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Body weight variability in midlife and risk for dementia in old age
Abstract
Objective:
To analyze the relationship between body weight variability and dementia more than 3 decades later.
Methods:
The measurement of body weight variability was based on 3 successive weight recordings taken from over 10,000 apparently healthy tenured working men participating in the Israel Ischemic Heart Disease study, in which cardiovascular risk factors and clinical status were assessed in 1963, 1965, and 1968, when subjects were 40-70 years of age. Groups of men were stratified according to quartiles of SD of weight change among 3 measurements (1963/1965/1968): ≤ 1.15 kg, 1.16-1.73 kg, 1.74-2.65 kg, and ≥ 2.66 kg. The prevalence of dementia was assessed more than 36 years later in approximately one-sixth of them who survived until 1999/2000 (minimum age 76 years) and underwent cognitive evaluation (n = 1,620).
Results:
Survivors' dementia prevalence rates were 13.4%, 18.4%, 20.1%, and 19.2% in the first to fourth quartiles of weight change SD, respectively (p for trend = 0.034). Compared to the first quartile of weight change SD and adjusted for diabetes mellitus, body height, and socioeconomic status, a multivariate analysis demonstrated that the odds ratio for dementia was 1.42 (95% confidence interval [CI] 0.95-2.13), 1.59 (95% CI 1.05-2.37), and 1.74 (95% CI 1.14-2.64) in quartiles 2-4 of weight change SD respectively. This relationship was independent of the direction of weight changes.
Conclusion:
Midlife variations in weight may antecede late-life dementia.
... Obesity can affect brain health through the actions of adipocytes, adipocyte-associated hormones, and cytokines. Moreover, these substances may cross the blood-brain barrier and influence various brain health, including energy homeostasis, learning, and memory [7,8]. However, even low body mass index (BMI) influences cognitive function and dementia risk [9,10]. ...
... These findings suggest that, besides the range of variability, the direction of variability may be a significant factor. BW variability can be driven by physical activity, intentional weight loss, and dietary factors, which may cause morbidities such as metabolic syndrome, cardiovascular disease, and cerebrovascular disease [7]. Significant BW fluctuation impaired glucose tolerance and insulin resistance, and thereby potentially accelerated atherogenesis [3,12,19]. ...
... Of the numerous studies on BW variability and parameters to calculate BW variability, most have mainly focused on investigating the effect of the magnitude of variability and have not considered the aspects of BW variability whereby the same magnitude of BW variability may pose different levels of health risk [7,20]. The results of this study clearly demonstrated that, even among the participants with high BW variability (highest quartile of VIM), the risk of dementia increased in a dose-response manner with higher levels and cycles of BW. ...
Background
Given the rising awareness of health-related lifestyle modifications, the impact of changes in body weight (BW) on cognitive function and dementia generates significant concern. This study aimed to investigate the association between BW changes and dementia in a middle-aged Korean population.
Methods
A retrospective, population-based longitudinal study was conducted utilizing data from the National Health Insurance Service (NHIS) database. Participants aged 40 years or older in 2011 who underwent at least five health checkups between 2002 and 2011 were followed-up for dementia until 2020. A total of 3,635,988 dementia-free Korean aged < 65 at baseline were examined. We analyzed the association between BW variability independent of the mean (VIM) with BW cycle, defined as either an upward or a downward direction of BW, and the risk of incident dementia.
Results
The results showed an increased risk of dementia in the highest quartile of VIM quartile (hazard ratio [HR] 1.52, 95% confidence interval [CI] 1.47–1.58) compared to the lowest quartile of VIM. Additionally, the results showed an even higher increased risk of dementia in the highest BW cycle (≥ 2 cycles of 10% BW = HR 2.00, 95% CI 1.74–1.29). Notably, the combined concept of VIM with BW cycle showed an even higher dementia risk (highest quartile of VIM with ≥ 2 cycles of 10% BW = HR 2.37, 95% CI 2.05–2.74) compared to the baseline group (lowest quartile of VIM with < 3% BW cycle).
Conclusions
The present study highlights the importance of considering BW changes with BW variability along with the BW cycle to assess dementia risk in detail, providing valuable insights for preventive strategies.
... The process of ageing is accompanied by fluctuations in homeostatic processes, resulting in intrinsic intraindividual variability in physiological parameters. For example, variability in weight, including both gaining and losing weight, is associated with significant increases in mortality [1,2]. Several observational studies have demonstrated that 'unintentional' weight loss in older adults is related to increased frailty and functional decline [2][3][4][5]. ...
... Current evidence on the association between cognitive decline and variation of weight mainly comes from studies that collected fewer weights measurements (≤3) than the present study (median of 11 measurements, IQR 11; 11) [1,6,19,20]. Furthermore, these studies did not calculate an average slope of weight change during follow-up as done in the present study. ...
... In line with our results, these studies demonstrate that greater variation in weight is associated with higher risk of dementia. In the present study, we used a battery of cognitive tests to examine various domains as opposed to solely the diagnosis of dementia [1,[19][20][21], adding nuance to our findings. Consistent with our findings, these studies suggest that larger variation in weight may function as a marker of risk of early cognitive impairment. ...
Objective
to investigate the association between variability and loss of body weight with subsequent cognitive performance and activities of daily living in older individuals.
Design
cross-sectional cohort study.
Setting
PROspective Study of Pravastatin in the Elderly at Risk, multicentre trial with participants from Scotland, Ireland and the Netherlands.
Subjects
4,309 participants without severe cognitive dysfunction (mean age 75.1 years, standard deviation (SD) = 3.3), at higher risk for cardiovascular disease (CVD).
Methods
body weight was measured every 3 months for 2.5 years. Weight loss was defined as an average slope across all weight measurements and as ≥5% decrease in baseline body weight during follow-up. Visit-to-visit variability was defined as the SD of weight measurements (kg) between visits. Four tests of cognitive function were examined: Stroop test, letter-digit coding test (LDCT), immediate and delayed picture-word learning tests. Two measures of daily living activities: Barthel Index (BI) and instrumental activities of daily living (IADL). All tests were examined at month 30.
Results
both larger body weight variability and loss of ≥5% of baseline weight were independently associated with worse scores on all cognitive tests, but minimally with BI and IADL. Compared with participants with stable weight, participants with significant weight loss performed 5.83 seconds (95% CI 3.74; 7.92) slower on the Stroop test, coded 1.72 digits less (95% CI −2.21; −1.13) on the LDCT and remembered 0.71 pictures less (95% CI -0.93; −0.48) on the delayed picture-word learning test.
Conclusion
in older people at higher risk for CVD, weight loss and variability are independent risk-factors for worse cognitive function.
... Body weight and its long-term change, especially weight loss, have been linked with dementia in later life (3)(4)(5)(6)(7), but the overall relation is still not fully understood (3,8,9). Unlike static body weight and body weight change, body weight variability (BWV) measures the fluctuations in body weight during a period and intraindividual instability and has been associated with higher risks of multiple chronic diseases and mortality (10)(11)(12)(13)(14). Recently, emerging evidence has also suggested that larger BWV was associated with a higher risk of dementia (15)(16)(17), underscoring the importance of long-term body weight management. However, the evidence is far from conclusive. ...
... Until now, investigations on the relation of BWV to dementia were scarce. In a cohort study of civil servants and municipal employees, midlife BWV was defined by three measurements of body weight, which suggested that remote BWV was associated with higher dementia risk after 36 years (15). Two nationwide cohort studies conducted in Korea also reported that BWV was related to higher risks of Alzheimer's and vascular dementia (16,17). ...
... To our knowledge, three observational studies have evaluated the relation of BWV to dementia, linking greater BWV to higher risk of dementia (15)(16)(17). Two nationwide studies conducted in the Korean population found that the highest BWV (calculated as VIM using repeated assessment of body weight) was associated with approximately 39%-42% higher risk of dementia onset in the follow-up period (16,17), and the association was stronger for participants who were underweight at baseline. ...
Background:
Body weight variability (BWV) refers to intraindividual weight loss and gain over a period. The association of long-term BWV with dementia remains unclear and whether this association is beyond body weight change is undetermined.
Methods:
In the Health and Retirement Study (HRS), a total of 5,547 dementia-free participants (56.7% women; mean [SD] age, 71.1 [3.2] years) at baseline (2008) were followed up to 8 years (mean=6.8 years) to detect incident dementia. Body weight was self-reported biennially from 1992-2008. BWV was measured as the coefficient of variation utilizing the body weight reported 9 times across 16 years before baseline. Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI).
Results:
Among the 5,547 participants, a total of 427 incident dementia cases were identified during follow-up. Greater long-term BWV was significantly associated with a higher risk of dementia (HR comparing extreme quartiles: 2.01, 95% CI: 1.48-2.72; HR of each SD increment: 1.21, 95% CI: 1.10-1.32; P-trend<0.001) independent of mean body weight and body weight change. This significant association was even observed for BWV estimated approximately 15 years preceding dementia diagnosis (HR of each SD increment: 1.13, 95% CI: 1.03-1.23) and was more pronounced for that closer to diagnosis.
Conclusions: Our prospective study suggested that greater BWV may be a novel risk factor for dementia.
... Unlike static body weight and body weight change, BWV mainly captures the instability of body weight over time and has been associated with higher risks of multiple morbidities and mortality [9][10][11][12][13]. Recently, emerging evidence has also suggested that larger body weight fluctuation was associated with a higher risk of dementia [14][15][16]. However, the evidence is far from conclusive. ...
... In an Israeli cohort study of tenured working men, midlife BWV defined by three measurements of body weight was associated with higher risk of dementia 36 years later independent of the direction of weight changes [14]. Two nationwide cohort studies conducted in Korea also reported that BWV was related to a higher risk of Alzheimer's and vascular dementia [15][16]. ...
... To our knowledge, three population-based studies have evaluated the relation between BWV and dementia, all reporting higher BWV being related to dementia [14][15][16]. Two nationwide studies conducted in Korean population respectively found that the highest BWV (calculated as VIM using repeated assessment of body weight) was associated with approximately 39%-42% higher risk of dementia onset in the follow-up period [15] , [16], and the association was stronger for participants who were underweight. ...
Introduction
We aimed to investigate whether long-term body weight variability (BWV) is associated with late-life dementia and to further assess their potential temporal relationships.
Methods
In 5,547 participants in Health and Retirement Study (HRS), a population-based prospective cohort, we quantified BWV as coefficient of variation using self-reported body weight from 1992 to 2008 and followed their dementia status from 2008 to 2016.
Results
A total of 427 incident dementia cases were identified. Larger long-term BWV was significantly associated with higher risk of dementia (HR comparing extreme quartiles: 2.01, 95% CI: 1.48-2.72; HR of each SD increment: 1.21, 95% CI,1.10-1.32; p-trend<0.001). This significant association was even observed for BWV estimated approximately 15 years preceding dementia diagnosis (HR of each SD increment: 1.13, 95% CI: 1.03-1.23) and was more pronounced for that closer to diagnosis.
Discussion
Our findings suggested that large BWV could be a novel risk factor for dementia.
... Incident disabling dementia was defined as the primary outcome according to the A c c e p t e d V e r s i o n 9 criteria of the LTCI system that has been implemented in Japan since April 2000. 18 The LTCI is a mandatory form of national social insurance to assist activities of daily living in the disabled elderly provided by municipalities. 19 People aged ≥ 40 years paying premiums and those aged ≥ 65 years are eligible to apply for formal caregiving services. ...
... ≥rank II) on the Dementia Scale (Degree of Independence in Daily Living for Elderly with Dementia), as entered on the DOP. This cutoff point for defining incident disabling dementia has been used in previous studies 18,20 The Dementia Scale were reported to have a satisfying sensitivity and specificity against clinical diagnoses by neuropsychiatrists. 21 ...
... 31 Additionally, it was reported that weight loss might be directly associated with changes in brain structure, 32-34 and one study suggested that the association between weight loss, AD biomarkers and brain atrophy in healthy participants remained significant even after the exclusion of subjects with progression to MCI or dementia in the follow-up. 34 Japanese adults may start to lose weight in late midlife because of less energy intake and physical activity, 35 but it remains unknown whether weight loss A c c e p t e d V e r s i o n 18 starting from midlife would also be related to dementia among Japanese. We analyzed the data stratified by age in 1994, (<60 y or ≥60 y) and observed that a ≤-4.5kg weight loss among younger individuals (i.e. ...
Background: Both weight loss and cognitive impairment are common in late-life, but it remains unknown whether weight change is associated with risk of incident dementia among elderly Japanese. Our study aimed to investigate the association between long-term weight change since midlife and risk of incident disabling dementia using a community-based cohort study of elderly Japanese.
Methods: In 2006, we conducted a cohort study of 6,672 disability-free Japanese adults aged ≥65 years. In both 1994 and 2006, the participants reported their weight using a self-reported questionnaire. Based on weight obtained at these two time points, participants were classified into: stable weight (-1.4 – +1.4kg), weight gain (≥ +1.5kg), and weight loss of -2.4 – -1.5kg, -3.4 – -2.5 kg, -4.4 – -3.5kg, -5.4 – -4.5kg, and ≤-5.5kg. Incident disabling dementia was retrieved from the public Long-term Care Insurance database. Participants were followed-up for 5.7 years (between April 2007 and November 2012). Cox proportional hazards model was used to estimate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (95% CIs) for incident disabling dementia.
Results: During 32,865 person-years of follow-up, 564 participants were ascertained as having incident disabling dementia. Compared with stable weight, the multivariable-adjusted HRs (95%CIs) were 0.97 (0.70, 1.34) for weight loss of -2.4 – -1.5kg, 0.98 (0.70, 1.38) for -3.4 – -2.5kg, 1.28 (0.91, 1.81) for -4.4 – -3.5kg,1.27 (0.92, 1.77) for -5.4 – -4.5kg, and 1.64 (1.29, 2.09) for ≤-5.5kg.
Conclusion: Our study suggested that a ≤-3.5kg weight loss over 12 years might be associated with higher risk of incident disabling dementia among elderly Japanese.
... We examined the 86 potentially eligible publications and two more retrieved from other sources as full texts, 30 reports met the inclusion criteria. Of these, 11 were excluded because midlife BMI was predicted rather than measured in midlife [14], was modeled as a continuous variable [36], as a covariate in multivariate models [37,38] 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 Rotterdam study (the Netherlands) [47] 2085 (58) 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 19 studies for further analysis (Launer LJ, personal communication, 2015) [10,[18][19][20]22,[29][30][31][44][45][46][47][48][49][50][51][52][53]. The selection process is shown in Fig. 1 and reported in detail in Appendix B and C. ...
... Except for one multicenter study [44], one in Israel [51], and one in Taiwan [46], studies were conducted in Northern European countries including the UK [10,18,20,22,[29][30][31]49,50,52,53], or the USA (Launer LJ, personal communication, 2015) [19,45,48,49]. Nine were purposely designed population-based prospective cohort studies (Launer LJ, personal communication, 2015) [10,20,29,30,47,48,51,52], the other 10 were cohort studies that used, to different extents, routinely collected health data of exposure status (i.e., height and weight measured during routine health checks or visits in midlife) or outcome (i.e., dementia diagnosis from hospital records or death certificates). ...
... Except for one multicenter study [44], one in Israel [51], and one in Taiwan [46], studies were conducted in Northern European countries including the UK [10,18,20,22,[29][30][31]49,50,52,53], or the USA (Launer LJ, personal communication, 2015) [19,45,48,49]. Nine were purposely designed population-based prospective cohort studies (Launer LJ, personal communication, 2015) [10,20,29,30,47,48,51,52], the other 10 were cohort studies that used, to different extents, routinely collected health data of exposure status (i.e., height and weight measured during routine health checks or visits in midlife) or outcome (i.e., dementia diagnosis from hospital records or death certificates). The sample sizes ranged from 651 [10] to 241,146 [53] for a total of 589,649 participants who were followed up for up to 42 years from midlife to late life [50]. ...
Introduction
We conducted a meta-analysis of the conflicting epidemiologic evidence on the association between midlife body mass index (BMI) and dementia.
Methods
We searched standard databases to identify prospective, population-based studies of dementia risk by midlife underweight, overweight, and obesity. We performed random-effects meta-analyses and meta-regressions of adjusted relative risk (RR) estimates and formally explored between-study heterogeneity.
Results
We included 19 studies on 589,649 participants (2040 incident dementia cases) followed up for up to 42 years. Midlife (age 35 to 65 years) obesity (BMI ≥ 30) (RR, 1.33; 95% confidence interval [CI], 1.08–1.63), but not overweight (25 < BMI < 30) (RR, 1.07; 95% CI, 0.96–1.20), was associated with dementia in late life. The association with midlife underweight (RR, 1.39; 95% CI, 1.13–1.70) was potentially driven by residual confounding (P from meta-regression = .004), selection (P = .046), and information bias (P = .007).
Discussion
Obesity in midlife increases the risk of dementia. The association between underweight and dementia remains controversial.
... For instance, obesity is related to cardiovascular diseases and metabolic disorders such as diabetes [18,19], which are risk factors for cognitive impairments in old age [20][21][22]. In line with this, obesity has been found to be associated with impaired cognitive functioning such as memory and executive functioning in old age [23][24][25]. ...
... The rationale underlying this view is that cognitive reserve may generally be a protective factor against disease-related cognitive changes in older adulthood. Specifically, cognitive stimulation may increase the brain's potential to effectively recruit neural networks and cognitive processes in situations in which the brain must react and adapt, such as in individuals at elevated risk for cognitive impairments in old age that is (at least partly) caused by obesity such as cardiovascular diseases and diabetes [18][19][20][21][22][23][24][25][26][27], thereby enhancing cognitive performance [3,4]. This view has been corroborated by evidence that cognitive reserve (operationalized as engaging in different social activities and head circumference) may have a preserving effect on cognitive functioning (such as lower risk for dementia), even when controlling for BMI [28,29]. ...
... The present study set out to investigate the association of obesity with different measures of cognitive performance and its interplay with key correlates of cognitive reserve in a large sample of older adults. First of all, confirming prior research [23][24][25], we found that obesity was related to lower performance in verbal abilities, processing speed, and cognitive flexibility. This further corroborates the view that obesity is a major health-related antecedent of impairments in multiple domains of cognitive functioning such as memory, processing speed, and executive functioning in old age [20][21][22]. ...
Aims:
The present study set out to investigate the relation of obesity to performance in verbal abilities, processing speed, and cognitive flexibility and its interplay with key correlates of cognitive reserve in a large sample of older adults.
Methods:
A total of 2,812 older adults served as a sample for the present study. Psychometric tests on verbal abilities, processing speed, and cognitive flexibility were administered. In addition, individuals were interviewed on their weight and height (to calculate body mass index; BMI), educational attainment, occupation, and engaging in different activities throughout adulthood.
Results:
Obesity (BMI ≥30) was significantly associated with a lower performance in verbal abilities, processing speed, and cognitive flexibility. Moderation analyses showed that obesity was related to lower processing speed and cognitive flexibility only in individuals with low engagement in activities and low education. Hierarchical regression analyses showed that obesity was not related to any of the three investigated cognitive performance measures when cognitive reserve in early and midlife was taken into account.
Conclusion:
Present data suggest that cognitive reserve accumulated during the life course may reduce the detrimental influences of obesity on cognitive functioning in old age.
... A better understanding of the association between body weight and dementia may be achieved by studying weight trajectories and patterns from midlife into old age (1). Studies with weight at multiple time points have generally reported weight loss (10)(11)(12)(13)(14)(15)(16)(17)(18) or weight instability (19) to be associated with increased dementia risk. However, many of these studies are limited to weight trajectories in old age and might be prone to reverse causality because dementia and preclinical dementia might cause weight loss (1,20). ...
... We identified five studies which have, like us, linked weight change from midlife into old age to dementia outcomes, and in accordance with our findings, weight loss was associated with increased dementia risk in all these studies (14,(17)(18)(19)28). First, in the Honolulu-Asia Aging Study, a study of Japanese American men, where age at inclusion was from late 40s and 60s and follow-up was until 90s for the oldest, it was reported that men who developed dementia lost significantly more weight during the years prior to the diagnosis, especially during the 3-year period prior to the diagnosis (14). ...
... Fourth, in the Finnish CAIDE study, both men and women were included, and weight loss between age 50 and 70 years was associated with 20% increased dementia risk per unit BMI loss, which was robust to adjustment for a range of health-related variables (17). Fifth, midlife weight variability (in either direction) was associated with increased dementia risk in the Israel Ischemic Heart Disease Project (19). Unfortunately, weight change and interaction with baseline weight status were not addressed in these five studies. ...
Background
The relationship between body mass index (BMI) and dementia is complex and controversial. This study investigates the association of weight change during midlife and later dementia-related mortality.
Methods
Two BMI measurements (average of 9.0 years apart) were available for 43,721 participants in the Norwegian Counties Study (NCS), with mean age 42 years at first BMI measurement and 51 at the final measurement. NCS was linked with the Cause of Death Registry until year 2015 (mean follow-up time 25.9 years). Cox regression with a conditional growth model was used.
Results
Our study comprised 1,205 dementia-related deaths. Weight loss was associated with increased dementia-related mortality, irrespectively of baseline BMI and confounders; those with 10% or more loss had hazard ratio (HR) = 1.52 (95% confidence interval [CI]: 1.09, 2.12) compared to those being stable (0%–2.5% BMI gain), and those with 5%–10% loss had HR = 1.38 (95% CI: 1.08, 1.76). Gaining weigh was associated with reduced dementia-related mortality. Associations with BMI change did not vary by baseline BMI.
Conclusions
Weight loss during midlife was associated with increased dementia-related mortality risk more than 3 decades later, while weight gain was associated with reduced risk. These associations held both for low and high baseline BMI. Weight loss was an independent risk factor for dementia-related mortality and more strongly related with dementia-related mortality than stable BMI (stable high or low). Overweight and obesity were associated with an increased risk for nondementia-related mortality, which was far more common than dementia-related mortality.
... A review in 2010 by Anstey et al. [11] did look by at BMI in mid-life and late life reporting that continuous BMI was not associated with dementia in late life, although that due to small numbers of studies the generalisability of the findings was reduced and that additional studies were warranted. More studies [12][13][14][15][16][17][18] have been published since the review by Anstey with regard to the possible association of overweight/obesity and the subsequent development of dementia, providing the opportunity to expand on previous analyses. ...
... Most of the studies were conducted either in the USA (n = 11) [3,4,13,15,16,[21][22][23][24][25] or in Europe (n = 7) [6,12,13,[26][27][28][29] with one in Japan [20], one in Australia [14] and one in Israel [17]. Ethnicity was reported in studies from the USA and included Japanese Americans [25], Latinos [24] and African Americans [4,15]. ...
... Ethnicity was reported in studies from the USA and included Japanese Americans [25], Latinos [24] and African Americans [4,15]. Sixteen reported on those aged 65 and over [4,6,13,15,16,18,[20][21][22][26][27][28][29] with five focusing on younger participants aged below 65 [3,12,17,23,29] and two on both [4,18]. For all those that were defined as late life, the mean age at baseline was over 65 (range 69-82 in those that reported this), for mid-life below 65 (range 42-50.4). ...
Scope: it has been suggested that overweight/obesity as a risk factor for incident dementia differs between mid-life and later life. We performed a systematic review and meta-analysis of the up-to-date current literature to assess this.
Search Methods: inclusion criteria included epidemiological longitudinal studies published up to September 2014, in participants without cognitive impairment based on evidence of cognitive assessment and aged 30 or over at baseline assessment with at least 2 years of follow-up. Pubmed, Medline, EMBASE, PsychInfo and the Cochrane Library were searched using combinations of the search terms: Dementia, Alzheimer disease, Vascular Dementia, Multi-Infarct Dementia, Cognitive decline, Cognitive impairment, Mild Cognitive Impairment/Obesity, Overweight, Adiposity, Waist circumference (limits: humans, English language). Handsearching of all papers meeting the inclusion criteria was performed. A random-effects model was used for the meta-analysis.
Results: of the 1,612 abstracts identified and reviewed, 21 completely met the inclusion criteria. Being obese below the age of 65 years had a positive association on incident dementia with a risk ratio (RR) 1.41 (95% confidence interval, CI: 1.20–1.66), but the opposite was seen in those aged 65 and over, RR 0.83 (95% CI: 0.74–0.94).
Conclusions: this systematic review and meta-analysis suggests a positive association between obesity in mid-life and later dementia but the opposite in late life. Whether weight reduction in mid-life reduces risk is worthy of further study.
... Obesity is defined as BMI ≥30 kg/m2. Higher BMI in midlife has been associated with poor cognitive outcomes in late life (4,5). Poorer performance in executive function (6) as well as working memory (7) and verbal fluency (8) have been consistently associated with higher BMI. ...
... Poorer performance in executive function (6) as well as working memory (7) and verbal fluency (8) have been consistently associated with higher BMI. We have shown that greater weight variability in midlife is associated with an increased risk of dementia three decades later (4). We have also found evidence for associations of greater variability in BMI over time with faster cognitive decline in late life (9). ...
Background
Adiposity has been previously associated with cognitive impairment and Alzheimer’s disease and related disorders (ADRD). Body mass index (BMI) is the most common measure of global adiposity, but inconsistent results were found since it is a global measurement. BMI does not represent regional fat distribution which differs between sexes, race, and age. Regional fat distribution may contribute differently to cognitive decline and Alzheimer’s disease (AD)-related brain changes. Fat-specific targeted therapies could lead to personalized improvement of cognition. The goal of this systematic review is to explore whether regional fat depots, rather than central obesity, should be used to understand the mechanism underlying the association between adiposity and brain.
Methods
This systematic review included 33 studies in the English language, conducted in humans aged 18 years and over with assessment of regional adiposity, cognitive function, dementia, and brain measures. We included only studies that have assessed regional adiposity using imaging technics and excluded studies that were review articles, abstract only or letters to editor. Studies on children and adolescents, animal studies, and studies of patients with gastrointestinal diseases were excluded. PubMed, PsychInfo and web of science were used as electronic databases for literature search until November 2022.
Results
Based on the currently available literature, the findings suggest that different regional fat depots are likely associated with increased risk of cognitive impairment, brain changes and dementia, especially AD. However, different regional fat depots can have different cognitive outcomes and affect the brain differently. Visceral adipose tissue (VAT) was the most studied regional fat, along with liver fat through non-alcoholic fatty liver disease (NAFLD). Pancreatic fat was the least studied regional fat.
Conclusion
Regional adiposity, which is modifiable, may explain discrepancies in associations of global adiposity, brain, and cognition. Specific regional fat depots lead to abnormal secretion of adipose factors which in turn may penetrate the blood brain barrier leading to brain damage and to cognitive decline.
... 45,46 Although there have been many studies about the association between Bwt variability and health status, very few studies have assessed the effects of Bwt variability on dementia. [47][48][49] A retrospective cohort study of 19,987 participants with a mean age of 73 years using Korean National Health Insurance Service data revealed that high Bwt variability was associated with increased risk of dementia in the elderly. 47 Low Bwt variability and obese BMI was associated with decreased risk of dementia. ...
... Another study of elderly women demonstrated that instability in Bwt over 5 years between ages 40 and 70 years was associated with increased risk for dementia independently of the direction of weight change. 48 In a very recent study, 49 participants with significant changes in BMI (increase or decrease of ≥5%) or who had greater variability in BMI, experienced faster cognitive decline. This pattern was consistent irrespective of BMI at baseline. ...
Obesity and obesity-associated morbidity continues to be a major public health issue worldwide. Dementia is also a major health concern in aging societies and its prevalence has increased rapidly. Many epidemiologic studies have shown an association between obesity and cognitive impairment, but this relationship is not as well established as other comorbidities. Conflicting results related to the age and sex of participants, and the methodology used to define obesity and dementia may account for the uncertainty in whether obesity is a modifiable risk factor for dementia. More recently, sarcopenia and sarcopenic obesity have been reported to be associated with cognitive impairment. In addition, new mediators such as the muscle-myokine-brain axis and gut-microbiota-brain axis have been suggested and are attracting interest. In this review, we summarize recent evidence on the link between obesity and cognitive impairment, especially dementia. In particular, we focus on various metrics of obesity, from body mass index to sarcopenia and sarcopenic obesity.
... Giving the shared modifiable risk factors for cardiovascular diseases and dementia [16][17][18] and the considerable overlaps between cerebrovascular and Alzheimer's pathology [19], large visit-to-visit metabolic fluctuation may have a role in risk for cognitive decline as well. There are studies suggesting that large weight fluctuations may be associated with cognitive decline [20][21][22][23]. However, it is unclear whether such association differs between participants with normal cognition and those with MCI; it is also undetermined whether the putative association of body weight fluctuation with cognitive decline is independent of the longitudinal trend in body weight change. ...
... This study extends the observations from previous cohorts [20][21][22][23] that linked large body weight fluctuation to higher dementia risk in several ways. First, we assessed the association in those with and without cognitive decline at baseline. ...
Background:
The evidence regarding dementia and late-life weight change is inconsistent, and data on body weight fluctuation and dementia are limited.
Objective:
To test the hypothesis that weight loss and substantial weight fluctuation predict cognitive decline independent of body weight and traditional risk factors of dementia.
Methods:
This study utilized longitudinal data from the National Alzheimer's Coordinating Center for 10,639 stroke- and dementia-free older adults (60.9%female, mean age 71.6 years, median follow-up 5.5 years). Trends in weight change and weight fluctuation were estimated for each individual by regressing repeated body weight measurements on time. Cognitive decline was examined as diagnostic progression from normal to mild cognitive impairment (MCI) or dementia and from MCI to dementia.
Results:
Compared to participants with stable weight, those with weight loss had increased odds of diagnostic progression (adjusted OR = 1.35, 95%CI [1.21, 1.51]). Also, large weight fluctuation was associated with increased odds of diagnostic progression (OR comparing the extreme quartiles = 1.20, 95%CI [1.04, 1.39]) after adjusting for traditional risk factors for dementia and body weight change. The magnitude of the association appeared larger among those older than 80 and those with 3 or more cardiometabolic risk factors at baseline (both p for interaction < 0.05).
Conclusion:
Weight loss and substantial weight fluctuation during late-life were associated with increased odds of cognitive decline independent of body weight and traditional risk factors of dementia. Our results suggested the linkage between late-life body weight instability and cognitive decline especially among those with greater age or higher cardiometabolic risk.
... 7 Similarly, mid-life variations in body weight that persist for over 5 years are associated with an increased risk of dementia. 8 Some studies have reported that high body weight ...
... This is a PDF file of an article accepted, but it is not yet the definitive version of record. 8 cognitive impairment (p for trend=0.016). A statistically significant association was observed between weight variability and development of mild cognitive impairment (K-MMSE ≤23), although a significant association was not noted for severe cognitive impairment (K-MMSE≤17). ...
Background:
Although several studies have assessed obesity and cognitive impairment, most of these studies focus on body mass index (BMI) and cognitive impairment. Therefore to better understand the importance of weight maintenance with aging, this study investigated the relationship between variations in weight and cognitive impairment using the Korean version of the Mini-Mental State Examination (K-MMSE) in individuals aged 45 years or older in Korea.
Methods:
Data on 3,477 adults with normal cognitive function (K-MMSE ≥24) at baseline were acquired from the Korean Longitudinal Study of Aging (KLoSA) 2006-2016. The association between weight variability and risk of cognitive impairment was assessed using multiple logistic regression models. We also assessed weight variability and change in cognitive function over the 6-year follow-up using multiple linear regression.
Results:
Overall, higher variations in BMI were associated with cognitive impairment. Patients in the quintile with the highest variation (Q5) in BMI (mean of BMI changes, 2.69) showed the greatest degree of cognitive impairments (adjusted odds ratio, 1.52; 95% CI, 1.08-2.14; P-trend=0.016). Furthermore, a higher frequency in the number of times (3 times) the patient's body weight changed was associated with a lower cognitive function (adjusted odds ratio, 3.42; 95% CI, 1.67-7.03; P-trend<0.001).
Conclusion:
In this nationally representative study, weight variability was associated with a higher risk of cognitive decline during mid- and late-life stages.
... Studies have evaluated either "change" in BMI, which is a difference between measurements at two time points, or "variability", which is a fluctuation of BMI among three or more measurements. Generally, BMI or weight loss and higher BMI or weight variability were associated with an increased risk for AD [11][12][13][14][15][16] . These studies concluded that weight loss precedes AD since weight loss may be a causal factor or a prodromal symptom of AD 11-14 . ...
... To our knowledge, there are very few studies, which investigated the association between body weight variability and risk for dementia. Previous studies reported that higher weight variability was associated with an increased risk for dementia 15,16 . In our study, we used ASV, the average absolute difference between BMI values of each measurement, as a measure of variability. ...
The effect of body mass index (BMI) changes and variability on the risk for Alzheimer’s disease (AD) remains unclear. We analyzed 45,076 participants, whose BMI were measured on phase 1 (2002–2003), phase 2 (2004–2005), and phase 3 (2006–2007), of the Korean National Health Insurance Service-Health Screening Cohort. We evaluated the effect of 2- and 4-year BMI changes and BMI variability on the risk of AD using Cox regression models. In men, association between 2-year BMI changes, BMI variability, and the risk of AD was not significant. Risk of AD was higher in men whose BMI had decreased 10.1–15.0% over 4 years. In women, aHRs and 95% CIs for AD were 1.14 (1.02–1.29), 1.44 (1.17–1.79), and 1.51 (1.09–2.09) when 2-year BMI loss was 5.1–10.0%, 10.1–15.0%, and > 15.0%. The HRs for AD in women significantly increased when 4-year BMI loss was > 5.0%. The aHR and 95% CI for AD was 1.31 (1.17–1.46) in the 4th quartile of average successive variability (ASV) compared with the 1st quartile of ASV in women. BMI loss over 2- and 4-year period was associated with increased risk for AD, and risk increased in women with higher BMI variability. Appropriate body weight management is recommended to prevent AD.
... (Field et al., 2004;Maruthur, Ma, & Delahanty, 2013) Dementia is also thought to be related to BWt variability; Ravona-Springer et al. showed that 40-to 70-year-old individuals with higher BWt variability had a higher risk of dementia after 36 years. (Ravona-Springer, Schnaider-Beeri, & Goldbourt, 2013) The present large-scale cohort study with a 5-year follow-up, which included more than 2,810,000 ...
... A previous study examined the association of BWt variability during midlife and the risk of dementia in men. (Ravona-Springer et al., (2013)) When the groups were stratified by SD quartiles of BWt change among three measurements in five years, the odds ratio for dementia after 36 years was 1.74 (95% CI, 1.14-2.64) in the highest SD quartile of BWt change compared to the lowest quartile, and no significant trend was observed across the direction of BWt change. ...
Background
While there have been disagreements concerning whether obesity and increase in body weight elevate the risk of dementia, variability in body weight has been recently recognized as a new biometric associated with a high risk for a number of diseases. This nationwide, population‐based cohort study examined the association between body weight variability and dementia.
Methods
A total of 2,812,245 adults (mean age, 51.7 years; standard deviation, 8.6) without a history of dementia who underwent at least three health examinations between 2005 and 2012 in a nationwide cohort were followed‐up until the date of dementia diagnosis (based on prescribed drugs and disease code) or until 2016 (median follow‐up duration, 5.38 years; interquartile range, 5.16–5.61). Cox regression models were used to evaluate the risk of Alzheimer's disease and vascular dementia according to body weight variability.
Results
The hazard ratios (95% confidence intervals) of the highest quartiles of variability were 1.42 (1.35–1.49) for Alzheimer's disease and 1.47 (1.32–1.63) for vascular dementia compared to the lowest quartile group as a reference. This association was consistent in various subgroup analyses and sensitivity analyses.
Conclusions
Body weight variability could predict Alzheimer's disease and vascular dementia, which may provide new insights into the prevention and management of dementia.
... A meta-analysis of 25 studies involving more than 400,000 participants, Bwt fluctuation was associated with a significant increase in risk of all-cause mortality, cardiovascular disease (CVD) mortality, and CVD (17). Although there have been some studies to examine the association between BMI at a specific time or Bwt changes with dementia (19)(20)(21), there has been no study to explore the effects of Bwt variability in late-life on the incidence of dementia. ...
... However, very few studies have assessed the effects of Bwt variability on dementia. One study suggested that midlife Bwt variability was associated increased risk of dementia late in life (20). Another study of elderly women reported that Bwt variability was associated with increased risk of cognitive impairment or dementia, but the risk was attenuated after adjusting for covariates (21). ...
Background: Recent growing evidences suggest that body weight (Bwt) variability, a repeated loss and regain of weight within a specific period, causes metabolic disturbances and can be a marker for poor homeostasis. Although there have been many studies about the association between Bwt variability and various health status, its association with the incidence of dementia among elderly people has not been examined. Methods: We performed a retrospective elderly cohort study from 19,987 participants with mean age 73 years old in the Korean National Health Insurance Service. We examined the risk of incident dementia, including Alzheimer's dementia and vascular dementia, according to the quartile of Bwt variability, represented as coefficient of variation (Bwt-CV), SD (Bwt-SD), and variability independent of the mean (Bwt-VIM). Results: In fully adjusted model, the group with the highest Bwt variability (Bwt-VIM Q4) showed an increased risk of all-cause dementia (hazard ratio [HR] 1.39, 95% confidence interval [CI] 1.206–1.603) and Alzheimer's dementia (HR 1.46, CI 1.240–1.724) compared to the lowest quartile (Bwt-VIM Q1). We also found that subjects with the highest Bwt variability (Q4) and underweight BMI had a significantly increased risk of developing dementia (HR 1.994, 95% CI 1.302–3.054), while subjects with low Bwt variability (Q1 and Q2) and obese BMI had decreased risk of dementia (HR 0.664, 95% CI 0.505–0.872 and HR 0.648, 95% CI 0.493–0.852, respectively) compared to reference group (lowest Bwt variability (Q1) with normal baseline BMI). The effect of Bwt variability on the incidence of dementia was more prominent in subjects <75 years old and abnormal BMI groups (P for interaction < 0.05). Conclusion: The present study revealed that high Bwt variability was associated with an increased risk of dementia in the elderly.
... [7]. Majority of the included studies in this meta-analysis were from the US and northern Europe, except for two small studies in Taiwan [28] and Israel [29]. Findings from the Whitehall II Study published after the meta-analysis also showed a higher risk of dementia for obesity at age 50, but in contrast to our findings, not at ages 60-70 years [30]. ...
... The relation between weight gain and cognitive impairment has also been mixed in epidemiological studies, with an inverse association observed with death due to dementia [17], null association with memory scores in one study [15], and a higher risk of cognitive impairment in other studies [14,16,37]. Moreover, the observed U-shaped association in our study is not unprecedented and changes in body weight at both directions have been associated with increased risk in previous studies [14,29]. The relationship between weight change and cognitive impairment in Asians was previously tested in 1814 Japanese with a comparable mean age (68.5) and BMI (23.0 kg/m 2 ) to our participants [16]. ...
Background:
Few prospective studies with long duration of follow-up have assessed the relations of body mass index (BMI) and weight change with cognitive function, especially in Asian populations.
Objective:
To investigate whether BMI and weight change in midlife are associated with cognitive impairment in old age.
Methods:
We used data from 14,691 participants in the Singapore Chinese Health Study and computed weight change as the difference between weight reported at baseline (1993-1998) at mean age of 53.0 years and follow-up 1 (1999-2004) at mean age of 58.6 years. Cognitive impairment was determined using education-specific cut-offs of the Singapore Modified Mini-Mental State Examination at follow-up 3 (2014-2016) at mean age of 72.9 years. We used multivariable logistic regression models to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations.
Results:
Obesity (as defined BMI ≥27.5 kg/m2) was associated with a higher risk of cognitive impairment at baseline (OR 1.33, 95% CI 1.12-1.58) and follow-up 1 (OR 1.30, 95% CI 1.10-1.54) compared to BMI of 18.5-22.9 kg/m2. Underweight (BMI <18.5 kg/m2) was not associated with a significant risk either at baseline (OR 0.91, 95% CI 0.73-1.13) or follow-up 1 (OR 1.05, 95% CI 0.85-1.28). Compared to participants with <5% weight change, the ORs (95% CIs) of cognitive impairment were 1.20 (1.03-1.41) for those with 5-9.9% weight loss, 1.53 (1.29-1.81) for ≥10% weight loss, 1.00 (0.85-1.17) for 5-9.9% weight gain, and 1.50 (1.28-1.75) for ≥10% weight gain.
Conclusion:
Obesity, weight loss, and excessive weight gain at midlife were associated with an increased risk of cognitive impairment at old age.
... These effects remained significant after adjusting for the mean levels of the parameters, suggesting that not only managing the absolute value but also reducing the fluctuation should be targeted to improve health outcomes. Intriguingly, higher variability in blood pressure [9][10][11][12][13], blood glucose [14], or body weight [15] was also associated with mild cognitive impairment, Alzheimer's disease, and dementia, suggesting a new avenue of risk modification. ...
... The effect of body weight variability on dementia is controversial. While one study identified midlife body weight variability as a risk for late-life dementia [15], another study of elderly women showed no significant association after adjustment of covariates [25]. In addition, we and others suggested cholesterol variability as a risk factor for mortality, cardiovascular outcomes, and end-stage renal disease in the general population or patients with coronary heart disease [6,26,27], whereas it was unknown whether the risk of dementia is associated with high cholesterol variability. ...
Background:
Variability in biological parameters has been reported to be associated with adverse health outcomes. We aimed to investigate the composite effect of the visit-to-visit variability in blood pressure, glucose, cholesterol, and body mass index on the risk of dementia.
Methods:
A population-based cohort study including 2,930,816 subjects without a history of dementia, hypertension, diabetes mellitus, and dyslipidemia who underwent ≥ 3 health examinations was performed. The coefficient of variation (CV), standard deviation, and variability independent of the mean were calculated as variability indices. High variability was defined as having values in the highest quartile for each parameter.
Results:
A total of 32,901 (1.12%) participants developed dementia, of which 74.4% and 11.0% were attributable to Alzheimer's disease and vascular dementia, respectively, during the median follow-up of 5.5 years. Individuals with higher variability of each parameter were at higher risk of future dementia. In the multivariable adjusted model, the hazard ratios and 95% confidence intervals of all-cause dementia were 1.22 (1.19-1.26) for one parameter, 1.39 (1.35-1.43) for two parameters, 1.54 (1.48-1.60) for three parameters, and 1.73 (1.60-1.88) for four parameters compared with subjects having no parameters of high variability measured as the CV. Consistent results were noted for Alzheimer's disease and vascular dementia, using other indices of variability and in various sensitivity and subgroup analyses.
Conclusions:
There was a linear association between the number of high variability parameters and risk of dementia. Reducing variability of metabolic parameters would be a target to preserve cognitive reserve in the general population.
... The outcome and exposure measures are comprehensively reported in S4 Table. Dementia & Cognition [44]; [48]; [47]; [133]; [46]; [131]; [50]; [45]; [51]; [52] [46]; [74]; [80]; [76]; [79]; [78]; [81]; [52] [26]; [126]; [134]; [125]; [129]; [52]; [132]; [127]; [124] [165]; [46]; [52]; [166]; [164]; [178] [ 184]; [46] [ 181]; [49]; [182]; [183] Overall mortality [46] No evidence was identified [46] No evidence was identified ...
... Dementia and cognition. There is limited evidence to suggest that weight change in midlife is related to dementia [178]. Those in the highest two quartiles of weight change had a significantly higher risk of dementia, independent of the direction of weight change. ...
Background
Smoking, alcohol consumption, poor diet and low levels of physical activity significantly contribute to the burden of illness in developed countries. Whilst the links between specific and multiple risk behaviours and individual chronic conditions are well documented, the impact of these behaviours in mid-life across a range of later life outcomes has yet to be comprehensively assessed. This review aimed to provide an overview of behavioural risk factors in mid-life that are associated with successful ageing and the primary prevention or delay of disability, dementia, frailty and non-communicable chronic conditions.
Methods
A literature search was conducted to identify cohort studies published in English since 2000 up to Dec 2014. Multivariate analyses and a minimum follow-up of five years were required for inclusion. Two reviewers screened titles, abstracts and papers independently. Studies were assessed for quality. Evidence was synthesised by mid-life behavioural risk for a range of late life outcomes.
Findings
This search located 10,338 individual references, of which 164 are included in this review. Follow-up data ranged from five years to 36 years. Outcomes include dementia, frailty, disability and cardiovascular disease. There is consistent evidence of beneficial associations between mid-life physical activity, healthy ageing and disease outcomes. Across all populations studied there is consistent evidence that mid-life smoking has a detrimental effect on health. Evidence specific to alcohol consumption was mixed. Limited, but supportive, evidence was available relating specifically to mid-life diet, leisure and social activities or health inequalities.
Conclusions
There is consistent evidence of associations between mid-life behaviours and a range of late life outcomes. The promotion of physical activity, healthy diet and smoking cessation in all mid-life populations should be encouraged for successful ageing and the prevention of disability and chronic disease.
... In addition, compared with the association of BWC, the association of BWV with CF has been less studied. For instance, BWV (measured by standard deviation [SD]) greater than approximately 2 kg over 5 years was related to a higher risk of dementia [17]. Aggregately, the independent associations between those measures of body weight dynamics (i.e., BWC and BWV) and long-term cognitive change remain unclear. ...
Objective:
The aim of this study was to investigate the associations of body weight change (BWC) and body weight variability (BWV) with changes in cognitive function.
Methods:
In 10,340 Health and Retirement Study participants (mean age: 68.0 years), body weight was reported biennially from 1993/1994 to 2016, and cognitive function was measured biennially from 1998 to 2016. We calculated BWC and BWV as the slope and root-mean-square error by regressing body weight on time for each individual. BWC was categorized by quintiles (Q): stable weight (Q2 to Q4), weight loss (Q1), and weight gain (Q5). BWV was categorized by tertiles. We used linear mixed regression models to assess associations with cognitive change.
Results:
Compared with stable weight (median: 0 kg/y), weight loss (median: -1.3 kg/y) predicted faster cognitive decline as demonstrated by mean difference of -0.023 (95% CI: -0.027 to -0.019) in cognitive change z score per year, whereas weight gain (median: 1 kg/y) was related to slower cognitive decline (β = 0.006; 95% CI: 0.003 to 0.009). Larger BWV was also associated with faster cognitive decline (β comparing the top with bottom tertile = -0.003; 95% CI: -0.006 to -0.0002). Similar associations were observed for episodic and working memory.
Conclusions:
Weight loss and large BWV over a long time independently predicted faster cognitive decline in middle-aged and older adults, underscoring the importance of long-term dynamic body weight monitoring.
... We are not aware of any published evidence on the association of carotid elastography and BMI. An additional aspect is variability in BMI over time, which has been associated with other old age outcomes such as dementia [46]. BMI variability over time in addition to the trajectory of BMI over time, might affect carotid atherosclerosis. ...
Background:
High body mass index (BMI) is a risk factor for type 2 diabetes and cardiovascular disease. However, its relationships with indices of carotid stiffness and plaque volume are unclear. We investigated associations of long-term measurements of BMI with indices of carotid stiffness and atherosclerosis among non-demented diabetes patients from the Israel Diabetes and Cognitive Decline (IDCD) study.
Methods:
Carotid ultrasound indices [carotid intima media thickness (cIMT), distensibility, elastography and plaque volume] were assessed in N = 471 participants. Mean BMI across all MHS diabetes registry measurements and trajectories of BMI were calculated. BMI was categorized into three trajectory groups representing: a relatively stable normal weight (n = 185, 44%), overweight trajectory (n = 188, 44.8%) and a trajectory of obesity (n = 47, 11.2%). Linear and logistic regressions estimated associations of carotid indices with mean BMI and BMI trajectories.
Results:
Compared to the normal weight trajectory, an obesity trajectory was associated with carotid distensibility (β = - 3.078, p = 0.037), cIMT (β = 0.095, p = 0.004), and carotid elastography (β = 0.181, p = 0.004) but not with plaque volume (β = 0.066, p = 0.858). Compared with the normal weight trajectory, an obesity trajectory was associated with increased odds for impaired carotid distensibility (OR = 2.790, p = 0.033), impaired cIMT (OR = 5.277, p = 0.001) and large carotid plaque volume (OR = 8.456, p = 0.013) but not with carotid elastography (OR = 1.956, p = 0.140). Mean BMI was linearly associated with Distensibility (β = - 0.275, p = 0.005) and cIMT (β = 0.005, p = 0.026).
Conclusions:
Long-term measurements of adiposity are associated with indices of carotid stiffness and plaque volume among older type 2 diabetes adults.
... An additional aspect is variability in BMI over time, which has been associated with other old age outcomes such as dementia 46 Several underlying biological mechanisms may link BMI with impaired vascular health. In our study obese patients were younger, yet had higher triglycerides and blood pressure, and lower HDL cholesterol, all independent risk factors for atherosclerosis in general and carotid artery atherosclerosis in particular 38 . ...
Background: High body mass index (BMI) is a risk factor for type 2 diabetes and cardiovascular disease. However, its relationships with indices of carotid stiffness and plaque volume are unclear. We investigated associations of long-term measurements of BMI with indices of carotid stiffness and atherosclerosis among non-demented diabetes patients from the Israel Diabetes and Cognitive Decline (IDCD) study.
Methods: Carotid ultrasound indices [carotid intima media thickness (cIMT), distensibility, elastography and plaque volume] were assessed in N=471 participants. Mean BMI across all MHS diabetes registry measurements and trajectories of BMI were calculated. BMI was categorized into three trajectory groups representing: a relatively stable normal weight (n=185, 44%), overweight trajectory (n=188, 44.8%) and a trajectory of obesity (n=47, 11.2%). Linear and logistic regressions estimated associations of carotid indices with mean BMI and BMI trajectories.
Results: Compared to the normal weight trajectory, an obesity trajectory was associated with carotid distensibility (β=-3.078, p=0.037), cIMT (β=0.095, p=0.004), and carotid elastography (β=0.181, p=0.004) but not with plaque volume (β=0.066, p=0.858). Compared with the normal weight trajectory, an obesity trajectory was associated with increased odds for impaired carotid distensibility (OR=2.790, p=0.033), impaired cIMT (OR=5.277, p=0.001) and large carotid plaque volume (OR=8.456, p=0.013) but not with carotid elastography (OR=1.956, p=0.140). Mean BMI was linearly associated with Distensibility (β=-0.275, p=0.005) and cIMT (β=0.005, p=0.026).
Conclusions: Long-term measurements of adiposity are associated with indices of carotid stiffness and plaque volume among older type 2 diabetes adults.
... Similarly, subjects with a larger number of highly variable metabolic parameters had a significantly higher cumulative incidence of all-cause dementia (16). More recently, the Taiwan Diabetes Study showed that visit-to-visit variations in fasting glucose and HbA1c were associated with increased risk of AD (32) and that midlife variation in body weight preceded late-life dementia (33). These previous studies all suggest that reducing fluctuations in Figure 3. Adjusted hazard ratios and 95% CI for the incidence of (A) all-cause dementia, (B) Alzheimer disease, and (C) vascular dementia according to groups categorized by baseline GGT levels and GGT variability assessed by ASV. ...
Context
Gamma-glutamyl transferase (GGT) has been associated with oxidative stress and inflammatory reactions. Variability in various biomarkers has emerged as a new clinical indicator for diseases including neurodegenerative disorders.
Objective
We investigated the association between GGT variability and dementia risk in patients with diabetes mellitus (DM).
Design, Participants, and Methods
We used the Korean National Health Insurance Service datasets of Claims and Health Check-ups from 2004 to 2016. The risk of incident dementia (all-cause dementia, Alzheimer disease, vascular dementia) was analyzed by quartiles of GGT variability in ≥ 40-year-old-DM individuals without baseline dementia.
Results
During 6.12 years of follow-up, 37,983 cases of dementia developed. In fully adjusted model, the group with the highest quartile of GGT variability had a 19 % increased risk of all-cause dementia when compared to the lowest quartile group [Hazard ratio, HR (95% confidence interval, CI): 1.19 (1.16-1.22)] with a small effect size (Cohens d’s= 0.14). Compared to the group with low baseline GGT level and the lowest quartiles of its variability, the group with high baseline GGT level and the highest quartile of its variability increased 27% of all-cause dementia. A 1 SD increment in the GGT variability was associated with a 3% increased risk of all-cause dementia. Subgroup analysis showed a more prominent association between increased GGT variability and dementia risk in men and < 60-year-old individuals (P for interaction ≤ 0.001).
Conclusions
In subjects with DM, high variability of GGT increased the risk of dementia independently of other factors, including baseline GGT levels.
... For instance, obesity is related to cardiovascular diseases and metabolic disorders such as diabetes (Bell, Kivimaki, & Hamer, 2014;Fan, Song, Chen, Hui, & Zhang, 2013), which are risk factors for cognitive impairments in midlife and old age (Carmichael, 2014;Reijmer et al., 2012;Schneider et al., 2015). Accordingly, several studies found that obesity is associated with impaired cognitive abilities such as executive functioning and memory in midlife and old age (Dahl et al., 2010;Ravona-Springer, Schnaider-Beeri, & Goldbourt, 2013;Stanek et al., 2013). Yet, the relationship between obesity and cognitive functioning is not always negative and may depend on the particular life phase studied. ...
Objectives: We investigated the longitudinal relationship between obesity and subsequent decline in executive functioning over six years as measured through performance changes in the Trail Making Test (TMT). We also examined whether this longitudinal relationship differed by key markers of cognitive reserve (education, occupation, and leisure activities), taking into account age, sex, and chronic diseases as covariates.
Method: We used latent change score modeling based on longitudinal data from 897 older adults tested on TMT parts A and B in two waves six years apart. Mean age in the first wave was 74.33 years. Participants reported their weight and height (to calculate BMI), education, occupation, leisure activities, and chronic diseases.
Results: There was a significant interaction of obesity in the first wave of data collection with leisure activities in the first wave on subsequent latent change. Specifically, obesity in the first wave significantly predicted a steeper subsequent decline in executive functioning over six years in individuals with a low frequency of leisure activities in the first wave. In contrast, in individuals with a high frequency of leisure activities in the first wave, this longitudinal relationship between obesity and subsequent decline in executive functioning was not significant.
Conclusion: The longitudinal relationship between obesity and subsequent decline in executive functioning may be attenuated in individuals who have accumulated greater cognitive reserve through an engaged lifestyle in old age. Implications for current cognitive reserve and gerontological research are discussed.
... demonstrating a link between type 2 diabetes and increased risk of cognitive decline (2,3), conversion of mild cognitive impairment to dementia (4), and development of dementia-spectrum disorders in general (5)(6)(7). Diabetes-related biological processes have been implicated in the genesis and maintenance of the pathophysiological mechanisms that give rise to neurodegenerative diseases, including Alzheimer's disease and vascular dementia (8)(9)(10)(11). ...
Background
To evaluate the effects of adaptive and tailored computerized cognitive training [CCT] on cognition and disease self-management in older adults with diabetes.
Methods
Single-blind trial. Eighty-four community-dwelling older adults with diabetes were randomized into a tailored and adaptive computerized cognitive training [TA-CCT] or a generic, non-tailored or adaptive CCT condition [GCCT]. Both groups trained for 8-weeks on the commercially-available CogniFit program and were supported by a range of behavior change techniques [BCTs]. Participants in each condition were further randomized into a global or cognition-specific self-efficacy [SE] intervention, or to a no-SE condition. The primary outcome was global cognition immediately following the intervention. Secondary outcomes included diabetes self-management, meta-memory, mood, and self-efficacy. Assessments were conducted at baseline, immediately after the training, and at a 6-month follow-up.
Results
Adherence and retention were lower in the GCCT condition, but the self-efficacy intervention was not associated with adherence. Moderate improvements in performance on a global cognitive composite at the post-treatment assessments were observed in both cognitive training conditions, with further small improvement observed at the 6-month follow-up. Results for diabetes self-management showed a modest improvement on self-rated diabetes care for both intervention conditions following the treatment, which was maintained at the 6-month follow-up.
Conclusions
Our findings suggest that older adults at higher dementia risk due to diabetes can show improvements in both cognition and disease self-management following home-based multi-domain computerized cognitive training. These findings also suggest that adaptive difficulty and individual task tailoring may not be critical components of such interventions.
... The present study did not consider biomarkers of dementia including neuroimaging, We also found that the SDs of some risk factors (i.e., BMI, blood pressure, fasting glucose, and total cholesterol) predicted all-cause dementia and Alzheimer's dementia. This means that the intra-individual variability of some risk factors influences the occurrence of dementia, consistent with the results of a previous study [34]. For instance, an individual with fluctuating body weight has a higher risk of developing dementia than an individual with stable body weight. ...
BACKGROUND
As the world’s population is aging, there are growing needs for prevention and prediction of dementia among the general population. The availability of national time-series health examination data in South Korea invites an opportunity to utilise deep learning, an artificial intelligence technology, to expedite the analysis of mass and sequential data.
OBJECTIVE
We aimed to compare the discriminative accuracy between a time-series deep learning algorithm and conventional statistical methods for predicting all-cause dementia and Alzheimer’s dementia using periodic health examination data.
METHODS
We used a South Korean national health examination cohort database to identify dementia and Alzheimer’s dementia based on diagnostic codes in medical claims data over a 10-year period. Our analyses of all-cause dementia and Alzheimer’s dementia included 479,845 and 465,081 individuals, respectively, who were 40–79 years of age and without dementia at baseline. We compared the predictive performance of three models: Cox proportional hazards model using only baseline data (HR-B), Cox proportional hazards model using repeated measurements (HR-R), and deep learning model using repeated measurements (DL-R).
RESULTS
The discrimination indices (95% confidence interval) for HR-B, HR-R, and DL-R models predicting all-cause dementia were 0.84 (0.83–0.85), 0.87 (0.86–0.88), and 0.90 (0.90–0.90), respectively. The discrimination indices for HR-B, HR-R, and DL-R models predicting Alzheimer’s dementia were 0.87 (0.86–0.88), 0.90 (0.88–0.91), and 0.91 (0.91–0.91), respectively. Thus, the DL-R model showed the best performance, followed by the HR-R model, in predicting both all-cause dementia and Alzheimer’s dementia. The DL-R model was superior to the HR-R model in all validation groups tested.
CONCLUSIONS
A deep learning algorithm using time-series data could be an accurate, cost-effective method of predicting dementia. A combination of deep learning and proportional hazards models could help enhance prevention strategies for dementia.
... More recently, attention has also been drawn to the negative relation between body weight and cognitive functioning. Deficits in cognitive functioning associated to obesity have been observed in children, adolescents, and adults [2][3][4], and epidemiological studies have pointed to obesity as one of the main risk factors for cognitive decline and dementia in elder people [5,6]. Furthermore, numerous studies have reported negative associations between BMI and executive functions, including attention shifting and flexibility [7][8][9], inhibition [8,9], and working memory [10][11][12][13][14]. ...
Background:
Obesity is a highly stigmatizing condition, and reduced cognitive functioning is a stereotypical trait ascribed to individuals with obesity. In the present work, we tested the hypothesis that stereotype threat (i.e., a depletion of working memory resources due to the fear of confirming a negative self-relevant stereotype when a stereotype-related ability is assessed) contributes to cognitive deficits in individuals with obesity.
Methods:
Computerized tests of (a) working memory and (b) probabilistic learning-an ability unrelated with working memory-were administered to a community sample of 131 adults. Stereotype threat was manipulated by altering the alleged nature of the tasks; the tasks were alternatively labeled as intelligence tests (high stereotype threat condition), memory and learning tests (standard instructions condition), or distraction games (low stereotype threat condition).
Results:
A negative relation between body mass index (BMI) and working memory emerged in both the high stereotype threat (95% CIs = -0.872, -0.175, p = 0.003) and the standard instructions conditions (95% CIs = -0.974, -0.153, p = 0.007), but not in the low stereotype threat condition (95% CIs = -0.266, 0.430, p = 0.643). No effect emerged on probabilistic learning.
Conclusion:
Stereotype threat is associated with impaired working memory of individuals with obesity. Implications for researchers and clinicians are discussed.
... 13,21 Thus, the subsequent studies proposed that waist circumference or visceral adiposity is a vital predictor of dementia in one's midlife. 14,[22][23][24][25] Fatty cell accumulation and endocrine regulation have been proposed as the underlying mechanisms of dementia occurrence. 26 Some feasible and noninvasive body measures can reflect certain proposed mechanisms; for example, neck circumference and waist circumference can represent fatty cell accumulation, and body height and limb length measures are related to hormone secretion and are indicators of in utero or early-age nutrition. ...
Objective
Few studies have investigated the relationship between specific body measures and dementia.
Methods
Three‐dimensional anthropometric body surface scanning data containing 38 body measures were collected from 6831 participants from the health examination department of a medical center in Taiwan during 2000 to 2008, and 236 dementia cases were identified during the 10‐year follow‐up. A multiple Cox regression analysis was performed.
Results
Specific body measures, namely chest width (hazard ratio [HR] = 0.90; 95% confidence interval [CI] = 0.83–0.98), and right thigh circumference (HR = 0.93; 95% CI = 0.90–0.96), were protective predictors to dementia occurrence. Waist circumference (HR = 1.03; 95% CI = 1.02–1.05) was a risk factor in dementia occurrence. Among the combinations, dementia risk was higher in participants with a larger waist circumference and a smaller right thigh circumference, with the highest HR of 2.49 (95% CI = 1.54–4.03).
Conclusion
The body measures provide clues for future applications and scientific merits in both clinical and preventive medicine.
... Among the potentially modifiable risk factors for dementia, chronic metabolic conditions such as type-2 diabetes have been repeatedly shown to be associated with increased risk of cognitive decline [4,5], conversion of mild cognitive impairment to dementia [6], and development of dementiarelated disorders in general [7][8][9]. Although it has been suggested that midlife onset of diabetes is more strongly associated with dementia relative to onset of diabetes in older age [10], others found no modulating effect of diabetes duration on dementia risk [11], and yet others reported that relative to nondiabetic older adults, cognitive compromise in older diabetic adults is independent of age [12]. ...
Introduction:
Older adults with type 2 diabetes are at high risk of cognitive decline and dementia and form an important target group for dementia risk reduction studies. Despite evidence that computerized cognitive training (CCT) may benefit cognitive performance in cognitively healthy older adults and those with mild cognitive impairment, whether CCT may benefit cognitive performance or improve disease self-management in older diabetic adults has not been studied to date. In addition, whether adaptive difficulty levels and tailoring of interventions to individuals' cognitive profile are superior to generic training remains to be established.
Methods:
Ninety community-dwelling older (age ≥ 65) diabetic adults are recruited and randomized into a tailored and adaptive computerized cognitive training condition or to a generic, nontailored, or adaptive CCT condition. Both groups complete an 8-week training program using the commercially available CogniFit program. The intervention is augmented by a range of behavior-change techniques, and participants in each condition are further randomized into a global or cognition-specific phone-based self-efficacy (SE) condition, or a no-SE condition. The primary outcome is global cognitive performance immediately after the intervention. Secondary outcomes include diabetes self-management, meta-memory, mood, and SE.
Discussion:
This pilot study is the first trial evaluating the potential benefits of home-based tailored and adaptive CCT in relation to cognitive and disease self-management in older diabetic adults. Methodological strengths of this trial include the double-blind design, the clear identification of the proposed active ingredients of the intervention, and the use of evidence-based behavior-change techniques. Results from this study will indicate whether CCT has the potential to lower the risk of diabetes-related cognitive decline. The outcomes of the trial will also advance our understanding of essential intervention parameters required to improve or maintain cognitive function and enhance disease self-management in this at-risk group.
... Among the potentially modifiable risk factors for dementia, chronic metabolic conditions such as type-2 diabetes have been repeatedly shown to be associated with increased risk of cognitive decline [4,5], conversion of mild cognitive impairment to dementia [6], and development of dementiarelated disorders in general [7][8][9]. Although it has been suggested that midlife onset of diabetes is more strongly associated with dementia relative to onset of diabetes in older age [10], others found no modulating effect of diabetes duration on dementia risk [11], and yet others reported that relative to nondiabetic older adults, cognitive compromise in older diabetic adults is independent of age [12]. ...
... For example, the combination of increased body mass and age predicts poorer EF and processing speed (PS) . Longitudinal studies have shown that obesity during midlife predicts higher incidence of dementia later in life (Ravona-Springer, Schnaider-Beeri, & Goldbourt, 2013). Another study found that only obesity in midlife, and not in late life, predicted cognitive deficits in late life (Dahl, 2013). ...
We examined the moderating effects of age and cognitive reserve on the relationship between body mass index (BMI) and processing speed, executive function, and working memory based on the literature suggesting that obese individuals perform more poorly on measures of these abilities. Fifty-six healthy, dementia-free community-dwelling older (mean age 65.72 ± 7.40) and younger (mean age 21.10 ± 2.33) adults completed a neuropsychological battery and reported height and weight. Mixed effects models were used to evaluate the interactive effects of age, education (a proxy for cognitive reserve), and BMI on cognitive scores. Higher education was protective for executive deficits in younger, but not older adults. Age differences in executive functions were reduced at higher education levels but increased in individuals with higher BMI. Results suggest the inter-relationships between cognitive reserve – as measured by education – and BMI differ across age, and that obesity may accelerate the cognitive aging process.
Objective
High BMI, which poorly represents specific fat depots, is linked to poorer cognition and higher dementia risk, with different associations between sexes. This study examined associations of abdominal fat depots with cognition and brain volumes and whether sex modifies this association.
Methods
A total of 204 healthy middle‐aged offspring of Alzheimer's dementia patients (mean age = 59.44, 60% females) underwent abdominal magnetic resonance imaging to quantify hepatic, pancreatic, visceral, and subcutaneous adipose tissue and to assess cognition and brain volumes.
Results
In the whole sample, higher hepatic fat percentage was associated with lower total gray matter volume ( β = −0.17, p < 0.01). Primarily in males, higher pancreatic fat percentage was associated with lower global cognition (males: β = −0.27, p = 0.03; females: β = 0.01, p = 0.93) executive function (males: β = −0.27, p = 0.03; females: β = 0.02, p = 0.87), episodic memory (males: β = −0.28, p = 0.03; females: β = 0.07, p = 0.48), and inferior frontal gyrus volume (males: β = −0.28, p = 0.02; females: β = 0.10, p = 0.33). Visceral and subcutaneous adipose tissue was inversely associated with middle frontal and superior frontal gyrus volumes in males and females.
Conclusions
In middle‐aged males at high Alzheimer's dementia risk, but not in females, higher pancreatic fat was associated with lower cognition and brain volumes. These findings suggest a potential sex‐specific link between distinct abdominal fat with brain health.
Background:
Obesity has been linked to cognitive impairment. However, how changes in body mass index (BMI) over the life course influence cognitive function remains unclear.
Objective:
The influence of distinct weight-change patterns from young adulthood to midlife and late adulthood on cognitive function in older adults was explored.
Methods:
A total of 5,809 individuals aged≥60 years were included and categorized into four groups on the basis of BMI change patterns. Cognitive function was assessed using four cognition tests in the baseline survey. The relationship between the weight-change patterns and cognition was evaluated using regression models.
Results:
In comparison with participants who remained at non-obese, those moving from the non-obese to obese weight-change pattern from young (25 years of age) to middle adulthood showed lower Digit Symbol Substitution Test (DSST) scores (β= -1.28; 95% confidence interval [CI]: -2.24 to -0.32). A non-obese to obese change pattern from age 25 years of age to 10 years before baseline was associated with a higher risk of DSST impairment (odds ratio = 1.40; 95% CI: 1.09 to 1.79). In comparison with participants whose heaviest weight was recorded after 60 years of age, those with the heaviest weight between 18 and 40 years of age had lower DSST scores (β= -1.46; 95% CI: -2.77 to -1.52).
Conclusion:
Our results suggest that the transition from the non-obese to obese category in early adulthood and appearance of the heaviest weight between 18 and 40 years of age are associated with lower cognitive function in later life.
Background and objective
To examine whether early weight change is associated with subsequent deterioration in cognitive function, including overall performance and specific domains, in Parkinson’s disease (PD).
Methods
This observational study used data from the Parkinson’s Progression Markers Initiative cohort. The patients underwent annual non-motor assessments covering neuropsychiatric, sleep-related, and autonomic symptoms for up to 8 years of follow-up. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) and detailed neuropsychological testing. Linear mixed-effects models were applied to investigate the association of early weight change with longitudinal evolution of cognitive and other non-motor symptoms.
Results
A total of 358 early PD patients were classified into weight loss (decrease of >3% body weight during the first year; n=98), weight maintenance (within ±3%; n=201), and weight gain (increase of >3%; n=59) groups. The weight loss group showed a significantly faster decline in MoCA scores than the weight maintenance group (β=−0.19, 95% confidence interval [CI] −0.28 to −0.10). With respect to specific cognitive domains, the weight loss group showed a steeper decline in sematic fluency test scores (β=−0.37, 95% CI −0.66 to −0.08) and MoCA phonemic fluency scores (β=−0.18, 95% CI −0.31 to −0.05) and, to a lesser extent, Letter-Number Sequencing scores (β=−0.07, 95% CI −0.14 to 0.01) compared to the weight maintenance group. Conversely, the weight gain group showed a slower decline in the Symbol-Digit Modalities Test scores (β=0.34, 95% CI 0.05 to 0.63), although no association was found with longitudinal changes in MoCA scores. We did not find any significant effects of weight change on the progression of other non-motor symptoms.
Discussion
Early weight loss was associated with a faster progression of decline in global cognitive function and executive function in PD patients, whereas early weight gain was associated with a slower progression of decline in processing speed and attention. The impact of early weight change on non-motor symptoms appeared to be specific to cognition.
Aims
This study aimed to examine the association between body weight variability and dementia risk using a large-scale cohort data of Korean patients with type 2 diabetes mellitus (T2DM).
Methods
A population-based cohort of 1,206,764 individuals with T2DM aged ≥40 years who underwent ≥3 Korean national health screenings were followed up until the end of 2019. Body weight variability was assessed using variability independent of the mean (VIM). A multivariate Cox proportional hazard regression was performed with calculating hazard ratios (HRs) with 95% confidence intervals (CIs) of dementia incidence.
Results
During a median follow-up of 7.9 years, 162,615 (13.4%) individuals developed dementia. Individuals with greater body weight variability tended to be associated with higher risk of all types of dementia (P for trend <0.001). Individuals in the highest quartile of VIM showed 26% (HR: 1.26, 95% CI: 1.24–1.28), 33% (HR: 1.33, 95% CI: 1.30–1.36) and 28% (HR: 1.28, 95% CI: 1.23–1.33) higher risk for all-cause dementia, Alzheimer’s disease, and vascular dementia, compared with those in the lowest quartile. These associations persisted in all body mass index categories (P for trend <0.001).
Conclusions
Maintaining an appropriate body weight may help mitigate dementia risk in patients with T2DM.
Background
Body weight variability (BWV) negatively affects the incidence and outcomes of various diseases, but the nature of the association between BWV and depression remains unclear. In this study, we aimed to test the hypothesis that BWV is associated with the risk of new-onset depression.
Methods
Data from a nationwide population-based cohort in the Korean National Health Insurance Service database were analyzed for 6 598 570 adults with no history of depression and reports of at least three health examinations. BWV was estimated using variability independent of the mean indices and divided into quartiles (Q1 lowest, Q4 highest BWV). Cox proportional hazard models were applied to assess the risk of depression according to the quartile of BWV.
Results
The incident rate for depression from Q1 to Q4 of BWV was 20.7, 20.3, 20.8, and 22.2 per 1000 person-years, respectively. BWV, especially high BWV, was associated with an increased risk of depression after adjusting for age, sex, smoking, alcohol consumption, physical activity, income, diabetes mellitus, hypertension, and dyslipidemia. The hazard ratio (HR) of new-onset depression was highest in Q4 relative to Q1 in the total population (HR 1.12, p < 0.0001) and was higher in women than in men (HR 1.72 v. 1.16, p < 0.0001). In stratified analyses, regardless of obesity or weight change status at baseline, the risk of depression was increased when bodyweight fluctuated highly during follow-up.
Conclusions
High BWV was associated with an increased risk of depression. Further studies need to evaluate the role of high BWV with respect to the onset of depression.
Objective:
Evidence on simultaneous changes in body mass index (BMI) and cognitive decline, which better reflect the natural course of both health phenomena, is limited.
Methods:
We capitalized on longitudinal data from 15,977 initially non-demented elderly from the Alzheimer's Disease Centers followed for 5 years on average. Changes in BMI were defined as (1) last minus first BMI, (2) mean of all follow-up BMIs minus first BMI, and (3) standard deviation of BMI change from baseline and all follow-up visits (representing variability).
Results:
Participants with significant changes in BMI (increase or decrease of ≥5%), or who had greater variability in BMI, had faster cognitive decline. This pattern was consistent irrespective of normal (BMI < 25; N = 5747), overweight (25 ≤ BMI < 30; N = 6302), or obese (BMI ≥ 30; N = 3928) BMI at baseline.
Conclusions:
Stability in BMI predicts better cognitive trajectories suggesting clinical value in tracking BMI change, which is simple to measure, and may point to individuals whose cognition is declining.
Background:
Although body weight variability has been associated with mortality, cardiovascular disease, and dementia, the relationship between body weight variability and Parkinson's disease (PD) has been rarely studied. We aimed to investigate the longitudinal association between body weight variability and PD incidence.
Methods:
A nationwide population-based, cohort study was conducted using the database from the Health Insurance Review and Assessment Service of the whole Korean population. We analyzed 2,815,135 participants (≥ 40 years old, mean age 51.7 (8.6) years, 66.8% men) without a previous PD diagnosis. We determined individual body weight variability from baseline weight and follow-up visits. We used Cox proportional hazards regression models.
Results:
The highest quartile group was associated with increased PD incidence compared with the lowest quartile group after adjustment for confounding factors (hazard ratio (HR), 1.18; 95% confidence interval (CI): 1.08-1.29). In contrast, baseline body mass index, baseline waist circumference, and waist circumference variability were not associated with increased PD incidence. In the body weight loss group, individuals within the quartile of the highest variation in body weight showed a higher HR of PD risk than those within other quartiles (HR, 1.41; 95% CI: 1.18-1.68).
Conclusion:
Body weight variability, especially weight loss, was associated with higher PD incidence. This finding has important implications for clinicians and supports the need for preventative measures and surveillance for PD in individuals with fluctuating body weight.
Introduction
The relationship between variability in cardiometabolic and inflammatory parameters and cognitive changes is unknown. This study investigates the association of visit-to-visit variability in BMI, mean arterial pressure, total cholesterol, triglycerides, HbA1c, high-sensitivity C-reactive protein, ferritin, and fibrinogen with cognitive decline.
Methods
This population-based cohort study included 2,260 individuals (mean age=63.0 [SD=7.5] years) free of cognitive diseases who underwent ≥3 clinical measurements from 2004 to 2019. Variability was expressed as variability independent of the mean across visits. Participants were divided on the basis of quartiles of variability score, a scoring system generated to explore the composite effect of parameter variability (range=0−24), where 0 points were assigned for Quartile 1, 1 point was assigned for Quartile 2, 2 points were assigned for Quartile 3, and 3 points were assigned for Quartile 4, each for the variability of 8 parameters measured as variability independent of the mean. Linear mixed models evaluated the longitudinal associations with cognitive decline in memory and verbal fluency. All analyses were conducted in 2020−2021.
Results
Higher BMI, mean arterial pressure, total cholesterol, HbA1c, and ferritin variability were linearly associated with cognitive decline irrespective of their mean values. In addition, participants in the highest quartile of variability score had a significantly worse cognitive decline rate in memory (−0.0224 points/year, 95% CI= −0.0319, −0.0129) and verbal fluency (−0.0088 points/year, 95% CI= −0.0168, −0.0008) than those in the lowest quartile.
Conclusions
A higher variability in cardiometabolic and inflammatory parameters was significantly associated with cognitive decline. Stabilizing these parameters may serve as a target to preserve cognitive functioning.
Rationale
Change in BMI is recognized as a key health indicator among midlife and older adults, though predictors of BMI change in this group have received little attention.
Objective
The aim of this study was to examine relations between hypothesized predictors (i.e., gender, cardiovascular disease [CVD] risk status, depressive symptoms, social support) and BMI change over 10 years, among midlife and older adults.
Methods
Participants were adults ages 50-74 at baseline (N = 5,688, 64% women) who completed four assessments over 10 years. Gender, CVD risk status (i.e., diagnosis of hypertension, type 2 diabetes, or both), depressive symptoms, and perceived social support were assessed at baseline, and BMI was calculated from height and weight reports at all assessments. Multilevel models tested for concurrent and prospective relations between predictors and BMI change (effect size estimates as semipartial correlation coefficients, sr), as well as whether observed relations were further moderated by baseline BMI category (underweight, healthy weight, overweight, or obese).
Results
Baseline BMI was higher among those with (vs. without) CVD risk, higher (vs. lower) depressive symptoms, and lower (vs. higher) social support; all of these relations were moderated by gender (ps < 0.05, srs 0.03-0.32). Moreover, BMI showed significant change over 10 years, and BMI variability during this time was higher among women (vs. men) and those with (vs. without) CVD risk (ps < 0.0001). BMI change also differed by CVD risk status, and this relation was moderated by gender, baseline depressive symptoms, and baseline BMI category (ps < 0.05, srs 0.03-0.08).
Conclusions
Although the predictors of interest were not associated with steady BMI decreases (which are associated with long term health risks for older adults), findings reveal unique patterns of change in BMI among subgroups of midlife and older adults, and may allow for early identification of those with noteworthy BMI changes after age 50.
Controversies persist about the associations of body mass index (BMI) with risk of cognitive impairment and dementia. This study aimed to evaluate these association from various aspects, in which Embase, PubMed and Cochrane databases were searched to identify prospective studies up to May 2019. Random-effects meta-analyses and dose-response meta-analysis were conducted, involving twenty-nine of 20,083 identified literatures. Meta-analysis showed that midlife underweight, obesity and late-life underweight conferred 1.39-, 1.31- and 1.64-fold excess risk for cognitive impairment and dementia, while late-life overweight and obesity conferred 21% and 25% reduced risk. In dose-response meta-analysis, all cause dementia (ACD), Alzheimer’s disease (AD) and vascular dementia (VaD) risk in midlife was significantly elevated when BMI surpassed 29, 30 and 32 kg/m². AD risk in late-life was decreased when BMI was under 27 kg/m², while this protection for VaD was absent when BMI surpassed 39 kg/m². Higher BMI produced opposite exerted opposite effects on dementia in mid- and late-age population. Firstly reported, a dose-response relationship further supports the guideline from the standpoint of dementia prevention.
Atherosclerotic heart disease remains a leading cause of morbidity and mortality worldwide. While extensive research supports cardiovascular risk factor reduction in the form of achieving evidence-based blood pressure, lipid, glucose, and body weight targets as a means to improve cardiovascular outcomes, residual risk remains. Emerging data have demonstrated that the intraindividual variability of these risk factor targets potentially contribute to this residual risk. It may therefore be time to define risk factor by not only its magnitude and duration as done traditionally, but perhaps also by the variability of that particular risk factor over time.
Objective
The aim of this study is to assess the association between midlife risk factors and dementia.
Methods
PubMed and Cochrane library were systematically searched on May 24, 2018, to retrieve prospective cohort studies. The summary relative risk (RR) and 95% confidence interval (CI) were calculated by the random-effect model to explore the association between midlife risk factors and dementia. Sensitivity analysis and meta-regression were conducted to explore the source of heterogeneity. Publication bias was examined using Begg's and Egger's tests.
Results
Thirty-four prospective cohort studies were included, among which 24 were eligible for meta-analysis. A total of 159,594 non-demented adults were enrolled at baseline before 65 years and 13,540 people were diagnosed with dementia after follow-up. The pooled results revealed that five factors could significantly increase the dementia risk by 41 to 78%, including obesity (RR, 1.78; 95% CI: 1.31-2.41), diabetes mellitus (RR, 1.69; 95% CI: 1.38-2.07), current smoking (RR, 1.61; 95%, CI: 1.32-1.95), hypercholesterolemia (RR, 1.57; 95% CI: 1.19-2.07), and hypertension (borderline blood pressure RR, 1.41; 95% CI: 1.23-1.62 and high systolic blood pressure (SBP) RR, 1.72; 95% CI: 1.25-2.37). However, the sensitivity analyses found that the results of hypercholesterolemia and high SBP were not reliable, which need to be confirmed by more high-quality studies. No influences due to publication bias were revealed. In the systematic review, another three factors (hyperhomocysteinemia, psychological stress, and heavy drinking) were found to be associated with elevated dementia risk. In addition, physical exercise, a healthy diet, and hormone therapy in middle age were associated with the reduction of dementia risk.
Conclusions
Middle-aged people with obesity, diabetes, hypertension, or hypercholesterolemia, and current smokers in midlife are at higher risk of developing dementia later in life.
Background:
With the increase in the world's aging population, there is a growing need to prevent and predict dementia among the general population. The availability of national time-series health examination data in South Korea provides an opportunity to use deep learning algorithm, an artificial intelligence technology, to expedite the analysis of mass and sequential data.
Objective:
This study aimed to compare the discriminative accuracy between a time-series deep learning algorithm and conventional statistical methods to predict all-cause dementia and Alzheimer dementia using periodic health examination data.
Methods:
Diagnostic codes in medical claims data from a South Korean national health examination cohort were used to identify individuals who developed dementia or Alzheimer dementia over a 10-year period. As a result, 479,845 and 465,081 individuals, who were aged 40 to 79 years and without all-cause dementia and Alzheimer dementia, respectively, were identified at baseline. The performance of the following 3 models was compared with predictions of which individuals would develop either type of dementia: Cox proportional hazards model using only baseline data (HR-B), Cox proportional hazards model using repeated measurements (HR-R), and deep learning model using repeated measurements (DL-R).
Results:
The discrimination indices (95% CI) for the HR-B, HR-R, and DL-R models to predict all-cause dementia were 0.84 (0.83-0.85), 0.87 (0.86-0.88), and 0.90 (0.90-0.90), respectively, and those to predict Alzheimer dementia were 0.87 (0.86-0.88), 0.90 (0.88-0.91), and 0.91 (0.91-0.91), respectively. The DL-R model showed the best performance, followed by the HR-R model, in predicting both types of dementia. The DL-R model was superior to the HR-R model in all validation groups tested.
Conclusions:
A deep learning algorithm using time-series data can be an accurate and cost-effective method to predict dementia. A combination of deep learning and proportional hazards models might help to enhance prevention strategies for dementia.
Conduct of research is an essential tool for the evaluation and improvement of health services. In Israel, research on persons with dementia is very limited, with the largest portion of such research involving a few surveys and examining risk factors for dementia. Very few studies describe clinical research, and those that do either include participants at early stages of dementia, or rely completely on caregivers’ perceptions and experiences, often without reference to any individual with dementia. This dearth of research is due, to a substantial extent, to Ministry of Health regulations which do not permit family proxy consent for research involving persons with dementia. Alternative models for regulation of consent for research exist in other countries, including the U.S., and these allow for proxy consent under certain conditions. This paper presents such a model and its underlying ethical principles. It contends that the current state of affairs, which stands in the way of clinical research concerning persons with advanced dementia, is contrary to the interests of such persons, their caregivers, and Israeli society. Therefore, this paper calls for a change in the present regulations and/or law in the cause of advancing knowledge and improving care for persons with dementia.
Objective:
To assess whether an average of 10 years of lifestyle intervention designed to reduce weight and increase physical activity lowers the prevalence of cognitive impairment among adults at increased risk due to type 2 diabetes and obesity or overweight.
Methods:
Central adjudication of mild cognitive impairment and probable dementia was based on standardized cognitive test battery scores administered to 3,802 individuals who had been randomly assigned, with equal probability, to either the lifestyle intervention or the diabetes support and education control. When scores fell below a prespecified threshold, functional information was obtained through proxy interview.
Results:
Compared with control, the intensive lifestyle intervention induced and maintained marked differences in weight loss and self-reported physical activity throughout follow-up. At an average (range) of 11.4 (9.5-13.5) years after enrollment, when participants' mean age was 69.6 (54.9-87.2) years, the prevalence of mild cognitive impairment and probable dementia was 6.4% and 1.8%, respectively, in the intervention group, compared with 6.6% and 1.8%, respectively, in the control group (p = 0.93). The lack of an intervention effect on the prevalence of cognitive impairment was consistent among individuals grouped by cardiovascular disease history, diabetes duration, sex, and APOE ε4 allele status (all p ≥ 0.50). However, there was evidence (p = 0.03) that the intervention effect ranged from benefit to harm across participants ordered from lowest to highest baseline BMI.
Conclusions:
Ten years of behavioral weight loss intervention did not result in an overall difference in the prevalence of cognitive impairment among overweight or obese adults with type 2 diabetes.
Clinicaltrialsgov identifier:
NCT00017953 (Action for Health in Diabetes).
Level of evidence:
This study provides Class II evidence that for overweight adults with type 2 diabetes, a lifestyle intervention designed to reduce weight and increase physical activity does not lower the risk of cognitive impairment.
Background:
The relationship of obesity with risk for dementia is complex and may change with age.
Objective:
To analyze the relationship between measures of obesity at age 40-65 and dementia prevalence in survivors 36 years later.
Methods:
Obesity-related measures of triceps and subscapular skinfold thickness were assessed in 1963 in n = 9,760 men aged 40-65 participating in the Israel Ischemic Heart Disease study. Cognitive evaluation and assessment of dementia prevalence were performed in n = 1,643 participants of the original cohort who survived until 1999/2000 (age ≥76 years) and had anthropometric measures in 1963.
Results:
Age-adjusted prevalence of dementia in survivors in 1999/2000 by baseline triceps skinfold quintile was 20.5%, 21.2%, 17.6%, 15.6%, and 14.5%, respectively, from lowest to highest (p = 0.006 in trend test). Using logistic regression, a 6-mm increment of triceps skinfold was associated with an age and BMI-adjusted odds ratio of 0.81 (95% CI, 0.70-0.94) for dementia prevalence among survivors. Age-adjusted risk for dementia by subscapular skinfold quintile demonstrated 20.5%, 17.1%, 15.7%, 19.4%, and 18.1%, respectively, in groups of subjects by subscapular skinfold quintile from lowest to highest (p = 0.6 in trend test).
Conclusions:
Lower triceps skinfold at age 40-65, reflecting diminished peripheral fat, was associated with higher dementia prevalence in late life, potentially suggesting a protective role of peripheral fat to brain health.
Background:
Whether life course anthropometric indices relate to cognitive function in midlife remains insufficiently explored. Rarely was socioeconomic position (SEP) adequately accounted for.
Objective:
To examine the association of the cumulative life course burden of high-ranked body mass index (BMI), its trajectory, and stature with cognitive function in midlife.
Methods:
Weight and height were measured from age 17 across a 33-year follow-up. 507 individuals completed a NeuroTrax computerized cognitive assessment at ages 48-52. Life course SEP was assessed by multiple methods. Using mixed models we calculated the area under the curve (AUC), representing both the life-course burden of BMI (total AUC) and trends in BMI (incremental AUC) from age 17 to midlife. The associations of BMI and height with global cognition and its five component domains were assessed by multiple regression.
Results:
Higher BMI in late adolescence and total AUC over the life course were associated with poorer global cognition (Standardized beta (Beta) = -0.111, p = 0.005 and Beta = -0.105, p = 0.018, respectively), adjusted for childhood and adulthood SEP, and demographic characteristics. The associations with higher adolescent and midlife BMI were both restricted to those with low childhood SEP (p < 0.05 for interaction). Short adolescent stature was related to poorer cognition (Beta = 0.084, p = 0.044), whereas late final growth in women was associated with better cognition (Beta = 0.213, p = 0.007).
Conclusion:
An adverse association of higher BMI with cognitive function began in adolescence and was restricted to low childhood SEP. Taller stature in both sexes and late growth in women were associated with better midlife cognitive performance.
Age-related dementia is increasingly recognized as having a mixed pathology, with contributions from both cerebrovascular factors and pathogenic factors associated with Alzheimer's disease (AD). Furthermore, there is accumulating evidence that vascular risk factors in midlife, e.g., obesity, diabetes, and hypertension, increase the risk of developing late-life dementia. Since obesity and changes in body weight/adiposity often drive diabetes and hypertension, understanding the relationship between adiposity and age-related dementia may reveal common underlying mechanisms. Here we offer a brief appraisal of how changes in body weight and adiposity are related to both AD and dementia on vascular basis, and examine the involvement of two key adipocyte-derived hormones: leptin and adiponectin. The evidence suggests that in midlife increased body weight/adiposity and subsequent changes in adipocyte-derived hormones may increase the long-term susceptibility to dementia. On the other hand, later in life, decreases in body weight/adiposity and related hormonal changes are early manifestations of disease that precede the onset of dementia and may promote AD and vascular pathology. Understanding the contribution of adiposity to age-related dementia may help identify the underlying pathological mechanisms common to both vascular dementia and AD, and provide new putative targets for early diagnosis and therapy.
To determine whether adhering to a healthy lifestyle in midlife may reduce the risk of dementia.
Case-control study nested in a prospective cohort.
The Honolulu-Asia Aging Study, Oahu, Hawaii.
Three thousand four hundred sixty-eight Japanese-American men (mean age 52 in 1965-1968) examined for dementia 25 years later.
Men at low risk were defined as those with the following midlife characteristics: nonsmoking, body mass index (BMI) less than 25.0 kg/m(2) , physically active, and having a healthy diet (based on alcohol, dairy, meat, fish, fruits, vegetables, cereals, and ratio of monounsaturated to saturated fat). Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for developing overall dementia, Alzheimer's disease (AD), and vascular dementia (VaD), adjusting for potential confounders.
Dementia was diagnosed in 6.4% of men (52.5% with AD, 35.0% with VaD). Examining the risk factors individually, BMI was most strongly associated with greater risk of overall dementia (OR = 1.87, 95% CI = 1.26-2.77; BMI > 25.0 vs <22.6 kg/m(2) ). All of the individual risk factors except diet score were significantly associated with VaD, whereas none were significantly associated with AD alone. Men with all four low-risk characteristics (7.2% of the cohort) had the lowest OR for overall dementia (OR = 0.36, 95% CI = 0.15-0.84). There were no significant associations between the combined low-risk characteristics and the risk of AD alone.
Among Japanese-American men, having a healthy lifestyle in midlife is associated with a lower risk of dementia in late life.
Personality traits contribute to health outcomes, in part through their association with major controllable risk factors, such as obesity. Body weight, in turn, reflects our behaviors and lifestyle and contributes to the way we perceive ourselves and others. In this study, the authors use data from a large (N = 1,988) longitudinal study that spanned more than 50 years to examine how personality traits are associated with multiple measures of adiposity and with fluctuations in body mass index (BMI). Using 14,531 anthropometric assessments, the authors modeled the trajectory of BMI across adulthood and tested whether personality predicted its rate of change. Measured concurrently, participants higher on Neuroticism or Extraversion or lower on Conscientiousness had higher BMI; these associations replicated across body fat, waist, and hip circumference. The strongest association was found for the impulsivity facet: Participants who scored in the top 10% of impulsivity weighed, on average, 11Kg more than those in the bottom 10%. Longitudinally, high Neuroticism and low Conscientiousness, and the facets of these traits related to difficulty with impulse control, were associated with weight fluctuations, measured as the variability in weight over time. Finally, low Agreeableness and impulsivity-related traits predicted a greater increase in BMI across the adult life span. BMI was mostly unrelated to change in personality traits. Personality traits are defined by cognitive, emotional, and behavioral patterns that likely contribute to unhealthy weight and difficulties with weight management. Such associations may elucidate the role of personality traits in disease progression and may help to design more effective interventions.
In people trying to lose weight, there are often repeated cycles of weight loss and regain. Weight cycling is, however, not limited to obese adults but affects people of normal weight, particularly young women, who are unhappy with their appearance. Furthermore, the onset of a pattern of weight cycling is shifting towards younger ages, owing to the increasing prevalence of overweight and obesity in children and adolescents, and the pressure from the media and society for a slim image even for normal weight children. Although there is still controversy whether weight cycling promotes body fat accumulation and obesity, there is mounting evidence from large population studies for increased cardiovascular risks in response to a behavior of weight cycling. Potential mechanisms by which weight cycling contributes to cardiovascular morbidity include hypertension, visceral fat accumulation, changes in adipose tissue fatty acid composition, insulin resistance and dyslipidemia. Moreover, fluctuations in blood pressure, heart rate, sympathetic activity, glomerular filtration rate, blood glucose and lipids that may occur during weight cycling--with overshoots above normal values during weight regain periods--put an additional load on the cardiovascular system, and may be easily overlooked if humans or animals are studied during a state of relatively stable weight. Overshoot of those risks factors, when repeated over time, will stress the cardiovascular system and probably contribute to the overall cardiovascular morbidity of weight cycling.
The prevalence of severe obesity is increasing markedly, as is prevalence of comorbid conditions such as hypertension and type 2 diabetes mellitus; however, apart from bariatric surgery and pharmacotherapy, few clinical trials have evaluated the treatment of severe obesity.
To determine the efficacy of a weight loss and physical activity intervention on the adverse health risks of severe obesity.
Single-blind randomized trial conducted from February 2007 through April 2010 at the University of Pittsburgh. Participants were 130 (37% African American) severely obese (class II or III) adult participants without diabetes recruited from the community.
One-year intensive lifestyle intervention consisting of diet and physical activity. One group (initial physical activity) was randomized to diet and physical activity for the entire 12 months; the other group (delayed physical activity) had the identical dietary intervention but with physical activity delayed for 6 months.
Changes in weight. Secondary outcomes were additional components comprising cardiometabolic risk, including waist circumference, abdominal adipose tissue, and hepatic fat content.
Of 130 participants randomized, 101 (78%) completed the 12-month follow-up assessments. Although both intervention groups lost a significant amount of weight at 6 months, the initial-activity group lost significantly more weight in the first 6 months compared with the delayed-activity group (10.9 kg [95% confidence interval {CI}, 9.1-12.7] vs 8.2 kg [95% CI, 6.4-9.9], P = .02 for group × time interaction). Weight loss at 12 months, however, was similar in the 2 groups (12.1 kg [95% CI, 10.0-14.2] vs 9.9 kg [95% CI, 8.0-11.7], P = .25 for group × time interaction). Waist circumference, visceral abdominal fat, hepatic fat content, blood pressure, and insulin resistance were all reduced in both groups. The addition of physical activity promoted greater reductions in waist circumference and hepatic fat content.
Among patients with severe obesity, a lifestyle intervention involving diet combined with initial or delayed initiation of physical activity resulted in clinically significant weight loss and favorable changes in cardiometabolic risk factors.
clinicaltrials.gov Identifier: NCT00712127.
The process by which emotions affect eating behavior emerges as one of the central unresolved questions in the field of emotional eating. The present studies address the hypothesis that the regulation strategies people use to deal with these emotions are responsible for increased eating. Negative emotions were induced and intake of comfort food and non-comfort food was measured by means of taste tests. Emotion induction was preceded by measuring individual differences in emotion regulation strategies (Study 1) or by instructions to regulate emotions in either an adaptive (reappraisal) or maladaptive (suppression) manner (Study 2). Study 3 also entailed a control condition without any regulation instructions. Relative to reappraisal and spontaneous expression, suppression led to increased food intake, but only of the comfort foods. Emotions themselves were not responsible for this effect. These findings provide new evidence that the way in which emotions are regulated affects eating behavior.
Dementia drug development aims to modulate pathological processes that cause clinical syndromes. Population data (epidemiological neuropathology) will help to model and predict the potential impact of such therapies on dementia burden in older people. Presently this can only be explored through post mortem findings. We report the attributable risks (ARs) for dementia at death for common age-related degenerative and vascular pathologies, and other factors, in the MRC Cognitive Function and Ageing Study (MRC CFAS).
A multicentre, prospective, longitudinal study of older people in the UK was linked to a brain donation programme. Neuropathology of 456 consecutive brain donations assessed degenerative and vascular pathologies. Logistic regression modelling, with bootstrapping and sensitivity analyses, was used to estimate AR at death for dementia for specific pathologies and other factors. The main contributors to AR at death for dementia in MRC CFAS were age (18%), small brain (12%), neocortical neuritic plaques (8%) and neurofibrillary tangles (11%), small vessel disease (12%), multiple vascular pathologies (9%), and hippocampal atrophy (10%). Other significant factors include cerebral amyloid angiopathy (7%) and Lewy bodies (3%).
Such AR estimates cannot be derived from the living population; rather they estimate the relative contribution of specific pathologies to dementia at death. We found that multiple pathologies determine the overall burden of dementia. The impact of therapy targeted to a specific pathology may be profound when the dementia is relatively "pure," but may be less impressive for the majority with mixed disease, and in terms of the population. These data justify a range of strategies, and combination therapies, to combat the degenerative and vascular determinants of cognitive decline and dementia. Please see later in the article for the Editors' Summary.
Controversy exists about whether weight cycling increases morbidity and mortality.
To assess the independent association of weight cycling with mortality, we conducted a prospective study of 44 882 middle-aged or older women in the Nurses' Health Study who provided information on intentional weight losses between 1972 and 1992, survived until at least 1994, had a body mass index (calculated as weight in kilograms divided by height in meters squared) of at least 17, and had no history of cancer (other than nonmelanoma skin cancer) or heart disease. Women who reported they had intentionally lost at least 9.1 kg at least 3 times were classified as severe weight cyclers. Women who had intentionally lost at least 4.5 kg at least 3 times but did not meet the criteria for severe weight cycling were classified as mild weight cyclers. All-cause mortality and cardiovascular mortality were assessed.
Between 1972 and 1992, approximately 18.8% of the women were mild weight cyclers, and 8.0% were severe weight cyclers. During 12 years of follow-up, 2884 women died; of their deaths, 425 were due to cardiovascular events. Weight cyclers gained more weight during follow-up than noncyclers (P < .001). After adjusting for BMI at age 18 years, physical activity, smoking, postmenopausal hormone replacement therapy, alcohol intake, net weight change from age 18 years, and change in physical activity, there was no increase in risk of all-cause mortality among mild (relative risk [RR], 0.83; 95% confidence interval [CI], 0.75-0.93) or severe cyclers (RR, 0.89; 95% CI, 0.77-1.04). Similar results were observed for cardiovascular mortality and among women 70 years or younger.
Repeated intentional weight losses were not predictive of greater all-cause or cardiovascular mortality.
Elevated rates of comorbidity between binge eating and alcohol use problems have been widely documented. Prior studies have examined specific personality traits associated with the co-occurrence of these problems. The current study explores comprehensive personality factors that are associated with the co-occurrence of binge eating and binge drinking among a diverse sample of 208 college undergraduates. Using the Five Factor Model of personality, the authors assessed both comprehensive personality factors and style of impulse control, a personality style defined by different combinations of neuroticism and conscientiousness. On the basis of responses to a screening instrument, college students were assigned to one of four groups: binge eat, binge drink, binge eat and drink, and non-binge. The binge eat and drink group reported a higher level of neuroticism than did students in the binge drink and non-binge groups. Additionally, the binge eat and drink group was more likely to report an undercontrolled style of impulse control than were other groups. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
It is still unclear whether weight gain from early to late adulthood affects longevity. Furthermore, no study has addressed its association with all-cause and cause-specific mortality in an Asian population.
We prospectively assessed the association between an increase in body mass index (BMI) category since age 20 years and risk of all-cause, cardiovascular disease (CVD) and cancer mortality. Self-reported information pertaining to BMI was collected from 38 080 Japanese men and women aged 40-79 years at study entry in 1994 after exclusion of participants with a BMI of <18.5 kg/m(2) at age 20 years or at study entry. We defined six patterns of increase in BMI category at age 20 years and study entry: stable normal, overweight and obese, normal to overweight or obese, and overweight to obese.
During 7 years of follow-up, 2617 participants died. After adjustment for potential confounders, we observed a significantly increased risk of all-cause mortality for the pattern of normal weight at age 20 years and obese at study entry and of stable obese compared with stable normal in BMI category, the multivariate HRs (95% confidence interval (CI)) being 1.42 (1.08-1.88) and 2.26 (1.45-3.51), respectively. For the pattern of overweight at age 20 years and obese at study entry, the multivariate hazard ratio (95% CI) was 1.35 (0.92-1.98). In contrast, we did not observe an increased risk of all-cause mortality for normal weight at age 20 years and overweight at study entry, and stable overweight. For CVD and cancer mortality, these results were consistently observed.
We observed an increased risk of all-cause mortality both among participants who had been persistently obese since early adulthood and participants who showed an increase in BMI category from normal to obese, compared with participants with a stable normal BMI category.
The prevalence of obesity and overweight increased in the United States between 1978 and 1991. More recent reports have suggested continued increases but are based on self-reported data.
To examine trends and prevalences of overweight (body mass index [BMI] > or = 25) and obesity (BMI > or = 30), using measured height and weight data.
Survey of 4115 adult men and women conducted in 1999 and 2000 as part of the National Health and Nutrition Examination Survey (NHANES), a nationally representative sample of the US population.
Age-adjusted prevalence of overweight, obesity, and extreme obesity compared with prior surveys, and sex-, age-, and race/ethnicity-specific estimates.
The age-adjusted prevalence of obesity was 30.5% in 1999-2000 compared with 22.9% in NHANES III (1988-1994; P<.001). The prevalence of overweight also increased during this period from 55.9% to 64.5% (P<.001). Extreme obesity (BMI > or = 40) also increased significantly in the population, from 2.9% to 4.7% (P =.002). Although not all changes were statistically significant, increases occurred for both men and women in all age groups and for non-Hispanic whites, non-Hispanic blacks, and Mexican Americans. Racial/ethnic groups did not differ significantly in the prevalence of obesity or overweight for men. Among women, obesity and overweight prevalences were highest among non-Hispanic black women. More than half of non-Hispanic black women aged 40 years or older were obese and more than 80% were overweight.
The increases in the prevalences of obesity and overweight previously observed continued in 1999-2000. The potential health benefits from reduction in overweight and obesity are of considerable public health importance.
Overweight and obesity are epidemic in Western societies and constitute a major public health problem because of adverse effects on vascular health. Vascular factors may play a role in the development of a rapidly growing disease of late life, Alzheimer disease (AD). Using body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters), we examined whether overweight is a risk factor for dementia and AD.
The relationship between BMI and dementia risk was investigated in a representative cohort of 392 nondemented Swedish adults who were followed up from age 70 to 88 years, with the use of neuropsychiatric, anthropometric, and other measurements. Multivariate Cox proportional hazards regression analyses included BMI, blood pressure, cardiovascular disease, cigarette smoking, socioeconomic status, and treatment for hypertension.
During the 18-year follow-up (4184.8 risk-years), 93 participants were diagnosed as having dementia. Women who developed dementia between ages 79 and 88 years were overweight, with a higher average BMI at age 70 years (27.7 vs 25.7; P =.007), 75 years (27.9 vs 25.0; P<.001), and 79 years (26.9 vs 25.1; P =.02) compared with nondemented women. A higher degree of overweight was observed in women who developed AD at 70 years (29.3; P =.009), 75 years (29.6; P<.001), and 79 years (28.2; P =.003) compared with nondemented women. For every 1.0 increase in BMI at age 70 years, AD risk increased by 36%. These associations were not found in men.
Overweight is epidemic in Western societies. Our data suggest that overweight at high ages is a risk factor for dementia, particularly AD, in women. This may have profound implications for dementia prevention.
Previous studies have shown that risk factors commonly associated with coronary disease, stroke, and other vascular disorders also predict dementia. We investigated the longitudinal relationship between body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) and risk of hospital discharge or death certificate diagnosis of dementia.
A total of 7402 men who were 47 to 55 years old in 1970 to 1973, without prior stroke or myocardial infarction, derived from a population sample of 9998 men were prospectively followed up until 1998. Two hundred fifty-four men (3.4%) had a hospital discharge diagnosis or a death certificate diagnosis of dementia: 176 with a primary diagnosis or cause of death and 78 with a secondary diagnosis.
The relationship between BMI and dementia as a primary diagnosis was J-shaped, and men with a BMI between 20.00 and 22.49 had the lowest risk. Subsequently, after adjustment for smoking, blood pressure, serum cholesterol level, diabetes mellitus, and social class, the risk increased linearly in men who had a BMI of 22.50 to 24.99 (multiple-adjusted hazard ratio [HR], 1.73; 95% confidence interval [CI], 0.92-3.25), 25.00 to 27.49 (HR, 1.93; 95% CI, 1.03-3.63), 27.50 to 29.99 (HR, 2.30; 95% CI, 1.18-4.47), and 30.00 or greater (HR, 2.54; 95% CI, 1.20-5.36) (P for linear trend = .03). Men with a BMI less than 20.00 had a nonsignificantly elevated risk (HR, 2.19; 95% CI, 0.77-6.25).
A J-shaped relationship was observed between BMI and dementia, such that a BMI less than 20 and an increasing BMI of 22.5 or greater were associated with increased risk from midlife to old age of a primary hospital diagnosis of dementia. Overweight and obesity could be major preventable factors in the development of dementia.
Weight loss in the obese improves risk factors for cardiovascular diseases and diabetes. However, several studies have shown inconsistent long-term effects of weight loss on mortality. We investigated the influence on mortality of intention to lose weight and subsequent weight changes among overweight individuals without known co-morbidities.
In 1975, a cohort of individuals reported height, weight, and current attempts (defined as "intention") to lose weight, and in 1981, they reported current weight. Mortality of the 2,957 participants with body mass index > or = 25 kg/m2 in 1975 and without pre-existing or current diseases was followed from 1982 through 1999, and 268 participants died. The association of intention to lose weight in 1975 and actual weight change until 1981 with mortality was analysed while controlling for behavioural and psychosocial risk factors and hypertension as possible confounders. Compared with the group not intending to lose and able to maintain stable weight, the hazard ratios (with 95% confidence intervals) in the group intending to lose weight were 0.84 (0.49-1.48) for those with stable weight, 1.86 (1.22-2.87) for those losing weight, and 0.93 (0.55-1.56) for those gaining weight. In the group not intending to lose weight, hazard ratios were 1.17 (0.82-1.66) for those who did lose weight, and 1.57 (1.08-2.30) for those gaining weight.
Deliberate weight loss in overweight individuals without known co-morbidities may be hazardous in the long term. The health effects of weight loss are complex, possibly composed of oppositely acting processes, and need more research.
Background:
Overweight and obesity in mid- and late-life may increase risk for dementia, whereas a decline in body weight or body mass index (BMI) and underweight in years preceding a clinical dementia diagnosis are also associated with dementia. Little is known about the modifying effect of the APOE genotype, a major susceptibility gene for Alzheimer's disease (AD), on the BMI-dementia adult life course trajectory.
Objective:
We evaluated the exposure, BMI, in relationship to the outcome, clinical dementia, over 37 years, considering the effect modification of the APOE ɛ4 allele.
Methods:
The Prospective Population Study of Women (PPSW) in Sweden is a systematic sample of 1462 women born 1908, 1914, 1918, 1922, and 1930 and aged 38-60 years at baseline. Examinations occurred in 1968, 1974, 1980, 1992, 2000, and 2005; 559 women had information on dementia, BMI, and APOE ɛ4 allele status, in addition to covariates. Statistical analyses were conducted using mixed effects regression models.
Results:
Trajectories of BMI over 37 years differed by APOE ɛ4 allele status. While women gained BMI similarly from mid-life to age 70 years, women with at least one APOE ɛ4 allele experienced BMI decline more quickly after age 70 years compared to women without an APOE ɛ4 allele. However, upon stratifying the sample by dementia occurrence, it appeared that dementia drove the overall BMI-trajectory. There was a main effect of age, interactions of age by APOE ɛ4 allele status, and age by presence versus absence of dementia.
Conclusions:
Women with similar average BMI at mid-life exhibited different BMI trajectories in relation to dementia occurrence. In addition, the pattern of BMI decline in late-life differed on the basis of APOE ɛ4 allele possession. Thus, these data suggest roles for both dementia- and APOE-associated changes in BMI during the adult life course.
Prior work has suggested that obesity and overweight as measured by body mass index (BMI) increases risk of dementia. It is unknown if there is a difference in the risk of developing Alzheimer disease (AD) versus vascular dementia (VaD) associated with high body weight. The goal of this study was to examine the association between midlife BMI and risk of both AD and VaD an average of 36 years later in a large (N= 10,136) and diverse cohort of members of a health care delivery system. Participants aged 40-45 participated in health exams between 1964 and 1968. AD and VaD diagnoses were obtained from Neurology visits between January 1, 1994 and June 15, 2006. Those with diagnoses of general dementia from primary care providers were excluded from the study. BMI was analyzed in WHO categories of underweight, overweight and obese, as well as in subdivisions of WHO categories. All models were fully adjusted for age, education, race, sex, marital status, smoking, hyperlipidemia, hypertension, diabetes, ischemic heart disease and stroke. Cox proportional hazard models showed that compared to those with a normal BMI (18.5-24.9), those obese (BMI > or = 30) at midlife had a 3.10 fold increase in risk of AD (fully adjusted model, Hazard Ratio=3.10, 95% CI 2.19-4.38), and a five fold increase in risk of VaD (fully adjusted model, HR=5.01, 95% CI 2.98-8.43) while those overweight ( BMI > or = 25 and <30) had a two fold increase in risk of AD and VaD (fully adjusted model, HR=2.09, 95% CI 1.69-2.60 for AD and HR=1.95, 95% CI 1.29-2.96 for VaD). These data suggest that midlife BMI is strongly predictive of both AD and VaD, independent of stroke, cardiovascular and diabetes co morbidities. Future studies need to unveil the mechanisms between adiposity and excess risk of AD and VaD.
BMI change and BMI at an early age have not been investigated as risks for dementia. This case-control study included 286 dementia patients and 268 controls from two medical centers between 2007 and 2009. BMI information was collected from medical records and questionnaires. Men and women with low BMIs at the time of the study, in their 20s, and in their 40s had significantly increased risks of Alzheimer's disease (AD) (odds ratio = OR = 2.62-3.97) and increased vascular dementia (VaD) risk (20s and 40s: OR = 6.23-11.11) compared with those with normal BMIs. High BMI in the 20s and 40s was associated with increased VaD risk (OR = 15.29 and 10.32) among women. For BMI changes from the 20s or 40s, the second and third tertiles were significantly associated with decreased AD risk among women (OR = 0.15-0.35) compared to the first tertile. The third tertile of BMI change from the 20s or 40s was associated with decreased VaD risk among women (OR = 0.06 and 0.14). Low BMIs in the 20s and 40s were stronger predictors of AD and VaD. There was a U-shaped association between BMI at different ages and dementia among participants with VaD.
To estimate the associations of weight dynamics with physical functioning and mortality in older adults.
Longitudinal cohort study using prospectively collected data on weight, physical function, and health status in four U.S. Communities in the Cardiovascular Health Study. Included were 3,278 participants (2,013 women and 541 African Americans), aged 65 or older at enrollment, who had at least five weight measurements. Weight was measured at annual clinic visits between 1992 and 1999, and summary measures of mean weight, coefficient of variation, average annual weight change, and episodes of loss and gain (cycling) were calculated. Participants were followed from 1999 to 2006 for activities of daily living (ADL) difficulty, incident mobility limitations, and mortality.
Higher mean weight, weight variability, and weight cycling increased the risk of new onset of ADL difficulties and mobility limitations. After adjustment for risk factors, the hazard ratio (95% confidence interval) for weight cycling for incident ADL impairment was 1.28 (1.12, 1.47), similar to that for several comorbidities in our model, including cancer and diabetes. Lower weight, weight loss, higher variability, and weight cycling were all risk factors for mortality, after adjustment for demographic risk factors, height, self-report health status, and comorbidities.
Variations in weight are important indicators of future physical limitations and mortality in the elderly and may reflect difficulties in maintaining homeostasis throughout older ages. Monitoring the weight of an older person for fluctuations or episodes of both loss and gain is an important aspect of geriatric care.
The reliability between 2 raters conducting independent family history interviews for Alzheimer disease-like and other dementias was investigated. The interviews were conducted at least 1 year apart with the second rater blind to the data collected by the first rater. Raters agreed on the presence and type of dementia in 153 relatives age 45 or older of 30 AD probands, the age at onset in secondary cases, and the age of nonaffected relatives.
Cognitive decline associated with old age and consistent with the diagnosis of primary degenerative dementia is a unique clinical syndrome with characteristic phenomena and progression. The authors describe a Global Deterioration Scale for the assessment of primary degenerative dementia and delineation of its stages. The authors have used the Global Deterioration Scale successfully for more than 5 years and have validated it against behavioral, neuroanatomic, and neurophysiologic measures in patients with primary degenerative dementia.
To determine whether self-reported physical activity predicts a decreased rate of coronary heart disease (CHD) and all-cause mortalities in middle-aged men when rates are adjusted for known confounders.
Cohort Analytic Study of Israeli government employees in 1963.
Eight thousand four hundred sixty-three Israeli male government employees, aged 40 years or older, representing six areas of birth, excluding those with known cardiovascular disease in either 1963 or 1965, from an original cohort of 10,059.
Comparison of rates of death due to CHD and all causes, determined from death certificates in 21 years of follow-up, for subjects with different baseline levels of self-reported leisure-time and work-related physical activities measured in 1965.
Self-reported leisure-time but not work-related physical activity was inversely related to both CHD (adjusted relative risk, 0.79; 95% confidence interval, 0.66 to 0.95) and all-cause mortalities (adjusted relative risk, 0.91; 95% confidence interval, 0.83 to 0.99). Most of the apparent benefit accrued was from light physical activity on less than a daily basis. These inverse relationships persisted after adjustment for age, systolic blood pressure, cigarette smoking, total and high-density lipoprotein cholesterol levels, body mass index, psychosocial factors, and other potential confounders.
Baseline levels of self-reported leisure-time physical activity predicted a decreased rate of CHD and all-cause mortalities in employed middle-aged Israeli men followed up prospectively for 21 years.
Synopsis
In the course of a large twin study of Alzheimer's disease we used a two-stage telephone screening procedure. The modified Telephone Interview for Cognitive Status (TICS-m) served as an initial screen for dementia in 12709 individuals. The telephone Dementia Questionnaire (DQ) was then asked of collateral informants for subjects with TICS-m scores below 28, as well as for samples of persons with higher TICS-m scores. Based upon DQ responses, individuals with cognitive impairment not attributable to focal causes underwent assessment for the clinical diagnosis of Alzheimer's disease (‘Alzheimer's dementia’), as did their twins. Well-defined Alzheimer's dementia was apparent in 39 subjects. Employing a cut-off of 27 or lower as indicative of cognitive impairment, the sensitivity of the TICS-m in the detection of Alzheimer's dementia was estimated at >99% and specificity at 86%. Inclusion of the DQ increased the specificity at the 27/28 cut-point to 99%. The TICS-m score was associated with an area under the receiver operating characteristic (ROC) curve of 0·88 (95% confidence interval 0·81 to 0·94). The maximum number of cases of Alzheimer's dementia remaining undetected in the sample was estimated to be 34.
To study the relation between changes in body weight and subsequent mortality.
Prospective follow-up study.
Population study.
6441 men aged 40-59 y at baseline participating in the European cohorts of the Seven Countries Study. The men were divided into groups depending on their weight pattern ascertained from three weight measurements with intervals of 5 years. They were also divided in quartiles according to the degree of weight variability.
All-cause and cause-specific mortality during 15 years following the last weight measurement. Deaths occurring during the first 5 years of follow-up were excluded.
Significantly elevated hazard ratios (RR) for death from all causes (RR = 1.3; 95% confidence interval (CI): 1.2-1.5), all cardiovascular diseases (RR = 1.2; 95% CI: 1.0-1.5) and other causes (RR = 1.6; 95% CI: 1.2-2.2) were found for men with a decreasing weight compared with men with a constant weight. A fluctuating weight was associated with an increased risk of all cause mortality (RR = 1.2; 95% CI: 1.0-1.4), coronary heart disease (RR = 1.5; 95% CI: 1.0-1.9) and myocardial infarction (RR = 1.5; 95% CI: 1.0-2.2). The group of men with an increasing body weight also had elevated hazard ratios for dying from coronary heart disease and myocardial infarction, but these were only significant when the total 15-year follow-up was analyzed. The risks of dying from all-causes, cardiovascular disease, cancer and other causes were increased in the upper quartile versus the lower quartile of weight variability.
The results of the present study show that a decreasing and a fluctuating body weight are associated with increased mortality. An average increase of 7 kg body weight was associated with an elevated risk of dying from coronary heart disease and myocardial infarction. Lowest mortality in these middle-aged men was found in those who maintained a constant body weight.
This paper presents a new test procedure for detecting trend in ordered 2 X K tables. Using an order-directed score statistic, the procedure does not require a set of scores preassigned to the ordinal categories under consideration. Thus the problem of varying p-values of linear rank tests, due to choices of different scoring systems, is avoided. The proposed test procedure can be easily generalized to handle stratified analysis where data are represented by several 2 x K tables. Examples are given to illustrate the method.
Standard methods for survival analysis, such as the time-dependent Cox model, may produce biased effect estimates when there exist time-dependent confounders that are themselves affected by previous treatment or exposure. Marginal structural models are a new class of causal models the parameters of which are estimated through inverse-probability-of-treatment weighting; these models allow for appropriate adjustment for confounding. We describe the marginal structural Cox proportional hazards model and use it to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study. In this study, CD4 lymphocyte count is both a time-dependent confounder of the causal effect of zidovudine on survival and is affected by past zidovudine treatment. The crude mortality rate ratio (95% confidence interval) for zidovudine was 3.6 (3.0-4.3), which reflects the presence of confounding. After controlling for baseline CD4 count and other baseline covariates using standard methods, the mortality rate ratio decreased to 2.3 (1.9-2.8). Using a marginal structural Cox model to control further for time-dependent confounding due to CD4 count and other time-dependent covariates, the mortality rate ratio was 0.7 (95% conservative confidence interval = 0.6-1.0). We compare marginal structural models with previously proposed causal methods.
Cardiovascular risk factors often cluster into a metabolic syndrome that may increase the risk of dementia. The objective of the present study was to assess the long-term association between clustered metabolic cardiovascular risk factors measured at middle age and the risk of dementia in old age. This prospective cohort study of cardiovascular disease was started in 1965 and was extended to a study of dementia in 1991. The subjects were Japanese-American men with an average age of 52.7+/-4.7 (mean+/-SD) years at baseline. Dementia was diagnosed in 215 men, according to international criteria, and was based on a clinical examination, neuropsychological testing, and an informant interview. The z scores were calculated for 7 risk factors (random postload glucose, diastolic and systolic blood pressures, body mass index, subscapular skinfold thickness, random triglycerides, and total cholesterol). The relative risk (RR [95% CI]) of dementia (subtypes) per 1 SD increase in the sum of the z scores was assessed after adjustment for age, education, occupation, alcohol consumption, cigarette smoking, and years of childhood lived in Japan. The z-score sum was higher in demented subjects than in nondemented subjects, indicating a higher risk factor burden (0.74 versus -0.06, respectively; P=0. 008). Per SD increase in the z-score sum, the risk of dementia was increased by 5% (RR 1.05, 95% CI 1.02 to 1.09). The z-score sum was specifically associated with vascular dementia (RR 1.11, 95% CI 1.05 to 1.18) but not with Alzheimer's disease (RR 1.00, 95% CI 0.94 to 1.05). Clustering of metabolic cardiovascular risk factors increases the risk of dementia (mainly, dementia of vascular origin).
To investigate whether long-term weight fluctuation is associated with the fasting serum insulin concentration.
Weight histories of 1932 male Japanese workers aged 40-59 y were analyzed in relation to their current fasting serum insulin concentration.
Individual weight fluctuation was calculated by root mean square error (RMSE) along the linear regression line of weight measured at five to six different ages.
The mean RMSE and fasting insulin concentration were 1.22 kg and 4.5 microU/ml, respectively. The multivariate adjusted insulin level became higher with the increase in weight fluctuation. Subanalysis stratified by current body mass index (BMI) showed that the multivariate adjusted insulin level in individuals in the top quartile of fluctuation was 4.3 microU/ml, against 3.9 microU/ml in those in the bottom quartile (P=0.018, analysis of covariance (ANCOVA)) in the normal weight subgroup with current BMI below 25 kg/m(2). In the overweight subgroup with BMI 25 kg/m(2) or above, the level was 6.9 microU/ml in individuals in the top quartile and 6.2 microU/ml in those in the bottom quartile (P=0.054, ANCOVA).
The results suggest that weight fluctuation increases the risk of developing hyperinsulinemia. Prospective observations together with measurement of changes in adiposity are needed for confirmation.
The validity of the Hebrew version of the Telephone Interview for Cognitive Status-Modified (TICS-m) for Mild Cognitive Impairment (MCI), for dementia, and for cognitive impairment (either MCI or dementia) was investigated.
Of the 10 059 who took part of the Israel Ischemic Heart Disease Cohort, 1902 of the 2901 survivors in 1999 had TICS-m interviews. Those with a score of 27 or below and a random sample with a score of 28 or 29 were invited to have a physician's examination for the diagnosis of dementia. The analysis was performed on the 576 who agreed.
Based on physician's diagnosis, 269 were diagnosed as suffering from dementia, 128 as suffering from MCI, and 179 were diagnosed with no cognitive impairment. The TICS-m Hebrew version's internal consistency was very high (Cronbach's alpha = 0.98) and showed a strong convergent validity with the MMSE (r = 0.82; p < 0.0005). The sensitivity was 100% for each of the conditions. Finally, after controlling for age, education and hearing impairment, TICS-m was a strong predictor of dementia, MCI and cognitive impairment.
At a cut-off of 27/50 the Hebrew version of the TICS-m is a useful screening instrument to identify subjects suffering from mild cognitive impairment, dementia and cognitive impairment (MCI or dementia).
To examine the association between diabetes in midlife (1963-1968) and dementia more than three decades later (1999-2001).
The authors characterized dementia using standard methods for 1,892 participants among 2,606 survivors of 10,059 participants in the Israeli Ischemic Heart Disease study, a longitudinal investigation of the incidence of and risk factors for cardiovascular disease among Jewish male civil servants in Israel. Face to face interviews were conducted with the 652 subjects identified as possibly demented by the Modified Telephone Interview for Cognitive Status. Logistic regression analysis was performed to assess the association of diabetes with dementia controlling for sociodemographic and cardiovascular variables compared to those with no cognitive impairment.
Of 1,892 assessed subjects (mean age 82 at assessment), 309 (16.3%) had dementia. Diabetic subjects had significantly more dementia than non-diabetic subjects (chi2 = 7.54, df = 1, p = 0.006, OR 2.83 [95% CI = 1.40 to 5.71]). Those who survived to the time of this study were younger and healthier than those who died.
Evidence for diabetes as a risk factor for dementia was found, similar to other epidemiologic studies. In contrast to the earlier studies, however, the authors linked diabetes in midlife to dementia more than three decades later in the very old survivors of a large male cohort.
The course of weight loss associated with dementia is unclear, particularly prior to and around the onset of the clinical syndrome.
To compare the natural history of weight change from mid to late life in men with and without dementia in late life.
The Honolulu-Asia Aging Study, a 32-year, prospective, population-based study of Japanese American men who had been weighed on 6 occasions between 1965 and 1999 and who had been screened for dementia 3 times between 1991 and 1999.
Of 1890 men (aged 77-98 years), 112 with incident dementia were compared with 1778 without dementia at the sixth examination (1997-1999).
Weight change up to and including the sixth examination was treated as the dependent variable and estimated using a repeated measures analysis.
Groups with and without dementia did not differ with respect to baseline weight or change in weight from mid to late life (first 26 years' follow-up). In the late-life examinations (final 6 years), mean age- and education-adjusted weight loss was -0.22 kg/y (95% confidence intervals, -0.26 to -0.18) in participants without dementia. Men with incident dementia at the same examination had an additional yearly weight loss of -0.36 kg (95% confidence interval, -0.53 to -0.19). This was not changed substantially with adjustment for risk factors for vascular disease or functional impairment and was significant for both Alzheimer disease and vascular dementia subtypes.
Dementia-associated weight loss begins before the onset of the clinical syndrome and accelerates by the time of diagnosis. The potential impact on prognosis should be considered in the case of elderly persons at risk for dementia.
Vascular risk factors play a role in the development of dementia, including Alzheimer disease (AD). However, little is known about the effect of body mass index and clustering of vascular risk factors on the development of dementia.
To investigate the relation between midlife body mass index and clustering of vascular risk factors and subsequent dementia and AD.
Participants of the Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) study were derived from random, population-based samples previously studied in a survey carried out in 1972, 1977, 1982, or 1987. After an average follow-up of 21 years, 1449 individuals (73%) aged 65 to 79 years participated in the reexamination in 1998.
Dementia and AD.
Obesity at midlife (body mass index>30 kg/m2) was associated with the risk of dementia and AD even after adjusting for sociodemographic variables (odds ratio [OR], 2.4 [95% confidence interval (CI), 1.2-5.1]). The association was somewhat modified by further adjusting for midlife blood pressure, total cholesterol level, and smoking (OR, 2.1 [95% CI, 1.0-4.6]) and also for apolipoprotein E genotype and history of vascular disorders (OR, 1.9 [95% CI, 0.8-4.6]). Midlife obesity, high total cholesterol level, and high systolic blood pressure were all significant risk factors for dementia with ORs of around 2 for each factor, and they increased the risk additively (OR, 6.2 for the combination).
Obesity at midlife is associated with an increased risk of dementia and AD later in life. Clustering of vascular risk factors increases the risk in an additive manner. The role of weight reduction for the prevention of dementia needs to be further investigated.
Insulin plays a key role in cognition and other aspects of normal brain function. Insulin resistance induces chronic peripheral insulin elevations, reduces insulin activity, and reduces brain insulin levels. The insulin resistance syndrome and associated conditions such as type 2 diabetes mellitus and hypertension, are associated with age-related memory impairment and Alzheimer disease. Our work has focused on potential mechanisms through which this association is forged, including the effects of peripheral hyperinsulinemia on memory, inflammation, and regulation of the beta-amyloid peptide. We have shown that raising plasma insulin to levels that characterize patients with insulin resistance invokes synchronous increases in levels of beta-amyloid and inflammatory agents. These convergent effects may impair memory and induce AD pathology. Therapeutic strategies focused on preventing or correcting insulin abnormalities may thus benefit adults with age-related memory impairment and AD.
To identify the midlife risk factors for subtypes of dementia newly developed later in life.
A nested case-control study was conducted on 157 demented cases and 628 comparison cases selected from 40,636 men and women who were enrolled from 1982 to 1992. Four comparison cases were frequency-matched on age, time at enrollment (within 6 months), gender, and residential township. Midlife risk factors included vascular risk factors (body mass index [BMI], total cholesterol, total triglycerides, blood glucose, cerebrovascular accident [CVA] history, diabetes mellitus history, and hypertension history), cigarette smoking, and alcohol consumption. Dementia assessments were ascertained through the computerized data linkage from National Health Insurance Database from 2000 to 2002 and clinically confirmed by neurologists or psychiatrists. Conditional logistic regression analysis was used to estimate the matched odds ratio (OR) and its 95% confidence intervals (CI) for each risk factor.
A J-shaped relationship was observed between BMI (kg/m(2)) and dementia. The multivariate-adjusted ORs (95% CI) of developing dementia were 1.84 (1.02-3.33), 1.87 (1.08-3.23) and 2.44 (1.39-4.28), respectively, for BMIs of <20.5, 23.0-25.4, >or=25.5 compared with a BMI of 20.5-22.9 as the referent group (OR = 1.0). Similar findings were observed for Alzheimer disease (AD) and vascular dementia (VaD). The association between obesity (BMI >or=25.5) and both AD and VaD was statistically significant among cigarette smokers but not among nonsmokers. Additionally, history of CVA was a significant risk factor for VaD, but not for AD.
Being underweight, being overweight, and a cerebrovascular accident in midlife may increase the risk of dementia in late life.
To investigate the effect of weight change and weight fluctuations on all-cause-mortality in men.
Within a prospective population-based cohort of 1,160 men aged 40-59 years at recruitment, complete weight change patterns from baseline and three follow-up examinations during a period of 15 years of follow-up was used to categorize the 505 men into stable obese, stable non-obese, weight gain, weight loss and weight fluctuation groups. For these men (age range: 55-74 years at start time of survival analysis) further survival was analyzed during the subsequent 15 years.
Overall, 183 deaths were observed among the 505 men. Only weight fluctuations had a clear significant impact on all-cause mortality. Adjusted hazard rate ratio (HRR (95%-CI)) was 1.86 (1.31-2.66) after adjustment for age group, pre-existing cardiovascular disease or diabetes mellitus, smoking and socio-economic status. The risk rate due to weight loss was borderline significant (HRR = 1.81 (0.99-3.31)). Risk of death due to weight gain (HRR = 1.15 (0.70-1.88)) or stable obesity (HRR = 1.16 (0.69-1.94)), however, were not significantly increased compared to men staying non-obese for the first 15 years after cohort recruitment.
Weight fluctuations are a major risk factor for all-cause mortality in middle aged men. Moreover, stable obesity does not increase further mortality in men aged 55-74 years in long-term follow-up.
Although several studies reported weight loss preceding the onset of dementia, other studies suggested that obesity in midlife or even later in life may be a risk factor for dementia.
The authors used the records-linkage system of the Rochester Epidemiology Project to ascertain incident cases of dementia in Rochester, MN, for the 5-year period 1990 to 1994. The authors defined dementia using the criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV). Each case was individually matched by age (+/-1 year) and sex to a person drawn randomly from the same population, and free from dementia in the index year (year of onset of dementia in the matched case). Weights were abstracted from the medical records in the system.
There were no differences in weight between cases and controls 21 to 30 years prior to the onset of dementia. However, women with dementia had lower weight than controls starting at 11 to 20 years prior to the index year, and the difference increased over time through the index year. We found a trend of increasing risk of dementia with decreasing weight in women both at the index year (test for linear trend; p < 0.001) and 9 to 10 years before the index year (test for linear trend; p = 0.001).
Even accounting for delays in diagnosis, weight loss precedes the diagnosis of dementia in women but not in men by several years. This loss may relate to predementia apathy, loss of initiative, and reduced olfactory function.
Adiposity, commonly measured as body mass index (BMI), may influence or be influenced by brain structures and functions involved in dementia processes. Adipose tissue changes in degree and intensity over the lifespan, and has been shown to influence brain development in relationship to early and late measures of cognitive function, intelligence, and disorders of cognition such as dementia. A lower BMI is associated with prevalent dementia, potentially due to underlying brain pathologies and correspondingly greater rates of BMI or weight decline observed during the years immediately preceding clinical dementia onset. However, high BMI during mid-life or at least approximately 5-10 years preceding clinical dementia onset may increase risk. The interplay of adiposity and the brain occurring over the course of the lifespan will be discussed in relationship to developmental origins, mid-life sequelae, disruptions in brain structure and function, and late-life changes in cognition and dementia. Characterizing the life course of adiposity among those who do and do not become demented enhances understanding of biological underpinnings relevant for understanding the etiologies of both dementia and obesity and their co-existence.
Preva-lence and trends in obesity among US adults
- Flegal
- Carroll Md Km
- Ogden
- Cl
- Johnson
- Cl
Flegal KM, Carroll MD, Ogden CL, Johnson CL. Preva-lence and trends in obesity among US adults, 1999-2000. JAMA 2002;288:1723–1727.