−1.22; 1.02)	0.863	−0.51 (−1.62; 0.60)	0.367
LTA	8.5 (7.2 - 9.7)	8.6 (7.2 - 10.3)	0.16 (−0.52; 0.84)	0.641	−0.09 (−0.94; 0.76)	0.834
MMP1	2462.3 (1366.4 - 4011.5)	2963.1 (1455.4- 4233.9)	579.48 (−188.15;1347.11)	0.138	−43.69 (−893.62; 806.25)	0.919
MMP12	349.8 (275.8 - 486.7)	393.5 (256.9 - 573.2)	61.96 (−8.24; 132.16)	0.083	23.74 (−50.64; 98.12)	0.530
OLR1	312.2 (156.2 - 488.0)	277.2 (132.9 - 530.1)	−31.31 (−130.86; 68.25)	0.536	70.38 (−60.58; 201.34)	0.290
OSM	7.8 (5.0 - 11.6)	7.9 (3.9 - 12.5)	0.43 (−1.54; 2.40)	0.667	1.26 (−0.96; 3.48)	0.264
TGFA	18.1 (12.4 - 25.9)	19.6 (11.4 - 31.9)	1.61 (−2.91; 6.12)	0.483	1.53 (−3.69; 6.75)	0.564
TNF	19.7 (16.2 - 23.9)	18.8 (15.9 - 22.4)	−0.78 (−2.90; 1.34)	0.469	−0.69 (−3.41; 2.04)	0.620
TNFSF10	505.6 (432.4 - 579.6)	489.4 (419.3 - 554.3)	−15.52 (−48.65; 17.62)	0.357	−4.36 (−53.31; 44.58)	0.861
TNFSF12	724.2 (599.6 - 890.3)	697.3 (602.5 - 907.1)	−26.83 (−99.60; 45.95)	0.468	21.79 (−62.84; 106.42)	0.612
VEGFA	606.0 (404.1 - 947.8)	528.8 (371.7 - 928.2)	−65.87 (−208.89; 77.16)	0.365	−130.32 (−302.93; 42.30)	0.138

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*Adjusted for sex at birth (male, female), age (50–59, 60 + years), employment status (student/worker, retiree/unemployed, unknown), time since HIV diagnosis (<10, 10–19, ≥ 20 years), current CD4/CD8 ratio (≤1, > 1), current HIV RNA < 50 copies/ml (no, yes), multimorbidity (no, yes), polypharmacy (no, yes), ever regular tobacco use (no, yes), and clinically significant depression (no, yes, unknown) and anxiety (no, yes, unknown).

Discussion

In our study, we did not observe differences in the analyzed markers between individuals experiencing loneliness and/or social isolation and those without loneliness or social isolation among PLWH aged >50 years. Nonetheless, previous studies have estimated that social disconnection, including loneliness, can increase inflammation in people without HIV [23,26,55–58]. However, a systematic review and meta-analysis examining associations between loneliness, social isolation, and some inflammatory biomarkers such as C-reactive protein (CRP), fibrinogen, interleukin(IL)-1RA, IL-6, and monocyte chemotactic protein-1 (MCP-1) [25], found a significant association between loneliness and the circulating IL-6 in adjusted analyses, with no association found between social isolation and IL-6. A minimally adjusted association was observed between social isolation and the acute-phase proteins CRP and fibrinogen, though caution was advised in interpreting these findings due to heterogeneity and limited robustness in some associations [25]. Similarly, van Bogart et al. [55] reported a significantly association between loneliness and higher CRP levels, yet found but no significant associations with cytokines. These findings underscore the needed for further research to clarify these relationships. Another meta-analysis [57] reported that social support and social integration were associated with lower levels of inflammatory cytokines.

Among PLWH, Ellis et al. [24] reported a link between social isolation and inflammation, showing that lower social support was associated with elevated plasma levels of MCP-1, IL-8 and vascular endothelial growth factor (VEGF), and higher cerebrospinal fluid levels of MCP-1 and IL-6 in a combined cohort of PLWH and HIV-negative individuals. Hussain et al. [30] found that loneliness in virologically suppressed PLWH (ages 36–69 years) was associated with increased coagulation (D-dimer) and monocyte activation (sCD14, CCL2/MCP-1). In contrast, a single-site study by Derry et al. [31] of PLWH aged 54–78 years reported no significant association between loneliness and composite cytokine levels or CRP levels. Our multicenter study similarly found no significant associations, and, to our knowledge, evaluated the largest panel of cytokines to date in this population.

Loneliness is highly correlated with depression [59]. Among PLWH, greater somatic depressive symptoms were linked to a higher monocyte activation (sCD14) and altered coagulation (D-dimer) [60]. Notably, different antidepressants influence inflammation distinctly: selective serotonin reuptake inhibitors were associated with lower levels of sCD14 and IL-6 levels, while tricyclic antidepressants were linked to higher levels of these markers [60].

Although we did not observe additional inflammatory elevation among PLWH reporting loneliness or social isolation, we caution against interpreting this as evidence against an underlying association. Such a relationship may have gone undetected because our cohort, clinically stable and engaged in care, shows limited variability in psychosocial exposures and inflammatory readouts; because exposure and biomarkers were assessed at a single time point, which may not align with the temporal dynamics of psychosocial stress and immune activation; and due to measurement or analytical constraints (e.g., limited power for small effects, residual confounding, non-linear or threshold relationships, and effect modification by demographic or clinical factors). Within this context, several biological and social factors may explain our findings: first, chronic HIV-related inflammation may approach a biological ‘ceiling’, blunting incremental effects of loneliness [61,62]; second; adaptation to persistent inflammation in PLWH may reduce the reactivity to psychosocial stressors, such as loneliness and SIL, limiting their additional inflammatory impact [61,62]; third supportive structures common in HIV care, such as peer support and multidisciplinary care teams, may mitigate the inflammatory impact of loneliness [63]; fourth, and HIV- and loneliness-related inflammation may involve partly distinct pathways, potentially limiting their additive effects in PLWH [64,65]. These considerations support a cautious interpretation of our findings and motivate longitudinal, multi-time-point studies with adequate power and explicit tests for non-linearity and effect modification.

Several limitations should be considered in interpreting these results. First, it is observational, so we cannot infer causality or tell which came first. Second, contrary to our expectations, were found no association between loneliness/social isolation and systemic inflammation; this may reflect unmeasured confounding that influences both social support and inflammation [24]. Third, effects may be non-linear, with stronger associations among those most lonely. Fourth, the use of internationally accepted brief screening questionnaires for loneliness and SIL [66–76], which, for example, do not assess duration, may have limited the detection of inflammation changes associated with prolonged exposure, whereas measures capturing such dimensions could reveal stronger associations with inflammatory markers. Fifth, our study did not include an HIV-negative control group because our focus was mechanisms within PLWH. Sixth, while systemic inflammation is essential for understanding chronic diseases, current measurement methods vary in cost, utility, and predictive validity [23]. Biomarkers employed in this study were those associated with HIV-related disease processes, yet emerging biomarkers (i.e., microbiota, soluble urokinase plasminogen activator receptor) may offer more reliable indicators of chronic inflammation than traditional markers like high-sensitivity CRP and IL-6 [23,77]. Chronic rather than acute inflammatory biomarkers may be particularly relevant to loneliness and/or social isolation research [23]. Seventh, loneliness may be a consequence of psychosocial stressors, such as HIV-related stigma [25]. Finally, non-ART factors not accounted here could potentially impact on the inflammatory state [60]. These points argue for longitudinal, mechanistic studies with harmonised measurement; despite them, our findings offer clinically relevant insight.

In summary, our findings contribute to the growing body of linking loneliness with inflammation. However, this study did no observe a significant increase in inflammatory markers among PLWH experiencing loneliness and/or social isolation. Further studies are needed to confirm these findings and to explore other potential factors that may influence these outcomes.

Supporting information

S1 Table. Detailed information on the Olink Target 48 Cytokine panel.

(XLS)

pone.0335949.s001.xls^{(31.5KB, xls)}

Acknowledgments

Other researchers from the Gesida 12021 study group

Hospital Clínico Universitario Lozano Blesa, Zaragoza (MJ. Crusells Canales; S. Letona Carbajo).

Hospital de Viladecans – Institut Català de la Salut (C. Imperiali-Rosario; M. Ruiz-Pombo)

Hospital General Universitario Dr Balmis – Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL) (J. Portilla Sogorb; E. Merino de Lucas; V. Boix Martínez; D. Torrús Tendero; S. Reus Bañuls, G. García Rodríguez; L. Paredes Arquiola; L. Giner Oncina; I. Agea Durán)

Hospital Universitario Clínico San Carlos, Madrid (V. Estrada Pérez)

Hospital Universitario de La Princesa, Madrid (L. García-Fraile)

Hospital Universitario La Paz, IdiPAZ, Madrid (M.L. Montes Ramírez; A. Delgado Hierro)

Hospital Universitario Río Hortega, Valladolid (B. Valentin Casado; M. González Fernández; M. Cazorla González; J. Gómez Barquero; RM. Lobo Valentin; Mª del Carmen Rebollo-Nájera)

Hospital Universitario San Pedro (E. Melús; M. Barrios)

Hospital Universitario Severo Ochoa, Leganés, Madrid (M. Cervero Jiménez; C. García-Lacalle; C. Córdoba-Chicote)

Hospital Universitario Virgen de la Arrixaca, Murcia (A. Castillo Navarro)

Data Availability

Individual-level data from human participants cannot be made publicly available due to privacy and informed-consent restrictions. De-identified data will be available to qualified researchers upon reasonable request and completion of a data-use agreement, subject to approval by the Institutional Research Ethics Committee (Comité de Ética de Investigación con medicamentos de La Rioja [CEImLAR]) from the CEIC secretary: secretaria.ceic@riojasalud.es.

Funding Statement

This study was supported by Gilead Science in the form of a grant awarded to J.R.B. (ISRES-18-10529) and Gilead Fellowship for Biomedical Research to J.R.B. (Beca Gilead a la Investigación Biomédica). The specific roles of this author are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0335949.r001

Decision Letter 0

Stanley Eneh

15 Aug 2025

Dear Dr. Blanco,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Additional Editor Comments:

The measurement of inflammatory biomarkers is not clearly described. It is important to specify the methods used for measuring these biomarkers.
Provide the ethical approval number for this study
Provide sample size justification

Kindly revise the manuscript and provide answers per the comments provided by the reviewer(s).

[Note: HTML markup is below. Please do not edit.]

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: In this study, the authors analysed data from 199 people living with HIV and observed that participants who experienced loneliness/social isolation did not show differences in peripheral inflammatory profiles compared to those who did not.

While this is a solid study with a well-considered methodology, I have a few suggestions and questions to improve the manuscript:

1. Please stick to consistent and accepted person-first terminology: “people with HIV” and “people without HIV”, not “HIV positive” or “HIV negative”.

2. Please clarify in the methods whether the questionnaires and the blood sampling was carried out within the same study visit, or if there was a time lag between these measurements.

3. Table 8 would be more readable if the inflammatory biomarkers were arranged alphabetically, or in some other logical order. At the moment, the order seems fairly random.

4. The discussion could benefit from citing some more relevant and recent reviews (in addition to individual studies), for instance see PMC10489482.

5. The authors have provided some reasonable explanations for why they may not have observed significant associations between inflammation and loneliness/social isolation in their study. However, I would like to see a more comprehensive discussion of this, since the association between these factors has been reported fairly consistently in several studies and even meta-analyses in the general population (which the authors have cited) as well as some individual studies in people with HIV. The potential explanations provided in the discussion at the moment assume that there is no true underlying association, and not the alternative explanation that the underlying relationship has simply not been captured in the current study, which may be due to (for instance) the study population characteristics or the single time-point measurement. Please discuss in more detail.

6. Finally, there is no justification offered for the lack of data availability. It is a growing expectation (and indeed standard policy for the specific journal where the manuscript has been submitted) that data is made publicly available in de-identified form. I am not sure why data has not been made available in this case.

Reviewer #2: The concepts of loneliness and social isolation are related but distinct. The authors need to describe each individually in the background and how they differ both in conceptualization and operationalization. The relationship between loneliness and inflammation has been described elsewhere and the background would benefit from an overview of this work and identifying the relationship between loneliness and inflammation on the causal pathway to other health outcomes.

The authors need to describe the variables included in the analysis in the Methods. Although this might be relevant to another published study this information is needed tin text to provide context for the information provided in the tables and results.

The authors describe the cut-offs (>6 3-item scale) as the presence of loneliness and isolation but what they are actually describing is the severity of loneliness. Relevant articles using similar cut-offs should be cited and the rationale for >6 (moderate loneliness) versus >7 (severe loneliness) should be included.

It was challenging to interpret any of the results because of the mixing of loneliness and social isolation. The tables and reporting in the Results currently mix both concepts and they should be described distinctly as they are distinct concepts. Given the mixing of the two main concepts it is challenging to meaningfully interpret the findings.

Reviewer #3: <overall evaluation=" ">

This study investigates the association between loneliness/social isolation and chronic inflammation in older people living with HIV (PLWH). Its multi-centre design, use of standardised rating scales, and comprehensive cytokine panel are noteworthy. The principal finding—that no clear association was detected—is itself valuable. The manuscript’s overall structure is appropriate, and the methodology appears sound. However, several issues, chiefly regarding the statistical analyses and the interpretation of results, require revision. Addressing these points will clarify the study’s scientific value and strengthen its suitability for publication in PLOS ONE.

Abstract

The concluding sentence (“Further research is needed to validate these findings and explore additional factors”) is rather general. Briefly touching on specific future research directions suggested by your results would be more helpful to readers.

Introduction

The research objective is clearly stated, but the authors do not present an explicit hypothesis (e.g., “PLWH with greater loneliness or social isolation will show higher levels of specific inflammatory markers than those without such experiences”). Stating a directional hypothesis would sharpen the study’s focus and guide interpretation of the results.

Materials and Methods

Participants and Data

Key details on participant selection/exclusion criteria and the handling of missing values should be included in the main text rather than referenced only in earlier studies.

Measurements

The study was conducted in Spain, yet the manuscript does not specify whether English or Spanish versions of the UCLA 3-item Loneliness Scale and the Lubben Social Network Scale-6 were used. If Spanish versions were employed, please cite validation studies and report their internal consistency (e.g., Cronbach’s α).

The rationale for the cut-offs (“UCLA ≥ 6” and “Lubben ≤ 20”) should also be provided.

Regarding inflammatory markers, the manuscript excludes markers for which more than 30 % of values fell below the detection limit (IL-33, IL-2, IL-1β, IL-4, TSLP, IL-13, CSF-2). Please explain why the 30 % threshold was chosen.

Statistical Analysis

The adjustment variables are described as “variables commonly associated with inflammatory markers.” A more detailed justification—supported by references—would help readers understand why each variable was considered a potential confounder. If the number of covariates is large relative to the sample size, model over-fitting or reduced power may result; please discuss this risk.

In addition to p-values, please report effect-size measures; these help readers judge clinical relevance regardless of statistical significance.

Results

Participant Characteristics

Several discrepancies exist between the narrative text and Table 1:

The text states that individuals without loneliness/isolation were more likely to be women (p = 0.023), yet Table 1 shows a higher proportion of men (77.4 %) in the “No” group and more women (37.9 %) in the “Yes” group.

Similarly, the text says that those without loneliness/isolation were more likely to be unemployed or retired (p < 0.001), whereas Table 1 indicates the opposite (higher employment in the “No” group).

The text references “…living alone unwillingly (p = 0.006)”, but this item is absent from Table 1.

Please revise the prose, correct the table, or add the missing variable as appropriate.

Multiple Comparisons

Across Tables 1–5, numerous variables are compared, generating many p-values. The risk of false-positive findings due to multiple testing should be acknowledged, and some form of adjustment (e.g., modified significance level or FDR control) or cautious interpretation is warranted. Reporting effect sizes would also help.

Discussion

Comparison with previous research needs to go beyond listing earlier findings. Where results diverge, discuss plausible reasons (e.g., differences in sample characteristics, loneliness measures, inflammatory marker assays, or statistical methods) and position your study accordingly (supporting, contradicting, or refining previous work). This will clarify both the contribution and the limitations of the present study.

The finding of no clear association between loneliness/social isolation and inflammatory markers in PLWH is scientifically valuable. By clarifying the methodology, refining the statistical analysis, ensuring consistency in the presentation of results, and deepening the discussion, the manuscript’s quality—and its contribution to the field—will be further enhanced. I look forward to reviewing a revised version.</conclusion></specific></overall>

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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PLoS One. 2025 Dec 17;20(12):e0335949. doi: 10.1371/journal.pone.0335949.r002

Author response to Decision Letter 1

17 Sep 2025

POINT-BY-POINT RESPONSE TO THE REFEREES’ COMMENTS

Reviewer #1:

1. Please stick to consistent and accepted person-first terminology: “people with HIV” and “people without HIV”, not “HIV positive” or “HIV negative”.

Thank you for your suggestion. We have updated the manuscript to ensure consistent use of person-first language throughout.

2. Please clarify in the methods whether the questionnaires and the blood sampling was carried out within the same study visit, or if there was a time lag between these measurements.

Thank you for this helpful comment. We have clarified in Methods that the questionnaires and blood sampling were conducted as part of the same study.

3. Table 8 would be more readable if the inflammatory biomarkers were arranged alphabetically, or in some other logical order. At the moment, the order seems fairly random.

Thank you for the suggestion. We understand the comment refers to Table 6, as there is no Table 8 in the manuscript. We have done it.

4. The discussion could benefit from citing some more relevant and recent reviews (in addition to individual studies), for instance see PMC10489482.

Thank you for your suggestion. We have added that citation and a few others.

Thank you for highlighting this point. We have revised the Discussion to acknowledge that, beyond explanations consistent with a true null additive effect, an underlying relationship may exist but was not identified.

Thank you for raising this point. Individual-level data from human participants cannot be made publicly available due to privacy and informed-consent restrictions. De-identified data will be available to qualified researchers upon reasonable request and completion of a data-use agreement, subject to approval by the Institutional Research Ethics Committee (Comité de Ética de Investigación con medicamentos de La Rioja [CEImLAR]).

Reviewer #2:

1. The concepts of loneliness and social isolation are related but distinct. The authors need to describe each individually in the background and how they differ both in conceptualization and operationalization. The relationship between loneliness and inflammation has been described elsewhere and the background would benefit from an overview of this work and identifying the relationship between loneliness and inflammation on the causal pathway to other health outcomes.

Thank you for your comment. We have done it.

2. The authors need to describe the variables included in the analysis in the Methods. Although this might be relevant to another published study this information is needed tin text to provide context for the information provided in the tables and results.

Thank you for your comment. We have done it.

3. The authors describe the cut-offs (>6 3-item scale) as the presence of loneliness and isolation but what they are actually describing is the severity of loneliness. Relevant articles using similar cut-offs should be cited and the rationale for >6 (moderate loneliness) versus >7 (severe loneliness) should be included.

Thank you for your comment. The cut-offs for the 3-item UCLA scale is described and justified in the Material and Method section.

4. It was challenging to interpret any of the results because of the mixing of loneliness and social isolation. The tables and reporting in the Results currently mix both concepts and they should be described distinctly as they are distinct concepts. Given the mixing of the two main concepts it is challenging to meaningfully interpret the findings.

Thank you for raising this important point. We agree that loneliness and social isolation are distinct constructs and that combining them can obscure interpretation. Initially, we treated them as separate exposures and classified participants into four mutually exclusive groups (neither, loneliness only, social isolation only, both). Because some strata were small and no meaningful differences in inflammatory markers were observed among the loneliness only, social isolation only, and both groups, we combined these categories into a single exposure (‘loneliness and/or social isolation’) to enhance statistical power. We have revised the Results section to clarify this.

Reviewer #3:

1. Abstract: The concluding sentence (“Further research is needed to validate these findings and explore additional factors”) is rather general. Briefly touching on specific future research directions suggested by your results would be more helpful to readers.

Thank you for your comment. We have revised it.

2. Introduction: The research objective is clearly stated, but the authors do not present an explicit hypothesis (e.g., “PLWH with greater loneliness or social isolation will show higher levels of specific inflammatory markers than those without such experiences”). Stating a directional hypothesis would sharpen the study’s focus and guide interpretation of the results.

Thank you for your suggestion. We have included it.

3. Materials and Methods: Participants and Data Key details on participant selection/exclusion criteria and the handling of missing values should be included in the main text rather than referenced only in earlier studies.

Thank you for your comment. We have revised it.

4. Measurements: The study was conducted in Spain, yet the manuscript does not specify whether English or Spanish versions of the UCLA 3-item Loneliness Scale and the Lubben Social Network Scale-6 were used. If Spanish versions were employed, please cite validation studies and report their internal consistency (e.g., Cronbach’s α). The rationale for the cut-offs (“UCLA ≥ 6” and “Lubben ≤ 20”) should also be provided.

Thank you for your observation. We used the validated Spanish versions. We have added the corresponding validation citations and report internal consistency in our study. The cut-offs for UCLA and Lubben are described and justified in the Material and Method section. Regarding inflammatory markers, we set the analyte-level threshold at 30% to balance data retention with reliability. First, this mirrors common practice in large Olink cohorts, where proteins are typically dropped once missingness/sub-LOD approaches one-third of observations (i.e., Walker et al., 2024; You et al., 2023). Second, 30% is a conservative choice within the widely used 30–50% range and helps avoid unstable inference under heavy left-censoring. Third, Olink recommends using detectability as a QC metric; requiring ≥70% detectable values provide a simple, platform-aware rule. For markers that passed this filter, sub-LOD values were imputed at the LOD to preserve information while minimizing downward bias. We now state these decisions explicitly in the Methods and cite the relevant sources.

5. Statistical Analysis: The adjustment variables are described as “variables commonly associated with inflammatory markers.” A more detailed justification—supported by references—would help readers understand why each variable was considered a potential confounder. If the number of covariates is large relative to the sample size, model over-fitting or reduced power may result; please discuss this risk. In addition to p-values, please report effect-size measures; these help readers judge clinical relevance regardless of statistical significance.

Thank you for this request. Measures of association (ie. differences in medians) were calculated to address the main objective of the study, evaluating whether inflammatory markers differed according to loneliness and/or social isolation, as shown in Table 6. We have added an explicit, literature-based rationale for covariate selection in the Statistical Analysis section.

6. Results: Participant Characteristics. Several discrepancies exist between the narrative text and Table 1: The text states that individuals without loneliness/isolation were more likely to be women (p = 0.023), yet Table 1 shows a higher proportion of men (77.4 %) in the “No” group and more women (37.9 %) in the “Yes” group. Similarly, the text says that those without loneliness/isolation were more likely to be unemployed or retired (p < 0.001), whereas Table 1 indicates the opposite (higher employment in the “No” group). The text references “…living alone unwillingly (p = 0.006)”, but this item is absent from Table 1. Please revise the prose, correct the table, or add the missing variable as appropriate.

Thank you for this helpful observation. We have corrected the text and added the missing data

7. Multiple Comparisons: Across Tables 1–5, numerous variables are compared, generating many p-values. The risk of false-positive findings due to multiple testing should be acknowledged, and some form of adjustment (e.g., modified significance level or FDR control) or cautious interpretation is warranted. Reporting effect sizes would also help.

Thank you for your comment. The sole purpose of Tables 1–5 was to characterize participants according to the presence or absence of loneliness and/or social isolation and to assess differences in these characteristics. While these tables focus on differences in percentages rather than statistical significance, measures of association were calculated to address the main objective of the study—evaluating whether inflammatory markers differed according to loneliness and/or social isolation, as shown in Table 6.

8. Discussion: Comparison with previous research needs to go beyond listing earlier findings. Where results diverge, discuss plausible reasons (e.g., differences in sample characteristics, loneliness measures, inflammatory marker assays, or statistical methods) and position your study accordingly (supporting, contradicting, or refining previous work). This will clarify both the contribution and the limitations of the present study.

Thank you for your suggestion. We have included it.

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PLoS One. doi: 10.1371/journal.pone.0335949.r003

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<p>Lack of Association Between Loneliness, Social Isolation and Inflammation in People Living with HIV Aged ≥50 Years: Results from the Sub-Study “No One Alone-Gesida Study”.

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PLoS One. doi: 10.1371/journal.pone.0335949.r004

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Supplementary Materials

S1 Table. Detailed information on the Olink Target 48 Cytokine panel.

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Data Availability Statement

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PERMALINK

Lack of association between loneliness, social isolation and inflammation in people living with HIV aged ≥50 years: Results from the sub-study “No One Alone-Gesida Study”

Jose-Ramon Blanco

Helena Albendin-Iglesias

Eugenia Negredo-Puigmal

Ana Mª Barrios-Blandino

Cristina Tomás-Jimenez

Isabel Sanjoaquin-Conde

María Saumoy

Verónica Pérez-Esquerdo

Inmaculada Gonzalez-Cuello

Ana María López-Lirola

María José Galindo

Noemí Cabello-Clotet

Jesica Abadía Otero

Dolores Merino-Muñoz

Joanna Cano-Smith

Magdalena Muelas-Fernandez

Javier De La Torre

Alicia González-Baeza

Lourdes Romero

Antonio Ocampo

Rafael Torres

Carmen Hidalgo

Herminia Esteban

Maria Angeles Fernandes-López

Jordi Puig

Lucio Garcia Fraile

Enrique Bernal Morell

Laura Perez-Martinez

Marta De Miguel Montero

Inma Jarrin

Julian Olalla

Roles

Introduction

Materials and methods

Results

Conclusion

Introduction

Materials and methods

Ethics statement

Study design and participants

Data collection

Loneliness and social isolation

Psychosocial measures

Inflammation biomarkers

Statistical Analysis

Results

Table 1. Characteristics of 199 people with HIV included in the study.

Table 2. Data related to HIV and coinfections, as a function of loneliness and/or social isolation.

Table 3. Comorbidities, as a function of loneliness and/or social isolation.

Table 4. Tobacco, alcohol and drug use as a function of loneliness and/or social isolation.

Table 5. Quality of life, depression and anxiety, and stigma, as a function of loneliness and/or social isolation.

Table 6. Medians (RI) and Median Difference (95%CI) of inflammatory markers as a function of having loneliness and/or social isolation versus having neither loneliness nor social isolation. Entries are listed in alphabetical order by biomarker name.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Stanley Eneh

Roles

Author response to Decision Letter 1

Decision Letter 1

Stanley Eneh

Roles

Acceptance letter

Stanley Eneh

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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