- Email: [email protected]
Determining Age Effects: The Unique Contributions Provided by Longitudinal Designs
EDITORIAL
Determining Age Effects: The Unique Contributions Provided by Longitudinal Designs Barnett S. Meyers, M.D.
I
nformation on the mental health effects of aging generated through cross-sectional studies is inherently limited. Single point in time comparisons of different age samples within a population cannot account for cohort effects or how risk factors may change over time and cannot, therefore, identify the effects of aging. Furthermore, clinical research that focuses on either mixed aged samples that exclude older persons or studies published from geriatric centers that exclude middle aged and young adults have are unable to directly assess aging effects.1 Longitudinal studies that investigate the effects of specific risk factors on psychopathology across a wide age range are best suited to investigate aging effects on relationships between specific predictors and outcomes. Importantly, longitudinal studies can identify associations but cannot demonstrate causation: The demonstration of associations between predictor factors and specific outcomes over time cannot test whether the factor present at baseline actually caused the event measured at a subsequent assessment. Old age increases the risk for Alzheimer disease (AD) but does not cause the disorder. Similarly, the impact of other AD predictors, whether heritable (e.g., APOE4) or those due to postnatal experiences (e.g., decreased education) can be expected to vary according to other life experiences and comorbid conditions in addition to aging; furthermore, genetic risks are moderated by epigenetic factors. Despite these caveats, the identification of predictors of the clinical states seen in
geriatric psychiatry and the factors that moderate disease expression can improve our understanding of the pathogenesis of specific disorders and generate hypotheses that can be tested through intervention studies designed to decrease the incidence and severity of geriatric disorders. In AD, pathological processes that occur over a long time period lead to the development of the clinical disorder. Snowdon et al.’s Nun Study2 demonstrated that impaired linguistic ability demonstrated in autobiographical material written in young adulthood predicted the development of autopsy-confirmed AD six decades later. The authors posited that low idea density is an early manifestation of underlying neuropathological changes that would be consistent with participants who had high idea density having greater brain reserve3 than those with low density who subsequently developed AD. Stern’s concept of cognitive reserve4,5 would argue that increased education and associated higher cognitive skills are indicative of greater resiliency that postpones the clinical expression of underlying neuropathology. The concept of cognitive reserve emphasizes enhanced adaptation to cognitive loss rather than viewing poorer cognitive ability in young adulthood as an early expression of neuropathology. The findings reported in this issue by Dekhytar et al.6 bear on the constructs of brain and cognitive reserve. The authors analyzed case registry data from the Uppsala Cohort Multigenerational Study to expand on and extend previous research7
Received May 28, 2015; accepted May 30, 2015. From New York Presbyterian Hospital - Weill Cornell Medical College, White Plains, NY. Send correspondence and reprint requests to Barnett S. Meyers, M.D., New York Presbyterian Hospital - Weill Cornell Medical College, White Plains, NY. e-mail: [email protected] Ó 2015 American Association for Geriatric Psychiatry http://dx.doi.org/10.1016/j.jagp.2015.05.013
Am J Geriatr Psychiatry 23:9, September 2015
881
Editorial demonstrating that childhood educational and work experiences affect the subsequent risk of dementia. They report that higher elementary school grades were associated with a lower the risk of late onset dementia. Although higher levels of schooling and job status added to the earlier protective effects of elementary school grades, the school grades were a stronger predictor of dementia risk; furthermore, the protective effects of work experience were specific: Employment involving complex work with data was protective but complex work with people or things did not affect dementia risk. The beneficial impact of early school performance is underscored by the fact that children in the lowest quintile of achieved grades who had highly data-complex occupations were not protected from dementia, whereas children with high grades were conferred protection regardless of their occupational complexity. Elementary school grades, the factor most distal from old age, was the strongest risk factor of those studied for incident dementia. Nevertheless, these data do not address the question of how genetic factors and the in utero environment affect subsequent educational achievement, perhaps by influencing the brain networks and neuroplasticity of the newborn. As suggested by Stern,5 prenatal factors may interact synergistically with educational and other postnatal life experiences to both protect against the clinical manifestations of later neuronal loss and to improve the individual’s ability to adapt to neuronal loss that does occur. Furthermore, the possibility that innate networks and capacity for neuroplasticity act synergistically with experience to enhance neuronal adaptive capacities merits consideration. Although the complex interactions between brain reserve driven by innate factors and life experiences that increase brain reserve to reduce the risk of AD need to be clarified, the data reported by Dekhytar and colleagues demonstrate that protective cognitive factors are identifiable during childhood. The authors note that reliance on a case registry methodology precluded the reliable differentiation of various types of dementia. Although they report a sensitivity of only 63% for their case registry approach for identifying dementia, the specificity of 98% indicates that cases were not misclassified. Also, missed cases would not cause a systematic bias that would weaken the significance of the authors’ findings. The authors could not reliably distinguish cases of AD from other types of dementia. Therefore, we
882
cannot know whether the effect of education would apply comparably across other different types of dementia. Diagnostic specificity is required to elucidate the mechanisms through which risk factors exert their effects. Snowden et al.8 use data from research participants at 34 AD centers to investigate the well-established crosssectional relationship between dementia and depressive syndromes.9 Baseline data demonstrated that the participants with both mild cognitive impairment (MCI) and dementia were 2.5 times more likely to meet both a clinical criterion for depression and exceed the depression cutoff on the short-form of the Geriatric Depression Scale10 than cognitively normal controls. Their longitudinal design allowed the authors to demonstrate that subjects with either MCI or dementia without depression at baseline were significantly more likely to meet depression criteria at follow-up than participants with intact cognition. The incidence of depression increased over time. By year four, 43% of participants with dementia without depression at baseline met study criteria for depression, compared with 38% of MCI participants and 20% of normal controls. As reviewed by Jorm,11 depression is a wellestablished risk factor for the subsequent development of dementia. The Snowden et al. findings demonstrate that the association between depression and cognitive impairment is not solely a consequence of the persistence of pre-existing depressive symptoms. Importantly, mechanisms underlying the depressionedementia relationship remain unclear. Evidence that cognitive impairment predicts incident depression cannot distinguish between depression being a consequence of the neuropathology of evolving dementia versus incident depression occurring as a psychological reaction to awareness of cognitive loss. Slavin et al.12 contribute valuable information comparing the informant’s memory and non-memory cognitive complaints to those of subjects. Using data from 620 non-demented elderly participants in in the Sydney Memory and Ageing Study, the authors found that informants’ ratings of cognitive symptoms were superior to participants’ self-ratings in predicting subsequent conversion from normal cognition to MCI or dementia 4 years later. The effect was small but statistically significant. The development and application of instruments to quantify informants’ observations as means of addressing the diminished reliability of subjective symptoms due to cognitive
Am J Geriatr Psychiatry 23:9, September 2015
Meyers loss is conceptually analogous to the use of the Cornell Scale for Depression in Dementia to quantify depression severity.13 Recruitment of non-impaired participants from a population based sample, the use of a continuous measures of both cognitive and non-cognitive symptoms, and determination of both neuropsychological and functional outcomes in addition to AD conversion rates strengthen the results. The demonstration of significant baseline correlations between informants’ ratings of both memory and non-memory symptoms and participants’ cognitive performance, which was not evident using participants’ ratings, indicates that informants’ ratings were more sensitive than those of participants. Also, informant assessments of participant memory predicted both global cognitive performance and general functioning among participants at follow-up but participant self-ratings did not, evidence that further supports the validity of informant observations. Interestingly, participants’ baseline ratings of their non-memory symptoms predicted poorer executive functioning, suggesting that subjects might be particularly sensitive to impairments in this nonmemory cognitive domain. Results demonstrated that informant reports of memory complaints significantly added to those of participants in predicting diagnostic conversion and cognitive and functional decline. Neither participant nor informant symptom scores predicted conversion from normal at baseline to either MCI or dementia at follow-up, perhaps because of the low frequency of these outcomes. Nevertheless, a model that included subjects who were normal or had MCI at baseline did predict conversion to dementia. As the authors note, these data add to a growing body of studies suggesting the validity of participants’ rating of memory and non-memory cognitive symptoms. Future research that incorporates subject and informant ratings of cognitive symptoms with both neuropsychological and neurobiological indices is needed to determine whether the sensitivity and specificity of predictors of incident neurocognitive disorders can be enhanced. The study by Cheung et al.14 advances our knowledge about aging effects on changes in Mini-Mental State Examination (MMSE) scores. The authors generated MMSE growth curves by longitudinally following a representative sample of 1,995 ethnic Chinese participants aged 55 to 84 years from
Am J Geriatr Psychiatry 23:9, September 2015
the Singapore Longitudinal Aging Study. Application of a mathematic model that incorporates each individuals’ educational background and age, previous modified MMSE score, and the interval between assessments was able to predict each individual’s next MMSE score. By excluding individuals with dementia or significant depressive symptoms, the study excluded individuals with known conditions that decrease cognition. The authors conceptualize the generated MMSE scores as “conditional” because they are based on each individual’s previous MMSE score and take educational level and the time between assessments into account. The generated MMSE growth curves provide conditional bandwidths of ranges or percentiles within which the next MMSE score for an individual is expected to fall. Thus, an individual whose subsequent MMSE score fell below the expected percentile range is considered to have a clinically suspicious score. The authors applied a quadratic approach to their analyses to better fit their data. Decreases in MMSE scores accelerated with increasing age, particularly between ages 75 and 84 years. Again, though, demonstrating this aging effect of accelerating decline does not explain why it occurs. It is possible that the increased incidence of evolving MCI and dementia at older ages lower the average MMSE score within the lower percentiles (e.g., the 5th) more than in the highest. Thus, the greater decline in average scores after age 80 years in the lower (e.g., 5th) than higher percentile bands (e.g., 75th) might be attributable to the inclusion of participants with poorer early performances on the modified MMSE because of evolving neurocognitive disorders in the low percentile groups. Also, MMSE scores in the lower percentile groups were more strongly affected by age and education (factors associated with dementia risk) than scores in the higher percentile groups. Additional data on high- and low-percentile subjects and diagnostic conversion rates within these groups would be informative, but an epidemiological study cannot provide specific clinical information. As the authors point out, the smaller numbers of subjects in the smallest percentile groups decreases the statistical power needed to further analyze data from these subgroups. Cohen and Ryu15 investigate the stability of the states of subsyndromal and syndromal depression in
883
Editorial a convenience sample of community residents with schizophrenia spectrum diagnoses. The authors subdivide their study sample of 104 individuals with chart diagnoses consistent with late-onset schizophrenia spectrum disorders into different depression categories using rating scale cutoffs on the Center for Epidemiological Studies Depression Scale16 (CES-D). Thus, the authors apply a scale originally developed for epidemiological use to investigate a smaller and less representative sample than those in the longitudinal studies discussed previously. The authors intend to build on previous work that demonstrated that significant depressive symptoms are prevalent in middle aged and older persons with schizophrenia and are associated with impairment in multiple domains of functioning.17 After grouping subjects into no depression (CES-D score <7), subsyndromal depression (CES-D scores 7e15), and syndromal (CES-D 16) groups, the authors determined the stability of membership in categories over a 4-year period and associated outcomes. The largest proportion of the subjects studied (44%) remained in the category that included syndromal and subsyndromal scores, followed by 30% who remained below the subsyndromal cutoff throughout the 4-year period of study. Therefore, measurable depressive symptoms
are frequently persistent in older persons with schizophrenia diagnoses, but a substantial minority of this population has minimal to no depression. The significance of these results for understanding the relationship between depression and late-life schizophrenia in the community is limited by the low average age of subjects (61 years), the relatively short follow-up period, and the use of chart diagnoses. These five studies demonstrate the valuable information that can be generated from longitudinal studies of older persons at risk for neurocognitive disorders, depression, and other aging-associated outcomes. Population-based studies that systematically assess baseline and outcome measures require government funding because of their enormous costs. Nevertheless, longitudinal studies that incorporate genetic factors, psychosocial experiences, and age measured as a continuous variable, are well-suited for improving our understanding of the effects of old age on clinically relevant outcomes. Results from these longitudinal studies can lead to intervention studies that will be designed to ameliorate the adverse effects of the risk factors associated with poor aging-related outcomes. The author has no disclosures to report.
References 1. Meyers BS, Jeste DV: Geriatric psychopharmacology: evolution of a discipline. J Clin Psychiatry 2010; 71:1416e1424 2. Snowdon DA, Kemper SJ, Mortimer JA, et al: Linguistic ability in early life and cognitive function in Alzheimer’s disease in late life. Findings from the Nun Study. JAMA 1996; 275:528e532 3. Katzman R, Terry R, DeTeresa R, et al: Clinical, pathological, and neurochemical changes in dementia: a subgroup with preserved mental status and numerous neocortical plaques. Ann Neurol 1988; 23:138e144 4. Stern Y, Gurland B, Tatemichi TK, et al: Influence and education and occupation on the incidence of Alzheimer’s disease. JAMA 1994; 271:1004e1010 5. Sterm Y: Cognitive reserve in aging and Alzheimer’s disease. Lancet Neurol 2012; 11:1006e1111 6. Dekhtyar S, Weng Hui-Xin, Scott K, et al: A life-course study of cognitive reserve in dementia. From childhood to old age. Am J Geriatr Psychiatry 2015; 23:885e896 7. Whalley LJ, Dick FD, Mcneill G: A life course approach to the aetiology of late-life dementias. Lancet Neurol 2006; 5:87e96 8. Snowden MB, Atkins DC, Steinman L, et al: Longitudinal association of dementia and depression. Am J Geriatr Psychiatry 2015; 23:897e905 9. Wragg RE, Jeste DV: Overview of depression and psychosis in Alzheimer’s disease. Am J Psychiatry 1989; 146:577e587 10. Sheikh JJ, Jesavage JA: Geriatric Depression Scale (GDS), in Recent evidence and development of a shorter version, in
884
11. 12.
13.
14.
15.
16.
17.
Clinical Gerontology: A Guide to Assessment and Intevention. Edited by Brink TL. New York, The Haworth Prss, Inc., 1986, pp 165e173 Jorm AF: History of depression as a risk factor for dementia: an updated review. Aust N Z J Psychiatry 2001; 35:776e781 Slavin MJ, Sachdev PS, Kochan NA, et al: Predicting cognitive, functional and diagnostic change over four years using baseline subjective cognitive complaints in the Sydney Memory and Ageing Study. Am J Geriatr Psychiatry 2015; 23:906e914 Alexopoulos GS, Abrams RC, Young RC, Shamoian CA: Cornell Scale for depression in dementia. Biol Psychiatry 1988; 23: 271e284 Cheung YB, Xu Y, Feng L, et al: Unconditional and conditional standards using cognitive function curves for the Modified MiniState Examination: Cross-sectional and longitudinal analyses in older Chinese in Singapore. Am J Geriatr Psychiatry 2015; 23:915e924 Cohen CI, Ryu HH: A longitudinal study of the outcome and associated factors of subsyndromal and syndromal depression in community-dwelling older adults with schizophrenia spectrum disorder. Am J Geriatr Psychiatry 2015; 23:925e933 Radloff LS: The CES-D Scale. A self-report depression scale for research in the general population. Appl Psychol Meas 1977; 3: 385e401 Zisook S, Montross L, Dasckow J, et al: Subsyndromal depressive symptoms in middle-aged and older persons with schizophrenia. Am J Geriatr Psychiatry 2007; 15:1005e1014
Am J Geriatr Psychiatry 23:9, September 2015
Determining Age Effects: The Unique Contributions Provided by Longitudinal Designs Barnett S. Meyers, M.D.
I
nformation on the mental health effects of aging generated through cross-sectional studies is inherently limited. Single point in time comparisons of different age samples within a population cannot account for cohort effects or how risk factors may change over time and cannot, therefore, identify the effects of aging. Furthermore, clinical research that focuses on either mixed aged samples that exclude older persons or studies published from geriatric centers that exclude middle aged and young adults have are unable to directly assess aging effects.1 Longitudinal studies that investigate the effects of specific risk factors on psychopathology across a wide age range are best suited to investigate aging effects on relationships between specific predictors and outcomes. Importantly, longitudinal studies can identify associations but cannot demonstrate causation: The demonstration of associations between predictor factors and specific outcomes over time cannot test whether the factor present at baseline actually caused the event measured at a subsequent assessment. Old age increases the risk for Alzheimer disease (AD) but does not cause the disorder. Similarly, the impact of other AD predictors, whether heritable (e.g., APOE4) or those due to postnatal experiences (e.g., decreased education) can be expected to vary according to other life experiences and comorbid conditions in addition to aging; furthermore, genetic risks are moderated by epigenetic factors. Despite these caveats, the identification of predictors of the clinical states seen in
geriatric psychiatry and the factors that moderate disease expression can improve our understanding of the pathogenesis of specific disorders and generate hypotheses that can be tested through intervention studies designed to decrease the incidence and severity of geriatric disorders. In AD, pathological processes that occur over a long time period lead to the development of the clinical disorder. Snowdon et al.’s Nun Study2 demonstrated that impaired linguistic ability demonstrated in autobiographical material written in young adulthood predicted the development of autopsy-confirmed AD six decades later. The authors posited that low idea density is an early manifestation of underlying neuropathological changes that would be consistent with participants who had high idea density having greater brain reserve3 than those with low density who subsequently developed AD. Stern’s concept of cognitive reserve4,5 would argue that increased education and associated higher cognitive skills are indicative of greater resiliency that postpones the clinical expression of underlying neuropathology. The concept of cognitive reserve emphasizes enhanced adaptation to cognitive loss rather than viewing poorer cognitive ability in young adulthood as an early expression of neuropathology. The findings reported in this issue by Dekhytar et al.6 bear on the constructs of brain and cognitive reserve. The authors analyzed case registry data from the Uppsala Cohort Multigenerational Study to expand on and extend previous research7
Received May 28, 2015; accepted May 30, 2015. From New York Presbyterian Hospital - Weill Cornell Medical College, White Plains, NY. Send correspondence and reprint requests to Barnett S. Meyers, M.D., New York Presbyterian Hospital - Weill Cornell Medical College, White Plains, NY. e-mail: [email protected] Ó 2015 American Association for Geriatric Psychiatry http://dx.doi.org/10.1016/j.jagp.2015.05.013
Am J Geriatr Psychiatry 23:9, September 2015
881
Editorial demonstrating that childhood educational and work experiences affect the subsequent risk of dementia. They report that higher elementary school grades were associated with a lower the risk of late onset dementia. Although higher levels of schooling and job status added to the earlier protective effects of elementary school grades, the school grades were a stronger predictor of dementia risk; furthermore, the protective effects of work experience were specific: Employment involving complex work with data was protective but complex work with people or things did not affect dementia risk. The beneficial impact of early school performance is underscored by the fact that children in the lowest quintile of achieved grades who had highly data-complex occupations were not protected from dementia, whereas children with high grades were conferred protection regardless of their occupational complexity. Elementary school grades, the factor most distal from old age, was the strongest risk factor of those studied for incident dementia. Nevertheless, these data do not address the question of how genetic factors and the in utero environment affect subsequent educational achievement, perhaps by influencing the brain networks and neuroplasticity of the newborn. As suggested by Stern,5 prenatal factors may interact synergistically with educational and other postnatal life experiences to both protect against the clinical manifestations of later neuronal loss and to improve the individual’s ability to adapt to neuronal loss that does occur. Furthermore, the possibility that innate networks and capacity for neuroplasticity act synergistically with experience to enhance neuronal adaptive capacities merits consideration. Although the complex interactions between brain reserve driven by innate factors and life experiences that increase brain reserve to reduce the risk of AD need to be clarified, the data reported by Dekhytar and colleagues demonstrate that protective cognitive factors are identifiable during childhood. The authors note that reliance on a case registry methodology precluded the reliable differentiation of various types of dementia. Although they report a sensitivity of only 63% for their case registry approach for identifying dementia, the specificity of 98% indicates that cases were not misclassified. Also, missed cases would not cause a systematic bias that would weaken the significance of the authors’ findings. The authors could not reliably distinguish cases of AD from other types of dementia. Therefore, we
882
cannot know whether the effect of education would apply comparably across other different types of dementia. Diagnostic specificity is required to elucidate the mechanisms through which risk factors exert their effects. Snowden et al.8 use data from research participants at 34 AD centers to investigate the well-established crosssectional relationship between dementia and depressive syndromes.9 Baseline data demonstrated that the participants with both mild cognitive impairment (MCI) and dementia were 2.5 times more likely to meet both a clinical criterion for depression and exceed the depression cutoff on the short-form of the Geriatric Depression Scale10 than cognitively normal controls. Their longitudinal design allowed the authors to demonstrate that subjects with either MCI or dementia without depression at baseline were significantly more likely to meet depression criteria at follow-up than participants with intact cognition. The incidence of depression increased over time. By year four, 43% of participants with dementia without depression at baseline met study criteria for depression, compared with 38% of MCI participants and 20% of normal controls. As reviewed by Jorm,11 depression is a wellestablished risk factor for the subsequent development of dementia. The Snowden et al. findings demonstrate that the association between depression and cognitive impairment is not solely a consequence of the persistence of pre-existing depressive symptoms. Importantly, mechanisms underlying the depressionedementia relationship remain unclear. Evidence that cognitive impairment predicts incident depression cannot distinguish between depression being a consequence of the neuropathology of evolving dementia versus incident depression occurring as a psychological reaction to awareness of cognitive loss. Slavin et al.12 contribute valuable information comparing the informant’s memory and non-memory cognitive complaints to those of subjects. Using data from 620 non-demented elderly participants in in the Sydney Memory and Ageing Study, the authors found that informants’ ratings of cognitive symptoms were superior to participants’ self-ratings in predicting subsequent conversion from normal cognition to MCI or dementia 4 years later. The effect was small but statistically significant. The development and application of instruments to quantify informants’ observations as means of addressing the diminished reliability of subjective symptoms due to cognitive
Am J Geriatr Psychiatry 23:9, September 2015
Meyers loss is conceptually analogous to the use of the Cornell Scale for Depression in Dementia to quantify depression severity.13 Recruitment of non-impaired participants from a population based sample, the use of a continuous measures of both cognitive and non-cognitive symptoms, and determination of both neuropsychological and functional outcomes in addition to AD conversion rates strengthen the results. The demonstration of significant baseline correlations between informants’ ratings of both memory and non-memory symptoms and participants’ cognitive performance, which was not evident using participants’ ratings, indicates that informants’ ratings were more sensitive than those of participants. Also, informant assessments of participant memory predicted both global cognitive performance and general functioning among participants at follow-up but participant self-ratings did not, evidence that further supports the validity of informant observations. Interestingly, participants’ baseline ratings of their non-memory symptoms predicted poorer executive functioning, suggesting that subjects might be particularly sensitive to impairments in this nonmemory cognitive domain. Results demonstrated that informant reports of memory complaints significantly added to those of participants in predicting diagnostic conversion and cognitive and functional decline. Neither participant nor informant symptom scores predicted conversion from normal at baseline to either MCI or dementia at follow-up, perhaps because of the low frequency of these outcomes. Nevertheless, a model that included subjects who were normal or had MCI at baseline did predict conversion to dementia. As the authors note, these data add to a growing body of studies suggesting the validity of participants’ rating of memory and non-memory cognitive symptoms. Future research that incorporates subject and informant ratings of cognitive symptoms with both neuropsychological and neurobiological indices is needed to determine whether the sensitivity and specificity of predictors of incident neurocognitive disorders can be enhanced. The study by Cheung et al.14 advances our knowledge about aging effects on changes in Mini-Mental State Examination (MMSE) scores. The authors generated MMSE growth curves by longitudinally following a representative sample of 1,995 ethnic Chinese participants aged 55 to 84 years from
Am J Geriatr Psychiatry 23:9, September 2015
the Singapore Longitudinal Aging Study. Application of a mathematic model that incorporates each individuals’ educational background and age, previous modified MMSE score, and the interval between assessments was able to predict each individual’s next MMSE score. By excluding individuals with dementia or significant depressive symptoms, the study excluded individuals with known conditions that decrease cognition. The authors conceptualize the generated MMSE scores as “conditional” because they are based on each individual’s previous MMSE score and take educational level and the time between assessments into account. The generated MMSE growth curves provide conditional bandwidths of ranges or percentiles within which the next MMSE score for an individual is expected to fall. Thus, an individual whose subsequent MMSE score fell below the expected percentile range is considered to have a clinically suspicious score. The authors applied a quadratic approach to their analyses to better fit their data. Decreases in MMSE scores accelerated with increasing age, particularly between ages 75 and 84 years. Again, though, demonstrating this aging effect of accelerating decline does not explain why it occurs. It is possible that the increased incidence of evolving MCI and dementia at older ages lower the average MMSE score within the lower percentiles (e.g., the 5th) more than in the highest. Thus, the greater decline in average scores after age 80 years in the lower (e.g., 5th) than higher percentile bands (e.g., 75th) might be attributable to the inclusion of participants with poorer early performances on the modified MMSE because of evolving neurocognitive disorders in the low percentile groups. Also, MMSE scores in the lower percentile groups were more strongly affected by age and education (factors associated with dementia risk) than scores in the higher percentile groups. Additional data on high- and low-percentile subjects and diagnostic conversion rates within these groups would be informative, but an epidemiological study cannot provide specific clinical information. As the authors point out, the smaller numbers of subjects in the smallest percentile groups decreases the statistical power needed to further analyze data from these subgroups. Cohen and Ryu15 investigate the stability of the states of subsyndromal and syndromal depression in
883
Editorial a convenience sample of community residents with schizophrenia spectrum diagnoses. The authors subdivide their study sample of 104 individuals with chart diagnoses consistent with late-onset schizophrenia spectrum disorders into different depression categories using rating scale cutoffs on the Center for Epidemiological Studies Depression Scale16 (CES-D). Thus, the authors apply a scale originally developed for epidemiological use to investigate a smaller and less representative sample than those in the longitudinal studies discussed previously. The authors intend to build on previous work that demonstrated that significant depressive symptoms are prevalent in middle aged and older persons with schizophrenia and are associated with impairment in multiple domains of functioning.17 After grouping subjects into no depression (CES-D score <7), subsyndromal depression (CES-D scores 7e15), and syndromal (CES-D 16) groups, the authors determined the stability of membership in categories over a 4-year period and associated outcomes. The largest proportion of the subjects studied (44%) remained in the category that included syndromal and subsyndromal scores, followed by 30% who remained below the subsyndromal cutoff throughout the 4-year period of study. Therefore, measurable depressive symptoms
are frequently persistent in older persons with schizophrenia diagnoses, but a substantial minority of this population has minimal to no depression. The significance of these results for understanding the relationship between depression and late-life schizophrenia in the community is limited by the low average age of subjects (61 years), the relatively short follow-up period, and the use of chart diagnoses. These five studies demonstrate the valuable information that can be generated from longitudinal studies of older persons at risk for neurocognitive disorders, depression, and other aging-associated outcomes. Population-based studies that systematically assess baseline and outcome measures require government funding because of their enormous costs. Nevertheless, longitudinal studies that incorporate genetic factors, psychosocial experiences, and age measured as a continuous variable, are well-suited for improving our understanding of the effects of old age on clinically relevant outcomes. Results from these longitudinal studies can lead to intervention studies that will be designed to ameliorate the adverse effects of the risk factors associated with poor aging-related outcomes. The author has no disclosures to report.
References 1. Meyers BS, Jeste DV: Geriatric psychopharmacology: evolution of a discipline. J Clin Psychiatry 2010; 71:1416e1424 2. Snowdon DA, Kemper SJ, Mortimer JA, et al: Linguistic ability in early life and cognitive function in Alzheimer’s disease in late life. Findings from the Nun Study. JAMA 1996; 275:528e532 3. Katzman R, Terry R, DeTeresa R, et al: Clinical, pathological, and neurochemical changes in dementia: a subgroup with preserved mental status and numerous neocortical plaques. Ann Neurol 1988; 23:138e144 4. Stern Y, Gurland B, Tatemichi TK, et al: Influence and education and occupation on the incidence of Alzheimer’s disease. JAMA 1994; 271:1004e1010 5. Sterm Y: Cognitive reserve in aging and Alzheimer’s disease. Lancet Neurol 2012; 11:1006e1111 6. Dekhtyar S, Weng Hui-Xin, Scott K, et al: A life-course study of cognitive reserve in dementia. From childhood to old age. Am J Geriatr Psychiatry 2015; 23:885e896 7. Whalley LJ, Dick FD, Mcneill G: A life course approach to the aetiology of late-life dementias. Lancet Neurol 2006; 5:87e96 8. Snowden MB, Atkins DC, Steinman L, et al: Longitudinal association of dementia and depression. Am J Geriatr Psychiatry 2015; 23:897e905 9. Wragg RE, Jeste DV: Overview of depression and psychosis in Alzheimer’s disease. Am J Psychiatry 1989; 146:577e587 10. Sheikh JJ, Jesavage JA: Geriatric Depression Scale (GDS), in Recent evidence and development of a shorter version, in
884
11. 12.
13.
14.
15.
16.
17.
Clinical Gerontology: A Guide to Assessment and Intevention. Edited by Brink TL. New York, The Haworth Prss, Inc., 1986, pp 165e173 Jorm AF: History of depression as a risk factor for dementia: an updated review. Aust N Z J Psychiatry 2001; 35:776e781 Slavin MJ, Sachdev PS, Kochan NA, et al: Predicting cognitive, functional and diagnostic change over four years using baseline subjective cognitive complaints in the Sydney Memory and Ageing Study. Am J Geriatr Psychiatry 2015; 23:906e914 Alexopoulos GS, Abrams RC, Young RC, Shamoian CA: Cornell Scale for depression in dementia. Biol Psychiatry 1988; 23: 271e284 Cheung YB, Xu Y, Feng L, et al: Unconditional and conditional standards using cognitive function curves for the Modified MiniState Examination: Cross-sectional and longitudinal analyses in older Chinese in Singapore. Am J Geriatr Psychiatry 2015; 23:915e924 Cohen CI, Ryu HH: A longitudinal study of the outcome and associated factors of subsyndromal and syndromal depression in community-dwelling older adults with schizophrenia spectrum disorder. Am J Geriatr Psychiatry 2015; 23:925e933 Radloff LS: The CES-D Scale. A self-report depression scale for research in the general population. Appl Psychol Meas 1977; 3: 385e401 Zisook S, Montross L, Dasckow J, et al: Subsyndromal depressive symptoms in middle-aged and older persons with schizophrenia. Am J Geriatr Psychiatry 2007; 15:1005e1014
Am J Geriatr Psychiatry 23:9, September 2015