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INTRODUCTION: Leveraging the nonmonolithic structure of Latin America, which represents a large variability in social determinants of health (SDoH) and high levels of genetic admixture, we aim to evaluate the relative contributions of SDoH and genetic ancestry in predicting dementia prevalence in Latin American populations. METHODS: Community-dwelling participants aged 65 and older (N = 3808) from Cuba, Dominican Republic, Mexico, and Peru completed the 10/66 protocol assessments. Dementia was diagnosed using the cross-culturally validated 10/66 algorithm. Multivariate linear regression models adjusted for SDoH were used in the main analysis. This study used cross-sectional data from the 1066 population-based study. RESULTS: Individuals with higher proportions of Native American (>70%) and African American (>70%) ancestry were more likely to exhibit factors contributing to worse SDoH, such as lower educational levels (p < 0.001), lower socioeconomic status (p < 0.001), and higher frequency of vascular risk factors (p < 0.001). After adjusting for measures of SDoH, there was no association between ancestry proportion and dementia probability, and ancestry proportions no longer significantly accounted for the variance in cognitive performance (African predominant p = 0.31 [-0.19, 0.59] and Native predominant p = 0.74 [-0.24, 0.33]). DISCUSSION: The findings suggest that social and environmental factors play a more crucial role than genetic ancestry in predicting dementia prevalence in Latin American populations. This underscores the need for public health strategies and policies that address these social determinants to effectively reduce dementia risk in these communities. HIGHLIGHTS: Countries in Latin America express a large variability in social determinants of health and levels of admixture. After adjustment for downstream societal factors linked to SDoH, genetic ancestry shows no link to dementia. Population ancestry profiles alone do not influence cognitive performance. SDoH are key drivers of racial disparities in dementia and cognitive performance.

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BACKGROUND: Population aging will lead to a dramatic increase in dementia prevalence, which will disproportionally affect racial minorities. The presence of racial differences in dementia prevalence has been widely reported in United States, but there are no relevant studies on this topic in low- and middle-income countries. METHODS: In a cross-sectional survey, 2944 older Cubans were recruited at a community-based level aimed to identify the effects of self-identified race and genetic admixture on cognitive performance. Dementia diagnosis was established using 10/66 Dementia and DSM-IV criteria. APOE-ε4 genotype was determined in 2511 (85%) and genetic admixture was completed for all dementia cases and in a randomly selected sample of cognitive healthy participants (218 dementia cases and 367 participants without dementia). RESULTS: The overall prevalence of dementia was 8.7%, without large or statistically significant differences on dementia prevalence (p = .12) by self-identified race. Mean cognitive scores were similar across racial groups (p = .46). After controlling for age, sex, and education, greater proportion of African ancestry was not associated with cognitive performance (p = .17). CONCLUSIONS: We found no evidence of an independent effect of self-identified race and/or population ancestry on dementia prevalence or cognitive performance. This suggests that observed differences in dementia prevalence among diverse populations may be driven primarily by social determinants of health.

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BACKGROUND: Rapid technological advances offer a possibility to develop cost-effective digital cognitive assessment tools. However, it is unclear whether these measures are suitable for application in populations from Low and middle-income countries (LMIC). OBJECTIVE: To examine the accuracy and validity of the Brain Health Assessment (BHA) in detecting cognitive impairment in a Cuban population. METHODS: In this cross-sectional study, 146 participants (cognitively healthy = 53, mild cognitive impairment (MCI) = 46, dementia = 47) were recruited at primary care and tertiary clinics. The main outcomes included: accuracy of the BHA and the Montreal Cognitive Assessment (MoCA) in discriminating between controls and cognitively impaired groups (MCI and dementia) and correlations between the BHA subtests of memory, executive functions, and visuospatial skills and criterion-standard paper-and-pencil tests in the same domains. RESULTS: The BHA had an AUC of 0.95 (95% CI: 0.91-0.98) in discriminating between controls and cognitively impaired groups (MCI and dementia, combined) with 0.91 sensitivity at 0.85 specificity. In discriminating between control and MCI groups only, the BHA tests had an AUC of 0.94 (95% CI: 0.90-0.99) with 0.71 sensitivity at 0.85 specificity. Performance was superior to the MoCA across all diagnostic groups. Concurrent and discriminant validity analyses showed moderate to strong correlations between the BHA tests and standard paper-and-pencil measures in the same domain and weak correlations with standard measures in unrelated domains. CONCLUSION: The BHA has excellent performance characteristics in detecting cognitive impairment including dementia and MCI in a Hispanic population in Cuba and outperformed the MoCA. These results support potential application of digital cognitive assessment for older adults in LMIC.

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OBJECTIVE: To evaluate the diagnostic performance of several biochemical predictors of insulin resistance (IR). DESIGN: A total of 90 nondiabetic subjects were tested with both the pancreatic suppression test (PST) and the oral glucose tolerance test (OGTT). Of them, 53 were non-insulin-resistant (NIR) subjects and the remaining 37 were insulin resistant subjects. RESULTS: All glucose and insulin values from the OGTT were positively correlated with the steady-state plasma glucose (SSPG) value of the PST. Among the OGTT values, basal insulin (I0) displayed a stronger correlation with SSPG (r = 0.604). Receiver operating characteristic analysis of the OGTT data demonstrated that I0 exhibited the highest area under the receiver operating characteristic curve (AUROC), compared with the rest of the OGTT data. However, the reduced sensitivity of this predictor precluded its clinical use.We then tested six potential predictors of IR derived from the OGTT values. Of them, the I0*G60 had a correlation coefficient of 0.697 with the SSPG and an AUROC of 0.867, surpassing the respective values of the traditional biochemical predictors of IR. Its cutoff predicting IR was >1110 mg/dL*µΙU/mL (>428 nM*pM), its sensitivity was 0.865, and its global accuracy was 0.822. We then selected the six best biochemical predictors of IR according to their posttest probability ratio. The order was as follows: I0*G60, ISI composite, AUC-Gl*In/', quantitative insulin sensitivity check index, homeostatic model assessment 1 (HOMA1), and HOMA2. CONCLUSION: We conclude that the I0*G60 is a promising, inexpensive, and easily calculable predictor of IR that outperforms the predictive power of the traditional predictors of IR, including the insulin sensitivity index composite.

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A cohort of 141 males (18-80 yo, 42.9 ± 12.9) strongly suspected of being Insulin Resistant (IR) was prospectively studied by determining their insulin sensitivity (Pancreatic Suppression Test, PST) and testicular function (total testosterone and SHBG). The subjects were labeled as IR when the Steady State Plasma Glucose (SSPG) was ≥150 mg/dL and Non-Insulin Resistant (NIR) when SSPG was <150 mg/dl; similarly, the subjects were labeled as Hypogonadal (HYPOG) when total testosterone was ≤3.0 ng/mL and Eugonadal (EUG) when total testosterone was >3.0 ng/mL. Two out of three subjects turned out to be IR, while around one in four subjects were HYPOG. Contingency analysis indicated a significant interdependence between insulin resistance and hypogonadism (chi-square was 4.69, p = 0.0303). Age (>43 yo) predicted hypogonadism (AUROC 0.606, p = 0.0308). Twice as many HYPOG subjects were IR as compared with EUG subjects. Also, HYPOG subjects exhibited higher SSPG values as compared with EUG subjects. Statistically, neither Weight nor BMI predicted hypogonadism, while Waist Circumference (>110 cm) was only a mediocre predictor (AUROC 0.640, p = 0.009). SSPG (>224 mg/dL) on the other hand, was the best predictor of hypogonadism (AUROC 0.709, p = 0.002), outperforming Waist Circumference (half of the subjects with an SSPG >224 mg/dL were HYPOG). Age did not predict insulin resistance, while Weight (>99 kg), BMI (>29), and especially, Waist Circumference (>99 cm, AUROC 0.812, p < 0.0001) were all predictors of insulin resistance. Almost 90% of the subjects with a waist circumference >99 cm was IR. As a logical consequence of the selection criteria (various clues suggesting insulin resistance), most subjects with normal weight in this cohort were IR (53.3%) while 20% were HYPOG. On the other hand, 13.6% of the obese subjects were NIR, and 2 out of 3 of them were both NIR and EUG. In conclusion, Waist Circumference predicted both insulin resistance (>99 cm) and hypogonadism (>110 cm), suggesting that the first hit of abdominal obesity is insulin resistance and the second hit is male hypogonadism. Normal weight did not protect from IR, while a relevant proportion of obese subjects were NIR (with 2/3 being also EUG).

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BACKGROUND: Polycystic ovary syndrome (PCOS) is non-uniformly associated with insulin resistance (IR). We examined IR in women with PCOS. METHODS: Sixty-nine PCOS women were subjected to the insulin suppression test (IST) to determine their steady-state plasma glucose (SSPG) as a direct measure of insulin sensitivity. RESULTS: SSPG exhibited a multimodal distribution suggesting the existence of subpopulations. The heterogeneous distribution of plasma glucose at 180 min (P = 0.011), with three modes, suggested differences in the plasma glucose level trajectories during the IST. Hence, the population was separated into three groups: (i) (n = 33), subjects with SSPG < or = 152.5 mg/dl, corresponding to the first to fifth deciles; (ii) (n = 29), subjects in the interval 152.5 mg/dl < SSPG < or = 300 mg/dl; (iii) (n = 7), subjects with SSPG > 300 mg/dl, corresponding to the tenth decile. Plasma glucose distributions at 180 min showed differences in their mean values and ranges among groups (P < 0.0001). The trajectories of the groups differed significantly during the IST (P < 0.0001). CONCLUSIONS: insulin sensitivity in our patients exhibited a discontinuous distribution, implying that PCOS is a heterogeneous disorder possessing subpopulations regarding IR.

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