Inflammatory pathways amongst people living with HIV in Malawi differ according to socioeconomic status

Christine Kelly; Willard Tinago; Dagmar Alber; Patricia Hunter; Natasha Luckhurst; Jake Connolly; Francesca Arrigoni; Alejandro Garcia Abner; Raphael Kamn’gona; Irene Sheha; Mishek Chammudzi; Kondwani Jambo; Jane Mallewa; Alicja Rapala; Patrick W G Mallon; Henry Mwandumba; Nigel Klein; Saye Khoo

doi:10.1371/journal.pone.0256576

. 2021 Aug 25;16(8):e0256576. doi: 10.1371/journal.pone.0256576

Inflammatory pathways amongst people living with HIV in Malawi differ according to socioeconomic status

Christine Kelly ^1,^2,^3,^*, Willard Tinago ¹, Dagmar Alber ⁴, Patricia Hunter ⁴, Natasha Luckhurst ⁵, Jake Connolly ⁵, Francesca Arrigoni ⁵, Alejandro Garcia Abner ¹, Raphael Kamn’gona ², Irene Sheha ², Mishek Chammudzi ², Kondwani Jambo ², Jane Mallewa ⁶, Alicja Rapala ⁷, Patrick W G Mallon ¹, Henry Mwandumba ², Nigel Klein ⁴, Saye Khoo ³

Editor: Olalekan Uthman⁸

¹Cente for Experimental Pathogen Host Research (CEPHR), University College Dublin, Dublin, Ireland

²Malawi Liverpool Wellcome Trust Clinical Research Program, Blantyre, Malawi

³Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom

⁴Institute of Child Health, University College London, London, United Kingdom

⁵Kingston University, London, United Kingdom

⁶College of Medicine, University of Malawi, Blantyre, Malawi

⁷Institute of Cardiovascular Science, University College London, London, United Kingdom

⁸The University of Warwick, UNITED KINGDOM

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: christine.kelly@ucd.ie

Roles

Christine Kelly: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

Willard Tinago: Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Dagmar Alber: Investigation, Writing – review & editing

Patricia Hunter: Data curation, Investigation, Methodology, Writing – review & editing

Natasha Luckhurst: Investigation, Writing – review & editing

Jake Connolly: Investigation, Writing – review & editing

Francesca Arrigoni: Investigation, Writing – review & editing

Alejandro Garcia Abner: Data curation, Methodology, Writing – review & editing

Raphael Kamn’gona: Data curation, Investigation, Methodology, Project administration, Writing – review & editing

Irene Sheha: Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing – review & editing

Mishek Chammudzi: Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing – review & editing

Kondwani Jambo: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – review & editing

Jane Mallewa: Conceptualization, Funding acquisition, Methodology, Project administration, Writing – review & editing

Alicja Rapala: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

Patrick W G Mallon: Conceptualization, Formal analysis, Methodology, Resources, Writing – review & editing

Henry Mwandumba: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Resources, Writing – review & editing

Nigel Klein: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Writing – review & editing

Saye Khoo: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Olalekan Uthman: Editor

PMCID: PMC8386842 PMID: 34432828

Abstract

Background

Non-communicable diseases (NCDs) are increased amongst people living with HIV (PLWH) and are driven by persistent immune activation. The role of socioeconomic status (SES) in immune activation amongst PLWH is unknown, especially in low-income sub-Saharan Africa (SSA), where such impacts may be particularly severe.

Methods

We recruited Malawian adults with CD4<100 cells/ul two weeks after starting ART in the REALITY trial (NCT01825031), as well as volunteers without HIV infection. Clinical assessment, socioeconomic evaluation, blood draw for immune activation markers and carotid femoral pulse wave velocity (cfPWV) were carried out at 2- and 42-weeks post-ART initiation. Socioeconomic risk factors for immune activation and arterial stiffness were assessed using linear regression models.

Results

Of 279 PLWH, the median (IQR) age was 36 (31–43) years and 122 (44%) were female. Activated CD8 T-cells increased from 70% amongst those with no education to 88% amongst those with a tertiary education (p = 0.002); and from 71% amongst those earning less than 10 USD/month to 87% amongst those earning between 100–150 USD/month (p = 0.0001). Arterial stiffness was also associated with higher SES (car ownership p = 0.003, television ownership p = 0.012 and electricity access p = 0.029). Conversely, intermediate monocytes were higher amongst those with no education compared to a tertiary education (12.6% versus 7.3%; p = 0.01) and trended towards being higher amongst those earning less than 10 USD/month compared to 100–150 USD/month (10.5% versus 8.0%; p = 0.08). Water kiosk use showed a protective association against T cell activation (p = 0.007), as well as endothelial damage (MIP1β, sICAM1 and sVCAM1 p = 0.047, 0.026 and 0.031 respectively).

Conclusions

Socioeconomic risk factors for persistent inflammation amongst PLWH in SSA differ depending on the type of inflammatory pathway. Understanding these pathways and their socioeconomic drivers will help identify those at risk and target interventions for NCDs. Future studies assessing drivers of inflammation in HIV should include an SES assessment.

Introduction

Non-communicable diseases (NCDs) are fast becoming the leading cause of mortality in low-income sub-Saharan Africa (SSA), with disability adjusted life years related to NCDs fast approaching those from communicable diseases [1]. Given the increased risk of NCDs amongst people living with HIV (PLWH) in high income countries, there is concern that the ageing HIV population will be particularly at-risk form NCD related morbidity and mortality in SSA, placing further strain on already under-resourced healthcare systems. Persistent inflammation and resultant endothelial damage amongst PLWH are implicated in the pathogenesis of NCDs amongst PLWH across healthcare settings [2–6]. Studies to date identify several drivers of inflammation, including microbial translocation through a compromised gut mucosa, subclinical coinfection, or low-level persistent HIV viraemia [7–9]. Aetiological mechanisms are often complex and overlapping, especially amongst those with a history of advanced immune suppression prior to treatment [10].

We previously demonstrated that heightened inflammation amongst PLWH in Malawi is heterogeneous in nature, with different immunological pathways predicting different trajectories in endothelial damage during the first year of antiretroviral therapy (ART) [5]. Carotid femoral pulse wave velocity (cfPWV) is a gold standard measurement of arterial stiffness and has been validated against clinical outcomes in high-income settings; a cfPWV in the top versus bottom tertile is associated with a >2-fold increased risk of myocardial infarction or stroke [11–13]. The 2012 European Society of Cardiology consensus guidelines propose a 10-m/second threshold as high risk for CVD events [14].

In this low-income setting, the picture of persistent inflammation amongst PLWH is likely to be further impacted by socio-economic factors such as poor water and housing quality, overcrowded living conditions, low incomes and uncertain food stability leading to increased risk of gastrointestinal infections, malaria, tuberculosis, malnutrition and decreasing ability to present for routine care for acute or chronic illness [15–20].

In order to modify NCD risk factors and reduce clinical burden, a multi-faceted approach involving reduction of traditional cardiovascular risk factors, pragmatic pharmacological strategies and public health messages will likely be required. Trials testing pharmacological interventions aimed at reducing inflammation amongst PLWH have so far shown modest effects and it is not clear how current strategies could be translated to low resource settings [21–23]. An understanding of the extent to which socio-economic factors contribute to inflammation will strengthen the existing model of chronic inflammation in the region and help identify where interventions should be targeted to most effectively reduce non-communicable comorbidity amongst PLWH. Here, we aim to characterise the relationship between socio-economic factors and immune activation in a cohort of PLWH.

Methods

ART-naïve adults with a new HIV diagnosis and CD4 <100 cells/uL were recruited prospectively from the ART clinic and voluntary HIV testing clinic at Queen Elizabeth Central Hospital, Blantyre, Malawi, (most HIV-positive patients were recruited from the REALITY trial NCT01825031), along with HIV negative adults with no evidence of infection within the previous 3 months. The enrolment visit for HIV-positive participants was 2 weeks after ART initiation to minimise visit burden in this unwell group. The cohort has been previously reported in detail [4]. In brief, participants underwent a detailed clinical assessment and blood draw for markers of immune activation at enrolment and 42 weeks post ART initiation. cfPWV was also carried out at both time points as a measurement of arterial stiffness. Further methods including inter-rater concordance can be found in [4]. Socio-economic evaluation was carried out at the baseline visit using a standardised questionnaire which included domains on housing, assets, household composition, education, employment, finances, transport and health and wellbeing. All participants provided informed written consent and ethical approval was granted by the College of Medicine Research and Ethics Committee (COMREC), University of Malawi (P.09/13/1464) and the University of Liverpool Research and Ethics Committee (UoL000996).

Characterisation of immune activation

Immune activation was characterised through cell surface immunophenotyping as well as quantification of plasma biomarkers of inflammation, as previously described [4]. Surface immunophenotyping of T-cells was performed on fresh peripheral blood mononuclear cells (PBMCs) using flow cytometry [4]. T-cell activation, exhaustion and senescence was defined as CD38/HLADR, PD1 and CD57 expression, respectively. Monocytes were defined as classical (CD14⁺⁺CD16^-), intermediate (CD14⁺⁺CD16⁺) or nonclassical (CD14⁺CD16⁺). Stored plasma was tested for 22 cytokines: Proinflammatory Panel-1 (interferon [IFN]-Ɣ, interleukin [IL]-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL12p70, IL-13, tumour necrosis factor [TNF]-α), Vascular Injury Panel-2 (serum amyloid A [SAA], C-reactive protein [CRP], vascular cell adhesion molecule [VCAM]-1, intercellular adhesion molecule [ICAM-1]), Chemokine Panel-1 (macrophage inflammatory protein [MIP]-1β, interferon γ-induced protein [IP]-10, monocyte chemoattractant protein [MCP]-1), Angiogenesis -Panel-1 (vascular endothelial growth factor [VEGF]-A, basic fibroblast growth factor [bFGF]) and single analyte assays for IL-1 receptor antagonist (IL-1Ra) and IL-7., all from Meso Scale Discovery (MSD, Rockland, MD, USA). Assays were performed following the manufacturer’s instructions and recommended dilutions for human plasma. Soluble CD163 was measured in plasma diluted at 1:20 using DuoSet antibodies (R&D Systems, Minneapolis, MN, USA) on MSD Multiarray plates. CMV viral loads were quantified by DNA PCR in a subset of participants with available plasma as described previously [24]. Values <100 copies/mL were less than 3 ct values higher than background fluorescence and were therefore considered negative.

Statistical analysis

All socioeconomic variables were categorical and were compared by HIV status using the chi square tests. Associations were tested for socioeconomic variables and immune activation markers as well as socioeconomic variables and cfPWV within the HIV cohort. Univariate relationships were tested using Wilcoxon ranksum or Kruskal-Wallis. Socioeconomic variables showing univariate associations with immune activation markers or cfPWV at a p value<0.1 were selected for reporting and entered into multivariate analysis by linear regression. Immune activation markers and cfPWV were log transformed for normality for linear regression analysis and back transformed for presentation of results. Regression models examining associations with socioeconomic variables were adjusted a priori for age and sex as confounders for immune activation markers and for age, sex, blood pressure and haemoglobin for cfPWV as previously identified [4]. Adjusted models with p<0.1 were selected for reporting. Analysis was undertaken using Stata 13.1 (StataCorp, College Station, USA).

Results

Clinical characteristics

Of 279 PLWH enrolled, the median age was 36 years (IQR 31–43) and 122 (44%) were female. The median (interquartile range (IQR)) nadir CD4 count and HIV viral load were 41 cells/μL (18–62) and 110,000 copies/mL (4000–290,000) respectively. One hundred and ten HIV-negative participants had comparable age (median [IQR] 35 years [31–41]), with 66(60%) women. Forty-five(16%) HIV-positive participants were diagnosed with an acute co-infection at study enrolment and CMV PCR was positive for 61 (32%) of 193 tested HIV-positive participants, with median (IQR) CMV viral load 928 copies/mL (412–3052). The median (IQR) cfPWV for the HIV-positive participants at baseline was 7.3 m/s (6.5–8.2). Fifteen (4%) participants died during the study.

Socio-economic characteristics

Socio-economic data were available for 267 participants living with HIV and 105 HIV negative participants. Overall, 212 (58%) of participants were renting or living with family and 316 (86%) used a pit latrine toilet. Two hundred and one (55%) had electricity and 95 (26%) had a private water tap at home. Seventy five (21%) were unemployed and of those employed, 108 (29%) earned less than 25 USD per month, although 97 (36%) of participants were not able to provide an estimate of monthly salary. Three hundred and thirty nine (92%) of participants felt that their household did not have enough food.

The main water source for participants differed according to HIV status. Comparing PLWH with participants without HIV, 105 (40%) compared to 18 (17%) used a shared domestic tap and only 59 (22%) compared to 46 (33%) used a water kiosk (p<0.0001, Table 1). There were higher proportions of participants unemployed or working in unskilled labour within the HIV cohort, who were also more likely to work 7 days per week and longer than 8 hours per day, and tended to be on lower salaries (p<0.0001, Table 1). People living with HIV were less likely to grow crops [31 (12%) versus 32 (30%); p<0.0001] and experienced lower rates of food security [13 (5%) vs 16(14%); p<0.0001].

Table 1. Socio-economic variables for 367 Malawian adults according to HIV status.

Socio-Economic Variables			HIV uninfected n = 107^*	PLWH n = 266^*	p value
Socio-Economic Variables			Frequency (%)	Frequency (%)	p value
Housing	Roof	Grass	7 (7)	14 (5)	0.81
		Corrugated	96 (91)	242 (92)
		Other	2 (2)	7 (3)
	Walls	Sundried brick	48 (46)	119 (45)	0.16
		Burnt brick	55 (52)	125 (48)
		Other	2 (2)	18 (7)
	Floor	Earth	14 (13)	41 (16)	0.11
		Brick	0 (0)	8 (3)
		Cement	92 (87)	198 (80)
	Tenure	Bought	10 (10)	25 (10)	0.72
		Built	32 (31)	71 (29)
		Renting	52 (51)	139 (56)
		Relatives	8 (8)	13 (5)
	Toilet	Flush	17 (16)	38 (14)	0.71
	Toilet	Latrine	90 (84)	226 (86)	0.71
	Number of bedrooms	1	12 (11)	16 (6)	0.038
		2	35 (33)	60 (23)
		3	29 (27)	77 (29)
		4	20 (19)	65 (24)
		5	8 (7)	21 (8)
		>5	3 (3)	26 (10)
	Kitchen as a separate room		64 (62)	163 (62)	0.77
Utilities	Cooking	Wood/Charcoal	94 (90)	232 (89)	0.79
	Cooking	Electricity/gas	11 (10)	31 (12)	0.79
	Electricity		57 (55)	144 (55)	0.91
	Water	Private domestic tap	31 (30)	64 (24)	<0.0001
		Shared domestic tap	18 (17)	105 (40)
		Communal Water kiosk	46 (44)	59 (22)
		Protected well	8 (8)	26 (10)
		Lake/unprotected well	2 (2)	10 (4)
Belongings	Fridge		41 (38)	65 (25)	0.14
	TV		40 (37)	120 (46)	<0.0001
	Video		38 (35)	87 (33)	0.003
	Radio		79 (73)	163 (62)	0.26
	Mobile phone		87 (81)	203 (77)	0.092
	Bicycle		19 (18)	27 (210)	0.31
	Car		5 (5)	11 (4)	0.11
Household	Number of adults	1	14 (13)	43 (16)	0.87
		2	56 (52)	132 (50)
		3	19 (18)	50 (19)
		>3	18 (17)	41 (16)
	Number of children	0	10 (9)	54 (20)	0.004
		1	21 (20)	44 (17)
		2	24 (22)	72 (27)
		3	21 (20)	58 (22)
		>3	31 (29)	38 (14)
Education/ employment	Education	None	5 (6)	22 (8)	0.032
		Primary incomplete	28 (26)	88 (34)
		Primary complete	10 (9)	33 (13)
		Secondary incomplete	40 (37)	56 (21)
		Secondary complete	16 (15)	51 (19)
		Tertiary complete	9 (8)	17 (6)
	Occupation	Unemployed	15 (14)	60 (23)	<0.0001
		Student	24 (22)	31 (12)
		Non-skilled labourer	56 (52)	141 (53)
		Skilled labourer	5 (5)	27 (10)
		Professional worker	8 (7)	7 (3)
	Employment status	Full time	68 (79)	123 (66)	0.12
		Part time	4 (5)	21 (11)
		Not working—ill health	2 (2)	10 (5)
		Not working—lack of employment	12 (14)	32 (17)
	Working days per week	4 or less	3 (5)	10 (10)	<0.0001
		5	31 (48)	18 (17)
		6	26 (240)	45 (43)
		7	5 (8)	32 (30)
	Working hours per day	Less than 8	13 (18)	19 (16)	<0.0001
		8	48 (66)	33 (27)
		9 to 12	12 (16)	58 (48)
		More than 12	0 (0)	12 (10)
Finances	Paid by Salary		55 (75)	78 (57)	0.007
	Participant income (USD/month)	<10	1 (1)	63 (37)	<0.0001
		10–25	15 (18)	29 (17)
		26–50	34 (40)	31 (18)
		51–100	21 (25)	25 (15)
		>100	13 (15)	23 (13)
	Household income (USD / month)	<10	1 (1)	61 (36)	<0.0001
		10–25	6 (8)	23 (13)
		26–50	31 (39)	26 (15)
		51–100	18 (23)	31 (18)
		>100	24 (30)	30 (18)
	In receipt of benefits		0 (0)	27 (15)	<0.0001
	In receipt of household credit		76 (70)	93 (37)	<0.0001
	Source of credit	Family	1 (1)	19 (22)	<0.0001
		Friends	40 (52)	33 (38)
		Bank	8 (10)	16 (18)
		Co-operative	28 (36)	20 (23)
	Household has enough food		16 (14)	13 (5)	<0.0001
	Household grows crops		32 (30)	31 (12)	<0.0001
	Households with a child that has left school due to lack of food		1 (1)	8 (3)	0.22
Transport to work	Mode of transport	Walking	16 (16)	32 (12)	0.66
		Bus	85 (83)	217 (84)
		Car	1 (1)	7 (3)
		Other	1 (1)	2 (1)
	Transport costs (Malawian Kwacha)	<200	2 (2)	2 (1)	0.047
		200–350	33 (37)	101 (46)
		350–600	27 (30)	37 (17)
		600–1000	15 (17)	30 (14)
		>1000	13 (14)	48 (22)
	Travel time	<10	2 (2)	6 (2)	<0.0001
		10–30	23 (22)	15 (6)
		31–60	45 (43)	91 (38)
		61–120	30 (29)	110 (46)
		>120	4 (4)	19 (8)
Health and Wellbeing	Health visual analogue score	0	31 (29)	77 (30)	0.094
		1	46 (43)	82 (32)
		2	25 (23)	60 (23)
		3	5 (5)	23 (9)
		4	0 (0)	12 (5)
		5	1 (1)	2 (1)
	Wellbeing visual analogue score	0	27 (25)	80 (31)	0.042
		1	52 (48)	88 (35)
		2	25 (23)	57 (22)
		3	2 (2)	21 (8)
		4	1 (1)	8 (3)
		5	1 (1)	1 (0.4)

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*Maximum number of SES questionnaires completed.

Some categories have missing values and total number of available answers for each category is inferred from percentage denominator. Denominator for employment indices were those eligible for employment and did not include students. PLWH = People living with HIV

Socio-economic predictors of cfPWV amongst PLWH

On univariate analysis of the HIV positive cohort, cfPWV was higher for the 16 (8%) participants who travelled to work in a car compared to those who did not [median (IQR) 8m/s (7.3 to 10.2) versus 7.2m/s (6.3 to 8.1); p = 0.008]; for those who owned a television [7.7 (6.7 to 8.5) versus 7.2 (6.2 to 7.8) p = 0.0005]; and for those who had an electricity supply at home [(7.4 (6.5 to 8.3) versus 7.2 (6.4 to 8.1) p = 0.093]. These factors remained associated with cfPWV when adjusted for age, sex, blood pressure and haemoglobin: car ownership [fold change 1.3m/s (95% CI 1.10 to 1.56); p = 0.003]; television ownership [1.12m/s (1.03 to 1.23); p = 0.012]; and electricity access [1.09m/s (1.01 to 1.17); p = 0.029]. Median values for plasma biomarkers are shown for each socio-economic variables and further detail on adjusted models are reported in S1 File.

Socio-economic predictors of immune activation amongst PLWH

Education level and income

Amongst the HIV positive cohort, increasing education level and income were associated with increasing proportion of activated CD8 T cells (Fig 1). Median (IQR) proportion of activated CD8 T cells ranged from 70% (63–78) amongst those with no education to 88% (74–94) amongst those with a tertiary education (p = 0.002); and 71% (55–81) amongst those earning less than 10 USD/month to 87% (86–93) amongst those earning between 100 to 150 USD/month(p = 0.0001). Interestingly, an inverse association was observed with intermediate monocytes, with higher median (IQR) amongst those with no education compared to a tertiary education [12.6% (5.4–15.5) versus 7.3% (5.3–9.8); p = 0.01] and amongst those earning less than 10 USD/month compared to 100–150 USD/month [10.5% (7.8–15.0) versus 8.0% (5.0–12.9); p = 0.08].

Fig 1 — *p<0.01 **p>0.001 ***p<0.0001 ns p>0.01.

Participants with a tertiary education tended to experience greater improvements in CD8 activation after 42 weeks of ART compared to those without any education [%median (IQR) change from baseline to 42 weeks -5.8 (-16.7 to 2.2) versus 2.4 (-7.9 to 12); p = 0.07], as did those with higher incomes [% change 6.0 (-8.2 to 20.1) versus -18.2 (-21.1 to -4.8) comparing lowest and highest income categories; p value = 0.0001]. However, neither educational level nor patient income predicted change in the proportion of intermediate monocytes on ART (p = 0.69 and 0.41 respectively).

Adjusting for age and sex, we first compared those with some education to those without any: IL7 was lower for those with some education [fold change 0.20μg/mL (0.07 to 0.59); p = 0.002] who also trended towards lower bFGF [fold change 0.22μg/mL (0.048 to 1.09); p = 0.056] and higher proportion of activated CD8 T cells [fold change 7.02% (-0.97 to 15.0); p = 0.085] (Figs 2 and 3). Amongst those earning more than 25 USD/month compared to those earning 25 USD/month or less, adjusted CD8 activation and exhaustion were both higher [activation fold change 12.7% (95% CI 7.75 to 17.78), p<0.0001; exhaustion 6.77% (1.30 to 12.24), p = 0.016]. Levels of IL6 and IL13 were also higher amongst people with higher incomes [fold change (95% CI) 2.9 μg/mL (1.13 to 7.6), p = 0.028; and 3.1 μg/mL (1.2 to 8.3), p = 0.025 respectively]. Proportions of nonclassical monocytes were lower amongst this higher income bracket with lower intermediate monocytes trending towards significance [nonclassical fold change -5.23 (95%CI -8.94 to -1.53), p = 0.006; intermediate -1.91 (-3.91 to 0.10), p = 0.063].

Fig 2 — Models for the effect of socio-economic variables on CD8 T cell and monocyte phenotypes, adjusted for age and sex, are shown. The x axis shows fold change with 95% confidence intervals for the following socio-economic comparisons: i) Income >25 USD per month compared to income</ = 25USD/month ii) some education compared to no education iii) water kiosk as source of water compared to all other water sources iv) grows household crops compared to doesn’t grow household crops.

Fig 3 — All inflammatory biomarkers measured in μg/mL apart from those marked * which are in pg/mL.

Household factors

Participants with HIV who grew crops at home had higher CD8 activation [median (IQR) 88% (80–91) versus 76% (64–86); p = 0.0004], and experienced greater improvements in CD8 activation after 42 weeks of ART [median (IQR) -15.8% (-19.9 to -1.9) versus -0.18 (-13.1 to 12.1); p = 0.01] over 42 weeks of ART. Baseline CD8 activation remained significantly higher amongst those who grew crops in adjusted analysis [fold change 12.1% (5.2 to 19.0);p = 0.001], with significantly higher adjusted levels of IL7 and IL8 [IL7 fold change 2.47pg/mL (95%CI 1.10 to 5.52),p = 0.028; IL8 2.97pg/mL (1.10 to 7.97),p = 0.031].

Compared to those with a brick floor, participants with an earth floor had higher proportions of nonclassical monocytes at presentation [mean (IQR) 16.9% (9.9 to 25.80) versus 10.3% (8.2–16.7); p = 0.04] and were also more likely to experience improvements after 42 weeks of ART [nonclassical monocyte mean (IQR) change -2.2% (-12.0 to 2.9) versus 5.8% (3.9 to 7.2); p = 0.026].

Water source

Amongst PLWH using a water kiosk compared to other water sources, the median (IQR) proportion of activated CD8 T cells at baseline was lower [mean (IQR) 70% (59 to 81) versus 81% (68 to 89), p = 0.002], and decreases in CD8 T cell activation after 42 weeks of ART were less marked [-3.78 (-15.79 to 7.18) versus -9.85 (-9.80 to 20.7); p = 0.039]. Water kiosk use was also associated with lower rates of CMV PCR positivity [5(7%) versus 57(31%); p<0.0001]. Use of a shared domestic tap was overrepresented as the main water source amongst those with CMV (shared domestic tap users compared to water kiosk; 32 (86%) versus 5 (14%); p<0.0001). For the four PLWH who used an unprotected well as their main water source, the proportion of intermediate monocytes was higher than those who did not [median% 14.2 (95%CI 11.5–17.6) versus 9.3 (5.7–13.0), p = 0.068].

After adjustment for confounders, CD8 activation remained lower amongst water kiosk users [fold change -7.05% (95%CI -12.1 to -1.97); p = 0.007]. MIP1β, sICAM1 and sVCAM1 were also all significantly lower amongst kiosk users [fold change 0.63pg/mL (95%CI 0.40 to 0.99), p = 0.047; 0.65μg/mL (0.44 to 0.59), p = 0.026; and 0.74 μg/mL (0.57 to 0.97), p = 0.031 respectively] but IL12p70 was higher [2.39 (1.34 to 4.28); p = 0.003].

Discussion

We show that, amongst PLWH in a low income SSA setting, socioeconomic variables associate with chronic immune activation along two different inflammatory pathways. Activated CD8 T cells were expanded amongst those with variables consistent with a higher socioeconomic status including higher incomes, higher education and having home grown crops. This population was also more likely to show improvements in CD8 T cell activation on ART. Conversely CD16 positive monocytes, both non-classical and intermediate, were expanded amongst those with variables consistent with a lower socio-economic status including lower income, less education and earth flooring. This expansion of CD16 positive monocytes did not resolve on ART.

The observation that participants in higher socioeconomic groups were more likely to improve on ART has several possible explanations. Firstly, inflammation in this group may be related to HIV itself, or viral co-infection, resulting in T cell activation. Secondly, there may be an element of the ART care effect, whereby other traditional risk factors are managed once engaged in care. Lastly, although the socioeconomic variables associated with arterial stiffness (car and TV ownership, electricity connection) were not the same as those associated with T cell activation, they were consistent with a higher socio-economic status and perhaps a more sedentary lifestyle. Taken together with previous data showing that T cell exhaustion is linked with arterial stiffness during the first 3 months of anti-retroviral therapy [4], there is a suggestion that there may be a relationship between T cell activation, sedentary lifestyles and increased cardiovascular risk amongst PLWH in low income SSA setting. The observation that T cell activation may be increased amongst PLWH of higher socioeconomic status has not been previously described. However, T cell activation, exhaustion and senescence have been previously linked to premature ageing and sedentary lifestyles [25, 26]. Studies for older populations show that lower levels of physical activity predispose to a shift towards Th2 responses and T cell senescence [26]. Non-communicable disease risk factors have previously been shown to vary across socio-economic backgrounds in sub-Saharan Africa, but understanding of this relationship is limited by heterogeneity in measurement and reporting of socio-economic status [27]. Urbanisation and epidemiological transition are likely to favour different non-communicable disease risks depending on socio-economic background; for example, sedentary lifestyle being more prominent in high income groups and poor diet in low-income groups [27]. Detailed characterisation of this relationship will be needed in future studies to provide context for the role of inflammation within these models.

Participants in lower socio-economic groups experience expanded CD16+ monocytes, which did not improve on ART. This suggests that this aspect of innate immune activation is not linked to HIV itself or co-infections that would normally improve on ART. Subclinical TB, untreated or recurrent acute bacterial and malarial infections, or malnutrition with persistent microbial translocation could play a role. Although this association has not been previously reported amongst PLWH, there is evidence to suggest that factors associated with lower socioeconomic status are related to immune activation in other fields. In paediatrics, lower parental socioeconomic status is associated with higher levels of CRP and enhancing nutrition can improve the effects of inflammation [28, 29]. A socioeconomic assessment of the Framingham Offspring Study showed that both higher lifetime socioeconomic status and education level predicted lower levels of inflammatory cytokines [30, 31]. Chronic life stressors might also contribute to higher inflammation levels amongst this more economically vulnerable group [32].

Water kiosk use was independently associated with lower levels of CD8 activation, inflammatory and endothelial cytokines and CMV viraemia. Water kiosks sell water in containers from a centralised source. It is possible that use may therefore reflect a lower infection-driven inflammatory risk profile due to improved water quality or better sanitation environments. Sima and colleagues in Jakatar showed that water kiosks produced good quality water and reduced diarrhoeal illness to a similar extent to bottled water when compared with using wells as a water source [33]. Shared domestic pipe use was more common among those who tested CMV PCR positive, perhaps reflecting overcrowding in urban settings leading to increased risk of close contact transmission. Intermediate monocytes were expanded amongst four PLWH who used an unprotected well as their main water source. Although this group is very small, this finding is in keeping with low socio-economic status and unsafe water sources being associated with inflammatory monocyte phenotypes and innate inflammatory pathways [34].

Study strengths include careful characterisation of socio-economic status in a longitudinal cohort study of clinically evaluated participants with and without HIV. We compare socioeconomic data with cell surface immunophenotyping and a comprehensive panel of inflammatory biomarkers allowing a biosocial assessment of chronic inflammation among PLWH in low income SSA.

Because our cohort of PLWH all had advanced immune suppression, these findings may not be generalisable to HIV populations as a whole. Also, there may be differences in reasons for late presentation to care amongst those from higher and lower socio-economic status backgrounds such as cultural pressures or ability to take time off work. The sample size for this study may not have allowed us to identify smaller but important effects of socioeconomic variables on inflammation and arterial stiffness. Analysis of a large number of inflammatory and socio-economic markers may have led to an increase in type 1 error, and although we have presented results in keeping with an a priori identified biologically plausible hypothesis, it is important to note that individual mechanisms will require further characterisation in larger clinical studies.

Researchers are increasingly recognising that immune activation in PLWH is a heterogenous process and interest is emerging around discrete clinical inflammatory phenotypes [5]. Here we show that socioeconomic risk factors for inflammation amongst PLWH have varied effects (summarised in Fig 4). Understanding the extent to which socioeconomic factors contribute to such phenotypes, especially in low income SSA, will help identify those most at risk of developing inflammation driven non-communicable disease, support policy to reduce non-communicable disease risk factors and aid efficient allocation of limited health care resources. Future studies assessing drivers of inflammation amongst PLWH should include an assessment of socioeconomic status.

Fig 4 — Hypothesis for the impact of socio-economic factors on inflammation mediated non-communicable diseases in people living with HIV in low-income settings. This builds on previously documented relationships between drivers of inflammation, and its effect on endothelial damage in this setting [4, 5]. Further research will be required to evaluate and improve our understanding of the factors driving immune activation and non-communicable disease in low-income settings.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(95.2KB, docx)}

Acknowledgments

The authors would like to acknowledge the patients and their families as well as the staff in the ART clinic and Department of Medicine at Queen Elizabeth Central Hospital, Blantyre, Malawi. We would also like to acknowledge Dr Elizabeth Tilley of the Malawi Polytechnic, for support with interpreting water supply data.

We would like to thank the REALITY trial group for their support with study design and implementation.

The REALITY trial group consists of:

Participating Centres: Joint Clinical Research Centre (JCRC), Kampala, Uganda (coordinating centre for Uganda): P Mugyenyi, C Kityo, V Musiime, P Wavamunno, E Nambi, P Ocitti, M Ndigendawani. JCRC, Fort Portal, Uganda: S Kabahenda, M Kemigisa, J Acen, D Olebo, G Mpamize, A Amone, D Okweny, A Mbonye, F Nambaziira, A Rweyora, M Kangah and V Kabaswahili. JCRC, Gulu, Uganda: J Abach, G Abongomera, J Omongin, I Aciro, A Philliam, B Arach, E Ocung, G Amone, P Miles, C Adong, C Tumsuiime, P Kidega, B Otto, F Apio. JCRC, Mbale, Uganda: K Baleeta, A Mukuye, M Abwola, F Ssennono, D Baliruno, S Tuhirwe, R Namisi, F Kigongo, D Kikyonkyo, F Mushahara, D Okweny, J Tusiime, A Musiime, A Nankya, D Atwongyeire, S Sirikye, S Mula, N Noowe. JCRC, Mbarara, Uganda: A Lugemwa, M Kasozi, S Mwebe, L Atwine, T Senkindu, T Natuhurira, C Katemba, E Ninsiima, M Acaku J Kyomuhangi, R Ankunda, D Tukwasibwe, L Ayesiga. University of Zimbabwe Clinical Research Centre, Harare, Zimbabwe: J Hakim, K Nathoo, M Bwakura-Dangarembizi, A Reid, E Chidziva, T Mhute, GC Tinago, J Bhiri, S Mudzingwa, M Phiri, J Steamer, R Nhema, C Warambwa, G Musoro, S Mutsai, B Nemasango, C Moyo, S Chitongo, K Rashirai, S Vhembo, B Mlambo, S Nkomani, B Ndemera, M Willard, C Berejena, Y Musodza, P Matiza, B Mudenge, V Guti. KEMRI Wellcome Trust Research Programme, Kilifi, Kenya: A Etyang, C Agutu, J Berkley, K Maitland, P Njuguna, S Mwaringa, T Etyang, K Awuondo, S Wale, J Shangala, J Kithunga, S Mwarumba, S Said Maitha, R Mutai, M Lozi Lewa, G Mwambingu, A Mwanzu, C Kalama, H Latham, J Shikuku, A Fondo, A Njogu, C Khadenge, B Mwakisha. Moi University Clinical Research Centre, Eldoret, Kenya: A Siika, K Wools-Kaloustian, W Nyandiko, P Cheruiyot, A Sudoi, S Wachira, B Meli, M Karoney, A Nzioka, M Tanui, M Mokaya, W Ekiru, C Mboya, D Mwimali, C Mengich, J Choge, W Injera, K Njenga, S Cherutich, M Anyango Orido, G Omondi Lwande, P Rutto, A Mudogo, I Kutto, A Shali, L Jaika, H Jerotich, M Pierre. Department of Medicine and Malawi-Liverpool Wellcome Trust Clinical Research Programme, College of Medicine, Blantyre, Malawi: J Mallewa, S Kaunda, J Van Oosterhout, B O’Hare, R Heyderman, C Gonzalez, N Dzabala, C Kelly, B Denis, G Selemani, L Nyondo Mipando, E Chirwa, P Banda, L Mvula, H Msuku, M Ziwoya, Y Manda, S Nicholas, C Masesa, T Mwalukomo, L Makhaza, I Sheha, J Bwanali, M Limbuni. Trial Coordination and Oversight: MRC Clinical Trials Unit at UCL, London, UK: D Gibb, M Thomason, AS Walker, S Pett, A Szubert, A Griffiths, H Wilkes, C Rajapakse, M Spyer, A Prendergast, N Klein. Data Management Systems: M Rauchenberger, N Van Looy, E Little, K Fairbrother. Social Science Group: F Cowan, J Seeley, S Bernays, R Kawuma, Z Mupambireyi.

Independent REALITY Trial Monitors: F Kyomuhendo, S Nakalanzi, J Peshu, S Ndaa, J Chabuka, N Mkandawire, L Matandika, C Kapuya. Trial Steering Committee: I Weller (Chair), E Malianga, C Mwansambo, F Miiro, P Elyanu, E Bukusi, E Katabira, O Mugurungi, D Gibb, J Hakim, A Etyang, P Mugyenyi, J Mallewa. Data Monitoring Committee: T Peto (Chair), P Musoke, J Matenga, S Phiri.

Endpoint Review Committee (independent members): H Lyall (Co-Chair), V Johnston (Co-Chair), F Fitzgerald, F Post, F Ssali, A Prendergast, A Arenas-Pinto, A Turkova, A Bamford.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The source of funding for this study was Wellcome Trust Training Fellowship grant number 099934/Z/12/A. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The grant holder, Christine Kelly, received a salary from this fellowship.

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PLoS One. doi: 10.1371/journal.pone.0256576.r001

Decision Letter 0

Olalekan Uthman

19 Mar 2021

PONE-D-20-21907

Inflammatory pathways amongst people living with HIV in Malawi differ according to socioeconomic status

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Reviewer #1: This is an interesting study which is one of the earliest to shed some light on socio-economic factors which contribute to inflammation amongst PLWH with the view to inform the management of non-communicable diseases. While the results are of importance to the field, there are many aspects that have to be addressed.

Abstract:

“Results: Of 279 PLWH, the median (IQR) age was 36 (31-43) years and 122 (44%) female” – should this be were female?

“and amongst those earning less than 10 USD/month compared to 100-150 USD/month (10.5% versus 8.0%; p=0.08)” I am not sure why this finding is specifically mentioned in the abstract since it was not statistically significant.

Line 45-7: “Non-communicable diseases (NCDs) are fast becoming the leading cause of mortality in low income sub-Saharan Africa (SSA), with DALYs related to NCDs fast approaching those from including communicable diseases(1).” – Please rephrase for clarity: what does from including communicable diseases mean?

Line 50-1: “Persistent inflammation and resultant endothelial damage amongst PLWH is implicated…” – This should be plural.

Some abbreviations are not explained e.g. DALYs, TB, PBMCs, IQR.

Line 89: Was cfPWV performed by a trained person? Was it performed by a single person? If not, was inter-rater concordance assessed?

Line 90: When was the socio-economic evaluation done? If done at both time points, which values were used?

Line 91: Has the standardised questionnaire for assessment of socio-economic status been validated for the sub-Saharan African setting?

Line 97-103: No information is provided about the instrument used for flow cytometry, the antibody manufacturer, the T-cell subsets examined, or the gating strategy used.

Line 104: “Proinflammatory Panel-1 (IFN-Ɣ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL12p70, IL-13, TNF-α)” – IL1Ra, IL-10 and IL-13 are anti-inflammatory markers.

Line 101-6: It would be better to write all the biomarker names out in full the first time they are used, since the abbreviations may not be familiar to all the readership of this journal.

Line 110-1: “CMV viral loads were quantified by DNA PCR in a subset of participants with available plasma as described previously(20).” Please explain who this subset was or what a decision to test for CMV was based on.

Write numbers in the beginning of a sentence out in full.

Line 137: Were HIV-uninfected participants also screened for CMV infection?

Line 138: Was cfPWV only performed for the HIV-positive participants?

Line 151: “HIV cohort, who were also more likely to work 7 hours per week…” – should this be 7 days per week?

Table 1: Please check all numbers in Table 1. For instance, the categories for the floor variable do not add up to 100% for the HIV-infected participants. In addition, 198/262 does not equal 80%; 71/262 does not equal 29%; 139/262 does not equal 56%; 90/105 does not equal 84%; 52/105 does not equal 51%; 64/105 does not equal 62%; etc, etc. Note that dark lines that divides the “mode of transport” category.

Line 158-9: “cfPWV was higher for the 16 (8%) participants who travelled to work in a car compared to those who did not [8m/s (7.3 to 10.2)”. It is not clear what is being presented: median and interquartile range? This is also true later in the manuscript e.g. when monocyte data are presented.

Line 161: “p0.0005” should be p=0.0005.

Line 162: “These factors remained predictive of cfPWV”. It seems that associated is the more appropriate word here since it does not appear as if predictive models had been constructed.

If “Carotid femoral pulse wave velocity (cfPWV) was also carried out at both time points as a

measurement of arterial stiffness”, which values are presented in lines 137 and 138, and which were used in the section “Socio-economic predictors of cfPWV amongst PLWH”? Had there been any change in cfPWV over time?

Since many readers will not be familiar with cfPWV, there should be some explanation about what higher values mean in terms of arterial stiffness.

Line 174: What is meant by “an indirect correlation”? According to the statistics, no correlations had been performed.

Line 181: “as did those on higher incomes” – should this be with higher incomes?

Lines 185-195: What is meant by the fold change values? Is this the change over time? Why was this selected rather than the median values for each time point? This is not explained in the statistical analysis section.

Line 191: Since IL-6 has been reported to be affected by obesity, was the association with higher income assessed for confounding with body mass index?

Line 191-3: “Levels of IL6 and IL13 were also higher amongst people with higher incomes [2.9 μg/mL (1.13 to 7.6), p=0.028; and 3.1 μg/mL (1.2 to 8.3), p=0.025 respectively].” Why are the median values (I presume these are medians and IQRs?) shown here and not the fold change? Which time point is referred to? Why are the comparator values not shown for participants with lower incomes? Which groups were combined to create the group “higher incomes”?

Line 193: What is meant by “Adjusted nonclassical and intermediate monocytes”?

Line 211-4: “Baseline CD8 activation remained significantly higher amongst those who grew crops in adjusted analysis [fold change 12.1% (5.2 to 213 19.0);p=0.001], with significantly higher adjusted levels of IL7 and IL8 [IL7 fold change 214 2.47pg/mL (95%CI 1.10 to 5.52),p=0.028; IL8 2.97pg/mL (1.10 to 7.97),p=0.031].” Where are comparator values for participants who did not grow crops? The same is true for lines 231-5.

A table with the descriptive results, as well as the unadjusted and adjusted results of the univariate and multivariable analysis of all the biomarkers tested would be very helpful and greatly improve understanding of the results. One would like to see the results of all the markers that had been tested. In addition, results should be displayed in a systematic fashion since now only results that the authors seemingly found interesting are displayed, even in the supplementary tables. This makes a full assessment of the results impossible.

Line 231: “After adjustment for confounders…”. It is not clear what confounding had been adjusted for with regards to CD8 activation.

Had any adjustment been made for CMV co-infection?

Monocytes were only defined as classical (CD14++CD16-), intermediate (CD14++CD16+) or nonclassical (CD14+CD16+) and no markers of activation had been included in the flow cytometric analysis. For this reason, any statement about monocyte activation is incorrect and should be rephrased and re-interpreted according to the phenotype observed. Just two examples where this is problematic are the following: “population did not show improvements in monocyte activation on ART.”; “Intermediate monocytes were expanded amongst four PLWH who used an unprotected well as their main water source. Although this group is very small, this finding is in keeping with low socio-economic status and unsafe water sources being associated with monocyte activation and innate inflammatory pathways(29).” As it stands, it is completely unclear what it means to have a higher proportion of intermediate monocytes, for instance.

It is not clear what the purpose of the control group is, since only socio-economic variables were compared between them and the HIV-infected participants.

Figure 1: there is no indication of what the asterisks mean.

Figure 2 is very difficult to understand and should be augmented with a footnote that describes what is depicted.

Figure 4 would be greatly strengthened by the inclusion of supporting references. It is not clear how viral antigen stimulation leads to lymph node fibrosis.

Why were CD4 cell markers not also assessed? Why are the data on T cell exhaustion and senescence not also shown?

Reviewer #2: In this paper, Kelly et al build upon previous published work describing distinct immunophenotypes in PLWHIV with low CD4 counts by examining the role of socio-economic status. This is a very important and frequently neglected question. It is particularly poorly studied in the setting of low income countries and I commend the authors on examining this question. Overall, the manuscript well-written and clear, with use of appropriate methodology. The authprs characterise SES and immune activation using multiple measures and report that PLWHIV of higher SES have higher proportions of activated CD8+ T-cells and higher cfPWV. These changes normalise with ART. In contrast, PLWHIV of lower SES have higher proprtions of intermediate monocytes, and do not experience a normalisation of this or a change in T-cell activation with ART. The work contributes to what is known on this topic, and the discussion sites the work in existing literature and appropriately presents limitations of the study. I have a few very minor suggestions but would recommend that the paper be accepted.

• What is a water kiosk? The authors report that it provides clean water but I would like a little more information on what it is.

• In line 151, should 7 hours be 7 days per week

• The authors could include a brief comment on what is known on the effect of repeated social stress on myelopoiesis (e.g. www.pnas.org/content/110/41/16574)

• I think a paragraph on what is known on SES and NCDs in low-income countries would also benefit the paper – e.g. referencing this article amongst others https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(17)30054-2/fulltext

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PLoS One. 2021 Aug 25;16(8):e0256576. doi: 10.1371/journal.pone.0256576.r002

Author response to Decision Letter 0

13 May 2021

Reviewer #1

Abstract:

“Results: Of 279 PLWH, the median (IQR) age was 36 (31-43) years and 122 (44%) female” – should this be were female?

Correction added to manuscript

Thank you for highlighting this inaccuracy. Correction added to manuscript.

Thank you for highlighting this typographical error. Corrected in manuscript.

Line 50-1: “Persistent inflammation and resultant endothelial damage amongst PLWH is implicated…” – This should be plural.

Corrected in manuscript.

Some abbreviations are not explained e.g. DALYs, TB, PBMCs, IQR.

Abbreviations now explained in the manuscript.

Line 89: Was cfPWV performed by a trained person? Was it performed by a single person? If not, was inter-rater concordance assessed?

Full training for cfPWV was provided by colleagues at the Institute of Cardiovascular Sciences, UCL (included on authorship). A sentence has been added to the manuscript to direct readers to a reference with a more detailed outline of how cfPWV was conducted for this cohort.

Line 90: When was the socio-economic evaluation done? If done at both time points, which values were used?

The assessment was carried out at baseline visit – this has been clarified in the manuscript.

Line 91: Has the standardised questionnaire for assessment of socio-economic status been validated for the sub-Saharan African setting?

The questionnaire has been used in HIV clinical trials across low-income sub-Saharan Africa, and was initially designed for use in the DART trial (Routine versus clinically driven laboratory monitoring of HIV antiretroviral therapy in Africa (DART): a randomised non-inferiority trial. January 2010. DOI: 10.1016/S0140-6736(09)62067-5).

Line 97-103: No information is provided about the instrument used for flow cytometry, the antibody manufacturer, the T-cell subsets examined, or the gating strategy used.

A reference has been inserted to refer the reader to the immunophenotyping protocol previously published.

Line 104: “Proinflammatory Panel-1 (IFN-Ɣ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL12p70, IL-13, TNF-α)” – IL1Ra, IL-10 and IL-13 are anti-inflammatory markers.

“Proinflammatory Panel 1” is the name of the panel according to the manufacturers (MSD) and has been used here to make it easier for the reader to replicate the panel.

Line 101-6: It would be better to write all the biomarker names out in full the first time they are used, since the abbreviations may not be familiar to all the readership of this journal.

The abbreviations used are standardised cytokine terminology and the authors would be concerned that writing them out in full might make the manuscript difficult to read.

This subset consisted of participants with stored plasma available for analysis. This sentence has been changed to provide clarity for the reader.

Write numbers in the beginning of a sentence out in full.

Corrected in the manuscript

Line 137: Were HIV-uninfected participants also screened for CMV infection?

Yes, participants without HIV also had CMV PCRs performed but did not have detectable virus.

Line 138: Was cfPWV only performed for the HIV-positive participants?

cfPWV was performed for all participants but the median for HIV positive patients is reported for context here. The effect of HIV on cfPWV was the subject of an earlier publication which has been referenced.

Line 151: “HIV cohort, who were also more likely to work 7 hours per week…” – should this be 7 days per week?

We thank the reviewer for highlighting this error which has now been corrected.

Table 1: Please check all numbers in Table 1. For instance, the categories for the floor variable do not add up to 100% for the HIV-infected participants.

We thank the reviewer for point out this rounding error – this has been corrected.

In addition, 198/262 does not equal 80%; 71/262 does not equal 29%; 139/262 does not equal 56%; 90/105 does not equal 84%; 52/105 does not equal 51%; 64/105 does not equal 62%; etc, etc. Note that dark lines that divides the “mode of transport” category.

Whilst the total number of observations for the socio-economic questionnaires is given at the top of the table, the denominator for the percentages in the table may change for variables where some data are missing. This denominator can be derived from the percentage.

This has been clarified in the manuscript.

Line 161: “p0.0005” should be p=0.0005.

Corrected in manuscript.

Line 162: “These factors remained predictive of cfPWV”. It seems that associated is the more appropriate word here since it does not appear as if predictive models had been constructed.

Corrected in manuscript.

If “Carotid femoral pulse wave velocity (cfPWV) was also carried out at both time points as a

The analyses presented here are baseline cfPWV values, this has been corrected in the manuscript. Where changes over time were associated with socio-economic variables, the relationship has been reported in the relevant results section. For example, second paragraph of ‘Education level and income section’ – “Participants with a tertiary education tended to experience greater improvements in CD8 activation after 42 weeks of ART compared to those without any education [%change -5.8 (-16.7 to 2.2) versus 2.4 (-7.9 to 12); p=0.07], as did those on higher incomes [% change 6.0 (-8.2 to 20.1) versus -18.2 (-21.1 to -4.8) comparing lowest and highest income categories; p value=0.0001]. However, neither educational level nor patient income predicted change in the proportion of intermediate monocytes on ART (p=0.69 and 0.41 respectively).”

Since many readers will not be familiar with cfPWV, there should be some explanation about what higher values mean in terms of arterial stiffness.

An explanatory paragraph on this has been added to the introduction.

Line 174: What is meant by “an indirect correlation”? According to the statistics, no correlations had been performed.

This has been amended in the manuscript.

Line 181: “as did those on higher incomes” – should this be with higher incomes?

Corrected in manuscript

Values here are median change over time between baseline and exit visits, to provide an indication of how the proportion of T cells have changed on ART. This has been clarified in the text.

Line 191: Since IL-6 has been reported to be affected by obesity, was the association with higher income assessed for confounding with body mass index?

For this analysis, the relationship between inflammatory markers and socioeconomic status were adjusted for age and sex. The effect of obesity would be outside the scope of this paper, but would certainly be important to look at in future studies.

These values are fold change with 95% CI, in keeping with the values reported in the previous sentence, this has been repeated for clarity. The time point is at baseline, as for all cross-sectional analysis throughout the manuscript. The comparator >25USD per month to <25 USD per month has been made clearer in the text.

Line 193: What is meant by “Adjusted nonclassical and intermediate monocytes”?

This has been clarified in the manuscript.

These values are fold change from regression analysis comparing values among those who did grow crops to values for those who did not. A separate comparator group is therefore not presented.

Due to the volume of data it is challenging to present all of the univariate and multivariate analyses together, and therefore we preferred to present those results that were statistically significant, biologically important and relevant to the message of the paper. To help provide clarity we have provided a new table in supplementary data showing median values of plasma biomarkers for each socio-economic variable.

Line 231: “After adjustment for confounders…”. It is not clear what confounding had been adjusted for with regards to CD8 activation.

CD8 activation is adjusted for age and sex, and is outlined in the methods statement as well as the supplementary material.

Had any adjustment been made for CMV co-infection?

Adjustment was not made for CMV as planned multi-variate analysis did not include CMV as a confounder or mediator.

On activation, monocytes express CD16, signally a proinflammatory phenotype. For clarity, the term monocyte activation has been replaced in the manuscript in the two instances that the term was used, as outlined by the reviewer.

It is not clear what the purpose of the control group is, since only socio-economic variables were compared between them and the HIV-infected participants.

The purpose of the control group here was to place socio-economic status for people living with HIV in the context of socio-economic status in Malawi.

Figure 1: there is no indication of what the asterisks mean.

A figure legend has been added to clarify this.

Figure 2 is very difficult to understand and should be augmented with a footnote that describes what is depicted.

A figure legend has been added to explain this.

Figure 4 would be greatly strengthened by the inclusion of supporting references. It is not clear how viral antigen stimulation leads to lymph node fibrosis.

A figure legend has been added to figure 4 with references and a clear explanation that it is a hypothesis which requires further research.

Why were CD4 cell markers not also assessed? Why are the data on T cell exhaustion and senescence not also shown?

Neither CD4 T cell data nor exhaustion / senescence markers demonstrated an association with socio-economic variables and are therefore not reported in line with the methodological approach outlined. It should be noted that for this population with low nadir CD4 count, few CD4 T cell events were captured.

Reviewer #2:

• What is a water kiosk? The authors report that it provides clean water but I would like a little more information on what it is.

We thank the reviewer for highlighting this. The functioning of a water kiosk has been clarified in the discussion.

• In line 151, should 7 hours be 7 days per week

This has been corrected in the manuscript. We thank the reviewer for raising this inconsistency.

• The authors could include a brief comment on what is known on the effect of repeated social stress on myelopoiesis (e.g. www.pnas.org/content/110/41/16574)

We thank the reviewer for this helpful suggestion. This has been added to the discussion.

Again, we thank the reviewer for this helpful suggestion and have added this point to the discussion.

Attachment

Submitted filename: Socioeconomic analysis replies to reviewers.docx

Click here for additional data file.^{(155.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0256576.r003

Decision Letter 1

Olalekan Uthman

21 Jul 2021

PONE-D-20-21907R1

Inflammatory pathways amongst people living with HIV in Malawi differ according to socioeconomic status

PLOS ONE

Dear Dr. Kelly,

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Olalekan Uthman, MD, MPH, PhD, FRSPH, FHEA

Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

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

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: No

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Abstract: Please correct the following sentence: “Water kiosk use showed a protective association protective against T cell activation…”

Introduction: Please include the abbreviation for carotid femoral pulse wave when the term is used for the first in line 61: “Carotid femoral pulse wave velocity is a gold standard measurement of arterial stiffness and has been validated against clinical outcomes in high-income settings…”. Do not introduce the term in line 95.

In the previous round of reviews, I have requested the authors to write all the biomarker names out in full the first time they are used, since the abbreviations may not be familiar to all the readership of this journal. The authors had responded that “The abbreviations used are standardised cytokine terminology and the authors would be concerned that writing them out in full might make the manuscript difficult to read.” I do not agree. Since the journal is not an Immunology journal and has a wide readership, the terms have to be explained. In fact, explaining the terms, as I have illustrated below, only adds 5 lines to the manuscript and provides the non-immunologist with better understanding.

CD38/HLADR, PD1 and CD57 expression, respectively. Monocytes were defined as classical (CD14++CD16- ), intermediate (CD14++CD16+ ) or nonclassical (CD14+CD16+). Stored plasma was tested for 22 cytokines: Proinflammatory Panel-1 (IFN-Ɣ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL12p70, IL-13, TNF-α), Vascular Injury Panel-2 (SAA, CRP, VCAM-1, ICAM-1), Chemokine Panel-1 (MIP-1β, IP-10, MCP-1), Angiogenesis -Panel-1 (VEGF-A, bFGF) and single analyte assays for IL1R antagonist and IL-7.

VERSUS

Cluster of differentiation (CD)38/Human Leukocyte Antigen – DR isotype (HLADR), programmed death (PD)-1 and CD57 expression, respectively. Monocytes were defined as classical (CD14++CD16- ), intermediate (CD14++CD16+ ) or nonclassical (CD14+CD16+). Stored plasma was tested for 22 cytokines: Proinflammatory Panel-1 (interferon [IFN]-Ɣ, interleukin [IL]-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL12p70, IL-13, tumour necrosis factor [TNF]-α), Vascular Injury Panel-2 (serum amyloid A [SAA], C-reactive protein [CRP], vascular cell adhesion molecule [VCAM]-1, intercellular adhesion molecule [ICAM-1]), Chemokine Panel-1 (macrophage inflammatory protein [MIP]-1β, interferon γ-induced protein [IP]-10, monocyte chemoattractant protein [MCP]-1), Angiogenesis -Panel-1 (

vascular endothelial growth factor [VEGF]-A, basic fibroblast growth factor [bFGF]) and single analyte assays for IL-1receptor antagonist (IL-1Ra) and IL-7.

As requested previously, please write numbers out in the beginning of sentences, e.g. “15 (4%) participants died during the study”; “201 (55%) had electricity and 95 (26%) had a private water tap at home. 75 (21%) were unemployed and of those employed, 108 (29%) earned less than 25 USD per month, although 97 (36%) of participants were not able to provide an estimate of monthly salary. 339 (92%) of participants felt that their household did not have enough food.”

One of my previous comments was about Table 1, where the percentages were not correct according to the denominator provided. The authors responded as follows: “Whilst the total number of observations for the socio-economic questionnaires is given at the top of the table, the denominator for the percentages in the table may change for variables where some data are missing. This denominator can be derived from the percentage.” It is good scientific practice to indicate where data are missing and the authors should please make these amendments to the table.

I still have difficulty in understanding this section: “On univariate analysis of the HIV positive cohort, cfPWV was higher for the 16 (8%) participants who travelled to work in a car compared to those who did not [median (IQR) 8m/s (7.3 to 10.2) versus 7.2m/s (6.3 to 8.1); p=0.008]; for those who owned a television [7.7 (6.7 to 8.5) versus 7.2 (6.2 to 7.8) p=0.0005]; and for those who had an electricity supply at home [(7.4 (6.5 to 8.3) versus 7.2 (6.4 to 8.1) p=0.093]. These factors remained associated with cfPWV when adjusted for age, sex, blood pressure and haemoglobin: car ownership [fold change 1.3m/s (95% CI 1.10 to 1.56); p=0.003]; television ownership [1.12m/s (1.03 to 14 174 1.23); p=0.012]; and electricity access [1.09m/s (1.01 to 1.17); p=0.029].” For the univariate analysis, it seems that the baseline cfPWV is used, but for the multivariable analysis, which I assume are the results given after adjusting for age, sex, blood pressure and haemoglobin, the fold change of the cfPWV is given. What is the rationale for that?

It is unclear how the authors decided whether a change in CD8 activation is an improvement or a deterioration: “Participants with a tertiary education tended to experience greater improvements in CD8 activation after 42 weeks of ART compared to those without any education [%median (IQR) change from baseline to 42 weeks -5.8 (-16.7 to 2.2) versus 2.4 (-7.9 to 12); p=0.07], as did those with higher incomes [% change 6.0 (-8.2 to 20.1) versus -18.2 (-21.1 to -4.8) comparing lowest and highest income categories; p value=0.0001].” This is especially true since no control values are shown and there are no reference values of CD8 activation one can refer to. So, while one group can have higher or lower levels, how does one know which one is better?

Similarly, how did the researchers decide what is an improvement in monocyte phenotype: “Compared to those with a brick floor, participants with an earth floor had higher proportions of nonclassical monocytes at presentation [mean (IQR) 16.9% (9.9 to 25.80) versus 10.3% (8.2 – 16.7); p=0.04] and were also more likely to experience improvements after 42 weeks of ART [nonclassical monocyte mean (IQR) change -2.2% (-12.0 to 2.9) versus 5.8% (3.9 to 7.2); p=0.026].”

“Adjusting for age and sex, we first compared those with some education to those without any: IL7 was lower for those with some education [fold change 0.20µg/mL (0.07 to 0.59); p=0.002] who also trended towards lower bFGF [fold change 0.22µg/mL (0.048 to 1.09); p=0.056] and higher proportion of activated CD8 T cells [fold change 7.02% (-0.97 to 15.0); p=0.085].” I am very sorry if I am just not understanding this. The authors state that IL-7 was lower, but show the fold change in support. Do they therefore mean that the fold change was greater in this group or do they mean that the exit value was lower? Even after re-reading the statistics section several times, I still fail to understand what the authors are trying to show.

“Interestingly, an indirect association was observed with intermediate monocytes, with higher median (IQR) amongst those with no education compared to a tertiary education [12.6% (5.4 – 15.5) versus 7.3% (5.3 – 9.8); p=0.01] …” Do the authors mean an inverse association instead of an indirect association?

A limitations that has to be mentioned is that there was no adjustment for other clinically relevant parameters such as obesity. This is especially important since some of the findings, such as “Levels of IL6 and IL13 were also higher amongst people with higher incomes [fold change (95% CI) 2.9 µg/mL (1.13 to 7.6), p=0.028; and 3.1 µg/mL (1.2 205 to 8.3), p=0.025 respectively]” could be severely confounded by body weight.

Since non-classical monocytes are associated with a less inflammatory phenotype and also with tissue repair, how do the authors explain the following finding? “Conversely CD16 positive monocytes, both non-classical and intermediate, were expanded amongst those with variables consistent with a lower socio-economic status including lower income, less education and earth flooring. This expansion of CD16 positive monocytes did not resolve on ART.” This is also contradicted in the following statement: “Participants in lower socio-economic groups experience expanded inflammatory monocytes, which did not improve on ART.” While monocyte phenotypes are extraordinarily complex and still a topic of debate, it is generally accepted that classical monocytes (CD14+ CD16-) are the most pro-inflammatory. See for instance Kapellos TS et al. Human Monocyte Subsets and Phenotypes in Major Chronic Inflammatory Diseases. Front. Immunol., 30 August 2019 | https://doi.org/10.3389/fimmu.2019.02035

It is not clear why CMV was tested for but not adjusted for in the multivariable analysis since CMV is known to significantly affect CD8+ T-cell and monocyte populations, and especially since there was an association in this study with water use, suggesting that some of the findings related to water use could be confounded by CMV. To list but two relevant references in this regard:

van den Berg SPH, Pardieck IN, Lanfermeijer J, et al. The hallmarks of CMV-specific CD8 T-cell differentiation. Med Microbiol Immunol. 2019;208(3-4):365-373. doi:10.1007/s00430-019-00608-7

Baasch S, Ruzsics Z, Henneke P. Cytomegaloviruses and Macrophages—Friends and Foes From Early on? Front. Immunol., 12 May 2020 | https://doi.org/10.3389/fimmu.2020.00793

At the very least, this should be stated as a limitation.

Can the authors hypothesize as to how the following markers can be reconciled, especially since higher levels of IL-12p70 are known to enhance the cytotoxic effects of CD8+ T-cells?

“After adjustment for confounders, CD8 activation remained lower amongst water kiosk users [fold change -7.05% (95%CI -12.1 to -1.97); p=0.007]. MIP1β, sICAM1 and sVCAM1 were also all significantly lower amongst kiosk users [fold change 0.63pg/mL (95%CI 0.40 to 254 0.99), p=0.047; 0.65µg/mL (0.44 to 0.59), p=0.026; and 0.74 µg/mL (0.57 to 0.97), p=0.03 respectively] but IL12p70 was higher [2.39 (1.34 to 4.28); 0=0.003].” Please also correct 0=0.003.

Reviewer #2: All comments have been addressed, and the ms is ready for publication. Congratulations to the authors.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2021 Aug 25;16(8):e0256576. doi: 10.1371/journal.pone.0256576.r004

Author response to Decision Letter 1

22 Jul 2021

Please see Response to Reviewers letter at the end of the PDF for review.

Attachment

Submitted filename: Socioeconomic analysis further replies (1).docx

Click here for additional data file.^{(155KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0256576.r005

Decision Letter 2

Olalekan Uthman

11 Aug 2021

Inflammatory pathways amongst people living with HIV in Malawi differ according to socioeconomic status

PONE-D-20-21907R2

Dear Dr. Kelly,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Olalekan Uthman, MD, MPH, PhD, FRSPH, FHEA

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0256576.r006

Acceptance letter

Olalekan Uthman

16 Aug 2021

PONE-D-20-21907R2

Inflammatory pathways amongst people living with HIV in Malawi differ according to socioeconomic status

Dear Dr. Kelly:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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PERMALINK

Inflammatory pathways amongst people living with HIV in Malawi differ according to socioeconomic status

Christine Kelly

Willard Tinago

Dagmar Alber

Patricia Hunter

Natasha Luckhurst

Jake Connolly

Francesca Arrigoni

Alejandro Garcia Abner

Raphael Kamn’gona

Irene Sheha

Mishek Chammudzi

Kondwani Jambo

Jane Mallewa

Alicja Rapala

Patrick W G Mallon

Henry Mwandumba

Nigel Klein

Saye Khoo

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Characterisation of immune activation

Statistical analysis

Results

Clinical characteristics

Socio-economic characteristics

Table 1. Socio-economic variables for 367 Malawian adults according to HIV status.

Socio-economic predictors of cfPWV amongst PLWH

Socio-economic predictors of immune activation amongst PLWH

Education level and income

Fig 1. Cell surface immune activation markers according to education and income category.

Fig 2. Adjusted fold change in cell surface immune activation markers according to socio-economic risk factors.

Fig 3. Adjusted fold change in plasma inflammatory markers according to socio-economic risk factors.

Household factors

Water source

Discussion

Fig 4. Hypothesis for the role of socio-economic determinants in chronic inflammation and endothelial damage amongst PLWH in low income SSA.

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

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Olalekan Uthman

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Author response to Decision Letter 0

Decision Letter 1

Olalekan Uthman

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Author response to Decision Letter 1

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Olalekan Uthman

Roles

Acceptance letter

Olalekan Uthman

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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