The influence of socioeconomic factors and apolipoprotein ε genotype on cognitive outcomes in a multi-ethnic sample living with HCV and HIV

Abstract

Background

This study examined associations between apolipoprotein (APOE) ε4 carrier status and Mini-Mental State Examination (MMSE) scores and whether this relationship varied among adults living with HIV/HCV, HIV, HCV, or neither. We also examined whether sociodemographic factors moderated associations between APOE ε4 carrier status and MMSE scores.

Methods

A secondary analysis of cognitive, psychological, and sociodemographic data from the Miami Adult Studies on HIV (MASH) cohort (n = 493) was completed. Banked blood samples underwent APOE genotyping. Bivariate analyses examined potential differences across participants by HIV and/or HCV statuses and by APOE ε4 carrier status. Multiple linear regression was conducted to test the adjusted moderation effect of each sociodemographic variable between APOE ε4 carrier status and MMSE scores within each group.

Results

Sociodemographic factors, such as income, education, food insecurity, and employment influenced associations between APOE ε4 carrier status and MMSE scores among participants with HIV/HCV. Participants with APOE ε4 genotypes living with HCV had higher MMSE scores than non-APOE ε4 carriers.

Conclusion

These results reinforce the need for future studies examining (1) the impact of APOE genotype on cognitive functioning while considering socioeconomic factors and (2) the influence of socioeconomic factors on cognitive scores among individuals living with HIV and or HCV.

Keywords

Cognition sociodemographic factors food insecurity hepatitis C apolipoprotein ε4

Despite access to antiviral treatment, both hepatitis C (HCV) and HIV infections have been linked to cognitive impairments across various domains of functioning,^1–3 highlighting the complex and persistent challenges in mitigating the cognitive effects of these viral infections. Deficits in domains such as attention and concentration, working memory, verbal fluency, and psychomotor speed have been observed in adults living with mild hepatitis C (PHCV).^4,5 Similarly, HIV-associated neurocognitive disorder (HAND) presents in up to 50% of those living with HIV (PHIV⁶) producing alterations in memory, executive function, and motor skills, although antiretroviral therapy (ART) has improved outcomes in HIV-related cognitive impairment.⁷

Researchers have identified multiple pathways through which these viral infections influence cognitive function. Evidence indicates that neuroinflammation, disruption to neurotransmitter systems, and direct viral invasion of the central nervous system led to changes in MRI-measured regional connectivity/functionality and reductions in cognitive performance.^1,8 Alterations in cerebral blood flow have also been observed in PHCV.⁸ A systematic review of cognitive function in cases of HIV/HCV coinfection observed more frequent impairment compared to HCV or HIV alone (n = 24 articles), while a meta-analysis identified greater global impairment and processing speed deficits suggesting a cumulative effect.⁹ Clarifying the influence of HIV/HCV coinfection on cognition and its relation to other physiological and psychosocial factors is critical: of the 1.2 million PHIV in the United States,¹⁰ an estimated 21% are also living with HCV.¹¹

Recently, researchers have begun to examine the influence of apolipoprotein ε4 carrier status on cognitive performance among PHCV and PHIV. APOE is a lipoprotein with essential tissue repair and lipid transport functions. While there are three isoforms, ε2, ε3, and ε4, the APOE ε4 genotype represents the most significant known risk factor for developing Alzheimer’s disease (AD)^12–14 as homozygous ε4 carriers (ε4/ε4) are significantly more likely to develop AD than heterozygous ε4 carriers (ε2/ε4, ε3/ε4).^15–19 Though understudied in relation to HIV and HCV, APOE genotype may directly affect cognitive and mental health and interact with behavioral and sociodemographic factors to influence cognition and mental health. Additionally, APOE genotype may impact HCV and HIV disease progression, treatment efficacy, and mortality.²⁰ The limited research available indicates that APOE ε4 may affect memory, verbal learning, and attention in PHCV.^21,22 APOE ε4 may have a protective effect with respect to severe liver disease: previous studies found that HCV patients with severe (vs mild) inflammation, fibrosis,²³ and viral persistence²⁴ were less likely to be ε4 carriers. APOE ε4 carrier status among people living with PHIV has been investigated more extensively, with evidence indicating that positive ε4 carrier status appears to increase the risk for HAND^25,26 though outcomes have not always been consistent.²⁷ Among PHIV, APOE ε4 may lead to decreased efficacy of ART or secondary complications,^28,29 which points to a potential intervention target. APOE ε4 carriers living with HIV may also experience a faster trajectory toward mortality,³⁰ implicating ε4 as a potential factor in HIV progression.³¹

While the role of APOE ε4 in health and well-being has been evaluated in cognitive and psychiatric contexts, limited research has sought to understand the influence of social and economic factors, including household income, employment status, educational attainment, or food security, and APOE in relation to cognitive functioning in adults diagnosed with HCV and/or HIV. Given the significant influence of poverty on HIV and HCV rates, which are 3.5 times higher in PHCV,³² and men living with HIV as well as 5.3 times higher in women living with HIV,³³ the role of economic and associated social factors on cognitive outcomes is critical to take into account. Food insecurity is an important socioeconomic factor yet is often excluded in the creation of this composite variable. Among PHIV experiencing food insecurity but not controls, poorer memory, learning, motor function, and processing speed outcomes have been identified.³⁴ Biomedical factors, including diabetes, heart disease, cerebrovascular disease, hypertension, or inflammation, have not explained cognitive decline in these cases.³⁵ Socioeconomic status has been associated with cognitive status. Still, participants with non-European heritage have been excluded from prior analyses³⁶ or collapsed into White samples when identifying as Native American or Hispanic.³⁴ However, minoritized populations are disproportionately affected by HIV in the U.S. African Americans represent 12% of the U.S. population, yet account for 42% of HIV diagnoses.¹⁰ The rate of HIV/AIDS infection in Hispanic/Latino populations is greater than fourfold that of non-Hispanic Whites.¹⁰ While African American race and Hispanic ethnicity have been associated with higher rates of HCV testing,³⁷ single nucleotide polymorphism genotyping during testing or regular clinical follow-up care has not been conducted concurrently or at all.

The current study addresses limitations in the previous literature by examining the moderating effect of socioeconomic status on the relations between APOE genotype and cognition in a representative majority-minority sample of PHIV, PHCV, people living with HIV/HCV, and people living with neither HIV nor HCV (as controls). This study had two aims: first, we examined the relationship between APOE ε4 and Mini-Mental State Examination (MMSE) scores and whether associations varied between the four HIV and HCV-related infection groups. Second, we sought to examine whether sociodemographic factors moderated correlations between APOE ε4 genotypes and MMSE scores across the four different infection groups.

Methods

Data for this secondary analysis were obtained from 493 participants in the Miami Adult Studies on HIV (MASH) cohort, collected between April 2017 and September 2019 as part of the NIDA grant U01-DA040381 in Miami, FL. The parent study, inclusion/exclusion criteria, and measures such as food insecurity³⁸ and cognition (MMSE scores³⁹) were previously described in Tamargo et al.⁴⁰ Study procedures, with the addition of APOE genotyping of banked blood samples, were previously described in Burke et al.⁴¹ The cohort is unique as the participants are predominantly African American. The Mini-Mental State Examination (MMSE) was used in this study to assess cognitive performance and impairment.³⁹ This brief tool (<10 minutes) has been in use since 1975 and continues to be one of the most widely used cognitive screeners.^42–44 The MMSE evaluates orientation, calculation, recall, language, and attention. The highest total score is 30 and scores of 24 and greater are usually associated with normal cognition, while scores less than 24 suggest the presence of cognitive impairment.^39,43,45

Statistical analysis

Descriptive statistics were assessed for all outcome measures. Bivariate analyses were conducted to evaluate differences between these variables across the four groups and by APOE ε4 carrier status. To assess continuous variables, one-way ANOVA or two-sample t-tests were conducted. The chi-square test was utilized to assess categorical variables.

We evaluated the moderation effect of HIV and/or HCV infection status on the relationship between the APOE ε4 genotype and the MMSE score by testing the adjusted interaction effect between APOE ε4 genotype and HIV and/or HCV status groups. Within each group, we tested the adjusted effect of each sociodemographic factor and APOE ε4 genotype on the MMSE scores. We tested the moderation effect of each sociodemographic factor on the relationship between APOE ε4 genotype and MMSE score, respectively, by testing the interaction effect between each sociodemographic factor and APOE ε4 genotype. Multiple linear regression models were used for the analyses while controlling age, sex, race, and Hispanic ethnicity. SAS 9.4 was used for all statistical analyses.

Results

Demographic composition of the sample

Of the 493 participants, 74 people were living with HIV/HCV, 174 were living with HIV, 78 were living with HCV, and 174 were living with neither HIV nor HCV. The average age was 55.54 (SD = 7.51) years, 55% of participants were men. Nearly 70% of the sample identified as Black/African American, 29% as White, and 2% as other races. The sample was mainly non-Hispanic (26.22% of participants identified as Hispanic/Latino). Nearly a third (32%) of the participants were APOE ε4 carriers. Almost half (49.3%) of the participants were on ART. Significant differences were found when comparing demographic composition variations between HIV and/or HCV statuses, including age, sex, race, educational attainment, antiretroviral medication, detectable HCV, and FIB-4 (Table 1). APOE e4 carriers and non-carriers differed significantly by racial group among PHIV and those with neither HCV nor HIV, as well as food insecurity among PHIV (Table 2).

Table 1.

Demographic composition of the sample.

	Overall sample	Infection status					APOE ε4 carrier
	Overall sample	HIV/HCV	HIV	HCV	No infection	p value	Yes	No	p value
N/%	493/100%	74/15.01%	171/34.69%	74/15.01%	174/35.29%		159/32.2%	334/67.75%
Sex						0.045			0.439
Male	272/55.17%	46/16.91%	92/33.82%	49/18.01%	85/31.25%		92/33.82%	180/66.18%
Female	221/44.83%	28/12.67%	79/35.75%	25/11.31%	89/40.27%		67/30.32%	154/69.68%
Race						0.023			0.001
White	141/28.66%	22/15.60%	38/26.95%	27/19.15%	54/38.30%		34/24.11%	107/75.89%
Black	341/69.31%	49/14.37%	126/36.95%	46/13.49%	120/35.19%		125/36.66%	216/63.34%
Other races	10/2.03%	2/20%	7/70%	1/10%	0/0%		0/0%	10/100%
Ethnicity						0.150			0.048
Hispanic/Latino	129/26.22%	23/17.83%	37/28.68%	16/12.40%	53/41.09%		32/24.81%	97/75.19%
Non-hispanic/Latino	363/73.78%	51/14.05%	134/36.91%	58/15.98%	120/33.06%		126/34.71%	237/65.29%
Antiretroviral therapy						0.001			0.096
Yes	243/49.3%	72/29.7%	169/69.5%	0/0%	2/0.8%		87/35.8%	156/64.2%
No	250/50.7%	2/0.8%	2/0.8%	74/29.6%	172/68.8%		72/28.8%	178/71.2%
Education						0.001			0.359
Lower than high school	193/39.15%	33/17.10%	74/38.34%	24/12.44%	62/32.12%		56/29.02%	137/70.98%
High school graduate	114/23.12%	12/10.53%	39/34.21%	16/14.04%	47/41.23%		39/34.21%	75/65.79%
GED or equivalent	35/7.10%	10/28.57%	12/34.29%	10/28.57%	3/8.57%		16/45.71%	19/54.29%
Some college, No degree	83/16.84%	13/15.66%	28/33.73%	16/19.28%	26/31.33%		28/33.73%	55/66.27%
Associate degree or higher	68/13.79%	6/8.82%	18/26.47%	8/11.76%	36/52.94%		20/29.41%	48/70.59%
Employment						<.0001			0.963
Working now	57/11.56%	6/10.53%	17/29.82%	8/14.04%	26/45.61%		18/3.65%	39/68.42%
Looking for work, unemployed	102/20.69%	13/12.75%	21/20.59%	17/16.67%	51/50%		34/33.33%	68/66.67%
Retired	47/9.53%	10/21.28%	6/12.77%	12/25.53%	19/40.43%		13/27.66%	34/72.34%
Disabled, permanently or temporarily	267/54.16%	44/16.48%	118/44.19%	32/11.99%	73/27.34%		87/32.58%	180/67.42%
Other (specify)	20/4.06%	1/5%	9/45%	5/25%	5/25%		7/35%	13/65%
Food insecurity						0.613			0.156
Food secure	354/71.81%	57/16.10%	126/35.59%	53/14.97%	118/33.33%		1151/32.49%	239/67.51%
Food insecure without hunger	70/14.20%	12/17.14%	18/25.71%	10/14.29%	30/42.86%		21/30.00%	49/70.00%
Food insecure without hunger, moderate	48/9.74%	3/6.25%	19/39.58%	8/16.67%	18/37.50%		12/25.00%	36/75.00%
Food insecure without hunger, severe	21/4.26%	2/9.52%	8/38.10%	3/14.29%	8/38.10%		11/52.38%	10/47.62%
HCV detected*						0.183*			0.214
>=15 IU/L detectable	66/13.20%	27/42.19%	0/0%	37/57.81%	0/0%		23/35.94%	41/64.06%
<15 IU/L detected	2/0.40%	1/50%	0/0%	1/50%	0/0%		0/0%	2/100%
<15 IU/L not detected or cleared	84/16.80%	44/53.66%	2/2.44%	36/43.90%	0/0%		33/40.24%	49/59.76%
Non-reactive or negative	348/69.60%	2/0.58%	169/48.99%	0/0%	174/50.43%		103/29.86%	242/70.14%
Mean/SD
Age (years)	55.54/7.51	54.60/9.21	54.20/7.27	57.93/7.83	56.23/6.47	0.001	55.39/7.89	55.61/7.33	0.759
Socioeconomic status
Family income ($)	13400.00/18535.04	10798.12/6207.27	14073.55/21604.75	13684.61/21735.03	13707.46/17266.42	0.658	13940.41/17187.66	13147.12/19154.43	0.670
Family size	1.82/1.29	1.63/1.25	1.88/1.44	1.72/0.91	1.89/1.28	0.395	1.94/1.39	1.77/1.23	0.155
CD4 cell count** (cells/μL)	605.60/364.64	589.05/313.12	611.68/388.95	N/A	N/A	0.661	592.48/409.55	611.55/343.66	0.700
FIB-4	1.40/1.23	1.98/1.94	1.29/1.12	1.74/1.14	1.06/0.66	<0.0001	1.53/1.64	1.31/0.92	0.059
The mini-mental state examination	27.26/3.10	27.34/2.59	27.29/2.93	26.97/4.44	27.31/2.77	0.863	27.36/3.94	27.21/2.95	0.636

# of missing cases: 5 cases for age; 1 case for race and ethnicity; 35 cases for family income; 2 cases for family size and food security; 2 cases for FIB-4.

**Only available for participants living with HIV. Bold indicates statistical significance.

Table 2.

Bivariate analyses by HIV/HCV infection status.

	HIV/HCV			HIV			HCV			No infection
	APOE ε4	No ε4	p	APOE ε4	No ε4	p	APOE ε4	No ε4	p	APOE ε4	No ε4	p
N/%	30/40.54%	44/59.46%		56/32.75%	115/67.25%		26/35.14%	48/64.86%		47/27.01%	127/72.99%
Sex			0.509			0.307			0.911			0.085
Race			0.468			0.045			0.648			0.039
White	8/36.36%	14/63.64%		9/23.68%	29/76.32%		8/29.63%	19/70.37%		9/16.67%	45/83.33%
Black	22/44.90%	27/55.10%		47/37.30%	79/62.70%		18/39.13%	28/60.87%		38/31.67%	82/68.33%
Other races	0/0%	2/100%		0/0%	7/100%		0/0%	1/100%		0/0%	0/0%
Ethnicity			0.498			0.402			0.713			0.057
Education			0.606			0.890			0.452			0.281
Employment			0.954			0.350			0.481			0.532
Food insecurity			0.494			0.022			0.473			0.212
Food secure	23/40.35%	34/59.65%		45/35.71%	81/64.29%		17/32.08%	36/67.92%		30/25.42%	88/74.58%
Food insecure without hunger	6/50.003%	6/50.00%		6/33.33%	12/66.67%		3/30.00%	7/70.00%		6/20.00%	24/80.00%
Food insecure without hunger, moderate	0/09%	3/100%		2/5.26%	18/94.74%		4/50.00%	5/50.00%		7/38.89%	11/61.11%
Food insecure without hunger, severe	1/50.00%	1/50.00%		4/50%	4/50%		2/66.67%	1/33.33%		4/50%	4/50%
HCV detected
>=15 detectable	12/44.44%	15/55.56%	0.943	0/0%	0/0%	0.549	11/29.73%	26/70.27%	0.568	N/A	N/A	N/A
<15 detected	0/0%	1/100%		0/0%	0/0%		0/0%	1/100%		N/A	N/A
<15 not detected or cleared	17/38.64%	27/61.36%		1/50%	1/50%		15/41.67%	21/58.33%		N/A	N/A
Non-reactive or negative	1/50.00%	1/50.00%		55/32.54%	114/67.46%		0/0%	0/0%		N/A	N/A
Mean/SD
Age (years)	56.77/10.22	53.09/8.21	0.094	53.27/8.31	54.66/6.69	0.241	58.08/5.50	57.85/8.86	0.894	55.60/6.11	56.46/6.60	0.436
Socioeconomic status
Family income ($)	13729.69/10984.08	14234.43/25124.56	0.860	19769.77/31247.56	10365.43/13486.16	0.171	11332.07/6038.42	10446.50/6365.39	0.569	12605.59/16924.88	14118.32/17445.41	0.621
Family size	2.05/1.47	1.80/1.43	0.282	1.85/1.05	1.65/.84	0.372	1.76/1.38	1.55/1.17	0.481	1.98/1.50	1.86/1.19	0.618
MMSE	27.17/2.52	27.45/2.65	0.642	27.18/2.91	27.35/2.95	0.724	27.58/5.80	26.645/3.51	0.460	27.58/6.29	27.21/2.82	0.446

# of missing cases: 5 cases for age; 1 case for race and ethnicity; 35 cases for family income; 2 cases for family size and food security; 2 cases for FIB-4.

Bold indicates statistical significance.

Results for the moderation effect of HIV/HCV infection status

APOE ε4 carriers living with HCV exhibited higher adjusted mean MMSE scores than non-ε4 carriers (28.84 vs 26.75, p = .003; Figure 1). Living with HCV moderated the association between APOE ε4 genotype and MMSE scores (p = .046) after adjusting for demographic covariates (see Table 3).

Figure 1.

Moderation effect of HIV/HCV infection status on the relationship between APOE e4 genotype and scores on the MMSE.

Table 3.

Moderation effects of HIV/HCV infection status on the relationship between APOE ε4 genotype and the score of the Mini-Mental State Examination.^a

	Mini-mental state examination
	β	SE	p value
Infection status			0.740
HIV/HCV	0.024	0.511	0.962
HIV	0.036	0.377	0.923
HCV	−0.663	0.490	0.177
No HIV/HCV	Ref
APOE ε4 carrier			0.079
Yes	0.301	0.492	0.540
No	Ref
Infection Status*APOE ε4 carrier			0.046
HIV/HCV*ε4	−0.385	0.839	0.646
HIV*ε4	−0.506	0.678	0.456
HCV*ε4	1.788	0.853	0.037

^aAdjusted by age, sex, race, and Hispanic ethnicity. β = Beta, SE = Standard Error. Bold indicates statistical significance.

The effect of socioeconomic factors and apolipoprotein ε genotype on the score of MMSE

APOE ε4 carriers living with HCV scored higher on the MMSE than non-ε4 carriers when adjusted by demographic covariates (β = 1.950, SE = 0.904, p = .036). Education (p = .0003) was found to be associated with MMSE scores for PHIV after adjustments. PHIV who graduated high school (β = 1.617, SE = 0.513, p = .002), obtained a GED or the equivalent (β = 1.711, SE = 0.760, p = .026), attended some college but did not earn a degree (β = 2.226, SE = 0.550, p < 0.0001), or earned an associate degree or higher (β = 1.638, SE = 0.679, p = .017), obtained higher scores than participants with lower than high school educations. For individuals living with neither HIV nor HCV, participants who earned an associate degree or higher (β = 1.433, SE = 0.712, p = .0463) scored higher on the MMSE than the participants with lower than a high school education. Food insecurity (p = .001) was also found to be associated with MMSE scores for PHIV after adjustments. PHIV with severe food insecurity (β = −3.875, SE = 0.949, p < .0001) scored lower on the MMSE than PHIV with food security. Participants living with neither HIV nor HCV who reported moderate food insecurity (β = 1.611, SE = 0.777, p = .040) scored higher on the MMSE than their food-secure counterparts (Table 4).

Table 4.

Main effects of socioeconomic factors and apolipoprotein E genotype on the score of the Mini-Mental State Examination.a

	HIV/HCV			HIV			HCV			No infection
	β	SE	p	β	SE	p	β	SE	p	β	SE	p
APOE ε4 carrier			0.779			0.893			0.036			0.826
Yes	0.213	0.755	0.779	0.058	0.432	0.983	1.950	0.904	0.036	0.116	0.530	0.826
No	Ref
Family income	0.0001	0.0001	0.203	<0.0001	<0.0001	0.105	<-0.0001	<0.0001	0.848	<0.0001	<0.0001	0.701
Family size	−0.411	0.304	0.184	−0.239	0.137	0.082	0.016	0.557	0.977	−0.245	0.208	0.242
Education			0.302			0.0003			0.544			0.130
Lower than high school	Ref
High school graduate	1.210	1.059	0.259	1.617	0.513	0.002	−1.961	1.310	0.141	−0.116	0.602	0.848
GED or equivalent	1.984	1.188	0.102	1.711	0.760	0.026	−0.784	1.342	0.562	1.549	1.672	0.356
Some college, No degree	2.350	1.323	0.083	2.226	0.550	<0.0001	0.146	1.230	0.906	1.274	0.751	0.092
Associate degree or higher	2.544	1.696	0.141	1.638	0.679	0.017	0.165	1.710	0.923	1.433	0.712	0.046
Employment			0.543			0.175			0.571			0.692
Food insecurity			0.341			0.001			0.469			0.128
Food secure	Ref
Food insecure without hunger	−1.714	0.953	0.079	−0.366	0.641	0.569	−1.806	1.245	0.154	0.938	0.632	0.140
Food insecure without hunger, moderate	0.279	1.644	0.866	−0.835	0.619	0.179	−1.076	1.424	0.454	1.611	0.777	0.040
Food insecure without hunger, severe	−0.904	2.273	0.693	−3.875	0.949	<0.0001	0.181	2.462	0.942	−0.037	1.056	0.972

^aAdjusted by age, sex, race, and Hispanic ethnicity. β = Beta, SE = Standard Error. Bold indicates statistical significance.

Food insecurity moderated the relationship between APOE ε4 genotype and MMSE scores among PHIV (p = .047). More specifically, compared to PHIV and food security, moderate food insecurity (β= −5.762, SE = 2.842, p = .044) increased the negative gap between APOE ε4 carriers and non-carriers on MMSE scores (Table 5).

Table 5.

Moderation effect of socioeconomic factors on the correlation between apolipoprotein E genotype and the score of the Mini-Mental State Examination.a

	HIV/HCV			HIV			HCV			No infection
	β	SE	p	β	SE	p	β	SE	p	β	SE	p
Family income
Family income	0.0001	0.0001	0.160	<0.0001	<0.0001	0.127	<0.0001	0.0001	0.721	<0.0001	<0.0001	0.251
APOE ε4	1.506	1.464	0.308	−0.015	0.689	0.983	2.115	1.021	0.043	0.585	0.666	0.381
Family Income*APOE ε4	−0.0001	0.0001	0.315	<0.0001	<0.0001	0.773	<0.0001	0.0001	0.991	<0.0001	<0.0001	0.556
Family size
Family size	0.180	0.358	0.616	−0.124	0.191	0.517	0.078	0.557	0.890	−0.094	0.223	0.675
APOE ε4	1.505	1.085	0.171	0.633	0.823	0.443	2.835	1.563	0.075	0.808	0.861	0.349
Family Size*APOE ε4	−0.870	0.528	0.104	−0.415	0.332	0.213	−0.403	0.784	0.610	−0.359	0.362	0.324
Education			0.257			0.001			0.851			0.090
APOE ε4	1.437	0.968	0.143	−0.083	0.720	0.909	1.055	1.404	0.456	0.893	0.902	0.324
Education Level*APOE ε4			0.314			0.718			0.799			0.625
Employment						0.179			0.417			0.140
APOE ε4				−0.329	0.581	0.573	2.836	1.145	0.016	0.614	0.741	0.408
Employment Status*APOE ε4						0.852			0.808			0.170
Food insecurity						<0.0001			0.802			0.106
Food secure	Ref
Food insecure without hunger	NA			−0.946	0.848	0.267	−2.072	1.290	0.114	1.160	0.673	0.087
Food insecure without hunger, moderate				−0.983	0.718	0.173	−2.324	1.650	0.164	1.944	0.940	0.040
Food insecure without hunger, severe				−5.818	1.408	<0.0001	−2.210	3.183	0.490	1.134	1.424	0.427
APOE ε4				−0.418	0.522	0.425	1.376	0.905	0.134	0.349	0.614	0.571
Food Insecurity*APOE ε4						0.047			0.468			0.618
Food Secure*APOE ε4	Ref
Food insecure without hunger *APOE ε4	NA			1.891	1.454	0.195	2.177	2.282	0.344	−0.699	1.515	0.645
Food insecure without hunger, moderate *APOE ε4				−5.762	2.842	0.044	3.037	2.328	0.197	−0.283	1.556	0.856
Food insecure without hunger, Severe*APOE ε4				2.871	2.010	0.155	2.899	4.509	0.523	−2.697	2.063	0.193

^aAdjusted by age, sex, race, and Hispanic ethnicity. β = Beta, SE = Standard Error.

NA- Not available due to the small sample size. Bold indicates statistical significance.

Discussion

This study sought to assess whether sociodemographic factors moderated associations between APOE ε4 carrier status and MMSE scores across four HIV/HCV status groups in a diverse, urban, middle-aged, and older adult sample. We found that income, education, food insecurity, and employment differentially influenced correlations between APOE ε4 carrier status and MMSE scores across HIV/HCV status groups. Our initial investigation of relationships between ε4 carrier status and cognitive performance revealed null results in the overall sample despite literature identifying a greater likelihood of mild deficits in cognitive performance, including spatial attention,⁴⁶ episodic memory, working memory,^47–51 global cognitive functioning,^52,53 and executive function¹⁸ in middle-aged and older ε4 carriers. Findings linking ε4 status and late-life cognitive performance have not been consistent, however.⁵⁴ The validated culturally-modified MMSE^55,56 used in this study may have uncovered a relationship between APOE ε4 carrier status and cognition in the ethnically diverse study sample.

Like Wozniak et al. (2016),²² we found that the ε4 genotype appeared to protect cognitive performance for PHCV who were ε4 carriers. This subsample produced the highest mean MMSE scores, adjusted by age, sex, race, and Hispanic ethnicity. Wozniak et al. (2016)²² found that working memory and attention were less altered among ε4 carriers compared to non-APOE ε4 carriers, though attention outcomes did not remain statistically significant after Bonferroni adjustment. Citing Chang et al.⁵⁷ they hypothesized that APOE ε4 carriers living with HCV demonstrated less cognitive impairment due to the role of APOE ε protein in HCV virion infectivity and production. Existing data indicates that APOE ε protein levels correlate with the lipoviral protein ratio found in HCV infection, and because apoE protein levels are lowest in APOE ε4 carriers, the APOE ε4 genotype may affect the ability of HCV to bind to cell surface receptors.^22,57 This process also influences blood-brain barrier permeability, thus ε4-carriers may have lower virion levels overall and experience less damage to brain tissues as fewer virions are present and active in their central nervous system.²²

Among PHIV, educational attainment was strongly associated with MMSE scores, even after adjustments. Compared to those with less than a high school education, participants at all educational levels had higher MMSE scores. There is considerable evidence supporting the association between lower levels of education and cognitive decline.^58–60 Researchers have sought to distinguish which cognitive domains, as reported by the MMSE, decline amid lower education levels. Higher scores on concentration, orientation, attention, and language have been associated with greater levels of education.^61,62 Low educational attainment has been investigated as a potential risk factor for the development of dementia,^63–65 but the literature provides little information on the relationship between educational attainment, HIV infection, and cognitive functioning. However, a recent study that examined risk factors associated with asymptomatic neurocognitive impairment in an Indian cohort found that low-education (<10 years) women living with HIV and one or more cardiovascular risk factors exhibited impaired-level functioning in two or more cognitive domains.⁶⁶

In this study, food insecurity (without hunger) was also associated with lower scores on the MMSE in PHIV and those living with neither HIV nor HCV when adjusted by study covariates. In previous research, food insecurity has been associated with poorer cognitive performance. In their systematic review examining the influence of food insecurity on cognitive functioning across the lifespan, Royer and colleagues⁶⁷ report that 88% of studies documented statistically significant inverse associations, most often in global cognition and executive function. Several studies have identified poorer cognitive outcomes in food-insecure adults living with HIV but not consistently.⁶⁸ Tamargo and colleagues⁴⁰ analysis of MASH data (n = 394) found that at baseline, cognitive impairment (≤24 on the MMSE) was considerably more likely in very low food-secure seropositive participants (OR: 3.23; 95% CI: 1.08, 9.65), while food insecurity, frequency of food insecurity, and persistent food insecurity correlated with MMSE score at 2-year follow-up. A domain-specific investigation of the Women’s Interagency HIV study data (n = 1324; ages 29–81 years; 100% female) documented differences in executive functioning and processing speed among food insecure participants in their predominantly Black sample after adjusting for covariates, substance use, health conditions, depression, and PTSD.⁶⁹

In contrast to our results, an analysis of a smaller sample of U.S. adults (n = 97) found that only those participants living with HIV exhibited differences in cognitive function. Food insecurity interacted with HIV status and produced lower scores in motor function, learning, memory, information processing, and global cognition.³⁴ Tan et al.⁶⁹ found similar results: HIV status moderated associations between food insecurity and motor function, learning, and memory. The current study identified a moderation effect of food insecurity (overall, moderate, and without hunger) on APOE ε4 status and global cognitive functioning in PHIV. This result is the first of its kind and suggests that a negative relation between APOE ε4 and cognitive performance in seropositive adults identified elsewhere,⁷⁰ may be strengthened by food insecurity.

Researchers have linked food insecurity to cognitive decline in the general population via stress and poor dietary habits.^71,72 In high-income countries, food insecurity has been linked to a higher body mass index (BMI).^73,74 Overweight individuals have demonstrated worse spatial abilities, semantic memory, and episodic memory.⁷⁵ Food insecurity is a prominent issue among PHIV^76–78 and poses serious health risks as malnutrition, weight loss, and an inadequate diet can accelerate disease progression.^79,80 Food insecurity may intensify HIV-related challenges to neuronal health as gastrointestinal symptoms reduce appetite and impair nutrient absorption critical for healthy organ functioning, including brain function.³⁴ Food insecurity has also predicted ART non-adherence, which worsens HAND symptoms,⁸¹ but further research is necessary to determine whether and to what degree this reflects systemic factors that intertwine with food insecurity (e.g., low income, loss of employment) as well as interpersonal (depression, stress) or intrapersonal factors such as lack of social support/positive health influence.⁸²

The use of the MASH data (n = 493) in this study allowed for the well-powered examination of multiple potential relations between sociodemographic factors, APOE ε4 carrier status, and important cognitive and psychosocial outcomes across four HIV/HCV status groups. Widely used, validated measures (e.g., MMSE) were incorporated, which bolsters confidence in the results.⁸³ Further, the sample in this study was predominantly Black/African American, a minoritized group that experiences considerable socioeconomic disparities in the U.S. and is underrepresented in public health research and overrepresented in HIV¹⁰ and HCV⁸⁴ prevalence rates.

Although this study has notable strengths, several limitations merit consideration. Data were extracted from the MASH cohort and used in a secondary data analysis, which did not allow additional or alternate measures to be included. While the MMSE has been recognized as the foremost valid and utilized neurocognitive impairment screening tool across various Hispanic ethnic groups⁸⁵ (which constitute roughly two-thirds (69.1%) of Miami-Dade County residents⁸⁶), the racial/ethnic makeup of the study sample was predominantly Black/African American. Future studies with majority Black/African American samples should further consider social and cultural factors impacting neurocognitive performance and scoring on the MMSE^87,88 or other commonly used cognitive screeners, such as the Montreal Cognitive Assessment (MoCA).^89,90 In addition, the heterogeneity of study populations of African ancestry and Hispanic ethnicity should be taken into consideration in future study designs due to the diversity of genetic pathways and loci involved in the pathogenesis of AD. Healthcare access and cultural norms regarding accessing care are other variables that have been highlighted as potential factors in the severity and age at AD onset,^56,91 however, these factors were not assessed in this study. The cross-sectional design of this study also prevented an evaluation of change across and at different points in time, which is relevant, as Black/African American and Hispanic/Latino PHIV are more likely to report heightened apprehensions about public attitudes upon disclosure of status and internalized stigma than non-Hispanic White individuals, which can lead to increased barriers in long-term physical and mental health outcomes.⁹²

Overall, our findings strengthen support for the development of research investigating the influence of various socioeconomic factors on relations between APOE ε4 genotype and cognitive functioning. This study expanded upon existing data, shedding light on the factors influencing the relationships between the APOE ε4 genotype and cognition within stigmatized minority populations. The study notably identified food insecurity as a moderating factor in the link between APOE ε4 genotype and cognitive scores. Subsequent research should consider examining how the frequency and duration of interactions between APOE genotype and sociodemographic factors influence cognitive functioning. A comprehensive grasp of the influence of sociodemographic variables is crucial to guide the development of targeted and impactful policies and interventions aimed at achieving equitable health outcomes in historically marginalized and stigmatized populations in the U.S.

Footnotes

Author contributions

SLB, AG, T.L., SSM, EJ, and DRJ participated in writing this manuscript. TL, SG, QL, NG, ZB were on the statistical analysis team. JH, ALC, ZB, and MKB are core members of the MASH team and were involved with primary data collection and managing biospecimens.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institutes of Health/National Institute of Minority Health and Health Disparities (3U54MD012393) and the National Institutes of Health/National Institute on Drug Abuse (U01DA040381).

Ethical statement

ORCID iDs

Shanna L Burke

Sabrina Sales Martinez

Daniel R Jimenez

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